Method and system for identifying saturated data signals in cell sorting - Patents.com

Abstract

Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. The method, according to certain embodiments, includes detecting light from particles in a flowstream, generating a plurality of data signals from the detected light, identifying one or more saturation data signals, generating a saturation signal index corresponding to the identified saturation data signals, and applying the saturation signal index to the particle classification index to generate an adjusted particle classification index. In some embodiments, the method includes determining one or more parameters of the particles (e.g., for use in particle sorting determinations) by calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes the one or more saturation data signals. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also provided. Non-transitory computer-readable storage media are also described.

Description

[Background technology]

[0001] Flow 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. In a flow particle sorting system, particles, such as molecules, analyte-bound beads, or individual cells in a fluid suspension, are passed in a stream through a detection region where a sensor detects particles of the type to be sorted contained in the stream.

[0002] When the sensor detects a particle of the type being sorted, it triggers a sorting mechanism that selectively isolates the particle of interest. Particle detection is typically performed by passing a fluid stream through a detection region where the particles are exposed to illumination from one or more lasers and their light scattering and fluorescence properties are measured.

[0003] Particles or components thereof can be labeled with fluorescent dyes to facilitate detection, and by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. Detection is performed using one or more optical sensors to facilitate independent measurement of the fluorescence of each different fluorescent dye.

[0004] To sort particles in a sample, a droplet charging mechanism imparts an electric charge to droplets in the flowstream containing particles of the type to be sorted at a flowstream breakoff point. The droplets are passed through an electrostatic field and deflected into one or more collection vessels based on the droplet's polarity and charge magnitude. Uncharged droplets are not deflected by the electrostatic field. Summary of the Invention

[0005] Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. The method, according to certain embodiments, includes detecting light from particles in a flowstream, generating a plurality of data signals from the detected light, identifying one or more saturation data signals, generating a saturation signal index corresponding to the identified saturation data signals, and applying the saturation signal index to the particle classification index to generate an adjusted particle classification index. In some embodiments, the method includes determining one or more parameters of the particles (e.g., for use in particle sorting determinations) by calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes the one or more saturation data signals. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also provided. Non-transitory computer-readable storage media are also described.

[0006] In some embodiments, the saturation signal index is a binary word identifying one or more detector channels that are saturated. For example, the saturation signal index may be a binary word consisting of 8 or more bits, such as a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or may include a 256-bit binary word. In embodiments, one or more parameters of particles in the flow stream are determined from the generated data signal. In some examples, determining the one or more parameters of the particles includes calculating a spectral separation matrix for the fluorescence of the particles. In certain examples, the sample of interest includes multiple fluorophores, and the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. For example, the fluorescence spectrum of each fluorophore may overlap with the fluorescence spectrum of at least one other fluorophore in the sample by 10 nm or more, for example, 25 nm or more, including 50 nm or more. In some examples, the fluorescence spectrum of one or more fluorophores in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by, for example, 10 nm or more, for example, 25 nm or more, including 50 nm or more. In other embodiments, the sample of interest comprises multiple fluorophores with non-overlapping fluorescence spectra, in which the fluorescence spectrum of each fluorophore is adjacent to at least one other fluorophore within 10 nm or less, such as 9 nm or less, for example 8 nm or less, such as 7 nm or less, for example 6 nm or less, such as 5 nm or less, for example 4 nm or less, such as 3 nm or less, for example 2 nm or less (including 1 nm or less).

[0007] In some embodiments, the method includes adjusting a spectral separation matrix for the fluorescence of the particles based on the calculated saturation signal index. For example, the spectral separation matrix may be adjusted by excluding one or more of the saturation data signals. In certain embodiments, to calculate one or more parameters of the particles in the sample, the method includes calculating a spectral separation matrix for the fluorescence of the particles, calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturation data signals, and comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix. In certain examples, the parameters of the particles in the sample are determined using the adjusted spectral separation matrix, in which one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0008] In some embodiments, the method includes classifying particles in the sample based on one or more of the determined parameters of the particles. In certain embodiments of the present disclosure, classifying particles in the sample includes using a bitmap gating strategy, and classifying the particles includes identifying, and in some instances removing, saturated data signals from the particle classification parameters. In other embodiments, classifying particles in the sample includes identifying saturated data signals and estimating true values ​​of the saturated data signals. In certain examples, to classify particles in the sample, the method includes generating a two-dimensional bitmap having regions of interest (ROIs) and determining whether particles should be assigned to the ROIs of the bitmap. In other examples, the method for classifying particles in the sample includes determining whether one or more bits of the ROIs of the bitmap include saturated data signals. In some embodiments, determining whether one or more bits of the particle assignment ROIs of the bitmap include saturated data signals includes applying a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, comparing the generated saturated signal bitmap with the particle assignment ROIs, and determining that one or more bits of the particle assignment ROIs are saturated. Boolean logic can be used to compare the saturated signal bitmap with the particle allocation ROI, for example, ANDing the saturated signal bitmap with the particle allocation ROI to determine whether one or more bits of the particle allocation ROI are saturated.

[0009] Aspects of the present disclosure also include a system having an optical detection system for characterizing particles of a sample (e.g., cells in a biological sample) in a flowstream. The system, according to certain embodiments, includes a light source configured to illuminate particles of the sample in the flowstream; an optical detection system having a photodetector that detects light from the particles in the sample and generates a plurality of data signals from the detected light; and a processor having a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to identify one or more saturation data signals, generate a saturation signal index including the identified saturation data signals, and apply the saturation signal index to a particle classification index to generate an adjusted particle classification index. In some embodiments, the saturation signal index used by the processor is a binary word that identifies which detector channel saturated the analog-to-digital converter. For example, the saturation signal index utilized by the subject systems may be an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word.

[0010] In embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to determine one or more parameters of particles in the flow stream from the generated data signals. In some embodiments, the memory includes instructions for calculating a spectral separation matrix for the fluorescence of the particles. In some examples, the memory includes instructions for calculating an adjusted spectral separation matrix using the calculated saturation signal index, such as when one or more saturated data signals are excluded. In other examples, the memory includes instructions for calculating an adjusted spectral separation matrix in which data signals from saturated detector channels are excluded. In particular examples, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate a spectral separation matrix for the fluorescence of the particles, calculate an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturated data signals, and compare the calculated spectral separation matrix to the calculated adjusted spectral separation matrix. In certain embodiments, the memory includes instructions for calculating a spectral separation matrix, in which one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0011] The memory may include instructions stored thereon that, when executed by the processor, cause the processor to classify particles based on one or more determined parameters of the particles. In some embodiments, the memory includes instructions for implementing a bitmap gating strategy for classifying particles. In some examples, the bitmap gating strategy implemented by the subject systems includes instructions for identifying and removing saturated data signals. In other examples, the bitmap gating strategy implemented by the subject systems includes instructions for identifying saturated data signals and estimating true values ​​of saturated data signals. In some embodiments, the memory includes instructions that, when executed by the processor, cause the processor to generate a two-dimensional bitmap having regions of interest (ROIs) and determine whether particles should be assigned to the ROIs of the bitmap. In other embodiments, the memory includes instructions that, when executed by the processor, cause the processor to determine whether one or more bits of the ROIs of the bitmap include saturated data signals. In some examples, the memory includes instructions where determining whether one or more bits of the particle allocation ROI of the bitmap include a saturated data signal includes applying a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, comparing the generated saturated signal bitmap with the particle allocation ROI, and determining that one or more bits of the particle allocation ROI are saturated. In particular examples, the processor operates using Boolean logic to compare the saturated signal bitmap with the particle allocation ROI. For example, the saturated signal bitmap may be ANDed with the particle allocation ROI to determine whether one or more bits of the particle allocation ROI are saturated.

[0012] In some embodiments, the subject systems may include one or more sorting decision modules configured to generate sorting decisions for the particles based on the classification of the particles. In certain embodiments, the system further includes a particle sorter (e.g., having a droplet deflector) for sorting particles from the flow stream based on the sorting decisions generated by the sorting decision modules.

[0013] Also provided is an integrated circuit device programmed to adjust a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. In embodiments, the integrated circuit device may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a complex programmable logic device (CPLD), or some other integrated circuit device. In some embodiments, the integrated circuit device is programmed to identify one or more saturation data signals from light detected from particles in the flowstream, generate a saturation signal index including the identified saturation data signals, and apply the saturation signal index to the particle classification index to generate an adjusted particle classification index. In some embodiments, the saturation signal index used by the integrated circuit is a binary word identifying a detector channel outputting a saturation data signal. For example, the saturation signal index utilized by the subject systems may be an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word.

[0014] In embodiments, a target integrated circuit device is programmed to determine one or more parameters of particles in a flow stream from the generated data signals. In some embodiments, the integrated circuit is programmed to calculate a spectral separation matrix for the fluorescence of the particles. In some examples, the integrated circuit is programmed to calculate an adjusted spectral separation matrix using the calculated saturation signal index. For example, the integrated circuit may be programmed to calculate a spectral separation matrix and exclude one or more saturated data signals. In other examples, the integrated circuit is programmed to calculate an adjusted spectral separation matrix that excludes data signals from saturated detector channels. In particular examples, the integrated circuit is programmed to calculate a spectral separation matrix for the fluorescence of the particles, calculate an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturated data signals, and compare the calculated spectral separation matrix to the calculated adjusted spectral separation matrix. In certain embodiments, the integrated circuit is programmed to calculate a spectral separation matrix, where one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0015] The integrated circuit is programmed to classify particles based on one or more determined parameters of the particles. In some embodiments, the integrated circuit is programmed to implement a bitmap gating strategy for classifying particles. In some examples, the bitmap gating strategy implemented by the integrated circuit includes programming for identifying and removing saturated data signals. In other examples, the bitmap gating strategy implemented by the integrated circuit includes programming for identifying saturated data signals and estimating true values ​​of saturated data signals. In some embodiments, the integrated circuit device is programmed to generate a two-dimensional bitmap having a region of interest (ROI) and determine whether a particle should be assigned to the ROI of the bitmap. In other embodiments, the integrated circuit is programmed to determine whether one or more bits of the ROI of the bitmap include saturated data signals. In some examples, the integrated circuit is programmed to determine whether one or more bits of the particle assignment ROI of the bitmap include saturated data signals by including programming for applying a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, comparing the generated saturated signal bitmap with the particle assignment ROI, and determining that one or more bits of the particle assignment ROI are saturated. In certain examples, the integrated circuit operates using Boolean logic to compare the saturation signal bitmap with the particle assignment ROI. For example, the saturation signal bitmap may be ANDed with the particle assignment ROI to determine whether one or more bits of the particle assignment ROI are saturated. In certain examples, the integrated circuit device is programmed to sort particles of the sample based on the adjusted particle classification.

[0016] Aspects of the present disclosure also include a non-transitory computer-readable storage medium for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. The non-transitory computer-readable storage medium according to certain embodiments includes instructions stored thereon, the instructions including: an algorithm for detecting light from particles in a flowstream; an algorithm for generating a plurality of data signals from the detected light; an algorithm for generating a saturation signal index corresponding to the identified saturation data signals; and an algorithm for applying the saturation signal index to the particle classification index to generate an adjusted particle classification index. The non-transitory computer-readable storage medium may also include an algorithm for calculating one or more parameters of the particles. In these embodiments, the computer-readable storage medium includes an algorithm for calculating a spectral separation matrix for the fluorescence of the particles; an algorithm for calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes the one or more saturation data signals; and an algorithm for comparing the calculated spectral separation matrix to the calculated adjusted spectral separation matrix. In certain embodiments, the non-transitory computer-readable storage medium may also include an algorithm for generating a two-dimensional bitmap including a region of interest (ROI) and an algorithm for assigning particles to the ROI of the two-dimensional bitmap. In certain examples, the non-transitory computer-readable storage medium may also include an algorithm for applying a saturation signal index to a second two-dimensional bitmap to generate a saturation signal bitmap, an algorithm for comparing the generated saturation signal bitmap with the particle assignment ROI, and an algorithm for identifying that one or more bits of the particle assignment ROI are saturated. In some examples, the non-transitory computer-readable storage medium includes an algorithm for generating a particle sorting decision based on the adjusted particle classification index. [Brief explanation of the drawings]

[0017] The invention can be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0018] [Figure 1] FIG. 1 is a functional block diagram illustrating an example of a sorting control system in accordance with certain embodiments. [Figure 2A] FIG. 1 is a schematic diagram illustrating a particle sorter system in accordance with certain embodiments. [Figure 2B] FIG. 1 is a schematic diagram illustrating a particle sorter system in accordance with certain embodiments. [Figure 3] FIG. 1 is a functional block diagram illustrating a particle analysis system for computer-based sample analysis and particle characterization, according to certain embodiments. [Figure 4] FIG. 1 illustrates a flow cytometer in accordance with certain embodiments. [Figure 5] FIG. 1 is a flow diagram for generating an adjusted particle classification index according to certain embodiments. [Figure 6] FIG. 10 is a flow diagram for using a multi-bit binary word saturated signal index to generate an adjusted particle classification, according to certain embodiments. [Figure 7] FIG. 1 is a flow diagram for classifying particles according to certain embodiments. [Figure 8] FIG. 1 is a block diagram illustrating a computing system in accordance with certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0019] Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. The method, according to certain embodiments, includes detecting light from particles in a flowstream, generating a plurality of data signals from the detected light, identifying one or more saturation data signals, generating a saturation signal index corresponding to the identified saturation data signals, and applying the saturation signal index to the particle classification index to generate an adjusted particle classification index. In some embodiments, the method includes determining one or more parameters of the particles (e.g., for use in particle sorting determinations) by calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes the one or more saturation data signals. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also provided. Non-transitory computer-readable storage media are also described.

[0020] Before the present invention is described in more detail, it is to be understood that the invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0021] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any other stated or intervening value 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 subject invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0022] Certain ranges are presented herein with numerical values ​​preceded by the term "about." The term "about" is used herein to provide literal support for the exact number it precedes, as well as a number that is near or approximately the number it precedes. In determining whether a number is near or approximately a specifically stated number, the number that is near or approximately the unstated number may be a number that, in the context in which it is presented, provides a substantial equivalent to the specifically stated number.

[0023] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are now described.

[0024] All publications and patents cited herein are incorporated by reference to the same extent as if each individual publication or patent was specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference 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 publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0025] It should be noted that, as used in this specification and the appended claims, the articles "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude optional elements. Accordingly, this statement is intended to serve as a prior basis for using exclusive terminology, such as "solely," "solely," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0026] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has distinct components and features which may be readily separated from or combined with the features of any of the other embodiments without departing from the scope or spirit of the invention. Any described method can be carried out in the order of events described or in any other order which is logically possible.

[0027] Although the apparatus and methods are described with functional descriptions for grammatical fluidity, it is expressly understood that unless expressly recited under 35 U.S.C. § 112, the claims should not necessarily be construed as limited by construction of "means" or "step" limitations, but should be accorded the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of legal equivalents, and that if the claims are expressly recited under 35 U.S.C. § 112, they should be accorded the full legal equivalents under 35 U.S.C. § 112.

[0028] As summarized above, the present disclosure provides methods for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. In further describing embodiments of the present disclosure, methods for generating a plurality of data signals from light detected from a sample in a flowstream, generating a saturation signal index from one or more identified saturation data signals, and generating an adjusted particle classification index using the saturation signal index are first described in more detail. Systems and integrated circuit devices programmed to implement the subject methods are then described. Non-transitory computer-readable storage media are also described.

[0029] Method for adjusting particle classification index in response to a saturation data signal - Patent Application 20070122997 Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. The phrase "saturation signal" is used herein in its conventional sense to refer to a signal that exceeds the maximum range that can be measured by one or more components of a light detection system (described in more detail below). In some embodiments, a saturation signal is a data signal output from a photodetector that has been exposed to an amount of light that exceeds the maximum amount that can be detected by the photodetector. In other embodiments, a saturation signal is a data signal that exceeds the maximum range of an analog-to-digital converter used to convert the analog signal from the photodetector to a digital signal. As described in more detail herein, the subject methods, according to certain embodiments, provide for identifying one or more saturation data signals from light detected from particles in a flowstream and removing the saturation data signals when classifying one or more particles in the flowstream. In other embodiments, the subject methods provide for identifying one or more saturation data signals and estimating the true value of the saturating data signals. Classifying particles according to the subject methods results in improved accuracy, such as improved accuracy in assigning particles to particle population clusters. When used as part of a particle sorting determination, the subject method can improve particle sorting yield as well as the purity of the sorted particles.

[0030] In carrying out the subject methods, a sample having particles is illuminated with a light source, and light from the sample is detected with a light detection system having one or more photodetectors. In some embodiments, the sample is a biological sample. The term "biological sample" is used in its conventional sense to refer to a whole organism, plant, fungus, or a subset of animal tissues, cells, or components that may be found in, for example, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic cord blood, urine, vaginal fluid, and semen. Thus, "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, plasma, serum, spinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. The biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some instances, the sample is a blood sample, including whole blood, such as blood obtained by venipuncture or fingerstick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0031] In certain embodiments, the source of the sample is a "mammal" or "mammalian," terms used broadly to refer to organisms within the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodents (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is a human. The methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult); in certain embodiments, the human subject is a juvenile, adolescent, or adult. It should be understood that while the present invention may be applied to samples from human subjects, it 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.

[0032] In embodiments, a sample (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source, emitting light having a wide range of wavelengths, e.g., ranging from 50 nm or greater, 100 nm or greater, 150 nm or greater, 200 nm or greater, 250 nm or greater, 300 nm or greater, 350 nm or greater, 400 nm or greater, or 500 nm or greater. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes irradiating with a broadband light source, the broadband light source protocol of interest may include, but is not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, an ultra-bright light emitting diode, a semiconductor light emitting diode, a broad spectrum LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0033] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, such as a light source that emits light in a narrow wavelength range, such as a range of 50 nm or less, 40 nm or less, 30 nm or less, 25 nm or less, 20 nm or less, 15 nm or less, 10 nm or less, 5 nm or less, or 2 nm or less, including light sources that emit light of a specific wavelength (i.e., monochromatic light). When the method includes irradiating with a narrowband light source, the subject narrowband light source protocol may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0034] In certain embodiments, the method includes irradiating the sample with one or more lasers. As noted above, the type and number of lasers will vary depending on the sample and the desired light to be collected, and may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other examples, the method includes irradiating the flowstream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the method includes irradiating the flowstream with a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In yet another example, the method includes irradiating the flowstream with a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thallium YAG laser, a ytterbium YAG laser, a Y2O3 laser, or a cerium-doped laser, and combinations thereof.

[0035] The sample may be illuminated with one or more of the above-mentioned light sources, 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, for example, ten or more light sources. The light sources may include any combination of light source types. 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.

[0036] The sample may be irradiated with a wavelength in the range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, e.g., 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample may be irradiated with a wavelength of 200 nm to 900 nm. In other examples where the light source includes multiple narrowband light sources, the sample may be irradiated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is irradiated with a specific wavelength in the range of 200 nm to 700 nm using a laser array having a gas laser, excimer laser, dye laser, metal vapor laser, and solid-state laser, as described above.

[0037] When two or more light sources are used, the sample can be illuminated with the light sources simultaneously or sequentially, or a combination thereof. For example, the sample may be illuminated with each light source simultaneously. In other embodiments, the flow stream is illuminated sequentially with each of the light sources. When two or more light sources are used to illuminate the sample sequentially, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method may include irradiating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, inclusive. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source can be the same or different.

[0038] The period between illumination by each light source may also vary as desired, being independently separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and 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.

[0039] The sample can be illuminated continuously or at discrete intervals. In some examples, the method includes continuously illuminating the sample in the sample with the light source. In other examples, the sample is illuminated with the light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds (including every 1000 milliseconds), or some other interval.

[0040] Depending on the light source, the sample may be illuminated from a distance that varies, including, 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, and 50 mm or more. Also, the angle or illumination may vary in the range of 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0041] In certain embodiments, the method includes irradiating the sample with two or more beams of frequency-shifted light. As described above, a light beam generator component having a laser and an acousto-optical device for frequency-shifting the laser light can be used. In these embodiments, the method includes irradiating the acousto-optical device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use in irradiating a sample in a flow stream), the laser may have a specific wavelength that varies over a range including 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, and 400 nm to 800 nm. The acousto-optical device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, e.g., ten or more lasers. The lasers may include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0042] When two or more lasers are used, the acousto-optical device can be illuminated with the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device may be illuminated with each of the lasers simultaneously. In other embodiments, the acousto-optical device is illuminated sequentially with each of the lasers. When two or more lasers are used to sequentially illuminate the acousto-optical device, the time for which each laser illuminates the acousto-optical device can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method may include irradiating the acousto-optical device with the laser for a duration ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, e.g., 5 microseconds to 10 microseconds. In embodiments in which the acousto-optic device is sequentially illuminated with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0043] The period between illumination by each laser may also vary as desired, being independently separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and 60 microseconds or more. For example, the period between illumination by each light source may range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and 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 by more than two (i.e., three or more) lasers, the delay between illumination by each laser may be the same or different.

[0044] The acousto-optic device can be illuminated continuously or at discrete intervals. In some examples, the method includes continuously illuminating the acousto-optic device with a laser. In other examples, the acousto-optic device is illuminated with a light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds (including every 1000 milliseconds), or some other interval.

[0045] Depending on the laser, the acousto-optic device may be illuminated from a distance that varies, including, for example, 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more. Also, the angle or illumination may vary in the range of 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, including 30° to 60°, for example an angle of 90°.

[0046] In an embodiment, a method includes applying radio frequency drive signals to an acousto-optic device to generate angularly deflected laser beams. Two or more radio frequency drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams, such as three or more radio frequency drive signals, four or more radio frequency drive signals, five or more radio frequency drive signals, six or more radio frequency drive signals, seven or more radio frequency drive signals, eight or more radio frequency drive signals, nine or more radio frequency drive signals, ten or more radio frequency drive signals, fifteen or more radio frequency drive signals, twenty-five or more radio frequency drive signals, fifty or more radio frequency drive signals, including one hundred or more radio frequency drive signals.

[0047] Each angularly deflected laser beam generated by the radio frequency drive signal has an intensity based on the amplitude of the applied radio frequency drive signal. In some embodiments, a method includes applying a radio frequency drive signal having an amplitude sufficient to generate an angularly deflected laser beam having a desired intensity. In some examples, each applied radio frequency drive signal independently has an amplitude ranging from about 0.001 V to about 500 V, e.g., from about 0.005 V to about 400 V, e.g., from about 0.01 V to about 300 V, e.g., from about 0.05 V to about 200 V, e.g., from about 0.1 V to about 100 V, e.g., from about 0.5 V to about 75 V, e.g., from about 1 V to about 50 V, e.g., from about 2 V to about 40 V, e.g., from 3 V to about 30 V, or including 5 V to about 25 V. In some embodiments, each applied high-frequency drive signal 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.

[0048] In these embodiments, the angularly deflected laser beams within the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, such as 100 μm or more, such as 500 μm or more, such as 1000 μm or more, or even 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0049] In certain examples, the flow stream is illuminated with multiple beams of frequency-shifted light, and cells within the flow stream are imaged by fluorescence imaging using radio frequency tagged emission (FIRE) to generate frequency-encoded images, such as that described in Diebold, et al. Nature Photonics Vol. 7(10);806-810 (2013), and in U.S. Patent Nos. 9,423,353, 9,784,661, and 10,006,852, and U.S. Patent Application Publication Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0050] As described above, in embodiments, light from the illuminated sample is transmitted to a light detection system and measured by multiple light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a range of wavelengths (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method comprises measuring a wavelength of light corresponding to the fluorescence peak wavelength of the fluorophore, hi some embodiments, the method comprises measuring light collected across the fluorescence spectrum of each fluorophore in the sample.

[0051] The collected light may be measured continuously or at discrete intervals. In some examples, the method includes measuring the light continuously. In other examples, the light is measured at discrete intervals, such as measuring the light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds (including every 1000 milliseconds), or some other interval.

[0052] Measurements of collected light may be made one or more times during the subject method, e.g., two or more times, e.g., three or more times, e.g., five or more times, e.g., ten or more times. In certain embodiments, light propagation is measured two or more times, and the data in certain instances is averaged.

[0053] The light from the sample may be measured at one or more wavelengths, for example 5 or more different wavelengths, for example 10 or more different wavelengths, for example 25 or more different wavelengths, for example 50 or more different wavelengths, for example 100 or more different wavelengths, for example 200 or more different wavelengths, for example 300 or more different wavelengths, and may include measuring the collected light at 400 or more different wavelengths.

[0054] The disclosed methods include adjusting a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. In embodiments, a plurality of data signals are generated from light detected from particles in a sample. The generated data signals may be analog or digital. When the data signals are analog, in some examples, the method includes converting the analog data signals to digital data signals, such as with an analog-to-digital converter. In performing the subject methods, one or more saturation data signals are identified. In some examples, the saturation data signals result from signals that exceed the range that the detector can measure. In other examples, the saturation data signals result from signals that exceed the range of the analog-to-digital converter. Depending on the number of photodetectors used to detect light from particles in the flowstream, the number of detector channels identified as outputting saturation data signals can vary, including, for example, one or more, for example, two or more, for example, four or more, for example, eight or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, and one or more than twenty-eight.

[0055] In embodiments, a saturation signal index is generated from the identified saturation data signal. In some embodiments, the saturation signal index is a binary word that identifies which detector channels have saturated the analog-to-digital converter. In some examples, the saturation signal index is a binary word consisting of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits, and including 256 or more bits. For example, the saturation signal index may be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word. In certain examples, the saturation signal index is a combination of binary words including two or more binary words, for example, three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, and ten or more. When the saturation signal index is a combination of binary words, each binary word may independently be a binary word consisting of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits (including 256 or more bits). In some embodiments, each binary word in the saturation signal index may independently be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word. For example, if the optical detection system includes 80 detector channels, the saturation signal index may be composed of a 128-bit binary word or three different 32-bit binary words.

[0056] In embodiments, the method includes determining one or more parameters of the particles in the flowstream from the generated data signals. In some embodiments, determining the one or more parameters of the particles includes decomposing light from multiple fluorophores in the sample, such as resolving light detected from fluorophores with overlapping fluorescence. In some embodiments, determining the parameters of the particles in the flowstream includes calculating a spectral unmixing matrix for fluorescence from the sample.

[0057] In some embodiments, spectrally resolving the light from the fluorophores in the sample comprises calculating a spectral unmixing matrix using a weighted least squares algorithm. In some examples, the weighted least squares algorithm is calculated according to the following formula:

[0058]

number

[0059]

number

[0060] In some embodiments, each W ii is calculated according to the following formula:

[0061]

number

[0062] where σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the constant noise at detector i. In one particular embodiment, the spectral separation matrix is ​​(X T WX) -1 X T In some examples, the method calculates the cell density (X) for each cell detected by the light detection system according to W.T WX) to calculate the spectral separation matrix. In some embodiments, the method includes calculating the solution to the spectral separation matrix by matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition) or singular value decomposition (SVD) using one or more of an iterative Newton-Raphson calculation, a Sherman-Morrison iterative inverse updater. In certain embodiments, the solution to the spectral separation matrix is ​​calculated as described in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, the disclosure of which is incorporated herein by reference.

[0063] In some embodiments, the method includes adjusting a spectral separation matrix of the fluorescence of particles in the sample based on the calculated saturation signal index. For example, the spectral separation matrix may be adjusted by excluding one or more of the saturation data signals. In certain embodiments, to calculate one or more parameters of particles in the sample, the method includes calculating a spectral separation matrix for the fluorescence of the particles, calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturation data signals, and comparing the calculated spectral separation matrix to the calculated adjusted spectral separation matrix. In certain examples, the spectral separation matrix is ​​adjusted by removing one or more rows of the matrix based on data signals from saturated input detector channels. In other examples, the saturation signal index is used to identify detector channels that are saturated and adjust the spectral separation matrix to compensate for the saturation signals. In one example, the spectral separation matrix solution is adjusted to use an estimate of the true value of the saturation signal. In these embodiments, the estimated true value of the saturation signal is first determined and input into the spectral separation matrix to generate the adjusted spectral separation matrix solution.

[0064] In some embodiments, the method includes classifying particles in the sample based on the one or more determined parameters of the particles. In some embodiments, classifying the particles includes assigning the particles to particle population clusters. In other embodiments, classifying the particles includes plotting the one or more parameters of the particles on a scatter plot.

[0065] In certain embodiments, classifying particles in a sample includes using a bitmap gating strategy, and classifying the particles includes identifying and removing saturated data signals identified from particle classification parameters. In other embodiments, classifying particles in a sample includes identifying saturated data signals and estimating true values ​​of the saturated data signals. In particular examples, to classify particles in a sample, a method includes generating a two-dimensional bitmap having regions of interest (ROIs) and determining whether particles should be assigned to the ROIs of the bitmap. In other examples, a method for classifying particles in a sample includes determining whether one or more bits of the ROIs of the bitmap include saturated data signals. In some embodiments, determining whether one or more bits of the particle assignment ROIs of the bitmap include saturated data signals includes applying a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, comparing the generated saturated signal bitmap with the particle assignment ROIs, and determining that one or more bits of the particle assignment ROIs are saturated.

[0066] If one or more bits of the bitmap ROI are saturated, the method according to some embodiments includes adjusting the particle classification index using the saturated signal index. In some examples, the coordinates used for the ROI are indices scaled to the addressable range of the bitmap. In some examples, one or more offsets are applied. In other examples, one or more scaling factors are applied. In particular examples, the method includes applying one or more offsets followed by one or more scaling factors. In certain embodiments, the method includes converting the saturated signal index to a scalar value before applying the saturated signal index to determine whether one or more bits of the bitmap particle assignment ROI contain saturated data signals. In some examples, the X and Y inputs to the ROI bitmap are selected from data frames from the data signal. In these examples, integers are subtracted (e.g., by an affine transformation) and then multiplied by a scale factor. In some embodiments, a contiguous subset of bits is used to select an index into the bitmap (e.g., bits 31-25 selected from a 64-bit multiplication product). In some embodiments, the saturated signal index is applied to the bitmap ROI. Boolean logic can be used to compare the saturated signal bitmap with the particle assignment ROI, for example, ANDing the saturated signal bitmap with the particle assignment ROI to determine whether one or more bits of the particle assignment ROI are saturated. In these embodiments, a true / false Boolean value can be obtained indicating whether an event is saturated in any detector channel. These results can be combined with the particle assignment ROI determination to include non-saturated status in the classification decision.

[0067] 5 shows a flow diagram for generating an adjusted particle classification index, according to certain embodiments. In step 501, light is detected from a sample in a flow stream. An optical detection system generates a plurality of data signals from the detected light in step 502, and one or more saturation data signals may be detected in step 503. A saturation data signal is the result of a detector exceeding the amount of light the detector can measure or when the input signal exceeds the range of an analog-to-digital converter. A saturation signal index in the form of a multi-bit binary word is generated in step 504 to identify detector channels that are saturated. In step 505, the multi-bit binary word saturation signal index is applied to generate a particle classification decision.

[0068] 6 shows a flow diagram for using a multi-bit binary word saturation signal index to generate an adjusted particle classification, according to certain embodiments. In step 601, a spectral separation matrix for the fluorescence of particles in a sample is calculated without applying a saturation signal index. In step 602, an adjusted spectral separation matrix solution is calculated by applying the saturation signal index to identify which detector channels are saturated. In step 603, the unadjusted spectral separation matrix is ​​compared to the adjusted spectral separation matrix, and one or more rows of the spectral separation matrix may be excluded based on the comparison between the unadjusted and adjusted spectral separation matrices.

[0069] 7 shows a flow diagram for classifying particles according to one specific embodiment. In step 701, a particle classification two-dimensional bitmap having a region of interest (ROI) is generated. In step 702, a saturated signal index is applied to generate a second two-dimensional bitmap ROI, the axis of which identifies the saturated channel. Using Boolean logic, the second two-dimensional bitmap ROI with the identified saturated channel is ANDed with the particle classification two-dimensional bitmap in step 703 to generate an adjusted particle classification that takes into account the saturated detector channel. In step 704, particles are sorted based on the determined particle classification.

[0070] In some embodiments, the method includes generating a sorting classification. In some examples, the sorting classification can be a particle sorting decision. For example, generating the sorting classification can include identifying a suitable sorting gate for sorting the particles based on the calculated parameters of the particles in combination with the determined saturation signal index. The particle sorting decision can be generated based on the overlap between the calculated parameters of the particles and the parameters of the particle classification adjusted by applying the saturation signal index. To select an appropriate gate, the method can further include plotting the parameters (e.g., on a scatter plot) to obtain the best possible separation of the subpopulations. This analysis can be transmitted to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0071] In some embodiments, a method for sorting components of a sample includes sorting particles (e.g., cells in a biological sample) using 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. In certain embodiments, cells of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Provisional Patent Application No. 62 / 803,264, filed February 8, 2019, the disclosure of which is incorporated herein by reference.

[0072] System for adjusting particle classification index in response to a saturation data signal - Patents.com As summarized above, aspects of the present disclosure include a system configured to adjust a particle classification index in response to one or more saturation data signals from light detected from particles in a flowstream. As noted above, the term “saturation signal” is used to refer to a signal that exceeds a maximum range that can be measured by one or more components of the light detection system described below. In some embodiments, a saturation signal is a data signal output from a photodetector that has been exposed to an amount of light that exceeds a maximum amount that can be detected by the photodetector. In other embodiments, a saturation signal is a data signal that exceeds a maximum range of an analog-to-digital converter used to convert an analog signal from the photodetector to a digital signal. A system according to certain embodiments includes a light source configured to illuminate particles of a sample in a flowstream, a light detection system having a photodetector that detects light from particles in the sample and generates a plurality of data signals from the detected light, and a processor having a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to identify one or more saturation data signals, generate a saturation signal index that includes the identified saturation data signals, and apply the saturation signal index to the particle classification index to generate an adjusted particle classification index.

[0073] In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit light with wavelengths ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and 400 nm to 800 nm. For example, the light source can include a broadband light source emitting light having a wavelength between 200 nm and 900 nm. In other examples, the light source can include a narrowband light source emitting a wavelength between 200 nm and 900 nm. For example, the light source can be a narrowband LED (1 nm to 25 nm) emitting light having a wavelength between 200 nm and 900 nm. In certain embodiments, the light source is a laser. In some examples, the subject systems include gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other examples, the subject systems include dye lasers such as stilbene lasers, coumarin lasers, or rhodamine lasers. In still 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 yet other examples, the subject systems include solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, slim YAG lasers, ytterbium YAG lasers, Y2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0074] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode, such as a broadband LED having a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broad spectrum LED white light source, an integrated multi-LED, etc. In some examples, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0075] The light source may be positioned at any suitable distance from the sample (e.g., a flow stream in a flow cytometer), such as a distance of 0.001 mm or more from the flow stream, such as a distance of 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 100 mm or more. Additionally, the light source illuminates the sample at any suitable angle (e.g., relative to the perpendicular axis of the flow stream), such as an angle ranging from 10° to 90°, 15° to 85°, 20° to 80°, 25° to 75° (including 30° to 60°), for example, a 90° angle.

[0076] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some examples, the system includes a light source configured to continuously illuminate the sample, such as with a continuous wave laser that continuously illuminates the flow stream at an interrogation point within the flow cytometer. In other examples, the subject systems include a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds (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 for providing intermittent illumination of the sample by the light source. For example, the subject systems in these embodiments may include one or more laser beam choppers, manual or computer-controlled beam stops for blocking and exposing the sample to the light source.

[0077] In some embodiments, the light source is a laser. Lasers of interest may include pulsed or continuous wave lasers. For example, lasers include gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof; 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-based lasers, and the like. solid-state lasers such as ruby, Nd:YAG, NdCrYAG, Er:YAG, Nd:YLF, Nd:YVO4, Nd:YCa4O(BO3)3, Nd:YCOB, titanium sapphire, thallium YAG, ytterbium YAG, ytterbium 2O3 or cerium doped lasers and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs) or frequency doubled or frequency tripled implementations of any of the aforementioned lasers.

[0078] In certain embodiments, the light source is an optical beam generator configured to generate two or more beams of frequency-shifted light. In some examples, the optical beam generator includes a laser and a radio-frequency generator configured to apply a radio-frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, lasers in optical beam generators of interest include gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof; dye lasers, such as stilbene, coumarin, or rhodamine lasers; and metal-vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, and helium-cerium (Ce) excimer lasers. The laser may be a rhenium (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, or a solid-state laser such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thallium YAG laser, a ytterbium YAG laser, a Y2O3 laser or a cerium-doped laser and combinations thereof.

[0079] The acousto-optic device can be any suitable acousto-optic device configured to frequency-shift laser light using applied acoustic waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the subject system is configured to generate an angularly deflected laser beam from light from a laser and an applied radio frequency drive signal. The radio frequency drive signal can be applied to the acousto-optic device using any suitable radio frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0080] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angularly deflected laser beams within the output laser beam, for example configured to apply three or more radio frequency drive signals, for example four or more radio frequency drive signals, for example five or more radio frequency drive signals, for example six or more radio frequency drive signals, for example seven or more radio frequency drive signals, for example eight or more radio frequency drive signals, for example nine or more radio frequency drive signals, for example ten or more radio frequency drive signals, for example fifteen or more radio frequency drive signals, for example twenty-five or more radio frequency drive signals, for example fifty or more radio frequency drive signals, including configured to apply one hundred or more radio frequency drive signals.

[0081] In some examples, 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 that varies in amplitude from about 0.001V to about 500V, for example, from about 0.005V to about 400V, for example, from about 0.01V to about 300V, for example, from about 0.05V to about 200V, for example, from about 0.1V to about 100V, for example, from about 0.5V to about 75V, for example, from about 1V to 50V, for example, from about 2V to 40V, for example, from 3V to about 30V, including 5V to about 25V. In some embodiments, each applied high-frequency drive signal 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, and about 5 MHz to about 50 MHz.

[0082] In certain embodiments, the controller includes 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 with an angularly deflected laser beam having a desired intensity profile. For example, the memory may include instructions to generate two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams having the same intensity, and the including memory may include instructions to generate one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the instructions may include instructions to generate two or more angularly deflected laser beams with different intensities, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more, angularly deflected laser beams having different intensities, and the including memory may include instructions to generate one hundred or more angularly deflected laser beams having different intensities.

[0083] In certain embodiments, the controller includes 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 that increases from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, 1% to about 90%, about 2% to about 85%, about 3% to about 80%, about 4% to about 75%, about 5% to about 70%, about 6% to about 65%, about 7% to about 60%, about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller includes 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 that increases from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, 1% to about 90%, about 2% to about 85%, about 3% to about 80%, about 4% to about 75%, about 5% to about 70%, about 6% to about 65%, about 7% to about 60%, about 8% to about 55%, or may include about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller comprises a processor having a memory operably 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 with a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory including 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.

[0084] In embodiments, the subject optical beam generator can be configured to generate angularly deflected laser beams within a spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly deflected laser beams can 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, or even 5000 μm or more. In some embodiments, the system is configured to generate angularly deflected laser beams within the output laser beam that overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam, such as with adjacent angularly deflected laser beams. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0085] In certain examples, the light beam generator configured to generate two or more beams of frequency-shifted light includes a laser excitation module such as those described in U.S. Patent Nos. 9,423,353, 9,784,661, and 10,006,852, and U.S. Patent Publication Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0086] In embodiments, the system includes a light detection system having one or more light detectors. Light detectors of interest may include, but are not limited to, light sensors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof, among other light detectors. In certain embodiments, light from the sample is measured using 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.

[0087] In some embodiments, the subject light detection system includes a plurality of light detectors. In some examples, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In particular examples, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include four or more light detectors, for example, ten or more light detectors, for example, twenty-five or more light detectors, for example, fifty or more light detectors, for example, one hundred or more light detectors, for example, two hundred or more light detectors, for example, five hundred or more light detectors, for example, seven hundred or more light detectors, and even one thousand or more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, for example, ten or more photodiodes, for example, twenty-five or more photodiodes, for example, fifty or more photodiodes, for example, one hundred or more photodiodes, for example, two hundred or more photodiodes, for example, five hundred or more photodiodes, for example, seven hundred or more photodiodes, and including one thousand or more photodiodes.

[0088] The photodetectors may be arranged in any geometric configuration as desired, including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregularly shaped configurations. The photodetectors within a photodetector array may be oriented relative to one another at angles ranging from 10° to 180° (as viewed in the XZ plane), e.g., 15° to 170°, e.g., 20° to 160°, e.g., 25° to 150°, e.g., 30° to 120°, and including 45° to 90°. The photodetector array may be any suitable shape, e.g., rectilinear shapes such as square, rectangular, trapezoidal, triangular, and hexagonal; curvilinear shapes such as circular and elliptical; and irregular shapes such as a parabolic base coupled to a planar top. In certain embodiments, the photodetector array has a rectangular active surface.

[0089] Each photodetector (e.g., photodiode) in the array can have an active surface with a width in the range of 5 μm to 250 μm, for example, 10 μm to 225 μm, for example, 15 μm to 200 μm, for example, 20 μm to 175 μm, for example, 25 μm to 150 μm, for example, 30 μm to 125 μm (inclusive) and a length in the range of 5 μm to 250 μm, for example, 10 μm to 225 μm, for example, 15 μm to 200 μm, for example, 20 μm to 175 μm, for example, 25 μm to 150 μm, for example, 30 μm to 125 μm (inclusive), and the surface area of ​​each photodetector (e.g., photodiode) in the array can be 25 μm 2 ~10,000 μm 2 , e.g., 50 μm 2 ~9000μm 2 , e.g., 75 μm 2 ~8000μm 2 , e.g., 100 μm 2 ~7000μm 2 , for example, 150 μm 2 ~6000μm 2 For example, the range is 200 to μm. 2 ~5000μm 2 The range includes:

[0090] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length ranging from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, and 5 mm to 25 mm. The width of the photodetector array may vary from 0.01 mm to 100 mm, for example 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, and 5 mm to 25 mm. Thus, the active surface of the photodetector array may be between 0.1 mm and 100 mm. 2 ~10,000mm 2 , e.g. 0.5 mm 2 ~5000mm 2 , e.g. 1 mm 2 ~1000mm 2 , e.g. 5mm 2 ~500mm 2 , and 10mm 2 ~100mm 2 It may be a range including.

[0091] The light detectors of interest are configured to measure light collected at one or more wavelengths, such as two or more wavelengths, for example five or more different wavelengths, such as ten or more different wavelengths, for example twenty-five or more different wavelengths, such as fifty or more different wavelengths, for example one hundred or more different wavelengths, such as two hundred or more different wavelengths, for example three hundred or more different wavelengths, including measuring light emitted by a sample in the flow stream at four hundred or more different wavelengths.

[0092] In some embodiments, the photodetector is configured to measure collected light over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector 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 wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the detector is configured to measure light from a sample in the flowstream 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 the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the photodetector may be configured to pair with a particular fluorophore, such as one used with a sample in a fluorescence assay, hi some embodiments, the photodetector is configured to measure the collected light across the fluorescence spectrum of each fluorophore in the sample.

[0093] The light detection system may be configured to measure light continuously or at discrete intervals. In some cases, the target light detector is configured to continuously measure collected light. In other cases, the light detection system is configured to measure at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds (including every 1000 milliseconds), or some other interval.

[0094] In embodiments, the system is configured to adjust the particle classification index in response to one or more saturation data signals from light detected from particles in the flow stream. The light detection system is configured to generate a plurality of data signals from light detected from particles in the sample. The generated data signals may be analog data signals or digital data signals. When the data signals are analog data signals, in some examples, the system further includes an analog-to-digital converter configured to convert the analog data signals to digital data signals. Systems of interest include a processor having a memory operably coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to identify one or more saturation data signals. In some examples, the saturation signals are data signals resulting from signals exceeding the range that a detector of the light detection system can measure. In other examples, the saturation signals are data signals resulting from signals exceeding the range of the analog-to-digital converter. Depending on the number of photodetectors in the subject photodetection system, the number of detector channels that may be identified as outputting a saturated data signal may vary, e.g., 1 or more, e.g., 2 or more, e.g., 4 or more, e.g., 8 or more, e.g., 16 or more, e.g., 32 or more, e.g., 64 or more, including 128 or more.

[0095] In some embodiments, the saturation signal index generated by the processor from the identified saturation data signals is a binary word that identifies which detector channels have saturated the analog-to-digital converter. In some examples, the saturation signal index is a binary word consisting of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits, and including 256 or more bits. For example, the saturation signal index may be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word. In particular examples, the saturation signal index is a combination of two or more binary words, for example, three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, and ten or more binary words. When the saturation signal index is a combination of binary words, each binary word may independently be a binary word consisting of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits (including 256 or more bits). In some embodiments, each binary word in the saturation signal index may independently be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word. For example, if the optical detection system includes 80 detector channels, the saturation signal index may be composed of a 128-bit binary word or three different 32-bit binary words.

[0096] In embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to determine one or more parameters of particles in the flow stream from the generated data signal. In some embodiments, the memory includes instructions for calculating a spectral separation matrix for the fluorescence of the particles. In some embodiments, the memory includes instructions for calculating the spectral separation matrix using a weighted least squares algorithm. In some examples, the weighted least squares algorithm is calculated according to the following formula:

[0097]

number

[0098]

number

[0099] In some embodiments, each W ii is calculated according to the following formula:

[0100]

number

[0101] where σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the constant noise at detector i. In one particular embodiment, the spectral separation matrix is ​​(X T WX) -1 X T In some examples, the memory stores the spectral separation matrix (X) for each cell detected by the optical detection system to calculate the spectral separation matrix. TWX). In some embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate a solution to a spectral separation matrix by matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition) or singular value decomposition (SVD) using one or more of an iterative Newton-Raphson calculation, a Sherman-Morrison iterative inverse updater. In certain embodiments, systems for calculating a solution to a spectral separation matrix include those described in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, the disclosure of which is incorporated herein by reference.

[0102] In some examples, the memory includes instructions for calculating an adjusted spectral separation matrix using the calculated saturation signal index, such as when one or more saturated data signals are excluded. In other examples, the memory includes instructions for calculating an adjusted spectral separation matrix in which data signals from saturated detector channels are excluded. In particular examples, the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to calculate a spectral separation matrix for the fluorescence of the particles, calculate an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturated data signals, and compare the calculated spectral separation matrix with the calculated adjusted spectral separation matrix. In certain embodiments, the memory includes instructions for calculating a spectral separation matrix, where one or more rows of the matrix are removed to exclude data signals from saturated input detector channels. In other embodiments, the memory includes instructions for identifying saturated detector channels and adjusting the spectral separation matrix to compensate for the saturated signals. In one example, the spectral separation matrix solution is adjusted to use an estimate of the true value of the saturated signal. In these embodiments, the estimated true value of the saturated signal is first determined and input into the spectral separation matrix to generate the adjusted spectral separation matrix solution.

[0103] The memory may include instructions stored thereon that, when executed by the processor, cause the processor to classify the particles based on one or more determined parameters of the particles. In some embodiments, the system is configured to classify the particles by assigning the particles to particle population clusters. In other embodiments, the system is configured to classify the particles by plotting the one or more parameters of the particles on a scatter plot.

[0104] In some embodiments, the memory includes instructions for implementing a bitmap gating strategy for classifying particles. In some examples, the bitmap gating strategy implemented by the subject systems includes instructions for identifying and removing saturated data signals. In other examples, the bitmap gating strategy implemented by the subject systems includes instructions for identifying saturated data signals and estimating true values ​​of saturated data signals. In some embodiments, the memory includes instructions that, when executed by a processor, cause the processor to generate a two-dimensional bitmap having regions of interest (ROIs) and determine whether particles should be assigned to the ROIs of the bitmap. In other embodiments, the memory includes instructions that, when executed by the processor, cause the processor to determine whether one or more bits of the ROIs of the bitmap include saturated data signals. In some examples, the memory includes instructions where determining whether one or more bits of the particle assignment ROIs of the bitmap include saturated data signals includes applying a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, comparing the generated saturated signal bitmap with the particle assignment ROIs, and determining that one or more bits of the particle assignment ROIs are saturated. In certain examples, the processor operates using Boolean logic to compare the saturated signal bitmap with the particle assignment ROI, for example, the saturated signal bitmap may be ANDed with the particle assignment ROI to determine whether one or more bits of the particle assignment ROI are saturated.

[0105] In some embodiments, the subject systems may include one or more sorting decision modules configured to generate particle sorting decisions based on the particle classifications. In certain embodiments, the systems further include a particle sorter (e.g., having a droplet deflector) for sorting particles from the flow stream based on the sorting decisions generated by the sorting decision module. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., non-cellular particles such as cells, biopolymers, etc.) and, in some instances, delivering the separated components to one or more sample collection vessels. For example, the subject systems may be configured to sort samples having two or more components, e.g., three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, and may include sorting samples having twenty-five or more components. One or more of the sample components may be separated from the sample and delivered to a sample collection container, for example, two or more sample components, for example, three or more sample components, for example, four or more sample components, for example, five or more sample components, for example, ten or more sample components, fifteen or more sample components, etc. may be separated from the sample and delivered to a sample collection container.

[0106] In some embodiments, a 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, a sorting determination module having multiple sorting determination units, such as that described in U.S. Provisional Patent Application No. 62 / 803,264, filed February 8, 2019, the disclosure of which is incorporated herein by reference, is used to sort particles (e.g., cells) of a sample. In some embodiments, a method for sorting components of a sample includes sorting particles (e.g., cells in a biological sample) using a particle sorting module having deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0107] 1 shows a functional block diagram of an example of a sorting control system for analyzing and displaying biological events, such as an analysis controller 100. Analysis controller 100 can be configured to implement various processes for controlling the graphical display of biological events.

[0108] The particle analyzer or sorting system 102 may be configured to acquire biological event data. For example, a flow cytometer may generate flow cytometry event data. The particle analyzer 102 may be configured to provide the biological event data to the analysis controller 100. A data communication channel may be included between the particle analyzer 102 and the analysis controller 100. The biological event data may be provided to the analysis controller 100 via the data communication channel.

[0109] The analysis controller 100 may be configured to receive biological event data from the particle analyzer 102. The biological event data received from the particle analyzer 102 may include flow cytometry event data. The analysis controller 100 may be configured to provide a graphical display including a first plot of the biological event data on the display device 106. The analysis controller 100 may be further configured to render a region of interest, for example, as a gate around a population of the biological event data shown by the display device 106, 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 saturation detector data.

[0110] Analysis controller 100 may further be configured to display biological event data within a gate differently from other events in the biological event data outside the gate on display device 106. For example, analysis controller 100 may be configured to render the color of biological event data contained within a gate distinct from the color of biological event data outside the gate. Display device 106 may be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0111] The analysis controller 100 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device may be implemented as a mouse 110. The mouse 110 may initiate a gate selection signal to the analysis controller 100 identifying a gate displayed on or manipulated via the display device 106 (e.g., by clicking on or within the desired gate when a cursor is positioned there). In some implementations, the first device may be implemented as a keyboard 108 or other means for providing input signals to the analysis controller 100, such as a touchscreen, a stylus, an optical detector, 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. 1, the mouse 110 may include a right mouse button and a left mouse button, each of which may generate a trigger event.

[0112] The trigger event may cause the analysis controller 100 to change how the data is displayed, what portions of the data are actually displayed on the display device 106, and / or provide input to further processing, such as selecting a population of interest for particle sorting.

[0113] In some embodiments, analysis controller 100 may be configured to detect when gate selection is initiated by mouse 110. Analysis controller 100 may further be configured to automatically modify the visualization of the plot to facilitate the gating process. The modification may be based on a particular distribution of the biological event data received by analysis controller 100.

[0114] The analysis controller 100 may be connected to a storage device 104. The storage device 104 may be configured to receive and store biological event data from the analysis controller 100. The storage device 104 may also be configured to receive and store flow cytometry event data from the analysis controller 100. The storage device 104 may further be configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 100.

[0115] The display device 106 may be configured to receive display data from the analysis controller 100. The display data may include a plot of the biological event data and a gate that outlines an area of ​​the plot. The display device 106 may be further configured to modify the presented information according to input received from the analysis controller 100 in conjunction with input from the particle analyzer 102, the storage device 104, the keyboard 108, and / or the mouse 110.

[0116] In some implementations, the analysis controller 100 may generate a user interface for receiving example events for filtering. For example, the user interface may include controls for receiving example events or example images. The example events or example images, or example gates, may be provided prior to collection of event data for the sample or based on an initial event set for a portion of the sample.

[0117] FIG. 2A is a schematic diagram of a particle sorter system 200 (e.g., particle analyzer 102) according to one embodiment presented herein. In some embodiments, particle sorter system 200 is a cell sorter system. As shown in FIG. 2A, a droplet-forming transducer 202 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 201, and fluid conduit 210 may be coupled to, include, or be a nozzle 203. Within fluid conduit 201, sheath fluid 204 hydrodynamically focuses sample fluid 206 containing particles 209 into a moving fluid column 208 (e.g., a stream). Within moving fluid column 208, particles 209 (e.g., cells) are aligned across a monitoring area 211 (e.g., where laser streams intersect) that is illuminated by an illumination source 212 (e.g., a laser). The vibration of the droplet forming transducer 202 causes the moving fluid column 208 to break up into multiple droplets 210 , some of which contain particles 209 .

[0118] During operation, the detection station 214 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring area 211. The detection station 214 feeds a timing circuit 228, which in turn feeds a flash charge circuit 230. At a droplet break-off point, signaled by a timed droplet delay (Δt), a flash charge can be applied to the moving fluid column 208, thus causing the droplet of interest to acquire 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 a well or microwell can be associated with a particular droplet of interest. As shown in FIG. 2A, the droplets can be collected in a drain container 238.

[0119] Detection system 216 (e.g., a droplet boundary detector) serves to automatically determine the phase of the droplet drive signal as a particle of interest passes through monitoring area 211. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. Detection system 216 allows the instrument to accurately calculate the position of each detected particle within the droplet. Detection system 216 may provide amplitude signal 220 and / or phase signal 218, which then provide amplitude control circuit 226 and / or frequency control circuit 224 (via amplifier 222). Amplitude control circuit 226 and / or frequency control circuit 224 then control droplet forming transducer 202. Amplitude control circuit 226 and / or frequency control circuit 224 may be included within a control system.

[0120] In some implementations, the sorting electronics (e.g., detection system 216, detection station 214, and processor 240) can be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decisions can be included in the event data for the particle. In some implementations, the detection system 216 and detection station 214 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 216 or detection station 214 and provided to a non-collection element.

[0121] FIG. 2B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 200 shown in FIG. 2B includes deflection plates 252 and 254. An electric charge can be applied via stream charging wires within the barbs, generating a stream of droplets 210 containing particles 210 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed by sorting electronics or other detection systems (not shown in FIG. 2B). Deflection plates 252 and 254 can be independently controlled to attract or repel charged droplets, directing them toward a desired collection vessel (e.g., one of 272, 274, 276, or 278). As shown in FIG. 2B, deflection plates 252 and 254 can be controlled to direct particles along first path 262 toward vessel 274 or along second path 268 toward vessel 278. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sort range), the deflector may allow the particle to continue along flow path 264. Such uncharged droplets may be diverted into a waste container, such as via aspirator 270.

[0122] Sorting electronics may be included to initiate the collection of measurements, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 2B includes a BD FACSAria® flow cytometer, commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0123] In some embodiments, one or more components described for particle sorter system 200 may be used to analyze and characterize particles, regardless of whether the particles are physically sorted into a collection container. Similarly, one or more components described below for particle analysis system 300 (FIG. 3) may be used to analyze and characterize particles, regardless of whether the particles are physically sorted into a collection container. For example, particles may be grouped or displayed in a tree including at least three groups as described herein using one or more of the components of particle sorter system 200 or particle analysis system 300.

[0124] FIG. 3 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 300 is a flow system. The particle analysis system 300 shown in FIG. 3 can be configured to perform, in whole or in part, the methods described herein. The particle analysis system 300 includes a fluidics system 302. The fluidics system 302 can include or be coupled to a sample tube 310 and a moving fluid column within the sample tube through which particles 330 (e.g., cells) of the sample move along a common sample path 320.

[0125] The particle analysis system 300 includes a detection system 304 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. A detection station 308 generally refers to a monitoring area 340 of the common sample path. Detection, in some implementations, may include detecting light or one or more other properties of the particle 330 as it passes through the monitoring area 340. In FIG. 3 , one detection station 308 is shown with one monitoring area 340. Some implementations of the particle analysis system 300 may include multiple detection stations. Additionally, some detection stations may monitor more than one area.

[0126] 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 multidimensional data points that include values ​​for each property measured for the particle. The detection system 304 is configured to collect a series of such data points over a first time interval.

[0127] The particle analysis system 300 may also include a control system 306. The control system 306 may include one or more processors, amplitude control circuitry 226, and / or frequency control circuitry 224, as shown in FIG. 2B. The illustrated control system 206 is operatively associated with the fluidics system 302. The control system 206 may be configured to generate a calculated signal frequency for at least a portion of a first time interval based on the Poisson distribution and the number of data points collected by the detection system 304 during the first time interval. The control system 306 may be further configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. Additionally, the control system 306 may compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0128] 4 shows a system 400 for flow cytometry in accordance with an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0129] Pump lasers 115a-c emit light in the form of laser beams. The wavelengths of the laser beams emitted from pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits UV light (light having a wavelength in the range of 10-400 nm) and reflects 488 nm and 633 nm light.

[0130] The laser beam is then directed to a focusing lens 420, which focuses the beam into a portion of the fluid stream where the sample particles are located, within a flow chamber 425. The flow chamber is part of a fluidics system that directs particles, typically one at a time, in the stream toward the focused laser beam for interrogation. The flow chamber may comprise a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0131] Light from the laser beam interacts with particles in the sample by diffraction, refraction, reflection, scattering, and absorption, and re-emits at a variety of different wavelengths depending on particle properties such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. Fluorescent emissions and diffracted, refracted, reflected, and scattered light can be sent 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 forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0132] The fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes it toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on each side of the center of the spectral band, from 500 nm to 520 nm. Shortpass filters transmit light with wavelengths below a specific wavelength. Longpass filters, such as longpass filters 455a-455b, transmit light with wavelengths 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 detector's specificity for a particular fluorochrome. The filter can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0133] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass. For example, beam splitter 445g is a 620 SP beam splitter, meaning that beam splitter 445g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g can comprise optical mirrors, such as dichroic mirrors.

[0134] The forward scatter detector 430 is positioned slightly off-axis from the direct beam passing through the flow cell and is configured to detect diffracted light, i.e., excitation light traveling primarily forward through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect light refracted and reflected from the particle's surface and internal structure, which tends to increase with increasing particle complexity. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, side scatter detector 435, and fluorescence detector can be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0135] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 4, but can include any flow cytometer known in the art. For example, a flow cytometer can have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0136] During operation, cytometer operation is controlled by controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by controller / processor 490. While not explicitly shown, controller / processor 490 may be coupled to detectors to receive output signals therefrom and may also be coupled to electrical and electromechanical components of flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided within the system. Memory 495, controller / processor 490, and I / O functionality 497 may be provided entirely as an integral part of flow cytometer 410. In such an embodiment, a display may also form part of I / O functionality 497 for presenting experimental data to a user of cytometer 400. Alternatively, memory 495 and controller / processor 490 and some or all of the I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of memory 495 and controller / processor 490 may be in wireless or wired communication with cytometer 410. Controller / processor 490, in conjunction with memory 495 and I / O functionality 497, can be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0137] The system shown in FIG. 4 includes six different detectors that detect fluorescence within six different wavelength bands (sometimes referred to herein as the “filter windows” of a given detector) defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The specific fluorescent labels used for an experiment and their associated fluorescence 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 fluorescence emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may lie within the filter window of one particular detector, it is generally the case that part of that label’s emission spectrum also overlaps with the filter windows of one or more other detectors. This may be referred to as spillover. I / O functions 497 can be configured to receive data regarding a flow cytometer experiment with a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. I / O functionality 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data (e.g., 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.

[0138] Systems according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory having stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control, such as, 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 system memory and / or program storage devices used in conjunction with 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 processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementation of hardware state machines to perform the functions described herein will be apparent to one skilled 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 via a communications channel, or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium, using any of these devices together with the memory. For example, a magnetic or optical disk may carry the programming and be readable by a disk writer / reader. The system of the present invention also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as 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 mobile phone (i.e., a smartphone).

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

[0144] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., physical ports or interfaces 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 communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, allowing the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that a user may use in conjunction with the subject system.

[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 a server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0148] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored within the subject system, e.g., within 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. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. The 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 achieved 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 various known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The one or more platforms present in the subject system are typically of a class of computers commonly referred to as servers, but may be any type of known or future-developed computer platform. Alternatively, they may be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication system, including wireless systems, either networked or not. They may be co-located or physically separated.In some cases, various operating systems may be employed on any of the computer platforms, depending on the type and / or configuration of the computer platform selected. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, etc.

[0150] FIG. 8 illustrates the general architecture of an exemplary computing device 800 according to a particular embodiment. The general architecture of computing device 800 illustrated in diagram 800 includes an arrangement of computer hardware and software components. Computing device 800 may include more (or fewer) elements than those shown in FIG. 8. However, it is not necessary to show all of these generally conventional elements to provide an enabling disclosure. As illustrated, computing device 800 includes a processing unit 810, a network interface 820, a computer-readable medium drive 830, an input / output device interface 840, a display 850, and input devices 860, all of which may communicate with each other via a communications bus. Network interface 820 may provide connectivity to one or more networks or computing systems. Thus, processing unit 810 may receive information and instructions from other computing systems or services over a network. Processing unit 810 also communicates with memory 870 and may further provide output information to optional display 850 via input / output device interface 840. The input / output device interface 840 can also accept input from optional input devices 860 such as a keyboard, mouse, digital pen, microphone, touch screen, gesture recognition system, voice recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0151] Memory 870 may include computer program instructions (grouped as modules or components in some embodiments) that processing unit 810 executes to implement one or more embodiments. Memory 870 generally includes RAM, ROM, and / or other persistent, secondary, or non-transitory computer-readable media. Memory 870 may store an operating system 872 that provides computer program instructions used by processing unit 810 in the general management and operation of computing device 800. Memory 870 may further include computer program instructions and other information for implementing aspects of the present disclosure.

[0152] For example, in one embodiment, memory 870 includes a saturation data signal identification module 874 for generating a saturation signal index and a particle classification module 876 for adjusting one or more parameters of particle classification, such as parameters of a sorting decision.

[0153] In certain embodiments, the subject system is a flow cytometry system that uses the above algorithms to analyze and adjust particle classification indices in response to one or more saturation data signals from light detected from particles in a flow stream. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997), Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997), Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995), Virgo, et al. (2012) Ann Clin Biochem. Jan;49(pt 1):17-28, Linden, et al., Semin Thromb Hemost. 2004 Oct;30(5):502-11, Alison, et al. J Pathol, 2010 Dec;222(4):335-344, and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular examples, flow cytometry systems of interest include the BD Biosciences FACSCanto™ II flow cytometer, BD Accuri™ 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 LSRFortess™ X-20 flow cytometer, and BD Biosciences FACSCalibur™ flow cytometer, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, and BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, and BD Biosciences Examples include a FACSMelody™ cell sorter.

[0154] In some embodiments, the subject particle sorting system may be configured with any of the methods described in U.S. Patent Nos. 9,952,076, 9,933,341, 9,726,527, 9,453,789, 9,200,334, 9,097,640, 9,095,494, 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,140,300, 7,544,326, 8 ... and flow cytometry systems such as those described in US Pat. Nos. 7,201,875, 7,129,505, 6,821,740, 6,813,017, 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, and 5,602,039, the disclosures of which are incorporated herein by reference in their entireties.

[0155] Integrated Circuit Devices Aspects of the present disclosure also include an integrated circuit device programmed to adjust a particle classification index in response to one or more saturation data signals from light detected from particles in the flowstream. In embodiments, the integrated circuit device may be a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), or a complex programmable logic device (CPLD), or some other integrated circuit device. In some embodiments, the integrated circuit device is programmed to identify one or more saturation data signals from the light detected from particles in the flowstream, generate a saturation signal index that includes the identified saturation data signals, and apply the saturation signal index to the particle classification index to generate an adjusted particle classification index.

[0156] In some embodiments, the saturation signal index used by the integrated circuit is a binary word that identifies the detector channel of the photodetection system that saturated the analog-to-digital converter. In some examples, the saturation signal index is a binary word consisting of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits, and including 256 or more bits. For example, the saturation signal index may be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word. In particular examples, the saturation signal index is a combination of two or more binary words, for example, three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, and ten or more binary words. When the saturation signal index is a combination of binary words, each binary word may independently be a binary word of 1 or more bits, for example, 2 or more bits, for example, 4 or more bits, for example, 8 or more bits, for example, 16 or more bits, for example, 32 or more bits, for example, 64 or more bits, for example, 128 or more bits (including 256 or more bits). In some embodiments, each binary word in the saturation signal index may independently be a 4-bit binary word, an 8-bit binary word, a 16-bit binary word, a 32-bit binary word, a 64-bit binary word, a 128-bit binary word, or a 256-bit binary word.

[0157] In some embodiments, the integrated circuit is programmed to determine one or more parameters of the particles from the data signal generated for the optical detection system. In some embodiments, the integrated circuit is programmed to calculate a spectral separation matrix for the fluorescence of the particles. In some embodiments, the integrated circuit is programmed to calculate the spectral separation matrix using a weighted least squares algorithm. In some examples, the weighted least squares algorithm is calculated according to the following formula:

[0158]

number

[0159]

number

[0160] In some embodiments, each W ii is calculated according to the following formula:

[0161]

number

[0162] where σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the constant noise at detector i. In one particular embodiment, the spectral separation matrix is ​​(X T WX) -1 X T In some examples, the integrated circuit calculates the spectral separation matrix (X) for each cell detected by the light detection system. T WX). In some embodiments, the integrated circuit is programmed to calculate a solution to the spectral separation matrix by matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition) or singular value decomposition (SVD) using one or more of an iterative Newton-Raphson calculation, a Sherman-Morrison iterative inverse updater. In certain embodiments, integrated circuits programmed to calculate a solution to the spectral separation matrix include those described in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, the disclosure of which is incorporated herein by reference.

[0163] In some examples, the integrated circuit is programmed to calculate an adjusted spectral separation matrix using the calculated saturation signal index, such as when one or more saturated data signals are excluded. In other examples, the integrated circuit is programmed to calculate an adjusted spectral separation matrix in which data signals from saturated detector channels are excluded. In particular examples, the integrated circuit is programmed to calculate a spectral separation matrix for particle fluorescence, calculate an adjusted spectral separation matrix for particle fluorescence that excludes one or more saturated data signals, and compare the calculated spectral separation matrix to the calculated adjusted spectral separation matrix. In certain embodiments, the integrated circuit is programmed to calculate a spectral separation matrix, where one or more rows of the matrix are removed to exclude data signals from saturated input detector channels. In other embodiments, the integrated circuit is programmed to identify saturated detector channels and adjust the spectral separation matrix to compensate for the saturated signals. In one example, the spectral separation matrix solution is adjusted to use an estimate of the true value of the saturated signal. In these embodiments, the estimated true value of the saturated signal is first determined and input into the spectral separation matrix to generate the adjusted spectral separation matrix solution.

[0164] The integrated circuit is programmed to classify the particles based on the one or more determined parameters of the particles. In some embodiments, the integrated circuit is programmed to classify the particles by assigning the particles to particle population clusters. In other embodiments, the integrated circuit is programmed to classify the particles by plotting the one or more parameters of the particles on a scatter plot.

[0165] In some embodiments, the integrated circuit is programmed to implement a bitmap gating strategy for classifying particles. In some examples, the bitmap gating strategy implemented by the integrated circuit includes identifying and removing saturated data signals. In other examples, the bitmap gating strategy implemented by the integrated circuit includes instructions for identifying saturated data signals and estimating true values ​​of saturated data signals. In some embodiments, the integrated circuit is programmed to generate a two-dimensional bitmap having regions of interest (ROIs) and determine whether particles should be assigned to the ROIs of the bitmap. In other embodiments, the integrated circuit is programmed to determine whether one or more bits of the ROIs of the bitmap include saturated data signals. In some examples, the integrated circuit is programmed to apply a saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, compare the generated saturated signal bitmap with the particle assignment ROI, and determine that one or more bits of the particle assignment ROI are saturated. In a particular example, the integrated circuit is programmed to use Boolean logic to compare the saturated signal bitmap with the particle assignment ROI. For example, the integrated circuit may be programmed to AND the saturation signal bitmap with the particle allocation ROI to determine whether one or more bits of the particle allocation ROI are saturated.

[0166] Computer-readable storage medium for adjusting a particle classification index in response to a saturation data signal - Patents.com Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for implementing the subject methods. The computer-readable storage medium may be employed on one or more computers for fully or partially automating systems 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," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), whether such devices are internal or external to the computer. Files containing information may be "stored" on a computer-readable medium, where "storing" means recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be executed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java (Oracle, Redwood Shores, CA), Visual Basic (Microsoft Corp., Redmond, WA), and C++ (AT&T Corp., Bedminster, NJ), as well as any of many other languages.

[0167] In some embodiments, a subject computer-readable storage medium includes a computer program stored thereon, the computer program including instructions that, when loaded into a computer, have an algorithm for detecting light from particles in a flow stream, an algorithm for identifying one or more saturation data signals, an algorithm for generating a saturation signal index corresponding to the identified saturation data signals, and an algorithm for applying the saturation signal index to a particle classification index to generate an adjusted particle classification index.

[0168] The computer-readable storage medium may include instructions for capturing one or more images of the flowstream, including two or more images, such as three or more images, such as four or more images, such as five or more images, such as ten or more images, such as fifteen or more images, and twenty-five or more images of the flowstream. In certain embodiments, the computer-readable storage medium includes instructions for optically adjusting the captured images, such as to increase the optical resolution of the images. In certain embodiments, the computer-readable storage medium may include instructions for increasing the resolution of the captured images by 5% or more, such as 10% or more, such as 25% or more, such as 50% or more, and may include increasing the resolution of the captured images by 75% or more.

[0169] In embodiments, a subject computer-readable storage medium includes an algorithm for classifying particles in a sample based on one or more of the determined parameters of the particles. In certain embodiments, the computer-readable storage medium includes an algorithm for classifying particles in a sample that includes using a bitmap gating strategy, where classifying the particles includes identifying, and in some instances removing, saturated data signals from the particle classification parameters. In other embodiments, the computer-readable storage medium includes an algorithm for classifying particles in a sample that includes identifying saturated data signals and estimating true values ​​of the saturated data signals.

[0170] The computer-readable storage medium also includes an algorithm for generating a two-dimensional bitmap having a region of interest (ROI) and an algorithm for determining whether particles should be assigned to the ROI of the bitmap. In some embodiments, the computer-readable storage medium of interest includes an algorithm for determining whether one or more bits of the ROI of the bitmap include saturated data signals. In other embodiments, the computer-readable storage medium of interest includes an algorithm for applying the saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap, an algorithm for comparing the generated saturated signal bitmap with the particle assignment ROI, and an algorithm for determining that one or more bits of the particle assignment ROI are saturated.

[0171] The non-transitory computer-readable storage medium may also include algorithms for calculating one or more parameters of the particles. In these embodiments, the computer-readable storage medium includes an algorithm for calculating a spectral separation matrix for the fluorescence of the particles, an algorithm for calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more saturation data signals, and an algorithm for comparing the calculated spectral separation matrix to the calculated adjusted spectral separation matrix.

[0172] The 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 stored instructions to perform the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in a well-known manner to facilitate the processor in coordinating and executing the functions of various computer programs, which may be written in a variety of 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.

[0173] kit Aspects of the present disclosure further include kits, which include one or more of the integrated circuits described herein. In some embodiments, the kits may further include programming for the subject systems, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, 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 within the subject kits in a variety of forms, one or more of which may be present within the kit. One form in which these instructions may be present is as printed information on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, in kit packaging, in a package insert, etc. Another form in which these instructions may be present is as a computer-readable medium having the information recorded thereon, such as a diskette, compact disc (CD), portable flash drive, etc. Another form in which these instructions may be present is as a website address that can be used via the Internet to access the information at a remote site.

[0174] Utilities The subject systems, methods, and computer systems find use in a variety of applications where it is desirable to analyze and sort particle components in a sample in a fluid medium, such as a biological sample. In some embodiments, the systems and methods described herein find use in flow cytometric characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods find use in emitted light spectroscopy. Additionally, the subject systems and methods find use in increasing the signal obtained from light collected from a sample (e.g., within a flow stream). In particular examples, the present disclosure finds use in improving measurements of light collected from a sample illuminated within a flow stream in a flow cytometer. 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.

[0175] Embodiments of the present disclosure also find use in applications where cells prepared from a biological sample may be desirable 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 fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used in therapy. The disclosed methods and devices enable the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems.

[0176] Notwithstanding the scope of the appended claims, the present disclosure is also defined by the following notes. 1. Detecting light from a particle-containing sample in a flow stream; generating a plurality of data signals from the detected light; identifying one or more saturated data signals; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to the particle classification index to generate an adjusted particle classification index; A method comprising: 2. The method of claim 1, wherein the saturated signal index comprises a binary word that identifies one or more detector channels that are saturated. 3. The method of claim 2, wherein the saturated signal index comprises a 32-bit binary word. 4. The method of claim 2, wherein the saturated signal index comprises a 128-bit binary word. 5. The method of any one of claims 1 to 4, further comprising determining one or more parameters of particles in the flow stream from the generated data signal.

[0177] 6. The method of claim 5, wherein determining one or more parameters of the particles includes calculating a spectral separation matrix for the fluorescence of the particles. 7. The method of claim 6, further comprising adjusting a spectral separation matrix for particle fluorescence based on a saturation signal index. 8. The method of claim 7, wherein adjusting the spectral separation matrix includes excluding one or more of the saturated data signals. 9. Calculating one or more parameters of a particle is calculating a spectral separation matrix for the fluorescence of the particles; calculating an adjusted spectral unmixing matrix for the fluorescence of the particles that excludes one or more saturation data signals; Comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix; 9. The method of claim 8, comprising: 10. The method of any one of claims 5 to 9, further comprising classifying the particles based on one or more determined parameters of the particles.

[0178] 11. Classifying particles is... Ch. generating a two-dimensional bitmap containing a region of interest (ROI); Assigning particles to ROIs in a two-dimensional bitmap 11. The method of claim 10, comprising: 12. The method of claim 11, further comprising determining whether one or more bits of the particle allocation ROI contain saturated data signals. 13. Determining whether one or more bits of the particle allocation ROI contain saturated data signals includes: applying the saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap; comparing the generated saturated signal bitmap with the particle assignment ROI; identifying that one or more bits of the particle allocation ROI are saturated; and 13. The method of claim 12, comprising: 14. The method of claim 13, wherein the saturated signal bitmap is compared to the particle assignment ROI using Boolean logic. 15. The method of claim 14, wherein the saturated signal bitmap is ANDed with the particle assignment ROI.

[0179] 16. The method of any one of claims 1 to 15, further generating a particle sorting decision based on the adjusted particle classification index. 17. The method of any one of claims 1 to 16, wherein detecting light from a sample in the flow stream comprises detecting light absorption, light scattering, fluorescence, or a combination thereof. 18. The method of any one of appendices 1 to 17, wherein the particle parameters are determined from light scattered from the particle. 19. The method of claim 18, wherein the scattered light includes forward scattered light. 20. The method of claim 18, wherein the scattered light includes side scattered light.

[0180] 21. The method of claim 18, wherein the particle parameters are calculated from fluorescence from the particles. 22. The method of claim 21, wherein the particle parameters are calculated from frequency-encoded fluorescence data from the particles. 23. The method of any one of claims 1 to 22, further comprising sorting the particles. 24. The method of any one of appendices 1 to 23, wherein the particle parameters are calculated by an integrated circuit device. 25. The method of claim 24, wherein the integrated circuit device is a field programmable gate array (FPGA).

[0181] 26. The method of claim 24, wherein the integrated circuit device is an application specific integrated circuit (ASIC). 27. The method of claim 24, wherein the integrated circuit device is a complex programmable logic device (CPLD). 28. The method of any one of claims 1 to 27, further comprising illuminating the flow stream with a light source. 29. The method of claim 28, wherein the flow stream is illuminated with a light source having a wavelength between 200 nm and 800 nm. 30. The method of claim 28 or 29, further comprising illuminating the flow stream with a first beam of frequency-shifted light and a second beam of frequency-shifted light.

[0182] 31. The method of claim 30, wherein the first frequency-shifted optical beam comprises a local oscillator (LO) beam and the second frequency-shifted optical beam comprises a high-frequency comb beam. 32. Applying a high frequency drive signal to an acousto-optic device; 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; 32. The method of claim 30 or 31, further comprising: 33. The method of claim 32, wherein the laser is a continuous wave laser.

[0183] 34. A light source configured to illuminate a particle-containing sample in a flow stream; a light detection system comprising a photodetector for detecting light from particles in the sample and generating a plurality of data signals from the detected light; a processor having a memory operatively coupled thereto; Equipped with The memory includes instructions stored thereon that, when executed by the processor, cause the processor to: identifying one or more saturated data signals; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to the particle classification index to generate an adjusted particle classification index; A system that is programmed to: 35. The system of claim 34, wherein the saturated signal index includes a binary word that identifies one or more detector channels that are saturated. 36. The system of claim 35, wherein the saturated signal index comprises a 32-bit binary word. 37. The system of claim 35, wherein the saturated signal index comprises a 128-bit binary word. 38. The system of any one of clauses 34-37, wherein the processor comprises a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to determine one or more parameters of particles in the flow stream from the generated data signal.

[0184] 39. The system of claim 38, wherein the processor comprises a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to calculate a spectral separation matrix for the fluorescence of the particles. 40. The system of claim 39, wherein the processor comprises a memory operably coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to adjust a spectral separation matrix of the fluorescence of the particles based on the saturation signal index. 41. The system of claim 40, wherein adjusting the spectral separation matrix includes filtering out one or more of the saturated data signals. 42. 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: calculating a spectral separation matrix for the fluorescence of the particles; calculating an adjusted spectral unmixing matrix for the fluorescence of the particles that excludes one or more saturation data signals; Comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix; 42. The system of claim 41, 43. The system of any one of appendices 38-42, wherein the processor comprises a memory operably coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to classify particles based on one or more determined parameters of the particles.

[0185] 44. A processor comprising a memory operatively coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: generating a two-dimensional bitmap containing a region of interest (ROI); Assigning particles to ROIs in a two-dimensional bitmap 44. The system of claim 43, 45. The system of claim 44, wherein the processor comprises a memory operably coupled to the processor, the memory including instructions stored thereon that, when executed by the processor, cause the processor to determine whether one or more bits of the particle assignment ROI include a saturated data signal. 46. ​​A processor comprising a memory operably coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to: applying the saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap; comparing the generated saturated signal bitmap with the particle assignment ROI; identifying that one or more bits of the particle allocation ROI are saturated; and 46. ​​The system of claim 45, 47. The system of any one of appendices 34 to 46, wherein the processor comprises a memory operably coupled to the processor, the memory including instructions stored thereon, the instructions, when executed by the processor, causing the processor to generate a particle sorting decision based on the adjusted particle classification index. 48. The system of any one of notes 34 to 47, wherein the optical detection system is configured to detect light absorption, light scattering, fluorescence, or a combination thereof.

[0186] 49. The system of any one of notes 34 to 48, 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. 50. The system of claim 49, wherein the optical beam generator includes an acousto-optic deflector. 51. The system of claim 49 or 50, wherein the optical beam generator includes a direct digital synthesizer (DDS) RF comb generator. 52. The system of any one of notes 49 to 51, wherein the optical beam generator component is configured to generate a frequency-shifted local oscillator beam. 53. The system of any one of notes 49 to 52, wherein the optical beam generator component is configured to generate a plurality of frequency-shifted comb beams.

[0187] 54. The system of any one of notes 34 to 53, wherein the light source includes a laser. 55. The system of claim 54, wherein the laser is a continuous wave laser. 56. A system described in any one of appendices 34 to 55, which is a flow cytometer. 57. The system of any one of appendices 34 to 56, further comprising a cell sorter. 58. The system of claim 57, wherein the cell sorter includes a droplet deflector.

[0188] 59. Identifying one or more saturation data signals from light detected from particles in the flow stream; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to the particle classification index to generate an adjusted particle classification index; an integrated circuit device programmed to perform the steps of: 60. The integrated circuit device of claim 59, wherein the saturated signal index includes a binary word that identifies one or more detector channels that are saturated. 61. The integrated circuit device of claim 60, wherein the saturation signal index comprises a 32-bit binary word. 62. The integrated circuit device of claim 60, wherein the saturation signal index comprises a 128-bit binary word. 63. The integrated circuit device of any one of clauses 59-62, wherein the integrated circuit is programmed to determine one or more parameters of particles in the flow stream from the generated data signals.

[0189] 64. The integrated circuit device of claim 63, wherein the integrated circuit is programmed to calculate a spectral separation matrix for the fluorescence of the particles. 65. The integrated circuit device of claim 64, wherein the integrated circuit is programmed to adjust a spectral separation matrix for particle fluorescence based on a saturation signal index. 66. The integrated circuit device of claim 65, wherein adjusting the spectral separation matrix includes filtering out one or more of the saturated data signals. 67. Integrated circuits are calculating a spectral separation matrix for the fluorescence of the particles; calculating an adjusted spectral unmixing matrix for the fluorescence of the particles that excludes one or more saturation data signals; Comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix; 67. The integrated circuit device of claim 66, programmed to: 68. The integrated circuit device of any one of notes 63 to 67, wherein the integrated circuit is programmed to classify particles based on one or more determined parameters of the particles.

[0190] 69. Integrated circuits are generating a two-dimensional bitmap containing a region of interest (ROI); Assigning particles to ROIs in a two-dimensional bitmap 69. The integrated circuit device of claim 68, programmed to: 70. The integrated circuit device of claim 69, wherein the integrated circuit is programmed to determine whether one or more bits of the particle assignment ROI include a saturated data signal. 71. Integrated circuits are applying the saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap; comparing the generated saturated signal bitmap with the particle assignment ROI; identifying that one or more bits of the particle allocation ROI are saturated; and 71. The integrated circuit device of claim 70, programmed to: 72. The integrated circuit device of any one of appendixes 59-71, wherein the integrated circuit is programmed to generate a particle sorting decision based on the adjusted particle classification index. 73. The integrated circuit device of any one of appendices 59 to 72, wherein the reconfigurable integrated circuit comprises a field programmable gate array (FPGA).

[0191] 74. The integrated circuit device of any one of appendices 59 to 72, wherein the integrated circuit comprises an application specific integrated circuit (ASIC). 75. The integrated circuit device of any one of appendices 59 to 72, wherein the integrated circuit comprises a complex programmable logic device (CPLD).

[0192] 76. A non-transitory computer-readable storage medium storing instructions for determining a drop delay of a flow stream in a flow cytometer, comprising: The command is, an algorithm for identifying one or more saturation data signals from the detected light from particles in the flowstream; an algorithm for generating a saturated signal index that includes the identified saturated data signals; An algorithm for applying the saturation signal index to the particle classification index to generate an adjusted particle classification index. 1. A non-transitory computer-readable storage medium comprising: 77. The non-transitory computer-readable storage medium of Claim 76, wherein the saturated signal index comprises a binary word that identifies one or more detector channels that are saturated. 78. The non-transitory computer-readable storage medium of claim 77, wherein the saturated signal index comprises a 32-bit binary word. 79. The non-transitory computer-readable storage medium of claim 77, wherein the saturated signal index comprises a 128-bit binary word. 80. The non-transitory computer-readable storage medium of any one of clauses 76-79, comprising an algorithm for determining one or more parameters of particles in a flow stream from the generated data signals.

[0193] 81. The non-transitory computer-readable storage medium of claim 80, comprising an algorithm for calculating a spectral separation matrix for the fluorescence of particles. 82. The non-transitory computer-readable storage medium of claim 81, comprising an algorithm for adjusting a spectral separation matrix of particle fluorescence based on a saturation signal index. 83. The non-transitory computer-readable storage medium of claim 82, including an algorithm for adjusting a spectral separation matrix, including filtering out one or more of the saturated data signals. 84. An algorithm for calculating a spectral separation matrix for particle fluorescence; an algorithm for calculating an adjusted spectral unmixing matrix for the fluorescence of the particles that excludes one or more saturating data signals; An algorithm for comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix; 84. The non-transitory computer-readable storage medium of claim 83, comprising: 85. The non-transitory computer-readable storage medium of any one of clauses 80-84, comprising an algorithm for classifying particles based on one or more determined parameters of the particles.

[0194] 86. An algorithm for generating a two-dimensional bitmap containing a region of interest (ROI); Algorithm for assigning particles to ROIs in a 2D bitmap and 86. The non-transitory computer-readable storage medium of claim 85, comprising: 87. The non-transitory computer-readable storage medium of claim 86, comprising an algorithm for determining whether one or more bits of a particle assignment ROI comprise a saturated data signal. 88. An algorithm for applying the saturated signal index to a second two-dimensional bitmap to generate a saturated signal bitmap; an algorithm for comparing the generated saturated signal bitmap with the particle assignment ROI; An algorithm for identifying when one or more bits of a particle allocation ROI are saturated; 88. The non-transitory computer-readable storage medium of claim 87, comprising: 89. The non-transitory computer-readable storage medium of any one of clauses 76-88, comprising an algorithm for generating a particle sorting decision based on an adjusted particle classification index.

[0195] 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.

[0196] Accordingly, the foregoing merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various arrangements, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language set forth herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts the inventors contributed to furthering the art, and should not be construed as being limited to such specifically described examples and conditions. Furthermore, all statements herein describing principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, such equivalents are intended to include both currently known equivalents and future-developed equivalents, 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 recited in the claims.

[0197] Accordingly, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention 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 as being invoked for a limitation in a claim only when the precise phrase "means for" or the precise phrase "step for" appears at the beginning of such limitation in the claim; if such precise phrases are not used in the limitation in the claim, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

[0198] 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. 62 / 982,604, filed February 27, 2020, the disclosure of which is incorporated herein by reference.

Claims

1. detecting light from a particle-containing sample in the flow stream; generating a plurality of data signals from the detected light; Identifying one or more saturated data signals; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to a particle classification index to generate an adjusted particle classification index; A method comprising:

2. The method of claim 1 , wherein the saturated signal index comprises a binary word that identifies one or more detector channels that are saturated.

3. The method of claim 1 or 2, further comprising determining one or more parameters of particles in the flow stream from the generated data signal.

4. The method of claim 3 , wherein determining one or more parameters of the particles comprises calculating a spectral separation matrix for fluorescence of the particles.

5. The method of claim 4 , further comprising adjusting the spectral separation matrix for the fluorescence of the particles based on the saturation signal index.

6. The method of claim 5 , wherein adjusting the spectral separation matrix comprises filtering out one or more of the saturated data signals.

7. Calculating one or more parameters of the particles includes: calculating a spectral separation matrix for the fluorescence of the particles; calculating an adjusted spectral separation matrix for the fluorescence of the particles that excludes one or more of the saturation data signals; comparing the calculated spectral separation matrix with the calculated adjusted spectral separation matrix; The method of claim 6, comprising:

8. The method of any one of claims 3 to 7, further comprising classifying the particles based on the determined one or more parameters of the particles.

9. The method of any one of claims 1 to 8, further comprising generating a particle sorting decision based on the adjusted particle classification index.

10. The method of any one of claims 1 to 9, wherein detecting light from the sample in the flow stream comprises detecting light absorption, light scattering, fluorescence, or a combination thereof.

11. The method of any one of claims 1 to 10, further comprising sorting the particles.

12. The method according to any one of the preceding claims, wherein the parameters of the particles are calculated by an integrated circuit device.

13. 13. The method of any one of claims 1 to 12, further comprising illuminating the flow stream with a light source, preferably illuminating the flow stream with a first beam of frequency-shifted light and a second beam of frequency-shifted light.

14. 1. A system comprising: a light source configured to illuminate a sample including particles within the flow stream; a light detection system comprising a light detector for detecting light from particles in the sample and generating a plurality of data signals from the detected light; a processor having a memory operatively coupled thereto; Equipped with The memory includes instructions stored thereon that, when executed by the processor, cause the processor to: Identifying one or more saturated data signals; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to a particle classification index to generate an adjusted particle classification index; A system that allows the user to:

15. identifying one or more saturation data signals from the light detected from the particles in the flow stream; generating a saturated signal index that includes the identified saturated data signals; applying the saturation signal index to a particle classification index to generate an adjusted particle classification index; 4. An integrated circuit device programmed to:

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