Advanced biophysical and biochemical cellular monitoring and quantification

Abstract

Described are methods, devices, and systems for measuring cellular responses to differential stimuli using one or more analyzers (e.g., optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic force-based analyzers). The methods may include receiving a selection of an initial samples (e.g., containing treated biological cells), performing measurements (e.g., force-based measurements) on the samples, and at least one of: developing a metric to describe the cellular fingerprint based on one or more optical or fluidic force-based parameters, or correlating the cellular response to a cellular property of interest (e.g., cell viability and / or vitality).

Description

ADVANCED BIOPHYSICAL AND BIOCHEMICAL CELLULAR MONITORING AND QUANTIFICATIONFIELD

[0001] Embodiments of the present disclosure relate generally to methods, systems, and devices for measuring cellular properties and / or responses to differential stimuli using optical and / or fluidic forces to describe a cellular fingerprint based and / or to correlate the cellular response to cellular property (e.g., viability and / or vitality).BACKGROUND

[0002] The serum virus neutralization assay is the gold standard for measuring the ability of in vivo-derived immunity to inhibit viral infection and / or replication. Neutralization assays are used to determine the efficacy of serum-derived antibodies to reduce or block viral infection and / or subsequent replication in cells in culture. Human or animal cells are treated in vitro with combinations of infectious viral agents and in vivo-derived serum antibodies in order to examine whether the serum-derived antibodies are specific for and effective against the infection and / or replication of the viral agent within the cells in vitro.

[0003] Additional analysis is required for these types of analytical experiments. The plaque assay and plaque reduction neutralization test (PRNT) both measure the number of infectious viral particles per unit volume of sample, the latter also measuring the reduction in infectious units as a result of a neutralizing serum or other agent. The assay involves placing a virus containing solution on growing adherent cells in a plate, applying an overlay (typically agarose) to prevent the free spread of virus and then waiting between 3 and 15 days for regions of dead or cleared cells (plaques) to develop as a result of a single infectious virus particle. Similarly, the tissue culture infectious dose 50 (TCID50) is a measure of the concentration of infectious virus in a specific volume by performing the endpoint dilution assay. The TCID50 is defined as the dilution of virus required to infect 50% of a given batch of inoculated wells of cells in culture.

[0004] Although these methods have been used for decades, there are inherent challenges to performing them with reliability and reproducibility of results between experiments and operators. There are also limitations of the assays with respect to analyzing cells in suspension, requirements for a high number of samples (for dilution calculations), time-consuming and subjective techniques for analysis and undesirable consequences such as cell deathand / or alteration of infection parameters resulting from cell manipulations. One reason for the large number of required dilutions is the limited dynamic range and high variability of current methodologies.

[0005] Known methods and apparatuses use optical density and various constraints to determine a neutralization titer such as analyzing and plotting the maximum optical density of each sample However, these techniques only use optical density and do not utilize microfluidic and / or optical forces, and neither do they incorporate automatic real-time grid search methods to determine which samples need to be read / analyzed to generate the results of the experiment. Another semi -automated system utilizes a light source, not optical forces, and is not fully automated.

[0006] Another method utilizes inactivated fluorescently-labeled virus monitored by flow cytometry in order to reduce the safety precautions required for experimental manipulation and / o the use of a pseudovirion reporter gene. However, these techniques are limited in that large numbers of samples must be analyzed due to cumbersome tagging or modification of sample cells or infectious agents used in the assays. As modification of cells and infectious agents has been shown to activate, differentiate, or alter infectivity and / or function, what is needed is label-free analysis that provides a direct measurement of biological particles (e.g., individual cells, clusters of cells, etc.).

[0007] Further known methods use a plaque transfer assay for detecting retrovirus and measuring neutralizing antibodies. However, such methods limit the experimenter to the use of mono layer cell types. In reality, not all viruses that infect cells form a mono layer. What is needed are methods and devices that enable the use of suspension or matrix-embedded cells for infection study and analysis thereby allowing a larger variety of cell types to be used in experimentation for viral infection.

[0008] Other methods use calibration objects (e.g., beads or cells), but such methods are limited to the use of calibration objects in the context of a time-delay -integration (TDI) detector. Functionality of the TDI detector relies on shifting the lines of photon-induced charge in the solid-state detector (such as a charge-couple device array) in synchronization of the flow of the specimen, and the calibration objects are used to enhance the performance of this system. Furthermore, not only are the calibration beads of the prior art limited to calibrating flow and aligning TDI detectors, they are not used to calibrate analytical information for data correction, normalization, quantitation, or calculations of physical or chemical information such as refractive index (ratio of refractive indices of bead / artificial cells, for example). What is lacking is the teaching or use of calibration objects that describemeasurements such as optical force, optical torque, optical dynamics, effective refractive index, size, shape, or related measurements wherein said objects are polymer, glass, biologic, lipid, vesicles, or cell (live or fixed) based. What is also lacking is a teaching of calibration objects having properties related to the particles of interest, yet not interfering with data collection on samples of interest.BRIEF SUMMARY OF THE ILLUSTRATIVE EMBODIMENTS

[0009] Described herein according to various embodiments are methods for measuring cellular properties and / or responses to differential stimuli using optical and / or fluidic forces, wherein the method comprises receiving a selection of initial samples comprising biological cells, performing optical and / or fluidic force-based measurements on the samples; and at least one of: developing a response metric (RM) to describe the cellular fingerprint based on one or more optical or fluidic force-based parameters; or correlating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.

[0010] Further described herein according to embodiments are methods for generating a calibration curve based on cellular changes during the production of a biologic molecule or other ongoing bioprocess that correlates the cellular response to cellular viability and / or vitality and then using the calibration to predict the results of a future process. The method may comprise adding treatments and incubating sample cells; analyzing by optical and / or fluidic force-based measurements a plurality of samples having cells and a known range of product concentrations, process outcomes, and / or product characteristics to determine a response metric; determining optimal response metric based on trend with dilution, and using generated data to predict cellular viability and / or vitality of future samples.

[0011] In embodiments are methods of using Laser Force Cytology (LFC), optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces for 1) measuring cell viable state, viability, vitality, cell health, and / or cellular function; 2) characterizing and / or sorting cells; 3) characterize and / or measure instantaneous viability / vitality; 4) for characterizing cells to create a predicting tool to measure cellular status and trends, including average and standard deviation; and / or 5) predicting future viability / vitality.

[0012] In further embodiments, described are methods using optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to create a functional cell-based assay to evaluate or correlate one or more of: a) stress on cells, including physical, chemical,biological, radiological stress, and measure a response; or b) correlate instantaneous response with future recovery of cell function, viability, and / or vitality.

[0013] Further described are methods for using optical, fluidic / hydrodynamic, electroki-netic, and / or acoustic forces to generate date concerning one or more of: a) treatment of a cell with H2O2and better response than traditional assay (trypan blue); b) T Cell activation disruption using a drug such as doxorubicin; or c) DMSO, Dox, fixing / permeability for disruption of viability and / or vitality.

[0014] In yet further embodiments, described herein are methods for identification of cells using differential stimuli using optical and / or fluidic forces. The methods may include receiving a selection of an initial samples comprising biological cells treated with varying known levels of stimuli or analyte; performing optical and / or fluidic force-based measurements on the samples; developing a response metric (RM) to describe the cellular response to the stimuli based on one or more optical or fluidic force-based parameters; and identification of cells by correlating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.

[0015] Further described herein are methods for identifying cells using optical and / or fluidic forces, wherein the method comprises: receiving a selection of initial samples comprising biological cells; performing optical and / or fluidic force-based measurements on the samples; developing a response metric (RM) to describe the cells based on one or more optical or fluidic force-based parameters; and identifying cells by correlating the parameters to a cellular property of interest, wherein the cellular property of interest comprises cellular viability and / or vitality.

[0016] In further embodiments, are methods for measuring cells using optical and / or fluidic forces of, wherein the method comprises: receiving a selection of initial samples comprising treated biological cells; performing optical and / or fluidic force-based measurements on the samples; developing a response metric (RM) to describe the cellular fingerprint based on one or more optical or fluidic force-based parameters; and correlating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.BRIEF SUMMARY OF THE DRAWINGS

[0017] The foregoing summary, as well as the following detailed description of illustrative embodiments of the present application, will be better understood when read in conjunction with the appended drawings. For the purposes of illustrating the present application, there is shown in the drawings illustrative embodiments of the disclosure. It should be understood, however, that the application is not limited to the precise arrangements and instrumentalities shown:

[0018] FIG. 1A is a representation of a methodology for selecting sequential dilutions and calculating TCID50 / mL or percent neutralization on cell culture well plates and defining the results as an Infection Metric.

[0019] FIG. 1B is a provides a flow chart demonstrating a methodology as used herein (IM refers to the Infection Metric, OLDR refers to the Optimal Linear Dynamic Range).

[0020] FIG. 2A is a schematic of a system for measuring cellular responses to differential stimuli using optical and / or fluidic forces according to various embodiments.

[0021] FIG. 2B is a schematic of a system for measuring cellular responses to differential stimuli using optical and / or fluidic forces including the addition of sample to cells (with incubation prior and / or post addition) according to various embodiments.

[0022] FIG.2C is a diagram depicting a microfluidic device suitable for use in the methods and systems described herein.

[0023] FIG.2D is a diagram depicting a microfluidic device suitable for use in the methods and systems described herein

[0024] FIG. 3A is a chart comparing the counts as a function of optical force index for a reference population to a sample of the same conditions measured using a laser force cytology analyzer according to embodiments herein.

[0025] FIG. 3B is a chart comparing the counts as a function of optical force index for a reference population to a biological sample having a higher fetal bovine serum (FBS) concentration as measured using a laser force cytology analyzer according to embodiments herein.

[0026] FIG. 3C is a chart comparing the counts as a function of optical force index for a reference population to a biological sample having no dimethyl sulfoxide (DMSO) as measured using a laser force cytology analyzer according to embodiments herein.

[0027] FIG. 3D is a chart comparing the counts as a function of optical force index for a reference population to a biological sample with uncontrolled freezing as measured using a laser force cytology analyzer according to embodiments herein.

[0028] FIG. 4 is a chart showing percent viability as a function of average optical force index when monitoring process / condition changes according to embodiments herein.

[0029] FIG. 5 is a chart showing precision monitoring for cellular fitness and function demonstrating the difference in viability vs. vitality according to embodiments herein for cell samples treated with different levels of reactive oxygen species (ROS) and sampled at different time points post treatment.

[0030] FIG. 6 is a chart showing cellular vitality through oxidative stress monitoring represented by a velocity and eccentricity threshold metric according to embodiments herein.

[0031] FIG.7 is a chart showing Vero cells treated with H2O2(oxidative stress) represented by a velocity / eccentricity threshold metric (%) according to embodiments herein and measured at different points during the viral production process.

[0032] FIG. 8 is a chart showing the change in optical force index (OFI) threshold between multiplicity of infection (MOI) 0.1 and uninfected cells represented by a change in OFI threshold metric (%) according to embodiments herein.

[0033] FIG. 9 is a chart showing a prediction of process success using laser force cytology (LFC) analysis of viability / vitality metrics according to embodiments herein.

[0034] FIG. 10 is a T cell activation process roadmap showing T-cell data across five (5) experiments and three (3) different donors using LFC data according to embodiments herein, creating a process average and boundary, and using that boundary to characterize samples from a deviated or changed process.

[0035] FIG. 11 is a chart showing direct detection and correlation of T Cell viability represented by the expected viability (Radiance®) as a function of the measured viability (trypan blue) according to embodiments herein.

[0036] FIG. 12 is a chart showing LFC measured vitality changes in T Cells for a variety of biological samples containing varying amounts of DMSO and doxorubicin (Dox) according to embodiments herein.

[0037] FIG. 13A is a chart showing the trypan blue measured viability of T Cells as a function of DMSO concentration and doxorubicin treatment according to embodiments herein.

[0038] FIG. 13B is a chart showing the principle component 1 score as a function of DMSO concentration and doxorubicin treatment according to embodiments herein.

[0039] FIG. 14A is a chart showing the LFC classification and single-cell measurement of cellular samples represented by size and optical force index for populations exposed to the cell cycle inhibitor (AmChl) compared to healthy (untreated) cells according to embodiments herein.

[0040] FIG. 14B is a chart showing the LFC classification and single-cell measurement of cellular samples represented by size and optical force index for populations exposed to the nutrient depletion (treated with D-PHE) compared to healthy (untreated) cells according to embodiments herein.

[0041] FIG. 14C is a chart showing the LFC classification and single-cell measurement of cellular samples represented by size and optical force index for populations exposed to oxidative stress from buthionine sulfoximine (BSO) treatment compared to health (untreated) cells according to embodiments herein.

[0042] FIG. 15 is a chart showing optical force index and size threshold metric as a function of cell health fingerprint for healthy (untreated) cells compared to multiple chemicals that fall under the categories of apoptosis modular, cell cycle inhibitor, metabolic stressor, nutrient depletion, and oxidative stress biological samples according to embodiments herein.

[0043] FIG. 16A is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 2X osmolality conditions according to embodiments herein.

[0044] FIG. 16B is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 3X osmolality conditions according to embodiments herein.

[0045] FIG. 16C is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 5X osmolality conditions according to embodiments herein.

[0046] FIG. 17 is a chart showing the average optical force index for HEK cell samples exposed to IX, 2X, 3X, and 5X osmolality conditions according to embodiments herein.

[0047] FIG. 18 is a chart showing the LFC analysis of vesicular stomatitis virus (VSV) infection of stressed Vero cells at different multiplicities of infection (MOIs) and the differentiation of cellular changes due to infection versus oxidative stress (H2O2) according to embodiments herein.

[0048] FIG. 19A is a chart showing the average velocity of human mesenchymal stem cell (hMSC) samples at varying ages (passages) as measured using LFC according to embodiments herein.

[0049] FIG. 19B is a chart showing the average complexity factor of human mesenchymal stem cell (hMSC) samples at varying ages (passages) as measured using LFC according to embodiments herein.

[0050] FIG. 20A is a principal component analysis (PCA) chart showing undifferentiated hMSC cell population changes resulting from different passages using LFC according to embodiments herein; the ellipse around the Passage 5 samples is statistically derived.

[0051] FIG. 20B is an image showing an hMSC cell sample after passage five (5).

[0052] FIG. 20C is an image showing an hMSC cell sample after passage eight (8).

[0053] FIG. 20D is an image showing an hMSC cell sample after passage 16.

[0054] FIG.21A is a PCA chart showing changes in mesenchymal stem cell samples based on age (passage number) using LFC according to embodiments herein.

[0055] FIG. 21B is a chart showing the mitochondrial activity of the mesenchymal stem cell samples using orthogonal assays in combination with LFC data according to embodiments herein.

[0056] FIG. 21C is a chart showing the P-galactosidase expression of the mesenchymal stem cell samples using orthogonal assays in combination with LFC data according to embodiments herein.

[0057] FIG. 22A is an image showing Oil Red O staining indicating differentiation to mature adipocytes cells at passage four (4).

[0058] FIG.22B is an image showing Oil Red O staining indicating poor differentiation to mature adipocytes cells at passage 10.

[0059] FIG. 22C is an image showing Alizarin Red staining indicating differentiation to mature osteocyte cells at passage four (4).

[0060] FIG. 22D is an image showing Alizarin Red staining indicating poor differentiation to mature osteocyte cells at passage 10.

[0061] FIG. 22E is an image showing a negative control.

[0062] FIG. 22F is a bar chart showing the absorbance at 540 nm for passages four (4) and 10 of the mature adipocyte samples compared to negative control samples, as a means of quantifying the differentiation based on Oil Red O.

[0063] FIG. 22G is a bar chart showing the optical density (OD) fold difference from the negative control for passages four (4) and 10 for the mature adipocyte samples compared to negative control samples.

[0064] FIG.22H is a bar chart showing the absorbance at 405 nm for passages four (4) and 10 for the mature osteocyte samples compared to negative control samples, as a means of quantifying Alizarin Red.

[0065] FIG. 221 is a bar chart showing the optical density (OD) fold difference from the negative control for passages four (4) and 10 for the mature osteocyte samples.

[0066] FIG. 23 is a PCA chart showing statistical differences between the cellular starting material of samples that resulted in high CAR T potency (B, E) versus those that resulted in low CAR T potency (A, C, D) using the same manufacturing process, demonstrating the ability to detect end of process differences by using LFC to analyze the cellular starting material using LFC according to embodiments herein.

[0067] FIG. 24 depicts a diagram of an illustrative example of a computing device implementing the systems and methods described herein.DEFINITIONS

[0068] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as would be commonly understood or used by one of ordinary skill in the art encompassed by this technology and methodologies.

[0069] Before describing various embodiments of the present invention in detail, it is to be understood that the terminology used in the specification is for the purpose of describing particular embodiments, and is not necessarily intended to be limiting. Although many methods, structures and materials similar, modified, or equivalent to those described herein can be used in the practice of the present invention without undue experimentation, preferred methods, structures and materials are described herein. In describing and claiming the present invention, the following terminology will be used in accordance with the definitions set out below.

[0070] As used herein, the singular forms “a,” “an,” and “the” include plural references unless the context clearly indicates otherwise. Thus, for example, reference to “a light source” includes a single light source as well two or more of the same or different light sources.

[0071] As used herein, the term “about” in connection with a measured quantity or time, refers to the normal variations in that measured quantity or time, as expected by one of ordinary skill in the art in making the measurement and exerci sing a level of care commensurate with the objective of measurement. In certain embodiments, the term “about” includes the recited number ±10%, such that “about 10” would include from 9 to 11, or “about 1 hour” would include from 54 minutes to 66 minutes.

[0072] The term “at least about” in connection with a measured quantity refers to the normal variations in the measured quantity, as expected by one of ordinary' skill in the art in making the measurement and exercising a level of care commensurate with the objective of measurement and precisions of the measuring equipment and any quantities higher than that. Incertain embodiments, the term “at least about” includes the recited number minus 10% and any quantity that is higher such that “at least about 10” would include 9 and anything greater than 9. This term can also be expressed as “about 10 or more.” Similarly, the term “less than about” typically includes the recited number plus 10% and any quantity that is lower such that “less than about 10” would include 11 and anything less than 11. This term can also be expressed as “about 10 or less ”

[0025] Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary? language (e.g., “such as”) provided herein, is intended merely to illustrate certain materials and methods and does not pose a limitation on scope.

[0073] As used herein, the term “and / or” includes any and all combinations of one or more of the associated listed items.

[0074] The term “biological particle” or “biological particles” as used herein refers to one or more biologic structure(s), including, but not limited to, raw materials for a biologic manufacturing process, material generated by a bioreactor, T-cells, engineered T-cells, chimeric antigen receptor (CAR) T or CAR-T cells, CAR macrophage (M) or CAR-M cells, tumorinfiltrating lymphocytes (TILs), natural killer (NK) cells, other engineered or effector cells, stem cells, nuclei, organelles, organoids, chloroplasts, mitochondria, islet cells, endocrine cells, P-cells, a-cells, 6-cells of the pancreas or other organs and / or FC cells, viruses, bacteria, mycoplasma, clusters thereof, cell clusters thereof, sub-cellular components thereof, fragments thereof, and / or combinations thereof. Cell clusters may be comprised of more than one cell of one or more biological cell types or classes joined together.

[0075] The term “well plate” as used herein refers to any well plate containing any number of wells, or pattern(s), or a vessel.

[0076] The term “Metric,” for example, a Viability Metric (“VM”), Fitness Metric (“FM”), Infection Metric (“IM”), or Response Metric (“RM”), refers to one or more specific parameters or values that take into account cell counts, velocity (including changes in velocity and position during flight time), optical force, size, shape, aspect ratio, eccentricity, deformability, orientation, rotation (frequency and position), refractive index, volume, roughness, cellular complexity, contrast based image measurements (e.g., spatial frequency, intensity variations in time or space), 3-D cell images or slices, laser scatter, fluorescence, Raman orother spectroscopic measurement and any combination thereof and / or other measurement made with respect to the cells or population that reflect differences and / or the level of cellular changes in a sample.

[0077] The term “viability” or “cell viability” as used herein refers to the measurement of cellular state, and in its most basic form, viability defines live vs. dead (e.g., membrane integrity via dye exclusion). The term “vitality” or “cell vitality” as used herein refers to a subset of viability, and is the measurement of the cell capacity to perform its various tasks, such as metabolic functions, reproduction (e.g., cell expansion), adaptation (e.g., response to environmental changes), and longevity (e.g., resilience to stress). Other vitality functions include the capacity to perform other fit-for-purpose or specific cellular functions, including but not limited to potency, activation, differentiation, proliferation, metabolism, gene expression, protein expression, metabolism, phagocytosis, cellular response, infection, transfection, transduction, cell-killing, and / or antigen recognition.DETAILED DESCRIPTION OF THE ILLUSTRATIVE EMBODIMENTS

[0078] Reference will now be made in detail to the various embodiments of the present disclosure illustrated in the accompanying drawings. Wherever possible, the same or like reference numbers will be used throughout the drawings to refer to the same or like features. It should be noted that the drawings are in simplified form and are not drawn to precise scale. In reference to the disclosure herein, for purposes of convenience and clarity only, directional terms such as top, bottom, left, right, above, below and diagonal, are used with respect to the accompanying drawings. Such directional terms used in conjunction with the following description of the drawings should not be construed to limit the scope of the present disclosure in any manner not explicitly set forth.

[0079] Embodiments are described with reference to particular embodiments having various features. It will be apparent to those skilled in the art that various modifications and variations can be made in the practice of the present invention without departing from the scope or spirit of the invention. One skilled in the art will recognize that these features may be used singularly or in any combination based on the requirements and specifications of a given application or design. One skilled in the art will recognize that the systems and devices of embodiments of the invention can be used with any of the methods of the invention and that any methods of the invention can be performed using any of the systems and devices of the invention. Embodiments comprising various features may also consist of or consist essentially of those various features. Other embodiments of the invention will be apparent tothose skilled in the art from consideration of the specification and practice of the invention. The description of the invention provided is merely exemplary in nature and, thus, variations that do not depart from the essence of the invention are intended to be within the scope of the invention.

[0080] Before explaining at least one embodiment in detail, it is to be understood that the invention is not limited in its application to the details of construction and the arrangement of the components set forth in the following description or illustrated in the drawings. The invention is capable of other embodiments or of being practiced or carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein is for the purpose of description and should not be regarded as limiting.

[0081] Viability assessment is a useful parameter in biopharmaceutical manufacturing, serving as an indicator of cell health and productivity throughout the production process. It is may be used to monitor the condition of cell cultures in upstream processing, where high viability correlates with optimal protein expression and product yield. Techniques such as trypan blue exclusion, flow cytometry, and label-free imaging technologies may be employed to quantify viable versus non-viable cells. In downstream processing, viability data may help guide harvest timing and purification strategies, ensuring product consistency and quality. Additionally, viability may play a role in process development, scale-up, and quality control, supporting regulatory compliance and risk mitigation. Viability is also useful in cell therapy, where the therapeutic efficacy directly depends on the viability of the final cell product. Overall, viability is a metric that is suitable to influence decision-making across the entire biomanufacturing lifecycle.

[0082] For the measurement of cellular state, viability has been used predominantly to assess cellular function. Viability is the measurement of cellular state and in its most simple form defines live / dead (e.g., membrane integrity via dye exclusion). Vitality is a subset of viability, the measurement of the cell capacity to perform its various tasks, such as metabolic functions, reproduction (e.g., cell expansion), adaptation (response to environmental changes), and longevity (e.g., resilience to stress). Other vitality functions include the capacity to perform other fit-for-purpose or specific cellular functions, including but not limited to potency, activation, differentiation, proliferation, metabolism, gene expression, protein expression, metabolism, phagocytosis, cellular response, infection, transfection, transduction, cell-killing, and antigen recognition.

[0083] The most common measurement used throughout the biopharmaceutical industry is the dye exclusion assay, typically using trypan blue or AO / DAPI. Viability can also bemeasured using apoptosis markers such as caspases, membrane markers, mitochondrial markers and DNA markers, among others. In later stage and QC release applications, fluorescent or flow cytometry assays are often used to simplify the measurement and improve results. While LFC can be used to correlate directly with cell viability, it has the ability to measure more subtle changes in cells, similar to apoptosis markers, versus a simple dye exclusion live / dead assay. It is here that LFC provides additional value because it is able to provide sensitive and holistic descriptive measurements of cell phenotypes. LFC is also useful to predict future cellular function or functional outcome of a bioprocess based on the cellular starting material or an intermediate cell sample. The ability to use machine learning (ML) and artificial intelligence (Al) in combination with LFC-based analysis for both predictive viability and / or vitality analyses (e.g., prediction of future process outcomes) and prescriptive analyses (e.g., actions to take to achieve process goals) provides a practical application of the systems, devices, and methods described herein.

[0084] Predictive analytics uses historical data, statistical algorithms, and machine learning techniques to identify the probability of future outcomes or describe the future properties of a given system. Prescriptive analytics goes a step beyond by recommending actions you can take to yield desired outcomes. It combines predictive ML models with optimization and simulation techniques. Multivariate models including, but not limited to principal component analysis (PCA) can be used to create process roadmaps, which are statistical time and unit operation dependent maps of a process focused on quality and are used to define out-of-trend (OOT) and out-of-specification (OOS) situations where product quality may be negatively affected. They can be a part of both predictive and prescriptive analytical control strategies. All of these strategies can be combined to contribute towards automated and agentic ML / Al process control strategies.

[0085] What is needed are improved methods and devices for efficiently characterizing biological components and systems with respect to numerous identifying aspects such as biophysical and biochemical profiles including cell fit-for-purpose viability and cell vitality. Currently available procedural and analytical methodologies for the characterization of biological cells and systems such as infectivity assays (e.g., neutralization assays, TCID50 and clinical sample manipulation) require extensive dilutions, potentially detrimental tagging procedures, and yield highly variable results making inter- and intra-experimental and trial comparisons challenging and downstream cellular applications limited. The current invention overcomes such limitations by providing novel methods related to biophysical and biochemical cellular monitoring and quantification. Such methods may implement artificialintelligence (Al) concepts including, but not limited to, machine learning, pattern recognition, fuzzy logic, and neural networks to provide automated scanning of un-tagged cell samples using optical and / or fluidic force-based technologies (e.g., laser force cytology). Such methods result in reduced requirements for sample dilutions, and ultimately sample specimens, as well as reduced time required for analysis and associated costs while enabling normalized and consistent evaluation of cells during analysis. Furthermore, embodiments of the present disclosure enables the use of suspension or matrix-embedded cells for analysis, expanding the dynamic range of infection models for neutralization or other functional assays as well as the ability to monitor, assess, and quantify adventitious agents from samples and cultures. Additionally, the inventive methods described herein may be computer-implemented thereby improving efficiency, reliability and reproducibility.

[0086] Embodiments described herein reduce the challenges associated with experimental subjectivity, time, and cost requirements while enhancing objective ease of use with regards to reading and analyzing samples. This is enabled by implementing methodologies such as feedback control, pattern recognition, machine learning, fuzzy logic, neural networks, etc. and associated hardware to scan and make dilution and / or titer determinations and requirements, independent of human calculation and enabled by computer-implemented processes in certain embodiments. Methodologies according to embodiments herein may utilize a complex set of instructions including fuzzy logic methods that encompass variable results such as infectivity and infection metrics (low, medium, or high infectivity ranges for example). The methodologies also may implement Al concepts such as neural networks (e.g., back propagation or probabilistic), machine learning, and / or pattern recognition to apply calibration data to the current samples to better predict the optimal grid search pattern for sampling. Embodiments described herein may reduce the number of dilutions required per experiment and thus save the experimenter resources, time, and the need for analysis of results by highly trained personnel, as well as eliminate the use of reporter genes, antibodies, or other staining / labeling mechanisms, as are currently required for quantification of neutralization assay titers.

[0087] Embodiments of the disclosure are suitable to measure cellular responses to differential stimuli using optical and / or fluidic forces for biophysical and biochemical cellular viability and / or vitality monitoring and quantification. Embodiments also enable the delivery of consistent and reliable characterization of biological systems. Methods, devices, and systems described herein may implement methodologies applicable to samples such as those derived from viral-based vaccination or drug discovery trials. Such embodiments enablewhole or depleted cell isolates to be examined for infectivity parameter deviations between cell types, between groups of subjects, and / or even between trials. Other sample treatments could include, the assessment of serum antibodies, antiviral compounds, antibacterial compounds, toxins, toxic industrial materials or chemicals (TIMs / TICs), parasites, and gene or cell therapy products such as CART-cells and oncolytic vaccines. Embodiments described herein may be suitable to provide neutralization assays for bacteria utilizing cells designed to be sensitive to bacteria (low response threshold) including cell lines or primary cells used to measure the infectivity of an infectious agent using multifaceted optical and / or fluidic force-based measurements. Such methods, devices, and systems may enable the determination of infectivity measurements useful for adventitious agent testing through the analysis of biomanufacturing liquids such as conditioned media or other samples of interest such as those obtained from bioreactors or other such vessels.

[0088] The described methods, devices, and systems may use optical and / or fluidic forcebased measurements, such as laser force cytology (LFC). Laser force cytology (LFC) utilizes a combination of microfluidics and light-induced pressure to take optical measurements including optical force, optical pressure, size, velocity, and other parameters on a per biological particle (e.g., cell) basis. While methods are described herein using LFC, other optical and / or fluidic force-based technologies may be implemented alone or in combination with LFC in the described methods. The application of LFC to the scanning and analysis of neutralization, TCID50, and other assays (i.e., for determining viral titer, infectivity, concentration, etc.) is performed by measuring changes in cellular properties or functions of interest. Such changes may be indicative of the cytopathic effects of cells co-cultured with serum containing antibodies and / or a virus of interest as compared to cells treated with nonimmunized serum alone (control or placebo). Additionally, cells co-cultured with a virus in the absence of serum can be used to determine the infection rate of cells derived from primary or cell culture sources. Hereinafter, any reference to neutralization assays will also be considered to include reference to TCID50 or plaque assay as the conventional application. It should be noted, however, that techniques other than LFC, including fluidic / hydrody-namic, electrokinetic, mechanical, magnetic, and / or acoustic forces may be used alone or in combination with LFC. Techniques that identify cells having cellular fitness for purpose, that is the identification of cells and cell populations that have the appropriate characteristics to result in a desired outcome of a biological manufacturing or other process / manipulation, also may be implemented alone or in combination with LFC.

[0089] In one or more embodiments, the methods, devices, and systems are implemented with methodologies applicable to samples such as those derived from drug discovery trials enabling whole or depleted cell isolates to be examined for desired cellular characteristics or biological fingerprints, including viability and / or vitality parameter deviations between cell types, between groups of subjects or even between trials. Such methods and devices are suitable to enable the determination of viability and / or vitality measurements useful for adventitious agent testing through the analysis of biomanufacturing liquids such as conditioned media or another samples of interest such as those obtained from bioreactors or other such vessels. Current viability and / or vitality methods are generally not fit-for-purpose, meaning they measure a property, such as the ability of a cell membrane to exclude a dye, that does not necessarily reflect the specific purpose of a cellular starting material or its intended use. For example, in the case of producing CART cells from healthy donor T cells, the viability and / or vitality of the donor T cells may or may not indicate the potential for manufacturing success of these cells. Manufacturing success could be measured in one or more ways, such as cytokine secretion or the ability of the manufactured CART cells to kill a target cell line. Thus, there is a need for fit-for-purpose cell-based assays that can identify specific properties of cells that indicate manufacturing or even clinical success.

[0090] Embodiments may optimize measurement of cellular properties and responses to differential stimuli using optical and / or fluidic forces, and enable the delivery of consistent and reliable characterization of biological systems. Samples include biological particles as defined herein, for example, including, but not limited to, cellular particles / particulates, individual cells, cell clusters, tissue, organoids, and / or combinations thereof. Examples include, but are not limited to, stem cells, cancer cells, white or red blood cells, immune cells, epithelial cells, microbial cells, bacteria, yeast cells, cells from solid tissues, CART-cells, B-cells, lymphocytes, tumor infiltrating lymphocytes, islet cells, modified or unmodified cells of organs, including human heart cells, brain cells, liver cells, kidney cells, Chinese hamster ovary (CHO) cells, Vero cells, baby hamster kidney cells (BHK), MRC-5 cells, human kidney fibroblast (324K) cells, and / or combinations thereof.

[0091] Embodiments may optimize measurement of cellular responses, such as viability and / or vitality, to differential stimuli including, but not limited to, process changes, purification, formulation, cryopreservation, reanimation using optical and / or fluidic forces, and / or combinations thereof. Embodiments are suitable to enable the delivery of consistent and reliable characterization of biological systems. Inherent differences in cellular starting material, for example differences between cell samples from two different donors, may also becharacterized. Furthermore, LFC may be used to measure cellular changes in cell lines or primary cells used as part of an analytical assay to improve performance or consistency.

[0092] FIG. 1A is a representation of a methodology 100 for selecting sequential dilutions and calculating TCID50 / mL or percent neutralization on cell culture well plates and generating an Infection Metric or Response Metric 120 based on the results. According to embodiments, methodology 100 may enable interpolation between dilutions and replicates using quantitative measurement of percent cytopathic effect (% CPE) of cells and analysis of the results 140. Referring to FIG. 1A, methodology 100 is configured to read (i.e., detect), analyze, and predict cellular changes including, but not limited to, CPE (e.g., % CPE for viral, bacterial, or toxin effects or other effects). Alternatively, any LFC measured parameter including, but not limited to, effective refractive index or size normalized velocity may be used to describe cellular changes instead of % CPE. The measured samples may be contained in a multi-well plate (e.g., a 96-well). Methods, devices, and systems described herein may be configured to initiate instrumental analysis and detection of cellular change, i.e. % CPE, in the starting well position. In embodiments, this starting position may be chosen by the user based on experience or other pre-programmed homing coordinates. Embodiments of the methods, devices and systems may subsequently automatically select a well with either a higher or lower dilution based on the observed data, the data trend, and / or the experiment layout previously stored in a memory of the devices or systems. Other methodologies, devices, and systems suitable for use herein are shown and described in international publication numbers WO 2019 / 183199 and WO 2019 / 125502, which are incorporated by reference herein in their entirety.

[0093] Sampling may begin at an intermediate dilution or untreated control based upon user input or instructions stored in a memory of the devices and systems. Measurements may be made at one or more points, including before, during, and after the manufacturing process. With reference to FIG. 2A, in an exemplary manufacturing process 200 sampling 206A-206E may be conducted at various stages of the process 200. During the pre-process phase 202, the biological parti cle(s) starting material 204 may be sampled 206A using a Biological Particle(s) Analyzer 212 such as LFC and / or neutralization assay and / or other suitable optical and / or fluidic force-based measurement technique as described herein. The starting material may include, but is not limited to, raw materials for a biologic manufacturing process (e.g., vaccine, protein, and / or antibody therapeutics, etc.), materials generated by a bioreactor, biological particle(s) received from another source, or combinations thereof.

[0094] Sampling 206B-206D may also be performed during the in-process phase 208 of the biological parti cle(s)-based process 210. Samples 206B-206D may be collected at various points within the biological parti cle(s)-based process 210. Process 210 may be a bioprocess within cell therapy, gene therapy, vaccine development, biologic manufacturing, a bioreactor, or other process involving the use and / or transformation of biological particle(s) into one or more products (e.g., biologic dosage forms, etc.). Methods, devices, and systems according to embodiments herein used, for example, in a cell therapy process are suitable to provide real-time, label-free cell characterization providing new process insights for autologous and allogenic cell therapies, stem cell, and phenotypic characterization of cells. In the context of gene therapy processes, embodiments provide accurate and precise AAV transfection and transduction monitoring enabling a more nuanced understanding of gene therapy potency to provide personalized and optimized therapeutic strategies. With respect to biomanufacturing processes, embodiments provide automated workflow and analysis that enables real-time monitoring of target product quality attributes and leads to improved product consistency. Regarding vaccine processes, embodiments provide rapid viral infectivity for vaccine research and development, process development and optimization, manufacturing formulations, potency, and neutralization assays.

[0095] As shown in FIG. 2A, sampling 206E also may be performed during the post-pro-cess phase 214. As such, the product containing the biological particle(s) may be sampled 206E with the analyzer 212 to test for quality, consistency, identity, purity, potency, and other target parameters.

[0096] As is apparent from FIG. 2B, some manufacturing processes 200 may include during the in-process phase 208, the addition of sample to the biological particle(s) with incubation prior and / or post addition. Sampling 220 of the incubated material may be performed by analyzer 212. Samples may be incubated to provide a controlled environment at particular temperature, humidity, and / or gas levels to encourage the growth, maintenance, and activity of microorganisms or cells. This controlled environment may help achieve optimal cell multiplication, protein expression, and other biological processes to maintain large-scale production.

[0097] Suitable microfluidic devices for use with the analyzers (e.g., LFC analyzers) described herein are set forth in WO 2019 / 125502. FIG.2C shows a microfluidic device 201 suitable for use in embodiments described herein. One or more biological particle inlet(s), sample(s) and / or substrate(s) (not shown) may be in operable communication with flow path 240. The device 201 is configured to receive in flow path 240 any type of sample that canmove through the device, such as a fluid, liquids, gas, plasma, serum, blood, cell(s), platelets, particle(s), etc. and / or combinations thereof. The one or more inlet(s), sample(s) and / or substrate(s) may be connected either directly (e.g., by a manifold) or indirectly, such as indirectly using air tubing (not shown) in operable communication with, for example, the underside of the chip. FIG. 2C shows that the biological parti cle(s) may be injected into flow path 240 at an exterior surface, such as an edge, of the microfluidic chip 201 (e.g., the XZ plane in FIG. 2C). Injection is shown in FIG. 2C along one of the faces shared by the smallest distance, such as the XZ face or the XY face. In this particular embodiment, the length of side X 260 is less than the length of side Y 270 and the length of side Z 290. Such a configuration allows a sample to have minimal deviation upon entering channels on the chip (e.g., no right turn is necessary as is generally the case in current state of the art).

[0098] In various embodiments, samples may be injected from the bottom of the microfluidic device 201 into flow path 240. By injecting the contents from the bottom of the chip 201, horizontal movement of the inlet tubing 240 and outlet tubing 245 may be minimized to avoid such problems as settlement of the particles or cells in the channel(s) 250. The outlet tubing 245 may be offset from the inlet tubing 240. In embodiments, the outlet tubing 245 is offset, not offset, straight, in-line, or angled in relation to the inlet tubing 240. Such a configuration obviates the need for serpentine or zigzag vertical channels because the current configuration resolves problems with settlement that occur in the prior art, such as in the case in which fluid combined with cells and / or particles is injected or pumped into the chip from the side in a horizontal direction that then must change direction and fluid dynamics to be pushed upwards. In one embodiment, pressure-based sample injection is used, for example, by using a pump, pressurized inert gas or other means to deliver the sample to inlet channel 240 under a suitable pressure.

[0099] Another embodiment of a microfluidic device 202 suitable for use in the methods and systems described herein is shown in FIG. 2D. As shown in FIG. 2D, a sample may travel upwards in a vertical direction through a first channel 211 in operable communication with a second horizontal channel 220. Another vertical channel 230 takes the sample even closer to the top of the chip 202 at which point a fourth channel 240 is horizontal and, as shown in FIG. 2D, may be an analysis channel. The channels are in operable communication with one another to allow for a sample to be moved through the system from one channel to another. In embodiments the sample can flow from the first channel to the second channel to the third channel to the fourth channel, or in the reverse, or combinations thereof. A pump and / or vacuum apparatus may provide positive and / or negative pressure at eitheror both the opening and the exit of the channels 220, 240 to enable movement of the substances through the channels. The analysis channel 240 may be close to one or more exterior surfaces of the device 202, such as the faces, edges, or sides of the chip 202. For example, the analysis channel 240, may be close to the top and side of the chip 202, thereby improving imaging and analysis through the substance of the microfluidic chip. In some embodiments, the analysis channel 240 may be from about 0.1 mm to about 100 mm from the top and side of the chip 202, about 0.1 mm to about 0.2 mm, about 0.2 mm to about 0.3 mm, about 0.3 mm to about 0.4 mm, about 1 mm to about 2 mm, or any individual value or sub-range within these ranges, from the top and side of the chip 202. In some embodiments, the analysis channel 2.40 may be disposed within the top 50%, 33%, 25%, 10%, or 5% of the microfluidic device 202. The length of the horizontal analysis channel 240 may be about 250 microns to about 10 mm, or any individual value or sub-range within this range.

[0100] One or more analyzer (not shown), may be positioned to suitably take a measurement (e.g., a force-based measurement using, e.g., optical and / or fluidic forces) of the sample while travelling through the analysis channel 240. In some embodiments, the analyzer may be position above the chip 202 and oriented orthogonally to the analysis channel 240. In some embodiments, an analyzer may be positioned to the side of the chip 202 and oriented orthogonally to the direction of the flow or diagonally to the direction of flow (such as above the channel, below the channel, or at angles to the side of the channel). In embodiments, the analyzer may be positioned such that the measurement is performed at any angle relative to the flow of the sample, such as orthogonal or about 0 degrees to about 90 degrees, about 10 degrees to about 80 degrees, about 30 degrees to about 60 degrees, about 90 degrees, or any individual value or sub-range within these ranges, relative to the flow of the one or more sample(s). In another embodiment, two or more analyzers may be used to measure biological particle(s) in the samples in the analysis channel 240. For example, an analyzer may be positioned above the chip 202. and be oriented orthogonally to the analysis channel 240. A second analyzer may be located to the side of the chip 202 and oriented orthogonally to the direction of flow or diagonally to the direction of the flow (such as above the channel, below the channel, or angles to the side of the channel).

[0101] In embodiments, one or more light sources (not shown) may be used to illuminate the analysis channel 240 The light sources may be positioned under the chip 202 and shining upward, to the side of the chip 202 and shining in the flow direction or opposite the flow direction, above the chip 2.02 and shining down, or diagonally to the analysis channel 240. A light source 280, such as a laser, may be used to affect cell flow. The laser 280 may beplaced in line with the sample flow, or opposing the sample flow. The laser 280 may also be placed and / or oriented orthogonally or diagonally to the sample flow. In at least one embodiment, a laser or other optical force 280 is from an LFC analyzer.

[0102] Microfluidic device 202 may include a substrate with a first channel 211 extending in a vertical direction in the substrate such that a first plane traverses first channel 211 substantially along its length and whereby the fluid sample is injected into the substrate / chip from the bottom of the chip and is forced by positive or negative pressure upwards. A second channel 220 is orthogonal to the first channel and thus disposed horizontally in the substrate such that a second plane traverses second channel 220 substantially along its length and the second plane is disposed orthogonal to the first plane. This second channel is in the horizontal direction of the chip 202. The second channel communicates directly or indirectly with the first channel. The second channel communicates directly or indirectly with a short upward vertical third channel 230 that takes the channel network closer to the top of the chip 202. The third channel communicates directly or indirectly with a fourth horizontal channel 240 that is located near the top and / or corner of the chip. The fourth channel 240 may be the channel closest to the top of the chip 202. In embodiments, the substrate can comprise one or more analysis channel, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, or 10 analysis channels.

[0103] According to further embodiments, described are methods of using the systems 200, devices 201, and methods described herein for analyzing a biological sample, sorting a biological sample, and / or preparing a therapeutic composition. In embodiments, a biological sample may be obtained from a subject (e.g., a human, animal) and / or from an external source such as a bioreactor, vial, etc. Suitable practical applications of the methods, systems and devices described herein are in the preparation of therapeutical compositions for cell therapy, gene therapy, vaccines, biologies,, or other process involving the use and / or transformation of biological particle(s) into one or more therapeutic compositions (e.g., a cell therapy, etc.). Methods, devices, and systems according to embodiments herein used, for example, in a cell therapy process are suitable to provide real-time, label-free cell characterization providing new process insights for autologous and allogenic cell therapies, stem cell, and phenotypic characterization of cells. In the context of gene therapy processes, embodiments provide accurate and precise AAV transfection and transduction monitoring enabling a more nuanced understanding of gene therapy potency to provide personalized and optimized therapeutic strategies. With respect to biomanufacturing processes, embodiments provide automated workflow and analysis that enables real-time monitoring of target product quality attributes and leads to improved product consistency. Regarding vaccineprocesses, embodiments provide rapid viral infectivity for vaccine research and development, process development and optimization, manufacturing formulations, potency, and neutralization assays.

[0104] The systems, devices, and methods are suitable to analyze biological particles and determine which particles (e.g., cells) are fit-for-purpose for the indicated application (i.e., gene therapy, cell therapy, etc.). The disclosed embodiments may determine cells that are viable, vital, and / or fit-for-purpose for a particular application. In embodiments, the systems, devices, and methods separate the vital and / or viable fit-for-purpose particles from the non-viable and / or viable but not fit-for-purpose particles to form a therapeutic composition. The resulting therapeutic composition would have a higher concentration of vital and viable fit-for-purpose particles than the originally collected biological sample. A weight ratio of the viable fit-for-purpose and / or vital particles to the non-viable and / or viable but not fit-for-purpose particles in the therapeutic composition may be about 5:1 to about 10000:1, or any individual value or sub-range within this range. Methods may further include analyzing the resulting product (e.g., therapeutic composition) for quality, consistency, identity, purity, potency, and other target parameters.

[0105] Embodiments described herein may reduce the number of sample dilutions required, as compared to the number required by conventional neutralization assays, decrease the time required for well plate analyses, provide a larger dynamic range afforded by the use of optical and / or fluidic force-based technologies. In at least one embodiment, the optical and / or force-based technology utilized comprises LFC. However, other technologies that may be used alone or in combination with LFC include, but are not limited to, optical chromatography, cross-type optical chromatography, laser separation, orthogonal laser separation, optical tweezers, optical trapping, holographic optical trapping, optical manipulation, laser radiation pressure, and / or combinations thereof.

[0106] LFC metrics with demonstrated correlations to viability, vitality, and / or other fit-for-purpose assays include optical force index in the case of Vero cells frozen using different procedures, velocity and eccentricity in cells subject to oxidative stress, and velocity and optical force in fixed and permeabilized primary human T cells. In various embodiments, a comprehensive multivariate approach that incorporates all LFC metrics may be implemented, with techniques including principal component analysis (PCA), regression including partial least squares (PLS), support vector machine (SVM), artificial neural networks (ANNs), and / or decision trees including gradient boosted decision trees.1

[0107] Embodiments described herein may reduce the number of sample dilutions required, as compared to the number required by conventional neutralization assays, decrease the time required for well plate analyses, provide a larger dynamic range afforded by the use of optical and / or fluidic force-based technologies. In at least one embodiment, the optical and / or force-based technology utilized comprises LFC. However, other technologies that may be used alone or in combination with LFC include, but are not limited to, optical chromatography, cross-type optical chromatography, laser separation, orthogonal laser separation, optical tweezers, optical trapping, holographic optical trapping, optical manipulation, laser radiation pressure, and / or combinations thereof.

[0108] LFC metrics with demonstrated correlations to viability, vitality, and / or other fit-for-purpose assays include optical force index in the case of Vero cells frozen using different procedures, velocity and eccentricity in cells subject to oxidative stress, and velocity and optical force in fixed and permeabilized primary human T cells. In various embodiments, a comprehensive multivariate approach that incorporates all LFC metrics may be implemented, with techniques including principal component analysis (PCA), regression including partial least squares (PLS), support vector machine (SVM), artificial neural networks (ANNs), and / or decision trees including gradient boosted decision trees.

[0109] Described herein according to various embodiments are methods for measuring cellular responses to differential stimuli using optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces. The methods may comprise receiving a selection of an initial samples comprising biological cells treated. The biological particles may be received from a source such as a subject (e.g., via a swab or blood draw of a human or animal, clinical sample, biobank, etc.), a bioreactor, a reservoir, a frozen cell sample, a biobank, a biologic manufacturing process, one or more sampling points of a process that generates and / or handles biological particles, biologic drugs, biological particle products generated by a and / or other biological source. The biological particles may be received in the sample inlet of a biological particle analyzer. Suitable biological particle analyzers may be configured to measure fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces.

[0110] The methods may further include performing force-based measurements (e.g., optical and / or fluidic force-based measurements) on the samples. Suitable optical and / or fluidic force-based biological particle analyzers may be configured to perform measurements using, but are not limited to, laser force cytology (e.g., Radiance® available from LumaCyte®), optical chromatography, cross-type optical chromatography, laser separation, orthogonal laser separation, optical tweezers, optical trapping, holographic optical trapping, opticalmanipulation, and / or laser radiation pressure. The measurement data received from the biological particle analyzer may be stored in a memory accessible to a processor suitable to perform further steps and analyses of the methods. Suitable fluidic / hydrodynamic forcebased analyzers include, but are not limited to, particle size analyzers (e.g., that use laser light scattering, light extinction, background membrane imagine, fluorescence membrane microscopy, etc.), fluidic force microscopy, flow imaging microscopy, electrozone sensing, microfluidic resistive pulse sensing, or combinations thereof. Suitable electrokinetic forcebased analyzers include, but are not limited to, absorbance fluorescence, luminescence, live cell assays, dielectrophoresis (DEP), electrophoresis, AC electrophoresis, or combinations thereof. Electrokinetic forces can efficiently concentrate cells from large sample volumes or isolate specific cell types, such as circulating tumor cells (CTCs) from blood. Microfluidic devices may be designed to count and sort cells in real-time by using electrokinetic focusing and flow switching. Combined electrokinetic techniques may be used to analyze biomarkers. For example, DEP may be used to capture extracellular vesicles (EVs), and electrophoresis can then isolate and analyze peptide fragments produced from protease activity on the EVs. The dielectric properties of cells, which can change with viability and / or vitality, are reflected in their DEP response. By analyzing this response, analyzers can gather information about the health of the cells. The small length scale of microfluidic devices, combined with high electric field gradients, allows for the manipulation of many cells per second, making it a high-throughput method for cell analysis. Suitable magnetic force analyzers may include, but are not limited to, magnetic tweezers for mechanical properties such as stiffness and fluidity, magnetic cell separation for isolation and counting, magnetic force microscopy (e.g., for high-resolution imaging), magnetic force spectroscopy, magnetic microposts, and / or combinations thereof. Suitable acoustic force-based analyzers include, but are not limited to, acoustic force spectroscopy (e.g., for single-cell mechanics), acoustic focusing in microfluidics (e.g., for high throughput cell counting and sizing), acoustic tweezers (e.g., to manipulate and pair cells), acoustic focusing for flow cytometry, acoustic scattering sensors (e.g., sound waves scattered by cells to measure properties without direct physical contact), and / or combinations thereof.

[0111] The described methods may further include developing a metric (e.g., a VM, RM, IM, FM) to describe the biological particle(s) (e.g., a cellular fingerprint) based on one or more optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic force-based parameters. Developing the metric may include calculating a titer and creating a calibration curve from an unknown or not well understood biological particle(s) (e.g., viral) systemwith a sample of unknown titer. The calibration curve may be stored in a memory and accessible via the processor. Measurements (e.g., force-based measurements, measurements of various parameters, etc.) may be performed in order to determine which parameters are suitable to use for the metric. The metric may include a parameter (or a set of parameters) that correlates well with the infectious viral titer over as wide a range of viral concentrations as possible.

[0112] For example, dilutions are made of viral stock and added to cell samples. The combined samples are incubated for a designated time period and the diluted samples are analyzed with replicates on one or more analyzers (e.g., LFC) to measure one or more parameters. The measurements are stored in the memory. Once the one or more analyzers have returned the measured parameters, the processor identifies the best metric(s) (and measurement time) based on the data trends for the parameters. In some embodiments, a single parameter is suitable and provides a univariate metric. In other embodiments, a multivariate metric may be used. A entire population histogram or subset may be used by the processor as part of an input vector to identify the most effective parameter along with K-means clustering to identify additional parameters. K-means clustering is an unsupervised machine learning algorithm that partitions a dataset into a specified number (k) of distinct, non-overlapping groups (clusters). It works by iteratively assigning data points to the nearest cluster center (centroid) and then updating the centroid's position to the mean of all points in the cluster, repeating this process until the centroids stabilize. This methodology creates clusters where points are as similar as possible to each other and as dissimilar as possible to points in other clusters. These analyses also may be combined with Partial Least Squares regression (PLS) to create a multivariate metric. PLS is a statistical method used to predict a set of dependent variables from a large set of independent variables, especially when they are highly correlated or when there are more predictors than observations. It combines features of both principal component analysis and multiple regression by creating a smaller set of uncorrelated, new variables called latent variables or components from the original predictors. The final regression is then performed on these components, which are chosen to maximize the covariance between the independent and dependent variables. The parameter (e.g., titer) may be determined by the processor based upon an absolute parameter algorithm and the parameter results may be used to determine dilution infectivity (titers). The processor may then generate the calibration curve, which is stored in the memory and useful for future analyses.

[0113] In one embodiment, a histogram of at least one parameter (e.g., as measured by LFC), such as size normalized velocity, may be generated from the collected data and plotted by the processor to show how it changes with respect to the amount of viruses added (i.e., multiplicity of infection or MOI). For example, cells may be infected with a virus and samples collected and measured over time. The parameter, that is, the size normalized velocity, may change as the MOI increases, providing minimum and maximum measured values. The processor may use the size normalized velocity, coupled with the standard deviation of the velocity, to develop the metric that correlates strongly with the MOI and thus viral concentration.

[0114] In another example of generating a metric, data from a viral system (e.g., human adenovirus 5 (Ad5) infecting human embryonic kidney (HEK 293) cells) may be generated by one or more analyzer(s) and stored in a memory. PLS may be used to analyze the data. As many parameters as needed may be measured and stored in a memory for the processor to add the data to the PLS model in order to develop a multivariate metric. The inputs for the PLS model may be population wide statistics, such as the average, standard deviation, or median for any parameter measured by the analyzer (e.g., LFC), but also more complex inputs, such as a population histogram for a particular parameter, such as velocity, determined by the processor. The processor may define the bins of the histogram based on a standard distance between the bins, or adjusted based on a clustering algorithm, such as K-means clustering. In the case of K-means clustering, the processor may define the number of bins as well as the parameter used. Either the entire population or a portion thereof may be used to define the population histogram.

[0115] The metric based on force-based measurement may be identified. Control (uninfected) and maximum values for the metric may be known for the biological particle(s) (e.g., virus / cell) combination. The type of fit for the calibration curve also may be known. The methods described herein may determine a value or values of the metric that maximize(s) the accuracy, precision, and signal to noise ratio of the infectious viral parameter (e.g., titer or infectivity). Providing a range of values for the metric may ensure this; the range of values may be determined based on previous data used to create the calibration curve The range may be the optimal linear dynamic range (OLDR) for the calibration curve and may be adjusted on a per virus / cell line basis In addition, it may be possible that multiple values are measured within the OLDR and are then all used to calculate the resultant infectious viral titer or infectivity.

[0116] An exemplary methodology for analyzing biological particle(s) using a metric according to embodiments herein is set forth in FIG. 1 B. The methodology may include measuring a sample, the first measurement of which is generally within the middle of the range of dilution values. If the value of the measurement is outside of the OLDR, then a different well is sampled, moving to a higher concentration of virus (analyte) if the measurement is too low, and moving to a lower concentration of virus (analyte) if the measurement is too high. Once the value of the measurement is within the OLDR, the system may confirm whether or not the sample is truly within the OLDR. The methods may include determining, by the processor, the optimal metric (i.e., best accuracy, precision, and signal to noise level) within the linear dynamic range of the calibration curve. One measurement may be suitable if that dilution is in the optimal linear dynamic range ODLR of the calibration curve (e.g., 40-60% of a maximum metric) or, if unsuitable, the processor may search the stored data for one or more additional metric within the ODLR. Once determined, an optimal dilution value is determined. The processor may use the signal-to-noise ratio to determine the suitability of a data point (i.e., calculate n in flat region of data and at peak metric signal). Several different dilutions (e.g., volumes of virus sample) may be combined and averaged by the processor to determine the target value. If the samples are within the ODLR, then the ODLR may be adjusted on a per virus / cell line basis.

[0117] In some cases (e.g., mid-titer), the values of the metric plateau for high concentrations of analyte, in which case there would be less potential for confusion as to whether or not a single measured value is actually within in the OLDR. In other cases (e.g., high titer), the value for the metric at very high concentrations, which are outside the OLDR, may be the same or even less than the values that are actually within the OLDR. Thus, the processor checks to ensure the values are within the OLDR. The first part of the check is to determine if other characteristics and measurements of the sample that are not necessarily part of the metric may be used to determine whether or not the sample is truly within the OLDR. This may be based on stored data related to the biology of the system as well as other measurements taken and stored in the memory. If other metrics are available to confirm the OLDR, then the method proceeds according to the results of that test. If the other metrics confirm the OLDR, then the measurement is complete and the titer (infectivity) can be calculated. If the other metrics cannot confirm the OLDR, then the metric is measured for the next highest concentration of virus (analyte). The same step may be performed if there are no other metrics available to confirm the OLDR. Based on the metric of the higher virus concentration, the method proceeds accordingly. If the metric changes by an expected amount based on theprevious knowledge of the calibration curve, then the processor confirms that the value is within the OLDR and the measurement is complete. If not, then the processor determines that the value is outside the OLDR and likely too high, so the next sample measured is three (3) steps lower in virus concentration.

[0118] Methods according to embodiments herein may further include correlating, by the processor, a cellular response to a cellular property of interest. In various embodiments, the cellular property of interest is cellular viability and / or vitality. The measured cellular response may be used by the processor to predict the results of a future treatment on subsequent samples comprising biological cells. For example, the processor may determine from stored and measured data, a correlation between a first parameter (e.g., optical force index) and a second parameter (e.g., viability and / or vitality).

[0119] Further described herein according to embodiments are methods for generating a calibration curve based on cellular changes during the production of a biologic molecule or other ongoing bioprocess that correlates the cellular response to cellular viability and / or vitality and then using the calibration to predict the results of a future process. The method may comprise adding treatments and incubating sample cells; analyzing by optical and / or fluidic force-based measurements a plurality of samples having cells and a known range of product concentrations, process outcomes, and / or product characteristics to determine a response metric; determining optimal response metric based on trend with dilution, and using generated data to predict cellular viability and / or vitality of future samples. In embodiments, the optical and / or fluidic force-based measurements may comprise laser force cytology. Laser force cytology may be used to measure one or more of linear velocity, perimeter, size (area, diameter, volume, etc.), number of trapped cells per sample, number of beam ejected cells per sample, number of aggregates (based upon size and / or shape or other parameters), number of debris-sized particles (based upon size and / or shape or other parameters), normalized velocity, minimum x position, optical retention time, optical trapping time, optical force, optical torque, orientation, optical and fluidic dynamics, effective refractive index, eccentricity, minor axis, major axis, deformability, eccentricity deformability, minor and major axis deformability, elongation factor, compactness factor, circularity factor, images including greyscale features, whole images, image components or image derived parameters, morphology characteristics, other laser force cytology derived parameters or combinations thereof. The sample may comprise one or more cellular particles / particulates, cells, cellular clusters, organoids, stem cells, cancer cells, white blood cells red blood cells, immune cells, epithelial cells, microbial cells, bacterial cells, yeast cells, cells derived fromsolid tissues, CART-cells, B-cells, lymphocytes tumor infiltrating lymphocytes, islet cells, modified or unmodified cells of organs, heart cells, brain cells, liver cells, kidney cells, CHO, Vero cells, baby hamster kidney cells (BHK), MRC- 5 cells, and Human kidney fibroblast (324K) cells.

[0120] Further described herein are methods of using Laser Force Cytology (LFC), fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces for 1) measuring cell viable state, viability, vitality, cell health, and / or cellular function; 2) characterizing and / or sorting cells; 3) characterize and / or measure instantaneous viability and / or vitality; 4) for characterizing cells to create a predicting tool to measure cellular status and trends, including average and standard deviation; and / or 5) predicting future viability and / or vitality.

[0121] In further embodiments, described are methods using optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to create a functional cell-based assay to evaluate or correlate one or more of a) stress on cells, including physical, chemical, biological, radiological stress, and measure a response; or b) correlate instantaneous response with future recovery of cell function, viability and / or vitality.

[0122] Further described are methods for using optical, fluidic / hydrodynamic, electrokinetic, and / or acoustic forces to generate date concerning one or more of a) treatment of a cell with H2O2and better response than traditional assay (trypan blue); b) T Cell activation disruption using a drug such as doxorubicin; or c) DMSO, Dox, fixing / permeability for disruption of viability and / or vitality. The optical force may be laser force cytology. The laser force cytology may comprise the assessment of parameters including one or more of linear velocity, size, perimeter, size (area, diameter, volume, etc.), number of trapped cells per sample, number of beam ejected cells per sample, number of aggregates (based upon size and / or shape or other parameters), number of debris-sized particles (based upon size and / or shape or other parameters), normalized velocity, minimum x position, optical retention time, optical trapping time, optical force, optical torque, orientation, optical and fluidic dynamics, effective refractive index, eccentricity, minor axis, major axis, deformability, eccentricity deformability, minor and major axis deformability, elongation factor, compactness factor, circularity factor, images including greyscale features, whole images, image components or image derived parameters, morphology characteristics, or other laser force cytology derived parameters.

[0123] In yet further embodiments, described herein are methods for identification of cells using differential stimuli using optical and / or fluidic forces. The methods may include receiving a selection of an initial samples comprising biological cells treated with varyingknown levels of stimuli or analyte; performing optical and / or fluidic force-based measurements on the samples; developing a metric (e.g., a VM, RM, IM, FM) to describe the cellular response to the stimuli based on one or more optical or fluidic force-based parameters; and identification of cells by correlating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.

[0124] A multivariate approach for determining a metric (e.g., a VM, IM, RM, FM) is demonstrated by FIGs. 3A-23. The metric may refer to the use of specific metrics for cellular properties or functions of interest, including thresholds, gates, and / or other selectors. Embodiments may use instantaneous LFC metrics, combinations, or multivariate sets or models to predict future cell performance, health, viability, and / or vitality. Cellular viability and / or vitality metrics may be measured and / or determined on a per cell type basis. Cell viability and / or vitality metrics may include, but are not limited to, velocity, optical force index, and / or any other LFC parameter or combination with other parameters or measurements as detailed herein. In various embodiments, use of optical, fluidic / hydrodynamic, electrokinetic, and / or acoustic forces may be used to generate data with respect to measured parameter (e.g., LFC, oxidative stress, T cell activation, etc.).

[0125] Suitable cell types for use in the methods, devices, and systems as described herein are shown in Table 1. The cellular properties suitable for use in the methods, devices, and systems described herein are shown in Table 2.Table 1 - Cell types and their propertiesTable 2 - Cellular properties suitable for the methods, devices, and systems

[0126] FIGs. 3A-3D are the results from a study that compared Vero cells (frozen with different methods) to a standard protocol. The cells were then analyzed using LFC immediately post thaw. FIG. 3A is a chart comparing the counts as a function of optical force index for a reference population to a sample of the same conditions measured using a laser force cytology analyzer. FIG. 3B is a chart comparing counts as a function of optical force index for a reference population to a biological sample having a higher fetal bovine serum (FBS) concentration as measured using a laser force cytology analyzer. FIG. 3C is a chart comparing counts as a function of optical force index for a reference population to a biological sample having no DMSO as measured using a laser force cytology analyzer. FIG. 3D is a chart comparing counts as a function of optical force index for a reference population to a biological sample with uncontrolled freezing as measured using a laser force cytologyanalyzer. The LFC analyzer used in this study was the Radiance® label-free single cell analysis instrument manufactured by LumaCyte®. The methods, analyzer, and systems, representative of the embodiments herein, rapidly monitored cell health conditions and characterized changes in cell populations to ensure consistent process performance. Significant changes were seen in optical force index for samples without DMSO (FIG. 3C) and rapid (uncontrolled) freezing (FIG. 3D).

[0127] FIG. 4 is a chart showing the cellular viability determined by monitoring process and condition changes. The percent viability is graphed as a function of average optical force index measured by the Radiance® LFC analyzer. In this study, optical and / or fluidic / hydrodynamic forces were used to generate data related to treatment of a cell with disruption using a drug such as DMSO. Several conditions were varied during cry opreservation FBS concentration, DMSO concentration, and freezing rate. As shown in FIG. 4, a correlation developed between optical force index and viability, both measured post thaw after five (5) weeks of storage. This study demonstrated how LFC data can be used to monitor cell viability and condition.

[0128] The results presented in FIG. 5 are from a study involving precision monitoring for cellular fitness and function according to embodiments herein. The cell samples were treated with different levels of reactive oxygen species (ROS) and sampled at different time points post treatment. FIG.5 is a principal component analysis chart that compares viability with vitality. Notably, viability of a biological particle (e.g., a cell) is not the same as vitality of a biological particle. Viability may indicate that the biological particle is alive, but not necessarily healthy. Vitality may indicate that the biological particle is alive, active, and healthy. Viability measurements are instantaneous whereas vitality assessments are forward looking (i.e., vitality asks what can cells do, given their current state?). The Radiance® LFC analyzer is suitable to measure phenotypes of cell fitness. This study demonstrated that LFC may be correlated to viability and / or vitality and is predictive of cellular function a) proliferation; b) activation; c) production capacity; d) potency; and e) clinical efficacy.

[0129] FIGs. 6, 7 and 8 are bar charts presenting the results of a cellular vitality study of Vero cells undergoing oxidative stress from various levels of hydrogen peroxide (H2O2). In FIG. 6, the velocity and eccentricity threshold metric is charted for several levels of H2O2. In this study, optical, and / or fluidic / hydrodynamic forces were used to generate data related to treatment of a cell with H2O2. The Vero cells were treated with various levels of H2O2to simulate oxidative stress felt during bioproduction. The Radiance® LFC analyzer was used to detect changes in velocity and eccentricity in all conditions compared to a control, withno statistical difference seen in viability between untreated cells and up to 300 pM H2O2. Caspase activity assay demonstrates that this LFC metric corresponds more closely with apoptosis, providing additional information beyond trypan blue viability. The results of FIG. 6 are summarized in Table 3.Table 3 - Viability (Trypan Blue) and Apoptosis (Caspase)

[0130] FIG. 7 is a bar chart showing the Vero cells treated with H2O2(oxidative stress) represented by a velocity / eccentricity threshold metric (%) at pre-freeze, post-thaw and infection stages. Measurements were taken at different points during the viral production process. Control samples had a significantly greater threshold metric at all collection times. Freezing impacted all cell samples. The control samples rebounded by the time of infection. Stress reduced the optical flow imaging (OFI) and vesicular stomatitis virus (VSV) metric delta between the control and infected samples (due to the control).

[0131] FIG. 8 is a bar chart showing the change in OFI threshold between multiplicity of infection (MOI) 0.1 and uninfected cells represented by a change in OFI threshold metric (%) according to embodiments herein. Infection was evident in all samples. The difference between uninfected and infected cells decreased with increasing concentration of H2O2. The OFI Threshold was greater than 30 s'1.

[0132] FIG. 9 is a principal component analysis chart showing a prediction of process success using LFC analysis of viability / vitality metrics according to embodiments herein. Activated T cells were evaluated at the 24 hour timepoint. T cells previously treated with 5 mM doxorubicin were also evaluated.

[0133] FIG. 10 is a principal component analysis chart providing a T cell activation process roadmap. T-cell data across five (5) experiments and three (3) different donors were used to create a process “road map” having a boundary using LFC data. The boundary was then used to characterize samples from a deviated or changed process. As shown in FIG. 10, the LFC metrics may be used to develop a process road map that tracks cellular changes over time. By tracking cellular changes throughout a process, an understanding of the ideal or desired pathway can be developed, and a statistical boundary can be used to define thispathway. A deviation in the process was detected outside the boundary as early as one (1) day post activation and continued down the alternate path. The deviation was represented by doxorubicin treated T Cells. The process average and standard deviation was 3.2 s in PCI and 3.0 s in PC2. This analysis allows for the identification of deviations from the ideal pathway by changes in the cell-based measurements that are located outside the ideal pathway.

[0134] FIG. 11 is a chart showing direct detection and correlation of T Cell viability represented by the expected viability (Radiance®) as a function of the measured viability (trypan blue) according to embodiments herein. Initial data used healthy donor T cells and two rapid treatments, Fix / Perm, and DMSO exposure. Treated and Untreated populations were mixed at varying ratios and analyzed using a Radiance® LFC analyzer. There was a strong multivariate correlation (PLS) developed between the Measured Viability (Trypan Blue) and the Calculated Viability using the LFC measurements.

[0135] FIG. 12 is a chart showing LFC measured vitality changes in T Cells for a variety of biological samples containing varying amounts of DMSO and doxorubicin according to embodiments herein. FIG. 13A is a chart showing the trypan blue measured viability of T Cells as a function of DMSO concentration and doxorubicin treatment. FIG. 13B is a chart showing the principle component 1 score as a function of DMSO concentration and doxorubicin treatment according to embodiments herein. The principal component analysis (PC A) chart of FIG. 12 represents a multivariate dimensionality reduction technique to easily visualize the multidimensional data. PCA model created using all data from all timepoints of prolonged treatments. As shown in FIGs. 13A and 13B, the LFC data showed detection of cell treatments even when viability did not. Other time points had a similar trend at 18 hours. A viability metric (VM) may be determined without the use of a dye or marker that distinguishes between live and dead cells. As noted in FIG. 12, in some multivariate embodiments, cell treatments may be detected even when viability does not detect such treatment.

[0136] FIG. 14A is a scatter plot showing the LFC classification and single-cell measurement of cellular samples represented by size and optical force index for populations exposed to the cell cycle inhibitor (AmChl) compared to healthy (untreated) cells. FIG. 14B is a scatter plot showing the LFC classification and single-cell measurement of cellular samples represented by size and optical force index for populations exposed to the nutrient depletion (D-PHE) compared to healthy (untreated) cells. FIG. 14C is a scatter plot showing the LFC classification and single-cell measurement of cellular samples represented by size andoptical force index for populations exposed to oxidative stress from buthionine sulfoximine (BSO) treatment compared to health (untreated) cells. FIG. 15 is a bar chart showing optical force index and size threshold metric as a function of cell health fingerprint for healthy (untreated) cells compared to multiple chemicals that fall under the categories of apoptosis modular, cell cycle inhibitor, metabolic stressor, nutrient depletion, and oxidative stress biological samples. The results of this study were used for classification of cellular stressors. All stressor groups showed separation from the healthy cells, with apoptosis modulators having the least impact. The greatest effect was observed with cell cycle inhibitors.

[0137] FIG. 16A is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 2X osmolality conditions. FIG. 16B is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 3X osmolality conditions. FIG. 16C is a chart showing the quantification of change in osmolality for human embryonic kidney (HEK) cells exposed to IX and 5X osmolality conditions. FIG. 17 is a bar chart showing the average optical force index for HEK cell samples exposed to IX, 2X, 3X, and 5X osmolality conditions. In this study, HEK cells were exposed to different osmolality conditions for 5 minutes prior to fixation at IX (culture medium), 2X, 3X, and 5X levels. Significant differences from control conditions were observed in all conditions across multiple LFC (Radiance®) metrics including optical force index. Changes in LFC metrics provided a sensitive profile of cell changes due to osmolality that cell size measurements alone cannot capture / resolve. It was determined that FIGs. 14A-17 may be used to create a cell health fingerprint.

[0138] FIG. 18 is a principal component analysis chart showing the LFC analysis of vesicular stomatitis virus (VSV) infection of stressed Vero cells at different multiplicities of infection (MOIs). The differentiation of cellular changes due to infection caused by viral infection versus oxidative stress (H2O2) were evaluated. Infection caused similar changes in stressed and unstressed cells (mostly PCI). Differentiation due to prior stress was seen mostly in PC2. This shows the ability of LFC to detect related forms of cellular stress (specifically apoptosis induced by viral infection and oxidative stress and subsequent cell damage).

[0139] In another study, hMSC changes were monitored due to age / passages through the system. FIG. 19A is a bar chart showing the average velocity of human mesenchymal stem cell (hMSC) samples at varying ages (passages) as measured using LFC. FIG. 19B is a bar chart showing the average complexity factor of human mesenchymal stem cell (hMSC) samples at varying ages (passages) as measured using LFC. Changes were seen in multipleLFC metrics, including both average velocity and average complexity factor, as the passage number increased. The velocity was proportional to the cell’s optical force, while complexity factor was a shape metric related to the cell’s circularity.

[0140] Multivariate methods may be used to measure population wide changes in multiple metrics. FIG. 20A is a PCA chart showing undifferentiated hMSC cell population changes resulting from different passages using LFC according to embodiments herein. The ellipse around the Passage 5 samples is statistically derived. PCA plot of undifferentiated cell populations at different passage numbers showed statistical separation between samples. LFC data was suitable to predict future differentiation quality. Differentiated Cell Images were generated (14 days post induction). FIG. 20B is an image showing an hMSC cell sample after passage five (5). FIG. 20C is an image showing an hMSC cell sample after passage eight (8). FIG. 20D is an image showing an hMSC cell sample after passage 16. These images show a general decrease in differentiation as passage number increases.

[0141] In another study, the detection of changes in mesenchymal stem cells based on age (passage number) was evaluated. FIG.21A is a PCA chart showing changes in mesenchymal stem cell (MSC) samples based on age (passage number) using LFC. FIG. 21B is a chart showing the mitochondrial activity of the mesenchymal stem cell samples using orthogonal assays in combination with LFC data according to embodiments herein. FIG.21C is a chart showing the P-galactosidase expression of the mesenchymal stem cell samples using orthogonal assays in combination with LFC data according to embodiments herein. As shown in FIG. 21 A, passage 10 cells showed very poor differentiation when compared to Passage 4 cells. The results of this study indicate a reduction in the ability to differentiate with age. Differentiation could be detected using differences in LFC data with orthogonal assay changes including mitochondria activity and P-galactosidase expression as shown in FIGs. 21B and 21C

[0142] The results of a study that investigated hMSC differentiation are shown in FIGs.22A-22I. FIG. 22A is an image showing Oil Red O staining indicating differentiation to mature adipocytes cells at passage four (4). FIG. 22B is an image showing Oil Red O staining indicating poor differentiation to mature adipocytes cells at passage 10. FIG. 22C is an image showing Alizarin Red staining indicating differentiation to mature osteocyte cells at passage four (4). FIG. 22D is an image showing Alizarin Red staining indicating poor differentiation to mature osteocyte cells at passage 10. FIG.22E is an image showing a negative control. FIG. 22F is a bar chart showing the absorbance at 540 nm for passages four (4) and 10 of the mature adipocyte samples compared to negative control samples, as ameans of quantifying the differentiation based on Oil Red Oas. FIG. 22G is a bar chart showing the optical density (OD) fold difference from the negative control for passages four (4) and 10 for the mature adipocyte samples. FIG. 22H is a bar chart showing the absorbance at 405 nm for passages four (4) and 10 for the mature osteocyte samples compared to negative control sample, as a means of quantifying Alizarin Red. FIG. 221 is a bar chart showing the optical density (OD) fold difference from the negative control for passages four (4) and 10 for the mature osteocyte samples. The results in FIG.s 22A-22E showed proper differentiation to either adipocytes or osteocytes for Passage 4 cells and poor differentiation for Passage 10 cells. The bar charts of FIGs. 22F-22I represent the quantitation of staining for each phenotype.

[0143] Cell types and the associated areas where determination or identification of cellular response using LFC were analyzed. FIG. 23 is a PCA chart showing statistical differences between the cellular starting material of samples that resulted in high CAR T potency (B, E) versus those that resulted in low CAR T potency (A, C, D) using the same manufacturing process. The results demonstrated the ability of methods, devices, and systems as described herein to detect end of process differences by using LFC to analyze the cellular starting material. The PCA model was created LFC (Radiance®) data collected from donor peripheral blood monocular cells (PBMC) samples. The ellipses are 2c boundaries. Table 5 summarizes the results.Table 5 - Percent Killing Results for Donors A-E

[0144] Donors B and E represented the high killing donors and the others represented the lower killing donors (see Table 5). The results were statistically different as shown in FIG.23. The results demonstrated that the LFC could be used to estimate the killing efficiency of donor T-cells prior to manufacturing of the therapy. PBMC isolated from whole blood (or whole blood), or T Cells obtained after apheresis may be measured and the resulting data used with a model to ascertain the future manufacturing efficacy, process step suitability, and / or clinical efficacy of the resulting T cell-based product. The same analytical logic and utility may be obtained for stem cells, cell lines, or other types of cells.

[0145] Further to the above studies, other cell types and cellular properties or cellular pro-cess / purpose for cells that may be modeled for classification or quantitation. Additionally, analyzers other than LFC, suitable for measuring other optical, fluidic, acoustic, magnetic, and / or other suitable properties may be implemented in the methods, devices, and systems described herein. The resulting models may be predictive or prescriptive models. Prescriptive models are ones that not only predict a manufacturing failure but go further and dictate the appropriate pathway required, given the state of cells measured using LFC (or other analysis techniques), to reach the desired outcome. The desired outcome may be manufacturing potency based, and / or clinical efficacy.

[0146] According to various embodiments, methods, devices, and systems for measuring cellular differences and responses to differential stimuli using various forces and / or other parameters may be configured to receive a selection of initial samples comprising biological particles treated with varying known levels of stimuli or analyte, perform optical and / or fluidic force-based measurements on the samples, develop a metric (e.g., a VM, IM, RM, IM) to describe the cellular response to the stimuli based on one or more optical or fluidic force-based parameters, and correlate the cellular response to a cellular property of interest (e.g., cellular viability, vitality, and / or other fit-for-purpose functions). As discussed, the methods, devices and systems as disclosed herein may be computer-implemented.

[0147] In various embodiments, the methods, devices, and systems may be configured to generate a calibration curve based on cellular changes during the production of a biologic molecule or other ongoing bioprocess that correlates the cellular response to cellular viability and / or vitality and then use the calibration to predict the results of a future process, add treatments and incubate sample cells, analyze by optical and / or fluidic force-based measurements a plurality of samples having cells and a known range of product outcomes to determine a response metric, determine optimal response metric based on trend with dilution and use generated data to predict cellular viability and / or vitality of future samples. One or more of laser force cytology, fluidic / hydrodynamic forces, electrokinetic forces, magnetic forces, and / or acoustic forces may be used for measurement of a cell viable state, viability, vitality, cell health, and / or cellular function. In some embodiments, such forces may be used to characterize and / or sort cells, and / or to characterize and / or measure instantaneous viability and / or vitality.

[0148] In some embodiments, one or more of the aforementioned forces may be used to create a predicting tool configured to measure cellular status and trends, including averageand standard deviation. In some embodiments, such forces may be used to predict future viability, vitality, and / or other cellular functions.

[0149] LFC-derived VM cell health metrics may be used for prediction of process or manufacturing success and / or clinical success. In various embodiments, LFC-derived VM cell health metrics may correlate with some function or property, potentially independent from a manufacturing process. LFC-derived VM cell health metrics also may be useful in the assessment of frozen samples (after thaw) for viability and / or vitality and prediction of performance during a manufacturing process as well as assessment of samples prior to cryopreservation to predict post-thaw performance as well as measurement of viability and / or vitality using LFC-derived VM metrics before, during, and after a manufacturing process or classification of change in fitness based on different stressors.

[0150] VM is particularly advantageous in therapies which involve the administration of cell-based therapy to a patient in need. In some embodiments, VM can be an indicator of the efficacy of the preparation or manufacturing process that creates a product to be administered to a patient in need. In certain embodiments, VM can be an indicator of the potency of a product to be administered to a patient in need, evaluated at a time before administration to a patient in need. In other embodiments, VM can act as a predictor of success of a treatment or product that is to be administered to a patient in need.

[0151] In certain embodiments, use of optical, fluidic / hydrodynamic, electrokinetic, mechanical, magnetic and / or acoustic forces to create a functional cell-based assay to evaluate or correlate one or more of stress on cells, including physical, chemical, biological, radiological stress, and measure a response correlate instantaneous response with future recovery of cell function, viability, and / or vitality.

[0152] In an embodiment, a device such as Radiance™ (a laser force cytology instrument available from LumaCyte™ (Charlottesville, Virginia, USA) is used for conducting optical force- based measurements, however, as would be evident to one skilled in the art, other devices and methods capable of optical force measurement including LFC would be suitable for use in connection with this invention. (For clarity Viability Metric (“VM”) and Response Metric (RM) may be used interchangeably depending on the type of measurement being made.)

[0153] In certain embodiments, the particles or cells for analysis in the present invention include any particle or cell where subtle changes in its physical properties can indicate a response, e.g. a biological response, can be analyzed. Adherent and suspensions of particles or cells, clusters, organoids, stem cells, cancer cells, other cells such as blood cells (like redor white blood cells), other immune cells, epithelial cells, microbial cells (bacteria, yeast), and cells from solid tissues that have been dissociated. In certain embodiments, cells which can be analyzed in accordance with the present invention include CART-cells, B- cells, lymphocytes, including tumor infiltrating lymphocytes, islet cells, modified or unmodified cells of organs of a mammal such as a human, including heart, brain, liver and kidney cells.

[0154] Other cells such as CHO cells can be analyzed as well as Vero cells, baby hamster kidney cells (BHK), MRC-5 cells, and Human kidney fibroblast (324K) cells. The panel is not limited to these cell particle or cell types and other existing types can be analyzed for viability and / or vitality in response to an intervention.

[0155] FIG. 24 illustrates a diagrammatic representation of a machine in the example form of a computer system 700 including a set of instructions executable by systems and devices as described herein (e.g., optical and / or fluidic force-based instruments) to perform any one or more methodologies (e.g., computer-implemented methods) as described herein In one or more implementation, systems comprising an optical and / or fluidic force-based instrument having an inlet for receiving biological particles, an outlet, for example, comprising a higher concentration of viable and / or vital fit-for-purpose biological particles than received in the inlet, and a computer system 700 in communication with the operable components of the system may include instructions to enable execution of the processes and corresponding components shown and described in this disclosure.

[0156] In alternative implementations, the systems may include a machine connected (e.g., networked) to other machines in a LAN, an intranet, an extranet, or the Internet. The machine may operate in the capacity of a server machine in client-server network environment. The machine may be a personal computer (PC), a neural computer, a set-top box (STB), Personal Digital Assistant (PDA), a cellular telephone, a server, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies described herein.

[0157] The example computer system 2400 can include a processing device (processor) 2402, a main memory 2404 (e.g., read-only memory (ROM), flash memory, dynamic random access memory (DRAM) such as synchronous DRAM (SDRAM), cloud-based memory'), a static memory 2406 (e.g., flash memory, static random access memory (SRAM)), and a data storage device 2418, which communicate with each other via a bus2430. In some implementations, the systems utilize AZURE BLOB STORAGE® for the cloud suitable store large quantities of unstructured data.

[0158] Processing device 2402 represents one or more general-purpose processing devices such as a microprocessor, central processing unit, or the like. More particularly, the processing device 2402 may be a complex instruction set computing (CISC) microprocessor, reduced instruction set computing (RISC) microprocessor, very long instruction word (VLIW) microprocessor, or a processor implementing other instruction sets or processors implementing a combination of instruction sets. The processing device 702 may also be one or more special-purpose processing devices such as an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), network processor, and the like. In various implementations of the present disclosure, the processing device 702 is configured to execute instructions for the devices or systems described herein for performing the operations and processes described herein.

[0159] The computer system 2400 may further include a network interface device 2408. The computer system 2400 also may include a video display unit 2410 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)), an alphanumeric input device 2412 (e.g., a keyboard), a cursor control device 2414 (e.g., a mouse), and a signal generation device 2416 (e.g., a speaker).

[0160] The data storage device 2418 may include a computer-readable medium 2428 on which is stored one or more sets of instructions of the devices and systems as described herein embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory 2404 and / or within processing logic 2426 of the processing device 2402 during execution thereof by the computer system 2400, the main memory 2404 and the processing device 2402 also constituting computer-readable media.

[0161] The instructions may further be transmitted or received over a network 2420 via the network interface device 2408. While the computer-readable storage medium 2428 is shown in an example implementation to be a single medium, the term ‘'computer-readable storage medium” should be taken to include a single medium or multiple media (e g., a centralized or distributed database, and / or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable storage medium” shall also be taken to include any medium that is capable of storing, encoding or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the present disclosure. The term “computer-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-state memories, optical media, and magnetic medi.

[0162] The preceding description sets forth numerous specific details such as examples of specific systems, components, methods, and so forth, in order to provide a good understanding of several implementations of the present disclosure. It will be apparent to one skilled in the art, however, that at least some implementations of the present disclosure may be practiced without these specific details. In other instances, well-known components or methods are not described in detail or are presented in simple block diagram format in order to avoid unnecessarily obscuring the present disclosure. Thus, the specific details set forth are merely presented as examples. Particular implementations may vary from these example details and still be contemplated to be within the scope of the present disclosure. In the above description, numerous details are set forth.

[0163] It will be apparent, however, to one of ordinary skill in the art. having the benefit of this disclosure, that implementations of the disclosure may be practiced without these specific details. In some instances, well-known structures and devices are shown in block diagram form, rather than in detail, in order to avoid obscuring the description.

[0164] Some portions of the detailed description are presented in terms of algorithms and symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and representations are the means used by those skilled in the data processing arts to most effectively convey the substance of their work to others skilled in the art. An algorithm is here, and generally, conceived to be a self-consistent sequence of steps leading to a desired result. The steps are those requiring physical manipulations of physical quantities. Usually, though not necessarily, these quantities take the form of electrical, magnetic, or optical signals capable of being stored, transferred, combined, compared, and otherwise manipulated. It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like.

[0165] It. should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the above discussion, it is appreciated that throughout the description, discussions utilizing terms such as “rating,” “selecting,” “comparing,” “adjusting,” or the like, refer to the actions and processes of a computer system, or similar electronic computing device, that manipulates and transforms data represented as physical (e.g., electronic) quantities within the computersystem’s registers and memories into other data similarly represented as physical quantities within the computer system memories or registers or other such information storage, transmission or display devices.

[0166] Implementations of the disclosure also relate to an apparatus for performing the operations herein. This apparatus may be specially constructed for the required purposes, or it may comprise a general purpose computer selectively activated or reconfigured by a computer program stored in the computer. Such a computer program may be stored in a computer readable storage medium, such as, but not limited to, any type of disk including floppy disks, optical disks, CD-ROMs, and magnetic-optical disks, read-only memories (ROMs), random access memories (RAMs), EPROMs, EEPROMs, magnetic or optical cards, or any type of media suitable for storing electronic instructions.

[0167] The algorithms and displays presented herein are not inherently related to any particular computer or other apparatus. Various general purpose systems may be used with programs in accordance with the teachings herein, or it may prove convenient to construct a more specialized apparatus to perform the required method steps The required structure for a variety of these systems will appear from the description below. In addition, the present disclosure is not described with reference to any particular programming language. It will be appreciated that a variety of programming languages may be used to implement the teachings of the disclosure as described herein.

[0168] The foregoing written description is considered to be sufficient to enable one skilled in the art to practice the invention. The present disclosure is not to be limited in scope by examples provided, since the examples are intended as a single illustration of one aspect of the invention and other functionally equivalent embodiments are within the scope of the invention. Various modifications of the invention in addition to those shown and described herein will become apparent to those skilled in the art from the foregoing description and fall within the scope of the appended claims. The advantages and objects of the invention are not necessarily encompassed by each embodiment of the invention. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Such equivalents are intended to be encompassed by the following claims.

[0169] One skilled in the art will recognize that the disclosed features may be used singularly, in any combination, or omitted based on the requirements and specifications of a given application or design. When an embodiment refers to “comprising” certain features, it is to be understood that the embodiments can alternatively “consist of or “consist essentially ofany one or more of the features. Other embodiments of the invention will be apparent to those skilled in the art from consideration of the specification and practice of the invention.

[0170] It is noted in particular that where a range of values is provided in this specification, each value between the upper and lower limits of that range is also specifically disclosed. The upper and lower limits of these smaller ranges may independently be included or excluded in the range as well. The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. It is intended that the specification and examples be considered as exemplary in nature and that variations that do not depart from the essence of the invention fall within the scope of the invention. Further, all of the references cited in this disclosure are each individually incorporated by reference herein in their entireties and as such are intended to provide an efficient way of supplementing the enabling disclosure of this invention as well as provide background detailing the level of ordinary skill in the art.

Claims

IN THE CLAIMSI / We claim:

1. A method for measuring cellular properties and / or responses to differential stimuli using optical and / or fluidic forces, wherein the method comprises:receiving a selection of initial samples comprising biological cells, performing optical and / or fluidic force-based measurements on the samples; andat least one of:developing a response metric (RM) to describe the cellular fingerprint based on one or more optical or fluidic force-based parameters; orcorrelating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.

2. The method of 1, wherein the measured cellular response is used to predict the results of a future treatment on subsequent samples comprising biological cells.

3. The method of claim 1 or 2, wherein the measured cellular response is used to predict the results of a future process outcome.

4. The method of claim 1 or 2, wherein the measured cellular response is used to prescribe the actions required to achieve a future process outcome.

5. A method for generating a calibration curve based on cellular changes during the production of a biologic molecule or other ongoing bioprocess that correlates the cellular response to cellular viability and / or vitality and then using the calibration to predict the results of a future process, the method comprising:adding treatments, performing manipulations, and / or incubating sample cells; analyzing by optical and / or fluidic force-based measurements a plurality of samples having cells and a known range of process outcomes or product characteristics to determine a response metric;determining an optimal response metric; andusing generated data to predict cellular viability and / or vitality of future samples.

6. The method of claims 1, 2 or 5, wherein the optical and / or force-based measurements comprise laser force cytology.

7. The method of claim 6, wherein laser force cytology comprises the assessment of parameters including one or more of linear velocity, size, perimeter, size (area, diameter, volume, etc.), number of trapped cells per sample, number of beam ejected cells per sample, number of aggregates (based upon size and / or shape or other parameters), number of debrissized particles (based upon size and / or shape or other parameters), normalized velocity, minimum x position, optical retention time, optical trapping time, optical force, optical torque, orientation, optical and fluidic dynamics, effective refractive index, eccentricity, minor axis, major axis, deformability, eccentricity deformability, minor and major axis deformability, elongation factor, compactness factor, circularity factor, images including greyscale features, whole images, image components or image derived parameters, morphology characteristics, other laser force cytology derived parameters or combinations thereof.

8. The method of any of claims 1, 2 or 5, wherein the sample comprises one or more cellular particles / particulates, cells, cellular clusters, organoids, stem cells, cancer cells, white blood cells red blood cells, immune cells, epithelial cells, microbial cells, bacterial cells, yeast cells, cells derived from solid tissues, CART-cells, B-cells, lymphocytes tumor infiltrating lymphocytes, islet cells, modified or unmodified cells of organs, heart cells, brain cells, liver cells, kidney cells, CHO, Vero cells, baby hamster kidney cells (BHK), MRC- 5 cells, and Human kidney fibroblast (324K) cells.

9. The method of claim 8, wherein the treatments comprise at least one of bio / chemical, thermal, electrical, magnetic or other treatment.

10. Use of Laser Force Cytology (LFC), fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces for measurement of cell viable state, viability, vitality, cell health, and / or cellular function.

11. Use of optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to characterize and / or sort cells.

12. Use of optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to characterize and / or measure instantaneous viability and / or vitality.

13. Use of optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to characterize to create a predicting tool to measure cellular status and trends, including average and standard deviation.

14. Use of optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to predict future viability and / or vitality.

15. Use of optical, fluidic / hydrodynamic, electrokinetic, magnetic and / or acoustic forces to create a functional cell-based assay to evaluate or correlate one or more of:a) stress on cells, including physical, chemical, biological, radiological stress, and measure a response; and / orb) correlate instantaneous response with future recovery of cell function and / or viability16. Use of optical, fluidic / hydrodynamic, electrokinetic, and / or acoustic forces to generate date concerning one or more of:c) treatment of a cell with H2O2and better response than traditional assay (trypan blue);d) T Cell activation disruption using a drug such as doxorubicin; and / or e) DMSO, Dox, fixing / permeability for disruption of viability17. The uses of any of claims 10 through 16, wherein the optical and / or fluidic force is laser force cytology.

18. The use of claim 17, wherein laser force cytology comprises the assessment of parameters including one or more of linear velocity, size, perimeter, size (area, diameter, volume, etc.), number of trapped cells per sample, number of beam ejected cells per sample, number of aggregates (based upon size and / or shape or other parameters), number of debrissized particles (based upon size and / or shape or other parameters), normalized velocity, minimum x position, optical retention time, optical trapping time, optical force, opticaltorque, orientation, optical and fluidic dynamics, effective refractive index, eccentricity, minor axis, major axis, deformability, eccentricity deformability, minor and major axis deformability, elongation factor, compactness factor, circularity factor, images including greyscale features, whole images, image components or image derived parameters, morphology characteristics, or other laser force cytology derived parameters.

19. A method for identification of cells using optical and / or fluidic forces, wherein the method comprises:receiving a selection of initial samples comprising biological cells; performing optical and / or fluidic force-based measurements on the samples; developing a response metric (RM) to describe the cells based on one or more optical or fluidic force-based parameters; andidentifying cells by correlating the parameters to a cellular property of interest, wherein the cellular property of interest comprises cellular viability and / or vitality.

20. A method for measuring cells using optical and / or fluidic forces of, wherein the method comprises:receiving a selection of initial samples comprising treated biological cells; performing optical and / or fluidic force-based measurements on the samples; developing a response metric (RM) to describe the cellular fingerprint based on one or more optical or fluidic force-based parameters; andcorrelating the cellular response to a cellular property of interest, wherein the cellular property of interest is cellular viability and / or vitality.

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