Method for determining a data filter for detecting particles in a sample, and system and method using same

Abstract

Aspects of the present disclosure include methods for determining and applying a data signal filter for detecting particles (e.g., small particles such as extracellular vesicles) in a particle analyzer. The method, according to certain embodiments, includes detecting light from particles in a flowstream with a light detection system, generating a data signal waveform in response to the detected light from the particles in the flowstream, determining characteristics of the data signal waveform, and calculating the data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the method includes determining a trigger metric for detecting particles in a sample based on the filtered data signal waveform. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

Description

[Technical Field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS Pursuant to 35 U.S.C. § 119(e), this application claims priority to the filing date of U.S. Provisional Patent Application No. 63 / 445,433, filed February 14, 2023, the disclosure of which is incorporated herein by reference in its entirety. [Background technology]

[0002] Introduction Flow-type particle sorting systems, such as sorting flow cytometers, are used to sort particles in a fluid sample based on at least one measured characteristic of the particles. Flow cytometers utilize fluorescence or scattered light to measure the physical and chemical properties of single cells. In flow-type particle sorting systems, particles, such as molecules, analyte-bound beads, or individual cells, in fluid suspension pass through a detection region where a sensor detects particles contained in the stream of the type to be sorted. Upon detecting particles of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particles of interest.

[0003] Particle detection is typically performed by passing a fluid stream through a detection region where particles are exposed to illumination from one or more lasers, and their light scattering and fluorescence properties are measured. Detection is performed using one or more photosensors to facilitate independent measurement of the fluorescence of each distinct fluorophore. Flow cytometers, with their high throughput and multivariate analysis capabilities, have recently been used to study small biological particles such as extracellular vesicles (EVs). EVs have been shown to play an important role in intercellular signaling. However, their tiny size results in weak scattering and fluorescence signals, making them difficult to separate from the noise and reliably identify. In flow cytometers, background noise and variations in system settings can lead to variations in light detection. Data analysis in flow cytometry assumes that noise, as well as changes to illumination and detector settings, are static and constant throughout an experiment. Summary of the Invention

[0004] Aspects of the present disclosure include methods for determining and applying a data signal filter for detecting particles (e.g., small particles such as extracellular vesicles) in a particle analyzer. The method, according to certain embodiments, includes detecting light from particles in a flowstream with a light detection system, generating a data signal waveform in response to the detected light from the particles in the flowstream, determining characteristics of the data signal waveform, and calculating the data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the method includes determining a trigger metric for detecting particles in a sample based on the filtered data signal waveform. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0005] In embodiments, the data signal waveform characteristics are used to calculate a data signal filter, for example, the data signal filter is used to optimize trigger performance for small particle detection by a particle analyzer. In some cases, the data signal waveform characteristics include a width parameter of the data signal waveform. In particular cases, the width parameter includes a ratio of waveform area to waveform height. In particular cases, the data signal waveform has a Gaussian distribution.

[0006] In some embodiments, the calculated data signal filter, when applied to the data signal from the light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the data signal filter matches a ground truth data signal waveform generated in response to the detected light. In particular cases, the data signal filter is calculated according to:

[0007]

number

[0008] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0009] In some embodiments, the data signal filter is an estimate of a ground truth data signal waveform. In some cases, the method includes calculating a linear analog data signal filter from the determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the method includes calculating a data signal filter from the determined characteristics of the data signal waveform, the data signal filter comprising one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0010] In some embodiments, the data signal filter is based on an aspect of the particle in the flow stream. In some cases, the data signal filter is based on the width of the particle. In some embodiments, the particle of interest is 1000 nm or less in diameter, e.g., in the range of 50 nm to 800 nm in diameter. In particular cases, the particle is an extracellular vesicle. In some embodiments, the generated data signal waveform is independent of particle size. In certain embodiments, the size of the particle (e.g., determined by the width of the particle) is smaller than the illumination beam profile of the light source.

[0011] In some embodiments, the method includes applying a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the method can include detecting light from particles of a sample in the flowstream with the optical detection system, generating a data signal waveform in response to the detected light, and applying a calculated data signal filter to the generated data signal waveform, the data signal filter being calculated based on determined characteristics of the data signal generated by the optical detection system. In some cases, a trigger metric for detecting particles in the flowstream by the optical detection system is determined based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise of the data signal waveform.

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

[0013] Aspects of the present disclosure also include systems for implementing the subject methods. The system, according to certain embodiments, includes a light source configured to illuminate particles in a flowstream, a light detection system having a plurality of light detectors, and a processor having a memory operably coupled to the processor, the memory including instructions that, when executed by the processor, cause the processor to generate a data signal waveform in response to the detected light from the particles in the flowstream, determine characteristics of the data signal waveform, and calculate a data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the memory includes instructions for determining a width parameter of the data signal waveform. In some cases, the memory includes instructions for determining one or more of a waveform area, a waveform height, and a ratio of the waveform area to the waveform height. In some cases, the data signal processed by the system has a Gaussian distribution.

[0014] In some embodiments, the memory includes instructions for calculating a data filter based on an aspect of particles in the flowstream. In some cases, the data signal filter is based on a width of the particles. In some embodiments, the memory includes instructions for calculating a data filter based on parameters of particles in the flowstream having a diameter of 1000 nm or less, such as when the diameter is between 50 nm and 800 nm.

[0015] In some embodiments, the memory includes instructions for calculating a data signal filter that, when applied to a data signal from the light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the memory includes instructions for matching the data signal filter to a ground truth data signal waveform produced in response to the detected light. In particular cases, the memory includes instructions for calculating a data signal filter according to:

[0016]

number

[0017] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0018] In some embodiments, the memory includes instructions for computing a data signal filter that is an estimate of the ground truth data signal waveform. In some cases, the memory includes instructions for computing a linear analog data signal filter from the determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the memory includes instructions for computing a data signal filter from the determined characteristics of the data signal waveform, the data signal filter including one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0019] In some embodiments, the system includes a memory having stored thereon instructions for applying a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the system may include a light source configured to illuminate sample particles in the flow stream, an optical detection system having a plurality of optical detectors, and a processor having a memory operably coupled to the processor, the memory including instructions that, when executed by the processor, cause the processor to generate a data signal waveform in response to the detected light and apply a data signal filter to the generated data signal waveform, the data signal filter being calculated based on determined characteristics of the data signal generated by the optical detection system. In some cases, the memory includes instructions for determining a trigger metric for detecting sample particles based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise of the data signal waveform.

[0020] An integrated circuit device programmed to apply a data signal filter to detect particles in a flowstream is also provided. According to certain embodiments, the integrated circuit is programmed to determine characteristics of a data signal waveform generated in response to light detected from an illuminated particle of a sample in the flowstream and calculate the data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the integrated circuit is programmed to determine a width parameter of the data signal waveform. In some cases, the integrated circuit is programmed to determine one or more of a waveform area, a waveform height, and a ratio of the waveform area and the waveform height. In some cases, the data signal processed by the system has a Gaussian distribution.

[0021] In some embodiments, the integrated circuit is programmed to calculate a data filter based on an aspect of the particles in the flow stream. In some cases, the data signal filter is based on a width of the particles. In some embodiments, the integrated circuit is programmed to calculate a data filter based on parameters of particles in the flow stream having a diameter of 1000 nm or less, such as when the diameter is between 50 nm and 800 nm.

[0022] In some embodiments, the integrated circuit is programmed to calculate a data signal filter that, when applied to a data signal from the light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the integrated circuit is programmed to match the data signal filter to a ground truth data signal waveform produced in response to the detected light. In particular cases, the integrated circuit is programmed to calculate the data signal filter according to:

[0023]

number

[0024] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0025] In some embodiments, the integrated circuit is programmed to calculate a data signal filter that is an estimate of the ground truth data signal waveform. In some cases, the integrated circuit is programmed to calculate a linear analog data signal filter from the determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the integrated circuit is programmed to calculate a data signal filter from the determined characteristics of the data signal waveform, the data signal filter including one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0026] In some embodiments, the integrated circuit is programmed to apply a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the integrated circuit is programmed to generate a data signal waveform in response to the detected light and apply a data signal filter to the generated data signal waveform, the data signal filter being calculated based on the determined characteristics of the data signal generated by the optical detection system. In some cases, the integrated circuit is programmed to determine a trigger metric for detecting particles in the sample based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise in the data signal waveform.

[0027] A non-transitory computer-readable storage medium having instructions with an algorithm for determining a data signal filter for detecting particles in a particle analyzer is also described. The non-transitory computer-readable storage medium according to certain embodiments comprises an algorithm for determining characteristics of a data signal waveform generated in response to light detected from illuminated particles of a sample in a flow stream and calculating a data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for determining a width parameter of the data signal waveform. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining one or more of a waveform area, a waveform height, and a ratio of the waveform area to the waveform height. In some cases, the data signal processed by the system has a Gaussian distribution.

[0028] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data filter based on an aspect of particles in the flowstream. In some cases, the data filter is based on a width of the particles. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data filter based on parameters of particles in the flowstream having a diameter of 1000 nm or less, such as when the diameter is between 50 nm and 800 nm.

[0029] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data signal filter that, when applied to a data signal from a light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the non-transitory computer-readable storage medium includes an algorithm for matching the data signal filter to a ground truth data signal waveform produced in response to the detected light. In particular cases, the non-transitory computer-readable storage medium includes an algorithm for calculating a data signal filter according to:

[0030]

number

[0031] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0032] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for computing a data signal filter that is an estimate of a ground truth data signal waveform. In some cases, the non-transitory computer-readable storage medium includes an algorithm for computing a linear analog data signal filter from determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the non-transitory computer-readable storage medium includes an algorithm for computing a data signal filter from determined characteristics of the data signal waveform, the data signal filter including one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0033] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm that applies a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the non-transitory computer-readable storage medium includes an algorithm that generates a data signal waveform in response to the detected light and applies a data signal filter to the generated data signal waveform, the data signal filter being calculated based on determined characteristics of the data signal generated by the optical detection system. In some cases, the non-transitory computer-readable storage medium includes an algorithm that determines a trigger metric for detecting particles in a sample based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise in the data signal waveform.

[0034] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which: [Brief explanation of the drawings]

[0035] [Figure 1A] 10 illustrates a data signal waveform generated in response to detected light from an illuminated particle according to certain embodiments. [Figure 1B] 10 illustrates data signal waveforms generated for particles having different widths in accordance with certain embodiments. [Figure 2] 1 illustrates a flowchart for calculating and applying a data signal filter to a data signal waveform in accordance with certain embodiments. [Figure 3A-1] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3A-2] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3B] 1 illustrates image-enabled particle sorting data processing in accordance with certain embodiments. [Figure 4A] FIG. 1 illustrates a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 4B]1 illustrates a flow cytometer according to certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of an example particle analyzer control system in accordance with certain embodiments. [Figure 6A] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 7] 1 illustrates a block diagram of a computing system in accordance with certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0036] Aspects of the present disclosure include methods for determining and applying a data signal filter for detecting particles (e.g., small particles such as extracellular vesicles) in a particle analyzer. The method, according to certain embodiments, includes detecting light from particles in a flowstream with a light detection system, generating a data signal waveform in response to the detected light from the particles in the flowstream, determining characteristics of the data signal waveform, and calculating the data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the method includes determining a trigger metric for detecting particles in a sample based on the filtered data signal waveform. Systems and integrated circuit devices (e.g., field programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0037] Before describing the present invention in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0038] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limit of that range, and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0039] Certain ranges are presented herein with the term "about" preceding the numerical value. The term "about" is used herein to provide literal support for the exact number preceded by the term, as well as a number that is close to or approximately the number preceded by the term. In determining whether a number is close to or approximately a specifically stated number, the unstated near or approximate number may be a number that, in the context in which it is presented, represents the substantial equivalent of the specifically stated number.

[0040] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are now described.

[0041] All publications and patents cited herein are incorporated by reference to disclose and describe the methods and / or materials for which the publications are cited, as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0042] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for use of exclusive terminology, such as "solely," "only," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0043] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has individual components and features that may be readily separated or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method can be carried out in the order of events recited or in any other order that is logically possible.

[0044] Although the apparatus and methods are described for grammatical fluidity with functional descriptions, it is to be clearly understood that the claims should not be construed as necessarily limited by "means" or "step" limitation constructions unless expressly formulated under 35 U.S.C. § 112, but rather should be given the full range of meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and that if a claim is expressly formulated under 35 U.S.C. § 112, then the full statutory equivalents under 35 U.S.C. § 112 should be given.

[0045] As summarized above, the present disclosure provides methods for determining and applying data signal filters for detecting particles in a particle analyzer (e.g., a flow cytometer). In further describing embodiments of the present disclosure, methods for determining data signal filters, such as by matching a calculated filter to a ground truth waveform generated from detected light from illuminated particles of a sample, or by calculating a data signal filter that is an estimate of the ground truth data signal waveform, are first described in more detail. Systems and integrated circuit devices programmed to implement the subject methods are then described. A non-transitory computer-readable storage medium is then provided.

[0046] Method for determining and applying a data signal filter for detecting particles in a flow stream - Patent Application 20070122997 Aspects of the present disclosure include methods for determining a data signal filter for detecting particles (e.g., small particles such as extracellular vesicles) in a particle analyzer. As described in more detail below, the subject methods provide for improving the sensitivity and accuracy of data signal measurements by an optical detection system. The methods described herein provide for calculating a data signal filter that can be used to improve trigger performance for small particle detection, in certain cases, including when no changes are made to the hardware components (e.g., optical detectors) of the particle analyzer system. In some cases, determining a data signal filter for particles of a sample can increase the sensitivity (e.g., increase the signal-to-noise ratio) of the data signal measurement by 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, including 99% or more. In some embodiments, the calculated data signal filter can be used to adjust and optimize a threshold for a trigger metric for detecting particles of a sample. In some cases, the methods described herein provide an increase in the amplitude-based threshold of the trigger metric of 5% or more, such as 10% or more, for example 15% or more, for example 25% or more, for example 50% or more, for example 75% or more, and 99% or more.

[0047] In some embodiments, the subject methods provide improved detection accuracy for small particles in a flow stream, such as when the particles have diameters of 1000 nm or less, e.g., 900 nm or less, e.g., 800 nm or less, e.g., 700 nm or less, e.g., 600 nm or less, e.g., 500 nm or less, e.g., 400 nm or less, e.g., 300 nm or less, including particles having diameters of 200 nm or less. In some cases, the particles of interest have diameters smaller than the width of the beam profile of illumination by the light source. In certain cases, techniques exist that allow for multiparametric analysis and identification and classification of extracellular vesicles, which are often unreliable in flow cytometry due to the inherent high noise levels of particles resulting from weak scattering and low fluorescence intensity signals.

[0048] In carrying out the subject method, a sample containing particles is illuminated with a light source, and light from the sample is detected by a light detection system having a plurality of 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 of tissues, cells, or components of a whole organism, plant, fungus, or animal, and in some cases includes blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, umniotic cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both intact organisms or subsets of their tissues, as well as homogenates made from organisms or subsets of their tissues, including, but not limited to, lysates or extracts, such as plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and genitourinary tract sections, tears, saliva, milk, blood cells, tumors, and organs. The biological sample can be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases, the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger stick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0049] In certain embodiments, the source of the sample is a "mammal" or "mammalian," a term used broadly to describe organisms belonging to the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is a human. The method may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. It should be understood that while the present invention may be applied to samples from human subjects, the method may also be performed on samples from other animal subjects (i.e., "non-human subjects"), including, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0050] In carrying out the subject methods, a particle-containing sample (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source, emitting light having a wide range of wavelengths, including those ranging from 50 nm or greater, e.g., 100 nm or greater, e.g., 150 nm or greater, e.g., 200 nm or greater, e.g., 250 nm or greater, e.g., 300 nm or greater, e.g., 350 nm or greater, e.g., 400 nm or greater, and 500 nm or greater. For example, one suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes irradiating with a broadband light source, broadband light source protocols of interest include, but are not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a wide spectrum LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0051] In other embodiments, the method comprises irradiating with a narrowband light source that emits a specific wavelength or narrow range of wavelengths, for example, a light source that emits light in a narrow range, such as a range of 50 nm or less, for example, 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less (including light sources that emit specific wavelengths of light (i.e., monochromatic light)). When the method comprises irradiating with a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, a narrow wavelength LED, a laser diode, or a broadband light source coupled to one or more optical bandpass filters, a diffraction grating, a monochromator, or any combination thereof.

[0052] In certain embodiments, the method includes irradiating the sample with one or more lasers. As noted above, the type and number of lasers will depend on the sample and the desired light to be collected, and may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other cases, the method includes irradiating the flowstream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In still other instances, the method includes irradiating the flowstream with a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In still other instances, the method includes irradiating the flowstream with a solid state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:YCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a ytterbium2O3 laser, or a cerium-doped laser, and combinations thereof.

[0053] The sample may be illuminated with one or more of the above-mentioned light sources, including two or more light sources, three or more light sources, four or more light sources, five or more light sources, etc., including ten or more light sources. The light source may include any combination of light source types. For example, in some embodiments, the method includes illuminating the sample of the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid state lasers.

[0054] The sample may be irradiated with a wavelength in the range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm (including 400 nm to 800 nm). For example, if the light source is a broadband light source, the sample may be irradiated with a wavelength in the range of 200 nm to 900 nm. In other cases, where the light source includes multiple narrowband light sources, the sample may be irradiated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is irradiated with a specific wavelength in the range of 200 nm to 700 nm, such as a laser array including the gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers described above.

[0055] When two or more light sources are used, the sample can be illuminated by the light sources simultaneously, sequentially, or a combination thereof. For example, each light source can illuminate the sample simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When two or more light sources are used to sequentially illuminate the sample, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include illuminating the sample with a light source (e.g., a laser) for a period ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, inclusive. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source can be the same or different.

[0056] The time between illumination by each light source can also be independently variable, optionally separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and 60 microseconds or more. For example, the time between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0057] The sample can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the sample in the sample with the light source. In other cases, the sample is illuminated by the light source at discrete intervals, including, for example, every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0058] Depending on the light source, the sample may be illuminated from a variety of distances including 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, such as 0.5 mm or more, for example 1 mm or more, such as 2.5 mm or more, for example 5 mm or more, such as 10 mm or more, for example 15 mm or more, such as 25 mm or more, and 50 mm or more. The angle of illumination may also be variable, ranging from 10° to 90°, such as 15° to 85°, for example 20° to 80°, such as 25° to 75°, for example 30° to 60°, such as 90°.

[0059] In certain embodiments, the method includes irradiating the sample with two or more frequency-shifted light beams. As described above, a light beam generator component having a laser and an acousto-optical device for frequency-shifting the laser light may be used. In these embodiments, the method includes irradiating the acousto-optical device with a laser. Depending on the desired wavelength of light produced in the output laser beam (e.g., for use in irradiating the sample in the flow stream), the laser may have a specific wavelength between 200 nm and 1500 nm, e.g., between 250 nm and 1250 nm, e.g., between 300 nm and 1000 nm, e.g., between 350 nm and 900 nm (including 400 nm and 800 nm). The acousto-optical device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, or may include ten or more lasers. The laser may include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0060] When two or more lasers are used, the acousto-optical device can be illuminated by the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be illuminated by each of the lasers simultaneously. In other embodiments, the acousto-optical device is illuminated sequentially by each of the lasers. When two or more lasers are used to sequentially illuminate the acousto-optical device, the time for which each laser illuminates the acousto-optical device can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include illuminating the acousto-optical device with the laser for a period ranging from 0.001 microseconds to 100 microseconds, e.g., from 0.01 microseconds to 75 microseconds, e.g., from 0.1 microseconds to 50 microseconds, e.g., from 1 microsecond to 25 microseconds, inclusive. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0061] The time period between illumination by each laser can also be independently variable, separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more (including 60 microseconds or more), as desired. For example, the time between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially by more than two (i.e., three or more) lasers, the delay between illumination by each laser can be the same or different.

[0062] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the acousto-optic device with a laser. In other cases, the acousto-optic device is illuminated with a laser at discrete intervals, including, for example, every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, every 1000 milliseconds, or some other interval.

[0063] Depending on the laser, the acousto-optic device may be illuminated from a variety of distances, including 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, such as 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more. The angle of illumination may also be variable, ranging from 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, for example 30° to 60°, for example 90°.

[0064] In an embodiment, the method includes applying a high frequency drive signal to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams (including, for example, 3 or more high frequency drive signals, such as 4 or more high frequency drive signals, for example 5 or more high frequency drive signals, for example 6 or more high frequency drive signals, for example 7 or more high frequency drive signals, for example 8 or more high frequency drive signals, for example 9 or more high frequency drive signals, for example 10 or more high frequency drive signals, for example 15 or more high frequency drive signals, for example 25 or more high frequency drive signals, for example 50 or more high frequency drive signals, and 100 or more high frequency drive signals).

[0065] The angularly deflected laser beams generated by the high frequency drive signals each have an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying a high frequency drive signal having an amplitude sufficient to produce an angularly deflected laser beam having a desired intensity. In some cases, each of the applied high frequency drive signals independently has an amplitude of about 0.001 V to about 500 V, e.g., about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to about 50 V, e.g., about 2 V to about 40 V, e.g., about 3 V to about 30 V, including about 5 V to about 25 V. In some embodiments, each of the applied high frequency drive signals has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0066] In these embodiments, the angularly deflected laser beams within the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly deflected laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more (including an overlap of 100 μm or more).

[0067] In certain instances, the optical fibers may be used in a variety of applications, such as those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as in U.S. Pat. Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and U.S. Pat. To generate a frequency-encoded image, such as those described in U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference, the flow stream is illuminated with multiple beams of wavenumber-shifted light to image cells in the flow stream by fluorescence imaging using radio frequency-tagged emission (FIRE).

[0068] As mentioned above, in various embodiments, light from the illuminated sample is conveyed to a light detection system and measured by a plurality of light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a wavelength range (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method comprises measuring a wavelength of light corresponding to the fluorescence peak wavelength of the fluorophore, hi some embodiments, the method comprises measuring light collected across the fluorescence spectrum of each fluorophore in the sample.

[0069] The collected light can be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other cases, the light is measured at discrete intervals, including measuring the light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0070] Measurements of the collected light may be made one or more times during the subject method, such as two or more times, such as three or more times, such as five or more times, and ten or more times. In certain embodiments, light propagation is measured two or more times, and the data for a particular instance is averaged.

[0071] The light from the sample may be measured at one or more wavelengths, for example 5 or more different wavelengths, such as 10 or more different wavelengths, for example 25 or more different wavelengths, such as 50 or more different wavelengths, for example 100 or more different wavelengths, such as 200 or more different wavelengths, for example 300 or more different wavelengths, including measuring light collected at 400 or more different wavelengths.

[0072] In embodiments, the method includes generating a data signal waveform in response to light detected from particles in the flowstream. In some cases, the data signal waveform is generated from one or more fluorescence detectors, including two or more, three or more, four or more, five or more, six or more, eight or more, twelve or more, sixteen or more, twenty-four or more, thirty-two or more, sixty-four or more, and even 128 or more fluorescence detectors. In some cases, light from the illuminated particles is detected in one or more photodetector channels, for example, two or more, for example, four or more, for example, eight or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, 128 or more photodetector channels. In some embodiments, the data signal waveform includes data components obtained (or derived) from light from other detectors, such as detected light absorption or detected light scattering. In some cases, one or more data components of the data signal waveform are generated from detected light absorption from the sample in a bright-field photodetector. In other cases, one or more data components of the data signal waveform are generated from light scatter detected from the sample, such as in a side scatter detector, a forward scatter detector, or a combination of a side scatter detector and a forward scatter detector.

[0073] The generated data signal waveforms are, in certain embodiments, plotted as a function of signal strength over time. In some cases, the data signal waveforms are collected over a time window of 0.000001 ms or more, including time windows of, for example, 0.000005 ms or more, such as 0.00001 ms or more, such as 0.00005 ms or more, such as 0.0001 ms or more, such as 0.0005 ms or more, such as 0.001 ms or more, such as 0.005 ms or more, such as 0.01 ms or more, such as 0.05 ms or more, such as 0.1 ms or more, such as 0.5 ms or more, such as 1 ms or more, such as 2 ms or more, such as 3 ms or more, such as 4 ms or more, such as 5 ms or more, such as 6 ms or more, such as 7 ms or more, such as 8 ms or more, such as 9 ms or more, such as 10 ms or more, and 100 ms or more. In certain embodiments, the data signal waveform is collected over a time frame of 0.000001 ms to 10 ms, such as 0.00001 ms to 9.5 ms, such as 0.0001 ms to 9 ms, such as 0.001 ms to 8.5 ms, such as 0.01 ms to 8 ms, inclusive.

[0074] In some cases, the data signal exhibits signal peaks having width parameters of 0.000001 ms or more, such as 0.000005 ms or more, such as 0.00001 ms or more, such as 0.00005 ms or more, such as 0.0001 ms or more, such as 0.0005 ms or more, such as 0.001 ms or more, such as 0.005 ms or more, such as 0.01 ms or more, such as 0.05 ms or more, such as 0.1 ms or more, such as 0.5 ms or more, such as 1 ms or more, for example 2 ms or more, such as 3 ms or more, for example 4 ms or more, such as 5 ms or more, for example 6 ms or more, such as 7 ms or more, for example 8 ms or more, such as 9 ms or more, such as 10 ms or more, and including width parameters of 100 ms or more. For example, the data signal waveform may have a width parameter in the range of 0.000001 ms to 10 ms, such as 0.00001 ms to 9.5 ms, such as 0.0001 ms to 9 ms, such as 0.001 ms to 8.5 ms, such as 0.01 ms to 8 ms, and including 0.1 ms to 7.5 ms.

[0075] In some embodiments, the data signal waveform includes a noise component. The term "noise" is used herein in its conventional sense to refer to signal measurements produced by a photodetector that result from components unrelated to the detected light from the illuminated particle and may include thermal noise components, shot noise components, dark current noise components, electronic noise components, or other random fluctuations in the detector signal. In some instances, the noise component is calculated as the root mean square of the data signal outside the signal peak regions of the data signal waveform.

[0076] In some embodiments, the data signal waveform includes measurement variance. Measurement variance refers to variations that occur during the data acquisition process, such as variations in light detection, data signal generation, or sample illumination. In some cases, measurement variance includes variance in photodetector gain for one or more of the photodetectors of the light detection system. In some cases, measurement variance includes variance in trigger thresholds for one or more of the photodetectors of the light detection system. In some cases, measurement variance includes variance in light detection time for each photodetector for each particle. In some cases, measurement variance includes variance in photonic shot noise detected by each photodetector for each particle.

[0077] In some embodiments, the data signal waveform includes a signal peak having a signal peak height and width. In some cases, the signal peak height is the intensity or amplitude of the data signal above the root mean square of the noise component. In other cases, the signal peak height is the intensity or amplitude of the data signal above a predetermined position within the root mean square of the noise component, such as the midpoint within the root mean square of the noise component. FIG. 1A shows a data signal waveform generated in response to detected light from an illuminated particle, plotted as a function of signal intensity or amplitude and time. In FIG. 1A, the data signal waveform generated in response to detected light from the illuminated particle includes a signal peak 101 having a peak height 102, a peak width 103, and a peak area 104. The data signal waveform also includes a noise component 105. The noise component, in some cases, is characterized by the noise root mean square (RMS) 106. In some cases, the signal peak height 102 of the data signal waveform 101 is determined from the upper limit of the root mean square of the noise to the peak amplitude of the data signal waveform 101. In some cases, the signal peak height 102 of the data signal waveform 101 is determined from a predetermined location within the noise rms, such as the midpoint within the noise rms.

[0078] In some cases, the method includes determining one or more of the height of the data signal waveform, the width of the data signal waveform, the area of ​​the data signal waveform, combinations thereof, or ratios of one or more of the width of the data signal waveform, the height of the data signal waveform, and the area of ​​the data signal waveform. In particular cases, the method includes determining a width parameter of the data signal waveform. In some embodiments, the width parameter is the ratio of the waveform area to the waveform height. In certain embodiments, the data signal waveform has a Gaussian distribution. In other embodiments, the data signal waveform has a super-Gaussian distribution.

[0079] In embodiments, the data signal filter is calculated from determined characteristics of the data signal waveform. In some instances, the characteristics are one or more of: data signal height, data signal width, data signal waveform area, or a ratio of one or more of data signal waveform width, data signal waveform height, and data signal waveform area. In some embodiments, the data signal filter is determined from the ratio of data signal waveform area to data signal waveform height.

[0080] In some embodiments, the data signal filter determined from the width parameter of the data signal waveform is a matched filter to the ground truth waveform generated by the optical detection system. In some cases, the matched filter is a calculated data signal filter that, when applied to the data signal from the optical detection system, produces a data signal with the maximum signal-to-noise ratio. In other words, the matched filter produces the best signal-to-noise ratio in the presence of some additive stochastic noise. In some cases, the matched filter has the same functional form as the ground truth signal. In some embodiments, the data signal filter is a linear filter that maximizes a trigger metric, as described in more detail below.

[0081] In some embodiments, the data signal filter is calculated by considering a time series signal x(t) corrupted by additive noise n(t), x(t)=s(t)+n(t), where s(t) is the underlying ground truth signal generated by the optical detection system. In particular cases, the data signal filter is calculated by function optimization according to:

[0082]

number

[0083] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0084] In some embodiments, the method includes calculating a data signal filter that is an estimate of the matched filter. In some cases, the method includes calculating a linear analog data signal filter from determined characteristics (e.g., a width parameter) of the data signal waveform. In some cases, the linear analog data signal filter includes a finite impulse response filter. In other cases, the linear analog data signal filter includes an infinite impulse response filter. In particular cases, the method includes calculating a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter from the determined characteristics of the data signal waveform.

[0085] In some embodiments, the data signal filter is calculated based on an aspect of the particle. In some cases, the data signal filter is based on spatial data of the particle. In some cases, the spatial data includes a horizontal size dimension of the particle, a vertical size dimension of the particle, a ratio of particle sizes along two different dimensions, or a relative size of a particle component (e.g., a ratio of a horizontal dimension of the nucleus to a horizontal dimension of the cytoplasm of a cell). In certain cases, the data signal filter is calculated based on a width of the particle. In some cases, the particle is an extracellular vesicle. In some embodiments the particles have a horizontal dimension of 2000 nm or less, such as 1900 nm or less, for example 1800 nm or less, such as 1700 nm or less, for example 1600 nm or less, such as 1500 nm or less, for example 1400 nm or less, such as 1300 nm or less, for example 1200 nm or less, such as 1100 nm or less, for example 1000 nm or less, such as 900 nm or less, for example 800 nm or less, such as 700 nm or less, for example 600 nm or less, such as 500 nm or less, for example 400 nm or less, such as 300 nm or less, including 250 nm or less. In some embodiments the particles have a vertical dimension of 2000 nm or less, such as 1900 nm or less, for example 1800 nm or less, such as 1700 nm or less, for example 1600 nm or less, such as 1500 nm or less, for example 1400 nm or less, such as 1300 nm or less, for example 1200 nm or less, such as 1100 nm or less, for example 1000 nm or less, such as 900 nm or less, for example 800 nm or less, such as 700 nm or less, for example 600 nm or less, such as 500 nm or less, for example 400 nm or less, such as 300 nm or less, including 250 nm or less.

[0086] In some embodiments, the size of the particle is smaller than the illumination beam size (i.e., the beam profile along the horizontal axis) of the light source. In other words, the beam profile (e.g., a laser light source) illuminates the entire particle. In some cases, the beam profile is 5% or more larger than the size of the particle, including 10% or more, such as 15% or more, such as 20% or more, such as 25% or more, such as 50% or more, such as 75% or more, such as 90% or more, such as 95% or more, or even 99% or more. In certain cases, the beam profile is 1.5 times or more, such as 2 times or more, such as 3 times or more, such as 4 times or more, including 5 times or more, the size of the particle.

[0087] In certain embodiments, the generated data signal waveform is independent of particle size. In some cases, the generated data signal waveform is independent of particle size when the particle has a vertical or horizontal dimension less than a predetermined threshold compared to the beam profile of the light source. For example, the generated data signal waveform may be independent of particle size, including when any one or more of the horizontal or vertical dimensions of the particle are smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 1% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 1% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 10% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 15% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 25% or more smaller than the beam profile of the light source. FIG. 1B illustrates data signal waveforms generated for particles having different widths, according to certain embodiments. In FIG. 1B, 200 nm and 800 nm particles are illuminated with a light source having a beam profile greater than 800 nm. As shown in FIG. 1B, the generated data signal waveform is independent of particle size.

[0088] Method aspects of the present disclosure also include applying a data signal filter to the data signal waveform generated by the optical detection system. As summarized above, applying a data signal filter to the data signal waveform generated by the optical detection system can, in certain embodiments, increase the sensitivity of the data signal measurement by 5% or more, such as 10% or more, such as 15% or more, such as 25% or more, such as 50% or more, such as 75% or more, and including 99% or more. In some embodiments, a method includes detecting light from particles of a sample in a flow stream with the optical detection system, generating a data signal waveform in response to the detected light, and applying the calculated data signal filter to the generated data signal waveform.

[0089] In some cases, the method includes calculating a trigger metric for detecting particles in the flow stream by the optical detection system based on the calculated data signal filter. The trigger metric, in some embodiments, is a ratio between the data signal waveform amplitude and a noise component of the data signal waveform. In some cases, the ratio is a ratio between the data signal waveform maximum and the noise component. In some embodiments, the noise component is calculated to be a root mean square value of the noise of the data signal waveform.

[0090] In some embodiments, the trigger threshold (e.g., to identify a positive event in the raw data waveform) is changed based on the calculated trigger metric. For example, the trigger threshold may be reduced by 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including when the trigger threshold is reduced by 2% or more. In certain cases, the trigger threshold is increased by 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, or 2% or more.

[0091] In some embodiments, one or more measurement parameters of the optical detection system are altered based on the calculated trigger metric. In some cases, the optical detection time is changed based on the calculated trigger metric, for example by 0.0001 μs or more, for example by 0.0005 μs or more, for example by 0. ...5 μs or more, for example by 0.01 μs or more, for example by 0.05 μs or more, for example by 0.1 μs or more, for example by 0.5 μs or more, for example by 1 μs or more, for example by 2 μs or more, for example by 3 μs or more, for example by 4 μs or more, for example by 5 μs or more, for example by 10 μs or more, for example by 50 μs or more, for example by 100 μs or more, for example by 500 μs or more, and for example by 1000 μs or more. For example, the light detection time can be increased by 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, including when the light detection time increases by 2% or more. In certain cases, the light detection time is decreased by 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, 2% or more.

[0092] The calculated trigger metric can be applied to data signal waveforms generated in one or more photodetector channels (e.g., fluorescence detector channels) including 5% or more of the photodetector channels of the photodetection system, e.g., 10% or more, 20% or more, 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, etc., and 99% or more of the photodetector channels of the photodetection system. In certain cases, the calculated trigger metric can be applied to data signal waveforms in all photodetector channels in the photodetection system.

[0093] FIG. 2 is a flowchart for calculating and applying a data signal filter to a data signal waveform according to certain embodiments. In step 201, a sample containing particles (e.g., cells or extracellular vesicles) is illuminated in a flow stream using a light source. Light from the illuminated particles is detected in multiple photodetector channels, such as one or more fluorescence detector channels (step 202). In step 203, a data signal waveform is generated in each photodetector channel having a signal component and a noise component. Features of the data signal waveform, such as the width component of the data signal (step 204), are used to calculate the data signal filter. In some cases, the data signal filter is calculated by a matching optimization algorithm to a ground truth waveform (step 205). In some cases, the data signal filter is an approximation of a matched filter, such as by calculating a linear analog data signal filter. In step 206, in some embodiments, the data signal filter can be applied to the data signal waveform, and in some cases, a trigger metric for detecting positive event data from light from the illuminated particles of the sample is determined.

[0094] In some embodiments, the method further includes sorting particles of the sample in the flow stream. In some cases, the method of sorting components of the sample includes sorting particles (e.g., cells in a biological sample) with a particle sorting module having deflection plates, such as described in U.S. Patent Application Publication No. 2017 / 0299493 (filed March 28, 2017, the disclosure of which is incorporated herein by reference). In certain embodiments, particles (e.g., cells) of the sample are sorted using a sorting determination module having multiple sorting determination units, such as described in U.S. Patent Application Publication No. 2020 / 0256781 (the disclosure of which is incorporated herein by reference). In some embodiments, a targeted system includes a particle sorting module with deflection plates, such as described in U.S. Patent Application Publication No. 2017 / 0299493 (filed March 28, 2017, the disclosure of which is incorporated herein by reference).

[0095] System for determining and applying a data signal filter for detecting particles in a flow stream Aspects of the present disclosure include methods for determining a data signal filter for detecting particles (e.g., small particles such as extracellular vesicles) in a particle analyzer. The system, according to certain embodiments, includes a light source configured to illuminate particles in a flowstream, a light detection system having a plurality of light detectors, and a processor having a memory operably coupled to the processor, the memory including instructions that, when executed by the processor, cause the processor to generate a data signal waveform in response to the detected light from the particles in the flowstream, determine characteristics of the data signal waveform, and calculate a data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the memory includes instructions for determining a width parameter of the data signal waveform.

[0096] In embodiments, the system includes a light source configured to illuminate a sample having particles in a flow stream. In embodiments, the light source may be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source may be configured to emit light at various wavelengths ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, including 400 nm to 800 nm. For example, the light source may include a broadband light source emitting light having a wavelength between 200 nm and 900 nm. In other cases, the light source may include a narrowband light source emitting a wavelength between 200 nm and 900 nm. For example, the light source may be a narrowband LED (1 nm to 25 nm) emitting light having a wavelength between 200 nm and 900 nm. In certain embodiments, the light source is a laser. In some cases, the systems of interest include gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other cases, the systems of interest include dye lasers such as stilbene, coumarin, or rhodamine lasers. In still other cases, the lasers of interest include metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof.In still other instances, the systems of interest include solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0097] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode such as a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integration, etc. In some cases, the non-laser light source is another light source, a stabilized fiber coupled broadband light source, a white light source, or any combination thereof.

[0098] The light source may be positioned at any suitable distance from the sample (e.g., the flow stream in a flow cytometer), for example, at a distance of 0.001 mm or more from the flow stream, for example, 0.005 mm or more, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 25 mm or more, including 100 mm or more. Further, the light source may illuminate the sample at any suitable angle (e.g., relative to the perpendicular axis of the flow stream), for example, at an angle in the range of 10° to 90°, for example, 15° to 85°, for example, 20° to 80°, for example, 25° to 75°, including an angle of 30° to 60°, for example, 90°.

[0099] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to continuously illuminate the sample, such as with a continuous wave laser that continuously illuminates the flow stream at an interrogation point within the flow cytometer. In other cases, systems of interest include a light source configured to illuminate the sample at discrete intervals, including, for example, every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to provide intermittent illumination of the sample by the light source. For example, systems of interest in these embodiments may include one or more laser beam choppers, manual or computer-controlled beam stops, for blocking and exposing the sample to the light source.

[0100] In some embodiments, the light source is a laser. Lasers of interest may include pulsed or continuous wave lasers. For example, the laser may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, or a fluorine laser. metal vapor lasers such as neon-copper (NeCu), copper, or gold lasers, and combinations thereof; solid-state lasers such as ruby, Nd:YAG, NdCrYAG, Er:YAG, Nd:YLF, Nd:YVO, Nd:YCaO(BO), Nd:YCOB, titanium sapphire, thulium YAG, ytterbium YAG, ytterbium O, and cerium-doped lasers, and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or frequency-doubled or -tripled implementations of any of the above lasers.

[0101] In certain embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some cases, the optical beam generator includes a laser and a radio-frequency generator configured to apply a radio-frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous-wave laser. For example, lasers in the optical beam generator of interest may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, xenon-fluorine (XeF) excimer lasers, or combinations thereof; dye lasers such as stilbene, coumarin, or rhodamine lasers; helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeS) lasers, or combinations thereof. e) Lasers, metal vapor lasers such as helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers or gold lasers and combinations thereof; solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, cerium-doped lasers and combinations thereof.

[0102] The acousto-optical device may be any convenient acousto-optical protocol configured to frequency-shift laser light using applied acoustic waves. In one specific embodiment, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the target system is configured to generate an angularly deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optical device using any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0103] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to produce a desired number of angularly deflected laser beams in the output laser beam, for example configured to apply three or more high frequency drive signals, for example four or more high frequency drive signals, for example five or more high frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals (including being configured to apply 100 or more high frequency drive signals).

[0104] In some cases, to produce an angularly deflected laser beam intensity profile within the output laser beam, the controller is configured to apply a high frequency drive signal having an amplitude that varies, for example, from about 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V (including from about 5 V to about 25 V). In some embodiments, each of the applied high frequency drive signals has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0105] In certain embodiments, the controller includes a processor having a memory operably coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity (e.g., 3 or more, e.g., 4 or more, e.g., 5 or more, e.g., 10 or more, e.g., 25 or more, e.g., 50 or more), or the including memory may include instructions for generating 100 or more angularly deflected laser beams having the same intensity. In other embodiments, the controller may include instructions for generating two or more angularly deflected laser beams having different intensities (e.g., 3 or more, e.g., 4 or more, e.g., 5 or more, e.g., 10 or more, e.g., 25 or more, e.g., 50 or more), or the including memory may include instructions for generating 100 or more angularly deflected laser beams having different intensities.

[0106] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge of the output laser beam to the center along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55% (including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis). In other embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55% (including about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis). In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to produce an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to produce an output laser beam having a top-hat intensity profile along a horizontal axis.

[0107] In embodiments, the objective optical beam generator may be configured to produce angularly polarized laser beams within the spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly polarized laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the system is configured to produce angularly polarized laser beams within the output laser beam that overlap with adjacent angularly polarized laser beams along the horizontal axis of the output laser beam, such as 5000 μm or more. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more (including an overlap of 100 μm or more).

[0108] In certain cases, an optical beam generator configured to generate two or more frequency-shifted optical beams is disclosed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, and 10,408,758. , 10,451,538, 10,620,111, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0109] In embodiments, the system includes a light detection system having a plurality of light detectors. The light detectors of interest may include optical sensors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APDs), image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other light detectors. In certain embodiments, light from the sample is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor.

[0110] In some embodiments, the light detection system of interest includes a plurality of light detectors. In some cases, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In particular cases, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include 4 or more light detectors, e.g., 10 or more light detectors, e.g., 25 or more light detectors, e.g., 50 or more light detectors, e.g., 100 or more light detectors, e.g., 250 or more light detectors, e.g., 500 or more light detectors, e.g., 750 or more light detectors, including 1000 or more light detectors. For example, the detector may be a photodiode array having 4 or more photodiodes, e.g., 10 or more photodiodes, e.g., 25 or more photodiodes, e.g., 50 or more photodiodes, e.g., 100 or more photodiodes, e.g., 250 or more photodiodes, e.g., 500 or more photodiodes, e.g., 750 or more photodiodes, including 1000 or more photodiodes.

[0111] The photodetectors can be arranged in any geometric configuration as desired, including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregularly patterned configurations. The photodetectors within the photodetector array may be oriented relative to one another at angles (referenced in the XZ plane) including angles between 10° and 180°, such as between 15° and 170°, such as between 20° and 160°, such as between 25° and 150°, such as between 30° and 120°, and such as between 45° and 90°. The photodetector array may be of any suitable shape, including rectilinear shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear shapes such as circular and elliptical, and irregular shapes such as a parabolic base coupled to a flat top. In certain embodiments, the photodetector array has an active surface that is rectangular in shape.

[0112] Each photodetector (e.g., photodiode) in the array may have an active surface with a width ranging from 5 μm to 250 μm, such as 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and a length ranging from 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and 2 ~10,000 μm 2 , e.g., 50 μm 2 ~9000μm 2 , e.g., 75 μm 2 ~8000μm 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 range of 200 μm 2 ~5000μm 2 Includes.

[0113] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length in the range of 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, including 5 mm to 25 mm. The width of the photodetector array may also vary in the range of 0.01 mm to 100 mm, for example 0.05 mm to 90 mm, for example 0.1 mm to 80 mm, for example 0.5 mm to 70 mm, for example 1 mm to 60 mm, for example 2 mm to 50 mm, for example 3 mm to 40 mm, for example 4 mm to 30 mm, including 5 mm to 25 mm. Thus, the active surface of the photodetector array may be 0.1 mm 2 ~10,000mm 2 , e.g. 0.5 mm 2 ~5000mm 2 , e.g. 1 mm2 ~1000mm 2 , e.g. 5mm 2 ~500mm 2 may be in the range of 10mm 2 ~100mm 2 Includes.

[0114] The subject photodetector is configured to measure light collected at one or more wavelengths, for example two or more wavelengths, for example five or more different wavelengths, such as ten or more different wavelengths, for example twenty-five or more different wavelengths, for example fifty or more different wavelengths, such as one hundred or more different wavelengths, for example two hundred or more different wavelengths, such as three hundred or more different wavelengths, including measuring light emitted by the sample in the flow stream at four hundred or more different wavelengths.

[0115] In some embodiments, the photodetector is configured to measure light collected over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a wavelength range. For example, a system may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from a sample in the flowstream at one or more specific wavelengths. For example, a system may include one or more detectors configured to measure light at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the photodetector may be configured to pair with a particular fluorophore, such as one used with the sample in a fluorescence assay, hi some embodiments, the photodetector is configured to measure the collected light across the fluorescence spectrum of each fluorophore in the sample.

[0116] The light detection system is configured to measure light continuously or at discrete intervals. In some cases, the target light detector is configured to continuously obtain measurements of the collected light. In other cases, the light detection system is configured to perform measurements at discrete intervals, such as measuring light every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0117] In some embodiments, the system is configured to identify and classify particles in a sample. In certain cases, the system is configured to sort identified or classified particles. In these embodiments, the system may include a computer control system further including one or more computers for fully or partially automating the system for performing the methods described herein. In embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program loaded onto the computer further including instructions for determining characteristics of a data signal waveform. In some embodiments, the memory includes instructions for determining one or more of the height of the data signal, the width of the data signal, the area of ​​the data signal waveform, combinations thereof, or ratios of one or more of the width of the data signal waveform, the height of the data signal waveform, and the area of ​​the data signal waveform. In certain cases, the memory includes instructions for determining a width parameter of the data signal waveform. In some embodiments, the width parameter is the ratio of the waveform area to the waveform height. In certain embodiments, the data signal waveform has a Gaussian distribution. In other embodiments, the data signal waveform has a super-Gaussian distribution.

[0118] In some embodiments, the memory includes instructions for calculating a data signal filter from identified characteristics of the data signal waveform. In some instances, the memory includes instructions for calculating a data signal filter from a data signal height, a data signal width, an area of ​​the data signal waveform, or a ratio of one or more of the data signal waveform width, the data signal waveform height, the data signal waveform area, or combinations thereof. In one particular embodiment, the memory includes instructions for calculating a data signal filter from a ratio of the data signal waveform area to the data signal waveform height.

[0119] In some embodiments, the memory includes instructions for determining a data signal filter that is a matched filter to a ground truth waveform generated by the optical detection system. In some cases, the matched filter is a calculated data signal filter that, when applied to the data signal from the optical detection system, produces a data signal with a maximum signal-to-noise ratio. In some cases, the memory includes instructions for determining a matched filter that results in the best signal-to-noise ratio in the presence of any additive stochastic noise. In some cases, the memory includes instructions for determining a matched filter that has the same functional form as the ground truth signal. In some embodiments, the memory includes instructions for determining a data signal filter that is a linear filter that maximizes a trigger metric.

[0120] In some embodiments, the system includes a computer having a computer-readable storage medium having stored thereon a computer program, the computer program further including, when loaded into the computer, instructions for computing a data signal filter that accounts for a time series signal x(t) corrupted by additive noise n(t), x(t)=s(t)+n(t), where s(t) is the underlying ground truth signal generated by the optical detection system. In particular cases, the memory includes instructions for computing the data signal filter by function optimization according to:

[0121]

number

[0122] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0123] In some embodiments, the memory includes instructions for computing a data signal filter that is an approximation of a matched filter. In some cases, the memory includes instructions for computing a linear analog data signal filter from determined characteristics (e.g., a width parameter) of the data signal waveform. In some cases, the linear analog data signal filter includes a finite impulse response filter. In other cases, the linear analog data signal filter includes an infinite impulse response filter. In particular cases, the memory includes instructions for computing a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter from determined characteristics of the data signal waveform.

[0124] In some embodiments, the memory includes instructions for calculating a data signal filter based on an aspect of the particle. In some cases, the data signal filter is based on spatial data of the particle. In some cases, the spatial data includes a horizontal size dimension of the particle, a vertical size dimension of the particle, a ratio of particle sizes along two different dimensions, or a relative size of a particle component (e.g., a ratio of a horizontal dimension of the nucleus to a horizontal dimension of the cytoplasm of a cell). In particular cases, the data signal filter is calculated based on a width of the particle. In some cases, the particle is an extracellular vesicle.

[0125] In certain embodiments, the memory includes instructions for generating a data signal waveform that is independent of particle size. In some cases, the generated data signal waveform is independent of particle size when the particle has a vertical or horizontal dimension less than a predetermined threshold compared to the beam profile of the light source. For example, the generated data signal waveform may be independent of particle size, including when any one or more of the horizontal or vertical dimensions of the particle are smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 1% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 1% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 2% or more smaller, such as when the horizontal or vertical dimension of the particle is 10% or more smaller than the beam profile of the light source, such as when the horizontal or vertical dimension of the particle is 15% or more smaller, such as when the horizontal or vertical dimension of the particle is 25% or more smaller than the beam profile of the light source.

[0126] In some embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program further including instructions, when loaded into the computer, for applying a data signal filter to a data signal waveform generated by the optical detection system. In some cases, the memory includes instructions for detecting light from particles of a sample in the flow stream with the optical detection system, instructions for generating a data signal waveform in response to the detected light, and instructions for applying the calculated data signal filter to the generated data signal waveform. In some cases, the memory includes instructions for calculating a trigger metric for detecting particles in the flow stream by the optical detection system based on the calculated data signal filter. The trigger metric, in some embodiments, is a ratio between the data signal waveform amplitude and a noise component of the data signal waveform. In some cases, the ratio is a ratio between the data signal waveform maximum and the noise component. In some embodiments, the memory includes instructions for calculating a noise component, which is the root-mean-square value of the noise of the data signal waveform.

[0127] In some embodiments, the memory includes instructions for modifying the trigger threshold (e.g., to identify a positive event in the raw data waveform) based on the calculated trigger metric. As an example, the memory may include instructions for lowering the trigger threshold by 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including instructions for lowering the trigger threshold by 2% or more. In another example, the memory includes instructions for increasing the trigger threshold by 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including 2% or more.

[0128] In some embodiments, the memory includes instructions for altering one or more measurement parameters of the optical detection system based on the calculated trigger metric. In some cases, the memory includes instructions for increasing or decreasing the optical detection time by 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, for example 0.005 μs or more, such as 0.01 μs or more, for example 0.05 μs or more, for example 0.1 μs or more, for example 0.5 μs or more, such as 1 μs or more, for example 2 μs or more, for example 3 μs or more, for example 4 μs or more, for example 5 μs or more, for example 10 μs or more, for example 50 μs or more, for example 100 μs or more, for example 500 μs or more, and 1000 μs or more based on the calculated trigger metric. For example, the light detection time can be increased by 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, including when the light detection time increases by 2% or more. In certain cases, the light detection time is decreased by 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, 2% or more.

[0129] In embodiments, the memory includes instructions for applying the calculated trigger metric to data signal waveforms generated in one or more photodetector channels (e.g., fluorescence detector channels), such as 5% or more, 10% or more, 20% or more, 30% or more, 40% or more, 50% or more, 60% or more, 70% or more, 80% or more, etc., of the photodetector channels of the photodetection system, and including 99% or more of the photodetector channels of the photodetection system. In certain cases, the memory includes instructions for applying the calculated trigger metric to data signal waveforms in all photodetector channels in the photodetection system.

[0130] Systems according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses memory in which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of the functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0131] System memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-and-write compact disk, flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or diskette drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or floppy diskette, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also called computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with the memory storage devices.

[0132] In some embodiments, a computer program product is described that includes a computer usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementing a hardware state machine to perform the functions described herein will be apparent to one skilled in the art.

[0133] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, fixed or portable). The processor may include a general-purpose digital microprocessor that is appropriately programmed from a computer-readable medium carrying the necessary program code. The programming may be provided to the processor remotely via a communications channel or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium using any of these devices in conjunction with the memory. For example, a magnetic or optical disk may record the program and be read by a disk writer / reader. The system of the present invention also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROMs, electrical storage media such as RAM, ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0134] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user is not in direct contact with the system but relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0135] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0136] In one embodiment, the communication interface is configured to include one or more communication ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port that allows data communication between the system of interest and other external devices, such as a computer terminal (e.g., in a doctor's office or hospital environment) configured for similar complementary data communication.

[0137] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the target system to communicate with computer terminals and / or other devices such as networks, communication-enabled mobile phones, personal digital assistants, or any other communication device that a user may use in conjunction with.

[0138] In one embodiment, the communication interface is configured to provide connectivity for data transfer using Internet Protocol (IP), Short Message Service (SMS) over a cellular network, a wireless connection to a personal computer (PC) in a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0139] In one embodiment, the subject system is configured to wirelessly communicate with a server device over a communications interface using common standards such as, for example, 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0140] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in any data storage unit with a system of interest, such as a network or server device, using one or more of the above communication protocols and / or mechanisms.

[0141] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also communicate information generated by the processing modules to a remote user, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be performed according to a variety of known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other formats of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The platform or platforms present in the system of interest can be any type of known or future developed computer platform, but they are typically computers of the class commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication system, including wireless systems, and may or may not be networked. They may be co-located or physically separated.In some cases, various operating systems may be employed on any computer platform depending on the type and / or manufacturer of the computer platform selected. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, etc.

[0142] In certain embodiments, systems of interest include one or more optical conditioning components for conditioning light, such as light irradiated onto a sample (e.g., from a laser) or light collected from a sample (e.g., scattering, fluorescence). For example, the optical conditioning may be increasing the size of the light, the focus of the light, or collimating the light. In some cases, the optical conditioning is an expansion protocol that increases the size of the light (e.g., beam spot), including increasing the size by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, and including increasing the size by 75% or more. In other embodiments, the optical conditioning includes focusing the light to reduce the size of the light, e.g., 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, including reducing the size of the beam spot by 75% or more. In certain embodiments, the optical conditioning includes collimating the light. The term "collimate" is used in its conventional sense to refer to optically adjusting the collinearity of light propagation or reducing the divergence of light from a common axis of propagation. In some cases, collimating involves narrowing the spatial cross-section of the light beam (e.g., reducing the beam profile of a laser). In some embodiments, the optical conditioning component is a focusing lens having a magnification of 0.1 to 0.95, e.g., a magnification of 0.2 to 0.9, e.g., a magnification of 0.3 to 0.85, e.g., a magnification of 0.35 to 0.8, e.g., a magnification of 0.5 to 0.75, including a magnification of 0.55 to 0.7, e.g., a magnification of 0.6. For example, the focusing lens is a double achromatic reduction lens having a magnification of about 0.6. The focal length of the focusing lens may vary from 5 mm to 20 mm, e.g., 6 mm to 19 mm, e.g., 7 mm to 18 mm, e.g., 8 mm to 17 mm, e.g., 9 mm to 16 mm, including focal lengths in the range of 10 mm to 15 mm. In certain embodiments, the focusing lens has a focal length of about 13 mm.

[0143] In other embodiments, the optical conditioning component is a collimator. The collimator may be any convenient collimating protocol, such as one or more mirrors or curved lenses, or a combination thereof. For example, the collimator may in certain cases be a single collimating lens. In other cases, the collimator is a collimating mirror. In still other cases, the collimator includes two lenses. In still other cases, the collimator includes a mirror and a lens. When the collimator includes one or more lenses, the focal length of the collimating lens may vary in a range from 5 mm to 40 mm, such as from 6 mm to 37.5 mm, such as from 7 mm to 35 mm, such as from 8 mm to 32.5 mm, such as from 9 mm to 30 mm, such as from 10 mm to 27.5 mm, such as from 12.5 mm to 25 mm, including focal lengths in the range from 15 mm to 20 mm.

[0144] In some embodiments, a subject system includes a flow cell nozzle having a nozzle orifice configured to direct a flow stream through the flow cell nozzle. The subject flow cell nozzle has an orifice that propagates a fluid sample to a sample interrogation region, and in some embodiments, the flow cell nozzle includes a proximal cylindrical portion defining a longitudinal axis and a distal frusto-conical portion terminating in a flat surface having a nozzle orifice transverse to the longitudinal axis. The length of the proximal cylindrical portion (as measured along the longitudinal axis) can vary from 1 mm to 15 mm, e.g., 1.5 mm to 12.5 mm, e.g., 2 mm to 10 mm, e.g., 3 mm to 9 mm, including 4 mm to 8 mm. The length of the distal frusto-conical portion (as measured along the longitudinal axis) can also vary from 1 mm to 10 mm, e.g., 2 mm to 9 mm, e.g., 3 mm to 8 mm, including 4 mm to 7 mm. The diameter of the flow cell nozzle chamber may in some embodiments vary in the range of 1 mm to 10 mm, such as 2 mm to 9 mm, such as 3 mm to 8 mm, including 4 mm to 7 mm.

[0145] In certain cases, the nozzle chamber does not include a cylindrical portion, and the entire flow cell nozzle chamber is frustoconical in shape. In these embodiments, the length of the frustoconical nozzle chamber (as measured along a longitudinal axis transverse to the nozzle orifice) may range from 1 mm to 15 mm, such as 1.5 mm to 12.5 mm, such as 2 mm to 10 mm, for example, 3 mm to 9 mm, including 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical nozzle chamber may range from 1 mm to 10 mm, such as 2 mm to 9 mm, for example, 3 mm to 8 mm, including 4 mm to 7 mm.

[0146] In some embodiments, the sample flow stream diverges from an orifice at the distal end of the flow cell nozzle. Depending on the desired characteristics of the flow stream, the flow cell nozzle orifice can be of any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes such as circular, elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. In certain embodiments, the flow cell nozzle of interest has a circular orifice. The size of the nozzle orifice may vary in some embodiments from 1 μm to 20,000 μm, such as from 2 μm to 17,500 μm, for example, from 5 μm to 15,000 μm, for example, from 10 μm to 12,500 μm, for example, from 15 μm to 10,000 μm, for example, from 25 μm to 7,500 μm, for example, from 50 μm to 5,000 μm, for example, from 75 μm to 1,000 μm, for example, from 100 μm to 750 μm, including from 150 μm to 500 μm. In one particular embodiment, the nozzle orifice is 100 μm.

[0147] In some embodiments, the flow cell nozzle includes a sample injection port configured to provide a sample to the flow cell nozzle. In embodiments, the sample injection system is configured to provide a suitable flow of sample to the flow cell nozzle chamber. Depending on the desired characteristics of the flow stream, the flow rate of the sample delivered by the sample injection port to the flow cell nozzle chamber can be 1 μL / sec or more, such as 2 μL / sec or more, for example 3 μL / sec or more, for example 5 μL / sec or more, for example 10 μL / sec or more, for example 15 μL / sec or more, for example 25 μL / sec or more, for example 50 μL / sec or more, for example 100 μL / sec or more, for example 150 μL / sec or more, for example 200 μL / sec or more, for example 250 μL / sec or more, for example 300 μL / sec or more, for example 350 μL / sec or more, for example 400 μL / sec or more, for example 450 μL / sec or more, and for example 500 μL / sec or more. For example, the sample flow rate may be in the range of 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, including 10 μL / sec to about 100 μL / sec.

[0148] The sample injection port may be an orifice disposed in the wall of the nozzle chamber, or may be a conduit disposed at the proximal end of the nozzle chamber. When the sample injection port is an orifice disposed in the wall of the nozzle chamber, the sample injection port orifice may have any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom joined to a flat top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, with an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm).

[0149] In certain cases, the sample injection port is a conduit located at the proximal end of the flow cell nozzle chamber. For example, the sample injection port may be a conduit positioned such that the sample injection port orifice is aligned with the flow cell nozzle orifice. When the sample injection port is a conduit aligned with the flow cell nozzle orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, including, but not limited to, linear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom joined to a flat top. In certain cases, the orifice of the conduit may have an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm), and may vary depending on the shape. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the sample injection port orifice may include a beveled tip having a bevel angle in the range of 1° to 10° (e.g., 2° to 9°, e.g., 3° ​​to 8°, e.g., 4° to 7°), including a bevel angle of 5°.

[0150] In some embodiments, the flow cell nozzle also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell nozzle. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell nozzle chamber, e.g., in conjunction with the sample, to produce a stacked flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the flow rate of the sheath fluid delivered to the flow cell nozzle chamber can be 25 μL / sec or more, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, including 2500 μL / sec or more. For example, the flow rate of the sheath fluid may be in the range of 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, including 10 μL / sec to about 100 μL / sec.

[0151] In some embodiments, the sheath fluid injection port is an orifice disposed in the wall of the nozzle chamber. The sheath fluid injection port orifice may have any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom joined to a flat top. The size of the sample injection port orifice may vary depending on the shape, with an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm), in certain cases.

[0152] The systems of interest, in certain instances, include a sample interrogation region in fluid communication with the flow cell nozzle orifice. In these examples, a sample flow stream emanates from an orifice at the distal end of the flow cell nozzle, and particles in the flow stream can be illuminated with a light source in the sample interrogation region. The size of the interrogation region can vary depending on characteristics of the flow nozzle, such as the size of the nozzle orifice and the size of the sample injection port. In embodiments, the interrogation region can have a width of 0.01 mm or more, e.g., 0.05 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 5 mm or more, including 10 mm or more. The length of the investigation region may also vary in some cases along 0.01 mm or more, such as 0.1 mm or more, for example 0.5 mm or more, such as 1 mm or more, for example 1.5 mm or more, such as 2 mm or more, for example 3 mm or more, such as 5 mm or more, for example 10 mm or more, such as 15 mm or more, for example 20 mm or more, such as 25 mm or more, including 50 mm or more.

[0153] The investigation region may be configured to facilitate illumination of a planar cross-section of the diverging flow stream, or may be configured to facilitate illumination of a diffuse field of a predetermined length (e.g., using a diffuse laser or lamp). In some embodiments, the investigation region includes a transparent window to facilitate illumination of a diverging flow stream of a predetermined length (e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 4 mm or more, e.g., 5 mm or more, including 10 mm or more). Depending on the light source used to illuminate the diverging flow stream (as described below), the investigation region may be configured to pass light in the range of 100 nm to 1500 nm, e.g., 150 nm to 1400 nm, e.g., 200 nm to 1300 nm, e.g., 250 nm to 1200 nm, e.g., 300 nm to 1100 nm, e.g., 350 nm to 1000 nm, e.g., 400 nm to 900 nm, including 500 nm to 800 nm. Therefore, the research area includes optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and other polymeric plastic materials including plastics such as polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (glycol-modified polyethylene terephthalate), polyesters, among which polyesters of interest include poly(ethylene terephthalate) (PET), bottle-grade PET (based on monoethylene glycol, terephthalic acid, and other comonomers such as isophthalic acid, cyclohexene dimethanol, etc.). polymers (alkylene adipates) such as polymer (ethylene adipate), polymer (1,4-butylene adipate), and polymer (hexamethylene adipate); polymers (alkylene suberates) such as polymer (ethylene suberate); polymers (alkylene sebacates) such as polymer (ethylene sebacate); polymers (ε-caprolactone) and polymer (β-propiolactone); polymers (alkylene isophthalates) such as polymer (ethylene isophthalate);Polymers (alkylene 2,6-naphthalenedicarboxylate) such as polymer (ethylene 2,6-naphthalenedicarboxylate); polymers (alkylenesulfonyl-4,4'-dibenzoates) such as polymer (ethylenesulfonyl-4,4'-dibenzoate); polymers (p-phenylene alkylene dicarboxylate) such as polymer (p-phenylene ethylene dicarboxylate); polymers (trans-1,4-cyclohexanediyl alkylene dicarboxylate) such as polymer (trans-1,4-cyclohexanediyl ethylene dicarboxylate); polymers (1,4-cyclohexanedimethylene alkylene dicarboxylate) such as polymer (1,4-cyclohexanedimethylene ethylene dicarboxylate); polymers ([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate) The optical filter can be formed from any transparent material that transmits the desired wavelength range, including, but not limited to, polymers such as ([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylate); lactic acid polymers and copolymers such as (S)-polylactide, (R,S)-polylactide, polymer(tetramethylglycolide), and polymer(lactide-co-glycolide); and polycarbonates of bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, and 3,3',5,5'-tetramethylbisphenol A; polyamides such as polymer(p-phenylene terephthalamide); polyesters, e.g., polyethylene terephthalate, e.g., Mylar™ polyethylene terephthalate, and the like. In some embodiments, the system of interest includes a cuvette positioned in the sample interrogation region. In some embodiments, the cuvette may transmit light in the range of 100 nm to 1500 nm, such as 150 nm to 1400 nm, such as 200 nm to 1300 nm, such as 250 nm to 1200 nm, such as 300 nm to 1100 nm, such as 350 nm to 1000 nm, such as 400 nm to 900 nm, including 500 nm to 800 nm;

[0154] In certain embodiments, a light detection system having multiple light detectors as described above is part of or located within a particle analyzer, such as a particle sorter, hi certain embodiments, the system of interest is a flow cytometry system that includes a photodiode and amplifier components as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden, et al., Semin Thromb Hemost. 2004 Oct; 30(5):502-11; Alison, et al. J Pathol, 2010 Dec; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular cases, the flow cytometry systems of interest are a BD Biosciences FACSCanto™ flow cytometer, a BD Biosciences FACSCanto™ II flow cytometer, a BD Accuri™ flow cytometer, a BD Accuri™ C6 Plus flow cytometer, a BD Biosciences FACSCelesta™ flow cytometer, a BD Biosciences FACSLyric™ flow cytometer, a BD Biosciences FACSVerse™ flow cytometer, a BD Biosciences FACSymphony™ flow cytometer, a BD Biosciences LSRFortessa™ flow cytometer, a BD Biosciences LSRFortessa™ X-20 flow cytometer, a BD Biosciences FACSPresto™ flow cytometer, a BD Biosciences FACSVia™ flow cytometer, and a BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, a BD Biosciences These include the FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, etc.

[0155] In some embodiments, the subject systems may be implemented using the same or similar technology as disclosed in U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469 ... ,481,074 specification, 10,302,545 specification, 10,145,793 specification, 10,113,967 specification, 10,006,852 specification, 9,95 Specification No. 2,076, Specification No. 9,933,341, Specification No. 9,726,527, Specification No. 9,453,789, Specification No. 9,200,334, Specification No. 9,097,640 9,095,494, 9,092,034, 8,975,595, 8,753,573, 8,233,146, 8,1 Specification No. 40,300, Specification No. 7,544,326, Specification No. 7,201,875, Specification No. 7,129,505, Specification No. 6,821,740, Specification No. 6,813,017 and flow cytometry systems such as those described in US Pat. Nos. 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766.

[0156] In some embodiments, a target system is a particle sorting system configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, a target system includes a particle sorting module with deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0157] In certain instances, the flow cytometry system of the present invention may be similar to that described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as those described in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, and 10,408 Nos. 10,451,538, 10,620,111, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0158] In certain embodiments, the subject systems are configured to sort one or more particles (e.g., cells) of a sample identified based on the estimated abundance of a fluorophore associated with the particle, as described above. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., cells, non-cellular particles such as biological macromolecules, etc.) and, in some cases, delivering the separated components to one or more sample collection vessels. For example, the subject systems may be configured to sort a sample having two or more components, e.g., three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, including sorting a sample having twenty-five or more components. One or more of the sample components may be separated from the sample and delivered to a sample collection vessel, e.g., two or more sample components, e.g., three or more sample components, e.g., four or more sample components, e.g., five or more sample components, e.g., ten or more sample components (including fifteen or more sample components).

[0159] In some embodiments, particle sorting systems of interest are configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, systems of interest include a particle sorting module with deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0160] In one specific embodiment, the system is a fluorescence imaging system using a radio frequency tagged luminescence imaging enabled particle sorter as shown in FIG. 3A. The particle sorter 300 includes an optical illumination component 300a including a light source 301 (e.g., a 488 nm laser) that generates an output beam of light 301a, which is split into beams 302a and 302b by a beam splitter 302. The light beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a having one or more angularly deflected light beams. In some cases, the output beam 303a generated from the acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. The light beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a having one or more angularly deflected light beams. In some cases, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 303a and 304a generated from acousto-optical devices 303 and 304, respectively, are combined in beam splitter 305 to generate output beam 305a, which is conveyed through optical components 306 (e.g., objective lens: Obj) to illuminate particles in flow cell 307. In one particular embodiment, acousto-optical device 303 (AOD) splits a single laser beam into an array of beamlets, each with a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam, which is then overlapped with the array of beamlets at beam combiner 305. In certain embodiments, the light irradiation system having a light source and an acousto-optical device may also include those described in Schraivogel, et al. ("High-speed fluorescence image-enabled cell sorting" Science (2022), 375 (6578): 315-320) and U.S. Patent Application Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0161] Output beam 305a illuminates sample particles 308 propagating through flow cell 307 (e.g., with sheath fluid 309) in illumination region 310. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high-frequency shifted optical beams shown as dots across illumination region 310) overlap with a reference local oscillator beam (shown as hatched across illumination region 310). Due to the different optical frequencies, the overlapping beams exhibit beating behavior, whereby each beamlet emits a distinct frequency f 1-n carries a sinusoidal modulation.

[0162] Light from the illuminated sample is conveyed to a light detection system 300b, which includes multiple light detectors. The light detection system 300b includes a forward scatter light detector 311 for generating a forward scatter image 311a and a side scatter light detector 312 for generating a side scatter image 312a. The light detection system 300b also includes a bright-field light detector 313 for generating a light loss image 313a. In some embodiments, the forward scatter detector 311 and the side scatter detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, the bright-field light detector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence detectors 314-317. In some cases, the light detectors 314-317 are photomultiplier tubes. The light from the illuminated sample is directed through a beam splitter 320 to the side scatter detection channel 312 and the fluorescence detection channels 314-317. Light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to light detectors 314-317. In some cases, optical component 321 is a 534 nm / 40 nm bandpass. In some cases, optical component 322 is a 586 nm / 42 nm bandpass. In some cases, optical component 323 is a 700 nm / 54 nm bandpass. In some cases, optical component 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number indicates the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0163] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright-field light detection channel 313, and fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a can be generated in each light detection channel based on the data signals generated by processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated by sorting trigger 352. Sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or to a waste stream 333. In some cases, sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Application Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0164] FIG. 3B illustrates image-enabled particle sorting data processing according to certain embodiments. In some instances, the image-enabled particle sorting data processing is a low-latency data processing pipeline. Each photodetector generates pulses with high-frequency modulation that encodes an image (waveform). Fourier analysis is performed to reconstruct the image from the modulated pulses. The image processing pipeline generates a set of image features (image analysis) that are combined with features derived from the pulse processing pipeline (event packets). Real-time sorting electronics then classify particles based on the image features and generate sort decisions that are used to selectively charge droplets.

[0165] In some embodiments, the system is a particle analyzer, and particle analysis system 401 (FIG. 4A) can be used to analyze and characterize particles, with or without physically sorting the particles into a collection vessel. FIG. 4A shows a functional block diagram of a particle analysis system for computationally based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 shown in FIG. 4A can be configured, in whole or in part, to perform methods, such as those described herein. Particle analysis system 401 includes a fluidic system 402. Fluidic system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0166] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. A detection station 408 generally refers to a monitoring area 407 of the common sample path. Detection, in some implementations, may include detecting light or one or more other characteristics of the particle 403 as it passes through the monitoring area 407. In FIG. 4A , one detection station 408 is shown with one monitoring area 407. Some implementations of the particle analysis system 401 may include multiple detection stations. Additionally, some detection stations may monitor more than one area.

[0167] Each signal is assigned a signal value to form a data point for each particle. This data may be referred to as event data, as described above. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect such data points continuously over a first time interval.

[0168] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may further be configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0169] 4B shows a system 400 for flow cytometry according to an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-g, one or more bandpass filters 450a-e, one or more longpass ("LP") filters 455a-b, and one or more fluorescence detectors 460a-f.

[0170] Pump lasers 115a-c emit light in the form of laser beams. The wavelengths of the laser beams emitted from pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system of FIG. 4B. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits ultraviolet light (light with wavelengths ranging from 10 nm to 400 nm) and reflects 488 nm and 633 nm light.

[0171] The laser beam is then directed onto a focusing lens 420, which focuses the beam onto a portion of the flow stream where the sample particles are located, within a flow chamber 425. The flow chamber is the part of a fluidic system that directs particles, typically one at a time, in a stream towards the focused laser beam for interrogation. A flow chamber can include a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0172] Light from the laser beam interacts with the particles of the sample by diffraction, refraction, reflection, scattering, and absorption by re-emission at a variety of different wavelengths, depending on particle characteristics such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light can be sent via one or more of beam splitters 445a-g, bandpass filters 450a-e, longpass filters 455a-b, and fluorescence collection lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0173] The fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes it toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number indicates the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, from 500 nm to 520 nm. Shortpass filters transmit light equal to or shorter than a specific wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths equal to or longer than a specific wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorescent dye. The filter can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0174] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g may include optical mirrors such as dichroic mirrors.

[0175] The forward scatter detector 430 is positioned slightly off-axis from the direct beam through the flow cell and is configured to detect diffracted light, or excitation light traveling primarily forward through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the particle's surface and internal structure, which tends to increase as the particle's structure becomes more complex. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, side scatter detector 435, and fluorescence detector can be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0176] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 4B, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0177] During operation, the operation of the cytometer is controlled by the controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by the controller / processor 490. Although not explicitly shown, the controller / processor 490 is coupled to the detectors to receive output signals from the detectors, and may also be coupled to electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided in the system. The memory 495, controller / processor 490, and I / O 497 may be provided entirely as an integral part of the flow cytometer 410. In such an embodiment, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the cytometer 400. Alternatively, some or all of the memory 495 and controller / processor 490 and I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 495 and controller / processor 490 may be in wireless or wired communication with the cytometer 410. Controller / processor 490, together with memory 495 and I / O 497, can be configured to perform a variety of functions related to the preparation and analysis of flow cytometer experiments.

[0178] The system shown in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (sometimes referred to herein as the "filter windows" of a given detector), as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to approximately match the filter windows of the detectors. However, as many detectors are provided and many labels are utilized, perfect correspondence between filter windows and fluorescence emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may fall within the filter window of one particular detector, it is generally true that a portion of that label's emission spectrum also overlaps with the filter window of one or more other detectors. This is sometimes referred to as spillover. The I / O 497 can be configured to receive data for a flow cytometer experiment involving a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. I / O 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. Controller / processor 490 may be configured to evaluate one or more assignments of labels to markers.

[0179] 5 shows a functional block diagram of an example particle analyzer control system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0180] The particle analyzer or sorting system 502 can be configured to acquire biological event data. For example, a flow cytometer can generate flow cytometry event data. The particle analyzer 502 can be configured to provide the biological event data to the analysis controller 500. A data communication channel can be included between the particle analyzer or sorting system 502 and the analysis controller 500. The biological event data can be provided to the analysis controller 500 via the data communication channel.

[0181] The analysis controller 500 can be configured to receive biological event data from a particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 can include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display including a first plot of the biological event data on a display device 506. The analysis controller 500 can be further configured to render a region of interest, for example, as a gate around a population of the biological event data shown by the display device 506, overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted on a histogram or bivariate plot of a single parameter. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0182] Analysis controller 500 can further be configured to display biological event data within a gate differently from other events within the biological event data outside the gate on display device 506. For example, analysis controller 500 can be configured to render the color of biological event data contained within a gate differently from the color of biological event data outside the gate. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0183] The analysis controller 500 can be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate a gate selection signal to the analysis controller 500 identifying a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking the desired gate when a cursor is positioned there). In some implementations, the first device can be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a stylus, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 can include a right mouse button and a left mouse button, each capable of generating a trigger event.

[0184] The trigger event can cause the analysis controller 500 to change how the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input for further processing, such as selecting a population for particle sorting purposes.

[0185] In some embodiments, the analysis controller 500 can be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0186] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0187] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can include a plot of the biological event data and a gate that delineates a section of the plot. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500, along with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0188] In some implementations, the analysis controller 500 can generate a user interface for receiving exemplary events for selection. For example, the user interface can include controls for receiving exemplary events or exemplary images. The exemplary events or images or exemplary gates can be provided prior to collection of event data for the sample or based on an initial set of events for a subset of the sample.

[0189] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which can be coupled to, include, or be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in single file across a monitoring area 611 (e.g., where a laser stream intersects) illuminated by an illumination source 612 (e.g., a laser). Vibration of droplet-forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0190] During operation, the detection station 614 (e.g., an event detector) identifies when a particle of interest (or cell of interest) crosses the monitoring area 611. The detection station 614 provides an input to a timing circuit 628, which in turn provides an input to a flash charge circuit 630. At a droplet separation point, signaled by a timed drop delay (Δt), a flash charge can be applied to the moving fluid column 608 so that the droplet of interest carries a charge. The droplet of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplet into a container, such as a collection tube or a multi-well or microwell sample plate, and a well or microwell can be associated with the particular droplet of interest. As shown in FIG. 6A, the droplets can be collected in a waste receptacle 638.

[0191] Detection system 616 (e.g., a droplet boundary detector) serves to automatically determine the phase of the drop drive signal when a particle of interest passes through monitoring area 611. An exemplary droplet boundary detector is described in U.S. Pat. No. 7,679,039, which is incorporated herein by reference in its entirety. Detection system 616 allows the instrument to accurately calculate the location of each detected particle within the droplet. Detection system 616 inputs amplitude signal 620 and / or phase 618 signals, which are input via amplifier 622 to amplitude control circuit 626 and / or frequency control circuit 624. Amplitude control circuit 626 and / or frequency control circuit 624 then control droplet forming transducer 602. Amplitude control circuit 626 and / or frequency control circuit 624 may be included in a control system.

[0192] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled to a memory configured to store detected events and sorting decisions based thereon. The sorting decisions can be included in the particle's event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by either the detection system 616 or the detection station 614 and provided to a non-collecting element.

[0193] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via stream charging wires within the barbs. This creates a stream of droplets 610 containing particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed, such as by sorting electronics or another detection system (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets and guide them toward a destination collection receptacle (e.g., any of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a receptacle 674 or along a second path 668 toward a receptacle 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particle to continue along flow path 664. Such uncharged droplets can enter a waste receptacle, such as via an aspirator 670.

[0194] Sorting electronics can be included to initiate collection of measurements, receive fluorescent signals for the particles, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 6B includes the BD FACSAria™ line of flow cytometers commercially offered by Becton, Dickinson and Company (Franklin Lakes, NJ).

[0195] Integrated Circuit Devices Aspects of the present disclosure also include integrated circuit devices programmed to perform the subject methods described herein, such as for calculating and applying data signal filters to detect particles in a flow stream. In some embodiments, the integrated circuit device of interest comprises a field programmable gate array (FPGA). In other embodiments, the integrated circuit device comprises an application specific integrated circuit (ASIC). In yet other embodiments, the integrated circuit device comprises a complex programmable logic device (CPLD).

[0196] According to certain embodiments, the integrated circuit is programmed to determine characteristics of a data signal waveform generated in response to light detected from illuminated particles of a sample in the flow stream and to calculate a data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the integrated circuit is programmed to determine a width parameter of the data signal waveform. In some cases, the integrated circuit is programmed to determine one or more of a waveform area, a waveform height, and a ratio of the waveform area and the waveform height. In some cases, the data signal processed by the system has a Gaussian distribution.

[0197] In some embodiments, the integrated circuit is programmed to calculate a data filter based on an aspect of the particles in the flow stream. In some cases, the data signal filter is based on a width of the particles. In some embodiments, the integrated circuit is programmed to calculate a data filter based on parameters of particles in the flow stream having a diameter of 1000 nm or less, such as when the diameter is between 50 nm and 800 nm.

[0198] In some embodiments, the integrated circuit is programmed to calculate a data signal filter that, when applied to a data signal from the light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the integrated circuit is programmed to match the data signal filter to a ground truth data signal waveform produced in response to the detected light. In particular cases, the integrated circuit is programmed to calculate the data signal filter according to:

[0199]

number

[0200] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0201] In some embodiments, the integrated circuit is programmed to calculate a data signal filter that is an estimate of the ground truth data signal waveform. In some cases, the integrated circuit is programmed to calculate a linear analog data signal filter from the determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the integrated circuit is programmed to calculate a data signal filter from the determined characteristics of the data signal waveform, the data signal filter including one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0202] In some embodiments, the integrated circuit is programmed to apply a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the integrated circuit is programmed to generate a data signal waveform in response to the detected light and apply a data signal filter to the generated data signal waveform, the data signal filter being calculated based on the determined characteristics of the data signal generated by the optical detection system. In some cases, the integrated circuit is programmed to determine a trigger metric for detecting particles in the sample based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise in the data signal waveform.

[0203] Non-transitory computer-readable storage medium Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for implementing the subject methods. The computer-readable storage medium may be used on one or more computers for fully or partially automating a system for implementing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), regardless of whether such devices are internal or external to the computer. A file containing information may be "stored" on a computer-readable medium, where "storing" means recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be performed using programming that may be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, as well as many others.

[0204] A non-transitory computer-readable storage medium according to certain embodiments comprises an algorithm for determining characteristics of a data signal waveform generated in response to light detected from illuminated particles of a sample in a flow stream and for calculating a data signal filter from the determined characteristics of the data signal waveform. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for determining a width parameter of the data signal waveform. In some cases, the non-transitory computer-readable storage medium includes an algorithm for determining one or more of a waveform area, a waveform height, and a ratio of the waveform area to the waveform height. In some cases, the data signal processed by the system has a Gaussian distribution.

[0205] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data filter based on an aspect of particles in the flowstream. In some cases, the data filter is based on a width of the particles. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data filter based on parameters of particles in the flowstream having a diameter of 1000 nm or less, such as when the diameter is between 50 nm and 800 nm.

[0206] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a data signal filter that, when applied to a data signal from a light detection system, produces a data signal having a maximum signal-to-noise ratio. In some cases, the non-transitory computer-readable storage medium includes an algorithm for matching the data signal filter to a ground truth data signal waveform produced in response to the detected light. In particular cases, the non-transitory computer-readable storage medium includes an algorithm for calculating a data signal filter according to:

[0207]

number

[0208] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, and sd(·) is the standard deviation. In some cases, the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering with the data signal filter. In some cases, the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering with the data signal filter.

[0209] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm for computing a data signal filter that is an estimate of a ground truth data signal waveform. In some cases, the non-transitory computer-readable storage medium includes an algorithm for computing a linear analog data signal filter from determined characteristics of the data signal waveform. In particular cases, the linear analog data signal filter is a finite impulse response filter. In other cases, the linear analog data signal filter is an infinite impulse response filter. In some cases, the non-transitory computer-readable storage medium includes an algorithm for computing a data signal filter from determined characteristics of the data signal waveform, the data signal filter including one or more of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0210] In some embodiments, the non-transitory computer-readable storage medium includes an algorithm that applies a data signal filter to a data signal waveform generated by the optical detection system. In these embodiments, the non-transitory computer-readable storage medium includes an algorithm that generates a data signal waveform in response to the detected light and applies a data signal filter to the generated data signal waveform, the data signal filter being calculated based on determined characteristics of the data signal generated by the optical detection system. In some cases, the non-transitory computer-readable storage medium includes an algorithm that determines a trigger metric for detecting particles in a sample based on the filtered data signal waveform. In some cases, the trigger metric is a ratio of the amplitude of the data signal to a noise component of the data signal waveform. In particular cases, the noise component is a root-mean-square value of the noise in the data signal waveform.

[0211] In certain cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating a sorting decision based on identified particles in a sample.

[0212] The non-transitory computer-readable storage medium may be used in one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory in which instructions for performing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, and an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known manners and facilitates the processor's coordination and execution of the functions of various computer programs, which may be written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0213] kit Aspects of the present disclosure further include kits, where the kits include one or more of the integrated circuits described herein. In some embodiments, the kits may further include programming for the subject systems, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, compact disc, DVD, Blu-ray disc, etc.), or instructions for downloading the programming from an Internet web protocol or cloud server. The kits may further include instructions for practicing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is information printed on a suitable medium or substrate, such as one or more sheets of paper with the information printed on them, kit packaging, a package insert, etc. Another form in which these instructions may be present is a computer-readable medium having the information recorded thereon, such as a diskette, a compact disc (CD), a portable flash drive, etc. Another form in which these instructions may be present is a website address that can be used via the Internet to access the information at a remote site.

[0214] Utilities The subject systems, methods, and computer systems find use in a variety of applications where it is desirable to analyze and separate particle components in a sample in a fluid medium, such as a biological sample. In some embodiments, the systems and methods described herein find use in flow cytometry characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods find use in spectroscopy of emitted light. Additionally, the subject systems and methods find use in increasing the signal obtained from light collected from a sample (e.g., in a flow stream). In particular cases, the present disclosure finds use in improving the measurement of light collected from a sample illuminated in a flow stream in a flow cytometer. Embodiments of the present disclosure find use where it is desirable to provide a flow cytometer with improved cell sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0215] Embodiments of the present disclosure also find use in applications where cells prepared from biological samples may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from a target fluid or tissue biological sample. For example, the subject methods and systems facilitate obtaining cells from fluid or tissue samples used as research or diagnostic samples for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used in therapy. The disclosed methods and devices enable the separation and recovery of cells from biological samples (e.g., organs, tissues, tissue fragments, fluids) with increased efficiency and reduced cost compared to conventional flow cytometry systems.

[0216] Notwithstanding the appended claims, the present disclosure is also defined by the following paragraphs.

[0217] 1. A method for determining a data signal filter for detecting particles in a particle analyzer, comprising: detecting light from particles in the flowstream with a light detection system; generating a data signal waveform in response to light detected from particles in the flow stream; determining a characteristic of a data signal waveform; calculating a data signal filter from the determined characteristics of the data signal waveform; A method comprising:

[0218] 2. The method of clause 1, wherein the data signal waveform characteristics include a width parameter of the data signal waveform.

[0219] 3. The method of clause 2, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0220] 4. The method of any one of clauses 1 to 3, wherein the data signal waveform has a Gaussian distribution.

[0221] 5. The method of any one of clauses 1 to 4, wherein the calculated data signal filter, when applied to the data signal from the optical detection system, produces a data signal having a maximum signal-to-noise ratio.

[0222] 6.Data signal filter

[0223]

number

[0224] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 6. The method of any one of clauses 1 to 5, wherein sd(·) is the standard deviation.

[0225] 7. The method of clause 6, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0226] 8. The method of any one of clauses 6 to 7, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0227] 9. The method of any one of clauses 1 to 8, comprising calculating a linear analog data signal filter from determined characteristics of the data signal waveform.

[0228] 10. The method of clause 9, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0229] 11. The method of clause 9, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0230] 12. The method of any one of clauses 9 to 11, comprising calculating from the determined characteristics of the data signal waveform a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0231] 13. The method of any one of clauses 1 to 12, wherein the data signal filter is based on particle behavior.

[0232] 14. The method of clause 13, wherein the aspect is the width of the particle.

[0233] 15. The method of any one of clauses 1 to 14, wherein the particles are extracellular vesicles.

[0234] 16. A method according to any one of clauses 13 to 15, wherein the generated data signal waveform is independent of particle size.

[0235] 17. The method of any one of clauses 1-16, further comprising illuminating the sample with a light source.

[0236] 18. The method according to clause 17, wherein the size of the particles is smaller than the size of the illumination beam of the light source.

[0237] 19. The method of any one of clauses 1-18, comprising applying a data signal filter to the data signal waveform produced by the optical detection system.

[0238] 20. The method of clause 19, further comprising determining a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0239] 21. The method of clause 20, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0240] 22. The method of clause 21, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0241] 23. A method comprising: detecting light from particles of the sample in the flowstream with a light detection system; generating a data signal waveform in response to the detected light; applying a data signal filter to the generated data signal waveform; Including, The data signal filter is calculated based on determined characteristics of the data signal generated by the optical detection system. method.

[0242] 24. The method of clause 23, further comprising determining a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0243] 25. The method of clause 24, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0244] 26. The method of clause 25, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0245] 27. The method of any one of clauses 23 to 26, wherein the data signal filter is based on particle behavior.

[0246] 28. The method of clause 27, wherein the aspect is the width of the particle.

[0247] 29. The method of any one of clauses 23 to 28, wherein the particles comprise extracellular vesicles.

[0248] 30. The method of any one of clauses 24 to 28, wherein the particles comprise extracellular vesicles.

[0249] 31. The method of any one of clauses 27 to 29, wherein the generated data signal waveform is independent of particle size.

[0250] 32. The method of any one of clauses 23-31, further comprising illuminating the sample with a light source.

[0251] 33. The method according to clause 32, wherein the size of the particles is smaller than the size of the illumination beam of the light source.

[0252] 34. The method of any one of clauses 23 to 33, wherein the data signal waveform characteristics include a width parameter of the data signal waveform.

[0253] 35. The method of clause 34, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0254] 36. The method of any one of clauses 23 to 35, wherein the data signal waveform has a Gaussian distribution.

[0255] 37. The method of any one of clauses 23 to 36, wherein the calculated data signal filter produces a data signal waveform having a maximum signal-to-noise ratio.

[0256] 38. A data signal filter is

[0257]

number

[0258] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 38. The method of any one of clauses 23 to 37, wherein sd(·) is the standard deviation.

[0259] 39. The method of clause 38, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0260] 40. The method of any one of clauses 38-39, wherein the denominator of the data signal filter kernel is the magnitude of the noise in the data signal waveform after filtering by the data signal filter.

[0261] 41. A method according to any one of clauses 23 to 40, comprising calculating a linear analogue data signal filter from determined characteristics of the data signal waveform.

[0262] 42. The method of clause 41, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0263] 43. The method of clause 42, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0264] 44. The method of any one of clauses 41 to 43, comprising calculating from the determined characteristics of the data signal waveform a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0265] 45. A system comprising: a light source configured to illuminate particles in the flow stream; a light detection system including a plurality of light detectors; a processor including a memory operatively coupled to the processor; which, when executed by a processor, causes the processor to: generating a data signal waveform in response to light detected from particles in the flow stream; determining a characteristic of the data signal waveform; Compute a data signal filter from determined characteristics of the data signal waveform The memory contains instructions for a system.

[0266] 46. ​​The system of clause 45, wherein the data signal waveform characteristics include a width parameter of the data signal waveform.

[0267] 47. The system of clause 46, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0268] 48. A system according to any one of clauses 45 to 47, wherein the data signal waveform has a Gaussian distribution.

[0269] 49. A system according to any one of clauses 45 to 48, wherein the calculated data signal filter, when applied to the data signal from the optical detection system, produces a data signal having a maximum signal-to-noise ratio.

[0270] 50. The memory is

[0271]

number

[0272] , wherein h(t) is the data signal filter kernel; n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 50. The system of any one of clauses 45 to 49, wherein sd(·) is the standard deviation.

[0273] 51. The system of clause 50, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0274] 52. A system described in any one of clauses 50-51, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0275] 53. A system according to any one of clauses 45 to 52, wherein the memory includes instructions for calculating a linear analogue data signal filter from determined characteristics of the data signal waveform.

[0276] 54. The system of clause 53, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0277] 55. The system of clause 53, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0278] 56. The system of any one of clauses 53-55, comprising calculating, from the determined characteristics of the data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal form filter, and a matched filter.

[0279] 57. A system according to any one of clauses 45 to 56, wherein the data signal filter is based on particle behavior.

[0280] 58. The system of clause 57, wherein the aspect is the width of the particle.

[0281] 59. A system described in any one of clauses 45 to 58, wherein the particles are extracellular vesicles.

[0282] 60. A system according to any one of clauses 57 to 59, wherein the generated data signal waveform is independent of particle size.

[0283] 61. The system according to clause 60, wherein the size of the particles is smaller than the illumination beam size of the light source.

[0284] 62. A system as described in any one of clauses 45 to 61, wherein the memory includes instructions for applying a data signal filter to the data signal waveform produced by the optical detection system.

[0285] 63. The system of clause 62, wherein the memory includes instructions for determining a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0286] 64. The system of clause 63, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0287] 65. The system of clause 64, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0288] 66. A system comprising: a light source configured to illuminate particles of the sample in the flow stream; a light detection system including a plurality of light detectors; a processor including a memory operatively coupled to the processor; which, when executed by a processor, causes the processor to: generating a data signal waveform in response to the detected light; Applying a data signal filter to the generated data signal waveform, the data signal filter being calculated based on the determined characteristics of the data signal generated by the optical detection system. The memory contains instructions for a system.

[0289] 67. The system of clause 66, wherein the memory includes instructions for determining a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0290] 68. The system of clause 67, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0291] 69. The system of clause 68, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0292] 70. A system described in any one of clauses 66 to 69, wherein the particles have a diameter of 1000 nm or less.

[0293] 71. The system described in clause 27, wherein the particles have a diameter of 50 nm to 800 nm.

[0294] 72. A system described in any one of clauses 66 to 71, wherein the particles comprise extracellular vesicles.

[0295] 73. A system according to any one of clauses 71-72, wherein the generated data signal waveform is independent of particle size.

[0296] 74. The system according to clause 73, wherein the size of the particles is smaller than the illumination beam size of the light source.

[0297] 75. A system according to any one of clauses 66 to 74, wherein the data signal waveform characteristics include a width parameter of the data signal waveform.

[0298] 76. The system of clause 75, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0299] 77. A system according to any one of clauses 66 to 76, wherein the data signal waveform has a Gaussian distribution.

[0300] 78. A system according to any one of clauses 66 to 77, wherein the calculated data signal filter produces a data signal waveform having a maximum signal-to-noise ratio.

[0301] 79. The memory is

[0302]

number

[0303] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 38. The method of any one of clauses 23 to 37, wherein sd(·) is the standard deviation.

[0304] 80. The system of clause 79, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0305] 81. A system described in any one of clauses 79-80, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0306] 82. A system according to any one of clauses 66 to 81, wherein the memory includes instructions for calculating a linear analog data signal filter from determined characteristics of the data signal waveform.

[0307] 83. The system of clause 82, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0308] 84. The system of any one of clauses 82-83, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0309] 85. The system of any one of clauses 82-84, wherein the memory includes instructions for calculating, from the determined characteristics of the data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal-form filter, and a matched filter.

[0310] 86. An integrated circuit for determining a data signal filter for detecting particles in a particle analyzer, the integrated circuit comprising: determining a characteristic of a data signal waveform generated in response to light detected from the illuminated particles of the sample flowing in the flow stream; an integrated circuit programmed to calculate a data signal filter from the determined data signal waveform characteristics;

[0311] 87. The integrated circuit of clause 86, wherein the data signal waveform characteristics include a width parameter of the data signal waveform.

[0312] 88. The integrated circuit of clause 87, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0313] 89. The integrated circuit of any one of clauses 86-88, wherein the data signal waveform has a Gaussian distribution.

[0314] 90. An integrated circuit according to any one of clauses 86 to 89, wherein the integrated circuit is programmed to calculate a data signal filter that produces a data signal waveform having a maximum signal-to-noise ratio.

[0315] 91. An integrated circuit has the following formula:

[0316]

number

[0317] , wherein h(t) is the data signal filter kernel; n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 91. The integrated circuit of any one of clauses 86 to 90, wherein sd(·) is the standard deviation.

[0318] 92. The integrated circuit of clause 91, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0319] 93. The integrated circuit of any one of clauses 91-92, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0320] 94. An integrated circuit according to any one of clauses 86 to 89, wherein the integrated circuit is programmed to calculate a linear analogue data signal filter from determined characteristics of the data signal waveform.

[0321] 95. The integrated circuit of clause 94, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0322] 96. The integrated circuit of clause 94, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0323] 97. An integrated circuit according to any one of clauses 94-95, wherein the integrated circuit is programmed to calculate, from determined characteristics of the data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal type filter, and a matched filter.

[0324] 98. The integrated circuit of any one of clauses 86-97, wherein the integrated circuit is programmed to apply a data signal filter to the data signal waveform produced by the optical detection system.

[0325] 99. The integrated circuit of clause 98, wherein the integrated circuit is programmed to determine a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0326] 100. The integrated circuit of clause 99, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0327] 101. The integrated circuit of clause 100, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0328] 102. An integrated circuit for applying a data signal filter to detect particles in a sample, the integrated circuit being programmed to apply a data signal filter to a data signal waveform generated in response to light detected from an illuminating particle of the sample in a flow stream, the data signal filter being calculated based on determined characteristics of the data signal generated by the light detection system.

[0329] 103. The integrated circuit of clause 102, wherein the integrated circuit is programmed to determine a trigger metric for detecting particles in the sample based on the filtered data signal waveform.

[0330] 104. The integrated circuit of clause 103, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0331] 105. The integrated circuit of clause 104, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0332] 106. The integrated circuit of any one of clauses 102-105, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0333] 107. The integrated circuit of clause 106, wherein the data signal waveform has a Gaussian distribution.

[0334] 108. An integrated circuit according to any one of clauses 102 to 107, wherein the integrated circuit is programmed to calculate a data signal filter that produces a data signal waveform having a maximum signal-to-noise ratio.

[0335] 109. An integrated circuit has the following formula:

[0336]

number

[0337] where h(t) is the data signal filter kernel, n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 109. The integrated circuit of any one of clauses 102 to 108, wherein sd(·) is the standard deviation.

[0338] 110. The integrated circuit of clause 109, wherein the numerator of the data signal filter kernel is a function of the maximum data signal waveform after filtering by the data signal filter.

[0339] 111. The integrated circuit of any one of clauses 109-110, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0340] 112. An integrated circuit according to any one of clauses 102 to 108, wherein the integrated circuit is programmed to calculate a linear analogue data signal filter from determined characteristics of the data signal waveform.

[0341] 113. The integrated circuit of clause 112, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0342] 114. The integrated circuit of clause 112, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0343] 115. An integrated circuit according to any one of clauses 112 to 114, wherein the memory includes instructions for calculating, from determined characteristics of the data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal-type filter, and a matched filter.

[0344] 116. A non-transitory computer-readable storage medium for determining a data signal filter for detecting particles in a particle analyzer, the non-transitory computer-readable storage medium having stored thereon determining a characteristic of a data signal waveform generated in response to light detected from the illuminated particles of the sample flowing in the flowstream; Computing a data signal filter from determined characteristics of the data signal waveform A non-transitory computer-readable storage medium comprising instructions for:

[0345] 117. The non-transitory computer-readable storage medium of clause 116, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0346] 118. The non-transitory computer-readable storage medium of clause 117, wherein the data signal waveform comprises a Gaussian distribution.

[0347] 119. A non-transitory computer readable storage medium according to any one of clauses 116 to 118, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a data signal filter that produces a data signal waveform having a maximum signal-to-noise ratio.

[0348] 120.An integrated circuit has the following formula:

[0349]

number

[0350] , wherein h(t) is the data signal filter kernel; n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 119. The non-transitory computer-readable storage medium of any one of clauses 116 to 119, wherein sd(·) is the standard deviation.

[0351] 121. The non-transitory computer-readable storage medium of clause 120, wherein the numerator of the data signal filter kernel is a function of a maximum data signal waveform after filtering by the data signal filter.

[0352] 122. A non-transitory computer-readable storage medium according to any one of clauses 120 to 121, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0353] 123. The non-transitory computer readable storage medium of any one of clauses 116-122, wherein the non-transitory computer readable storage medium includes an algorithm for calculating a linear analog data signal filter from determined characteristics of the data signal waveform.

[0354] 124. The non-transitory computer-readable storage medium of clause 123, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0355] 125. The non-transitory computer-readable storage medium of clause 123, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0356] 126. The non-transitory computer readable storage medium of any one of clauses 124-125, wherein the non-transitory computer readable storage medium includes an algorithm for calculating, from determined characteristics of a data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L-filter, a Linkwitz-Riley filter, an ideal-form filter, and a matched filter.

[0357] 127. A non-transitory computer readable storage medium according to any one of clauses 116 to 126, wherein the non-transitory computer readable storage medium includes an algorithm for applying a data signal filter to a data signal waveform generated by the optical detection system.

[0358] 128. The non-transitory computer readable storage medium of clause 127, wherein the non-transitory computer readable storage medium includes an algorithm for determining a trigger metric for detecting particles in a sample based on the filtered data signal waveform.

[0359] 129. The non-transitory computer-readable storage medium of clause 128, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0360] 130. The non-transitory computer-readable storage medium of clause 129, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0361] 131. A non-transitory computer readable storage medium for determining a data signal filter for detecting particles in a particle analyzer, the non-transitory computer readable storage medium including an algorithm for applying the data signal filter to a data signal waveform generated in response to light detected from an illuminated particle of a sample in a flow stream, the data signal filter being calculated based on determined characteristics of the data signal generated by the light detection system.

[0362] 132. The non-transitory computer readable storage medium of clause 131, wherein the non-transitory computer readable storage medium includes an algorithm for determining a trigger metric for detecting particles in a sample based on the filtered data signal waveform.

[0363] 133. The non-transitory computer-readable storage medium of clause 132, wherein the trigger metric comprises a ratio of the data signal amplitude to the noise component of the data signal waveform.

[0364] 134. The non-transitory computer-readable storage medium of clause 133, wherein the noise component comprises a root mean square value of the noise in the data signal waveform.

[0365] 135. The non-transitory computer-readable storage medium of any one of clauses 131-134, wherein the width parameter comprises a ratio of corrugation area to corrugation height.

[0366] 136. The non-transitory computer-readable storage medium of clause 135, wherein the data signal waveform comprises a Gaussian distribution.

[0367] 137. A non-transitory computer readable storage medium according to any one of clauses 131 to 136, wherein the non-transitory computer readable storage medium comprises an algorithm for calculating a data signal filter that produces a data signal waveform having a maximum signal-to-noise ratio.

[0368] 138. A non-transitory computer-readable storage medium comprising:

[0369]

number

[0370] , wherein h(t) is the data signal filter kernel; n(t) is the noise component of the data signal, s(t) is the data signal waveform produced by the photodetection system; 138. The non-transitory computer-readable storage medium of any one of clauses 131 to 137, wherein sd(·) is the standard deviation.

[0371] 139. The non-transitory computer-readable storage medium of clause 138, wherein the numerator of the data signal filter kernel is a function of a maximum data signal waveform after filtering by the data signal filter.

[0372] 140. The non-transitory computer-readable storage medium of any one of clauses 138-139, wherein the denominator of the data signal filter kernel is the magnitude of noise in the data signal waveform after filtering by the data signal filter.

[0373] 141. The non-transitory computer readable storage medium of any one of clauses 131 to 140, wherein the non-transitory computer readable storage medium includes an algorithm for calculating a linear analog data signal filter from determined characteristics of the data signal waveform.

[0374] 142. The non-transitory computer-readable storage medium of clause 141, wherein the linear analog data signal filter comprises a finite impulse response filter.

[0375] 143. The non-transitory computer-readable storage medium of clause 141, wherein the linear analog data signal filter comprises an infinite impulse response filter.

[0376] 144. An integrated circuit according to any one of clauses 141 to 143, wherein the memory includes instructions for calculating, from determined characteristics of the data signal waveform, a data signal filter selected from the group consisting of a Butterworth filter, a Chebyshev filter, an elliptic Cauer filter, a Bessel filter, a Gaussian filter, an optimal L filter, a Linkwitz-Riley filter, an ideal-type filter, and a matched filter.

[0377] Although the foregoing invention has been described in some detail by way of illustration and example for clarity of understanding, it will be readily apparent to those skilled in the art in light of the teachings of the invention that certain changes and modifications can be made without departing from the spirit or scope of the appended claims.

[0378] Accordingly, the foregoing merely illustrates the principles of the present invention. It will be appreciated that those skilled in the art will be able to devise various configurations, not explicitly described or shown herein, which embody the principles of the present invention and are within its spirit and scope. Furthermore, all examples and conditional language recited herein are intended primarily to aid the reader in understanding the principles of the present invention and the concepts the inventors have contributed to advancing the art, and should not be construed as being limited to such specifically recited examples and conditions. Furthermore, all statements herein reciting principles, aspects, and embodiments of the present invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure. Furthermore, nothing disclosed herein is intended as a dedication to the public, regardless of whether such disclosure is expressly recited in the claims.

[0379] Accordingly, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention is embodied by the appended claims. In the claims, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is expressly defined as being invoked for a limitation in a claim only when the precise phrase "means for" or the precise phrase "step for" is recited at the beginning of such limitation in the claim; if such precise phrases are not used in a claim limitation, 35 U.S.C. 112(f) or 35 U.S.C. 112(6) is not invoked.

Claims

1. A method for determining a data signal filter for detecting particles in a particle analyzer, Using a photodetection system, detect light from particles in a flow stream, To generate a data signal waveform in response to the detected light from the particles in the flow stream, To determine the characteristics of the aforementioned data signal waveform, Calculating a data signal filter from the determined characteristics of the data signal waveform, A method that includes this.

2. The method according to claim 1, wherein the features of the data signal waveform include a width parameter of the data signal waveform.

3. The method according to claim 2, wherein the width parameter includes the ratio of the waveform area to the waveform height.

4. The method according to any one of claims 1 to 3, wherein the data signal waveform has a Gaussian distribution.

5. The method according to any one of claims 1 to 3, wherein when the calculated data signal filter is applied to the data signal from the photodetection system, it generates a data signal having the maximum signal-to-noise ratio.

6. The aforementioned data signal filter is defined by the following equation [Math 1] The hat (^)h(t) is calculated according to the above and is the kernel of the data signal filter. n(t) is the noise component of the data signal, s(t) is the data signal waveform generated by the optical detection system, The method according to any one of claims 1 to 3, wherein sd(•) is the standard deviation.

7. The method according to claim 6, wherein the numerator of the kernel of the data signal filter is a function of the maximum data signal waveform after filtering by the data signal filter.

8. The method according to claim 6, wherein the denominator of the kernel of the data signal filter is the magnitude of the noise in the data signal waveform after filtering by the data signal filter.

9. The method according to any one of claims 1 to 3, wherein the method comprises calculating a linear analog data signal filter from the determined features of the data signal waveform.

10. The method according to any one of claims 1 to 3, wherein the data signal filter is based on the characteristics of the particles.

11. The method according to any one of claims 1 to 3, wherein the particle is an extracellular vesicle.

12. The method according to any one of claims 1 to 3, wherein the method comprises applying a data signal filter to the data signal waveform generated by the photodetection system.

13. It is a method, Using a photodetection system, light is detected from sample particles in a flow stream, To generate a data signal waveform in response to the detected light, Applying a data signal filter to the generated data signal waveform Includes, The data signal filter is calculated based on the determined characteristics of the data signal generated by the photodetection system. method.

14. It is a system, A light source configured to irradiate particles in a flowstream, A photodetection system equipped with multiple photodetectors, A processor including memory that is operablely coupled to the processor The processor is equipped with, and when executed by the processor, the processor, A data signal waveform is generated in response to the detected light from the particles in the flow stream. Determine the characteristics of the aforementioned data signal waveform. The data signal filter is calculated from the determined characteristics of the data signal waveform. The system includes instructions for this purpose in the memory.

15. It is a system, A light source configured to irradiate sample particles in a flowstream, A photodetection system equipped with multiple photodetectors, A processor including memory that is operablely coupled to the processor The processor is equipped with, and when executed by the processor, the processor, A data signal waveform is generated in response to the detected light. A data signal filter is applied to the generated data signal waveform. The memory includes instructions for this purpose, The data signal filter is calculated based on the determined characteristics of the data signal generated by the photodetection system. system.

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