Systems and methods for t-cell characterization based on cell dynamics

Abstract

An exemplary embodiment of the present disclosure provides a method of characterizing a plurality of cells, comprising: providing a sample comprising the plurality of cells; obtaining a plurality of images of the plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterizing the plurality of cells based, at least in part, on the image values.

Description

SYSTEMS AND METHODS FOR T-CELL CHARACTERIZATION BASED ON CELL DYNAMICSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 528,139, filed on 21 July 2023, which is incorporated herein by reference in its entirety as if fully set forth below.GOVERNMENT LICENSE RIGHTS

[0002] This invention was made with government support under Agreement No. R35GM147437, awarded by National Institutes of Health, and Agreement No. 1648035, awarded by National Science Foundation. The government has certain rights in the invention.FIELD OF THE DISCLOSURE

[0003] The various embodiments of the present disclosure relate generally to imaging systems and more particularly to imaging systems for characterizing biological cells.BACKGROUND

[0004] Cell therapies hold great promise to treat a number of diseases. One such therapy uses CAR T cells (Chimeric Antigen Receptor T cells) to treat cancer, for example. In CAR T cell therapy, T cells from patients are extracted and altered to produce the CAR protein, which helps T cells identify and then kill cancer cells. Altered T cells need to be expanded to ultimately produce a biologic product that has millions CAR T cells. The altered and expanded cells are then infused back into the patient. Characterization of T cells during expansion (or manufacturing) and prior to infusion into the patient is critical. Current methods to characterize T cells include flow cytometry, immunofluorescence and immunohistochemistry imaging / sensing. However, with these such methods, the cells that are used for characterization cannot be injected back into the patient and are thus destructive. Accordingly, there is a need for a simple, non-destructive, and label-free method to characterize cells, including health (live, dead, apoptotic), activation and Cluster of Differentiation (“CD”) receptors (CD3, CD45, CD8, CD4). The present disclosure provides such methods.BRIEF SUMMARY

[0005] An exemplary embodiment of the present disclosure provides a method of characterizing a plurality of cells, comprising: providing a sample comprising the plurality ofcells; obtaining a plurality of images of the plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterizing the plurality of cells based, at least in part, on the image values.

[0006] In any of the embodiments disclosed herein, the plurality of images can indicate a movement of one or more components of the cell over time.

[0007] In any of the embodiments disclosed herein, the plurality of images can be quantitative phase images.

[0008] In any of the embodiments disclosed herein, the plurality of images can be imaged at a frequency of between 0.1-1000 Hz.

[0009] In any of the embodiments disclosed herein, the plurality of images can be ultraviolet images.

[0010] In any of the embodiments disclosed herein, each image value can correspond to an optical phase.

[0011] In any of the embodiments disclosed herein, each image value can correspond to a refractive index.

[0012] In any of the embodiments disclosed herein, each image value can correspond to an attenuation.

[0013] In any of the embodiments disclosed herein, each image value can correspond to a back scattered signal.

[0014] In any of the embodiments disclosed herein, each image value can correspond to an autofluorescence.

[0015] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise obtaining an autocorrelation of image values for each pixel over the points in time.

[0016] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise obtaining a Fourier transform of image values for each pixel over the points in time.

[0017] In any of the embodiments disclosed herein, characterizing the plurality of cells can further comprise obtaining an absolute value of the Fourier transforms.

[0018] In any of the embodiments disclosed herein, characterizing the plurality of cells can further comprise performing a phasor analysis on the Fourier transforms.

[0019] In any of the embodiments disclosed herein, the phasor analysis can comprise decomposing the Fourier transforms into real and imaginary values.

[0020] In any of the embodiments disclosed herein, characterizing the plurality of cells can further comprise plotting the real and imaginary values on a histogram representative of a phasor space.

[0021] In any of the embodiments disclosed herein, characterizing the plurality of cells can further comprise analyzing the structure and dynamics of cells shown in histogram.

[0022] In any of the embodiments disclosed herein, the plurality of cells can be T cells.

[0023] In any of the embodiments disclosed herein, the plurality of cells can be CAR T cells.

[0024] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise processing the plurality of images with a neural network.

[0025] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise determining phenotypes for the plurality of cells.

[0026] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise determining whether each of the plurality of cells are activated or quiescent T-cells.

[0027] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise determining that one or more of the plurality of cells is a CD4 T-cell or a CD8 T-cell.

[0028] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise differentiating B-cells and T-cells in the plurality of cells.

[0029] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise determining whether each of the plurality of cells is dead or alive.

[0030] In any of the embodiments disclosed herein, characterizing the plurality of cells can comprise determining a response of each of the plurality of cells to a predetermined stimulus.

[0031] Another embodiment of the present disclosure provides a system for characterizing a plurality of cells. The system can comprise one or more processors configured to, individually or collectively: obtain a plurality of images of a sample comprising a plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterize the plurality of cells based, at least in part, on the image values.

[0032] These and other aspects of the present disclosure are described in the Detailed Description below and the accompanying drawings. Other aspects and features of embodiments will become apparent to those of ordinary skill in the art upon reviewing the following description of specific, exemplary embodiments in concert with the drawings. While features of the present disclosure may be discussed relative to certain embodiments and figures, allembodiments of the present disclosure can include one or more of the features discussed herein. Further, while one or more embodiments may be discussed as having certain advantageous features, one or more of such features may also be used with the various embodiments discussed herein. In similar fashion, while exemplary embodiments may be discussed below as device, system, or method embodiments, it is to be understood that such exemplary embodiments can be implemented in various devices, systems, and methods of the present disclosure.BRIEF DESCRIPTION OF THE DRAWINGS

[0033] The following detailed description of specific embodiments of the disclosure will be better understood when read in conjunction with the appended drawings. For the purpose of illustrating the disclosure, specific embodiments are shown in the drawings. It should be understood, however, that the disclosure is not limited to the precise arrangements and instrumentalities of the embodiments shown in the drawings.

[0034] FIG. 1 provides a flow chart of a method of characterizing a plurality of biological cells, in accordance with some embodiments of the present disclosure.

[0035] FIGS. 2A-D provide a workflow for analyzing T cell dynamics using phasor analysis, in accordance with some embodiments of the present disclosure.

[0036] FIG. 3 provides a plot showing differentiation of CD3+CD45+CD8+ T cells and CD3+CD45+CD4+ T cells using an exemplary dynamic analysis of the present disclosure versus flow cytometry, in accordance with some embodiments of the present disclosure.

[0037] FIGS. 4A-E illustrate a method of characterizing a plurality of biological cells using phasor analysis to reveal intracellular dynamics, in accordance with some embodiments of the present disclosure.

[0038] FIG. 5 provide a block diagram an exemplary computing device for implementing the methods (or portions of the methods) disclosed herein.DETAILED DESCRIPTION

[0039] Although preferred exemplary embodiments of the disclosure are explained in detail, it is to be understood that other exemplary embodiments are contemplated. Accordingly, it is not intended that the disclosure is limited in its scope to the details of construction and arrangement of components set forth in the following description or illustrated in the drawings. The disclosure is capable of other exemplary embodiments and of being practiced or carried out invarious ways. Also, in describing the preferred exemplary embodiments, specific terminology will be resorted to for the sake of clarity.

[0040] To facilitate an understanding of the principles and features of the present disclosure, various illustrative embodiments are explained below. The components, steps, and materials described hereinafter as making up various elements of the embodiments disclosed herein are intended to be illustrative and not restrictive. Many suitable components, steps, and materials that would perform the same or similar functions as the components, steps, and materials described herein are intended to be embraced within the scope of the disclosure. Such other components, steps, and materials not described herein can include, but are not limited to, similar components or steps that are developed after development of the embodiments disclosed herein.

[0041] As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise.

[0042] Also, in describing the preferred exemplary embodiments, terminology will be resorted to for the sake of clarity. It is intended that each term contemplates its broadest meaning as understood by those skilled in the art and includes all technical equivalents which operate in a similar manner to accomplish a similar purpose.

[0043] Ranges can be expressed herein as from “about” or “approximately” one particular value and / or to “about” or “approximately” another particular value. When such a range is expressed, another exemplary embodiment includes from the one particular value and / or to the other particular value.

[0044] Similarly, as used herein, “substantially free” of something, or “substantially pure”, and like characterizations, can include both being “at least substantially free” of something, or “at least substantially pure”, and being “completely free” of something, or “completely pure”.

[0045] By ‘ ‘comprising” or “containing” or “including” is meant that at least the named compound, member, particle, or method step is present in the composition or article or method, but does not exclude the presence of other compounds, materials, particles, method steps, even if the other such compounds, material, particles, method steps have the same function as what is named.

[0046] Mention of one or more method steps does not preclude the presence of additional method steps or intervening method steps between those steps expressly identified. Similarly, it is also to be understood that the mention of one or more components in a device or systemdoes not preclude the presence of additional components or intervening components between those components expressly identified.

[0047] The materials described as making up the various members of the invention are intended to be illustrative and not restrictive. Many suitable materials that would perform the same or a similar function as the materials described herein are intended to be embraced within the scope of the invention. Such other materials not described herein can include, but are not limited to, for example, materials that are developed after the time of the development of the invention.

[0048] Reference will now be made in detail to exemplary embodiments of the disclosed technology, examples of which are illustrated in the accompanying drawings and disclosed herein. Wherever convenient, the same references numbers will be used throughout the drawings to refer to the same or like parts.

[0049] Described below are non-destructive, and label-free systems and methods for characterizing cells, including health (live, dead, apoptotic), activation and CD receptors (CD3, CD45, CD8, CD4). Embodiments disclose herein can use microscopy (e.g., phase contrast, brightfield, back reflectance) to monitor cell dynamics, which can correlate to certain cell characteristics, e.g., cell phenotypes.

[0050] As shown in FIG. 1, an exemplary embodiment of the present disclosure provides a method 100 of characterizing a plurality of cells in a sample. The method 100 can comprise providing a sample comprising the plurality of cells 105. The cells can contain many different cells types known in the art, including, but not limited to, naive (unactivated, immature cells) T-cells, activated T-cells and corresponding subtypes (e.g., CD3 / 4 / 8 / etc.) (with and without Chimeric antigen receptor T cells (CAR-T cells)), B cells, and the like.

[0051] The method 100 can further comprising obtaining a plurality of images of the plurality of cells 110. The images can be many different types of images known in the art, including, but not limited to quantitative phase images, ultraviolet images, phase contrast, differential interference microscopy (“DIC”), differential phase contrast (“DPC”) oblique illumination, oblique back illumination (“OBM”) quantitative OBM (“qOBM”), confocal microscopy, bright field microscopy, dark field microscopy, mutltiphoton microscopy, photothermal microscopy, photoacoustical microscopy, and the like. In some embodiments, quantitate phase imaging (QPI) is used to image cells. Each of the images can be taken at a different point in time. The period of time over which the plurality of cells are imaged can vary from less than asecond, to multiple seconds, to one or more minutes, hours, days, etc., in accordance with various embodiments of the present disclosure. The images can be taken at many different frequencies. In some embodiments, the images can be taken of a frequency of at least 0.01 Hz, 0.05 Hz, 0.1 Hz, 0.5 Hz, 1 Hz, 5 Hz, 10 Hz, 50 Hz, 100 Hz, 200 Hz, 300 Hz, 400 Hz, 500 Hz, or 1000 Hz. In some embodiments, the images can be taken at a frequency of no more than 1000 Hz, 500 Hz, 400 Hz, 300 Hz, 200 Hz, 100 hz, 50 Hz, 10 Hz, 5 Hz, 1 Hz, 0.5 Hz, 0.1 Hz, 0.05 Hz, or 0.01 Hz. The present disclosure further contemplates frequency ranges including any of the above upper and lower limits, e.g., 0.01-1000 Hz, 5-500 Hz, 200-400 Hz, and the like. By taking each image at a distinct point in time, the plurality of images can indicate a movement of one or more components of the cell over time, e.g., subcellular components and molecules, including lipids, mitochondria, nucleoid acids (e.g., DNA, RNA), chromatin, nucleotides, proteins, cytochromes, flavins, nuclear membrane / envelope, cell nucleus, cell vesicles (membrane-derived “bubbles” used for intracellular transport, and the like.

[0052] Each image of the images can be represented by a digital file and can comprise a plurality of pixels. Each pixel can be represented by an image value. The image values can correspond to many different quantitative values, including, but not limited to an optical phase, a refractive index, an attenuation, a back scattered signal, an autofluorescence, and the like.

[0053] The method 100 can further comprise characterizing the plurality of cells based, at least in part, on the image values of each of the plurality of images 115. The characterization can make many different determinations about the cells, in accordance with various embodiments of the present disclosure, including, but not limited to, determining phenotypes for the plurality of cells, determining whether each of the plurality of cells are activated or quiescent T-cells, determining that one or more of the plurality of cells is a CD4 T-cell or a CD8 T-cell, differentiating B-cells and T-cells in the plurality of cells, determining whether each of the plurality of cells is dead or alive, determining a response of each of the plurality of cells to a predetermined stimulus (e.g., pharmaceutical or biological agent), and the like.

[0054] Characterizing the plurality of cells can comprise one or more steps, in accordance with various embodiments of the present disclosure. For example, in some embodiments, characterizing the plurality of cells can comprise obtaining an autocorrelation of image values for each pixel over the points in time. In some embodiments, characterizing the plurality of cells can comprise utilize a frequency response of the images, such as by obtaining a Fourier transform of image values for each pixel over the points in time. In some embodiments, oncethe Fourier transforms are obtained, characterizing the plurality of cells can further comprise obtaining an absolute value of the Fourier transforms. In some embodiments, once the Fourier transforms are obtained, characterizing the plurality of cells can further comprise performing a phasor analysis on the Fourier transforms. In some embodiments, the phasor analysis can comprise decomposing the Fourier transforms into real and imaginary values. Once the real and imaginary values are obtained, in some embodiments, characterizing the plurality of cells can further comprise plotting the real and / or imaginary values on a histogram representative of a phasor space. Once the real and / or imaginary values are plotted on a histogram, characterizing the plurality of cells can further comprise analyzing the structure and dynamics of cells shown in histogram.

[0055] In some embodiments, characterizing the plurality of cells, or one of more steps thereof, can be performed with a neural network. The neural network can be many different neural networks known in the art.

[0056] In an exemplary embodiment of the present disclosure, the frequency response of the images can be utilized to characterize the cells. For example, the absolute value of the Fourier transform (FT) can be taken of the dynamic signal on a spatial pixel-by-pixel manner. The frequency response typically exhibits an exponential-like response, which can then be analyzed using phasor analysis. In phasor analysis, the signal (here the FT of the phase values), can be decomposed into the real and imaginary values of the FT at a particular frequency (effectively a cosine and sine decomposition at a given frequency). This can reduce the dimensionality of the temporal signal to two values, referred to herein as “g” and “s.” These values can then be plotted against one another and a 2D histogram generated. Pixels in the image that have similar dynamic behavior can cluster in similar regions in this 2D histogram called phasor space. Cells can then be segmented and classified based on their dynamics as well as their structure. By analyzing the structure of the cell (e.g., size, subcellular texture, and values), along with the dynamics (using the “g” and “s” values, for example) the t-cells can be characterized. Though the above disclosure contemplates quantification of dynamics using histograms, the disclosure is not so limited. Rather, other methods, include, but are not limited to, analyzing the autocorrelation of the temporal dynamics, using linear, exponential, or logarithmic egression fitting coefficients, and the like.

[0057] FIGS. 2A-D illustrate an exemplary workflow for analyzing T cell dynamics using phasor analysis, in accordance with some embodiments of the present disclosure. In FIG. 2A,multiple images 201A-E are taken of the plurality of cells over a period of time. FIG. 2B provides a plot of representative temporal dynamics from a single spatial pixel in the images 202 and the absolute value of its Fourier Transform 203. FIG. 2C provides a plot of a phasor analysis. FIG. 2D provide a plot of cell segmentation and characterization based on the phasor analysis.

[0058] FIG. 3 provides representative results of an exemplary method showing the ability to identify CD3+CD45+CD4+ and CD3+CD45+CD8+ T cells. Here, 21 independent quantitative phase images, from a quantitative oblique back illumination microscopy (qOBM) system, taken over 21 days of culture were compared to flow cytometry. Results show near perfect agreement. A similar analysis can be used to identify live and dead cells, as well apoptotic and activated T cells.

[0059] FIGS. 4A-E illustrate an exemplary method of characterizing cells, in accordance with some embodiments of the present disclosure. In FIG. 4A, a plurality of images (500) are taken over a period of time (0.1-100 Hz). In FIG. 4B, pixelwise intensity temporal response is plotted. In FIG. 4C, pixelwise intensity frequency response (Fourier Transform) is plotted. In FIG. 4D, real and imaginary parts of the Fourier Transform are calculated. In FIG. 4E, a phasor plot of the real and imaginary parts is performed.

[0060] The embodiments disclosed herein provide many advantages and improvements over conventional technologies. Existing methods to characterize T-cells typically require cell fixation, staining, labeling and / or destruction. These methods thus may not be able to preserve the cells that are analyzed. In contrast, the methods disclosed herein can enable T cell analysis and characterization in a manner that allows re-utilization of the cells to continue expanding or to be infused into the patient. The method can also be simple and label-free, such that it can be used to monitor cells during expansion at- line or in-line, and to monitor cells prior to infusion.

[0061] The images and / or histograms discussed above can be output to many different locations. For example, in some embodiments, the images and / or histograms can be output and stored in memory, transmitted to a remote device, displayed on a display, and the like.

[0062] FIG. 5 illustrates an exemplary computing device 220 that can be used to implement the methods (or one or more steps of the methods) disclosed herein. Additionally, the computing device 220 can be used to implement one or more aspects of a neural network for characterizing the plurality of cells, as described herein. As will be appreciated by one of skill in the art, the computing device 220 can be configured to implement all or some of the featuresdescribed in relation to the methods 1000 1100. As shown, the computing device 220 may include a processor 222, an input / output (“I / O”) device 224, a memory 230 containing an operating system (“OS”) 232 and a program 236. In certain example implementations, the computing device 220 may be a single server or may be configured as a distributed computer system including multiple servers or computers that interoperate to perform one or more of the processes and functionalities associated with the disclosed embodiments. In some embodiments, computing device 220 may be one or more servers from a serverless or scaling server system. In some embodiments, the computing device 220 may further include a peripheral interface, a transceiver, a mobile network interface in communication with the processor 222, a bus configured to facilitate communication between the various components of the computing device 220, and a power source configured to power one or more components of the computing device 220.

[0063] A peripheral interface, for example, may include the hardware, firmware and / or software that enable(s) communication with various peripheral devices, such as media drives (e.g., magnetic disk, solid state, or optical disk drives), other processing devices, or any other input source used in connection with the disclosed technology. In some embodiments, a peripheral interface may include a serial port, a parallel port, a general-purpose input and output (GPIO) port, a game port, a universal serial bus (USB), a micro-USB port, a high definition multimedia interface (HD MI) port, a video port, an audio port, a Bluetooth™ port, a near-field communication (NFC) port, another like communication interface, or any combination thereof.

[0064] In some embodiments, a transceiver may be configured to communicate with compatible devices and ID tags when they are within a predetermined range. A transceiver may be compatible with one or more of: radio-frequency identification (RFID), near-field communication (NFC), Bluetooth™, low-energy Bluetooth™ (BLE), WiFi™, ZigBee™, ambient backscatter communications (ABC) protocols or similar technologies.

[0065] A mobile network interface may provide access to a cellular network, the Internet, or another wide-area or local area network. In some embodiments, a mobile network interface may include hardware, firmware, and / or software that allow(s) the processor(s) 222 to communicate with other devices via wired or wireless networks, whether local or wide area, private or public, as known in the art. A power source may be configured to provide an appropriate alternating current (AC) or direct current (DC) to power components.

[0066] The processor 222 may include one or more of a microprocessor, microcontroller, digital signal processor, co-processor or the like or combinations thereof capable of executing stored instructions and operating upon stored data. The memory 230 may include, in some implementations, one or more suitable types of memory (e.g. such as volatile or non-volatile memory, random access memory (RAM), read only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), magnetic disks, optical disks, floppy disks, hard disks, removable cartridges, flash memory, a redundant array of independent disks (RAID), and the like), for storing files including an operating system, application programs (including, for example, a web browser application, a widget or gadget engine, and or other applications, as necessary), executable instructions and data. In one embodiment, the processing techniques described herein may be implemented as a combination of executable instructions and data stored within the memory 230.

[0067] The processor 222 may be one or more known processing devices, such as, but not limited to, a microprocessor from the Pentium™ family manufactured by Intel™ or the Turion™ family manufactured by AMD™. The processor 222 may constitute a single core or multiple core processor that executes parallel processes simultaneously. For example, the processor 222 may be a single core processor that is configured with virtual processing technologies. In certain embodiments, the processor 222 may use logical processors to simultaneously execute and control multiple processes. The processor 222 may implement virtual machine technologies, or other similar known technologies to provide the ability to execute, control, run, manipulate, store, etc. multiple software processes, applications, programs, etc. The processor 222 may also comprise multiple processors, each of which is configured to implement one or more features / steps of the disclosed technology. One of ordinary skill in the art would understand that other types of processor arrangements could be implemented that provide for the capabilities disclosed herein.

[0068] In accordance with certain example implementations of the disclosed technology, the computing device 220 may include one or more storage devices configured to store information used by the processor 222 (or other components) to perform certain functions related to the disclosed embodiments. In one example, the computing device 220 may include the memory 230 that includes instructions to enable the processor 222 to execute one or more applications, such as server applications, network communication processes, and any other type ofapplication or software known to be available on computer systems. Alternatively, the instructions, application programs, etc. may be stored in an external storage or available from a memory over a network. The one or more storage devices may be a volatile or non-volatile, magnetic, semiconductor, tape, optical, removable, non-removable, or other type of storage device or tangible computer-readable medium.

[0069] In one embodiment, the computing device 220 may include a memory 230 that includes instructions that, when executed by the processor 222, perform one or more processes consistent with the functionalities disclosed herein. Methods, systems, and articles of manufacture consistent with disclosed embodiments are not limited to separate programs or computers configured to perform dedicated tasks. For example, the computing device 220 may include the memory 230 that may include one or more programs 236 to perform one or more functions of the disclosed embodiments.

[0070] The processor 222 may execute one or more programs located remotely from the computing device 220. For example, the computing device 220 may access one or more remote programs that, when executed, perform functions related to disclosed embodiments.

[0071] The memory 230 may include one or more memory devices that store data and instructions used to perform one or more features of the disclosed embodiments. The memory 230 may also include any combination of one or more databases controlled by memory controller devices (e.g., server(s), etc.) or software, such as document management systems, Microsoft™ SQL databases, SharePoint™ databases, Oracle™ databases, Sybase™ databases, or other relational or non-relational databases. The memory 230 may include software components that, when executed by the processor 222, perform one or more processes consistent with the disclosed embodiments. In some examples, the memory 230 may include a database 234 configured to store various data described herein. For example, the database 234 can be configured to store the software repository 102 or data generated by the repository intent model 104 such as synopses of the computer instructions stored in the software repository 102, inputs received from a user (e.g., responses to questions or edits made to synopses), or other data that can be used to train the repository intent model 104.

[0072] The computing device 220 may also be communicatively connected to one or more memory devices (e.g., databases) locally or through a network. The remote memory devices may be configured to store information and may be accessed and / or managed by the computing device 220. By way of example, the remote memory devices may be document managementsystems, Microsoft™ SQL database, SharePoint™ databases, Oracle™ databases, Sybase™ databases, or other relational or non-relational databases. Systems and methods consistent with disclosed embodiments, however, are not limited to separate databases or even to the use of a database.

[0073] The computing device 220 may also include one or more I / O devices 224 that may comprise one or more user interfaces 226 for receiving signals or input from devices and providing signals or output to one or more devices that allow data to be received and / or transmitted by the computing device 220. For example, the computing device 220 may include interface components, which may provide interfaces to one or more input devices, such as one or more keyboards, mouse devices, touch screens, track pads, trackballs, scroll wheels, digital cameras, microphones, sensors, and the like, that enable the computing device 220 to receive data from a user.

[0074] In example embodiments of the disclosed technology, the computing device 220 may include any number of hardware and / or software applications that are executed to facilitate any of the operations. The one or more I / O interfaces may be utilized to receive or collect data and / or user instructions from a wide variety of input devices. Received data may be processed by one or more computer processors as desired in various implementations of the disclosed technology and / or stored in one or more memory devices.

[0075] While the computing device 220 has been described as one form for implementing the techniques described herein, other, functionally equivalent, techniques may be employed. For example, some or all of the functionality implemented via executable instructions may also be implemented using firmware and / or hardware devices such as application specific integrated circuits (ASICs), programmable logic arrays, state machines, etc. Furthermore, other implementations of the computing device 220 may include a greater or lesser number of components than those illustrated.

[0076] The present disclosure further discloses the following embodiments:

[0077] Embodiment 1 : A method of characterizing a plurality of cells, comprising: providing a sample comprising the plurality of cells; obtaining a plurality of images of the plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterizing the plurality of cells based, at least in part, on the image values.

[0078] Embodiment 2 : The method of Embodiment 1 , wherein the plurality of images indicate a movement of one or more components of the cell over time.

[0079] Embodiment 3: The method of any of Embodiments 1-2, wherein the plurality of images are imaged at a frequency ofbetween 0.1-1000 Hz.

[0080] Embodiment 4: The method of any of Embodiments 1-2, wherein the plurality of images are quantitative phase images.

[0081] Embodiment 5: The method of any of Embodiments 1-2, wherein the plurality of images are ultraviolet images.

[0082] Embodiment 6: The method of any of Embodiments 1-5, wherein each image value corresponds to an optical phase.

[0083] Embodiment 7 : The method of any of Embodiments 1-5, wherein each image value corresponds to a refractive index.

[0084] Embodiment 8: The method of any of Embodiments 1-5, wherein each image value corresponds to an attenuation.

[0085] Embodiment 9: The method of any of Embodiments 1-5, wherein each image value corresponds to a back scattered signal.

[0086] Embodiment 10: The method of any of Embodiments 1-5, wherein each image value corresponds to an autofluorescence.

[0087] Embodiment 11 : The method of any of Embodiments 1-10, wherein characterizing the plurality of cells comprises obtaining an autocorrelation of image values for each pixel over the points in time.

[0088] Embodiment 12: The method of any of Embodiments 1-11, wherein characterizing the plurality of cells comprises obtaining a Fourier transform of image values for each pixel over the points in time.

[0089] Embodiment 13: The method of any of Embodiments 1-12, wherein characterizing the plurality of cells further comprises obtaining an absolute value of the Fourier transforms.

[0090] Embodiment 14: The method of any of Embodiments 1-13, wherein characterizing the plurality of cells further comprises performing a phasor analysis on the Fourier transforms.

[0091] Embodiment 15: The method of any of Embodiments 1-14, wherein the phasor analysis comprises decomposing the Fourier transforms into real and imaginary values.

[0092] Embodiment 16: The method of any of Embodiments 1-15, wherein characterizing the plurality of cells further comprises plotting the real and imaginary values on a histogram representative of a phasor space.

[0093] Embodiment 17: The method of any of Embodiments 1-16, wherein characterizing the plurality of cells further comprises analyzing the structure and dynamics of cells shown in histogram.

[0094] Embodiment 18: The method of any of Embodiments 1-17, wherein the plurality of cells are T cells.

[0095] Embodiment 19: The method of any of Embodiments 1-8, wherein the plurality of cells are CAR T cells.

[0096] Embodiment 20: The method of any of Embodiments 1-19, wherein characterizing the plurality of cells comprises processing the plurality of images with a neural network.

[0097] Embodiment 21 : The method of any of Embodiments 1 -20, wherein characterizing the plurality of cells comprises determining phenotypes for the plurality of cells.

[0098] Embodiment 22: The method of any of Embodiments 1-21, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells are activated or quiescent T-cells.

[0099] Embodiment 23: The method of any of Embodiments 1-22, wherein characterizing the plurality of cells comprises determining that one or more of the plurality of cells is a CD4 T- cell or a CD8 T-cell.

[0100] Embodiment 24: The method of any of Embodiments 1-23, wherein characterizing the plurality of cells comprises differentiating B-cells and T-cells in the plurality of cells.

[0101] Embodiment 25: The method of any of Embodiments 1-24, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells is dead or alive.

[0102] Embodiment 26: The method of any of Embodiments 1-25, wherein characterizing the plurality of cells comprises determining a response of each of the plurality of cells to a predetermined stimulus.

[0103] It is to be understood that the embodiments and claims disclosed herein are not limited in their application to the details of construction and arrangement of the components set forth in the description and illustrated in the drawings. Rather, the description and thedrawings provide examples of the embodiments envisioned. The embodiments and claims disclosed herein are further capable of other embodiments and of being practiced and carried out in various ways. Also, it is to be understood that the phraseology and terminology employed herein are for the purposes of description and should not be regarded as limiting the claims.

[0104] Accordingly, those skilled in the art will appreciate that the conception upon which the application and claims are based may be readily utilized as a basis for the design of other structures, methods, and systems for carrying out the several purposes of the embodiments and claims presented in this application. It is important, therefore, that the claims be regarded as including such equivalent constructions.

[0105] Furthermore, the purpose of the foregoing Abstract is to enable the United States Patent and Trademark Office and the public generally, and especially including the practitioners in the art who are not familiar with patent and legal terms or phraseology, to determine quickly from a cursory inspection the nature and essence of the technical disclosure of the application. The Abstract is neither intended to define the claims of the application, nor is it intended to be limiting to the scope of the claims in any way.

Claims

CLAIMSWhat is claimed is:

1. A method of characterizing a plurality of cells, comprising: providing a sample comprising the plurality of cells; obtaining a plurality of images of the plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterizing the plurality of cells based, at least in part, on the image values.

2. The method of claim 1, wherein the plurality of images indicate a movement of one or more components of the cell over time.

3. The method of claim 1, wherein the plurality of images are quantitative phase images.

4. The method of claim 1, wherein the plurality of images are imaged at a frequency of between 0.1-1000 Hz.

5. The method of claim 1, wherein the plurality of images are ultraviolet images.

6. The method of claim 1, wherein each image value corresponds to an optical phase.

7. The method of claim 1, wherein each image value corresponds to a refractive index.

8. The method of claim 1, wherein each image value corresponds to an attenuation.

9. The method of claim 1, wherein each image value corresponds to a back scattered signal.

10. The method of claim 1, wherein each image value corresponds to an auto fluorescence.

11. The method of claim 1 , wherein characterizing the plurality of cells comprises obtaining an autocorrelation of image values for each pixel over the points in time.

12. The method of claim 1 , wherein characterizing the plurality of cells comprises obtaining a Fourier transform of image values for each pixel over the points in time.

13. The method of claim 12, wherein characterizing the plurality of cells further comprises obtaining an absolute value of the Fourier transforms.

14. The method of claim 12, wherein characterizing the plurality of cells further comprises performing a phasor analysis on the Fourier transforms.

15. The method of claim 14, wherein the phasor analysis comprises decomposing the Fourier transforms into real and imaginary values.

16. The method of claim 15, wherein characterizing the plurality of cells further comprises plotting the real and imaginary values on a histogram representative of a phasor space.

17. The method of claim 16, wherein characterizing the plurality of cells further comprises analyzing the structure and dynamics of cells shown in histogram.

18. The method of claim 1, wherein the plurality of cells are T cells.

19. The method of claim 18, wherein the plurality of cells are CAR T cells.

20. The method of claim 1, wherein characterizing the plurality of cells comprises processing the plurality of images with a neural network.

21. The method of claim 1, wherein characterizing the plurality of cells comprises determining phenotypes for the plurality of cells.

22. The method of claim 1, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells are activated or quiescent T-cells.

23. The method of claim 1, wherein characterizing the plurality of cells comprises determining that one or more of the plurality of cells is a CD4 T-cell or a CD8 T-cell.

24. The method of claim 1, wherein characterizing the plurality of cells comprises differentiating B-cells and T-cells in the plurality of cells.

25. The method of claim 1, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells is dead or alive.

26. The method of claim 1, wherein characterizing the plurality of cells comprises determining a response of each of the plurality of cells to a predetermined stimulus.

27. A system for characterizing a plurality of cells, the system comprising one or more processors configured to, individually or collectively: obtain a plurality of images of a sample comprising a plurality of cells, each image taken at a different point in time, each image comprising a plurality of pixels, each pixel represented by an image value; and characterize the plurality of cells based, at least in part, on the image values.

28. The system of claim 27, wherein the plurality of images indicate a movement of one or more components of the cell over time.

29. The system of claim 27, wherein the plurality of images are quantitative phase images.

30. The system of claim 27, wherein the plurality of images are imaged at a frequency of between 0.1-1000 Hz.

31. The system of claim 27, wherein the plurality of images are ultraviolet images.

32. The system of claim 27, wherein each image value corresponds to an optical phase.

33. The system of claim 27, wherein each image value corresponds to a refractive index.

34. The system of claim 27, wherein each image value corresponds to an attenuation.

35. The system of claim 27, wherein each image value corresponds to a back scattered signal.

36. The system of claim 27, wherein each image value corresponds to an autofluorescence.

37. The system of claim 27, wherein characterizing the plurality of cells comprises obtaining an autocorrelation of image values for each pixel over the points in time.

38. The system of claim 27, wherein characterizing the plurality of cells comprises obtaining a Fourier transform of image values for each pixel over the points in time.

39. The system of claim 38, wherein characterizing the plurality of cells further comprises obtaining an absolute value of the Fourier transforms.

40. The system of claim 38, wherein characterizing the plurality of cells further comprises performing a phasor analysis on the Fourier transforms.

41. The system of claim 40, wherein the phasor analysis comprises decomposing the Fourier transforms into real and imaginary values.

42. The system of claim 41, wherein characterizing the plurality of cells further comprises plotting the real and imaginary values on a histogram representative of a phasor space.

43. The system of claim 42, wherein characterizing the plurality of cells further comprises analyzing the structure and dynamics of cells shown in histogram.

44. The system of claim 27, wherein the plurality of cells are T cells.

45. The system of claim 44, wherein the plurality of cells are CAR T cells.

46. The system of claim 27, wherein characterizing the plurality of cells comprises processing the plurality of images with a neural network.

47. The system of claim 27, wherein characterizing the plurality of cells comprises determining phenotypes for the plurality of cells.

48. The system of claim 27, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells are activated or quiescent T-cells.

49. The system of claim 27, wherein characterizing the plurality of cells comprises determining that one or more of the plurality of cells is a CD4 T-cell or a CD8 T-cell.

50. The system of claim 27, wherein characterizing the plurality of cells comprises differentiating B-cells and T-cells in the plurality of cells.

51. The system of claim 27, wherein characterizing the plurality of cells comprises determining whether each of the plurality of cells is dead or alive.

52. The system of claim 27, wherein characterizing the plurality of cells comprises determining a response of each of the plurality of cells to a predetermined stimulus.

Images