Methods for label-free cell sorting and systems for the same

Abstract

SOLUTION: Aspects of the present disclosure include methods for label-free particle sorting. Methods according to certain embodiments include measuring light from a sample having label-free particles in a flow stream, generating an image of one or more of the particles from the measured light, calculating image parameters from the generated image of the one or more particles, and generating a particle sort determination based on the calculated image parameters. In some embodiments, sorting gates are determined based on image parameters calculated from the particle and ground-truth image classification parameters. 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: None

Description

[Background technology]

[0001] 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. In flow-type particle sorting systems, particles, such as analyte-bound beads or individual cells in a fluid suspension, are passed in a stream through a detection region where a sensor detects particles contained in the stream of the type to be sorted. Upon detecting particles of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particles of interest.

[0002] Particle detection is typically performed by passing a fluid stream through a detection region where particles are exposed to radiation from one or more lasers, and fluorescence from the particles is measured. Particles or components thereof can be labeled with fluorescent dyes to facilitate detection; by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. Detection is performed using one or more photosensors to facilitate independent measurement of the fluorescence of each distinct fluorescent dye.

[0003] Data generated from the detected light can be used to record the distribution of components and to sort the desired material. To sort particles in a sample, a droplet charging mechanism charges droplets in the flowstream containing the particle type to be sorted with an electric charge at the breakoff 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 label-free particle sorting. The method, according to certain embodiments, includes measuring light from a sample having label-free particles in a flow stream, generating one or more images of the particles from the measured light, calculating image parameters from the generated images of the one or more particles, and generating a particle sorting decision based on the calculated image parameters. In some embodiments, the sorting gate is determined based on the image parameters calculated from the particles and ground truth image classification parameters. 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, image parameters are calculated from the generated images of unlabeled particles 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 the particle, forward scattered light by the particle, and side scattered light by the particle. In some examples, particles from the sample are classified based on one or more of the calculated image parameters. In some examples, the method includes classifying particles using five or more calculated image parameters.

[0006] In some embodiments, the particles are classified by comparing one or more of the calculated image parameters to a ground truth image classification parameter. In some examples, classifying the particle is based on a threshold between the calculated image parameter of the particle and the ground truth image classification parameter. In some examples, the method further includes determining the ground truth image classification parameter. In particular examples, the ground truth image classification parameter is determined by contacting particles from the sample with one or more fluorescent labels, illuminating the fluorescently labeled particles with a light source, measuring fluorescence from the illuminated particles, identifying particles from the sample based on the measured fluorescence, determining image parameters of the identified particles, and generating ground truth image classification parameters for the identified particles. In some examples, the particles are contacted with four or more different fluorescent labels. In some examples, the method includes generating an image of the fluorescently labeled particles. In particular examples, the image is generated from a frequency-encoded data signal. In some embodiments, the ground truth image classification parameter is generated from the image of the fluorescently labeled particles.

[0007] In some embodiments, the ground truth image classification parameters use a dynamic algorithm that updates based on the determined image parameters of the fluorescently labeled particles. In some examples, the imaging parameters generated from the fluorescently labeled particles are used in a machine learning algorithm to generate the ground truth image classification parameters. In some examples, the images generated from the fluorescently labeled particles are used as training data to generate the ground truth image classification parameters.

[0008] In some examples, classifying particles of the sample includes assigning particles to one or more particle population clusters. In some examples, particles are assigned to particle population clusters based on a comparison between ground truth image classification parameters of each particle population cluster and calculated image parameters of the particles. In some embodiments, one or more sorting gates are determined for the classified particles of the sample. In some examples, the sorting gates are determined using the ground truth image classification parameters of each particle population cluster. In some examples, the one or more sorting gates capture particles of target particle population clusters and exclude particles of non-target particle population clusters. In some examples, the sorting gates maximize the inclusion yield of particles of the target particle population clusters. In some examples, the sorting gates maximize the exclusion of particles of the non-target particle population clusters. In some examples, the sorting gates exclude particles of the non-target particle population clusters based on a calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster. In certain embodiments, generating sorting gates includes calculating an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. 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.

[0009] 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).

[0010] Aspects of the present disclosure also include systems for practicing the subject methods. The system, according to certain embodiments, includes a light source configured to illuminate unlabeled particles of a sample, a light detection system having a plurality of photodetectors for measuring light from the particles, 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 one or more images of the particles from the measured light, calculate image parameters from the generated images of the particles, and generate particle sorting decisions based on the calculated image parameters.

[0011] In some embodiments, the memory includes instructions for classifying particles based on one or more of the calculated image parameters. In some examples, the memory includes instructions for classifying particles using five or more calculated image parameters. In some examples, the calculated image parameters for the particles include center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the particle, forward scattered light by the particle, side scattered light by the particle, and combinations thereof.

[0012] In some embodiments, the memory includes instructions for classifying particles by comparing one or more of the calculated image parameters to ground truth image classification parameters. In some examples, the memory includes instructions for classifying particles based on a threshold between the calculated image parameters of the particles and the ground truth image classification parameters. In some embodiments, the memory includes instructions for determining ground truth image classification parameters. In some examples, the memory includes instructions that, when executed by the processor, cause the processor to illuminate fluorescently labeled particles from a sample with a light source, measure fluorescence from the illuminated particles, identify particles from the sample based on the measured fluorescence, determine image parameters of the identified particles, and generate ground truth image classification parameters for the identified particles. In some examples, the memory includes instructions for generating ground truth image classification parameters from images of fluorescently labeled particles.

[0013] In some embodiments, the memory includes instructions for generating ground truth image classification parameters using a dynamic algorithm, such that the classification parameters are updated based on the determined image parameters of the fluorescently labeled particles. In some examples, the memory includes a machine learning algorithm for generating the ground truth image classification parameters, such that the determined image parameters are used as training data for the machine learning algorithm. In some examples, the generated images are used as training data for the machine learning algorithm.

[0014] In some embodiments, the memory includes instructions for classifying particles of the sample by assigning the particles to one or more particle population clusters. In some examples, the memory includes instructions for assigning particles to particle population clusters based on a comparison between ground truth image classification parameters of each particle population cluster and calculated image parameters of the particles. In some embodiments, the memory includes instructions for generating sorting gates using the ground truth image classification parameters of each particle population cluster. In some examples, the one or more sorting gates capture particles of target particle population clusters and exclude particles of non-target particle population clusters. In some examples, the sorting gates maximize the inclusion yield of particles of the target particle population clusters. In some examples, the sorting gates maximize the exclusion of particles of non-target particle population clusters. In some examples, the memory includes instructions for determining the sorting gates by excluding particles of non-target particle population clusters based on the calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster. 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 particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. 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.

[0015] 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.

[0016] A non-transitory computer-readable storage medium having instructions with an algorithm for label-free sorting of particles in a sample is also described. The non-transitory computer-readable storage medium according to certain embodiments has an algorithm for measuring light from a sample containing label-free particles in a flow stream, an algorithm for generating one or more images of the particles from the measured light, an algorithm for calculating image parameters from the generated images of the one or more particles, and an algorithm for generating a particle sorting decision based on the calculated image parameters. In some examples, the image parameters include one or more of the following: center of mass, delta center of mass, diffusivity, eccentricity, major axis moment, maximum intensity, radial moment, minor axis moment, size, total intensity, light loss by the particle, forward scattered light by the particle, and side scattered light by the particle. In some examples, particles from the sample are classified based on one or more of the calculated image parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles based on five or more calculated image parameters.

[0017] In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles by comparing one or more of the calculated image parameters with ground truth image classification parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles based on a threshold between the calculated image parameters of the particles and the ground truth image classification parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining ground truth image classification parameters. In particular examples, the non-transitory computer-readable storage medium includes an algorithm for contacting particles from a sample with one or more fluorescent labels, an algorithm for illuminating the fluorescently labeled particles with a light source, an algorithm for measuring fluorescence from the illuminated particles, an algorithm for identifying particles from the sample based on the measured fluorescence, an algorithm for determining image parameters of the identified particles, and an algorithm for generating ground truth image classification parameters for the identified particles. In some examples, the particles are labeled with four or more different fluorophores. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating images of labeled particles from a frequency-encoded data signal. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating ground truth image classification parameters from an image of fluorescently labeled particles.

[0018] In some embodiments, the non-transitory computer-readable storage medium is programmed with a dynamic algorithm that updates based on the determined image parameters of the fluorescently labeled particles. In some examples, the non-transitory computer-readable storage medium is programmed with a machine learning algorithm to generate ground truth image classification parameters from the fluorescently labeled particles. In some examples, images generated from the fluorescently labeled particles are used as training data for generating the ground truth image classification parameters.

[0019] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for classifying particles of a sample by assigning the particles to one or more particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for assigning particles to particle population clusters based on a comparison between ground truth image classification parameters of each particle population cluster and calculated image parameters of the particles. In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for determining one or more sorting gates for the classified particles of the sample. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining one or more sorting gates that capture particles of a target particle population cluster and exclude particles of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining the sorting gates using the ground truth image classification parameters of each particle population cluster. In some examples, the sorting gates maximize the inclusion yield of particles of the target particle population cluster. In some examples, the sorting gates maximize the exclusion of particles of the non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining sorting gates that exclude particles of non-target particle population clusters based on calculated Mahalanobis distances from the ground truth image classification parameters of each particle population cluster. In some examples, the non-transitory computer-readable storage medium includes an algorithm for calculating an Fβ score, where the Fβ score includes a weighted harmonic mean of the inclusion of particles of the target particle population cluster and the exclusion of particles of the non-target particle population cluster. 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 determination uses eight or fewer sorting gates, for example, four or fewer sorting gates. [Brief explanation of the drawings]

[0020] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0021] [Figure 1] 1 shows a flow chart for sorting unlabeled particles according to certain embodiments. [Figure 2A] 1 illustrates the activation of cells from a sample and the acquisition of flow cytometry data to generate an image classification parameter training dataset, according to certain embodiments. [Figure 2B] 1 illustrates the generation of ground truth imaging training datasets for activated and non-activated cells, according to certain embodiments. [Figure 2C] 10 illustrates computing a gating strategy using an image classification training dataset of activated and non-activated cells according to certain embodiments. [Figure 2D] 10 illustrates sorting of label-free activated and non-activated cells using a gating strategy generated using ground truth imaging training data, according to certain embodiments. [Figure 2E] 1 illustrates triggering cell death in a cell sample and acquiring flow cytometry data to generate an image classification parameter training dataset, according to certain embodiments. [Figure 2F] 1 illustrates the generation of ground truth imaging training datasets for dead and live cells according to certain embodiments. [Figure 2G] 10 illustrates computing a gating strategy using an image classification training dataset of live and dead cells according to certain embodiments. [Figure 2H] 10 illustrates sorting of label-free live and dead cells using a gating strategy generated using ground truth imaging training data according to certain embodiments. [Figure 3A] 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

[0022] Aspects of the present disclosure include methods for label-free particle sorting. The method, according to certain embodiments, includes measuring light from a sample having label-free particles in a flow stream, generating one or more images of the particles from the measured light, calculating image parameters from the generated images of the one or more particles, and generating a particle sorting decision based on the calculated image parameters. In some embodiments, the sorting gate is determined based on the image parameters calculated from the particles and ground truth image classification parameters. 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.

[0023] 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.

[0024] 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.

[0025] Certain ranges are described herein by numerical values ​​preceded by the term "about." The term "about" is used herein to provide literal support for 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] 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.

[0030] 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.

[0031] As summarized above, the present disclosure provides label-free sorting of particles of a sample. In further describing embodiments of the present disclosure, methods for generating image parameters, ground truth image classification parameters, and sorting gates are first described in more detail. Next, systems, integrated circuit devices, and non-transitory computer-readable storage media having programming for implementing the subject methods by calculating sorting gates for sorting label-free particles of a sample are also provided.

[0032] How to sort unlabeled particles from a sample Aspects of the present disclosure include methods for label-free particle sorting. The subject methods provide for sorting particles without any type of label, such as a fluorophore. 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, among others. For example, labeling may include 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 "label-free" is used herein in its conventional sense to refer to the absence of a detection component added, coupled, or otherwise conjugated (e.g., via one or more non-covalent or covalent bonds) to a particle of interest in a sample. Thus, the methods provide for generating a sorting strategy to sort particles of a sample that are not bound or coupled to a detection marker, such as a fluorophore, radioisotope, or contrast agent. In some embodiments, the particles of sample that have coupled or conjugated detection markers (e.g., 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, no particles in the sample have coupled or conjugated detection markers.

[0033] In some examples, the label-free sorting method of the present disclosure provides for isolating rare cell populations that may be damaged or have reduced viable cell quality due to labeling with fluorophores. In some examples, label-free sorting provides for isolating cells of a sample so that the cell quality remains suitable for downstream transcriptome analysis and downstream cell manufacturing, such as for use in adoptive cell therapy or drug discovery. As described in more detail below, particles of a sample are sorted based on image parameters generated from images of particles in the sample. Therefore, there is no need to treat particles with fluorescent or other types of markers to identify and isolate them.

[0034] In some embodiments, the subject methods provide for increasing the sensitivity and accuracy of particle classification without the use of detectable labels. In some examples, the accuracy of clustering particles of a sample is increased. In particular examples, the accuracy of particle sorting gates is increased when applying the particle sorting algorithms described herein. In some examples, the methods provide for generating image parameters that can be used to improve classification in cluster analysis, including when no changes are made to the hardware components (e.g., photodetectors) of the particle analyzer system. In some examples, the determined image parameters can increase the accuracy of label-free particle classification 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 (including 99% or more). In some embodiments, the determined image parameters can be used to adjust and optimize thresholds for trigger metrics in detecting particles of a sample. For example, the number of particles that are misclassified (e.g., particles are inaccurately identified or classified) when applying image parameters generated by the subject method to cluster analysis is reduced 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 (including 99% or more).

[0035] In embodiments, a sorting strategy is generated for unlabeled particles of a sample based on image parameters determined from one or more images of the particles. In some examples, the image parameters are determined solely from the images 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 generated images of the particles and data signal waveforms generated in response to light measured from the illuminated particles.

[0036] In carrying out the subject methods according to certain embodiments, light from a sample having unlabeled particles in a flow stream (e.g., by illuminating the sample with a light source) is measured using a light detection system having a photodetector. In some embodiments, the sample is a biological sample. The term "biological sample" is used in its conventional sense and refers to a whole organism, a whole plant, a whole fungus, or a subset of animal tissues, cells, or component parts, as may be found in certain examples, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic fluid, umbilical cord blood, urine, vaginal fluid, and semen. Thus, "biological sample" refers to both intact organisms or subsets of their tissues, as well as homogenates made from organisms or subsets of their tissues, including, but not limited to, lysates or extracts, such as plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and genitourinary tract sections, tears, saliva, milk, blood cells, tumors, and organs. The 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 examples, 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).

[0037] 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), Rodents (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. While the present invention 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.

[0038] In some embodiments, a sample (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, e.g., 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 (including spanning 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.

[0039] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, for example, 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, laser diode, or broadband light source coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0040] 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 Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0041] 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.

[0042] 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.

[0043] 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, inclusive. 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 can be the same or different.

[0044] 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.

[0045] 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, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0046] 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, 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), and the angle or illumination may be variable between 10° and 90°, for example 15° and 85°, for example 20° and 80°, for example 25° and 75° (including 30° and 60°, for example 90°).

[0047] 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, and e.g., 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.

[0048] 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, e.g., 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, and e.g., 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.

[0049] The time between illumination by each laser can also be independently variable, 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, and e.g., 60 microseconds or more, as desired. 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.

[0050] 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 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0051] Depending on the laser, the acousto-optic device 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, 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, and for example 50 mm or more, and the angle or illumination may be variable between 10° and 90°, for example 15° and 85°, for example 20° and 80°, for example 25° and 75°, including angles between 30° and 60°, for example 90°.

[0052] In an embodiment, the method includes applying a high frequency drive signal to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals 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, three or more high frequency drive signals, for example, four or more high frequency drive signals, for example, five or more high frequency drive signals, for example, six or more high frequency drive signals, for example, seven or more high frequency drive signals, for example, eight or more high frequency drive signals, for example, nine or more high frequency drive signals, for example, ten or more high frequency drive signals, for example, fifteen or more high frequency drive signals, for example, twenty-five or more high frequency drive signals, for example, fifty or more high frequency drive signals, and one hundred or more high frequency drive signals).

[0053] 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.

[0054] 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.

[0055] 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 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 flow stream is irradiated with multiple frequency-shifted light beams to image particles in the flow stream.

[0056] As described above, light from particles in the sample is conveyed to a light detection system and measured by multiple light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of the following wavelengths: 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.

[0057] 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.

[0058] Measurements of the collected light may be made one or more times during the subject method, e.g., two or more times, e.g., three or more times, e.g., 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.

[0059] Light from the sample may be measured at one or more wavelengths, for example 5 or more different wavelengths, for example 10 or more different wavelengths, for example 25 or more different wavelengths, for example 50 or more different wavelengths, for example 100 or more different wavelengths, for example 200 or more different wavelengths, for example 300 or more different wavelengths, including measuring light collected at 400 or more different wavelengths.

[0060] 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.

[0061] In embodiments, one or more images of particles 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 from 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 side scatter detectors and forward scatter detectors. 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.

[0062] One or more images can be generated from the measured light. In some embodiments, a single image is generated for each particle from each form of detected light. In other embodiments, multiple images are generated for each particle (e.g., including two or more, e.g., three or more, e.g., five or more, e.g., ten or more, and twenty-five or more particle images). For example, a first image of the particle is generated from detected light absorption, a second image of the cell is generated from detected light scattering, and a third image of the cell is generated from detected light emission. In other embodiments, two or more images are generated from each form of detected light (e.g., including three or more, e.g., four or more, e.g., five or more, and ten or more images, or a combination thereof).

[0063] In some embodiments, frequency-encoded data (e.g., frequency-encoded spatial data) is generated from light measured from particles 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 with one or more detection channels (e.g., 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 frequency-encoded data is phase-corrected. In some examples, the frequency-encoded data used to generate an image of the particle 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.

[0064] In some examples, the method includes generating one or more grayscale images of the particles. The term "grayscale" is used herein in its conventional sense to refer to an image of cells in a flow stream 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 particle. In particular examples, the image of the particle is a binary pixel image of the 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).

[0065] In embodiments, one or more image parameters are calculated from the generated particle images. 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 major 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 minor axis moment image parameter is calculated from the generated images. In some examples, a particle size image parameter is calculated from the generated images. In some examples, a total intensity image parameter is calculated from the generated images. In some examples, a particle light loss image parameter 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, an image moment is 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 the image. In other examples, the orientation of the cell may be calculated from the image moments of the image. In yet other examples, the eccentricity of the cell may be calculated from the image moments of the image.

[0066] In certain embodiments, imaging parameters (described below) calculated from the images generated for use in generating the gating strategy are summarized in Table 1.

[0067] [Table 1-1]

[0068] [Table 1-2]

[0069] In some embodiments, the particles are classified based on one or more of the calculated image parameters. In some examples, the particles are classified using a plurality of calculated image parameters, including classifying particles based on a combination of two or more (e.g., three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, for example, ten or more, for example, fifteen or more, for example, twenty or more, for example, twenty-five or more, for example, thirty or more, for example, forty or more) image parameters, and classifying particles based on fifty or more image parameters determined from the generated images of the particles.

[0070] In some examples, particles are classified by comparing one or more of the calculated image parameters with ground truth image classification parameters. The term "ground truth" is used herein to refer to previously determined image parameters for identified or classified particles, such as image parameters determined using fluorescently labeled particles, as described in more detail below. In certain embodiments, the method includes generating ground truth classification parameters for particles of the sample. In some examples, particles from the sample (e.g., particles from a portion of the sample) are contacted with one or more fluorophores to generate a composition containing fluorescently labeled particles. The particles may be labeled with one or more fluorophores, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, and include eight or more fluorophores.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] In some examples, the fluorescent dye is a polymer dye (e.g., a fluorescent polymer dye). The fluorescent polymer dyes used in the subject methods and systems vary. 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, with π electrons able to move from one bond to the other. 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 can form "rigid rod" polymer backbones, experiencing limited twist (e.g., bending) 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.

[0075] 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.

[0076] In some embodiments, fluorescently labeled particles are illuminated with a light source (e.g., as described above) and fluorescence is measured from the illuminated particles in one or more fluorescence photodetector channels (e.g., two or more, e.g., three or more, e.g., four or more, e.g., eight or more, e.g., sixteen or more, e.g., thirty-two or more), including 64 different fluorescence photodetector channels. In some examples, one or more of light absorption and light scattering are also measured for the fluorescently labeled particles.

[0077] In some embodiments, images of the fluorescently labeled particles are generated based on the measured fluorescence from the illuminated particles. In some examples, the images are generated using light absorption from the fluorescently labeled particles. In some examples, the images are generated using light scattering (e.g., forward scattered light and side scattered light) from the fluorescently labeled particles. In some examples, multiple images of each fluorescently labeled particle are generated, e.g., two or more images are generated for each particle based on the measured fluorescence, e.g., three or more, e.g., four or more, e.g., five or more, including ten or more images.

[0078] In some embodiments, ground truth image classification parameters are calculated using images generated from fluorescently labeled particles. In some examples, ground truth centroid image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth delta centroid image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth diffuse image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth eccentricity image classification parameters are calculated from the generated images. In some examples, ground truth major axis moment image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth maximum intensity image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth radial moment image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth minor axis moment image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth particle size image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth total intensity image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth particle light loss image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth forward scattered light image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth side scattered light image classification parameters are calculated from images generated using fluorescently labeled particles. In some examples, ground truth image moment classification parameters are calculated from images generated using fluorescently labeled particles.

[0079] In some embodiments, one or more of the ground truth image classification parameters are calculated by inputting images of fluorescently labeled particles generated from measured fluorescence from the illuminated particles into a machine learning algorithm as one or more training data sets. In some examples, the machine learning algorithm is a dynamic algorithm that updates based on determined image parameters of the fluorescently labeled particles. 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.

[0080] In some examples, the training data set input to the machine learning algorithm has two or more images of fluorescently labeled particles, for example, three or more, for example, five or more, for example, ten or more, for example, fifteen or more, for example, twenty or more, for example, twenty-five or more, for example, fifty or more, for example, one hundred or more, for example, two hundred or more, for example, fifty or more, for example, five hundred or more, for example, seven hundred or more, including inputting a training data set having 1000 or more images of fluorescently labeled particles into the machine learning algorithm. In certain examples, the training data set may include one or more images of particle clusters, such as particle clusters that form doublets in the generated data signal.

[0081] In some embodiments, ground truth image classification parameters calculated from images of fluorescently labeled particles by a machine learning algorithm are determined for one or more photodetector channels of the optical detection system. In certain examples, ground truth image classification parameters are determined for all photodetector channels of the optical detection system. For example, ground truth image classification parameters may be determined for scattered light photodetector channels (e.g., forward scattered light, side scattered light), light absorption photodetector channels, and fluorescence photodetector channels.

[0082] In some embodiments, adjustments to one or more of the ground truth image classification parameters are calculated. In some examples, calculating the adjustments to one or more of the ground truth image classification parameters includes determining accuracy and loss statistics of the generated dynamic particle classification algorithm. In some examples, the accuracy and loss statistics of the dynamic particle classification algorithm are calculated by an iterative optimization approach. In particular examples, the iterative optimization approach is a first-order optimization algorithm. In some examples, the accuracy and loss statistics of the dynamic particle classification algorithm are calculated by a gradient descent algorithm. In particular examples, the accuracy and loss statistics of the dynamic particle classification algorithm are calculated by backpropagation. In some embodiments, the method includes adjusting one or more of the ground truth image classification parameters based on the calculated accuracy and loss statistics. In some examples, each of the ground truth image classification parameters is iteratively adjusted for each photodetector channel to converge to an optimized set of image classification parameters for the dynamic particle classification algorithm.

[0083] In some embodiments, the method includes applying the determined ground truth image classification parameters to classify unlabeled particles of a sample in the flow stream. In some examples, classifying the unlabeled particles of a sample includes assigning the unlabeled particles to particle population clusters based on a comparison between the ground truth image classification parameters of each particle population cluster and the particles' calculated image parameters. As used herein, a "population" or "subpopulation" of classified unlabeled particles (e.g., cells) refers to a group of analytes having image parameters (e.g., delta center of mass, radial moment, eccentricity) such that the measured parameter data form a cluster in data space. In embodiments, a population cluster may be formed using multiple different image parameters, such as two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, etc., including 20 or more different image parameters. Thus, the populations are recognized as clusters in the data. Conversely, each particle population cluster may be interpreted as corresponding to a particular type of cell or compound population of an analyte, although clusters corresponding to noise or background are also typically observed. Particle population clusters can be defined, for example, in terms of a subset of measured image parameters, in a subset of dimensions, which correspond to complex populations that differ only in a subset of the measured image parameters or features extracted from the cell or particle measurements.

[0084] In some embodiments, the method includes determining one or more sorting gates for the classified particles 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 delimit 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 define boundaries for classifying populations based on ground truth image parameters. 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 particle population clusters from one or more different samples that have been previously determined (e.g., by a user) to correspond to a property of interest.

[0085] In some examples, one or more sorting gates capture particles of the target particle population cluster and exclude particles of the non-target particle population cluster. In some examples, the sorting gates are determined using ground truth image classification parameters for each particle population cluster. In some examples, the sorting gates are configured to maximize an inclusion yield of particles of the target particle population cluster, for example, a sorting gate configured to generate an inclusion yield of particles of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate an inclusion yield of particles 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 particles of the target particle population cluster, for example, the sorting gate is configured to produce a purity yield of particles 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, such as 70% or more, for example 75% or more, for example 80% or more, such as 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 particles of the target particle population cluster of 99.9% or more.

[0086] In some examples, the sorting gates are configured to maximize exclusion of particles of the non-target particle populations, for example, the sorting gates are configured to exclude 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 of the particles of the non-target particle population clusters, including determining a sorting gate configured to exclude 99.9% or more of the particles of the non-target particle population clusters. In some examples, the sorting gates exclude particles of the non-target particle population clusters based on calculated Mahalanobis distances from the ground truth image classification parameters of each particle population cluster.

[0087] 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 sorting gate of interest generated has an Fβ score of greater than or equal to 1. In some instances, the sorting gate of interest generated has an Fβ score of less than 1.

[0088] In some examples, generating particle sorting decisions in accordance with the present disclosure includes a gating strategy of eight or fewer sorting gates, e.g., seven or fewer sorting gates, e.g., six or fewer sorting gates, e.g., five or fewer sorting gates, e.g., four or fewer sorting gates, e.g., three or fewer sorting gates, including two 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 the determined ground truth image classification parameters to the image parameters determined for the unlabeled particles.

[0089] 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 particles in a particle population cluster is compared to a predetermined threshold.

[0090] 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).

[0091] FIG. 1 shows a flowchart for sorting unlabeled particles according to certain embodiments. In step 101, light from a sample having unlabeled particles is measured using a photodetector. In some examples, the method includes illuminating particles of the sample in a flow stream, such as with a frequency-modulated beam of laser light. In some examples, a frequency-encoded data signal is generated from the measured light. Images of the unlabeled particles are generated in step 102, and image parameters (e.g., radial moment, eccentricity) are calculated from the generated images in step 103. In some examples, the method includes calculating multiple image parameters for each particle, such as 50 or more image parameters. In step 104, a gating strategy is determined for each unlabeled particle based on the calculated image parameters for each particle and ground truth image classification parameters. In some embodiments, the method includes determining ground truth image classification parameters for generating the gating strategy. To determine the ground truth image classification parameters, particles of the sample are contacted with one or more fluorophores in step 110. Fluorescently labeled particles are illuminated, and fluorescence from the illuminated particles is measured in step 111. Fluorescently labeled particles are identified based on the measured fluorescence (e.g., by identifying biomarkers) (step 112). In some examples, one or more images of the fluorescently labeled particles are generated from the measured fluorescence. In step 113, image parameters of the identified particles are determined, and ground truth image classification parameters are calculated from the determined image parameters (step 114). In some examples, one or more of 1) the images of the fluorescently labeled particles, 2) the image parameters generated from the identified fluorescently labeled particles, and 3) the ground truth image classification parameters are used as training data in a machine learning algorithm. In some embodiments, the machine learning algorithm is used to classify unlabeled particles of the sample based on the calculated image parameters and ground truth image classification parameters of the unlabeled particles.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 sorting decisions in step 105. In some examples, the unlabeled particles are sorted into different containers (step 106), such as using a droplet sorter (e.g., having a droplet charging device, a piezoelectric transducer to generate separate droplets with unlabeled particles, and deflection plates to deflect the droplets into different sample containers).

[0092] In certain embodiments, the method includes sorting one or more unlabeled particles (e.g., cells) of a sample identified based on the estimated abundance of a fluorophore associated with the particle. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., droplets containing cells, 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.

[0093] 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 with 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.

[0094] 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).

[0095] A system for sorting unlabeled particles from a sample As summarized above, aspects of the present disclosure include a system for sorting unlabeled particles of a sample. As described above, the system is configured to sort particles without any type of label, such as a fluorophore. Accordingly, the system is configured to generate a sorting strategy to sort particles of the sample that are not bound or coupled to a detection marker, such as a fluorophore, radioisotope, or contrast agent. In some embodiments, the system is configured to generate the sorting strategy for unlabeled particles of the sample based on image parameters determined from one or more images of the particles. In some examples, the system is configured to determine the image parameters solely from the images of the particles, rather than from another data source, such as a data signal waveform. In other examples, the system is configured to generate the image parameters based on a combination of generated images of the particles and data signal waveforms generated in response to measured light.

[0096] Certain embodiments of the system include a light source configured to illuminate unlabeled particles in the sample. In certain embodiments, the light source may be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), 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 another example, the subject system includes 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 Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0097] 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.

[0098] 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°.

[0099] 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 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or at 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.

[0100] 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, or a fluorine laser. metal vapor lasers, such as 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, Yb2O3 lasers, and 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.

[0101] 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 (H metal vapor lasers such as a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof; solid-state lasers 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 Yb2O3 laser, a cerium-doped laser, and combinations thereof.

[0102] 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.

[0103] 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 three or more high frequency drive signals, for example four or more high frequency drive signals, for example five or more high frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals, including being configured to apply one hundred or more high frequency drive signals.

[0104] 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.

[0105] 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 (e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more), or the including memory may include instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the controller may include instructions for generating two or more angularly deflected laser beams having different intensities (e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more), or the including memory may include instructions for generating one hundred or more angularly deflected laser beams having different intensities.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] In embodiments, the system includes a light detection system having a plurality of photodetectors for measuring light from unlabeled particles. 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, photovoltaic cells, 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.

[0110] 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 four or more light detectors, e.g., ten or more light detectors, e.g., twenty-five or more light detectors, e.g., fifty or more light detectors, e.g., one hundred or more light detectors, e.g., two hundred or more light detectors, e.g., five hundred or more light detectors, e.g., seven hundred or more light detectors, e.g., seven hundred or more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, e.g., ten or more photodiodes, e.g., twenty-five or more photodiodes, e.g., fifty or more photodiodes, e.g., one hundred or more photodiodes, e.g., two hundred or more photodiodes, e.g., five hundred or more photodiodes, e.g., seven hundred or more photodiodes, e.g., one thousand or more photodiodes.

[0111] 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 the photodetector array may be oriented relative to one another at angles (referenced in 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, and hexagonal, curvilinear shapes such as circular and elliptical, and irregular shapes such as a parabolic base coupled to a flat top. In certain embodiments, the photodetector array has a rectangular active surface.

[0112] 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 , e.g., 50 μm 2 ~9000μm 2 , e.g., 75 μm 2 ~8000μm 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 range of 200 μm 2 ~5000μm 2 Includes.

[0113] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length ranging from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, 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 from 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.

[0114] The optical detector of interest is 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 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.

[0115] 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.

[0116] 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 every 1000 milliseconds, or some other interval.

[0117] In embodiments, the system is configured to generate one or more images of particles in a 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.

[0118] 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 particle 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 particle (e.g., including two or more, e.g., three or more, e.g., five or more, e.g., ten or more, and twenty-five or more particle images). For example, the memory includes instructions for generating a first image of the particle from detected light absorption; instructions for generating a second image of the cell from detected light scattering; and instructions for generating a third image of the cell from detected light emission. In other embodiments, two or more images are generated from each form of detected light (e.g., including three or more, e.g., four or more, e.g., five or more, and ten or more images, or a combination thereof).

[0119] In some embodiments, the system includes a memory having instructions for generating frequency-encoded data (e.g., frequency-encoded spatial data) measured from light particles in a 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 (e.g., 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 particle 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.

[0120] In some examples, the memory includes instructions for generating one or more grayscale images of the particles. The term "grayscale" is used herein in its conventional sense to refer to an image of cells in a flow stream 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 used to generate the image of the particle. In particular examples, the image of the particle is a binary pixel image of the 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).

[0121] In embodiments, the system includes a memory having instructions for calculating one or more image parameters from the generated images of the particles. 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 a cell orientation from the image moments of the images. In yet other examples, the memory includes instructions for calculating a cell eccentricity from the image moments of the images.

[0122] In some embodiments, the memory includes instructions for classifying particles based on one or more of the calculated image parameters. In some examples, the memory includes instructions for using a plurality of the calculated image parameters to classify particles with a combination of two or more (e.g., three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, for example, ten or more, for example, fifteen or more, for example, twenty or more, for example, twenty-five or more, for example, thirty or more, for example, forty or more) image parameters, including classifying particles based on fifty or more image parameters determined from the generated images of the particles.

[0123] In some examples, the memory includes instructions for classifying particles by comparing one or more of the calculated image parameters with ground truth image classification parameters. In certain embodiments, the memory includes instructions for generating ground truth classification parameters for particles of the sample. In some examples, particles from the sample (e.g., particles from a portion of the sample) are fluorescently labeled particles (e.g., by contacting particles from the sample with fluorophores). The particles may be labeled with one or more fluorophores, for example, two or more, for example, three or more, for example, four or more, for example, five or more, including eight or more fluorophores, for example, those listed above.

[0124] In some embodiments, the memory includes instructions for illuminating the fluorescently labeled particles with a light source (e.g., as described above), and fluorescence is measured from the illuminated particles in one or more fluorescence photodetector channels (e.g., two or more, e.g., three or more, e.g., four or more, e.g., eight or more, e.g., sixteen or more, e.g., thirty-two or more), including 64 different fluorescence photodetector channels. In some examples, one or more of light absorption and light scattering are also measured for the fluorescently labeled particles.

[0125] In some embodiments, the memory includes instructions for generating images of the fluorescently labeled particles based on the measured fluorescence from the illuminated particles. In some examples, the memory includes instructions for generating images using light absorption from the fluorescently labeled particles. In some examples, the memory includes instructions for generating images using light scattering (e.g., forward scattered light and side scattered light) from the fluorescently labeled particles. In some examples, the memory includes instructions for generating multiple images of each fluorescently labeled particle, e.g., two or more images are generated for each particle based on the measured fluorescence, e.g., three or more, e.g., four or more, e.g., five or more, including ten or more images.

[0126] In some embodiments, the memory includes instructions for calculating ground truth image classification parameters using images generated from fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth center of mass image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth delta center of mass image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth diffuse image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth eccentricity image classification parameters from the generated images. In some examples, the memory includes instructions for calculating ground truth major axis moment image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth maximum intensity image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth radial moment image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth minor axis moment image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth particle size image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth total intensity image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth particle light loss image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth forward scattered light image classification parameters from images generated using fluorescently labeled particles. In some examples, the memory includes instructions for calculating ground truth side scattered light image classification parameters from images generated using fluorescently labeled particles.In some examples, the memory includes instructions for calculating ground truth image moment classification parameters from images generated using fluorescently labeled particles.

[0127] In some embodiments, the memory includes instructions for calculating one or more ground truth image classification parameters by inputting images of the fluorescently labeled particles generated from the measured fluorescence from the illuminated particles as one or more training data sets into a machine learning algorithm. In some examples, the system includes a processor having memory for executing a dynamic algorithm that updates the machine learning algorithm based on the determined image parameters of the fluorescently labeled particles. 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 Python scripts.

[0128] In some examples, the training data set input to the machine learning algorithm has two or more images of fluorescently labeled particles, for example, three or more, for example, five or more, for example, ten or more, for example, fifteen or more, for example, twenty or more, for example, twenty-five or more, for example, fifty or more, for example, one hundred or more, for example, two hundred or more, for example, fifty or more, for example, five hundred or more, for example, seven hundred or more, including inputting a training data set having 1000 or more images of fluorescently labeled particles into the machine learning algorithm. In certain examples, the training data set may include one or more images of particle clusters, such as particle clusters that form doublets in the generated data signal.

[0129] In some embodiments, ground truth image classification parameters calculated from images of fluorescently labeled particles by a machine learning algorithm are determined for one or more photodetector channels of the optical detection system. In certain examples, ground truth image classification parameters are determined for all photodetector channels of the optical detection system. For example, ground truth image classification parameters may be determined for scattered light photodetector channels (e.g., forward scattered light, side scattered light), light absorption photodetector channels, and fluorescence photodetector channels.

[0130] In some embodiments, the memory includes instructions for adjusting one or more of the ground truth image classification parameters. In some examples, the memory includes instructions for calculating adjustments to one or more of the ground truth image classification parameters by determining accuracy and loss statistics of the generated dynamic particle classification algorithm. In some examples, the memory includes instructions for calculating the accuracy and loss statistics of the dynamic particle classification algorithm by an iterative optimization approach. In particular examples, the iterative optimization approach is a first-order optimization algorithm. In some examples, the memory includes instructions for calculating the accuracy and loss statistics of the dynamic particle classification algorithm by a gradient descent algorithm. In particular examples, the memory includes instructions for calculating the accuracy and loss statistics of the dynamic particle classification algorithm by backpropagation. In some embodiments, the memory includes instructions for adjusting one or more of the ground truth image classification parameters based on the calculated accuracy and loss statistics. In some examples, the memory includes instructions for iteratively adjusting each of the ground truth image classification parameters in each photodetector channel to converge to an optimized set of image classification parameters for the dynamic particle classification algorithm.

[0131] In some embodiments, the memory includes instructions for applying the determined ground truth image classification parameters to classify unlabeled particles of the sample in the flow stream. In some examples, the memory includes instructions for classifying unlabeled particles of the sample by assigning the unlabeled particles to particle population clusters based on a comparison between the ground truth image classification parameters of each particle population cluster and the calculated image parameters of the particles.

[0132] In some embodiments, the memory includes instructions for determining one or more sorting gates for the sorted particles of the sample. In some examples, the memory includes instructions for generating one or more sorting gates that capture particles of target particle population clusters and exclude particles of non-target particle population clusters. In some examples, the memory includes instructions for determining the sorting gates using ground truth image classification parameters for each particle population cluster. In some examples, the memory includes instructions for configuring the sorting gates to maximize an inclusion yield of particles of the target particle population cluster, for example, the sorting gates are configured to generate an inclusion yield of particles of the target particle population cluster 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 when the sorting gates are configured to generate an inclusion yield of particles of the target particle population cluster of 99.9% or more. In some examples, the memory includes instructions for configuring the sorting gate to maximize a purity yield of particles of the target particle population cluster, for example, the sorting gate is configured to produce a purity yield of particles 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, such as 70% or more, for example 75% or more, for example 80% or more, such as 85% or more, for example 90% or more, for example 95% or more, for example 97% or more, such as 99% or more, including when the sorting gate is configured to produce a purity yield of particles of the target particle population cluster of 99.9% or more.

[0133] In some examples, the memory includes instructions for configuring the sorting gate to maximize rejection of particles of the non-target particle populations, for example, the sorting gate is configured to reject 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 of the particles of the non-target particle populations, including when the sorting gate is configured to reject 99.9% or more of the particles of the non-target particle population clusters. In some examples, the memory includes instructions for configuring the sorting gate to reject particles of the non-target particle population clusters based on the calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster.

[0134] In certain embodiments, the memory includes instructions for generating sorting gates by performing 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.

[0135] In some examples, the memory includes instructions for generating particle sorting decisions including a gating strategy of eight or fewer sorting gates, e.g., seven or fewer sorting gates, e.g., six or fewer sorting gates, e.g., five or fewer sorting gates, e.g., four or fewer sorting gates, e.g., three or fewer sorting gates, including two or fewer sorting gates. In some embodiments, the memory includes instructions for generating the sorting gates using a graphical display displaying one or more analysis algorithms for applying the determined ground truth image classification parameters to the image parameters determined for the unlabeled particles. In some embodiments, the system includes a display for visualizing the gating strategy on a graphical user interface.

[0136] 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).

[0137] 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).

[0138] 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.

[0139] 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 particles (e.g., unlabeled particles or fluorescently labeled particles). In some examples, the integrated circuit includes programming for calculating image parameters from the generated images of the particles. In some examples, the integrated circuit includes programming for calculating one or more ground truth image classification parameters. In some examples, the integrated circuit includes programming for classifying the unlabeled particles based on the calculated image parameters and the ground truth image classification 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).

[0140] 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 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 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.

[0141] 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 magnetic 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.

[0142] 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.

[0143] 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 invention also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. The programming according to the present invention 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 magnetic 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.

[0144] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning 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).

[0145] 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).

[0146] 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.

[0147] 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.

[0148] 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 WiFi hotspot.

[0149] 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.

[0150] 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.

[0151] 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 user 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 provide 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 data in other formats. 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.

[0152] 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).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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 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 (including 1.25 mm to 1.75 mm, e.g., 1.5 mm).

[0160] 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 may have 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 (including 1.25 mm to 1.75 mm, e.g., 1.5 mm), and may vary depending on the shape. 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° (e.g., 2° to 9°, e.g., 3° ​​to 8°, e.g., 4° to 7°), including a bevel angle of 5°.

[0161] 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 carried by that stream 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.

[0162] 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 specific examples having 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 (including 1.25 mm to 1.75 mm, e.g., 1.5 mm).

[0163] 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.

[0164] 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 of a predetermined length (e.g., using a diffuse laser or lamp). In some embodiments, the investigation region includes a transparent window to facilitate illumination of a 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. Therefore, the research area includes optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, as well as 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 other comonomers such as isophthalic acid, cyclohexene dimethanol). poly(alkylene adipates) such as poly(ethylene adipate), poly(1,4-butylene adipate), poly(hexamethylene adipate); poly(alkylene suberates) such as poly(ethylene suberate); poly(alkylene sebacates) such as poly(ethylene sebacate); poly(ε-caprolactone) and poly(β-propiolactone); poly(alkylene isophthalates) such as poly(ethylene isophthalate);Poly(alkylene 2,6-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;

[0165] 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.

[0166] 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.

[0167] 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., 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Light from the illuminated sample is conveyed to a light detection system 300b, which includes multiple photodetectors. The light detection system 300b includes a forward-scattered light photodetector 311 for generating a forward-scattered light image 311a and a side-scattered light photodetector 312 for generating a side-scattered light image 312a. The light detection system 300b also includes a bright-field photodetector 313 for generating a light loss image 313a. In some embodiments, the forward-scattered light detector 311 and the side-scattered light detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some examples, the bright-field photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence photodetectors 314-317. In some examples, the photodetectors 314-317 are photomultiplier tubes. The light from the illuminated sample is directed through a beam splitter 320 to the side-scattered light 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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, can 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 can include multiple detection stations. Additionally, some detection stations can monitor more than one area.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] Pump lasers 115a-115c 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.

[0180] The laser beam is then directed onto a focusing lens 420, which focuses the beam onto a portion of the flow stream where the sample particles are located, within a flow chamber 425. The flow chamber is the part of a fluidic system that directs particles, typically one at a time, in a stream towards the focused laser beam for interrogation. A flow chamber can include a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0181] 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.

[0182] The fluorescence collection lens 440 collects light emitted from the laser beam's interaction with the particle and routes it 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 of light. 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 fluorescent dye. 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.

[0183] 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 620SP 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 can include optical mirrors such as dichroic mirrors.

[0184] 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.

[0185] 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.

[0186] 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 the 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.

[0187] The system shown in FIG. 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 a portion 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 marker density, 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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 an input signal 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 may 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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 .

[0199] 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.

[0200] 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 signal 618, which then (via amplifier 622) provides 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.

[0201] 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.

[0202] 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.

[0203] 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).

[0204] 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 magnetic 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.

[0205] According to certain embodiments, a non-transitory computer-readable storage medium has an algorithm for measuring light from a sample containing unlabeled particles in a flow stream, an algorithm for generating one or more images of the particles from the measured light, an algorithm for calculating image parameters from the generated images of the one or more particles, and an algorithm for generating a particle sorting decision based on the calculated image parameters. 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 the particle, forward scattered light by the particle, and side scattered light by the particle. In some examples, particles from the sample are classified based on one or more of the calculated image parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles based on five or more calculated image parameters.

[0206] In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles by comparing one or more of the calculated image parameters with ground truth image classification parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for classifying particles based on a threshold between the calculated image parameters of the particles and the ground truth image classification parameters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining ground truth image classification parameters. In particular examples, the non-transitory computer-readable storage medium includes an algorithm for contacting particles from a sample with one or more fluorescent labels, an algorithm for illuminating the fluorescently labeled particles with a light source, an algorithm for measuring fluorescence from the illuminated particles, an algorithm for identifying particles from the sample based on the measured fluorescence, an algorithm for determining image parameters of the identified particles, and an algorithm for generating ground truth image classification parameters for the identified particles. In some examples, the particles are labeled with four or more different fluorophores. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating images of labeled particles from a frequency-encoded data signal. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating ground truth image classification parameters from an image of fluorescently labeled particles.

[0207] In some embodiments, the non-transitory computer-readable storage medium is programmed with a dynamic algorithm that updates based on the determined image parameters of the fluorescently labeled particles. In some examples, the non-transitory computer-readable storage medium is programmed with a machine learning algorithm to generate ground truth image classification parameters from the fluorescently labeled particles. In some examples, images generated from the fluorescently labeled particles are used as training data for generating the ground truth image classification parameters.

[0208] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for classifying particles of a sample by assigning the particles to one or more particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for assigning particles to particle population clusters based on a comparison between ground truth image classification parameters of each particle population cluster and calculated image parameters of the particles. In certain embodiments, the non-transitory computer-readable storage medium includes an algorithm for determining one or more sorting gates for the classified particles of the sample. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining one or more sorting gates that capture particles of a target particle population cluster and exclude particles of non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining the sorting gates using the ground truth image classification parameters of each particle population cluster. In some examples, the sorting gates maximize the inclusion yield of particles of the target particle population cluster. In some examples, the sorting gates maximize the exclusion of particles of the non-target particle population clusters. In some examples, the non-transitory computer-readable storage medium includes an algorithm for determining sorting gates that exclude particles of non-target particle population clusters based on calculated Mahalanobis distances from the ground truth image classification parameters of each particle population cluster. In some examples, the non-transitory computer-readable storage medium includes an algorithm for calculating an Fβ score, where the Fβ score includes a weighted harmonic mean of the inclusion of particles of the target particle population cluster and the exclusion of particles of the non-target particle population cluster. 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 determination uses eight or fewer sorting gates, for example, four or fewer sorting gates.

[0209] 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 to facilitate 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.

[0210] 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.

[0211] Utilities The subject systems, methods, and computer systems are used in a variety of applications where it is desirable to analyze and sort particle components in a sample in a fluid medium, such as a biological sample. In some embodiments, the systems and methods described herein are used for flow cytometric characterization of biological samples where it is desirable to leave the particles of the sample unlabeled. Embodiments of the present disclosure are used where it is desirable to provide a flow cytometer with improved cell sorting accuracy, increased particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0212] Embodiments of the present disclosure are also useful in applications where cells prepared from biological samples may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from target fluid or tissue biological samples. For example, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used for therapeutic purposes. The disclosed methods and devices enable the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems. [Example]

[0213] 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.

[0214] Label-free cell sorting of resting and activated T cells (T cell activation model) Peripheral blood mononuclear cells (PBMCs) were isolated from fresh whole blood using density gradient centrifugation. The isolated PBMCs were treated with human T cell activator CD3 / 28 beads for 3 days to obtain a mixture of activated and non-activated T cells. Stimulated cells were harvested and stained with a four-color panel including live / dead markers, CD3, CD69, and CD25, to assess T cell activation. The stained cells were acquired using a FACSDiscover Cell Sorter to generate a training dataset. Figure 2A illustrates the activation of cells from a sample and the acquisition of flow cytometry data to generate an image classification training dataset, according to certain embodiments. Figure 2B illustrates the generation of ground truth imaging training data for activated and non-activated cells. Activation (CD3 + CD25 + ) and non-activated (CD3+ CD25 - ) A mixture of T cells was observed in the training dataset. Figure 2C shows the gating strategy was calculated using the image classification training dataset of activated and non-activated cells. The training dataset was then transferred to a different computer for the HyperFinder workflow. Unsupervised dimensionality reduction was performed on the total cells, and clusters were identified using FlowSOM, including non-activated and activated T cell clusters. The HyperFinder algorithm was applied to the two clusters to generate an optimal gating strategy (β was set to 1, and the F-score was approximately 0.6 for both populations). The HyperFinder gating strategy was then imported back into the instrument's flow cytometer software, and cell sorting was performed accordingly. Figure 2D shows the sorting of unlabeled activated and non-activated cells using the gating strategy generated using the ground truth imaging training data. The sorted cell populations were stained with a four-color panel to confirm their phenotype based on CD25 expression.

[0215] Label-free cell sorting of live and dead cells (cell apoptosis model) PBMCs were isolated from fresh whole blood using density gradient centrifugation. The isolated PBMCs were treated with anti-Fas antibodies to trigger cell death. Figure 2E shows the triggering of cell death in a cell sample and the acquisition of flow cytometry data to generate an image classification parameter training dataset. After overnight treatment, all cells were collected. A small portion of the cells was stained with the DNA dye DAPI to distinguish live from dead cells. After acquisition using the FACSDiscover system, a training dataset was generated. Figure 2F shows the generation of a ground truth imaging training dataset of dead and live cells. More than 5% dead cells were observed in the cell mixture, and morphological differences were observed in the FSC imaging channel. Figure 2G shows the calculation of a gating strategy using the image classification training dataset of live and dead cells. In this example, the HyperFinder algorithm was applied to both the live and dead cell populations to generate an optimal gating strategy (β was set to 1, and the F-score was approximately 0.5 for both populations). Live and dead cells were then sorted based on the gating strategy. Figure 2H shows the sorting of unlabeled live and dead cells using the generated gating strategy. The two sorted populations were stained with DAPI to confirm successful execution of the workflow.

[0216] 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 therein without departing from the spirit or scope of the appended claims.

[0217] 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 with which 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.

[0218] 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.

Claims

1. 1. A method for label-free particle sorting, comprising: measuring light from a sample containing unlabeled particles in a flow stream; generating an image of one or more of the particles from the measured light; calculating image parameters from the generated images of the one or more particles; and generating a particle sorting decision based on said calculated image parameters; A method comprising:

2. 2. The method of claim 1, 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 the particle, forward scattered light by the particle, side scattered light by the particle, and combinations thereof.

3. The method of any one of claims 1 to 2, wherein the method comprises classifying the particles based on one or more of the calculated image parameters.

4. The method of claim 3 , wherein the particles are classified based on five or more calculated image parameters.

5. The method of any one of claims 3 to 4, wherein the particles are classified by comparing one or more of the calculated image parameters with ground truth image classification parameters.

6. The method of claim 5 , wherein classifying the particle is based on a threshold between the calculated image parameter of the particle and the ground truth image classification parameter.

7. The method of any one of claims 5 to 6, wherein the method further comprises determining the ground truth image classification parameters.

8. The ground truth image classification parameters are: contacting particles from said sample with one or more fluorescent labels; illuminating the fluorescently labeled particles with a light source; measuring fluorescence from the illuminated particles; identifying the particles from the sample based on the measured fluorescence; determining image parameters of the identified particles; and generating ground truth image classification parameters for the identified particles; The method of claim 7, wherein the value is determined by

9. 9. The method of claim 8, wherein the particles from the sample are contacted with four or more different fluorescent labels.

10. The method of any one of claims 8 to 9, wherein generating ground truth image classification parameters further comprises generating images of the fluorescently labeled particles.

11. The method of claim 10 , wherein the ground truth image classification parameters are generated from the images of the fluorescently labeled particles.

12. The method of any one of claims 8 to 11, wherein generating ground truth image classification parameters comprises a dynamic algorithm that updates based on determined image parameters of the fluorescently labeled particles.

13. The method of any one of claims 8 to 12, wherein generating ground truth image classification parameters comprises a machine learning algorithm.

14. The method of any one of claims 3 to 13, wherein classifying the particles comprises assigning the particles to one or more particle population clusters.

15. The method of claim 14 , wherein the particles are assigned to particle population clusters based on a comparison between the ground truth image classification parameters of each particle population cluster and the calculated image parameters of the particles.

16. The method of any one of claims 3 to 15, wherein the method comprises determining one or more sorting gates for the classified particles of the sample.

17. 17. The method of claim 16, wherein the one or more sorting gates capture particles of a target particle population cluster and exclude particles of a non-target particle population cluster.

18. The method of any one of claims 16 to 17, wherein the sorting gates are determined using the ground truth image classification parameters of each particle population cluster.

19. The method of any one of claims 16 to 18, wherein the sorting gate maximizes the inclusion yield of particles in the target particle population cluster.

20. The method of any one of claims 16 to 19, wherein the sorting gate maximizes the exclusion of particles of non-target particle population clusters.

21. 21. The method of claim 20, wherein the sorting gate excludes particles of non-target particle population clusters based on a calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster.

22. 22. The method of claim 16, wherein generating the sorting gate comprises calculating an Fβ score, the Fβ score comprising a weighted harmonic mean of the inclusion of particles of target particle population clusters and the exclusion of particles of non-target particle population clusters.

23. 23. The method of claim 22, wherein the generated sorting gate comprises one or more Fβ scores.

24. 23. The method of claim 22, wherein the generated sorting gate comprises an Fβ score less than 1.

25. The method of any one of claims 16 to 24, wherein the particle sorting decision comprises eight or fewer sorting gates.

26. 26. The method of claim 25, wherein the particle sorting determination comprises four or fewer sorting gates.

27. The method of any one of claims 1 to 26, wherein the method further comprises sorting the particles of the sample into a plurality of sample vessels.

28. The method of any one of claims 1 to 27, wherein measuring light from the particles in the flow stream comprises detecting light absorption, light scattering, or a combination thereof.

29. 30. The method of claim 28, wherein the light measured from the particles in the flow stream consists of light absorption and light scattering.

30. A method according to any preceding claim, wherein the image parameters of the particles are calculated from scattered light from the particles.

31. 31. The method of claim 30, wherein the scattered light comprises forward scattered light.

32. 32. The method of claim 31 , wherein the scattered light comprises side scattered light.

33. 29. The method of claim 28, wherein the parameters of the particles are calculated from frequency-encoded fluorescence data from the particles.

34. The method of any one of claims 1 to 33, wherein the image parameters of the particles are calculated by an integrated circuit device.

35. 35. The method of claim 34, 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).

36. The method of any one of claims 1 to 35, further comprising illuminating the flow stream with a light source.

37. 37. The method of claim 36, wherein the flow stream is illuminated with a light source at a wavelength between 200 nm and 800 nm.

38. 37. The method of claim 36, comprising irradiating the flow stream with a first frequency-shifted light beam and a second frequency-shifted light beam.

39. 39. The method of claim 38, 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.

40. applying a high frequency drive signal to the acousto-optic device; and irradiating the acousto-optic device with a laser to generate the first frequency-shifted light beam and the second frequency-shifted light beam; 40. The method of any one of claims 38 to 39, further comprising:

41. 41. The method of claim 40, wherein the laser is a continuous wave laser.

42. a light source configured to illuminate a sample containing unlabeled particles in the flow stream; a light detection system including a light detector for measuring light from the unlabeled particles; and 1. A system including a processor including a memory operatively coupled to the processor, The memory stores instructions that, when executed by the processor, cause the processor to: generating an image of one or more of the particles from the measured light; calculating image parameters from the generated images of the one or more particles; and generating a particle sorting decision based on said calculated image parameters; system.

43. 43. The system of claim 42, wherein the light source comprises a light beam generator component configured to generate at least a first frequency-shifted light beam and a second frequency-shifted light beam.

44. 44. The system of claim 43, wherein the optical beam generator comprises an acousto-optic deflector.

45. The system of any one of claims 43 to 44, wherein the optical beam generator comprises a direct digital synthesizer (DDS) RF comb generator.

46. A system according to any one of claims 43 to 45, wherein the optical beam generator component is configured to generate a local oscillator beam.

47. 47. The system of any one of claims 43 to 46, wherein the optical beam generator component is configured to generate a plurality of frequency-shifted comb beams.

48. The system of any one of claims 42 to 47, wherein the light source comprises a laser.

49. 49. The system of any one of claims 42 to 48, 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 the particle, forward scattered light by the particle, side scattered light by the particle, and combinations thereof.

50. A system according to any one of claims 42 to 49, wherein the memory includes instructions for classifying the particles based on one or more of the calculated image parameters.

51. 51. The system of claim 50, wherein the memory includes instructions for classifying the particles based on five or more calculated image parameters.

52. 52. The system of claim 51, wherein the memory includes instructions for classifying the particles by comparing one or more of the calculated image parameters to ground truth image classification parameters.

53. 53. The system of claim 52, wherein the memory includes instructions for classifying the particle based on a threshold between the calculated image parameter of the particle and the ground truth image classification parameter.

54. 54. The system of any one of claims 52 to 53, wherein the memory includes instructions for determining the ground truth image classification parameters.

55. The memory stores instructions that, when executed by the processor, cause the processor to: illuminating the light source onto fluorescently labeled particles from the sample; measuring fluorescence from the illuminated particles; identifying the particles from the sample based on the measured fluorescence; determining image parameters of the identified particles; and generating ground truth image classification parameters for the identified particles; 55. The system of claim 54.

56. 56. The system of claim 55, wherein the particles of the sample are labeled with four or more different fluorescent labels.

57. 57. The system of any one of claims 55 to 56, wherein the memory includes instructions for generating an image of the fluorescently labeled particles.

58. 58. The system of claim 57, wherein the memory includes instructions for generating the ground truth image classification parameters from images of the fluorescently labeled particles.

59. 59. The system of any one of claims 55 to 58, wherein the memory includes instructions for generating ground truth image classification parameters using a dynamic algorithm that updates based on determined image parameters of the fluorescently labeled particles.

60. 59. The system of any one of claims 55 to 58, wherein the memory includes instructions for generating ground truth image classification parameters using a machine learning algorithm.

61. 61. The system of any one of claims 50 to 60, wherein the memory includes instructions for classifying the particles by assigning the particles to one or more particle population clusters.

62. 62. The system of claim 61 , wherein the memory comprises instructions for assigning particles to particle population clusters based on a comparison between the ground truth image classification parameters of each particle population cluster and the calculated image parameters of the particles.

63. 63. The system of any one of claims 50 to 62, wherein the memory has stored therein instructions that, when executed by the processor, cause the processor to determine one or more sorting gates for the classified particles of the sample.

64. 64. The system of claim 63, wherein the one or more sorting gates capture particles of a target particle population cluster and exclude particles of a non-target particle population cluster.

65. 65. The system of any one of claims 63 to 64, wherein the memory includes instructions for determining the sorting gates using the ground truth image classification parameters of each particle population cluster.

66. 66. The system of any one of claims 63 to 65, wherein the sorting gate maximizes the inclusion yield of particles in a target particle population cluster.

67. 67. The system of any one of claims 63 to 66, wherein the sorting gate maximizes the exclusion of particles of non-target particle population clusters.

68. 68. The system of claim 67, wherein the memory comprises instructions for determining the sorting gate by filtering out particles of non-target particle population clusters based on a calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster.

69. 69. The system of any one of claims 63-68, wherein the memory comprises instructions for generating the sorting gate by calculating an Fβ score, the Fβ score comprising a weighted harmonic mean of the inclusion of particles of target particle population clusters and the exclusion of particles of non-target particle population clusters.

70. 70. The system of claim 69, wherein the generated sorting gate comprises one or more Fβ scores.

71. 70. The system of claim 69, wherein the generated sorting gate comprises an Fβ score less than 1.

72. 72. The system of any one of claims 63 to 71, wherein the particle sorting decision comprises eight or fewer sorting gates.

73. 73. The system of claim 72, wherein the particle sorting decision comprises four or fewer sorting gates.

74. The system of any one of claims 42 to 73, further comprising a display configured to display a graphical user interface.

75. 75. The system of claim 74, wherein the graphical user interface is configured for manual input of one or more of the sorting gates.

76. 76. The system of claim 75, wherein manually inputting the sorting gate comprises drawing the sorting gate on a scatter plot of the particle population cluster.

77. 77. The system of claim 76, wherein the sorting gate is a hyper-rectangular sorting gate.

78. 78. The system of any one of claims 42 to 77, further comprising a cell sorter.

79. 79. The system of claim 78, wherein the cell sorter comprises a droplet deflector.

80. The system of any one of claims 42 to 79, wherein the system is a flow cytometer.

81. The system of any one of claims 42 to 80, wherein the system comprises an integrated circuit device.

82. 82. The system of claim 81, 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).

83. A non-transitory computer-readable storage medium having instructions stored thereon, comprising: an algorithm for measuring light from a sample containing unlabeled particles in a flow stream; an algorithm for generating an image of one or more of the particles from the measured light; an algorithm for calculating image parameters from the generated image of the one or more particles; and an algorithm for generating particle sorting decisions based on said calculated image parameters; 1. A non-transitory computer-readable storage medium comprising:

84. 84. The non-transitory computer-readable storage medium of claim 83, 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 the particle, forward scattered light by the particle, side scattered light by the particle, and combinations thereof.

85. 85. The non-transitory computer readable storage medium of any one of claims 83 to 84, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying the particles based on one or more of the calculated image parameters.

86. 86. The non-transitory computer readable storage medium of claim 85, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying the particles based on five or more calculated image parameters.

87. 87. The non-transitory computer readable storage medium of any one of claims 85-86, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying the particles by comparing one or more of the calculated image parameters to ground truth image classification parameters.

88. 88. The non-transitory computer-readable storage medium of claim 87, wherein the non-transitory computer-readable storage medium comprises an algorithm for classifying the particle based on a threshold between the calculated image parameters of the particle and the ground truth image classification parameters.

89. 89. The non-transitory computer readable storage medium of any one of claims 87 to 88, wherein the non-transitory computer readable storage medium comprises an algorithm for determining the ground truth image classification parameters.

90. the non-transitory computer-readable storage medium comprising: an algorithm for contacting particles from said sample with one or more fluorescent labels; an algorithm for illuminating the fluorescently labeled particles with a light source; an algorithm for measuring fluorescence from said illuminated particles; an algorithm for identifying the particles from the sample based on the measured fluorescence; an algorithm for determining image parameters of the identified particles; and an algorithm for generating ground truth image classification parameters for the identified particles; 90. The non-transitory computer-readable storage medium of claim 89, comprising:

91. 91. The non-transitory computer readable storage medium of claim 90, wherein the non-transitory computer readable storage medium includes an algorithm for generating an image of the fluorescently labeled particles.

92. 92. The non-transitory computer readable storage medium of claim 91, wherein the non-transitory computer readable storage medium comprises an algorithm for generating the ground truth image classification parameters from the images of the fluorescently labeled particles.

93. 93. The non-transitory computer readable storage medium of any one of claims 83 to 92, wherein the non-transitory computer readable storage medium comprises an algorithm for determining one or more sorting gates for the classified particles of the sample.

94. 94. The non-transitory computer-readable storage medium of claim 93, wherein the one or more sorting gates capture particles of a target particle population cluster and exclude particles of a non-target particle population cluster.

95. 95. The non-transitory computer readable storage medium of any one of claims 93 to 94, wherein the non-transitory computer readable storage medium comprises an algorithm for determining sorting gates using the ground truth image classification parameters of each particle population cluster.

96. 96. The non-transitory computer-readable storage medium of any one of claims 93 to 95, wherein the sorting gate maximizes inclusion yield of particles in a target particle population cluster.

97. 97. The non-transitory computer-readable storage medium of any one of claims 93 to 96, wherein the sorting gate maximizes the exclusion of particles of non-target particle population clusters.

98. 98. The non-transitory computer-readable storage medium of claim 97, wherein the sorting gate excludes particles of non-target particle population clusters based on a calculated Mahalanobis distance from the ground truth image classification parameters of each particle population cluster.

99. 99. The non-transitory computer-readable storage medium of any one of claims 83 to 98, wherein the non-transitory computer-readable storage medium comprises an algorithm for calculating an Fβ score, the Fβ score comprising a weighted harmonic average of the inclusion of particles of target particle population clusters and the exclusion of particles of non-target particle population clusters.

100. 100. The non-transitory computer-readable storage medium of claim 99, wherein the generated sorting gates comprise one or more Fβ scores.

101. 100. The non-transitory computer-readable storage medium of claim 99, wherein the generated sorting gates include Fβ scores less than 1.

102. 102. The non-transitory computer readable storage medium of any one of claims 93 to 101, wherein the non-transitory computer readable storage medium comprises an algorithm for generating a particle sorting decision comprising eight or fewer sorting gates.

103. 103. The non-transitory computer readable storage medium of claim 102, wherein the non-transitory computer readable storage medium comprises an algorithm for generating a particle sorting decision that includes four or fewer sorting gates.

Images