Methods for assessing cell nuclei morphology and systems for the same

Abstract

To provide methods for assessing morphology of isolated cell nuclei (e.g., in order to determine viability of the cell nuclei) in a sample.SOLUTION: Methods according to certain embodiments include measuring light from a sample having isolated cell nuclei in a flow stream, generating an image of the cell nuclei from the measured light, and assessing morphology of the cell nuclei based on the generated images of the cell nuclei. In some embodiments, sorting gates are determined based on images or image parameters calculated for the cell nuclei in the sample. Systems and integrated circuit devices (e.g., a field programmable gate array) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Nuclei isolation is a common biological process used by scientists for downstream applications requiring nuclear content. Applications such as cell cycle analysis or molecular biology assays such as ChIP-seq, Hi-C, and ATAC-seq all require nuclei as starting material. Despite the widespread adoption of these applications, workflows for easy implementation suffer from many drawbacks. The quality of isolated nuclei has long been known to be crucial for these molecular biology assays. However, few simple assays are available to quickly assess the quality of isolated nuclei before embarking on time-consuming downstream molecular biology workflows that often end with expensive sequencing. While high-resolution microscopes (e.g., 60X) can be used to visualize cell nuclei in samples, focusing the microscope can be challenging for such visualization, and the limited number of images may not be representative of the morphology and quality of the isolated nuclei.

[0002] Flow-type particle sorting systems, such as sorting flow cytometers, are used to sort particles in a fluid sample based on at least one measured characteristic of the particles. Particles or components thereof can be labeled with fluorescent dyes for ease of detection, and by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. In flow-type particle sorting systems, particles, such as analyte-bound beads or individual cells, in a fluid suspension pass in a stream through a detection region. In the detection region, a sensor detects particles of the type to be sorted contained in the stream. Upon detecting particles of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particles of interest.

[0003] Data generated from the detected light can be used to record the distribution of components and sort the desired material. To sort particles in a sample, a droplet charging mechanism charges droplets in the flowstream containing the particle type to be sorted with an electric charge at the break-off point of the flowstream. The droplets pass through an electrostatic field and are deflected into one or more collection vessels based on the polarity and magnitude of the charge on the droplet. Uncharged droplets are not deflected by the electrostatic field. Summary of the Invention

[0004] Aspects of the present disclosure include methods for assessing the morphology of isolated cell nuclei in a sample (e.g., to determine the viability of the cell nuclei). The method, according to certain embodiments, includes measuring light from a sample having isolated cell nuclei in a flow stream, generating an image of the cell nuclei from the measured light, and assessing the morphology of the cell nuclei based on the generated image of the cell nuclei. In some embodiments, sorting gates are determined based on the image or image parameters calculated for the cell nuclei in the sample. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0005] In embodiments, the morphology of the cell nucleus is evaluated based on at least an image of the isolated cell nucleus. In some examples, evaluating the morphology includes determining the viability of the cell nucleus. In some examples, evaluating the morphology includes determining the ploidy of the cell nucleus. In some examples, evaluating the morphology includes evaluating the size of the cell nucleus. In some examples, evaluating the morphology includes evaluating the shape of the cell nucleus, such as where the shape may be spherical, spindle-shaped, oval, elongated, and flattened. In some examples, evaluating the morphology includes evaluating the elasticity of the nuclear envelope.

[0006] In some embodiments, image parameters are calculated from the generated images of cell nuclei in the flow stream. In some examples, the image parameters include one or more 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 particles, forward scattered light by cell nuclei, and side scattered light by cell nuclei. In some examples, cell nuclei from the sample are classified based on one or more of the calculated image parameters. In some examples, the method includes classifying cell nuclei using five or more calculated image parameters.

[0007] In some examples, the cell nuclei are unlabeled. In other examples, the method includes labeling the cell nuclei, such as with a fluorescent label. In some examples, the method includes generating an image of the unlabeled cell nuclei. In other examples, the method includes generating an image of the fluorescently labeled cell nuclei. In particular examples, the image is generated from a frequency-encoded data signal.

[0008] In some examples, the morphology is assessed using a dynamic algorithm that updates using images of the nuclei (e.g., reference images) or the determined image parameters. In some examples, the morphology is assessed using a machine learning algorithm that uses images of the nuclei or the determined image parameters as a training dataset.

[0009] In some examples, classifying cell nuclei of the sample includes assigning the cell nuclei to one or more particle population clusters. In some examples, the cell nuclei are assigned to the particle population clusters based on a comparison of the generated image of the cell nuclei with parameters of the particle population clusters (e.g., determined using a reference image or calculated image parameters). In some embodiments, one or more sorting gates are determined for the sorted cell nuclei of the sample. In some examples, the one or more sorting gates capture cell nuclei of target particle population clusters and exclude cell nuclei of non-target particle population clusters. In some examples, the sorting gates maximize the inclusion yield of cell nuclei of the target particle population clusters. In some examples, the sorting gates maximize the exclusion of cell nuclei of non-target particle population clusters. In some examples, the sorting gates exclude cell nuclei of non-target particle population clusters based on a calculated Mahalanobis distance from the target particle population cluster. In some embodiments, the target particle population cluster includes cell nuclei having an assessed viability greater than a predetermined threshold. In some embodiments, the target particle population cluster includes cell nuclei having a predetermined shape. In some embodiments, the target particle population cluster includes cell nuclei having a predetermined size. In some embodiments, the non-target particle population clusters comprise non-nuclear cellular debris, hi some embodiments, the non-target particle population clusters comprise cell nuclei having assessed viability that is below a predetermined threshold.

[0010] In certain embodiments, generating sorting gates includes calculating an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of cell nuclei in the target particle population clusters and the exclusion of cell nuclei in the non-target particle population clusters. In certain examples, the generated sorting gates have an Fβ score of 1 or greater. In certain examples, the generated sorting gates have an Fβ score of less than 1. In some examples, the particle sorting decision uses eight or fewer sorting gates, for example, four or fewer sorting gates.

[0011] In some embodiments, the method includes sorting nuclei of the sample using the generated sorting gate. In some examples, sorting the nuclei into a plurality of sample containers. In some examples, the nuclei are sorted based on generated images of the nuclei. In some examples, the nuclei are sorted based on calculated image parameters of the nuclei.

[0012] In some embodiments, the method includes illuminating the sample with a light source. In some examples, the sample is illuminated by the light source in a flow stream. In some examples, the light source includes one or more lasers. In some examples, the light is detected by 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 charge-coupled device (CCD).

[0013] Aspects of the present disclosure also include systems for practicing the subject methods, including a light source configured to illuminate nuclei of a sample, a light detection system having a plurality of photodetectors for measuring light from the nuclei, and a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate images of the nuclei from the measured light and evaluate morphology of the nuclei based on the generated images of the nuclei.

[0014] In some embodiments, the memory includes instructions for evaluating the morphology of isolated cell nuclei based on at least an image of the cell nuclei. In some examples, the memory includes instructions for evaluating morphology by determining the viability of the cell nuclei. In some examples, the memory includes instructions for evaluating morphology by determining the ploidy of the cell nuclei. In some examples, the memory includes instructions for evaluating morphology by assessing the size of the cell nuclei. In some examples, the memory includes instructions for evaluating morphology by assessing the shape of the cell nuclei, such as where the shape may be spherical, spindle-shaped, oval, elongated, and flattened. In some examples, the memory includes instructions for evaluating morphology by assessing the elasticity of the nuclear envelope.

[0015] In some embodiments, the memory includes instructions for calculating image parameters of the cell nuclei from the generated image. In some examples, the image parameters include one or more 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 particles, forward scattered light by the cell nuclei, and side scattered light by the cell nuclei. In some examples, the memory includes instructions for classifying the isolated cell nuclei based on one or more of the calculated image parameters. In some examples, the memory includes instructions for classifying the isolated cell nuclei using five or more calculated image parameters.

[0016] In some examples, the memory includes instructions for generating an image of unlabeled nuclei from measurement light from the illuminated unlabeled nuclei. In some examples, the memory includes instructions for generating an image of fluorescently labeled nuclei from measurement light from the illuminated nuclei. In particular examples, the image is generated from a frequency-encoded data signal.

[0017] In some embodiments, the memory includes instructions for assessing morphology of the cell nuclei using a dynamic algorithm that updates using an image of the cell nuclei (e.g., a reference image) or the determined image parameters. In some examples, the memory includes instructions for assessing morphology of the cell nuclei using a machine learning algorithm that uses the reference image of the cell nuclei or the determined image parameters as a training dataset.

[0018] In some embodiments, the memory includes instructions for classifying cell nuclei of the sample by assigning the cell nuclei to one or more particle population clusters. In some examples, the memory includes instructions for assigning cell nuclei to particle population clusters based on a comparison of a generated image of the cell nuclei to parameters of the particle population clusters (e.g., determined using a reference image or calculated image parameters).

[0019] In some embodiments, the memory includes instructions for determining one or more sorting gates for sorted cell nuclei of the sample. In some examples, the memory includes instructions for generating one or more sorting gates that capture cell nuclei of a target particle population cluster and exclude cell nuclei of non-target particle population clusters. In some examples, the memory includes instructions for generating sorting gates that maximize the inclusion yield of cell nuclei of the target particle population cluster. In some examples, the memory includes instructions for generating sorting gates that maximize the exclusion of cell nuclei of non-target particle population clusters. In some examples, the memory includes instructions for generating sorting gates that exclude cell nuclei of non-target particle population clusters based on a calculated Mahalanobis distance from the target particle population cluster. In some embodiments, the memory includes instructions for capturing cell nuclei in the target particle population cluster that have an assessed viability greater than a predetermined threshold. In some embodiments, the memory includes instructions for capturing cell nuclei having a predetermined shape in the target particle population cluster. In some embodiments, the memory includes instructions for capturing cell nuclei having a predetermined size in the target particle population cluster. In some embodiments, the non-target particle population clusters include non-nuclear cell debris. In some embodiments, the non-target particle population clusters include cell nuclei having an assessed viability that is below a predetermined threshold.

[0020] In certain embodiments, the memory includes instructions for generating sorting gates by calculating an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of cell nuclei in target particle population clusters and the exclusion of cell nuclei in non-target particle population clusters. In certain examples, the generated sorting gates have an Fβ score of 1 or greater. In certain examples, the generated sorting gates have an Fβ score of less than 1. In some examples, the particle sorting decision uses eight or fewer sorting gates, for example, four or fewer sorting gates.

[0021] In certain embodiments, the system includes a display for displaying a graphical user interface. In some examples, the graphical user interface is configured to manually input one or more of the sorting gates. In some examples, 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 examples, the sorting gates are hyper-rectangular sorting gates.

[0022] Also described is a non-transitory computer-readable storage medium having instructions with algorithms for evaluating the morphology of cell nuclei in a sample. The non-transitory computer-readable storage medium according to certain embodiments has an algorithm for measuring light from a sample having isolated cell nuclei in a flow stream, an algorithm for generating an image of the cell nuclei from the measured light, and an algorithm for evaluating the morphology of the cell nuclei based on the generated image of the cell nuclei.

[0023] In embodiments, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology based on at least one image of an isolated cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology by determining the viability of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology by determining the ploidy of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology by evaluating the size of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology by evaluating the shape of the cell nucleus, such as where the shape may be spherical, spindle-shaped, oval, elongated, and flattened. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing morphology by evaluating the elasticity of the nuclear envelope.

[0024] In some embodiments, the non-transitory computer-readable storage medium has an algorithm for calculating image parameters of cell nuclei from the generated image. In some examples, the image parameters include one or more 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 particles, forward scattered light by cell nuclei, and side scattered light by cell nuclei. In some examples, the non-transitory computer-readable storage medium has an algorithm for classifying isolated cell nuclei based on one or more of the calculated image parameters. In some examples, the non-transitory computer-readable storage medium has an algorithm for classifying isolated cell nuclei using five or more calculated image parameters.

[0025] In some examples, the memory includes instructions for generating an image of unlabeled nuclei from measurement light from the illuminated unlabeled nuclei. In some examples, the memory includes instructions for generating an image of fluorescently labeled nuclei from measurement light from the illuminated nuclei. In particular examples, the image is generated from a frequency-encoded data signal.

[0026] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for assessing the morphology of a cell nucleus using a dynamic algorithm that updates using an image of the cell nucleus (e.g., a reference image) or the determined image parameters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing the morphology of a cell nucleus using a machine learning algorithm that uses the reference image of the cell nucleus or the determined image parameters as a training dataset.

[0027] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for classifying cell nuclei of a sample by assigning the cell nuclei to one or more particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assigning cell nuclei to particle population clusters based on a comparison of a generated image of the cell nuclei to parameters of the particle population clusters (e.g., determined using a reference image or calculated image parameters).

[0028] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for determining one or more sorting gates for sorted cell nuclei of a sample. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating one or more sorting gates that capture cell nuclei of a target particle population cluster and exclude cell nuclei of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that maximize the inclusion yield of cell nuclei of a target particle population cluster. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that maximize the exclusion of cell nuclei of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that exclude cell nuclei of non-target particle population clusters based on a calculated Mahalanobis distance from the target particle population cluster. In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for capturing cell nuclei in a target particle population cluster that have an assessed viability greater than a predetermined threshold. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for capturing cell nuclei having a predetermined shape in a target particle population cluster. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for capturing cell nuclei having a predetermined size in a target particle population cluster. In some embodiments, the non-target particle population cluster comprises non-nuclear cellular debris. In some embodiments, the non-target particle population cluster comprises cell nuclei having an assessed viability that is below a predetermined threshold.

[0029] In certain embodiments, the non-transitory computer-readable storage medium has an algorithm for generating sorting gates by calculating an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of cell nuclei in target particle population clusters and the exclusion of cell nuclei in non-target particle population clusters. In certain examples, the generated sorting gates have an Fβ score of 1 or greater. In certain examples, the generated sorting gates have an Fβ score of less than 1. In some examples, the particle sorting decision uses eight or fewer sorting gates, for example, four or fewer sorting gates.

[0030] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which: [Brief explanation of the drawings]

[0031] [Figure 1] 1 shows a flow chart for assessing cell nuclear morphology of a sample according to certain embodiments. [Figure 2A] 1 illustrates the assessment of nuclear morphology of cell nuclei in viable samples using imaging flow cytometry, according to certain embodiments. [Figure 2B] 1 shows the assessment of nuclear morphology of cell nuclei in low viability samples using imaging flow cytometry, according to certain embodiments. [Figure 2C] 1 shows analysis of polymerase chain reaction (PCR) products using an electrophoretic bioanalyzer for cell nuclei in viable and low viability samples, according to certain embodiments. [Figure 2D] 10 shows an assessment of nuclear morphology of stimulated CD4 T cells exhibiting different particle population clusters according to certain embodiments. [Figure 2E] 1 shows a comparison of four different imaging parameters for small and large nuclei, according to certain embodiments. [Figure 2F] 1 shows an analysis of four imaging parameters using representative cell nuclei images of a sample of stimulated CD4 T cells, according to certain embodiments. [Figure 2G]10 shows a calculated gating strategy using imaging parameters for samples with small and large nuclei from a stimulated CD4 T cell sample, according to certain embodiments. [Figure 2H] 10 illustrates a gating strategy for isolating nuclei with different morphologies from samples of stimulated and unstimulated CD4 T cells using images and imaging parameters according to certain embodiments. [Figure 2I] 10 shows clustering of different nuclei using imaging parameters from unstimulated and stimulated CD4 T cells, according to certain embodiments. [Figure 3A-1] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3A-2] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3B] 1 illustrates image-enabled particle sorting data processing in accordance with certain embodiments. [Figure 4A] FIG. 1 illustrates a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 4B] 1 illustrates a flow cytometer according to certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of an example particle analyzer control system in accordance with certain embodiments. [Figure 6A] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 illustrates 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 certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0032] Aspects of the present disclosure include methods for assessing the morphology of isolated cell nuclei in a sample (e.g., to determine the viability of the cell nuclei). The method, according to certain embodiments, includes measuring light from a sample having isolated cell nuclei in a flow stream, generating an image of the cell nuclei from the measured light, and assessing the morphology of the cell nuclei based on the generated image of the cell nuclei. In some embodiments, sorting gates are determined based on the image or image parameters calculated for the cell nuclei in the sample. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0033] Before describing the present invention in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such 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 present invention will be limited only by the appended claims.

[0034] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any other stated or intervening value in that 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.

[0035] Certain ranges are described herein by numerical values ​​preceded by the term "about." The term "about" is used herein to literally support the exact number it precedes, as well as a number that is near or approximately the number preceded by the term. In determining whether a number is near or approximately a specifically stated number, the near or approximately unstated number may be a number that, in the context in which it is presented, provides a substantial equivalent to the specifically stated number.

[0036] 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. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are now described.

[0037] All publications and patents cited herein are incorporated by reference to disclose and describe the methods and / or materials for which the publications are cited, as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. 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 publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0038] 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 drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for use of exclusive terminology, such as "solely," "only," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0039] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has individual components and features which may be readily separated or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0040] Although the apparatus and methods are described for grammatical fluidity with functional descriptions, it is to be clearly understood that the claims should not be construed as necessarily limited by "means" or "step" limitation constructions unless expressly formulated under 35 U.S.C. § 112, but rather should be given the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and that if a claim is expressly formulated under 35 U.S.C. § 112, the full statutory equivalents under 35 U.S.C. § 112 should be given.

[0041] As summarized above, the present disclosure provides for evaluating the morphology of isolated cell nuclei of a sample.In further describing the embodiments of the present disclosure, the method for generating image parameters, evaluating cell nuclei, and generating a sorting gate will first be described in more detail.Then, a system, an integrated circuit device, and a non-transitory computer-readable storage medium are also provided, which have programming for implementing the subject method by evaluating morphology and calculating a sorting gate (for example, unlabeled or fluorescently labeled cell nuclei) for sorting cell nuclei.

[0042] Method for assessing the morphology of isolated cell nuclei in flowstream samples - Patent Application 20070122999 Aspects of the present disclosure include methods for assessing the morphology of cell nuclei in a sample. In some embodiments, the subject methods provide for determining the viability of cell nuclei, such as for use in downstream biological assay protocols (e.g., ChIP-seq, Hi-C, and ATAC-seq protocols) that rely on high-quality cell nuclei samples. In some examples, assessing the morphology of cell nuclei as described herein can increase the accuracy of determining the quality of a cell nuclei sample by 5% or more, e.g., 10% or more, e.g., 15% or more, e.g., 25% or more, e.g., 50% or more, e.g., 75% or more, and including 99% or more. In some examples, the present disclosure provides for rapid determination of the quality of cell nuclei in a sample. For example, the subject methods can provide an assessment of a sample of isolated cell nuclei in 10 minutes or less, e.g., 9 minutes or less, e.g., 8 minutes or less, e.g., 7 minutes or less, e.g., 6 minutes or less, e.g., 5 minutes or less, e.g., 4 minutes or less, e.g., 3 minutes or less, e.g., 2 minutes or less, including 1 minute or less. As described in more detail below, in some examples, the assessed cell nuclei can be sorted and a sample having high quality cell nuclei can be prepared, including, for example, when it is determined that 50% or more (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) of the cell nuclei in the sample are suitable for use in downstream biological assays requiring high quality cell nuclei, and that 99% or more of the cell nuclei in the sorted sample are suitable for use as a high quality cell nuclei sample.

[0043] In embodiments, the morphology of the cell nuclei is evaluated based on at least an image of the cell nuclei. In some embodiments, the morphology of the cell nuclei is evaluated based on image parameters determined from the image of the cell nuclei and one or more images of the particles. In some examples, the image parameters are determined only from the image of the particles and not from another data source, such as a data signal waveform. In some examples, the image parameters are determined using a combination of the generated image of the particle and a data signal waveform generated in response to measurement light from the illuminated particle.

[0044] In carrying out the subject methods according to certain embodiments, light from a sample having isolated cell nuclei in a flow stream (e.g., the sample is illuminated with a light source) is measured using a light detection system having a photodetector. The term "isolated cell nuclei" is used herein in its conventional sense to refer to cell nuclei extracted from cells of a biological sample. Thus, the cell nuclei are freely present in the sample and are no longer intracellular. In other words, the cell nuclei samples described herein include cell nuclei that are no longer within the cell membrane of intact cells. Cell nuclei samples can be prepared by any convenient protocol, including tissue dissociation or cell line dissociation, to isolate cell nuclei from cells of a sample. In some examples, isolated cell nuclei are prepared from single-cell compositions by treating the cells with digestive enzymes or other membrane-dissociating or separating compounds (e.g., detergents). The phrase "single cells" is used herein to refer to compositions having distinct, separated cells of dissociated tissue. A "biological sample" can refer to, in certain examples, a whole organism, a whole plant, a whole fungus, or a subset of animal tissues, cells, or component parts that may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both an intact organism or a subset of its tissues, as well as homogenates, lysates, or extracts made from an organism or a subset of its tissues, including, but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and genitourinary tract sections, tears, saliva, milk, blood cells, tumors, and 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 venipuncture or a finger stick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0045] In certain embodiments, the source of the sample is a "mammal," a term used broadly to describe organisms belonging to the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The present methods may be applied to samples obtained from human subjects of both genders and at any developmental stage (i.e., newborn, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. While the present disclosure may be applied to samples from human subjects, it should be understood that the methods may also be performed 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.

[0046] In some embodiments, a sample of isolated cell nuclei (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, emitting light having a wide range of wavelengths, including, for example, spanning 50 nm or more, 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, or 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 irradiating with a broadband light source, the broadband light source protocol of interest can include, but is not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0047] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, for example, irradiating with a light source that emits light in a narrow range, such as a range of 50 nm or less, for example, 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 light sources that emit specific wavelengths of light (i.e., monochromatic light)). When the method includes irradiating with a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, 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.

[0048] In certain embodiments, the method includes irradiating the sample with one or more lasers. As noted above, the type and number of lasers will depend on the sample and the desired light to be collected, and may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO 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. In other examples, the method includes irradiating the flowstream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the method includes irradiating the flowstream 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 yet another example, the method includes irradiating the flowstream with a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:yCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a ytterbium 2O3 laser, or a cerium-doped laser, and combinations thereof.

[0049] The sample may be illuminated with one or more of the above-mentioned light sources, including two or more light sources, three or more light sources, four or more light sources, five or more light sources, etc., including ten or more light sources. The light source may include any combination of light source types. For example, in some embodiments, the method includes illuminating the sample of 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.

[0050] The sample may be irradiated with a wavelength 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, 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 examples where 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 (e.g., 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 including the gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers described above.

[0051] When two or more light sources are used, the sample can be illuminated by the light sources simultaneously, sequentially, or a combination thereof. For example, each light source can illuminate the sample simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When two or more light sources are used to sequentially illuminate the sample, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include irradiating the sample with a light source (e.g., a laser) for a period ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, and 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.

[0052] The time between illumination by each light source can also be independently variable, optionally separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the time between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the time 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 can be the same or different.

[0053] The sample can be illuminated continuously or at discrete intervals. In some examples, the method includes continuously illuminating the sample in the sample with a light source. In other examples, the sample is illuminated by the light source at discrete intervals, such as every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or at some other interval.

[0054] Depending on the light source, the sample may be illuminated from a variety of distances, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 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, for example 15 mm or more, for example 25 mm or more, including 50 mm or more. The illumination angle may also be variable, ranging from 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, including 30° to 60°, such as 90°.

[0055] In certain embodiments, 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-optical device for frequency-shifting the laser light may be used. In these embodiments, the method includes irradiating the acousto-optical device with a laser. Depending on the desired wavelength of light produced in the output laser beam (e.g., for use in irradiating the sample in the flow stream), the laser may have a specific wavelength between 200 nm and 1500 nm, e.g., between 250 nm and 1250 nm, e.g., between 300 nm and 1000 nm, e.g., between 350 nm and 900 nm, including between 400 nm and 800 nm. The acousto-optical 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, or may include ten or more lasers. The lasers may include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optical 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.

[0056] When two or more lasers are used, the acousto-optical device can be illuminated by the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be illuminated by each of the lasers simultaneously. In other embodiments, the acousto-optical device is illuminated sequentially by each of the lasers. When two or more lasers are used to sequentially illuminate the acousto-optical device, the time for which each laser illuminates the acousto-optical device can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include illuminating the acousto-optical device with the laser for a period ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0057] The time between illumination by each laser can also be independently variable, optionally separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the time between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the time between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially by more than two (i.e., three or more) lasers, the delay between illumination by each laser can be the same or different.

[0058] The acousto-optic device can be illuminated continuously or at discrete intervals. In some examples, the method includes continuously illuminating the acousto-optic device with a laser. In other examples, the acousto-optic device is illuminated with a laser at discrete intervals, such as every 0.001 millisecond, 0.01 millisecond, 0.1 millisecond, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0059] Depending on the laser, the acousto-optic device may be illuminated from a variety of distances, for example 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, including 50 mm or more. The illumination angle may also be variable, ranging from 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, including 30° to 60°, for example 90°.

[0060] In an embodiment, a 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 may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams, including, for example, 3 or more high frequency drive signals, such as 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, and 100 or more high frequency drive signals.

[0061] The angularly deflected laser beams produced 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 high frequency drive signals having an amplitude sufficient to produce an angularly deflected laser beam having a desired intensity. In some examples, each of the applied high frequency drive signals independently has an amplitude of about 0.001 V to about 500 V, e.g., about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to about 50 V, e.g., about 2 V to about 40 V, e.g., about 3 V to about 30 V, including about 5 V to about 25 V. In some embodiments, each of the applied high frequency drive signals 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.

[0062] In these embodiments, the angularly deflected laser beams within 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 be spaced apart 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 angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0063] Specific examples include Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Patent 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, 10,620,111, and and U.S. Patent Application Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference, a sample is irradiated in the flowstream with multiple frequency-shifted light beams to generate images of cell nuclei in the flowstream.

[0064] As described above, light from illuminated cell nuclei in the sample is conveyed to a light detection system and measured by multiple photodetectors, as described in more detail below. In some embodiments, the method includes measuring collected light over a wavelength range (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring collected light at one or more specific wavelengths. For example, 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.

[0065] The collected light may be measured continuously or at discrete intervals. In some examples, the method includes measuring the light continuously. In other examples, the light is measured at discrete intervals, such as measuring the light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or at some other interval.

[0066] Measurement of the collected light may be made one or more times during the subject method, for example, two or more times, for example, three or more times, for example, five or more times, including ten or more times. In certain embodiments, the light from the sample is measured two or more times, and in certain instances the data is averaged.

[0067] Light from cell nuclei in the sample may be measured at one or more wavelengths, such as 5 or more different wavelengths, for example 10 or more different wavelengths, such as 25 or more different wavelengths, for example 50 or more different wavelengths, such as 100 or more different wavelengths, for example 200 or more different wavelengths, such as 300 or more different wavelengths, including measuring light collected at 400 or more different wavelengths.

[0068] In some embodiments, the method includes further conditioning the light from the sample before detecting the light. For example, the light from the sample source can pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some examples, the collected light passes through one or more focusing lenses, for example, to reduce the light profile. In other examples, the emitted light from the sample passes through one or more collimators to reduce the divergence of the light beam.

[0069] In embodiments, one or more images of cell nuclei in the sample are generated from the measured light. The images may be generated from detected light absorption, detected light scattering, detected light emission, or any combination thereof. In some examples, the images are generated from detected light absorption from the sample, such as by a brightfield light detector. In these examples, the images are generated based on brightfield image data from particles in the flow stream. In other examples, the images are 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 examples, the images are generated based on scattered light image data. In yet other examples, the images are generated from emitted light from the sample. In still other examples, the images are generated from a combination of detected light absorption, detected light scattering, and detected light emission.

[0070] One or more images can be generated from the measured light. In some embodiments, a single image is generated for each cell nucleus in the sample from each form of detected light. In other embodiments, multiple images are generated for each cell nucleus, e.g., two or more, e.g., three or more, e.g., five or more, e.g., ten or more, including 25 or more images for each cell nucleus. For example, a first image of the cell nucleus is generated from detected light absorption, a second image of the cell nucleus is generated from detected light scattering, and a third image of the cell nucleus is generated from detected light emission. In other embodiments, more than one image is generated from each form of detected light, e.g., three or more, e.g., four or more, e.g., five or more, ten or more, or a combination thereof.

[0071] In some embodiments, frequency-encoded data (e.g., frequency-encoded spatial data) is generated from measured light from cell nuclei in the flow stream. In some examples, one or more images are generated from the frequency-encoded data. The frequency-encoded data may be generated from one or more detection channels, including, for example, two or more, for example, three or more, for example, four or more, for example, five or more, for example, six or more, and 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 examples, the frequency-encoded data is phase-corrected. In some examples, the frequency-encoded data used to generate an image of the cell nuclei 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) on the frequency-encoded data. In another example, the frequency encoding is phase-corrected by performing a discrete Fourier transform (DFT) on the frequency-encoded data. In yet another example, the frequency-encoded data is phase-corrected by performing a short-time Fourier transform (STFT) on the frequency-encoded data. In certain embodiments, the method includes performing a transformation of the frequency-encoded data without performing 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.

[0072] In some examples, the method includes generating one or more grayscale images of the cell nuclei. The term "grayscale" is used herein in its conventional sense to refer to an image of the cell nuclei 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 cell nuclei. In particular examples, the image of the cell nuclei is a binary pixel image of the cell nuclei, e.g., each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel exceeds a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0073] In some embodiments, one or more image parameters are calculated from the generated images of the nuclei. In some examples, a center of mass image parameter is calculated from the generated images. In some examples, a delta center of mass image parameter is calculated from the generated images. In some examples, a diffuse image parameter is calculated from the generated images. In some examples, an eccentricity image parameter is calculated from the generated images. In some examples, a long axis moment image parameter is calculated from the generated images. In some examples, a maximum intensity image parameter is calculated from the generated images. In some examples, a radial moment image parameter is calculated from the generated images. In some examples, a short axis moment image parameter is calculated from the generated images. In some examples, a size image parameter of the nuclei is calculated from the generated images. In some examples, a total intensity image parameter is calculated from the generated images. In some examples, a light loss image parameter of the nuclei is calculated from the generated images. In some examples, a forward scatter image parameter is calculated from the generated images. In some examples, a side scatter image parameter is calculated from the generated images. In some examples, image moments are calculated from the generated images. The term "image moment" is used herein in its conventional sense to refer to a weighted average of pixel intensities in an image. In some examples, the center of mass may be calculated from the image moments of an image. In other examples, the orientation of a nucleus may be calculated from the image moments of an image. In yet other examples, the eccentricity of a nucleus may be calculated from the image moments of an image.

[0074] In certain embodiments, imaging parameters (described below) calculated from the images generated for use in generating the gating strategy are summarized in Table 1.

[0075] [Table 1-1]

[0076] [Table 1-2]

[0077] In embodiments, the morphology of cell nuclei is evaluated based on one or more of the generated images of the cell nuclei and image parameters of the cell nuclei calculated from the generated images. In some examples, evaluating the morphology includes determining the viability of cell nuclei in the sample. In some embodiments, the method includes determining the number of viable cell nuclei in the sample. In some embodiments, the method includes determining the percentage of viable cell nuclei in the sample, such as determining that 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 cell nuclei are viable in the sample, and determining that 99.5% or more of the cell nuclei in the sample are viable. In some embodiments, the method includes determining whether a predetermined number of cell nuclei in the sample are viable based on the images of the cell nuclei. In some examples, the method includes determining that the number of viable cell nuclei in the sample exceeds a predetermined threshold. In other examples, the method includes determining that the number of viable cell nuclei in the sample is below a predetermined threshold. In some examples, the method includes determining that the sample is a high quality sample of isolated cell nuclei if the number or percentage of viable cell nuclei in the sample exceeds a predetermined threshold, such as a threshold where 50% or more, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more cell nuclei are viable, including a threshold where 90% or more cell nuclei are viable.

[0078] In some examples, evaluating the morphology of cell nuclei includes determining the ploidy of cell nuclei. In some examples, one or more cell nuclei in the sample are determined to be polyploid. In other examples, one or more cell nuclei in the sample are determined to be monoploid. In some examples, the method includes determining the number of polyploid cell nuclei in the sample. In some examples, the method includes determining the percentage of polyploid cell nuclei in the sample, such as when 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, for example 98% or more, for example 99% or more of the cell nuclei in the sample are polyploid, including determining that 99.5% or more of the cell nuclei in the sample are polyploid. In some examples, the method includes determining whether the number or percentage of polyploid cell nuclei in the sample exceeds a predetermined threshold. In some examples, the method includes determining that the sample is a high quality sample of isolated cell nuclei if the number or percentage of cell nuclei that are polyploid in the sample exceeds a predetermined threshold.

[0079] In some embodiments, evaluating the morphology of cell nuclei includes evaluating the size of cell nuclei. In some examples, measuring the size of each cell nucleus in a sample. In some examples, determining the size of each cell nucleus in a sample includes comparing the size of the cell nuclei to a predetermined reference size (using a reference image of the cell nuclei), e.g., determining that the size of each cell nuclei is 50% or more, 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 cell nuclei in the sample having a size within 15% or less, e.g., 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 of the predetermined reference size, including within 0.5% or less of the predetermined reference size. In some examples, the method includes determining that the sample is a high-quality sample of isolated cell nuclei if the cell nuclei in the sample have a size within the reference size range.

[0080] In some embodiments, evaluating the morphology of cell nuclei includes evaluating the shape of cell nuclei. In some examples, the method includes determining that the cell nuclei have a shape selected from spherical, spindle-shaped, oval, elongated, and flattened. In some examples, the method includes determining the percentage of cell nuclei having a particular shape. In one example, the percentage of flat cell nuclei in a sample can be determined. In another example, the percentage of elongated cell nuclei in a sample can be determined. In another example, the percentage of spherical cell nuclei in a sample can be determined. In certain examples, depending on the type of cell nuclei in the sample and the desired shape of the cell nuclei, the method includes determining that the sample is a high quality sample of isolated cell nuclei if the percentage of cells having a particular desired shape exceeds a predetermined threshold, for example, if 50% or more, such as 60% or more, for example 70% or more, such as 80% or more, for example 90% or more, such as 95% or more, for example 97% or more, such as 98% or more, for example 99% or more of the cell nuclei in the sample have the desired shape (spherical, spindle-shaped, elongated), including when 99.5% or more of the cell nuclei in the sample have the particular desired shape.

[0081] In some embodiments, assessing the morphology of the cell nuclei includes assessing the elasticity of the nuclear envelope of the cell nuclei based on the generated image. In some examples, the method of assessing the elasticity of the nuclear envelope of each cell nucleus in the sample includes comparing the generated image of the cell nuclei to a reference image. In some examples, the method includes determining that the sample is a high-quality sample of isolated cell nuclei if a number or percentage of cell nuclei in the sample above a predetermined threshold have a desirable nuclear envelope elasticity (based on the generated image).

[0082] In some examples, the method includes classifying cell nuclei of the sample based on one or more calculated image parameters. In some examples, the particles are classified using a plurality of calculated image parameters, such as a combination of two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., fifteen or more, e.g., twenty or more, e.g., twenty-five or more, e.g., thirty or more, e.g., forty or more image parameters, and includes classifying the particles based on fifty or more image parameters determined from the generated images of the particles.

[0083] In some instances, the cell nuclei are not labeled. The term "label" is used herein in its conventional sense to refer to the coupling or conjugation of a particle of a sample to one or more detection components, such as components detectable by luminescence (e.g., fluorescence, phosphorescence, etc.), contrast agents, or radioisotopes. For example, labeling may involve coupling a particle to a detection component through non-covalent interactions, such as hydrogen bonding, dipole-dipole bonding, or ionic bonding, or through one or more covalent bonds. The term "unlabeled" is used herein in its conventional sense to refer to the absence of a detection component that is added, coupled, or otherwise conjugated (e.g., via one or more non-covalent or covalent bonds) to a particle of interest in a sample. In some embodiments, the cell nuclei of the sample that have coupled or conjugated detectable markers (for example, fluorophores) are present in the sample at 5% or less, for example, 2% or less, for example, 1% or less, for example, 0.5% or less, for example, 0.1% or less, for example, 0.05% or less, for example, 0.01% or less, for example, 0.005% or less, for example, 0.001% or less, for example, 0.0005% or less, for example, 0.0001% or less, for example, 0.00005% or less, for example, 0.00001% or less, including 0.000001% or less.In certain examples, the cell nuclei that have coupled or conjugated detectable markers to the components of cell nuclei are not present in the sample.

[0084] In other examples, the method includes labeling the cell nucleus with a fluorescent label, etc. Fluorescent dyes of interest 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, eurodine 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, squaraines, bodipy, squarinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetrapyrroles, and combinations thereof. In certain embodiments, the conjugate may comprise two or more dyes, for example, 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, eurodine 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, squaraines, bodipy, squarinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetrapyrroles, and combinations thereof.

[0085] In certain embodiments, fluorescent dyes 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 particular examples, the fluorescent dye may include one or more of 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)ethenyl]-benzene), C510 dye, C6 dye, Nile Red dye, T614 dye (e.g., N-[7-(methanesulfonamido)-4-oxo-6-phenoxycyclomen-3-yl]formamide), LDS 821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylene-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dye (e.g., mFluor Red dye such as mFluor 780NS).

[0086] Fluorescent dyes 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, TruRed, 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, and 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, Cerulean, mCFP, ECFP, CyPet, Y66W, dKeima-Red, mKeima-Red, TagCFP, AmCyan1, mTFP1(Teal), S65A, Midoriishi-Cyan, Wild TypeGFP, S65C, TurboGFP, TagGFP, TagGFP2, AcGFP1, S65L, Emerald, S65T, EGFP, Azami-Green, ZsGreen1, Dronpa-Green, TagYFP, EYFP, Top az, Venus, mCitrine, YPet, TurboYFP, PhiYFP, PhiYFP-m, ZsYellow1, mBanana, Kusabira-Orange, mOrange, mOrange2, mKO, TurboRFP, 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 HyperFluor. In some embodiments, the fluorescent dye is 7-AAD, Alexa Fluor 488, Alexa Fluor 647, Alexa Fluor 700, AmCyan, 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 BlueAM, CFSE, DAPI, DRAQ5, DRAQ7, FITC, Fluo-4 AM, FVS440UV, FVS450, FVS510, FVS520, FVS570, FVS575V, FVS620, FVS660, FVS700, FVS780, Indo-1 Hi, Indo-1 Lo、JC-1、MythStatus Network、MythStatus 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 Network、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, NFR 685 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 YG 581, Spark YG 593 and tdTomato.

[0087] In some examples, the fluorescent dye is a polymer dye (e.g., a fluorescent polymer dye). A variety of fluorescent polymer dyes find use in the subject methods and systems. In some examples of the present methods, the polymer dye comprises a conjugated polymer. A conjugated polymer (CP) is characterized by a delocalized electronic structure, comprising 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 another. In this manner, the conjugated backbone can impart an extended, linear structure to the polymer dye, with limited bond angles between repeating units of the polymer. For example, proteins and nucleic acids are also polymers, but in some instances do not form extended rod structures but rather fold into highly ordered three-dimensional shapes. Furthermore, CPs form "rigid rod" polymer backbones, which can exhibit limited twist (e.g., torsion) angles between repeating monomer units along the polymer backbone chain. In some examples, the polymer dye comprises a CP with a rigid rod structure. The structural features of the polymer dye can affect the fluorescent properties of the molecule.

[0088] Polymeric dyes of interest include those disclosed 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, 8,969,509, 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, as well as those disclosed in Gaylord et al. 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. Soc., 2011, 133(32), pp. 12600-12607, the disclosures of which are incorporated herein by reference in their entireties. Specific polymer dyes that can be used include, but are not limited to, BD Horizon Brilliant™ Dyes, such as BD Horizon Brilliant™ Violet Dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786); BD Horizon Brilliant™ Ultraviolet Dyes (e.g., BUV395, BUV496, BUV737, BUV805); BD Horizon Brilliant™ Blue Dyes (e.g., BB515) (BD Biosciences, San Jose, CA). Any fluorescent dye known to those skilled in the art (including, but not limited to, those listed above) or yet to be discovered may be used in the subject methods.

[0089] In some embodiments, the cell nuclei of the sample are classified by a machine learning algorithm that uses a reference image of the cell nuclei or one or more reference image parameters as a training dataset. In some examples, the machine learning algorithm is a dynamic algorithm that updates based on the generated image of the cell nuclei and the determined image parameters. Any convenient machine learning algorithm can be implemented, including, but 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 examples, 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 examples, the machine learning algorithm is a Python script.

[0090] In some embodiments, classifying the cell nuclei of a sample includes assigning each cell nucleus in the sample to a particle population cluster. As used herein, a "population" or "subpopulation" of classified cell nuclei refers to a group of analytes with related parameters (e.g., delta center of mass, radial moment, eccentricity) such that the measured data form a cluster in data space. Thus, the populations are recognized as clusters in the data. Conversely, each particle population cluster can be interpreted as corresponding to a compound population of a particular type of cell nuclei, although clusters corresponding to noise or background are also typically observed. Particle population clusters can be defined in a subset of dimensions, for example, with respect to a subset of measured image parameters, which correspond to composite populations that differ only in a subset of the measured image parameters or features extracted from particle measurements.

[0091] In some embodiments, the method includes determining one or more sorting gates for the classified cell nuclei of the sample. The term "gate" is used herein in its conventional sense to refer to a classifier boundary that identifies a subset of data of interest. In some examples, a gate can demarcate a group of events of particular interest. Furthermore, "gating" may refer to the process of classifying data using gates defined for a given data set, where the gates can be one or more regions of interest combined with Boolean logic. In some embodiments, the gates identify particles that exhibit the same image parameters. Examples of methods for gating are described in, for example, U.S. Patent Nos. 4,845,653; 5,627,040; 5,739,000; 5,795,727; 5,962,238; 6,014,904; 6,944,338; and 8,990,047, the disclosures of which are incorporated herein by reference. In some embodiments, the gate bounds a particle population cluster from one or more different samples previously determined (eg, by a user) to correspond to a property of interest.

[0092] In some examples, one or more sorting gates capture cell nuclei of the target particle population cluster and exclude cell nuclei of the non-target particle population cluster. In some examples, the sorting gates are configured to maximize an inclusion yield of cell nuclei of the target particle population cluster, for example, the sorting gates are configured to generate an inclusion yield of cell nuclei of the target particle population cluster of 50% or more, for example, 55% or more, for example, 60% or more, for example, 65% or more, for example, 70% or more, for example, 75% or more, for example, 80% or more, for example, 85% or more, for example, 90% or more, for example, 95% or more, for example, 97% or more, for example, 99% or more, including determining a sorting gate configured to generate an inclusion yield of cell nuclei of the target particle population cluster of 99.9% or more. In some examples, the sorting gate is configured to maximize a purity yield of cell nuclei of the target particle population cluster, for example, the sorting gate is configured to produce a purity yield of cell nuclei of the target particle population cluster of 50% or more, for example 55% or more, for example 60% or more, for example 65% or more, for example 70% or more, for example 75% or more, for example 80% or more, for example 85% or more, for example 90% or more, for example 95% or more, for example 97% or more, for example 99% or more, including determining a sorting gate configured to produce a purity yield of cell nuclei of the target particle population cluster of 99.9% or more.

[0093] In some examples, the sorting gate is configured to maximize exclusion of particles from the non-target particle population. Particles from the non-target particle population include, in some examples, cell nuclei with a viability below a predetermined threshold, cell nuclei of an improper size or undesirable shape, or non-nuclear debris. In some examples, the sorting gate is configured to exclude 50% or more particles from the non-target particle population, for example, 55% or more, for example, 60% or more, for example, 65% or more, for example, 70% or more, for example, 75% or more, for example, 80% or more, for example, 85% or more, for example, 90% or more, for example, 95% or more, for example, 97% or more, or for example, 99% or more, including determining a sorting gate configured to exclude 99.9% or more particles from the non-target particle population cluster. In some examples, the sorting gate excludes particles from the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0094] In certain embodiments, generating a sorting gate involves an Fβ score, which is a weighted harmonic mean 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 sample yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles). 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 to 10, e.g., 0.2 to 9.5, e.g., 0.3 to 9, e.g., 0.4 to 8.5, e.g., 0.5 to 8, e.g., 0.5 to 7, e.g., 0.5 to 6, e.g., 0.5 to 5, e.g., 0.5 to 4, e.g., 0.5 to 3, or e.g., 0.5 to 2. In some instances, the generated sorting gate of interest has an Fβ score of greater than or equal to 1. In some instances, the generated sorting gate has an Fβ score of less than 1.

[0095] In some examples, 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 made based on a metric indicating the accuracy of the sorting gates according to the desired sorting strategy. In some examples, the metric may be an accuracy (e.g., purity) metric in which purity is compared to a predetermined threshold. The metric may be generated based on the confidence of a classifier included in the sorting strategy. In other examples, the metric may be a yield metric in which the yield of target cell nuclei in a particle population cluster is compared to a predetermined threshold.

[0096] In some embodiments, the method includes generating a sorting decision based on sorting gates determined for cell nuclei of the sample. In some examples, generating a particle sorting decision in accordance with the present disclosure includes a gating strategy of 8 or fewer sorting gates, e.g., 7 or fewer sorting gates, e.g., 6 or fewer sorting gates, e.g., 5 or fewer sorting gates, e.g., 4 or fewer sorting gates, e.g., 3 or fewer sorting gates, including 2 or fewer sorting gates. In some embodiments, the method includes generating the sorting gates using a graphical display (described below) that displays one or more analysis algorithms for applying classification parameters to image parameters determined for the cell nuclei.

[0097] In some embodiments, the method includes displaying the gating strategy on a graphical user interface. For example, the analysis algorithm may be one or more of a spectral compensation matrix, a clustering algorithm, and a t-distributed stochastic neighbor embedding (t-SNE) algorithm. In some examples, 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 examples, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In particular examples, the analysis algorithm is a spectral unmixing algorithm such 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 such as described in U.S. Pat. No. 11,513,054 (the disclosures of which are incorporated herein by reference). In certain examples, sorting gates may 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 in a separate computing system (e.g., a different computer system or network) and transmitted to the flow cytometer to implement the gating strategy, for example, using the particle sorter of the flow cytometer (e.g., with a sorting determination module).

[0098] FIG. 1 shows a flowchart for assessing the morphology of cell nuclei of a sample according to certain embodiments. In step 101, light from a sample having isolated cell nuclei in a flow stream is measured with a photodetector. In some examples, the method includes illuminating particles of the sample in the flow stream with a frequency-modulated beam of laser light, for example. In some examples, a frequency-encoded data signal is generated from the measured light. In step 102, images of the cell nuclei are generated. In some examples, image parameters (e.g., radial moment, eccentricity) are calculated from the images generated in step 102a. In some examples, the method includes calculating multiple image parameters for each cell nucleus, such as 50 or more image parameters. The morphology of the cell nuclei of the sample is assessed in step 104. Assessing the morphology may include one or more of assessing cell viability of the cell nuclei in the sample, determining ploidy of the cell nuclei in the sample, assessing the size of the cell nuclei, assessing the shape of the cell nuclei, and assessing the elasticity of the nuclear envelope of the cell nuclei.

[0099] If the cell nuclei are to be sorted, such as to isolate specific cell nuclei of interest, a gating strategy is determined for the cell nuclei in step 104. A gating strategy having one or more applied gates (e.g., 4-8 applied gates) can be developed, and the gating strategy can be used to generate the sorting determination in step 105. In some examples, the cell nuclei are sorted into different containers (step 106), such as using a droplet sorter (e.g., having a droplet charging device, a piezoelectric transducer for generating separate droplets with the cell nuclei, and deflection plates for deflecting the droplets into different sample containers).

[0100] In certain embodiments, the method includes sorting one or more of the identified cell nuclei of the sample based on the estimated abundance of a fluorophore associated with the cell nuclei (e.g., in the case of fluorescently labeled cell nuclei). The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., droplets containing cell nuclei, droplets containing non-cellular particles such as biological macromolecules) and, in some instances, delivering the separated components to one or more sample collection vessels. For example, the method may include sorting two or more components of a sample, e.g., 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, including sorting 25 or more components of a sample.

[0101] In sorting particles identified based on the abundance of a fluorophore associated with the particle, the method includes data acquisition, analysis, and recording, such as by a computer, where multiple data channels record data from each detector used to obtain overlapping spectra of the multiple fluorophores associated with the particle. In these embodiments, the analysis includes spectrally decomposing light from the multiple fluorophores having overlapping spectra associated with the particle (e.g., by calculating a spectral unmixing matrix) and identifying the particle based on the estimated abundance of each fluorophore associated with the particle. This analysis can be conveyed to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0102] In some embodiments, a method for sorting components of a sample includes sorting particles (e.g., cells in a biological sample) with a particle sorting module having a deflector plate, such as described in U.S. Patent Application 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 those described in U.S. Patent Application Publication No. 2020 / 0256781 (the disclosure of which is incorporated herein by reference). In some embodiments, a subject system includes a particle sorting module having a deflector plate, such as described in U.S. Patent Application Publication No. 2017 / 0299493 (filed March 28, 2017, the disclosure of which is incorporated herein by reference).

[0103] System for assessing cell nuclear morphology in a sample - Patent Application 20070122997 As summarized above, aspects of the present disclosure include a system for assessing the morphology of isolated cell nuclei in a sample in a flow stream. The system, according to certain embodiments, includes a light source configured to illuminate cell nuclei of the sample, a light detection system having a plurality of photodetectors for measuring light from the cell nuclei, and a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate images of the cell nuclei from the measured light and assess the morphology of the cell nuclei based on the generated images of the cell nuclei.

[0104] In embodiments, the light source may be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., the type of cell nucleus), the light source may be configured to emit light at various wavelengths, 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. For example, the light source may include a broadband light source emitting light having a wavelength between 200 nm and 900 nm. In other examples, the light source may include a narrowband light source emitting a wavelength between 200 nm and 900 nm. For example, the light source may be a narrowband LED (1 nm to 25 nm) emitting light having a wavelength between 200 nm and 900 nm. In certain embodiments, the light source is a laser. In some examples, the target system includes a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other examples, the target system includes a dye laser such as a stilbene, coumarin, or rhodamine laser. In still other examples, the target laser includes 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 yet other examples, the subject systems include solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers or cerium-doped lasers, and combinations thereof.

[0105] 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 having a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integration, etc. In some examples, 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.

[0106] The light source may be positioned at any suitable distance from the sample (e.g., the flow stream in a flow cytometer), for example, at a distance of 0.001 mm or more from the flow stream, for example, 0.005 mm or more, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 25 mm or more, including 100 mm or more. Further, the light source may illuminate the sample at any suitable angle (e.g., relative to the perpendicular axis of the flow stream), for example, at an angle in the range of 10° to 90°, for example, 15° to 85°, for example, 20° to 80°, for example, 25° to 75°, including an angle of 30° to 60°, for example, 90°.

[0107] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some examples, the system includes a light source configured to continuously illuminate the sample, such as with a continuous wave laser that continuously illuminates the flow stream at an interrogation point within the flow cytometer. In other examples, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 milliseconds, every 10 milliseconds, every 100 milliseconds, and 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 provide intermittent illumination of the sample by the light source. For example, the system of interest in these embodiments may include one or more laser beam choppers, manual or computer-controlled beam stops, for blocking and exposing the sample to the light source.

[0108] In some embodiments, the light source is a laser. Lasers of interest may include pulsed or continuous wave lasers. For example, the laser may be a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, 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; a dye laser such as a stilbene, coumarin, or rhodamine laser; 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 laser, or a fluorine laser. metal vapor lasers such as on-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; solid state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thrim YAG lasers, ytterbium YAG lasers, ytterbium 2O3 lasers, cerium doped lasers, and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or frequency doubled or frequency tripled implementations of any of the above lasers.

[0109] In certain embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some examples, 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 may be a pulsed laser or a continuous-wave laser. For example, the laser in the optical beam generator of interest may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, 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; a dye laser, such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeS) laser, or a combination thereof. e) Lasers, metal vapor lasers such as helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers or gold lasers and combinations thereof; solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:yCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thorium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, cerium-doped lasers and combinations thereof.

[0110] The acousto-optical device may be any convenient acousto-optical protocol configured to frequency-shift laser light using applied acoustic waves. In a specific embodiment, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the target system is configured to generate an angularly 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 using any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0111] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to produce a desired number of angularly deflected laser beams in the output laser beam, including being 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.

[0112] In some examples, to produce an intensity profile of the angularly deflected laser beam within the output laser beam, the controller is configured to apply a high frequency drive signal having an amplitude that varies, for example, 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. In some embodiments, each of the applied high frequency drive signals 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.

[0113] In certain embodiments, the controller includes a processor having a memory operably coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, or the memory may include instructions for generating 100 or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more angularly deflected laser beams having different intensities, such as 3 or more, such as 4 or more, such as 5 or more, such as 10 or more, such as 25 or more, such as 50 or more, or the memory may include instructions for generating 100 or more angularly deflected laser beams having different intensities.

[0114] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In another embodiment, the controller includes a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly 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 storing instructions that, when executed by the processor, cause the processor to produce an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam having a top-hat intensity profile along a horizontal axis.

[0115] In embodiments, the objective optical beam generator may be configured to produce angularly polarized laser beams within the spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly polarized 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 produce angularly polarized laser beams within the output laser beam that overlap with adjacent angularly polarized laser beams along the horizontal axis of the output laser beam, such as 5000 μm or more. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0116] In certain examples, an optical beam generator configured to generate two or more frequency-shifted optical beams may be used, for example, as disclosed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Patent 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, 10,423,353, 10,784,661, 10 ... Nos. 0,451,538, 10,620,111, and U.S. Patent Application Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0117] In embodiments, the system includes a light detection system having a plurality of photodetectors for measuring light from isolated cell nuclei in the sample. In some examples, one or more photodetectors of the light detection system are light-loss photodetectors. In some examples, one or more photodetectors of the light detection system are configured to measure scattered light. In certain examples, one or more photodetectors of the light detection system are configured to measure side-scattered light. In certain examples, one or more photodetectors of the light detection system are configured to measure forward-scattered light. In certain examples, one or more photodetectors of the light detection system are configured to measure back-scattered light. Photodetectors of interest may include optical sensors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APDs), image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other photodetectors. 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.

[0118] In some embodiments, the subject light detection system includes a plurality of light detectors. In some examples, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In particular examples, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include 4 or more light detectors, e.g., 10 or more light detectors, e.g., 25 or more light detectors, e.g., 50 or more light detectors, e.g., 100 or more light detectors, e.g., 250 or more light detectors, e.g., 500 or more light detectors, e.g., 750 or more light detectors, including 1000 or more light detectors. For example, the detector may be a photodiode array having 4 or more photodiodes, e.g., 10 or more photodiodes, e.g., 25 or more photodiodes, e.g., 50 or more photodiodes, e.g., 100 or more photodiodes, e.g., 250 or more photodiodes, e.g., 500 or more photodiodes, e.g., 750 or more photodiodes, including 1000 or more photodiodes.

[0119] The photodetectors can be arranged in any geometric configuration as desired, including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, non-angular, decagonal, dodecagonal, circular, elliptical, and irregularly patterned configurations. The photodetectors within a photodetector array may be oriented relative to one another at angles (referenced to the XZ plane) including angles between 10° and 180°, such as between 15° and 170°, such as between 20° and 160°, such as between 25° and 150°, such as between 30° and 120°, and such as between 45° and 90°. The photodetector array may be of any suitable shape, including rectilinear shapes such as square, rectangular, trapezoidal, triangular, hexagonal, and the like, curvilinear shapes such as circular and elliptical, as well as irregular shapes such as a parabolic base coupled to a flat top. In certain embodiments, the photodetector array has a rectangular active surface.

[0120] Each photodetector (e.g., photodiode) in the array may have an active surface with a width ranging from 5 μm to 250 μm, such as 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and a length ranging from 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and 2 ~10,000 μm 2 , for example, 50 μm 2 ~9000μm 2 , for example, 75 μm 2 ~8000μm 2 , for example, 100 μm 2 ~7000μm 2 , for example, 150 μm 2 ~6000μm 2 The range is 200 to μm 2 ~5000μm 2 Includes:

[0121] 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 in the range of 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, including 5 mm to 25 mm. The width of the photodetector array may also vary in the range of 0.01 mm to 100 mm, for example 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, including 5 mm to 25 mm. Thus, the active surface of the photodetector array may be 0.1 mm 2 ~10,000mm 2 , e.g. 0.5 mm 2 ~5000mm 2 , e.g. 1 mm2 ~1000mm 2 , e.g. 5mm 2 ~500mm 2 may be in the range of 10mm 2 ~100mm 2 Includes:

[0122] The optical detector of interest may be configured to measure light collected at one or more wavelengths, such as two or more wavelengths, for example five or more different wavelengths, such as ten or more different wavelengths, for example twenty-five or more different wavelengths, such as fifty or more different wavelengths, for example one hundred or more different wavelengths, such as two or more different wavelengths, for example two hundred or more different wavelengths, for example three hundred or more different wavelengths, including measuring light emitted by the sample in the flow stream at four hundred or more different wavelengths.

[0123] In some embodiments, the photodetector is configured to measure light collected over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a wavelength range. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from a 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.

[0124] The light detection system may be configured to measure light continuously or at discrete intervals. In some examples, the target light detector is configured to continuously obtain measurements of the collected light. In other examples, the light detection system may be configured to perform measurements at discrete intervals, such as measuring light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0125] In embodiments, the system is configured to generate one or more images of cell nuclei in the sample. In some embodiments, the system includes a memory having stored thereon instructions for generating an image from detected light absorption, detected light scattering, detected light emission, or any combination thereof. In some examples, 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 examples, 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 examples, the memory includes instructions for generating an image from emitted light from the sample. In other examples, the memory includes instructions for generating an image from a combination of detected light absorption and detected light scattering.

[0126] In some embodiments, the system includes a memory having instructions for generating one or more images from the light measured by the light detection system. In some examples, the memory includes instructions for generating a single image for each cell nucleus from each form of detected light (e.g., scattered light, light loss, etc.). In other examples, the memory includes instructions for generating multiple images for each cell nucleus, including, for example, 25 or more images of each cell nucleus, such as 2 or more, e.g., 3 or more, e.g., 5 or more, e.g., 10 or more. For example, the memory includes instructions for generating a first image of each cell nucleus from detected light absorption, instructions for generating a second image of each cell nucleus from detected light scattering, and instructions for generating a third image of each cell nucleus from detected light emission. In other embodiments, more than one image is generated from each form of detected light, e.g., 3 or more, e.g., 4 or more, e.g., 5 or more, 10 or more, or a combination thereof.

[0127] 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 cell nuclei in the flow stream. In some examples, 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, including two or more, three or more, four or more, five or more, six or more, and 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 examples, the memory includes instructions for phase-correcting the frequency-encoded data. In some examples, the memory includes instructions for generating a phase-corrected image of the cell nuclei 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 a particular embodiment, the memory includes instructions for performing a transformation of the frequency-encoded data without performing imaginary mathematical calculations (i.e., performing only calculations for the real mathematical calculations of the transformation) to generate an image from the frequency-encoded data.

[0128] In some examples, the memory includes instructions for generating one or more grayscale images of the cell nuclei. The term "grayscale" is used herein in its conventional sense to refer to an image of the cell nuclei 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, which is used to convert each pixel to a binary value that is used to generate the image of the cell nuclei. In particular examples, the image of the cell nuclei is a binary pixel image of a particle, e.g., each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel exceeds a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0129] In embodiments, the system includes a memory having instructions for calculating one or more image parameters from the generated images of cell nuclei. In some examples, the memory includes instructions for calculating a center of mass image parameter from the generated images. In some examples, the memory includes instructions for calculating a delta center of mass image parameter from the generated images. In some examples, the memory includes instructions for calculating a diffuse image parameter from the generated images. In some examples, the memory includes instructions for calculating an eccentricity image parameter from the generated images. In some examples, the memory includes instructions for calculating a major axis moment image parameter from the generated images. In some examples, the memory includes instructions for calculating a maximum intensity image parameter from the generated images. In some examples, the memory includes instructions for calculating a radial moment image parameter from the generated images. In some examples, the memory includes instructions for calculating a minor axis moment image parameter from the generated images. In some examples, the memory includes instructions for calculating a particle size image parameter from the generated images. In some examples, the memory includes instructions for calculating a total intensity image parameter from the generated images. In some examples, the memory includes instructions for calculating a particle light loss image parameter from the generated images. In some examples, the memory includes instructions for calculating a forward scatter light image parameter from the generated images. In some examples, the memory includes instructions for calculating side scatter image parameters from the generated images. In some examples, the memory includes instructions for calculating image moments from the generated images. The term "image moment" is used herein in its conventional sense to refer to a weighted average of pixel intensities in an image. In some examples, the memory includes instructions for calculating a center of mass from the image moments of the images. In other examples, the memory includes instructions for calculating an orientation of a cell nucleus from the image moments of the images. In yet other examples, the memory includes instructions for calculating an eccentricity of a cell nucleus from the image moments of the images.

[0130] In embodiments, the memory includes instructions for evaluating the morphology of isolated cell nuclei based on one or more of the generated images of the cell nuclei and image parameters of the cell nuclei calculated from the generated images. In some examples, the memory includes instructions for evaluating morphology by determining the viability of cell nuclei in the sample. In some embodiments, the memory includes instructions for determining the number of viable cell nuclei in the sample. In some embodiments, the memory includes instructions for determining the percentage of viable cell nuclei in the sample, for example, determining that 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, for example 98% or more, for example 99% or more of the cell nuclei are viable in the sample, and determining that 99.5% or more of the cell nuclei in the sample are viable. In some embodiments, the memory includes instructions for determining whether a predetermined number of cell nuclei in the sample are viable based on the images of the cell nuclei. In some examples, the memory includes instructions for determining that the number of viable cell nuclei in the sample exceeds a predetermined threshold. In other examples, the memory includes instructions for determining that the number of viable cell nuclei in the sample is below a predetermined threshold. In some examples, the memory includes instructions for determining that the sample is a high quality sample of isolated cell nuclei if the number or percentage of viable cell nuclei in the sample exceeds a predetermined threshold, such as a threshold where 50% or more, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more cell nuclei are viable, including a threshold where 90% or more cell nuclei are viable.

[0131] In some examples, the memory includes instructions for evaluating the morphology of cell nuclei by determining the ploidy of the cell nuclei. In some examples, the memory includes instructions for determining that one or more cell nuclei in the sample are polyploid. In other examples, the memory includes instructions for determining that one or more cell nuclei in the sample are monoploid. In some examples, the memory includes instructions for determining the number of polyploid cell nuclei in the sample. In some examples, the memory includes instructions for determining the percentage of polyploid cell nuclei in the sample, such as when 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, for example 98% or more, for example 99% or more of the cell nuclei in the sample are polyploid, and includes instructions for determining that 99.5% or more of the cell nuclei in the sample are polyploid. In some examples, the memory includes instructions for determining whether the number or percentage of polyploid cell nuclei in the sample exceeds a predetermined threshold. In some examples, the memory includes instructions for determining that the sample is a high quality sample of isolated cell nuclei if the number or percentage of polyploid cell nuclei in the sample exceeds a predetermined threshold.

[0132] In some embodiments, the memory includes instructions for evaluating the morphology of cell nuclei by assessing their size. In some examples, the memory includes instructions for measuring the size of each cell nucleus in the sample. In some examples, the memory includes instructions for determining the size of each cell nucleus in the sample by comparing the size of the cell nuclei to a predetermined reference size (using a reference image of the cell nuclei), e.g., determining that the size of each cell nucleus is 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, or such as 99% or more of the predetermined reference size. In some examples, the memory includes instructions for determining the number or percentage of cell nuclei in the sample having a size within 15% or less, 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 of the predetermined reference size, including within 0.5% or less of the predetermined reference size. In some examples, the memory includes instructions for determining that the sample is a high quality sample of isolated cell nuclei if the cell nuclei in the sample have a size within a reference size range.

[0133] In some embodiments, the memory includes instructions for evaluating the morphology of cell nuclei by evaluating the shape of the cell nuclei. In some examples, the memory includes instructions for determining that the cell nuclei have a shape selected from spherical, spindle-shaped, oval, elongated, and flattened. In some examples, the memory includes instructions for determining the percentage of cell nuclei having a particular shape. In one example, the memory includes instructions for determining the percentage of flattened cell nuclei in a sample. In another example, the memory includes instructions for determining the percentage of elongated cell nuclei in a sample. In another example, the memory includes instructions for determining the percentage of spherical cell nuclei in a sample. In some embodiments, the memory includes instructions for determining that a sample is a high quality sample of isolated cell nuclei if the percentage of cells having a particular desired shape exceeds a predetermined threshold, for example, if more than 50%, such as more than 60%, such as more than 70%, such as more than 80%, such as more than 90%, such as more than 95%, such as more than 97%, such as more than 98%, such as more than 99% of the cell nuclei in the sample have a desired shape (spherical, spindle-shaped, elongated), including when more than 99.5% of the cell nuclei in the sample have a particular desired shape.

[0134] In some embodiments, the memory includes instructions for evaluating the morphology of cell nuclei by evaluating the elasticity of the nuclear envelope of the cell nuclei based on the generated images. In some examples, the memory includes instructions for evaluating the elasticity of the nuclear envelope of each cell nucleus in the sample by comparing the generated images of the cell nuclei to a reference image. In some examples, the memory includes instructions for determining that the sample is a high-quality sample of isolated cell nuclei if a number or percentage of cell nuclei in the sample above a predetermined threshold have a desirable nuclear envelope elasticity (based on the generated images).

[0135] In some embodiments, the memory comprises instructions for classifying cell nuclei of the sample based on one or more calculated image parameters. In some examples, the memory comprises instructions for using a plurality of calculated image parameters to classify particles with a combination of 2 or more, such as 3 or more, such as 4 or more, such as 5 or more, such as 6 or more, such as 7 or more, such as 8 or more, such as 9 or more, such as 10 or more, such as 15 or more, such as 20 or more, such as 25 or more, such as 30 or more, such as 40 or more image parameters, including classifying particles based on 50 or more image parameters determined from generated images of the particles.

[0136] In some examples, the system includes a processor having memory for executing a machine learning algorithm, which is a dynamic algorithm for classifying cell nuclei of a sample. The system may be configured to implement any convenient machine learning algorithm, including, but 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 examples, 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 examples, the system is configured to implement a Python script.

[0137] In some embodiments, the memory includes instructions for determining one or more sorting gates for cell nuclei of the sample. In some examples, the memory includes instructions for generating one or more sorting gates that capture cell nuclei of target particle population clusters and exclude cell nuclei of non-target particle population clusters. In some examples, the memory includes instructions for determining a sorting gate configured to maximize an inclusion yield of cell nuclei of the target particle population clusters, for example, a sorting gate configured to generate an inclusion yield of cell nuclei of the target particle population clusters of 50% or more, 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, including determining a sorting gate configured to generate an inclusion yield of cell nuclei of the target particle population clusters of 99.9% or more. In some examples, the memory includes instructions for determining a sorting gate configured to maximize a purity yield of cell nuclei of the target particle population cluster, for example, a sorting gate configured to generate a purity yield of cell nuclei of the target particle population cluster of 50% or more, such as 55% or more, for example 60% or more, for example 65% or more, for example 70% or more, for example 75% or more, for example 80% or more, for example 85% or more, for example 90% or more, for example 95% or more, for example 97% or more, for example 99% or more, including determining a sorting gate configured to generate a purity yield of cell nuclei of the target particle population cluster of 99.9% or more.

[0138] In some examples, the memory includes instructions for determining a sorting gate configured to maximize exclusion of particles of the non-target particle population. Particles of the non-target particle population include, in some examples, cell nuclei with a viability below a predetermined threshold, cell nuclei of an improper size or undesirable shape, or non-nuclear debris. In some examples, the memory includes instructions for determining a sorting gate configured to exclude 50% or more particles of the non-target particle population, for example, 55% or more, for example, 60% or more, for example, 65% or more, for example, 70% or more, for example, 75% or more, for example, 80% or more, for example, 85% or more, for example, 90% or more, for example, 95% or more, for example, 97% or more, or for example, 99% or more, or instructions for determining a sorting gate configured to exclude 99.9% or more particles of the non-target particle population cluster. In some examples, the memory includes instructions for determining a sorting gate that excludes particles of the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0139] In certain embodiments, the memory includes instructions for generating sorting gates using an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of particles in target particle population clusters and the exclusion of particles in non-target particle population clusters. The beta (β) parameter of the Fβ score biases the gating strategy toward either sample yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles). 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 to 10, e.g., 0.2 to 9.5, e.g., 0.3 to 9, e.g., 0.4 to 8.5, e.g., 0.5 to 8, e.g., 0.5 to 7, e.g., 0.5 to 6, e.g., 0.5 to 5, e.g., 0.5 to 4, e.g., 0.5 to 3, e.g., 0.5 to 2. In some examples, the generated sorting gate of interest has an Fβ score of 1 or greater. In some examples, the generated sorting gate has an Fβ score of less than 1.

[0140] In some examples, 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 made based on a metric indicating the accuracy of the sorting gates according to the desired sorting strategy. In some examples, the metric may be an accuracy (e.g., purity) metric in which purity is compared to a predetermined threshold. The metric may be generated based on the confidence of a classifier included in the sorting strategy. In other examples, the metric may be a yield metric in which the yield of target cell nuclei in a particle population cluster is compared to a predetermined threshold.

[0141] In some embodiments, the memory comprises instructions for generating a sorting decision based on sorting gates determined for cell nuclei of the sample. In some examples, the memory comprises instructions for generating a particle sorting decision using a gating strategy of 8 or fewer sorting gates, e.g., 7 or fewer sorting gates, e.g., 6 or fewer sorting gates, e.g., 5 or fewer sorting gates, e.g., 4 or fewer sorting gates, e.g., 3 or fewer sorting gates, including 2 or fewer sorting gates. In some embodiments, the memory comprises instructions for generating the sorting gates using a graphical display that displays one or more analysis algorithms for applying classification parameters to image parameters determined for the cell nuclei.

[0142] 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 ground truth image classification parameters to the determined image parameters for the cell nuclei. In some embodiments, the system includes a display for visualizing the gating strategy on a graphical user interface.

[0143] In some examples, the graphical user interface applies an analysis algorithm to generate the gating strategy. For example, the analysis algorithm may be one or more of a spectral compensation matrix, a clustering algorithm, and a t-distributed stochastic neighbor embedding (t-SNE) algorithm. In some examples, 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 examples, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In particular examples, the analysis algorithm is a spectral unmixing algorithm such 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).

[0144] In some examples, the system includes a memory having instructions for determining a gating strategy using a computational sorting algorithm, such as that described in U.S. Patent No. 11,513,054 (the disclosure of which is incorporated herein by reference). In certain examples, 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 in a separate computing system (e.g., a different computer system or network) and transmitted to the flow cytometer to implement the gating strategy, for example, using the particle sorter of the flow cytometer (e.g., with a sorting determination module).

[0145] In some embodiments, the system for generating a gating strategy is part of or operably coupled to a particle analyzer system (e.g., a flow cytometer) for generating the flow cytometer data described herein.

[0146] In some examples, the system includes an integrated circuit device programmed to perform one or more of the above-described methods. In some examples, the integrated circuit includes programming for generating images of cell nuclei. In some examples, the integrated circuit includes programming for calculating image parameters from the generated images of cell nuclei. In some examples, the integrated circuit includes programming for evaluating the morphology of cell nuclei according to one or more of the above parameters (e.g., viability, size, shape, ploidy). In some examples, the integrated circuit includes programming for classifying cell nuclei based on the generated images and / or the calculated image parameters. 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 still other embodiments, the integrated circuit device includes a complex programmable logic device (CPLD).

[0147] Systems 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 memory storing instructions for executing the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, 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 or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of 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 negative feedback control.

[0148] System memory may 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-and-write compact disk, flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or diskette 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 diskette, 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 known as computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with the memory storage devices.

[0149] In some embodiments, a computer program product is described that includes a computer-usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementing a hardware state machine to perform the functions described herein will be apparent to one skilled in the art.

[0150] 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, fixed or portable). The processor may include a general-purpose digital microprocessor that is appropriately programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor via a communications channel or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium using any of these devices in conjunction with the memory. For example, a magnetic or optical disk may carry the program and be read by a disk writer / reader. The system of the present disclosure also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. Programming according to the present disclosure may be recorded on a computer-readable medium, e.g., 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-ROM, storage media such as RAM, ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0151] The processor may also have access to a communication channel for communicating with a user in a remote location, where remote location means that the user is not in direct contact with the system but 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 mobile phone (i.e., a smartphone).

[0152] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0153] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port that enables data communication between the system of interest and other external devices, such as a computer terminal (e.g., in a doctor's office or hospital environment) configured for similar complementary data communication.

[0154] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the target system to communicate with computer terminals and / or other devices such as networks, communication-enabled mobile phones, personal digital assistants, or any other communication device that a user can integrate and use.

[0155] In one embodiment, the communication interface is configured to provide connectivity for data transfer using Internet Protocol (IP) over a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) in a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a Wi-Fi hotspot.

[0156] In one embodiment, the target system is configured to wirelessly communicate with a 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, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, 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 touchscreen.

[0157] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in the target system, e.g., the optional data storage unit, with a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0158] 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. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of graphical elements. A graphical user interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing the user's input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also communicate information generated by the processing modules to a user at a remote location, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be performed according to various known techniques. As some examples, the data may include SQL, HTML, or XML documents, email or other files, or other formats of data. The data may also 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 of any type of known or future-developed computer platform, but they are typically of a class of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication system, including wireless systems, and may or may not be networked. They may be co-located or physically separated.In some cases, various operating systems may be employed on any computer platform depending on the type and / or manufacturer of the computer platform selected. 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, etc.

[0159] In certain embodiments, the subject systems include one or more optical conditioning components for conditioning light, such as light irradiated onto a sample (e.g., from a laser) or light collected from a sample (e.g., scattering, fluorescence). For example, the optical conditioning may be increasing the dimensions of the light, the focus of the light, or collimating the light. In some examples, the optical conditioning is an expansion protocol that increases the dimensions of the light (e.g., beam spot), including increasing the dimensions by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, and including increasing the dimensions by 75% or more. In other embodiments, the optical conditioning includes focusing the light to reduce the dimensions of the light, e.g., 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, including reducing the dimensions of the beam spot by 75% or more. In certain embodiments, the optical conditioning includes collimating the light. The term "collimate" is used in its conventional sense to refer to optically adjusting the collinearity of light propagation or reducing the divergence of light from a common axis of propagation. In some examples, collimating includes narrowing the spatial cross-section of the light beam (e.g., reducing the beam profile of a laser).

[0160] In some embodiments, the optical conditioning component is a focusing lens having a magnification ratio of 0.1 to 0.95, e.g., a magnification ratio of 0.2 to 0.9, e.g., a magnification ratio of 0.3 to 0.85, e.g., a magnification ratio of 0.35 to 0.8, e.g., a magnification ratio of 0.5 to 0.75, e.g., a magnification ratio of 0.55 to 0.7, e.g., a magnification ratio of 0.6. For example, the focusing lens is a dual achromatic demagnifying lens, in a particular example, having a magnification ratio of approximately 0.6. The focal length of the focusing lens may vary in the range of 5 mm to 20 mm, e.g., 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 mm, including focal lengths in the range of 10 mm to 15 mm. In a particular embodiment, the focusing lens has a focal length of approximately 13 mm.

[0161] In other embodiments, the optical conditioning component is a collimator. The collimator may 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 may vary in a range of 5 mm to 40 mm, e.g., 6 mm to 37.5 mm, e.g., 7 mm to 35 mm, e.g., 8 mm to 32.5 mm, e.g., 9 mm to 30 mm, e.g., 10 mm to 27.5 mm, e.g., 12.5 mm to 25 mm, including focal lengths in a range of 15 mm to 20 mm.

[0162] 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 has an orifice that propagates a fluid sample to a sample interrogation region, and in some embodiments, the flow cell nozzle includes a proximal cylindrical portion defining a longitudinal axis and a distal frusto-conical portion terminating in a flat surface having a nozzle orifice transverse to the longitudinal axis. The length of the proximal cylindrical portion (as measured along the longitudinal axis) may vary from 1 mm to 15 mm, e.g., 1.5 mm to 12.5 mm, e.g., 2 mm to 10 mm, e.g., 3 mm to 9 mm, including 4 mm to 8 mm. The length of the distal frusto-conical portion (as measured along the longitudinal axis) may also vary from 1 mm to 10 mm, e.g., 2 mm to 9 mm, e.g., 3 mm to 8 mm, including 4 mm to 7 mm. The diameter of the flow cell nozzle chamber may in some embodiments vary in the range of 1 mm to 10 mm, such as 2 mm to 9 mm, such as 3 mm to 8 mm, including 4 mm to 7 mm.

[0163] In certain examples, the nozzle chamber does not include a cylindrical portion, and the entire flow cell nozzle chamber is frustoconical in shape. In these embodiments, the length of the frustoconical nozzle chamber (as measured along a longitudinal axis transverse to the nozzle orifice) may range from 1 mm to 15 mm, such as 1.5 mm to 12.5 mm, such as 2 mm to 10 mm, for example, 3 mm to 9 mm, including 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical nozzle chamber may range from 1 mm to 10 mm, such as 2 mm to 9 mm, for example, 3 mm to 8 mm, including 4 mm to 7 mm.

[0164] In some embodiments, the sample flow stream diverges 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 cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes such as circular, elliptical, and irregular shapes such as a parabolic bottom coupled to a flat 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, such as 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, including from 150 μm to 500 μm. In a particular embodiment, the nozzle orifice is 100 μm.

[0165] 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 sample to the flow cell nozzle chamber. Depending on the desired characteristics of the flow stream, the rate of sample delivered by the sample injection port to the flow cell nozzle chamber may be 1 μL / sec or more, for example, 2 μL / sec or more, for example, 3 μL / sec or more, for example, 5 μL / sec or more, for example, 10 μL / sec or more, for example, 15 μL / sec or more, for example, 25 μL / sec or more, for example, 50 μL / sec or more, for example, 100 μL / sec or more, for example, 150 μL / sec or more, for example, 200 μL / sec or more, for example, 250 μL / sec or more, for example, 300 μL / sec or more, for example, 350 μL / sec or more, for example, 400 μL / sec or more, for example, 450 μL / sec or more, including 500 μL / sec or more. For example, the sample flow rate may be in the range of 1 μL / sec to about 500 μL / sec, such as 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, including 10 μL / sec to about 100 μL / sec.

[0166] The sample injection port may be an orifice disposed in the wall of the nozzle chamber or a conduit disposed at the proximal end of the nozzle chamber. When the sample injection port is an orifice disposed in the wall of the nozzle chamber, the orifice may have any desired cross-sectional shape, including, but not limited to, straight cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom coupled to a flat 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, with openings ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, and may range from 1.25 mm to 1.75 mm, e.g., 1.5 mm.

[0167] In certain examples, the sample injection port is a conduit located at the proximal end of the flow cell nozzle chamber. For example, the sample injection port may be a conduit positioned so that the orifice of the sample injection port is aligned with the flow cell nozzle orifice. When the sample injection port is a conduit aligned with the flow cell nozzle orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, including, but not limited to, linear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom coupled to a flat top. In certain examples, the orifice of the conduit has an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, and may vary depending on the shape, including 1.25 mm to 1.75 mm, e.g., 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 in the range of 1° to 10°, for example, 2° to 9°, for example, 3° to 8°, for example, 4° to 7°, including a bevel angle of 5°.

[0168] 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 stacked 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, 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, including 2500 μL / sec or more. For example, the sheath fluid flow rate may be in the range of 1 μL / sec to about 500 μL / sec, such as 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, including 10 μL / sec to about 100 μL / sec.

[0169] In some embodiments, the sheath fluid injection port is an orifice disposed in the wall of the nozzle chamber. The sheath fluid injection port orifice may have any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom coupled to a flat top. The size of the sample injection port orifice may vary depending on the shape, with openings ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm, in particular examples, including 1.25 mm to 1.75 mm, e.g., 1.5 mm.

[0170] The subject systems, in certain examples, include a sample interrogation region in fluid communication with the flow cell nozzle orifice. In these examples, a sample flow stream diverges 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 interrogation region. The size of the interrogation region can vary depending on characteristics of the flow nozzle, such as the size of the nozzle orifice and the size of the sample injection port. In embodiments, the interrogation region can have a width of 0.01 mm or more, e.g., 0.05 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 5 mm or more, including 10 mm or more. The length of the interrogation region can also vary in some examples along a length of 0.01 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, e.g., 1 mm or more, e.g., 1.5 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 5 mm or more, e.g., 10 mm or more, e.g., 15 mm or more, e.g., 20 mm or more, e.g., 25 mm or more, including 50 mm or more.

[0171] The investigation region may be configured to facilitate illumination of a planar cross-section of the diverging flow stream, or may be configured to facilitate illumination of a diffuse field (e.g., using a diffuse laser or lamp) of a predetermined length. In some embodiments, the investigation region includes a transparent window to facilitate illumination of the diverging flow stream of a predetermined length, e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 4 mm or more, e.g., 5 mm or more, including 10 mm or more. Depending on the light source used to illuminate the diverging flow stream (as described below), the investigation region may be configured to pass light in the range of 100 nm to 1500 nm, e.g., 150 nm to 1400 nm, e.g., 200 nm to 1300 nm, e.g., 250 nm to 1200 nm, e.g., 300 nm to 1100 nm, e.g., 350 nm to 1000 nm, e.g., 400 nm to 900 nm, including 500 nm to 800 nm. Thus, the research area includes optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and other polymeric plastic materials including plastics, such as polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (glycol-modified polyethylene terephthalate), polyesters, among which polyesters of interest include poly(ethylene terephthalate) (PET), bottle-grade PET (monoethylene glycol, terephthalic acid, and isophthalic acid, cyclohexene dimethanol, and other polymeric materials). Poly(alkylene terephthalates) such as poly(ethylene adipate), poly(1,4-butylene adipate), poly(hexamethylene adipate) and the like; 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-naphthalenedicarboxylates) such as poly(ethylene 2,6-naphthalenedicarboxylate); 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-cyclohexanediyl alkylene dicarboxylates) such as poly(trans-1,4-cyclohexanediyl ethylene dicarboxylate); poly(1,4-cyclohexanedimethylene alkylene dicarboxylates) such as poly(1,4-cyclohexanedimethylene ethylene dicarboxylate); poly([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate) The optical filter can be formed from any transparent material that transmits the desired wavelength range, including, but not limited to, poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylate), such as poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylate); lactic acid polymers and copolymers, such as (S)-polylactide, (R,S)-polylactide, poly(tetramethylglycolide), and poly(lactide-co-glycolide); and polycarbonates of bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, and 3,3',5,5'-tetramethylbisphenol A; polyamides, such as poly(p-phenylene terephthalamide); polyesters, such as polyethylene terephthalate, e.g., Mylar™ polyethylene terephthalate, and the like. In some embodiments, the system of interest includes a cuvette positioned in the sample interrogation region. In some embodiments, the cuvette may transmit light in the range of 100 nm to 1500 nm, such as 150 nm to 1400 nm, such as 200 nm to 1300 nm, such as 250 nm to 1200 nm, such as 300 nm to 1100 nm, such as 350 nm to 1000 nm, such as 400 nm to 900 nm, including 500 nm to 800 nm;

[0172] In certain embodiments, a light detection system having a plurality of light detectors as described above is part of or disposed within a particle analyzer, such as a particle sorter, hi certain embodiments, the system of interest is a flow cytometry system that includes a photodiode and amplifier component as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University 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 particular examples, flow cytometry systems of interest include a BD Biosciences FACSCanto™ flow cytometer, a BD Biosciences FACSCanto™ II flow cytometer, a BD Accuri™ flow cytometer, a BD Accuri™ C6 Plus flow cytometer, a BD Biosciences FACSCelesta™ flow cytometer, a BD Biosciences FACSLyric™ flow cytometer, a BD Biosciences FACSVerse™ flow cytometer, a BD Biosciences FACSymphony™ flow cytometer, a BD Biosciences LSRFortessa™ flow cytometer, a BD Biosciences LSRFortessa™ X-20 flow cytometer, a BD Biosciences FACSPresto™ flow cytometer, a BD Biosciences FACSVia™ flow cytometer, and a BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, a BD Biosciences These include the FACSLyric™ cell sorter, 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.

[0173] In some embodiments, the subject system is a hybrid system as described 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 specification, 10,145,793 specification, 10,113,967 specification, 10,006,852 specification, 9,952,076 specification, 9,933, Specification No. 341, Specification No. 9,726,527, Specification No. 9,453,789, Specification No. 9,200,334, Specification No. 9,097,640, Specification No. 9,095,494 Specification, Specification No. 9,092,034, Specification No. 8,975,595, Specification No. 8,753,573, Specification No. 8,233,146, Specification No. 8,140,300, Specification No. 7,544,326, Specification No. 7,201,875, Specification No. 7,129,505, Specification No. 6,821,740, Specification No. 6,813,017, Specification No. 6, and flow cytometry systems such as those described in US Pat. Nos. 809,804, 6,372,506, 5,700,692, 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 herein by reference in their entireties.

[0174] In some embodiments, the system includes a particle sorter component. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., cell nuclei, cells, non-cellular particles such as biological macromolecules, etc.) and, in some instances, delivering the separated components to one or more sample collection containers. For example, the subject systems may be configured to sort samples having two or more components, e.g., 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, including sorting samples having twenty-five or more components. One or more of the sample components may be separated from the sample and delivered to a sample collection container, 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 (including fifteen or more sample components).

[0175] In some embodiments, particle sorting systems of interest are configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application 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 Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, systems of interest include a particle sorting module with deflector plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0176] In certain embodiments, the system is an image-enabled particle sorter using frequency-encoded data, as shown in FIG. 3A. Particle sorter 300 includes an optical illumination component 300a including a light source 301 (e.g., a 488 nm laser) generating an output beam of light 301a, which is split into beams 302a and 302b by a beam splitter 302. Light beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate output beam 303a having one or more angularly deflected light beams. In some examples, output beam 303a generated from acousto-optic device 303 includes a local oscillator beam and multiple high-frequency comb beams. Light beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate output beam 304a having one or more angularly deflected light beams. In some examples, output beam 304a generated from acousto-optic device 304 includes a local oscillator beam and multiple high-frequency comb beams. Output beams 303a and 304a generated from acousto-optical devices 303 and 304, respectively, are combined with beam splitter 305 to generate output beam 305a, which is conveyed through optical component 306 (e.g., an objective lens) to illuminate particles in flow cell 307. In certain embodiments, acousto-optical device 303 (AOD) splits a single laser beam into an array of beamlets, each having a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam, which is then overlapped with the array of beamlets at beam combiner 305. In certain embodiments, the light illumination system having a light source and acousto-optical device can 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 Application Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0177] Output beam 305a illuminates sample particles 308 propagating through flow cell 307 (e.g., with sheath fluid 309) in illumination region 310. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high-frequency shifted optical beams shown as dots across illumination region 310) overlap with a reference local oscillator beam (shown as hatched across illumination region 310). Due to their different optical frequencies, the overlapping beams exhibit beat behavior, whereby each beamlet emits at a distinct frequency f 1-n carries a sinusoidal modulation.

[0178] Light from the illuminated sample is conveyed to a light detection system 300b, which includes multiple light detectors. The light detection system 300b includes a forward scatter light detector 311 for generating a forward scatter image 311a and a side scatter light detector 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 examples, 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 examples, 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 channels 314-317. Light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to photodetectors 314-317. In some examples, optical component 321 is a 534 nm / 40 nm bandpass. In some examples, optical component 322 is a 586 nm / 42 nm bandpass. In some examples, optical component 323 is a 700 nm / 54 nm bandpass. In some examples, 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 extends 10 nm on either side of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0179] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright-field light detection channel 313, and fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a can 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 sorting trigger 352. Sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or to a waste stream 333. In some examples, sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493 (filed March 28, 2017, the disclosure of which is incorporated herein by reference). In certain embodiments, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0180] FIG. 3B illustrates image-enabled particle sorting data processing according to certain embodiments. In some examples, the image-enabled particle sorting data processing is a low-latency data processing pipeline. Each photodetector produces pulses 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 produces 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 produces sort decisions that are used to classify particles based on the image features and selectively charge droplets.

[0181] In some embodiments, the system is a particle analyzer, and particle analysis system 401 (FIG. 4A) can be used to analyze and characterize particles, with or without physically sorting the particles into a collection vessel. FIG. 4A shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 shown in FIG. 4A can be configured, in whole or in part, to perform methods, such as those described herein. Particle analysis system 401 includes a fluidic system 402. Fluidic system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0182] 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. A detection station 408 generally refers to a monitoring area 407 of the common sample path. Detection, in some implementations, may include detecting light or one or more other characteristics of the particle 403 as it passes through the monitoring area 407. In FIG. 4A , one detection station 408 is shown with one monitoring area 407. Some implementations of the particle analysis system 401 may include multiple detection stations. Additionally, some detection stations may monitor more than one area.

[0183] Each signal is assigned a signal value to form a data point for each particle. This data may be referred to as event data, as described above. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect such data points continuously over a first time interval.

[0184] The particle analysis system 401 may also include a control system 406. 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 fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0185] 4B shows a system 400 for flow cytometry according to an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, 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-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0186] Pump lasers 115a-515c emit light in the form of laser beams. In the exemplary system of FIG. 4B, the wavelengths of the laser beams emitted from pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits ultraviolet light (light with wavelengths ranging from 10 nm to 400 nm) and reflects 488 nm and 633 nm light.

[0187] The laser beam is then directed to focusing lens 420, which focuses the beam onto a portion of the flow stream where the sample particles are located, within flow chamber 425. The flow chamber is the part of the fluidic system that directs particles, typically one at a time, in a stream toward the focused laser beam for investigation. The flow chamber may comprise a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0188] Light from the laser beam interacts with particles of the sample by diffraction, refraction, reflection, scattering, and absorption by re-emission at a variety of different wavelengths, depending on particle characteristics such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light can be sent via one or more of beam splitters 445a-445g, bandpass filters 450a-450e, longpass filters 455a-455b, and fluorescence collection lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-460f.

[0189] The 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 extends 10 nm on either side of the center of the spectral band, from 500 nm to 520 nm. Shortpass filters transmit light equal to or shorter than a specific wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths of light equal to or longer than a specific wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorochrome. The filter can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0190] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass. For example, beam splitter 445g is a 620 SP beam splitter, meaning that beam splitter 445g 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.

[0191] 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, or excitation light traveling primarily forward 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 refracted and reflected light from the particle's surface and internal structure, which tends to increase as the particle's structure becomes more complex. Fluorescence emission from fluorescent molecules associated with the particle can 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, side scatter detector 435, and fluorescence detector can be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0192] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown 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.

[0193] During operation, the operation of the 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 490 may be coupled to the detectors to receive output signals from the detectors, and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided in the system. The memory 495, controller / processor 490, and I / O 497 may be provided entirely as an integral part of the flow cytometer 410. In such embodiments, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the cytometer 400. Alternatively, some or all of the memory 495 and 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, some or all of the memory 495 and controller / processor 490 may be in wireless or wired communication with the cytometer 410. Controller / processor 490, together with memory 495 and I / O 497, can be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0194] The system shown in Figure 4B includes six different detectors that detect fluorescence in six different wavelength bands (sometimes referred to herein as the "filter windows" of 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 emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to approximately match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescence emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may fall within the filter window of one particular detector, it is generally true that part of that label's emission spectrum also overlaps with the filter windows of one or more other detectors. This is sometimes referred to as spillover. The I / O 497 can be configured to receive data for a flow cytometer experiment involving a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. I / O 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, data on the densities of the markers, 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.

[0195] 5 shows a functional block diagram of an example 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.

[0196] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel.

[0197] The analysis controller 500 can be configured to receive biological event data from the particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 can include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display including a first plot of the biological event data on the display device 506. The analysis controller 500 can be further configured to render a region of interest, for example, as a gate around a population of the biological event data shown by the display device 506, overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted on a histogram or bivariate plot of a single parameter. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0198] Analysis controller 500 can be further configured to display biological event data within the gate on display device 506 differently from other events within the biological event data outside the gate. For example, analysis controller 500 can be configured to render the color of biological event data contained within the gate differently from the color of biological event data outside the gate. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0199] The analysis controller 500 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device may be implemented as a mouse 510. The mouse 510 may initiate a gate selection signal to the analysis controller 500 identifying a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking the desired gate when a cursor is positioned there). In some implementations, the first device may be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a stylus, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 may include a right mouse button and a left mouse button, each capable of generating a trigger event.

[0200] The trigger event can cause the analysis controller 500 to change how 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 for particle sorting purposes.

[0201] In some embodiments, the analysis controller 500 can be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0202] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0203] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can include plots of the biological event data and gates that delineate sections of the plot. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500, along with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0204] In some implementations, the analysis controller 500 can generate a user interface for receiving 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 the sample or based on an initial set of events for a portion of the sample.

[0205] 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, include, or 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 a laser stream intersects) illuminated by an illumination source 612 (e.g., a laser). Vibration of droplet-forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0206] During operation, the detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitoring area 611. The detection station 614 is fed to a timing circuit 628, which in turn feeds a flash charge circuit 630. At a drop breakoff point, signaled by a timed drop delay (Δt), a flash charge can be applied to the moving fluid column 608 so that the droplets of interest carry a charge. The droplets 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 collection tube or a container, such as a multi-well or microwell sample plate, and a well or microwell can be associated with the particular droplet of interest. As shown in FIG. 6A, the droplets can be collected in a waste receptacle 638.

[0207] Detection system 616 (e.g., a droplet boundary detector) helps automatically determine the phase of the droplet drive signal when a particle of interest passes through monitoring area 611. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is incorporated herein by reference in its entirety. Detection system 616 allows the instrument to accurately calculate the location of each detected particle in the droplet. Detection system 616 can provide amplitude signal 620 and / or phase 618 signals, which then (via amplifier 622) provide to amplitude control circuit 626 and / or frequency control circuit 624. Amplitude control circuit 626 and / or frequency control circuit 624 then control droplet forming transducer 602. Amplitude control circuit 626 and / or frequency control circuit 624 can be included in a control system.

[0208] 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 sorting decisions based thereon. The sorting decisions can be included in the particle's 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 either the detection system 616 or the detection station 614 and provided to a non-collecting element.

[0209] 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 stream charging wires within the barbs. This creates a stream of droplets 610 containing 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. The information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, directing them toward a destination collection vessel (e.g., any of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a receptacle 674 or along a second path 668 toward a receptacle 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particle to continue along flow path 664. Such uncharged droplets can enter a waste receptacle, such as via an aspirator 670.

[0210] Sorting electronics can be included to initiate measurement collection, receive particle fluorescent signals, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 6B includes the BD FACSAria™ line of flow cytometers commercially offered by Becton, Dickinson and Company (Franklin Lakes, NJ).

[0211] Non-transitory computer-readable storage medium Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for implementing the subject methods. The computer-readable storage media may be used on one or more computers for fully or partially automating systems for implementing 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," and 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, Blu-ray disks, solid-state disks, and network-attached storage (NAS), whether such devices are internal or external to the computer. Files containing information may be "stored" on a computer-readable medium, where "storage" refers to recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be executed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, as well as many others.

[0212] A non-transitory computer-readable storage medium according to certain embodiments has an algorithm for measuring light from a sample having isolated cell nuclei in a flow stream, an algorithm for generating an image of the cell nuclei from the measured light, and an algorithm for evaluating the morphology of the cell nuclei based on the generated image of the cell nuclei.

[0213] In embodiments, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology based on at least one image of an isolated cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology by determining the viability of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology by assessing the ploidy of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology by assessing the size of the cell nucleus. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology by assessing the shape of the cell nucleus, such as where the shape may be spherical, spindle-shaped, oval, elongated, and flattened. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for evaluating morphology by assessing the elasticity of the nuclear envelope.

[0214] In some embodiments, the non-transitory computer-readable storage medium has an algorithm for calculating image parameters of cell nuclei from the generated image. In some examples, the image parameters include one or more 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 particles, forward scattered light by cell nuclei, and side scattered light by cell nuclei. In some examples, the non-transitory computer-readable storage medium has an algorithm for classifying isolated cell nuclei based on one or more of the calculated image parameters. In some examples, the non-transitory computer-readable storage medium has an algorithm for classifying isolated cell nuclei using five or more calculated image parameters.

[0215] In some examples, the memory includes instructions for generating an image of unlabeled nuclei from measurement light from the illuminated unlabeled nuclei. In some examples, the memory includes instructions for generating an image of fluorescently labeled nuclei from measurement light from the illuminated nuclei. In particular examples, the image is generated from a frequency-encoded data signal.

[0216] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for assessing the morphology of a cell nucleus using a dynamic algorithm that updates using an image of the cell nucleus (e.g., a reference image) or the determined image parameters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assessing the morphology of a cell nucleus using a machine learning algorithm that uses the reference image of the cell nucleus or the determined image parameters as a training dataset.

[0217] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for classifying cell nuclei of a sample by assigning the cell nuclei to one or more particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for assigning cell nuclei to particle population clusters based on a comparison of a generated image of the cell nuclei to parameters of the particle population clusters (e.g., determined using a reference image or calculated image parameters).

[0218] In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for determining one or more sorting gates for sorted cell nuclei of a sample. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating one or more sorting gates that capture cell nuclei of a target particle population cluster and exclude cell nuclei of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that maximize the inclusion yield of cell nuclei of a target particle population cluster. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that maximize the exclusion of cell nuclei of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium comprises an algorithm for generating sorting gates that exclude cell nuclei of non-target particle population clusters based on a calculated Mahalanobis distance from the target particle population cluster. In some embodiments, the non-transitory computer-readable storage medium comprises an algorithm for capturing cell nuclei in a target particle population cluster that have an assessed viability greater than a predetermined threshold. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for capturing cell nuclei having a predetermined shape in a target particle population cluster. In some embodiments, the non-transitory computer readable storage medium comprises an algorithm for capturing cell nuclei having a predetermined size in a target particle population cluster. In some embodiments, the non-target particle population cluster comprises non-nuclear cellular debris. In some embodiments, the non-target particle population cluster comprises cell nuclei having an assessed viability that is below a predetermined threshold.

[0219] In certain embodiments, the non-transitory computer-readable storage medium has an algorithm for generating sorting gates by calculating an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of cell nuclei in target particle population clusters and the exclusion of cell nuclei in non-target particle population clusters. In certain examples, the generated sorting gates have an Fβ score of 1 or greater. In certain examples, the generated sorting gates have an Fβ score of less than 1. In some examples, the particle sorting decision uses eight or fewer sorting gates, for example, four or fewer sorting gates.

[0220] The non-transitory computer-readable storage medium may be used in 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, etc. The processing module includes a processor that accesses a memory in which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, 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 or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known manners and facilitates the processor's coordination and execution of the 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 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.

[0221] 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., a flash drive, USB storage, a compact disc, a DVD, a Blu-ray disc, etc.), or instructions for downloading the programming from an Internet web protocol or cloud server. The kits may further include instructions for implementing 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 the instructions may be present is information printed on a suitable medium or substrate, such as one or more pieces of paper with the information printed on them, kit packaging, a package insert, etc. Another form in which the instructions may be present is a computer-readable medium on which the information is recorded, such as a diskette, a compact disc (CD), a portable flash drive, etc. Another form in which the instructions may be present is a website address that can be used via the Internet to access the information at the removed site.

[0222] Utilities The subject systems, methods, and computer systems are used as upstream evaluations of cell nuclei, such as for applications in which isolated nuclei may be used in molecular biology assays (e.g., ChIP-seq, Hi-C, and ATAC-seq). In some examples, the present disclosure provides high-quality and pure cell nuclei isolated from heterogeneous nuclear samples. Additionally, the subject systems and methods are used in a variety of applications in which it is desirable to analyze and sort particle components in a sample in a fluid medium. In some embodiments, the systems and methods described herein are used for flow cytometric characterization of cell nuclei isolates, including cases in which it is desirable to keep the cell nuclei of a sample unlabeled. Embodiments of the present disclosure are used in cases in which it is desirable to provide a flow cytometer with improved cell nuclei sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell nuclei sorting.

[0223] Embodiments of the present disclosure are also useful in applications where cell nuclei prepared from biological samples may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices can facilitate obtaining individual cell nuclei prepared from target fluid or tissue biological samples. For example, the subject methods and systems facilitate obtaining cell nuclei from fluid or tissue samples used as research or diagnostic specimens. The disclosed methods and devices enable the separation and collection of cell nuclei from biological samples (e.g., organs, tissues, tissue fragments, body fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems. [Example]

[0224] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the invention, and are not intended to limit the scope of what the inventors regard as their invention.

[0225] Assessing the quality of cell nuclei by assessing morphology prior to use in downstream biological assays Highly viable cell samples and low-quality cell samples with only approximately 50% viability were collected for cell nuclei isolation. Approximately 5,000 cell nuclei were acquired for each sample, and the quality of the nuclei was visualized by imaging flow cytometry. Figure 2A shows the evaluation of cell nuclei morphology for viable samples using imaging flow cytometry (Becton Dickinson FACSGover). Scatter plots were generated using side scatter and light loss (violet) imaging parameters. The scatter plots of viable cells showed three particle cluster populations and no cell debris. Mononuclear, binuclear, and trinuclear clusters were identified, and images of each cell nucleus were generated. Figure 2A shows representative images of each particle cluster cluster from a viable cell sample. Figure 2B shows the evaluation of cell nuclei morphology for cell nuclei in a low-viability sample using imaging flow cytometry, according to certain embodiments. The scatter plot of the low-viability cell nuclei sample shows four distinct particle clusters, including a mononuclear cluster, two enlarged cell nuclei clusters from dead cells, and a cell debris cluster. Figure 2B also shows representative images of each of the particle clusters. Differences between the two samples were observed using the scatter imaging parameter, light loss (violet) versus side scatter imaging. Each cluster can be correlated with the different ploidy states of nuclei in the highly viable sample based on the images generated (Figure 2A). For the low-quality sample, no distinct replicative states of nuclei were observed (Figure 2B). Enlarged nuclei were observed, suggesting a cell death process. These results, shown in Figures 2A and 2B, demonstrate that nuclear morphology can be distinguished based on images generated according to the methods described herein.

[0226] Each sample was used for downstream ATAC-Seq by DNA fragmentation followed by PCR amplification. The resulting PCR products were run using an automated electrophoresis instrument bioanalyzer. Correlating with the results above, we observed a good distribution of free DNA, mononucleosomes, binucleosomes, and trinucleosomes in highly viable cell samples. In contrast, low-quality samples showed increased levels of free DNA and reduced multinucleosome peaks, which can result in suboptimal ATAC-Seq data (Figure 2C). Collectively, these data demonstrate that analysis of cell nuclear morphology from generated images can be used to determine the quality of isolated cell nuclei, for example, for use in downstream molecular biology workflows.

[0227] Assessment of cell nuclear morphology and label-free sorting of nuclei based on images and image parameters Nuclei were isolated from stimulated CD4 T cells, and nuclear morphology was visualized by imaging flow cytometry. Figure 2D shows an evaluation of nuclear morphology of stimulated CD4 T cells, showing distinct particle population clusters, according to certain embodiments. Using side scatter and light loss (violet) imaging parameters, both small and large nuclei were observed in a heterogeneous sample. A scatter plot of stimulated CD4 T cells showed two particle cluster populations. Small mononuclear and large (binuclear) clusters were identified, and images of each nucleus were generated. Figure 2D also shows representative images for each of the small and large particle population clusters. Figure 2E shows a comparison of four different imaging parameters for small and large nuclei, according to certain embodiments. Based on the generated images, four imaging parameters (total intensity (light loss (imaging)), total intensity (SSC (imaging)), SSC (imaging)-A, and SSC (violet)-A) were calculated, showing a greater than two-fold difference between small and large nuclei. Figure 2F shows the analysis of each of the four imaging parameters using representative images of small and large cell nuclei. The four imaging parameters were able to distinguish between small and large nuclei. Figure 2G shows the calculated gating strategy using the imaging parameters of a sample according to certain embodiments. Based on the four imaging parameters, a sequential gating strategy was developed to exclude cell nuclei doublets and isolate large singlets. The imaging parameters were generated based on images from two different cell nuclei particle populations, and the sorting strategy was generated using the image parameters calculated with unlabeled particles.

[0228] Figure 2H shows another gating strategy calculated to isolate nuclei with distinct morphologies from stimulated and unstimulated CD4 T cell samples using images and imaging parameters according to certain embodiments. The gating strategy includes a first gate to remove debris from identified nuclei and a second gate to separate multinuclear (binuclear and trinuclear) morphologies from singlet nuclei. An additional gate is implemented to separate single nuclei from nuclei exhibiting enlarged morphologies (e.g., from replicating cells). Figure 2I shows clustering of distinct nuclei using imaging parameters from unstimulated and stimulated CD4 T cells according to certain embodiments. Unsupervised clustering using the t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm separated nuclei from unstimulated and stimulated T cells, which correlates with the biology of the samples. Heat maps were used to rank the top imaging parameters that drove separation based on the fold change between the two samples. Some imaging parameters showed up to a 2.4-fold difference between stimulated and unstimulated cell populations. This example demonstrates that cell nuclei with different morphologies can be isolated based on a combination of images and imaging parameters.

[0229] Notwithstanding the scope of the appended claims, the present disclosure is also defined by the following clauses. 1. A method comprising: measuring light from a sample containing isolated cell nuclei in a flow stream; generating an image of the cell nucleus from the measured light; and assessing the morphology of the cell nucleus based on the generated image of the cell nucleus. 2. The method of clause 1, wherein assessing the morphology of the cell nucleus includes determining the viability of the cell nucleus. 3. The method of clause 2, wherein assessing the morphology of the cell nucleus includes determining the ploidy of the cell nucleus. 4. The method of any one of clauses 1 to 3, wherein assessing the morphology of the cell nucleus comprises assessing the size of the cell nucleus. 5. The method of any one of clauses 1 to 4, wherein assessing the morphology of the cell nucleus comprises assessing the shape of the cell nucleus.

[0230] 6. The method according to clause 5, wherein the shape of the cell nucleus is selected from the group consisting of spherical, spindle-shaped, oval, elongated and flattened. 7. The method of any one of clauses 1 to 6, wherein assessing the morphology of the cell nucleus comprises assessing the elasticity of the nuclear envelope. 8. The method of any one of clauses 1 to 7, further comprising calculating image parameters from the generated images of the cell nuclei. 9. The method of clause 8, wherein 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 nuclei, forward scatter by nuclei, side scatter by nuclei, and combinations thereof. 10. The method of any one of clauses 1-9, comprising classifying cell nuclei based on the generated image, the calculated image parameters, or a combination thereof.

[0231] 11. The method of clause 10, wherein classifying the cell nuclei includes assigning the cell nuclei to one or more particle population clusters. 12. The method of any one of clauses 1 to 11, wherein the cell nuclei are not labeled. 13. The method of any one of clauses 1 to 11, wherein the cell nuclei are fluorescently labeled. 14. The method of any one of clauses 10 to 13, comprising determining one or more sorting gates for sorted cell nuclei of the sample. 15. The method of clause 14, wherein one or more sorting gates capture cell nuclei of a target particle population cluster and exclude particles of a non-target particle population cluster.

[0232] 16. The method of clause 15, wherein the target particle population cluster comprises cell nuclei having an assessed viability greater than a predetermined threshold. 17. The method of clause 15, wherein the target particle population clusters comprise cell nuclei having a predetermined shape. 18. The method of clause 15, wherein the target particle population cluster comprises cell nuclei having a predetermined size. 19. The method of any one of clauses 15-18, wherein the non-target particle population clusters comprise non-nuclear cellular debris. 20. The method of any one of clauses 15-18, wherein the non-target particle population cluster comprises cell nuclei having an assessed viability that is below a predetermined threshold.

[0233] 21. The method of any one of clauses 14 to 50, wherein sorting gates are determined using calculated image parameters of each cell nucleus population cluster. 22. The method of any one of clauses 14 to 21, wherein the sorting gate maximizes the inclusion yield of cell nuclei in the target particle population cluster. 23. The method of any one of clauses 14 to 21, wherein the sorting gate maximizes the exclusion of particles in non-target particle population clusters. 24. The method of clause 23, wherein the sorting gate excludes particles of non-target particle population clusters based on their calculated Mahalanobis distance from the target particle population cluster. 25. The method of any one of clauses 14 to 24, wherein generating the sorting gate comprises calculating an Fβ score, the Fβ score comprising a weighted harmonic mean of the inclusion of cell nuclei in the target particle population cluster and the exclusion of particles in the non-target particle population cluster.

[0234] 26. The method of clause 25, wherein the generated sorting gates include an Fβ score of 1 or greater. 27. The method of clause 25, wherein the generated sorting gates include Fβ scores less than 1. 28. The method of any one of clauses 1-27, further comprising sorting cell nuclei of the sample into a plurality of sample containers. 29. The method of any one of clauses 1-28, wherein measuring light from cell nuclei in the flow stream comprises detecting light absorption, scattered light, emitted light, or a combination thereof. 30. The method of clause 29, wherein the scattered light comprises forward scattered light.

[0235] 31. The method of clause 29, wherein the scattered light comprises side scattered light. 32. The method of any one of clauses 8 to 31, wherein the image parameters of the cell nuclei are calculated from frequency-encoded fluorescence data from fluorescently labeled cell nuclei. 33. The method of any one of clauses 1-32, further comprising illuminating the sample containing the cell nuclei in the flow stream with a light source. 34. The method of clause 33, wherein the flow stream is illuminated with a light source having a wavelength between 200 nm and 800 nm. 35. The method of any one of clauses 33-34, comprising irradiating the flow stream with a first frequency-shifted light beam and a second frequency-shifted light beam.

[0236] 36. The method of clause 35, wherein the first frequency-shifted optical beam comprises a local oscillator (LO) beam and the second frequency-shifted optical beam comprises a high-frequency comb beam. 37. The method according to any one of clauses 35-36, applying a high frequency drive signal to the acousto-optic device; and The method further includes irradiating the acousto-optic device with a laser to generate the first frequency-shifted light beam and the second frequency-shifted light beam. 38. The method of clause 37, wherein the laser is a continuous wave laser.

[0237] 39. A system comprising: a light source configured to illuminate a sample containing isolated cell nuclei in the flow stream; a light detection system including a photodetector for measuring light from the cell nucleus; and A processor including a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to: generating an image of the cell nucleus from the measured light; and The system allows evaluation of the morphology of the cell nucleus based on the generated image of the cell nucleus. 40. The system of clause 39, wherein the memory includes instructions for assessing the morphology of cell nuclei by determining the viability of the cell nuclei. 41. A system described in any one of clauses 39 to 40, wherein the memory includes instructions for assessing the morphology of a cell nucleus by determining the ploidy of the cell nucleus. 42. A system described in any one of clauses 39 to 41, wherein the memory includes instructions for assessing the morphology of a cell nucleus by assessing the size of the cell nucleus. 43. A system described in any one of clauses 39 to 42, wherein the memory includes instructions for assessing the morphology of cell nuclei by assessing the shape of the cell nuclei. 44. The system according to clause 43, wherein the shape of the cell nucleus is selected from the group consisting of spherical, spindle-shaped, oval, elongated and flattened.

[0238] 45. A system described in any one of clauses 39 to 44, wherein the memory includes instructions for assessing the morphology of a cell nucleus by assessing the elasticity of the nuclear envelope. 46. ​​A system according to any one of clauses 39 to 45, wherein the memory includes instructions for calculating image parameters from the generated images of the cell nuclei. 47. The system of clause 46, 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 by cell nuclei, forward scatter by cell nuclei, side scatter by cell nuclei, and combinations thereof. 48. The system of any one of clauses 39-47, wherein the memory includes instructions for classifying cell nuclei based on the generated image, the calculated image parameters, or a combination thereof. 49. The system of clause 48, wherein the memory includes instructions for classifying cell nuclei by assigning the cell nuclei to one or more particle population clusters. 50. A system according to any one of clauses 39 to 49, wherein the cell nuclei are not labeled.

[0239] 51. A system described in any one of clauses 39 to 49, wherein the cell nuclei are fluorescently labeled. 52. A system described in any one of clauses 49 to 51, wherein the memory includes instructions for determining one or more sorting gates for the sorted cell nuclei of the sample. 53. The system of clause 52, wherein one or more sorting gates capture cell nuclei of a target particle population cluster and exclude particles of a non-target particle population cluster. 54. The system of clause 53, wherein the target particle population cluster comprises cell nuclei having an assessed viability greater than a predetermined threshold. 55. The system of clause 53, wherein the target particle population clusters include cell nuclei having a predetermined shape.

[0240] 56. The system of clause 53, wherein the target particle population cluster comprises cell nuclei having a predetermined size. 57. A system described in any one of clauses 53 to 56, wherein the non-target particle population clusters comprise non-nuclear cellular debris. 58. A system described in any one of clauses 53 to 57, wherein the non-target particle population cluster comprises cell nuclei having an assessed viability that is below a predetermined threshold. 59. A system described in any one of clauses 48 to 58, wherein the memory includes instructions for determining one or more sorting gates using the calculated image parameters of each cell nucleus population cluster. 60. The system of any one of clauses 53-59, wherein the memory includes instructions for determining a sorting gate that maximizes the inclusion yield of cell nuclei in the target particle population cluster.

[0241] 61. A system according to any one of clauses 53 to 59, wherein the memory comprises instructions for determining a sorting gate that maximizes the rejection of particles of non-target particle population clusters. 62. The system of clause 61, wherein the memory includes instructions for determining a sorting gate that excludes particles of a non-target particle population cluster based on the calculated Mahalanobis distance from the target particle population cluster. 63. The system of any one of clauses 53-62, wherein the memory includes instructions for generating sorting gates by calculating an Fβ score, the Fβ score comprising a weighted harmonic mean of inclusion of cell nuclei in target particle population clusters and exclusion of particles in non-target particle population clusters. 64. The system of clause 63, wherein the generated sorting gates include an Fβ score of 1 or greater. 65. The system of clause 63, wherein the generated sorting gates include Fβ scores less than 1.

[0242] 66. The system of any one of clauses 39 to 65, further comprising a display configured to display a graphical user interface. 67. The system of clause 66, wherein the graphical user interface is configured to manually input one or more of the sorting gates. 68. The system of clause 67, wherein manually inputting the sorting gates includes drawing the sorting gates on a scatter plot of particle population clusters. 69. The system of any one of clauses 39-68, further comprising a cell sorter. 70. The system of clause 69, wherein the cell sorter includes a droplet deflector.

[0243] 71. A system described in any one of clauses 39 to 70, wherein the light source includes a light beam generator component configured to generate at least a first frequency-shifted light beam and a second frequency-shifted light beam. 72. The system of clause 71, wherein the optical beam generator comprises an acousto-optic deflector. 73. The system of any one of clauses 71-72, wherein the optical beam generator comprises a direct digital synthesizer (DDS) RF comb generator. 74. A system according to any one of clauses 71 to 73, wherein the optical beam generator component is configured to generate a local oscillator beam. 75. The system of any one of clauses 71-74, wherein the optical beam generator component is configured to generate a plurality of frequency-shifted comb beams.

[0244] 76. A system according to any one of clauses 71 to 75, wherein the light source comprises a laser. 77. The system of clause 76, wherein the laser is a continuous wave laser. 78. A system according to any one of clauses 39 to 77, wherein the system is a flow cytometer. 79. A system according to any one of clauses 39 to 78, including an integrated circuit device. 80. The system of clause 79, 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).

[0245] 81. A non-transitory computer-readable storage medium having instructions stored thereon, comprising: an algorithm for measuring light from a sample containing isolated cell nuclei in a flow stream; an algorithm for generating an image of the cell nucleus from the measured light; A non-transitory computer-readable storage medium comprising an algorithm for assessing the morphology of a cell nucleus based on a generated image of the cell nucleus. 82. The non-transitory computer-readable storage medium of clause 81, comprising an algorithm for assessing the morphology of cell nuclei by determining the viability of the cell nuclei. 83. A non-transitory computer-readable storage medium according to any one of clauses 81-82, comprising an algorithm for assessing the morphology of a cell nucleus by determining the ploidy of the cell nucleus. 84. A non-transitory computer-readable storage medium according to any one of clauses 81 to 83, comprising an algorithm for assessing the morphology of a cell nucleus by assessing the size of the cell nucleus.

[0246] 85. A non-transitory computer-readable storage medium according to any one of clauses 81 to 84, comprising an algorithm for assessing the morphology of a cell nucleus by assessing the shape of the cell nucleus. 86. The non-transitory computer-readable storage medium of clause 85, wherein the shape of the cell nucleus is selected from the group consisting of spherical, spindle-shaped, oval, elongated and flattened. 87. A non-transitory computer-readable storage medium according to any one of clauses 81 to 86, comprising an algorithm for assessing the morphology of a cell nucleus by assessing the elasticity of the nuclear envelope. 88. A non-transitory computer-readable storage medium according to any one of clauses 81 to 87, comprising an algorithm for calculating image parameters from the generated images of cell nuclei. 89. The non-transitory computer-readable storage medium of clause 88, 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 by cell nuclei, forward scatter by cell nuclei, side scatter by cell nuclei, and combinations thereof.

[0247] 90. A non-transitory computer-readable storage medium according to any one of clauses 81-89, comprising an algorithm for classifying cell nuclei based on generated images, calculated image parameters, or a combination thereof. 91. The non-transitory computer-readable storage medium of clause 90, comprising an algorithm for classifying cell nuclei by assigning the cell nuclei to one or more particle population clusters. 92. The non-transitory computer-readable storage medium of any one of clauses 81-91, wherein the cell nuclei are unlabeled. 93. The non-transitory computer-readable storage medium of any one of clauses 81-91, wherein the cell nuclei are fluorescently labeled. 94. A non-transitory computer-readable storage medium according to any one of clauses 91 to 93, comprising an algorithm for determining one or more sorting gates for sorted cell nuclei of a sample. 95. The non-transitory computer-readable storage medium of clause 94, wherein one or more sorting gates capture cell nuclei of a target particle population cluster and exclude particles of a non-target particle population cluster.

[0248] 96. The non-transitory computer-readable storage medium of clause 95, wherein the target particle population cluster comprises cell nuclei having an assessed viability greater than a predetermined threshold. 97. The non-transitory computer-readable storage medium of clause 95, wherein the target particle population clusters include cell nuclei having a predetermined shape. 98. The non-transitory computer-readable storage medium of clause 95, wherein the target particle population clusters include cell nuclei having a predetermined size. 99. The non-transitory computer-readable storage medium of any one of clauses 95-98, wherein the non-target particle population clusters include non-nuclear cellular debris. 100. The non-transitory computer-readable storage medium of any one of clauses 95-99, wherein the non-target particle population clusters include cell nuclei having an assessed viability that is less than a predetermined threshold.

[0249] 101. A non-transitory computer-readable storage medium according to any one of clauses 88 to 100, comprising an algorithm for determining one or more sorting gates using the calculated image parameters of each cell nucleus population cluster. 102. A non-transitory computer-readable storage medium according to any one of clauses 95-101, comprising an algorithm for determining sorting gates that maximize the inclusion yield of cell nuclei in the target particle population cluster. 103. A non-transitory computer-readable storage medium according to any one of clauses 95-101, comprising an algorithm for determining a sorting gate that maximizes the rejection of particles of non-target particle population clusters. 104. The non-transitory computer-readable storage medium of clause 103, comprising an algorithm for determining a sorting gate that excludes particles of a non-target particle population cluster based on their calculated Mahalanobis distance from the target particle population cluster.

[0250] 105. The non-transitory computer readable storage medium of any one of clauses 94-104, wherein the non-transitory computer readable storage medium comprises an algorithm for generating sorting gates by calculating an Fβ score, the Fβ score comprising a weighted harmonic mean of inclusion of cell nuclei in target particle population clusters and exclusion of particles in non-target particle population clusters. 106. The non-transitory computer-readable storage medium of clause 105, wherein the generated sorting gate comprises an Fβ score of 1 or greater. 107. The non-transitory computer-readable storage medium of clause 104, wherein the generated sorting gates include Fβ scores less than 1.

[0251] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of the invention that certain changes and modifications can be made without departing from the spirit or scope of the appended claims.

[0252] Accordingly, the foregoing merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various configurations, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts the inventors have contributed to advancing the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended as a dedication to the public, regardless of whether such disclosure is expressly recited in the claims.

[0253] Accordingly, 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 are embodied by the appended claims. In the claims, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) is expressly defined as being invoked for a limitation in a claim only if the exact phrase "means for" or the exact phrase "step" appears at the beginning of such limitation in the claim. If such exact phrases are not used in a claim limitation, 35 U.S.C. §112(f) or 35 U.S.C. §112(6) is not invoked.

[0254] 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 / 631,674, filed April 9, 2024, the disclosure of which is incorporated herein by reference in its entirety.

Claims

1. measuring light from a sample containing isolated cell nuclei in a flow stream; generating an image of the nucleus from the measured light; and assessing the morphology of the cell nuclei based on the generated images of the cell nuclei; A method comprising:

2. The method of claim 1 , wherein assessing the morphology of the cell nucleus comprises determining the viability of the cell nucleus.

3. 3. The method of claim 2, wherein assessing the morphology of the cell nucleus comprises determining the ploidy of the cell nucleus.

4. The method of any one of claims 1 to 3, wherein assessing the morphology of the cell nucleus comprises assessing the size of the cell nucleus.

5. The method of any one of claims 1 to 4, wherein assessing the morphology of the cell nucleus comprises assessing the shape of the cell nucleus.

6. 6. The method of claim 5, wherein the shape of the cell nucleus is selected from the group consisting of spherical, spindle-shaped, oval, elongated, and flattened.

7. The method of any one of claims 1 to 6, wherein assessing the morphology of the cell nucleus comprises assessing the elasticity of the nuclear envelope.

8. The method of any one of claims 1 to 7, further comprising calculating image parameters from the generated images of the cell nuclei.

9. The method of any one of claims 1 to 8, comprising classifying the cell nuclei based on the generated image, the calculated image parameters, or a combination thereof.

10. The method of any one of claims 1 to 9, wherein the cell nuclei are unlabeled or fluorescently labeled.

11. The method of any one of claims 1 to 10, further comprising sorting the cell nuclei of the sample into a plurality of sample vessels.

12. 12. The method of any one of claims 1 to 11, wherein measuring light from the cell nuclei in the flow stream comprises detecting light absorption, scattered light, emitted light, or a combination thereof.

13. The method of any one of claims 1 to 12, further comprising illuminating the sample containing cell nuclei in the flow stream with a light source.

14. a light source configured to illuminate a sample containing isolated cell nuclei in the flow stream; a light detection system including a light detector for measuring light from the cell nuclei; and 1. A system including a processor including a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to: generating an image of the nucleus from the measured light; and evaluating the morphology of the cell nuclei based on the generated images of the cell nuclei; system.

15. A non-transitory computer-readable storage medium having instructions stored thereon, comprising: an algorithm for measuring light from a sample containing isolated cell nuclei in a flow stream; an algorithm for generating an image of the nuclei from the measured light; an algorithm for assessing the morphology of the cell nucleus based on the generated image of the cell nucleus; 1. A non-transitory computer-readable storage medium comprising:

Images