Method for detecting and selecting image base, and system therefor

Abstract

To provide a method for calculating one or more cell parameters for a cell in a sample.SOLUTION: A method includes steps of: irradiating a sample containing a cell in a flow stream with a frequency-modulated light beam; measuring light from the cell in the sample; generating image data for the cell from the measured light; and calculating one or more cell parameters on the basis of the generated image data. The cell parameter identifies a mitotic phase of a cell selected from an interphase, a prophase, a metaphase, an anaphase, a telophase and a cytokinesis.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Sorting Flow-based particle sorting systems, such as flow cytometers, are used to sort particles in a fluid sample based on at least one measured characteristic of the particles.

[0002] In a flow-based particle sorting system, particles such as analyte-bound beads or individual cells in fluid suspension are passed in a stream through a detection region where a sensor detects particles of the type to be sorted contained in the stream, and upon detection of a particle of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particle of interest.

[0003] Particle sensing is typically performed by passing a fluid stream through a detection region where particles are exposed to illumination from one or more lasers and fluorescence from the particles is measured. Particles or components thereof can be labeled with fluorescent dyes to facilitate detection, and by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. Detection is performed using one or more optical sensors to facilitate independent measurement of the fluorescence of each different fluorescent dye.

[0004] Data generated from the detected light can be used to record the distribution of components and to sort the desired material. To sort particles in a sample, a droplet charging mechanism imparts an electric charge to droplets in the flow stream containing the particle type to be sorted at a flow stream break-off point. The droplets are passed through an electrostatic field and are deflected into one or more collection vessels based on the droplet's polarity and charge magnitude. Uncharged droplets are not deflected by the electrostatic field. Summary of the Invention

[0005] Aspects of the present disclosure include methods for calculating one or more cellular parameters of cells in a sample. A method according to certain embodiments includes irradiating a sample containing cells in a flow stream with a frequency modulated light beam, measuring light from the cells in the sample, generating cellular image data from the measured light, and calculating one or more cellular parameters based on the generated image data. Systems and integrated circuit devices (e.g., field programmable gate arrays) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.

[0006] In embodiments, the image data includes one or more waveforms generated in response to the measured light. In some cases, an image of the cell is generated from the image data. In some cases, the method includes calculating an image parameter based on one or more of the image data and the image of the cell. In some cases, calculating the cell parameter includes evaluating a cell interaction parameter. In some cases, the cell interaction parameter is an interaction between two or more cells in the sample. In some cases, calculating the cell interaction parameter includes evaluating phagocytosis by one or more cells in the sample. In some cases, calculating the cell interaction parameter includes evaluating tumor cell killing by one or more cells in the sample. In some cases, calculating the cell interaction parameter includes evaluating internalization of a cell or cell component by one or more cells in the sample. In some cases, calculating the cell interaction parameter includes evaluating co-localization between two or more cells in the sample.

[0007] In some cases, the cellular parameter includes a translocation parameter of a cellular constituent. In some cases, calculating the cellular parameter includes assessing the translocation of a cellular constituent within the cell (e.g., displacement of a cellular constituent, such as a protein, from one location within the cell to another location within the cell). In some cases, the method includes assessing nuclear translocation of a cellular constituent within the cell.

[0008] In some embodiments, the cellular parameter is a cellular mitotic phase parameter. In some cases, calculating the cellular parameter includes determining a mitotic phase of one or more cells in the sample. In some cases, the cell is identified as being in interphase. In some cases, the cell is identified as being in prophase. In some cases, the cell is identified as being in metaphase. In some cases, the cell is identified as being in anaphase. In some cases, the cell is identified as being in telophase. In certain cases, the cell is identified as being in cytokinesis.

[0009] In some embodiments, calculating the cell parameters includes identifying the cell as a plant cell. In some cases, identifying the plant cell includes evaluating cell morphology. In some cases, evaluating one or more of cell size, cell shape, and cell punctation. In some cases, the cell is identified as a plant cell by distinguishing the cell from cell debris. In some cases, the cell is identified as a plant cell by distinguishing the cell from a cell doublet.

[0010] In some cases, the method includes calculating one or more image parameters from the generated image of the cell. In some cases, the cell parameter is calculated based on the image parameter. In some cases, the image parameter is a quantitative image parameter. In some cases, the parameter is selected from center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the cell nucleus, forward scatter by the cell nucleus, side scatter by the cell nucleus, and combinations thereof. In some cases, the image parameter is calculated from the generated image based on fluorescence measured from the cell, light loss measured for the cell, forward scatter measured for the cell, side scatter measured for the cell, and combinations thereof.

[0011] In some embodiments, the method includes classifying cells of the sample based on the calculated cell parameters. In some cases, the cells are classified based on a waveform generated from the measured light, a generated image of the cells, image parameters calculated from the generated image of the cells, or a combination thereof. In some cases, classifying the cells includes assigning the cells to one or more particle population clusters. In some cases, the method includes determining one or more sorting gates for the classified cells of the sample. In some cases, the one or more sorting gates capture cells of the target particle population clusters and exclude particles of the non-target particle population clusters. In some cases, the sorting gates maximize the inclusion yield of cells of the target particle population clusters. In some cases, the sorting gates maximize the exclusion of cells of the non-target particle population clusters.

[0012] In some cases, the method includes sorting cells of the sample into a plurality of sample containers. In some cases, the cells are sorted based on generated images of the cells. In some cases, the cells are sorted based on calculated image parameters.

[0013] In some embodiments, the method includes illuminating the sample with a frequency modulated light beam. In some cases, the sample is illuminated within the flow stream. In some cases, the light source includes one or more lasers. In some cases, the light is detected using a light detection system having multiple light detectors. In some embodiments, one or more of the light detectors are photomultiplier tubes. In some embodiments, one or more of the light detectors are photodiodes (e.g., avalanche photodiodes, APDs). In certain embodiments, the light detection system includes a light detector array, such as a light detector array having multiple photodiodes or a charged coupled device (CCD).

[0014] The present disclosure also includes systems for practicing the subject methods. The system according to certain embodiments includes a light source configured to irradiate cells of a sample in a flow stream with a frequency modulated light beam, a light detection system having a photodetector for measuring light from the cells, and a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate image data of the cells from the measured light and calculate one or more cellular parameters based on the generated image data.

[0015] In some embodiments, the image data includes one or more waveforms generated in response to the measured light. In some cases, the memory includes instructions for generating an image of a cell from the image data. In some cases, the memory includes instructions for calculating an image parameter based on one or more of the image data and the image of the cell. In some cases, the memory includes instructions for evaluating a cell interaction parameter. In some cases, the memory includes instructions for evaluating an interaction between two or more cells in the sample. In some cases, the memory includes instructions for evaluating phagocytosis by one or more cells in the sample. In some cases, the memory includes instructions for evaluating tumor cell killing by one or more cells in the sample. In some cases, the memory includes instructions for calculating evaluating co-localization between two or more cells in the sample. In some cases, the memory includes instructions for evaluating translocation of a cellular component within a cell. In some cases, the memory includes instructions for evaluating nuclear translocation of a cellular component within a cell.

[0016] In some embodiments, the memory comprises instructions for determining the mitotic phase of one or more cells in the sample. In some cases, the cell is identified as being in interphase. In some cases, the cell is identified as being in prophase. In some cases, the cell is identified as being in metaphase. In some cases, the cell is identified as being in anaphase. In some cases, the cell is identified as being in telophase. In certain cases, the cell is identified as being in cytokinesis.

[0017] In some embodiments, the memory includes instructions for identifying the cell as a plant cell. In some cases, the memory includes instructions for identifying the plant cell by evaluating cell morphology. In some cases, the memory includes instructions for evaluating one or more of cell size, cell shape, and cell punctation.

[0018] In some embodiments, the memory includes instructions for calculating the cell parameters based on image parameters generated from the images of the cells. In some cases, the image parameters are quantitative image parameters. In some cases, the image parameters are selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the cell nucleus, forward scatter by the cell nucleus, side scatter by the cell nucleus, and combinations thereof. In some cases, the memory includes instructions for calculating the image parameters from the images generated based on the fluorescence measured from the cells, the light loss measured for the cells, the forward scatter measured for the cells, the side scatter measured for the cells, and combinations thereof.

[0019] In some cases, the memory includes instructions for classifying cells based on the image data, the generated images of cells, and the calculated image parameters, or a combination thereof. In some cases, the memory includes instructions for classifying cells by assigning cells to one or more particle population clusters. In some embodiments, the memory includes instructions for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells of a target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the memory includes instructions for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the memory includes instructions for calculating a sorting gate that maximizes the exclusion of cells of the non-target particle population clusters.

[0020] In certain embodiments, the system includes a display for displaying a graphical user interface. In some cases, the graphical user interface is configured to manually input one or more of the sorting gates. In some cases, the sorting gates are manually input into the graphical user interface by drawing the sorting gates on a scatter plot of the particle population clusters. In some cases, the sorting gates are hyper-rectangular sorting gates.

[0021] A non-transitory computer readable storage medium having instructions with an algorithm for calculating cellular parameters for cells of a sample is also described. The non-transitory computer readable storage medium according to certain embodiments has an algorithm for irradiating a sample containing cells in a flow stream with a frequency modulated light beam, an algorithm for measuring light from the cells in the sample, an algorithm for generating image data of the cells from the measured light, and an algorithm for calculating one or more cellular parameters based on the generated image data. In some cases, the image data includes one or more waveforms generated in response to the measured light.

[0022] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for generating an image of a cell from the image data. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter based on one or more of the image data and the image of the cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating a cellular interaction parameter. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating an interaction between two or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating phagocytosis by one or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating tumor cell killing by one or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating evaluating co-localization between two or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating translocation of a cellular component within a cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating nuclear translocation of a cellular component within a cell.

[0023] In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for determining the mitotic phase of one or more cells in a sample. In some cases, the cell is identified as being in interphase. In some cases, the cell is identified as being in prophase. In some cases, the cell is identified as being in metaphase. In some cases, the cell is identified as being in anaphase. In some cases, the cell is identified as being in telophase. In certain cases, the cell is identified as being in cytokinesis.

[0024] In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for identifying a cell as a plant cell. In some cases, the non-transitory computer readable storage medium comprises an algorithm for identifying a plant cell by evaluating cell morphology. In some cases, the non-transitory computer readable storage medium comprises an algorithm for evaluating one or more of cell size, cell shape, and cell punctation.

[0025] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating a cell parameter based on an image parameter generated from an image of the cell. In some cases, the image parameter is a quantitative image parameter. In some cases, the image parameter is selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the cell nucleus, forward scatter by the cell nucleus, side scatter by the cell nucleus, and combinations thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter from an image generated based on fluorescence measured from the cell, light loss measured for the cell, forward scatter measured for the cell, side scatter measured for the cell, and combinations thereof.

[0026] In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells based on the image data, the generated images of the cells, and the calculated image parameters, or a combination thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells by assigning the cells to one or more particle population clusters. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells of a target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the exclusion of cells of the non-target particle population clusters. [Brief description of the drawings]

[0027] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawing figures, which include: [Figure 1] 1 depicts a flowchart for calculating cellular parameters for cells of a sample, according to certain embodiments. [Figure 2A] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts the calculation of cell-cell interaction parameters by immune synapse testing; [Figure 2B] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts visualization of a tumor cell killing assay by an imaging flow cytometer, according to certain embodiments; [Figure 2C]1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts visualization of T cell-monocyte cell-cell interaction complexes, according to certain embodiments; [Figure 2D] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts the calculation of a sorting gate for T cell-monocyte complexes, according to certain embodiments; [Figure 2E] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts sorting of T cell-monocyte complexes using a 20-color spectral panel as imaging parameters, according to certain embodiments; [Figure 2F] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts the calculation of cell-cell interaction parameters for T cell-monocyte complexes with a multi-color fluorescent panel, according to certain embodiments; [Figure 2G] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments. 2 depicts sorting and sorting of extracellular vesicle-CD4 T cell complexes based on cell-cell interaction parameters, according to certain embodiments. [Figure 2H] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts images of different complexes formed between EVs and CD4+ T cells, according to certain embodiments; [Figure 2I] 1 depicts cellular parameters determined using an imaging cellular flow cytometer for cells in a sample, according to certain embodiments;1 depicts the calculation of cellular parameters for gating and sorting cells in different mitotic phases, according to certain embodiments; [Figure 3A] 1 depicts an image enabled particle sorter in accordance with certain embodiments. [Figure 3B] 1 illustrates image-based particle sorting data processing in accordance with certain embodiments. [Figure 4A] 1 depicts a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 4B] 1 depicts a flow cytometer in accordance with certain embodiments. [Diagram 5] 1 illustrates a functional block diagram for one example of a particle analyzer control system in accordance with certain embodiments. [Figure 6A] 1 depicts a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 depicts a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 7] 1 illustrates a block diagram of a computing system in accordance with a particular embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0028] Aspects of the present disclosure include methods for calculating one or more cellular parameters of cells in a sample. A method according to certain embodiments includes irradiating a sample containing cells in a flow stream with a frequency modulated light beam, measuring light from the cells in the sample, generating cellular image data from the measured light, and calculating one or more cellular parameters based on the generated image data. Systems and integrated circuit devices (e.g., field programmable gate arrays) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.

[0029] Before describing the invention in more detail, it is to be understood that the invention is not limited to the particular embodiments described, which may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the invention will be limited only by the appended claims.

[0030] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is to be understood that each intermediate value between the upper and lower limit of that range, to the tenth of the unit of the lower limit, and any other stated or intermediate value within this stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0031] In this specification, certain ranges are presented with the term "about" before numerical values. The term "about" is used in this specification to provide literal support for the exact number it precedes, as well as a number that is close to or approximately the number it precedes. When determining whether a number is close to or approximately a specifically stated number, the number that is close to or approximately the unstated number may be a number that provides substantial equivalence to the specifically stated number in the context in which it is presented.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, but representative illustrative methods and materials are described below.

[0033] All publications and patents cited herein are incorporated by reference as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0034] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be designed to exclude any optional element. Thus, this statement is intended to serve as a predicate to the use of such exclusive terminology, such as "solely," "only," or the use of a "negative" limitation in connection with the recitation of claim elements.

[0035] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the various other embodiments without departing from the scope or spirit of the invention. Any described method may be carried out in the order of events described or in any other order which is logically possible.

[0036] Although the systems and methods have been or will be described for grammatical fluidity with functional descriptions, it is expressly understood that the claims, unless expressly formulated under 35 U.S.C. ...

[0037] As summarized above, the present disclosure corresponds to calculating cell parameters for cells of a sample in a flow stream. In further describing embodiments of the present disclosure, methods for generating one or more of image data, images from image data, and calculated image parameters, calculating cell parameters, and in some embodiments, generating sorting gates for sorting cells having specific characteristics are first described in more detail. Then, a system, an integrated circuit device, and a non-transitory computer-readable storage medium having programming for implementing the subject method by evaluating cell parameters and calculating sorting gates for sorting cells are also provided.

[0038] Method for calculating cell parameters for cells of a sample in a flow stream - Patents.com Aspects of the present disclosure include methods for calculating cellular parameters for cells in a sample. In some embodiments, the subject methods provide a high-throughput, robust, yet simplified protocol for determining quantitative image data, including generating information-rich images and calculating image parameters in multiple photodetector channels. The subject methods accommodate applications such as evaluating cell morphology, as well as cell organelles, cell interaction assays (e.g., tumor cell killing assays, internalization assays, cell-cell interaction assays, colocalization determinations, and phagocytosis assays). In addition, the subject methods accommodate determining the cell cycle phase of each individual cell in a sample in a high-throughput manner, and can be used to gate and sort cells at the determined cell cycle phase. In certain instances, the subject methods can be used to differentiate and sort plant cells of a sample, as well as to identify and sort rare cells (e.g., circulating tumor cells or diseased cells).

[0039] In some embodiments, calculating the cellular parameters described herein can increase the accuracy of characterizing the cells of a sample for cell abundance, activation, or status (e.g., to monitor disease progression), including by, for example, 5% or more, for example, 10% or more, for example, 15% or more, for example, 25% or more, for example, 50% or more, for example, 75% or more, and 99% or more. In some cases, the present disclosure provides high throughput assays of cell samples, such as those in which imaging data can be used to determine cell-cell interactions for a large number of cells. For example, the subject methods can accommodate calculating a plurality of cellular parameters for a sample of cells in a time of 10 minutes or less, for example, 9 minutes or less, for example, 8 minutes or less, for example, 7 minutes or less, for example, 6 minutes or less, for example, 5 minutes or less, for example, 4 minutes or less, for example, 3 minutes or less, for example, 2 minutes or less, including 1 minute or less. As described in more detail below, in some cases, the assessed cells may be sorted and a cell sample having a cellular parameter of interest may be prepared where cells comprising, for example, 50% or more, for example, 60% or more, for example, 70% or more, for example, 80% or more, for example, 90% or more, for example, 95% or more, for example, 97% or more, and 99% of the cells in the sample are suitable for use in downstream biological assays (e.g., gene sequencing protocols).

[0040] In embodiments, the cellular parameters are evaluated based on at least image data of the cells. In some cases, the image data includes a waveform generated in response to light measured from a sample illuminated with frequency modulated light (as described in more detail below). In some cases, the image data includes an image generated from the waveform. In some cases, the image data includes image parameters calculated from the waveform, the generated image, and a combination thereof. In some cases, the cellular parameters are determined solely from the calculated image parameters (e.g., radial moment, eccentricity, etc.). In some cases, the cellular parameters are determined solely from the image of the particles and not from another data source, such as, for example, a data signal waveform. In some cases, the cellular parameters are determined using a combination of the generated image of the particles and the data signal waveform generated in response to the measured light from the illuminated particles.

[0041] In practicing the subject methods according to certain embodiments, light from a sample having cells in a flow stream (e.g., the sample is illuminated with a light source) is measured with a light detection system having a light detector. A "biological sample" can refer to a whole organism, a plant, a fungus, or a subset of animal tissues, cells, or components that may be found in certain cases, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, for example, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, organs. A biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some instances, the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0042] In certain embodiments, the sample source is a "mammal" or "mammalian animal," terms used broadly to describe organisms within mammals, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders, at any stage of development (i.e., neonates, infants, juveniles, adolescents, adults), and in certain embodiments, the human subjects are juveniles, adolescents, or adults. It is understood that the present invention may be applied to samples from human subjects, but may also be practiced on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0043] In some embodiments, a sample having cells (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a broad range of wavelengths, e.g., ranging from 50 nm or more, such as, e.g., 100 nm or more, e.g., 150 nm or more, e.g., 200 nm or more, e.g., 250 nm or more, e.g., 300 nm or more, e.g., 350 nm or more, e.g., 400 nm or more, and 500 nm or more. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes illuminating with a broadband light source, broadband light source protocols of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, broadband LEDs with continuous spectra, ultra-bright light emitting diodes, semiconductor light emitting diodes, broad spectrum LED white light sources, multi-LED integrated white light sources, or any combination thereof, among other broadband light sources.

[0044] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or a narrow range of wavelengths, for example, a light source that emits light in a narrow range of wavelengths, such as a range of 50 nm or less, for example, a light source that emits light in a wavelength of 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less, including a light source that emits light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, narrowband light source protocols of interest may include, but are not limited to, a laser, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0045] In certain embodiments, the method includes irradiating the sample with one or more lasers. As explained above, the type and number of lasers can vary depending on the sample and the desired light to be collected, and include Helium Neon lasers, Argon lasers, Krypton lasers, Xenon lasers, Nitrogen lasers, CO lasers, etc. 2 The laser may be a gas laser, such as a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the method includes irradiating the flow stream with a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, the method includes irradiating the flow stream with a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other cases, the method includes irradiating the flow stream with a metal-vapor laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, Ti:Sapphire laser, Slim YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 This involves irradiating the flow stream with a solid state laser, such as a laser, or a cerium doped laser, and combinations thereof.

[0046] The sample may be illuminated with one or more of the light sources mentioned above, for example, two or more light sources, for example, three or more light sources, for example, four or more light sources, for example, five or more light sources, including ten or more light sources. The light sources may include any type of combination of light sources. For example, in some embodiments, the method includes illuminating the sample in the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0047] The sample may be irradiated with a wavelength in the range of 200 nm to 1500 nm, such as 250 nm to 1250 nm, such as 300 nm to 1000 nm, such as 350 nm to 900 nm, including 400 nm to 800 nm, with a diode laser. For example, if the light source is a broadband light source, the sample may be irradiated with a wavelength in the range of 200 nm to 900 nm. In other cases, if the light source includes multiple narrowband light sources, the sample may be irradiated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm) each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (such as a laser array) and the sample is irradiated with a specific wavelength in the range of 200 nm to 700 nm, such as a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid state lasers, as described above.

[0048] When two or more light sources are employed, the sample may be illuminated with the light sources simultaneously or sequentially, or a combination thereof. For example, the sample may be illuminated with each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially with each of the light sources. When two or more light sources are employed to sequentially illuminate the sample, the time for which each light source illuminates may independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method may include illuminating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source may be the same or different.

[0049] The period between illumination by each light source may also vary as desired, independently separated by a delay of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as up to 10 microseconds or more, such as up to 15 microseconds or more, such as up to 30 microseconds or more, including 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source may be the same or different.

[0050] The sample may be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the sample in the sample with the light source continuously. In other cases, the sample is illuminated with the light source at discrete intervals, such as illuminating every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 milliseconds, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0051] Depending on the light source, the sample may be illuminated from different distances, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 2.5 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 25 mm or more, including 50 mm or more. The angle or illumination may also vary from 10° to 90°, such as 15° to 85°, such as 20° to 80°, such as 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0052] In an embodiment, the method includes irradiating the cell sample with a frequency modulated light beam. In some instances, the method includes irradiating the sample with two or more frequency shifted light beams. As described above, a light beam generator component having a laser and an acousto-optic device for frequency shifting the laser light may be employed. In these embodiments, the method includes irradiating the acousto-optic device with a laser. Depending on the desired wavelength of light to be generated in the output laser beam (e.g., for use in irradiating the sample in the flow stream), the laser may have a specific wavelength ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, including 400 nm to 800 nm. The acousto-optic device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and ten or more lasers. The laser may include any combination of multiple laser types. For example, in some embodiments, the method includes irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0053] When two or more lasers are employed, the acousto-optic device may be irradiated with the lasers simultaneously or sequentially, or a combination thereof. For example, the acousto-optic device may be irradiated with each of the lasers simultaneously. In other embodiments, the acousto-optic device is irradiated with each of the lasers sequentially. When two or more lasers are employed to sequentially irradiate the acousto-optic device, the time for irradiating the acousto-optic device with each laser may independently be for a time period of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method may include irradiating the acousto-optic device with the laser for a duration ranging from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is sequentially illuminated with two or more lasers, the duration of illumination of the acousto-optic device with each laser may be the same or different.

[0054] The period between illumination by each laser may also be varied as necessary and may be independently separated by a delay of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 15 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with more than two (i.e., three or more) lasers, the delay between illumination by each laser may be the same or different.

[0055] The acousto-optical device may be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the acousto-optical device with a laser continuously. In other cases, the acousto-optical device is illuminated with a laser at discrete intervals, e.g., every 0.001 milliseconds, e.g., every 0.01 milliseconds, e.g., every 0.1 milliseconds, e.g., every 1 millisecond, e.g., every 10 milliseconds, e.g., every 100 milliseconds, including every 1000 milliseconds, or some other interval.

[0056] Depending on the laser, the acousto-optical device may be illuminated from different distances, for example 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, such as 0.5 mm or more, for example 1 mm or more, such as 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, such as 15 mm or more, for example 25 mm or more, including 50 mm or more. The angle or illumination may also vary from 10° to 90°, such as 15° to 85°, such as 20° to 80°, such as 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0057] In an embodiment, the method includes applying a high frequency drive signal to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals can be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams, for example, 3 or more high frequency drive signals, for example, 4 or more high frequency drive signals, for example, 5 or more high frequency drive signals, for example, 6 or more high frequency drive signals, for example, 7 or more high frequency drive signals, for example, 8 or more high frequency drive signals, for example, 9 or more high frequency drive signals, for example, 10 or more high frequency drive signals, for example, 15 or more high frequency drive signals, for example, 25 or more high frequency drive signals, for example, 50 or more high frequency drive signals, including 100 or more high frequency drive signals.

[0058] The angularly deflected laser beams generated by the high frequency drive signals each have an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying a high frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam at a desired intensity. In some cases, each applied high frequency drive signal independently has an amplitude of about 0.001V to about 500V, e.g., about 0.005V to about 400V, e.g., about 0.01V to about 300V, e.g., about 0.05V to about 200V, e.g., about 0.1V to about 100V, e.g., about 0.5V to about 75V, e.g., about 1V to about 50V, e.g., 2V to about 40V, e.g., 3V to about 30V, including about 5V to about 25V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0059] In these embodiments, the angularly deflected laser beams in the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may have a separation of 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, such as 100 μm or more, such as 500 μm or more, such as 1000 μm or more, including 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams, for example, along the horizontal axis of the output laser beam. The overlap between adjacent angle deflected laser beams (such as overlap of beam spots) can be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, such as an overlap of 0.01 μm or more, such as an overlap of 0.05 μm or more, such as an overlap of 0.1 μm or more, such as an overlap of 0.5 μm or more, such as an overlap of 1 μm or more, such as an overlap of 5 μm or more, such as an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0060] In certain instances, the present invention may be used in a variety of applications, including, but not limited to, the following: Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620, 10,720, 10,820, 10,920, 10,102, ... As described in U.S. Pat. No. 6,111, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the sample is illuminated with a plurality of frequency-shifted light beams in the flow stream to generate images of cellular mitochondria in the flow stream, the disclosures of which are incorporated herein by reference.

[0061] Light from the illuminated cells in the sample is transmitted to a light detection system and measured by a plurality of light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof.

[0062] The collected light may be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, such as measuring the light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0063] Measurements of the collected light may be made one or more times during the subject methods, including, for example, two or more times, such as three or more times, such as five or more times, and ten or more times. In certain embodiments, the light from the sample is measured two or more times, and the data in a particular instance is averaged.

[0064] Light from cells in a sample may be measured at one or more wavelengths, for example, at 5 or more different wavelengths, such as at 10 or more different wavelengths, for example, at 25 or more different wavelengths, such as at 50 or more different wavelengths, for example, at 100 or more different wavelengths, such as at 200 or more different wavelengths, for example, at 300 or more different wavelengths, including measurements of collected light at 400 or more different wavelengths.

[0065] In some embodiments, the method includes further conditioning the light from the sample prior to detecting the light. For example, the light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses to reduce the light profile. In other cases, the light emitted from the sample passes through one or more collimators to reduce the light beam divergence.

[0066] In some embodiments, image data is generated from the measured light. In some cases, the image data includes data waveforms generated at one or more photodetector channels. In some embodiments, one or more images of cells in the sample are generated. The image may be generated from detected fluorescence, detected light absorption, detected light scattering, detected light emission, or any combination thereof. In some cases, the image is generated from fluorescence from labeled mitochondria. In some cases, the image is generated from detected light absorption from the sample, such as from a brightfield light detector. In these cases, the image is generated based on brightfield image data from particles in the flow stream. In other cases, the image is generated from detected light scattering from the sample, such as from a side scatter detector, a forward scatter detector, or a combination of a side scatter detector and a forward scatter detector. In these cases, the image is generated based on scattered light image data. In still other cases, the image is generated from light emitted from the sample. In still other cases, the image is generated from a combination of detected light absorption, detected light scattering, and detected light emission. In certain cases, an image of cellular mitochondria is generated from fluorescence from labeled mitochondria and one or more of light loss, side scattered light, and forward scattered light.

[0067] One or more images may be generated from the measured light. In some embodiments, a single image is generated for each cell in the sample from each form of detected light. In other embodiments, multiple images are generated for each cell, e.g., two or more, e.g., three or more, e.g., five or more, e.g., ten or more, including twenty-five or more images. For example, a first image of the cell is generated from fluorescence, a second image of the cell is generated from detected light absorption, and a third image is generated from detected light scattering. In other embodiments, two or more images may be generated from each form of detected light, e.g., three or more, e.g., four or more, e.g., five or more, including ten or more images, or a combination thereof.

[0068] In certain embodiments, frequency-encoded data (e.g., frequency-encoded spatial data) is generated from measured light from fluorescently labeled cells in the flow stream. In some cases, one or more images are generated from the frequency-encoded data. The frequency-encoded data may be generated in one or more detection channels, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, including eight or more detection channels. In some embodiments, the frequency-encoded data includes data components obtained (or derived) from light from different detectors, such as fluorescence detection channels, detected light absorption, or detected light scattering. In some cases, the frequency-encoded data is phase-corrected. In some cases, the frequency-encoded data used to generate an image of a cell is phase-corrected by performing a transform on the frequency-encoded data. In one example, the frequency-encoded data is phase-corrected by performing a Fourier transform (FT) of the frequency-encoded data. In another example, the frequency-encoded data is phase-corrected by performing a discrete Fourier transform (DFT) of the frequency-encoded data. In yet another example, the frequency-encoded data is phase-corrected by performing a short-time Fourier transform (STFT) of the frequency-encoded data. In one particular embodiment, the method includes performing a transformation of the frequency-encoded data without performing any mathematical imaginary calculations (i.e., performing only calculations for the mathematical real calculations of the transformation) to generate an image from the frequency-encoded data.

[0069] In some cases, the method includes generating one or more grayscale images of the cells. The term "grayscale" is used herein in the conventional sense of the term to refer to an image of the cells in the flow stream that is composed of various shades of gray based on the intensity of light at each pixel. In some embodiments, a pixel intensity threshold is determined from the grayscale image, and the pixel intensity threshold is used to convert each pixel to a binary value that is used to generate the image of the cells. In certain cases, the image of the cells is a binary pixel image of the cells, such that each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel is above a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0070] In some embodiments, one or more image parameters are calculated from the generated images of the cells. In some cases, a center of mass image parameter is calculated from the generated images. In some cases, a delta center of mass image parameter is calculated from the generated images. In some cases, a diffusivity image parameter is calculated from the generated images. In some cases, an eccentricity image parameter is calculated from the generated images. In some cases, a long axis moment image parameter is calculated from the generated images. In some cases, a maximum intensity image parameter is calculated from the generated images. In some cases, a radial moment image parameter is calculated from the generated images. In some cases, a short axis moment image parameter is calculated from the generated images. In some cases, a particle size image parameter is calculated from the generated images. In some cases, a total intensity image parameter is calculated from the generated images. In some cases, a particle light loss image parameter is calculated from the generated images. In some cases, a forward scatter image parameter is calculated from the generated images. In some cases, a side scatter image parameter is calculated from the generated images. In some cases, image moments are calculated from the generated images. The term "image moment" is used herein in the conventional sense of the term to refer to a weighted average of pixel intensities in an image. In some cases, the center of mass may be calculated from the image moments of the images. In other cases, the orientation of the cell may be calculated from the image moments of the images. In yet other cases, the eccentricity of the cell may be calculated from the image moments of the images.

[0071] In certain embodiments, imaging parameters are calculated from the generated images for use in generating the gating strategies summarized in Table 1 (as described below). [Table 1-1] [Table 1-2]

[0072] In some embodiments, the method includes contacting the cells with one or more fluorophores to fluorescently label the cells. The term "labeling" is used herein in the conventional sense of the term to refer to the coupling or conjugation of one or more components of the cells of the sample with one or more detection components, such as components detectable by luminescence (e.g., fluorescence, phosphorescence, etc.), contrast agents, radioisotopes, etc. For example, labeling can include coupling a particle to a detection component by non-covalent interactions such as hydrogen bonds, dipole-dipole bonds, ionic bonds, etc., or via one or more covalent bonds.

[0073] In certain examples, fluorochromes of interest may include, but are not limited to, bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurhodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squalenes, bodipy, squalinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetraplyls, and combinations thereof. In certain embodiments, the conjugate may comprise two or more dyes, such as two or more dyes selected from bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurhodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazoline dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squalenes, bodipy, squalinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetraplyls, and combinations thereof.

[0074] In certain embodiments, fluorochromes of interest may include, but are not limited to, fluorescein isothiocyanate (FITC), phycoerythrin (PE) dyes, peridinin chlorophyll protein-cyanine dyes (e.g., PerCP-Cy5.5), phycoerythrin-cyanine (PE-Cy) dyes (PE-Cy7), allophycocyanin (APC) dyes (e.g., APC-R700), allophycocyanin-cyanine dyes (e.g., APC-Cy7), coumarin dyes (e.g., V450 or V500). In certain cases, the fluorochrome may include one or more of 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)ethenyl]-benzene), C510 dyes, C6 dyes, Nile Red dyes, T614 dyes (e.g., N-[7-(methanesulfonamido)-4-oxo-6-phenoxycyclomen-3-yl]formamide), LDS821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylene-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dyes (e.g., mFluor Red dyes such as mFluor 780NS).

[0075] Fluorochromes of interest include, but are not limited to, fluorescein, hydroxycoumarin, aminocoumarin, methoxycoumarin, cascade blue, pacific blue, pacific orange, lucifer yellow, NBD, R-phycoerythrin (PE), PE-Cy5 conjugates, PE-Cy7 conjugates, red 613, PerCP, Tru Red, FluorX, BODIPY-FL, TRITC, X-rhodamine, Lissamine rhodamine B, Texas Red, allophycocyanin (APC), APC-Cy7 conjugates, Cy2, Cy3, Cy3B, Cy3.5, Cy5, Cy5.5, Cy7, Hoechst 33342, DAPI, Hoechst 33258, SYTOX Blue, Chromomycin A3, Mithramycin, YOYO-1, Ethidium Bromide, Acridine Orange, SYTOX Green, TOTO-1, TO-PRO-1, Thiazole Orange, Propidium Iodide (PI), LDS 751, 7-AAD, SYTOX Orange, TOTO-3, TO-PRO-3, DRAQ5, Indo-1, Fluo-3, DCFH, DHR, SNARF, Y66H, Y66F, EBFP, EBFP2, Azurite, GFPuv, T Sapphire, TagBFP, Cytolian, mCFP, ECFP, CyPet, Y66W, dKeima-Red, mKeima-Red, TagCFP, Am Cyan 1,mTFP1(Teal), S65A, Acropora-Cyan, Wild-type GFP, S65C, TurboGFP, TagGFP, TagGFP2, AcGFP1, S65L, Emerald, S65T, EGFP, Thistle Green, ZsGreen1, Doronpa Green, TagYFP, EYFP, Topaz, Venus, mCitrine, YPet, TurboYFP, PhiYFP, PhiYFP-m, Zs Yellow 1, mBanana, Kusabira-Orange, mOrange, mOrange 2, mKO, Turbo boRFP, tdTomato, DsRed-Express2, TagRFP, DsRed Monomer, DsRed2 ("RFP"), mStrawberry, TurboFP602, AsRed2, mRFP1, J-Red, mCherry, HcRed1, mKate2, Katushka (TurboFP635), mKate (TagFP635), TurboFP635, mPlum, mRaspberry, mNeptune, E2-Crimson, Monochlorobimane, Calcein, Alexa Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750, Alexa Fluor 790, and HyPer. In some embodiments, the fluorochrome is 7-AAD, Alexa Fluor 488, Alexa Fluor 647, Alexa Fluor 700, Am Cyan, APC, APC-Cy7, APC-H7, APC-R700, BB660-P2, BB790-P, BUV395, BUV615, BUV661, BV570, BV605, BV650, BV711, BV750, BV786, BYG584-P, calcein AM, calcein blue AM, CFSE, DAPI, DRAQ5, DRAQ7, FITC, Fluo-4AM, FVS440UV, FVS450, FVS510, FVS520, FVS570, FVS575V, FVS620, FVS660, FVS700, FVS780, Indo-1 Hi, Indo-1Lo, JC-1, MitoStatus Red, MitoStatus TMRE, Pacific Blue, PE, PE-CF594, PE-Cy5, PE-Cy7, PerCP, PerCP-Cy5.5, PI, R718, RB545, RB613, RB744, RB780, RY586, RY610, V450, V500, Via-Probe Green, Via-Probe Red, VPD450, Alexa Fluor 532, Alexa Fluor 561, Alexa Fluor 660, APC-eFluor 780, APC / Fire 750, APC / Fire 810, BV785, eBFP, eCFP, eFluor 450, eFluor 506, eFluor 660, eGFP, eYFP, Hoechst 33258, Kiravia Blue 520, mCherry, NFB510, NFB530, NFB555, NFB585, NFB610-70S, NFB660-120S, NFR660, NFR685, NFR700, NFR710, NFY570, NFY590, NFY610, NFY660, NFY690, NFY700, NFY730, Pacific Orange, PE-Cy5.5, PE-eFluor 610, PE / Dazzle 594, PE / Fire 640, PE / Fire 700, PE / Fire 810, PerCP-eFluor 710, SB436, SB600, SB645, SB702, SB780, Spark Blue 550, Spark Blue 574, Spark NIR 685, Spark UV 387, Spark Violet 423, Spark Violet 538, Spark YG581, Spark YG593, and tdTomato.

[0076] In some cases, the fluorochrome is a polymer dye (e.g., a fluorescent polymer dye). The fluorescent polymer dyes that find use in the subject methods and systems are varied. In some cases of the methods, the polymer dye comprises a conjugated polymer. Conjugated polymers (CPs) are characterized by a delocalized electronic structure that includes a backbone of alternating unsaturated (e.g., double and / or triple) and saturated (e.g., single) bonds, where π electrons can move from one bond to the other. Thus, the conjugated backbone can limit the bond angles between the repeat units of the polymer, imparting an extended linear structure to the polymer dye. For example, proteins and nucleic acids are also polymers, but in some cases do not form extended rod structures, but rather fold into highly ordered three-dimensional shapes. In addition, CPs can form "rigid rod" polymer backbones and experience limited twist (e.g., twist) angles between monomer repeat units along the polymer backbone chain. In some cases, the polymer dye comprises a CP with a rigid rod structure. The structural properties of the polymer dye can affect the fluorescent properties of the molecule.

[0077] Polymeric dyes of interest include, but are not limited to, those described in U.S. Pat. Nos. 7,270,956, 7,629,448, 8,158,444, 8,227,187, 8,455,613, 8,575,303, 8,802,450, 8,969, Nos. 509, 9,139,869, 9,371,559, 9,547,008, 10,094,838, 10,302,648, 10,458,989, 10,641,775, and 10,962,546, the disclosures of which are incorporated herein by reference in their entireties. et al., J. Am. Chem. Soc., 2001, 123(26), pp 6417-6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411-2419; and Traina et al., J Am. Chem., 2011, 133(32), pp 12600-12607. Specific polymeric dyes that may be employed include, but are not limited to, the BD Horizon Brilliant™ dyes, such as the BD Horizon Brilliant™ Violet dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786), the BD Horizon Brilliant™ Ultraviolet dyes (e.g., BUV395, BUV496, BUV737, BUV805), and the BD Horizon Brilliant™ Blue dyes (e.g., BB515) (BD Biosciences, San Jose, Calif.). Any fluorochrome known to one of skill in the art, including but not limited to those described above or yet to be discovered, may be employed in the subject methods.

[0078] In some embodiments, calculating a cellular parameter according to the present disclosure includes evaluating a cellular interaction parameter. The term "interaction" is used herein in the conventional sense of the term to refer to a physical or functional coupling of two or more cells. In some cases, the cells are in physical contact with each other. In some cases, the cells are co-localized with each other in the generated image, such as being located within 1 μm or less of each other, e.g., within 0.5 μm or less, e.g., 0.1 μm or less, e.g., 0.05 μm or less, e.g., 0.01 μm or less, e.g., 0.005 μm or less, e.g., 0.001 μm or less, including 0.0001 μm or less. In some cases, one or more cells are internalized within the cell.

[0079] In some cases, the cellular interaction is an active interaction between cells, such as when two or more cells actively communicate with each other (e.g., via active synapses). In other cases, the cellular interaction is a passive interaction between cells, such as when two or more cells passively communicate with each other (e.g., via passive synapses). In other cases, the cellular interaction is a coincidence doublet in the generated image of the cells. In other cases, the cellular interaction is an engagement doublet in the generated image of the cells. In some cases, calculating the cellular interaction parameter includes evaluating internalization of a cell or cellular component by one or more cells in the sample. In some cases, calculating the cellular interaction parameter includes evaluating co-localization between two or more cells in the sample. In some cases, calculating the cellular parameter includes evaluating phagocytic cellular interactions.

[0080] In certain cases, calculating the cellular parameters includes evaluating a tumor cell killing interaction, such as when one of the cells is a cancer cell and the interacting cell is a lymphocyte. In some cases, evaluating the tumor cell killing interaction includes a heterogeneous bi-lymphocyte and a cancer cell. In some cases, evaluating the tumor cell killing interaction includes a homogeneous bi-lymphocyte and a lymphocyte.

[0081] In some embodiments, calculating the cellular parameter includes assessing a translocation parameter of a cellular component within the cells of the sample. In some cases, calculating the cellular parameter includes determining the displacement of a cellular component between different organelles within the cell. In some cases, the cellular parameter characterizes the type of component that is translocated within the cell. For example, in some cases, calculating the cellular parameter includes assessing protein translocation within the cell. In certain cases, the method includes assessing transcription factor translocation. In some cases, calculating the cellular parameter includes assessing lipid translocation within the cell. In some cases, calculating the cellular parameter includes assessing polysaccharide translocation within the cell. In some cases, calculating the cellular parameter includes assessing nucleic acid translocation within the cell. In certain cases, calculating the cellular parameter includes assessing translocation of a cell signaling compound. In other cases, calculating the cellular parameter includes assessing translocation of a cell growth factor within the cell. In certain embodiments, calculating the cellular parameter includes measuring nuclear translocation, such as when a cellular component (e.g., NF-κβ transcription factor) moves from the cytoplasm to the nucleus. In some cases, calculating the cellular parameter includes measuring translocation of a component from a nucleus to a cytoplasm. In some cases, calculating the cellular parameter includes measuring translocation of a component from a cytoplasm to a mitochondria. In some cases, calculating the cellular parameter includes measuring translocation of a component from a mitochondria to a cytoplasm.

[0082] In some embodiments, the cellular parameter is a cellular mitotic phase parameter. In some cases, calculating the cellular parameter includes determining a mitotic phase of one or more cells in the sample. In some cases, the method includes identifying the cell as being in interphase. In some cases, the method includes identifying the cell as being in prophase. In some cases, the method includes identifying the cell as being in metaphase. In some cases, the method includes identifying the cell as being in anaphase. In some cases, the method includes identifying the cell as being in telophase. In certain cases, the method includes identifying the cell as being in cytokinesis. In certain cases, the mitotic phase parameter is evaluated from a generated image of cellular mitochondria. In some cases, the mitotic phase parameter is evaluated using image parameters calculated from a generated image of mitochondria.

[0083] In some cases, the morphology of the cells' mitochondria is assessed to determine the mitotic stage of the cells. In some cases, the morphology of the cells' mitochondria is determined based on radial moment image parameters. In some cases, the image parameters include the radial moment of a fluorophore used to stain the mitochondria. In some cases, the radial moment image parameters are calculated from an image generated based on fluorescence measured from the cells' mitochondria, light loss measured for the cells' mitochondria, forward light scatter measured for the cells' mitochondria, side light scatter measured for the cells' mitochondria, and combinations thereof.

[0084] In some embodiments, mitochondria are labeled with one or more labels. In some embodiments, mitochondria are labeled with one or more lipophilic cationic fluorophores. In some cases, mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial protein. In some cases, the lipophilic cationic fluorophore has a thiol-reactive moiety for attaching the fluorophore to a mitochondrial protein. In some cases, mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial lipid. In some cases, mitochondria are labeled with a fluorophore that stains the inner mitochondrial membrane. In certain embodiments, mitochondria are labeled with a mitochondrial-specific dye, such as a MitoTracker dye. In some cases, mitochondria are labeled with a dye that is or includes the following dye components: MitoTracker Green FM dye, MitoTracker Red FM dye, MitoTracker Deep Red dye, MitoTracker Orange dye, MitoTracker Red dye, MitoTracker Deep Red FM dye, MitoTracker Red CMXRos dye, or a combination thereof. In some embodiments, mitochondria are labeled with a mitochondrial labeling dye that is or includes a dye moiety from one or more of the following: 10-N-nonyl-acridine orange, BrdU, CellLight mitochondrial GFP, Chomabody, Cox8 Snap Tag, DiOC 6 , IraZolve-Mito, JC-1, JC-9, mito-Dronpa, mito-PA-mCherryl, Mito-QC, Mito Blue, Mitorotor-1, MitoSOX, MitoTimer, Mt-Keima, PK Mito Orange (PKMO), Rhodamine 123, SNARF-1 dye, SPLICS, styryl dye DASPMI, terpyridyl cyclometalate Ir(III) complex, tetra-methylrhodamine methyl ester, mitochondrial fluorescent protein (red, yellow, orange, green, blue, and cyan), ZP1-HaloTag, and combinations thereof.

[0085] In some cases, mitotic phase parameters are calculated from the generated images of the cells. In some cases, mitochondrial size image parameters are calculated from the generated images. In some cases, mitochondrial fluorescence radial moment image parameters are calculated from the generated images. In some cases, mitochondrial light loss image parameters are calculated from the generated images. In some cases, forward scatter radial moment image parameters are calculated from the generated images. In some cases, side scatter radial moment image parameters are calculated from the generated images. In some cases, image moments are calculated from the generated images. The term image moment is used herein in the conventional sense of the term to refer to a weighted average of pixel intensities in an image. In some cases, a center of mass of mitochondrial cells may be calculated from image moments of an image. In other cases, an orientation of mitochondrial cells may be calculated from image moments of an image. In still other cases, an eccentricity of mitochondrial cells may be calculated from image moments of an image.

[0086] In some embodiments, the image parameter calculated is a radial moment of the fluorophore. In some cases, the method includes calculating a pixel density of the generated image of the cellular mitochondria. In some cases, the number of pixels in a horizontal row of the image of the cellular mitochondria is calculated. In some cases, the number of pixels in a vertical column of the image of the cellular mitochondria is calculated. In certain cases, the absolute number of pixels found in the image of the cellular mitochondria is determined. In some cases, a center of gravity of the cellular mitochondria is determined from one or more of the generated images. In some cases, the image parameter is calculated by determining a sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria.

[0087] In some embodiments, the method includes determining a cellular parameter for evaluating plant cell biology. In some cases, calculating the cellular parameter according to the present disclosure includes identifying cells in the sample as plant cells. In some cases, calculating the cellular parameter corresponds to distinguishing plant cells in the sample from cell debris. In some cases, the method includes calculating the cellular parameter to characterize different sizes of plant cells. In some cases, calculating the cellular parameter includes identifying the plant cell as a parenchyma cell. In some cases, calculating the cellular parameter includes identifying the plant cell as a sclerenchyma cell. In some cases, calculating the cellular parameter includes identifying the plant cell as a sclerenchyma cell. In some cases, calculating the cellular parameter includes identifying the plant cell as a xylem cell (e.g., aerial). In some cases, calculating the cellular parameter includes identifying the plant cell as a phloem cell. In certain cases, calculating the cellular parameter includes identifying the cells of the sample as comprising protoplasts.

[0088] In some cases, calculating the cellular parameters includes determining a developmental stage of the plant cell. In some cases, the method includes determining that the plant cell is undergoing cell division. In some cases, the method includes determining that the plant cell is undergoing cell elongation. In some cases, the method includes determining that the plant cell is undergoing cell differentiation.

[0089] In some embodiments, calculating the cellular parameters includes evaluating the morphology of the plant cells. In some cases, evaluating the morphology of the plant cells includes evaluating the size of the plant cells. In some cases, the size of each plant cell in the sample is measured. In some cases, a method for determining the size of each plant cell in the sample includes comparing the size of the plant cells (using a reference image of the plant cell of interest) to a predetermined reference size, e.g., determining that the size of each cell is 50% or more of the predetermined reference size, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 98% or more, e.g., 99% or more of the predetermined reference size. In some examples, the method includes determining the number or percentage of cells in the sample having a size within 15% or less of a predetermined reference size range, such as within 10% or less, such as within 5% or less, such as within 4% or less, such as within 3% or less, such as within 2% or less, such as within 1% or less, including within 0.5% or less, of the predetermined reference size range. In some cases, the method includes determining that the sample is a plant cell sample when cells in the sample have a size within the reference size range.

[0090] In some embodiments, assessing the morphology of the cells includes assessing the punctatility of the cells. In some cases, the punctatility of each cell in the sample is measured. In some cases, the method for determining the punctatility of each cell in the sample includes comparing the punctatility of the cells (using a reference image of the cells) to a predetermined reference punctatility, e.g., the punctatility of each cell is determined to be 50% or more of the predetermined reference punctatility, e.g., 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, 98% or more, 99% or more of the predetermined reference punctatility. In some examples, the method includes determining the number or percentage of cells in the sample that have a punctatility within 15% or less of a predetermined reference size range, e.g., within 10% or less, e.g., within 5% or less, e.g., within 4% or less, e.g., within 3% or less, e.g., within 2% or less, e.g., within 1% or less, including within 0.5% or less. In some cases, the method includes determining that the sample is a plant cell sample when cells in the sample have punctatility within a reference punctatility range.

[0091] In some embodiments, evaluating the morphology of the plant cells includes evaluating the shape of the cells. In some cases, the method includes determining the percentage of cells having a particular shape. In certain cases, depending on the type of cell sample and the desired shape of the cells, the method includes determining that the cells of the sample are plant cells when the percentage of cells having the particular desired shape exceeds a predetermined threshold, including when, for example, 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 98% or more, such as 99% or more of the cells in the sample have the desired shape, including when 99.5% or more of the cells have the particular desired shape.

[0092] In some embodiments, calculating the cellular parameters includes determining that the cell belongs to a rare cell population. The term "rare cell" is used herein in the conventional sense of the term to refer to cells in a sample where the abundance of this type of cell is, for example, 0.1% or less of the cell population, for example, 0.05% or less of the cell population, for example, 0.01% or less of the cell population, for example, 0.005% or less of the cell population, for example, 0.001% or less of the cell population, for example, 0.0005% or less of the cell population, including when this type of cell is 0.0001% or less of the cell population. In some cases, calculating the cellular parameters includes determining that the rare cell is a stem cell. In some cases, calculating the cellular parameters includes determining that the rare cell is a circulating endothelial cell. In some cases, calculating the cellular parameters includes determining that the rare cell is a circulating tumor (malignant or benign) cell. In some cases, calculating the cellular parameters includes determining that the rare cell is a residual disease cell, such as a blood or bone marrow disease.

[0093] In some embodiments, the method includes classifying the cells of the sample based on the image data (e.g., waveforms generated at each photodetector channel), the generated images of the cells, the calculated image parameters, or a combination thereof. In some cases, classifying the cells includes assigning the cells to one or more particle population clusters. In some embodiments, the cells of the sample are classified by a machine learning algorithm that uses a reference image of the cells or one or more reference image parameters as a training data set. In some cases, the machine learning algorithm is a dynamic algorithm that updates based on the generated images and the determined image parameters of the cells. Any convenient machine learning algorithm may be implemented, and machine learning algorithms of interest may include, but are not limited to, a linear regression algorithm, a logistic regression algorithm, a Naive Bayes algorithm, a k-nearest neighbor (kNN) algorithm, a random forest algorithm, a decision tree algorithm, a support vector machine algorithm, a gradient boosting algorithm, and a clustering algorithm. In certain embodiments, the machine learning algorithm is a neural network. In some cases, the machine learning algorithm is a neural network, such as an artificial neural network, a convolutional neural network, or a recurrent neural network. In certain cases, the machine learning algorithm is a Python script.

[0094] In some embodiments, classifying the cells of the sample includes assigning each cell in the sample to a particle population cluster. As used herein, a "population" or "subpopulation" of classified cells refers to a group of cells that possess related parameters (e.g., delta center of mass, radial moment, eccentricity) such that the measured data form a cluster in the data space. Thus, the populations are recognized as clusters in the data. Conversely, each particle population cluster may be interpreted as corresponding to a compound population of a particular category of cells (e.g., exhibiting cell-cell interactions, internalization, phagocytosis, tumor cell killing, cellular component translocation, cell morphology, cell division phase, plant cell development stage, etc.), although clusters that typically correspond to noise or background are also observed. Particle population clusters may be defined in a subset of dimensions, e.g., with respect to a subset of measured image parameters that correspond to composite populations that differ only in a subset of measured image parameters or features extracted from particle measurements.

[0095] In some embodiments, the method includes determining one or more sorting gates for sorted cells of the sample. In some cases, the method includes determining a sorting gate for cells at a particular mitotic stage. In some cases, the method includes determining a sorting gate for a rare cell population. In some cases, the method includes determining a sorting gate for identified plant cells. In some cases, the method includes determining a sorting gate for plant cells having an identified developmental stage. In some cases, the method includes determining a sorting gate for cells that exhibit translocation of cellular components. In some cases, the method includes determining a sorting gate for cells having a desired morphology. In some cases, the method includes determining a sorting gate for a cell-cell interaction (e.g., tumor cell killing interaction, internalization interaction, phagocytosis interaction, passive synaptic interaction, active synaptic interaction, colocalization).

[0096] The term "gate" is used herein in the conventional sense of the term to refer to a classifier boundary that identifies a subset of data of interest. In some instances, a gate can distinguish a group of events of particular interest. In addition, "gating" refers to the process of classifying data using a gate defined for a given data set, where the gate can be one or more regions of interest combined with Boolean logic. In some embodiments, the gate identifies particles that exhibit the same image parameters. Examples of methods for gating are described, for example, in U.S. Pat. Nos. 4,845,653, 5,627,040, 5,739,000, 5,795,727, 5,962,238, 6,014,904, and 6,944,338, as well as 8,990,047, the disclosures of which are incorporated herein by reference. In some embodiments, the gate distinguishes particle population clusters from one or more different samples that have been previously determined (eg, by a user) to correspond to a property of interest.

[0097] In some cases, one or more sorting gates capture cells of the target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the sorting gates maximize the inclusion yield of cells of the target particle population cluster. In some cases, the sorting gates are configured to maximize the inclusion yield of cells of the target particle population cluster, for example, the sorting gates are configured to generate an inclusion yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate an inclusion yield of cells of the target particle population cluster of 99.9% or more. In some cases, the sorting gate is configured to maximize a purity yield of target particle population cluster cells, e.g., the sorting gate is configured to generate a purity yield of target particle population cluster cells of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate a purity yield of target particle population cluster cells of 99.9% or more.

[0098] In some cases, the sorting gate is configured to maximize the rejection of particles of the non-target particle population. In some cases, the sorting gate is configured to reject 50% or more of the particles of the non-target particle population cluster, including determining a sorting gate configured to reject 99.9% or more of the particles of the non-target particle population cluster, such as 55% or more, such as 60% or more, such as 65% or more, such as 70% or more, such as 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more. In some cases, the sorting gate rejects particles of the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0099] In certain embodiments, generating a sorting gate includes an Fβ score, which is a weighted harmonic average of the inclusion of particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. The beta (β) parameter of the Fβ score biases the gating strategy toward either yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles) of the sample. For example, when the Fβ score is equal to 1, purity and yield have equal contributions to the gating strategy. When the Fβ score is equal to 2, the gating strategy emphasizes yield over purity. When the Fβ score is equal to 0.5, the gating strategy emphasizes purity over yield. In some embodiments, the Fβ score is in the range of 0.1-10, e.g., 0.2-9.5, e.g., 0.3-9, e.g., 0.4-8.5, e.g., 0.5-8, e.g., 0.5-7, e.g., 0.5-6, e.g., 0.5-5, e.g., 0.5-4, e.g., 0.5-3, e.g., 0.5-2. In some cases, the generated sorting gate of interest has an Fβ score of 1 or greater. In some cases, the generated sorting gate has an Fβ score of less than 1.

[0100] In some cases, the method includes evaluating the sorting gates of the gating strategy and adjusting one or more of the generated sorting gates. The adjustment may be based on an evaluation criterion indicating the accuracy of the sorting gates according to the desired sorting strategy. In some cases, the evaluation criterion may be an accuracy (e.g., purity) criterion, in which the purity is compared to a predetermined threshold. The evaluation criterion may be generated based on the confidence of the classifiers included in the sorting strategy. In other cases, the evaluation criterion may be a yield criterion, in which the yield of target cells in the particle population cluster is compared to a predetermined threshold.

[0101] In some embodiments, the method includes generating a sorting decision based on the determined sorting gates of the cells of the sample. In some cases, generating a particle sorting decision according to the present disclosure includes a sorting gate gating strategy including 8 or less sorting gates, e.g., 7 or less sorting gates, e.g., 6 or less sorting gates, e.g., 5 or less sorting gates, e.g., 4 or less sorting gates, e.g., 3 or less sorting gates, and including 2 or less sorting gates. In some embodiments, the method includes generating the sorting gates using a graphical display (as described below) that displays one or more analysis algorithms for applying classification parameters to the image parameters determined for the cells.

[0102] In some embodiments, the method includes displaying the gating strategy on a graphical user interface. For example, the analysis algorithm can be one or more of a spectral compensation matrix, a clustering algorithm, and a t-SNE algorithm. In some cases, the analysis algorithm is applied to the particle population cluster by dragging an icon of the analysis algorithm onto the particle population cluster. In other cases, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In certain cases, the analysis algorithm is a spectral unmixing algorithm as described in U.S. Pat. No. 11,009,400 and International Patent Application No. PCT / US2021 / 46741, filed August 19, 2021, the disclosures of which are incorporated herein by reference. In some examples, the gating strategy is determined using a computational sorting algorithm as described in U.S. Pat. No. 11,513,054, the disclosures of which are incorporated herein by reference. In certain cases, the sorting gates can be determined using computer software such as HyperFinder (e.g., as described in Bonavia, et al. Frontiers in Immunology 2022;13:1007016) and Computational Sorting with HyperFinder, FlowJo Software and BD FACSDiva Software (Becton Dickinson, 2021), the disclosures of which are incorporated herein by reference. In certain embodiments, the gating strategy is developed on a separate computing system (e.g., a different computer system or network) and communicated to the flow cytometer to implement the gating strategy, such as by using a particle sorter (e.g., having a sorting decision module) of the flow cytometer.

[0103] FIG. 1 depicts a flow chart for calculating cellular parameters for cells of a sample, according to certain embodiments. In step 101, light from cells in a sample illuminated with a frequency modulated light beam in a flow stream is measured using a photodetector. In some cases, the method includes illuminating cells of the sample in the flow stream, such as with a high frequency shifted beam of laser light. In some cases, a frequency encoded data signal is generated from the measured light. In step 102, image data of the cells (e.g., digital waveforms generated by a light detection system in one or more photodetector channels) is generated. In some cases, an image of the cells is generated (step 102a). In some cases, the image visualizes cell-cell interactions (colocalization, internalization, phagocytosis, tumor cell killing). In some cases, the image visualizes translocation of a cellular component (e.g., nuclear translocation of a transcription factor from the cytoplasm to the nucleus). In some cases, the image visualizes cells of a rare cell population. In some cases, the image visualizes plant cells. In some cases, the images visualize one or more mitotic phases (e.g., anaphase, telophase, etc.) In some cases, the images visualize a developmental stage of a cell.

[0104] In some cases, in step 102a, an image parameter (e.g., radial moment) is calculated from the generated image. In some cases, the method includes calculating multiple image parameters for each cell, e.g., 50 or more image parameters. In step 103, cell parameters are calculated for the cells of the sample. If the cells are to be sorted to isolate cells with a particular cell parameter of interest (e.g., internalized cells or cells in a particular mitotic stage), a gating strategy for the cells is determined in step 104. A gating strategy having one or more applied gates (e.g., 4 to 8 applied gates) is developed, and a sorting decision in step 105 can be generated using the gating strategy. In some cases, the cells are sorted into different containers (step 106), such as a droplet sorter (e.g., having a droplet filling device, a piezoelectric transducer for generating droplets different from the cells, and a deflection plate for deflecting the droplets into different sample containers).

[0105] In certain embodiments, the method includes sorting one or more of the cells of the sample based on the calculated cell parameters. The term "sorting" is used herein in the conventional sense of the term to refer to separating components of the sample (e.g., droplets containing cells, droplets containing non-cellular particles such as biopolymers), and in some cases delivering the separated components to one or more sample collection vessels. For example, the method may include sorting two or more components of the sample, such as three or more components, such as four or more components, such as five or more components, such as ten or more components, such as fifteen or more components, including sorting 25 or more components of the sample.

[0106] In sorting the particles, the method includes data acquisition, analysis, and recording, such as by a computer, where multiple data channels record data from each detector used in acquiring overlapping spectra of multiple fluorophores associated with the particle. In these embodiments, the analysis includes spectrally resolving (e.g., by calculating a spectral unmixing matrix) light from multiple fluorophores having overlapping spectra associated with the particle, and identifying the particle based on the estimated abundance of each fluorophore associated with the particle. This analysis can be communicated to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0107] In some embodiments, the method for sorting components of a sample includes sorting cells of the sample using a particle sorting module having a deflection plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, cells of the sample are sorted using a sorting determination module having a plurality of sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having a deflection plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0108] System for calculating cell parameters for cells of a sample in a flow stream - Patents.com As summarized above, aspects of the present disclosure include a system for calculating cellular parameters for cells in a sample in a flow stream. The system according to certain embodiments includes a light source configured to irradiate cells of the sample in the flow stream with a frequency modulated light beam, a light detection system having a light detector for measuring light from the cells, and a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate image data of the cells from the measured light and to calculate one or more cellular parameters based on the generated image data.

[0109] In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells having cell-cell interactions, cells exhibiting intracellular translocation, etc.), the light source can be configured to emit wavelengths of light that vary in the range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, including 400 nm to 800 nm. For example, the light source can include a broadband light source that emits light having a wavelength of 200 nm to 900 nm. In other cases, the light source includes a narrowband light source that emits wavelengths spanning the range of 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) that emits light having a wavelength range of 200 nm to 900 nm. In certain embodiments, the light source is a laser. In some examples, the subject systems can be lasers such as helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, or the like. 2Lasers of interest include gas lasers such as CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers, or xenon fluorine (XeF) excimer lasers, or combinations thereof. In other examples, the subject systems include dye lasers such as stilbene lasers, coumarin lasers, or rhodamine lasers. In yet other examples, lasers of interest include metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof. In still other examples, the subject systems include ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, Ti:Sapphire laser, Slim YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 Lasers include solid state lasers such as lasers, fluorine-doped lasers, or cerium-doped lasers, and combinations thereof.

[0110] In other embodiments, the light source is a non-laser light source, such as a lamp including, but not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode such as a broadband LED with a continuous spectrum, a superluminescent diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integration, etc. In some cases, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0111] The light source may be positioned at any suitable distance from the sample, such as, for example, 0.001 mm or more, such as 0.005 mm or more, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 5 mm or more, such as 10 mm or more, such as 25 mm or more, including distances of 100 mm or more from the flow stream. In addition, the light source illuminates the sample at any suitable angle (e.g., relative to the normal axis of the flow stream), such as an angle ranging from 10° to 90°, such as, for example, 15° to 85°, such as, for example, 20° to 80°, such as, for example, 25° to 75°, including, for example, 30° to 60°, such as, for example, an angle of 90°.

[0112] The light source may be configured to illuminate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to illuminate the sample continuously, such as with a continuous wave laser that is continuously illuminated into the flow stream at an inspection point in the flow cytometer. In other cases, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, such as every 0.01 milliseconds, such as every 0.1 milliseconds, such as every 1 milliseconds, such as every 10 milliseconds, such as every 100 milliseconds, including every 1000 milliseconds, or some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to accommodate intermittent illumination of the sample with the light source. For example, the subject systems in these embodiments may include one or more laser beam choppers and manual or computer controlled beam stops for blocking the sample and exposing the sample to the light source.

[0113] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous wave lasers. For example, lasers may include helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO 2Lasers, gas lasers such as CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers or xenon fluorine (XeF) excimer lasers, dye lasers such as stilbene, coumarin or rhodamine lasers, helium cadmium (HeCd) lasers, helium mercury (HeHg) lasers, helium selenium (HeSe) lasers, helium silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers or gold lasers, and combinations thereof, ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, Ti:sapphire laser, Thulium YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 The laser may be a frequency-doubled or frequency-tripled implementation of any of the lasers mentioned above, such as a laser or a cerium-doped laser, as well as combinations thereof, a semiconductor diode laser, an optically pumped semiconductor laser (OPSL), or a frequency-doubled or frequency-tripled implementation of any of the lasers mentioned above.

[0114] In some embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some cases, the optical beam generator includes a laser and a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous wave laser. For example, lasers in the optical beam generator of interest can be gas lasers such as helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers, or xenon fluorine (XeF) excimer lasers, or combinations thereof; dye lasers such as stilbene, coumarin, or rhodamine lasers; metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa4O(BO 3 ) 3 Laser, Nd:YCOB laser, Titanium sapphire laser, Thulium YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 The laser may be a solid state laser such as a laser or a cerium doped laser, as well as combinations thereof.

[0115] The acousto-optical device may be any convenient acousto-optical protocol configured to frequency shift laser light using applied acoustic waves. In certain embodiments, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the subject system is configured to generate an angle-deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optical device with any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0116] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams in the output laser beam, for example, configured to apply 3 or more high frequency drive signals, for example, 4 or more high frequency drive signals, for example, 5 or more high frequency drive signals, for example, 6 or more high frequency drive signals, for example, 7 or more high frequency drive signals, for example, 8 or more high frequency drive signals, for example, 9 or more high frequency drive signals, for example, 10 or more high frequency drive signals, for example, 15 or more high frequency drive signals, for example, 25 or more high frequency drive signals, for example, 50 or more high frequency drive signals, including being configured to apply 100 or more high frequency drive signals.

[0117] In some cases, to generate an angularly deflected laser beam intensity profile in the output laser beam, the controller is configured to apply a high frequency drive signal having an amplitude ranging from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V, including from about 5 V to about 25 V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0118] In certain embodiments, the controller comprises a processor having a memory operatively coupled to the processor such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam including an angularly deflected laser beam having a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity, including instructions for generating three or more, such as four or more, such as five or more, such as ten or more, such as twenty-five or more, such as fifty or more, angularly deflected laser beams having the same intensity, including instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more angle-deflected laser beams having different intensities, such as including instructions for generating three or more, such as four or more, such as five or more, such as ten or more, such as twenty-five or more, such as fifty or more, angle-deflected laser beams having different intensities, including may include instructions for generating 100 or more angle-deflected laser beams having different intensities.

[0119] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from an edge to a center of the output laser beam along a horizontal axis. In these cases, the intensity of the angle-deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99% of the intensity of the angle-deflected laser beam at the edge of the output laser beam along the horizontal axis, such as, for example, 0.5% to about 95%, such as, for example, 1% to about 90%, such as, for example, about 2% to about 85%, such as, for example, about 3% to about 80%, such as, for example, about 4% to about 75%, such as, for example, about 5% to about 70%, such as, for example, about 6% to about 65%, such as, for example, about 7% to about 60%, such as, for example, about 8% to about 55%, including, for example, about 10% to about 50%, of the intensity of the angle-deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from an edge to a center of the output laser beam along a horizontal axis. In these cases, the intensity of the angle-deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99% of the intensity of the angle-deflected laser beam at the center of the output laser beam along the horizontal axis, such as, for example, 0.5% to about 95%, such as, for example, 1% to about 90%, such as, for example, about 2% to about 85%, such as, for example, about 3% to about 80%, such as, for example, about 4% to about 75%, such as, for example, about 5% to about 70%, such as, for example, about 6% to about 65%, such as, for example, about 7% to about 60%, such as, for example, about 8% to about 55%, including, for example, about 10% to about 50%, of the intensity of the angle-deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile having a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along a horizontal axis.

[0120] In embodiments, the optical beam generator of interest may be configured to generate angularly deflected laser beams in the spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the system is configured to generate angularly deflected laser beams in the output laser beam that overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam, etc. The overlap between adjacent angle deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, including 100 μm or more.

[0121] In certain cases, a light beam generator configured to generate two or more frequency shifted light beams may be used as disclosed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,45 Nos. 1,538, 10,620,111, and the laser excitation modules described in U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0122] In an embodiment, the system includes a light detection system having a plurality of light detectors for measuring light from cells in the sample. In some cases, one or more light detectors of the light detection system are light loss light detectors. In some cases, one or more light detectors of the light detection system are configured to measure scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure side scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure forward scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure back scattered light. Detectors of interest may include optical sensors or light detectors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APD), image sensors, charge-coupled devices (CCD), intensified charge-coupled devices (ICCD), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultipliers, phototransistors, quantum dot photoconductors, or photodiodes, as well as combinations thereof, among other light detectors. In certain embodiments, the light from the sample is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor.

[0123] In some embodiments, the light detection module of interest includes a plurality of light detectors. In some cases, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In certain cases, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include a light detector having 4 or more light detectors, such as 10 or more light detectors, such as 25 or more light detectors, such as 50 or more light detectors, such as 100 or more light detectors, such as 250 or more light detectors, such as 500 or more light detectors, such as 750 or more light detectors, including 1000 or more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, such as a photodiode array having photodiodes including 10 or more photodiodes, such as 25 or more photodiodes, such as 50 or more photodiodes, such as 100 or more photodiodes, such as 250 or more photodiodes, such as 500 or more photodiodes, such as 750 or more photodiodes, including 1000 or more photodiodes.

[0124] The photodetectors may be arranged in any desired geometric configuration, configurations of interest include, but are not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregular configurations. The photodetectors in the photodetector array may be oriented relative to one another at angles ranging from 10° to 180° (referenced in the XZ plane), such as 15° to 170°, such as 20° to 160°, such as 25° to 150°, such as 30° to 120°, including 45° to 90°. The photodetector array may be of any suitable shape, including rectilinear shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear shapes, such as circular, elliptical, and irregular shapes, such as a parabolic lower portion joined to a planar upper portion. In one particular embodiment, the photodetector array has a rectangular active surface.

[0125] Each photodetector (e.g., photodiode) in the array may have an active surface having a width in the range of 5 μm to 250 μm, e.g., 10 μm to 225 μm, e.g., 15 μm to 200 μm, e.g., 20 μm to 175 μm, e.g., 25 μm to 150 μm, e.g., 30 μm to 125 μm, inclusive, of 50 μm to 100 μm, and a length in the range of 5 μm to 250 μm, e.g., 10 μm to 225 μm, e.g., 15 μm to 200 μm, e.g., 20 μm to 175 μm, e.g., 25 μm to 150 μm, e.g., 30 μm to 125 μm, inclusive, of 50 μm to 100 μm. For example, the surface area of ​​each photodetector (e.g., photodiode) in the array may be in the range of 25 μm to 30 μm. 2 ~10000μm 2 For example, 50 μm 2 ~9000m 2 , e.g., 75 μm 2 ~8000m 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 and 200 μm 2 ~5000μm 2 The range includes:

[0126] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length ranging from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. The width of the photodetector array may also vary from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. Thus, the active surface of the photodetector array may vary from 0.1 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. 2~10000mm 2 For example, 0.5 mm 2 ~5000mm 2 , e.g., 1 mm 2 ~1000mm 2 , e.g., 5 mm 2 ~500mm 2 and 10 mm 2 ~100mm 2 The range may include.

[0127] Optical detectors of interest are configured to measure collected light at one or more wavelengths, for example, measuring collected light at 2 or more wavelengths, 5 or more different wavelengths, for example, 10 or more different wavelengths, for example, 25 or more different wavelengths, for example, 50 or more different wavelengths, for example, 100 or more different wavelengths, for example, 200 or more different wavelengths, for example, 300 or more different wavelengths, including measuring light emitted by a sample in the flow stream at 400 or more different wavelengths.

[0128] In some embodiments, the photodetector is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a range of wavelengths. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from the sample in the flow stream at one or more specific wavelengths. For example, the system may include one or more detectors configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof.

[0129] The light detection system is configured to measure light continuously or at discrete intervals. In some cases, the light detector of interest is configured to make measurements of the collected light continuously. In other cases, the light detection system is configured to make measurements at discrete intervals, such as every 0.001 milliseconds, such as every 0.01 milliseconds, such as every 0.1 milliseconds, such as every 1 millisecond, such as every 10 milliseconds, such as every 100 milliseconds, including every 1000 milliseconds, or some other interval.

[0130] In embodiments, the system is configured to generate one or more images of cells in the sample. In some embodiments, the system includes a memory, the memory having instructions stored thereon for generating an image from detected light absorption, detected light scattering, detected light emission, or any combination thereof. In some cases, the memory includes instructions for generating an image from detected light absorption from the sample, such as from a bright field light detector. In some cases, the memory includes instructions for generating an image from detected light scattering from the sample, such as from a side scatter detector, a forward scatter detector, or a combination of a side scatter detector and a forward scatter detector. In some cases, the memory includes instructions for generating an image from light emitted from the sample. In other cases, the memory includes instructions for generating an image from a combination of detected light absorption and detected light scattering. In certain cases, the memory includes instructions for generating an image of cell mitochondria from fluorescence from labeled mitochondria and one or more of light loss, side scatter light, and forward scatter light (e.g., to calculate the division stage of the cell).

[0131] In some cases, the memory includes instructions for generating image data, such as digital waveforms generated at one or more photodetector channels. In some cases, the memory includes instructions for calculating image parameters directly from the waveforms. In some cases, the memory includes instructions for calculating image parameters from the image data, the generated images, or a combination thereof. In some cases, the memory includes instructions for determining cellular parameters only from the calculated image parameters (e.g., radial moment, eccentricity, etc.). In some cases, the memory includes instructions for determining cellular parameters only from the images of the particles, and not from another data source, such as, for example, a data signal waveform. In some cases, the memory includes instructions for determining cellular parameters using a combination of the generated images of the particles and a data signal waveform generated in response to measured light from the illuminated particles.

[0132] In some embodiments, the memory includes instructions for generating one or more images from the measured light. In some embodiments, the memory includes instructions for generating a single image for each cell in the sample from each form of detected light. In other embodiments, the memory includes instructions for generating a plurality of images, e.g., two or more, e.g., three or more, e.g., five or more, e.g., ten or more, including twenty-five or more, for each cell. For example, the memory includes instructions for generating a first image of the cells from detected fluorescence from the labeled cells, instructions for generating a second image of the cells from detected light absorption, and instructions for generating a third image from detected light scattering. In other embodiments, the memory includes instructions for generating two or more images, including ten or more, such as three or more, such as four or more, such as five or more, or a combination thereof, from each form of detected light.

[0133] In some embodiments, the system includes a memory having instructions for generating frequency encoded data (e.g., frequency encoded spatial data) from measured light from cells of a sample in a flow stream. In some cases, the memory includes instructions for generating one or more images from the frequency encoded data. The frequency encoded data may be generated in one or more detection channels, such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, including eight or more detection channels. In some embodiments, the frequency encoded data includes data components obtained (or derived) from light from different detectors, such as detected light absorption or detected light scattering. In some cases, the memory includes instructions for phase correcting the frequency encoded data. In some cases, the memory includes instructions for generating a phase corrected image of the cells by performing a transform on the frequency encoded data. In one example, the memory includes instructions for phase correcting the frequency encoded data by performing a Fourier transform (FT) of the frequency encoded data. In another example, the memory includes instructions for phase correcting the frequency encoded data by performing a discrete Fourier transform (DFT) of the frequency encoded data. In yet another example, the memory includes instructions for phase correcting the frequency-encoded data by performing a short-time Fourier transform (STFT) of the frequency-encoded data. In one particular embodiment, the memory includes instructions for performing a transformation of the frequency-encoded data without performing any mathematical imaginary calculations (i.e., performing only calculations for the mathematical real calculations of the transformation) to generate an image from the frequency-encoded data.

[0134] In some cases, the memory includes instructions for generating one or more grayscale images of cells of the sample. The term "grayscale" is used herein in the conventional sense of the term to refer to an image of cells in a flow stream that is composed of various shades of gray based on the intensity of light at each pixel. In some embodiments, the memory includes instructions for determining a pixel intensity threshold from the grayscale image, where the pixel intensity threshold is used to convert each pixel to a binary value that is used to generate an image of the cell. In certain cases, the images of cells and cell mitochondria are binary pixel images of the particles such that each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel is above a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0135] In some embodiments, the system includes a memory having instructions for calculating one or more image parameters from the generated images of the cells. In some cases, the memory includes instructions for calculating a center of mass image parameter from the generated images. In some cases, the memory includes instructions for calculating a delta center of mass image parameter from the generated images. In some cases, the memory includes instructions for calculating a diffusivity image parameter from the generated images. In some cases, the memory includes instructions for calculating an eccentricity image parameter from the generated images. In some cases, the memory includes instructions for calculating a long axis moment image parameter from the generated images. In some cases, the memory includes instructions for calculating a maximum intensity image parameter from the generated images. In some cases, the memory includes instructions for calculating a radial moment image parameter from the generated images. In some cases, the memory includes instructions for calculating a short axis moment image parameter from the generated images. In some cases, the memory includes instructions for calculating a particle size image parameter from the generated images. In some cases, the memory includes instructions for calculating a total intensity image parameter from the generated images. In some cases, the memory includes instructions for calculating a particle light loss image parameter from the generated images. In some cases, the memory includes instructions for calculating forward scatter image parameters from the generated images. In some cases, the memory includes instructions for calculating side scatter image parameters from the generated images. In some cases, the memory includes instructions for calculating image moments from the generated images. In some cases, the memory includes instructions for calculating a center of mass from the image moments of the images. In other cases, the memory includes instructions for calculating an orientation of the cell from the image moments of the images. In yet other cases, the memory includes instructions for calculating an eccentricity of the cell from the image moments of the images.

[0136] In some embodiments, the memory includes instructions for calculating cellular parameters by evaluating cellular interaction parameters. The term "interaction" is used herein in the conventional sense of the term to refer to a physical or functional coupling of two or more cells. In some cases, the cells are in physical contact with each other. In some cases, the cells are co-localized with each other in the generated image, e.g., the cells are located within 1 μm or less of each other, e.g., 0.5 μm or less, e.g., 0.1 μm or less, e.g., 0.05 μm or less, e.g., 0.01 μm or less, e.g., 0.005 μm or less, e.g., 0.001 μm or less, including 0.0001 μm or less. In some cases, one or more cells are internalized within the cell.

[0137] In some cases, the cellular interaction is an active interaction between cells, such as when two or more cells are actively in communication with each other (e.g., via an active synapse). In other cases, the cellular interaction is a passive interaction between cells, such as when two or more cells are passively in communication with each other (e.g., via a passive synapse). In other cases, the cellular interaction is a coincidence doublet in the generated image of the cells. In other cases, the cellular interaction is an engagement doublet in the generated image of the cells. In some cases, the memory includes instructions for calculating a cellular interaction parameter by evaluating internalization of a cell or a cellular component by one or more cells in the sample. In some cases, the memory includes instructions for calculating a cellular interaction parameter by evaluating colocalization between two or more cells in the sample. In some cases, the memory includes instructions for calculating a cellular parameter by phagocytic cellular interaction.

[0138] In certain cases, the memory includes instructions for calculating the cellular parameter by evaluating the tumor cell killing interaction, such as when one of the cells is a cancer cell and the interacting cell is a lymphocyte. In some cases, the evaluating the tumor cell killing interaction includes a heterogeneous bi-lymphocyte and a cancer cell. In some cases, the evaluating the tumor cell killing interaction includes a homogeneous bi-lymphocyte and a lymphocyte.

[0139] In some embodiments, the memory includes instructions for calculating a cellular parameter by assessing a translocation parameter of a cellular component within a cell of the sample. In some cases, the memory includes instructions for calculating a cellular parameter by determining a displacement of a cellular component between different organelles within the cell. In some cases, the cellular parameter characterizes a type of component translocated within the cell. For example, in some cases, the memory includes instructions for calculating a cellular parameter by assessing protein translocation within the cell. In certain cases, the memory includes instructions for assessing transcription factor translocation. In some cases, the memory includes instructions for calculating a cellular parameter by assessing lipid translocation within the cell. In some cases, the memory includes instructions for calculating a cellular parameter by assessing polysaccharide translocation within the cell. In some cases, the memory includes instructions for calculating a cellular parameter by assessing nucleic acid translocation within the cell. In certain cases, the memory includes instructions for calculating a cellular parameter by assessing translocation of a cell signaling compound. In other cases, the memory includes instructions for calculating a cellular parameter by assessing translocation of a cellular growth factor within the cell. In certain embodiments, the memory includes instructions for calculating a cellular parameter by measuring nuclear translocation, such as when a cellular component (e.g., NF-κβ transcription factor) is translocated from the cytoplasm to the nucleus. In some cases, the memory includes instructions for calculating a cellular parameter by measuring translocation of a component from the nucleus to the cytoplasm. In some cases, the memory includes instructions for calculating a cellular parameter by measuring translocation of a component from the cytoplasm to mitochondria. In some cases, the memory includes instructions for calculating a cellular parameter by measuring translocation of a component from the mitochondria to the cytoplasm.

[0140] In some embodiments, the cellular parameter is a cellular mitotic phase parameter. In some cases, the memory includes instructions for calculating the cellular parameter by determining the mitotic phase of one or more cells in the sample. In some cases, the memory includes instructions for identifying the cell as being in interphase. In some cases, the memory includes instructions for identifying the cell as being in prophase. In some cases, the memory includes instructions for identifying the cell as being in metaphase. In some cases, the memory includes instructions for identifying the cell as being in anaphase. In some cases, the memory includes instructions for identifying the cell as being in telophase. In certain cases, the memory includes instructions for identifying the cell as being in cytokinesis. In certain cases, the memory includes instructions for evaluating the mitotic phase parameter from the generated image of the cellular mitochondria. In some cases, the memory includes instructions for evaluating the mitotic phase parameter using the image parameters calculated from the generated image of the mitochondria.

[0141] In some cases, the memory includes instructions for evaluating cellular mitochondrial morphology to determine the mitotic stage of the cell. In some cases, the memory includes instructions for determining cellular mitochondrial morphology based on radial moment image parameters. In some cases, the image parameters include radial moment of a fluorophore used to stain the mitochondria. In some cases, the memory includes instructions for calculating radial moment image parameters from an image generated based on fluorescence measured from the cellular mitochondria, light loss measured for the cellular mitochondria, forward scatter measured for the cellular mitochondria, side scatter measured for the cellular mitochondria, and combinations thereof.

[0142] In some cases, the memory includes instructions for calculating mitotic phase parameters from the generated image of the cell. In some cases, the memory includes instructions for calculating a cellular mitochondrial size image parameter from the generated image. In some cases, the memory includes instructions for calculating a cellular mitochondrial fluorescence radial moment image parameter from the generated image. In some cases, the memory includes instructions for a cellular mitochondrial light loss image parameter from the generated image. In some cases, the memory includes instructions for a forward scatter radial moment image parameter from the generated image. In some cases, the memory includes instructions for a side scatter radial moment image parameter from the generated image. In some cases, the memory includes instructions for image moments from the generated image. In some cases, a center of mass of the cellular mitochondria can be calculated from the image moments of the image. In other cases, an orientation of the cellular mitochondria can be calculated from the image moments of the image. In still other cases, an eccentricity of the cellular mitochondria can be calculated from the image moments of the image.

[0143] In some embodiments, the image parameter calculated is the radial moment of the fluorophore. In some cases, the memory includes instructions for calculating a pixel density of the generated image of the cellular mitochondria. In some cases, the memory includes instructions for calculating a number of pixels in a horizontal row of the image of the cellular mitochondria. In some cases, the memory includes instructions for calculating a number of pixels in a vertical column of the image of the cellular mitochondria. In certain cases, the memory includes instructions for calculating an absolute number of pixels found in the image of the cellular mitochondria. In some cases, the memory includes instructions for calculating a center of gravity of the cellular mitochondria from one or more of the generated images. In some cases, the memory includes instructions for calculating the image parameter by determining a sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria.

[0144] In some embodiments, the memory includes instructions for determining a cellular parameter for evaluating plant cell biology of one or more cells of the sample. In some cases, the memory includes instructions for calculating a cellular parameter by identifying cells in the sample as plant cells. In some cases, the memory includes instructions for calculating a cellular parameter by distinguishing plant cells from cellular debris in the sample. In some cases, the memory includes instructions for calculating a cellular parameter for characterizing different sizes of plant cells. In some cases, the memory includes instructions for identifying the plant cells as parenchyma cells. In some cases, the memory includes instructions for identifying the plant cells as sclerenchyma cells. In some cases, the memory includes instructions for identifying the plant cells as sclerenchyma cells. In some cases, the memory includes instructions for identifying the plant cells as xylem cells (e.g., aerial). In some cases, the memory includes instructions for identifying the plant cells as phloem cells. In certain cases, the memory includes instructions for identifying the cells of the sample as comprising protoplasts.

[0145] In some cases, the memory includes instructions for determining a developmental stage of the plant cell. In some cases, the memory includes instructions for determining that the plant cell is undergoing cell division. In some cases, the memory includes instructions for determining that the plant cell is undergoing cell elongation. In some cases, the memory includes instructions for determining that the plant cell is undergoing cell differentiation.

[0146] In some embodiments, the memory includes instructions for evaluating the morphology of the plant cells. In some cases, the memory includes instructions for evaluating the morphology of the plan cells by evaluating the size of the plant cells. In some cases, the size of each plant cell in the sample is measured. In some cases, the memory includes instructions for determining the size of each plant cell in the sample by comparing the size of the plant cell to a predetermined reference size (using a reference image of the plant cell of interest), e.g., the size of each cell is determined to be 50% or more of the predetermined reference size, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 98% or more, e.g., 99% or more of the predetermined reference size. In some cases, the memory includes instructions for determining the number or percentage of cells in the sample having a size within 15% or less of a predetermined reference size range, e.g., within 10% or less, e.g., within 5% or less, e.g., within 4% or less, e.g., within 3% or less, e.g., within 2% or less, e.g., within 1% or less, including within 0.5% or less of the predetermined reference size range. In some cases, the memory includes instructions for determining that the sample is a plant cell sample when cells in the sample have a size within the reference size range.

[0147] In some embodiments, the memory includes instructions for evaluating cell morphology by evaluating the punctatility of the cells. In some cases, the punctatility of each cell in the sample is measured. In some cases, the memory includes instructions for determining the punctatility of each cell in the sample by comparing the punctatility of the cells (using a reference image of the cells) to a predetermined reference punctatility, e.g., the punctatility of each cell is determined to be 50% or more of the predetermined reference punctatility, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 98% or more, e.g., 99% or more of the predetermined reference punctatility. In some cases, the memory includes instructions for determining the number or percentage of cells in the sample having a punctatility of 15% or less within a predetermined reference size range, including, e.g., within a predetermined reference size range of 10% or less, e.g., 5% or less, e.g., 4% or less, e.g., 3% or less, e.g., 2% or less, e.g., 1% or less, and 0.5% or less. In some cases, the memory includes instructions for determining that the sample is a plant cell sample when cells in the sample have punctiformity within a reference punctiformity range.

[0148] In some embodiments, the memory includes instructions for evaluating the morphology of plant cells by evaluating the shape of the cells. In some cases, the memory includes instructions for determining the percentage of cells having a particular shape. In certain cases, depending on the type of cell sample and the desired shape of the cells, the memory includes instructions for determining that cells of the sample are plant cells when the percentage of cells having the particular desired shape exceeds a predetermined threshold, including when 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 98% or more, such as 99% or more of the cells in the sample have the particular desired shape, including when 99.5% or more of the cells in the sample have the particular desired shape.

[0149] In some embodiments, the memory includes instructions for calculating a cellular parameter by determining that the cell belongs to a rare cell population. The term "rare cell" is used herein in the conventional sense of the term to refer to a cell in a sample where the abundance of this type of cell is 0.1% or less of the cell population, including, for example, 0.05% or less of the cell population, such as 0.01% or less of the cell population, such as 0.005% or less of the cell population, such as 0.001% or less of the cell population, such as 0.0005% or less of the cell population, including when this type of cell is 0.0001% or less of the cell population. In some cases, the memory includes instructions for calculating a cellular parameter by determining that the rare cell is a stem cell. In some cases, the memory includes instructions for calculating a cellular parameter by determining that the rare cell is a circulating endothelial cell. In some cases, the memory includes instructions for calculating a cellular parameter by determining that the rare cell is a circulating tumor (malignant or benign) cell. In some cases, the memory includes instructions for calculating a cellular parameter by determining that the rare cell is a residual disease cell, such as a blood or bone marrow disease.

[0150] In some cases, the system includes a processor having a memory for executing a machine learning algorithm, which is a dynamic algorithm for classifying cells of a sample. The system may be configured to implement any convenient machine learning algorithm, where the machine learning algorithm of interest may include, but is not limited to, a linear regression algorithm, a logistic regression algorithm, a Naive Bayes algorithm, a k-nearest neighbor (kNN) algorithm, a random forest algorithm, a decision tree algorithm, a support vector machine algorithm, a gradient boosting algorithm, and a clustering algorithm. In certain embodiments, the system is configured to implement a neural network. In some cases, the machine learning algorithm is a neural network, such as an artificial neural network, a convolutional neural network, or a recurrent neural network. In certain cases, the system is configured to implement a Python script.

[0151] In some embodiments, the memory includes instructions for determining one or more sorting gates for cells of the sample, such as for classifying cells of the sample by assigning each cell in the sample to a particle population cluster. In some cases, the memory includes instructions for generating one or more sorting gates that capture cells of a target particle population cluster and exclude cells of non-target particle population clusters. In some cases, the memory includes instructions for determining a sorting gate configured to maximize an inclusion yield of cells of the target particle population cluster, where, for example, the sorting gate is configured to generate an inclusion yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more. In some cases, the memory includes instructions for determining a sorting gate configured to maximize a purity yield of cells of the target particle population cluster, where, for example, the sorting gate is configured to generate a purity yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate a purity yield of cells of the target particle population cluster of 99.9% or more.

[0152] In some cases, the memory includes instructions for determining a sorting gate configured to maximize the exclusion of cells of the non-target particle population. In some cases, the memory includes instructions for determining a sorting gate configured to exclude 50% or more of the cells of the non-target particle population, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more of the cells of the non-target particle population, including determining a sorting gate configured to exclude 99.9% or more. In some cases, the memory includes instructions for determining a sorting gate that excludes cells of the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0153] In certain embodiments, the memory includes instructions for generating a sorting gate using an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. The beta (β) parameter of the Fβ score biases the gating strategy toward either yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles) of the sample. For example, when the Fβ score is equal to 1, purity and yield have equal contributions to the gating strategy. When the Fβ score is equal to 2, the gating strategy emphasizes yield over purity. When the Fβ score is equal to 0.5, the gating strategy emphasizes purity over yield. In some embodiments, the Fβ score is in the range of 0.1-10, e.g., 0.2-9.5, e.g., 0.3-9, e.g., 0.4-8.5, e.g., 0.5-8, e.g., 0.5-7, e.g., 0.5-6, e.g., 0.5-5, e.g., 0.5-4, e.g., 0.5-3, e.g., 0.5-2. In some cases, the generated sorting gate of interest has an Fβ score of 1 or greater. In some cases, the generated sorting gate has an Fβ score of less than 1.

[0154] In some cases, the memory includes instructions for evaluating the sorting gates of the gating strategy and adjusting one or more of the generated sorting gates. The adjustment may be based on an evaluation criterion indicating the accuracy of the sorting gates according to the desired sorting strategy. In some cases, the evaluation criterion may be an accuracy (e.g., purity) criterion, in which the purity is compared to a predetermined threshold. The evaluation criterion may be generated based on the confidence of the classifiers included in the sorting strategy. In other cases, the evaluation criterion may be a yield criterion, in which the yield of target cells in the particle population cluster is compared to a predetermined threshold.

[0155] In some embodiments, the memory includes instructions for generating a sorting decision based on the sorting gates determined for the cells of the sample. In some cases, the memory includes instructions for generating a particle sorting decision using a sorting gate gating strategy including 8 or less sorting gates, e.g., 7 or less sorting gates, e.g., 6 or less sorting gates, e.g., 5 or less sorting gates, e.g., 4 or less sorting gates, e.g., 3 or less sorting gates, and including 2 or less sorting gates. In some embodiments, the memory includes instructions for generating the sorting gates using a graphical display that displays one or more analysis algorithms for applying classification parameters to the image parameters determined for the cells.

[0156] In some embodiments, the memory includes instructions for generating sorting gates using a graphical display that displays one or more analysis algorithms for applying the determined classification parameters to the determined image parameters for the cells. In some embodiments, the system includes a display for visualizing the gating strategy on a graphical user interface.

[0157] In some cases, the graphical user interface applies an analysis algorithm to generate the gating strategy. For example, the analysis algorithm can be one or more of a spectral compensation matrix, a clustering algorithm, and a t-SNE algorithm. In some cases, the analysis algorithm is applied to the particle population cluster by dragging an icon of the analysis algorithm onto the particle population cluster. In other cases, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In one particular case, the analysis algorithm is a spectral unmixing algorithm as described in U.S. Pat. No. 11,009,400 and International Patent Application No. PCT / US2021 / 46741, filed Aug. 19, 2021, the disclosures of which are incorporated herein by reference.

[0158] In some examples, the system includes a memory having instructions for determining a gating strategy using a computational sorting algorithm such as described in U.S. Patent No. 11,513,054, the disclosure of which is incorporated herein by reference. In certain cases, the system includes a memory having computer software for determining sorting gates, such as HyperFinder (e.g., as described in Bonavia, et al. Frontiers in Immunology 2022;13:1007016) and Computational Sorting with HyperFinder, FlowJo Software and BD FACSDiva Software (Becton Dickinson, 2021), the disclosures of which are incorporated herein by reference. In certain embodiments, the gating strategy is developed on a separate computational system (e.g., a different computer system or network) and communicated to the flow cytometer to implement the gating strategy, such as by using a particle sorter (e.g., having a sorting decision module) of the flow cytometer.

[0159] In some embodiments, the system for generating a gating strategy is part of or operatively coupled to a particle analyzer system (e.g., a flow cytometer) for generating the flow cytometer data described herein.

[0160] In some cases, the system includes an integrated circuit device programmed to implement one or more of the methods described above. In some cases, the integrated circuit includes programming for generating an image of the cell. In some cases, the integrated circuit includes programming for calculating image parameters from the generated image of the cell. In some embodiments, the integrated circuit device of interest includes a field programmable gate array (FPGA). In other embodiments, the integrated circuit device includes an application specific integrated circuit (ASIC). In yet other embodiments, the integrated circuit device includes a complex programmable logic device (CPLD).

[0161] The system according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having instructions stored therein to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control, such as, for example, negative feedback control.

[0162] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write compact disk, flash memory devices, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or disk drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or floppy disk, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in conjunction with the memory storage devices.

[0163] In some embodiments, a computer program product is described that includes a computer usable medium having control logic (a computer software program including program code) stored thereon. The control logic, when executed by a processor, causes the computer to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. The implementation of hardware state machines to perform the functions described herein will be apparent to one of ordinary skill in the relevant art.

[0164] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor over a communication channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices together with the memory. For example, a magnetic or optical disk may carry the programming and can be read by a disk writer / reader. The system of the present invention also includes programming, for example in the form of a computer program product, algorithms for use in practicing the methods described above. The programming according to the present invention may be recorded on a computer-readable medium, for example any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0165] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a cellular phone (i.e., a smartphone).

[0166] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0167] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment) configured for similar complementary data communications.

[0168] In one embodiment, the communications interface is configured for infrared communications, Bluetooth® communications, or any other suitable wireless communications protocol to enable the subject system to communicate with other devices, such as a computer terminal and / or network, a communications-enabled mobile phone, a personal digital assistant, or any other communications device that a user may use in conjunction with.

[0169] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing the Internet Protocol (IP) via a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0170] In one embodiment, the subject system is configured to communicate wirelessly with the server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, a personal digital assistant (PDA) or a notebook computer, or a larger device, such as a desktop computer, an appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0171] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., in the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0172] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. Where one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite network, in accordance with known techniques. Presentation of data by the output manager may be performed in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system may be any type of known or future developed computer platform, but they are typically of the class of computers commonly referred to as servers. However, they may be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication systems, including wireless systems, either networked or not. They may be co-located or physically separated.In some cases, depending on the type and / or configuration of the computer platform selected, various operating systems may be employed on any of the computer platforms. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, and the like.

[0173] In certain embodiments, the subject systems include one or more optical conditioning components for conditioning light, such as light projected onto the sample (e.g., from a laser) or light collected from the sample (e.g., scattered light, fluorescence). For example, the optical conditioning can be increasing the size of the light, the focal length of the light, or collimating the light. In some examples, the optical conditioning is an expansion protocol to increase the size of the light (e.g., beam spot), such as increasing the size by 5% or more, including increasing the size by 10% or more, such as 25% or more, such as 50% or more, and 75% or more. In other embodiments, the optical conditioning includes focusing the light to reduce the size of the light, such as by 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, including reducing the size of the beam spot by 75% or more. In certain embodiments, the optical conditioning includes collimating the light. The term "collimate" is used in the traditional sense to refer to optically adjusting the collinearity of light propagation or reducing the divergence of the light from a common propagation axis. In some examples, collimating includes narrowing the spatial cross-section of the light beam (eg, reducing the beam profile of a laser).

[0174] In some embodiments, the opening in the particle sorting module housing includes a focusing lens having a magnification, e.g., a magnification of 0.1 to 0.95, e.g., a magnification of 0.2 to 0.9, e.g., a magnification of 0.3 to 0.85, e.g., a magnification of 0.35 to 0.8, e.g., a magnification of 0.5 to 0.75, including a magnification of 0.55 to 0.7, e.g., a magnification of 0.6. For example, the focusing lens is a dichromatic reduction lens having a magnification of about 0.6 in one particular example. The focal length of the focusing lens can vary in the range of 5 mm to 20 mm, e.g., including focal lengths in the range of 6 mm to 19 mm, e.g., 7 mm to 18 mm, e.g., 8 mm to 17 mm, e.g., 9 mm to 16, and 10 mm to 15 mm. In one particular embodiment, the focusing lens has a focal length of about 13 mm.

[0175] In other embodiments, the optical conditioning component is a collimator. The collimator can be any convenient collimating protocol, such as one or more mirrors or curved lenses, or a combination thereof. For example, the collimator is a single collimating lens in certain instances. In other instances, the collimator is a collimating mirror. In yet other instances, the collimator includes two lenses. In yet other instances, the collimator includes a mirror and a lens. When the collimator includes one or more lenses, the focal length of the collimating lens can vary in the range of, for example, 5 mm to 40 mm, including focal lengths in the range of, for example, 6 mm to 37.5 mm, for example, 7 mm to 35 mm, for example, 8 mm to 32.5 mm, for example, 9 mm to 30 mm, for example, 10 mm to 27.5 mm, for example, 12.5 mm to 25 mm, and 15 mm to 20 mm.

[0176] In some embodiments, the subject systems include a flow cell nozzle having a nozzle orifice configured to direct a flow stream through the flow cell nozzle. The subject flow cell nozzle includes a flow cell nozzle having an orifice that propagates a fluid sample to a sample testing region, and in some embodiments, the flow cell nozzle has a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having the nozzle orifice, transverse to the longitudinal axis. The length of the proximal cylindrical portion (measured along the longitudinal axis) can vary, for example, from 1 mm to 15 mm, including 1.5 mm to 12.5 mm, for example, from 2 mm to 10 mm, including 3 mm to 9 mm, and 4 mm to 8 mm. The length of the distal frustoconical portion (measured along the longitudinal axis) can similarly vary, for example, from 1 mm to 10 mm, including 2 mm to 9 mm, including 3 mm to 8 mm, and 4 mm to 7 mm. The diameter of the flow cell nozzle chamber can vary in some embodiments, for example, from 1 mm to 10 mm, including, for example, from 2 mm to 9 mm, such as from 3 mm to 8 mm, and from 4 mm to 7 mm.

[0177] In certain instances, the nozzle chamber does not include a cylindrical portion, and the entire flow cell nozzle chamber is formed in a frustoconical shape. In these embodiments, the length of the frustoconical nozzle chamber (measured along a longitudinal axis transverse to the nozzle orifice) may range, for example, from 1 mm to 15 mm, including 1.5 mm to 12.5 mm, for example, from 2 mm to 10 mm, for example, from 3 mm to 9 mm, and from 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical nozzle chamber may range, for example, from 1 mm to 10 mm, including 2 mm to 9 mm, for example, from 3 mm to 8 mm, and from 4 mm to 7 mm.

[0178] In embodiments, the sample flow stream originates from an orifice at the distal end of the flow cell nozzle. Depending on the desired characteristics of the flow stream, the flow cell nozzle orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, such as, for example, square, rectangular, trapezoidal, triangular, hexagonal, curvilinear cross-sectional shapes, such as, for example, circular, elliptical, as well as irregular shapes, such as, for example, a parabolic bottom joined to a planar top. In certain embodiments, the flow cell nozzle of interest has a circular orifice. The size of the nozzle orifice may vary in some embodiments from 1 μm to 20,000 μm, including, for example, from 2 μm to 17,500 μm, for example, from 5 μm to 15,000 μm, for example, from 10 μm to 12,500 μm, for example, from 15 μm to 10,000 μm, for example, from 25 μm to 7,500 μm, for example, from 50 μm to 5,000 μm, for example, from 75 μm to 1,000 μm, for example, from 100 μm to 750 μm, and from 150 μm to 500 μm. In one particular embodiment, the nozzle orifice is 100 μm.

[0179] In some embodiments, the flow cell nozzle includes a sample injection port configured to provide a sample to the flow cell nozzle. In embodiments, the sample injection system is configured to provide a suitable flow of the sample to the flow cell nozzle chamber. Depending on the desired characteristics of the flow stream, the rate of the sample delivered by the sample injection port to the flow cell nozzle chamber can be 1 μL / sec or more, such as 2 μL / sec or more, such as 3 μL / sec or more, such as 5 μL / sec or more, such as 10 μL / sec or more, such as 15 μL / sec or more, such as 25 μL / sec or more, such as 50 μL / sec or more, such as 100 μL / sec or more, such as 150 μL / sec or more, such as 200 μL / sec or more, such as 250 μL / sec or more, such as 300 μL / sec or more, such as 350 μL / sec or more, such as 400 μL / sec or more, such as 450 μL / sec or more, including 500 μL / sec or more. For example, the flow rate of the sample may be in the range of 1 μL / sec to about 500 μL / sec, including, for example, 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, and 10 μL to about 100 μL / sec.

[0180] The sample injection port may be an orifice located in the wall of the nozzle chamber or may be a conduit located at the proximal end of the nozzle chamber. When the sample injection port is an orifice located in the wall of the nozzle chamber, the sample injection port orifice may be of any suitable shape, including cross-sectional shapes of interest, including but not limited to rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes, such as circular, elliptical, and irregular shapes, such as a parabolic bottom joined to a planar top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, and in certain examples has an opening ranging from 0.1 mm to 5.0 mm, including, for example, 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm.

[0181] In certain examples, the sample injection port is a conduit positioned at the proximal end of the flow cell nozzle chamber. For example, the sample injection port can be a conduit positioned to have the orifice of the sample injection port aligned with the flow cell nozzle orifice. When the sample injection port is a conduit positioned in line with the flow cell nozzle orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, and cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes such as circular, elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. The orifice of the conduit may vary in shape, and in certain instances may have an opening ranging from 0.1 mm to 5.0 mm, including, for example, 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip having a bevel angle ranging from 1° to 10°, including, for example, a bevel angle of 2° to 9°, such as 3° to 8°, such as 4° to 7°, and 5°.

[0182] In some embodiments, the flow cell nozzle also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell nozzle. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell nozzle chamber, e.g., in conjunction with the sample, to produce a laminated flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered to the flow cell nozzle chamber can be 25 μL / sec or more, including, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, and 2500 μL / sec or more. For example, the flow rate of the sheath fluid may be in the range of 1 μL / sec to about 500 μL / sec, including, for example, 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, and 10 μL / sec to about 100 μL / sec.

[0183] In some embodiments, the sheath fluid injection port is an orifice located in the wall of the nozzle chamber. The sheath fluid injection port orifice may be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, such as, for example, square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes, such as, for example, circular, elliptical, as well as irregular shapes, such as, for example, a parabolic bottom portion joined to a planar top portion. The size of the sample injection port orifice may vary depending on the shape, and in certain cases has an opening of, for example, 1.5 mm, ranging from, for example, 0.2 to 3.0 mm, such as, for example, 0.5 mm to 2.5 mm, 0.75 mm to 2.25 mm, 1 mm to 2 mm, including 1.25 mm to 1.75 mm.

[0184] The subject systems, in certain instances, include a sample inspection region in fluid communication with the cell nozzle orifice. In these instances, a sample flow stream originates from an orifice at the distal end of the flow cell nozzle, and particles in the flow stream can be illuminated with a light source in the sample inspection region. The size of the inspection region can vary depending on the characteristics of the flow nozzle, such as the size of the nozzle orifice and the sample injection port size. In embodiments, the inspection region can have a width that is 0.01 mm or greater, including, for example, 0.05 mm or greater, such as 0.1 mm or greater, such as 0.5 mm or greater, such as 1 mm or greater, such as 2 mm or greater, such as 3 mm or greater, such as 5 mm or greater, and 10 mm or greater. The length of the inspection region may similarly vary, and in some instances may range from 0.01 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 1.5 mm or more, such as 2 mm or more, such as 3 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 20 mm or more, such as 25 mm or more, including 50 mm or more.

[0185] The inspection region may be configured to facilitate illumination of a planar cross-section of the generating flow stream, or may be configured to facilitate illumination of a diffuse field (e.g., by a diffuse laser or lamp) of a predetermined length. In some embodiments, the inspection region includes a transparent window to facilitate illumination of a predetermined length of the generating flow stream, such as, for example, 1 mm or more, such as, for example, 2 mm or more, such as, for example, 3 mm or more, such as, for example, 4 mm or more, such as, for example, 5 mm or more, and 10 mm or more. Depending on the light source used to illuminate the generating flow stream (as described below), the inspection region may be configured to pass light in the range of 100 nm to 1500 nm, such as, for example, 150 nm to 1400 nm, such as, for example, 200 nm to 1300 nm, such as, for example, 250 nm to 1200 nm, such as, for example, 300 nm to 1100 nm, such as, for example, 350 nm to 1000 nm, such as, for example, 400 nm to 900 nm, including, for example, 500 nm to 800 nm. Thus, the inspection region may be formed from any transparent material that passes the desired range of wavelengths, including, but not limited to, optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and copolymers of these thermoplastics, such as, for example, polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (glycol modified polyethylene terephthalate), including polyesters, among other polymeric plastic materials, polyesters of interest include, but are not limited to, poly(ethylene terephthalate) (PET), bottle grade PET (copolymers made based on monoethylene glycol, terephthalic acid, and other comonomers such as isophthalic acid, cyclohexanedimethanol), poly(butylene terephthalate) (PBT), and poly(alkylene terephthalates), such as poly(hexamethylene terephthalate), poly(ethylene adipate), poly(1,poly(alkylene adipates) such as poly(4-butylene adipate) and poly(hexamethylene adipate), poly(alkylene suberates) such as poly(ethylene suberate), poly(alkylene sebacates) such as poly(ethylene sebacate), poly(ε-caprolactone) and poly(β-propiolactone), poly(alkylene isophthalates) such as poly(ethylene isophthalate), poly(alkylene 2,6-naphthalene dicarboxylates) such as poly(ethylene 2,6-naphthalene dicarboxylate), poly(alkylenesulfonyl-4,4'-dibenzoates) such as poly(ethylenesulfonyl-4,4'-dibenzoate), poly(p-phenylene alkylene dicarboxylates) such as poly(p-phenylene ethylene dicarboxylate), poly(trans-1,4-cyclohexyl 1,4-phenylene ... poly(trans-1,4-cyclohexanediyl alkylene dicarboxylates), such as poly(1,4-cyclohexane-dimethylene alkylene dicarboxylates), such as poly(1,4-cyclohexane-dimethylene ethylene dicarboxylate), poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylates), such as poly([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate), lactic acid polymers and copolymers, such as (S)-polylactide, (R,S)-polylactide, poly(tetramethylglycolide), and poly(lactide-co-glycolide), and bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, 3,3',5,Examples of suitable materials include polycarbonate of 5'-tetramethylbisphenol A, polyamides such as poly(p-phenylene terephthalamide), polyesters such as polyethylene terephthalate, Mylar™ polyethylene terephthalate, and the like. In some embodiments, the subject system includes a cuvette positioned within the sample inspection region. In embodiments, the cuvette may transmit light in the range of 100 nm to 1500 nm, including, for example, 150 nm to 1400 nm, for example, 200 nm to 1300 nm, for example, 250 nm to 1200 nm, for example, 300 nm to 1100 nm, for example, 350 nm to 1000 nm, for example, 400 nm to 900 nm, and 500 nm to 800 nm.

[0186] In certain embodiments, a light detection system having multiple light detectors as described above is part of or positioned within a particle analyzer, such as a particle sorter. In certain embodiments, the subject system is a flow cytometry system that includes a photodiode and amplifier components as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan; 49(pt 1): 17-28; Linden, et al., Semin Thromb Hemost. 2004 Oct; 30(5): 502-11; Alison, et al. J Pathol, 2010 Dec; 222(4): 335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In certain examples, flow cytometry systems of interest include BD Biosciences FACSCanto™ flow cytometers, BD Biosciences FACSCanto™ II flow cytometers, BD Accuri™ flow cytometers, BD Accuri™ C6 Plus flow cytometers, BD Biosciences FACSCelesta™ flow cytometers, BD Biosciences FACSLyric™ flow cytometers, BD Biosciences FACSVerse™ flow cytometers, BD Biosciences FACSymphony™ flow cytometers, BD Biosciences LSRFortessa™ flow cytometers, BD Biosciences LSRFortessa™ X-20 flow cytometers, BD Biosciences FACSPresto™ flow cytometers, BD Biosciences FACSVia™ flow cytometers, as well as BD Biosciences FACSCalibur™ cell sorters, BD Biosciences FACSCount™ cell sorters, BD Biosciences FACSLyric™ cell sorters, BD These include BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, etc.

[0187] In some embodiments, the subject systems are disclosed in U.S. Pat. Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,585,031 ...85,031, 10,585,031, 10,585,031, 10,585,031, 10,585,031, 10,585,031, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. 9, No. 726,527, No. 9,453,789, No. 9,200,334, No. 9,097,640, No. 9,095,494, No. 9,092,034, No. 8,975,595, No. 8,753,573, No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,201,8 No. 75, No. 7,129,505, No. 6,821,740, No. 6,813,017, No. 6,809,804, No. 6,372,506, No. 5,7 Nos. 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766, the disclosures of which are incorporated by reference in their entireties.

[0188] In some embodiments, the system includes a particle sorter component. The term "sorting" is used herein in the conventional sense of the term to refer to separating components of a sample (e.g., target cells having mitochondrial morphology of interest, non-target cells having mitochondrial morphology not of interest, non-cellular particles such as biopolymers), and in some cases, delivering the separated components to one or more sample collection containers. For example, the subject systems can be configured to sort a sample having two or more components, including, for example, soring a sample having three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, and twenty-five or more components. One or more of the sample components, e.g., two or more sample components, e.g., three or more sample components, e.g., four or more sample components, e.g., five or more sample components, e.g., ten or more sample components, can be separated from the sample and delivered to a sample collection container, and fifteen or more sample components can be separated from the sample and delivered to a sample collection container.

[0189] In some embodiments, the particle sorting system of interest is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosures of which are incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having deflection plates, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0190] In one particular embodiment, the system is an image-enabled particle sorter using frequency-encoded data, as depicted in FIG. 3A. The particle sorter 300 includes an illumination component 300a including a light source 301 (e.g., a 488 nm laser) generating an output light beam 301a that is split into beams 302a and 302b by a beam splitter 302. The light beam 302a is propagated through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a having one or more angularly deflected light beams. In some cases, the output beam 303a generated from the acousto-optic device 303 includes a local oscillator beam and multiple high frequency comb beams. The light beam 302b is propagated through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a having one or more angularly deflected light beams. In some cases, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple high frequency comb beams. The output beams 303a and 304a generated from the acousto-optic devices 303 and 304, respectively, are combined with a beam splitter 305 to generate an output beam 305a that is transmitted through an optical component 306 (e.g., an objective lens) to illuminate particles in a flow cell 307. In one particular embodiment, the acousto-optic device 303 (AOD) splits a single laser beam into an array of beamlets, each beamlet having a different optical frequency and angle. The second AOD 304 tunes the optical frequency of a reference beam, which is then superimposed with the array of beamlets in the beam combiner 305. In certain embodiments, the light illumination system having a light source and an acousto-optical device may also include those described in Schraivogel, et al. ("High-speed fluorescence image-enabled cell sorting" Science (2022), 375(6578):315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0191] Output beam 305a is illuminated (e.g., with sheath fluid 309) at illumination region 310 onto sample particles 308 propagating through flow cell 307. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high frequency shifted optical beams depicted as dots across illumination region 310) overlap with a reference local oscillator beam (depicted as a shaded line across illumination region 310). The overlapped beams exhibit a beat behavior due to their different optical frequencies, such that each beamlet is oscillated at a different frequency f 1-n delivers sinusoidal modulation.

[0192] Light from the illuminated sample is transmitted to a light detection system 300b that includes multiple light detectors. The light detection system 300b includes a forward scatter light photodetector 311 for generating a forward scatter image 311a and a side scatter light photodetector 312 for generating a side scatter image 312a. The light detection system 300b also includes a bright field light detector 313 for generating a light loss image 313a. In some embodiments, the forward scatter detector 311 and the side scatter detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright field light detector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence light detectors 314-317. In some cases, the light detectors 314-317 are photomultiplier tubes. The light from the illuminated sample is directed through a beam splitter 320 to the side scatter detection channel 312 and the fluorescence detection channel 314-317. The light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to the light detectors 314-317. In some cases, the optical component 321 is a 534 nm / 40 nm bandpass. In some cases, the optical component 322 is a 586 nm / 42 nm bandpass. In some cases, the optical component 323 is a 700 nm / 54 nm bandpass. In some cases, the optical component 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter spans 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm.

[0193] Data signals generated in response to light detected in the scattered light detection channels 311 and 312, the brightfield light detection channel 313, and the fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a may be generated in each light detection channel based on the data signals generated by processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated by a sorting trigger 352. The sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or into a waste stream 333. In some embodiments, the sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In one particular embodiment, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0194] FIG. 3B depicts image-enabled particle sorting data processing according to certain embodiments. In some cases, the image-enabled particle sorting data processing is a low-latency data processing pipeline. Each photodetector generates a pulse with high frequency modulation that encodes an image (waveform). Fourier analysis is performed to reconstruct the image from the modulated pulses. The image processing pipeline generates a set of image features (image analysis) that are combined with features derived from the pulse processing pipeline (event packets). Real-time sorting electronics then classify particles based on the image features and generate sorting decisions that are used to selectively charge droplets.

[0195] In some embodiments, the system is a particle analyzer that can analyze and characterize particles with or without physically sorting the particles into a collection vessel using a particle analysis system 401 (FIG. 4A). FIG. 4A shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 shown in FIG. 4A can be configured in whole or in part to perform the methods described herein. The particle analysis system 401 includes a fluidics system 402. The fluidics system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which the particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0196] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection station 408 generally refers to a monitoring area 407 of the common sample path. In some embodiments, detection may include detecting light, or one or more other characteristics, of the particles 403 as they pass through the monitoring area 407. In FIG. 4A, one detection station 408 is shown with one monitoring area 407. Some embodiments of the particle analysis system 401 may include multiple detection stations. Additionally, some detection stations may monitor more than one region.

[0197] Each signal is assigned a signal value to form a data point for each particle. As explained above, this data may be referred to as event data. The data points may be multi-dimensional data points that include values ​​of each property measured for the particle. The detection system 404 is configured to collect a series of such data points at a first time interval.

[0198] The particle analysis system 401 may also include a control system 306. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluidics system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of a first time period based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time period. The control system 406 may further be configured to generate an empirical signal frequency based on the number of data points in the portion of the first time period. The control system 406 may additionally compare the empirical signal frequency to that of the calculated signal frequency or a predetermined signal frequency.

[0199] 4B shows a system 400 for flow cytometry, according to an illustrative embodiment of the invention. The system 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. The flow cytometer 410 includes one or more excitation lasers 415a-c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-g, one or more bandpass filters 450a-e, one or more longpass ("LP") filters 455a-b, and one or more fluorescence detectors 460a-f.

[0200] The pump lasers 115a-115c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4B. The laser beams are first directed through one or more of the beam splitters 445a and 445b. The beam splitter 445a transmits light at 488 nm and reflects light at 633 nm. The beam splitter 445b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects light at 488 nm and 633 nm.

[0201] The laser beam is then directed to a focusing lens 420, which focuses the beam onto the portion of the fluid stream where the sample particles are located, in a flow chamber 425. The flow chamber is the part of the fluidics system that directs particles in the stream, usually one at a time, into the focused laser beam for inspection. The flow chamber can comprise a flow cell in a benchtop flow cytometer, or a nozzle tip in a stream-in-air cytometer.

[0202] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, with re-emission at a variety of different wavelengths depending on the particle's properties, such as the particle's size, internal structure, and the presence of one or more fluorescent molecules attached or naturally present on or in the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light may be routed through one or more of beam splitters 445a-g, bandpass filters 450a-e, longpass filters 455a-b, and fluorescence collection lens 440 to one or more of a forward scatter detector 430, a side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0203] Fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes the light toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter spans 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light below a specified wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths of light above a specified wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorochrome. The filters may be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0204] Beam splitters direct light of different wavelengths in different directions. Beam splitters may be characterized by filter properties such as short pass and long pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that it transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g may include optical mirrors such as dichroic mirrors.

[0205] The forward scatter detector 430 is positioned slightly off-axis from the direct beam through the flow cell and is configured to detect diffracted light, excitation light traveling mostly in a forward direction through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect diffracted and reflected light from the surface and internal structure of the particle, which tends to increase as particle structures become more complex. Fluorescent emissions from fluorescent molecules associated with the particle may be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, the side scatter detector 435, and the fluorescence detector may be converted to electronic signals (voltages) by the detectors. This data may provide information about the sample.

[0206] Those skilled in the art will recognize that a flow cytometer according to an embodiment of the present invention is not limited to the flow cytometer depicted in Figure 4B, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0207] During operation, the operation of the flow cytometer is controlled by the controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by the controller / processor 490. Although not explicitly shown, the controller / processor 190 may be coupled to the detectors to receive output signals therefrom, and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, and the like. Input / output (I / O) functionality 497 may also be provided within the system. The memory 495, controller / processor 490, and I / O 497 may be provided as an integral part of the flow cytometer 410 collectively. In such an embodiment, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the flow cytometer 400. Alternatively, the memory 495 and some or all of the controller / processor 490 and I / O functionality may be part of one or more external devices, such as a general purpose computer. In some embodiments, memory 495 and some or all of controller / processor 490 may be in wireless or wired communication with flow cytometer 410. In conjunction with memory 495 and I / O 497, controller / processor 490 may be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0208] The system illustrated in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for a given detector) as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment will emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment, and their associated fluorescent emission bands, may be selected to generally match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescent emission spectra is not possible. Although the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, it is generally true that a portion of the emission spectrum of that label will also overlap with the filter windows of one or more other detectors. This may be referred to as a spillover signal. The I / O 497 may be configured to receive data relating to a flow cytometer experiment having a panel of fluorescent labels, and a plurality of cell populations having a plurality of markers, each cell population having a subset of the plurality of markers. I / O 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. Controller / processor 490 may be configured to evaluate one or more assignments of labels to markers.

[0209] 5 shows a functional block diagram of one embodiment of a particle analyzer control system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0210] The particle analyzer or sorting system 502 may be configured to acquire bio-event data. For example, a flow cytometer may generate flow cytometric event data. The particle analyzer 502 may be configured to provide the bio-event data to the analysis controller 500. A data communication channel may be included between the particle analyzer or sorting system 502 and the analysis controller 500. The bio-event data may be provided to the analysis controller 500 via the data communication channel.

[0211] The analysis controller 500 may be configured to receive bio-event data from a particle analyzer or sorting system 502. The bio-event data received from the particle analyzer or sorting system 502 may include flow cytometric event data. The analysis controller 500 may be configured to provide a graphical display including a first plot of the bio-event data on a display device 506. The analysis controller 500 may be further configured to render a region of interest as a gate around a population of the bio-event data shown by the display device 506, e.g., overlaid on the first plot. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest depicted on a single parameter histogram or bivariate plot. In some embodiments, the display may be used to display particle parameters or saturated detector data.

[0212] Analysis controller 500 may further be configured to display the bioevent data within the gate on display device 506 differently from other events within the bioevent data outside the gate. For example, analysis controller 500 may be configured to render the color of the bioevent data contained within the gate distinct from the color of the bioevent data outside the gate. Display device 506 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0213] Analysis controller 500 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, first input device may be implemented as a mouse 510. This mouse 510 may initiate a gate selection signal (e.g., by clicking on a desired gate when positioning a cursor thereon) to analysis controller 500 that identifies the gate to be displayed or manipulated via display device 506. In some implementations, the first device may be implemented as a keyboard 508 or other means for providing input signals to analysis controller 500, such as a touch screen, an input pen, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in FIG. 5, mouse 510 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0214] This triggering event may cause the analysis controller 500 to change the way the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input for further processing, such as selecting a population of interest for particle sorting.

[0215] In some embodiments, the analysis controller 500 may be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 may further be configured to automatically modify the visualization of the plot to facilitate the gating process. This modification may be based on a particular distribution of the bio-event data received by the analysis controller 500.

[0216] The analysis controller 500 may be connected to a storage device 504. The storage device 504 may be configured to receive and store bio-event data from the analysis controller 500. The storage device 504 may also be configured to receive and store flow cytometric event data from the analysis controller 500. The storage device 504 may be further configured to enable retrieval of bio-event data, such as flow cytometric event data, by the analysis controller 500.

[0217] The display device 506 may be configured to receive display data from the analysis controller 500. The display data may include a plot of the biological event data and a gate delineating a section of the plot. The display device 506 may be further configured to modify the presented information according to input received from the analysis controller 500 in conjunction with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0218] In some embodiments, the analysis controller 500 can generate a user interface to receive exemplary events for sorting. For example, the user interface can include controls for receiving exemplary events or exemplary images. The exemplary events or images, or exemplary gates, can be provided prior to collection of event data for a sample, or based on an initial set of events for a portion of a sample.

[0219] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, may include, or may be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in single file across a monitoring area 611 (e.g., where laser streams intersect) and are illuminated by an illumination source 612 (e.g., a laser). Vibration of the droplet forming transducer 602 causes the moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0220] In operation, the detection station 614 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring area 611. The detection station 614 feeds into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At the droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 608, thus causing the droplet of interest to carry a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplets into a container, such as a collection tube or a multi-well or microwell sample plate, where wells or microwells may be associated with a particular droplet of interest. As shown in FIG. 6A, the droplets may be collected in a drain container 638.

[0221] The detection system 616 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitoring area 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 616 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 616 may input an amplitude signal 620 and / or a phase 618 signal, which then inputs (via amplifier 622) to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624 then control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be included within a control system.

[0222] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store the detected events and the sorting decisions based thereon. The sorting decisions can be included in the particle event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by one of the detection system 616 or detection station 614 and provided to a non-collection element.

[0223] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream charging wire in the barb. This creates a stream of droplets 610 containing the particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. Information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). The deflection plates 652 and 654 can be independently controlled to attract or repel the charged droplets and direct them toward a desired collection vessel (e.g., one of 672, 674, 676, or 678). As shown in FIG. 6B, the deflection plates 652 and 654 can be controlled to direct the particles along a first path 662 toward vessel 674 or along a second path 668 toward vessel 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector may allow the particle to continue along flow path 664. Such uncharged droplets may be diverted into a waste container, such as via aspirator 670.

[0224] Sorting electronics may be included to initiate the collection of measurements, receive fluorescent signals about the particles, and determine how to adjust the deflection plates to cause particle sorting. Exemplary implementations of the embodiment shown in Figure 6B include the BD FACSAria™ line of flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0225] Non-transitory computer-readable storage medium Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for practicing the subject methods. The computer-readable storage medium may be employed on one or more computers for full or partial automation of a system for practicing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," in which case the term "computer-readable medium," as used herein, refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blue-ray disks, solid-state disks, and network-attached storage devices (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording the information such that the information can be accessed and retrieved at a later date by the computer. The computer-implemented methods described herein may be performed using programming that may be written in one or more of any number of computer programming languages, including, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as many others.

[0226] A non-transitory computer readable storage medium having instructions including an algorithm for calculating cellular parameters for cells of a sample is also described. The non-transitory computer readable storage medium according to certain embodiments has an algorithm for irradiating a sample containing cells in a flow stream with a frequency modulated light beam, an algorithm for measuring light from the cells in the sample, an algorithm for generating image data of the cells from the measured light, and an algorithm for calculating one or more cellular parameters based on the generated image data. In some cases, the image data includes one or more waveforms generated in response to the measured light.

[0227] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for generating an image of a cell from the image data. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter based on one or more of the image data and the image of the cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating a cellular interaction parameter. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating an interaction between two or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating phagocytosis by one or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating tumor cell killing by one or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating evaluating co-localization between two or more cells in a sample. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating translocation of a cellular component within a cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating nuclear translocation of a cellular component within a cell.

[0228] In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for determining the mitotic phase of one or more cells in a sample. In some cases, the cell is identified as being in interphase. In some cases, the cell is identified as being in prophase. In some cases, the cell is identified as being in metaphase. In some cases, the cell is identified as being in anaphase. In some cases, the cell is identified as being in telophase. In certain cases, the cell is identified as being in cytokinesis.

[0229] In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for identifying a cell as a plant cell. In some cases, the non-transitory computer readable storage medium comprises an algorithm for identifying a plant cell by evaluating cell morphology. In some cases, the non-transitory computer readable storage medium comprises an algorithm for evaluating one or more of cell size, cell shape, and cell punctation.

[0230] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating a cellular parameter by determining that the cell belongs to a rare cell population. A rare cell may be a cell in a sample where the abundance of this type of cell is 0.05% or less of the cell population, e.g., 0.01% or less of the cell population, e.g., 0.005% or less of the cell population, e.g., 0.001% or less of the cell population, e.g., 0.0005% or less of the cell population, including 0.0001% or less of the cell population where this type of cell is. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a cellular parameter by determining that the rare cell is a stem cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a cellular parameter by determining that the rare cell is a circulating endothelial cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a cellular parameter by determining that the rare cell is a circulating tumor (malignant or benign) cell. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a cellular parameter by determining that the rare cell is a residual disease cell, such as a blood or bone marrow disease.

[0231] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating a cell parameter based on an image parameter generated from an image of the cell. In some cases, the image parameter is a quantitative image parameter. In some cases, the image parameter is selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the cell nucleus, forward scatter by the cell nucleus, side scatter by the cell nucleus, and combinations thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter from an image generated based on fluorescence measured from the cell, light loss measured for the cell, forward scatter measured for the cell, side scatter measured for the cell, and combinations thereof.

[0232] In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells based on the image data, the generated images of the cells, and the calculated image parameters, or a combination thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells by assigning the cells to one or more particle population clusters. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells of a target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the exclusion of cells of the non-target particle population clusters.

[0233] The non-transitory computer readable storage medium may be employed on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, and the like. The processing module includes a processor that accesses a memory having stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0234] kit Aspects of the present disclosure further include kits, which include one or more of the integrated circuits described herein. In some embodiments, the kits may further include programming for the subject systems, such as in the form of a computer-readable medium (e.g., flash drive, USB storage, compact disc, DVD, Blu-ray disc, etc.) or instructions for downloading programming from an Internet web protocol or cloud server. The kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as on a piece of paper on which the information is printed, in the kit packaging, in a package insert, etc. Yet another form in which these instructions may be present is a computer-readable medium on which the information is recorded, such as a diskette, compact disc (CD), portable flash drive, etc. Yet another form in which these instructions may be present is a website address that may be used via the Internet to access the information at a remote site.

[0235] Utilities The subject systems, methods, and computer systems find use as upstream evaluation of cells, such as in molecular biology assays (e.g., gene expression analysis), applications in which cells with specific morphology, cell division stage, cell development stage, cell interactions, intracellular translocations, cell types (e.g., rare cells) may be used. In some cases, the present disclosure provides high quality and high purity cells isolated from heterogeneous samples. In addition, the subject systems, methods, and computer systems find use in a variety of applications in which it is desirable to analyze and sort particle components in samples in fluid media. In some embodiments, the systems and methods described herein find use in flow cytometric characterization of cell isolation. Embodiments of the present disclosure are used where it is desirable to provide a flow cytometer with improved cell sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0236] The disclosed embodiments are also used in applications where cells prepared from a biological sample may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from a fluid or tissue sample to be used as a research or diagnostic specimen. The disclosed methods and devices allow for the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems. EXAMPLES

[0237] The following examples are put forward so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention.

[0238] Evaluation of cell-cell interaction parameters using an imaging flow cytometer Cell samples were stained with multiple (up to 20 different colors) different fluorescent dyes and visualized using an imaging flow cytometer (FACSDiscover™, Bection Dickinson Biosciences, San Jose CA). Cells were visualized by generating images of the cells. FIG. 2A depicts the calculation of cell-cell interaction parameters by immune synapse testing. The cell images showed engaged and matched doublets that were gated and classified. Engaged doublets included cells with active synapses, potential active synapses, and passive synapses that demonstrate cell-cell interactions via physical contact. Matched doublets were not in physical contact. Sample cells included B cell-T cell interactions, T cell-monocyte interactions, B cell-monocyte interactions, and cancer cell-T cell interactions.

[0239] FIG. 2B depicts visualization of a tumor cell killing assay by imaging flow cytometry according to certain embodiments. The tumor cell killing assay was gated for the imaging parameters of radial moment (light loss) and eccentricity (light loss). Lysed whole blood was spiked with Epcam+ cell line and heterogeneous and homogeneous doublets were visualized. To determine the imaging parameters of lymphocyte-cancer cell interactions, heterogeneous doublets were gated and visualized. To determine the imaging parameters of lymphocyte-lymphocyte interactions, homogeneous doublets were gated and visualized.

[0240] FIG. 2C depicts visualization of T cell-monocyte cell-cell interactions according to certain embodiments. As shown in the cartoon depiction, adhesion molecules expressed on leukocytes and monocytes correspond to the formation of complexes between lymphocytes and monocytes that can be visualized by a panel of fluorophores (e.g., BUV395, BV780, RB780, and BB515). Sample cells were sorted into population clusters using the image parameters of radial moment (light loss) and eccentricity (light loss). Gating on T cell-monocyte complexes generated light loss images that were used to determine cell-cell interaction parameters of these complexes based on the generated images and image parameters. FIG. 2D depicts calculation of sorting gates for T cell-monocyte complexes. The sorting gates separated T cell-monocyte complexes that exhibited correlated interactions, complexes in physical contact, and coincident complexes. Sorting gates were generated using imaging parameters of correlation between fluorescence detected for CD54 (fluorophore BB515) and CD11a (fluorophore RB780) and delta center of mass (Delta COM) of CD54 and CD11a. FIG. 2E depicts sorting T cell-monocyte complexes using a 20-color spectral panel as imaging parameters. Population clusters generated by sorting using side scatter, light loss imaging parameters, followed by gating on radial moment and eccentricity, were gated using fluorescence from a multicolor spectral panel. This multicolor panel corresponds to calculating cell-cell interaction parameters for T cell-monocyte complexes using fluorescence imaging parameters. The multicolor panel was further used to visualize T cell-monocyte complexes exhibiting correlated interactions, physically contacting complexes, and coincident complexes, as shown in FIG. 2F.

[0241] FIG. 2G depicts sorting and sorting of extracellular vesicles-CD4 T cell complexes based on cell-cell interaction parameters according to certain embodiments. Samples containing extracellular vesicles (EVs) that were in physical contact with and bound to CD4 T cells were analyzed using an imaging flow cytometer. HeLa-derived EVs were labeled with PKH67 and incubated with PBMCs. The supernatant from Tu167 cell line cells was run to collect the EV fraction. Polymer-based precipitation was performed by mixing the EV fraction with ExoQuick O / N and freezing the pellet until ready for use or ultrafiltration. Ultrafiltration was followed by staining of EVs with PKH67 and then incubated with purified T cells or PBMCs for 2 hours. Population clusters were generated by sorting using side scatter and light loss image parameters followed by gating on differential fluorescence (CD3 RB780, CD4 BV421-A and EV PKH67-A). FIG. 2H depicts images of different complexes formed between EVs and CD4+ T cells, according to certain embodiments. Using imaging parameters for EVs and assessing the size of the EVs, each of the different complexes can be gated and sorted using these parameters.

[0242] Assessment of mitotic parameters by imaging flow cytometry The mitotic phase of cells in the sample was visualized using an imaging flow cytometer. Samples of cells undergoing mitosis were stained using a mitochondrial labeling dye (e.g., MitoTracker dye) and images of the cells were generated to identify the mitotic stage of the cells. Figure 2I depicts the calculation of cell parameters for gating and sorting cells in different mitotic phases. Each cell in the sample was identified as being in interphase, prophase, prometaphase, metaphase, anaphase, telophase, or undergoing cytokinesis. Each population cluster was gated using different image parameters used to identify each mitotic phase. Mitochondrial morphology was used to verify the mitotic phase of the cells. Image parameters including DAPI fluorescence (DNA stain), histone H2B and AF647 (anti-pS10H3 phospho-histone H3), along with the radial moment, maximum intensity and eccentricity of the histone H2B dye, were used to classify the cells of the sample. Using this gating strategy, samples containing cells in different mitotic phases were collected.

[0243] Notwithstanding the scope of the appended claims, the present disclosure is also defined by the following paragraphs. Aspect 1. A method comprising: illuminating a sample containing cells in a flow stream with a frequency modulated light beam; Measuring light from cells in the sample; generating image data of the cell from the measured light; and and calculating one or more cellular parameters based on the generated image data. 2. The method of claim 1, wherein the image data includes one or more waveforms generated in response to the measured light. 3. The method of claim 1 or 2, wherein the method comprises generating an image of the cell from the image data. 4. The method of claim 3, wherein the method includes calculating image parameters based on one or more of the image data and the image of the cell. 5. The method of any one of clauses 1 to 4, wherein calculating a cellular parameter comprises evaluating a cellular interaction parameter. 6. The method of claim 5, wherein calculating a cellular interaction parameter comprises evaluating an interaction between two or more cells in the sample. 7. The method of claim 6, wherein calculating the cell interaction parameter comprises assessing phagocytosis by one or more cells in the sample. 8. The method of clause 6 or 7, wherein calculating the cell interaction parameter comprises assessing tumor cell killing by one or more cells in the sample. 9. The method of claim 6, wherein calculating the cellular interaction parameter comprises assessing internalization of a cell or cellular component by one or more cells in the sample. 10. The method of claim 6, wherein calculating a cellular interaction parameter comprises assessing colocalization between two or more cells in the sample. 11. The method of any one of clauses 1 to 4, wherein calculating a cellular parameter comprises assessing translocation of a cellular component within the cell. 12. The method of claim 11, wherein the method comprises assessing nuclear translocation of a cellular component in a cell. 13. The method of any one of clauses 1 to 4, wherein calculating a cellular parameter comprises determining the division phase of the cell. 14. The method of claim 13, wherein calculating the cellular parameters comprises identifying the cells as exhibiting a mitotic phase selected from interphase, prophase, metaphase, anaphase, telophase, and cytokinesis. 15. The method of any one of clauses 1 to 4, wherein calculating the cell parameters includes identifying the cell as a plant cell. 16. The method of claim 15, wherein identifying the plant cell comprises evaluating cell morphology. 17. The method of claim 15 or 16, wherein identifying the plant cells comprises assessing one or more of cell size, cell shape, and cell punctation. 18. The method of any one of clauses 1 to 17, wherein the cell parameters are calculated based on image parameters generated from an image of the cell. 19. The method according to claim 18, wherein the image parameter is a quantitative image parameter. 20. The method of claim 18 or 19, wherein the image parameter is selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, long axis moment, maximum intensity, radial moment, short axis moment, size, total intensity, light loss by nuclei, forward scatter by nuclei, side scatter by nuclei, and combinations thereof. 21. A method according to any one of clauses 18 to 20, wherein the image parameters are calculated from an image generated based on fluorescence measured from the cells, light loss measured for the cells, forward scatter measured for the cells, side scatter measured for the cells, and combinations thereof. 22. The method of any one of clauses 1 to 21, wherein the method comprises classifying the cells based on the calculated cell parameters. 23. The method of claim 22, wherein classifying the cells comprises assigning the cells to one or more particle population clusters. 24. The method of claim 22 or 23, wherein the method further comprises determining one or more sorting gates for the sorted cells of the sample. 25. The method of claim 24, wherein one or more sorting gates capture cells of a target particle population cluster and exclude cells of a non-target particle population cluster. 26. The method of claim 24 or 25, wherein the sorting gate maximizes the inclusion yield of cells in the target particle population cluster. 28. The method of any one of clauses 24 to 26, wherein the sorting gate maximizes exclusion of cells in non-target particle population clusters. 29. The method of any one of clauses 1-28, wherein the method further comprises sorting cells of the sample into a plurality of sample containers. 30. The method of any one of clauses 1-29, wherein measuring light from cells in the flow stream comprises detecting fluorescence, light absorption, scattered light, or a combination thereof. 31. The method of any one of clauses 1 to 31, wherein the image data is calculated from frequency-encoded fluorescence data from fluorescently labeled cells. 32. The method of any one of clauses 1-31, wherein the frequency modulated light beam comprises a first beam of frequency shifted light and a second beam of frequency shifted light. 33. The method of clause 32, wherein the first beam of frequency-shifted light comprises a local oscillator (LO) beam and the second beam of frequency-shifted light comprises a high frequency comb beam. 34. applying a high frequency drive signal to an acousto-optic device; 34. The method of claim 32 or 33, further comprising illuminating the acousto-optic device with a laser to generate a first beam of frequency-shifted light and a second beam of frequency-shifted light. 35. The method of claim 34, wherein the laser is a continuous wave laser. 36. A system comprising: a light source configured to irradiate cells of a sample in the flow stream with a frequency modulated light beam; a light detection system comprising a light detector for measuring light from the cells; a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: generating image data of the cell from the measured light; The system calculates one or more cellular parameters based on the generated image data. 37. The system of clause 36, wherein the image data includes one or more waveforms generated in response to the measured light. 38. The system of clause 36 or 37, wherein the memory includes instructions for generating an image of the cell from the image data. 39. The system of clause 38, wherein the memory includes instructions for calculating image parameters based on one or more of the image data and the image of the cell. 40. A system according to any one of clauses 36 to 39, wherein the memory comprises instructions for evaluating a cellular interaction parameter. 41. The system of clause 40, wherein the memory comprises instructions for evaluating interactions between two or more cells in a sample. 42. The system of clause 41, wherein the memory comprises instructions for assessing phagocytosis by one or more cells in the sample. 43. The system according to one or more of clauses 41-42, wherein the memory comprises instructions for assessing tumor cell killing by one or more cells in the sample. 44. The system of clause 41, wherein the memory includes instructions for calculating assessing colocalization between two or more cells in a sample. 45. The system of any one of clauses 36 to 39, wherein the memory comprises instructions for assessing translocation of a cellular component within a cell. 46. ​​The system of clause 45, wherein the memory includes instructions for assessing nuclear translocation of a cellular component within the cell. 47. A system according to any one of clauses 36 to 39, wherein the memory comprises instructions for determining the division stage of a cell. 48. The system of clause 47, wherein the memory includes instructions for identifying that the cell exhibits a mitotic phase selected from interphase, prophase, metaphase, anaphase, telophase, and cytokinesis. 49. A system according to any one of clauses 36 to 39, wherein the memory comprises instructions for identifying the cell as a plant cell. 50. The system of clause 49, wherein the memory includes instructions for identifying plant cells by evaluating cell morphology. 51. The system of clause 50, wherein the memory includes instructions for evaluating one or more of cell size, cell shape, and cell punctiformity. 52. A system described in any one of clauses 36 to 51, wherein the memory includes instructions for calculating cell parameters based on image parameters generated from an image of the cell. 53. The system of claim 52, wherein the image parameter is a quantitative image parameter. 54. The system described in clause 53, wherein the image parameters are selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, long axis moment, maximum intensity, radial moment, short axis moment, size, total intensity, light loss by cell nuclei, forward scatter by cell nuclei, side scatter by cell nuclei, and combinations thereof. 55. The system of clause 53 or 54, wherein the memory includes instructions for calculating image parameters from an image generated based on fluorescence measured from the cells, light loss measured for the cells, forward scatter light measured for the cells, side scatter light measured for the cells, and combinations thereof. 56. The system of any one of clauses 36-55, wherein the memory includes instructions for classifying cells by assigning the cells to one or more particle population clusters. 57. The system of clause 56, wherein the memory includes instructions for determining one or more sorting gates for sorted cells of the sample. 58. The system of clause 57, wherein one or more sorting gates capture cells of a target particle population cluster and exclude cells of a non-target particle population cluster. 59. The system of any one of clauses 56-58, wherein the memory includes instructions for calculating a sorting gate that maximizes the inclusion yield of cells in the target particle population cluster. 60. The system of any one of clauses 56-59, wherein the memory includes instructions for calculating a sorting gate that maximizes exclusion of cells of non-target particle population clusters. 61. A system according to any one of clauses 36 to 60, wherein the system further comprises a display configured to display a graphical user interface. 62. The system of clause 61, wherein the graphical user interface is configured to manually input one or more of the sorting gates. 63. The system of clause 62, wherein manually inputting the sorting gate comprises drawing the sorting gate on a scatter plot of the particle population clusters. 64. The system of any one of clauses 36 to 63, further comprising a cell sorter. 65. The system of claim 64, wherein the cell sorter comprises a droplet deflector. 66. A system described in any one of clauses 36 to 65, wherein the light source includes a light beam generator component configured to generate at least a first beam of frequency-shifted light and a second beam of frequency-shifted light. 67. The system of claim 66, wherein the optical beam generator includes an acousto-optic deflector. 68. The system of clause 66 or 67, wherein the optical beam generator comprises a direct digital synthesiser (DDS) RF comb generator. 69. A system as described in any one of clauses 66-68, wherein the optical beam generator component is configured to generate a local oscillator beam. 70. The system of any one of clauses 66-69, wherein the optical beam generator component is configured to generate a plurality of frequency-shifted comb beams. 71. A system according to any one of clauses 36 to 70, wherein the light source comprises a laser. 72. The system of claim 71, wherein the laser is a continuous wave laser. 73. A system according to any one of clauses 36 to 72, wherein the system is an imaging flow cytometer. 74. A system according to any one of clauses 36 to 73, wherein the system comprises an integrated circuit device. 75. The system of clause 74, wherein the integrated circuit device is a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a complex programmable logic device (CPLD). 76. A non-transitory computer-readable storage medium having instructions stored thereon, the non-transitory computer-readable storage medium comprising: an algorithm for irradiating a sample containing cells in a flow stream with a frequency modulated light beam; an algorithm for measuring light from cells in a sample; an algorithm for generating image data of the cell from the measured light; and an algorithm for calculating one or more cellular parameters based on the generated image data. 77. The non-transitory computer-readable storage medium of clause 76, wherein the image data includes one or more waveforms generated in response to the measured light. 78. The non-transitory computer readable storage medium of clause 76 or 77, wherein the non-transitory computer readable storage medium comprises an algorithm for generating an image of a cell from image data. 79. The non-transitory computer readable storage medium of clause 78, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating image parameters based on one or more of the image data and the image of the cell. 80. The non-transitory computer readable storage medium of any one of clauses 76 to 79, wherein the non-transitory computer readable storage medium comprises an algorithm for evaluating cellular interaction parameters. 81. The non-transitory computer readable storage medium of clause 80, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing interactions between two or more cells in a sample. 82. The non-transitory computer readable storage medium of clause 81, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing phagocytosis by one or more cells in a sample. 83. A non-transitory computer-readable storage medium according to one or more of clauses 81-82, wherein the non-transitory computer-readable storage medium comprises an algorithm for assessing tumor cell killing by one or more cells in a sample. 84. The non-transitory computer readable storage medium of clause 81, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating assessing co-localization between two or more cells in a sample. 85. The non-transitory computer readable storage medium of any one of clauses 76 to 79, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing translocation of a cellular component within a cell. 86. The non-transitory computer readable storage medium of clause 85, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing nuclear translocation of a cellular component within a cell. 87. The non-transitory computer readable storage medium of any one of clauses 76 to 79, wherein the non-transitory computer readable storage medium comprises an algorithm for determining the division phase of a cell. 88. The non-transitory computer readable storage medium of clause 87, wherein the non-transitory computer readable storage medium comprises an algorithm for identifying that a cell exhibits a mitotic phase selected from interphase, prophase, metaphase, anaphase, telophase, and cytokinesis. 89. The non-transitory computer readable storage medium of any one of clauses 76 to 79, wherein the non-transitory computer readable storage medium comprises an algorithm for identifying a cell as a plant cell. 90. The non-transitory computer readable storage medium of clause 79, wherein the non-transitory computer readable storage medium comprises an algorithm for identifying plant cells by evaluating cell morphology. 91. The non-transitory computer readable storage medium of clause 90, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing one or more of cell size, cell shape, and cell punctiformity. 92. A non-transitory computer readable storage medium according to any one of clauses 76 to 91, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating cellular parameters based on image parameters generated from an image of the cell. 93. The non-transitory computer-readable storage medium of clause 92, wherein the image parameter is a quantitative image parameter. 94. The non-transitory computer readable storage medium of claim 93, wherein the image parameters are selected from the group consisting of center of mass, delta center of mass, diffusivity, eccentricity, long axis moment, maximum intensity, radial moment, short axis moment, size, total intensity, light loss by cell nuclei, forward scatter by cell nuclei, side scatter by cell nuclei, and combinations thereof. 95. The non-transitory computer readable storage medium of clause 93 or 94, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating image parameters from an image generated based on fluorescence measured from the cells, light loss measured for the cells, forward scatter light measured for the cells, side scatter light measured for the cells, and combinations thereof. 96. A non-transitory computer readable storage medium according to any one of clauses 76 to 95, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying cells by assigning the cells to one or more particle population clusters. 97. The non-transitory computer readable storage medium of clause 96, wherein the non-transitory computer readable storage medium comprises an algorithm for determining one or more sorting gates for sorted cells of a sample. 98. The non-transitory computer-readable storage medium of clause 97, wherein one or more sorting gates capture cells of a target particle population cluster and exclude cells of a non-target particle population cluster. 99. A non-transitory computer readable storage medium according to any one of clauses 96 to 98, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells in the target particle population cluster. 100. The non-transitory computer readable storage medium of any one of clauses 96-99, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a sorting gate that maximizes the exclusion of cells of non-target particle population clusters.

[0244] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those skilled in the art that, in light of the teachings of this invention, certain changes and modifications can be made thereto without departing from the spirit or scope of the appended claims.

[0245] Thus, the above merely illustrates the principles of the present invention. It will be understood that those skilled in the art can devise various arrangements that, although not explicitly described or shown herein, embody the principles of the present invention and are included within its spirit and scope. Furthermore, all examples and conditional language described herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts that the inventors contribute to further the art, and should not be construed as being limited to such specifically described examples and conditions. Furthermore, all statements herein that describe the principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. In addition, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, regardless of structure, i.e., any elements developed to perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly set forth in the claims.

[0246] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is expressly defined to be invoked to limit a claim only if the precise phrase "means for" or the precise phrase "step for" appears at the beginning of the limitation to such claim, and if such precise phrase is not used in the limitation to the claim, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0247] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. §119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 465,059, filed May 9, 2023, the entire disclosure of which is incorporated herein by reference.

Claims

1. 1. A method comprising: illuminating a sample containing cells in a flow stream with a frequency modulated light beam; measuring light from the cells in the sample; generating image data of the cells from the measured light; and and calculating one or more cellular parameters based on the generated image data.

2. The method of claim 1 , wherein the image data comprises one or more waveforms generated in response to the measured light.

3. The method of claim 1 or 2, wherein the method comprises generating an image of the cell from the image data.

4. The method of claim 3 , wherein the method comprises calculating image parameters based on one or more of the image data and the image of the cell.

5. The method of any one of claims 1 to 4, wherein calculating the cellular parameter comprises evaluating a cellular interaction parameter.

6. 6. The method of claim 5, wherein calculating the cellular interaction parameters comprises evaluating interactions between two or more cells in the sample.

7. The method of claim 6, wherein calculating the cellular interaction parameters comprises assessing phagocytosis by one or more cells in the sample.

8. The method of claim 6 or 7, wherein calculating the cellular interaction parameters comprises assessing tumor cell killing by one or more cells in the sample.

9. The method of claim 6, wherein calculating the cellular interaction parameters comprises assessing internalization of a cell or cellular component by one or more cells in the sample.

10. 7. The method of claim 6, wherein calculating the cellular interaction parameters comprises assessing co-localization between two or more cells in the sample.

11. The method of any one of claims 1 to 4, wherein calculating the cellular parameter comprises assessing the translocation of a cellular component within the cell.

12. The method of claim 11 , wherein the method comprises assessing nuclear translocation of a cellular component within the cell.

13. The method of any one of claims 1 to 4, wherein calculating the cellular parameter comprises determining the division phase of the cell.

14. 14. The method of claim 13, wherein calculating the cellular parameters comprises identifying the cell as exhibiting a mitotic phase selected from interphase, prophase, metaphase, anaphase, telophase, and cytokinesis.

15. The method of any one of claims 1 to 4, wherein calculating the cell parameters comprises identifying the cell as a plant cell.

16. The method of claim 15 , wherein identifying the plant cell comprises evaluating the morphology of the cell.

17. 17. The method of claim 15 or 16, wherein identifying the plant cells comprises assessing one or more of the size of the cells, the shape of the cells, and the punctation of the cells.

18. The method according to any one of claims 1 to 17, wherein the cell parameters are calculated based on image parameters generated from an image of the cell.

19. The method of claim 18 , wherein the image parameter is a quantitative image parameter.

20. 20. The method of claim 18 or 19, wherein the image parameters are selected from the group consisting of: center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss due to cell nuclei, forward scatter by the cell nuclei, side scatter by the cell nuclei, and combinations thereof.

21. 21. The method of any one of claims 18 to 20, wherein the image parameters are calculated from an image generated based on fluorescence measured from the cells, light loss measured for the cells, forward scatter light measured for the cells, side scatter light measured for the cells, and combinations thereof.

22. The method of any one of claims 1 to 21, wherein the method comprises classifying the cells based on the calculated cell parameters.

23. 23. The method of claim 22, wherein classifying the cells comprises assigning the cells to one or more particle population clusters.

24. 24. The method of claim 22 or 23, wherein the method further comprises determining one or more sorting gates for the sorted cells of the sample.

25. 25. The method of claim 24, wherein the one or more sorting gates capture cells of a target particle population cluster and exclude cells of a non-target particle population cluster.

26. 25. The method of claim 24, wherein the sorting gate maximizes the inclusion yield of cells in a target particle population cluster.

27. 26. The method of claim 25, wherein the sorting gate maximizes the inclusion yield of cells in a target particle population cluster.

28. The method of any one of claims 24 to 26, wherein the sorting gate maximizes exclusion of cells of non-target particle population clusters.

29. The method of any one of claims 1 to 28, wherein the method further comprises sorting cells of the sample into a plurality of sample vessels.

30. 30. The method of any one of claims 1 to 29, wherein measuring light from the cells in the flow stream comprises detecting fluorescence, light absorption, scattered light, or a combination thereof.

31. The method according to any preceding claim, wherein said image data is calculated from frequency-encoded fluorescence data from fluorescently labeled cells.

32. A method according to any preceding claim, wherein the frequency modulated light beam comprises a first beam of frequency shifted light and a second beam of frequency shifted light.

33. 33. The method of claim 32, wherein the first beam of frequency-shifted light comprises a local oscillator (LO) beam and the second beam of frequency-shifted light comprises a high frequency comb beam.

34. applying a high frequency drive signal to an acousto-optic device; 34. The method of claim 32 or 33, further comprising illuminating the acousto-optic device with a laser to generate a first beam of frequency-shifted light and a second beam of frequency-shifted light.

35. 35. The method of claim 34, wherein the laser is a continuous wave laser.

36. 1. A system comprising: a light source configured to irradiate cells of a sample in the flow stream with a frequency modulated light beam; a light detection system comprising a light detector for measuring light from the cells; a processor comprising a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: generating image data of the cell from the measured light; The system calculates one or more cellular parameters based on the generated image data.

37. 1. A non-transitory computer readable storage medium comprising instructions stored thereon, the non-transitory computer readable storage medium comprising: an algorithm for irradiating a sample containing cells in a flow stream with a frequency modulated light beam; an algorithm for measuring light from the cells in the sample; an algorithm for generating image data of the cell from the measured light; and and an algorithm for calculating one or more cellular parameters based on the generated image data.

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