Machine learning methods for predicting cell phenotypes using holographic imaging

Abstract

Provided herein is a method for determining the cell phenotype, for example, activation state, of a T cell population.In some embodiments, provided is a label-free method.In some embodiments, provided can be used to monitor the cell phenotype, for example, activation state, of T cell culture.Also provided herein is a computing device for use in implementing provided method.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Application No. 63 / 431,635, filed December 9, 2022, entitled "MACHINE LEARNING METHODS FOR PREDICTING CELL PHENOTYPE USING HOLOGRAPHIC IMAGING," and U.S. Provisional Application No. 63 / 537,467, filed September 8, 2023, entitled "MACHINE LEARNING METHODS FOR PREDICTING CELL PHENOTYPE USING HOLOGRAPHIC IMAGING," the disclosures of which are incorporated by reference in their entireties for all purposes.

[0002] Incorporation by reference to sequence listing This application is submitted with a Sequence Listing in electronic format. The Sequence Listing is provided as a file entitled 735042024740SeqList.xml, created on December 7, 2023, and is 38,941 bytes in size. The information in the electronic format of the Sequence Listing is incorporated by reference in its entirety.

[0003] The present disclosure relates to a method for determining the cell phenotype, for example, activation state, of a T cell population. In some embodiments, the provided method is a label-free method. In some embodiments, the provided method can be used to monitor the cell phenotype, for example, activation state, of T cell culture. Also provided herein is a computing device for use in implementing the provided method. [Background technology]

[0004] For example, existing methods for determining the cellular phenotype, e.g., activation status, of T cells during in vitro or ex vivo culture can be time-consuming or require specialized reagents, equipment, or trained operators. In some cases, existing methods involve directly manipulating or labeling T cells, for example, by incubating with immunoaffinity-based reagents that can interfere with the quality or function of T cells. Such methods can pose a risk of cell contamination or damage before any downstream processing. Improved methods for accurately determining the cellular phenotype, e.g., activation status, of T cells are needed. Such methods are useful, for example, in the production of cell products containing T cells, e.g., cell therapy products. Methods and computing devices that meet this need are provided herein. Summary of the Invention

[0005] In some embodiments, provided herein are methods for determining a cell phenotype of a T cell population, the method comprising determining, for a cell population comprising T cells, a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest, wherein the population-level output measure is determined based on a plurality of population-level statistical values, each population-level statistical value being a statistical value of one or more input measures for a cell feature among a plurality of cell features derived from holographic information obtained for the cell population, and wherein the plurality of population-level statistical values ​​includes one or more population-level statistical values ​​for each of the plurality of cell features, and each input measure is derived from an individual cell of the cell population.

[0006] In some of any of the embodiments, the method is for determining the activation state of a T cell population, and the marker is expressed by activated T cells. In some of any of the embodiments, the marker is CD137 (4-1BB).

[0007] In some of any of the embodiments, the method is for determining the memory phenotype of a T cell population, and the marker, e.g., CCR7, is expressed by T cells with a central memory phenotype or a stem cell memory phenotype.

[0008] Also provided herein in some embodiments is a method for determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the population-level output measure is determined based on a plurality of population-level statistical values, each population-level statistical value being a statistical value of one or more input measures for a cell feature among a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of population-level statistical values ​​includes one or more population-level statistical values ​​for each of the plurality of cellular features, and each input measure is derived from an individual cell of the cell population.

[0009] In some of any of the embodiments, the method is for determining recombinant receptor expression in a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0010] Also provided herein in some embodiments is a method of determining recombinant receptor expression in a T cell population, comprising: determining, for a cell population comprising T cells, a population-level output measure of expression of a recombinant receptor introduced into T cells of the T cell population, wherein the population-level output measure is determined based on a plurality of population-level statistical values, each population-level statistical value being a statistical value of one or more input measures for a cell feature of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of population-level statistical values ​​includes one or more population-level statistical values ​​for each of the plurality of cellular features, wherein each input measure is derived from an individual cell of the cell population.

[0011] In some optional embodiments, the method includes determining a plurality of population-level statistics from one or more input measures for each of a plurality of cellular features.

[0012] In some optional embodiments, the method includes determining one or more input measures for each of the plurality of cellular features from the holographic information.

[0013] In some of the optional embodiments, the method includes obtaining holographic information.

[0014] Also provided herein in some embodiments is a method for determining a cell phenotype of a T cell population, the method comprising: (a) obtaining holographic information about a cell population comprising T cells; (b) determining one or more input measures for each of a plurality of cellular features derived from the holographic information, where each input measure is derived from an individual cell in the cell population; (c) determining a plurality of population-level statistical values, where each population-level statistical value is a statistical value of the one or more input measures for a cell feature among the plurality of cellular features, and the plurality of population-level statistical values ​​comprises one or more population-level statistical values ​​for each of the plurality of cellular features; and (d) determining a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest of the cell population based on the plurality of population-level statistical values.

[0015] In some of any of the embodiments, the method is for determining the activation state of a T cell population, and the marker is expressed by activated T cells. In some of any of the embodiments, the marker is CD137 (4-1BB).

[0016] Also provided herein in some embodiments are methods for determining the activation state of a T cell population, the method comprising: (a) obtaining holographic information about a cell population comprising T cells; (b) determining one or more input measures for each of a plurality of cellular features derived from the holographic information, where each input measure is derived from an individual cell in the cell population; (c) determining a plurality of population-level statistical values, where each population-level statistical value is a statistical value of the one or more input measures for a cell feature among the plurality of cellular features, and the plurality of population-level statistical values ​​comprises one or more population-level statistical values ​​for each of the plurality of cellular features; and (d) determining a population-level output measure of expression of markers expressed by activated T cells of the cell population based on the plurality of population-level statistical values.

[0017] In some of any of the embodiments, the method is for determining recombinant receptor expression in a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0018] Also provided herein in some embodiments is a method of determining recombinant receptor expression in a T cell population, the method comprising: (a) obtaining holographic information about the cell population comprising T cells; (b) determining one or more input measures for each of a plurality of cellular features derived from the holographic information, where each input measure is derived from an individual cell in the cell population; (c) determining a plurality of population-level statistical values, where each population-level statistical value is a statistical value of the one or more input measures for a cell feature among the plurality of cellular features, and the plurality of population-level statistical values ​​comprises one or more population-level statistical values ​​for each of the plurality of cellular features; and (d) determining a population-level output measure of expression for the cell population of a recombinant receptor introduced into T cells of the cell population based on the plurality of population-level statistical values.

[0019] In some of any of the embodiments, the method further comprises manipulating the T cell population after determining the population-level output measure.

[0020] In some of any of the embodiments, the holographic information is obtained by differential digital holographic microscopy (DDHM). In some of any of the embodiments, obtaining the holographic information comprises imaging the cell population using DDHM.

[0021] In some of any of the embodiments, the one or more population-level statistical values ​​for at least one, and optionally each, of the plurality of cellular features comprises one or more quantiles of one or more input measures of the cellular feature. In some of any of the embodiments, the one or more population-level statistical values ​​for each of the plurality of cellular features comprises one or more quantiles of one or more input measures of the cellular feature. In some of any of the embodiments, the one or more quantiles are selected from (including one or more of) the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more input measures of the cellular feature.

[0022] In some embodiments, the one or more population-level statistical values ​​for at least one, and optionally each, of the plurality of cellular features are determined by applying a distribution-based pooling filter to one or more input measures of the cellular features. In some embodiments, the one or more population-level statistical values ​​for each of the plurality of cellular features are determined by applying a distribution-based pooling filter to one or more input measures of the cellular features.

[0023] In some of any of the embodiments, the population-level output measure is the number of cells, the percentage of cells, the proportion of cells, or the density of cells in the cell population that express the marker.

[0024] In some of any of the embodiments, the cell population is derived from a cell culture that has been cultured in vitro or ex vivo.

[0025] In some of the optional embodiments, the holographic information is obtained during in vitro or ex vivo culturing of the cell culture.

[0026] In some of any of the embodiments, the population-level output measure is a measure of expression of a marker during in vitro or ex vivo culturing of the cell culture.

[0027] In some of any of the embodiments, the in vitro or ex vivo culturing is performed under conditions that allow the T cells in the cell culture to expand.

[0028] In some of any of the embodiments, the in vitro or ex vivo culturing is carried out in a bioreactor.

[0029] In some of any of the embodiments, the cell population is incubated under T cell stimulating conditions before obtaining holographic information. In some of any of the embodiments, the method includes incubating the cell population under T cell stimulating conditions before obtaining holographic information.

[0030] In some of any of the embodiments, the incubation is prior to in vitro or ex vivo culture.

[0031] In some embodiments, the T cell stimulatory conditions include incubation in the presence of a T cell stimulator that induces a primary activation signal and a costimulatory signal in the T cells. In some embodiments, the T cell stimulator includes an anti-CD3 antibody or antibody fragment. In some embodiments, the T cell stimulator includes an anti-CD28 antibody or antibody fragment. In some embodiments, the T cell stimulator is immobilized on beads.

[0032] In some of any of the embodiments, the T cell stimulator is immobilized on a streptavidin mutein reagent in oligomeric form.

[0033] In some of any of the embodiments, the recombinant receptor is introduced into the T cells of the cell population before obtaining the holographic information. In some of any of the embodiments, the method includes introducing the recombinant receptor into the T cells of the cell population before obtaining the holographic information.

[0034] In some of any of the embodiments, the introducing comprises contacting the cell population with an agent comprising a polynucleotide encoding the recombinant receptor.

[0035] In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0036] In some of any of the embodiments, the cell population is enriched for T cells, in some of any of the embodiments, at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the cell population are T cells.

[0037] In some of any of the embodiments, the population-level output measure is determined by providing a plurality of population-level statistics as input to a machine learning model trained to predict a population-level output measure of expression of the marker based on the population-level statistical values ​​of the plurality of cellular features.

[0038] In some of any of the embodiments, the machine learning model is trained using a dataset of reference population-level statistics, where for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics includes one or more reference population-level statistics for each of a plurality of cellular features, each of the reference population-level statistics being a statistical value of one or more reference input measures for a cellular feature among the plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population.

[0039] In some of any of the embodiments, the machine learning model is trained using a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of markers for the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations.

[0040] Also, in some embodiments, there is provided a method of training a machine learning model to predict a cellular phenotype of a T cell population, comprising: (i) a dataset of reference population-level statistics, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among the plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population; and (ii) a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among the plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population. Provided herein are methods comprising training a machine learning model using, for each of the cell populations, the dataset of reference population-level output measures comprising reference population-level output measures of expression of markers expressed by T cells having a cell phenotype of interest in the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein a reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as a reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measures of expression of the markers based on population-level statistical values ​​of the plurality of cellular features.

[0041] In some of any of the embodiments, the method is for determining the activation state of a T cell population, and the marker is expressed by activated T cells. In some of any of the embodiments, the marker is CD137 (4-1BB).

[0042] Also, in some embodiments, there is provided a method of training a machine learning model to predict the activation state of a T cell population, comprising: (i) a dataset of reference population-level statistics, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among a plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population; and (ii) a dataset of reference population-level output measures, wherein a second plurality of reference cell populations comprising T cells are included; Provided herein are methods comprising training a machine learning model using a dataset of reference population-level output measures, for each of a number of reference cell populations, comprising reference population-level output measures of expression of markers expressed by activated T cells of the reference cell populations, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein a reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as a reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict a population-level output measure of expression of the markers based on population-level statistical values ​​of a plurality of cellular features.

[0043] In some of any of the embodiments, the method is for determining recombinant receptor expression in a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0044] Also, in some embodiments, there is provided a method of training a machine learning model to predict recombinant receptor expression in a T cell population, comprising: (i) a dataset of reference population-level statistics, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among a plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population; and (ii) a dataset of reference population-level output measures, wherein a second plurality of reference cell populations comprising T cells comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among a plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population. Provided herein are methods comprising training a machine learning model using a dataset of reference population-level output measures, for each of a number of reference cell populations, comprising a reference population-level output measure of the reference cell population's expression of a recombinant receptor introduced into T cells of the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measure of the expression of the recombinant receptor based on population-level statistical values ​​of the plurality of cellular features.

[0045] In some optional embodiments, the plurality of cellular features include one or more of intensity skewness, intensity correlation, intensity uniformity, intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

[0046] In some of any of the embodiments, the plurality of cellular features are selected from (including one or more of) intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean. Also provided herein in some embodiments is a method of determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, expression of a marker expressed by activated T cells, where the marker is CD137, wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features are selected from (including one or more of) intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean, wherein each input measure is derived from an individual cell of the cell population.

[0047] In some optional embodiments, the plurality of cell features includes an intensity maximum, an intensity minimum, an intensity entropy, an intensity contrast, a phase entropy, a cell area, and a radius mean.

[0048] In some embodiments, provided herein are methods for determining the activation state of a T cell population, the method comprising: determining, for a cell population comprising T cells, expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean, wherein each input measure is derived from an individual cell of the cell population.

[0049] Also provided herein in some embodiments is a method of determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean, wherein each input measure is derived from an individual cell of the cell population.

[0050] In some of any of the embodiments, the plurality of cellular features comprises one or more of intensity skewness, intensity correlation, and intensity uniformity. In some of any of the embodiments, the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity.

[0051] Also provided herein in some embodiments is a method for determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising intensity skewness, intensity correlation, and intensity uniformity, wherein each input measure is derived from an individual cell of the cell population.

[0052] Also provided herein in some embodiments is a method for determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity, and wherein each input measure is derived from an individual cell of the cell population.

[0053] In some of any of the embodiments, the plurality of cellular features comprises one or more of an intensity maximum, an intensity minimum, an intensity entropy, an intensity contrast, a phase entropy, a cell area, and a radius mean. In some of any of the embodiments, the plurality of cellular features comprises an intensity maximum, an intensity minimum, an intensity entropy, an intensity contrast, a phase entropy, a cell area, and a radius mean.

[0054] In some of any of the embodiments, the plurality of cellular features comprises one or more of: peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height. In some of any of the embodiments, the plurality of cellular features comprises peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height.

[0055] In some of any of the embodiments, the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter. In some of any of the embodiments, the plurality of cellular features comprises cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

[0056] Also provided herein in some embodiments is a method for determining the memory phenotype of a T cell population, comprising determining, for a cell population comprising T cells, expression of a marker expressed by central memory T cells or stem cell memory T cells, wherein the marker is CCR7, and wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter, and wherein each input measure is derived from an individual cell of the cell population.

[0057] In some of the embodiments, the plurality of cellular features includes one or more of: intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean. In some of the embodiments, the plurality of cellular features includes one or more of: peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height.

[0058] In some optional embodiments, the plurality of cellular features includes peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height.

[0059] In some of any of the embodiments, the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter. In some of any of the embodiments, the plurality of cellular features comprises cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

[0060] Also provided herein in some embodiments is a method for determining the memory phenotype of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by central memory T cells or stem cell memory T cells, wherein the marker is CCR7, and wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter, and wherein each input measure is derived from an individual cell of the cell population.

[0061] In some embodiments, the plurality of cellular features includes cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

[0062] Also provided herein in some embodiments is a method of determining recombinant receptor expression in a T cell population, comprising: determining, for a cell population comprising T cells, expression of a recombinant receptor introduced into the T cells of the cell population, wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features include peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height, wherein each input measure is derived from an individual cell of the cell population.

[0063] In some of any of the embodiments, the one or more input measures for at least one, and optionally each, of the plurality of cellular features are determined by providing holographic information about individual cells of the cell population to a convolutional neural network, the plurality of cellular features being cellular features extracted by the convolutional neural network, and the one or more input measures being determined from the convolutional neural network. In some of any of the embodiments, the one or more input measures for each of the plurality of cellular features are determined by providing holographic information about individual cells of the cell population to a convolutional neural network, the plurality of cellular features being cellular features extracted by the convolutional neural network, and the one or more input measures being determined from the convolutional neural network.

[0064] In some of any of the embodiments, the one or more reference input measures for at least one, and optionally each, of the plurality of cellular features are determined by providing holographic information about individual cells of the reference cell population to a convolutional neural network, the plurality of cellular features being cellular features extracted by the convolutional neural network, and the one or more reference input measures being determined from the convolutional neural network. In some of any of the embodiments, the one or more reference input measures for each of the plurality of cellular features are determined by providing holographic information about individual cells of the reference cell population to a convolutional neural network, the plurality of cellular features being cellular features extracted by the convolutional neural network, and the one or more reference input measures being determined from the convolutional neural network.

[0065] In some of any of the embodiments, the convolutional neural network is trained using a dataset of reference holographic information, where for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population.

[0066] In some of any of the embodiments, the one or more reference population-level statistical values ​​for at least one, and optionally each, of the plurality of cellular features comprises one or more quantiles of the one or more reference input measures of the cellular feature. In some of any of the embodiments, the one or more reference population-level statistical values ​​for each of the plurality of cellular features comprises one or more quantiles of the one or more reference input measures of the cellular feature. In some of any of the embodiments, the one or more quantiles are selected from the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more reference input measures of the cellular feature.

[0067] In some embodiments, the one or more reference population-level statistical values ​​for at least one, and optionally each, of the plurality of cellular features are determined by applying a distribution-based pooling filter to one or more reference input measures of the cellular features. In some embodiments, the one or more reference population-level statistical values ​​for each of the plurality of cellular features are determined by applying a distribution-based pooling filter to one or more reference input measures of the cellular features.

[0068] Also, in some embodiments, a method of training a machine learning model to predict a cellular phenotype of a T cell population includes: (a) training a convolutional neural network using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population; (b) determining from the convolutional neural network one or more reference input measures for each cellular feature of a plurality of cellular features derived from the holographic information, wherein the plurality of cellular features are cellular features extracted by the convolutional neural network, and each reference input measure is derived from an individual cell of the reference cell population; (c) determining a dataset of reference population-level statistics, wherein the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of the plurality of cellular features, wherein each reference population-level statistical value is derived from a plurality of cellular features, and wherein the plurality of cellular features are cellular features extracted by the convolutional neural network; and (d) training a machine learning model using a dataset of reference population-level statistics and a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of markers expressed by T cells having a cell phenotype of interest of the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measure of expression of the markers based on the population-level statistical values ​​of the plurality of cellular features.

[0069] In some of any of the embodiments, the method is for determining the activation state of a T cell population, and the marker is expressed by activated T cells. In some of any of the embodiments, the marker is CD137 (4-1BB).

[0070] Also, in some embodiments, a method of training a machine learning model to predict activation states of T cell populations includes: (a) training a convolutional neural network using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population; (b) determining from the convolutional neural network one or more reference input measures for each cellular feature of a plurality of cellular features derived from the holographic information, wherein the plurality of cellular features are cellular features extracted by the convolutional neural network, and each reference input measure is derived from an individual cell of the reference cell population; and (c) determining a dataset of reference population-level statistics, wherein the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of the plurality of cellular features, wherein each of the reference population-level statistics is derived from a distribution-based pooling filter. and (d) training a machine learning model using a dataset of reference population-level statistics and a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of markers expressed by activated T cells of the reference cell population, the first and second plurality of reference cell populations being derived from reference cell cultures that have been cultured in vitro or ex vivo, and the reference cell populations from the first plurality of reference cell populations being derived from the same reference cell culture as the reference cell populations from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measures of expression of the markers based on the population-level statistical values ​​of the plurality of cellular features.

[0071] In some of any of the embodiments, the method is for determining recombinant receptor expression in a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0072] Also, in some embodiments, a method of training a machine learning model to predict recombinant receptors for a T cell population includes: (a) training a convolutional neural network using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population; (b) determining from the convolutional neural network one or more reference input measures for each cellular feature of a plurality of cellular features derived from the holographic information, wherein the plurality of cellular features are cellular features extracted by the convolutional neural network, and each reference input measure is derived from an individual cell of the reference cell population; and (c) determining a dataset of reference population-level statistics, wherein the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of the plurality of cellular features, wherein each of the reference population-level statistics is derived from a distribution-based pooling filter. and (d) training a machine learning model using a dataset of reference population-level statistics and a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises a reference population-level output measure of a reference cell population of expression of a recombinant receptor introduced into T cells of the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measure of expression of the recombinant receptor based on the population-level statistical values ​​of the plurality of cellular features.

[0073] In some of any of the embodiments, the one or more input measures for at least one, and optionally each, of the plurality of cellular features are obtained from a fully connected layer of a convolutional neural network. In some of the embodiments, the one or more input measures for each of the plurality of cellular features are obtained from a fully connected layer of a convolutional neural network.

[0074] In some of any of the embodiments, the holographic information about the first plurality of reference cell populations is obtained by DDHM.

[0075] In some optional embodiments, the holographic information includes phase information and intensity information.

[0076] In some of any of the embodiments, the reference population level output measure is the number of cells, the percentage of cells, the proportion of cells, or the density of cells in the reference cell population that express the marker.

[0077] In some of the optional embodiments, the dataset of reference population-level statistics and the dataset of reference population-level output measures are time-concordant.

[0078] In some of any of the embodiments, the in vitro or ex vivo culturing of the reference cell culture is performed under the same or similar conditions as the in vitro or ex vivo culturing of the cell culture.

[0079] In some of any of the embodiments, the holographic information about the first plurality of reference cell populations is obtained during in vitro or ex vivo culturing of the reference cell cultures.

[0080] In some of any of the embodiments, the dataset of reference population-level output measures is a measure of expression of markers during or after in vitro or ex vivo culture of the reference cell culture.

[0081] In some of any of the embodiments, the dataset of reference population-level output measures is determined using fluorescent imaging of the second plurality of reference cell populations, hi some of the embodiments, the fluorescent imaging is by flow cytometry.

[0082] In some of any of the embodiments, the method is for determining the activation state of a T cell population, and the marker is expressed by activated T cells.

[0083] In some of any of the embodiments, the marker is CD137 (4-1BB).

[0084] In some embodiments, the method is for determining the memory phenotype of a T cell population, and the marker is expressed by T cells with a central memory phenotype or a stem cell memory phenotype. In some embodiments, the marker is expressed by T cells with a central memory phenotype. In some embodiments, the marker is expressed by T cells with a stem cell memory phenotype. In some embodiments, the marker is CCR7.

[0085] In some of any of the embodiments, the method is for determining recombinant receptor expression of a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0086] In some of any of the embodiments, the first plurality of reference cell populations is incubated under T cell stimulating conditions prior to obtaining holographic information about the first plurality of reference cell populations.

[0087] In some of any of the embodiments, the incubation of the first plurality of reference cell populations is performed under the same or similar conditions as the incubation of the cell population.

[0088] In some of any of the embodiments, the incubation of the first plurality of reference cell populations is prior to the in vitro or ex vivo culturing of the reference cell culture.

[0089] In some of any of the embodiments, the first and / or second plurality of reference cell populations are enriched for T cells. In some of any of the embodiments, the first and second plurality of reference cell populations are each enriched for T cells.

[0090] In some of any of the embodiments, at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the first and / or second plurality of reference cell populations are T cells. In some of any of the embodiments, at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of each of the first and second plurality of reference cell populations are T cells.

[0091] Also provided herein, in some embodiments, are methods for monitoring a cell phenotype of a T cell culture, the methods comprising determining, for a cell population derived from a cell culture comprising T cells that have been cultured in vitro or ex vivo, a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest, wherein the population-level output measure is determined according to any portion of the provided methods.

[0092] In some of any of the embodiments, the method is for monitoring the activation state of a T cell population and the marker is expressed by activated T cells. In some of any of the embodiments, the marker is CD137 (4-1BB).

[0093] In some optional embodiments,

[0094] Also provided herein, in some embodiments, is a method of monitoring the activation state of a T cell culture, comprising determining a population-level output measure of expression of a marker expressed by activated T cells for a cell population derived from a cell culture comprising T cells that have been cultured in vitro or ex vivo, wherein the population-level output measure is determined according to any portion of the provided methods.

[0095] In some of any of the embodiments, the method is for monitoring recombinant receptor expression in a T cell population, and the marker is a recombinant receptor that was introduced into T cells of the cell population prior to obtaining the holographic information. In some of any of the embodiments, the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

[0096] Also provided herein, in some embodiments, are methods for monitoring recombinant receptor expression in a T cell culture, the methods comprising determining, for a cell population derived from a cell culture comprising T cells that have been cultured in vitro or ex vivo, a population-level output measure of expression of a recombinant receptor that has been introduced into the T cells of the cell culture, wherein the population-level output measure is determined according to any portion of the provided methods.

[0097] Also provided herein, in some embodiments, is a computing device comprising in memory instructions for performing a portion of any of the provided methods, the instructions including: (a) instructions for receiving holographic information for an individual cell of a cell population comprising T cells, one or more input measures for each of a plurality of cellular features of an individual cell of a cell population comprising T cells, or a plurality of population-level statistical values ​​for a cell population comprising T cells; and (b) instructions for determining a population-level output measure for the cell population from the holographic information, the one or more input measures for each of the plurality of cellular features, or the plurality of population-level statistical values ​​according to the method.

[0098] In some of any of the embodiments, the computing device further includes in memory a machine learning model trained according to any of the methods provided, and the population-level output measure for the cell population is determined using the machine learning model. [Brief explanation of the drawings]

[0099] [Figure 1] Figures 1 and 2 show the results of a validation study of a machine learning method for determining the overall activation status (e.g., CD137 positivity) of T cell populations using holographic imaging. The T cell populations in Figures 1 and 2 were subjected to different stimulation processes. [Figure 2] Same as above.

[0100] [Figure 3] Figure 3 shows the results of validation of a machine learning method for determining recombinant receptor expression in T cell populations using holographic imaging.

[0101] [Figure 4] Figure 4 shows the results of validation of a machine learning method for determining the memory phenotype of T cell populations using holographic imaging. DETAILED DESCRIPTION OF THE INVENTION

[0102] Provided herein are methods for determining the cell phenotype, e.g., activation state, of a cell population containing T cells. In some aspects, the provided methods do not require labeling of cells. In some aspects, the provided methods do not include labeling of T cells. In some aspects, the provided methods are label-free methods. In some aspects, the provided methods can be used to monitor the cell phenotype, e.g., activation state, of a cell culture containing T cells, for example, during in vitro or ex vivo culture of T cells and before downstream processing steps. In some embodiments, determining the cell phenotype, e.g., activation state, is performed using machine learning. Also provided herein are methods for training a machine learning model to predict the cell phenotype, e.g., activation state, of a cell population containing T cells, as well as computing devices for use in performing any of the provided methods.

[0103] For example, existing methods for determining the cell phenotype, such as activation state, of T cells during in vitro or ex vivo culture can be time-consuming or require specialized reagents, equipment, or trained operators. In some cases, existing methods involve directly manipulating or labeling T cells, including incubating with immunoaffinity-based reagents that can interfere with the quality or function of T cells. Such methods can pose the risk of cell contamination or damage before any downstream processing.

[0104] The provided methods allow for label-free determination and monitoring of the cellular phenotype, e.g., activation state, of T cells. In some aspects, the provided methods include determining activation state based on T cell expression of CD137 (4-1BB). The results demonstrated herein indicate that CD137 is a particularly useful marker for predicting overall activation state, as the results are consistent with findings that CD137 expression is associated with characteristics that can be monitored using holographic imaging, e.g., changes in size of activated T cells. In some aspects, without wishing to be bound by theory, the extent to which CD137 expression dynamically changes over time during activation contributes to its usefulness as a marker for predicting activation state during cellular monitoring by holographic imaging. In some such embodiments, fluctuations in CD137 expression over time are associated with changes in cell size. In some such embodiments, fluctuations in CD137 expression are associated with one or more of cell size, intensity, texture (or smoothness), and circularity. In some embodiments, increased CD137 expression is associated with one or more (e.g., at least one, at least two, at least three, or all) of increased intensity, increased size (e.g., increased cell area and / or increased cell radius), increased texture (decreased cell smoothness), and decreased circularity. In some embodiments, increased CD137 expression is associated with one or more (e.g., at least one, at least two, at least three, or all) of decreased intensity, decreased size (e.g., cell area and / or cell radius), increased cell smoothness, and increased circularity.

[0105] The provided methods also allow for the label-free determination and monitoring of other cell phenotypes of T cells, e.g., memory phenotypes. In some aspects, the provided methods include determining the memory phenotype of a T cell population. In some embodiments, the determination is based on the expression of CCR7 on T cells, with CCR7-positive cells typically considered to be cells with an early memory phenotype, e.g., a central memory or stem cell memory phenotype, and CCR7-negative cells typically considered to have an effector memory phenotype. See Blaeschke et al., Cancer Immunol Immunother, 67: 1053-1066 (2018). Early memory phenotypes, e.g., stem cell memory or central memory phenotypes, have been reported to result in sustained in vivo responses to CART cell therapy given their proliferative and effector capabilities in both hematological and solid tumor environments. See Gargett et al., Cytotherapy, 21: 593-602 (2019). This makes CCR7 a particularly relevant marker for therapeutic T cell populations. The results demonstrated herein demonstrate that CCR7 expression is associated with changes in properties that can be monitored using holographic imaging. In some such embodiments, variations in CCR7 expression are associated with one or more of cell size, intensity, texture (or smoothness), and circularity. In some embodiments, increased CCR7 expression is associated with one or more (e.g., at least two, at least three, at least four, or all) of the following characteristics: cell area, intensity mean, perimeter, equivalent peak diameter, and normalized peak area. In some embodiments, increased CCR7 expression is associated with one or more (e.g., at least two, at least three, at least four, or all) of the following characteristics: (i) increased cell size, (ii) decreased mean intensity, (iii) increased phase correlation, and (iv) decreased normalized radial variance. In some embodiments, increased cell size is indicated by increased area, increased perimeter, and / or increased diameter.

[0106] In some embodiments, the methods provided further comprise manipulating the phenotyped and / or monitored T cells to produce a cell therapy product.

[0107] In some aspects, provided methods include determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by T cells, e.g., a marker expressed by activated T cells. For example, in some embodiments, the population-level output measure is the number of cells, the percentage of cells, the proportion of cells, or the density of cells in the cell population that express the marker.

[0108] In some aspects, the population-level output measure is determined based on holographic information obtained for individual cells of the cell population. In some aspects, the population-level output measure is determined based on cellular features derived from the holographic information for the individual cells. In some aspects, the population-level output measure is determined based on population-level statistics of the cellular features. In some aspects, the population-level statistics summarize input measures of the cellular features across individual cells of the cell population. For example, in some embodiments, the population-level statistics include quantile values ​​of input measures across individual cells of the cell population for the cellular feature.

[0109] In some aspects, population-level statistics of cell features derived from holographic information obtained for individual cells of a reference T cell population are used to train a machine learning model to predict a population-level output measure of marker expression. In some embodiments, the machine learning model is trained using a reference population-level output measure of marker expression in the reference cell population. In some embodiments, the reference population-level output measure is determined using fluorescent imaging, for example, by immunoaffinity-based fluorescent labeling for the marker.

[0110] As demonstrated herein, marker expression, e.g., activation marker expression, of individual cells was not required or used to develop the provided methods, thereby eliminating the need for any specialized equipment or reagents, e.g., for capturing coincident holographic and fluorescent images of individual cells. Furthermore, because the holographically imaged cells did not need to be fluorescently labeled as part of the provided methods, the impact on cell feature measurements and model prediction accuracy of any random or systematic morphometric changes that may occur in labeled cells compared to unlabeled cells was avoided.

[0111] Furthermore, the same holographic imaging system used in accordance with the provided methods to obtain holographic information for training a machine learning model can also be used to obtain holographic information for prediction using the trained machine learning model. This is in contrast to other methods that may use different imaging systems to obtain holographic information for model training and use, such as a dual fluorescence holographic imaging system for model training and a holographic imaging-only system for model use. Thus, in some embodiments, the machine learning model of the provided methods is highly transferable for use in subsequent application of the model after model training, compared to other methods that involve, for example, using different imaging systems before and after model training.

[0112] In some embodiments, the holographic information is provided as input to a convolutional neural network. In some embodiments, selection or design of cellular features derived from the holographic information is not necessary prior to model training. Instead, in some embodiments, cellular features may be automatically extracted from the holographic information as part of model training, e.g., extracted by a convolutional neural network. In some embodiments, the automatically extracted cellular features include those that may not be detectable by humans or that may not be known to be predictive of marker expression. In some embodiments, the automatically extracted cellular features are not detectable by humans based on visual inspection of the holographic image. In some embodiments, one or more of the automatically extracted cellular features are not known to be predictive of marker (e.g., activation marker) expression. Thus, in some aspects, the provided methods are accurate, efficient, objective, and unbiased methods for predicting cellular phenotypes, e.g., activation states.

[0113] In some embodiments, the methods described herein can be used to monitor and characterize T cell phenotype, e.g., activation state or memory state, using a non-destructive, non-damaging approach that allows T cells (e.g., unlabeled T cells) to be imaged without damaging them (e.g., cells can be removed from a bioreactor and returned to the bioreactor intact). The provided methods can be used to monitor the dynamics of T cell phenotype, e.g., activation state or memory state, during culture without frequent cell sampling or difficult analytical techniques. In some embodiments, the provided methods can be used to identify relationships between cell phenotype (e.g., activation state or memory state) and outcome, e.g., how cells evolve over time.

[0114] In some embodiments, the provided methods can be used to predict the quality of T cells that have undergone a manufacturing process, e.g., the quality of T cells during or after the manufacturing process. In some aspects, the cell phenotype (e.g., activation state or memory state) as predicted by the provided methods can be used, e.g., as a readout during manufacturing of successful manufacturing or of the success of a manufacturing step (e.g., cell stimulation). In some embodiments, the provided methods can be used to monitor whether T cells are sufficiently activated, e.g., for expansion of T cells to a desired threshold number, e.g., the number required for a clinical dose of T cells for T cell therapy.

[0115] In some aspects, T cell activation can lead to T cell differentiation. A high proportion of early memory T cells, e.g., naive-like T cells, in T cell therapy can improve patient outcomes (see, e.g., Jiang et al., Journal of Pharmaceutical Sciences (2021) 110:1871-1876). In some embodiments, the provided methods can be used to monitor the memory state of T cells directly or by monitoring the activation state of T cells. In some embodiments, the activation state of T cells is monitored to predict the memory state of T cells.

[0116] In some embodiments, cell phenotype information obtained by the provided methods can be used during process development to optimize the duration or other conditions of a manufacturing process or steps thereof to improve the quality of the processed T cells. In some examples, this information can be used to develop process control strategies, e.g., if a predicted cell phenotype, e.g., activation state, falls outside a predetermined range, conditions of one or more (e.g., current or subsequent) manufacturing steps can be altered, e.g., the duration of a current or subsequent manufacturing step can be altered to improve the final quality of the T cells being produced. For example, if a predicted cell phenotype, e.g., activation state, falls outside a predetermined range during culture, subsequent culture can, in some examples, be performed under perfusion conditions and / or in the presence of small molecules, e.g., to modulate the T cell phenotype toward a desired profile.

[0117] In some embodiments, the cell phenotypic information obtained by the provided methods can be used to assess or reduce batch-to-batch variability of T cells that have undergone a manufacturing process. In some embodiments, the cell phenotypic information can be used to assess or reduce batch-to-batch variability of a drug product produced using the manufacturing process. For example, by ensuring that T cells across different cell therapy manufacturing runs are in a comparable activation state, the differentiation and memory state of T cells can be maintained constant. This can reduce variability (e.g., patient-to-patient variability) in the resulting T cell therapy (see, e.g., Jiang et al., Journal of Pharmaceutical Sciences (2021) 110:1871-1876).

[0118] In some embodiments, the provided methods can be used to monitor the activation state of T cells before or after manipulation of the T cells. In some aspects, transgene expression may be higher in activated versus non-activated T cells, e.g., after viral transduction of T cells (see, e.g., Ghassemi et al., Nature Biomedical Engineering (2022) 6:118-128). In some aspects, electroporation efficiency for manipulation may be higher in activated versus non-activated T cells (see, e.g., Zhang et al., BMC Biotechnology (2018) 18:4). In some embodiments, T cells are monitored according to the provided methods before manipulation, e.g., so that manipulation can begin when the provided methods predict that the T cells are sufficiently activated for improved transgene expression. In some embodiments, T cells are monitored according to the provided methods after manipulation, e.g., after manipulation to improve transgene expression, to determine whether the T cells are sufficiently activated or remain sufficiently activated.

[0119] All publications, including patent documents, scientific articles, and databases, referenced in this application are incorporated herein by reference in their entirety for all purposes to the same extent as if each individual publication was individually incorporated by reference. To the extent that a definition set forth herein contradicts or otherwise conflicts with a definition set forth in a patent, application, published application, or other publication incorporated herein by reference, the definition set forth herein takes precedence over the definition incorporated herein by reference.

[0120] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0121] Methods for determining IT cell phenotype In some embodiments, provided methods include determining a cell phenotype, e.g., activation state, of a cell population containing T cells. In some embodiments, provided methods are for determining a cell phenotype, e.g., activation state, of a cell population containing T cells. Exemplary cell populations are described in Section II. In some embodiments, provided methods include performing any of the cell processing steps described in Section II on the cell population.

[0122] In some embodiments, provided herein is a method for determining a cell phenotype of a T cell population, the method comprising: determining, for a cell population comprising T cells, a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest, wherein the population-level output measure is determined based on a plurality of population-level statistical values, each population-level statistical value being a statistical value of one or more input measures for a cell feature among a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of population-level statistical values ​​including one or more population-level statistical values ​​for each of the plurality of cellular features, wherein each input measure is derived from an individual cell of the cell population.

[0123] Also provided herein in some embodiments is a method for determining a cell phenotype of a T cell population, the method comprising: (a) obtaining holographic information about a cell population comprising T cells; (b) determining one or more input measures for each of a plurality of cell features derived from the holographic information, where each input measure is derived from an individual cell in the cell population; (c) determining a plurality of population-level statistical values, where each population-level statistical value is a statistical value of the one or more input measures for a cell feature among the plurality of cell features, and the plurality of population-level statistical values ​​comprises one or more population-level statistical values ​​for each of the plurality of cell features; and (d) determining a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest of the cell population based on the plurality of population-level statistical values. In some embodiments, the term "determining" refers to predicting.

[0124] Thus, in some embodiments, provided herein is a method of predicting a cell phenotype of a T cell population, the method comprising: (a) obtaining holographic information about a cell population comprising T cells; (b) predicting one or more input measures for each of a plurality of cellular features derived from the holographic information, where each input measure is derived from an individual cell in the cell population; (c) predicting a plurality of population-level statistical values, where each population-level statistical value is a statistical value of the one or more input measures for a cell feature among the plurality of cellular features, and the plurality of population-level statistical values ​​comprises one or more population-level statistical values ​​for each of the plurality of cellular features; and (d) predicting a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest of the cell population based on the plurality of population-level statistical values.

[0125] Also, in some embodiments, there is provided a method of training a machine learning model to predict a cellular phenotype of a T cell population, comprising: (i) a dataset of reference population-level statistics, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among a plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population; and (ii) a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features, each reference population-level statistical value being a statistical value of one or more reference input measures for a cellular feature among a plurality of cellular features derived from holographic information obtained for the reference cell population, each reference input measure being derived from an individual cell of the reference cell population. Provided herein are methods comprising training a machine learning model using, for each of the cell populations, the dataset of reference population-level output measures comprising reference population-level output measures of expression of markers expressed by T cells having a cell phenotype of interest in the reference cell populations, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein a reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as a reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measures of expression of the markers based on population-level statistical values ​​of the plurality of cellular features.

[0126] Also provided herein in some embodiments is a method of determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean, wherein each input measure is derived from an individual cell of the cell population.

[0127] Also provided herein in some embodiments is a method for determining the activation state of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity, and wherein each input measure is derived from an individual cell of the cell population.

[0128] In some embodiments, the term "determining" means predicting.

[0129] Thus, in some embodiments, provided herein is a method for predicting the activation state of a T cell population, comprising predicting, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is predicted based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean, wherein each input measure is derived from an individual cell of the cell population.

[0130] Also provided herein in some embodiments is a method of predicting the activation state of a T cell population, comprising predicting, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is predicted based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity, and wherein each input measure is derived from an individual cell of the cell population.

[0131] Also provided herein in some embodiments is a method for determining the memory phenotype of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by central memory T cells or stem cell memory T cells, where the marker is CCR7, and the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, where the plurality of cellular features include one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter, and each input measure is derived from an individual cell of the cell population. In some embodiments, the term "determining" refers to predicting.

[0132] Thus, in some embodiments, provided herein is a method of predicting the memory phenotype of a T cell population, comprising predicting, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by central memory T cells or stem cell memory T cells, wherein the marker is CCR7, and wherein the population-level output measure is predicted based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, wherein the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter, and wherein each input measure is derived from an individual cell of the cell population.

[0133] Also, in some embodiments, a method of training a machine learning model to predict a cellular phenotype of a T cell population includes: (a) training a convolutional neural network using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population; (b) determining from the convolutional neural network one or more reference input measures for each cellular feature of a plurality of cellular features derived from the holographic information, wherein the plurality of cellular features are cellular features extracted by the convolutional neural network, and each reference input measure is derived from an individual cell of the reference cell population; (c) determining a dataset of reference population-level statistics, wherein the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of the plurality of cellular features, wherein each reference population-level statistical value is derived from a plurality of cellular features, and wherein the plurality of cellular features are cellular features extracted by the convolutional neural network; and (d) training a machine learning model using a dataset of reference population-level statistics and a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of markers expressed by T cells having a cell phenotype of interest of the reference cell population, wherein the first and second plurality of reference cell populations are derived from reference cell cultures that have been cultured in vitro or ex vivo, and wherein the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations, whereby the machine learning model is trained to predict the population-level output measure of expression of the markers based on the population-level statistical values ​​of the plurality of cellular features.

[0134] Also provided herein in some embodiments is a method for determining recombinant receptor expression in a T cell population, comprising: determining, for a cell population comprising T cells, expression of a recombinant receptor introduced into the T cells of the cell population, wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising peak area, phase-averaged uniformity, intensity geometric mean, minimum optical height, and normalized optical height, wherein each input measure is derived from an individual cell of the cell population. In some embodiments, the term "determining" refers to predicting.

[0135] Thus, in some embodiments, provided herein is a method of predicting recombinant receptor expression in a T cell population, comprising: predicting, for a cell population comprising T cells, expression of a recombinant receptor introduced into T cells of the cell population, wherein determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population, the plurality of cellular features comprising peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height, wherein each input measure is derived from an individual cell of the cell population.

[0136] In some embodiments, the cell phenotype is the expression of a marker by cells of the cell population. In some embodiments, the marker is expressed on the surface of T cells. In some embodiments, the provided methods comprise determining the presence or absence of marker-expressing cells in the cell population, the extent to which marker-expressing cells are present in the cell population, or the extent to which marker-expressing cells are present among T cells of the cell population. In some embodiments, the presence or absence of marker-expressing cells in the cell population is determined. In some embodiments, the extent to which marker-expressing cells are present ... among T cells of the cell population is determined.

[0137] In some embodiments, the cell phenotype is based on the expression of one or more of the combination of markers by cells of the cell population. In some embodiments, the cell phenotype is based on a population-level output measure of the expression of one or more of the combination of markers by cells of the cell population. In some embodiments, the markers are expressed on the surface of T cells. In some embodiments, the cell phenotype is expression of some of the markers in the combination of markers and non-expression of the remaining markers in the combination of markers. In some embodiments, the cell phenotype is a population-level output measure of expression of some of the markers in the combination of markers and non-expression of the remaining markers in the combination of markers. In some embodiments, the cell phenotype is the expression of each of the combination of markers. In some embodiments, the cell phenotype is a population-level output measure of the expression of each of the combination of markers.

[0138] In some embodiments, references herein to "markers" refer to combinations of markers, e.g., any combination of markers described herein. In some embodiments, references herein to "marker expression," "marker-expressing cells," "cells expressing markers," or the like, also refer to combined marker expression and / or non-expression consistent with a cell phenotype of interest. For example, for cell phenotype CCR7+CD45RA-, "marker-expressing cells" can refer to CCR7+CD45RA- cells. Similarly, for cell phenotype CCR7+CD27+, "marker-expressing cells" can refer to CCR7+CD27+ cells.

[0139] In some embodiments, provided methods include determining the activation state of a cell population. In some embodiments, "activation state" refers to the presence or absence of activated T cells in a cell population, the extent to which activated T cells are present in a cell population, or the extent to which activated T cells are present among the T cells of a cell population. In some embodiments, the presence or absence of activated T cells in a cell population is determined. In some embodiments, the extent to which activated T cells are present in a cell population is determined. In some embodiments, the extent to which activated T cells are present among the T cells of a cell population is determined. In some embodiments, such a determination is predictive.

[0140] In some embodiments, the provided methods include determining the memory state of a cell population. In some embodiments, "memory state" refers to the presence or absence of T cells of a particular memory phenotype in a cell population, the degree to which T cells of a particular memory phenotype are present in a cell population, or the degree to which T cells of a particular memory phenotype are present among T cells of a cell population. In some embodiments, the presence or absence of T cells of a particular memory phenotype in a cell population is determined. In some embodiments, the degree to which T cells of a particular memory phenotype are present among T cells of ...

[0141] In some embodiments, the memory phenotype is that of naive-like T cells. In some embodiments, the presence or absence of naive-like T cells in the cell population is determined. In some embodiments, the extent to which naive-like T cells are present in the cell population is determined. In some embodiments, the extent to which naive-like T cells are present among the T cells of the cell population is determined. In some embodiments, the memory phenotype is a central memory phenotype or a stem cell memory phenotype. In some embodiments, the presence or absence of T cells having a central memory phenotype or a stem cell memory phenotype in the cell population is determined. In some embodiments, the extent to which T cells having a central memory phenotype or a stem cell memory phenotype are present among the T cells of the cell population is determined. In some embodiments, the determination is a prediction.

[0142] In some embodiments, the memory phenotype is that of central memory T cells. In some embodiments, the presence or absence of central memory T cells in a cell population is determined. In some embodiments, the extent to which central memory T cells are present in a cell population is determined. In some embodiments, the extent to which central memory T cells are present in T cells of a cell population is determined.

[0143] In some embodiments, the memory phenotype is that of effector memory T cells. In some embodiments, the presence or absence of effector memory T cells in a cell population is determined. In some embodiments, the extent to which effector memory T cells exist in a cell population is determined. In some embodiments, the extent to which effector memory T cells exist in the T cells of a cell population is determined.

[0144] In some embodiments, the memory phenotype is of well-differentiated T cells. In some embodiments, the presence or absence of well-differentiated T cells in a cell population is determined. In some embodiments, the extent to which well-differentiated T cells are present in a cell population is determined. In some embodiments, the extent to which well-differentiated T cells are present among T cells of the cell population is determined. In some embodiments, the provided methods comprise determining a population-level output measure for the cell population that is indicative of a cell phenotype, e.g., an activation state, of the cell population. In some embodiments, the population-level output measure is indicative of the presence or absence of marker-expressing cells in the cell population. In some embodiments, the population-level output measure is indicative of the extent to which marker-expressing cells are present ... among T cells of the cell population.

[0145] In some embodiments, the population-level output measure is the number, percentage, proportion, or density of marker-expressing cells in the cell population. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of marker-expressing cells among T cells of the cell population. In some embodiments, the population-level output measure is the percentage of marker-expressing cells among T cells of the cell population.

[0146] In some embodiments, the marker is expressed on the surface of a T cell.

[0147] In some embodiments, the marker is CD3. In some embodiments, the marker is CD4. In some embodiments, the marker is CD8.

[0148] In some embodiments, the marker is a recombinant protein. In some embodiments, the recombinant protein is a recombinant receptor. In some embodiments, the recombinant receptor is a chimeric antigen receptor (CAR). Exemplary CARs are described in Section II-C-2.

[0149] In some embodiments, the recombinant receptor is a T cell receptor (TCR). Exemplary TCRs are described in section II-C-3.

[0150] In some embodiments, provided methods include determining a population-level output measure for a cell population that is indicative of the activation state of the cell population. In some embodiments, the population-level output measure is indicative of the presence or absence of activated T cells in the cell population. In some embodiments, the population-level output measure is indicative of the extent to which activated T cells are present in the cell population. In some embodiments, the population-level output measure is indicative of the extent to which activated T cells are present among the T cells of the cell population.

[0151] In some embodiments, the population-level output measure is the number, percentage, proportion, or density of activated T cells in the cell population. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of activated T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of activated T cells among the T cells of the cell population.

[0152] In some embodiments, provided methods include determining, for a cell population, a population-level output measure of expression of a marker indicative of T cell activation. In some embodiments, "activation state" refers to the presence of T cells in a cell population that express a marker, the degree to which T cells that express a marker are present in a cell population, or the degree to which T cells that express a marker are present among T cells of a cell population.

[0153] In some embodiments, the marker is a marker expressed by activated T cells (an "activation marker"). In some embodiments, "activated T cells" refers to T cells that express such a marker. In some embodiments, the marker is CD137 (4-1BB). In some embodiments, the marker is CD25. In some embodiments, the marker is CD69. In some embodiments, the marker is a combination of more than two activation markers, such as two or more of CD137, CD25, and CD69. In some embodiments, the marker includes CD137. In some embodiments, the marker includes CD137 and one or more additional activation markers. In some embodiments, the one or more additional activation markers include CD25 and / or CD69.

[0154] In other embodiments, the marker is a marker expressed by non-activated T cells. In some embodiments, "activated T cells" refers to T cells that do not express such markers.

[0155] In some embodiments, the provided methods include determining a population-level output measure for a cell population that indicates the memory state of the cell population. In some embodiments, the population-level output measure indicates the presence or absence of T cells of a particular memory phenotype in the cell population. In some embodiments, the population-level output measure indicates the extent to which T cells of a particular memory phenotype are present in the cell population. In some embodiments, the population-level output measure indicates the extent to which T cells of a particular memory phenotype are present among T cells of the cell population. Early memory phenotypes, such as stem cell memory or central memory phenotypes, have been reported to result in sustained in vivo responses to CART cell therapy given their proliferative and effector capabilities in both hematological and solid tumor environments. See Gargett et al., Cytotherapy, 21: 593-602 (2019). CCR7-positive cells are typically considered to be of a more early memory phenotype.

[0156] In some embodiments, the population-level output measure is the number of T cells of a particular memory phenotype, the percentage of T cells of a particular memory phenotype, the proportion of T cells of a particular memory phenotype, or the density of T cells of a particular memory phenotype among the T cells of the cell population. In some embodiments, the population-level output measure is the number of T cells of a particular memory phenotype, the percentage of T cells of a particular memory phenotype, the proportion of T cells of a particular memory phenotype, or the density of T cells of a particular memory phenotype among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of T cells of a particular memory phenotype among the T cells of the cell population.

[0157] In some embodiments, the memory phenotype is that of naive-like T cells. In some embodiments, the population-level output measure is the number of naive-like T cells, the percentage of naive-like T cells, the proportion of naive-like T cells, or the density of naive-like T cells of the cell population. In some embodiments, the population-level output measure is the number of naive-like T cells, the percentage of naive-like T cells, the proportion of naive-like T cells, or the density of naive-like T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of naive-like T cells among the T cells of the cell population.

[0158] In some embodiments, the memory phenotype is of a central memory phenotype or a stem cell memory phenotype.

[0159] In some embodiments, the memory phenotype is that of central memory T cells. In some embodiments, the population-level output measure is the number of central memory T cells, the percentage of central memory T cells, the proportion of central memory T cells, or the density of central memory T cells in the cell population. In some embodiments, the population-level output measure is the number of central memory T cells, the percentage of central memory T cells, the proportion of central memory T cells, or the density of central memory T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of central memory T cells among the T cells of the cell population.

[0160] In some embodiments, the memory phenotype is that of stem cell memory T cells. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of stem cell memory T cells in the cell population. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of stem cell memory T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of stem cell memory T cells among the T cells of the cell population.

[0161] In some embodiments, the memory phenotype is that of effector memory T cells. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of effector memory T cells in the cell population. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of effector memory T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of effector memory T cells among the T cells of the cell population.

[0162] In some embodiments, the memory phenotype is that of well-differentiated T cells. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of well-differentiated T cells in the cell population. In some embodiments, the population-level output measure is the number, percentage, proportion, or density of well-differentiated T cells among the T cells of the cell population. In some embodiments, the population-level output measure is the percentage of well-differentiated T cells among the T cells of the cell population.

[0163] In some embodiments, provided methods include determining, for a cell population, a population-level output measure of expression of a marker that is indicative of a T cell memory state. In some embodiments, "memory state" refers to the presence of T cells in a cell population that express a marker, the degree to which T cells that express a marker are present in a cell population, or the degree to which T cells that express a marker are present among T cells of a cell population.

[0164] In some embodiments, the marker is a marker expressed by naive-like T cells ("naive-like marker"). In some embodiments, "naive-like T cells" refers to T cells that express such markers. In some embodiments, the marker is CCR7. In some embodiments, the marker is CD27. In some embodiments, the marker is CD45RA.

[0165] In other embodiments, the marker is a marker expressed by non-naive-like T cells. In some embodiments, "naive-like T cells" refers to T cells that do not express such markers.

[0166] In some embodiments, the combination of markers is any combination of markers described herein.

[0167] In some embodiments, the combination of markers is a combination of markers that is indicative of a memory phenotype of the T cell, hi some embodiments, the combination of markers is a combination of markers that is expressed by T cells of a particular memory phenotype.

[0168] In some embodiments, the memory phenotype is of an earlier memory phenotype. In some embodiments, the earlier memory phenotype is a central memory phenotype or a stem cell memory phenotype. In some embodiments, the earlier memory phenotype is a central memory phenotype. In some embodiments, the earlier memory phenotype is a stem cell memory phenotype. In some embodiments, the marker expressed by an earlier memory phenotype, for example, a central memory phenotype or a stem cell memory phenotype, is CCR7.

[0169] In some embodiments, the memory phenotype is that of a naive-like T cell. In some embodiments, the naive-like T cell is CCR7+CD27+. In some embodiments, the naive-like T cell is CCR7+CD45RA+. In some embodiments, the naive-like T cell is CD27+CD45RA+.

[0170] In some embodiments, the memory phenotype is that of a central memory T cell. In some embodiments, the central memory T cell is CCR7+CD45RA-.

[0171] In some embodiments, the memory phenotype is that of an effector memory T cell. In some embodiments, the effector memory T cell is CCR7-CD45RA-.

[0172] In some embodiments, the memory phenotype is that of a terminally differentiated T cell. In some embodiments, the effector memory T cell is CCR7-CD45RA+.

[0173] In some embodiments, the population-level output measure indicates the presence of T cells expressing the marker in the cell population. In some embodiments, the population-level output measure indicates the extent to which T cells expressing the marker are present in the cell population. In some embodiments, the population-level output measure indicates the extent to which T cells expressing the marker are present among the T cells of the cell population.

[0174] In some embodiments, the population-level output measure is the number of cells, percentage of cells, proportion of cells, or density of cells in the cell population that express the marker. In some embodiments, the population-level output measure is the number of cells, percentage of cells, proportion of cells, or density of cells in the T cells of the cell population that express the marker. In some embodiments, the population-level output measure is the percentage of cells in the T cells of the cell population that express the marker.

[0175] In some embodiments, a cell phenotype, e.g., activation state, is determined based on holographic information obtained for a cell population, e.g., for individual cells of the cell population. In some embodiments, a population-level output measure is determined based on holographic information obtained from a cell population, e.g., for individual cells of the cell population. In some embodiments, a provided method includes obtaining holographic information for a cell population, e.g., for individual cells of the cell population. In some embodiments, the holographic information includes holographic information for one or more individual cells of the cell population. In some embodiments, the holographic information includes holographic information for each of a plurality of individual cells of the cell population. Exemplary holographic information and methods for obtaining it are described in Section IA.

[0176] Also, in some embodiments, a cell phenotype, e.g., activation state, is determined based on non-holographic information obtained for the cell population. In some embodiments, a population-level output measure is determined based on the non-holographic information obtained for the cell population. In some embodiments, the non-holographic information includes non-holographic information for one or more individual cells of the cell population. In some embodiments, the non-holographic information includes non-holographic information for each of a plurality of individual cells of the cell population.

[0177] In some embodiments, a cell phenotype, e.g., activation state, is determined based on one or more input measures for each of one or more cell features. In some embodiments, a population-level output measure is determined based on one or more input measures for each of one or more cell features. In some embodiments, a "cell feature" refers to a property of an individual cell. In some embodiments, one or more cell features are derived from holographic information. In some embodiments, each cell feature is derived from holographic information obtained for an individual cell.

[0178] In some embodiments, "input measure" refers to a value determined (e.g., measured or calculated) for a feature (e.g., a cellular feature). In some embodiments, provided methods include determining one or more input measures for each of one or more cellular features. In some embodiments, each input measure is determined from holographic information obtained for a cell population, e.g., for individual cells of the cell population. In some embodiments, each input measure is derived from individual cells of the cell population. In some embodiments, each input measure is determined from holographic information obtained for individual cells of the cell population. In some embodiments, each input measure is measured from holographic information. In some embodiments, each input measure is calculated using holographic information. Exemplary cellular features and methods for determining their input measures are described in Section IB.

[0179] In some embodiments, the cell phenotype, e.g., activation state, is also determined based on one or more other input measures for each of one or more other features derived from non-holographic information. In some embodiments, the population-level output measure is also determined based on one or more other input measures for each of one or more other features derived from non-holographic information. In some embodiments, each of the other features is a property of an individual cell. In some embodiments, each of the other features is derived from non-holographic information obtained for the individual cells.

[0180] In some embodiments, a cellular phenotype, e.g., activation state, is determined based on at least one population-level statistical value. In some embodiments, a population-level output measure is determined based on at least one population-level statistical value. In some embodiments, each population-level statistical value is a statistical value of one or more input measures of a cell feature of the one or more cellular features. In some embodiments, each population-level statistical value describes one or more input measures. In some embodiments, each population-level statistical value describes a distribution of one or more input measures. In some embodiments, a provided method includes determining each population-level statistical value from one or more input measures. In some embodiments, each population-level statistical value is calculated using one or more input measures. Exemplary population-level statistical values ​​are described in Section IC. Exemplary methods for determining a population-level output measure based on at least one population-level statistical value are described in Section ID.

[0181] In some embodiments, the cell phenotype, e.g., activation state, is determined based on one or more other population-level statistical values. In some embodiments, a population-level output measure is determined based on one or more other population-level statistical values. In some embodiments, the one or more other population-level statistical values ​​are statistics of one or more other input measures for each of one or more other features derived from non-holographic information.

[0182] In some embodiments, any number of the steps of the provided methods are performed in a closed system. In some embodiments, any number of the steps of the provided methods are automated.

[0183] A. Holographic Information In some embodiments, a cell phenotype, e.g., activation state, of a cell population is determined based on holographic information obtained for the cell population, e.g., for individual cells of the cell population. In some embodiments, a population-level output measure is determined based on holographic information obtained for the cell population, e.g., for individual cells of the cell population. In some embodiments, the holographic information obtained for the cell population includes holographic information for each of a plurality of individual cells of the cell population. In some embodiments, the holographic information for each of the plurality of individual cells is obtained simultaneously or near simultaneously.

[0184] In some embodiments, the holographic information is obtained using a method that does not involve recording a projection image of the cell population. In some embodiments, the holographic information comprises a obtained hologram of the cell population. In some embodiments, the holographic information is obtained using a wavefront of light of interest and a reference wavefront. In some embodiments, the holographic information comprises phase and intensity information obtained about the cell population, e.g., as described in Alm et al. (2013), "Cells and Holograms - Holograms and Digital Holographic Microscopy as a Tool to Study the Morphology of Living Cells" in Holography: Basic Principles and Contemporary Applications. In some embodiments, the phase and intensity information is obtained using a wavefront of light of interest and a reference wavefront.

[0185] In some embodiments, an image of a cell population can be reconstructed from holographic information. In some embodiments, an image of a cell population can be reconstructed from a hologram. In some embodiments, an image of a cell population can be reconstructed from phase and intensity information. In some embodiments, the reconstruction is performed using a computer and a numerical algorithm.

[0186] In some embodiments, provided methods include obtaining holographic information about a cell population, e.g., about individual cells of the cell population. In some embodiments, the holographic information is obtained by microscopy. In some embodiments, the holographic information is obtained by imaging the cell population, e.g., individual cells of the cell population, using a microscope. In some embodiments, the holographic information about individual cells of the cell population is obtained by segmenting the holographic information obtained about the cell population.

[0187] In some embodiments, holographic information is obtained by repeated imaging of cells, e.g., individual cells or samples of cells, removed from a bioreactor holding a population of cells (e.g., a population of cells in suspension).

[0188] In some embodiments, "holographic information" refers to a 2D image of a cell population or of an individual cell obtained using holographic imaging. In some embodiments, the holographic information comprises multiple 2D images of a cell population or individual cell obtained using holographic imaging. In some embodiments, the holographic information comprises a 2D phase image of a cell population or individual cell. In some embodiments, the holographic information comprises a 2D intensity image of a cell population or individual cell. In some embodiments, the holographic information comprises a 2D phase image and a 2D intensity image of a cell population or individual cell.

[0189] In some embodiments, the holographic information itself can be used to train a machine learning model, e.g., a convolutional neural network, or the holographic information can be provided as input to a machine learning model, e.g., a convolutional neural network, according to any of the methods provided. In other embodiments, cellular features derived from holographic information, such as any of those described in Section IB, can be used to train a machine learning model, or can be provided as input to a machine learning model according to any of the methods provided.

[0190] Imaging techniques may use a digital device to capture, e.g., record, the output (e.g., results) of the imaging process. In some embodiments, the digital device is a charge-coupled device (e.g., a CCD camera). In some embodiments, the digital device is a complementary metal-oxide semiconductor device (e.g., a CMOS camera). Thus, in some embodiments, image data is obtained using a digital device. In some embodiments, the digital device interfaces with a computer to store and / or analyze the results (e.g., output) of the imaging process.

[0191] To obtain holographic information associated with T cells in a non-damaging, non-destructive manner, T cells can be contained in a liquid, e.g., culture medium. In some embodiments, T cells can be suspended in a liquid, e.g., culture medium, for imaging. Thus, in some embodiments, the imaging technique is capable of imaging T cells contained and / or suspended in a liquid.

[0192] In some embodiments, the holographic information is obtained by interference microscopy, optical coherence tomography, diffraction phase microscopy, or digital holographic microscopy (DHM). In some embodiments, the holographic information is obtained by imaging a cell population using interference microscopy, optical coherence tomography, diffraction phase microscopy, or DHM.

[0193] In some embodiments, the holographic information is obtained by DHM (see, e.g., Carl et al., Applied Optics (2004) 43(36):6536-6544 and Marquet et al., Optics Letters (2005) 30(5):468-470). In some embodiments, the holographic information is obtained by imaging a cell population, e.g., individual cells of a cell population, using DHM. In some embodiments, the DHM is traditional DHM, in-line DHM, or differential DHM (DDHM).

[0194] DHM is a technique that can record 3D samples or objects without the need to scan the sample layer by layer. In this respect, DHM may be a superior technique to confocal microscopy. In DHM, holographic information can be recorded by a digital camera, such as a CCD or CMOS camera, which can then be stored or processed by a computer. Various DHM techniques, including DDHM, and microscope configurations and components are described in US Pat. No. 7,362,449, US Pat. No. 9,684,281-B2, US Pat. No. 1,057,8541-B2, US Pat. No. 2,015,248,109-A1, US Pat. No. 9,846,151-B2, US Pat. No. 2,014,193,850-A1, US Pat. No. 2,016,184,817-A1, US Pat. No. 1,067,379-B2, US Pat. No. 2,014,195,568-A1, and US Pat. No. 2,021,142,472-A1.

[0195] To create a holographic representation or hologram, a highly coherent light source, such as a laser, is traditionally used to illuminate a sample. The light from the source may be split into two beams: an object beam and a reference beam. The object beam is sent to the sample through an optical system and interacts with it, thereby changing the phase and amplitude of the light depending on the optical properties and 3D shape of the object. The object beam reflected from or transmitted through the sample is then interfered with the reference beam (e.g., by a set of mirrors and / or beam splitters), resulting in an interference pattern that can be digitally recorded. Because holograms can be more accurate when the object and reference beams have similar amplitudes, an absorbing element can be introduced into the reference beam that can reduce its amplitude to the level of the object beam without changing the phase of the reference beam, or at most changing its phase entirely. The recorded interference pattern can contain information about the phase and amplitude changes that vary depending on the optical properties and 3D shape of the object.

[0196] An alternative method for creating holograms is by using in-line holographic techniques. In some embodiments, in-line DHM is similar to traditional DHM, but does not split the beam, at least not by a beam splitter or other external optical element. For example, in-line DHM can be used to examine particles in a fluid, such as a solution of cells, so that a portion of at least partially coherent light passes through the sample without interacting with the particles (reference beam) and interferes with the light that has interacted with the particles (object beam), producing an interference pattern that can be digitally recorded and processed. In-line DHM can be used in transmission mode.

[0197] Another DHM technique is DDHM. In some embodiments, DDHM differs from other techniques in its use of reference and object beams. In a preferred DDHM setup, a sample can be illuminated by an illumination means containing at least partially coherent light in a reference or transmission mode. The reflected or transmitted sample beam can be sent through an objective lens and then split into two by a beam splitter and sent along different paths in, for example, a Michelson or Mach-Zehnder differential interferometer. A beam-bending element or tilting means, such as a transparent wedge, can be inserted in one of the paths. The two beams can then be made to interfere with each other in the focal plane of a focusing lens, and the interference pattern in this focal plane can be digitally recorded and stored, for example, by a CCD camera connected to a computer. Due to the beam-bending element, the two beams can be shifted slightly in a controlled manner, and the interference pattern can change depending on the amount of shift. The beam-bending element can then be rotated, thereby changing the amount of shift. A new interference pattern can also be recorded. This can be done several times (N), and from these N interference patterns, the gradient (or spatial derivative) of the phase at the focal plane of the focusing lens can be estimated by a computer. This is called the phase stepping method, but other methods for obtaining the phase gradient are known, such as Fourier transform data processing techniques. The gradient of the phase can be integrated to obtain the phase as a function of position. The amplitude of the light as a function of position can sometimes, but not necessarily, be calculated by a computer from a weighted average of the amplitudes of the N recorded interference patterns. Because the phase and amplitude are thus known, the same information can be obtained as in direct holographic methods (using a reference beam and an object beam), and subsequent 3D reconstruction of the object can be performed.

[0198] In DDHM, the illumination means may include spatially and temporally partially coherent light. In some embodiments, this is in contrast to other DHM methods, which may use highly correlated laser light. Spatially and temporally partially coherent light can be generated, for example, by an LED. LEDs are less expensive than lasers and can generate light with a spectrum centered around a known wavelength, which may be spatially and temporally partially coherent, e.g., less coherent than laser light, but still sufficiently coherent to generate a holographic image of sufficient quality for the application at hand. Also, in some embodiments, LEDs may have the advantage of being available in many different wavelengths, being extremely small, and being easy to use or replace as needed. Thus, in some embodiments, methods using spatially and temporally partially coherent light to obtain holographic images may lead to more cost-effective devices for implementing such methods. In some embodiments, the illumination means is a red LED.

[0199] The holographic image may undergo object segmentation and further analysis to obtain multiple cellular features that quantitatively describe the imaged object (e.g., T cells, cellular debris). As such, various features, e.g., cellular features, described in Section IB can be assessed or calculated directly from the DDHM using, for example, the steps of image acquisition, image processing, image segmentation, and feature extraction. In some embodiments, a digital recording device is used to record the holographic image. In some embodiments, a computer containing algorithms for analyzing the holographic image can be used. In some embodiments, a monitor and / or computer can be used to display the results of the holographic image analysis. In some embodiments, the analysis is automated (e.g., can be performed without user input).

[0200] Any type of DHM can be used according to the provided methods. In some embodiments, the DHM is a traditional DHM. In some embodiments, the DHM is an in-line DHM. In some embodiments, the DHM is a differential DHM.

[0201] Exemplary DHM systems for use in the provided methods, such as the Ovizio iLine F (Ovizio Imaging Systems NV / SA, Brussels, Belgium), include those that can be used with bioreactors for incubating T cells. In some embodiments, the cell population from which holographic information is obtained is derived from a cell culture that has been cultured in vitro or ex vivo, e.g., as described in Section II-D. In some embodiments, the in vitro or ex vivo culture is in a bioreactor. In some embodiments, the cell population is removed from the bioreactor for imaging. In some embodiments, imaging is performed using a digital holographic microscope connected to the bioreactor. The microscope can be any capable of obtaining phase information of a fluid sample and having illumination means. The microscope can be any used for DHM, including any for traditional DHM, in-line DHM, or DDHM.

[0202] In some embodiments, one or more fluidic systems capable of guiding fluid from the bioreactor to the microscope are connected to the bioreactor and the microscope. In some embodiments, at least one fluidic system contains one or more tubes that can come into direct contact with the fluid from the bioreactor. In some embodiments, at least one tube contains a portion that is at least partially transparent to the microscope's illumination means for obtaining holographic information of the fluid sample. In some embodiments, the tube contains a portion that is at least partially transparent to the microscope's illumination means and contains a flow cell and / or microfluidic system. In some embodiments, the flow cell and / or microfluidic system contains a cross-section whose height and / or width varies along the cross-section. In some aspects, this allows for obtaining clear holographic images of objects of various concentrations suspended in the fluid. A high concentration of suspended objects can lead to multiple objects being stacked on top of each other, which can lead to difficulties in obtaining holographic images, especially when the microscope is operating in transmission mode. A low concentration can cause the microscope to obtain a holographic image of only the fluid medium and not of the objects suspended in the medium. When the concentration is high, the holographic image can be obtained at a position with a small height or width, thereby ensuring that not too many objects are stacked in the illumination beam. When the concentration is low, the holographic image can be obtained at a position with a large height or width, thereby ensuring that at least one suspended object is in the illumination beam. In some embodiments, the microfluidic system contains a tube branching into multiple tubes of different cross-sections, diameters, heights, and / or widths. Such an arrangement can make it possible to obtain clear holographic images of objects of various concentrations suspended in the fluid. In some embodiments, the cross-section, diameter, height, and / or width of the flow cell and / or microfluidic system are selected as a function of the size of the suspended objects and / or the size of the microscope's illumination beam.In some embodiments, the narrowest cross-sectional dimension of the flow cell and / or microfluidic system is greater than 10 micrometers, more preferably greater than 30 micrometers, even more preferably greater than 50 micrometers, and / or the largest cross-sectional dimension of the flow cell and / or microfluidic system is less than 5000 micrometers, more preferably less than 3000 micrometers, even more preferably less than 2500 micrometers. In some embodiments, the microfluidic system is attached to a substrate, e.g., to facilitate fabrication and / or to provide stability to the microfluidic system.

[0203] Although not necessarily required, it may be desirable for fluid flow to exist in at least one of the fluidic systems. This may allow for sampling the contents of the bioreactor over time and monitoring different samples to gain a better understanding of the state and / or reaction of the bioreactor. Fluid flow may exist due to natural phenomena such as convection, conduction, or radiation; for example, due to density or pressure differences induced by reactions occurring in the bioreactor or thermal gradients; gravity; etc. When fluid flow is desired but does not occur spontaneously, or when the flow needs to be controlled, one or more pumping systems may be connected to the fluidic systems to induce flow in the systems. Thus, in some embodiments, at least one pumping system is connected to one or more fluidic systems and is capable of inducing fluid flow in the fluidic systems.

[0204] In some embodiments, at least one fluid system contains a fluid-tight flexible portion that, when compressed, pulled, and / or pushed, causes fluid flow in the fluid system. As such, fluid flow can be induced in the fluid system without a high risk of leakage and without contamination of the flow actuator. In some embodiments, a pumping system is connected to the fluid system and is capable of pulling and / or pushing the fluid-tight flexible portion to induce fluid flow in the fluid system.

[0205] In some aspects, the contents of the bioreactor can be non-destructively monitored, allowing the population of cells observed under the microscope to be reintroduced into the bioreactor. Thus, in some embodiments, at least one fluidic system forms a closed circuit between the bioreactor and the microscope and back to the bioreactor, e.g., the fluidic system can guide fluid from the bioreactor to the microscope and back.

[0206] B. Cell characteristics In some embodiments, the cellular phenotype, e.g., activation state, of a cell population is determined based on one or more input measures for each of one or more cellular features. In some embodiments, a population-level output measure is determined based on one or more input measures for each of one or more cellular features. In some embodiments, one or more cellular features are derived from holographic information. In some embodiments, each input measure is determined from holographic information obtained for individual cells of the cell population. In some embodiments, determining one or more input measures for each of one or more cellular features comprises one or more steps selected from holographic information acquisition, holographic information processing, holographic information segmentation, and cellular feature extraction. In some embodiments, image segmentation is used to determine holographic information associated with individual cells. Segmentation processes are known in the art, for example, from watershed procedures, image thresholds based on clustering, and using neural networks. For watershed procedures, various algorithms exist, including Meyer's flooding algorithm and optimal spanning forest algorithm (watershed cut). Cluster-based image thresholding may use the Otsu method. In some embodiments, the holographic information comprises a segmented image.

[0207] In some embodiments, the one or more input measures are multiple input measures. In some embodiments, the activation state is determined based on the multiple input measures of each of the one or more cellular features. In some embodiments, a population-level output measure is determined based on the multiple input measures of each of the one or more cellular features.

[0208] In some embodiments, the one or more cellular features are a plurality of cellular features. In some embodiments, the activation state is determined based on one or more input measures for each of the plurality of cellular features. In some embodiments, the population-level output measure is determined based on one or more input measures for each of the plurality of cellular features.

[0209] In some embodiments, the activation state is determined based on a plurality of input measures of each of the plurality of cellular features. In some embodiments, a population-level output measure is determined based on a plurality of input measures of each of the plurality of cellular features.

[0210] In some embodiments, some or all of the input measures of the one or more cellular features are obtained for individual cells, hi some embodiments, the input measure of each of the one or more cellular features is obtained for individual cells.

[0211] In some embodiments, the one or more input measures of a cell feature of the one or more cell features include input measures from one or more individual cells of the cell population. In some embodiments, the one or more input measures of a cell feature of the one or more cell features include input measures from a plurality of individual cells of the cell population. In some embodiments, the input measure of a first cell feature is for a first plurality of individual cells, and the input measure of a second cell feature is for a second, distinct plurality of individual cells that may or may not contain some of the first plurality of individual cells. In some embodiments, the input measure of the first cell feature is for the first plurality of individual cells, and the input measure of the second cell feature is for the first plurality of individual cells. In some embodiments, all input measures of a cell feature are derived from the same plurality of individual cells.

[0212] In some embodiments, the one or more input measures of one of the one or more cellular features include input measures derived from some or all individual cells of the cell population. In some embodiments, the one or more input measures of one of the one or more cellular features include input measures derived from only viable cells. In some embodiments, viable cells are classified as viable using an automated method. In some embodiments, viable cells are classified as viable by analysis of holographic images of cells of the cell population, for example, as exemplified herein (e.g., using OsOne software for viable cell classification). In some embodiments, the one or more input measures of one of the one or more cellular features include input measures derived only from cells of at least a certain size. In some embodiments, size is determined by a radius mean cellular feature. In some embodiments, size is determined by a cell area cellular feature. In some embodiments, the one or more input measures of one of the one or more cellular features include input measures derived only from cells of sufficient circularity, for example, as determined using a circularity cellular feature. In some embodiments, the one or more input measures for a cell feature of the one or more cell features include input measures derived only from cells having an input measure for the intensity smoothness cell feature below a certain threshold. In some embodiments, the one or more input measures for a cell feature of the one or more cellular features include input measures derived only from cells that meet all of the above criteria.

[0213] In some embodiments, the one or more input measures of a cell feature of the one or more cell features include one or more input measures derived only from cells that meet one or more (e.g., one, two, three, four, or all) of the following criteria: (i) are classified as viable, (ii) have a radius mean cell feature measure >5, (iii) have an intensity smoothness cell feature <0.03, (iv) have a cell area cell feature measure >60, and (v) have a circularity cell feature measure >0.5. In some embodiments, the cell meets all of the foregoing criteria.

[0214] In some embodiments, the one or more input measures of a cell feature of the one or more cellular features comprise input measures derived from each individual cell of the cell population.

[0215] In some embodiments, one of the one or more cellular features is derived from phase information of the holographic information. In some embodiments, one of the one or more cellular features is derived from intensity information of the holographic information. In some embodiments, one of the one or more cellular features is derived from phase information and intensity information. In some embodiments, one of the one or more cellular features is derived from images of individual cells reconstructed from the holographic information. Exemplary methods for determining cellular features from holographic information are described in US-9684281, US-20140193850, US-20140195568-A1, US-10578541-B2, US-9904248-B2, US-11067379-B2, and US-2021142472-A1. Exemplary software for determining cell features from holographic information includes Ovizio OsOne (Ovizio Imaging Systems NV / SA, Brussels, Belgium).

[0216] In some embodiments, the one or more cellular features include one or more morphological features, one or more optical features, one or more intensity texture features, one or more phase texture features, or any combination thereof. In some embodiments, the one or more cellular features include one or more morphological features, one or more intensity texture features, and one or more phase texture features. Exemplary cellular features and their descriptions are listed in Table E1.

[0217] In some embodiments, the one or more cellular features include one or more morphological features. In some embodiments, the one or more morphological features describe one or more properties of the physical shape of an individual cell. In some embodiments, the one or more morphological features are selected from aspect ratio, cell area, circularity, compactness, elongation, elongation, diameter, Hu moment 1, Hu moment 2, Hu moment 3, Hu moment 4, Hu moment 5, Hu moment 6, Hu moment 7, perimeter, radius mean, radius variance, and normalized radius variance. In some embodiments, the one or more morphological features include aspect ratio. In some embodiments, the one or more morphological features include cell area. In some embodiments, the one or more morphological features include circularity. In some embodiments, the one or more morphological features include compactness. In some embodiments, the one or more morphological features include elongation. In some embodiments, the one or more morphological features include diameter. In some embodiments, the one or more morphological features include Hu moment 1. In some embodiments, the one or more morphological features include Hu moment 2. In some embodiments, the one or more morphological features include Hu moment 3. In some embodiments, the one or more morphological features include Hu moment 4. In some embodiments, the one or more morphological features include Hu moment 5. In some embodiments, the one or more morphological features include Hu moment 6. In some embodiments, the one or more morphological features include Hu moment 7. In some embodiments, the one or more morphological features include perimeter. In some embodiments, the one or more morphological features include radius mean. In some embodiments, the one or more morphological features include radius variance. In some embodiments, the one or more morphological features include normalized radius variance.

[0218] In some embodiments, the one or more morphological characteristics include cell area and mean radius.

[0219] In some embodiments, the one or more cellular features include one or more optical features. In some embodiments, the one or more optical features describe one or more optical properties of an image of an individual cell. In some embodiments, the one or more optical features are selected from a refraction peak diameter, an intensity maximum, an average intensity, an intensity minimum, a mass eccentricity, a maximum optical height (radians), a maximum optical height (μm), an average optical height (radians), an average optical height (μm), a normalized optical height, a minimum optical height (radians), a minimum optical height (μm), an optical volume, a refraction peak surface, a normalized peak area, an aggregate size, a refraction peak intensity, and a normalized peak height. In some embodiments, the one or more optical features include a refraction peak diameter. In some embodiments, the one or more optical features include an intensity maximum. In some embodiments, the one or more optical features include an average intensity. In some embodiments, the one or more optical features include an intensity minimum. In some embodiments, the one or more optical features include a mass eccentricity. In some embodiments, the one or more optical features include a maximum optical height (radian). In some embodiments, the one or more optical features include a maximum optical height (μm). In some embodiments, the one or more optical features include an average optical height (radian). In some embodiments, the one or more optical features include an average optical height (μm). In some embodiments, the one or more optical features include a normalized optical height. In some embodiments, the one or more optical features include a minimum optical height (radian). In some embodiments, the one or more optical features include a minimum optical height (μm). In some embodiments, the one or more optical features include an optical volume. In some embodiments, the one or more optical features include a refractive peak surface. In some embodiments, the one or more optical features include a normalized peak area. In some embodiments, the one or more optical features include an aggregate size. In some embodiments, the one or more optical features include a refractive peak intensity. In some embodiments, the one or more optical features include a normalized peak height.

[0220] In some embodiments, the one or more cellular features include one or more intensity texture features. In some embodiments, the one or more intensity texture features describe one or more characteristics of the intensity information of individual cells. In some embodiments, the one or more intensity texture features are selected from intensity variance, intensity mean contrast, intensity mean entropy, intensity mean, intensity mean uniformity, intensity contrast, intensity correlation, intensity entropy, intensity uniformity, intensity uniformity, intensity skewness, and intensity smoothness. In some embodiments, the one or more intensity texture features include intensity variance. In some embodiments, the one or more intensity texture features include intensity mean contrast. In some embodiments, the one or more intensity texture features include intensity mean entropy. In some embodiments, the one or more intensity texture features include intensity mean uniformity. In some embodiments, the one or more intensity texture features include intensity contrast. In some embodiments, the one or more intensity texture features include intensity correlation. In some embodiments, the one or more intensity texture features include intensity entropy. In some embodiments, the one or more intensity texture features include intensity uniformity. In some embodiments, the one or more intensity texture features include intensity uniformity. In some embodiments, the one or more intensity texture features include intensity skewness. In some embodiments, the one or more intensity texture features include intensity smoothness.

[0221] In some embodiments, the one or more intensity texture features include maximum intensity, minimum intensity, intensity entropy, and intensity contrast.

[0222] In some embodiments, the one or more cellular features comprise one or more phase texture features. In some embodiments, the one or more phase texture features describe one or more properties of the phase information of individual cells. In some embodiments, the one or more phase texture features are selected from optical height variance (radian), optical height variance (μm), phase average contrast, phase average entropy, phase average, phase average uniformity, phase contrast, phase correlation, phase entropy, phase uniformity, phase skewness, phase smoothness, and phase uniformity. In some embodiments, the one or more phase texture features comprise optical height variance (radian). In some embodiments, the one or more phase texture features comprise optical height variance (μm). In some embodiments, the one or more phase texture features comprise phase average contrast. In some embodiments, the one or more phase texture features comprise phase average entropy. In some embodiments, the one or more phase texture features comprise phase average. In some embodiments, the one or more phase texture features comprise phase average uniformity. In some embodiments, the one or more phase texture features comprise phase contrast. In some embodiments, the one or more phase texture features comprise phase correlation. In some embodiments, the one or more phase texture features include phase entropy. In some embodiments, the one or more phase texture features include phase uniformity. In some embodiments, the one or more phase texture features include phase skewness. In some embodiments, the one or more phase texture features include phase smoothness. In some embodiments, the one or more phase texture features include phase uniformity.

[0223] In some embodiments, the cell phenotype is that of an activated T cell.

[0224] In some embodiments, the plurality of cellular features comprises one or more (e.g., any combination of two, three, four, five, six, seven, eight, nine, or all) of intensity skewness, intensity correlation, intensity uniformity, intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean. In some embodiments, the plurality of cellular features comprises intensity skewness, intensity correlation, intensity uniformity, intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

[0225] In some embodiments, the plurality of cellular features are selected from (including one or more of) maximum intensity, minimum intensity, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean. In some embodiments, the plurality of cellular features comprises maximum intensity, minimum intensity, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

[0226] In some embodiments, the one or more cellular features include one or more (e.g., any combination of two, three, four, five, six, or all of) intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

[0227] In some embodiments, the plurality of cellular features comprises one or more (e.g., any combination of two or all) of intensity skewness, intensity correlation, and intensity uniformity. In some embodiments, the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity.

[0228] In some embodiments, the cell phenotype is a memory phenotype. In some embodiments, the memory phenotype is an early memory phenotype, for example, a stem cell memory phenotype or a central memory phenotype. In some embodiments, the cell phenotype is a central memory phenotype. In some embodiments, the cell phenotype is a stem cell memory phenotype. In some embodiments, the marker is CCR7. In some embodiments, the cell phenotype is a memory phenotype, for example, a stem cell memory phenotype or a central memory phenotype, and the marker is CCR7.

[0229] Thus, in some embodiments, the disclosed method is for determining the collective memory phenotype of T cells, and the marker is expressed by T cells with a central memory phenotype or a stem cell memory phenotype. In some embodiments, the marker is expressed by T cells with a central memory phenotype. In some embodiments, the marker is expressed by T cells with a stem cell memory phenotype. In some embodiments, the marker is CCR7.

[0230] In some embodiments, the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter. In some embodiments, the plurality of cellular features comprises cell area. In some embodiments, the plurality of cellular features comprises perimeter. In some embodiments, the plurality of cellular features comprises mean intensity. In some embodiments, the plurality of cellular features comprises normalized peak area. In some embodiments, the plurality of cellular features comprises equivalent peak diameter. In some embodiments, the plurality of cellular features comprises cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

[0231] In some embodiments, the cell phenotype is recombinant receptor expression, e.g., CAR or TCR expression.

[0232] In some embodiments, the plurality of cellular features comprises one or more (e.g., any combination of two, three, four, or all) of peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height. In some embodiments, the plurality of cellular features comprises peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height. In some embodiments, the plurality of cellular features comprises one or more of an increase in a feature reflecting cell size (area, perimeter, diameter), a decrease in mean intensity feature, an increase in phase correlation feature, and a decrease in normalized radial variance feature. In some embodiments, the plurality of cellular features comprises one or more of an increase in cell area, an increase in cell perimeter, an increase in cell diameter, a decrease in intensity mean, an increase in phase correlation, and a decrease in normalized radial variance.

[0233] In some embodiments, the plurality of cellular features are cellular features extracted by a machine learning model. In some embodiments, the extraction is automatic, e.g., without prior design or engineering of the cellular features. In some embodiments, the one or more input measures of at least one of the plurality of cellular features are determined by providing holographic information about individual cells of a cell population to the machine learning model. In some embodiments, the one or more input measures for each of the plurality of cellular features are determined by providing holographic information about individual cells of a cell population to the machine learning model. In some embodiments, the one or more input measures are determined from the machine learning model.

[0234] In some embodiments, the machine learning model is a deep learning model. In some embodiments, the machine learning model is a convolutional neural network. Advantages of convolutional neural networks include their ability to automatically detect and extract features important for classification or regression without human supervision, as well as their ability to detect features that may not be detectable by humans.

[0235] In some embodiments, the one or more input measures for at least one of the plurality of cellular features are obtained from a fully connected layer of a convolutional neural network. In some embodiments, the one or more input measures for each of the plurality of cellular features are obtained from a fully connected layer of a convolutional neural network.

[0236] The construction of a convolutional neural network suitable for feature extraction can be identified and designed by one skilled in the art. In some embodiments, the convolutional neural network is LeNet, AlexNet, ResNet, GoogleNet / InceptionNet, MobileNetV1, ZfNet, Depth-based, Highway Network, Wide ResNet, VGG, PolyNet, Inception v2, Inception v3, Inception v4, Inception-ResNet, DenseNet, Pyramidal Net, Xception, channel boosting, residual attention neural network, attention-based, feature map exploitation-based, squeeze-and-excitation or competitive squeeze-and-excitation convolutional neural network.

[0237] In some embodiments, the machine learning model, e.g., a convolutional neural network, is trained using a dataset of reference holographic information. In some embodiments, for each of the reference cell populations containing the first plurality of T cells, the dataset of reference holographic information contains holographic information obtained for the reference cell population, e.g., for individual cells of the reference cell population. Exemplary reference cell populations are described in Section ID-1.

[0238] In some embodiments, the machine learning model, e.g., a convolutional neural network, is trained using non-holographic images. In some embodiments, the machine learning model, e.g., a convolutional neural network, is trained using holographic and non-holographic images.

[0239] C. Population-level statistics In some embodiments, a cell phenotype, e.g., an activation state, is determined based on at least one population-level statistical value. In some embodiments, a population-level output measure is determined based on at least one population-level statistical value. In some embodiments, each population-level statistical value is a statistical value of one or more input measures of a cell feature of the one or more cell features. In some embodiments, each population-level statistical value describes one or more input measures of a cell feature of the one or more cell features. In some embodiments, each population-level statistical value describes the distribution of one or more input measures of a cell feature of the one or more cell features.

[0240] In some embodiments, the at least one population level statistical value comprises one or more population level statistical values ​​for each of the one or more cellular features. In some embodiments, the at least one population level statistical value is a plurality of population level statistical values. In some embodiments, the plurality of population level statistical values ​​comprises one or more population level statistical values ​​for each of the one or more cellular features. In some embodiments, the plurality of population level statistical values ​​comprises a plurality of population level statistical values ​​for each of the one or more cellular features.

[0241] In some embodiments, one or more population-level statistical values ​​of a first cellular feature are different statistical values ​​from one or more population-level statistical values ​​of a second cellular feature. For example, in some embodiments, one or more population-level statistical values ​​of a first cellular feature include an average of one or more input features of the first cellular feature, and one or more population-level statistical values ​​of a second cellular feature do not include an average of one or more input features of the second cellular feature. In some embodiments, one or more population-level statistical values ​​of a first cellular feature are the same statistical value as the population-level statistical value of a second cellular feature. In some embodiments, the one or more population-level statistical values ​​are the same statistical value for all cellular features.

[0242] Exemplary population-level statistical values ​​are described in this section and are known in the art. The one or more population-level statistical values ​​for each of the one or more cellular features can be independently selected from any such population-level statistical values, for example, from any of the exemplary population-level statistical values ​​described. In some embodiments, the one or more population-level statistical values ​​for each of the one or more cellular features are the same and are selected from any of the exemplary population-level statistical values ​​described.

[0243] In some embodiments, the one or more population-level statistical values ​​of a cell feature of the one or more cellular features comprises a population-level statistical value that summarizes one or more input measures of the cell feature. In some embodiments, the population-level statistical value is a summary statistical value.

[0244] In some embodiments, the one or more population-level statistical values ​​of a cellular feature of the one or more cellular features comprises a measure of central tendency of one or more input measures of the cellular feature, hi some embodiments, the one or more population-level statistical values ​​comprise one or more of the mean, median, and mode of the one or more input measures of the cellular feature.

[0245] In some embodiments, the one or more population-level statistical values ​​of a cellular feature of the one or more cellular features comprise a measure of statistical dispersion of one or more input measures of the cellular feature, hi some embodiments, the one or more population-level statistical values ​​comprise one or more of the following: standard deviation, interquartile range, range, mean absolute difference, median absolute deviation, mean absolute deviation, distance standard deviation of the one or more input measures of the cellular feature.

[0246] In some embodiments, the one or more population-level statistics of a cell feature of the one or more cellular features comprise a measure of the shape of the distribution of the one or more input measures of the cell feature, hi some embodiments, the one or more population-level statistics comprise one or both of skewness and kurtosis of the one or more input measures of the cell feature.

[0247] In some embodiments, the one or more population-level statistical values ​​of a cellular feature of the one or more cellular features comprise one or more quantiles of one or more input measures of the cellular feature. In some embodiments, the one or more quantiles are selected from (including one or more of) the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more input measures of the cellular feature. In some embodiments, the one or more quantiles comprise the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more input measures of the cellular feature. In some embodiments, the one or more population-level statistical values ​​of each cellular feature of the one or more cellular features comprise the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more input measures of the cellular feature.

[0248] In some embodiments, the one or more population-level statistical values ​​for a cellular feature of the one or more cellular features are determined by applying a pooling filter to one or more input measures of the cellular feature, hi some embodiments, the one or more population-level statistical values ​​for each cellular feature of the one or more cellular features are determined by applying a pooling filter to one or more input measures of the cellular feature.

[0249] In some embodiments, the pooling filter summarizes one or more input measures of the cellular features. In some embodiments, the pooling filter is a point estimate based pooling filter. In some embodiments, the pooling filter is an average pooling filter. In some embodiments, the pooling filter is a max pooling filter.

[0250] In some embodiments, the pooling filter is a distribution-based pooling filter. In some embodiments, the distribution-based pooling filter is based on estimated marginal distributions of one or more input measures of cell features, as described in Oner et al., arXiv:2006.01561. In some embodiments, the estimated marginal distributions are calculated using kernel density estimation, such as with a Gaussian kernel.

[0251] D. Determining group-level output measures In some embodiments, the cell phenotype, e.g., activation state, of the cell population is determined based on a population-level output measure determined for the cell population. In some embodiments, the population-level output measure is determined based on one or more input measures for each of one or more cellular characteristics. In some embodiments, the population-level output measure is determined based on at least one population-level statistical value determined for the cell population.

[0252] In some embodiments, each population-level statistical value is compared to a corresponding threshold. For some population-level statistical thresholds, a population-level statistical value above the threshold may indicate the presence of marker-expressing cells. For other population-level statistical thresholds, a population-level statistical value below the threshold may indicate the presence of marker-expressing cells in the cell population. In some embodiments, the threshold value for one of the one or more cellular features is a value represented by at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of a reference cell population containing a plurality of T cells. In some embodiments, each of the plurality of reference cell populations contains marker-expressing cells. In some embodiments, each of the plurality of reference cell populations contains marker-expressing T cells. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of each of the plurality of reference cell populations are marker-expressing cells. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of each of the plurality of reference cell populations are marker-expressing T cells. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the T cells of each of the plurality of reference cell populations are marker-expressing T cells. Exemplary reference cell populations are described in Section ID-1.

[0253] In some embodiments, the population-level output measure indicates whether at least one population-level statistical value indicates the presence of marker-expressing cells in the cell population. In some embodiments, the population-level output measure indicates whether at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the population-level statistical values ​​indicate the presence of marker-expressing cells in the cell population. In some embodiments, the population-level output measure indicates whether a majority of the population-level statistical values ​​indicate the presence of marker-expressing cells in the cell population. In some embodiments, the population-level output measure indicates whether each population-level statistical value indicates the presence of marker-expressing cells in the cell population.

[0254] In some embodiments, each population-level statistical value is compared to a corresponding threshold. For some population-level statistical thresholds, a population-level statistical value above the threshold may indicate the presence of activated T cells. For other population-level statistical thresholds, a population-level statistical value below the threshold may indicate the presence of activated T cells in the cell population. In some embodiments, the threshold value for one of the one or more cellular features is a value represented by at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of a reference cell population containing a plurality of T cells. In some embodiments, each of the plurality of reference cell populations contains activated T cells. In some embodiments, each of the plurality of reference cell populations contains T cells expressing a marker indicative of T cell activation. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of each of the plurality of reference cell populations are activated T cells. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of each of the plurality of reference cell populations are T cells that express the marker. In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the T cells of each of the plurality of reference cell populations are T cells that express the marker. Exemplary reference cell populations are described in Section ID-1.

[0255] In some embodiments, the population-level output measure indicates whether at least one population-level statistical value indicates the presence of activated T cells in the cell population. In some embodiments, the population-level output measure indicates whether at least 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the population-level statistical values ​​indicate the presence of activated T cells in the cell population. In some embodiments, the population-level output measure indicates whether a majority of the population-level statistical values ​​indicate the presence of activated T cells in the cell population. In some embodiments, the population-level output measure indicates whether each population-level statistical value indicates the presence of activated T cells in the cell population.

[0256] In some embodiments, the population-level output measure is determined using a trained machine learning model. Exemplary machine learning models and methods for training them are described in Hastie et al., The Elements of Statistical Learning (2016); and Abu-Mostafa et al., Learning from Data (2012). Exemplary machine learning models are also described in Hastie et al., The Elements of Statistical Learning (2016); and Abu-Mostafa et al., Learning from Data (2012).

[0257] In some embodiments, the population-level output measure is an output of, or is derived from, a trained machine learning model. In some embodiments, one or more input measures for each of one or more cellular features are provided as inputs to the trained machine learning model or to a process including the trained machine learning model. In some embodiments, at least one population-level statistical value for each of one or more cellular features is provided as inputs to the trained machine learning model or to a process including the trained machine learning model. In some embodiments, as part of being provided as inputs to the trained machine learning model, the input measures or population-level statistical values ​​undergo one or more preprocessing steps. In some embodiments, the preprocessing step includes a normalization step. In some embodiments, the preprocessing step includes a dimensionality reduction step.

[0258] In some embodiments, the machine learning model is an unsupervised machine learning model. In some embodiments, the machine learning model is a semi-supervised machine learning model. In some embodiments, the machine learning model is a supervised machine learning model.

[0259] In some embodiments, the machine learning model is trained to predict the presence or absence of marker-expressing cells in a cell population containing T cells, the extent to which marker-expressing cells are present in a cell population containing T cells, or the extent to which marker-expressing cells are present in T cells of a cell population containing T cells. In some embodiments, the machine learning model is trained to predict the presence or absence of marker-expressing T cells in a cell population containing T cells, the extent to which marker-expressing T cells are present in a cell population containing T cells, or the extent to which marker-expressing T cells are present in T cells of a cell population containing T cells.

[0260] In some embodiments, the machine learning model is trained to predict the presence or absence of activated T cells in a cell population containing T cells, the extent to which activated T cells are present in a cell population containing T cells, or the extent to which activated T cells are present among T cells of a cell population containing T cells. In some embodiments, the machine learning model is trained to predict the presence or absence of T cells expressing a marker indicative of T cell activation in a cell population containing T cells, the extent to which T cells expressing a marker indicative of T cell activation are present among T cells of a cell population containing T cells.

[0261] In some embodiments, the machine learning model is trained to make predictions based on input measures of one or more cell features.In some embodiments, the machine learning model is trained to make predictions based on population-level statistics of one or more cell features.In some embodiments, the machine learning model is trained to make predictions based on features including one or more cell features and other features, for example, features of cell populations that are not based on the properties of individual cells.

[0262] In some embodiments, the population-level output measure is determined by providing a plurality of population-level statistics as inputs to a machine learning model, hi some embodiments, the machine learning model is trained to predict the population-level output measure of expression of the marker based on the population-level statistics of the plurality of cellular features.

[0263] In some embodiments, the machine learning model is a classification model. In some embodiments, the machine learning model is a regression model. For any of the example machine learning models described herein, both classification and regression versions of the machine learning model are disclosed.

[0264] In some embodiments, the machine learning model is a linear model. In some embodiments, the machine learning model is a non-linear model. In some embodiments, the machine learning model is a Bayesian model.

[0265] In some embodiments, the machine learning model is a regularization model. In some embodiments, the machine learning model is a lasso regularization model. In some embodiments, the machine learning model is a ridge regularization model. In some embodiments, the machine learning model is an elastic-net regularization model.

[0266] In some embodiments, the machine learning model is an artificial neural network. In some embodiments, the machine learning model is a support vector machine. In some embodiments, the machine learning model is an ensemble model. In some embodiments, the machine learning model comprises a decision tree. In some embodiments, the machine learning model comprises a boosted decision tree. In some embodiments, the machine learning model is a random forest model. In some embodiments, the machine learning model is an ensemble model including any combination of the machine learning models described herein. In some embodiments, a first machine learning model of the ensemble model is trained to predict a population-level output measure of a first marker, e.g., any of those described herein, and a second machine learning model of the ensemble model is trained to predict a population-level output measure of a second marker different from the first marker, e.g., any of those described herein. In some embodiments, a first machine learning model of the ensemble model is trained to predict a population-level output measure of a marker, e.g., any of those described herein, and a second machine learning model of the ensemble model is trained to predict a population-level output measure of the same marker.

[0267] In some embodiments, the machine learning model is a multi-output model. In some embodiments, the multi-output model is a deep learning model, such as any of those described herein, for example, any of the convolutional neural networks described in Section IB. In some embodiments, the multi-output model is trained to predict population-level output measures for each of a plurality of different markers, which can be independently selected from any of the markers described herein.

[0268] 1. Reference Dataset In some embodiments, the threshold is determined based on a reference input scale dataset.In some embodiments, the machine learning model is trained using a reference input scale dataset.In some embodiments, for each of the reference cell populations that contain a first plurality of T cells, the reference input scale dataset comprises one or more reference input scales for each of one or more cell features.

[0269] In some embodiments, the threshold is determined based on a data set of reference population-level statistics. In some embodiments, the machine learning model is trained using a data set of reference population-level statistics. In some embodiments, for each of the reference cell populations containing the first plurality of T cells, the data set of reference population-level statistics comprises one or more reference population-level statistics for each of one or more cellular features. In some embodiments, each of the reference population-level statistics is the statistical value of one or more reference input measures for one cellular feature of the plurality of cellular features.

[0270] In some embodiments, provided methods are for training a machine learning model to predict a cellular phenotype, e.g., activation state, of a T cell population. In some embodiments, provided methods include a dataset of reference input measures. In some embodiments, for each of a reference cell population containing a first plurality of T cells, the dataset of reference input measures includes one or more reference input measures for each of one or more cellular features. Exemplary machine learning models are described in Section ID.

[0271] In some embodiments, the provided method includes a dataset of reference population-level statistics. In some embodiments, for each of the reference cell populations containing a first plurality of T cells, the dataset of reference population-level statistics includes one or more reference population-level statistics for each of one or more cellular features. In some embodiments, each of the reference population-level statistics is a statistical value of one or more reference input measures of one of the one or more cellular features. Exemplary machine learning models are described in Section ID.

[0272] In some embodiments, the threshold is determined based on a dataset of reference population level output measures. In some embodiments, the machine learning model is trained using a dataset of reference population level output measures. In some embodiments, the provided method includes a dataset of reference population level output measures. In some embodiments, for each of a second plurality of reference cell populations, the dataset of reference population level output measures includes a reference population level output measure of the reference cell population.

[0273] In some embodiments, the machine learning model is a multi-instance learning model. In some embodiments, the machine learning model is a deep multi-instance learning model.

[0274] In some embodiments, a provided method includes (a) training a convolutional neural network using a dataset of reference holographic information, where for each of a reference cell population containing a first plurality of T cells, the dataset of reference holographic information contains holographic information obtained for individual cells of the reference cell population; (b) determining from the convolutional neural network one or more reference input measures for each cellular feature of a plurality of cellular features derived from the holographic information, where the plurality of cellular features are cellular features extracted by the convolutional neural network, and each reference input measure is derived from an individual cell of the reference cell population; (c) determining a dataset of reference population-level statistics, where the dataset of reference population-level statistics contains one or more reference population-level statistics for each of the plurality of cellular features; and (d) training a machine learning model using the dataset of reference population-level statistics and the dataset of reference population-level output measures, where for each of a second plurality of reference cell populations, the dataset of reference population-level output measures contains reference population-level output measures of expression of markers of the reference cell population. In some embodiments, the markers are markers expressed by activated T cells.

[0275] In some embodiments, a machine learning model is trained to predict a population-level output measure of expression of a marker based on population-level statistics of a plurality of cellular features.

[0276] In some embodiments, the first and second plurality of reference cell populations are identical.In some embodiments, the first and second plurality of reference cell populations are different from each other.In some embodiments, the dataset of reference population level statistics and the dataset of reference population level output measure are time-matched, for example, whether from the same reference cell population or from different reference cell populations, comprise population level statistics and population level output measure that are measured at the same time or within 1 hour, 30 minutes, 20 minutes, 10 minutes or 5 minutes.

[0277] In some embodiments, for each of the first plurality of reference cell populations, each reference input measure is derived from holographic information obtained for the reference cell population, e.g., for individual cells of the cell population. In some embodiments, each reference input measure is derived from individual cells of the reference cell population.

[0278] In some embodiments, the first and / or second plurality of reference cell populations comprises at least 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, or 100 reference cell populations. In some embodiments, each of the first and / or second plurality of reference cell populations is any of the cell populations described in Section II. In some embodiments, the provided methods include performing any of the cell processing steps described in Section II for each of the first and / or second plurality of reference cell populations. In some embodiments, the first and / or second plurality of reference cell populations are each enriched for T cells. In some embodiments, at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of each of the first and / or second plurality of reference cell populations are T cells.

[0279] In some embodiments, the first and / or second plurality of reference cell populations are derived from a reference cell culture that has been cultured in vitro or ex vivo. In some embodiments, the first and second plurality of reference cell populations comprise reference populations derived from the same reference cell culture. In some embodiments, the provided method includes culturing the reference cell culture. Exemplary methods and conditions for culturing are described in Section II-D. In some embodiments, the in vitro or ex vivo culturing of the reference cell culture is performed under the same or similar conditions as the in vitro or ex vivo culturing of the cell culture.

[0280] In some embodiments, the first and / or second plurality of reference cell populations are incubated under T cell stimulating conditions. In some embodiments, the incubation occurs before obtaining holographic information about the first plurality of reference cell populations. In some embodiments, the provided method includes incubating the first and / or second plurality of reference cell populations under T cell stimulating conditions. Exemplary methods and conditions for stimulating T cells are described in Section II-B. In some embodiments, the incubation of the first and / or second plurality of reference cell populations is performed under the same or similar conditions as the incubation of the cell population.

[0281] In some embodiments, the first and / or second plurality of reference cell populations are genetically engineered to express a recombinant protein. In some embodiments, the provided methods include genetically engineering the first and / or second plurality of reference cell populations to express a recombinant protein. Exemplary engineering methods are described in Section II-C. In some embodiments, the engineering of the first and / or second plurality of reference cell populations is performed under the same or similar conditions as the engineering of the cell population. In some embodiments, the first and / or second plurality of reference cell populations are engineered to express the same recombinant protein that the cell population is engineered to express.

[0282] In some embodiments, the holographic information for the first plurality of reference cell populations is obtained according to any of the methods described in Section IA. In some embodiments, the provided method includes obtaining holographic information for the first plurality of reference cell populations. In some embodiments, the holographic information for the first plurality of reference cell populations is obtained during in vitro or ex vivo culture of the reference cell culture. In some embodiments, the multiple reference cell populations of the first plurality of reference cell populations are derived from the same reference cell culture. In some embodiments, the holographic information for the multiple reference cell populations is obtained at multiple time points during in vitro or ex vivo culture of the reference cell culture.

[0283] In some embodiments, the holographic information for the first plurality of reference cell populations is obtained according to a method used to obtain holographic information for the cell populations. In some embodiments, the holographic information for the first plurality of reference cell populations is obtained by DHM.

[0284] In some embodiments, the reference input measure is determined according to any of the methods described in Section IB. In some embodiments, the provided method includes determining a reference input measure. In some embodiments, the reference input measure or the dataset of reference population-level statistics includes only one or more cellular features for which the input measure or population-level statistics for the cell population have been obtained. In some embodiments, the dataset of reference input measures or reference population-level statistics includes other features in addition to the one or more cellular features. The other features may be determined from holographic or non-holographic information obtained for the first plurality of reference cell populations. The other features may or may not be based on properties of individual cells in the first plurality of reference cell populations.

[0285] In some embodiments, the one or more reference population-level statistical values ​​for each of the one or more cellular features are any of the population-level statistical values ​​described in Section IC. In some embodiments, the provided method includes determining one or more reference population-level statistical values ​​for each of the one or more cellular features. In some embodiments, for a cellular feature of the one or more cellular features, the one or more reference population-level statistical values ​​are identical to one or more population-level statistical values ​​of the cell population. In some embodiments, the one or more reference population-level statistical values ​​of a cellular feature of the one or more cellular features comprise one or more quantiles of one or more reference input measures for the cellular feature. In some embodiments, the one or more quantiles are selected from the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more reference input measures for the cellular feature. In some embodiments, the one or more quantiles comprise the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more reference input measures for the cellular feature. In some embodiments, the one or more reference population-level statistics for each cellular feature of the one or more cellular features include the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of the one or more reference input scales for the cellular feature.

[0286] In some embodiments, one or more reference population-level statistical values ​​for a cellular feature of the one or more cellular features are determined by applying a pooling filter to one or more reference input measures for the cellular feature. In some embodiments, each of the reference population-level statistical values ​​is determined by applying a distribution-based pooling filter to one or more reference input measures for the cellular feature of the plurality of cellular features. In some embodiments, the pooling filter is a point estimate-based pooling filter. In some embodiments, the pooling filter is an average pooling filter. In some embodiments, the pooling filter is a max pooling filter. In some embodiments, the pooling filter is a distribution-based pooling filter.

[0287] In some embodiments, for each of the second plurality of reference cell populations, the reference population level output measure indicates the cell phenotype of the reference cell population. In some embodiments, the reference population level output measure indicates the presence or absence of marker-expressing cells in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which marker-expressing cells are present in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which marker-expressing cells are present in the T cells of the reference cell population.

[0288] In some embodiments, the reference population-level output measure is the number, percentage, proportion, or density of marker-expressing cells in the reference cell population. In some embodiments, the reference population-level output measure is the number, percentage, proportion, or density of marker-expressing cells in T cells of the reference cell population. In some embodiments, the reference population-level output measure is the percentage of marker expression in T cells of the reference cell population.

[0289] In some embodiments, for each of the second plurality of reference cell populations, the reference population level output measure indicates the activation state of the reference cell population. In some embodiments, the reference population level output measure indicates the presence or absence of activated T cells in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which activated T cells are present in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which activated T cells are present among the T cells of the reference cell population.

[0290] In some embodiments, the reference population-level output measure is the number, percentage, proportion, or density of activated T cells in the reference cell population. In some embodiments, the reference population-level output measure is the number, percentage, proportion, or density of activated T cells among the T cells of the reference cell population. In some embodiments, the reference population-level output measure is the percentage of activated T cells among the T cells of the reference cell population.

[0291] In some embodiments, the reference population level output measure is a measure of expression of a marker indicative of T cell activation by T cells of the reference cell population. In some embodiments, the reference population level output measure indicates the presence of cells expressing the marker in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which T cells expressing the marker are present in the reference cell population. In some embodiments, the reference population level output measure indicates the extent to which T cells expressing the marker are present among the T cells of the reference cell population.

[0292] In some embodiments, the reference population level output measure is the number of cells, percentage of cells, proportion of cells, or density of cells in the reference cell population that express the marker. In some embodiments, the reference population level output measure is the number of cells, percentage of cells, proportion of cells, or density of cells in the T cells of the reference cell population that express the marker. In some embodiments, the reference population level output measure is the percentage of cells in the T cells of the reference cell population that express the marker.

[0293] In some embodiments, the dataset of reference population level output measures is a measure of marker expression during or after in vitro or ex vivo culturing of the reference cell culture.In some embodiments, the multiple reference cell populations of the second multiple reference cell populations are derived from the same reference cell culture.In some embodiments, the reference population level output measures of the multiple reference cell populations are a measure of marker expression at multiple time points during in vitro or ex vivo culturing of the reference cell culture.In some embodiments, for the reference cell population, the reference population level output measure is a measure of marker expression at the same time point when holographic information for a reference cell population of the first multiple reference cell populations is obtained.

[0294] In some embodiments, the dataset of reference population-level output measures is determined using fluorescent imaging of cells of the reference cell culture. In some embodiments, the provided method includes determining the dataset of reference population-level output measures using fluorescent imaging of a second plurality of reference cell populations. In some embodiments, the fluorescent imaging is by flow cytometry.

[0295] In some embodiments, one or more preparative and / or affinity-based cell separation steps are performed prior to labeling of cells for flow cytometry. In some embodiments, cells are washed, centrifuged, and / or incubated in the presence of one or more reagents, for example, to remove unwanted components, enrich for desired components, or lyse or remove cells sensitive to a particular reagent. In some embodiments, cells are separated based on one or more properties, such as density, adhesion properties, size, sensitivity, and / or resistance to a particular component. In some embodiments, the method includes a density-based cell separation method.

[0296] In some embodiments, cells are labeled with one or more fluorescent markers (e.g., one or more fluorophores) that generate a fluorescent signal that can be measured by a flow cytometer. In some embodiments, cells are labeled by incubating the cells with one or more staining reagents, each containing a fluorescent signal or marker (e.g., a fluorophore). A staining reagent can be any reagent for characterizing, selecting, or isolating a particular cell type or subtype of cells. In some embodiments, a staining reagent contains an immunoaffinity-based reagent, e.g., an antibody.

[0297] In some embodiments, the staining reagent stains cells based on the cellular expression or expression level of a marker, for example, a marker indicating T cell activation. In some embodiments, the staining reagent contains one or more fluorescent markers that can be attached to a binding agent that can bind, for example, specifically bind, to the marker, for example, by chemical conjugation. In some embodiments, the binding agent is a protein. In some embodiments, the binding agent is an antibody or an antigen-binding fragment. The fluorescent marker can be conjugated to the binding agent, for example, an antibody, by any method known in the art.

[0298] As is well known in the art, an "antibody" is an immunoglobulin (Ig) molecule capable of specifically binding to a target, such as a carbohydrate, polynucleotide, lipid, or polypeptide, via at least one epitope recognition site located in the variable region of the Ig molecule. As used herein, the term encompasses not only intact polyclonal or monoclonal antibodies, but also fragments thereof, such as dAb, Fab, Fab', F(ab')2, Fv), single-chain (scFv), synthetic variants thereof, naturally occurring variants thereof, fusion proteins containing antibody portions with antigen-binding fragments of the required specificity, chimeric antibodies, nanobodies, and any other modified configurations of immunoglobulin molecules containing antigen-binding sites or fragments (epitope-recognition sites) of the required specificity. Minibodies containing scFvs containing a CH3 domain are also included herein.

[0299] A binding agent, e.g., an antibody, that "specifically binds" or "preferentially binds" (used interchangeably herein) to a marker is a term well understood in the art. A molecule is said to exhibit "specific binding" or "preferential binding" if it reacts or binds with a particular marker more frequently, more rapidly, for a longer period, and / or with greater affinity than it reacts or binds with a surrogate marker. An antibody specifically binds or preferentially binds to a target if it binds with greater affinity, avidity, more rapidly, and / or for a longer period to other substances. It is also understood that specific or preferential binding does not necessarily require (although it can include) exclusive binding. Methods for determining such specific or preferential binding, e.g., immunoassays, are well known in the art.

[0300] The antibody for flow cytometry can be selected based on the marker detected in multiple reference cell populations, for example, based on the marker indicating T cell activation. In some embodiments, the marker is CD137 (4-1BB). Exemplary CD137 binders for flow cytometry, for example, anti-CD137 antibodies, include clone 4B4-1 available from Miltenyi Biotec and clone 17B5 available from ThermoFisher Scientific.

[0301] In some embodiments, the marker is CD3. In some embodiments, the marker is CD4. In some embodiments, the marker is CD8. Exemplary CD3-, CD4-, and CD8-binding agents, e.g., antibodies, are described in Section II-A.

[0302] In some embodiments, the binding agent, e.g., an antibody, is conjugated to a fluorescent marker, e.g., a fluorophore. For example, cells can be incubated with one or more fluorescently labeled antibodies. In some embodiments, any fluorescent marker or fluorophore suitable for use with flow cytometry analysis can be used. Exemplary fluorescent markers include fluorescent proteins (e.g., GFP, YFP, RFP), fluorescent moieties (e.g., fluorescein isothiocyanate (FITC), phycoerythrin (PE), allophycocyanin (APC), and Alexa Fluor (AF)), nucleic acid stains (e.g., 4',6-diamidino-2-phenylindole (DAPI), SYT016, and propidium iodide (PI)), cell membrane stains (e.g., FMI-43), cell functional dyes (e.g., Fluo-4 and Indo-1), and synthetic dyes (e.g., Brilliant Violet (BV)).Exemplary fluorophores include hydroxycoumarin, Cascade Blue, Dylight 405 Pacific Orange, Alexa Fluor 430, fluorescein, Oregon Green, Alexa Fluor 488, BODIPY 493, 2,7-dichlorofluorescein, ATTO 488, Chromeo 488, Dylight 488, HiLyte 488, Alexa Fluor 532, Alexa Fluor 555, ATTO 550, BODIPY TMR-X, CF 555, Chromeo 546, Cy3, TMR, TRITC, Dy547, Dy548, Dy549, HiLyte 555, Dylight 550, BODIPY 564, Alexa Fluor 568, Alexa Fluor 594, rhodamine, Texas Red, Alexa Fluor 610, Alexa Fluor 633, Dylight 633, Alexa Fluor 647, APC, ATTO 655, CF633, CF640R, Chromeo642, Cy5, Dylight 650, Alexa Fluor 680, IRDye 680, Alexa Fluor 700 (AF700), Cy5.5, ICG, Alexa Fluor 750, Dylight 755, IRDye 750, Cy7, PE-Cy7, Cy7.5, Alexa Fluor 790, Dylight 800, IRDye 800, BV421, BV510, BV570, BV605, BV650, BV711, BV750, BV785, Qdot® 525, Qdot® 565, Qdot® 605, Qdot® 655, Qdot® 705 and Qdot® 800.

[0303] In some embodiments, cell staining for flow cytometry involves incubation with a staining reagent, which in some embodiments is followed by a washing step and separation of cells bound to the staining reagent from cells not bound to the staining reagent. In some embodiments, a fixed volume of cells is mixed with the desired amount of staining reagent and incubated under conditions for cell staining. In some embodiments, staining is performed at a temperature between 0°C and 25°C, e.g., at or about 4°C. In some embodiments, staining is performed for more than 5 minutes, typically more than 15 minutes. In some embodiments, staining is performed for between 15 minutes and 6 hours, e.g., between 30 minutes and 2 hours. In some embodiments, staining is performed for, e.g., 15 minutes, 30 minutes, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, or about 15 minutes, about 30 minutes, about 1 hour, about 1.5 hours, about 2 hours, about 2.5 hours, about 3 hours, or any value between any of the foregoing. In some embodiments, one or more washing steps are performed before introducing the cells into a flow cytometer for analysis. In some embodiments, the stained cells are introduced into a flow cytometer.

[0304] In some embodiments, cells are collected at 1 x 10 single cells to allow the cells to pass through a flow cytometer for reading. 6 pieces~1×10 7 The cell sample is prepared by suspending the sample at a density of 100 cells / mL. In some embodiments, this concentration of cells is referred to as the fluid sheath. In some embodiments, the fluid sheath influences the speed of flow sorting, which typically proceeds at approximately 2,000-20,000 cells per second. The fluid sheath of the cell sample can be made in a phosphate-buffered saline solution, although other solutions are available as known and understood by those skilled in the art.

[0305] II. T Cell Populations Exemplary cell populations containing T cells and cell processing steps comprising the cell populations are described in this section. In some embodiments, each of the reference cell populations referred to in section ID-1 is individually selected from any of the cell populations containing T cells described. In some embodiments, the provided method comprises performing any of the described cell processing steps for each of a plurality of reference cell populations. In some embodiments, the same or similar cell processing steps are performed for each of a plurality of reference cell populations. In some embodiments, the same or similar cell processing steps are performed for each of a plurality of reference cell populations and cell populations.

[0306] In some embodiments, at least 2%, 4%, 6%, 8%, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, or 90% of the cell population are T cells. In some embodiments, the cell population is enriched for T cells. In some embodiments, at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, or 100% of the cell population are T cells. For example, exemplary methods for selecting T cells from a mixed population of cells are described in Section IA.

[0307] In some embodiments, the cell population is incubated under T cell stimulating conditions. In some embodiments, the provided methods include incubating the cell population under T cell stimulating conditions. Exemplary T cell stimulating conditions are described in Section II-B.

[0308] In some embodiments, the cell population is genetically engineered to express a recombinant protein. In some embodiments, provided methods include genetically engineering a cell population to express a recombinant protein. Exemplary engineering methods are described in Section II-C.

[0309] In some embodiments, the cell population is derived from a cell culture that has been cultured in vitro or ex vivo. In some embodiments, the provided methods include culturing the cell culture. Exemplary conditions for in vitro or ex vivo culture are described in Section II-D.

[0310] In some embodiments, holographic information about individual cells of a cell population is obtained during in vitro or ex vivo culture of the cell culture. In some embodiments, the cell phenotype, e.g., activation state, of the cell population during in vitro or ex vivo culture is determined. In some embodiments, a population-level output measure indicates the cell phenotype, e.g., activation state, of the cell population during in vitro or ex vivo culture. In some embodiments, expression of a marker by the cell population during in vitro or ex vivo culture, e.g., a marker indicative of T cell activation, is determined. In some embodiments, the population-level output measure is a measure of marker expression during in vitro or ex vivo culture of the cell culture.

[0311] In some embodiments, the cell culture is incubated under T cell stimulating conditions. In some embodiments, the provided method comprises incubating the cell culture under T cell stimulating conditions. Exemplary T cell stimulating conditions are described in Section II-B.

[0312] In some embodiments, incubating under T cell stimulating conditions is before obtaining holographic information about individual cells of the cell population. In some embodiments, incubating under T cell stimulating conditions is after obtaining holographic information about individual cells of the cell population. In some embodiments, incubating under T cell stimulating conditions is before and after obtaining holographic information about individual cells of the cell population.

[0313] Sections II-A through II-C each describe exemplary methods and reagents for selecting, stimulating, and culturing cell populations containing T cells. In some embodiments, the provided methods include one or more of the described steps of selecting, stimulating, and culturing T cells. In some embodiments, the provided methods include stimulating the T cells. In some embodiments, the provided methods include culturing the T cells. In some embodiments, the provided methods include stimulating and culturing the T cells.

[0314] In some embodiments, the step of selecting the T cells is performed before the step of stimulating or culturing the T cells. In some embodiments, the step of selecting the T cells is performed after the step of stimulating or culturing the T cells.

[0315] In some embodiments, incubating under T cell stimulating conditions is before in vitro or ex vivo culture. In some embodiments, at least a portion of the in vitro or ex vivo culture is performed under T cell stimulating conditions. In some embodiments, all of the in vitro or ex vivo culture is performed under T cell stimulating conditions. In some embodiments, incubating under T cell stimulating conditions is after in vitro or ex vivo culture.

[0316] In some embodiments, some or all of the steps of selecting, stimulating, and culturing the T cells are carried out at a temperature above room temperature, e.g., at physiological temperature. In some embodiments, the temperature is between about 35°C and about 39°C, e.g., 37°C or about 37°C.

[0317] In some embodiments, any number of the steps of the provided methods are performed in a closed system. In some embodiments, any number of the steps of the provided methods are automated.

[0318] A. Selection In some embodiments, the cell population is derived from a biological sample. In some embodiments, the cell population is a primary cell. In some embodiments, the primary cell is a primary cell obtained from a human subject. Biological samples can include tissues, fluids, and other samples taken directly from a subject. Biological samples can be samples obtained directly from a biological source or samples to be processed. Exemplary biological samples include body fluids, such as blood, plasma, serum, cerebrospinal fluid, synovial fluid, urine, and sweat, tissue and organ samples, and processed samples derived therefrom. Exemplary biological samples also include whole blood, peripheral blood mononuclear cells (PBMCs), white blood cells, bone marrow, thymus, tissue biopsy, tumor, leukemia, lymphoma, lymph node, gut-associated lymphoid tissue, mucosa-associated lymphoid tissue, spleen, other lymphoid tissue, liver, lung, stomach, intestine, colon, kidney, pancreas, breast, bone, prostate, cervix, testis, ovary, tonsil, or other organ or cell derived therefrom.

[0319] In some instances, cells are obtained from the subject's circulating blood, for example, by apheresis or leukapheresis. The biological sample may contain lymphocytes, including T cells, monocytes, granulocytes, B cells, other nucleated leukocytes, red blood cells and / or platelets, and in some embodiments, contains cells other than red blood cells and platelets.

[0320] In some embodiments, the biological sample is a sample containing T cells. In some embodiments, the biological sample is a whole blood sample, a buffy coat sample, a peripheral blood mononuclear cell (PBMC) sample, an unfractionated T cell sample, a lymphocyte sample, a white blood cell sample, an apheresis product, or a leukapheresis product. In some embodiments, the biological sample is an apheresis product. In some embodiments, the biological sample is a leukapheresis product.

[0321] In some embodiments, cells obtained from a subject are washed, e.g., to remove the plasma fraction and to place the cells in an appropriate buffer or medium for subsequent processing steps. In some embodiments, the cells are washed with phosphate-buffered saline (PBS). In some embodiments, the wash solution lacks calcium, magnesium, and / or many or all divalent cations. In some aspects, the wash step is accomplished using a semi-automated "flow-through" centrifuge (e.g., Cobe 2991 cell processor, Baxter) according to the manufacturer's instructions. In some aspects, the wash step is accomplished by tangential flow filtration (TFF) according to the manufacturer's instructions. In some embodiments, after washing, the cells are washed in various biocompatible buffers, e.g., Ca 2+ / Mg 2+ In some embodiments, the components of the blood cell biological sample are removed and the cells are resuspended directly in culture medium. In some embodiments, the biological sample (e.g., an apheresis product or leukapheresis product) is washed to remove one or more anticoagulants, such as heparin, added during apheresis or leukapheresis.

[0322] In some embodiments, cell selection involves one or more preparation and / or affinity-based cell separation steps. In some examples, cells are washed, centrifuged, and / or incubated in the presence of one or more reagents, for example, to remove unwanted components, enrich for desirable components, or lyse or remove cells sensitive to a particular reagent. In some examples, cells are separated based on one or more properties, such as density, adhesion properties, size, sensitivity and / or resistance to a particular component. In some embodiments, the method involves a density-based cell separation method, such as preparing white blood cells from peripheral blood by lysis of red blood cells and centrifugation through a Percoll or Ficoll gradient.

[0323] In some embodiments, a cryopreserved and / or cryoprotected apheresis product or leukapheresis product is thawed. In some embodiments, the thawed cell composition is diluted (e.g., with serum-free medium) and / or washed (e.g., with serum-free medium), which in some cases can remove or reduce unwanted or undesirable components. In some cases, dilution and / or washing removes or reduces the presence of cryoprotectants, such as DMSO, contained in the thawed sample that may otherwise negatively affect cell viability, yield, or recovery rate during prolonged exposure to room temperature. In some embodiments, dilution and / or washing allows for medium exchange of the thawed cryopreserved product with serum-free medium, such as that described in US-20210207080.

[0324] Exemplary methods and reagents for the selection of T cells to generate a cell population containing T cells, including by positive or negative selection, are described in US-11400115, US-20190112576, US-10228312, US-20200354677, US-20200384025, US-20210163893, US-20220002669, US-5985658, US-9023604, US-20030175850, US-20040082012, US-20080255004, US-20130059288, and US-9678061.

[0325] In some embodiments, at least a portion of the selection step involves incubation of cells with a selection reagent. In some embodiments, for example, incubation with a selection reagent(s) as part of a selection method can be performed using one or more selection reagents for the selection of one or more different cell types based on the expression or presence of one or more specific molecules, e.g., surface markers, e.g., surface proteins, intracellular markers, or nucleic acids, in or on the cells. In some embodiments, any known method using a selection reagent(s) for separation based on such markers can be used. In some embodiments, the selection reagent(s) results in a separation that is affinity- or immunoaffinity-based separation. For example, selection, in some aspects, involves incubation with a reagent(s) for separating cells and cell populations based on the cellular expression or expression level of one or more markers, usually cell surface markers, e.g., by incubation with a binding partner, e.g., an antibody, that specifically binds to such marker, typically followed by a washing step and separation of cells bound to the binding partner from cells not bound to the binding partner.

[0326] In some aspects of such processes, a certain volume of cells is mixed with a certain amount of the desired affinity-based selection reagent. Immunoaffinity-based selection can be performed using any system or method that results in favorable energy interactions between the cells being separated and molecules that specifically bind to markers on the cells, such as binding partners on a solid surface, e.g., particles. In some embodiments, the method is performed using particles, e.g., beads, e.g., magnetic beads, that are coated with a selection agent (e.g., antibody) specific to the marker of the cells. The particles (e.g., beads) can be incubated or mixed with the cells in a container, such as a tube or bag, with shaking or mixing, with a certain cell density to particle (e.g., bead) ratio to help promote energetically favorable interactions.

[0327] In some embodiments, the total duration of incubation with the selection reagent is at or about 5 minutes to 6 hours, e.g., 30 minutes to 3 hours, e.g., at least or about at least 30 minutes, 60 minutes, 120 minutes, or 180 minutes.

[0328] In some embodiments, after incubation and / or mixing of the cells and selection reagent(s), the incubated cells undergo separation to select for cells based on the presence or absence of a particular selection reagent(s). In some embodiments, after incubation with the selection reagent, the incubated cells, including the selection reagent-bound cells, are transferred into a system for immunoaffinity-based separation of cells. In some embodiments, the system for immunoaffinity-based separation is or contains a magnetic separation column.

[0329] Such separation steps may be based on positive selection, where cells that bind a selection reagent, e.g., an antibody, are retained for further use, and / or negative selection, where cells that do not bind a selection reagent, e.g., an antibody, are retained. In some examples, both fractions are retained for further use. In some embodiments, the process steps further comprise negative and / or positive selection of the incubated cells, e.g., using a system or device capable of performing affinity-based selection. In some embodiments, isolation is performed by enriching for a particular cell population by positive selection, or depleting a particular cell population by negative selection. In some embodiments, positive or negative selection separates cells from a population of cells that are expressed, or at relatively high levels (markers) on the positively or negatively selected cells, respectively. high ) is achieved by incubating with one or more antibodies or other binding agents that specifically bind to the expressed (marker+) one or more surface markers.

[0330] Separation does not necessarily result in 100% enrichment or removal of a particular cell population or cells expressing a particular selection marker. For example, positive selection or enrichment of a particular type of cell, such as those expressing a selection marker, refers to increasing the number or percentage of such cells, but does not necessarily result in the complete absence of cells that do not express the selection marker. Similarly, negative selection, removal, or depletion of a particular type of cell, such as those expressing a selection marker, refers to reducing the number or percentage of such cells, but does not necessarily result in the complete removal of all such cells.

[0331] In some cases, multiple rounds of separation steps are performed, and the positively or negatively selected fraction from one step is subjected to another separation step, such as a subsequent positive or negative selection. In some cases, a single separation step can simultaneously deplete cells expressing multiple markers by incubating cells with multiple antibodies or other binding partners, each specific for a marker targeted for negative selection. Similarly, multiple cell types can be simultaneously positively selected by incubating cells with multiple antibodies or other binding partners expressed in various cell types. In certain embodiments, the separation step is repeated and / or performed more than once, and the positively or negatively selected fraction from one step is subjected to the same separation step, for example, repeated positive or negative selection. In some cases, a single separation step is repeated and / or performed more than once, for example, to increase the purity of the selected cells and / or to further remove and / or deplete negatively selected cells from the negatively selected fraction. In certain embodiments, one or more separation steps are performed 2, 3, 4, 5, 6, 7, 8, 9, 10 or more than 10 times. In certain embodiments, one or more selection steps are performed and / or repeated between 1 and 10 times, between 1 and 5 times, or between 3 and 5 times.

[0332] In some embodiments, T cells are separated from PBMC, apheresis, or leukapheresis samples by negative selection of markers expressed on non-T cells, e.g., B cells, monocytes, or other leukocytes, e.g., CD14. In some aspects, a CD3+ selection step is used to generate a population enriched in CD3+ T cells from a starting sample, e.g., a PBMC, apheresis, or leukapheresis sample, where the starting sample has not undergone positive or negative selection based on another marker, e.g., CD4 and / or CD8. In some embodiments, the starting sample is selected for CD3 without any prior, concurrent, or subsequent selection for another marker (e.g., CD4 and / or CD8) to generate a population enriched in CD3+ T cells. In some embodiments, a cell population is enriched in CD3+ T cells, and the population is not subjected to further selection, e.g., CD4+ and / or CD8+ selection, e.g., before undergoing a step of stimulating the cell population. In certain embodiments, the enriched population of CD3+ T cells has a ratio of CD4+ T cells to CD8+ T cells of between 1:10 and 10:1, between 1:5 and 5:1, between 4:1 and 1:4, between 1:3 and 3:1, between 2:1 and 1:2, between 1.5:1 and 1:1.5, between 1.25:1 and 1:1.25, between 1.2:1 and 1:1.2, between 1.1:1 and 1:1.1, or about 1:1 or 1:1.

[0333] In some embodiments, T cells are separated from PBMCs, apheresis, or leukapheresis samples by negative selection of markers expressed on non-T cells, such as B cells, monocytes, or other leukocytes, e.g., CD14. In some aspects, a CD4+ or CD8+ selection step is used to separate CD4+ helper and CD8+ cytotoxic T cells. Such CD4+ and CD8+ populations can be further sorted into subpopulations by positive or negative selection for markers expressed on, or relatively highly expressed in, one or more naive-like, memory, and / or effector T cell subpopulations.

[0334] In certain embodiments, a biological sample, such as a PBMC, apheresis, or leukapheresis sample, is subjected to selection of CD4+ T cells, which retains both the negative and positive fractions. In certain embodiments, CD8+ T cells are selected from the negative fraction. In some embodiments, a biological sample is subjected to selection of CD8+ T cells, which retains both the negative and positive fractions. In certain embodiments, CD4+ T cells are selected from the negative fraction.

[0335] In some aspects, a CD8-based positive selection step is used to generate a population enriched in CD8+ T cells from a starting sample, e.g., a PBMC, apheresis, or leukapheresis sample, which has not been subjected to selection based on another marker, e.g., CD3+ and / or CD4+. In some embodiments, both the negative and positive fractions from the CD8 positive selection step are retained, and the CD8 negative fraction is further subjected to a CD4-based positive selection step to generate a population enriched in CD4+ T cells. In some embodiments, cells from the CD8+ T cell-enriched population and cells from the CD4+ T cell-enriched population are mixed, combined, and / or pooled to generate a population containing CD4+ T cells and CD8+ T cells.

[0336] In some aspects, a CD4-based positive selection step is used to generate a population enriched in CD4+ T cells from a starting sample, e.g., a PBMC, apheresis, or leukapheresis sample, which has not been subjected to selection based on another marker, e.g., CD3+ and / or CD8+. In some embodiments, both the negative and positive fractions from the CD4 positive selection step are retained, and the CD4 negative fraction is further subjected to a CD8-based positive selection step to generate a population enriched in CD8+ T cells. In some embodiments, cells from the CD4+ T cell-enriched population and cells from the CD8+ T cell-enriched population are mixed, combined, and / or pooled to generate a population containing CD8+ T cells and CD4+ T cells.

[0337] In certain embodiments, the enriched population of CD4+ T cells and the enriched population of CD8+ T cells are pooled, mixed, and / or combined prior to stimulating the cells, e.g., culturing the cells under stimulatory conditions as described in Section IB. In certain embodiments, the pooled, mixed, and / or combined cells or populations have a ratio of CD4+ T cells to CD8+ T cells of between 1:10 and 10:1, between 1:5 and 5:1, between 4:1 and 1:4, between 1:3 and 3:1, between 2:1 and 1:2, between 1.5:1 and 1:1.5, between 1.25:1 and 1:1.25, between 1.2:1 and 1:1.2, between 1.1:1 and 1:1.1, or about 1:1 or 1:1. In certain embodiments, the cells or populations are pooled, mixed, and / or combined to have a ratio of CD4+ T cells to CD8+ T cells in the pooled, mixed, and / or combined cell composition of 1:1 or about 1:1.

[0338] In some embodiments, a selection agent that specifically binds CD4 and a selection agent that specifically binds CD8 are used to generate a population enriched in CD4+ T cells and a population enriched in CD8+ T cells, respectively. In some embodiments, the potencies of the CD4-specific selection agent and the CD8-specific selection agent are the same or substantially the same, e.g., a unit volume or unit weight of selection agent (e.g., ClinicMACS CD4 selection reagent and CD8 selection reagent) can be used to select CD4 or CD8 cells from the same number of total cells. In some embodiments, a greater amount of CD4-specific selection agent can be used than a CD8-specific selection agent with the same or substantially the same potency. For example, the volume or weight of the CD4-specific selection agent and the CD8-specific selection agent can be in a ratio of about 5:1, 4:1, 3:1, 2:1, or 1:5:1.

[0339] In some embodiments, the incubated sample or population of cells to be separated is incubated with a selection reagent containing a small, magnetizable, or magnetically responsive material, e.g., magnetically responsive particles or microparticles, e.g., paramagnetic beads (e.g., Dynalbeads or MACS® beads, etc.). The magnetically responsive material, e.g., particles, are generally attached, directly or indirectly, to a binding partner, e.g., an antibody, that specifically binds to a molecule, e.g., a surface marker, present on the population of cells desired to be separated, e.g., negatively or positively selected.

[0340] In some embodiments, the magnetic particles, e.g., beads, contain a magnetically responsive material bound to a specific binding member, e.g., an antibody or other binding partner. Many well-known magnetically responsive materials are known for use in magnetic separation methods, such as those described in US-4,452,773 and EP-452,342. Colloidal-sized particles, such as those described in US-4,795,698 and US-5,200,084, can also be used.

[0341] Incubation can be carried out under conditions in which an antibody or other binding partner, e.g., a secondary antibody or other reagent, that specifically binds to the antibody or other binding partner, such as that attached to the magnetic particles, e.g., beads, specifically binds to the cell surface molecule, if present on cells in the sample.

[0342] In certain embodiments, magnetically responsive particles are coated with a primary antibody or other binding partner, a secondary antibody, a lectin, an enzyme, or streptavidin. In certain embodiments, magnetic particles are attached to cells via coating with a primary antibody specific to one or more markers. In certain embodiments, cells, rather than beads, are labeled with a primary antibody or binding partner, and then magnetic particles coated with a cell-type-specific secondary antibody or other binding partner (e.g., streptavidin) are added. In certain embodiments, streptavidin-coated magnetic particles are used together with biotinylated primary or secondary antibodies.

[0343] In some embodiments, separation is achieved by placing a sample in a magnetic field, and cells with magnetically responsive or magnetizable particles attached are attracted to the magnet and separated from unlabeled cells. For positive selection, cells that are attracted to the magnet are retained, and for negative selection, cells that are not attracted (unlabeled cells) are retained. In some embodiments, a combination of positive and negative selection is performed during the same selection step, and positive and negative fractions are retained and further processed or subjected to additional separation steps.

[0344] In some embodiments, affinity-based selection is via magnetically activated cell sorting (MACS) (Miltenyi Biotech, Auburn, CA). Magnetically activated cell sorting (MACS), for example, the CliniMACS system, allows for high-purity selection of cells attached to magnetized particles. In certain embodiments, MACS operates in a manner in which non-target and target species are sequentially eluted after applying an external magnetic field. That is, cells attached to magnetized particles remain in place, while unattached species are eluted. Then, after this first elution step is completed, species trapped in the magnetic field and prevented from elution are released in some manner so that they can be eluted and recovered. In certain embodiments, non-target cells are labeled and depleted from a heterogeneous population of cells.

[0345] In some embodiments, the separation and / or isolation step is performed using magnetic beads to which immunoaffinity reagents are reversibly bound via peptide ligand interactions with streptavidin mutant proteins, e.g., as described in US-20170037369. Exemplary of such magnetic beads are Streptamers®. In some embodiments, the separation and / or isolation step is performed using magnetic beads, such as those commercially available from Miltenyi Biotec.

[0346] In some embodiments, T cells are isolated, selected, or enriched by chromatographic isolation, for example, by column chromatography, including affinity chromatography or gel permeation chromatography. Such methods may also be described as (traceless) cell affinity chromatography techniques (CATCH) and may include any of the methods or techniques described in US-10228312 and US-20170037369. Generally, the chromatographic method is fluid chromatography, usually liquid chromatography. In some aspects, chromatography can be performed in a flow-through mode, for example, by gravity flow or by a pump at one end of a column containing a chromatography matrix, and the fluid sample exits the column at the other end. Furthermore, chromatography can be performed in a "up and down" mode, for example, by a pipette at one end of a column containing a chromatography matrix packed in a pipette tip, and the fluid sample enters and exits the chromatography matrix / pipette tip at the other end of the column. Alternatively, chromatography can be performed in a batch mode in which a chromatography matrix (e.g., a stationary phase) is incubated with the cell-containing sample, e.g., under shaking, rotation, or repeated contact and removal of the fluid sample, e.g., by means of a pipette.

[0347] In some embodiments, the selection agent is contained in a chromatography column, e.g., directly or indirectly bound to a chromatography matrix (e.g., stationary phase). In some embodiments, the selection agent is present on the chromatography matrix (e.g., stationary phase) when the sample is added to the column. In some embodiments, the selection agent can be indirectly bound to the chromatography matrix (e.g., stationary phase) via a reagent, e.g., a selection reagent. In some embodiments, the selection reagent is covalently or non-covalently bound to the stationary phase of the column. In some embodiments, the selection reagent is reversibly immobilized on the chromatography matrix (e.g., stationary phase). In some cases, the selection reagent is immobilized on the chromatography matrix (e.g., stationary phase) by a covalent bond. In some aspects, the selection reagent is reversibly immobilized on the chromatography matrix (e.g., stationary phase) non-covalently.

[0348] In some embodiments, the selection agent may be present on the chromatography matrix (e.g., stationary phase) at the time the sample is added to the chromatography column (e.g., stationary phase), e.g., bound directly (e.g., covalently or non-covalently) or indirectly via a selection reagent. Thus, upon addition of the sample, T cells may be bound by the selection agent and immobilized on the chromatography matrix (e.g., stationary phase) of the column. Alternatively, in some embodiments, the selection agent may be added to the sample. In this method, the selection agent binds to T cells in the sample, and the sample may then be added to a chromatography matrix (e.g., stationary phase) containing a selection reagent, whereby the selection agent already bound to the T cells binds to the selection reagent, thereby immobilizing the target cells to the chromatography matrix (e.g., stationary phase).

[0349] In some embodiments, one or both of the first and / or second selections can use multiple affinity chromatography matrices and / or antibodies, whereby the multiple matrices and / or antibodies are connected in series. In some embodiments, the affinity chromatography matrix(ies) used in the selections can be at least about 50×10 6 Individual cells / mL, 100×10 6 Individual cells / mL, 200×10 6 cells / mL or 400 x 10 6 The matrix can adsorb or select or enrich for cells / mL. In some embodiments, the adsorption capacity can be modulated based on the diameter and / or length of the matrix. In some embodiments, the starting culture ratio of the selected or enriched population is achieved by, for example, selecting a sufficient amount of matrix and / or a sufficient relative amount to achieve the assumed starting culture ratio based on the adsorption capacity of the matrix for cell selection.

[0350] In some embodiments, the chromatography matrix / stationary phase is a non-magnetic or non-magnetizable material. Such materials can include derivatized silica or cross-linked gels. Cross-linked gels (usually manufactured in bead form) can be based on natural polymers such as cross-linked polysaccharides. Suitable examples include agarose gels or cross-linked dextran gels. Cross-linked gels can also be based on synthetic polymers, e.g., non-naturally occurring polymer classes. Typically, such synthetic polymers on which stationary phases for cell separation are based are polymers that have polar monomer units and are therefore themselves polar.

[0351] Illustrative examples of suitable synthetic polymers include polyacrylamide, styrene-divinylbenzene gels, and copolymers of acrylates and diols or acrylamide and diols. Illustrative examples include polymethacrylate gels commercially available as Fractogel®. A further example is a copolymer of ethylene glycol and methacrylate commercially available as Toyopearl®. In some embodiments, the stationary phase can also include natural and synthetic polymer components, such as composite matrices or composite materials or copolymers of polysaccharides and agarose, e.g., polyacrylamide / agarose composites, or polysaccharides and N,N'-methylenebisacrylamide. Illustrative examples of copolymers of dextran and N,N'-methylenebisacrylamide include the Sephacryl® series of materials mentioned above. Derivatized silica can include silica particles coupled to synthetic or natural polymers. Examples of such embodiments include polysaccharide-grafted silica, polyvinylpyrrolidone-grafted silica, polyethylene oxide-grafted silica, poly(2-hydroxyethylaspartamide) silica, and poly(N-isopropylacrylamide)-grafted silica.

[0352] In some embodiments, the isolation and / or selection results in one or more populations of enriched T cells, e.g., CD3+ T cells, CD4+ T cells, and / or CD8+ T cells. In some embodiments, two or more distinct enriched T cell populations are isolated, selected, enriched, or obtained from a single biological sample. In some embodiments, the distinct populations are isolated, selected, enriched, and / or obtained from separate biological samples collected, harvested, and / or obtained from the same subject.

[0353] In certain embodiments, the isolation and / or selection results in one or more populations of enriched T cells comprising at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% or about 100% CD3+ T cells. In certain embodiments, the enriched population of T cells consists essentially of CD3+ T cells.

[0354] In certain embodiments, the isolation and / or enrichment results in an enriched population of CD4+ T cells comprising at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% or about 100% CD4+ T cells. In certain embodiments, the population of CD4+ T cells comprises less than 40%, less than 35%, less than 30%, less than 25%, less than 20%, less than 15%, less than 10%, less than 5%, less than 1%, less than 0.1%, or less than 0.01% CD8+ T cells, and / or contains no CD8+ T cells, and / or is free or substantially free of CD8+ T cells. In some embodiments, the enriched population of T cells consists essentially of CD4+ T cells.

[0355] In certain embodiments, the isolation and / or enrichment results in an enriched population of CD8+ T cells comprising at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, at least 99%, at least 99.5%, at least 99.9%, or 100% or about 100% CD8+ T cells. In certain embodiments, the population of CD8+ T cells contains less than 40%, less than 35%, less than 30%, less than 25%, less than 20%, less than 15%, less than 10%, less than 5%, less than 1%, less than 0.1%, or less than 0.01% CD4+ T cells, and / or no CD4+ T cells, and / or is free or substantially free of CD4+ T cells. In some embodiments, the enriched T cell population consists essentially of CD8+ T cells.

[0356] In some embodiments, the selection marker may be CD4, and the selection agent specifically binds to CD4. In some aspects, the selection agent that specifically binds to CD4 may be selected from the group consisting of an anti-CD4 antibody, a bivalent antibody fragment of an anti-CD4 antibody, a monovalent antibody fragment of an anti-CD4 antibody, and a proteinaceous CD4-binding molecule with antibody-like binding properties. In some embodiments, the anti-CD4 antibody, bivalent antibody fragment, or monovalent antibody fragment (e.g., an anti-CD4 Fab fragment) can be derived from antibody 13B8.2 or a functionally active mutant of 13B8.2 that retains specific binding to CD4. For example, exemplary mutants of antibody 13B8.2 or m13B8.2 are described in US Pat. No. 7,482,000, US Pat. No. 20140295458, US Pat. No. 10,228,312, and Bes et al., J. Biol. Chem. (2003) 278:14265-14273. The mutant Fab fragment, designated "ml3B8.2," retains the variable domain of the CD4-binding murine antibody 13B8.2, as described in US Pat. No. 7,482,000, and a constant domain containing a gamma-type constant human CH1 domain of the heavy chain and a kappa-type constant human light chain domain. In some embodiments, a mutant anti-CD4 antibody, bivalent antibody fragment, or monovalent antibody fragment, e.g., antibody 13B8.2, contains the amino acid substitution H91A in the variable light chain, Y92A in the variable light chain, H35A in the variable heavy chain, and / or R53A in the variable heavy chain, according to Kabat numbering. In some aspects, compared to the variable domain of the 13B8.2 Fab fragment in ml3B8.2, the His residue at position 91 of the light chain (position 93 in SEQ ID NO: 1) is mutated to Ala, and the Arg residue at position 53 of the heavy chain (position 55 in SEQ ID NO: 2) is mutated to Ala. In some embodiments, the reagent that reversibly binds to an anti-CD4 antibody or fragment thereof is commercially available or derived from a commercially available reagent (e.g., catalog numbers 6-8000-206, 6-8000-205, or 6-8002-100; IBA GmbH, Gottingen, Germany). In some embodiments, the selection agent comprises an anti-CD4 Fab fragment.In some embodiments, the anti-CD4 Fab fragment contains a variable heavy chain having the sequence set forth in SEQ ID NO: 2 and a variable light chain having the sequence set forth in SEQ ID NO: 1. In some embodiments, the anti-CD4 Fab fragment contains the CDRs of the variable heavy chain having the sequence set forth in SEQ ID NO: 2 and the CDRs of the variable light chain having the sequence set forth in SEQ ID NO: 1.

[0357] In some embodiments, the selection marker may be CD8, and the selection agent specifically binds to CD8. In some aspects, the selection agent that specifically binds to CD8 may be selected from the group consisting of an anti-CD8 antibody, a bivalent antibody fragment of an anti-CD8 antibody, a monovalent antibody fragment of an anti-CD8 antibody, and a proteinaceous CD8-binding molecule with antibody-like binding properties. In some embodiments, the anti-CD8 antibody, bivalent antibody fragment, or monovalent antibody fragment (e.g., an anti-CD8 Fab fragment) can be derived from the antibody OKT8 (e.g., ATCC CRL-8014) or a functionally active mutant thereof that retains specific binding to CD8. In some embodiments, the reagent that reversibly binds to anti-CD8 or a fragment thereof is commercially available or derived from a commercially available reagent (e.g., catalog number 6-8003 or 6-8000-201; IBA GmbH, Gottingen, Germany). In some embodiments, the selection agent contains an anti-CD8 Fab fragment. In some embodiments, the anti-CD8 Fab fragment contains a variable heavy chain having the sequence set forth in SEQ ID NO: 3 and a variable light chain having the sequence set forth in SEQ ID NO: 4. In some embodiments, the anti-CD8 Fab fragment contains the CDRs of the variable heavy chain having the sequence set forth in SEQ ID NO: 3 and the CDRs of the variable light chain having the sequence set forth in SEQ ID NO: 4.

[0358] In some embodiments, the selection marker may be CD3, and the selection agent specifically binds to CD3. In some aspects, the selection agent that specifically binds to CD3 may be selected from the group consisting of an anti-CD3 antibody, a bivalent antibody fragment of an anti-CD3 antibody, a monovalent antibody fragment of an anti-CD3 antibody, and a proteinaceous CD3-binding molecule with antibody-like binding properties. In some embodiments, the anti-CD3 antibody, bivalent antibody fragment, or monovalent antibody fragment (e.g., an anti-CD3 Fab fragment) can be derived from the antibody OKT3 (e.g., ATCC CRL-8001; see, e.g., Stemberger et al., PLoS One (2012) 7(4):e35798) or a functionally active mutant thereof that retains specific binding to CD3. In some embodiments, the reagent that reversibly binds to the anti-CD3 antibody or fragment thereof is commercially available or derived from a commercially available reagent (e.g., catalog number 6-8000-201 or 6-8001-100; IBA GmbH, Gottingen, Germany). In some embodiments, the selection agent comprises an anti-CD3 Fab fragment. In some embodiments, the anti-CD3 Fab fragment comprises a variable heavy chain having the sequence set forth in SEQ ID NO:5 and a variable light chain having the sequence set forth in SEQ ID NO:6. In some embodiments, the anti-CD3 Fab fragment comprises the CDRs of the variable heavy chain having the sequence set forth in SEQ ID NO:5 and the CDRs of the variable light chain having the sequence set forth in SEQ ID NO:6.

[0359] In any of the above examples, the bivalent antibody fragment may be a F(ab')2 fragment or a bivalent single-chain Fv fragment. In some embodiments, the monovalent antibody fragment may be selected from the group consisting of a Fab fragment, an Fv fragment, and a single-chain Fv fragment (scFv). In any of the above examples, the proteinaceous binding molecule with antibody-like binding properties may be an aptamer, a mutein based on a lipocalin family polypeptide, a glubody, an ankyrin scaffold-based protein, a crystalline scaffold-based protein, an adnectin, or an avimer.

[0360] B. stimulation In some embodiments, the cell population is incubated under T cell stimulatory conditions. In some embodiments, the provided methods comprise incubating the cell population under T cell stimulatory conditions.

[0361] Exemplary methods and stimulatory reagents for stimulation of T cells are described in US-6040177, US-6352694, US-11400115, US-20190112576, US-20190136186, US-11274278, US-20210032297, US-20200354677, US-20200384025, US-20210163893 and US-20220002669.

[0362] In some embodiments, conditions for T cell stimulation may include one or more of a particular medium, temperature, oxygen content, carbon dioxide content, time, agents such as nutrients, amino acids, antibiotics, ions, and / or stimulatory factors such as cytokines, chemokines, antigens, binding partners, fusion proteins, recombinant soluble receptors, and other agents designed to stimulate T cells.

[0363] In some embodiments, the incubation is in a basal medium. In some embodiments, the basal medium is serum-free. In some embodiments, the basal medium does not contain human-derived serum. In some embodiments, the basal medium contains a mixture of inorganic salts, sugars, amino acids, and optionally vitamins, organic acids, and / or buffers or other well-known cell culture nutrients. In addition to nutrients, the basal medium may also help maintain pH and osmolality. A wide variety of commercially available basal media are well known to those skilled in the art, including Dulbecco's Modified Eagle's Medium (DMEM), Roswell Park Memorial Institute Medium (RPMI), Iscove's Modified Dulbecco's Medium, and Ham's Medium. In some embodiments, the basal medium is Iscove's Modified Dulbecco's Medium, RPMI-1640, or α-MEM.

[0364] In some embodiments, the basal medium is a balanced salt solution (e.g., PBS, DPBS, HBSS, or EBSS). In some embodiments, the basal medium is selected from Dulbecco's Modified Eagle's Medium (DMEM), Minimum Essential Medium (MEM), Basal Medium Eagle's (BME), F-10, F-12, RPMI 1640, Glasgow's Minimum Essential Medium (GMEM), alpha Minimum Essential Medium (alphaMEM), Iscove's Modified Dulbecco's Medium, and M199. In some embodiments, the basal medium is a complex medium (e.g., RPMI-1640 or IMDM). In some embodiments, the basal medium is OpTmizer™ CTS™ T Cell Expansion Basal Medium (ThermoFisher).

[0365] In some embodiments, the basal medium is supplemented with additional additives. In some embodiments, the basal medium is not supplemented with any additional additives. Additives to cell culture media include nutrients, sugars such as glucose, amino acids, vitamins, and additives such as ATP and NADH.

[0366] In some embodiments, incubation is in serum-free medium, such as that described in US-20210207080.

[0367] In some embodiments, the incubation is carried out in the presence of one or more recombinant cytokines. In some embodiments, the one or more recombinant cytokines are human recombinant cytokines. In certain embodiments, the one or more recombinant cytokines bind to and / or are capable of binding to a receptor expressed by and / or endogenous to T cells. In certain embodiments, the one or more recombinant cytokines comprise a member of the four-alpha-helical bundle family of cytokines. Members of the four-alpha-helical bundle family of cytokines include interleukin-2 (IL-2), interleukin-4 (IL-4), interleukin-7 (IL-7), interleukin-9 (IL-9), interleukin-12 (IL-12), interleukin-15 (IL-15), granulocyte colony-stimulating factor (G-CSF), and granulocyte-macrophage colony-stimulating factor (GM-CSF). In some embodiments, the one or more recombinant cytokines are selected from IL-2, IL-15, and IL-7. In some embodiments, the incubation is carried out in the presence of IL-2, IL-15 and IL-7.

[0368] In certain embodiments, the amount or concentration of one or more recombinant cytokines is measured and / or quantified using International Units (IU). International units can be used to quantify vitamins, hormones, cytokines, vaccines, blood products, and similar biologically active substances. In some embodiments, an IU is or includes a unit of measurement of the potency of a biological preparation by comparison to an international reference standard of specific gravity and strength, e.g., the WHO First International Standard for Human IL-2, 86 / 504. International units are published, are the only recognized, standardized method for reporting biological activity units, and are derived from international collaborative research efforts. In certain embodiments, the IU of a cytokine population, sample, or source can be obtained by product comparison testing using a similar WHO standard product. For example, in some embodiments, the IU / mg of a population, sample, or source of human recombinant IL-2, IL-7, or IL-15 is compared to a WHO standard IL-2 product (NIBSC code: 86 / 500), a WHO standard IL-17 product (NIBSC code: 90 / 530), and a WHO standard IL-15 product (NIBSC code: 95 / 554), respectively.

[0369] In some embodiments, the biological activity in IU / mg is (ED50 in ng / mL)-1×10 6 In certain embodiments, the ED of recombinant human IL-2 or IL-15 is equivalent to 50is equivalent to the concentration required for half-maximal stimulation of cell proliferation (XTT cleavage) using CTLL-2 cells. In certain embodiments, the ED50 of recombinant human IL-7 is equivalent to the concentration required for half-maximal stimulation of proliferation of PHA-activated human peripheral blood lymphocytes. Details related to IL-2 assays and IU calculations are discussed in Wadhwa et al., Journal of Immunological Methods (2013) 379 (1-2): 1-7; and Gearing and Thorpe, Journal of Immunological Methods (1988) 114 (1-2): 3-9, and details related to IL-15 assays and IU calculations are discussed in Soman et al., Journal of Immunological Methods (2009) 348 (1-2): 83-94.

[0370] In some embodiments, the cells are stimulated or stimulated in the presence of a recombinant cytokine, e.g., a recombinant human cytokine, at a concentration of between 1 IU / mL and 1,000 IU / mL, between 10 IU / mL and 50 IU / mL, between 50 IU / mL and 100 IU / mL, between 100 IU / mL and 200 IU / mL, between 100 IU / mL and 500 IU / mL, between 250 IU / mL and 500 IU / mL, or between 500 IU / mL and 1,000 IU / mL.

[0371] In some embodiments, the cells are stimulated or stimulated in the presence of recombinant IL-2, e.g., human recombinant IL-2, at a concentration of between 1 IU / mL and 500 IU / mL, between 10 IU / mL and 250 IU / mL, between 50 IU / mL and 200 IU / mL, between 50 IU / mL and 150 IU / mL, between 75 IU / mL and 125 IU / mL, between 100 IU / mL and 200 IU / mL, or between 10 IU / mL and 100 IU / mL. In certain embodiments, the cells are administered at or about 50 IU / mL, 60 IU / mL, 70 IU / mL, 80 IU / mL, 90 IU / mL, 100 IU / mL, 110 IU / mL, 120 IU / mL, 130 IU / mL, 140 IU / mL, 150 IU / mL, 160 IU / mL, 170 IU / mL, 180 IU / mL, 190 IU / mL or 100 IU / mL. In some embodiments, the cells are stimulated or allowed to stimulate in the presence of recombinant IL-2 at a concentration of about 70 IU / mL, about 80 IU / mL, about 90 IU / mL, about 100 IU / mL, about 110 IU / mL, about 120 IU / mL, about 130 IU / mL, about 140 IU / mL, about 150 IU / mL, about 160 IU / mL, about 170 IU / mL, about 180 IU / mL, about 190 IU / mL, or about 100 IU / mL. In some embodiments, the cells are stimulated or allowed to stimulate in the presence of 100 IU / mL or about 100 IU / mL of recombinant IL-2, e.g., human recombinant IL-2.

[0372] In some embodiments, the cells are stimulated or stimulated in the presence of recombinant IL-7, e.g., human recombinant IL-7, at a concentration of between 100 IU / mL and 2,000 IU / mL, between 500 IU / mL and 1,000 IU / mL, between 100 IU / mL and 500 IU / mL, between 500 IU / mL and 750 IU / mL, between 750 IU / mL and 1,000 IU / mL, or between 550 IU / mL and 650 IU / mL. In certain embodiments, the cells are at a concentration of 50 IU / mL, 100 IU / mL, 150 IU / mL, 200 IU / mL, 250 IU / mL, 300 IU / mL, 350 IU / mL, 400 IU / mL, 450 IU / mL, 500 IU / mL, 550 IU / mL, 600 IU / mL, 650 IU / mL, 700 IU / mL, 750 IU / mL, 800 IU / mL, 750 IU / mL, 750 IU / mL, or 1,000 IU / mL or about 50 IU / mL, about 100 IU / mL In certain embodiments, the cells are stimulated or are stimulated in the presence of IL-7 at a concentration of about 150 IU / mL, about 200 IU / mL, about 250 IU / mL, about 300 IU / mL, about 350 IU / mL, about 400 IU / mL, about 450 IU / mL, about 500 IU / mL, about 550 IU / mL, about 600 IU / mL, about 650 IU / mL, about 700 IU / mL, about 750 IU / mL, about 800 IU / mL, about 750 IU / mL, about 750 IU / mL, or about 1,000 IU / mL. In certain embodiments, the cells are stimulated or are stimulated in the presence of 600 IU / mL or about 600 IU / mL of recombinant IL-7, e.g., human recombinant IL-7.

[0373] In some embodiments, the cells are stimulated or stimulated in the presence of recombinant IL-15, e.g., human recombinant IL-15, at a concentration of between 1 IU / mL and 500 IU / mL, between 10 IU / mL and 250 IU / mL, between 50 IU / mL and 200 IU / mL, between 50 IU / mL and 150 IU / mL, between 75 IU / mL and 125 IU / mL, between 100 IU / mL and 200 IU / mL, or between 10 IU / mL and 100 IU / mL. In certain embodiments, the cells are administered at a concentration of 50 IU / mL, 60 IU / mL, 70 IU / mL, 80 IU / mL, 90 IU / mL, 100 IU / mL, 110 IU / mL, 120 IU / mL, 130 IU / mL, 140 IU / mL, 150 IU / mL, 160 IU / mL, 170 IU / mL, 180 IU / mL, 190 IU / mL, or 200 IU / mL or about 50 IU / mL, about 60 IU / mL In some embodiments, the cells are stimulated or are stimulated in the presence of recombinant IL-15 at a concentration of about 100 IU / mL, about 70 IU / mL, about 80 IU / mL, about 90 IU / mL, about 100 IU / mL, about 110 IU / mL, about 120 IU / mL, about 130 IU / mL, about 140 IU / mL, about 150 IU / mL, about 160 IU / mL, about 170 IU / mL, about 180 IU / mL, about 190 IU / mL, or about 200 IU / mL. In some embodiments, the cells are stimulated or are stimulated in the presence of 100 IU / mL or about 100 IU / mL of recombinant IL-15, e.g., human recombinant IL-15.

[0374] In some embodiments, the incubation is carried out in the absence of recombinant cytokines.

[0375] In certain embodiments, stimulation is performed under static conditions, e.g., conditions that do not involve centrifugation, shaking, rotation, rocking, or perfusion of the medium, e.g., continuous or semi-continuous perfusion. In some embodiments, before or immediately after the start of stimulation, e.g., within 5, 15, or 30 minutes, the cells are transferred (e.g., transferred under sterile conditions) to a container, e.g., a bag or vial, and placed in an incubator. In certain embodiments, the incubator is set to 16°C, 24°C, or 35°C, about 16°C, about 24°C, or about 35°C, or at least 16°C, at least 24°C, or at least 35°C. In some embodiments, the incubator is set to 37°C, about 37°C, or 37°C ± 2°C, ± 1°C, ± 0.5°C, or ± 0.1°C. In certain embodiments, stimulation under static conditions is performed in a cell culture bag placed in an incubator. In some embodiments, the culture bag is constructed from a single web of polyolefin gas-permeable film, when present, that allows monocytes to adhere to the bag surface.

[0376] In some embodiments, the T cell stimulatory conditions include incubation in the presence of a T cell stimulator. In some embodiments, the T cell stimulator binds to a molecule expressed on the surface of the T cell. In some embodiments, one of the T cell stimulators induces a primary activation signal in the T cell. In some embodiments, one of the T cell stimulators induces a costimulatory signal in the T cell. In some embodiments, the T cell stimulator induces a primary activation signal and a costimulatory signal in the T cell.

[0377] In some embodiments, the T cell stimulator that induces the primary activation signal binds to a member of the TCR / CD3 complex in T cells. In some embodiments, the T cell stimulator binds to CD3. In some embodiments, the T cell stimulator is an anti-CD3 antibody, a bivalent antibody fragment of an anti-CD3 antibody, a monovalent antibody fragment of an anti-CD3 antibody, or a proteinaceous CD3-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD3 antibody or antibody fragment. In some embodiments, the anti-CD3 antibody, bivalent antibody fragment of an anti-CD3 antibody, or monovalent antibody fragment of an anti-CD3 antibody (e.g., an anti-CD3 Fab fragment) is derived from the antibody OKT3 (e.g., ATCC CRL-8001; see, e.g., Stemberger et al., PLoS One (2012) 7(4):e35798) or a functionally active mutant thereof that retains specific binding to CD3. In some embodiments, the T cell stimulator is an anti-CD3 Fab. In some embodiments, the anti-CD3 Fab contains a variable heavy chain having the sequence set forth in SEQ ID NO: 5 and a variable light chain having the sequence set forth in SEQ ID NO: 6. In some embodiments, the anti-CD3 Fab contains the CDRs of the variable heavy chain having the sequence set forth in SEQ ID NO: 5 and the CDRs of the variable light chain having the sequence set forth in SEQ ID NO: 6.

[0378] In some embodiments, the T cell stimulator that induces the costimulatory signal binds to a costimulatory molecule in the T cell. In some embodiments, the costimulatory molecule is CD28, CD90 (Thy-1), CD95 (Apo- / Fas), CD137 (4-1BB), CD154 (CD40L), ICOS, LAT, CD27, OX40, or HVEM.

[0379] In some embodiments, the costimulatory molecule is CD28. In some embodiments, the T cell stimulator is an anti-CD28 antibody, a bivalent antibody fragment of an anti-CD28 antibody, a monovalent antibody fragment of an anti-CD28 antibody, or a proteinaceous CD28-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD28 antibody or antibody fragment. In some embodiments, the anti-CD28 antibody, a bivalent antibody fragment of an anti-CD28 antibody, or a monovalent antibody fragment of an anti-CD28 antibody (e.g., an anti-CD28 Fab fragment) is derived from the antibody CD28.3 (deposited as a synthetic single-chain Fv construct under GenBank accession number AF451974.1; see also Vanhove et al., Blood (2003) 102(2):564-570), whose variable heavy and light chains contain the amino acid sequences set forth in SEQ ID NOs: 7 and 8, respectively. In some embodiments, the T cell stimulator is an anti-CD28 Fab. In some embodiments, the anti-CD28 Fab contains a variable heavy chain having the sequence set forth in SEQ ID NO: 7 and a variable light chain having the sequence set forth in SEQ ID NO: 8. In some embodiments, the anti-CD28 Fab contains the CDRs of the variable heavy chain having the sequence set forth in SEQ ID NO: 7 and the CDRs of the variable light chain having the sequence set forth in SEQ ID NO: 8.

[0380] In some embodiments, the costimulatory molecule is CD90. In some embodiments, the T cell stimulator is an anti-CD90 antibody, a bivalent antibody fragment of an anti-CD90 antibody, a monovalent antibody fragment of an anti-CD90 antibody, or a proteinaceous CD90 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD90 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD90 Fab. In some embodiments, the anti-CD90 antibody, a bivalent antibody fragment of an anti-CD90 antibody, or a monovalent antibody fragment of an anti-CD90 antibody (e.g., an anti-CD90 Fab fragment) is derived from the anti-CD90 antibody G7 (Biolegend, catalog number 105201).

[0381] In some embodiments, the costimulatory molecule is CD95. In some embodiments, the T cell stimulator is an anti-CD95 antibody, a bivalent antibody fragment of an anti-CD95 antibody, a monovalent antibody fragment of an anti-CD95 antibody, or a proteinaceous CD95 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD28 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD95 Fab. In some embodiments, the anti-CD95 antibody, bivalent antibody fragment of an anti-CD95 antibody, or monovalent antibody fragment of an anti-CD95 antibody (e.g., an anti-CD95 Fab fragment) is derived from monoclonal mouse anti-human CD95 CH11 (Upstate Biotechnology, Lake Placid, NY), anti-CD95 mAb 7C11, or anti-APO-1, such as those described in Paulsen et al., Cell Death & Differentiation (2011) 18(4):619-631.

[0382] In some embodiments, the costimulatory molecule is CD137. In some embodiments, the T cell stimulator is an anti-CD137 antibody, a bivalent antibody fragment of an anti-CD137 antibody, a monovalent antibody fragment of an anti-CD137 antibody, or a proteinaceous CD137 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD137 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD137 Fab. In some embodiments, the anti-CD137 antibody, a bivalent antibody fragment of an anti-CD137 antibody, or a monovalent antibody fragment of an anti-CD137 antibody (e.g., an anti-CD137 Fab fragment) is derived from LOB12, IgG2a, or LOB12.3, IgG1, as described in Taraban et al., Eur J Immunol. (2002) 32(12):3617-27. See also, e.g., US-6569997, US-6303121 and Mittler et al., Immunol Res. (2004) 29(1-3):197-208.

[0383] In some embodiments, the costimulatory molecule is CD40. In some embodiments, the T cell stimulator is an anti-CD40 antibody, a bivalent antibody fragment of an anti-CD40 antibody, a monovalent antibody fragment of an anti-CD40 antibody, or a proteinaceous CD40 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD40 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD40 Fab.

[0384] In some embodiments, the costimulatory molecule is CD40L. In some embodiments, the T cell stimulator is an anti-CD40L antibody, a bivalent antibody fragment of an anti-CD40L antibody, a monovalent antibody fragment of an anti-CD40L antibody, or a proteinaceous CD40L-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD40L antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD40L Fab. In some embodiments, the anti-CD40L antibody, a bivalent antibody fragment of an anti-CD40L antibody, or a monovalent antibody fragment of an anti-CD40L antibody (e.g., an anti-CD40L Fab fragment) is derived from Hu5C8 as described in Blair et al., JEM (2000) 19(4):651-660. See also, e.g., US-7563445, US20010026932, US7547438, and US-7172759.

[0385] In some embodiments, the costimulatory molecule is ICOS. In some embodiments, the T cell stimulator is an anti-ICOS antibody, a bivalent antibody fragment of an anti-ICOS antibody, a monovalent antibody fragment of an anti-ICOS antibody, or a proteinaceous ICOS-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-ICOS antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-ICOS Fab. In some embodiments, the anti-ICOS antibody, a bivalent antibody fragment of an anti-ICOS antibody, or a monovalent antibody fragment of an anti-ICOS antibody (e.g., an anti-ICOS Fab fragment) is derived from any of the antibodies described in US-20080279851 and Deng et al., Hybrid Hybridomics (2004) 23(3):176-82.

[0386] In some embodiments, the costimulatory molecule is a linker for activation of T cells (LAT). In some embodiments, the T cell stimulator is an anti-LAT antibody, a bivalent antibody fragment of an anti-LAT antibody, a monovalent antibody fragment of an anti-LAT antibody, or a proteinaceous LAT-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-LAT antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-LAT Fab.

[0387] In some embodiments, the costimulatory molecule is CD27. In some embodiments, the T cell stimulator is an anti-CD27 antibody, a bivalent antibody fragment of an anti-CD27 antibody, a monovalent antibody fragment of an anti-CD27 antibody, or a proteinaceous CD27 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-CD27 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-CD27 Fab. In some embodiments, the anti-CD27 antibody, a bivalent antibody fragment of an anti-CD27 antibody, or a monovalent antibody fragment of an anti-CD27 antibody (e.g., an anti-CD27 Fab fragment) is derived from any of the antibodies described in US-8,481,029.

[0388] In some embodiments, the costimulatory molecule is OX40. In some embodiments, the T cell stimulator is an anti-OX40 antibody, a bivalent antibody fragment of an anti-OX40 antibody, a monovalent antibody fragment of an anti-OX40 antibody, or a proteinaceous OX40 binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-OX40 antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-OX40 Fab. In some embodiments, the anti-OX40 antibody, a bivalent antibody fragment of an anti-OX40 antibody, or a monovalent antibody fragment of an anti-OX40 antibody (e.g., an anti-OX40 Fab fragment) is derived from any of the antibodies described in US-9475880 and Melero et al., Clin Cancer Res. (2013) 19(5):1044-53.

[0389] In some embodiments, the costimulatory molecule is HVEM. In some embodiments, the T cell stimulator is an anti-HVEM antibody, a bivalent antibody fragment of an anti-HVEM antibody, a monovalent antibody fragment of an anti-HVEM antibody, or a proteinaceous HVEM-binding molecule with antibody-like binding properties. In some embodiments, the T cell stimulator is an anti-HVEM antibody or antibody fragment. In some embodiments, the T cell stimulator is an anti-HVEM Fab. In some embodiments, the anti-HVEM antibody, bivalent antibody fragment of an anti-HVEM antibody, or monovalent antibody fragment of an anti-HVEM antibody (e.g., an anti-HVEM Fab fragment) is derived from any of the antibodies described in WO-2006054961, US-8188232, and Park et al., Cancer Immunol Immunother. (2012) 61(2):203-14.

[0390] In some embodiments, the T cell stimulator is immobilized on a solid support. In some embodiments, the solid support is a bead. In some embodiments, the bead is biocompatible, e.g., composed of a material suitable for biological use. In some embodiments, the bead is non-toxic to T cells.

[0391] In some embodiments, the beads have a diameter of greater than about 0.001 μm, greater than about 0.01 μm, greater than about 0.1 μm, greater than about 1.0 μm, greater than about 10 μm, greater than about 50 μm, greater than about 100 μm, or greater than about 1000 μm, up to about 1500 μm. In some embodiments, the beads have a diameter of about 1.0 μm to about 500 μm, about 1.0 μm to about 150 μm, about 1.0 μm to about 30 μm, about 1.0 μm to about 10 μm, about 1.0 μm to about 5.0 μm, about 2.0 μm to about 5.0 μm, or about 3.0 μm to about 5.0 μm. In some embodiments, the beads have a diameter of about 3 μm to about 5 μm. In some embodiments, the beads have a diameter of at least about or at least about 0.001 μm, 0.01 μm, 0.1 μm, 0.5 μm, 1.0 μm, 1.5 μm, 2.0 μm, 2.5 μm, 3.0 μm, 3.5 μm, 4.0 μm, 4.5 μm, 5.0 μm, 5.5 μm, 6.0 μm, 6.5 μm, 7.0 μm, 7.5 μm, 8.0 μm, 8.5 μm, 9.0 μm, 9.5 μm, 10 μm, 12 μm, 14 μm, 16 μm, 18 μm, or 20 μm. In certain embodiments, the beads have a diameter of 4.5 μm or about 4.5 μm. In certain embodiments, the beads have a diameter of 2.8 μm or about 2.8 μm.

[0392] In some embodiments, the beads have a density of 0.001 g / cm 3 Greater than 0.01g / cm 3 Greater than 0.05g / cm 3 Greater than 0.1g / cm 3 Greater than 0.5g / cm 3 Greater than 0.6g / cm 3 Greater than 0.7g / cm 3 Greater than 0.8g / cm 3 Greater than 0.9g / cm 3 Greater than 1g / cm 3 Greater than 1.1g / cm 3 Greater than 1.2g / cm 3 Greater than 1.3g / cm 3 Larger, 1.4g / cm 3 Greater than 1.5g / cm3 Greater than 2g / cm 3 Greater than 3g / cm 3 Greater than 4g / cm 3 Greater than or equal to 5g / cm 3 In some embodiments, the beads have a density greater than about 0.001 g / cm 3 to about 100 g / cm 3 , about 0.01g / cm 3 to about 50 g / cm 3 , about 0.1g / cm 3 to about 10 g / cm 3 , about 0.1g / cm 3 to approximately 0.5g / cm 3 , about 0.5g / cm 3 to about 1 g / cm 3 , about 0.5g / cm 3 to about 1.5 g / cm 3 , about 1g / cm 3 to about 1.5 g / cm 3 , about 1g / cm 3 to about 2 g / cm 3 or about 1 g / cm 3 to about 5 g / cm 3 In some embodiments, the beads have a density of between about 0.5 g / cm 3 , about 0.5g / cm 3 , about 0.6g / cm 3 , about 0.7g / cm 3 , about 0.8g / cm 3 , about 0.9g / cm 3 , about 1.0g / cm 3 , about 1.1g / cm 3 , about 1.2g / cm 3 , about 1.3g / cm 3 , approximately 1.4 g / cm 3 , about 1.5g / cm 3 , about 1.6g / cm 3 , about 1.7g / cm 3 , about 1.8g / cm 3 , about 1.9g / cm 3 or approximately 2.0 g / cm 3 In one particular embodiment, the beads have a density of about 1.6 g / cm 3In certain embodiments, the beads have a density of about 1.5 g / cm 3 In one particular embodiment, the beads have a density of about 1.3 g / cm 3 It has a density of

[0393] In some embodiments, the plurality of beads has a uniform density, hi some embodiments, the uniform density has a density standard deviation of less than 10%, less than 5%, or less than 1% of the average bead density.

[0394] In some embodiments, the beads respond in a magnetic field. In some embodiments, the beads are magnetic beads. In some embodiments, the beads are paramagnetic. In certain embodiments, the beads are superparamagnetic. In certain embodiments, the beads do not exhibit any magnetic properties until exposed to a magnetic field.

[0395] In certain embodiments, the beads contain a magnetic core. In certain embodiments, the beads contain a paramagnetic core. In certain embodiments, the beads contain a superparamagnetic core. In some embodiments, the core contains a metal. In some embodiments, the metal can be iron, nickel, copper, cobalt, gadolinium, manganese, tantalum, zinc, zirconium, or any combination thereof. In certain embodiments, the core contains a metal oxide (e.g., iron oxide), a ferrite (e.g., manganese ferrite, cobalt ferrite, nickel ferrite, etc.), hematite, and / or a metal alloy (e.g., CoTaZn). In some embodiments, the core contains one or more of ferrite, a metal, a metal alloy, iron oxide, and chromium dioxide. In some embodiments, the core contains elemental iron or a compound thereof. In some embodiments, the core contains one or more of magnetite (FeO), maghemite (γFeO), and greigite (FeS). In some embodiments, the core contains iron oxide (e.g., FeO).

[0396] In some embodiments, the beads contain at least one material on or near the bead surface that can be coupled, linked, or conjugated to an agent. In some embodiments, the T cell stimulator is immobilized on the bead via this material. In some embodiments, the beads are surface-functionalized, e.g., have functional groups that can form covalent bonds with binding molecules, e.g., polynucleotides or polypeptides. In certain embodiments, the beads have surface-exposed carboxyl, amino, hydroxyl, tosyl, epoxy, and / or chloromethyl groups. In certain embodiments, the beads have surface-exposed agarose and / or sepharose. In some embodiments, the beads have surface-exposed protein A, protein G, or biotin.

[0397] In certain embodiments, the beads contain a magnetic, paramagnetic, and / or superparamagnetic core covered with a surface-functionalized coat or coating. In some embodiments, the coat may contain materials that may include polymers, polysaccharides, silica, fatty acids, proteins, carbon, agarose, sepharose, or combinations thereof. In some embodiments, the polymer may be polyethylene glycol, polylactic-co-glycolic acid, polyglutaraldehyde, polyurethane, polystyrene, or polyvinyl alcohol. In certain embodiments, the outer coat or coating comprises polystyrene. In certain embodiments, the outer coating is surface-functionalized.

[0398] In some embodiments, the cell population containing T cells is incubated with a stimulatory reagent at a bead-to-cell ratio of 3:1, 2.5:1, 2:1, 1.5:1, 1.25:1, 1.2:1, 1.1:1, 1:1, 0.9:1, 0.8:1, 0.75:1, 0.67:1, 0.5:1, 0.3:1, or 0.2:1 or about 3:1, about 2.5:1, about 2:1, about 1.5:1, about 1.25:1, about 1.2:1, about 1.1:1, about 1:1, about 0.9:1, about 0.8:1, about 0.75:1, about 0.67:1, about 0.5:1, about 0.3:1, or about 0.2:1. In certain embodiments, the ratio of beads to cells is between 2.5:1 and 0.2:1, between 2:1 and 0.5:1, between 1.5:1 and 0.75:1, between 1.25:1 and 0.8:1, or between 1.1:1 and 0.9:1. In certain embodiments, the ratio of beads to cells is about 1:1 or is 1:1.

[0399] In some embodiments, the T cell stimulator is not immobilized on a solid support, for example, not immobilized on a bead. In some embodiments, the T cell stimulator is part of a stimulating reagent that is in a soluble form. Exemplary soluble forms of T cell stimulating reagents are described in US2021 / 0032297 (see also Poltorak et al., Scientific Reports (2020)).

[0400] In some embodiments, the T cell stimulator is immobilized on the streptavidin mutein reagent in oligomeric form. In some embodiments, the T cell stimulator is reversibly immobilized on the streptavidin mutein reagent in oligomeric form. In some embodiments, the streptavidin mutein reagent in oligomeric form is an oligomer or polymer of streptavidin mutein.

[0401] In some embodiments, the T cell stimulator comprises a binding partner that is reversibly bound to a streptavidin mutein molecule of the oligomeric form of the streptavidin mutein reagent, hi some embodiments, the binding partner is fused to the C-terminus of the heavy chain of the T cell stimulator.

[0402] In some embodiments, the binding partner reversibly binds to the biotin-binding site of the streptavidin mutant protein. In some embodiments, the binding of the binding partner to the streptavidin mutant protein is hindered by the presence of biotin. In some embodiments, the biotin is D-biotin.

[0403] In some embodiments, one or both of the binding partners is biotin, a biotin analog, or a streptavidin-binding peptide. In some embodiments, one or both of the binding partners is a streptavidin-binding peptide. In some embodiments, the streptavidin-binding peptide comprises the sequence set forth in any of SEQ ID NOs: 9-15. In some embodiments, the sequence of the streptavidin-binding peptide is set forth in any of SEQ ID NOs: 9-15. In some embodiments, the streptavidin-binding peptide comprises the sequence set forth in SEQ ID NO: 15. In some embodiments, the sequence of the streptavidin-binding peptide is set forth in SEQ ID NO: 15.

[0404] In some embodiments, the streptavidin mutein reagent in oligomeric form comprises between or about 2,000 and 3,000 tetramers of streptavidin mutein, hi some embodiments, the streptavidin mutein reagent in oligomeric form comprises about 2,400 tetramers of streptavidin mutein.

[0405] In some embodiments, the streptavidin mutant protein reagent in oligomeric form has a radius of between or about 90 and 110 nm. In some embodiments, the streptavidin mutant protein reagent in oligomeric form has a radius of about 100 nm. In some embodiments, the radius is the hydrodynamic radius.

[0406] In some embodiments, the individual molecules of the streptavidin mutein reagent in oligomeric form are cross-linked by a bifunctional linker. In some embodiments, the bifunctional linker is a heterobifunctional linker. In some embodiments, the bifunctional linker is an amine-thiol linker.

[0407] In some embodiments, the streptavidin mutant protein reversibly binds to biotin, a biotin analog, or a streptavidin-binding peptide. In some embodiments, the streptavidin mutant protein reversibly binds to a streptavidin-binding peptide. In some embodiments, the streptavidin-binding peptide comprises a sequence set forth in any of SEQ ID NOs: 9-15. In some embodiments, the sequence of the streptavidin-binding peptide is set forth in any of SEQ ID NOs: 9-15.

[0408] In some embodiments, the streptavidin mutein comprises one or more mutations compared to wild-type streptavidin. In some embodiments, the streptavidin mutein comprises one or more mutations compared to the amino acid sequence set forth in SEQ ID NO: 16.

[0409] In some embodiments, the streptavidin mutein comprises one or more mutations compared to minimal streptavidin. In some embodiments, the minimal streptavidin begins at the N-terminus in the region of amino acid positions 10-16 and ends at the C-terminus in the region of amino acid positions 133-142 compared to the sequence set forth in SEQ ID NO: 16. In some embodiments, the streptavidin mutein begins at the N-terminus in the region of amino acid positions 10-16 and ends at the C-terminus in the region of amino acid positions 133-142 compared to the sequence set forth in SEQ ID NO: 16.

[0410] In some embodiments, the sequence of the minimal streptavidin is from position Ala13 to Ser139 of the sequence of amino acids set forth in SEQ ID NO: 16. In some embodiments, the minimal streptavidin has an N-terminal methionine residue in place of Ala13.

[0411] In some embodiments, the sequence of a minimal streptavidin is set forth in SEQ ID NO: 17. In some embodiments, the streptavidin mutein comprises one or more mutations compared to the sequence of amino acids set forth in SEQ ID NO: 17.

[0412] In some embodiments, the sequence of a minimal streptavidin is set forth in SEQ ID NO: 18. In some embodiments, the streptavidin mutein comprises one or more mutations compared to the sequence of amino acids set forth in SEQ ID NO: 18.

[0413] In some embodiments, the streptavidin mutein comprises the amino acid sequence Val44-Thr45-Ala46-Arg47 (SEQ ID NO: 19) at positions corresponding to positions 44 to 47 of the amino acid sequence set forth in SEQ ID NO: 16. In some embodiments, the streptavidin mutein comprises the amino acid sequence Ile44-Gly45-Ala46-Arg47 (SEQ ID NO: 20) at positions corresponding to positions 44 to 47 of the amino acid sequence set forth in SEQ ID NO: 16.

[0414] In some embodiments, the streptavidin mutein further comprises the amino acid substitutions Glu117, Gly120, and Try121 ​​at sequence positions corresponding to the positions in the sequence of amino acids set forth in SEQ ID NO:16.

[0415] In some embodiments, the streptavidin mutein comprises a sequence of amino acids set forth in any of SEQ ID NOs: 21-28. In some embodiments, the streptavidin mutein comprises a sequence of amino acids set forth in SEQ ID NO: 21. In some embodiments, the sequence of the streptavidin mutein is set forth in SEQ ID NO: 21.

[0416] In some embodiments, incubating under T cell stimulating conditions is carried out for at least 12 hours. In some embodiments, incubating under T cell stimulating conditions is carried out for 48, 42, 36, 30, 24, 22, 20, 18, 16, or 12 hours, about 48, 42, 36, 30, 24, 22, 20, 18, 16, or 12 hours, or for less than 48, 42, 36, 30, 24, 22, 20, 18, 16, or 12 hours. In some embodiments, incubation is carried out for 16 to 24 hours or for about 16 to 24 hours. In certain embodiments, the incubation is for between or about 12 to 36 hours, 18 to 30 hours, or 24 hours. In some embodiments, the incubation is for 2 days, about 2 days, or less than 2 days. In some embodiments, the incubation is for 1 day, about 1 day, or less than 1 day.

[0417] C. Genetic manipulation In some embodiments, the cell population is genetically engineered to express a recombinant protein. In some embodiments, the provided methods include genetically engineering a cell population to express a recombinant protein. In some embodiments, a heterologous or recombinant polynucleotide encoding the recombinant protein is introduced into cells of the cell population. Any method of introducing a heterologous or recombinant polynucleotide into the genome of a cell, such as a T cell, that will result in integration of the polynucleotide encoding the recombinant protein can be used, including viral and non-viral methods of genetic engineering. Introduction of a polynucleotide encoding a recombinant protein, e.g., a heterologous or recombinant polynucleotide, into a cell can be carried out using any of several known vectors. Exemplary vectors are described in Section II-C-1. Such vectors include viral systems, including lentiviruses and gammaretroviruses. Exemplary methods include those for the introduction of a heterologous polynucleotide encoding a recombinant protein, including via viral, e.g., retroviral or lentiviral transduction.

[0418] Exemplary methods for genetic engineering of cells are described in US-11400115, US-20190112576, US-20190136186, US-11274278, US-20210032297, US-20200354677, US-20200384025, US-20210163893 and US-20220002669.

[0419] In some embodiments, heterologous or recombinant polynucleotides encoding recombinant proteins are introduced using non-viral methods, such as electroporation, calcium phosphate transfection, protoplast fusion, cationic liposome-mediated transfection, nanoparticles, e.g., lipid nanoparticles, tungsten particle-facilitated microparticle bombardment, strontium phosphate DNA co-precipitation, or other approaches described, for example, in US-10654928 and US-7446190. Transposon-based systems are also contemplated.

[0420] In certain embodiments, the cells are genetically engineered, transformed, or transduced after the cells have been stimulated, such as by any of the methods described herein, for example, in Section II-B. In certain embodiments, the cells are genetically engineered, transformed, or transduced within 72, 60, 48, 36, 24, or 12 hours, inclusive, about 72, 60, 48, 36, 24, or 12 hours, or within 72, 60, 48, 36, 24, or 12 hours, inclusive, from the start of stimulation. In certain embodiments, the cells are genetically engineered, transformed, or transduced within 3, 2, or 1 day, inclusive, about 3, 2, or 1 day, or within 3, 2, or 1 day, inclusive, from the start of stimulation. In certain embodiments, the cells are engineered, transformed, or transduced between or about 12 to 48 hours, 16 to 36 hours, or 18 to 30 hours after the initiation of stimulation. In certain embodiments, the cells are engineered, transformed, or transduced between or about 12 to 48 hours, 16 to 36 hours, or 18 to 30 hours after the initiation of stimulation. In certain embodiments, the cells are engineered, transformed, or transduced between or about 18 to 30 hours after the initiation of stimulation. In certain embodiments, the cells are engineered, transformed, or transduced at or about 16, 18, 20, 22, or 24 hours after the initiation of stimulation.

[0421] In some embodiments, the operation, e.g., transduction, is performed for 24 to 48 hours, 36 to 12 hours, 18 to 30 hours, or 24 hours or about 24 hours. In some embodiments, the operation, e.g., transduction, is performed for 24, 48, or 72 hours, or for about 24, 48, or 72 hours, or for 1, 2, or 3 days, or for about 1, 2, or 3 days, respectively. In certain embodiments, the operation, e.g., transduction, is performed for 24 hours ± 6 hours, 48 ​​hours ± 6 hours, or 72 hours ± 6 hours, or for about 24 hours ± 6 hours, 48 ​​hours ± 6 hours, or 72 hours ± 6 hours. In certain embodiments, the operation, e.g., transduction, is performed for 72 hours, 72 ± 4 hours, or for about 72 hours, about 72 ± 4 hours, or for 3 days or for about 3 days.

[0422] In certain embodiments, the genetic engineering method is carried out by contacting one or more cell populations with a nucleic acid molecule or polynucleotide encoding a recombinant protein or by introducing a nucleic acid molecule or polynucleotide encoding a recombinant protein into one or more cell populations. In certain embodiments, the nucleic acid molecule or polynucleotide is heterologous to the cell. In certain embodiments, the heterologous nucleic acid molecule or heterologous polynucleotide is not native to the cell. In certain embodiments, the heterologous nucleic acid molecule or heterologous polynucleotide encodes a protein, such as a recombinant protein, that is not naturally expressed by the cell. In certain embodiments, the heterologous nucleic acid molecule or polynucleotide is or contains a nucleic acid sequence not found in the cell prior to contact or introduction.

[0423] In some embodiments, cells are engineered, e.g., transduced, in the presence of a transduction adjuvant. Exemplary transduction adjuvants include polycations, fibronectin or fibronectin-derived fragments or variants, and RetroNectin. In certain embodiments, cells are engineered in the presence of polycations, fibronectin or fibronectin-derived fragments or variants, and / or RetroNectin. In certain embodiments, cells are engineered in the presence of a polycation that is polybrene, DEAE-dextran, protamine sulfate, poly-L-lysine, or cationic liposomes. In certain embodiments, cells are engineered in the presence of protamine sulfate.

[0424] In some embodiments, the genetic manipulation, e.g., transduction, is performed in any of the media described in Section II-B. In some embodiments, the genetic manipulation, e.g., transduction, is performed in serum-free media, e.g., any of those described in US-20210207080.

[0425] In some embodiments, the genetic manipulation, e.g., transduction, is performed in the presence of one or more recombinant cytokines, hi some embodiments, the genetic manipulation, e.g., transduction, is performed in the presence of any of the recombinant cytokines described in Section II-B, at any of the concentrations described in Section II-B.

[0426] In some embodiments, the cells are genetically engineered, transformed, or transduced in the presence of the same or similar medium as that present during stimulation. In some embodiments, the cells are genetically engineered, transformed, or transduced in a medium having the same cytokines as the medium present during stimulation. In certain embodiments, the cells are genetically engineered, transformed, or transduced in a medium having the same cytokines at the same concentrations as the medium present during stimulation.

[0427] In some embodiments, genetically engineering a cell is or includes introducing a polynucleotide, e.g., a heterologous or recombinant polynucleotide, into the cell by transduction. In some embodiments, the cell is transduced or undergoes transduction using a viral vector. In certain embodiments, the cell is transduced or undergoes transduction using a viral vector. In some embodiments, the virus is a retroviral vector, e.g., a gamma retroviral vector or a lentiviral vector. Methods for lentiviral transduction are known. Exemplary methods are described, for example, in Wang et al., J. Immunother. (2012) 35(9):689-701; Cooper et al., Blood (2003) 101:1637-1644; Verhoeyen et al., Methods Mol Biol. (2009) 506: 97-114; and Cavalieri et al., Blood. (2003) 102(2):497-505.

[0428] In some embodiments, transduction is carried out by contacting one or more cell populations with a nucleic acid molecule encoding a recombinant protein. In some embodiments, contacting can be achieved using centrifugation, such as spinoculation (e.g., centrifugal inoculation). Such methods include any of those described in US Pat. No. 1,042,835. Exemplary centrifuge chambers include those produced and sold by Biosafe SA, such as those for use with the Sepax® and Sepax® 2 systems, e.g., the A-200 / F and A-200 centrifuge chambers and various kits for use with such systems. Exemplary chambers, systems, and processing equipment and cabinets are described, for example, in US Pat. Nos. 6,123,655, 6,733,433, 2008,017,1951, and 6,733,433. Exemplary kits for use with such systems include single-use kits sold by BioSafe SA under the product names CS-430.1, CS-490.1, CS-600.1 and CS-900.2.

[0429] In certain embodiments, genetic engineering, such as by transforming (e.g., transducing) cells with a viral vector, further comprises one or more steps of incubating the cells after introducing or contacting the cells with the viral vector. In some embodiments, the cells, e.g., cells of a transformed cell population (also called "transformed cells"), are incubated following the process of genetically engineering, transforming, transducing, or transfecting the cells to introduce the viral vector into the cells.

[0430] In some embodiments, cells, e.g., transformed cells, are incubated after introduction of a heterologous or recombinant polynucleotide, e.g., a viral vector particle, is performed without further processing of the cells. In certain embodiments, prior to incubation, the cells are washed to remove or substantially remove exogenous or residual polynucleotides, e.g., viral vector particles, encoding the heterologous or recombinant polynucleotide, e.g., those remaining in the medium after the genetic engineering process following spinoculation.

[0431] In some embodiments, further incubation is performed under conditions that allow the viral vector to be integrated into the host genome of one or more cells. For example, further incubation provides time for the viral vector that can be attached to T cells after transduction, for example, by spinoculation, to be integrated into the genome of the cells and deliver the gene of interest. In some aspects, further incubation is performed under conditions that allow cells, for example, transformed cells, to rest or recover, and the culture of cells during incubation supports or maintains the health of the cells. In certain embodiments, cells are incubated under static conditions, for example, without centrifugation, shaking, rotation, rocking, or perfusion of medium, for example, continuous or semi-continuous perfusion.

[0432] It is within the level of ordinary skill in the art to assess or determine whether incubation has resulted in integration of the viral vector particles into the host genome, and therefore empirically determine the conditions for further incubation. In some embodiments, integration of the viral vector into the host genome can be assessed by measuring the expression level of a recombinant protein, such as a heterologous protein, encoded by a nucleic acid contained in the genome of the viral vector particle after incubation. Several well-known methods for assessing the expression level of a recombinant molecule may be used, for example, affinity-based methods, such as detection by immunoaffinity-based methods, such as flow cytometry, in the context of cell surface proteins. In some examples, expression is measured by detecting a transduction marker and / or a reporter construct. In some embodiments, a nucleic acid encoding a truncated surface protein is included in the vector and used as a marker for expression and / or its enhancement.

[0433] In certain embodiments, incubation is carried out under static conditions, e.g., conditions that do not involve centrifugation, shaking, rotation, rocking, or perfusion, e.g., continuous or semi-continuous perfusion, of the medium. In some embodiments, before or immediately after the start of incubation, e.g., within 5, 15, or 30 minutes, the cells are transferred (e.g., transferred under sterile conditions) into a container, e.g., a bag or vial, and placed in an incubator. In some embodiments, the cells are transferred into a container under closed or sterile conditions. In some embodiments, the container, e.g., a vial or bag, is then placed in an incubator for all or part of the incubation. In certain embodiments, the incubator is set at 16°C, 24°C, or 35°C, at about 16°C, about 24°C, or about 35°C, or at least 16°C, at least 24°C, or at least 35°C. In some embodiments, the incubator is set at 37°C, at about 37°C, or at 37°C ± 2°C, ± 1°C, ± 0.5°C, or ± 0.1°C.

[0434] In some embodiments, the incubation is carried out in a serum-free medium. In some embodiments, the serum-free medium is a defined and / or well-defined cell culture medium. In certain embodiments, the serum-free medium is a controlled culture medium that has been treated, for example, filtered, to remove inhibitors and / or growth factors. In some embodiments, the serum-free medium contains proteins. In certain embodiments, the serum-free medium may contain serum albumin, hydrolysates, growth factors, hormones, carrier proteins, and / or attachment factors.

[0435] In some embodiments, the further incubation is carried out in any of the media described in Section II-B. In some embodiments, the further incubation is carried out in serum-free media, such as any of those described in US-20210207080.

[0436] In some embodiments, the further incubation is carried out in the presence of one or more recombinant cytokines, hi some embodiments, the further incubation is carried out in the presence of any of the recombinant cytokines described in Section II-B at any of the concentrations described in Section II-B.

[0437] In some embodiments, the further incubation is in the presence of the same or similar medium as that present during manipulation. In some embodiments, the further incubation is in medium having the same cytokines as the medium present during manipulation. In certain embodiments, the further incubation is in medium having the same cytokines at the same concentrations as the medium present during manipulation.

[0438] In certain embodiments, the cells are further incubated in the absence of cytokines. In certain embodiments, the cells are further incubated in the absence of any recombinant cytokines. In certain embodiments, the cells are further incubated in the absence of recombinant IL-2, IL-7, and IL-15.

[0439] In certain embodiments, the cells are further incubated for 18 hours, 24 hours, 30 hours, 36 hours, 40 hours, 48 ​​hours, 54 hours, 60 hours, 72 hours, 84 hours, 96 hours or more than 96 hours, about 18 hours, about 24 hours, about 30 hours, about 36 hours, about 40 hours, about 48 hours, about 54 hours, about 60 hours, about 72 hours, about 84 hours, about 96 hours or more than 96 hours, or at least 18 hours, at least 24 hours, at least 30 hours, at least 36 hours, at least 40 hours, at least 48 hours, at least 54 hours, at least 60 hours, at least 72 hours, at least 84 hours, at least 96 hours or at least more than 96 hours after introduction of a polynucleotide encoding a heterologous or recombinant protein, e.g., a viral vector. In certain embodiments, the cells are further incubated for 1 day, 2 days, 3 days, 4 days, or more than 4 days, about 1 day, about 2 days, about 3 days, about 4 days, or more than 4 days, or at least 1 day, at least 2 days, at least 3 days, at least 4 days, or more than 4 days after introduction of a polynucleotide encoding a heterologous or recombinant protein, e.g., a viral vector. In some embodiments, the further incubation is carried out for an amount of time between 30 minutes and 2 hours, between 1 hour and 8 hours, between 6 hours and 12 hours, between 12 hours and 18 hours, between 16 hours and 24 hours, between 18 hours and 30 hours, between 24 hours and 48 hours, between 24 hours and 72 hours, between 42 hours and 54 hours, between 60 hours and 120 hours, between 96 hours and 120 hours, between 90 hours and 1 day and 7 days, between 3 days and 8 days, between 1 day and 3 days, between 4 days and 6 days, or between 4 days and 5 days prior to genetic manipulation. In some embodiments, the further incubation is for between or about 18 and 30 hours, hi certain embodiments, the further incubation is for at or about 24 hours, or at or about 1 day.

[0440] In certain embodiments, the total duration of the further incubation is about 12 hours, 18 hours, 24 hours, 30 hours, 36 hours, 42 hours, 48 ​​hours, 54 hours, 60 hours, 72 hours, 84 hours, 96 hours, 108 hours, or 120 hours, or at least 12 hours, at least 18 hours, at least 24 hours, at least 30 hours, at least 36 hours, at least 42 hours, at least 48 hours, at least 54 hours, at least 60 hours, at least 72 hours, at least 84 hours, at least 96 hours, at least 108 hours, or at least 120 hours. In certain embodiments, the total duration of the further incubation is 1 day, 2 days, 3 days, 4 days, or 5 days, about 1 day, about 2 days, about 3 days, about 4 days, or about 5 days, or at least 1 day, at least 2 days, at least 3 days, at least 4 days, or at least 5 days. In certain embodiments, the further incubation is completed in 120 hours, 108 hours, 96 hours, 84 hours, 72 hours, 60 hours, 54 hours, 48 ​​hours, 42 hours, 36 hours, 30 hours, 24 hours, 18 hours, or 12 hours, in about 120 hours, about 108 hours, about 96 hours, about 84 hours, about 72 hours, about 60 hours, about 54 hours, about 48 hours, about 42 hours, about 36 hours, about 30 hours, about 24 hours, about 18 hours, or about 12 hours, or within 120 hours, 108 hours, 96 hours, 84 hours, 72 hours, 60 hours, 54 hours, 48 ​​hours, 42 hours, 36 hours, 30 hours, 24 hours, 18 hours, or 12 hours. In certain embodiments, the further incubation is completed within 1 day, 2 days, 3 days, 4 days, or 5 days, about 1 day, about 2 days, about 3 days, about 4 days, or about 5 days, or within 1 day, 2 days, 3 days, 4 days, or 5 days.In some embodiments, the total duration of the further incubation is between or about 12 to 120 hours, 18 to 96 hours, 24 to 72 hours, or 24 to 48 hours, inclusive. In some embodiments, the total duration of the further incubation is between or about 12 to 120 hours, 18 to 96 hours, 24 to 72 hours, or 24 to 48 hours, inclusive. In some embodiments, the total duration of the further incubation is between or about 1 to 48 hours, 4 to 36 hours, 8 to 30 hours, or 12 to 24 hours, inclusive. In certain embodiments, the further incubation is carried out for or about 24 hours, about 48 hours, or about 72 hours, or for or about 1 day, 2 days, or 3 days, respectively. In certain embodiments, the further incubation is carried out for 24 hours ± 6 hours, 48 ​​hours ± 6 hours, or 72 hours ± 6 hours. In certain embodiments, the further incubation is carried out for at or about 72 hours or for at or about 3 days.

[0441] In some embodiments, the further incubation is completed between or about 24 to 120, 36 to 108, 48 to 96, or 48 to 72 hours, inclusive, after the start of stimulation. In some embodiments, the further incubation is completed at 120, 108, 96, 72, 48, or 36 hours, at about 120, 108, 96, 72, 48, or 36 hours, or within 120, 108, 96, 72, 48, or 36 hours from the start of stimulation. In some embodiments, the further incubation is completed within 5 days, 4.5 days, 4 days, 3 days, 2 days, or 1.5 days, or within about 5 days, about 4.5 days, about 4 days, about 3 days, about 2 days, or about 1.5 days from the start of stimulation, or within 5 days, 4.5 days, 4 days, 3 days, 2 days, or 1.5 days. In certain embodiments, the further incubation is completed 24 hours ± 6 hours, 48 ​​hours ± 6 hours, or 72 hours ± 6 hours after the start of stimulation. In some embodiments, the further incubation is completed after 72 hours or about 72 hours, or after 3 days or about 3 days.

[0442] 1. Virus particles In some embodiments, the nucleic acid sequence encoding the recombinant protein is contained in a viral particle. In some embodiments, the viral particle is a recombinant infectious viral particle. In some embodiments, the viral particle is a viral vector, such as a vector derived from simian virus 40 (SV40), adenovirus, or adeno-associated virus (AAV). In some embodiments, the viral particle is a recombinant lentiviral or retroviral vector, e.g., a gammaretroviral vector (see, e.g., Koste et al., Gene Therapy (2014) doi: 10.1038 / gt.2014.25; Carlens et al., Exp Hematol (2000) 28(10): 1137-46; Alonso-Camino et al. (2013) Mol Ther Nucl Acids 2, e93; Park et al., Trends Biotechnol. 2011 November 29(11): 550-557. In some embodiments, the viral particle is a recombinant lentiviral vector.

[0443] In some embodiments, the retroviral vector has a long terminal repeat (LTR), such as a retroviral vector derived from Moloney murine leukemia virus (MoMLV), myeloproliferative sarcoma virus (MPSV), murine embryonic stem cell virus (MESV), murine stem cell virus (MSCV), spleen focus-forming virus (SFFV), or adeno-associated virus (AAV). Many retroviral vectors are derived from murine retroviruses. In some embodiments, retroviruses include those derived from any avian or mammalian cell source. Retroviruses can be amphotropic, meaning that they can infect host cells of several species, including humans. In one embodiment, the expressed gene replaces the retroviral gag, pol, and / or env sequences. Many exemplary retroviral systems have been described [e.g., U.S. Pat. Nos. 5,219,740; 6,207,453; 5,219,740; Miller and Rosman (1989) BioTechniques 7:980-990; Miller, AD (1990) Human Gene Therapy 1:5-14; Scarpa et al. (1991) Virology 180:849-852; Bums et al. (1993) Proc. Natl. Acad. Sci. USA 90:8033-8037; and Boris-Lawrie and Temin (1993) Cur. Opin. Genet. Develop. 3: 102-109].

[0444] The viral vector genome can be constructed in the form of a plasmid that can be transfected into a packaging or producer cell line. In some embodiments, a nucleic acid encoding a recombinant protein, e.g., a recombinant receptor, is inserted or located in a region of the viral vector, e.g., a non-essential region of the viral genome. In some embodiments, the nucleic acid is inserted into the viral genome in place of a specific viral sequence, generating a virus that is replication-deficient.

[0445] Any of a variety of known methods can be used to generate retroviral particles whose genome contains the RNA copy of the viral vector genome.In some embodiments, the construction of virus-based gene delivery systems involves at least two components: first, packaging plasmids that contain structural proteins and the enzymes required to generate viral vector particles; and second, the viral vector itself, for example, the genetic material to be transferred.Biosafety protection can be incorporated into the design of one or both of these components.

[0446] In some embodiments, the packaging plasmid may contain all retroviral, e.g., HIV-1, proteins other than the envelope proteins (Naldini et al., 1998). In other embodiments, the viral vector may lack additional viral genes, such as those associated with pathogenesis, e.g., vpr, vif, vpu, and nef, and / or Tat, the major transactivator of HIV. In some embodiments, lentiviral vectors, e.g., HIV-based lentiviral vectors, contain only three genes of the parent virus: gag, pol, and rev, reducing or eliminating the possibility of wild-type virus reconstitution via recombination.

[0447] In some embodiments, the viral vector genome is introduced into a packaging cell line that contains all the components necessary for packaging the viral genome RNA transcribed from the viral vector genome into viral particles. Alternatively, the viral vector genome may contain one or more sequences of interest, such as one or more genes encoding viral components, in addition to the recombinant nucleic acid. However, in some embodiments, to prevent genome replication in target cells, the endogenous viral genes required for replication are removed and separately provided to the packaging cell line.

[0448] In some embodiments, packaging cell lines are transfected with one or more plasmid vectors containing the components necessary to produce particles. In some embodiments, packaging cell lines are transfected with a plasmid containing a viral vector genome, including a nucleic acid encoding an LTR, a cis-acting packaging sequence, and a sequence of interest, i.e., an antigen receptor, such as CAR, and one or more helper plasmids encoding viral enzymatic and / or structural components, such as Gag, pol, and / or rev. In some embodiments, multiple vectors are used to separate the various genetic components that produce retroviral vector particles. In some such embodiments, providing separate vectors to packaging cells reduces the chance of recombination events that could otherwise produce replication-competent viruses. In some embodiments, a single plasmid vector carrying all of the retroviral components can be used.

[0449] In some embodiments, retroviral vector particles, e.g., lentiviral vector particles, are pseudotyped to increase the efficiency of transduction of host cells. For example, in some embodiments, retroviral vector particles, e.g., lentiviral vector particles, are pseudotyped with the VSV-G glycoprotein, which provides a broader cellular host range, expanding the range of cell types that can be transduced. In some embodiments, packaging cell lines are transfected with a plasmid or polynucleotide encoding a non-native envelope glycoprotein, such as a xenotropic, polytropic, or amphotropic envelope, e.g., Sindbis virus envelope, GALV, or VSV-G.

[0450] In some embodiments, the packaging cell line provides components, including viral regulatory and structural proteins, required in trans for packaging viral genomic RNA into lentiviral vector particles. In some embodiments, the packaging cell line can be any cell line capable of expressing lentiviral proteins and producing functional lentiviral vector particles. In some aspects, suitable packaging cell lines include 293 (ATCC CCL X), 293T, HeLA (ATCC CCL 2), D17 (ATCC CCL 183), MDCK (ATCC CCL 34), BHK (ATCC CCL-10), and Cf2Th (ATCC CRL 1430) cells.

[0451] In some embodiments, the packaging cell line stably expresses viral protein(s). For example, in some aspects, packaging cell lines can be constructed that contain gag, pol, rev, and / or other structural genes, but lack the LTRs and packaging components. In some embodiments, packaging cell lines can be transiently transfected with nucleic acid molecules encoding one or more viral proteins, along with a viral vector genome containing a nucleic acid molecule encoding a heterologous protein and / or a nucleic acid encoding an envelope glycoprotein.

[0452] In some embodiments, the viral vector and packaging and / or helper plasmid are introduced into a packaging cell line via transfection or infection. The packaging cell line can produce viral vector particles containing the viral vector genome. Transfection or infection methods are well known. Examples include calcium phosphate, DEAE-dextran, and lipofection, electroporation, and microinjection.

[0453] When the recombinant plasmid and retroviral LTR and packaging sequence are introduced into a particular cell line (e.g., by calcium phosphate precipitation), the packaging sequence can allow the RNA transcript of the recombinant plasmid to be packaged into viral particles, which can then be secreted into the culture medium. In some embodiments, the medium containing the recombinant retrovirus is then collected, optionally concentrated, and used for gene transfer. For example, in some aspects, after co-transfection of the packaging plasmid and transfer vector into a packaging cell line, viral vector particles are recovered from the culture medium and titered by standard methods used by those skilled in the art.

[0454] In some embodiments, retroviral vectors, e.g., lentiviral vectors, can be produced in a packaging cell line, e.g., an exemplary HEK 293T cell line, by introducing a plasmid that allows for the production of lentiviral particles. In some embodiments, the packaging cells are transfected and / or contain polynucleotides encoding gag and pol and a recombinant receptor, e.g., a polynucleotide encoding an antigen receptor, e.g., a CAR. In some embodiments, the packag...

Claims

1. 1. A method for determining the cell phenotype of a T cell population, comprising: (a) obtaining holographic information about a cell population that includes T cells; (b) determining one or more input measures for each of a plurality of cellular features derived from the holographic information, wherein each input measure is derived from an individual cell in the cell population; (c) determining a plurality of population-level statistical values, wherein each population-level statistical value is a statistical value of one or more input measures for a cell feature of the plurality of cellular features, and the plurality of population-level statistical values ​​includes one or more population-level statistical values ​​for each of the plurality of cellular features; and (d) determining a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest in the cell population based on the plurality of population-level statistics; A method comprising:

2. 1. A method for determining a cell phenotype of a T cell population, comprising determining, for a cell population comprising T cells, a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest, wherein the population-level output measure is determined based on a plurality of population-level statistics; each population-level statistical value is a statistical value of one or more input measures for one cell feature of the plurality of cell features derived from holographic information obtained for the cell population, the plurality of population-level statistical values ​​including one or more population-level statistical values ​​for each of the plurality of cell features; and Each input measure originates from an individual cell in the cell population. method.

3. 3. The method of claim 2, comprising determining a plurality of population-level statistics from one or more input measures for each of a plurality of cellular features.

4. 4. The method of claim 2 or claim 3, comprising determining one or more input measures for each of a plurality of cellular features from the holographic information.

5. 5. A method according to any one of claims 2 to 4, comprising obtaining holographic information.

6. 6. The method according to claim 1, wherein the holographic information is obtained by differential digital holographic microscopy (DDHM).

7. 7. The method of claim 4, wherein obtaining holographic information comprises imaging the cell population using DDHM.

8. 8. The method of any one of claims 1 to 7, wherein the one or more population-level statistics for at least one, and optionally each, of the plurality of cellular features comprises one or more quantiles of one or more input measures of the cellular feature.

9. 9. The method of claim 8, wherein the one or more quantiles comprise one or more of the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of one or more input scales of the cell features.

10. 8. The method of claim 1, wherein one or more population-level statistics for at least one, and optionally each, of a plurality of cellular features are determined by applying a distribution-based pooling filter to one or more input measures of the cellular features.

11. 11. The method of any one of claims 1 to 10, wherein the population-level output measure is the number of cells, the percentage of cells, the proportion of cells or the density of cells in the cell population that express the marker.

12. 12. The method of any one of claims 1 to 11, wherein the cell population is derived from a cell culture that has been cultivated in vitro or ex vivo.

13. 13. The method of claim 12, wherein the holographic information is obtained during in vitro or ex vivo culturing of the cell culture.

14. 14. The method of claim 12 or claim 13, wherein the population-level output measure is a measure of expression of a marker during in vitro or ex vivo culturing of the cell culture.

15. 15. The method of any one of claims 12 to 14, wherein the in vitro or ex vivo culturing is carried out under conditions that allow the T cells of the cell culture to expand.

16. 16. The method according to any one of claims 12 to 15, wherein the in vitro or ex vivo culturing is carried out in a bioreactor.

17. 17. The method of any one of claims 1 to 16, wherein the cell population is incubated under T cell stimulating conditions before obtaining holographic information.

18. 18. The method of any one of claims 1 to 17, comprising incubating the cell population under T cell stimulating conditions prior to obtaining holographic information.

19. 19. The method of claim 17 or claim 18, wherein the incubation is before in vitro or ex vivo culture.

20. 20. The method of any one of claims 17 to 19, wherein the T cell stimulating conditions comprise incubation in the presence of a T cell stimulator that induces a primary activation signal and a costimulatory signal in the T cell.

21. 21. The method of claim 20, wherein the T cell stimulator comprises an anti-CD3 antibody or antibody fragment.

22. 22. The method of claim 20 or claim 21, wherein the T cell stimulator comprises an anti-CD28 antibody or antibody fragment.

23. 23. The method of any one of claims 20 to 22, wherein the T cell stimulator is immobilized on beads.

24. 23. The method of any one of claims 20 to 22, wherein the T cell stimulator is immobilized on a streptavidin mutein reagent in oligomeric form.

25. 25. The method of any one of claims 1 to 24, wherein recombinant receptors are introduced into T cells of the cell population before obtaining the holographic information.

26. 26. The method of any one of claims 1 to 25, comprising introducing a recombinant receptor into T cells of the cell population before obtaining the holographic information.

27. 27. The method of claim 25 or claim 26, wherein introducing comprises contacting the cell population with an agent comprising a polynucleotide encoding the recombinant receptor.

28. 28. The method of claim 26 or claim 27, wherein the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

29. 29. The method of any one of claims 1 to 28, wherein at least 50, 55, 60, 65, 70, 75, 80, 85, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99 or 100% of the cell population are T cells.

30. 30. The method of any one of claims 1 to 29, wherein the population-level output measure is determined by providing the plurality of population-level statistics as inputs to a machine learning model trained to predict the population-level output measure of expression of the marker based on the population-level statistical values ​​of the plurality of cellular features.

31. a machine learning model is trained using a dataset of reference population-level statistics; for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features; Each of the reference population level statistics is a statistical value of one or more reference input measures for one cell feature of the plurality of cell features derived from holographic information obtained for the reference cell population; and Each reference input measure is derived from an individual cell of the reference cell population; 31. The method of claim 30.

32. a machine learning model is trained using a dataset of reference population-level output measures, wherein for each of a second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of the markers of the reference cell population; the first and second plurality of reference cell populations are derived from a reference cell culture that has been cultured in vitro or ex vivo; 32. The method of claim 31 , wherein the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations.

33. 1. A method of training a machine learning model to predict a cell phenotype of a T cell population, comprising: (i) a dataset of reference population-level statistics, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference population-level statistics comprises one or more reference population-level statistics for each of a plurality of cellular features; each of the reference population level statistics is a statistical value of one or more reference input measures for one cell feature of the plurality of cell features derived from holographic information obtained for the reference cell population; and Each reference input measure is derived from an individual cell of the reference cell population; and (ii) a dataset of reference population-level output measures, wherein for each of the second plurality of reference cell populations, the dataset of reference population-level output measures comprises a reference population-level output measure of expression of markers expressed by T cells having the cell phenotype of interest in the reference cell population; the first and second plurality of reference cell populations are derived from a reference cell culture that has been cultured in vitro or ex vivo; and the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations; training a machine learning model using A method whereby a machine learning model is trained to predict a population-level output measure of expression of a marker based on population-level statistics of a plurality of cellular features.

34. 34. The method of any one of claims 1 to 33, wherein the plurality of cellular features comprises one or more of intensity skewness, intensity correlation, intensity uniformity, intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

35. 35. The method of any one of claims 1 to 34, wherein the plurality of cellular features comprises intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

36. 1. A method for determining the activation state of a T cell population, comprising: determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population; The plurality of cellular features include an intensity maximum, an intensity minimum, an intensity entropy, an intensity contrast, a phase entropy, a cell area, and a radius mean; and A method in which each input measure is derived from an individual cell of a cell population.

37. 37. The method of any one of claims 1 to 36, wherein the plurality of cellular features comprises intensity skewness, intensity correlation, and intensity uniformity.

38. 1. A method for determining the activation state of a T cell population, comprising: determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by activated T cells, wherein the marker is CD137 (4-1BB), wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population; The plurality of cellular features include intensity skewness, intensity correlation, and intensity uniformity; and A method in which each input measure is derived from an individual cell of a cell population.

39. 39. The method of claim 38, wherein the plurality of cellular features comprises intensity maximum, intensity minimum, intensity entropy, intensity contrast, phase entropy, cell area, and radius mean.

40. 34. The method of any one of claims 1 to 33, wherein the plurality of cellular features comprises one or more of peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height.

41. 41. The method of any one of claims 1 to 33 and 40, wherein the plurality of cellular features comprises peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height.

42. 34. The method of any one of claims 1 to 33, wherein the plurality of cellular features comprises one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

43. 43. The method of any one of claims 1 to 33 and 42, wherein the plurality of cellular features comprises cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

44. 1. A method for determining the memory phenotype of a T cell population, comprising: determining, for a cell population comprising T cells, a population-level output measure of expression of a marker expressed by central memory T cells or stem cell memory T cells, wherein the marker is CCR7, wherein the population-level output measure is determined based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population; The plurality of cellular features includes one or more of cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter; and A method in which each input measure is derived from an individual cell of a cell population.

45. 45. The method of claim 44, wherein the plurality of cellular features comprises cell area, perimeter, mean intensity, normalized peak area, and equivalent peak diameter.

46. 1. A method for determining recombinant receptor expression in a T cell population, comprising: determining, for a cell population comprising T cells, expression of a recombinant receptor introduced into the T cells of the cell population, wherein the determining is based on one or more input measures for each of a plurality of cellular features derived from holographic information obtained for the cell population; The plurality of cellular features include peak area, phase average uniformity, intensity geometric mean, minimum optical height, and normalized optical height; and A method in which each input measure is derived from an individual cell of a cell population.

47. one or more input measures for at least one, and optionally each, of the plurality of cellular features are determined by providing holographic information about individual cells of the cell population to a convolutional neural network; The plurality of cellular features are cellular features extracted by a convolutional neural network; and 34. The method of any one of claims 1 to 33, wherein the one or more input measures are determined from a convolutional neural network.

48. one or more reference input measures for at least one, and optionally each, of the plurality of cellular features are determined by providing holographic information for individual cells of the reference cell population to a convolutional neural network; The plurality of cellular features are cellular features extracted by a convolutional neural network; and 48. The method of any one of claims 31 to 33 and 47, wherein the one or more reference input measures are determined from a convolutional neural network.

49. 49. The method of claim 47 or claim 48, wherein the convolutional neural network is trained using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population.

50. 50. The method of any one of claims 31-35, 37, 40-43, and 47-49, wherein the one or more reference population-level statistics for at least one, and optionally each, of the plurality of cellular features comprises one or more quantiles of one or more reference input measures of the cellular features.

51. 51. The method of claim 50, wherein one or more quantiles are selected from the 0.01, 0.1, 0.5, 0.9, and 0.99 quantiles of one or more reference input scales of said cellular features.

52. 50. The method of any one of claims 31-35, 37, 40-43, and 47-49, wherein one or more reference population-level statistics for at least one, and optionally each, of the plurality of cellular features are determined by applying a distribution-based pooling filter to one or more reference input measures of the cellular features.

53. 1. A method of training a machine learning model to predict a cell phenotype of a T cell population, comprising: (a) training a convolutional neural network using a dataset of reference holographic information, wherein for each of a first plurality of reference cell populations comprising T cells, the dataset of reference holographic information comprises holographic information obtained for individual cells of the reference cell population; (b) determining, from the convolutional neural network, one or more reference input measures for each cellular feature of the plurality of cellular features derived from the holographic information, where the plurality of cellular features are cellular features extracted by the convolutional neural network and each reference input measure is derived from an individual cell of the reference cell population; (c) determining a dataset of reference population-level statistics, wherein the dataset of reference population-level statistics includes one or more reference population-level statistics for each of the plurality of cellular features, each of the reference population-level statistics being determined by applying a distribution-based pooling filter to one or more reference input measures for a cellular feature of the plurality of cellular features; and (d) training a machine learning model using the dataset of reference population-level statistics and the dataset of reference population-level output measures, wherein for each of the second plurality of reference cell populations, the dataset of reference population-level output measures comprises reference population-level output measures of expression of markers expressed by T cells having the cell phenotype of interest of the reference cell population; the first and second plurality of reference cell populations are derived from a reference cell culture that has been cultured in vitro or ex vivo; and the reference cell population from the first plurality of reference cell populations is derived from the same reference cell culture as the reference cell population from the second plurality of reference cell populations; Including, A method whereby a machine learning model is trained to predict a population-level output measure of expression of a marker based on population-level statistics of a plurality of cellular features.

54. 54. The method of any one of claims 47-53, wherein the one or more input measures for at least one, and optionally each, of the plurality of cellular features are obtained from a fully connected layer of a convolutional neural network.

55. 55. The method of any one of claims 31 to 35, 37, 40 to 43, and 47 to 54, wherein the holographic information for the first plurality of reference cell populations is obtained by DDHM.

56. 56. The method of any one of claims 1 to 55, wherein the holographic information comprises phase information and intensity information.

57. 57. The method of any one of claims 32 to 35, 37, 40 to 43, and 47 to 56, wherein the reference population level output measure is the number of cells, the percentage of cells, the proportion of cells, or the density of cells of the reference cell population that express the marker.

58. 58. The method of any one of claims 32-35, 37, 40-43, and 47-57, wherein the dataset of reference population level statistics and the dataset of reference population level output measures are time-matched.

59. 59. The method of any one of claims 32 to 35, 37, 40 to 43, and 47 to 58, wherein the in vitro or ex vivo culturing of the reference cell culture is performed under the same or similar conditions as the in vitro or ex vivo culturing of the cell culture.

60. 60. The method of any one of claims 32 to 35, 37, 40 to 43, and 47 to 59, wherein the holographic information about the first plurality of reference cell populations is obtained during in vitro or ex vivo culturing of the reference cell culture.

61. 61. The method of any one of claims 32 to 35, 37, 40 to 43, and 47 to 60, wherein the dataset of reference population-level output measures is a dataset of measures of expression of markers during or after in vitro or ex vivo culture of the reference cell culture.

62. 62. The method of any one of claims 32-35, 37, 40-43, and 47-61, wherein the dataset of reference population-level output measures is determined using fluorescent imaging of a second plurality of reference cell populations.

63. 63. The method of claim 62, wherein the fluorescent imaging is by flow cytometry.

64. 64. The method of any one of claims 1 to 35, 37, and 47 to 63, wherein the method is for determining the activation state of a T cell population and the marker is expressed by activated T cells.

65. 65. The method of any one of claims 1 to 35, 37, and 47 to 64, wherein the marker is CD137 (4-1BB).

66. 64. The method of any one of claims 1 to 35 and 42 to 63, wherein the method is for determining the memory phenotype of a T cell population and the marker is expressed by T cells having a central memory phenotype or a stem cell memory phenotype.

67. 67. The method of claim 66, wherein the marker is expressed by T cells with a central memory phenotype.

68. 67. The method of claim 66, wherein the marker is expressed by T cells having a stem cell memory phenotype.

69. 69. The method of any one of claims 1 to 35, 42 to 63, and 66 to 68, wherein the marker is CCR7.

70. 64. A method according to any one of claims 1 to 33 and 40 to 63, wherein the method is for determining recombinant receptor expression in a T cell population and the marker is a recombinant receptor introduced into the T cells of the cell population prior to obtaining holographic information.

71. 71. The method of any one of claims 46 to 63 and 70, wherein the recombinant receptor is a chimeric antigen receptor (CAR) or an engineered T cell receptor (TCR).

72. 72. The method of any one of claims 31-35, 37, 40-43, and 47-71, wherein the first plurality of reference cell populations is incubated under T cell stimulating conditions prior to obtaining holographic information about the first plurality of reference cell populations.

73. 73. The method of claim 72, wherein the incubation of the first plurality of reference cell populations is performed under the same or similar conditions as the incubation of the cell population.

74. 74. The method of claim 72 or claim 73, wherein the incubation of the first plurality of reference cell populations is prior to the in vitro or ex vivo culturing of the reference cell culture.

75. 75. The method of any one of claims 31-35, 37, 40-43, and 47-74, wherein the first and / or second plurality of reference cell populations are enriched for T cells.

76. 96, 97, 98, 99, or 100% of the first and / or second plurality of reference cell populations are T cells.

77. 77. A method for monitoring a cell phenotype of a T cell culture, comprising determining, for a cell population derived from a cell culture comprising T cells that have been cultured in vitro or ex vivo, a population-level output measure of expression of markers expressed by T cells having a cell phenotype of interest, wherein the population-level output measure is determined according to the method of any one of claims 1-32, 34, 35, 37, 40-43, 47-52, and 54-76.

78. 10. A computing device comprising in a memory instructions for carrying out the method of any one of claims 1 to 32, 34, 35, 37, 40 to 43, 47 to 52, and 54 to 76, the instructions comprising: (a) instructions for receiving holographic information for individual cells of a cell population comprising T cells, one or more input measures for each of a plurality of cellular characteristics of individual cells of a cell population comprising T cells, or a plurality of population-level statistics for a cell population comprising T cells; and (b) instructions for determining a population-level output measure for the cell population from the holographic information, the one or more input measures for each of the plurality of cellular features, or the plurality of population-level statistics according to the method; a computing device,

79. 79. The computing device of claim 78, further comprising in memory a machine learning model trained according to the method of any one of claims 33-35, 37, 40-43, and 47-76, wherein a population-level output measure for the cell population is determined using the machine learning model.

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