Methods for assessing cell mitochondrial morphology and systems for the same

Abstract

To provide methods for assessing the morphology of cell mitochondria to be used to determine viability of cells of a sample.SOLUTION: A method includes: measuring light from cells in a sample having fluorescently labeled mitochondria; generating images of the cell mitochondria from the measured light; calculating an image parameter from the generated images of the cell mitochondria; and assessing the morphology of the cell mitochondria based on the calculated image parameter.SELECTED DRAWING: Figure 1

Description

[Background technology]

[0001] Mitochondria are highly dynamic organelles that undergo morphological changes and regulate energy homeostasis in cells. Dynamic changes in mitochondrial morphology are characterized by continuous cycles of fusion (conjugation) and fission (division), and play a role in cellular metabolism and function in disease progression.

[0002] It has been known that the quality of isolated cells is very important for performing molecular biology assays in downstream analysis. However, there is no simple assay available to quickly determine the morphological state of mitochondria in cell isolates before focusing on the time-consuming downstream molecular biology workflow, which often requires costly sequencing.

[0003] High-resolution microscopy can be used to visualize cellular mitochondria in a sample, however, focusing the microscope for such visualization can be difficult and the limited number of images may not represent the mitochondrial morphology and quality of the isolated cells.

[0004] Flow-based particle sorting systems, such as flow cytometers, are used to sort particles in a fluid sample based on at least one measured characteristic of the particles. Particles or components thereof can be labeled with fluorescent dyes to facilitate detection, and by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. In flow-based particle sorting systems, particles such as analyte-bound beads or individual cells in fluid suspension pass in a stream through a detection region where a sensor detects particles of the type to be sorted contained in the stream. Upon detecting a particle of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particle of interest.

[0005] Data generated from the detected light can be used to record the distribution of components and the desired material can be sorted. To sort particles in a sample, a droplet charging mechanism applies an electric charge to droplets in the flow stream containing the particle type to be sorted at a flow stream break-off point. The droplets are passed through an electrostatic field and are deflected into one or more collection vessels based on the droplet's polarity and charge magnitude. Uncharged droplets are not deflected by the electrostatic field. Summary of the Invention

[0006] Aspects of the present disclosure include methods for assessing cellular mitochondrial morphology (e.g., for use in determining cellular viability of a sample). A method according to certain embodiments includes measuring light from cells in a sample having fluorescently labeled mitochondria, generating an image of the cellular mitochondria from the measured light, calculating image parameters from the generated image of the cellular mitochondria, and assessing the cellular mitochondrial morphology based on the calculated image parameters. Systems and integrated circuit devices (e.g., field programmable gate arrays) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.

[0007] In embodiments, the morphology of cellular mitochondria in a sample in a flow stream is assessed based on at least an image parameter of cellular mitochondria. In some cases, the image parameter is a quantitative image parameter. In some cases, the morphology of cellular mitochondria is assessed based on a radial moment image parameter. In some cases, the image parameter includes a radial moment of a fluorophore used to stain the mitochondria. In some cases, the radial moment image parameter is calculated from an image generated based on fluorescence measured from the cellular mitochondria, light loss measured for the cellular mitochondria, forward light scatter measured for the cellular mitochondria, side light scatter measured for the cellular mitochondria, and combinations thereof.

[0008] In some cases, assessing morphology includes determining the size of the cellular mitochondria. In some cases, assessing morphology includes determining the punctateness of the cellular mitochondria. In some cases, assessing morphology includes determining the shape of the cellular mitochondria, which can be, for example, spherical, spindle, elliptical, elongated, and flattened in shape.

[0009] In some embodiments, mitochondria are labeled with one or more lipophilic cationic fluorophores. In some cases, mitochondria are labeled with fluorophores that form covalent bonds with mitochondrial proteins. In some cases, the lipophilic cationic fluorophores have thiol-reactive moieties for binding the fluorophores to mitochondrial proteins. In some cases, mitochondria are labeled with fluorophores that form covalent bonds with mitochondrial lipids. In some cases, mitochondria are labeled with fluorophores that stain the inner mitochondrial membrane.

[0010] In some embodiments, the image parameter calculated is the radial moment of the fluorophore. In some cases, the method includes calculating a pixel density of the generated image of the cellular mitochondria. In some cases, the image parameter is calculated by determining a sum of pixels in the generated image weighted by the distance of each pixel from the centroid of the cellular mitochondria. In some cases, the morphology of the cellular mitochondria is evaluated by comparing a weighted sum of mitochondrial pixels in the generated image to a predetermined threshold. In certain cases, the cell is determined to be activated when the weighted sum of mitochondrial pixels in the generated image is equal to or exceeds the predetermined threshold. In certain cases, the cell is determined to be not activated when the weighted sum of mitochondrial pixels in the generated image is less than the predetermined threshold.

[0011] In some embodiments, the method includes classifying cells based on the generated images of cellular mitochondria and the calculated image parameters, or a combination thereof. In some cases, classifying the cells includes assigning the cells to one or more particle population clusters. In some cases, the method includes determining one or more sorting gates for the classified cells of the sample. In some cases, the one or more sorting gates capture cells having mitochondria of the target particle population cluster and exclude particles of the non-target particle population clusters. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting punctatity equal to or greater than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity less than a predetermined threshold. In some cases, the sorting gate maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the sorting gate maximizes the exclusion of cells of the non-target particle population clusters. In some cases, the method includes sorting cells of the sample into a plurality of sample containers. In some cases, the cells are sorted based on the generated images of the cellular mitochondria. In some cases, the cells are sorted based on a calculated image parameter (e.g., the radial moment of a mitochondrially labeled fluorophore).

[0012] In some embodiments, the method includes illuminating the sample with a light source. In some cases, the sample is illuminated by the light source within the flow stream. In some cases, the light source includes one or more lasers. In some cases, the light is detected using a light detection system having multiple light detectors. In some embodiments, one or more of the light detectors are photomultiplier tubes. In some embodiments, one or more of the light detectors are photodiodes (e.g., avalanche photodiodes, APDs). In certain embodiments, the light detection system includes a light detector array, such as a light detector array having multiple photodiodes or a charged coupled device (CCD).

[0013] The present disclosure also includes systems for practicing the subject methods. The system according to certain embodiments includes a light source configured to irradiate cells having fluorescently labeled mitochondria in a flow stream, a photodetector for measuring light from the cells, and a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate images of the cellular mitochondria from the measured light, calculate image parameters from the generated images of the cellular mitochondria, and assess the cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria.

[0014] In some embodiments, the memory includes instructions for calculating an image parameter that is a quantitative image parameter. In some cases, the memory includes instructions for assessing cellular mitochondrial morphology based on a radial moment image parameter. In some cases, the memory includes instructions for calculating an image parameter from an image generated based on measured fluorescence from cellular mitochondria, measured light loss for cellular mitochondria, measured forward light scatter for cellular mitochondria, measured side light scatter for cellular mitochondria, and combinations thereof.

[0015] In some embodiments, the memory includes instructions for assessing cellular mitochondrial morphology by determining the size of the cellular mitochondria. In some instances, the memory includes instructions for assessing cellular mitochondrial morphology by determining the punctateness of the cellular mitochondria. In some instances, the memory includes instructions for assessing cellular mitochondrial morphology by determining the shape of the cellular mitochondria. In some instances, the memory includes instructions for determining whether the shape of the mitochondria is spherical, spindle-shaped, elliptical, elongated, and flat.

[0016] In some embodiments, the memory includes instructions for calculating the radial moment of the fluorophore. In some cases, the memory includes instructions for calculating pixel density of the generated image of the cellular mitochondria. In some cases, the memory includes instructions for calculating image parameters by determining a sum of pixels in the generated image weighted by the distance of each pixel from the centroid of the cellular mitochondria. In some cases, the memory includes instructions for evaluating the morphology of the cellular mitochondria by comparing a weighted sum of mitochondrial pixels in the generated image to a predetermined threshold. In certain cases, the memory includes instructions for determining that the cell is activated when the weighted sum of mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. In certain cases, the memory includes instructions for determining that the cell is not activated when the weighted sum of mitochondrial pixels in the generated image is less than a predetermined threshold.

[0017] In some cases, the memory includes instructions for classifying cells based on the generated images of cellular mitochondria and the calculated image parameters, or a combination thereof. In some cases, the memory includes instructions for classifying cells by assigning cells to one or more particle population clusters. In some embodiments, the memory includes instructions for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells having mitochondria of a target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting punctatity equal to or greater than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity below a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity below a predetermined threshold. In some cases, the memory includes instructions for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the memory includes instructions for calculating a sorting gate that maximizes the exclusion of cells of the non-target particle population cluster.

[0018] In certain embodiments, the system includes a display for displaying a graphical user interface. In some cases, the graphical user interface is configured to manually input one or more of the sorting gates. In some cases, the sorting gates are manually input into the graphical user interface by drawing the sorting gates on a scatter plot of the particle population clusters. In some cases, the sorting gates are hyper-rectangular sorting gates.

[0019] A non-transitory computer readable storage medium having instructions including algorithms for assessing cellular mitochondrial morphology in a cell sample is also described. The non-transitory computer readable storage medium according to certain embodiments has an algorithm for measuring light from cells in a sample that includes fluorescently labeled mitochondria, an algorithm for generating an image of the cellular mitochondria from the measured light, an algorithm for calculating image parameters from the generated image of the cellular mitochondria, and an algorithm for assessing cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria.

[0020] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter that is a quantitative image parameter. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating cellular mitochondrial morphology based on radial moment image parameters. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter from an image generated based on measured fluorescence from cellular mitochondria, measured light loss for cellular mitochondria, measured forward scatter light for cellular mitochondria, measured side scatter light for cellular mitochondria, and combinations thereof.

[0021] In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a size of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a punctateness of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a shape of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for determining whether the shape of the mitochondria is spherical, spindle-shaped, elliptical, elongated, and flat.

[0022] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating the radial moment of the fluorophore. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating the pixel density of the generated image of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter by determining the sum of pixels in the generated image weighted by the distance of each pixel from the centroid of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating the morphology of the cellular mitochondria by comparing a weighted sum of the mitochondrial pixels in the generated image to a predetermined threshold. In certain cases, the non-transitory computer readable storage medium includes an algorithm for determining that the cell is activated when the weighted sum of the mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. In certain cases, the non-transitory computer readable storage medium includes an algorithm for determining that the cell is not activated when the weighted sum of the mitochondrial pixels in the generated image is less than a predetermined threshold.

[0023] In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells based on the generated images of cellular mitochondria, the calculated image parameters, or a combination thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells by assigning cells to one or more particle population clusters. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells having mitochondria of the target particle population cluster and exclude particles of the non-target particle population clusters. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting punctatity equal to or greater than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria smaller than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity less than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity less than a predetermined threshold. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the exclusion of cells of non-target particle population clusters. [Brief description of the drawings]

[0024] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawing figures, which include the following figures:

[0025] [Figure 1] 1 depicts a flow chart for assessing cellular mitochondrial morphology of a sample, according to certain embodiments. [Figure 2A]1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments;1 illustrates the preparation of a cell population for observing cellular mitochondrial morphology, according to certain embodiments; [Figure 2B] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments. 1 illustrates the visualization of mitochondrial morphology in light loss and fluorescence photodetector channels, according to certain embodiments. [Figure 2C] 1 depicts the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells according to certain embodiments. 2 depicts cluster diagrams of CD3 T cells, CD4 T cells, and CD8 T cells. [Figure 2D] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments;12 illustrates a cluster diagram of two-color imaging of cell nuclei and mitochondria, according to certain embodiments; [Figure 2E] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments. 2 illustrates images generated using light loss and fluorescence photodetector channels, according to certain embodiments. [Figure 2F] 1 depicts the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells according to certain embodiments. 2 depicts cluster diagrams of activated and non-activated T cells, as well as images of mitochondrial morphology for each group of cells. [Figure 2G] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments;FIG. 2 illustrates radial moment parameters of generated images for mitochondria, according to certain embodiments; [Figure 2H]1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells according to certain embodiments, and depicts the radial moment of MitoTracker and the difference in morphology between activated and non-activated T cells. [Figure 2I] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells according to certain embodiments. 2 illustrates the radial moment in activated and non-activated T cells as determined in the light loss and side scatter light detector channels. [Figure 2J] 1 depicts the assessment of mitochondrial morphology in cells of samples having activated and non-activated cells, according to certain embodiments. 2 depicts the radial moment of activated and non-activated T cells in stimulated and non-stimulated cell samples, according to certain embodiments. [Figure 2K] 1 illustrates the assessment of mitochondrial morphology in cells of samples having activated and non-activated cells according to certain embodiments. 2 illustrates a summary of radial moment of activated and non-activated T cells in stimulated and non-stimulated cell samples. [Figure 2L] 1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments;FIG. 2 illustrates sorting cells based on the assessment of mitochondrial morphology, according to certain embodiments; [Figure 2M] 1 depicts the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments. 2 depicts isolated cells with low and high radial moment, according to certain embodiments. [Figure 2N]1 illustrates the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments. 2 illustrates a workflow for using a sample having assessed mitochondrial morphology in gene expression analysis, according to certain embodiments. [Figure 2O] 1 depicts the assessment of mitochondrial morphology in cells of samples having activated and non-activated cells according to certain embodiments. 2 depicts gene expression results from PCR array characterization of mitochondria-related genes comparing cell samples exhibiting high and low radial moment image parameters. [Figure 3A] 1 depicts an image enabled particle sorter in accordance with certain embodiments. [Figure 3B] 1 illustrates image-based particle sorting data processing in accordance with certain embodiments. [Figure 4A] 1 depicts a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 4B] 1 depicts a flow cytometer in accordance with certain embodiments. [Diagram 5] 1 illustrates a functional block diagram for one example of a particle analyzer control system in accordance with certain embodiments. [Figure 6A] 1 depicts a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 depicts a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 7] 1 illustrates a block diagram of a computing system in accordance with a particular embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0026] Aspects of the present disclosure include methods for assessing cellular mitochondrial morphology (e.g., for use in determining cellular viability of a sample). A method according to certain embodiments includes measuring light from cells in a sample having fluorescently labeled mitochondria, generating an image of the cellular mitochondria from the measured light, calculating image parameters from the generated image of the cellular mitochondria, and assessing the cellular mitochondrial morphology based on the calculated image parameters. Systems and integrated circuit devices (e.g., field programmable gate arrays) for practicing the subject methods are also described. Non-transitory computer readable storage media are also provided.

[0027] Before describing the invention in more detail, it is to be understood that the invention is not limited to the particular embodiments described, which may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the invention will be limited only by the appended claims.

[0028] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is to be understood that each intermediate value between the upper and lower limits of that range, to the tenth of the unit of the lower limit, and any other stated or intermediate value within this stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges, and are also encompassed within the invention, subject to any specific 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.

[0029] In this specification, certain ranges are presented using numerical values ​​preceded by the term "about". In this specification, the term "about" is used to provide literal support for the exact number it precedes, as well as a number that is close to or approximately the number it precedes. When determining whether a number is close to or approximately a specifically recited number, the close or approximate unrecited number may be a number that provides a substantial equivalent to the specifically recited number in the context in which it is presented.

[0030] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. In addition, any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, but representative illustrative methods and materials are described below.

[0031] All publications and patents cited herein are incorporated by reference as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated by reference herein to disclose and describe the methods and / or materials in connection with which the publications are cited. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the dates of publication provided may be different from the actual publication dates, which may need to be independently confirmed.

[0032] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be designed to exclude any optional element. As such, this statement is intended to serve as a predicate to the use of such exclusive terminology, such as "solely," "only," or the use of a "negative" limitation in connection with the recitation of claim elements.

[0033] As will be apparent to those skilled in the art upon reading this disclosure, each of the separate embodiments described and illustrated herein has separate components and features which may be readily separated from or combined with the features of any of the other various embodiments without departing from the scope or spirit of the invention. Any recited method may be carried out in the order of events recited or in any other order which is logically possible.

[0034] Although the systems and methods have been or will be described for grammatical fluidity with functional descriptions, it is expressly understood that the claims, unless expressly formulated under 35 U.S.C. ...

[0035] As summarized above, the present disclosure addresses assessing cellular mitochondrial morphology in cells of a sample. In further describing embodiments of the present disclosure, methods for generating image parameters and determining cellular mitochondria, as well as, in some embodiments, methods for generating sorting gates for sorting cells having a particular mitochondrial morphology, are first described in more detail. Then, systems, integrated circuit devices, and non-transitory computer-readable storage media having programming for implementing the subject methods by assessing mitochondrial morphology and calculating sorting gates for sorting cells are also provided.

[0036] Method for assessing mitochondrial morphology in cells of flow stream samples Aspects of the present disclosure include methods for assessing cellular mitochondrial morphology. In some embodiments, the subject methods correspond to determining cellular viability, status, or activation, such as for use in downstream biological assay protocols or for determining disease progression of cells in a sample. In some cases, the subject methods correspond to characterizing mitochondrial dynamics. In some embodiments, assessing cellular mitochondrial morphology as described herein can increase the accuracy of determining cellular mitochondrial dynamics of a sample for cellular viability, activation, or status (e.g., for monitoring disease progression), by a percentage including, for example, 5% or more, for example, 10% or more, for example, 15% or more, for example, 25% or more, for example, 50% or more, for example, 75% or more, and 99% or more. In some cases, the present disclosure corresponds to rapid determination of cellular mitochondrial dynamics of a sample. For example, the subject method can accommodate evaluation of a sample of cells in 10 minutes or less, such as 9 minutes or less, such as 8 minutes or less, such as 7 minutes or less, such as 6 minutes or less, such as 5 minutes or less, such as 4 minutes or less, such as 3 minutes or less, such as 2 minutes or less, including 1 minute or less. As described in more detail below, in some cases, the evaluated cells can be sorted to prepare a cell sample having cellular mitochondria of interest, where the cells in the sample include, for example, 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more, such as 95% or more, such as 97% or more, and 99% of the cells are suitable for use in downstream biological assays (e.g., gene sequencing protocols).

[0037] In embodiments, the morphology of the cell's mitochondria is evaluated based on at least an image of the cell's mitochondria. In some embodiments, the morphology of the cell's mitochondria is evaluated based on an image parameter determined from the image of the cell's mitochondria and one or more images of the cell. In some cases, the image parameter is determined only from the image of the particle and not from another data source, such as, for example, a data signal waveform. In some cases, the image parameter is determined using a combination of the generated image of the particle and a data signal waveform generated in response to the measured light from the illuminated particle.

[0038] In practicing the subject methods according to certain embodiments, light from a sample having cells in a flow stream (e.g., the sample is illuminated with a light source) is measured with a light detection system having a light detector. A "biological sample" can refer to a whole organism, a plant, a fungus, or a subset of animal tissues, cells, or components that may be found in certain cases, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from an organism or a subset of its tissues, including, but not limited to, for example, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, organs. A biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some instances, the sample is a blood sample, including whole blood, such as blood obtained from a venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0039] In certain embodiments, the sample source is a "mammal" or "mammalian animal," terms used broadly to describe organisms within mammals, including carnivores (e.g., dogs and cats), rodents (e.g., mice, guinea pigs, and rats), and primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders, at any stage of development (i.e., neonate, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. It is understood that the present invention may be applied to samples from human subjects, but may also be practiced on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0040] In some embodiments, a sample having cells (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a broad range of wavelengths, e.g., ranging from 50 nm or more, such as, e.g., 100 nm or more, e.g., 150 nm or more, e.g., 200 nm or more, e.g., 250 nm or more, e.g., 300 nm or more, e.g., 350 nm or more, e.g., 400 nm or more, and 500 nm or more. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes illuminating with a broadband light source, broadband light source protocols of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, broadband LEDs with continuous spectra, ultra-bright light emitting diodes, semiconductor light emitting diodes, broad spectrum LED white light sources, multi-LED integrated white light sources, or any combination thereof, among other broadband light sources.

[0041] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or a narrow range of wavelengths, for example, a light source that emits light in a narrow range of wavelengths, such as a range of 50 nm or less, for example, a light source that emits light in a wavelength of 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less, including a light source that emits light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, narrowband light source protocols of interest may include, but are not limited to, a laser, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0042] In certain embodiments, the method includes irradiating the sample with one or more lasers. As discussed above, the type and number of lasers may vary depending on the sample and the desired light to be collected, and may include Helium Neon lasers, Argon lasers, Krypton lasers, Xenon lasers, Nitrogen lasers, CO lasers, etc. 2 The laser may be a gas laser, such as a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other cases, the method includes irradiating the flow stream with a dye laser, such as a stilbene laser, a coumarin laser, or a rhodamine laser. In still other cases, the method includes irradiating the flow stream with a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other cases, the method includes irradiating the flow stream with a metal-vapor laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO 4 Laser, Nd:yCa 4 O(BO3 ) 3 Laser, Nd:YCOB laser, Ti:Sapphire laser, Slim YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 This involves irradiating the flow stream with a solid state laser, such as a laser, or a cerium doped laser, and combinations thereof.

[0043] The sample may be illuminated with one or more of the light sources mentioned above, for example, two or more light sources, for example, three or more light sources, for example, four or more light sources, for example, five or more light sources, including ten or more light sources. The light sources may include any type of combination of light sources. For example, in some embodiments, the method includes illuminating the sample in the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0044] The sample may be irradiated with a wavelength in the range of 200 nm to 1500 nm, such as 250 nm to 1250 nm, such as 300 nm to 1000 nm, such as 350 nm to 900 nm, including 400 nm to 800 nm, with a diode laser. For example, if the light source is a broadband light source, the sample may be irradiated with a wavelength in the range of 200 nm to 900 nm. In other cases, if the light source includes multiple narrowband light sources, the sample may be irradiated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm) each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (such as a laser array) and the sample is irradiated with a specific wavelength in the range of 200 nm to 700 nm, such as a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid state lasers, as described above.

[0045] When two or more light sources are employed, the sample may be illuminated with the light sources simultaneously or sequentially, or a combination thereof. For example, the sample may be illuminated with each of the light sources simultaneously. In other embodiments, the flow stream is illuminated sequentially with each of the light sources. When two or more light sources are employed to sequentially illuminate the sample, the time for which each light source illuminates may independently be 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method may include illuminating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source may be the same or different.

[0046] The period between illumination by each light source may also vary as desired, independently separated by a delay of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as up to 10 microseconds or more, such as up to 15 microseconds or more, such as up to 30 microseconds or more, including 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source may be the same or different.

[0047] The sample may be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the sample in the sample with the light source continuously. In other cases, the sample is illuminated by the light source at discrete intervals, such as illuminating every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 milliseconds, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0048] Depending on the light source, the sample may be illuminated from different distances, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 2.5 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 25 mm or more, including 50 mm or more. The angle or illumination may also vary from 10° to 90°, such as 15° to 85°, such as 20° to 80°, such as 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0049] 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-optic device for frequency-shifting the laser light may be employed. In these embodiments, the method includes irradiating the acousto-optic device with a laser. Depending on the desired wavelength of light to be generated in the output laser beam (e.g., for use in irradiating the sample in the flow stream), the laser may have a particular wavelength ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, inclusive, from 400 nm to 800 nm. The acousto-optic device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and ten or more lasers. The laser may include any combination of multiple laser types. For example, in some embodiments, the method includes irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0050] When two or more lasers are employed, the acousto-optic device may be irradiated with the lasers simultaneously or sequentially, or a combination thereof. For example, the acousto-optic device may be irradiated with each of the lasers simultaneously. In other embodiments, the acousto-optic device is irradiated with each of the lasers sequentially. When two or more lasers are employed to sequentially irradiate the acousto-optic device, the time for irradiating the acousto-optic device with each laser may independently be for a time period of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the method may include irradiating the acousto-optic device with the laser for a duration ranging from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, such as 0.1 microseconds to 50 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is sequentially illuminated with two or more lasers, the duration of illumination of the acousto-optic device with each laser may be the same or different.

[0051] The period between illumination by each laser may also be varied as necessary and may be independently separated by a delay of 0.001 microseconds or more, such as 0.01 microseconds or more, such as 0.1 microseconds or more, such as 1 microseconds or more, such as 5 microseconds or more, such as 10 microseconds or more, such as 15 microseconds or more, such as 30 microseconds or more, including 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, including 5 microseconds to 10 microseconds. In certain embodiments, the period between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially with more than two (i.e., three or more) lasers, the delay between illumination by each laser may be the same or different.

[0052] The acousto-optical device may be illuminated continuously or at discrete intervals. In some cases, the method includes illuminating the acousto-optical device with a laser continuously. In other cases, the acousto-optical device is illuminated with a laser at discrete intervals, e.g., every 0.001 milliseconds, e.g., every 0.01 milliseconds, e.g., every 0.1 milliseconds, e.g., every 1 millisecond, e.g., every 10 milliseconds, e.g., every 100 milliseconds, including every 1000 milliseconds, or some other interval.

[0053] Depending on the laser, the acousto-optical device may be illuminated from different distances, for example 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, such as 0.5 mm or more, for example 1 mm or more, such as 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, such as 15 mm or more, for example 25 mm or more, including 50 mm or more. The angle or illumination may also vary from 10° to 90°, such as 15° to 85°, such as 20° to 80°, such as 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0054] In an embodiment, the method includes applying a high frequency drive signal to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals can be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams, for example, 3 or more high frequency drive signals, for example, 4 or more high frequency drive signals, for example, 5 or more high frequency drive signals, for example, 6 or more high frequency drive signals, for example, 7 or more high frequency drive signals, for example, 8 or more high frequency drive signals, for example, 9 or more high frequency drive signals, for example, 10 or more high frequency drive signals, for example, 15 or more high frequency drive signals, for example, 25 or more high frequency drive signals, for example, 50 or more high frequency drive signals, including 100 or more high frequency drive signals.

[0055] The angularly deflected laser beams generated by the high frequency drive signals each have an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying a high frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam at a desired intensity. In some cases, each applied high frequency drive signal independently has an amplitude of about 0.001V to about 500V, e.g., about 0.005V to about 400V, e.g., about 0.01V to about 300V, e.g., about 0.05V to about 200V, e.g., about 0.1V to about 100V, e.g., about 0.5V to about 75V, e.g., about 1V to about 50V, e.g., 2V to about 40V, e.g., 3V to about 30V, including about 5V to about 25V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0056] In these embodiments, the angularly deflected laser beams in the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, including 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams, for example, along the horizontal axis of the output laser beam. The overlap between adjacent angle deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, including 100 μm or more.

[0057] In certain instances, the present invention may be used in a variety of applications, including, but not limited to, the following: Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620, 10,720, 10,820, 10,920, 10,102, ... As described in U.S. Pat. No. 6,111, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the sample is illuminated with a plurality of frequency-shifted light beams in the flow stream to generate images of cellular mitochondria in the flow stream, the disclosures of which are incorporated herein by reference.

[0058] As discussed above, light from the illuminated cells in the sample is transmitted to a light detection system and measured by a plurality of light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof.

[0059] The collected light may be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, such as measuring the light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, or some other interval, including every 1000 milliseconds.

[0060] Measurements of the collected light may be made one or more times during the subject methods, including, for example, two or more times, such as three or more times, such as five or more times, and ten or more times. In certain embodiments, the light from the sample is measured two or more times, and the data in a particular instance is averaged.

[0061] Light from cells in a sample may be measured at one or more wavelengths, for example, at 5 or more different wavelengths, such as at 10 or more different wavelengths, for example, at 25 or more different wavelengths, such as at 50 or more different wavelengths, for example, at 100 or more different wavelengths, such as at 200 or more different wavelengths, for example, at 300 or more different wavelengths, including measurements of collected light at 400 or more different wavelengths.

[0062] In some embodiments, the method includes further conditioning the light from the sample prior to detecting the light. For example, the light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, optical refractors, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses to reduce the light profile. In other cases, the light emitted from the sample passes through one or more collimators to reduce the light beam divergence.

[0063] In embodiments, one or more images of cellular mitochondria in a sample are generated from the measured light. The images may be generated from detected fluorescence, detected light absorption, detected light scattering, detected luminescence, or any combination thereof. In some cases, the images are generated from fluorescence from labeled mitochondria. In some cases, the images are generated from detected light absorption from the sample, such as from a bright field light detector. In these cases, the images are generated based on bright field image data from particles in the flow stream. In other cases, the images are generated from detected light scattering from the sample, such as from a side scatter detector, a forward scatter detector, or a combination of a side scatter detector and a forward scatter detector. In these cases, the images are generated based on scattered light image data. In still other cases, the images are generated from light emitted from the sample. In still other cases, the images are generated from a combination of detected light absorption, detected light scattering, and detected luminescence. In certain cases, the images of cellular mitochondria are generated from fluorescence from labeled mitochondria and one or more of light loss, side scattered light, and forward scattered light.

[0064] One or more images may be generated from the measured light. In some embodiments, a single image is generated for each cell mitochondrion in the sample from each form of detected light. In other embodiments, multiple images are generated for each cell mitochondrion, for example, two or more, for example, three or more, for example, five or more, for example, ten or more, including twenty-five or more images. For example, a first image of the cell mitochondrion is generated from the detected fluorescence from the labeled mitochondria, a second image of the cell mitochondrion is generated from the detected light absorption, and a third image is generated from the detected light scattering. In other embodiments, two or more images may be generated from each form of detected light, for example, three or more, for example, four or more, for example, five or more, including ten or more, or a combination thereof.

[0065] In some embodiments, frequency encoded data (e.g., frequency encoded spatial data) is generated from measured light from cells having fluorescently labeled mitochondria in the flow stream. In some cases, one or more images are generated from the frequency encoded data. The frequency encoded data may be generated in one or more detection channels, such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, including eight or more detection channels. In some embodiments, the frequency encoded data includes data components obtained (or derived) from light from different detectors, such as fluorescence detection channels, detected light absorption, or detected light scattering. In some cases, the frequency encoded data is phase corrected. In some cases, the frequency encoded data used to generate an image of cellular mitochondria is phase corrected by performing a transform on the frequency encoded data. In one example, the frequency encoded data is phase corrected by performing a Fourier transform (FT) of the frequency encoded data. In another example, the frequency encoded data is phase corrected by performing a discrete Fourier transform (DFT) of the frequency encoded data. In yet another example, the frequency encoded data is phase corrected by performing a short-time Fourier transform (STFT) of the frequency encoded data. In one particular embodiment, the method includes performing a transformation of the frequency-encoded data without performing any mathematical imaginary calculations (i.e., performing only calculations for the mathematical real calculations of the transformation) to generate an image from the frequency-encoded data.

[0066] In some cases, the method includes generating one or more grayscale images of the cellular mitochondria. The term "grayscale" is used herein in the conventional sense of the term to refer to an image of the cellular mitochondria in a flow stream that is composed of various shades of gray based on the intensity of light at each pixel. In some embodiments, a pixel intensity threshold is determined from the grayscale image, and the pixel intensity threshold is used to convert each pixel to a binary value that is used to generate the image of the cellular mitochondria. In certain cases, the image of the cellular mitochondria is a binary pixel image of the cellular mitochondria, such that each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel is above a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0067] In some embodiments, one or more image parameters are calculated from the generated images of the cellular mitochondria. In some cases, the image parameters are quantitative image parameters. In some cases, the morphology of the cellular mitochondria is evaluated based on radial moment image parameters. In some cases, the image parameters include radial moments of fluorophores used to stain the mitochondria. In some cases, the radial moment image parameters are calculated from the generated images based on fluorescence measured from the cellular mitochondria, light loss measured for the cellular mitochondria, forward light scatter measured for the cellular mitochondria, side light scatter measured for the cellular mitochondria, and combinations thereof.

[0068] In some cases, a cellular mitochondrial size image parameter is calculated from the generated images. In some cases, a cellular mitochondrial fluorescence radial moment image parameter is calculated from the generated images. In some cases, a cellular mitochondrial light loss image parameter is calculated from the generated images. In some cases, a forward scatter radial moment image parameter is calculated from the generated images. In some cases, a side scatter radial moment image parameter is calculated from the generated images. In some cases, an image moment is calculated from the generated images. The term image moment is used herein in the conventional sense of the term to refer to a weighted average of pixel intensities in an image. In some cases, a center of mass of a cellular mitochondrion may be calculated from the image moments of the image. In other cases, an orientation of a cellular mitochondrion may be calculated from the image moments of the image. In still other cases, an eccentricity of a cellular mitochondrion may be calculated from the image moments of the image.

[0069] In some embodiments, the image parameter calculated is a radial moment of the fluorophore. In some cases, the method includes calculating a pixel density of the generated image of the cellular mitochondria. In some cases, the number of pixels in a horizontal row of the image of the cellular mitochondria is calculated. In some cases, the number of pixels in a vertical column of the image of the cellular mitochondria is calculated. In certain cases, the absolute number of pixels found in the image of the cellular mitochondria is determined. In some cases, a center of gravity of the cellular mitochondria is determined from one or more of the generated images. In some cases, the image parameter is calculated by determining a sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria.

[0070] In some embodiments, evaluating the morphology of cell mitochondria includes determining the shape of cell mitochondria. In some cases, the size of each cell mitochondria in the sample is measured. In some cases, the method for determining the size of each cell mitochondria in the sample includes comparing the size of the cell mitochondria to a predetermined standard size (using a reference image of the cell mitochondria), for example, determining that the size of each cell mitochondria is 50% or more of the predetermined standard size, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 98% or more, e.g., 99% or more of the predetermined standard size. In some examples, the method includes determining the number or percentage of cell mitochondria in the sample having a size within 15% or less of the predetermined standard size range, e.g., within 10% or less, e.g., within 5% or less, e.g., within 4% or less, e.g., within 3% or less, e.g., within 2% or less, e.g., within 1% or less, including within 0.5% or less. In some cases, the method includes determining that the sample is an activated cell sample when cells in the sample have a size within a reference size range.

[0071] In some embodiments, assessing the morphology of cell mitochondria includes determining the punctateness of cell mitochondria. In some cases, the punctateness of each cell mitochondria in the sample is measured. In some cases, the method for determining the punctateness of each cell mitochondria in the sample includes comparing the punctateness of the cell mitochondria (using a reference image of the cell mitochondria) to a predetermined reference punctateness, for example, determining that the punctateness of each cell mitochondria is 50% or more of the predetermined reference punctateness, for example, 60% or more, 70% or more, 80% or more, 90% or more, 95% or more, 97% or more, 98% or more, 99% or more of the predetermined reference punctateness. In some examples, the method includes determining the number or percentage of cell mitochondria in the sample that have a punctateness within 15% or less of a predetermined reference size range, for example, within 10% or less, for example, within 5% or less, for example, within 4% or less, for example, within 3% or less, for example, within 2% or less, for example, within 1% or less, including within 0.5% or less. In some cases, the method includes determining that the sample is an activated cell sample when cellular mitochondria in the sample have punctativity within a reference punctativity range.

[0072] In some embodiments, assessing the morphology of cellular mitochondria includes determining the shape of cellular mitochondria. In some cases, the method includes determining that the cellular mitochondria have a shape selected from a spherical, spindle, elliptical, elongated, and flat shape. In some cases, the method includes determining the percentage of cellular mitochondria having a particular shape. In one example, the percentage of flat cellular mitochondria in the sample may be determined. In another example, the percentage of elongated cellular mitochondria in the sample may be determined. In another example, the percentage of spherical cellular mitochondria in the sample may be determined. In certain cases, depending on the type of cellular mitochondria in the sample and the desired shape of the cellular mitochondria, determining that the sample is an activated cell sample includes determining when the percentage of cells having a particular desired shape exceeds a predetermined threshold, for example, when more than 50%, for example more than 60%, for example more than 70%, for example more than 80%, for example more than 90%, for example more than 95%, for example more than 97%, for example more than 98%, for example more than 99% of the cellular mitochondria in the sample have the desired shape, including when more than 99.5% of the cells have the particular desired shape (spherical, spindle, elongated).

[0073] In an embodiment, mitochondria are labeled with one or more labels. The term "label" is used herein in the conventional sense of the term to refer to the coupling or conjugation of one or more components of the mitochondria of a sample with one or more detection components, such as components detectable by luminescence (e.g., fluorescence, phosphorescence, etc.), contrast agents, radioisotopes, etc. For example, labeling can include coupling a particle to a detection component by non-covalent interactions such as hydrogen bonds, dipole-dipole bonds, ionic bonds, etc., or via one or more covalent bonds.

[0074] In some embodiments, the method includes labeling cellular mitochondria with one or more fluorescent labels. In certain examples, fluorochromes of interest may include, but are not limited to, bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurhodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squalenes, bodipy, squalinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetraplyls, and combinations thereof. In certain embodiments, the conjugate may comprise two or more dyes, such as two or more dyes selected from bodipy dyes, coumarin dyes, rhodamine dyes, acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes, chlorophyll-containing dyes, triarylmethane dyes, azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurhodine dyes, safranine dyes, indamines, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazoline dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronine dyes, fluorine dyes, rhodamine dyes, phenanthridine dyes, squalenes, bodipy, squalinoloxitanes, naphthalenes, coumarins, oxadiazoles, anthracenes, pyrenes, acridines, arylmethines, or tetraplyls, and combinations thereof.

[0075] In certain embodiments, fluorochromes of interest may include, but are not limited to, fluorescein isothiocyanate (FITC), phycoerythrin (PE) dyes, peridinin chlorophyll protein-cyanine dyes (e.g., PerCP-Cy5.5), phycoerythrin-cyanine (PE-Cy) dyes (PE-Cy7), allophycocyanin (APC) dyes (e.g., APC-R700), allophycocyanin-cyanine dyes (e.g., APC-Cy7), coumarin dyes (e.g., V450 or V500). In certain cases, the fluorochrome may include one or more of 1,4-bis-(o-methylstyryl)-benzene (bis-MSB 1,4-bis[2-(2-methylphenyl)ethenyl]-benzene), C510 dyes, C6 dyes, Nile Red dyes, T614 dyes (e.g., N-[7-(methanesulfonamido)-4-oxo-6-phenoxycyclomen-3-yl]formamide), LDS821 dye ((2-(6-(p-dimethylaminophenyl)-2,4-neopentylene-1,3,5-hexatrienyl)-3-ethylbenzothiazolium perchlorate), mFluor dyes (e.g., mFluor Red dyes such as mFluor 780NS).

[0076] Fluorochromes of interest include, but are not limited to, fluorescein, hydroxycoumarin, aminocoumarin, methoxycoumarin, cascade blue, pacific blue, pacific orange, lucifer yellow, NBD, R-phycoerythrin (PE), PE-Cy5 conjugates, PE-Cy7 conjugates, red 613, PerCP, Tru Red, FluorX, BODIPY-FL, TRITC, X-rhodamine, Lissamine rhodamine B, Texas Red, allophycocyanin (APC), APC-Cy7 conjugates, Cy2, Cy3, Cy3B, Cy3.5, Cy5, Cy5.5, Cy7, Hoechst 33342, DAPI, Hoechst 33258, SYTOX Blue, Chromomycin A3, Mithramycin, YOYO-1, Ethidium Bromide, Acridine Orange, SYTOX Green, TOTO-1, TO-PRO-1, Thiazole Orange, Propidium Iodide (PI), LDS751, 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, Cell Lian, mCFP, ECFP, CyPet, Y66W, dKeima-Red, mKeima-Red, TagCFP, Am Cyan 1, mTFP1 (Teal), S65A, Acropora Cyan, Wild-type GFP, S65C, TurboGFP, TagGFP, TagGFP2, AcGFP1, S65L, Emerald, S65T, EGFP, Azami Green, ZsGreen1, Doronpa Green, TagYFP, EY FP, Topaz, Venus, mCitrine, YPet, TurboYFP, PhiYFP, PhiYFP-m, Zs Yellow 1, m Banana, Kusabira-Orange, mOrange, mOrange 2, mKO, TurboRFP, td Tomato, Ds Red-Express2, TagRFP, Ds Red Monomer, Ds Red 2 ("RFP"), mStrawberry, TurboFP602, As Red 2, mRFP1, J-Red, mCherry, Hc Red 1, mKate2, Katushka (TurboFP635), mKate (TagFP635), TurboFP635, mPlum, mRaspberry, mNeptune, E2-Crimson, Monochlorobimane, Calcein, Alexa Fluor 350, Alexa Fluor 405, Alexa Fluor 430, Alexa Fluor 488, Alexa Fluor 500, Alexa Fluor 514, Alexa Fluor 532, Alexa Fluor 546, Alexa Fluor 555, Alexa Fluor 568, Alexa Fluor 594, Alexa Fluor 610, Alexa Fluor 633, Alexa Fluor 647, Alexa Fluor 660, Alexa Fluor 680, Alexa Fluor 700, Alexa Fluor 750, Alexa Fluor 790, and HyPer. In some embodiments, the fluorochrome is 7-AAD, Alexa Fluor 488, Alexa Fluor 647, Alexa Fluor700, Am Cyan, APC, APC-Cy7, APC-H7, APC-R700, BB660-P2, BB790-P, BUV395, BUV615, BUV661, BV570, BV60 5, BV650, BV711, BV750, BV786, BYG584-P, Calcein AM, Calcein Blue AM, CFSE, DAPI, DRAQ5, DRAQ7, FITC, Fluo-4 AM, FVS440UV, FVS450, FVS510, FVS520, FVS570, FVS575V, FVS620, FVS660, FVS700, FVS780, Indo-1 Hi, Indo-1Lo, JC-1, MitoStatus Red, MitoStatus TMRE, Pacific Blue, PE, PE-CF594, PE-Cy5, PE-Cy7, PerCP, PerCP-Cy5.5, PI, R718, RB545, RB613, RB744, RB780, RY586, RY610, V450, V500, Via-Probe Green, Via-Probe Red, VPD450, Alexa Fluor 532, Alexa Fluor 561, Alexa Fluor 660, APC-eFluor 780, APC / Fire 750, APC / Fire 810, BV785, eBFP, eCFP, eFluor 450, eFluor 506, eFluor 660, eGFP, eYFP, Hoechst 33258, Kiravia Blue 520, mCherry, NFB510, NFB530, NFB555, NFB585, NFB610-70S, NFB660-120S, NFR660, NFR685, NFR700, NFR710, NFY570, NFY590, NFY610, NFY660, NFY690, NFY700, NFY730, Pacific Orange, PE-Cy5.5, PE-eFluor 610, PE / Dazzle 594, PE / Fire 640, PE / Fire 700, PE / Fire 810, PerCP-eFluor 710, SB436, SB600, SB645, SB702, SB780, Spark Blue 550, Spark Blue 574, Spark NIR 685, Spark UV 387, Spark Violet 423, Spark Violet 538, Spark YG581, Spark YG593, and tdTomato.

[0077] In some cases, the fluorochrome is a polymer dye (e.g., a fluorescent polymer dye). The fluorescent polymer dyes that find use in the subject methods and systems are varied. In some cases of the methods, the polymer dye comprises a conjugated polymer. Conjugated polymers (CPs) are characterized by a delocalized electronic structure that includes a backbone of alternating unsaturated (e.g., double and / or triple) and saturated (e.g., single) bonds, where π electrons can move from one bond to the other. Thus, the conjugated backbone can limit the bond angles between the repeat units of the polymer, imparting an extended linear structure to the polymer dye. For example, proteins and nucleic acids are also polymers, but in some cases do not form extended rod structures, but rather fold into highly ordered three-dimensional shapes. In addition, CPs can form "rigid rod" polymer backbones and experience limited twist (e.g., twist) angles between monomer repeat units along the polymer backbone chain. In some cases, the polymer dye comprises a CP with a rigid rod structure. The structural properties of the polymer dye can affect the fluorescent properties of the molecule.

[0078] Polymeric dyes of interest include, but are not limited to, those described in U.S. Pat. Nos. 7,270,956, 7,629,448, 8,158,444, 8,227,187, 8,455,613, 8,575,303, 8,802,450, 8,969, Nos. 509, 9,139,869, 9,371,559, 9,547,008, 10,094,838, 10,302,648, 10,458,989, 10,641,775, and 10,962,546, the disclosures of which are incorporated herein by reference in their entireties. et al., J. Am. Chem. Soc., 2001, 123(26), pp 6417-6418; Feng et al., Chem. Soc. Rev., 2010, 39, 2411-2419; and Traina et al., J Am. Chem., 2011, 133(32), pp 12600-12607. Specific polymeric dyes that may be employed include, but are not limited to, the BD Horizon Brilliant™ dyes, such as the BD Horizon Brilliant™ Violet dyes (e.g., BV421, BV510, BV605, BV650, BV711, BV786), the BD Horizon Brilliant™ Ultraviolet dyes (e.g., BUV395, BUV496, BUV737, BUV805), and the BD Horizon Brilliant™ Blue dyes (e.g., BB515) (BD Biosciences, San Jose, Calif.). Any fluorochrome known to one of skill in the art, including but not limited to those described above or yet to be discovered, may be employed in the subject methods.

[0079] In some embodiments, mitochondria are labeled with one or more lipophilic cationic fluorophores. In some cases, mitochondria are labeled with fluorophores that form covalent bonds with mitochondrial proteins. In some cases, the lipophilic cationic fluorophores have a thiol-reactive moiety for attaching the fluorophore to mitochondrial proteins. In some cases, mitochondria are labeled with fluorophores that form covalent bonds with mitochondrial lipids. In some cases, mitochondria are labeled with fluorophores that stain the inner mitochondrial membrane. In certain embodiments, mitochondria are labeled with mitochondrial-specific dyes, such as MitoTracker dyes. In some cases, mitochondria are labeled with dyes that are or include the following dye components: MitoTracker Green FM dye, MitoTracker Red FM dye, MitoTracker Deep Red dye, MitoTracker Orange dye, MitoTracker Red dye, MitoTracker Deep Red FM dye, MitoTracker Red CMXRos dye, or combinations thereof. In some embodiments, mitochondria are labeled with mitochondrial labeling dyes that are or include dye components from one or more of the following: 10-N-nonyl-acridine orange, BrdU, CellLight mitochondrial GFP, Choma body, Cox8 SNAP tag, DiOC 6 , IraZolve-Mito, JC-1, JC-9, mito-Dronpa, mito-PA-mCherryl, Mito-QC, Mito Blue, Mitorotor-1, MitoSOX, MitoTimer, Mt-Keima, PK Mito Orange (PKMO), Rhodamine 123, SNARF-1 dye, SPLICS, styryl dye DASPMI, terpyridyl cyclometalate Ir(III) complex, tetra-methylrhodamine methyl ester, mitochondrial fluorescent protein (red, yellow, orange, green, blue, and cyan), ZP1-HaloTag, and combinations thereof.

[0080] In some embodiments, the method includes classifying the cells based on the generated images of the cell mitochondria and the calculated image parameters, or a combination thereof. In some cases, classifying the cells includes assigning the cells to one or more particle population clusters. In some embodiments, the cells of the sample are classified by a machine learning algorithm that uses a reference image of the cell mitochondria or one or more reference image parameters as a training data set. In some cases, the machine learning algorithm is a dynamic algorithm that updates based on the generated images and the determined image parameters of the cell mitochondria. Any convenient machine learning algorithm may be implemented, and machine learning algorithms of interest may include, but are not limited to, a linear regression algorithm, a logistic regression algorithm, a Naive Bayes algorithm, a k-nearest neighbor (kNN) algorithm, a random forest algorithm, a decision tree algorithm, a support vector machine algorithm, a gradient boosting algorithm, and a clustering algorithm. In certain embodiments, the machine learning algorithm is a neural network. In some cases, the machine learning algorithm is a neural network, such as an artificial neural network, a convolutional neural network, or a recurrent neural network. In certain cases, the machine learning algorithm is a Python script.

[0081] In some embodiments, classifying the cells of the sample includes assigning each cell in the sample having a particular mitochondrial morphology to a particle population cluster. As used herein, a "population" or "subpopulation" of classified cells having imaged mitochondria refers to a group of cells possessing related parameters (e.g., delta center of mass, radial moment, eccentricity) such that the measured data form a cluster in the data space. Thus, the populations are recognized as clusters in the data. Conversely, each particle population cluster may be interpreted as corresponding to a compound population of a particular category (e.g., punctiformity, size, shape, etc.) of cellular mitochondria, although clusters that typically correspond to noise or background are also observed. Particle population clusters may be defined in a subset of dimensions, e.g., with respect to a subset of measured image parameters that correspond to composite populations that differ only in a subset of measured image parameters or features extracted from particle measurements.

[0082] In some embodiments, the method includes determining one or more sorting gates for classified cells of the sample, such as cells with mitochondria of interest. The term "gate" is used herein in the conventional sense of the term to refer to a classifier boundary that identifies a subset of data of interest. In some instances, a gate can distinguish a group of events of particular interest. In addition, "gating" refers to the process of classifying data using gates defined for a given data set, where the gates can be one or more regions of interest combined with Boolean logic. In some embodiments, the gates identify particles that exhibit the same image parameters. Examples of methods for gating are described, for example, in U.S. Patent Nos. 4,845,653, 5,627,040, 5,739,000, 5,795,727, 5,962,238, 6,014,904, and 6,944,338, and 8,990,047, the disclosures of which are incorporated herein by reference. In some embodiments, the gate distinguishes particle population clusters from one or more different samples that have been previously determined (e.g., by a user) to correspond to a property of interest.

[0083] In some cases, the one or more sorting gates capture cells having mitochondria of the target particle population cluster and exclude particles of non-target particle population clusters. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting punctiformity above a predetermined threshold. In some cases, the sorting gates maximize the inclusion yield of cells of the target particle population cluster. In some cases, the sorting gates are configured to maximize the inclusion yield of cells of the target particle population cluster, such as configured to generate an inclusion yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate an inclusion yield of cells of the target particle population cluster of 99.9% or more. In some cases, the sorting gate is configured to maximize a purity yield of target particle population cluster cells, e.g., the sorting gate is configured to generate a purity yield of target particle population cluster cells of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate a purity yield of target particle population cluster cells of 99.9% or more.

[0084] In some cases, the sorting gate is configured to maximize the exclusion of particles of the non-target particle population. In some cases, the non-target particle population cluster includes cells with cellular mitochondria smaller than a predetermined threshold. In some cases, the non-target particle population cluster includes cells with cellular mitochondria exhibiting punctateness below a predetermined threshold. In some cases, the sorting gate is configured to exclude 50% or more of the particles of the non-target particle population cluster, such as 55% or more, such as 60% or more, such as 65% or more, such as 70% or more, such as 75% or more, such as 80% or more, such as 85% or more, such as 90% or more, such as 95% or more, such as 97% or more, such as 99% or more, including determining a sorting gate configured to exclude 99.9% or more of the particles of the non-target particle population cluster. In some cases, the sorting gate excludes particles of the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0085] In certain embodiments, generating a sorting gate includes an Fβ score, which is a weighted harmonic average of the inclusion of particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. The beta (β) parameter of the Fβ score biases the gating strategy toward either yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles) of the sample. For example, when the Fβ score is equal to 1, purity and yield have equal contributions to the gating strategy. When the Fβ score is equal to 2, the gating strategy emphasizes yield over purity. When the Fβ score is equal to 0.5, the gating strategy emphasizes purity over yield. In some embodiments, the Fβ score is in the range of 0.1-10, e.g., 0.2-9.5, e.g., 0.3-9, e.g., 0.4-8.5, e.g., 0.5-8, e.g., 0.5-7, e.g., 0.5-6, e.g., 0.5-5, e.g., 0.5-4, e.g., 0.5-3, e.g., 0.5-2. In some cases, the generated sorting gate of interest has an Fβ score of 1 or greater. In some cases, the generated sorting gate has an Fβ score of less than 1.

[0086] In some cases, the method includes evaluating the sorting gates of the gating strategy and adjusting one or more of the generated sorting gates. The adjustment may be based on an evaluation criterion indicating the accuracy of the sorting gates according to the desired sorting strategy. In some cases, the evaluation criterion may be an accuracy (e.g., purity) criterion, in which the purity is compared to a predetermined threshold. The evaluation criterion may be generated based on the confidence of the classifiers included in the sorting strategy. In other cases, the evaluation criterion may be a yield criterion, in which the yield of target cells in the particle population cluster is compared to a predetermined threshold.

[0087] In some embodiments, the method includes generating a sorting decision based on the determined sorting gates of the cells of the sample. In some cases, generating a particle sorting decision according to the present disclosure includes a sorting gate gating strategy including 8 or less sorting gates, e.g., 7 or less sorting gates, e.g., 6 or less sorting gates, e.g., 5 or less sorting gates, e.g., 4 or less sorting gates, e.g., 3 or less sorting gates, and including 2 or less sorting gates. In some embodiments, the method includes generating the sorting gates using a graphical display (as described below) that displays one or more analysis algorithms for applying classification parameters to the image parameters determined for the cells.

[0088] In some embodiments, the method includes displaying the gating strategy on a graphical user interface. For example, the analysis algorithm can be one or more of a spectral compensation matrix, a clustering algorithm, and a t-SNE algorithm. In some cases, the analysis algorithm is applied to the particle population cluster by dragging an icon of the analysis algorithm onto the particle population cluster. In other cases, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In certain cases, the analysis algorithm is a spectral unmixing algorithm as described in U.S. Pat. No. 11,009,400 and International Patent Application No. PCT / US2021 / 46741, filed August 19, 2021, the disclosures of which are incorporated herein by reference. In some examples, the gating strategy is determined using a computational sorting algorithm as described in U.S. Pat. No. 11,513,054, the disclosures of which are incorporated herein by reference. In certain cases, the sorting gates can be determined using computer software such as HyperFinder (e.g., as described in Bonavia, et al. Frontiers in Immunology 2022;13:1007016) and Computational Sorting with HyperFinder, FlowJo Software and BD FACSDiva Software (Becton Dickinson, 2021), the disclosures of which are incorporated herein by reference. In certain embodiments, the gating strategy is developed on a separate computing system (e.g., a different computer system or network) and communicated to the flow cytometer to implement the gating strategy, such as by using a particle sorter (e.g., having a sorting decision module) of the flow cytometer.

[0089] FIG. 1 illustrates a flow chart for evaluating the morphology of cellular mitochondria of a sample, according to certain embodiments. In step 101, light from cells in the sample having fluorescently labeled mitochondria in a flow stream is measured using a photodetector. In some cases, the method includes illuminating particles of the sample in the flow stream with a frequency modulated laser light beam or the like. In some cases, a frequency encoded data signal is generated from the measured light. In step 102, an image of the cellular mitochondria is generated. In some cases, in step 102a, an image parameter (e.g., radial moment) is calculated from the generated image. In some cases, the method includes calculating multiple image parameters for each cell, for example, 50 or more image parameters. In step 104, the morphology of the cellular mitochondria of the sample is evaluated. Evaluating the morphology can include one or more of determining a size of the cellular mitochondria, determining a shape of the cellular mitochondria, and determining a punctateness of the cellular mitochondria.

[0090] If the cells are to be sorted to isolate cells having a particular cellular mitochondria of interest, a gating strategy for the cells is determined in step 104. A gating strategy having one or more applied gates (e.g., 4 to 8 applied gates) can be developed and a sorting decision in step 105 can be generated using the gating strategy. In some cases, the cells having the fluorescently labeled mitochondria are sorted into different containers (step 106), such as a droplet sorter (e.g., having a droplet filling device, a piezoelectric transducer for generating different droplets from the cells, and a deflection plate for deflecting the droplets into different sample containers).

[0091] In certain embodiments, the method includes sorting one or more of the cells of the sample that are identified based on the estimated abundance of a fluorophore associated with cellular mitochondria. The term "sorting" is used herein in the conventional sense of the term to refer to separating components of the sample (e.g., droplets containing cells, droplets containing non-cellular particles such as biopolymers), and in some cases delivering the separated components to one or more sample collection vessels. For example, the method may include sorting two or more components of the sample, such as three or more components, such as four or more components, such as five or more components, such as ten or more components, such as fifteen or more components, including sorting 25 or more components of the sample.

[0092] 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 in acquiring overlapping spectra of the multiple fluorophores associated with the particle. In these embodiments, the analysis includes spectrally resolving (e.g., by calculating a spectral unmixing matrix) light from the multiple fluorophores having overlapping spectra associated with the particle, and identifying the particle based on the estimated abundance of each fluorophore associated with the particle. This analysis can be communicated to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0093] In some embodiments, the method for sorting components of a sample includes sorting particles (e.g., cells in a biological sample) using a particle sorting module having a deflection plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having a plurality of sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having a deflection plate, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0094] System for assessing cellular mitochondrial morphology in cells of a sample - Patent Application 20070229333 As summarized above, aspects of the disclosure include a system for assessing mitochondrial morphology of cells in a sample in a flow stream. The system according to certain embodiments includes a light source configured to irradiate cells having fluorescently labeled mitochondria in the flow stream, a light detector for measuring light from the cells, and a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate images of the cellular mitochondria from the measured light, calculate image parameters from the generated images of the cellular mitochondria, and assess the cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria.

[0095] In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the constituents in the sample (e.g., the type of cellular mitochondria, etc.), the light source can be configured to emit wavelengths of light that vary in the range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, including 400 nm to 800 nm. For example, the light source can include a broadband light source that emits light having a wavelength of 200 nm to 900 nm. In other cases, the light source includes a narrowband light source that emits wavelengths that span the range of 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) that emits light having a wavelength range of 200 nm to 900 nm. In certain embodiments, the light source is a laser. In some examples, the subject systems can be lasers such as helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, or the like. 2 Lasers of interest include gas lasers such as CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers, or xenon fluorine (XeF) excimer lasers, or combinations thereof. In other examples, the subject systems include dye lasers such as stilbene lasers, coumarin lasers, or rhodamine lasers. In yet other examples, lasers of interest include metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof. In still other examples, the subject systems include ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, Ti:Sapphire laser, Slim YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3Lasers include solid state lasers such as lasers, fluorine-doped lasers, or cerium-doped lasers, and combinations thereof.

[0096] In other embodiments, the light source is a non-laser light source, such as a lamp including, but not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode such as a broadband LED with a continuous spectrum, a superluminescent diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integration, etc. In some cases, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0097] The light source may be positioned at any suitable distance from the sample, such as, for example, 0.001 mm or more, such as 0.005 mm or more, such as 0.01 mm or more, such as 0.05 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 5 mm or more, such as 10 mm or more, such as 25 mm or more, including distances of 100 mm or more from the flow stream. In addition, the light source illuminates the sample at any suitable angle (e.g., relative to the normal axis of the flow stream), such as an angle ranging from 10° to 90°, such as, for example, 15° to 85°, such as, for example, 20° to 80°, such as, for example, 25° to 75°, including, for example, 30° to 60°, such as, for example, an angle of 90°.

[0098] The light source may be configured to illuminate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to illuminate the sample continuously, such as with a continuous wave laser that is continuously illuminated into the flow stream at an inspection point in the flow cytometer. In other cases, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, such as every 0.01 milliseconds, such as every 0.1 milliseconds, such as every 1 milliseconds, such as every 10 milliseconds, such as every 100 milliseconds, including every 1000 milliseconds, or some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to accommodate intermittent illumination of the sample with the light source. For example, the subject systems in these embodiments may include one or more laser beam choppers and manual or computer controlled beam stops for blocking the sample and exposing the sample to the light source.

[0099] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous wave lasers. For example, lasers may include helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO 2 Lasers, gas lasers such as CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers or xenon fluorine (XeF) excimer lasers, dye lasers such as stilbene, coumarin or rhodamine lasers, helium cadmium (HeCd) lasers, helium mercury (HeHg) lasers, helium selenium (HeSe) lasers, helium silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers or gold lasers, and combinations thereof, ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3Laser, Nd:YCOB laser, Ti:sapphire laser, Thulium YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3 The laser may be a frequency-doubled or frequency-tripled implementation of any of the lasers mentioned above, such as a laser or a cerium-doped laser, as well as combinations thereof, a semiconductor diode laser, an optically pumped semiconductor laser (OPSL), or a frequency-doubled or frequency-tripled implementation of any of the lasers mentioned above.

[0100] In certain embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some cases, the optical beam generator includes a laser and a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous wave laser. For example, lasers in the optical beam generator of interest can be gas lasers such as helium neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon fluorine (ArF) excimer lasers, krypton fluorine (KrF) excimer lasers, xenon chlorine (XeCl) excimer lasers, or xenon fluorine (XeF) excimer lasers, or combinations thereof; dye lasers such as stilbene, coumarin, or rhodamine lasers; metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO 4 Laser, Nd:yCa 4 O(BO 3 ) 3 Laser, Nd:YCOB laser, Titanium sapphire laser, Thulium YAG laser, Ytterbium YAG laser, Ytterbium 2 O 3The laser may be a solid state laser such as a laser or a cerium doped laser, as well as combinations thereof.

[0101] The acousto-optical device may be any convenient acousto-optical protocol configured to frequency shift laser light using applied acoustic waves. In certain embodiments, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the subject system is configured to generate an angle-deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optical device with any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0102] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams in the output laser beam, for example, configured to apply 3 or more high frequency drive signals, for example, 4 or more high frequency drive signals, for example, 5 or more high frequency drive signals, for example, 6 or more high frequency drive signals, for example, 7 or more high frequency drive signals, for example, 8 or more high frequency drive signals, for example, 9 or more high frequency drive signals, for example, 10 or more high frequency drive signals, for example, 15 or more high frequency drive signals, for example, 25 or more high frequency drive signals, for example, 50 or more high frequency drive signals, including being configured to apply 100 or more high frequency drive signals.

[0103] In some cases, to generate an angularly deflected laser beam intensity profile in the output laser beam, the controller is configured to apply a high frequency drive signal having an amplitude ranging from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V, including from about 5 V to about 25 V. Each applied high frequency drive signal, in some embodiments, has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0104] In certain embodiments, the controller comprises a processor having a memory operatively coupled to the processor such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam including an angularly deflected laser beam having a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity, including instructions for generating three or more, such as four or more, such as five or more, such as ten or more, such as twenty-five or more, such as fifty or more, angularly deflected laser beams having the same intensity, including instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the memory may include instructions for generating two or more angle-deflected laser beams having different intensities, such as including instructions for generating three or more, such as four or more, such as five or more, such as ten or more, such as twenty-five or more, such as fifty or more, angle-deflected laser beams having different intensities, including may include instructions for generating 100 or more angle-deflected laser beams having different intensities.

[0105] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from an edge to a center of the output laser beam along a horizontal axis. In these cases, the intensity of the angle-deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99% of the intensity of the angle-deflected laser beam at the edge of the output laser beam along the horizontal axis, such as, for example, 0.5% to about 95%, such as, for example, 1% to about 90%, such as, for example, about 2% to about 85%, such as, for example, about 3% to about 80%, such as, for example, about 4% to about 75%, such as, for example, about 5% to about 70%, such as, for example, about 6% to about 65%, such as, for example, about 7% to about 60%, such as, for example, about 8% to about 55%, including, for example, about 10% to about 50%, of the intensity of the angle-deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon, which when executed by the processor, cause the processor to generate an output laser beam that increases in intensity from an edge to a center of the output laser beam along a horizontal axis. In these cases, the intensity of the angle-deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99% of the intensity of the angle-deflected laser beam at the center of the output laser beam along the horizontal axis, such as, for example, 0.5% to about 95%, such as, for example, 1% to about 90%, such as, for example, about 2% to about 85%, such as, for example, about 3% to about 80%, such as, for example, about 4% to about 75%, such as, for example, about 5% to about 70%, such as, for example, about 6% to about 65%, such as, for example, about 7% to about 60%, such as, for example, about 8% to about 55%, including, for example, about 10% to about 50%, of the intensity of the angle-deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile having a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along a horizontal axis.

[0106] In embodiments, the optical beam generator of interest may be configured to generate angularly deflected laser beams in the spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the system is configured to generate angularly deflected laser beams in the output laser beam that overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam, etc. The overlap between adjacent angle deflected laser beams (such as overlap of beam spots) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, including 100 μm or more.

[0107] In certain cases, a light beam generator configured to generate two or more frequency shifted light beams may be used as disclosed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,45 Nos. 1,538, 10,620,111, and the laser excitation modules described in U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0108] In an embodiment, the system includes a light detection system having a plurality of light detectors for measuring light from cells in the sample. In some cases, one or more light detectors of the light detection system are light loss light detectors. In some cases, one or more light detectors of the light detection system are configured to measure scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure side scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure forward scattered light. In certain cases, one or more light detectors of the light detection system are configured to measure back scattered light. Detectors of interest may include optical sensors or light detectors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APD), image sensors, charge-coupled devices (CCD), intensified charge-coupled devices (ICCD), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, solar cells, photodiodes, photomultipliers, phototransistors, quantum dot photoconductors, or photodiodes, as well as combinations thereof, among other light detectors. In certain embodiments, the light from the sample is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide-semiconductor (CMOS) image sensor, or an N-type metal-oxide-semiconductor (NMOS) image sensor.

[0109] In some embodiments, the light detection module of interest includes a plurality of light detectors. In some cases, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In certain cases, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include a light detector having 4 or more light detectors, such as 10 or more light detectors, such as 25 or more light detectors, such as 50 or more light detectors, such as 100 or more light detectors, such as 250 or more light detectors, such as 500 or more light detectors, such as 750 or more light detectors, including 1000 or more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, such as a photodiode array having photodiodes including 10 or more photodiodes, such as 25 or more photodiodes, such as 50 or more photodiodes, such as 100 or more photodiodes, such as 250 or more photodiodes, such as 500 or more photodiodes, such as 750 or more photodiodes, including 1000 or more photodiodes.

[0110] The photodetectors may be arranged in any desired geometric configuration, configurations of interest include, but are not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregular configurations. The photodetectors in the photodetector array may be oriented relative to one another at angles ranging from 10° to 180° (referenced in the XZ plane), such as 15° to 170°, such as 20° to 160°, such as 25° to 150°, such as 30° to 120°, including 45° to 90°. The photodetector array may be of any suitable shape, including rectilinear shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curvilinear shapes, such as circular, elliptical, and irregular shapes, such as a parabolic lower portion joined to a planar upper portion. In one particular embodiment, the photodetector array has a rectangular active surface.

[0111] Each photodetector (e.g., photodiode) in the array may have an active surface having a width in the range of 5 μm to 250 μm, e.g., 10 μm to 225 μm, e.g., 15 μm to 200 μm, e.g., 20 μm to 175 μm, e.g., 25 μm to 150 μm, e.g., 30 μm to 125 μm, inclusive, of 50 μm to 100 μm, and a length in the range of 5 μm to 250 μm, e.g., 10 μm to 225 μm, e.g., 15 μm to 200 μm, e.g., 20 μm to 175 μm, e.g., 25 μm to 150 μm, e.g., 30 μm to 125 μm, inclusive, of 50 μm to 100 μm. For example, the surface area of ​​each photodetector (e.g., photodiode) in the array may be in the range of 25 μm to 30 μm. 2 ~10000μm 2 For example, 50 μm 2 ~9000m 2 , e.g., 75 μm 2 ~8000m 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 and 200 μm 2 ~5000μm 2 The range includes:

[0112] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length ranging from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. The width of the photodetector array may also vary from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. Thus, the active surface of the photodetector array may vary from 0.1 mm to 100 mm, such as 0.05 mm to 90 mm, such as 0.1 mm to 80 mm, such as 0.5 mm to 70 mm, such as 1 mm to 60 mm, such as 2 mm to 50 mm, such as 3 mm to 40 mm, such as 4 mm to 30 mm, inclusive. 2~10000mm 2 For example, 0.5 mm 2 ~5000mm 2 , e.g., 1 mm 2 ~1000mm 2 , e.g., 5 mm 2 ~500mm 2 and 10 mm 2 ~100mm 2 The range may include.

[0113] Optical detectors of interest are configured to measure collected light at one or more wavelengths, for example, measuring collected light at 2 or more wavelengths, 5 or more different wavelengths, for example, 10 or more different wavelengths, for example, 25 or more different wavelengths, for example, 50 or more different wavelengths, for example, 100 or more different wavelengths, for example, 200 or more different wavelengths, for example, 300 or more different wavelengths, including measuring light emitted by a sample in the flow stream at 400 or more different wavelengths.

[0114] In some embodiments, the photodetector is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a range of wavelengths. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more of the wavelength ranges of 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from the sample in the flow stream at one or more specific wavelengths. For example, the system may include one or more detectors configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof.

[0115] The light detection system is configured to measure light continuously or at discrete intervals. In some cases, the light detector of interest is configured to make measurements of the collected light continuously. In other cases, the light detection system is configured to make measurements at discrete intervals, such as every 0.001 milliseconds, such as every 0.01 milliseconds, such as every 0.1 milliseconds, such as every 1 millisecond, such as every 10 milliseconds, such as every 100 milliseconds, including every 1000 milliseconds, or some other interval.

[0116] In embodiments, the system is configured to generate one or more images of cells in the sample. In some embodiments, the system includes a memory, the memory having instructions stored thereon for generating an image from detected light absorption, detected light scattering, detected light emission, or any combination thereof. In some cases, the memory includes instructions for generating an image from detected light absorption from the sample, such as from a bright field light detector. In some cases, the memory includes instructions for generating an image from detected light scattering from the sample, such as from a side scatter detector, a forward scatter detector, or a combination of a side scatter detector and a forward scatter detector. In some cases, the memory includes instructions for generating an image from light emitted from the sample. In other cases, the memory includes instructions for generating an image from a combination of detected light absorption and detected light scattering. In certain cases, the memory includes instructions for generating an image of cell mitochondria from fluorescence from labeled mitochondria and one or more of light loss, side scatter light, and forward scatter light.

[0117] In some embodiments, the memory includes instructions for generating one or more images from the measured light. In some embodiments, the memory includes instructions for generating a single image for each cellular mitochondrion in the sample from each form of detected light. In other embodiments, the memory includes instructions for generating a plurality of images, e.g., two or more, e.g., three or more, e.g., five or more, e.g., ten or more, including twenty-five or more, for each cellular mitochondrion. For example, the memory includes instructions for generating a first image of the cellular mitochondrion from detected fluorescence from the labeled mitochondria, instructions for generating a second image of the cellular mitochondrion from detected light absorption, and instructions for generating a third image from detected light scattering. In other embodiments, the memory includes instructions for generating two or more images, including ten or more, such as three or more, such as four or more, such as five or more, or a combination thereof, from each form of detected light.

[0118] In some embodiments, the system includes a memory having instructions for generating frequency encoded data (e.g., frequency encoded spatial data) from measured light from cellular mitochondria in the flow stream. In some cases, the memory includes instructions for generating one or more images from the frequency encoded data. The frequency encoded data may be generated in one or more detection channels, such as two or more, such as three or more, such as four or more, such as five or more, such as six or more, including eight or more detection channels. In some embodiments, the frequency encoded data includes data components obtained (or derived) from light from different detectors, such as detected light absorption or detected light scattering. In some cases, the memory includes instructions for phase correcting the frequency encoded data. In some cases, the memory includes instructions for generating a phase corrected image of cellular mitochondria by performing a transform on the frequency encoded data. In one example, the memory includes instructions for phase correcting the frequency encoded data by performing a Fourier transform (FT) of the frequency encoded data. In another example, the memory includes instructions for phase correcting the frequency encoded data by performing a discrete Fourier transform (DFT) of the frequency encoded data. In yet another example, the memory includes instructions for phase correcting the frequency-encoded data by performing a short-time Fourier transform (STFT) of the frequency-encoded data. In one particular embodiment, the memory includes instructions for performing a transformation of the frequency-encoded data without performing any mathematical imaginary calculations (i.e., performing only calculations for the mathematical real calculations of the transformation) to generate an image from the frequency-encoded data.

[0119] In some cases, the memory includes instructions for generating one or more grayscale images of cellular mitochondria. The term "grayscale" is used herein in the conventional sense of the term to refer to an image of cellular mitochondria in a flow stream that is composed of various shades of gray based on the intensity of light at each pixel. In some embodiments, the memory includes instructions for determining a pixel intensity threshold from the grayscale image, where the pixel intensity threshold is used to convert each pixel to a binary value that is used to generate an image of the cell. In certain cases, the images of cells and cellular mitochondria are binary pixel images of the particles, such that each pixel is assigned a binary pixel value of 1 (e.g., if the intensity of the pixel is above a predetermined threshold) or a binary pixel value of 0 (e.g., if the intensity of the pixel is below a predetermined threshold).

[0120] In embodiments, the system includes a memory having instructions for calculating one or more image parameters from a generated image of the cellular mitochondria. In some embodiments, the memory includes instructions for calculating an image parameter that is a quantitative image parameter. In some cases, the morphology of the cellular mitochondria is evaluated based on a radial moment image parameter. In some cases, the image parameter includes a radial moment of a fluorophore used to stain the mitochondria. In some cases, the radial moment image parameter is calculated from an image generated based on fluorescence measured from the cellular mitochondria, light loss measured for the cellular mitochondria, forward light scatter measured for the cellular mitochondria, side light scatter measured for the cellular mitochondria, and combinations thereof.

[0121] In some cases, the memory includes instructions for calculating a cellular mitochondrial size image parameter from the generated image. In some cases, the memory includes instructions for calculating a cellular mitochondrial fluorescence radial moment image parameter from the generated image. In some cases, the memory includes instructions for calculating a cellular mitochondrial light loss image parameter from the generated image. In some cases, the memory includes instructions for calculating a forward scatter radial moment image parameter from the generated image. In some cases, the memory includes instructions for calculating a side scatter radial moment image parameter from the generated image. In some cases, the memory includes instructions for calculating image moments from the generated image. The term image moment is used herein in the conventional sense of the term to refer to a weighted average of pixel intensities in an image. In some cases, the memory includes instructions for calculating a center of mass of a cellular mitochondrion from the image moments of the image. In other cases, the memory includes instructions for calculating an orientation of a cellular mitochondrion from the image moments of the image. In still other cases, the memory includes instructions for calculating an eccentricity of a cellular mitochondrion from the image moments of the image.

[0122] In some embodiments, the memory includes instructions for calculating a radial moment of the fluorophore. In some cases, the memory includes instructions for calculating a pixel density of the generated image of the cellular mitochondria. In some cases, the memory includes instructions for calculating a number of pixels in a horizontal row of the image of the cellular mitochondria. In some cases, the memory includes instructions for calculating a number of pixels in a vertical column of the image of the cellular mitochondria. In certain cases, the memory includes instructions for determining an absolute number of pixels found in the image of the cellular mitochondria. In some cases, the memory includes instructions for determining a center of gravity of the cellular mitochondria from one or more of the generated images. In some cases, the memory includes instructions for calculating an image parameter by determining a sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria.

[0123] In an embodiment, the memory includes instructions for evaluating cellular mitochondrial morphology based on one or more of the generated image and image parameters calculated from the generated image. In some embodiments, the memory includes instructions for evaluating cellular mitochondrial morphology by determining a size of the cellular mitochondria. In some cases, the memory includes instructions for measuring a size of each cellular mitochondrion in the sample. In some cases, the memory includes instructions for determining a size of each cellular mitochondrion in the sample by comparing the size of the cellular mitochondrion to a predetermined reference size (using a reference image of the cellular mitochondria), e.g., the size of each cellular mitochondrion is determined to be 50% or more of the predetermined reference size, e.g., 60% or more, e.g., 70% or more, e.g., 80% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 98% or more, e.g., 99% or more of the predetermined reference size. In some cases, the memory includes instructions for determining the number or percentage of cellular mitochondria in the sample having a size within 15% or less of a predetermined reference size range, e.g., within 10% or less, e.g., within 5% or less, e.g., within 4% or less, e.g., within 3% or less, e.g., within 2% or less, e.g., within 1% or less, including within 0.5% or less of the predetermined reference size range. In some cases, the memory includes instructions for determining that the sample is an activated cell sample when cellular mitochondria in the sample have a size within the reference size range.

[0124] In some embodiments, the memory includes instructions for evaluating the morphology of cell mitochondria by determining the punctateness of cell mitochondria. In some cases, the memory includes instructions for measuring the punctateness of each cell mitochondria in the sample. In some cases, the memory includes instructions for determining the punctateness of each cell mitochondria in the sample by comparing the punctateness of the cell mitochondria with a predetermined reference punctateness (using a reference image of the cell mitochondria), for example, the punctateness of each cell mitochondria is determined to be 50% or more of the predetermined reference punctateness, for example, 60% or more, for example, 70% or more, for example, 80% or more, for example, 90% or more, for example, 95% or more, for example, 97% or more, for example, 98% or more, for example, 99% or more of the predetermined reference punctateness. In some cases, the memory includes instructions for determining the number or percentage of cellular mitochondria in the sample having a punctateness within a size of 15% or less of a predetermined reference size range, e.g., within 10% or less, e.g., within 5% or less, e.g., within 4% or less, e.g., within 3% or less, e.g., within 2% or less, e.g., within 1% or less, including within 0.5% or less, of the predetermined reference size range. In some cases, the memory includes instructions for determining that the sample is an activated cell sample when cellular mitochondria in the sample have a punctateness within the reference punctate range.

[0125] In some embodiments, the memory includes instructions for assessing cellular mitochondrial morphology by determining the shape of cellular mitochondria. In some cases, the memory includes instructions for determining that cellular mitochondria have a shape selected from spherical, spindle, elliptical, elongated, and flat. In some cases, the memory includes instructions for determining the percentage of cellular mitochondria having a particular shape. In one example, the memory includes instructions for determining the percentage of flat cellular mitochondria in a sample. In another example, the memory includes instructions for determining the percentage of elongated cellular mitochondria in a sample. In another example, the memory includes instructions for determining the percentage of spherical cellular mitochondria in a sample. In certain cases, depending on the type of cellular mitochondria and the desired shape of the cellular mitochondria, the memory includes instructions for determining that the sample is an activated cell sample when the percentage of cells having the particular desired shape exceeds a predetermined threshold, including when, for example, 50% or more, for example 60% or more, for example 70% or more, for example 80% or more, for example 90% or more, for example 95% or more, for example 97% or more, for example 98% or more, for example 99% or more of the cellular mitochondria in the sample have the particular desired shape (spherical, spindle, elongated), and when 99.5% or more of the cellular mitochondria in the sample have the particular desired shape.

[0126] In some embodiments, the memory includes instructions for classifying cells of the sample based on one or more calculated image parameters of the cellular mitochondria. In some cases, the memory includes instructions for classifying cells using a plurality of calculated image parameters, including classifying cells based on 50 or more image parameters determined from the generated images of the cellular mitochondria, e.g., using a combination of 2 or more, e.g., 3 or more, e.g., 4 or more, e.g., 5 or more, e.g., 6 or more, e.g., 7 or more, e.g., 8 or more, e.g., 9 or more, e.g., 10 or more, e.g., 15 or more, e.g., 20 or more, e.g., 25 or more, e.g., 30 or more, e.g., 40 or more image parameters.

[0127] In some cases, the system includes a processor having a memory for executing a machine learning algorithm, which is a dynamic algorithm for classifying cells of a sample. The system may be configured to implement any convenient machine learning algorithm, where the machine learning algorithm of interest may include, but is not limited to, a linear regression algorithm, a logistic regression algorithm, a Naive Bayes algorithm, a k-nearest neighbor (kNN) algorithm, a random forest algorithm, a decision tree algorithm, a support vector machine algorithm, a gradient boosting algorithm, and a clustering algorithm. In certain embodiments, the system is configured to implement a neural network. In some cases, the machine learning algorithm is a neural network, such as an artificial neural network, a convolutional neural network, or a recurrent neural network. In certain cases, the system is configured to implement a Python script.

[0128] In some embodiments, the memory includes instructions for determining one or more sorting gates for cells of the sample, such as for classifying cells of the sample by assigning each cell in the sample having a particular mitochondrial morphology to a particle population cluster. In some cases, the memory includes instructions for generating one or more sorting gates that capture cells of a target particle population cluster and exclude cells of non-target particle population clusters. In some cases, the memory includes instructions for determining a sorting gate configured to maximize an inclusion yield of cells of the target particle population cluster, where, for example, the sorting gate is configured to generate an inclusion yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more. In some cases, the memory includes instructions for determining a sorting gate configured to maximize a purity yield of cells of the target particle population cluster, where, for example, the sorting gate is configured to generate a purity yield of cells of the target particle population cluster of 50% or more, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more, including determining a sorting gate configured to generate a purity yield of cells of the target particle population cluster of 99.9% or more. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting a punctateness above a predetermined threshold.

[0129] In some cases, the memory includes instructions for determining a sorting gate configured to maximize the exclusion of cells of the non-target particle population. In some cases, the non-target particle population cluster includes cells with cellular mitochondria smaller than a predetermined threshold. In some cases, the non-target particle population cluster includes cells with cellular mitochondria exhibiting punctateness below a predetermined threshold. In some cases, the memory includes instructions for determining a sorting gate configured to exclude 50% or more of the cells of the non-target particle population, e.g., 55% or more, e.g., 60% or more, e.g., 65% or more, e.g., 70% or more, e.g., 75% or more, e.g., 80% or more, e.g., 85% or more, e.g., 90% or more, e.g., 95% or more, e.g., 97% or more, e.g., 99% or more of the cells of the non-target particle population, including determining a sorting gate configured to exclude 99.9% or more. In some cases, the memory includes instructions for determining a sorting gate to exclude cells of the non-target particle population cluster based on a calculated Mahalanobis distance from the target particle population cluster.

[0130] In certain embodiments, the memory includes instructions for generating a sorting gate using an Fβ score, where the Fβ score is a weighted harmonic average of the inclusion of particles in the target particle population cluster and the exclusion of particles in the non-target particle population cluster. The beta (β) parameter of the Fβ score biases the gating strategy toward either yield (e.g., increased inclusion of target particles) or purity (e.g., increased exclusion of non-target particles) of the sample. For example, when the Fβ score is equal to 1, purity and yield have equal contributions to the gating strategy. When the Fβ score is equal to 2, the gating strategy emphasizes yield over purity. When the Fβ score is equal to 0.5, the gating strategy emphasizes purity over yield. In some embodiments, the Fβ score is in the range of 0.1-10, e.g., 0.2-9.5, e.g., 0.3-9, e.g., 0.4-8.5, e.g., 0.5-8, e.g., 0.5-7, e.g., 0.5-6, e.g., 0.5-5, e.g., 0.5-4, e.g., 0.5-3, e.g., 0.5-2. In some cases, the generated sorting gate of interest has an Fβ score of 1 or greater. In some cases, the generated sorting gate has an Fβ score of less than 1.

[0131] In some cases, the memory includes instructions for evaluating the sorting gates of the gating strategy and adjusting one or more of the generated sorting gates. The adjustment may be based on an evaluation criterion indicating the accuracy of the sorting gates according to the desired sorting strategy. In some cases, the evaluation criterion may be an accuracy (e.g., purity) criterion, in which the purity is compared to a predetermined threshold. The evaluation criterion may be generated based on the confidence of the classifiers included in the sorting strategy. In other cases, the evaluation criterion may be a yield criterion, in which the yield of target cells in the particle population cluster is compared to a predetermined threshold.

[0132] In some embodiments, the memory includes instructions for generating a sorting decision based on the sorting gates determined for the cells of the sample. In some cases, the memory includes instructions for generating a particle sorting decision using a sorting gate gating strategy including 8 or less sorting gates, e.g., 7 or less sorting gates, e.g., 6 or less sorting gates, e.g., 5 or less sorting gates, e.g., 4 or less sorting gates, e.g., 3 or less sorting gates, and including 2 or less sorting gates. In some embodiments, the memory includes instructions for generating the sorting gates using a graphical display that displays one or more analysis algorithms for applying classification parameters to the image parameters determined for the cells.

[0133] In some embodiments, the memory includes instructions for generating sorting gates using a graphical display that displays one or more analysis algorithms for applying the determined classification parameters to the determined image parameters for the cells. In some embodiments, the system includes a display for visualizing the gating strategy on a graphical user interface.

[0134] In some cases, the graphical user interface applies an analysis algorithm to generate the gating strategy. For example, the analysis algorithm can be one or more of a spectral compensation matrix, a clustering algorithm, and a t-SNE algorithm. In some cases, the analysis algorithm is applied to the particle population cluster by dragging an icon of the analysis algorithm onto the particle population cluster. In other cases, the particle population cluster is selected and the analysis algorithm is applied by selecting from a drop-down menu. In one particular case, the analysis algorithm is a spectral unmixing algorithm as described in U.S. Pat. No. 11,009,400 and International Patent Application No. PCT / US2021 / 46741, filed Aug. 19, 2021, the disclosures of which are incorporated herein by reference.

[0135] In some examples, the system includes a memory having instructions for determining a gating strategy using a computational sorting algorithm such as described in U.S. Patent No. 11,513,054, the disclosure of which is incorporated herein by reference. In certain cases, the system includes a memory having computer software for determining sorting gates, such as HyperFinder (e.g., as described in Bonavia, et al. Frontiers in Immunology 2022;13:1007016) and Computational Sorting with HyperFinder, FlowJo Software and BD FACSDiva Software (Becton Dickinson, 2021), the disclosures of which are incorporated herein by reference. In certain embodiments, the gating strategy is developed on a separate computational system (e.g., a different computer system or network) and communicated to the flow cytometer for implementation of the gating strategy, such as by using a particle sorter (e.g., having a sorting decision module) of the flow cytometer.

[0136] In some embodiments, the system for generating a gating strategy is part of or operatively coupled to a particle analyzer system (e.g., a flow cytometer) for generating the flow cytometer data described herein.

[0137] In some cases, the system includes an integrated circuit device programmed to implement one or more of the methods described above. In some cases, the integrated circuit includes programming for generating an image of cellular mitochondria. In some cases, the integrated circuit includes programming for calculating image parameters from the generated image of cellular mitochondria. In some cases, the integrated circuit includes programming for evaluating cellular mitochondrial morphology according to one or more of the parameters (e.g., size, shape, punctiformity) as described above. In some cases, the integrated circuit includes programming for classifying cellular mitochondria based on the generated image and / or the calculated image parameters. In some embodiments, the integrated circuit device of interest includes a field programmable gate array (FPGA). In other embodiments, the integrated circuit device includes an application specific integrated circuit (ASIC). In yet other embodiments, the integrated circuit device includes a complex programmable logic device (CPLD).

[0138] The system according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having instructions stored therein to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor coordinating and executing the functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control, such as, for example, negative feedback control.

[0139] The system memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-write compact disk, flash memory devices, or other memory storage devices. The memory storage device can be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or disk drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or floppy disk, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also referred to as computer control logic, are typically stored in the system memory and / or in program storage devices used in conjunction with the memory storage devices.

[0140] 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, causes the computer to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. The implementation of hardware state machines to perform the functions described herein will be apparent to one of ordinary skill in the relevant art.

[0141] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, either fixed or portable). The processor may include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The programming may be provided remotely to the processor over a communication channel, or may be pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices together with the memory. For example, a magnetic or optical disk may carry the programming and can be read by a disk writer / reader. The system of the present invention also includes programming, for example in the form of a computer program product, algorithms for use in practicing the above methods. The programming according to the present invention may be recorded on a computer-readable medium, for example any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0142] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a cellular phone (i.e., a smartphone).

[0143] In some embodiments, a system according to the present disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0144] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communications between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment) configured for similar complementary data communications.

[0145] In one embodiment, the communications interface is configured for infrared communications, Bluetooth® communications, or any other suitable wireless communications protocol to enable the subject system to communicate with other devices, such as a computer terminal and / or network, a communications-enabled mobile phone, a personal digital assistant, or any other communications device that a user may use in conjunction with.

[0146] In one embodiment, the communication interface is configured to provide a connection for data transfer utilizing the Internet Protocol (IP) via a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0147] In one embodiment, the subject system is configured to communicate wirelessly with the server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, a personal digital assistant (PDA) or a notebook computer, or a larger device, such as a desktop computer, an appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touch screen.

[0148] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., in the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0149] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. Where one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be accomplished in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite network, in accordance with known techniques. Presentation of data by the output manager may be performed in accordance with a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user may retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system may be any type of known or future developed computer platform, but they are typically of the class of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication systems, including wireless systems, either networked or not. They may be co-located or physically separated.In some cases, depending on the type and / or configuration of the computer platform selected, various operating systems may be employed on any of the computer platforms. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, and the like.

[0150] In certain embodiments, the subject systems include one or more optical conditioning components for conditioning light, such as light projected onto the sample (e.g., from a laser) or light collected from the sample (e.g., scattered light, fluorescence). For example, the optical conditioning can be increasing the size of the light, the focal length of the light, or collimating the light. In some examples, the optical conditioning is an expansion protocol to increase the size of the light (e.g., beam spot), such as increasing the size by 5% or more, including increasing the size by 10% or more, such as 25% or more, such as 50% or more, and 75% or more. In other embodiments, the optical conditioning includes focusing the light to reduce the size of the light, such as by 5% or more, such as by 10% or more, such as by 25% or more, such as by 50% or more, including reducing the size of the beam spot by 75% or more. In certain embodiments, the optical conditioning includes collimating the light. The term "collimate" is used in the traditional sense to refer to optically adjusting the collinearity of light propagation or reducing the divergence of the light from a common propagation axis. In some examples, collimating includes narrowing the spatial cross-section of the light beam (eg, reducing the beam profile of a laser).

[0151] In some embodiments, the opening in the particle sorting module housing includes a focusing lens having a magnification, e.g., a magnification of 0.1 to 0.95, e.g., a magnification of 0.2 to 0.9, e.g., a magnification of 0.3 to 0.85, e.g., a magnification of 0.35 to 0.8, e.g., a magnification of 0.5 to 0.75, including a magnification of 0.55 to 0.7, e.g., a magnification of 0.6. For example, the focusing lens is a dichromatic reduction lens having a magnification of about 0.6 in one particular example. The focal length of the focusing lens can vary in the range of 5 mm to 20 mm, e.g., including focal lengths in the range of 6 mm to 19 mm, e.g., 7 mm to 18 mm, e.g., 8 mm to 17 mm, e.g., 9 mm to 16, and 10 mm to 15 mm. In one particular embodiment, the focusing lens has a focal length of about 13 mm.

[0152] In other embodiments, the optical conditioning component is a collimator. The collimator can be any convenient collimating protocol, such as one or more mirrors or curved lenses, or a combination thereof. For example, the collimator is a single collimating lens in certain instances. In other instances, the collimator is a collimating mirror. In yet other instances, the collimator includes two lenses. In yet other instances, the collimator includes a mirror and a lens. When the collimator includes one or more lenses, the focal length of the collimating lens can vary in the range of, for example, 5 mm to 40 mm, including focal lengths in the range of, for example, 6 mm to 37.5 mm, for example, 7 mm to 35 mm, for example, 8 mm to 32.5 mm, for example, 9 mm to 30 mm, for example, 10 mm to 27.5 mm, for example, 12.5 mm to 25 mm, and 15 mm to 20 mm.

[0153] In some embodiments, the subject systems include a flow cell nozzle having a nozzle orifice configured to direct a flow stream through the flow cell nozzle. The subject flow cell nozzle includes a flow cell nozzle having an orifice that propagates a fluid sample to a sample testing region, and in some embodiments, the flow cell nozzle has a proximal cylindrical portion defining a longitudinal axis and a distal frustoconical portion terminating in a flat surface having the nozzle orifice, transverse to the longitudinal axis. The length of the proximal cylindrical portion (measured along the longitudinal axis) can vary, for example, from 1 mm to 15 mm, including 1.5 mm to 12.5 mm, for example, from 2 mm to 10 mm, including 3 mm to 9 mm, and 4 mm to 8 mm. The length of the distal frustoconical portion (measured along the longitudinal axis) can similarly vary, for example, from 1 mm to 10 mm, including 2 mm to 9 mm, including 3 mm to 8 mm, and 4 mm to 7 mm. The diameter of the flow cell nozzle chamber can vary in some embodiments, for example, from 1 mm to 10 mm, including, for example, from 2 mm to 9 mm, such as from 3 mm to 8 mm, and from 4 mm to 7 mm.

[0154] In certain instances, the nozzle chamber does not include a cylindrical portion, and the entire flow cell nozzle chamber is formed in a frustoconical shape. In these embodiments, the length of the frustoconical nozzle chamber (measured along a longitudinal axis transverse to the nozzle orifice) may range, for example, from 1 mm to 15 mm, including 1.5 mm to 12.5 mm, for example, from 2 mm to 10 mm, for example, from 3 mm to 9 mm, and from 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical nozzle chamber may range, for example, from 1 mm to 10 mm, including 2 mm to 9 mm, for example, from 3 mm to 8 mm, and from 4 mm to 7 mm.

[0155] In embodiments, the sample flow stream originates from an orifice at the distal end of the flow cell nozzle. Depending on the desired characteristics of the flow stream, the flow cell nozzle orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, such as, for example, square, rectangular, trapezoidal, triangular, hexagonal, curvilinear cross-sectional shapes, such as, for example, circular, elliptical, as well as irregular shapes, such as, for example, a parabolic bottom joined to a planar top. In certain embodiments, the flow cell nozzle of interest has a circular orifice. The size of the nozzle orifice may vary in some embodiments from 1 μm to 20,000 μm, including, for example, from 2 μm to 17,500 μm, for example, from 5 μm to 15,000 μm, for example, from 10 μm to 12,500 μm, for example, from 15 μm to 10,000 μm, for example, from 25 μm to 7,500 μm, for example, from 50 μm to 5,000 μm, for example, from 75 μm to 1,000 μm, for example, from 100 μm to 750 μm, and from 150 μm to 500 μm. In one particular embodiment, the nozzle orifice is 100 μm.

[0156] In some embodiments, the flow cell nozzle includes a sample injection port configured to provide a sample to the flow cell nozzle. In embodiments, the sample injection system is configured to provide a suitable flow of the sample to the flow cell nozzle chamber. Depending on the desired characteristics of the flow stream, the velocity of the sample delivered by the sample injection port to the flow cell nozzle chamber can be 1 μL / sec or more, including, 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, and 500 μL / sec or more. For example, the flow rate of the sample may be in the range of 1 μL / sec to about 500 μL / sec, including, for example, 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, and 10 μL to about 100 μL / sec.

[0157] The sample injection port may be an orifice located in the wall of the nozzle chamber or may be a conduit located at the proximal end of the nozzle chamber. When the sample injection port is an orifice located in the wall of the nozzle chamber, the sample injection port orifice may be of any suitable shape, including cross-sectional shapes of interest, including but not limited to rectilinear cross-sectional shapes, such as square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes, such as circular, elliptical, and irregular shapes, such as a parabolic bottom joined to a planar top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, and in certain examples has an opening ranging from 0.1 mm to 5.0 mm, including, for example, 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm.

[0158] In certain examples, the sample injection port is a conduit positioned at the proximal end of the flow cell nozzle chamber. For example, the sample injection port can be a conduit positioned to have the orifice of the sample injection port aligned with the flow cell nozzle orifice. When the sample injection port is a conduit positioned in line with the flow cell nozzle orifice, the cross-sectional shape of the sample injection tube can be any suitable shape, and cross-sectional shapes of interest include, but are not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes such as circular, elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. The orifice of the conduit may vary in shape, and in certain instances may have an opening ranging from 0.1 mm to 5.0 mm, including, for example, 0.2 to 3.0 mm, such as 0.5 mm to 2.5 mm, such as 0.75 mm to 2.25 mm, such as 1 mm to 2 mm, and 1.25 mm to 1.75 mm, such as 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip having a bevel angle ranging from 1° to 10°, including, for example, a bevel angle of 2° to 9°, such as 3° to 8°, such as 4° to 7°, and 5°.

[0159] In some embodiments, the flow cell nozzle also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell nozzle. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell nozzle chamber, e.g., in conjunction with the sample, to produce a laminated flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered to the flow cell nozzle chamber can be 25 μL / sec or more, including, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, and 2500 μL / sec or more. For example, the flow rate of the sheath fluid may be in the range of 1 μL / sec to about 500 μL / sec, including, for example, 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, and 10 μL / sec to about 100 μL / sec.

[0160] In some embodiments, the sheath fluid injection port is an orifice located in the wall of the nozzle chamber. The sheath fluid injection port orifice may be of any suitable shape, with cross-sectional shapes of interest including, but not limited to, rectilinear cross-sectional shapes, such as, for example, square, rectangular, trapezoidal, triangular, hexagonal, curved cross-sectional shapes, such as, for example, circular, elliptical, as well as irregular shapes, such as, for example, a parabolic bottom portion joined to a planar top portion. The size of the sample injection port orifice may vary depending on the shape, and in certain cases has an opening of, for example, 1.5 mm, ranging from, for example, 0.2 to 3.0 mm, such as, for example, 0.5 mm to 2.5 mm, 0.75 mm to 2.25 mm, 1 mm to 2 mm, including 1.25 mm to 1.75 mm.

[0161] The subject systems, in certain instances, include a sample inspection region in fluid communication with the cell nozzle orifice. In these instances, a sample flow stream originates from an orifice at the distal end of the flow cell nozzle, and particles in the flow stream can be illuminated with a light source in the sample inspection region. The size of the inspection region can vary depending on the characteristics of the flow nozzle, such as the size of the nozzle orifice and the sample injection port size. In embodiments, the inspection region can have a width that is 0.01 mm or greater, including, for example, 0.05 mm or greater, such as 0.1 mm or greater, such as 0.5 mm or greater, such as 1 mm or greater, such as 2 mm or greater, such as 3 mm or greater, such as 5 mm or greater, and 10 mm or greater. The length of the inspection region may similarly vary, and in some instances may range from 0.01 mm or more, such as 0.1 mm or more, such as 0.5 mm or more, such as 1 mm or more, such as 1.5 mm or more, such as 2 mm or more, such as 3 mm or more, such as 5 mm or more, such as 10 mm or more, such as 15 mm or more, such as 20 mm or more, such as 25 mm or more, including 50 mm or more.

[0162] The inspection region may be configured to facilitate illumination of a planar cross-section of the generating flow stream, or may be configured to facilitate illumination of a diffuse field (e.g., by a diffuse laser or lamp) of a predetermined length. In some embodiments, the inspection region includes a transparent window to facilitate illumination of a predetermined length of the generating flow stream, such as, for example, 1 mm or more, such as, for example, 2 mm or more, such as, for example, 3 mm or more, such as, for example, 4 mm or more, such as, for example, 5 mm or more, and 10 mm or more. Depending on the light source used to illuminate the generating flow stream (as described below), the inspection region may be configured to pass light in the range of 100 nm to 1500 nm, such as, for example, 150 nm to 1400 nm, such as, for example, 200 nm to 1300 nm, such as, for example, 250 nm to 1200 nm, such as, for example, 300 nm to 1100 nm, such as, for example, 350 nm to 1000 nm, such as, for example, 400 nm to 900 nm, including, for example, 500 nm to 800 nm. Thus, the inspection region may be formed from any transparent material that passes the desired range of wavelengths, including, but not limited to, optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and copolymers of these thermoplastics, such as, for example, polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (glycol modified polyethylene terephthalate), including polyesters, among other polymeric plastic materials, polyesters of interest include, but are not limited to, poly(ethylene terephthalate) (PET), bottle grade PET (copolymers made based on monoethylene glycol, terephthalic acid, and other comonomers such as isophthalic acid, cyclohexanedimethanol), poly(butylene terephthalate) (PBT), and poly(alkylene terephthalates), such as poly(hexamethylene terephthalate), poly(ethylene adipate), poly(1,poly(alkylene adipates) such as poly(4-butylene adipate) and poly(hexamethylene adipate), poly(alkylene suberates) such as poly(ethylene suberate), poly(alkylene sebacates) such as poly(ethylene sebacate), poly(ε-caprolactone) and poly(β-propiolactone), poly(alkylene isophthalates) such as poly(ethylene isophthalate), poly(alkylene 2,6-naphthalene dicarboxylates) such as poly(ethylene 2,6-naphthalene dicarboxylate), poly(alkylenesulfonyl-4,4'-dibenzoates) such as poly(ethylenesulfonyl-4,4'-dibenzoate), poly(p-phenylene alkylene dicarboxylates) such as poly(p-phenylene ethylene dicarboxylate), poly(trans-1,4-cyclohexyl 1,4-phenylene ... poly(trans-1,4-cyclohexanediyl alkylene dicarboxylates), such as poly(1,4-cyclohexane-dimethylene alkylene dicarboxylates), such as poly(1,4-cyclohexane-dimethylene ethylene dicarboxylate), poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylates), such as poly([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate), lactic acid polymers and copolymers, such as (S)-polylactide, (R,S)-polylactide, poly(tetramethylglycolide), and poly(lactide-co-glycolide), and bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, 3,3',5,Examples of suitable materials include polycarbonate of 5'-tetramethylbisphenol A, polyamides such as poly(p-phenylene terephthalamide), polyesters such as polyethylene terephthalate, Mylar™ polyethylene terephthalate, and the like. In some embodiments, the subject system includes a cuvette positioned within the sample inspection region. In embodiments, the cuvette may transmit light in the range of 100 nm to 1500 nm, including, for example, 150 nm to 1400 nm, for example, 200 nm to 1300 nm, for example, 250 nm to 1200 nm, for example, 300 nm to 1100 nm, for example, 350 nm to 1000 nm, for example, 400 nm to 900 nm, and 500 nm to 800 nm.

[0163] In certain embodiments, a light detection system having multiple light detectors as described above is part of or positioned within a particle analyzer, such as a particle sorter. In certain embodiments, the subject system is a flow cytometry system that includes a photodiode and amplifier components as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include, but are not limited to, those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan; 49(pt 1): 17-28; Linden, et. al., Semin Thromb Hemost. 2004 Oct; 30(5): 502-11; Alison, et al. J Pathol, 2010 Dec; 222(4): 335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In certain examples, flow cytometry systems of interest are BD Biosciences FACSCanto™ flow cytometer, BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ flow cytometer, BD Accuri™ C6 Plus flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortessa™ X-20 flow cytometer, BD Biosciences FACSPresto™ flow cytometer, BD Biosciences FACSVia™ flow cytometer, as well as BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD These include BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, etc.

[0164] In some embodiments, the subject systems are disclosed in U.S. Pat. Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,302,545, 10,585,031, 10,578,542, 10,578,469, 10,481,074, 10,585,031 ...85,031, 10,585,031, 10,585,031, 10,585,031, 10,585,031, 10,585,031, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. 9, No. 726,527, No. 9,453,789, No. 9,200,334, No. 9,097,640, No. 9,095,494, No. 9,092,034, No. 8,975,595, No. 8,753,573, No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,201,8 No. 75, No. 7,129,505, No. 6,821,740, No. 6,813,017, No. 6,809,804, No. 6,372,506, No. 5,7 Nos. 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766, the disclosures of which are incorporated herein by reference in their entireties.

[0165] In some embodiments, the system includes a particle sorter component. The term "sorting" is used herein in the conventional sense of the term to refer to separating components of a sample (e.g., target cells having mitochondrial morphology of interest, non-target cells having mitochondrial morphology not of interest, non-cellular particles such as biopolymers), and in some cases, delivering the separated components to one or more sample collection containers. For example, the subject systems can be configured to sort a sample having two or more components, including, for example, soring a sample having three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, and twenty-five or more components. One or more of the sample components, e.g., two or more sample components, e.g., three or more sample components, e.g., four or more sample components, e.g., five or more sample components, e.g., ten or more sample components, can be separated from the sample and delivered to a sample collection container, and fifteen or more sample components can be separated from the sample and delivered to a sample collection container.

[0166] In some embodiments, the particle sorting system of interest is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosures of which are incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having deflection plates, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0167] In one particular embodiment, the system is an image-enabled particle sorter using frequency-encoded data, as depicted in FIG. 3A. The particle sorter 300 includes an illumination component 300a including a light source 301 (e.g., a 488 nm laser) generating an output light beam 301a that is split into beams 302a and 302b by a beam splitter 302. The light beam 302a is propagated through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a having one or more angularly deflected light beams. In some cases, the output beam 303a generated from the acousto-optic device 303 includes a local oscillator beam and multiple high frequency comb beams. The light beam 302b is propagated through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a having one or more angularly deflected light beams. In some cases, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple high frequency comb beams. The output beams 303a and 304a generated from the acousto-optic devices 303 and 304, respectively, are combined with a beam splitter 305 to generate an output beam 305a that is transmitted through an optical component 306 (e.g., an objective lens) to illuminate particles in a flow cell 307. In one particular embodiment, the acousto-optic device 303 (AOD) splits a single laser beam into an array of beamlets, each beamlet having a different optical frequency and angle. The second AOD 304 tunes the optical frequency of a reference beam, which is then superimposed with the array of beamlets in the beam combiner 305. In certain embodiments, the light irradiation system having a light source and an acousto-optical device may include those described in Schraivogel, et al. ("High-speed fluorescence image-enabled cell sorting" Science (2022), 375(6578):315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0168] Output beam 305a is illuminated (e.g., with sheath fluid 309) at illumination region 310 onto sample particles 308 propagating through flow cell 307. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high frequency shifted optical beams depicted as dots across illumination region 310) overlap with a reference local oscillator beam (depicted as a shaded line across illumination region 310). The overlapped beams exhibit a beat behavior due to their different optical frequencies, such that each beamlet is oscillated at a different frequency f 1-n delivers sinusoidal modulation.

[0169] Light from the illuminated sample is transmitted to a light detection system 300b that includes multiple light detectors. The light detection system 300b includes a forward scatter light photodetector 311 for generating a forward scatter image 311a and a side scatter light photodetector 312 for generating a side scatter image 312a. The light detection system 300b also includes a bright field light detector 313 for generating a light loss image 313a. In some embodiments, the forward scatter detector 311 and the side scatter detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright field light detector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence light detectors 314-317. In some cases, the light detectors 314-317 are photomultiplier tubes. The light from the illuminated sample is directed through a beam splitter 320 to the side scatter detection channel 312 and the fluorescence detection channel 314-317. The light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to the light detectors 314-317. In some cases, the optical component 321 is a 534 nm / 40 nm bandpass. In some cases, the optical component 322 is a 586 nm / 42 nm bandpass. In some cases, the optical component 323 is a 700 nm / 54 nm bandpass. In some cases, the optical component 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter spans 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm.

[0170] Data signals generated in response to light detected in the scattered light detection channels 311 and 312, the brightfield light detection channel 313, and the fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a may be generated in each light detection channel based on the data signals generated by processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated by a sorting trigger 352. The sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or into a waste stream 333. In some embodiments, the sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In one particular embodiment, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0171] FIG. 3B depicts image-enabled particle sorting data processing according to certain embodiments. In some cases, the image-enabled particle sorting data processing is a low-latency data processing pipeline. Each photodetector generates a pulse with high frequency modulation that encodes an image (waveform). Fourier analysis is performed to reconstruct the image from the modulated pulses. The image processing pipeline generates a set of image features (image analysis) that are combined with features derived from the pulse processing pipeline (event packets). Real-time sorting electronics then classify particles based on the image features and generate sorting decisions that are used to selectively charge droplets.

[0172] In some embodiments, the system is a particle analyzer that can analyze and characterize particles with or without physically sorting the particles into a collection vessel using a particle analysis system 401 (FIG. 4A). FIG. 4A shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 shown in FIG. 4A can be configured in whole or in part to perform the methods described herein. The particle analysis system 401 includes a fluidics system 402. The fluidics system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which the particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0173] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection station 408 generally refers to a monitoring area 407 of the common sample path. In some embodiments, detection may include detecting light, or one or more other characteristics, of the particles 403 as they pass through the monitoring area 407. In FIG. 4A, one detection station 408 is shown with one monitoring area 407. Some embodiments of the particle analysis system 401 may include multiple detection stations. Additionally, some detection stations may monitor more than one region.

[0174] Each signal is assigned a signal value to form a data point for each particle. As explained above, this data may be referred to as event data. The data points may be multi-dimensional data points that include values ​​of each property measured for the particle. The detection system 404 is configured to collect a series of such data points at a first time interval.

[0175] The particle analysis system 401 may also include a control system 306. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluidics system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of a first time period based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time period. The control system 406 may further be configured to generate an empirical signal frequency based on the number of data points in the portion of the first time period. The control system 406 may additionally compare the empirical signal frequency to that of the calculated signal frequency or a predetermined signal frequency.

[0176] 4B shows a system 400 for flow cytometry, according to an illustrative embodiment of the invention. The system 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. The flow cytometer 410 includes one or more excitation lasers 415a-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 445c-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0177] The pump lasers 115a-115c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4B. The laser beams are first directed through one or more of the beam splitters 445a and 445b. The beam splitter 445a transmits light at 488 nm and reflects light at 633 nm. The beam splitter 445b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects light at 488 nm and 633 nm.

[0178] The laser beam is then directed to a focusing lens 420, which focuses the beam onto the portion of the fluid stream where the sample particles are located, in a flow chamber 425. The flow chamber is the part of the fluidics system that directs particles in the stream, usually one at a time, into the focused laser beam for inspection. The flow chamber can comprise a flow cell in a benchtop flow cytometer, or a nozzle tip in a stream-in-air cytometer.

[0179] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, with re-emission at a variety of different wavelengths depending on the particle's properties, such as the particle's size, internal structure, and the presence of one or more fluorescent molecules attached or naturally present on or in the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light may be routed through one or more of beam splitters 445a-g, bandpass filters 450a-e, longpass filters 455a-b, and fluorescence collection lens 440 to one or more of a forward scatter detector 430, a side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0180] Fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes the light toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter spans 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm. Shortpass filters transmit wavelengths of light below a specified wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths of light above a specified wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the specificity of the detector for a particular fluorochrome. The filters may be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0181] Beam splitters direct light of different wavelengths in different directions. Beam splitters may be characterized by filter properties such as short pass and long pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that it transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g may include optical mirrors such as dichroic mirrors.

[0182] The forward scatter detector 430 is positioned slightly off-axis from the direct beam through the flow cell and is configured to detect diffracted light, excitation light traveling mostly in a forward direction through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect diffracted and reflected light from the surface and internal structure of the particle, which tends to increase as particle structures become more complex. Fluorescent emissions from fluorescent molecules associated with the particle may be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, the side scatter detector 435, and the fluorescence detector may be converted to electronic signals (voltages) by the detectors. This data may provide information about the sample.

[0183] Those skilled in the art will recognize that a flow cytometer according to an embodiment of the present invention is not limited to the flow cytometer depicted in Figure 4B, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0184] During operation, the operation of the flow cytometer is controlled by the controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by the controller / processor 490. Although not explicitly shown, the controller / processor 190 may be coupled to the detectors to receive output signals therefrom, and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, and the like. Input / output (I / O) functionality 497 may also be provided within the system. The memory 495, controller / processor 490, and I / O 497 may collectively be provided as an integral part of the flow cytometer 410. In such an embodiment, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the flow cytometer 400. Alternatively, the memory 495 and some or all of the controller / processor 490 and I / O functionality may be part of one or more external devices, such as a general purpose computer. In some embodiments, memory 495 and some or all of controller / processor 490 may be in wireless or wired communication with flow cytometer 410. In conjunction with memory 495 and I / O 497, controller / processor 490 may be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0185] The system illustrated in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as "filter windows" for a given detector) as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment will emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment, and their associated fluorescent emission bands, may be selected to generally match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, perfect correspondence between filter windows and fluorescent emission spectra is not possible. Although the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, it is generally true that a portion of the emission spectrum of that label will also overlap with the filter windows of one or more other detectors. This may be referred to as a spillover signal. The I / O 497 may be configured to receive data relating to a flow cytometer experiment having a panel of fluorescent labels, and a plurality of cell populations having a plurality of markers, each cell population having a subset of the plurality of markers. I / O 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker concentration data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. Controller / processor 490 may be configured to evaluate one or more assignments of labels to markers.

[0186] 5 shows a functional block diagram of one embodiment of a particle analyzer control system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0187] The particle analyzer or sorting system 502 may be configured to acquire bio-event data. For example, a flow cytometer may generate flow cytometric event data. The particle analyzer 502 may be configured to provide the bio-event data to the analysis controller 500. A data communication channel may be included between the particle analyzer or sorting system 502 and the analysis controller 500. The bio-event data may be provided to the analysis controller 500 via the data communication channel.

[0188] The analysis controller 500 may be configured to receive bio-event data from a particle analyzer or sorting system 502. The bio-event data received from the particle analyzer or sorting system 502 may include flow cytometric event data. The analysis controller 500 may be configured to provide a graphical display including a first plot of the bio-event data on a display device 506. The analysis controller 500 may be further configured to render a region of interest as a gate around a population of the bio-event data shown by the display device 506, e.g., overlaid on the first plot. In some embodiments, the gate may be a logical combination of one or more graphical regions of interest depicted on a single parameter histogram or bivariate plot. In some embodiments, the display may be used to display particle parameters or saturated detector data.

[0189] Analysis controller 500 may further be configured to display the bioevent data within the gate on display device 506 differently from other events within the bioevent data outside the gate. For example, analysis controller 500 may be configured to render the color of the bioevent data contained within the gate distinct from the color of the bioevent data outside the gate. Display device 506 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0190] Analysis controller 500 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, first input device may be implemented as a mouse 510. This mouse 510 may initiate a gate selection signal (e.g., by clicking on a desired gate when positioning a cursor thereon) to analysis controller 500 that identifies the gate to be displayed or manipulated via display device 506. In some implementations, the first device may be implemented as a keyboard 508 or other means for providing input signals to analysis controller 500, such as a touch screen, an input pen, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in FIG. 5, mouse 510 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0191] This trigger event may cause the analysis controller 500 to change the way the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input for further processing, such as selecting a population of interest for particle sorting.

[0192] In some embodiments, the analysis controller 500 may be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 may further be configured to automatically modify the visualization of the plot to facilitate the gating process. This modification may be based on a particular distribution of the bio-event data received by the analysis controller 500.

[0193] The analysis controller 500 may be connected to a storage device 504. The storage device 504 may be configured to receive and store bio-event data from the analysis controller 500. The storage device 504 may also be configured to receive and store flow cytometric event data from the analysis controller 500. The storage device 504 may be further configured to enable retrieval of bio-event data, such as flow cytometric event data, by the analysis controller 500.

[0194] The display device 506 may be configured to receive display data from the analysis controller 500. The display data may include a plot of the biological event data and a gate delineating a section of the plot. The display device 506 may be further configured to modify the presented information according to input received from the analysis controller 500 in conjunction with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0195] In some embodiments, the analysis controller 500 can generate a user interface to receive exemplary events for sorting. For example, the user interface can include controls for receiving exemplary events or exemplary images. The exemplary events or images, or exemplary gates, can be provided prior to collection of event data for a sample, or based on an initial set of events for a portion of a sample.

[0196] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, may include, or may be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in single file across a monitoring area 611 (e.g., where laser streams intersect) and are illuminated by an illumination source 612 (e.g., a laser). Vibration of the droplet forming transducer 602 causes the moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0197] In operation, the detection station 614 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring area 611. The detection station 614 feeds into a timing circuit 628, which in turn feeds into a flash charge circuit 630. At the droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 608, thus causing the droplet of interest to carry a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplets into a container, such as a collection tube or a multi-well or microwell sample plate, where wells or microwells may be associated with a particular droplet of interest. As shown in FIG. 6A, the droplets may be collected in a drain container 638.

[0198] The detection system 616 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through the monitoring area 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 616 allows the instrument to accurately calculate the position of each detected particle in the droplet. The detection system 616 may input an amplitude signal 620 and / or a phase 618 signal, which then inputs (via amplifier 622) to an amplitude control circuit 626 and / or a frequency control circuit 624. The amplitude control circuit 626 and / or the frequency control circuit 624 then control the droplet forming transducer 602. The amplitude control circuit 626 and / or the frequency control circuit 624 may be included within a control system.

[0199] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store the detected events and the sorting decisions based thereon. The sorting decisions can be included in the particle event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by one of the detection system 616 or detection station 614 and provided to a non-collection element.

[0200] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream charging wire in the barb. This creates a stream of droplets 610 containing the particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. Information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). The deflection plates 652 and 654 can be independently controlled to attract or repel the charged droplets and direct them toward a desired collection vessel (e.g., one of 672, 674, 676, or 678). As shown in FIG. 6B, the deflection plates 652 and 654 can be controlled to direct the particles along a first path 662 toward vessel 674 or along a second path 668 toward vessel 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector may allow the particle to continue along flow path 664. Such uncharged droplets may be diverted into a waste container, such as via aspirator 670.

[0201] Sorting electronics may be included to initiate the collection of measurements, receive fluorescent signals about the particles, and determine how to adjust the deflection plates to cause particle sorting. Exemplary implementations of the embodiment shown in Figure 6B include the BD FACSAria™ line of flow cytometers, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0202] Non-transitory computer-readable storage medium Aspects of the present disclosure further include a non-transitory computer-readable storage medium having instructions for practicing the subject methods. The computer-readable storage medium may be employed on one or more computers for full or partial automation of a system for practicing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," in which case the term "computer-readable medium," as used herein, refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blue-ray disks, solid-state disks, and network-attached storage devices (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording the information such that the information can be accessed and retrieved at a later date by the computer. The computer-implemented methods described herein may be performed using programming that may be written in one or more of any number of computer programming languages, including, for example, Python, Java, Java Script, C, C#, C++, Go, R, Swift, PHP, as well as any of many others.

[0203] A non-transitory computer readable storage medium according to certain embodiments has an algorithm for measuring light from cells in a sample including fluorescently labeled mitochondria, an algorithm for generating an image of the cellular mitochondria from the measured light, an algorithm for calculating image parameters from the generated image of the cellular mitochondria, and an algorithm for assessing cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria.

[0204] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter that is a quantitative image parameter. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating cellular mitochondrial morphology based on radial moment image parameters. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter from an image generated based on measured fluorescence from cellular mitochondria, measured light loss for cellular mitochondria, measured forward scatter light for cellular mitochondria, measured side scatter light for cellular mitochondria, and combinations thereof.

[0205] In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a size of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a punctateness of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for assessing cellular mitochondrial morphology by determining a shape of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for determining whether the shape of the mitochondria is spherical, spindle-shaped, elliptical, elongated, and flat.

[0206] In some embodiments, the non-transitory computer readable storage medium includes an algorithm for calculating the radial moment of the fluorophore. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating the pixel density of the generated image of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating an image parameter by determining the sum of pixels in the generated image weighted by the distance of each pixel from the centroid of the cellular mitochondria. In some cases, the non-transitory computer readable storage medium includes an algorithm for evaluating the morphology of the cellular mitochondria by comparing a weighted sum of the mitochondrial pixels in the generated image to a predetermined threshold. In certain cases, the non-transitory computer readable storage medium includes an algorithm for determining that the cell is activated when the weighted sum of the mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. In certain cases, the non-transitory computer readable storage medium includes an algorithm for determining that the cell is not activated when the weighted sum of the mitochondrial pixels in the generated image is less than a predetermined threshold.

[0207] In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells based on the generated images of cellular mitochondria, the calculated image parameters, or a combination thereof. In some cases, the non-transitory computer readable storage medium includes an algorithm for classifying cells by assigning cells to one or more particle population clusters. In some embodiments, the non-transitory computer readable storage medium includes an algorithm for determining one or more sorting gates for the sorted cells of the sample. In some cases, the one or more sorting gates capture cells having mitochondria of the target particle population cluster and exclude particles of the non-target particle population clusters. In some cases, the target particle population cluster includes cells having cellular mitochondria with a predetermined size. In some cases, the target particle population cluster includes cells having cellular mitochondria exhibiting punctatity equal to or greater than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria smaller than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity less than a predetermined threshold. In some cases, the non-target particle population cluster includes cells having cellular mitochondria exhibiting punctatity less than a predetermined threshold. In some cases, the non-transitory computer readable storage medium includes an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells of the target particle population cluster. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating a sorting gate that maximizes the exclusion of cells of non-target particle population clusters. The non-transitory computer-readable storage medium may be employed on one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having instructions stored therein to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices.The processor may be a commercially available processor or one of other processors that are or will become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor's coordination and execution of functions of various computer programs, which may be written in a variety of programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0208] kit Aspects of the present disclosure further include kits, which include one or more of the integrated circuits described herein. In some embodiments, the kits may further include programming for the subject systems, such as in the form of a computer-readable medium (e.g., flash drive, USB storage, compact disc, DVD, Blu-ray disc, etc.) or instructions for downloading programming from an Internet web protocol or cloud server. The kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as on a piece of paper on which the information is printed, in the kit packaging, in a package insert, etc. Yet another form in which these instructions may be present is a computer-readable medium on which the information is recorded, such as a diskette, compact disc (CD), portable flash drive, etc. Yet another form in which these instructions may be present is a website address that may be used via the Internet to access the information at a remote site.

[0209] Utilities The subject systems, methods, and computer systems find use as upstream evaluation of cells, such as in molecular biology assays (e.g., gene expression analysis), applications in which cells with specific mitochondrial morphology may be used. In some cases, the present disclosure provides high quality and high purity cells isolated from heterogeneous samples. In addition, the subject systems, methods, and computer systems find use in a variety of applications in which it is desirable to analyze and sort particle components in samples in fluid media. In some embodiments, the systems and methods described herein find use in flow cytometric characterization of cell isolation. Embodiments of the present disclosure are used where it is desirable to provide a flow cytometer with improved cell sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0210] The disclosed embodiments are also used in applications where cells prepared from a biological sample may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from a fluid or tissue sample to be used as a research or diagnostic specimen. The disclosed methods and devices allow for the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems. EXAMPLES

[0211] The following examples are put forward so as to provide those of ordinary skill in the art with a complete disclosure and description of how to make and use the present invention, and are not intended to limit the scope of what the inventors regard as their invention.

[0212] Assessment of cellular mitochondrial morphology by imaging flow cytometry Cell samples were stained using the mitochondria-specific dye MitoTracker and visualized using an imaging flow cytometer (FACSDiscover™, Bection Dickinson Biosciences, San Jose CA). Individual cells were visualized by generating images of the cells. Samples of activated and non-activated T cells in the same sample were visualized. FIG. 2 depicts the assessment of mitochondrial morphology in cells of a sample having activated and non-activated cells, according to certain embodiments. FIG. 2A depicts the preparation of a cell population for observing cellular mitochondrial morphology, according to certain embodiments. PBMCs from whole blood were stimulated by treatment with anti-CD3 / 28 beads + IL2. Stimulated PBMCs were treated for 4 days and non-stimulated PBMCs were left untreated for 4 days. Cellular mitochondria were stained with non-imaging dyes (BV421, BV605, BV786, RY586, PE-Cy7, APC and APC-H7) and imaging dyes (MitoTracker). The mitochondria of each cell population can be imaged in each photodetector channel, including light loss, light scatter (SSC, FSC), and fluorescence photodetector channels. FIG. 2B illustrates visualization of mitochondrial morphology in light loss and fluorescence photodetector channels. The light loss photodetector channel illustrates the shape of the cell, and the fluorescence photodetector channel illustrates the mitochondrial morphology in the cell. The overlay of the light loss and fluorescence channels illustrates the location of the mitochondria in the cell. FIG. 2B illustrates an example of using a graphical user interface to visualize images of mitochondrial morphology from each cell, as well as overlay images generated using different photodetector channels. FIG. 2C illustrates cluster diagrams of CD3, CD4, and CD8 T cells showing unstimulated and stimulated PBMCs. Two-color imaging can be used to visualize mitochondrial structure. A cell nuclear stain (e.g., DRAQ5™) and MitoTracker mitochondrial staining dye can be used in combination to generate overlaid images of cells showing nuclear and mitochondrial structure.FIG. 2D depicts a cluster view of two-color imaging of a cell nucleus and mitochondria, according to certain embodiments. An image of the cell structure is generated from measured light from a light-loss photodetector channel, while the nuclear and mitochondrial structures are depicted using a different fluorescence photodetector channel. FIG. 2E depicts an image generated using light-loss and fluorescence photodetector channels, according to certain embodiments. An overlay of the images from each of the photodetector channels provides two-color imaging of the cell.

[0213] FIG. 2F depicts cluster diagrams of activated and non-activated T cells, as well as images of mitochondrial morphology for each group of cells. Activated T cells exhibited changes in mitochondrial morphology compared to non-activated T cells. In particular, activated T cells were enlarged (larger median mitochondrial diameter) and had increased punctate structures. FIG. 2G depicts radial moment parameters of generated images for mitochondria, according to certain embodiments. MitoTracker radial moment correlated with changes in mitochondrial morphology in activated T cells compared to non-activated T cell morphology. As shown in FIG. 2G, there was an increase in radial moment in activated T cells compared to MitoTracker radial moment in non-activated T cells. FIG. 2H depicts the difference in MitoTracker radial moment and morphology between activated and non-activated T cells. Cells with higher radial moment exhibited enlarged mitochondria compared to cells with lower radial moment, demonstrating that radial moment can be used to identify cells with unique mitochondrial morphology. FIG. 2I depicts the radial moment in activated and non-activated T cells as determined in the light loss and side scatter light detector channels. As shown in FIG. 2I, activated T cells exhibited greater radial moment in both the light loss and side scatter light detector channels. Cells in the population of activated cells also exhibited enlarged mitochondria and increased punctate structures compared to cells in the population of non-activated cells.

[0214] The radial moment of MitoTracker can be used to evaluate mitochondrial morphology in activated and non-activated T cells in stimulated PBMCs and in non-activated T cells in non-stimulated PBMCs. FIG. 2J depicts the radial moment of activated and non-activated T cells in stimulated and non-stimulated cell samples according to certain embodiments. The cellular mitochondria of non-stimulated and non-activated T cells in stimulated PBMCs showed similar radial moment, while activated T cells in stimulated PBMCs exhibited increased radial moment. Thus, this demonstrates that radial moment can be used to evaluate mitochondrial morphological features, as activated T cells in stimulated PBMCs exhibit mitochondrial enlargement and increased punctate structures compared to cellular mitochondria of non-stimulated and non-activated T cells of stimulated PBMCs. FIG. 2K depicts a summary of the radial moment of activated and non-activated T cells in stimulated and non-stimulated cell samples.

[0215] FIG. 2L depicts sorting cells based on assessment of mitochondrial morphology according to certain embodiments. A cell sample of PBMC was stimulated as described above and cluster views of activated and non-activated T cells were gated. The total intensity of MitoTracker fluorescence was plotted using imaging side scatter and radial moment imaging parameters were determined from images of cellular mitochondria. Cells exhibiting low radial moment were gated from cells exhibiting high radial moment. FIG. 2M depicts isolated cells with low and high radial moment. These images of isolated cells with low and high radial moment correspond to a purity check of cells sorted using the calculated gating strategy.

[0216] Taken together, these data demonstrate that analysis of cellular mitochondrial morphology from generated images can be used to characterize cellular samples, such as for use in downstream molecular biology workflows. FIG. 2N depicts a workflow for using samples with assessed mitochondrial morphology in gene expression analysis according to certain embodiments. Cell samples with cells with various mitochondrial morphologies are assessed as described herein, images of mitochondrially labeled cells are generated, and radial moment image parameters are calculated for each visualized mitochondrion. Gating strategies can be calculated and cells exhibiting low radial moments can be sorted and separated from cells exhibiting high radial moments. Cellular components such as RNA can be isolated from each population of cells, and gene expression analysis can be performed on mitochondrial genes from cells exhibiting high radial moments (e.g., activated cells) and low radial moments (e.g., non-activated cells). FIG. 2O depicts gene expression results from PCR array characterization of mitochondria-related genes comparing cell samples exhibiting high and low radial moment image parameters.

[0217] Notwithstanding the scope of the appended claims, the present disclosure is also defined by the following paragraphs. Aspect 1. A method comprising: measuring light from cells in the sample that contain fluorescently labeled mitochondria; generating an image of the cell's mitochondria from the measured light; and Calculating image parameters from the generated images of the cellular mitochondria; and assessing cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria. 2. The method according to claim 1, wherein the image parameter is a quantitative image parameter. 3. The method of claim 1 or 2, wherein cellular mitochondrial morphology is assessed based on radial moment image parameters. 4. The method of claim 3, wherein the radial moment image parameters are calculated from images generated based on fluorescence measured from the cell mitochondria, light loss measured for the cell mitochondria, forward scattered light measured for the cell mitochondria, side scattered light measured for the cell mitochondria, and combinations thereof. 5. The method of any one of clauses 1 to 4, wherein assessing the morphology of cell mitochondria comprises determining the size of cell mitochondria. 6. The method of any one of clauses 1 to 5, wherein assessing the morphology of cell mitochondria comprises determining the punctateness of cell mitochondria. 7. The method of any one of clauses 1 to 6, wherein assessing the morphology of cell mitochondria comprises determining the shape of cell mitochondria. 8. The method of claim 7, wherein the shape of the cells' mitochondria is selected from the group consisting of spherical, spindle-shaped, elliptical, elongated, and flattened. 9. The method of any one of clauses 1 to 8, wherein the mitochondria are labeled with one or more lipophilic cationic fluorophores. 10. The method according to clause 9, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial protein. 11. The method of claim 10, wherein the lipophilic cationic fluorophore comprises a thiol-reactive moiety for attaching the fluorophore to a mitochondrial protein. 12. The method according to clause 9, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with mitochondrial lipids. 13. The method of any one of clauses 1 to 12, wherein the mitochondria are labeled with a fluorophore that stains the inner mitochondrial membrane. 14. The method of any one of clauses 1-13, wherein the calculated image parameters include the radial moment of the fluorophore. 15. The method of any one of clauses 1 to 14, wherein the method further comprises calculating pixel density of the generated image of the cellular mitochondria. 16. The method of any one of clauses 1 to 15, wherein the image parameters are calculated by determining the sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria. 17. The method according to clause 16, wherein the cellular mitochondrial morphology is assessed by comparing a weighted sum of mitochondrial pixels in the generated image with a predetermined threshold. 18. The method according to claim 16, wherein the cell is determined to be activated when a weighted sum of mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. 19. The method according to claim 16, wherein the cell is determined to be not activated when a weighted sum of mitochondrial pixels in the generated image is less than a predetermined threshold. 20. The method of any one of clauses 1-19, wherein the method comprises classifying cells based on the generated images of cellular mitochondria, the calculated image parameters, or a combination thereof. 21. The method of claim 20, wherein classifying the cells comprises assigning the cells to one or more particle population clusters. 22. The method of clause 20 or 21, wherein the method further comprises determining one or more sorting gates for the sorted cells of the sample. 23. The method of claim 22, wherein one or more sorting gates capture cells containing mitochondria of a target particle population cluster and exclude particles of a non-target particle population cluster. 24. The method of claim 23, wherein the target particle population cluster comprises cells having cellular mitochondria having a predetermined size. 25. The method of claim 23, wherein the target particle population cluster comprises cells having cellular mitochondria exhibiting punctateness above a predetermined threshold. 26. The method of claim 23, wherein the non-target particle population cluster comprises cells having cellular mitochondria smaller than a predetermined threshold. 27. The method of claim 23, wherein the non-target particle population cluster comprises cells having cellular mitochondria exhibiting punctiformity below a predetermined threshold. 28. The method of any one of clauses 23-27, wherein the sorting gate maximizes the inclusion yield of cells in the target particle population cluster. 29. The method of any one of clauses 23-27, wherein the sorting gate maximizes exclusion of cells in non-target particle population clusters. 30. The method of any one of clauses 1-29, wherein the method further comprises sorting cells of the sample into a plurality of sample vessels. 31. The method of any one of clauses 1 to 30, wherein measuring light from cells in the flow stream comprises detecting fluorescence, light absorption, scattered light, or a combination thereof. 32. The method according to any one of clauses 1 to 31, wherein the image parameters are calculated from frequency-encoded fluorescence data from fluorescently labeled cellular mitochondria. 33. The method of any one of clauses 1-32, further comprising illuminating the sample in the flow stream with a light source. 34. The method of claim 33, wherein the flow stream is illuminated with a light source having a wavelength between 200 nm and 800 nm. 35. The method of clause 33 or 34, wherein the method includes irradiating the flow stream with a first beam of frequency-shifted light and a second beam of frequency-shifted light. 36. The method of claim 35, wherein the first beam of frequency-shifted light comprises a local oscillator (LO) beam and the second beam of frequency-shifted light comprises a high frequency comb beam. 37. Applying a high frequency drive signal to an acousto-optic device; 37. The method of claim 35 or 36, further comprising illuminating the acousto-optic device with a laser to generate a first beam of frequency-shifted light and a second beam of frequency-shifted light. 38. The method of claim 37, wherein the laser is a continuous wave laser. 39. A system comprising: a light source configured to illuminate cells containing fluorescently labeled mitochondria in the flow stream; a light detection system comprising a light detector for measuring light from the cells; and a processor comprising a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: The measured light is used to generate an image of the cell's mitochondria. calculating image parameters from the generated images of the cellular mitochondria; The system allows assessment of cellular mitochondrial morphology based on calculated image parameters of the cellular mitochondria. 40. The system of claim 39, wherein the image parameter is a quantitative image parameter. 41. The system of clause 39 or 40, wherein the memory includes instructions for assessing cellular mitochondrial morphology based on radial moment image parameters. 42. The system described in clause 41, wherein the memory includes instructions for calculating radial moment image parameters from an image generated based on fluorescence measured from the cell mitochondria, light loss measured for the cell mitochondria, forward scattered light measured for the cell mitochondria, side scattered light measured for the cell mitochondria, and combinations thereof. 43. A system described in any one of clauses 39 to 42, wherein the memory includes instructions for assessing cellular mitochondrial morphology by assessing cellular mitochondrial size. 44. A system described in any one of clauses 39 to 42, wherein the memory includes instructions for assessing cellular mitochondrial morphology by determining the punctateness of cellular mitochondria. 45. A system described in any one of clauses 39 to 42, wherein the memory includes instructions for assessing cellular mitochondrial morphology by determining the shape of cellular mitochondrial. 46. ​​The system described in clause 45, wherein the shape of the cellular mitochondria is selected from the group consisting of spherical, spindle-shaped, elliptical, elongated, and flattened. 47. A system according to any one of clauses 39 to 46, wherein the mitochondria are labeled with one or more lipophilic cationic fluorophores. 48. The system according to clause 47, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial protein. 49. The system according to clause 48, wherein the lipophilic cationic fluorophore comprises a thiol-reactive moiety for attaching the fluorophore to a mitochondrial protein. 50. The system according to clause 47, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with mitochondrial lipids. 51. A system according to any one of clauses 39 to 50, wherein the mitochondria are labelled with a fluorophore that stains the inner mitochondrial membrane. 52. A system described in any one of clauses 39 to 51, wherein the memory includes instructions for calculating the radial moment of the fluorophore. 53. A system described in any one of clauses 39 to 52, wherein the memory includes instructions for calculating pixel density of the generated image of cellular mitochondria. 54. A system described in any one of clauses 39 to 53, wherein the memory includes instructions for calculating image parameters by determining the sum of pixels in the generated image weighted by the distance of each pixel from the center of gravity of the cellular mitochondria. 55. The system described in clause 54, wherein the memory includes instructions for evaluating cellular mitochondrial morphology by comparing a weighted sum of mitochondrial pixels in the generated image to a predetermined threshold. 56. The system described in clause 54, wherein the memory includes instructions for determining that a cell is activated when a weighted sum of mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. 57. The system of clause 54, wherein the memory includes instructions for determining that the cell is not activated when a weighted sum of mitochondrial pixels in the generated image is less than a predetermined threshold. 58. The system of any one of clauses 39 to 57, wherein the memory includes instructions for classifying cells based on the generated images of cellular mitochondria, the calculated image parameters, or a combination thereof. 59. The system of clause 58, wherein the memory includes instructions for classifying cells by assigning the cells to one or more particle population clusters. 60. The system of clause 58 or 59, wherein the memory includes instructions for determining one or more sorting gates for sorted cells of the sample. 61. The system of clause 60, wherein one or more sorting gates capture cells containing mitochondria of a target particle population cluster and exclude particles of a non-target particle population cluster. 62. The system of claim 61, wherein the target particle population cluster comprises cells having cellular mitochondria having a predetermined size. 63. The system described in clause 61, wherein the target particle population cluster comprises cells having cellular mitochondria exhibiting punctateness above a predetermined threshold. 64. The system of clause 61, wherein the non-target particle population cluster comprises cells having cellular mitochondria smaller than a predetermined threshold. 65. The system of claim 61, wherein the non-target particle population cluster comprises cells having cellular mitochondria exhibiting punctiformity below a predetermined threshold. 66. The system of any one of clauses 60-65, wherein the memory includes instructions for calculating a sorting gate that maximizes the inclusion yield of cells in the target particle population cluster. 67. A system according to any one of clauses 60 to 65, wherein the memory comprises instructions for calculating a sorting gate that maximizes the exclusion of cells of non-target particle population clusters. 68. A system according to any one of clauses 39 to 67, wherein the system further comprises a display configured to display a graphical user interface. 69. The system of clause 68, wherein the graphical user interface is configured to manually input one or more of the sorting gates. 70. The system of clause 69, wherein manually inputting the sorting gate includes drawing the sorting gate on a scatter plot of the particle population clusters. 71. The system of any one of clauses 39 to 70, further comprising a cell sorter. 72. The system of claim 71, wherein the cell sorter comprises a droplet deflector. 73. A system described in any one of clauses 39 to 72, wherein the light source includes a light beam generator component configured to generate at least a first beam of frequency-shifted light and a second beam of frequency-shifted light. 74. The system of claim 73, wherein the optical beam generator includes an acousto-optic deflector. 75. The system of any one of clauses 73-74, wherein the optical beam generator includes a direct digital synthesiser (DDS) RF comb generator. 76. A system according to any one of clauses 73 to 75, wherein the optical beam generator component is configured to generate a local oscillator beam. 77. The system of any one of clauses 73-76, wherein the optical beam generator component is configured to generate a plurality of frequency-shifted comb beams. 78. A system according to any one of clauses 73 to 77, wherein the light source comprises a laser. 79. The system of claim 78, wherein the laser is a continuous wave laser. 80. A system according to any one of clauses 39 to 79, wherein the system is an imaging flow cytometer. 81. A system according to any one of clauses 39 to 80, wherein the system comprises an integrated circuit device. 82. The system of clause 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, the non-transitory computer-readable storage medium comprising: an algorithm for measuring light from cells in a sample that includes fluorescently labeled mitochondria; an algorithm for generating an image of the cell's mitochondria from the measured light; an algorithm for calculating image parameters from the generated images of the cellular mitochondria; and an algorithm for evaluating cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria. 84. The non-transitory computer-readable storage medium of clause 83, wherein the image parameter is a quantitative image parameter. 85. The non-transitory computer readable storage medium of clause 83, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing cellular mitochondrial morphology based on radial moment image parameters, preferably the non-transitory computer readable storage medium comprises an algorithm for calculating radial moment image parameters from an image generated based on fluorescence measured from the cellular mitochondria, light loss measured for the cellular mitochondria, forward scattered light measured for the cellular mitochondria, side scattered light measured for the cellular mitochondria, and combinations thereof. 86. A non-transitory computer readable storage medium according to any one of clauses 83 to 85, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing the morphology of cell mitochondria by determining the size of cell mitochondria. 87. A non-transitory computer readable storage medium according to any one of clauses 83 to 86, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing cellular mitochondrial morphology by determining the punctateness of cellular mitochondria. 88. A non-transitory computer readable storage medium according to any one of clauses 83 to 87, wherein the non-transitory computer readable storage medium comprises an algorithm for assessing cellular mitochondrial morphology by determining the shape of cellular mitochondria. 89. The non-transitory computer-readable storage medium of claim 88, wherein the shape of the cellular mitochondria is selected from the group consisting of spherical, spindle-shaped, elliptical, elongated, and flattened. 90. The non-transitory computer-readable storage medium of any one of clauses 83 to 89, wherein the mitochondria are labeled with one or more lipophilic cationic fluorophores. 91. The non-transitory computer-readable storage medium according to clause 90, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial protein. 92. The non-transitory computer-readable storage medium of clause 91, wherein the lipophilic cationic fluorophore comprises a thiol-reactive moiety for attaching the fluorophore to a mitochondrial protein. 93. The non-transitory computer-readable storage medium of claim 90, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial lipid. 94. The non-transitory computer-readable storage medium of any one of clauses 83 to 93, wherein the mitochondria are labeled with a fluorophore that stains the inner mitochondrial membrane. 95. A non-transitory computer readable storage medium according to any one of clauses 83 to 94, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating the radial moment of a fluorophore. 96. A non-transitory computer readable storage medium according to any one of clauses 83 to 95, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating pixel density of the generated image of cellular mitochondria. 97. A non-transitory computer readable storage medium according to any one of clauses 83 to 96, comprising an algorithm for calculating image parameters by determining the sum of pixels in the generated image weighted by the distance of each pixel from the centroid of a cellular mitochondrion. 98. The non-transitory computer readable storage medium of clause 97, wherein the non-transitory computer readable storage medium includes an algorithm for evaluating cellular mitochondrial morphology by comparing a weighted sum of mitochondrial pixels in the generated image with a predetermined threshold. 99. The non-transitory computer readable storage medium of clause 98, wherein the non-transitory computer readable storage medium comprises an algorithm for determining that a cell is activated when a weighted sum of mitochondrial pixels in the generated image is equal to or exceeds a predetermined threshold. 100. The non-transitory computer readable storage medium of clause 98, wherein the non-transitory computer readable storage medium comprises an algorithm for determining that a cell is not activated when a weighted sum of mitochondrial pixels in the generated image is less than a predetermined threshold. 101. A non-transitory computer readable storage medium according to any one of clauses 83 to 100, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying cells based on generated images of cellular mitochondria, calculated image parameters, or a combination thereof. 102. The non-transitory computer readable storage medium of clause 101, wherein the non-transitory computer readable storage medium comprises an algorithm for classifying cells by assigning the cells to one or more particle population clusters. 103. The non-transitory computer readable storage medium of clause 101 or 102, wherein the non-transitory computer readable storage medium comprises an algorithm for determining one or more sorting gates for sorted cells of the sample. 104. The non-transitory computer-readable storage medium of clause 103, wherein the one or more sorting gates capture cells containing mitochondria of the target particle population cluster and exclude particles of the non-target particle population cluster. 105. The non-transitory computer-readable storage medium of clause 104, wherein the target particle population cluster comprises cells having cellular mitochondria having a predetermined size. 106. The non-transitory computer-readable storage medium of clause 105, wherein the target particle population cluster comprises cells having cellular mitochondria exhibiting punctiformity above a predetermined threshold. 107. The non-transitory computer-readable storage medium of clause 104, wherein the non-target particle population cluster comprises cells having cellular mitochondria smaller than a predetermined threshold. 108. The non-transitory computer-readable storage medium of clause 104, wherein the non-target particle population cluster comprises cells having cellular mitochondria exhibiting punctiformity below a predetermined threshold. 109. A non-transitory computer readable storage medium according to any one of clauses 103 to 108, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a sorting gate that maximizes the inclusion yield of cells in the target particle population cluster. 110. The non-transitory computer readable storage medium of any one of clauses 103 to 108, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a sorting gate that maximizes the exclusion of cells of non-target particle population clusters. 111. A non-transitory computer readable storage medium according to any one of clauses 83 to 110, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating image parameters from frequency-encoded fluorescence data from fluorescently labeled cellular mitochondria.

[0218] Although the foregoing invention has been described in some detail by way of illustration and example for purposes of clarity of understanding, it will be readily apparent to those skilled in the art that, in light of the teachings of this invention, certain changes and modifications can be made thereto without departing from the spirit or scope of the appended claims.

[0219] Thus, the above merely illustrates the principles of the present invention. It will be understood that those skilled in the art can devise various arrangements that embody the principles of the present invention and are included within its spirit and scope, although not explicitly described or shown herein. Furthermore, all examples and conditional language described herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts that the inventors contribute to further the art, and should not be construed as being limited to such specifically described examples and conditions. Furthermore, all statements herein that describe the principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. In addition, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, regardless of structure, i.e., any elements developed to perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended to be dedicated to the public, regardless of whether such disclosure is expressly set forth in the claims.

[0220] Therefore, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is expressly defined to be invoked to limit a claim only if the precise phrase "means for" or the precise phrase "step for" appears at the beginning of the limitation to such claim, and if such precise phrase is not used in the limitation to the claim, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0221] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. §119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 465,066, filed May 9, 2023, the entire disclosure of which is incorporated herein by reference.

Claims

1. 1. A method comprising: measuring light from cells in the sample that contain fluorescently labeled mitochondria; generating an image of the cell's mitochondria from the measured light; and calculating image parameters from the generated images of the cellular mitochondria; and and assessing a morphology of the cell's mitochondria based on the calculated image parameters of the cell's mitochondria.

2. The method of claim 1 , wherein the image parameter is a quantitative image parameter.

3. 3. The method of claim 1 or 2, wherein the morphology of the cell's mitochondria is assessed based on radial moment image parameters.

4. 4. The method of claim 3, wherein the radial moment image parameters are calculated from images generated based on fluorescence measured from the cell mitochondria, light loss measured for the cell mitochondria, forward scatter light measured for the cell mitochondria, side scatter light measured for the cell mitochondria, and combinations thereof.

5. 5. The method of claim 1, wherein assessing the morphology of the cellular mitochondria comprises determining the size of the cellular mitochondria.

6. 6. The method of claim 1, wherein assessing the morphology of the cell's mitochondria comprises determining the punctateness of the cell's mitochondria.

7. 7. The method of claim 1, wherein said evaluating the morphology of said cellular mitochondria comprises determining the shape of said cellular mitochondria.

8. 8. The method of claim 7, wherein the shape of the cellular mitochondria is selected from the group consisting of spherical, spindle-shaped, oval, elongated, and flattened.

9. 9. The method of any one of claims 1 to 8, wherein the mitochondria are labeled with one or more lipophilic cationic fluorophores.

10. 10. The method of claim 9, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial protein.

11. 11. The method of claim 10, wherein the lipophilic cationic fluorophore comprises a thiol-reactive moiety for attaching the fluorophore to the mitochondrial protein.

12. 10. The method of claim 9, wherein the mitochondria are labeled with a fluorophore that forms a covalent bond with a mitochondrial lipid.

13. The method of any one of claims 1 to 12, wherein the mitochondria are labeled with a fluorophore that stains the inner mitochondrial membrane.

14. The method of any one of claims 1 to 13, wherein the calculated image parameters comprise the radial moments of the fluorophores.

15. The method of any one of claims 1 to 14, wherein the method comprises calculating pixel density of the generated image of the cell mitochondria.

16. 16. The method of any one of claims 1 to 15, wherein the image parameters are calculated by determining the sum of pixels in the generated images weighted by the distance of each pixel from the centroid of the cell's mitochondria.

17. 17. The method of claim 16, wherein the morphology of the cellular mitochondria is assessed by comparing a weighted sum of the pixels of the mitochondria in the generated image to a predetermined threshold.

18. 17. The method of claim 16, wherein the cell is determined to be activated when the weighted sum of the pixels of the mitochondria in the generated image is equal to or exceeds the predetermined threshold.

19. 17. The method of claim 16, wherein the cell is determined to be not activated when the weighted sum of the pixels of the mitochondria in the generated image is less than the predetermined threshold.

20. 20. The method of any one of claims 1 to 19, wherein the method comprises classifying the cell based on the generated image of the cellular mitochondria, the calculated image parameters, or a combination thereof.

21. 21. The method of claim 20, wherein classifying the cells comprises assigning the cells to one or more particle population clusters.

22. 22. The method of claim 20 or 21, wherein the method further comprises determining one or more sorting gates for the sorted cells of the sample.

23. 23. The method of claim 22, wherein the one or more sorting gates capture cells containing mitochondria of a target particle population cluster and exclude particles of a non-target particle population cluster.

24. 24. The method of claim 23, wherein the target particle population cluster comprises cells having cellular mitochondria having a predetermined size.

25. 24. The method of claim 23, wherein the target particle population cluster comprises cells having cellular mitochondria exhibiting punctateness above a predetermined threshold.

26. 24. The method of claim 23, wherein the non-target particle population cluster comprises cells having cellular mitochondria smaller than a predetermined threshold.

27. 24. The method of claim 23, wherein the non-target particle population cluster comprises cells having cellular mitochondria exhibiting punctiformity below a predetermined threshold.

28. 28. The method of any one of claims 23 to 27, wherein the sorting gate maximizes the inclusion yield of cells of a target particle population cluster.

29. 28. The method of any one of claims 23 to 27, wherein the sorting gate maximizes exclusion of cells of non-target particle population clusters.

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

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

32. The method of any preceding claim, wherein the image parameters are calculated from frequency-encoded fluorescence data from fluorescently labeled cellular mitochondria.

33. The method of any one of claims 1 to 32, further comprising illuminating the sample in the flow stream with a light source.

34. 34. The method of claim 33, wherein the flow stream is illuminated with a light source having a wavelength between 200 nm and 800 nm.

35. 35. The method of claim 33 or 34, wherein the method comprises irradiating the flow stream with a first beam of frequency-shifted light and a second beam of frequency-shifted light.

36. 36. The method of claim 35, wherein the first beam of frequency-shifted light comprises a local oscillator (LO) beam and the second beam of frequency-shifted light comprises a high frequency comb beam.

37. applying a high frequency drive signal to an acousto-optic device; 37. The method of claim 35 or 36, further comprising illuminating the acousto-optic device with a laser to generate a first beam of frequency-shifted light and a second beam of frequency-shifted light.

38. 38. The method of claim 37, wherein the laser is a continuous wave laser.

39. 1. A system comprising: a light source configured to illuminate cells containing fluorescently labeled mitochondria in the flow stream; a light detection system comprising a light detector for measuring light from the cells; a processor comprising a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: The measured light is used to generate an image of the cell's mitochondria. calculating image parameters from the generated images of the cellular mitochondria; The system evaluates cellular mitochondrial morphology based on the calculated image parameters of the cellular mitochondria.

40. 1. A non-transitory computer readable storage medium comprising instructions stored thereon, the non-transitory computer readable storage medium comprising: an algorithm for measuring light from cells in a sample that includes fluorescently labeled mitochondria; an algorithm for generating an image of the cell's mitochondria from the measured light; an algorithm for calculating image parameters from the generated images of the cellular mitochondria; and an algorithm for evaluating a morphology of the cell's mitochondria based on the calculated image parameters of the cell's mitochondria.

Images