Method for identifying saturated data signals in cell sorting and system therefor

Abstract

Aspects of the present disclosure include methods for adjusting a particle classification index in response to one or more saturated data signals based on detected light from particles in a fluid stream. Methods according to certain embodiments include detecting light from particles in a fluid stream, generating a plurality of data signals based on the detected light, identifying one or more saturated data signals, generating a saturation signal index corresponding to the identified saturated data signals, and applying the saturation signal index to a particle classification index to generate an adjusted particle classification index. In some embodiments, the methods include determining one or more parameters of a particle (e.g., for a particle sorting decision) by calculating an adjusted spectral unmixing matrix for fluorescence of the particle that excludes the one or more saturated 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

[0001] Cross-reference to related applications

[0002] Pursuant to 35 U.S.SC §119(e), this application claims priority to U.S. Provisional Patent Application Serial No. 62 / 982,604, filed February 27, 2020, the disclosure of which is incorporated herein by reference. Background Technology

[0003] Flow cytometry particle sorting systems, such as sorting flow cytometers, are used to sort particles in fluid samples based on at least one measured characteristic of the particles. In a flow cytometry particle sorting system, particles such as molecules, analyte-bound beads, or individual cells in a fluid suspension pass through a detection zone in the flow, where sensors detect the type of particles to be sorted contained in the flow. When the sensor detects particles of the type to be sorted, it triggers a sorting mechanism that selectively separates the particles of interest.

[0004] Particle sensing is typically performed by flowing fluid through a detection zone in which particles are exposed to illumination light from one or more lasers, and the light scattering and fluorescence properties of the particles are measured. Particles or components thereof can be labeled with fluorescent dyes to facilitate detection, and multiple different particles or components can be detected simultaneously by using fluorescent dyes with different spectral signatures to label different particles or components. Detection is performed using one or more photoelectric sensors to facilitate independent measurement of the fluorescence of each different fluorescent dye.

[0005] To sort particles from a sample, a drop charging mechanism charges droplets containing the particle types to be sorted at the break point in the liquid flow using electrical charges. The droplets pass through an electrostatic field and are deflected into one or more collection containers based on the polarity and quantity of the charge on the droplets. The electrostatic field does not deflect uncharged droplets. Summary of the Invention

[0006] This disclosure includes a method for adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. According to some embodiments, the method includes: detecting light from particles in a fluid stream; generating a plurality of data signals based on 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 a particle (e.g., for particle sorting decisions) by calculating a spectral unmixing matrix that excludes one or more saturation data signals for adjusting the fluorescence of the particle. Systems and integrated circuit devices (e.g., field-programmable gate arrays) for implementing the subject method are also provided. Non-transitory computer-readable storage media are also described.

[0007] In some embodiments, the saturation signal index is a binary word identifying one or more saturated detector channels. For example, the saturation signal index can 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 including a 256-bit binary word. In embodiments, one or more parameters of the particles in the flow are determined based on the generated data signal. In some instances, determining one or more parameters of the particles includes calculating a spectral unmixing matrix for the fluorescence of the particles. In some instances, the sample of interest includes multiple fluorophores, wherein 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 (e.g., 25 nm or more and including 50 nm or more). In some instances, the fluorescence spectra of one or more fluorophores in the sample overlap with the fluorescence spectra of two different fluorophores in the sample by, for example, 10 nm or more, for example, 25 nm or more, and including 50 nm or more. In other embodiments, the sample of interest includes multiple fluorophores with non-overlapping fluorescence spectra. In these embodiments, the fluorescence spectrum of each fluorophore is within 10 nm or closer (e.g. within 9 nm or closer, e.g. within 8 nm or closer, e.g. within 7 nm or closer, e.g. within 6 nm or closer, e.g. within 5 nm or closer, e.g. within 4 nm or closer, e.g. within 3 nm or closer, e.g. within 2 nm or closer, and including within 1 nm or closer) adjacent to at least one other fluorophore.

[0008] In some embodiments, the method includes: adjusting a spectral unmixing matrix for the fluorescence of particles based on a calculated saturation signal index. For example, the spectral unmixing matrix can be adjusted by excluding one or more of the saturated data signals. In some embodiments, to calculate one or more parameters of particles in a sample, the method includes: calculating a spectral unmixing matrix for the fluorescence of particles; calculating an adjusted spectral unmixing matrix for the fluorescence of particles excluding one or more of the saturated data signals; and comparing the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some instances, the adjusted spectral unmixing matrix is ​​used to determine the parameters of particles in the sample, wherein one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0009] In some embodiments, the method includes classifying particles in a sample based on one or more of determined parameters of the particles. In some embodiments of this disclosure, classifying particles in a sample includes using a bitmap gating strategy, wherein classifying particles includes identifying saturated data signals based on particle classification parameters and, in some instances, removing them. In other embodiments, classifying particles in a sample includes identifying saturated data signals and estimating the true value of the saturated data signals. In some instances, to classify particles in a sample, the method includes generating a two-dimensional bitmap with a region of interest (ROI); and determining whether particles should be assigned to the ROI of the bitmap. In other instances, the method for classifying particles in a sample includes determining whether one or more bits of the ROI of the bitmap include a saturated data signal. In some embodiments, determining whether one or more bits of the ROI of a designated particle in 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 ROI of the designated particle; and determining that one or more bits of the ROI of the designated particle are saturated. To compare a saturation signal bitmap with the ROI of a specified particle, Boolean logic can be used, such as ANDing the saturation signal bitmap with the ROI of the specified particle to determine whether one or more bits of the ROI of the specified particle are saturated.

[0010] This disclosure also includes a system having a light detection system for characterizing particles (e.g., cells in a biological sample) of a sample in a fluid stream. A system according to some embodiments includes: a light source configured to illuminate particles of a sample in a fluid stream; a light detection system having a photodetector that detects light from particles in the sample and generates a plurality of data signals based on the detected light; and a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: identify one or more saturated data signals; generate a saturated signal index including the identified saturated data signals; and apply the saturated signal index to a particle classification index to generate an adjusted particle classification index. In some embodiments, the saturated signal index used by the processor is a binary word identifying which detector channels have saturated the analog-to-digital converter. For example, the saturated signal index used by the subject system 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.

[0011] In embodiments, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to: determine one or more parameters of the particles in the flow based on the generated data signals. In some embodiments, the memory includes instructions for calculating a spectral unmixing matrix for the fluorescence of the particles. In some instances, the memory includes instructions for calculating an adjusted spectral unmixing matrix using calculated saturation signal indices, for example, excluding one or more saturated data signals from the adjusted spectral unmixing matrix. In other instances, the memory includes instructions for calculating an adjusted spectral unmixing matrix where data signals from saturated detector channels are excluded. In some instances, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to: calculate a spectral unmixing matrix for the fluorescence of the particles; calculate an adjusted spectral unmixing matrix for the fluorescence of the particles excluding one or more of the saturated data signals; and compare the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some embodiments, the memory includes instructions for calculating a spectral unmixing matrix in which one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0012] The memory may include instructions stored thereon that, when executed by a 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 instances, the bitmap gating strategy implemented by the subject system includes instructions for identifying and removing saturated data signals. In other instances, the bitmap gating strategy implemented by the subject system includes instructions for identifying saturated data signals and estimating the true value of the 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 with a region of interest (ROI); and determine whether a particle should be assigned to the ROI of the bitmap. In other embodiments, the memory includes instructions that, when executed by a processor, cause the processor to determine whether one or more bits of the ROI of the bitmap include a saturated data signal. In some instances, the memory includes instructions for determining whether one or more bits of the ROI of a specified particle in a bitmap include a saturation data signal, comprising: applying a saturation signal index to a second two-dimensional bitmap to generate a saturation signal bitmap; comparing the generated saturation signal bitmap with the ROI of the specified particle; and determining that one or more bits of the ROI of the specified particle are saturated. In some instances, the processor uses Boolean logic to perform operations to compare the saturation signal bitmap with the ROI of the specified particle. For example, the saturation signal bitmap may be ANDed with the ROI of the specified particle to determine whether one or more bits of the ROI of the specified particle are saturated.

[0013] In some embodiments, the system of interest may include one or more sorting decision modules configured to generate sorting decisions for particles based on particle classification. In some embodiments, the system may also include a particle sorter (e.g., with droplet deflectors) for sorting particles from the liquid stream based on the sorting decisions generated by the sorting decision modules.

[0014] An integrated circuit device is also provided, programmed to adjust a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. 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 based on detected light from particles in a fluid stream; 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 that outputs a saturation data signal. For example, the saturation signal index used by the subject system 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.

[0015] In embodiments, the integrated circuit device of interest is programmed to determine one or more parameters of particles in the fluid stream based on the generated data signal. In some embodiments, the integrated circuit is programmed to calculate a spectral unmixing matrix for the fluorescence of the particles. In some instances, the integrated circuit is programmed to calculate an adjusted spectral unmixing matrix using a calculated saturation signal index. For example, the integrated circuit may be programmed to calculate the spectral unmixing matrix and exclude one or more saturated data signals. In other instances, the integrated circuit is programmed to calculate an adjusted spectral unmixing matrix in which data signals from saturated detector channels are excluded. In some instances, the integrated circuit is programmed to calculate a spectral unmixing matrix for the fluorescence of the particles; calculate an adjusted spectral unmixing matrix for the fluorescence of the particles excluding one or more of the saturated data signals; and compare the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some embodiments, the integrated circuit is programmed to calculate a spectral unmixing matrix in which one or more rows of the matrix are removed to exclude data signals from saturated input detector channels.

[0016] The integrated circuit is programmed to classify particles based on one or more defined parameters. In some embodiments, the integrated circuit is programmed to implement a bitmap gating strategy for classifying particles. In some instances, the bitmap gating strategy implemented by the integrated circuit includes being programmed to identify and remove saturated data signals. In other instances, the bitmap gating strategy implemented by the integrated circuit includes being programmed to identify saturated data signals and estimate the true value of the saturated data signals. In some embodiments, the integrated circuit device is programmed to generate a two-dimensional bitmap with 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 assigned particle in the bitmap include a saturated data signal. In some instances, the integrated circuit is programmed to determine whether one or more bits of the ROI of the assigned particle in the bitmap include a saturated data signal by including: 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 ROI of the assigned particle; and determining that one or more bits of the ROI of the assigned particle are saturated. In some instances, integrated circuits use Boolean logic to compare a saturation signal bitmap with the ROI of a specified particle. For example, the saturation signal bitmap can be ANDed with the ROI of a specified particle to determine whether one or more bits of the ROI of the specified particle are saturated. In some instances, integrated circuit devices are programmed to sort sample particles based on an adjusted particle classification.

[0017] This disclosure also includes a non-transitory computer-readable storage medium for adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. According to some embodiments, the non-transitory computer-readable storage medium includes instructions stored thereon having: an algorithm for detecting light from particles in a fluid stream; an algorithm for generating multiple data signals based on the detected light; an algorithm for generating a saturation signal index corresponding to 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 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 unmixing matrix for the fluorescence of the particles; an algorithm for calculating an adjusted spectral unmixing matrix for the fluorescence of the particles excluding one or more of the saturation data signals; and an algorithm for comparing the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some embodiments, the non-transitory computer-readable storage medium may also include algorithms for generating a two-dimensional bitmap including a region of interest (ROI) and algorithms for assigning particles to the ROI of the two-dimensional bitmap. In some instances, the non-transitory computer-readable storage medium may further 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 ROI of a specified particle; and an algorithm for identifying the saturation of one or more bits of the ROI of the specified particle. In some instances, the non-transitory computer-readable storage medium includes an algorithm for generating particle sorting decisions based on an adjusted particle classification index. Attached Figure Description

[0018] The invention can be best understood based on the following detailed description when read in conjunction with the accompanying drawings. The drawings include the following figures:

[0019] Figure 1 A functional block diagram of an example sorting control system according to certain embodiments is depicted.

[0020] Figure 2A A schematic diagram of a particle sorter system according to certain embodiments is depicted.

[0021] Figure 2B A schematic diagram of a particle sorter system according to certain embodiments is depicted.

[0022] Figure 3 A functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization, according to certain embodiments, is depicted.

[0023] Figure 4 A flow cytometer according to certain embodiments is described.

[0024] Figure 5 A flowchart for generating an adjusted particle classification index is depicted according to certain embodiments.

[0025] Figure 6 A flowchart is depicted illustrating the use of multi-bit binary word saturation signal indexes to generate adjusted particle classifications according to certain embodiments.

[0026] Figure 7 A flowchart for classifying particles according to certain embodiments is depicted.

[0027] Figure 8 A block diagram of a computing system according to certain embodiments is depicted. Detailed Implementation

[0028] This disclosure includes a method for adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. The method according to some embodiments includes: detecting light from particles in a fluid stream; generating a plurality of data signals based on 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 a particle (e.g., for particle sorting decisions) by calculating a spectral unmixing matrix that excludes one or more saturation data signals for adjusting the fluorescence of the particle. Systems and integrated circuit devices (e.g., field-programmable gate arrays) for implementing the subject method are also provided. Non-transitory computer-readable storage media are also described.

[0029] Before describing the invention in more detail, it should be understood that the invention is not limited to the certain embodiments described, and therefore variations are of course possible. It should also be understood that the terminology used herein is for the purpose of describing certain embodiments only and is not intended to be limiting, as the scope of the invention will be limited only by the appended claims.

[0030] When a range of values ​​is provided, it should be understood that, unless the context clearly specifies otherwise, every intermediate value between the upper and lower limits of the range, accurate to one-tenth of the unit of the lower limit, and any other specified value or intermediate value within the range, is included in this invention. Smaller upper and lower limits may be independently included in these smaller ranges and are also included in this invention, subject to any specific exclusions within the range. If the range includes one or both limits, the invention also includes ranges that do not include one or both of these included limits.

[0031] Certain ranges are presented herein in numerical form, defined by the term “about”. The term “about” is used herein to provide textual support for the exact figures defined thereto, as well as figures that are close to or approximate to the figures defined by the term. In determining whether a figure is close to or approximates an explicitly listed figure, an unlisted figure that is close to or approximates may be a figure that is substantially equivalent to the explicitly listed figure in the context in which it is presented.

[0032] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. While any methods and materials similar to or equivalent to those described herein may also be used in the practice or testing of this invention, representative illustrative methods and materials are described hereafter.

[0033] All publications and patents referenced in this specification are incorporated herein by reference as if each individual publication or patent were expressly and individually indicated to be incorporated by reference, and are incorporated herein by reference to disclose and describe methods and / or materials in conjunction with the cited publications. References to any publication refer to its publication prior to the date of this application and should not be construed as an admission that the invention is not entitled to a prior art invention prior to that publication. Furthermore, the publication dates provided may differ from the actual publication dates, which may require separate confirmation.

[0034] It should be noted that, unless the context clearly specifies otherwise, the singular forms “a” and “an” as used herein and in the appended claims include plural references. It should also be noted that claims can be drafted to exclude any optional elements. Therefore, this description is intended to serve as a priori basis for using exclusive terms such as “only” or “just” in conjunction with the enumeration of the elements of the claim, or for using a negative limitation.

[0035] Those skilled in the art will understand upon reading this disclosure that each of the individual embodiments described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of the invention. Any of the enumerated methods may be performed in the order of the enumerated events or in any other logically possible order.

[0036] Although the apparatus and method have been described or will be described for grammatical fluency and functional description, it is to be clearly understood that, unless expressly provided under 35 U.SC §112, the claims need not be interpreted as limiting by any means of constructing an “apparatus” or “step,” but should conform to the full scope of the meaning and equivalent meaning provided by the claims under the judicial interpretation of equivalent claims, and where the claims are expressly stated under 35 U.SC §112, they should conform to the full statutory equivalent meaning under 35 U.SC §112.

[0037] In summary, this disclosure provides a method for adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a liquid stream. In embodiments further described in this disclosure, the method for: generating multiple data signals based on detected light from a sample in a liquid stream; generating a saturation signal index based on one or more identified saturation data signals; and generating an adjusted particle classification index using the saturation signal index. Next, systems and integrated circuit devices programmed to implement the subject method are described. A non-transitory computer-readable storage medium is also described.

[0038] A method for adjusting the particle classification index in response to saturation data signals.

[0039] This disclosure includes a method for adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a liquid stream. The phrase "saturation signal" is used herein in its conventional sense to refer to a signal exceeding a maximum range that can be measured by one or more components of a light detection system (described in more detail below). In some embodiments, the saturation signal is a data signal output by a photodetector exposed to an amount of light exceeding the maximum amount that can be detected by a photodetector. In other embodiments, the saturation signal is a data signal exceeding a maximum range for an analog-to-digital converter used to convert an analog signal from a photodetector into a digital signal. As described in more detail herein, the subject method according to certain embodiments provides identification of one or more saturation data signals based on detected light from particles in a liquid stream and removal of the saturation data signals when classifying one or more particles in the liquid stream. In other embodiments, the subject method provides: identifying one or more saturation data signals; and estimating the true value of the saturated data signals. Classifying particles according to the subject method results in higher accuracy, such as higher accuracy when assigning particles to particle swarms. When used as part of a particle sorting decision, the subject method can improve the yield of particle sorting and the purity of the sorted particles.

[0040] In implementing the subject method, a sample containing particles is illuminated using a light source, and the light from the sample is detected using 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 subset, cell, or component of a whole organism, plant, fungus, or animal tissue that can be found in certain instances in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, sheep cord blood, urine, vaginal fluid, and semen. Thus, "biological sample" refers to both a native organism or a subset of its tissues, and to homogenates, lysates, or extracts prepared based on a subset of an organism or its tissues, including but not limited to, for example, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular and genitourinary tract, tears, saliva, milk, blood cells, tumors, and organs. Biological samples can be any type of organic tissue, including both healthy and diseased tissues (e.g., cancerous, malignant, necrotic, etc.). In some embodiments, the biological sample is a liquid sample, such as blood or its derivatives, such as plasma, tears, urine, semen, etc. In some instances, the sample is a blood sample, including whole blood, such as blood obtained by venipuncture or finger-prick sampling (wherein the blood may or may not be mixed with any reagents such as preservatives, anticoagulants, etc. before testing).

[0041] In some embodiments, the sample source is "mammal" (or "mammalian"), a term used broadly to describe organisms in the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some instances, the subject is human. The method can be applied to samples obtained from human subjects of both sexes and at any developmental stage (i.e., newborns, infants, adolescents, young adults, and adults), wherein in some embodiments, the human subject is an adolescent, young adult, or adult. While the invention can be applied to samples from human subjects, it should be understood that the method can also be performed on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0042] In embodiments, a sample is illuminated (e.g., in a flow cytometer) using light from a light source. In some embodiments, the light source is, for example, a broadband light source that emits light with a wide wavelength range, such as spanning 50 nm or more, for example 100 nm or more, for example 150 nm or more, for example 200 nm or more, for example 250 nm or more, for example 300 nm or more, for example 350 nm or more, for example 400 nm or more, and including spanning 500 nm or more. For example, a suitable broadband light source emits light with wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light with wavelengths from 400 nm to 1000 nm. When the method involves illumination using a broadband light source, the broadband light source protocols of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stable fiber-coupled broadband light sources, broadband LEDs, superluminescent diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, other broadband light sources, or any combination thereof.

[0043] In other embodiments, the method includes illuminating the light source with a narrow band light source that emits a specific wavelength or a narrow wavelength range, such as a light source emitting light in a narrow wavelength range, for example, in the range of 50 nm or less, such as 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less, and including light sources that emit light of a specific wavelength (i.e., monochromatic light). When the method includes illuminating the light source with a narrow band light source, the narrow band light source protocol of interest may include, but is not limited to, narrow-wavelength LEDs, laser diodes, or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0044] In some embodiments, the method includes irradiating the sample with one or more lasers. As discussed above, the type and number of lasers will vary depending on the sample and the light to be acquired, and may be gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon fluoride (ArF) excimer lasers, krypton fluoride (KrF) excimer lasers, xenon chloride (XeCl) excimer lasers, or xenon fluoride (XeF) excimer lasers, or combinations thereof. In other instances, the method includes irradiating the liquid stream with dye lasers (e.g., stilbene, coumarin, or rhodamine lasers). In still other instances, the method includes irradiating the liquid stream with metal vapor lasers (e.g., 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, or combinations thereof). In other instances, the method involves irradiating the liquid stream with a solid-state laser (e.g., a ruby ​​laser, an Nd:YAG laser, an NdCrYAG laser, an Er:YAG laser, an Nd:YLF laser, an Nd:YVO4 laser, an Nd:YCa4O(BO3)3 laser, an Nd:YCOB laser, a titanite laser, a thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, or combinations thereof).

[0045] The sample can be illuminated using one or more of the above-described light sources, such as two or more light sources, three or more light sources, four or more light sources, five or more light sources, or even ten or more light sources. The light sources can include any combination of various types of light sources. For example, in some embodiments, the method includes illuminating the sample in the liquid stream using 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.

[0046] The sample can be illuminated using wavelengths ranging from 200 nm to 1500 nm (e.g., from 250 nm to 1250 nm, from 300 nm to 1000 nm, from 350 nm to 900 nm, and including from 400 nm to 800 nm). For example, when the light source is a broadband light source, wavelengths from 200 nm to 900 nm can be used to illuminate the sample. In other instances, when the light source comprises multiple narrowband light sources, specific wavelengths within the range of 200 nm to 900 nm can be used to illuminate the sample. For example, the light source can be multiple narrowband LEDs (1 nm–25 nm) that each independently emit light having a wavelength range between 200 nm and 900 nm. In other embodiments, the narrowband light source comprises one or more lasers (e.g., a laser array) and illuminates the sample using specific wavelengths ranging from 200 nm to 700 nm, such as laser arrays having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers as described above.

[0047] When using more than one light source, the sample can be illuminated simultaneously, sequentially, or in combination using the light sources. For example, each of the light sources can be used to illuminate the sample simultaneously. In other embodiments, each of the light sources is used to illuminate the liquid flow sequentially. When illuminating the sample sequentially using more than one light source, the duration of illumination for each light source can be independently 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the method may include illuminating the sample with a light source (e.g., a laser) for a duration ranging from 0.001 microseconds to 100 microseconds, such as from 0.01 microseconds to 75 microseconds, from 0.1 microseconds to 50 microseconds, from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In embodiments, when illuminating the sample sequentially using two or more light sources, the duration of illumination for each light source can be the same or different.

[0048] The time interval between illuminations from each light source can also vary as needed, with independent delays of 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 15 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the range of time intervals between illuminations from each light source can be from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, such as from 0.1 microseconds to 35 microseconds, such as from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In some embodiments, the time interval between illuminations from each laser is 10 microseconds. In embodiments, when a sample is sequentially illuminated by more than two (i.e., three or more) light sources, the delay between illuminations from each light source can be the same or different.

[0049] The sample can be illuminated continuously or at discrete intervals. In some instances, the method involves illuminating the sample continuously using a light source. In other instances, the sample is illuminated at discrete intervals using a light source, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds or at some other interval.

[0050] Depending on the light source, the sample can be illuminated at varying distances, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 2.5 mm or more, 5 mm or more, 10 mm or more, 15 mm or more, 25 mm or more, and up to 50 mm or more. Furthermore, the illumination angle can vary from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and from 30° to 60°, for example, at a 90° angle.

[0051] In some embodiments, the method includes irradiating a sample with two or more frequency-shifted beams. As described above, a beam generator assembly having a laser and an acousto-optic device for frequency-shifting the laser can be used. In these embodiments, the method includes irradiating the acousto-optic device with a laser. Depending on the desired wavelength of light generated in the output laser beam (e.g., for use when irradiating a sample in a liquid stream), the laser can have a specific wavelength varying from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm, and including from 400 nm to 800 nm. The acousto-optic device can be irradiated with one or more lasers, such as two or more lasers, such as three or more lasers, such as four or more lasers, such as five or more lasers, and including ten or more lasers. The lasers can include any combination of various types of lasers. For example, in some embodiments, the method includes irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0052] When using more than one laser, the lasers can be used to simultaneously, sequentially, or in combination to irradiate the acousto-optic device. For example, each laser can be used to simultaneously irradiate the acousto-optic device. In other embodiments, each laser is used to sequentially irradiate the acousto-optic device. When using more than one laser to sequentially irradiate the acousto-optic device, the duration for which each laser irradiates the acousto-optic device can independently be 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the method may include irradiating the acousto-optic device with lasers for durations ranging from 0.001 microseconds to 100 microseconds, such as from 0.01 microseconds to 75 microseconds, from 0.1 microseconds to 50 microseconds, from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In an embodiment, when two or more lasers are used to sequentially irradiate the acousto-optic device, the duration for which each laser irradiates the acousto-optic device may be the same or different.

[0053] The time interval between irradiations by each laser can also vary as needed, with independent delays of 0.001 microseconds or longer, such as 0.01 microseconds or longer, 0.1 microseconds or longer, 1 microsecond or longer, 5 microseconds or longer, 10 microseconds or longer, 15 microseconds or longer, 30 microseconds or longer, and including 60 microseconds or longer. For example, the range of time intervals between irradiations by each light source can be from 0.001 microseconds to 60 microseconds, such as from 0.01 microseconds to 50 microseconds, from 0.1 microseconds to 35 microseconds, from 1 microsecond to 25 microseconds, and including from 5 microseconds to 10 microseconds. In some embodiments, the time interval between irradiations by each laser is 10 microseconds. In embodiments, when the acousto-optic device is sequentially irradiated by more than two (i.e., three or more) lasers, the delay between irradiations by each laser can be the same or different.

[0054] The acousto-optic device can be illuminated continuously or at discrete intervals. In some instances, the method includes illuminating the acousto-optic device continuously using a laser. In other instances, the acousto-optic device is illuminated using a laser at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds or at some other interval.

[0055] Depending on the laser, the acousto-optic device can be irradiated at varying distances, such as 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 2.5 mm or more, 5 mm or more, 10 mm or more, 15 mm or more, 25 mm or more, and including 50 mm or more. Furthermore, the irradiation angle can vary within a range from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and including from 30° to 60°, for example, at a 90° angle.

[0056] In an embodiment, the method includes applying radio frequency (RF) drive signals to an acousto-optic device to generate an angularly deflected laser beam. Two or more RF drive signals may be applied to the acousto-optic device to generate an output laser beam with a desired number of angularly deflected laser beams, such as three or more RF drive signals, four or more RF drive signals, five or more RF drive signals, six or more RF drive signals, seven or more RF drive signals, eight or more RF drive signals, nine or more RF drive signals, ten or more RF drive signals, fifteen or more RF drive signals, 25 or more RF drive signals, 50 or more RF drive signals, and including 100 or more RF drive signals.

[0057] The angularly deflected laser beams generated by the radio frequency (RF) drive signal each have an intensity based on the amplitude of the applied RF drive signal. In some embodiments, the method includes applying an RF drive signal having an amplitude sufficient to produce an angularly deflected laser beam with a desired intensity. In some instances, each applied RF drive signal independently has an 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, and including from about 5V to about 25V. In some embodiments, each applied radio frequency drive signal has a frequency from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and including from about 5 MHz to about 50 MHz.

[0058] In these embodiments, the angularly deflected laser beams in the output laser beam are spatially separated. Depending on the applied RF drive signal and the desired illumination distribution of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and including 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with, for example, adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between laser beams deflected at adjacent angles (e.g., overlap of beam points) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and including 100 μm or more.

[0059] In some instances, the flow of fluid is illuminated with multiple frequency-shifted beams and the cells in the flow are imaged using fluorescence imaging with radio frequency labeled emission (FIRE) to generate frequency-encoded images, such as those described in Diebold et al., Nature Photonics, Vol. 7(10), 806-810 (2013); and those described in U.S. Patent Nos. 9,423,353, 9,784,661, and 10,006,852 and U.S. Patent Publications 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0060] As discussed above, in embodiments, as described in more detail below, light from an illuminated sample is transmitted to a photodetector system and measured by multiple photodetectors. In some embodiments, the method includes measuring light collected over a wavelength range (e.g., 200 nm–1000 nm). For example, the method may include collecting the spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In 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, and any combination thereof: 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. In some embodiments, the method includes measuring the wavelength of light corresponding to the fluorescence peak wavelength of a fluorophore. In some embodiments, the method includes measuring the light collected across the entire fluorescence spectrum of each luciferase in the sample.

[0061] The collected light can be measured continuously or at discrete intervals. In some instances, the method involves measuring the light continuously. In other instances, the light is measured at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds or some other interval.

[0062] Measurements of the acquired light during the main method may be performed once or multiple times, such as two or more times, three or more times, five or more times, and including ten or more times. In some embodiments, light propagation is measured two or more times, and in some instances, the data are averaged.

[0063] Light from a sample can be measured at one or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and including measuring the collected light at 400 or more different wavelengths.

[0064] The method disclosed herein includes adjusting a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. In embodiments, multiple data signals are generated based on detected light from particles in a sample. The generated data signals can be analog or digital. When the data signals are analog, in some instances, the method includes, for example, converting the analog data signals into digital data signals using an analog-to-digital converter. In implementing the subject method, one or more saturation data signals are identified. In some instances, the property of a saturation data signal is that the signal exceeds the range that the detector can measure. In other instances, the property of a saturation data signal is that the signal exceeds the range of the analog-to-digital converter. Depending on the number of photodetectors used to detect light from particles in the fluid stream, the number of detector channels identified as outputting saturation data signals can vary, for example, one or more, two or more, four or more, eight or more, 16 or more, 32 or more, 64 or more, and including 128 or more.

[0065] In embodiments, a saturation signal index is generated based on the identified saturated 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 instances, the saturation signal index is a binary word consisting of one or more bits, such as two or more bits, four or more bits, eight or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. For example, the saturation signal index can 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 some instances, the saturation signal index is a combination of two or more binary words, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more binary words. When the saturation signal index is a combination of binary words, each binary word can be independently composed of one or more bits, such as two or more bits, four or more bits, eight or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. In some embodiments, each binary word in the saturation signal index can be independently 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, when the optical detection system includes 80 detector channels, the saturation signal index can be composed of a 128-bit binary word or three different 32-bit binary words.

[0066] In some embodiments, the method includes determining one or more parameters of particles in a liquid stream based on a generated data signal. In some embodiments, determining one or more parameters of the particles includes: resolving light from a plurality of luciferates in a sample, for example, resolving detected fluorescence from luciferates with overlapping fluorescence. In some embodiments, determining the parameters of the particles in the liquid stream includes calculating a spectral unmixing matrix for fluorescence from the sample.

[0067] In some embodiments, resolving the light from fluorophores in a sample spectrally includes calculating the spectral unmixing matrix using a weighted least squares algorithm. In some instances, the weighted least squares algorithm is calculated according to the following:

[0068]

[0069] Where y is the measured detector value from multiple photodetectors of the photodetector system for each cell; a is the estimated luciferase abundance; X is spillover; and W is... In some embodiments, each W ii according to Perform the calculation.

[0070] in, The variance at detector i; y i The signal is at detector i; and λ i This is constant noise at detector i. In some embodiments, the spectral unmixing matrix is ​​based on (X... T WX) -1 X T W is used for calculation. In some instances, the method includes: calculating the spectral unmixing matrix for each cell pair (X) detected by the light detection system. T The method involves performing an inversion operation on the WX (Warranty Matrix). In some embodiments, the method includes: computing a solution to the spectral unmixing matrix using an iterative Newton-Raphson computation, a Sherman-Morrison iterative inversion updater, or by one or more of matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition) or singular value decomposition (SVD). In some embodiments, a solution to the spectral unmixing matrix is ​​computed according to the description in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, which is incorporated herein by reference.

[0071] In some embodiments, the method includes adjusting a spectral unmixing matrix for the fluorescence of particles in a sample based on a calculated saturation signal index. For example, the spectral unmixing matrix can be adjusted by excluding one or more saturated data signals. In some embodiments, to calculate one or more parameters of particles in a sample, the method includes: calculating a spectral unmixing matrix for the fluorescence of particles; calculating an adjusted spectral unmixing matrix for the fluorescence of particles excluding one or more saturated data signals; and comparing the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some instances, the spectral unmixing matrix is ​​adjusted by removing one or more rows of the matrix based on data signals from saturated input detector channels. In other instances, a saturation signal index is used to identify saturated detector channels and the spectral unmixing matrix is ​​adjusted to compensate for the saturation signal. In one example, the spectral unmixing 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 unmixing matrix to generate the adjusted spectral unmixing matrix solution.

[0072] In some embodiments, the method includes classifying particles based on one or more defined parameters of particles in a sample. In some embodiments, classifying particles includes assigning particles to particle clusters. In other embodiments, classifying particles includes plotting one or more parameters of the particles on a scatter plot.

[0073] In some embodiments, classifying particles in a sample includes using a bitmap gating strategy, wherein classifying particles includes: identifying them based on particle classification parameters; and removing identified saturated data signals. In other embodiments, classifying particles in a sample includes: identifying saturated data signals; and estimating the true value of the saturated data signals. In some instances, to classify particles in a sample, a method includes: generating a two-dimensional bitmap with a region of interest (ROI); and determining whether a particle should be assigned to the ROI of the bitmap. In other instances, a method for classifying particles in a sample includes: determining whether one or more bits of the ROI of the bitmap include a saturated data signal. In some embodiments, determining whether one or more bits of the ROI of a designated particle in 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 ROI of the designated particle; and determining that one or more bits of the ROI of the designated particle are saturated.

[0074] When one or more bits of a ROI of a bitmap are saturated, a method according to some embodiments includes adjusting the particle classification index using a saturation signal index. In some instances, the coordinates for the ROI are indices scaled into the addressable range of the bitmap. In some instances, one or more offsets are applied. In other instances, one or more scaling factors are applied. In some instances, the method includes applying one or more offsets after applying one or more scaling factors. In some embodiments, the method includes converting the saturation signal index to a scalar value before applying the saturation signal index to determine whether one or more bits of the ROI of a specified particle in the bitmap include a saturated data signal. In some instances, the X and Y inputs to the ROI bitmap are selected from a data frame based on the data signal. In these instances, an integer is subtracted after multiplying by a scaling factor (e.g., via an affine transformation). In some embodiments, a consecutive subset of bits is used to select the indices of the inputs to the bitmap (e.g., bits 31-25 are selected from a 64-bit product). In some embodiments, a saturation signal index is applied to the ROI of the bitmap. To compare the saturation signal bitmap with the ROI of a specified particle, Boolean logic can be used, for example, when ANDing the saturation signal bitmap with the ROI of the specified particle to determine whether one or more bits of the ROI of the specified particle are saturated. In these embodiments, a Boolean type of true or false indicating the presence of a saturation event in any detector channel can be obtained. These results can be combined with the ROI determination of the specified particle to include unsaturated cases for classification decisions.

[0075] Figure 5 A flowchart illustrating the generation of an adjusted particle classification index according to certain embodiments is depicted. At step 501, light from a sample in the liquid stream is detected. At step 502, the light detection system generates multiple data signals based on the detected light, and at step 503, one or more saturated data signals may be detected. The saturated data signals may be the result of detecting light exceeding the amount that the detector can measure or the input signal exceeding the range of the analog-to-digital converter. At step 504, a saturated signal index in the form of a multi-bit binary word, along with detector channels identifying saturation, is generated. At step 505, the multi-bit binary word saturated signal index is applied to the generation of particle classification decisions.

[0076] Figure 6A flowchart illustrating the use of multi-bit binary saturation signal indexes to generate adjusted particle classification, according to certain embodiments, is provided. At step 601, a spectral unmixing matrix for the fluorescence of particles in the sample is calculated without applying the saturation signal index. At step 602, an adjusted spectral unmixing matrix solution is calculated by applying the saturation signal index to identify which detector channels are saturated. At step 603, the unadjusted spectral unmixing matrix is ​​compared with the adjusted spectral unmixing matrix, and one or more rows in the spectral unmixing matrix may be excluded based on the comparison between the unadjusted and adjusted spectral unmixing matrices.

[0077] Figure 7 A flowchart for particle classification according to certain embodiments is depicted. At step 701, a two-dimensional bitmap of particle classification with regions of interest (ROIs) is generated. At step 702, a saturation signal index is applied to generate a second two-dimensional bitmap ROI, where the coordinate axes of the ROI identify saturated channels. At step 703, Boolean logic is used to AND the second two-dimensional bitmap ROI with the identified saturated channels to generate a particle classification that takes into account adjustments to the saturated detector channels. At step 704, particles are sorted based on the determined particle classification.

[0078] In some embodiments, the method includes generating a sorting classification. In some instances, the sorting classification can be a particle sorting decision. For example, generating a sorting classification can include identifying a suitable sorting gate for sorting particles based on a combination of calculated particle parameters and determined saturation signal indices. The particle sorting decision can be generated based on the overlap between the calculated particle parameters and the parameters of the particle classification adjusted by applying the saturation signal indices. To select an appropriate gate, the method may also include plotting the parameters (e.g., on a scatter plot) to obtain the potentially optimal subgroup separation. This analysis can be passed to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0079] 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 with deflection plates, such as those described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, whose disclosure is incorporated herein by reference. In some embodiments, a sorting decision module having multiple sorting decision units is used to sort cells in the sample, such as those described in U.S. Provisional Patent Application No. 62 / 803,264, filed February 8, 2019, whose disclosure is incorporated herein by reference.

[0080] A system for adjusting particle classification index in response to saturation data signals.

[0081] In summary, aspects of this disclosure include a system configured to adjust a particle classification index in response to one or more saturation data signals based on detected light from particles in a liquid stream. As described above, the term "saturation signal" is used to refer to a signal exceeding a maximum range that can be measured by one or more components of the optical detection system described below. In some embodiments, the saturation signal is a data signal output by a photodetector exposed to an amount of light exceeding a maximum amount that can be detected by a photodetector. In other embodiments, the saturation signal is a data signal exceeding a maximum range for an analog-to-digital converter used to convert an analog signal from the photodetector into a digital signal. A system according to some embodiments includes: a light source configured to illuminate particles of a sample in a liquid stream; an optical detection system having a photodetector that detects light from particles in the sample and generates a plurality of data signals based on the detected light; and a processor having a memory operatively coupled to the processor, wherein the memory includes 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 the particle classification index to generate an adjusted particle classification index.

[0082] In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the composition of the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit light with wavelengths varying from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, for example from 300 nm to 1000 nm, for example from 350 nm to 900 nm, and including from 400 nm to 800 nm. For example, the light source can include a broadband light source emitting light with wavelengths from 200 nm to 900 nm. In other instances, the light source includes a narrowband light source emitting wavelengths from 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm–25 nm) emitting light with wavelengths ranging from 200 nm to 900 nm. In some embodiments, the light source is a laser. In some instances, the subject system includes gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon fluoride (ArF) excimer lasers, krypton fluoride (KrF) excimer lasers, xenon chloride (XeCl) excimer lasers, or xenon fluoride (XeF) excimer lasers, or combinations thereof. In other instances, the subject system includes dye lasers, such as stilbene, coumarin, or rhodamine lasers. In still other instances, 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, or combinations thereof. In other instances, the subject system includes 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, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers and combinations thereof.

[0083] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, and light-emitting diodes (e.g., broadband LEDs with continuous spectrum, superluminescent diodes, semiconductor light-emitting diodes, broadband LED white light sources, and integrated multi-LEDs). In some instances, the non-laser light source is a stable fiber-coupled broadband light source, a white light source, other light sources, or any combination thereof.

[0084] The light source can be located at any suitable distance from the sample (e.g., the fluid flow in a flow cytometer), for example, at a distance of 0.001 mm or more, such as 0.005 mm or more, 0.01 mm or more, 0.05 mm or more, 0.1 mm or more, 0.5 mm or more, 1 mm or more, 5 mm or more, 10 mm or more, 25 mm or more, and including distances of 100 mm or more. Furthermore, the light source illuminates the sample at any suitable angle (e.g., relative to the vertical axis of the fluid flow), such as at angles ranging from 10° to 90°, such as from 15° to 85°, 20° to 80°, 25° to 75°, and including angles from 30° to 60°, such as at a 90° angle.

[0085] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some instances, the system includes a light source configured to continuously illuminate the sample, such as a continuous-wave laser having continuously illuminating the fluid flow at the interrogation point of a flow cytometer. In other instances, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds or at some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components for providing intermittent illumination of the sample using the light source. For example, the subject system in these embodiments may include: one or more laser beam choppers for blocking the sample and the light source, and an artificially or computer-controlled beam stop for exposing the sample to the light source.

[0086] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous-wave lasers. For example, the laser may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon fluoride (ArF) excimer laser, a krypton fluoride (KrF) excimer laser, a xenon chloride (XeCl) excimer laser, or a xenon fluoride (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; or 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, or a neon-copper (N) laser. eCu ​​lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or second or third harmonics of any of the above lasers.

[0087] In some embodiments, the light source is a beam generator configured to generate two or more frequency-shifted beams. In some instances, the beam generator includes: a laser; and a radio frequency generator configured to apply radio frequency drive signals to an acousto-optic device to generate laser beams deflected at two or more angles. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, the laser in the beam generator of interest can be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon fluoride (ArF) excimer laser, a krypton fluoride (KrF) excimer laser, a xenon chloride (XeCl) excimer laser, or a xenon fluoride (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; or a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, or a helium-selenium laser. (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titania-sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, or cerium-doped lasers and combinations thereof.

[0088] The acousto-optic device can be any convenient acousto-optic protocol configured to frequency-shift a laser using applied acoustic waves. In some 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 based on light from a laser and an applied radio frequency (RF) drive signal. The RF drive signal can be applied to the acousto-optic device using any suitable RF drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0089] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a laser beam deflected at a desired number of angles in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0090] In some instances, in order to generate an intensity distribution of the laser beam with angular deflection in the output laser beam, the controller is configured to apply an RF drive signal with amplitudes varying from approximately 0.001V to approximately 500V, for example from approximately 0.005V to approximately 400V, for example from approximately 0.01V to approximately 300V, for example from approximately 0.05V to approximately 200V, for example from approximately 0.1V to approximately 100V, for example from approximately 0.5V to approximately 75V, for example from approximately 1V to approximately 50V, for example from approximately 2V to approximately 40V, for example from approximately 3V to approximately 30V, and including approximately 5V to approximately 25V. In some embodiments, each applied radio frequency drive signal has a frequency from about 0.001 MHz to about 500 MHz, for example from about 0.005 MHz to about 400 MHz, for example from about 0.01 MHz to about 300 MHz, for example from about 0.05 MHz to about 200 MHz, for example from about 0.1 MHz to about 100 MHz, for example from about 0.5 MHz to about 90 MHz, for example from about 1 MHz to about 75 MHz, for example from about 2 MHz to about 70 MHz, for example from about 3 MHz to about 65 MHz, for example from about 4 MHz to about 60 MHz, and including from about 5 MHz to about 50 MHz.

[0091] In some embodiments, the controller includes a processor and a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with angularly deflected laser beams having a desired intensity distribution. For example, the memory may include instructions for generating two or more angularly deflected laser beams of equal intensity, such as three or more, four or more, five or more, ten or more, 25 or more, 50 or more, and may also include instructions for generating 100 or more angularly deflected laser beams of equal intensity. In other embodiments, instructions may be included for generating two or more angularly deflected laser beams of different intensities, such as three or more, four or more, five or more, ten or more, 25 or more, 50 or more, and may also include instructions for generating 100 or more angularly deflected laser beams of different intensities.

[0092] In some embodiments, the controller includes a processor and a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity that gradually increases from the edge of the output laser beam along the horizontal axis toward the center. In these examples, the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis can range from 0.1% to 99% of the intensity of the angularly deflected laser beam at the edge, for example, 0.5% to 95%, for example, about 1% to 90%, for example, about 2% to 85%, for example, about 3% to 80%, for example, about 4% to 75%, for example, about 5% to 70%, for example, about 6% to 65%, for example, about 7% to 60%, for example, about 8% to 55%, and includes about 10% to 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 and a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an intensity that gradually increases from the edge of the output laser beam along the horizontal axis toward the center. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam along the horizontal axis can range from 0.1% to 99% of the intensity of the angularly deflected laser beam at the center, for example, 0.5% to 95%, for example, 1% to 90%, for example, about 2% to 85%, for example, about 3% to 80%, for example, about 4% to 75%, for example, about 5% to 70%, for example, about 6% to 65%, for example, about 7% to 60%, for example, about 8% to 55%, and includes about 10% to 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In some other embodiments, the controller includes a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having a Gaussian intensity distribution along a horizontal axis. In still other embodiments, the controller includes a processor having a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having a top hat intensity distribution along a horizontal axis.

[0093] In embodiments, the beam generator of interest can be configured to generate spatially separated, angularly deflected laser beams within the output laser beam. Depending on the applied radio frequency drive signal and the desired illumination distribution of the output laser beam, the angularly deflected laser beams can be separated by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and including 5000 μm or more. In some embodiments, the system is configured to generate overlapping angularly deflected laser beams within the output laser beam, for example, overlapping with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between laser beams deflected at adjacent angles (e.g., overlap of beam points) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and including 100 μm or more.

[0094] In some instances, a beam generator configured to generate two or more frequency-shifted beams includes a laser excitation module, the disclosure of which is incorporated herein by reference in U.S. Patent Nos. 9,423,353, 9,784,661, and 10,006,852, and U.S. Patent Publications 2017 / 0133857 and 2017 / 0350803.

[0095] In some embodiments, the system includes a light detection system with one or more photodetectors. Photodetectors 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), enhancement charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, thermoelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or combinations thereof, and other types of photodetectors. In some embodiments, light from a 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.

[0096] In some embodiments, the light detection system of interest includes a plurality of photodetectors. In some instances, the light detection system includes a plurality of solid-state detectors such as photodiodes. In some instances, the light detection system includes an array of photodetectors, such as an array of photodiodes. In these embodiments, the photodetector array may include four or more photodetectors, such as 10 or more photodetectors, such as 25 or more photodetectors, such as 50 or more photodetectors, such as 100 or more photodetectors, such as 250 or more photodetectors, such as 500 or more photodetectors, such as 750 or more photodetectors, and including 1000 or more photodetectors. For example, the detector may be a photodiode array having four or more photodiodes, such as 10 or more photodiodes, such as 25 or more photodiodes, such as 50 or more photodiodes, such as 100 or more photodiodes, such as 250 or more photodiodes, such as 500 or more photodiodes, such as 750 or more photodiodes, and including 1000 or more photodiodes.

[0097] The photodetectors can be arranged in any geometric configuration as needed, including but not limited to square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregular shapes. The photodetectors in the photodetector array can be oriented relative to the others (with reference in the XZ plane) at angles ranging from 10° to 180°, such as from 15° to 170°, from 20° to 160°, from 25° to 150°, from 30° to 120°, and including from 45° to 90°. The photodetector array can be any suitable shape and can be a shape composed of straight lines, such as square, rectangular, trapezoidal, triangular, hexagonal, etc.; a shape composed of curves, such as circular, elliptical; and an irregular shape, such as a parabola with a base coupled to a flat top. In some embodiments, the photodetector array has an effective surface of a rectangular shape.

[0098] Each photodetector (e.g., a photodiode) in the array may have the following effective surface: a width ranging from 5 μm to 250 μm, for example from 10 μm to 225 μm, for example from 15 μm to 200 μm, for example from 20 μm to 175 μm, for example from 25 μm to 150 μm, for example from 30 μm to 125 μm, and including a width ranging from 50 μm to 100 μm; and a length ranging from 5 μm to 250 μm, for example from 10 μm to 225 μm, for example from 15 μm to 200 μm, for example from 20 μm to 175 μm, for example from 25 μm to 150 μm, for example from 30 μm to 125 μm, and including a length ranging from 50 μm to 100 μm, wherein the surface area of ​​each photodetector (e.g., a photodiode) in the array ranges from 25 μm... 2 Up to 10000μm 2 For example, from 50μm 2 Up to 9000μm 2 For example, from 75μm 2 Up to 8000μm 2 For example, from 100μm 2 Up to 7000μm 2 For example, from 150μm 2 Up to 6000μm 2 and including 200μm 2 Up to 5000μm 2 .

[0099] The size of the photodetector array can vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and can have lengths ranging from 0.01 mm to 100 mm, such as from 0.05 mm to 90 mm, from 0.1 mm to 80 mm, from 0.5 mm to 70 mm, from 1 mm to 60 mm, from 2 mm to 50 mm, from 3 mm to 40 mm, from 4 mm to 30 mm, and including from 5 mm to 25 mm. The width of the photodetector array can also vary from 0.01 mm to 100 mm, such as from 0.05 mm to 90 mm, from 0.1 mm to 80 mm, from 0.5 mm to 70 mm, from 1 mm to 60 mm, from 2 mm to 50 mm, from 3 mm to 40 mm, from 4 mm to 30 mm, and including from 5 mm to 25 mm. Therefore, the effective area of ​​the photodetector array can range from 0.1 mm² to 100 mm². 2 Up to 10000mm 2 For example, from 0.5mm 2 Up to 5000mm 2 For example, from 1mm 2 Up to 1000mm 2For example, from 5mm 2 Up to 500mm 2 and including 10mm 2 Up to 100mm 2 .

[0100] The photodetector of interest is configured to measure light of one or more wavelengths, such as two or more wavelengths, five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths, and including measuring light of 400 or more different wavelengths emitted by a sample in a liquid flow.

[0101] In some embodiments, the photodetector is configured to measure light acquired over a wavelength range (e.g., 200 nm–1000 nm). In some embodiments, the photodetector of interest is configured to acquire the spectrum of light over a wavelength range. For example, the system may include one or more detectors configured to acquire the spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the photodetector of interest is configured to measure light from one or more specific wavelengths from a sample in a liquid stream. 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 some embodiments, the photodetector may be configured to match a specific luciferase (e.g., those used in binding samples during fluorescence assays). In some embodiments, the photodetector is configured to measure light across the entire fluorescence spectrum of each luciferase in the acquired sample.

[0102] The light detection system is configured to measure light continuously or at discrete intervals. In some instances, the photodetector of interest is configured to measure the collected light continuously. In other instances, the light detection system is configured to measure at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds or some other interval.

[0103] In one embodiment, the system is configured to adjust a particle classification index in response to one or more saturation data signals based on detected light from particles in a liquid stream. The optical detection system is configured to generate multiple data signals based on detected light from particles in a sample. The generated data signals can be analog or digital. When the data signals are analog, in some instances, the system further includes an analog-to-digital converter configured to convert the analog data signals into digital data signals. A system of interest includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to identify one or more saturation data signals. In some instances, a saturation signal is a data signal whose property is that the signal exceeds the range measurable by the detector of the optical detection system. In other instances, a saturation signal is a data signal whose property is that the signal exceeds the range of the analog-to-digital converter. Depending on the number of photodetectors in the subject light detection system, the number of detector channels that can be identified as outputting saturated data signals can vary, for example, one or more, two or more, four or more, eight or more, 16 or more, 32 or more, 64 or more, and including 128 or more.

[0104] In some embodiments, the saturation signal index generated by the processor based on the identified saturation data signal is a binary word that identifies which detector channels have saturated the analog-to-digital converter. In some instances, the saturation signal index is a binary word consisting of one or more bits, such as two or more bits, four or more bits, eight or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. For example, the saturation signal index can 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 some instances, the saturation signal index is a combination of two or more binary words, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more binary words. When the saturation signal index is a combination of binary words, each binary word can be independently composed of one or more bits, such as two or more bits, four or more bits, eight or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. In some embodiments, each binary word in the saturation signal index can be independently 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, when the optical detection system includes 80 detector channels, the saturation signal index can be composed of a 128-bit binary word or three different 32-bit binary words.

[0105] In some embodiments, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to: determine one or more parameters of the particles in the fluid stream based on the generated data signal. In some embodiments, the memory includes instructions for calculating a spectral unmixing matrix for the fluorescence of the particles. In some embodiments, the memory includes instructions for calculating the spectral unmixing matrix using a weighted least squares algorithm. In some instances, the weighted least squares algorithm is calculated according to the following:

[0106]

[0107] Where y is the measured detector value from multiple photodetectors of the light detection system for each cell; X is the estimated luciferase abundance; and W is... In some embodiments, each Wii according to Perform the calculation.

[0108] in, The variance at detector i; y i The signal is at detector i; and λ i This is constant noise at detector i. In some embodiments, the spectral unmixing matrix is ​​based on (X... T WX) -1 X T W is used for calculation. In some instances, the memory includes memory for each cell pair (X) detected by the light detection system in order to calculate the spectral unmixing matrix. T Instructions for performing inversion operations (WX). In some embodiments, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to compute a solution to the spectral unmixing matrix using one or more of iterative Newton-Raphson computation, a Sherman-Morrison iterative inversion updater, matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition), or singular value decomposition (SVD). In some embodiments, the system for computing a solution to the spectral unmixing matrix includes those described in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, whose disclosure is incorporated herein by reference.

[0109] In some instances, the memory includes instructions for calculating an adjusted spectral unmixing matrix (e.g., where one or more saturated data signals are excluded) using a calculated saturation signal index. In other instances, the memory includes instructions for calculating an adjusted spectral unmixing matrix (where data signals from saturated detector channels are excluded). In some instances, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to: calculate a spectral unmixing matrix for particle fluorescence; calculate an adjusted spectral unmixing matrix for particle fluorescence excluding one or more saturated data signals; and compare the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix. In some embodiments, the memory includes instructions for calculating a spectral unmixing matrix in which 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 unmixing matrix to compensate for saturated signals. In one example, the spectral unmixing matrix solution is adjusted to use an estimate of the true value of the saturated signal. In these embodiments, the true value of the estimated saturation signal is first determined and then input into the spectral unmixing matrix to generate an adjusted spectral unmixing matrix solution.

[0110] The memory may include instructions stored thereon that, when executed by a processor, cause the processor to classify particles based on one or more defined parameters of the particles. In some embodiments, the system is configured to classify particles by assigning them to particle clusters. In other embodiments, the system is configured to classify particles by plotting one or more parameters of the particles on a scatter plot.

[0111] In some embodiments, the memory includes instructions for implementing a bitmap gating strategy for classifying particles. In some instances, the bitmap gating strategy implemented by the subject system includes instructions for identifying and removing saturated data signals. In other instances, the bitmap gating strategy implemented by the subject system includes instructions for identifying saturated data signals and estimating the true value of the 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 with a region of interest (ROI); and determine whether a particle should be assigned to the ROI of the bitmap. In other embodiments, the memory includes instructions that, when executed by a processor, cause the processor to: determine whether one or more bits of the ROI of the bitmap include a saturated data signal. In some instances, the memory includes instructions for determining whether one or more bits of the ROI of a designated particle in the bitmap include a saturated data signal, including: 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 ROI of the designated particle; and determining that one or more bits of the ROI of the designated particle are saturated. In some instances, the processor uses Boolean logic to compare a saturation signal bitmap with the ROI of a specified particle. For example, the saturation signal bitmap can be ANDed with the ROI of a specified particle to determine whether one or more bits of the ROI of the specified particle are saturated.

[0112] In some embodiments, the system of interest may include one or more sorting decision modules configured to generate sorting decisions for particles based on particle classification. In some embodiments, the system further includes a particle sorter (e.g., with droplet deflectors) for sorting particles from the liquid stream based on the sorting decisions generated by the sorting decision modules. The term “sorting” is used herein in its conventional sense to refer to the separation of components of a sample (e.g., cells, non-cellular particles such as biological macromolecules) and, in some instances, the transfer of separated components to one or more sample collection containers. For example, the system may be configured to sort samples having two or more components, such as three or more components, four or more components, five or more components, ten or more components, fifteen or more components, and including sorting samples having 25 or more components. One or more sample components can be separated from a sample and transferred to a sample collection container, such as two or more sample components, three or more sample components, four or more sample components, five or more sample components, ten or more sample components, and including the ability to separate 15 or more sample components from a sample and transfer them to a sample collection container.

[0113] In some embodiments, the particle sorting system of interest is configured to sort particles using enclosed particle sorting modules (e.g., those described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, which is incorporated herein by reference). In some embodiments, particles (e.g., cells) of a sample are sorted using a sorting decision module having multiple sorting decision units (e.g., described in U.S. Provisional Patent Application No. 62 / 803,264, filed February 8, 2019, which is incorporated herein by reference). 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 (e.g., described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, which is incorporated herein by reference).

[0114] Figure 1 A functional block diagram of an example sorting control system (e.g., analysis controller 100) for analyzing and displaying biological events is shown. The analysis controller 100 can be configured to implement various processes for controlling the graphical display of biological events.

[0115] The particle analyzer or sorting system 102 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 102 can be configured to provide 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. Biological event data can be provided to the analysis controller 100 via the data communication channel.

[0116] Analysis controller 100 can be configured to receive biological event data from particle analyzer 102. The biological event data received from particle analyzer 102 may include flow cytometry event data. Analysis controller 100 can be configured to provide a graphical display of a first plot including the biological event data to display device 106. For example, analysis controller 100 can also be configured to render regions of interest as gates near clusters of biological event data superimposed on the first plot and displayed by display device 106. In some embodiments, a gate may be a logical combination of one or more graphical regions of interest plotted based on a single-parameter histogram or a bivariate plot. In some embodiments, the display may be used to display particle parameters or saturated detector data.

[0117] The analysis controller 100 can also be configured to display on the display device 106 other biological event data inside the door that differs from the events in the biological event data outside the door. For example, the analysis controller 100 can be configured to render the colors of the biological event data contained within the door differently from the colors of the biological event data outside the door. The display device 106 can be implemented as a monitor, tablet computer, smartphone, or other electronic device with a graphical interface.

[0118] The analysis controller 100 can be configured to receive a door selection signal identifying a door from a first input device. For example, the first input device can be implemented as a mouse 110 (e.g., by clicking when the cursor is over or in the desired door). The mouse 110 can send a door selection signal to the analysis controller 100 identifying a door to be displayed on or manipulated via the display device 106. In some implementations, the first device can be implemented as a keyboard 108 or other tool, such as a touchscreen, stylus, light detector, or voice recognition system, for providing input signals to the analysis controller 100. Some input devices may include multiple input functions. In such implementations, all input functions can be considered as input devices. For example, such as... Figure 1 As shown, mouse 110 may include a right mouse button and a left mouse button, both of which can generate trigger events.

[0119] Triggering events can cause the analysis controller 100 to change the way the data is displayed, which parts of the data are actually displayed on the display device 106, and / or provide input for further processing, such as selecting groups of interest for particle sorting.

[0120] In some embodiments, the analysis controller 100 may be configured to detect when gate selection is initiated via mouse 110. The analysis controller 100 may also be configured to automatically modify the visualization plot to facilitate the gating process. The modification may be based on a specific distribution of biological event data received by the analysis controller 100.

[0121] The analysis controller 100 can be connected to the storage device 104. The storage device 104 can be configured to receive and store biological event data from the analysis controller 100. The storage device 104 can also be configured to receive and store flow cytometry event data from the analysis controller 100. The storage device 104 can also be configured to allow retrieval of biological event data, such as flow cytometry event data, via the analysis controller 100.

[0122] Display device 106 can be configured to receive display data from analysis controller 100. The display data may include graphs of biological event data and cross-sectional views of delineated plots. Display device 106 can also be configured to change the presented information based on the combination of input received from analysis controller 100 and input from particle analyzer 102, storage device 104, keyboard 108 and / or mouse 110.

[0123] In some implementations, the analysis controller 100 may generate a user interface to receive sample events for sorting. For example, the user interface may include controls for receiving sample events or sample images. Sample events, images, or sample gates may be provided before the event data of the sample is acquired or based on an initial set of events from a portion of the sample.

[0124] Figure 2A This is a schematic diagram of a particle sorter system 200 (e.g., particle analyzer 102) according to one embodiment presented herein. In some embodiments, the particle sorter system 200 is a cell sorter system. Figure 2AAs shown, a droplet-forming transducer 202 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 201, which may be coupled to, may include, or may be a nozzle 203. Within the fluid conduit 201, a sheath fluid 204 hydrodynamically concentrates a sample fluid 206, including particles 209, into a moving fluid column 208 (e.g., a stream). Within the moving fluid column 208, particles 209 (e.g., cells) align in a single file across a monitoring area 211 (e.g., where the laser-stream crosses) irradiated by an irradiation source 212 (e.g., a laser). Vibration of the droplet-forming transducer 202 causes the moving fluid column 208 to split into multiple droplets 210, some of which contain particles 209.

[0125] In operation, when a particle of interest (or cell of interest) crosses monitoring area 211, a detection stage 214 (e.g., an event detector) identifies it. The detection stage 214 is fed into timing circuitry 228, which in turn feeds into flash charging circuitry 230. At a drop interruption point notified by a timed drop delay (Δt), flash charging can be applied to the moving fluid column 208 to charge the drop of interest. The drop of interest may include one or more particles or cells to be sorted. The charged drop can then be sorted by activating a deflection plate (not shown) to deflect the drop into a container (e.g., a collection tube or a multi-well or micro-well sample tray, where wells or micro-wells may be associated with the drop of particular interest). Figure 2A As shown, drips can be collected in the discharge container 238.

[0126] Detection system 216 (e.g., a drop boundary detector) is used to automatically determine the phase of the drop drive signal as a particle of interest passes through monitoring region 211. An exemplary drop boundary detector is described in U.S. Patent 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 particle detected in the drop. Detection system 216 may receive amplitude signal 220 and / or phase signal 218 as inputs, which are then (via amplifier 222) input to amplitude control circuitry 226 and / or frequency control circuitry 224. Amplitude control circuitry 226 and / or frequency control circuitry 224 then control the drop forming transducer 202. Amplitude control circuitry 226 and / or frequency control circuitry 224 may be included in a control system.

[0127] In some implementations, sorting electronics (e.g., detection system 216, detection stage 214, and processor 240) may be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decisions may be included in the particle event data. In some implementations, detection system 216 and detection stage 214 may be implemented as a single detection unit or communicatively coupled so that event measurements can be acquired by one of detection system 216 or detection stage 214 and provided to non-acquisition elements.

[0128] Figure 2B This is a schematic diagram of a particle sorter system according to an embodiment presented herein. Figure 2B The particle sorting system 200 shown includes deflection plates 252 and 254. Charge can be applied via flow-charged wires in the barbs. This generates a flow of droplets 210 containing particles 210 to be analyzed. The particles can be illuminated using one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. This information is then processed by, for example, sorting electronics or other detection systems. Figure 2B (Not shown in the image) Analyze particle information. Deflection plates 252 and 254 can be independently controlled to attract or repel charged droplets to guide them to the target collection container (e.g., one of 272, 274, 276, or 278). Figure 2B As shown, deflection plates 252 and 254 can be controlled to direct particles toward container 274 along a first path 262 or along a second path 268. If the particles are of no interest (e.g., do not exhibit scattering or illuminance information within a specific sorting range), the deflection plates can allow the particles to continue along flow path 264. Such uncharged droplets can, for example, enter the waste container via a suction device 270.

[0129] It may include sorting electronics to initiate the acquisition of measurement results, receive the fluorescence signal of the particles, and determine how to adjust the deflection plate to result in the sorting of the particles. Figure 2B The example implementation shown includes the commercially available BD FACSAria from Becton, Dickinson (Franklin Lakes, New Jersey). TM A series of flow cytometers.

[0130] In some embodiments, one or more components of the particle sorter system 200 described below can be used to analyze and characterize particles by physically sorting them into a collection container, with or without the aid of physical sorting. Similarly, the particle analysis system 300 described below ( Figure 3One or more components of the particle sorter system 200 or particle analysis system 300 can be used to analyze and characterize particles by physically sorting them into a collection container, with or without the aid of physical sorting. For example, using one or more components of the particle sorter system 200 or particle analysis system 300, particles can be grouped or displayed in a tree that includes at least three groups as described herein.

[0131] Figure 3 A functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization is shown. In some embodiments, the particle analysis system 300 is a flow cytometry system. Figure 3 The particle analysis system 300 shown can be configured to perform the methods described herein, either fully or partially. The particle analysis system 300 includes a fluid system 302. The fluid system 302 may include or be coupled to a sample tube 310, and a moving fluid column containing particles 330 (e.g., cells) of a sample within the sample tube moves along a common sample path 320.

[0132] The particle analysis system 300 includes a detection system 304 configured to acquire signals from each particle as it passes one or more detection stations along a common sample path. A detection station 308 typically refers to a monitoring area 340 of the common sample path. In some implementations, detection may include detecting light or one or more of their other properties as particles 330 pass through the monitoring area 340. Figure 4 Figure A shows a detection station 308 with a monitoring area 340. Some implementations of the particle analysis system 300 may include multiple detection stations. Furthermore, some detection stations can monitor more than one area.

[0133] Each signal is assigned a signal value to form a data point for each particle. As mentioned above, this data can be referred to as event data. The data point can be a multidimensional data point that includes values ​​of various properties measured for the particle. The detection system 304 is configured to acquire a sequence of such data points in a first time interval.

[0134] The particle analysis system 300 may also include a control system 306. For example... Figure 2B As shown, the control system 306 may include one or more processors, amplitude control circuitry 226, and / or frequency control circuitry 224. The illustrated control system 206 may be operatively associated with the fluid system 302. The control system 206 may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on a Poisson distribution and the number of data points acquired by the detection system 304 during the first time interval. The control system 306 may also be configured to generate an experimental signal frequency based on the number of data points in that portion of the first time interval. The control system 306 may also compare the experimental signal frequency with a calculated signal frequency or a predetermined signal frequency.

[0135] Figure 4 A system 400 for flow cytometry according to an illustrative embodiment of the present invention is shown. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. The flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scattering detector 430, a side scattering detector 435, a fluorescence acquisition lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more long-pass (“LP”) filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0136] The laser 115a-c is excited to emit light in the form of a laser beam. Figure 4 In the example system, the laser beams emitted from excitation lasers 415a-415c have wavelengths of 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits UV light (light with wavelengths in the range of 10 to 400 nm) and reflects both 488 nm and 633 nm light.

[0137] The laser beam is then directed to a focusing lens 420, which concentrates the beam onto the portion of the fluid flow sample within a flow chamber 425 where the particles are located. A flow chamber is a component of a fluid system that directs particles (typically one at a time) in a flow to a concentrated laser beam for probing. A flow chamber may include the flow chamber of a benchtop cytometer or the nozzle tip of an airflow cytometer.

[0138] Depending on the characteristics of the particles (e.g., their size, internal structure) and the presence of one or more fluorescent molecules attached to or naturally present on or within the particles, light from a laser beam interacts with the particles in the sample through diffraction, refraction, reflection, scattering, absorption, and re-emission at various wavelengths. Fluorescence emission, as well as diffracted, refracted, reflected, and scattered light, can be routed through one or more of beam splitters 445a-445g, bandpass filters 450a-450e, longpass filters 455a-455b, and fluorescence acquisition lens 440 to one or more of front-scatter detector 430, side-scatter detector 435, and one or more fluorescence detectors 460a-460f.

[0139] The fluorescence collecting lens 440 collects light emitted due to particle-laser beam interactions and routes this light to one or more beamsplitters 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 indicates the center of the spectral band. The second number provides the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on each side of the center of the spectral band, or from 500 nm to 520 nm. Short-pass filters transmit light with wavelengths equal to or shorter than a specific wavelength. Long-pass filters, such as long-pass filters 455a-455b, transmit light with wavelengths equal to or longer than a specific wavelength. For example, long-pass filter 455a, a 670 nm long-pass filter, transmits light equal to or longer than 670 nm. Filters are typically selected to optimize the detector's characteristics for a specific fluorescent dye. The filter can be configured so that the spectral band of the light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0140] A beam splitter directs light of different wavelengths in different directions. The beam splitter can be defined as, for example, a short-pass or long-pass beam splitter, depending on the properties of the filter. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits light of 620 nm or shorter wavelengths and reflects light of longer wavelengths than 620 nm in different directions. In one embodiment, beam splitters 445a-445g may include optical mirrors, such as dichroic mirrors.

[0141] A forward-scattering detector 430 is positioned slightly off-center from the beam orientation along the axis of the flow cell and is configured to detect diffracted light, the excitation light traveling primarily in the forward direction through or near the particle. The intensity of the light detected by the forward-scattering detector depends on the overall size of the particle. The forward-scattering detector may include a photodiode. A side-scattering detector 435 is configured to detect light based on refraction and reflection from the particle's surface and internal structure, and tends to increase with increasing particle structural complexity. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side-scattering detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected at the forward-scattering detector 430, the side-scattering detector 435, and the fluorescence detector can be converted into electrical signals (voltages) by the detectors. This data can provide information about the sample.

[0142] Those skilled in the art will recognize that the flow cytometer according to embodiments of the present invention is not limited to Figure 4 The flow cytometer described 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 employing various wavelengths and different configurations.

[0143] During operation, the cytometer is controlled by a controller / processor 490, and measurement data from the detector can be stored in a memory 495 and processed by the controller / processor 490. Although not explicitly shown, the controller / processor 490 is coupled to the detector to receive output signals from it, and can also be coupled to the electrical and electromechanical components of the flow cytometer 400 to control the laser, fluid flow parameters, etc. An input / output (I / O) capability 497 can also be provided in the system. The memory 495, controller / processor 490, and I / O 497 can all be provided as integrated components of the flow cytometer 410. In such embodiments, a display can also form part of the I / O capability 497 for presenting experimental data to a user of the cytometer 400. Alternatively, some or all of the memory 495, controller / processor 490, and I / O capability can be components of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 495 and controller / processor 490 can communicate wirelessly or wiredly with the cytometer 410. The controller / processor 490, which combines memory 495 and I / O 497, can be configured to perform various functions related to the preparation and analysis of flow cytometry experiments.

[0144] Based on the definition of the configuration of filters and / or beam splitters in the optical path from flow chamber 425 to each detector Figure 4 The system shown includes six different detectors (which may be referred to herein as “filter windows” for specifying detectors) for detecting fluorescence in six different wavelength bands. Different fluorescent molecules used in flow cytometry experiments will emit light in their own characteristic wavelength bands. Specific fluorescent labels used for the experiment and its associated fluorescence emission bands can be selected to generally coincide with the filter windows of the detectors. However, due to the availability of more detectors and the use of more labels, a perfect correspondence between filter windows and fluorescence emission spectra is impossible. In reality, while the peaks of the emission spectrum of a particular fluorescent molecule may lie within the filter window of a particular detector, some emission spectra of that label will also overlap with the filter windows of one or more other detectors. This can be referred to as overflow. I / O 497 can be configured to receive data on flow cytometry experiments having groups of fluorescent labels and multiple cell populations with multiple labels, each cell population having a subset of multiple labels. I / O 497 can also be configured to receive biological data assigning one or more labels to one or more cell populations, label density data, emission spectrum data, data on label assignment to one or more labels, and cytometry configuration data. Flow cytometry experimental data, such as label profile characteristics and flow cytometry configuration data, can also be stored in memory 495. Controller / processor 490 can be configured to evaluate one or more label-to-flag assignments.

[0145] The system according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor having access to memory having instructions stored thereon for performing steps of a subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, buffer memory, data backup units, and many other devices. The processor may be a commercially available processor or may be one of other processors that are available or will become available. The processor executes the operating system, and the operating system is connected to firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of the functions of various computer programs that can be written in various programming languages ​​(e.g., Java, Perl, C++, other high-level or low-level languages ​​known in the art) and combinations thereof. The operating system, which typically cooperates with the processor, coordinates and executes the functions of 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 entirely according to known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control (e.g., negative feedback control).

[0146] 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 resident hard disks or magnetic tapes), optical media (such as optical discs), flash memory devices, or other memory storage devices. Memory storage devices can be any of a variety of known or future devices, including optical disc drives, magnetic tape drives, removable hard disk drives, or floppy disk drives. These types of memory storage devices typically read from and / or write to program storage media (not shown), such as optical discs, magnetic tapes, removable hard disks, or floppy disks, respectively. Any of these or other program storage media currently in use or that may be developed later can be considered as computer program products. It will be understood that these program storage media typically store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0147] In some embodiments, a computer program product is described as including a computer-usable medium having a computer software program storing control logic (including program code). When the control logic is executed by a processor / computer, it causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementing a hardware state machine to perform the functions described herein will be apparent to those skilled in the art.

[0148] The memory can be any suitable device that the processor can store and receive data, such as magnetic, optical, or solid-state storage devices (including magnetic disks or optical discs or magnetic tapes or RAM or any other suitable device, either stationary or portable). The processor can include a general-purpose digital microprocessor that is appropriately programmed based on a computer-readable medium carrying the necessary program code. The program can be provided to the processor remotely via a communication channel or pre-stored in a computer program product (e.g., memory or some other portable or stationary computer-readable storage medium using any of those devices combined with memory). For example, a magnetic disk or optical disc can carry the program and can be read by a disk writer / reader. The system of the present invention also includes programs, for example, in the form of computer program products or algorithms used in practicing the methods described above. The program according to the invention can 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 tapes; optical storage media such as CD-ROMs; electrical storage media such as RAM and ROMs; portable flash drives; and mixtures of these kinds, such as magnetic / optical storage media.

[0149] The processor may also have access to a communication channel to communicate with a user at a remote location. A remote location indicates that the user is not in direct contact with the system and that input information from external devices is relayed to the input manager, such as computers connected to a wide area network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones).

[0150] In some embodiments, the system according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication, such as RFID, Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), and Bluetooth. Communication protocols, and cellular communications, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0151] In one embodiment, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces, such as USB ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices, such as (e.g., in a doctor's office or in a hospital environment) computer terminals configured for similar complementary data communication.

[0152] In one embodiment, the communication interface is configured for infrared communication and Bluetooth. Communication or any other suitable wireless communication protocol to enable 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 the user can use in combination).

[0153] In one embodiment, the communication interface is configured to provide data transmission via: Internet Protocol (IP) via a mobile network, Short Message Service (SMS), wireless connection to a personal computer (PC) connected to a local area network (LAN) connected to the Internet, or WiFi connection to the Internet at a WiFi hotspot.

[0154] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communication interface, such as using common standards like 802.11 or Bluetooth. The protocol is either RF or IrDA infrared. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop computer; or a larger device, such as a desktop computer, instrument, etc. In some embodiments, the server device has: a display, such as a liquid crystal display (LCD); and input devices, such as buttons, keyboard, mouse, or touch screen.

[0155] In some embodiments, the communication interface is configured to automatically or semi-automatically transmit data stored in the subject system (e.g., in an optional data storage unit) to a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0156] The output controller may include any of a variety of known display devices for presenting information to a user, regardless of whether the user is human or machine, local or remote. If 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. In alternative embodiments, some of these communications may be implemented using a network or other types of remote communication. According to known techniques, 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. Presenting data through the output manager may be implemented according to a variety of known techniques. According to 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 URLs to allow the user to retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the subject system may be any type of known computer platform or a type to be developed in the future, but it will typically be a category commonly referred to as a server in computers. However, it can also be a mainframe computer, workstation, or other computer type. It can be connected via any known or future type of cable or other communication system, including wireless systems, whether networked or otherwise. It can be co-located or physically separated. Depending on the type and / or brand of the chosen computer platform, various operating systems can be used on any of these platforms. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, ZorinOS, etc.

[0157] Figure 8 The overall architecture of an example computing device 800 according to certain embodiments is described. Figure 8 The depicted computing device 800's overall architecture includes the arrangement of computer hardware and software components. The computing device 800 may include... Figure 8The diagram shows more (or fewer) of those components. However, it is not necessary to show all of these generally conventional components to provide a achievable disclosure. As shown, computing device 800 includes processing unit 810, network interface 820, computer-readable media drive 830, input / output device interface 840, display 850, and input device 860, all of which can communicate with each other via a communication bus. Network interface 820 can provide connectivity to one or more networks or computing systems. Processing unit 810 can therefore receive information and instructions from other computing systems or services via the network. Processing unit 810 can also communicate bidirectionally with memory 870 and also provide output information to optional display 850 via input / output device interface 840. Input / output device interface 840 can also receive input from optional input device 860 (e.g., keyboard, mouse, digital pen, microphone, touchscreen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope, or other input device).

[0158] Memory 870 may contain computer program instructions (grouped into modules or components in some embodiments), which processing unit 810 executes to implement one or more embodiments. Memory 870 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 870 may store operating system 872, which provides computer program instructions for use by processing unit 810 in the general management and operation of computing device 800. Memory 870 may also include computer program instructions and other information for implementing aspects of this disclosure.

[0159] 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 (e.g., parameters of sorting decisions).

[0160] In some embodiments, the subject system is a flow cytometry system that uses the aforementioned algorithm to analyze and adjust the particle classification index in response to one or more saturation data signals based on detected particles from the flow. Suitable flow cytometry systems may include, but are not limited to, those described below: Ormerod (ed.), FlowCytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology, Vol. 91, Humana Press (1997); Practical Flow Cytometry, 3rd Edition, Wiley-Liss (1995); Virgo et al. (2012), Ann Clin Biochem, Jan., 49(Part 1): 17-28; Linden et al., Semin Throm Hemost, Oct. 2004, 30(5): 502-11; Alison et al., J. Pathol, December 2010, 222(4):335-344; and Herbig et al. (2007) Crit Rev Ther DrugCarrier Syst, 24(3):203-255; the contents of which are incorporated herein by reference. In some instances, flow cytometry systems of interest include BD Biosciences FACSCanto TM II flow cytometer, BD Accuri TM Flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BDBiosciences FACSVerse TM Flow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BDBiosciences LSRFortessa TM Flow cytometer, BD Biosciences LSRFortess TM X-20 flow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TMCell sorter, BD Biosciences FACSLyric TM Cell sorter, and BD BiosciencesVia TM Cell sorter, BDBiosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorter, and BD Biosciences FACSMelody TM Cell sorters, etc.

[0161] In some embodiments, the subject particle sorting system is a flow cytometry system, such as those 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; 8,233,1 46; 8,140,300; 7,544,326; 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; 5,602,039; The entire contents of these publications are incorporated herein by reference.

[0162] Integrated circuit devices

[0163] This disclosure also includes (and provides) an integrated circuit device programmed to adjust a particle classification index in response to one or more saturation data signals based on detected light from particles in a fluid stream. 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 based on detected light from particles in a fluid stream; 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.

[0164] In some embodiments, the saturation signal index used by the integrated circuit is a binary word that identifies which detector channels of the optical detection system have saturated the analog-to-digital converter. In some instances, the saturation signal index is a binary word consisting of one or more bits, such as two or more bits, four or more bits, eight or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. For example, the saturation signal index can 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 some instances, the saturation signal index is a combination of two or more binary words, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, and including ten or more binary words. When the saturation signal index is a combination of binary words, each binary word can be a binary word consisting of 1 or more bits, such as 2 or more bits, 4 or more bits, 8 or more bits, 16 or more bits, 32 or more bits, 64 or more bits, 128 or more bits, and including 256 or more bits. In some embodiments, each binary word in the saturation signal index can 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.

[0165] In some embodiments, the integrated circuit is programmed to determine parameters of one or more particles based on data signals generated by a photodetector system. In some embodiments, the integrated circuit is programmed to calculate a spectral unmixing matrix for the fluorescence of the particles. In some embodiments, the integrated circuit is programmed to calculate the spectral unmixing matrix using a weighted least squares algorithm. In some instances, the weighted least squares algorithm is calculated according to the following:

[0166]

[0167] Where y is the detector value measured by multiple photodetectors from the light detection system for each cell; X is the estimated luciferase abundance; and W is... In some embodiments, each W ii according to Perform the calculation.

[0168] in, The variance at detector i; yi The signal is at detector i; and λ i This is constant noise at detector i. In some embodiments, the spectral unmixing matrix is ​​based on (X... T WX) -1 X T W is used for calculation. In some instances, the integrated circuit is programmed to perform calculations for each cell pair (X) detected by the optical detection system in order to calculate the spectral unmixing matrix. T The inversion operation is performed using WX. In some embodiments, the integrated circuit is programmed to compute solutions to the spectral unmixing matrix using one or more of iterative Newton-Raphson computation, Sherman-Morrison iterative inversion updater, matrix decomposition (e.g., LU matrix decomposition, Gaussian elimination, modified Cholesky decomposition), or singular value decomposition (SVD). In some embodiments, integrated circuits programmed to compute solutions to the spectral unmixing matrix include those described in U.S. Patent Application No. 16 / 725,799, filed December 23, 2019, whose disclosure is incorporated herein by reference.

[0169] In some instances, the integrated circuit is programmed to: compute an adjusted spectral unmixing matrix using a calculated saturation signal index, for example, where one or more saturated data signals are excluded. In other instances, the integrated circuit is programmed to: compute an adjusted spectral unmixing matrix in which data signals from saturated detector channels are excluded. In some instances, the integrated circuit is programmed to: compute a spectral unmixing matrix for particle fluorescence; compute an adjusted spectral unmixing matrix for particle fluorescence excluding one or more of the saturated data signals; and compare the computed spectral unmixing matrix with the computed adjusted spectral unmixing matrix. In some embodiments, the integrated circuit is programmed to: compute a spectral unmixing matrix in which 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 unmixing matrix to compensate for saturated signals. In one example, the spectral unmixing 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 unmixing matrix to generate the adjusted spectral unmixing matrix solution.

[0170] The integrated circuit is programmed to classify particles based on one or more defined parameters. In some embodiments, the integrated circuit is programmed to classify particles by assigning them to particle clusters. In other embodiments, the integrated circuit is programmed to classify particles by plotting one or more parameters of the particles on a scatter plot.

[0171] In some embodiments, the integrated circuit is programmed to implement a bitmap gating strategy for classifying particles. In some instances, the bitmap gating strategy implemented by the integrated circuit includes identifying and removing saturated data signals. In other instances, the bitmap gating strategy implemented by the integrated circuit includes instructions for identifying saturated data signals and estimating the true value of the saturated data signals. In some embodiments, the integrated circuit is programmed to generate a two-dimensional bitmap with 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 a saturated data signal. In some instances, 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 ROI of the assigned particle; and determine that one or more bits of the ROI of the assigned particle are saturated. In some instances, the integrated circuit is programmed to use Boolean logic to compare the saturated signal bitmap with the ROI of the assigned particle. For example, an integrated circuit can be programmed to AND a saturation signal bitmap with the ROI of a specified particle to determine whether one or more bits of the ROI of the specified particle are saturated.

[0172] Computer-readable storage medium for adjusting particle classification index in response to saturation data signal

[0173] This disclosure also includes a non-transitory computer-readable storage medium having instructions for implementing the subject methods. The computer-readable storage medium can be used on one or more computers for automating or partially automating the implementation of a system for implementing the methods described herein. In some embodiments, the instructions according to the methods described herein can be encoded on a computer-readable medium in a “programmed” form, wherein, as used herein, the term “computer-readable medium” refers to any non-transitory storage medium that participates 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 discs, solid-state drives, and network-attached storage (NAS), regardless of whether these devices are internal or external to a computer. Files containing information can be “stored” on a computer-readable medium, wherein “stored” means recording information so that it can be accessed and retrieved by a computer later. The computer-implemented methods described herein can be executed using a program, which can be written in one or more of any number of computer programming languages. These languages ​​include, for example, Java (Oracle, Redwood Shores, CA), Visual Basic (Microsoft, Redmond, WA), and C++ (ATT, Bedminster, NJ).

[0174] In some embodiments, the computer-readable storage medium of interest includes a computer program stored thereon, wherein, when the computer program is loaded onto a computer, it includes instructions having: an algorithm for detecting light from particles in a liquid stream; an algorithm for identifying one or more saturated data signals; an algorithm for generating a saturated signal index corresponding to the identified saturated data signals; and an algorithm for applying the saturated signal index to a particle classification index to generate an adjusted particle classification index.

[0175] A computer-readable storage medium may include instructions for capturing one or more images of a fluid flow, such as two or more images of the fluid flow, for example, three or more images, for example, four or more images, for example, five or more images, for example, ten or more images, for example, fifteen or more images, and including 25 or more images. In some embodiments, the computer-readable storage medium includes instructions for optical adjustments to the captured images (e.g., for increasing the optical resolution of the images). In some embodiments, the computer-readable storage medium may include instructions for increasing the resolution of the captured images by 5% or more, for example, 10% or more, for example, 25% or more, for example, 50% or more, and including increasing the resolution of the captured images by 75% or more.

[0176] In embodiments, the computer-readable storage medium of interest includes an algorithm for classifying particles in a sample based on one or more determined parameters of the particles. In some embodiments, the computer-readable storage medium includes an algorithm for classifying particles in a sample using a bitmap gating strategy, wherein classifying the particles includes: identifying saturated data signals based on particle classification parameters; and removing them in some instances. In other embodiments, the computer-readable storage medium includes an algorithm for classifying particles in a sample, which includes: identifying saturated data signals; and estimating the true value of the saturated data signals.

[0177] The computer-readable storage medium further includes: an algorithm for generating a two-dimensional bitmap having a region of interest (ROI); and an algorithm for determining whether a particle 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 a saturated data signal. In other embodiments, the computer-readable storage medium of interest includes: an algorithm for applying a 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 ROI of the assigned particle; and an algorithm for determining that one or more bits of the ROI of the assigned particle are saturated.

[0178] The non-transitory computer-readable storage medium may also include algorithms for calculating one or more parameters of the particle. In these embodiments, the computer-readable storage medium includes: an algorithm for calculating a spectral unmixing matrix for the fluorescence of the particle; an algorithm for calculating an adjusted spectral unmixing matrix for the fluorescence of the particle, excluding one or more saturated data signals; and an algorithm for comparing the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix.

[0179] Computer-readable storage media can be used on one or more computer systems having a display and an operator input device. The operator input device can be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses memory having instructions stored thereon for executing steps of a subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor or one of other processors that may be available or to be available. The processor executes the operating system, and the operating system interacts with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of the functions of various computer programs written in various programming languages ​​(e.g., Java, Perl, C++), other high-level or low-level languages, and combinations thereof, as known in the art. The operating system, which typically cooperates with the processor, coordinates and executes the functions of 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 according to known techniques.

[0180] kit

[0181] This disclosure also includes kits, which include one or more of the integrated circuits described herein. In some embodiments, the kit may also include, for example, a program for the subject system or instructions for downloading the program via an Internet web protocol or cloud server, in the form of a computer-readable medium (e.g., a flash drive, USB storage, optical disc, DVD, Blu-ray disc, etc.). The kit may also include instructions for implementing the subject method. These instructions may exist in various forms within the subject kit, including one or more forms. One form of these instructions may be printed information on a suitable medium or substrate (e.g., on one or more sheets of paper with printed information, in the kit's packaging, in a packaging insert, etc.). Another form of these instructions is a computer-readable medium on which information is recorded, such as a disk, optical disc (CD), portable flash drive, etc. Yet another form of these instructions may be a website address that can be used via the Internet to access information at a deleted site.

[0182] practicality

[0183] The systems, methods, and computer systems described herein are applied in various applications where it is desirable to analyze and sort the particle composition of samples (e.g., biological samples) in a fluid medium. In some embodiments, the systems and methods described herein are applied in flow cytometry characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods are applied in the spectral study of transmitted light. Furthermore, the systems and methods are applied when it is desirable to increase the signal obtainable based on light acquired from (e.g., in a flow stream) a sample. In some instances, this disclosure is applied when it is used to enhance the measurement of light acquired from a sample in a flow stream within an irradiated flow cytometer. Embodiments of this disclosure are applied when it is desirable to provide flow cytometry with better cell sorting accuracy, better particle acquisition, particle charging efficiency, more precise particle charging, and better particle deflection during cell sorting.

[0184] Embodiments of this disclosure are also applicable to applications where it may be desirable to use cells prepared from biological samples for research, laboratory testing, or therapeutic purposes. In some embodiments, the subject methods and apparatus can facilitate the acquisition of corresponding cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate the acquisition of cells from fluid or tissue samples used as research or diagnostic samples for a disease (e.g., cancer). Similarly, the subject methods and systems can facilitate the acquisition of cells from fluid or tissue samples intended for use in therapy. Compared to conventional flow cytometry systems, the methods and apparatus of this disclosure allow for the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, fluids) with greater efficiency and lower cost.

[0185] Despite the appended claims, this disclosure is further defined by the following:

[0186] 1. A method comprising:

[0187] Detecting light from a sample containing particles in a liquid stream;

[0188] Multiple data signals are generated based on the detected light;

[0189] Identify one or more saturation data signals;

[0190] Generate a saturation signal index that includes the identified saturated data signal; and

[0191] The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

[0192] 2. The method according to item 1, wherein the saturation signal index comprises a binary word identifying one or more detector channels that are saturated.

[0193] 3. The method according to item 2, wherein the saturation signal index comprises a 32-bit binary word.

[0194] 4. The method according to item 2, wherein the saturation signal index comprises a 128-bit binary word.

[0195] 5. The method according to any one of items 1-4, wherein the method includes determining one or more parameters of particles in the fluid flow based on the generated data signal.

[0196] 6. The method according to item 5, wherein determining one or more parameters of the particle comprises: calculating a spectral unmixing matrix for the fluorescence of the particle.

[0197] 7. The method according to item 6 further comprises: adjusting the spectral unmixing matrix for the fluorescence of the particle based on the saturation signal index.

[0198] 8. The method according to item 7, wherein adjusting the spectral unmixing matrix comprises: excluding one or more of the saturated data signals.

[0199] 9. The method according to item 8, wherein calculating one or more parameters of the particle comprises:

[0200] Calculate the spectral unmixing matrix for the fluorescence of the particles;

[0201] Calculate the spectral unmixing matrix for adjusting the fluorescence of the particles by excluding one or more of the saturated data signals; and

[0202] The calculated spectral unmixing matrix is ​​compared with the calculated adjusted spectral unmixing matrix.

[0203] 10. The method according to any one of items 5-9, further comprising: classifying the particles based on one or more determined parameters of the particles.

[0204] 11. The method according to item 10, wherein classifying particles comprises:

[0205] Generate a 2D bitmap including the region of interest (ROI); and

[0206] Assign the particles to the ROI of the two-dimensional bitmap.

[0207] 12. The method according to item 11, wherein the method further comprises: determining whether one or more bits of the ROI of the specified particle include a saturation data signal.

[0208] 13. The method according to item 12, wherein determining whether one or more bits of the ROI of the specified particle include a saturation data signal comprises:

[0209] Apply a saturation signal index to the second two-dimensional bitmap to generate a saturation signal bitmap;

[0210] Compare the generated saturation signal bitmap with the ROI of the specified particles; and

[0211] One or more bits are saturated to identify the ROI of the specified particle.

[0212] 14. The method according to item 13, wherein Boolean logic is used to compare the saturation signal bitmap with the ROI of the specified particle.

[0213] 15. The method according to item 14, wherein the saturation signal bitmap is ANDed with the ROI of the specified particle.

[0214] 16. The method described in any one of items 1-15 further generates a particle sorting decision based on the adjusted particle classification index.

[0215] 17. The method according to any one of items 1-16, wherein detecting light from the sample in the liquid stream comprises: detecting light absorption, light scattering, fluorescence, or a combination thereof.

[0216] 18. The method according to any one of items 1-17, wherein the parameters of the particle are determined based on the scattered light from the particle.

[0217] 19. The method according to item 18, wherein the scattered light includes forward scattered light.

[0218] 20. The method according to item 18, wherein the scattered light includes side-scattered light.

[0219] 21. The method according to entry 18, wherein the parameters of the particle are calculated based on fluorescence from the particle.

[0220] 22. The method according to item 21, wherein the parameters of the particle are calculated based on fluorescence data encoded from the particle at frequency.

[0221] 23. The method according to any one of items 1-22 further includes sorting the particles.

[0222] 24. The method according to any one of items 1-23, wherein the parameters of the particle are calculated by an integrated circuit device.

[0223] 25. The method according to item 24, wherein the integrated circuit device is a field-programmable gate array (FPGA).

[0224] 26. The method according to item 24, wherein the integrated circuit device is an application-specific integrated circuit (ASIC).

[0225] 27. The method according to item 24, wherein the integrated circuit device is a complex programmable logic device (CPLD).

[0226] 28. The method according to any one of items 1-27 further comprises: irradiating the liquid stream with a light source.

[0227] 29. The method according to item 28, wherein the liquid stream is irradiated with a light source of wavelength from 200 nm to 800 nm.

[0228] 30. The method according to any one of items 28-29, wherein the method comprises: irradiating the liquid flow with a first frequency-shifted beam and a second frequency-shifted beam.

[0229] 31. The method according to item 30, wherein the first frequency-shifted beam comprises a local oscillator (LO) beam and the second frequency-shifted beam comprises a radio frequency comb beam.

[0230] 32. The method according to any one of items 30-31 further comprises:

[0231] Applying radio frequency drive signals to acousto-optic devices; and

[0232] The acousto-optic device is irradiated with a laser to generate the first frequency-shifted beam and the second frequency-shifted beam.

[0233] 33. The method according to item 32, wherein the laser is a continuous wave laser.

[0234] 34. A system comprising:

[0235] A light source is configured to illuminate a sample, including particles, in a liquid stream;

[0236] A light detection system includes a photodetector for detecting light from particles in the sample and generating multiple data signals based on the detected light; and

[0237] A processor, including memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0238] Identify one or more saturation data signals;

[0239] Generate a saturation signal index that includes the identified saturated data signal; and

[0240] The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

[0241] 35. The system according to item 34, wherein the saturation signal index comprises a binary word identifying one or more detector channels that are saturated.

[0242] 36. The system according to item 35, wherein the saturation signal index comprises a 32-bit binary word.

[0243] 37. The system according to item 35, wherein the saturation signal index comprises a 128-bit binary word.

[0244] 38. The system according to any one of items 34-37, wherein the processor includes a memory operatively coupled to the processor, wherein 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 fluid stream based on generated data signals.

[0245] 39. The system according to item 38, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate a spectral unmixing matrix for the fluorescence of the particles.

[0246] 40. The system according to item 39, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust a spectral unmixing matrix for the fluorescence of the particles based on the saturation signal index.

[0247] 41. The system according to item 40, wherein adjusting the spectral unmixing matrix comprises: excluding one or more of the saturated data signals.

[0248] 42. The system according to item 41, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0249] Calculate the spectral unmixing matrix for the fluorescence of the particles;

[0250] Calculate the spectral unmixing matrix for adjusting the fluorescence of the particles by excluding one or more of the saturated data signals; and

[0251] The calculated spectral unmixing matrix is ​​compared with the calculated adjusted spectral unmixing matrix.

[0252] 43. The system according to any one of items 38-42, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes 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.

[0253] 44. The system according to item 43, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0254] Generate a 2D bitmap including the region of interest (ROI); and

[0255] Assign the particle to the ROI of the two-dimensional bitmap.

[0256] 45. The system according to item 44, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to determine whether one or more bits of the ROI of the specified particle include a saturation data signal.

[0257] 46. ​​The system according to item 45, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to:

[0258] Apply a saturation signal index to the second two-dimensional bitmap to generate a saturation signal bitmap;

[0259] Compare the generated saturation signal bitmap with the ROI of the specified particles; and

[0260] One or more bits are saturated to identify the ROI of the specified particle.

[0261] 47. The system according to any one of items 34-46, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate a particle sorting decision based on an adjusted particle classification index.

[0262] 48. The system according to any one of items 34-47, wherein the optical detection system is configured to detect light absorption, light scattering, fluorescence, or a combination thereof.

[0263] 49. The system according to any one of items 34-48, wherein the light source includes: a beam generator assembly configured to generate at least a first frequency-shifted beam and a second frequency-shifted beam.

[0264] 50. The system according to item 49, wherein the beam generator includes an acousto-optic deflector.

[0265] 51. The system according to any one of items 49-50, wherein the beam generator includes a direct digital synthesis (DDS) RF comb generator.

[0266] 52. The system according to any one of items 49-51, wherein the beam generator assembly is configured to generate a frequency-shifted local oscillator beam.

[0267] 53. The system according to any one of items 49-52, wherein the beam generator assembly is configured to generate a plurality of frequency-shifted comb beams.

[0268] 54. The system according to any one of items 34-53, wherein the light source includes a laser.

[0269] 55. The system according to item 54, wherein the laser is a continuous wave laser.

[0270] 56. The system according to any one of items 34-55, wherein the system is a flow cytometer.

[0271] 57. The system according to any one of items 34-56 further includes a cell sorter.

[0272] 58. The system according to item 57, wherein the cell sorter includes a droplet deflector.

[0273] 59. An integrated circuit device, programmed to:

[0274] Identify one or more saturated data signals of light based on detected particles in the liquid stream;

[0275] Generate a saturation signal index that includes the identified saturated data signal; and

[0276] The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

[0277] 60. The integrated circuit device according to item 59, wherein the saturation signal index comprises a binary word identifying one or more detector channels that are saturated.

[0278] 61. The integrated circuit device according to item 60, wherein the saturation signal index comprises a 32-bit binary word.

[0279] 62. The integrated circuit device according to item 60, wherein the saturation signal index comprises a 128-bit binary word.

[0280] 63. An integrated circuit device according to any one of items 59-62, wherein the integrated circuit is programmed to determine one or more parameters of particles in the fluid flow based on generated data signals.

[0281] 64. The integrated circuit device according to item 63, wherein the integrated circuit is programmed to: calculate a spectral unmixing matrix for the fluorescence of the particles.

[0282] 65. The integrated circuit device according to item 64, wherein the integrated circuit is programmed to adjust the spectral unmixing matrix for the fluorescence of the particles based on a saturation signal index.

[0283] 66. The integrated circuit device according to item 65, wherein adjusting the spectral unmixing matrix includes: excluding one or more of the saturated data signals.

[0284] 67. The integrated circuit device according to item 66, wherein the integrated circuit is programmed to:

[0285] Calculate the spectral unmixing matrix for the fluorescence of the particles;

[0286] Calculate the spectral unmixing matrix for adjusting the fluorescence of the particles by excluding one or more of the saturated data signals; and

[0287] The calculated spectral unmixing matrix is ​​compared with the calculated adjusted spectral unmixing matrix.

[0288] 68. An integrated circuit device according to any one of items 63-67, wherein the integrated circuit is programmed to classify the particles based on one or more defined parameters of the particles.

[0289] 69. The integrated circuit device according to item 68, wherein the integrated circuit is programmed to:

[0290] Generate a 2D bitmap including the region of interest (ROI); and

[0291] Assign the particles to the ROI of the two-dimensional bitmap.

[0292] 70. The integrated circuit device according to item 69, wherein the integrated circuit is programmed to determine whether one or more bits of the ROI of a specified particle include a saturated data signal.

[0293] 71. The integrated circuit device according to item 70, wherein the integrated circuit is programmed to:

[0294] Apply a saturation signal index to the second two-dimensional bitmap to generate a saturation signal bitmap;

[0295] Compare the generated saturation signal bitmap with the ROI of the specified particles; and

[0296] One or more bits are saturated to identify the ROI of the specified particle.

[0297] 72. An integrated circuit device according to any one of items 59-71, wherein the integrated circuit is programmed to generate particle sorting decisions based on an adjusted particle classification index.

[0298] 73. An integrated circuit device according to any one of items 59-72, wherein the reconfigurable integrated circuit includes a field-programmable gate array (FPGA).

[0299] 74. An integrated circuit device according to any one of items 59-72, wherein the integrated circuit includes an application-specific integrated circuit (ASIC).

[0300] 75. An integrated circuit device according to any one of items 59-72, wherein the integrated circuit includes a complex programmable logic device (CPLD).

[0301] 76. A non-transitory computer-readable storage medium comprising instructions stored thereon for determining a droplet delay in a flow cytometer, the instructions comprising:

[0302] An algorithm for identifying one or more saturated data signals of light based on detected particles from a fluid stream; an algorithm for generating a saturated signal index including the identified saturated data signals; and

[0303] An algorithm for applying the saturation signal index to the particle classification index to generate an adjusted particle classification index.

[0304] 77. The non-transitory computer-readable storage medium according to item 76, wherein the saturation signal index comprises a binary word identifying one or more detector channels that are saturated.

[0305] 78. The non-transitory computer-readable storage medium according to item 77, wherein the saturation signal index comprises a 32-bit binary word.

[0306] 79. The non-transitory computer-readable storage medium according to item 77, wherein the saturation signal index comprises a 128-bit binary word.

[0307] 80. The non-transitory computer-readable storage medium according to any one of items 76-79, wherein the non-transitory computer-readable storage medium includes an algorithm for determining one or more parameters of particles in the fluid flow based on the generated data signal.

[0308] 81. The non-transitory computer-readable storage medium according to item 80, wherein the non-transitory computer-readable storage medium includes an algorithm for calculating a spectral unmixing matrix for the fluorescence of the particles.

[0309] 82. The non-transitory computer-readable storage medium according to item 81, wherein the non-transitory computer-readable storage medium includes an algorithm for adjusting a spectral unmixing matrix for the fluorescence of particles based on the saturation signal index.

[0310] 83. The non-transitory computer-readable storage medium according to item 82, wherein the non-transitory computer-readable storage medium includes: an algorithm for adjusting a spectral unmixing matrix, including excluding one or more of the saturated data signals.

[0311] 84. The non-transitory computer-readable storage medium according to item 83, wherein the non-transitory computer-readable storage medium comprises:

[0312] An algorithm for calculating the spectral unmixing matrix for the fluorescence of the particles;

[0313] An algorithm for calculating a spectral unmixing matrix for adjusting the fluorescence of the particles by excluding one or more of the saturated data signals; and

[0314] An algorithm for comparing the calculated spectral unmixing matrix with the calculated adjusted spectral unmixing matrix.

[0315] 85. A non-transitory computer-readable storage medium according to any one of items 80-84, wherein the non-transitory computer-readable storage medium includes an algorithm for classifying particles based on one or more determined parameters of the particles.

[0316] 86. The non-transitory computer-readable storage medium according to item 85, wherein the non-transitory computer-readable storage medium comprises:

[0317] An algorithm for generating a two-dimensional bitmap including a region of interest (ROI); and

[0318] An algorithm for assigning particles to the ROI of a two-dimensional bitmap.

[0319] 87. The non-transitory computer-readable storage medium according to item 86, wherein the non-transitory computer-readable storage medium includes an algorithm for determining whether one or more bits of the ROI of a specified particle include a saturated data signal.

[0320] 88. The non-transitory computer-readable storage medium according to item 87, wherein the non-transitory computer-readable storage medium comprises:

[0321] An algorithm for applying a saturation signal index to a second two-dimensional bitmap to generate a saturation signal bitmap;

[0322] An algorithm for comparing the generated saturated signal bitmap with the ROI of a specified particle; and

[0323] An algorithm for identifying one or more bit saturations of the ROI of a specified particle.

[0324] 89. The non-transitory computer-readable storage medium according to any one of items 76-88, wherein the non-transitory computer-readable storage medium includes an algorithm for generating particle sorting decisions based on an adjusted particle classification index.

[0325] Although the invention has been described in detail by way of illustration and example for the purpose of clear understanding, it will be readily apparent to those skilled in the art, based on the teachings of the invention, that certain changes and modifications may be made thereto without departing from the spirit and scope of the appended claims.

[0326] Therefore, the foregoing only illustrates the principles of the invention. It will be understood that those skilled in the art will be able to design various arrangements embodying the principles of the invention and included within its spirit and scope, but not expressly described or shown herein. Furthermore, all examples and conditional descriptions listed herein are primarily intended to help the reader understand the principles of the invention and the concepts beyond the prior art contributed by the inventors, and are to be construed as not being limited to these expressly listed examples and conditions. Moreover, all statements herein listing the principles, aspects, and embodiments of the invention, as well as certain examples thereof, are intended to include both structural and functional equivalents. Furthermore, these equivalents are intended to include both currently known equivalents and future-developed equivalents, i.e., any structurally independent element developed to perform the same function. Furthermore, regardless of whether the disclosures are expressly listed in the claims, this document is not intended to specifically disclose such disclosures to the public.

[0327] Therefore, the scope of the invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the invention are embodied by the appended claims. In the claims, 35U.SC §112(f) or 35U.SC §112(6) expressly defines that such limitation is made in the claims only when the exact phrase “means for…” or the exact phrase “step for…” is referenced at the beginning of the limitation of the claims, and if such exact phrases are not used in the limitation of the claims, 35U.SC §112(f) or 35U.SC §112(6) is not invoked.

Claims

1. A method for adjusting a particle classification index, comprising: A sample containing particles in a liquid stream is passed through the detection zone of a particle analyzer, where the particles are exposed to illumination light from a light source. A light detection system including a photodetector is used to detect light from particles in the sample, wherein detecting light from particles in the sample includes detecting light absorption, light scattering, fluorescence, or a combination thereof; Multiple data signals are generated based on the light detected by the optical detection system; One or more saturated data signals are identified from the plurality of data signals, wherein the saturated data signals exceed the maximum range that one or more components of the optical detection system can measure; Generate a saturation signal index that includes the identified saturated data signal; and The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

2. The method according to claim 1, wherein, The saturation signal index includes a binary word that identifies one or more detector channels that are saturated.

3. The method according to claim 1, wherein, The method includes determining one or more parameters of particles in the fluid flow based on the generated data signal.

4. The method according to claim 3, wherein, Determining one or more parameters of the particle includes calculating a spectral unmixing matrix for the fluorescence of the particle.

5. The method according to claim 4, further comprising: The spectral unmixing matrix used for the fluorescence of the particle is adjusted based on the saturation signal index.

6. The method according to claim 5, wherein, Adjusting the spectral unmixing matrix includes excluding one or more saturated data signals.

7. The method according to claim 6, wherein, Calculating one or more parameters of the particle includes: Calculate the spectral unmixing matrix for the fluorescence of the particles; Calculate the adjusted spectral unmixing matrix for the fluorescence of the particles, excluding one or more of the saturated data signals; and The calculated spectral unmixing matrix is ​​compared with the calculated adjusted spectral unmixing matrix.

8. The method according to any one of claims 3-7, further comprising: The particles are classified based on one or more defined parameters.

9. The method according to any one of claims 1-7, further comprising generating a particle sorting decision based on the adjusted particle classification index.

10. The method according to any one of claims 1-7, wherein, Detecting light from the sample in the liquid stream includes detecting light absorption, light scattering, fluorescence, or a combination thereof.

11. The method according to any one of claims 1-7, further comprising sorting the particles.

12. The method according to any one of claims 1-7, wherein, The parameters of the particles are calculated using integrated circuit devices.

13. The method according to any one of claims 1-7, further comprising: The liquid flow is illuminated using a light source.

14. The method according to any one of claims 1-7, wherein, The method includes irradiating the liquid flow using a first frequency-shifted beam and a second frequency-shifted beam.

15. A system for adjusting a particle classification index, comprising: A light source is configured to illuminate a sample, including particles, in a liquid stream; A light detection system includes a photodetector for detecting light from particles in the sample and generating multiple data signals based on the light detected by the light detection system, wherein detecting light from particles in the sample includes detecting light absorption, light scattering, fluorescence, or a combination thereof; as well as A processor, including memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to: One or more saturated data signals are identified from the plurality of data signals, wherein the saturated data signals exceed the maximum range that one or more components of the optical detection system can measure; Generate a saturation signal index that includes the identified saturated data signal; and The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

16. An integrated circuit device, programmed to: Identify one or more saturated data signals of light from particles in a liquid flow, based on light detected by an optical detection system, wherein the saturated data signals exceed the maximum range that one or more components of the optical detection system can measure; Generate a saturation signal index that includes the identified saturated data signal; and The saturation signal index is applied to the particle classification index to generate an adjusted particle classification index.

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