Method and system for calibrating spectral data signals in flow cytometry data

Abstract

The present invention provides a method for calibrating spectral data signals from a flow cytometer, enabling comparisons between assays at arbitrary unit scales and relative intensities between populations; a system configured to perform the method; and a non-temporary computer-readable storage medium. [Solution] Aspects of the present disclosure include a method for calibrating a spectral data signal (e.g., flow cytometer data). A method according to a particular embodiment includes irradiating particles of a sample having fluorophores in a flow stream with a light source; detecting the light from the irradiated particles with a photodetector in a photodetector system; generating a spectral data signal in response to the detected light; and calibrating the generated spectral data signal based on a calibration scaling factor and a calculated abundance of fluorophores.

Description

[Background technology]

[0001] Introduction The characterization of biological fluid analytes is a crucial part of biological research, medical diagnosis, and the assessment of a patient's overall health and wellness. Detecting biological fluid analytes, such as human blood or blood-derived products, can yield results that can play a role in determining treatment protocols for patients with various disease conditions.

[0002] Flow cytometry is a technique used to characterize and often sort biological materials, such as cells in a blood sample or particles of interest contained in another type of biological or chemical sample. A flow cytometer typically includes a sample reservoir for receiving a fluid sample, such as a blood sample, and a sheath reservoir for containing a sheath fluid. The flow cytometer directs the sheath fluid towards a flow cell while transporting particles (including cells) in the fluid sample to the flow cell as a stream of cells. Light is irradiated onto the flow stream to characterize its components. Variations in the material in the flow stream, such as the presence of morphology or fluorescent labels, can cause variations in the observed light, which enable characterization and separation. To characterize the components in the flow stream, light must strike and collect from the flow stream. The light source in the flow cytometer can vary and may include one or more broad-spectrum lamps, light-emitting diodes, and single-wavelength lasers. The light source is aligned with the flow stream, and the optical response from the illuminated particles is collected and quantified.

[0003] The isolation of biological particles has been achieved by adding sorting or collection capabilities to flow cytometers. Particles in an isolated stream, detected as possessing one or more desired characteristics, are individually isolated from the sample stream by mechanical or electrical removal. A common flow sorting technique utilizes droplet sorting, in which a fluid stream containing linearly isolated particles is divided into droplets. Droplets containing the particles of interest are charged and deflected into a collection tube by passing through an electric field. Typically, linearly isolated particles in the stream are characterized as they pass through an observation point located directly below the nozzle tip. Once a particle is identified as meeting one or more desired criteria, the time it will reach the droplet departure point and detach from the stream to become a droplet can be predicted. Ideally, a charge is briefly applied to the fluid stream just before the droplet containing the selected particle detaches from the fluid stream, and then grounded immediately after the droplet detaches. The sorted droplet retains its charge when detaching from the fluid stream, while all other droplets remain uncharged.

[0004] Flow cytometers scale the measured photodetector data to arbitrary units. The position of a given input optical signal on this arbitrary unit scale can be increased or decreased by changing the detector gain setting. The ability of a flow cytometer to perform consistently day after day (defined as producing the same output signal for the same input sample) depends on several factors, such as temperature and optics-mechanical alignment, which can change randomly over time. To maintain performance, manufacturers have established detector gain settings based on daily quality control (QC) procedures to date. [Overview of the project]

[0005] The inventors have recognized that the results of this methodology involve scaling the data to an arbitrary unit scale, regardless of the number of molecules stained on particles such as cells. Consequently, this biologically relevant information is lost, and end-users must instead rely on the relative intensity of the population to understand the abundance of the marker. Furthermore, comparisons between instrument platforms become excessively difficult, especially when detecting the same combination of markers using different fluorophores, because the arbitrary unit scale and relative intensity between populations are not quantitative units that enable comparisons between assays. Embodiments of the present invention address, among other things, the above problems.

[0006] Aspects of this disclosure include methods for calibrating spectral data signals (e.g., flow cytometer data). A method according to a particular embodiment includes irradiating particles of a sample having fluorophores in a flow stream with a light source; detecting light from the irradiated particles with a photodetector in a photodetector system; generating a spectral data signal in response to the detected light; and calibrating the generated spectral data signal based on a calibration scaling factor and a calculated abundance of fluorophores. A system configured to perform the method in question and a non-temporary computer-readable storage medium are also provided.

[0007] In some embodiments, the generated spectral data signal is the sum of data signals collected across multiple photodetector channels. In some cases, the generated data signal is the data signal collected on a single photodetector channel, for example, a photodetector channel showing the maximum signal intensity of a fluorophore.

[0008] In some embodiments, the method includes calculating a calibration scaling factor. In some cases, the method includes determining fluorophore abundance. In some cases, the fluorophore abundance is the amount (e.g., number) of fluorophore moieties present on the particles. In some cases, the calibration scaling factor is calculated by generating a reference data signal in one or more photodetector channels in response to light detected from an irradiated reference sample and calculating the calibration scaling factor based on the sum of the reference data signals and the abundance in the fluorophore reference sample. In some cases, the reference sample is a single stain control. In some cases, the calibration scaling factor is calculated using the sum of the medians of the fluorophore-specific signals for each photodetector channel. In certain cases, one or more of the calibration scaling factor and the fluorophore abundance are calculated using a linear regression of the total signal from the reference sample and the fluorophore abundance.

[0009] In some embodiments, the generated spectral data signal is calibrated prior to spectral decomposition of the generated spectral data signal. In some cases, the signal intensity is rescaled to be equivalent to the intensity of a single fluorophore. In some cases, rescaling the signal intensity to the intensity of a single fluorophore includes dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore. In some cases, the generated spectral data signal is calibrated according to the following.

[0010]

Number

[0011] Where X arb is the single stain reference spectrum, CSF is the calibration scaling factor, and f cal is the vector of fluorophore abundance, and y arb is the detector signal vector. In some embodiments, the spectral data signal is spectrally decomposed. In some cases, the spectral data signal is spectrally decomposed before calibration. In some cases, the reference data signal is normalized to 1. In some cases, the decomposed data signal is rescaled. In some cases, the prevalence of a single-stain control is determined. In some cases, the generated spectral data signal is calibrated according to the following:

[0012]

number

[0013] In the formula, X † is the inverse problem spectrum, CSF is the calibration scaling coefficient, and f cal is a vector of fluorophore abundances, and S is the sum of the data signals generated by multiple photodetector channels (S sum ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. max ) is selected from, y arb This is the detector signal vector.

[0014] In some embodiments, the particles have multiple different fluorophores. In some cases, the particles are multispectral beads. In some cases, the particles are cells labeled with multiple different fluorophores. In some cases, a calibration scaling factor is calculated for each of the different fluorophores. In some cases, the calibration scaling factor for each fluorophore is calculated using the sum of the median values ​​of the fluorophore-specific signals per photodetector channel for each fluorophore. In some cases, the calibration scaling factor is calculated by interpolating the entire signal with a reference calibration scaling factor to determine the equivalent fluorophore abundance.

[0015] Aspects of the present disclosure also include systems for carrying out the method in question, for example, to calibrate the spectral data signal of a flow cytometer. A system according to a particular embodiment includes a light source configured to irradiate particles of a sample having fluorophores in a flow stream; a photodetection system having a photodetector for detecting light from the irradiated particles; and a processor having memory, the memory operably coupled to the processor, the memory storing instructions, which, when executed by the processor, cause the processor to generate a spectral data signal in response to the detected light, and to calibrate the generated spectral data signal based on a calibration scaling factor and a calculated abundance of fluorophores.

[0016] In some embodiments, the light source is a light-emitting diode. In some embodiments, the system includes a light source having one or more lasers, such as multiple lasers. In some cases, the photodetector system includes multiple photodetectors. In some cases, the photodetector includes one or more photomultiplier tubes. In some cases, the photodetector system includes a photodetector array. In certain cases, one or more photodetectors in the array are photodiodes (e.g., avalanche photodiodes). In certain cases, one or more photodetectors in the array are charge-coupled devices.

[0017] In some cases, the system is configured to detect light by a photodetection system in multiple photodetector channels. In some embodiments, the generated spectral data signal is the sum of the data signals collected across multiple photodetector channels. In some cases, the generated data signal is the data signal collected in a single photodetector channel, for example, a photodetector channel showing the maximum signal intensity of a fluorophore.

[0018] In some embodiments, the memory includes instructions for calculating a calibration scaling factor. In some cases, the memory includes instructions for generating a reference data signal in one or more photodetector channels in response to light detected from an irradiated reference sample, and for calculating a calibration scaling factor based on the sum of the reference data signals and the abundance in the fluorophore reference sample. In some cases, the reference sample is a single-staining control. In some cases, the memory includes instructions for calculating a calibration scaling factor using the sum of the medians of the fluorophore-specific signals for each photodetector channel. In some cases, the memory includes instructions for calculating one or more of a calibration scaling factor and a fluorophore abundance using a linear regression of the total signal and fluorophore abundance from the reference sample. In a particular case, the fluorophore abundance is the number of fluorophores present on the particle.

[0019] In some embodiments, the memory includes instructions for calibrating the generated spectral data signal prior to spectral decomposition of the generated spectral data signal. In some cases, the memory includes instructions for rescaling the signal intensity to be equivalent to the intensity of a single fluorophore. In some cases, the memory includes instructions for rescaling the signal intensity to the intensity of a single fluorophore by dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore. In some embodiments, the memory includes instructions for calibrating the generated spectral data signal according to the following.

[0020] <00,00134> [Number] ]

[0021] <0000!41>Where X arb is a single-staining reference spectrum, CSF is a calibration scaling factor, and f cal is a vector of fluorophore abundances, and y arb is a detector signal vector.

[0022] In some embodiments, the memory includes instructions for spectrally decomposing a spectral data signal to generate a decomposed data signal. In some cases, the memory includes instructions for normalizing a reference data signal to 1. In some cases, the memory includes instructions for rescaling the decomposed spectral data signal. In some cases, the memory includes instructions for determining the occurrence rate of a single stained control. In some embodiments, the memory includes instructions for calibrating the generated spectral data signal according to the following:

[0023]

number

[0024] In the formula, X † is the inverse problem spectrum, CSF is the calibration scaling coefficient, and f cal is a vector of fluorophore abundances, and S is the sum of the data signals generated by multiple photodetector channels (S sum ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. max ) is selected from, y arb This is the detector signal vector.

[0025] In some embodiments, the particles have multiple different fluorophores. In some cases, the particles are multispectral beads. In some cases, the particles are cells labeled with multiple different fluorophores. In some cases, the memory includes instructions for calculating a calibration scaling factor for each of the different fluorophores. In some cases, the memory includes instructions for calculating a calibration scaling factor for each fluorophore by using the sum of the median values ​​of the fluorophore-specific signals per photodetector channel for each fluorophore. In some cases, the memory includes instructions for calculating a calibration scaling factor by interpolating the entire signal with a reference calibration scaling factor to determine the equivalent fluorophore abundance.

[0026] Aspects of the present disclosure also include non-temporary computer-readable storage media for carrying out, for example, one or more computer implementation methods described herein. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for irradiating particles of a sample containing fluorophores in a flow stream with a light source; an algorithm for detecting light from the irradiated particles using a photodetection system including a photodetector; an algorithm for generating a spectral data signal in response to the detected light; and an algorithm for calibrating the generated spectral data signal based on a calibration scaling coefficient and a calculated abundance of fluorophores.

[0027] In some cases, a non-temporary computer-readable storage medium includes an algorithm for detecting light by a photodetection system in multiple photodetector channels. In some embodiments, the generated spectral data signal is the sum of data signals collected across multiple photodetector channels. In some cases, the generated data signal is the data signal collected in a single photodetector channel, for example, a photodetector channel showing the maximum signal intensity of a fluorophore.

[0028] In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor based on the sum of the reference data signals and the abundance of fluorophores in the reference sample, which are generated in one or more photodetector channels in response to light detected from an irradiated reference sample. In some cases, the reference sample is a single-stained control. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating one or more calibration scaling factors and fluorophore abundances using linear regression of the total signal from the reference sample and the fluorophore abundance. In certain specific cases, the fluorophore abundance is the number of fluorophores present on a particle.

[0029] In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal before spectral decomposition of the generated spectral data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the signal intensity to be equivalent to the intensity of a single fluorophore. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the signal intensity to the intensity of a single fluorophore by dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal according to the following:

[0030]

number

[0031] In the formula, X arb is the single-stain reference spectrum, CSF is the calibration scaling factor, and f cal is a vector of fluorophore abundances, and y arb This is the detector signal vector. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for spectrally decomposing a spectral data signal to generate a decomposed data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for normalizing a reference data signal to 1. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the decomposed spectral data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for determining the occurrence rate of a single stained control. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal according to the following:

[0032]

number

[0033] In the formula, X † is the inverse problem spectrum, CSF is the calibration scaling coefficient, and f cal is a vector of fluorophore abundances, and S is the sum of the data signals generated by multiple photodetector channels (S sum ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. max ) is selected from, y arb This is the detector signal vector.

[0034] In some embodiments, the particles have multiple different fluorophores. In some cases, the particles are multispectral beads. In some cases, the particles are cells labeled with multiple different fluorophores. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor for each of the different fluorophores. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor for each fluorophore by using the sum of the median values ​​of the fluorophore-specific signals per photodetector channel for each fluorophore. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor by interpolating the entire signal with a reference calibration scaling factor to determine an equivalent fluorophore abundance.

[0035] This disclosure can be best understood from the following detailed description when read in conjunction with the attached drawings. The drawings include the following figures. [Brief explanation of the drawing]

[0036] [Figure 1A] This is a flowchart for calibrating spectral data signals according to a specific embodiment. [Figure 1B] This figure shows an exemplary workflow for spectral calibration according to a particular embodiment. [Figure 1C] This figure shows an exemplary workflow for spectral crosscalibration according to a particular embodiment. [Figure 2] This figure shows a flow cytometry system according to a specific embodiment. [Figure 3A-1] This figure shows an image-enabled particle sorting machine according to a specific embodiment. [Figure 3A-2] This figure shows an image-enabled particle sorting machine according to a specific embodiment. [Figure 3B] This figure shows image-based particle sorting data processing according to a specific embodiment. [Figure 4A] This is a functional block diagram of a particle analysis system according to a specific embodiment. [Figure 4B] This figure shows a flow cytometer according to a specific embodiment. [Figure 5] This is a functional block diagram of an example of a control system according to a specific embodiment. [Figure 6A] This is a schematic diagram of a particle sorting machine system according to a specific embodiment. [Figure 6B] This is a schematic diagram of a particle sorting machine system according to a specific embodiment. [Figure 7] This figure shows an embodiment of a computer-controlled system according to a particular designation. [Modes for carrying out the invention]

[0037] Aspects of this disclosure include methods for calibrating spectral data signals (e.g., flow cytometer data). A method according to a particular embodiment includes irradiating particles of a sample having fluorophores in a flow stream with a light source; detecting light from the irradiated particles with a photodetector in a photodetector system; generating a spectral data signal in response to the detected light; and calibrating the generated spectral data signal based on a calibration scaling factor and a calculated abundance of fluorophores. A system configured to perform the method in question and a non-temporary computer-readable storage medium are also provided.

[0038] Before describing this disclosure in detail, please understand that this disclosure is not limited to the specific embodiments described and is therefore naturally subject to change. Since the scope of this disclosure is limited only by the appended claims, please also understand that the terms used herein are intended solely to describe specific embodiments and are not intended to limit them.

[0039] When a range of values ​​is presented, unless explicitly indicated in the context, it is understood that the values ​​between the upper and lower limits of that range, up to one-tenth of the lower limit unit, and any other values ​​or values ​​within that stated range are included in this disclosure. These smaller upper and lower limits may be independently included in the smaller range, subject to any specifically excluded limits within the stated range, and are also included in this disclosure. If the stated range includes one or both limits, the range excluding one or both of those limits is also included in this disclosure.

[0040] In this specification, certain ranges are indicated by numbers preceded by the term “approximately.” The term “approximately” is used herein to provide literal support for the exact number preceded by the term, as well as for numbers that are close to or nearly close to the number preceded by the term. In determining whether a number is close to or approximates a specifically stated number, a number not explicitly stated as close to or approximates may, in the context in which it is presented, present a substantial equivalent of the specifically stated number.

[0041] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those generally understood by those skilled in the art to which this disclosure belongs. Any methods and materials similar or equivalent to those described herein may also be used in the implementation or testing of this disclosure, but representative exemplary methods and materials are described below.

[0042] All publications and patents referenced herein are incorporated herein by reference in such a way that it specifically and individually indicates that each individual publication or patent is incorporated by reference, and are incorporated herein by reference to disclose and describe the manner and / or materials by which the publications are referenced. Any reference to a publication is for the purpose of making that disclosure prior to the filing date, and this disclosure should not be construed as an acknowledgment that such publication has no prior rights by prior disclosure. Furthermore, the publication dates presented may differ from the actual publication dates and may need to be independently verified.

[0043] In this specification and the appended claims, the singular forms “a,” “an,” and “the” refer to multiple subjects unless the context clearly indicates otherwise. Furthermore, it should be noted that claims may be written to exclude any optional element. Therefore, this statement is intended to serve as a prior basis for using exclusive terms such as “alone” and “only” in relation to the enumeration of elements in the claims or the use of “negative” limitations.

[0044] As will be obvious to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has separate components and features that can be readily separated or combined with features of any of several other embodiments without departing from the scope or spirit of this disclosure. Any of the listed methods may be performed in the order of the listed events, or in any other logically possible order.

[0045] While systems and methods are described for grammatical fluidity with functional descriptions, claims should not necessarily be interpreted as being limited by constructing a limitation of “means” or “steps” unless explicitly formulated under 35 U.S. SC § 112, and the full meaning of the definitions and the scope of equivalents provided by the claims should be given under the doctrine of equivalents, and if the claims are explicitly formulated under 35 U.S. SC § 112, the full legal equivalents should be given under 35 U.S. SC § 112.

[0046] Method for calibrating spectral data signals Aspects of this disclosure include a method for calibrating spectral data signals, for example, data generated by detecting light from particles in a flow stream of a flow cytometer. As described above, earlier flow cytometers scale the measured photodetector data in arbitrary units. The position of a given input optical signal on this arbitrary unit scale can be adjusted by changing the detector gain setting. The result of this methodology is that the data is scaled on an arbitrary unit scale regardless of the number of molecules stained on the particles. Thus, this biologically relevant information is lost, and the end user must instead rely on the relative intensity of the population to understand the abundance of the marker. In embodiments of this disclosure, the method in question provides for converting spectral data from a flow cytometer at an arbitrary unit scale to a standard unit scale having a quantitative metric of the particles being characterized. In some cases, the spectral data is calibrated to preserve fluorophore abundance data. In certain particular cases, the generated spectral data provides an accurate determination of the absolute intensity produced by the particles, and does not require determining the relative intensity of the population to determine the abundance of the biological marker.

[0047] In some cases, the method in question provides a way to determine the abundance of fluorophores in a sample, particularly when the sample contains two or more different fluorophores, e.g., three or more, e.g., four or more, and e.g., five or more different fluorophores. In certain embodiments, the method in question provides a way to compensate for instrument or inter-instrument variations by taking into account tolerances such as temperature changes, optics-mechanical alignment, particle velocity, and laser intensity. In some cases, this provides a way to easily and efficiently cross-instrument analysis using the same instrument acquisition and data analysis templates, with faster and more accurate downstream internal platform analysis and longitudinal analysis. Furthermore, when calibrating spectral data signals according to the method in certain embodiments, the performance resolution of the cytometer is maintained over time, e.g., for more than one day, e.g., for more than three days, e.g., for more than seven days, e.g., for more than two weeks, e.g., for more than four weeks, e.g., for more than three months, e.g., for more than six months, e.g., for more than nine months, and, for example, for more than one year, the consistency within the instrument is maintained.

[0048] In certain embodiments, the method of the Act provides optimized photodetector system performance, such as an increase in the signal-to-noise ratio of the photodetector system. In certain cases, the method of the Act improves the consistency of the output from the photodetector of the photodetector system. For example, the signal-to-noise ratio of a photodetector system using the method of the Act herein can be increased by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, e.g., 75% or more, e.g., 90% or more, and e.g., 99% or more. In certain cases, the method of the Act increases the signal-to-noise ratio by more than 2 times, e.g., 3 times or more, e.g., 4 times or more, e.g., 5 times or more, and e.g., 10 times or more. In some embodiments, the method of the Act increases the consistency of the output from the photodetector of the photodetector system by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, e.g., 75% or more, e.g., 90% or more, and e.g., 99% or more. In certain cases, the method in question increases the consistency of the output from the photodetector of the photodetector system by more than two times, for example more than three times, for example more than four times, for example more than five times, and for example more than ten times.

[0049] The term “analyte data” is used herein in its conventional sense and refers to data obtained by evaluating a particular analyte for certain properties. In some embodiments, the analyte data is spectral data. The term “spectral data” is used herein in its conventional sense and refers to a data signal extending over one or more predetermined wavelength ranges. The spectral data signal may be generated by measuring light from particles in a flow stream at, for example, 400 or more different wavelengths, In certain cases, light from a sample is spectrally separated into two or more spectral ranges, e.g., three or more spectral ranges, e.g., four or more, e.g., eight or more, e.g., 16 or more, e.g., 32 or more, e.g., 64 or more, e.g., 128 or more, e.g., 256 or more, and e.g., 512 or more, and each spectral range is detected by one or more photodetector channels. Thus, the generated data signal responds to light including spectral ranges of 1 nm or more, e.g., 2 nm or more, e.g., 5 nm or more, e.g., 10 nm or more, e.g., 25 nm or more, e.g., 50 nm or more, e.g., 100 nm or more, e.g., 150 nm or more, e.g., 200 nm or more, and e.g., 250 nm or more. As will be described in more detail below, each spectral data signal may be generated by one or more photodetector channels, e.g., two or more, e.g., three or more, e.g., four or more, e.g., eight or more, e.g., 12 or more, e.g., 16 or more, e.g., 32 or more, and e.g., 64 or more different photodetector channels.

[0050] In some cases, analyte data is flow cytometer data. “Flow cytometer data” means information about the characteristics of sample particles collected by any number of detectors in a particle analyzer. For the purposes of this specification, “particle analyzer” is an analytical tool (e.g., a flow cytometer) that enables the characterization of particles based on certain (e.g., optical) parameters. “Particles” means separate components of a biological sample, such as molecules, analyte-bound beads, or individual cells. While this disclosure primarily describes flow cytometer data, its applicability is not limited to flow cytometer data. In certain specific cases, this disclosure may be applicable to other types of data, such as nucleic acid data.

[0051] Flow cytometer data may be received from any suitable source. In some embodiments, flow cytometer data is received from the memory of a storage device. In such embodiments, flow cytometer data may be pre-generated and stored in the memory of the storage device for subsequent reading and analysis. In other embodiments, flow cytometer data is received in real time. In other words, flow cytometer data generated during the operation of the flow cytometer may then (e.g., immediately) be read into data space (e.g., a two-dimensional plot). In embodiments, flow cytometer data is received from a forward scatter detector. In some cases, the forward scatter detector may provide information about the overall size of the particles. In embodiments, flow cytometer data is received from a side scatter detector. The side scatter detector may be configured to detect refracted and reflected light from the surface and internal structure of the particles, which in some cases tend to increase as the particle structure becomes more complex.

[0052] When performing the method in question, a sample containing particles (for example, in the flow stream of a flow cytometer) is irradiated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide range of wavelengths, for example, above 50 nm, above 100 nm, above 150 nm, above 200 nm, above 250 nm, above 300 nm, above 350 nm, above 400 nm, and above 500 nm. For example, a suitable broadband light source emits light having wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having wavelengths from 400 nm to 1000 nm. If the method involves irradiating with a broadband light source, the broadband light source protocol of interest may include, but is not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, broadband LEDs with a continuous spectrum, superluminescent light-emitting diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, or any combination thereof, among other broadband light sources.

[0053] In other embodiments, the method includes irradiating with a narrowband light source that emits a specific wavelength or a narrow range of wavelengths, for example, a light source that emits light with wavelengths such as 40 nm or less, 30 nm or less, 25 nm or less, 20 nm or less, 15 nm or less, 10 nm or less, 5 nm or less, or 2 nm or less, for example, a light source that emits a specific wavelength of light (i.e., monochromatic light). If the method includes irradiating with a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, a narrow-wavelength LED, laser diode, or broadband light source coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0054] In certain embodiments, the method involves irradiating a sample with one or more lasers. As discussed above, the type and number of lasers vary depending on the sample and the desired light to be collected, and may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other cases, the method involves irradiating a flowstream with dye lasers such as stilbene, coumarin, or rhodamine lasers. In further cases, the method involves irradiating the flowstream with a metallic 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, or combinations thereof. In yet another case, the method involves 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 trimmyrm YAG laser, a ytterbium YAG laser, a ytterbium-2O3 laser, or a cerium-doped laser, or combinations thereof.

[0055] The sample may be irradiated with one or more of the above-mentioned light sources, for example, two or more light sources, for example, three or more light sources, for example, four or more light sources, for example, five or more light sources, and for example, ten or more light sources. The light sources may include any combination of light sources of any type. For example, in some embodiments, the method includes irradiating the sample in the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers and one or more solid-state lasers.

[0056] The sample may be irradiated with wavelengths in the range of 200 nm to 1500 nm, for example 250 nm to 1250 nm, for example 300 nm to 1000 nm, for example 350 nm to 900 nm, and for example 400 nm to 800 nm. For example, if the light source is a broadband light source, the sample may be irradiated with wavelengths 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 specific wavelengths in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm) each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (such as a laser array), and the sample is irradiated with specific wavelengths 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 mentioned above.

[0057] When two or more light sources are used, the sample may be irradiated by the light sources simultaneously, sequentially, or in combination thereof. For example, the sample may be irradiated by each of the light sources simultaneously. In other embodiments, the flow stream is irradiated sequentially by each of the light sources. When two or more light sources are used to sequentially irradiate the sample, the duration for which each light source irradiates the sample may be independently 0.001 microseconds or more, e.g., 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 30 microseconds or more, and e.g., 60 microseconds or more. For example, the method may include irradiating the sample with a light source (e.g., a laser) for durations ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In embodiments in which two or more light sources sequentially irradiate the sample, the duration for which each light source irradiates the sample may be the same or different.

[0058] The time between irradiations from each light source may also be independently and variable, as desired, by delays of 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 15 microseconds or more, for example, 30 microseconds or more, and for example, 60 microseconds or more. For example, the interval between irradiations from each light source may be in the range of 0.001 microseconds to 60 microseconds, for example, 0.01 microseconds to 50 microseconds, for example, 0.1 microseconds to 35 microseconds, for example, 1 microsecond to 25 microseconds, and for example, 5 microseconds to 10 microseconds. In a particular embodiment, the interval between irradiations from each light source is 10 microseconds. In embodiments in which more than two (i.e., three or more) light sources are sequentially irradiated onto the sample, the delays between irradiations from each light source may be the same or different.

[0059] The sample may be irradiated continuously or at discrete intervals. In some cases, the method involves continuously irradiating the sample with a light source. In other cases, the sample is irradiated with a light source at discrete intervals, for example, every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and for example every 1000 milliseconds, or any other interval.

[0060] Depending on the light source, the sample may be illuminated from various distances, such as 0.01 mm or more, for example 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and for example 50 mm or more. The illumination angle may also be variable within the range of 10° to 90°, for example 15° to 85°, for example 20° to 80°, for example 25° to 75°, and for example 30° to 60°, for example 90°.

[0061] In certain embodiments, the method includes irradiating a 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 generated by the output laser beam (for use when irradiating a sample in a flow stream, for example), the laser may have specific wavelengths that vary at 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and e.g., 400 nm to 800 nm. The acousto-optical device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, and e.g., ten or more lasers. The lasers may include any combination of lasers. For example, in some embodiments, the method includes irradiating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers and one or more solid-state lasers.

[0062] When two or more lasers are used, the acousto-optical device may be irradiated by the lasers simultaneously, sequentially, or in combination thereof. For example, the acousto-optical device may be irradiated by each of the lasers simultaneously. In other embodiments, the acousto-optical device is irradiated by each of the lasers sequentially. When two or more lasers are used to sequentially irradiate the acousto-optical device, the time for each laser to irradiate the acousto-optical device may be independently 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, and e.g., 60 microseconds or more. For example, the method may include irradiating the acousto-optical device with lasers for durations ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In embodiments in which two or more lasers are sequentially irradiated onto an acoustic-optical device, the duration for which each laser irradiates the acoustic-optical device may be the same or different.

[0063] The time between irradiations by each laser may also be independently and variable, as desired, by delays of 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 15 microseconds or more, for example, 30 microseconds or more, and for example, 60 microseconds or more. For example, the time between irradiations by each light source may be in the range of 0.001 microseconds to 60 microseconds, for example, 0.01 microseconds to 50 microseconds, for example, 0.1 microseconds to 35 microseconds, for example, 1 microsecond to 25 microseconds, and for example, 5 microseconds to 10 microseconds. In a particular embodiment, the time between irradiations by each laser is 10 microseconds. In embodiments in which more than two (i.e., three or more) lasers are sequentially irradiated onto the acousto-optical device, the delays between irradiations by each laser may be the same or different.

[0064] Acousto-optical devices may be illuminated continuously or at discrete intervals. In some cases, the method involves continuously illuminating the acousto-optical device with a laser. In other cases, the acousto-optical device is illuminated with a laser at discrete intervals, for example, every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, for example, every 1000 milliseconds, or at any other interval.

[0065] Depending on the laser, the acousto-optic device may be irradiated from various distances, such as 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., 2.5 mm or more, e.g., 5 mm or more, e.g., 10 mm or more, e.g., 15 mm or more, e.g., 25 mm or more, and e.g., 50 mm or more. The irradiation angle may also be variable within the range of 10° to 90°, e.g., 15° to 85°, e.g., 20° to 80°, e.g., 25° to 75°, and e.g., 30° to 60°, e.g., 90°.

[0066] In some embodiments, the method includes applying high-frequency drive signals to an acousto-optical device to generate an angularly deflected laser beam. Two or more high-frequency drive signals, for example, three or more high-frequency drive signals, for example, four or more high-frequency drive signals, for example, five or more high-frequency drive signals, for example, six or more high-frequency drive signals, for example, seven or more high-frequency drive signals, for example, eight or more high-frequency drive signals, for example, nine or more high-frequency drive signals, for example, ten or more high-frequency drive signals, for example, fifteen or more high-frequency drive signals, for example, twenty-five or more high-frequency drive signals, for example, fifty or more high-frequency drive signals, and for example, one hundred or more high-frequency drive signals may be applied to the acousto-optical device to generate an output laser beam having a desired number of angularly deflected laser beams.

[0067] Each angle-deflected laser beam generated by a high-frequency drive signal has 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 sufficient amplitude to generate an angle-deflected laser beam having a desired intensity. In some cases, each of the applied high-frequency drive signals independently has an amplitude of about 0.001V to about 500V, e.g., about 0.005V to about 400V, e.g., about 0.01V to about 300V, e.g., about 0.05V to about 200V, e.g., about 0.1V to about 100V, e.g., about 0.5V to about 75V, e.g., about 1V to 50V, e.g., about 2V to 40V, e.g., 3V to about 30V, and e.g., about 5V to about 25V. In some cases, each of the applied high-frequency drive signals independently has an amplitude of approximately 0.001V to 100V, for example, approximately 0.001V to 200V, for example, 0.001V to 300V, for example, 0.001V to 400V, and for example, 0.001V to 500V. In some embodiments, each of the applied high-frequency drive signals has a frequency of approximately 0.001MHz to approximately 500MHz, for example, approximately 0.005MHz to approximately 400MHz, for example, approximately 0.01MHz to approximately 300MHz, for example, approximately 0.05MHz to approximately 200MHz, for example, approximately 0.1MHz to approximately 100MHz, for example, approximately 0.5MHz to approximately 90MHz, for example, approximately 1MHz to approximately 75MHz, for example, approximately 2MHz to approximately 70MHz, for example, approximately 3MHz to approximately 65MHz, for example, approximately 4MHz to approximately 60MHz, and for example, approximately 5MHz to approximately 50MHz. In some embodiments, each of the applied high-frequency drive signals has a frequency of approximately 0.001 MHz to approximately 100 MHz, for example 0.001 MHz to 200 MHz, for example 0.001 MHz to 300 MHz, for example 0.001 MHz to 400 MHz, for example 0.001 MHz to 500 MHz.

[0068] In these embodiments, the angle-deflected laser beams within the output laser beam are spatially separated. Depending on the applied high-frequency drive signal and the desired irradiation profile of the output laser beam, the angle-deflected laser beams may be separated by distances of 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, e.g., 5000 μm or more, and e.g., 5000 μm or more. In some embodiments, the angle-deflected laser beams overlap with adjacent angle-deflected laser beams along the horizontal axis of the output laser beam, etc. The overlap between adjacent angle-deflected laser beams (such as beam spot overlap) may be 0.001 μm or larger, for example 0.005 μm or larger, for example 0.01 μm or larger, for example 0.05 μm or larger, for example 0.1 μm or larger, for example 0.5 μm or larger, for example 1 μm or larger, for example 5 μm or larger, for example 10 μm or larger, and for example 100 μm or larger.

[0069] In certain cases, see 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, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, 10,684,211, 10,845,295, and 10,935,482. As described in Patent Nos. 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369, and 11,946,851, a flowstream is irradiated with multiple frequency-shifted light beams, and particles in the flowstream are imaged, the disclosures of which are incorporated herein by reference.

[0070] When the method of the subject is performed, light from each particle is detected by a photodetector system. In embodiments, the photodetector system includes one or more photodetectors, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, and e.g., ten or more photodetectors. The photodetector for performing the method of the subject may include, but is not limited to, any convenient photodetector protocol, including, among other photodetectors, avalanche photodiodes (APDs), active pixel sensors (APS), quadrant photodiodes, image sensors, charge-coupled devices (CCDs), sensitized charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors or photodiodes and combinations thereof. In certain embodiments, the photodetector is 0.01 cm 2 ~10cm2 For example, 0.05 cm 2 ~9cm 2 For example, 0.1 cm 2 ~8cm 2 For example, 0.5 cm 2 ~7cm 2 , and for example, 1 cm 2 ~5cm 2 The photomultiplier tubes, such as photomultiplier tubes, have an active detection surface area in each region within the range of [specify range]. In one embodiment, light is detected in one or more photodetector channels, for example, two or more photodetectors, for example, three or more, for example, four or more, for example, five or more, for example, six or more, for example, seven or more, for example, eight or more, for example, nine or more, for example, ten or more, for example, twelve or more, for example, sixteen or more, for example, twenty-four or more, for example, twenty-four or more, for example, thirty-two or more, for example, sixty-four or more, for example, twelve-five-six or more, and for example, five-one or more photodetector channels.

[0071] Light may be measured by a photodetector at one or more wavelengths, e.g., two or more wavelengths, e.g., five or more different wavelengths, e.g., ten or more different wavelengths, e.g., twenty-five or more different wavelengths, e.g., fifty or more different wavelengths, e.g., one hundred or more different wavelengths, e.g., two hundred or more different wavelengths, e.g., three hundred or more different wavelengths, e.g., four

[0072] In certain embodiments, the light detected from a sample is scattered light. The term “scattered light” is used herein in its conventional sense and refers to the propagation of light energy from particles in a sample (e.g., flowing through a flowstream) that have been deflected from the incident beam path by reflection, refraction, or deflection of the light beam. In certain cases, the scattered light detected from particles in a flowstream is forward scattered light (FSC). In other cases, the scattered light detected from particles in a flowstream is side scattered light (SSC). In yet another case, the scattered light detected from particles in a flowstream is back scattered light (BSC).

[0073] In some embodiments, the light detected from each particle is emission such as particle luminescence (i.e., fluorescence or phosphorescence). In these embodiments, each particle may contain one or more fluorophores that emit fluorescence in response to irradiation by two or more light sources. For example, each particle may contain two or more fluorophores, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, and e.g., ten or more fluorophores. In some cases, each particle contains fluorophores that emit fluorescence in response to irradiation by a light source. In some embodiments, the fluorophores of interest may include, but are not limited to, dyes suitable for use in analytical applications (e.g., flow cytometry, imaging, etc.), such as acridine dyes, anthraquinone dyes, arylmethane dyes, diarylmethane dyes (e.g., diphenylmethane dyes), chlorophyll-containing dyes, triarylmethane dyes (e.g., triphenylmethane dyes), azo dyes, diazonium dyes, nitro dyes, nitroso dyes, phthalocyanine dyes, cyanine dyes, asymmetric cyanine dyes, quinoneimine dyes, azine dyes, eurodin dyes, safranin dyes, indamine, indophenol dyes, fluorine dyes, oxazine dyes, oxazone dyes, thiazine dyes, thiazole dyes, xanthene dyes, fluorene dyes, pyronin dyes, fluorine dyes, rhodamine dyes, phenanthoridine dyes, as well as dyes combining two or more of the aforementioned dyes (e.g., in tandem), polymer dyes having one or more monomer dye units, and mixtures of two or more of the aforementioned dyes. Numerous dyes are commercially available from various suppliers, such as Molecular Probes (Eugene, OR), Dyomics GmbH (Jena, Germany), Sigma-Aldrich (St. Louis, MO), Sirigen, Inc. (Santa Barbara, CA), Becton Dickinson (BD) and Company (Franklin Lakes, NJ), and Exciton (Dayton, OH). For example, fluorophores include 4-acetamido-4'-isothiocyanatostilbene-2,2'disulfonic acid;Acridine, and derivatives such as acridine, acridine orange, acridine yellow, acridine red, and acridine isothiocyanate; allophycocyanin (APC), phycoerythrin (PE), peridinin-chlorophyll protein, 5-(2'-aminoethyl)aminonaphthalene-1-sulfonic acid (EDANS); 4-amino-N-[3-(vinylsulfonyl)phenyl]naphthalimide-3,5 disulfonate (Lucifer Yellow VS); N-(4-anilino-1-naphthyl)maleimide; anthranilamide; brillianto Yellow; coumarin, and derivatives such as coumarin, 7-amino-4-methylcoumarin (AMC, coumarin 120), 7-amino-4-trifluoromethylcruarin (coumaran 151); cyanine, and derivatives such as cyanosine, Cy3, Cy3.5, Cy5, Cy5.5 and Cy7; 4',6-diaminidino-2-phenylindole (DAPI); 5',5”-dibromovyrrogallol-sulfonphthalein (bromovyrrogallol red); 7-diethylamino-3-(4'-isothiocyanatophenyl)-4-methylcoumarin; diethylamino Coumarin; diethylenetriaminepentaacetate; 4,4'-diisothiocyanatodihydrostilbene-2,2'-disulfonic acid; 4,4'-diisothiocyanatostilbene-2,2'-disulfonic acid; 5-[dimethylamino]naphthalene-1-sulfonyl chloride (DNS, dansyl chloride); 4-(4'-dimethylaminophenylazo)benzoic acid (DABCYL); 4-dimethylaminophenylazophenyl-4'-isothiocyanate (DABITC); eosin, and derivatives such as eosin and eosin isothiocyanate; erythrosine , as well as derivatives such as erythrosine B and erythrosine isothiocyanate; ethidium; fluorescein, and derivatives such as 5-carboxyfluorescein (FAM), 5-(4,6-dichlorotriazine-2-yl)aminofluorescein (DTAF), 2'7'-dimethoxy-4'5'-dichloro-6-carboxyfluorescein (JOE), fluorescein isothiocyanate (FITC), fluorescein chlorotriazinyl, naphthofluorescein, and QFITC ​​(XRITC); fluorescein; IR144; IR1446;Green fluorescent protein (GFP); coral fluorescent protein (RCFP); Lisamin®; Lisamin Rhodamine, Lucifer Yellow; malachite green isothiocyanate; 4-methylumbelliferone; orthocresolphthalein; nitrotyrosine; pararoseaniline; Nile Red; Oregon Green; phenol Red; β-phycoerythrin; o-phthalidaldehyde; pyrene, and derivatives such as pyrene, pyrenebutyrate and succinimimidyl 1-pyrenebutyrate; Reactive Red 4 (Cibacron® Brilliant Red3B-A); Rhodamine, as well as 6-carboxy-X-rhodamine (ROX), 6-carboxyrhodamine (R6G), 4,7-dichlororhodamine lysamine, rhodamine B sulfonyl chloride, rhodamine (Rhod), rhodamine B, rhodamine 123, rhodamine X isothiocyanate, sulforhodamine B, sulforhodamine 101, sulforhodamine 101 sulfonyl chloride derivatives (Texas Dye-conjugated polymers (i.e., polymer-bound dyes) such as red, N,N,N',N'-tetramethyl-6-carboxyrhodamine (TAMRA), tetramethylrhodamine, and tetramethylrhodamine isothiocyanate (TRITC); riboflavin; rosolic acid and terbium chelate derivatives; xanthenes; fluorescein isothiocyanate-dextran, and dyes that combine two or more dyes (e.g., tandem dyes or protein complex tandem dyes), e.g., phycoerythrin (PE) tandem dye or The material may include allophycocyanin (APC) tandem dyes, such as phycoerythrin-CF594 (PE-CF594) tandem, phycoerythrin-cyanine 5 tandem (PE-Cy5), phycoerythrin-cyanine 5.5 tandem (PE-Cy5.5), phycoerythrin-cyanine 7 tandem (PE-Cy7), allophycocyanin-R700 tandem (APC-R700), allophycocyanin-cyanine 7 (APC-Cy7), polymer dyes having one or more monomer dye units, and mixtures or combinations thereof of two or more of the aforementioned dyes.

[0074] In some cases, the fluorophore (i.e., dye) is a fluorescent polymer dye. The fluorescent polymer dyes found to be used in the methods and systems of interest are diverse. In some cases of the methods, the polymer dyes include conjugated polymers. Conjugated polymers (CPs) are characterized by a delocalized electronic structure containing an alternating backbone of unsaturated (e.g., double and / or triple) and saturated (e.g., single) bonds, where π electrons can move from one bond to the other. Thus, the conjugated backbone may confer an elongated linear structure on the polymer dye, where the bond angles between the repeating units of the polymer are restricted. For example, proteins and nucleic acids are polymers, but in some cases do not form elongated rod structures, but rather fold into higher-order three-dimensional shapes. Furthermore, CPs may form a “rigid rod” polymer backbone, experiencing restricted twist (e.g., torsion) angles between monomer repeating units along the polymer backbone chain. In some cases, the polymer dyes include CPs having a rigid rod structure. As summarized above, the structural characteristics of polymer dyes can influence the fluorescence properties of the molecules.

[0075] Any suitable polymer dye may be used in the method and system of interest. In some cases, the polymer dye is a multichromophore having a structure that can collect light to amplify the fluorescence output of a fluorophore. In some cases, the polymer dye can collect light and efficiently convert it into emission of longer wavelengths. In some cases, the polymer dye has a light-gathering multichromophore system that can efficiently transfer energy to a nearby emission species (e.g., a "signal-transferring chromophore"). Mechanisms for energy transfer include, for example, resonance energy transfer (e.g., Forster (or fluorescence) resonance energy transfer, FRET) and quantum charge exchange (Dexter energy transfer). In some cases, these energy transfer mechanisms are relatively short-range, i.e., proximity of the light-gathering multichromophore system to the signal-transferring chromophore provides efficient energy transfer. Under conditions for efficient energy transfer, amplification of emission from a signal-transmitting chromophore occurs when there are many individual chromophores in a light-gathering multichromophore system; that is, emission from a signal-transmitting chromophore is stronger when the incident light ("excitation light") is at a wavelength absorbed by the light-gathering multichromophore system than when the signal-transmitting chromophore is directly excited by the pump light.

[0076] The multichromophore may be a conjugated polymer. Conjugated polymers (CPs) are characterized by their delocalized electronic structure and may be used as highly responsive optical reporters for chemical and biological targets. Because the effective conjugation length is significantly shorter than the polymer chain length, the backbone contains numerous closely spaced conjugated segments. Therefore, conjugated polymers are efficient at light harvesting and enable light amplification via energy transfer.

[0077] In some cases, the polymer may be used as a direct fluorescent reporter, such as a fluorescent polymer having a high absorption coefficient, high brightness, etc. In some cases, the polymer may be used as a potent chromophore, where the color or optical density is used as an indicator.

[0078] The polymer dyes of interest include U.S. Patent Applications Publications 20040142344, 20080293164, 20080064042, 20100136702, 20110256549, 20120028828, 20120252986, 20130190193 and 20160025735 by Gaylord et al. (these disclosures are incorporated herein by reference in their entirety), as well as Gaylord et al., J.Am.Chem.Soc., 2001, 123(26), pp 6417-6418; Feng et al., Chem.Soc.Rev., 2010, 39, 2411-2419 and Traina et al. This includes, but is not limited to, the dyes described in al., J.Am.Chem.Soc., 2011, 133(32), pp 12600-12607 (these disclosures are incorporated herein by reference in their entirety).

[0079] In carrying out the method of the subject according to a particular embodiment, a spectral data signal is generated in response to light detected by a photodetector system. In some cases, the generated spectral data signal is summed across multiple photodetector channels, for example, the spectral data signal is the sum of data signals across two or more photodetector channels, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., twelve or more, e.g., sixteen or more, e.g., twenty-four or more, e.g., twenty-four or more, e.g., thirty-two or more, e.g., sixteen or more, e.g., twelve In other embodiments, the generated spectral data signal is summed across multiple photodetector channels that exceed a predetermined intensity threshold, for example, each of the multiple photodetector channels exceeds the predetermined intensity threshold by 1%, e.g., 5%, e.g., 10%, and e.g., 25% or more. In some cases, the generated spectral data signal is summed across multiple photodetector channels that exceed a predetermined signal-to-noise ratio, for example, each of the multiple photodetector channels exceeds the predetermined signal-to-noise ratio by 1%, e.g., 5%, e.g., 10%, and e.g., 25% or more.

[0080] In some embodiments, the spectral data signal is calibrated using a calibration scaling factor. In certain embodiments, calibrating the generated spectral data signal based on the calibration scaling factor allows for improvements in photodetector system performance, e.g., an increase in the signal-to-noise ratio and / or an increase in photodetector output consistency. In some embodiments, the method includes calculating the calibration scaling factor. In some cases, calculating the calibration scaling factor includes generating a reference data signal in one or more photodetector channels in response to light detected from an irradiated reference sample, and calculating the calibration scaling factor based on the sum of the reference data signals and the abundance of fluorophores in the reference sample. In certain embodiments, calibrating the generated spectral data signal based on the calibration scaling factor and the abundance of fluorophores allows for improvements in photodetector system performance, e.g., an increase in the signal-to-noise ratio and / or an increase in photodetector output consistency. In some cases, the abundance of fluorophores in the sample is the number (i.e., amount) of fluorophore portions present on the particle. In some cases, the reference sample is a single-stained control. In certain cases, the reference particle is a multispectral fluorescent bead. In certain cases, multispectral fluorescent beads contain a fluorescent dye component covalently bonded to the particle. In some embodiments, the particles are beads for use in flow cytometry, having structures with diameters in the nanometer to micrometer range, e.g., 0.01 to 1,000 μm, e.g., 0.1 to 100 μm, e.g., 1 to 100 μm, and e.g., about 1 to 100 μm. Such particles may have any shape, and in some cases are approximately spherical.Such particles can be made from any suitable material (or combination thereof), including but not limited to polymers such as polystyrene; polystyrene containing other copolymers such as divinylbenzene; polymethyl methacrylate (PMMA); polyvinyltoluene (PVT); copolymers such as styrene / butadiene and styrene / vinyltoluene; latex; glass; or other materials, such as silica (e.g., SiO2). In some embodiments, the particles of interest are beads, such as glass beads, which have low or no autofluorescence.

[0081] In some embodiments, the beads are organic polymer matrices having a metal-organic polymer matrix having a skeletal structure containing metals such as aluminum, barium, antimony, calcium, chromium, copper, erbium, germanium, iron, lead, lithium, phosphorus, potassium, silicon, tantalum, tin, titanium, vanadium, zinc, or zirconium. In some embodiments, the porous metal-organic matrix is ​​an organosiloxane polymer, including but not limited to polymers of methyltrimethoxysilane, dimethyldimethoxysilane, tetraethoxysilane, methacryloxypropyltrimethoxysilane, bis(triethoxysilyl)ethane, bis(triethoxysilyl)butane, bis(triethoxysilyl)pentane, bis(triethoxysilyl)hexane, bis(triethoxysilyl)heptane, bis(triethoxysilyl)octane, and combinations thereof.

[0082] The particles of interest may be porous or nonporous. In some embodiments, the particles are nonporous. In other embodiments, the particles are porous, for example, particles having pores with diameters in the range of 0.01 nm to 1000 nm, e.g., 0.05 nm to 750 nm, e.g., 0.1 nm to 500 nm, e.g., 0.5 nm to 250 nm, e.g., 1 nm to 100 nm, e.g., 5 nm to 75 nm, and e.g., particles having pores with diameters in the range of 10 nm to 50 nm.

[0083] In certain embodiments, the fluorescently labeled beads of interest include, but are not limited to, fluorescently labeled polystyrene beads, fluorescein beads, rhodamine beads, and other beads tagged with fluorescent dyes. Further examples of fluorescently labeled beads are described in U.S. Patents 6,350,619, 7,738,094, and 8,248,597, each of which disclosures are incorporated herein by reference in their entirety.

[0084] In some cases, the calibration scaling factor is calculated using the sum of the median fluorophore-specific signals for each photodetector channel. In some cases, the calibration scaling factor is calculated using a linear regression of the total signal from a reference sample and a given or known fluorophore abundance. In some cases, the fluorophore abundance is determined using a linear regression of the total signal from a reference sample. In some cases, the calibration scaling factor is interpolated from the linear regression. In some cases, the fluorophore abundance is interpolated from the linear regression. In some cases, the regression or scaling factor is derived using the sum of the median fluorophore-specific signals for each channel for each population against a reference value assigned to each population. For example, suppose the total signal from a reference bead is 300,000 arbitrary units and the number of molecules on its surface is 50,000. The calibration scaling factor is calculated using the following:

[0085]

number

[0086] Next, this regression or scaling factor is used to derive the number of molecules on a single stained control used as the reference spectrum for the decomposition, and then the number of molecules to which it is equivalent. For example, if the reference spectrum has a total MFI equivalent to 360,000 arbitrary units and the calibration scaling factor is 6, then it is equivalent to 60,000 MESF (molecules of equivalent soluble fluorescent dye).

[0087]

number

[0088] In some embodiments, the generated spectral data signal is spectrally decomposed. In some cases, the spectral data signal is calibrated before spectral decomposition. In some cases, the signal intensity of the generated data signal is rescaled to be equivalent to the intensity of a single fluorophore. In some cases, rescaling the signal intensity to the intensity of a single fluorophore involves dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore. In the above example, if the spectral data is calibrated before decomposition, the single-stain control reference spectrum is divided by the total number of fluorophores (60,000) to make the magnitude of a single fluorophore equivalent. The result of spectral decomposition then yields the total number of detected fluorophores. In some embodiments, the generated spectral data signal is calibrated according to the following:

[0089]

number

[0090] During the ceremony: X arb This is a single-stain reference spectrum, CSF is the calibration scaling factor. f cal This is a vector of fluorophore abundances, and y arb This is the detector signal vector.

[0091] In some embodiments, the method includes spectrally decomposing a spectral data signal and calibrating the spectrally decomposed data signal. In these embodiments, the method may include spectrally decomposing the light from each fluorophore in the sample (e.g., using a weighted least squares algorithm or a generalized least squares algorithm). In some embodiments, the overlap between each different fluorophore is determined and the contribution of each fluorophore to the overlapping fluorescence is calculated. In some embodiments, spectral decomposition of light includes calculating a spectral decomposition matrix of the fluorescence spectrum for each of a plurality of fluorophores in the sample having overlapping fluorescence detected by a photodetector system. For example, spectral decomposition of light by the method described herein may include the Moore-Penrose inverse or pseudo-inverse of the spectral matrix. In some cases, the algorithm for spectral decomposition is characterized by Cholesky decomposition of the decomposition matrix. In some embodiments, the decomposed spectral data signal is calibrated using calibration scaling factors and fluorophore abundances.

[0092] In some cases, the abundance of each fluorophore in a sample can be estimated by spectrally decomposing the light from each fluorophore (e.g., by calculating the spectral decomposition matrix for each fluorophore). In certain embodiments, the abundance of each fluorophore related to a target particle can be determined. In certain embodiments, the spectral data signal is spectrally decomposed by decomposition algorithms such as those described in U.S. Patent Application No. 11,009,400, U.S. Patent Application Publication No. 2024 / 0192122 filed December 12, 2023, and U.S. Patent Application No. 18 / 986,295 filed December 18, 2024, the disclosures of which are incorporated herein by reference.

[0093] In some cases, calibrating the degraded spectral data signal involves determining the occurrence rate of single-stained controls. In some cases, the reference data signal is normalized, for example, the spectral data signal is normalized to 1. In some cases, the degraded spectral data signal is rescaled. For example, if the degradation process is performed using the raw generated spectral data signal, single-stained controls are normalized to 1, and then the signal sum is used to rescale after spectral degradation, the resulting degraded signal will be equivalent to the number of times single-stained controls were found. In the example above, the calibration scaling factor for the sum of single-stained controls was 6, so the abundance of MESF (equivalently soluble fluorescent dye molecules) can be derived by dividing the resulting degraded data by 6.

[0094] In some cases, the generated spectral data signal is calibrated as follows:

[0095]

number

[0096] During the ceremony: X † This is the inverse problem spectrum, CSF is the calibration scaling factor. f cal This is a vector of fluorophore abundances, S is the sum of the data signals generated by multiple photodetector channels (S sum ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. max ) selected from, and y arb This is the detector signal vector.

[0097] In some embodiments, by calibrating the spectral data signal as described above, the average fluorescence intensity of the sample becomes constant regardless of changes in system detector settings, temperature settings, and optics-mechanical alignment, for example, the change in average fluorescence intensity between the same system or between different systems is less than or equal to 10%, e.g. less than or equal to 9%, e.g. less than or equal to 8%, e.g. less than or equal to 7%, e.g. less than or equal to 6%, e.g. less than or equal to 5%, e.g. less than or equal to 4%, e.g. less than or equal to 3%, e.g. less than or equal to 2%, e.g. less than or equal to 1%, e.g. less than or equal to 0.5%, e.g. less than or equal to 0.1%, and e.g. less than or equal to 0.01%.

[0098] Figure 1A shows a flowchart for calibrating a spectral data signal according to a particular embodiment. In step 101, a light source is applied to particles of a sample having fluorophores in a flow stream. In step 102, light is detected from the irradiated particles using a photodetector system with a photodetector. A spectral data signal is generated in response to the wavelength range of light measured by the photodetector (step 103). In some cases, the entire spectral data signal across multiple photodetector channels is summed (step 103a). In other cases, the spectral data signal used to calibrate the data signal is from a single photodetector channel, e.g., the photodetector channel with the highest intensity for fluorophores (step 103b). The calibration scaling factor is calculated in step 104a by regression using the sum of signals from the calibration signal of a reference sample (e.g., a single-stained control). The fluorophore abundance (e.g., the number of fluorophore portions present on the particle) is determined in step 104b. In step 105, the generated spectral data signal is calibrated using the calibration scaling factor and the fluorophore abundance.

[0099] Figure 1B shows an exemplary workflow for spectral calibration according to a particular embodiment. A reference sample having a reference particle (e.g., a single-stained control) is illuminated with a light source, and light from the emitting fluorophore is detected in step 1. Spectral data signals are generated in multiple photodetector channels, and digitized signals are determined in each photodetector channel. In step 2, the sum of the digital signals across the fluorescence channels is calculated. Here, using signals of 10 and 2 generated in two different fluorescence channels, the total signal is calculated to be 12. In step 3, a regression is generated using the sum of signals from the calibration signal. The total signal of the reference signal fluorophore is interpolated with the calibration signal to derive the number of molecules it is equivalent to (step 4). The generated data signal can be calibrated before or after spectral decomposition. In step 5a, the decomposed signal is rescaled based on the digitized reference signal (total signal), and the decomposed intensity is rescaled using the number of molecules it was equivalent to. This generates the decomposed raw signal. In step 5b, the reference signal is rescaled to be equivalent to the intensity of a single molecule, and the rescaled reference signal is obtained.

[0100] Figure 1C shows an exemplary workflow for spectral crosscalibration according to a particular embodiment. Multispectral bead (MSB) signals are generated by irradiating a sample containing a multispectral bead composition with a light source and detecting light in multiple photodetector channels (Step 1). Calibrator signals for a single stained control are also acquired as described above (Step 2). In Step 3, the total signal of the MSB signal fluorophores is interpolated with the calibrator signal to derive the number of molecules to which it is equivalent. This process can be repeated to crosscalibrate each of the MSB fluorophores with a reference calibrator dye signal. Each of these crosscalibrations can be used to calibrate spectral data signals as described above for Figure 1B.

[0101] In some cases, the samples analyzed in this method are biological samples. The term “biological sample” is used in its conventional sense to refer to an entire organism, an entire plant, an entire fungus, or, in certain cases, a subset of animal tissues, cells, or components that may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, amniotic umbilical cord blood, urine, vaginal fluid, and semen. Thus, “biological sample” refers to, but is not limited to, both a naturally occurring organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from a subset of an organism or its tissues, including, for example, plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular and urogenital tract sections, tears, saliva, milk, blood cells, tumors, and organs. A biological sample may be any type of living tissue, including both healthy tissue and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or its derivatives, e.g., plasma, tears, urine, semen, and in some cases, the sample is a blood sample containing whole blood, such as blood obtained from a venipuncture or fingertip puncture (the blood may or may not be combined with any reagents such as preservatives and anticoagulants before the assay).

[0102] In certain embodiments, the source of the sample is “mammal” or “mammalian,” and these terms are used broadly to describe organisms belonging to the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs and rats), and Primates (e.g., humans, chimpanzees and monkeys). In some cases, the subject is human. The method may be applied to samples obtained from human subjects of both sexes and any developmental stage (i.e., neonatal, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is juvenile, adolescent, or adult. While this disclosure may be applied to samples derived from human subjects, it should be understood that the method may also be performed on samples from other animal subjects (i.e., “non-human subjects”), such as, for example, birds, mice, rats, dogs, cats, livestock, and horses, etc., but not limited to these.

[0103] Cells of interest may be targeted for characterization by various parameters, such as phenotypic features identified by attaching specific fluorescent labels to the cells of interest. In some embodiments, the system is configured to deflect analyzed droplets determined to contain target cells. Various cells may be characterized using the method of interest. Target cells of interest include, but are not limited to, stem cells, T cells, dendritic cells, B cells, granulocytes, leukemia cells, lymphoma cells, viral cells (e.g., HIV cells), NK cells, macrophages, monocytes, fibroblasts, epithelial cells, endothelial cells, and erythroid cells. Target cells of interest include cells having favorable cell surface markers or antigens that can be captured or labeled by favorable affinity factors or their coupling. For example, target cells may contain cell surface antigens such as CD11b, CD123, CD14, CD15, CD16, CD19, CD193, CD2, CD25, CD27, CD3, CD335, CD36, CD4, CD43, CD45RO, CD56, CD61, CD7, CD8, CD34, CD1c, CD23, CD304, CD235a, T cell receptor alpha / beta, T cell receptor gamma / delta, CD253, CD95, CD20, CD105, CD117, CD120b, Notch4, Lgr5 (N-terminus), SSEA-3, TRA-1-60 antigen, disialoganglioside GD2, and CD71. In some embodiments, target cells are selected from HIV-containing cells, Treg cells, antigen-specific T cell populations, tumor cells, or hematopoietic progenitor cells (CD34+) from whole blood, bone marrow, or umbilical cord blood.

[0104] When performing the method of the subject according to a particular embodiment, a certain amount of initial fluid sample is injected into a flow cytometer. The amount of sample injected into the particle sorting module may vary, and may be in the range of, for example, 0.001 mL to 1000 mL, 0.005 mL to 900 mL, 0.01 mL to 800 mL, 0.05 mL to 700 mL, 0.1 mL to 600 mL, 0.5 mL to 500 mL, 1 mL to 400 mL, 2 mL to 300 mL, or 5 mL to 100 mL.

[0105] In some embodiments, the method includes counting labeled particles (e.g., target cells) in a sample and selectively sorting them. When performing the method in question, the fluid sample containing the particles is first introduced into the system's flow nozzle. Exiting the flow nozzle, the particles pass through the sample interrogation region substantially one at a time, where each particle is illuminated by a light source, and light scattering parameters and, in some cases, desired fluorescence emission measurements (e.g., two or more light scattering parameters and one or more fluorescence emission measurements) are recorded separately for each particle. Depending on the characteristics of the interrogated flowstream, the light may be irradiated to a length of 0.001 mm or more of the flowstream, e.g., 0.005 mm or more, e.g., 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 of the flowstream. In certain embodiments, the method includes irradiating a planar cross section of the flowstream within the sample interrogation region with a laser or the like (as described above). In other embodiments, the method includes irradiating a flow stream within a sample interrogation region for a predetermined length, for example, a length corresponding to the irradiation profile of a diffuse laser beam or lamp.

[0106] In certain embodiments, the method includes irradiating the flowstream at or near the flow cell nozzle orifice. For example, the method may include irradiating the flowstream at a position of approximately 0.001 mm or more from the nozzle orifice, e.g., 0.005 mm or more, e.g., 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, and e.g., 1 mm or more from the nozzle orifice. In certain embodiments, the method includes irradiating the flowstream immediately adjacent to the flow cell nozzle orifice.

[0107] In embodiments of the method, a detector such as a photomultiplier tube (PMT) is used to record the light passing through each particle (in certain cases called forward scattering), the light reflected perpendicular to the direction of particle flow through the detection region (in some cases called orthogonal or side scattering), and, if the particles are labeled with a fluorescent marker, the fluorescence emitted from the particles as they pass through the detection region and are illuminated by an energy source. Each of forward scattering (FSC), side scattering (SSC), and fluorescence emission involves distinct parameters for each particle (or each “event”). Thus, for example, two, three, or four parameters can be collected (and recorded) from particles labeled with two different fluorescent markers. The data recorded for each particle can, if desired, be analyzed in real time or stored in data storage and analysis means such as a computer.

[0108] In certain embodiments, particles are detected and uniquely identified, as desired, by exposing the particles to excitation light and measuring the fluorescence of each particle in one or more detection channels. The fluorescence emitted in the detection channels used to identify the particles and their associated binding complexes may be measured after excitation by a single light source or separately after excitation by individual light sources. If separate excitation light sources are used to excite particle labels, the labels may be selected so that all labels are excitable by each of the excitation light sources used.

[0109] In certain embodiments, the method includes data acquisition, analysis, and recording using a computer or the like, with multiple data channels recording data from each detector about the light scattering and fluorescence emitted by each particle as it passes through the sample interrogation area of ​​the particle sorting module. In these embodiments, the analysis includes sorting and counting the particles so that each particle is presented as a set of digitized parameter values. The system under consideration may be set up with triggers on selected parameters to distinguish the target particles from background and noise. A “trigger” refers to a preset threshold for detecting a parameter and may be used as a means to detect the passage of particles through a light source. Detection of an event exceeding the threshold of the selected parameter triggers the acquisition of light scattering and fluorescence data for the particles. For particles or other components in the medium being assayed that cause a response below the threshold, no data is acquired. The trigger parameter may be the detection of forward scattered light caused by the passage of particles through a light beam. A flow cytometer then detects and collects the light scattering and fluorescence data for the particles.

[0110] Next, a specific subpopulation of interest is further analyzed by “gating” based on data collected for the entire population. To select an appropriate gate, the data is plotted to obtain the best possible separation of the subpopulation. This procedure may be carried out by plotting forward light scattering (FSC) versus side (i.e., orthogonal) light scattering (SSC) on a two-dimensional dot plot. Then, a subpopulation of particles is selected (i.e., their cells in the gate), and particles not in the gate are excluded. If desired, the gate may be selected by drawing a line around the desired subpopulation using a cursor on a computer screen. Then, only those particles in the gate are further analyzed by plotting other parameters of these particles, such as fluorescence. If desired, the above analysis may be configured to yield a count of the particles of interest in the sample.

[0111] The methods of interest may further include the use of particles in research, laboratory testing, or treatment. In some embodiments, the methods of interest include obtaining individual cells prepared from a biological sample of a target fluid or tissue. For example, the methods of interest include obtaining cells from a fluid or tissue sample used as a research or diagnostic specimen for a disease such as cancer. Similarly, the methods of interest include obtaining cells from a fluid or tissue sample used in treatment. Cell therapy protocols are protocols in which viable cellular material, including cells and tissues, is prepared and can be introduced into a subject as a therapeutic procedure. Conditions that can be treated by administration of samples sorted by flow cytometry include, but are not limited to, blood disorders, immune system disorders, and organ damage.

[0112] A typical cell therapy protocol may include the following steps: sample collection, cell isolation, genetic modification, culture and in vitro growth, cell harvesting, sample volume reduction and washing, biopreservation, storage, and introduction of cells into the subject. The protocol may begin with the collection of viable cells and tissues from the subject's source tissue to generate cell and / or tissue samples. Samples may be collected by any appropriate procedure, including, for example, administering a cell recruiter to the subject, drawing blood from the subject, or removing bone marrow from the subject. After sample collection, cell enrichment may be performed by several methods, including, for example, centrifugation-based methods, filter-based methods, elutriation, magnetic separation, and fluorescence-activated cell sorting (FACS). In some cases, enriched cells may be genetically modified by any convenient method, for example, nuclease-mediated gene editing. Genetically modified cells can be cultured, activated, and grown in vitro. In some cases, the cells may be stored, for example by cryopreservation, and then thawed and stored for future use, and subsequently administered to a patient, for example, the cells may be injected into the patient.

[0113] system Aspects of the present disclosure also include systems for carrying out the method in question to calibrate spectral data signals, for example, spectral data signals generated by a flow cytometer. A system according to a particular embodiment includes a light source configured to irradiate particles of a sample having fluorophores in a flow stream; a photodetection system having a photodetector for detecting light from the irradiated particles; and a processor having memory, the memory operably coupled to the processor, the memory storing instructions, which, when executed by the processor, cause the processor to generate spectral data signals in response to detected light, and to calibrate the generated spectral data signals based on calibration scaling factors and calculated abundances of fluorophores.

[0114] A system according to a particular embodiment includes a light source configured to irradiate particles in a sample. In an embodiment, 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 with wavelengths varying in the ranges 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, and e.g., 400 nm to 800 nm. For example, the light source may include a broadband light source that emits light having wavelengths in the range of 200 nm to 900 nm. In other cases, the light source may include a narrowband light source that emits wavelengths in the range of 200 nm to 900 nm. For example, the light source may be a narrowband LED (1 nm to 25 nm) that emits light having wavelengths in the range of 200 nm to 900 nm. In a particular embodiment, the light source is a laser. In some cases, the systems in question include gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other cases, the systems in question include dye lasers such as stilbene, coumarin, or rhodamine lasers. In yet another case, 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 other cases, the systems in question 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, trimmylated YAG lasers, ytterbium YAG lasers, ytterbium-2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0115] 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, or a xenon arc lamp; a light-emitting diode such as a broadband LED having a continuous spectrum; a superluminescent light-emitting diode; a semiconductor light-emitting diode; a broadband LED white light source; or a multi-LED integrated light source. In some cases, the non-laser light source is a stabilized fiber-coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0116] The light source may be placed at any suitable distance from the sample (e.g., in the flow stream in a flow cytometer), including, for example, distances of 0.001 mm or more from the flow stream, e.g., 0.005 mm or more, e.g., 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., 5 mm or more, e.g., 10 mm or more, e.g., 25 mm or more, and e.g., 100 mm or more. Furthermore, the light source irradiates the sample at any suitable angle (e.g., with respect to the vertical axis of the flow stream), e.g., angles in the range of 10° to 90°, e.g., 15° to 85°, e.g., 20° to 80°, e.g., 25° to 75°, and e.g., 30° to 60°, e.g., 90°.

[0117] The light source may be configured to irradiate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to irradiate the sample continuously, such as a continuous-wave laser that continuously irradiates the flow stream at an interrogation point in a flow cytometer. In other cases, the system of interest includes a light source configured to irradiate the sample at discrete intervals, 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 any other interval. If the light source is configured to irradiate the sample at discrete intervals, the system may include one or more additional components to provide intermittent irradiation of the sample by the light source. For example, the system of interest in these embodiments may include one or more laser beam choppers, manual or computer-controlled beam stops for blocking the sample and exposing it to the light source.

[0118] In some embodiments, the light source is a laser. The laser of interest may include pulsed lasers or continuous-wave lasers. For example, the laser may be a gas laser or a combination thereof, such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon-chlorine (XeCl) excimer laser, xenon-fluorine (XeF) excimer laser; 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, a neo Metal vapor lasers such as n-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, ytterbium 2O3 lasers, cerium-doped lasers, and combinations thereof; semiconductor diode lasers, photo-excited semiconductor lasers (OPSLs), or any of the above-mentioned lasers in a double or triple frequency implementation form.

[0119] In certain embodiments, the light source is a light beam generator configured to produce two or more frequency-shifted light beams. In some cases, the light beam generator includes a laser and a high-frequency generator configured to apply a high-frequency drive signal to an acousto-optical device to produce two or more angle-deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous-wave laser. For example, the laser in the light beam generator of interest may be a gas laser or a combination thereof, such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon-chlorine (XeCl) excimer laser, xenon-fluorine (XeF) excimer laser; a dye laser, such as a stilbene, coumarin or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (He Metal vapor lasers such as Se) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof; solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium 2O3 lasers, cerium-doped lasers, and combinations thereof.

[0120] The acousto-optic device may be any convenient acousto-optic protocol configured to frequency-shift laser light using applied sound waves. In a particular embodiment, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in the system of interest 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-optic device using any suitable high-frequency drive signal source, such as a direct digital combiner (DDS), arbitrary waveform generator (AWG), or electrical pulse generator.

[0121] In one embodiment, the controller is configured to apply high-frequency drive signals to an acoustic-optical device to generate a laser beam deflected by a desired number of angles to the output laser beam, and is configured to apply 3 or more high-frequency drive signals, for example 4 or more high-frequency drive signals, for example 5 or more high-frequency drive signals, for example 6 or more high-frequency drive signals, for example 7 or more high-frequency drive signals, for example 8 or more high-frequency drive signals, for example 9 or more high-frequency drive signals, for example 10 or more high-frequency drive signals, for example 15 or more high-frequency drive signals, for example 25 or more high-frequency drive signals, for example 50 or more high-frequency drive signals, and is configured to apply 100 or more high-frequency drive signals.

[0122] In some cases, to generate an intensity profile of the angularly deflected laser beam within the output laser beam, the controller is configured to apply a high-frequency drive signal having an amplitude that varies, for example, from about 0.001V to about 500V, for example, from about 0.005V to about 400V, for example, from about 0.01V to about 300V, for example, from about 0.05V to about 200V, for example, from about 0.1V to about 100V, for example, from about 0.5V to about 75V, for example, from about 1V to 50V, for example, from about 2V to 40V, for example, from about 3V to about 30V, for example, from about 5V to about 25V. In some embodiments, each of the applied high-frequency drive signals has a frequency of approximately 0.001 MHz to approximately 500 MHz, for example approximately 0.005 MHz to approximately 400 MHz, for example approximately 0.01 MHz to approximately 300 MHz, for example approximately 0.05 MHz to approximately 200 MHz, for example approximately 0.1 MHz to approximately 100 MHz, for example approximately 0.5 MHz to approximately 90 MHz, for example approximately 1 MHz to approximately 75 MHz, for example approximately 2 MHz to approximately 70 MHz, for example approximately 3 MHz to approximately 65 MHz, for example approximately 4 MHz to approximately 60 MHz, and for example approximately 5 MHz to approximately 50 MHz.

[0123] In one particular embodiment, the controller has a processor having memory, the memory being operably coupled to the processor, and the memory storing instructions, which, when executed by the processor, cause the processor to generate an output laser beam having an angle-deflected laser beam having a desired intensity profile. For example, the memory may include instructions for generating two or more angle-deflected laser beams having the same intensity, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., 25 or more, e.g., 50 or more, for example, the memory may include instructions for generating 100 or more angle-deflected laser beams having the same intensity. In another embodiment, the memory may include instructions for generating two or more angle-deflected laser beams having different intensities, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., 25 or more, e.g., 50 or more, for example, the memory may include instructions for generating 100 or more angle-deflected laser beams having different intensities.

[0124] In certain embodiments, the controller has a processor having memory, the memory being operably coupled to the processor, and the memory storing instructions, which, when executed by the processor, cause the processor to generate an output laser beam whose intensity increases from the edges of the output laser beam towards the center along the horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edges of the output laser beam along the horizontal axis, for example, 0.5% to about 95%, 1% to about 90%, 2% to about 85%, 3% to about 80%, 4% to about 75%, 5% to about 70%, 6% to about 65%, 7% to about 60%, 8% to about 55%, and 10% to about 50%. In other embodiments, the controller has a processor, and memory is operably coupled to the processor, and the memory stores instructions, which, when executed by the processor, cause the processor to generate an output laser beam whose intensity increases from the edge to the center along the horizontal axis. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis, for example, 0.5% to about 95%, 1% to about 90%, 2% to about 85%, 3% to about 80%, 4% to about 75%, 5% to about 70%, 6% to about 65%, 7% to about 60%, 8% to about 55%, and 10% to about 50%. In yet another embodiment, the controller has a processor having memory, the memory being operably coupled to the processor, the memory storing instructions, which, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile having a Gaussian distribution along the horizontal axis.In yet another embodiment, the controller has a processor having memory, the memory being operably coupled to the processor, and the memory storing instructions, which, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along the horizontal axis.

[0125] In some embodiments, the light beam generator of interest may be configured to generate an angularly deflected laser beam within a spatially separated output laser beam. Depending on the applied high-frequency drive signal and the desired irradiation 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, e.g., 5000 μm or more, e.g., 1000 μm or more, e.g., 1000 μm or more, e.g., 5000 μm or more. In some embodiments, the system is configured to generate an angularly deflected laser beam within an output laser beam that overlaps with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (such as beam spot overlap) may be 0.001 μm or larger, for example 0.005 μm or larger, for example 0.01 μm or larger, for example 0.05 μm or larger, for example 0.1 μm or larger, for example 0.5 μm or larger, for example 1 μm or larger, for example 5 μm or larger, for example 10 μm or larger, and for example 100 μm or larger.

[0126] In certain cases, a light beam generator configured to produce two or more frequency-shifted light beams is described in Diebold, et al. Nature Photonics Vol.7(10);806-810(2013) and U.S. Patents No. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, 10,684,211, and 10,845. This includes laser excitation modules described in Patent Nos. 295, 10,935,482, 10,935,485, 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369 and 11,946,851 (these disclosures are incorporated herein by reference).

[0127] In some embodiments, the system includes a photodetector having a photodetector configured to detect light emitted by an irradiated particle. In some embodiments, the photodetector is configured to detect scattered light. In some cases, the photodetector includes a side-scatter light detector. In some cases, the photodetector includes a forward-scatter light detector. In other embodiments, the photodetector includes multiple scattered light detectors, e.g., two or more, e.g., three or more, e.g., four or more, and e.g., five or more. In some embodiments, the photodetector in question also includes a fluorescence detector configured to detect one or more fluorescence wavelengths of light. In other embodiments, the photodetector includes multiple fluorescence detectors, e.g., two or more, e.g., three or more, e.g., four or more, five or more, ten or more, fifteen or more, and e.g., twenty or more.

[0128] Detectors of interest include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors or photodiodes, and combinations thereof. In certain embodiments, the collected light is measured by 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. In certain embodiments, the detector is a photomultiplier tube, e.g., 0.01 cm² 2 ~10cm 2 For example, 0.05 cm 2 ~9cm 2 For example, 0.1 cm 2 ~8cm 2 For example, 0.5 cm 2 ~7cm 2 , and for example, 1 cm 2 ~5cm 2 This is a photomultiplier tube having an activity detection surface area in each region within the specified range.

[0129] If the system in question includes multiple fluorescence detectors, each fluorescence detector may be the same, or the collection of fluorescence detectors may be a combination of different types of detectors. For example, if the system in question includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD device and the second fluorescence detector (or imaging sensor) is a CMOS device. In other embodiments, both the first and second fluorescence detectors are CCD devices. In yet another embodiment, both the first and second fluorescence detectors are CMOS devices. In yet another embodiment, the first fluorescence detector is a CCD device and the second fluorescence detector is a photomultiplier tube (PMT). In yet another embodiment, the first fluorescence detector is a CMOS device and the second fluorescence detector is a photomultiplier tube. In yet another embodiment, both the first and second fluorescence detectors are photomultiplier tubes.

[0130] In embodiments of the present disclosure, the fluorescence detector of interest is configured to measure light emitted by a sample in a flow stream at, for example, 400 or more different wavelengths, to measure light collected at one or more wavelengths, for example, two or more wavelengths, for example, five or more different wavelengths, for example, ten or more different wavelengths, for example, 25 or more different wavelengths, for example, 50 or more different wavelengths, for example, 100 or more different wavelengths, for example, 200 or more different wavelengths, for example, 300 or more different wavelengths. In some embodiments, two or more detectors in the module described herein are configured to measure the same or overlapping wavelengths of collected light.

[0131] In some embodiments, the fluorescence detector of interest is configured to measure light collected over a range of wavelengths (e.g., 200 nm to 1000 nm). In certain embodiments, the detector of interest is configured to collect the spectrum of light over a range of wavelengths. For example, a flow cytometer may include one or more detectors configured to collect the spectrum of light over one or more wavelengths in the 200 nm to 1000 nm range. In yet another embodiment, the detector of interest is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a module 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, one or more detectors may be configured to pair with specific fluorophores, such as those used with a sample in a fluorescence assay.

[0132] In one embodiment, the system is configured to detect light from emitted particles in a flow stream in one or more photodetector channels, for example, two or more photodetectors, for example three or more, for example four or more, for example five or more, for example six or more, for example seven or more, for example eight or more, for example nine or more, for example ten or more, for example twelve or more, for example sixteen or more, for example twenty-four or more, for example twenty-four or more, for example thirty-two or more, for example sixteen or more, for example sixteen or more, for example twelve or more, for example twenty-four or more, for example thirty-four or more, for example sixteen or more, for example twelve or more, for example twenty-four or more, and for example five-thousand or more photodetector channels.

[0133] In certain embodiments, the photodetection system described herein is part of a flow cytometer. The flow cytometer may include any suitable mechanism for supplying sheath fluid and sample fluid to a sample fluid inlet coupler and the sheath fluid inlet coupler. For example, the sample fluid inlet coupler may be fluidically connected to a sample fluid line (e.g., a tube) fluidically connected to a sample fluid reservoir. Similarly, the sheath fluid inlet coupler may be fluidically connected to a sheath fluid line fluidically connected to a sheath fluid reservoir. Similarly, the flow cytometer may include any suitable mechanism for managing waste from the flow stream. The fluid discharge coupler may be fluidically connected to a waste line fluidically connected to a waste reservoir. A fluid management system that may be adapted for use in the flow cytometer in question is provided in U.S. Patent Application Publication No. 2022 / 0341838, the disclosure of which is incorporated herein by reference in its entirety.

[0134] In some embodiments, a flow cytometer includes a flow cell. The flow cell of interest includes a cuvette configured to transport particles in a flow stream. As described herein, “flow cell” is described in its conventional sense, referring to a component that houses a channel for a liquid flow stream for transporting particles in a sheath fluid. The cuvette of interest has a through passage (i.e., a channel). The flow stream in which the channel is formed may include a liquid sample injected from a sample tube. In certain cases, the flow cell includes a light-accessible channel. The cuvette may be made of, for example, quartz, glass, transparent plastic, etc. In some embodiments, the cuvette is formed from silica, such as fused silica. In some cases, the flow cell is configured to irradiate light from a light source at one or more interrogation points. As described herein, “interrogation point” refers to a region within the flow cell where particles are irradiated with light from a light source, for example, for analysis. The size of the interrogation points may vary as desired. For example, if 0 μm represents the optical axis of light emitted by the light source, the interrogation points may be in the range of -50 μm to 50 μm, e.g., -25 μm to 40 μm, and e.g., -15 μm to 30 μm. Depending on certain considerations (e.g., the number and arrangement of lasers), multiple irradiation points may exist within the flow cell.

[0135] In some embodiments, the flow cell includes, or is configured to be used with, a sample injection port configured to supply a sample to the flow cell. In embodiments, the sample injection system is configured to provide a suitable flow of the sample into the internal chamber (i.e., flow path) of the flow cell. Depending on the desired characteristics of the flowstream, the rate at which the sample is delivered to the flow cell chamber by the sample injection port may be 1 μL / min or more, e.g., 2 μL / min or more, e.g., 3 μL / min or more, e.g., 5 μL / min or more, e.g., 10 μL / min or more, e.g., 15 μL / min or more, e.g., 25 μL / min or more, e.g., 50 μL / min or more, and e.g., 100 μL / min or more. In some cases, the rate at which the sample is delivered to the flow cell chamber by the sample injection port may be 1 μL / second or more, e.g., 2 μL / second or more, e.g., 3 μL / second or more, e.g., 5 μL / second or more, e.g., 10 μL / second or more, e.g., 15 μL / second or more, e.g., 25 μL / second or more, e.g., 50 μL / second or more, and e.g., 100 μL / second or more.

[0136] The sample injection port may be an orifice located in the wall of the internal chamber, or a conduit located at the proximal end of the internal chamber. If the sample injection port is an orifice located in the wall of the internal chamber, the sample injection port orifice may be any suitable shape, including, but not limited to, linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, hexagons, etc., curved cross-sectional shapes such as circles, ellipses, etc., and irregular shapes such as parabolic bottoms coupled to a flat top. In a particular embodiment, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, in a particular case, 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, and e.g., 1.25 mm to 1.75 mm, e.g., having an opening of 1.5 mm.

[0137] In certain cases, the sample injection port is a conduit located at the proximal end of the internal chamber of the flow cell. For example, the sample injection port may be a conduit positioned so that the orifice of the sample injection port is aligned with the flow cell orifice. If the sample injection port is a conduit positioned in line with the flow cell orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, including, but not limited to, straight cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons, curved cross-sectional shapes such as circles and ellipses, and irregular shapes such as a parabolic bottom joined to a flat top. The orifice of the conduit may vary depending on the shape, and in certain cases, it may have an opening in the range of 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, and e.g., 1.25 mm to 1.75 mm, e.g., 1.5 mm. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a slanted tip having an inclination angle in the range of 1° to 10°, for example 2° to 9°, for example 3° to 8°, for example 4° to 7°, and for example 5°.

[0138] In some embodiments, the flow cell also includes a sheath fluid injection port configured to supply sheath fluid to the flow cell. In embodiments, the sheath fluid injection system is configured to supply a flow of sheath fluid to the internal chamber of the flow cell, for example, together with the sample, to generate a layered flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered to the flow cell chamber by the sheath fluid injection port may 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, and e.g., 2500 μL / sec or more.

[0139] In some embodiments, the sheath fluid injection port is an orifice located in the wall of the internal chamber. The sheath fluid injection port orifice may have any suitable cross-sectional shape of interest, including, but not limited to, linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons, curved cross-sectional shapes such as circles and ellipses, and irregular shapes such as a parabolic bottom coupled to a flat top. The size of the sheath fluid injection port orifice may vary depending on the shape, in certain cases 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, and e.g., 1.25 mm to 1.75 mm, e.g., having an opening of 1.5 mm.

[0140] In some embodiments, the system includes a flow cytometer or is operably coupled to a flow cytometer. Appropriate flow cytometry systems include: Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1997); Jaroszeski et al. (ed.), 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 Throm 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 Examples of disclosures included, but not limited to, those described in Syst.24(3):203-255, are incorporated herein by reference.In certain cases, the flow cytometry systems of interest include the BD Biosciences FACSCanto® flow cytometer, BD Biosciences FACSCanto® II flow cytometer, BD Accuri® flow cytometer, BD Accuri® C6 Plus flow cytometer, BD Biosciences FACSCelesta® flow cytometer, BD Biosciences FACSLyric® flow cytometer, BD Biosciences FACSVerse® flow cytometer, BD Biosciences FACSymphony® flow cytometer, BD Biosciences LSRFortessa® flow cytometer, BD Biosciences LSRFortessa® X-20 flow cytometer, BD Biosciences FACSPresto® flow cytometer, BD Biosciences FACSVia® flow cytometer, and BD Biosciences FACSCalibur® cell sorter, BD Biosciences FACSCount® cell sorter, and BD Biosciences This includes FACSLyric™ cell sorters, BD Biosciences Via™ cell sorters, BD Biosciences Influx™ cell sorters, BD Biosciences Jazz™ cell sorters, BD Biosciences Aria™ cell sorters, BD Biosciences FACSAria™ II cell sorters, BD Biosciences FACSAria™ III cell sorters, BD Biosciences FACSAria™ Fusion cell sorters, and BD Biosciences FACSMelody™ cell sorters, BD Biosciences FACSymphony™ S6 cell sorters, BD Biosciences FACSDiscover™ cell sorters, and others.

[0141] In some embodiments, the system in question is specified 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, 10,481,074, and 10,302,54 No. 5, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. 9,952,076, No. 9,933,341, No. 9 , 726,527, 9,453,789, 9,200,334, 9,097,640, 9,095,494, 9,092,034 , No. 8,975,595, No. 8,753,573, No. 8,233,146, No. 8,140,300, No. 7,544,326, No. 7,201 ,875, No. 7,129,505, No. 6,821,740, No. 6,813,017, No. 6,809,804, No. 6,372,506, No. 5 Flow cytometry systems such as those described in Patent Nos. 700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766 (these disclosures are incorporated herein in their entirety by reference).

[0142] In some embodiments, the flow cytometer is configured as an imaging flow cytometer. For example, in a particular case, the system in question is based on 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, 10,036,699, 10,078,045, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, 10,684,211, 10,845,295, 10,935,482, 10,935,485, and A flow cytometry system configured to image particles in a flow stream by fluorescence imaging using radiofrequency tagged emission (FIRE), as described in Patent Nos. 11,105,728, 11,280,718, 11,327,016, 11,366,052, 11,371,937, 11,692,926, 11,630,053, 11,774,343, 11,940,369 and 11,946,851 (these disclosures are incorporated herein by reference).

[0143] Figure 2 shows a system 200 for flow cytometry according to an exemplary embodiment of the present disclosure. The system 200 includes a laser 201 configured to irradiate particles 211 in a flow stream 214 at an interrogation point 215 within a flow cell 210. Although the example in Figure 2 shows a single laser, it will be understood that multiple lasers may also be used. The laser beam from laser 201 is directed to a focusing lens 202, which focuses the beam onto a portion of the fluid stream where the particles 211 of the sample in the flow cell 210 are located. The flow cell 210 is part of a fluid system that guides particles in the stream to the focused laser beam, typically one at a time, for interrogation. Alternatively, a nozzle top may be used if the flow cytometer is a stream-in-air cytometer.

[0144] As shown in Figure 2, the flow cell 210 is fluidly connected to a sheath fluid reservoir 203 containing sheath fluid and a sample fluid reservoir 204 containing sample fluid. The sheath fluid from the sheath fluid reservoir 203 is supplied to at least one sheath fluid injection port 208 via a conduit (i.e., sheath fluid line) 207. In addition, the sample fluid containing particles 211 from the sample fluid reservoir 204 is supplied to a sample injection port 206 via a conduit (i.e., sample fluid line) 205. The sample injection port 206 is fluidly connected to a sample injector 213 (e.g., a sample injection needle) configured to introduce particles 211 into the interior of the flow cell 210. The particles 211 are hydrodynamically focused via the sheath fluid entering from the sheath fluid injection port 208 so that a flowstream 214 is formed downstream of the tapered portion 212 of the flow cell 210. Particles emitted at the distal end of the flow cell 210 may be disposed of and / or collected via any suitable protocol. For example, depending on the type of flow cytometry performed, particles may be collected at the distal end of the flow cell 210, for example, via a waste line. Alternatively, the particles may be sorted.

[0145] Light from the laser beam interacts with particles 211 in the sample by diffraction, refraction, reflection, scattering, and absorption, accompanied by re-emission at various different wavelengths, depending on the particle's characteristics, such as its size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescence emission, as well as the diffracted, refracted, reflected, and scattered light, may be sent to one or more detectors. In particular, forward scatter (FSC) is sent to a forward scatter detector 223. The forward scatter detector 223 is positioned slightly off-axis from the direct beam passing through the flow cell 210 and is configured to detect the diffracted light, i.e., the excitation light that travels mainly forward through or around the particle. The intensity of the light detected by the forward scatter detector 223 depends on the overall size of the particle. The forward scatter detector may include, for example, a photodiode. An optical filter 221a and a scattering bar 222 are positioned between the forward scatter detector 223 and the beam. The optical filter 221a may be configured to filter out non-FSC light of at least one wavelength, while the scattering bar 222 may be configured to prevent the incident beam from the laser 201 (i.e., non-scattered light) from being detected by the forward scatter light detector 223.

[0146] Furthermore, side-scattered light (SSC) is detected by the side-scattered light detector 224. In other words, the side-scattered light detector 224 is configured to detect refracted and reflected light from the surface and internal structure of the particle 211, which tends to increase as the complexity of the particle structure increases. In the example in Figure 2, the flow cytometer 200 includes a dichroic mirror 220a configured to reflect SSC light to the side-scattered light detector 224 and allow non-SSC light (e.g., fluorescence) to pass through. An optical filter 221b is configured to prevent non-SSC light of at least one wavelength from being detected by the side-scattered light detector 224. Fluorescence detectors 225a-225c, each configured to detect fluorescence of various wavelengths, are also shown. For example, the dichroic mirror 220b may be configured to reflect fluorescence (FL) corresponding to a first wavelength (or wavelength range) to the fluorescence detector 225a and allow light of other wavelengths to pass through. The optical filter 221c may be configured to prevent at least one wavelength of light that does not correspond to a first wavelength (or wavelength range) from being detected by the fluorescence detector 225a. Similarly, the dichroic mirror 220c is configured to reflect FL light corresponding to a second wavelength (or wavelength range) to the fluorescence detector 225b and to allow light of a third wavelength (or wavelength range) to pass through for detection by the fluorescence detector 225c. The optical filter 221d is configured to prevent at least one wavelength of light that does not correspond to a second wavelength (or wavelength range) from being detected by the fluorescence detector 225b. Furthermore, the optical filter 221e is configured to prevent at least one wavelength of light that does not correspond to a third wavelength (or wavelength range) from being detected by the fluorescence detector 225c.

[0147] Those skilled in the art will recognize that the flow cytometer according to the embodiments of this disclosure is not limited to the flow cytometer shown in Figure 2, but may include any flow cytometer known in the art. For example, the flow cytometer may have any number of lasers, beam splitters, filters, and detectors of various wavelengths and various different configurations. For example, the embodiment in Figure 2 shows three fluorescence detectors for illustrative purposes, but it will be understood that any suitable number of fluorescence detectors can be used.

[0148] During operation, the operation of the cytometer is controlled by the controller / processor 290, and measurement data from the detector may be stored in memory 295 and processed by the controller / processor 290. Although not explicitly shown, the controller / processor 290 may be coupled to the detector to receive output signals from the detector, and may also be coupled to the electrical and electromechanical components of the flow cytometer to control the laser 201, fluid flow parameters, etc. An input / output (I / O) function 297 may also be provided in the system. The memory 295, controller / processor 290, and I / O 297 may be provided as a single integrated part of the flow cytometer. In such embodiments, a display may also form part of the I / O function 297 for presenting experimental data to the user of the cytometer 200. Alternatively, some or all of the memory 295, controller / processor 290, and I / O functions may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 295 and controller / processor 290 can communicate with the cytometer 210 wirelessly or via a wired connection. In conjunction with memory 295 and I / O 297, the controller / processor 290 can be configured to perform various functions related to the preparation and analysis of flow cytometer experiments.

[0149] Different fluorescent molecules in a fluorescent dye panel used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The specific fluorescent labels used in the experiment, and their associated fluorescence emission bands, may be selected to substantially match the detector's filter window. I / O297 can be configured to receive data for flow cytometer experiments having a panel of fluorescent labels, and for multiple cell populations having multiple markers, with each cell population having a subset of multiple markers. I / O297 can also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectral data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experiment data, such as label spectral characteristics and flow cytometer configuration data, can also be stored in memory 295. The controller / processor 290 can be configured to evaluate the assignment of one or more labels to markers.

[0150] In some embodiments, the system in question is a particle sorting system configured to sort particles using a sealed particle sorting module, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed on 28 March 2017, whose disclosure is incorporated herein by reference. In certain embodiments, particles of a sample (e.g., cells) are sorted using a sorting decision module having multiple sorting decision units, such as that described in U.S. Patent Application Publication No. 2020 / 0256781, filed on 23 December 2019, whose disclosure is incorporated herein by reference. In some embodiments, the system for sorting components of a sample includes a particle sorting module having deflection plates, such as that described in U.S. Patent Application Publication No. 2017 / 0299493, filed on 28 March 2017, whose disclosure is incorporated herein by reference.

[0151] In a particular embodiment, the system is fluorescence imaging using a radiofrequency tagged emission image-enabled particle sorter as shown in Figure 3. The particle sorter 300 includes an optical illumination component 300a, which includes a light source 301 (e.g., a 488 nm laser) that generates an output optical beam 301a, which is split into beam 302a and beam 302b using a beam splitter 302. The optical beam 302a is propagated through an acousto-optical device (e.g., an acousto-optic deflector, AOD) 303 to generate an output beam 303a having one or more angularly deflected optical beams. In some cases, the output beam 303a generated from the acousto-optical device 303 includes a local oscillator beam and multiple radiofrequency comb beams. The optical beam 302b is propagated through an acousto-optical device (e.g., an acousto-optic deflector, AOD) 304 to generate an output beam 304a having one or more angularly deflected optical beams. In some cases, the output beam 304a generated from the acousto-optic device 304 includes a local oscillator beam and multiple high-frequency comb beams. The output beams 303a and 304a generated from the acousto-optic devices 303 and 304, respectively, are combined with a beam splitter 305 to produce an output beam 305a, which is transported through an optical component 306 (e.g., an objective lens) to irradiate particles in the flow cell 307. In a particular embodiment, the acousto-optic device 303 (AOD) splits a single laser beam into an array of beamlets, each having a different optical frequency and angle. A second AOD 304 adjusts the optical frequency of a reference beam, which is then superimposed with the array of beamlets in a beam combiner 305. In certain embodiments, the light irradiation system having a light source and an acoustic-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.

[0152] The output beam 305a irradiates sample particles 308 propagating through the flow cell 307 (e.g., using the sheath fluid 309) in the irradiation area 310. As shown in the irradiation area 310, multiple beams (e.g., angle-deflected high-frequency shifted optical beams shown as dots across the irradiation area 310) are superimposed on the reference local oscillator beam (shown as diagonal lines across the irradiation area 310). Due to their different optical frequencies, the overlapping beams exhibit beat behavior, thereby giving each beamlet a distinct frequency f 1-n This is used to carry a sine wave modulation signal.

[0153] Light from the irradiated sample is delivered to a photodetector system 300b, which includes multiple photodetectors. The photodetector system 300b includes a forward scatter photodetector 311 for generating a forward scatter image 311a and a side scatter photodetector 312 for generating a side scatter image 312a. The photodetector system 300b also includes a bright-field photodetector 313 for generating an optical 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 photodetector 313 is a photomultiplier tube (PMT). Fluorescence from the irradiated sample is also detected by fluorescence detectors 314-317. In some cases, the photodetectors 314-317 are photomultiplier tubes. Light from the irradiated sample is directed through a beam splitter 320 to the side scatter detection channel 312 and the fluorescence detection channels 314-317. The photodetector system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for propagating light of a predetermined wavelength to photodetectors 314-317. In some cases, optical component 321 is 534 nm / 40 nm bandpass. In some cases, optical component 322 is 586 nm / 42 nm bandpass. In some cases, optical component 323 is 700 nm / 54 nm bandpass. In some cases, optical component 324 is 783 nm / 56 nm bandpass. The first digit represents the center of the spectral band. The second digit indicates the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on both sides of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0154] Data signals generated in response to light detected by 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-responsive sorting is performed in response to sorting signals generated by sorting trigger 352. The sorting component 300c houses deflection plates 331 for deflecting particles into the sample container 332 or into the waste stream 333. In some cases, the 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, whose disclosure is incorporated herein by reference. In certain embodiments, the sorting component 300c includes a sorting decision module having a plurality of sorting decision units, as described in U.S. Patent Application Publication No. 2020 / 0256781, which is incorporated herein by reference.

[0155] In some embodiments, the system is a particle analyzer, and the particle analysis system 401 (Figure 4) can be used to analyze and characterize particles, whether or not the particles are physically sorted into a collection container. Figure 4 shows a functional block diagram of the particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, the particle analysis system 401 is a flow system. The particle analysis system 401 includes a fluid system 402. The fluid system 402 includes or can include a sample tube 405 and a moving fluid column in the sample tube through which sample particles 403 (e.g., cells) move along a common sample path 409.

[0156] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. The detection station 408 generally refers to a monitoring area 407 of the common sample path. In some implementations, detection may include detecting light or one or more other properties of particle 403 as it passes through the monitoring area 407. Figure 4 shows one detection station 408 with one monitoring area 407. Some implementations of the particle analysis system 401 may include multiple detection stations. Furthermore, some detection stations may monitor two or more areas.

[0157] Each signal is assigned a signal value to form a data point for each particle. As mentioned above, this data can be called event data. The data points can be multidimensional data points containing the values ​​of each characteristic measured for each particle. The detection system 404 is configured to collect such data points sequentially at a first time interval.

[0158] The particle analysis system 401 may also include a control system 406. The control system 406 may include one or more processors, amplitude control circuits and / or frequency control circuits. The illustrated control system may be operably associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of a first time interval based on a Poisson distribution and the number of data points collected by the detection system 404 during a first time interval. The control system 406 may be further configured to generate an experimental signal frequency based on the number of data points in a portion of the first time interval. The control system 406 may further compare the experimental signal frequency with that of a calculated signal frequency or a predetermined signal frequency.

[0159] Figure 5 shows a functional block diagram of an example particle analyzer control system, such as an analysis controller (e.g., processor) 500, for analyzing and displaying biological events. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0160] 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. The analysis controller 500 may be a processor configured to perform the method of the present invention by, for example, applying a distance-based classification model to determine a density distinction threshold in a size-based analyte feature space, applying a density-based clustering algorithm to separate the analyte data into high-density and low-density clusters based on the density threshold, and classifying the analyte data based on high-density and low-density clusters based on a size-based analyte feature space.

[0161] 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 may include flow cytometry event data. The analysis controller 500 can be configured to provide a display device 506 with a graphic display including a first plot of the biological event data. The analysis controller 500 can be further configured to render a region of interest overlaid on the first plot, for example, as a gate around the collection of biological event data shown by the display device 506. In some embodiments, the gate may be a logical combination of one or more graphic regions of interest depicted in a histogram or bivariate plot of a single parameter. In some embodiments, the display may be used to display particle parameters or saturation detector data.

[0162] The analysis controller 500 can further be configured to display biological event data within the gate on the display device 506 in a different manner than other events in the biological event data outside the gate. For example, the analysis controller 500 can be configured to render the colors of the biological event data contained within the gate differently from the colors of the biological event data outside the gate. The display device 506 can be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0163] The analysis controller 500 can be configured to receive gate selection signals from a first input device that identify gates. For example, the first input device can be implemented as a mouse 510. The mouse 510 can initiate gate selection signals to the analysis controller 500 that identify gates displayed on the display device 506 or operated via the display device 506 (for example, by clicking on a desired gate when the cursor is positioned there). In some implementations, the first device can be implemented as a keyboard 508, or as other means for providing input signals to the analysis controller 500, such as a touchscreen, stylus, photodetector, or speech recognition system. Some input devices can include multiple input functions. In such implementations, each of those input functions can be considered an input device. For example, as shown in Figure 5, the mouse 510 may include a right mouse button and a left mouse button, each capable of generating a trigger event.

[0164] A trigger event can provide input to the analysis controller 500 for further processing, such as changing how the data is displayed, which parts of the data are actually displayed on the display device 506, and / or selecting a population of interest for particle sorting.

[0165] In some embodiments, the analysis controller 500 can be configured to detect when gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the plot visualization to facilitate the gating process. The modification can be based on a specific distribution of biological event data received by the analysis controller 500.

[0166] 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 allow the analysis controller 500 to retrieve biological event data, such as flow cytometry event data.

[0167] The display device 506 can be configured to receive display data from the analysis controller 500. The display data may include plots of biological event data and gates that outline sections of the plots. The display device 506 can be further configured to change the information presented according to input received from the analysis controller 500, along with input from the particle analyzer 502, the memory device 504, the keyboard 508, and / or the mouse 510.

[0168] In some implementations, the analysis controller 500 can generate a user interface for receiving exemplary events for selection. For example, the user interface may include controls for receiving exemplary events or exemplary images. The exemplary events or images or exemplary gates may be provided before the collection of event data for the sample, or based on events from an initial set of the sample.

[0169] Figure 6A is a schematic diagram of a particle sorting system 600 (e.g., a particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorting system 600 is a cell sorting system. As shown in Figure 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to a nozzle 603, may include a nozzle 603, or may be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are arranged in a line across a monitoring area 611 (e.g., where laser streams intersect) that is irradiated by an irradiation source 612 (e.g., a laser). The vibration of the droplet-forming transducer 602 causes the moving fluid column 608 to decompose into multiple droplets 610, some of which contain particles 609.

[0170] During operation, a detection station 614 (e.g., an event detector) identifies when a particle (or cell) of interest crosses the monitoring area 611. The detection station 614 supplies power to a timing circuit 628, which supplies power to a flash charge circuit 630. A flash charge can be applied to the moving fluid column 608 so that the droplet of interest becomes charged at the droplet departure point, which is indicated by a timed drop delay (Δt). The droplet of interest may contain one or more particles or cells to be sorted. The charged droplet can then be sorted by activating a deflection plate (not shown) to deflect the droplet into a collection tube or a container such as a multi-well or microwell sample plate, and the wells or microwells can be associated with specific droplets of interest. As shown in Figure 6A, the droplets can be collected in a drain receptacle 638.

[0171] The detection system 616 (e.g., a droplet boundary detector) plays a role in automatically determining the phase of the droplet drive signal as particles of interest pass through the monitoring area 611. An exemplary droplet boundary detector is described in U.S. Patent No. 7,679,039, which is incorporated herein by reference in its entirety. The detection system 616 enables the instrument to accurately calculate the location of each detected particle in the droplet. The detection system 616 can supply amplitude signals 620 and / or phase signals 618, which supply amplitude control circuits 626 and / or frequency control circuits 624 (via amplifier 622). The amplitude control circuits 626 and / or frequency control circuits 624 control the droplet formation transducer 602. The amplitude control circuits 626 and / or frequency control circuits 624 may be included in a control system.

[0172] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store detected events and sorting decisions based thereon. The sorting decisions can be included in the particle event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit, or they can be communicatively coupled so that either the detection system 616 or the detection station 614 can collect event measurements and provide them to non-collecting elements.

[0173] Figure 6B is a schematic diagram of a particle sorting system according to one embodiment presented herein. The particle sorting system 600 shown in Figure 6B includes deflection plates 652 and 654. An electric charge can be applied via a stream-charging wire in a barb. This creates a stream of droplets 610 containing particles 6609 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. Information about the particles is analyzed by sorting electronics or other detection systems (not shown in Figure 6B). The deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets, guiding the droplets toward a target collection receptacle (e.g., one of 672, 674, 676, or 678). As shown in Figure 6B, deflection plates 652 and 654 can be used to direct particles toward receptacle 674 along the first path 662 or toward receptacle 678 along the second path 668. If the particles are not of interest (e.g., do not exhibit scattering or illumination information within a specified sorting range), the deflection plates may allow the particles to continue along the flow path 664. Such uncharged droplets can enter the waste receptacle via an aspirator 670 or the like.

[0174] Sorting electronics may be included to initiate measurement data collection, receive fluorescence signals related to particles, and determine how to adjust the deflection plates to induce particle sorting. An exemplary implementation of the embodiment shown in Figure 6B includes the BD FACSAria® line of flow cytometers commercially available from Becton, Dickinson and Company (Franklin Lakes, NJ).

[0175] Computer control system The system may include a display and an operator input device. The operator input device may be, for example, a keyboard or mouse. The processing module includes a processor that can access memory containing instructions for carrying out steps of the method in question. The processing module may also 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 several other processors that are available or will be available. The processor runs the operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor coordinating and executing the functions of various computer programs that can be written in various programming languages, such as Java, Perl, C++, Python, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system usually works with the processor to coordinate and execute the functions of other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known technology. In some embodiments, the processor includes analog electronic equipment that provides feedback control, such as negative feedback control.

[0176] System memory may be any of the various known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as permanent hard disks or tapes, optical media such as read-and-write compact disks, flash memory devices, or other memory storage devices. Memory storage devices may be any of the various known or future devices, including compact disk drives, tape drives, or floppy disk drives. Such types of memory storage devices typically read from and / or write to program storage media such as compact disks (not shown). Any of these program storage media, or others currently in use or to be developed in the future, may be considered computer program products. As is understood, these program storage media typically store computer software programs and / or data. Computer software programs, also called computer control logic, are typically stored in program storage devices used in conjunction with system memory and / or memory storage devices.

[0177] In some embodiments, a computer program product is described that includes a computer-usable medium on which control logic (a computer software program including program code) is stored. When the control logic is executed by the computer's processor, it causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using a hardware state machine. It will be obvious to those skilled in the art that a hardware state machine is implemented to perform the functions described herein.

[0178] Memory may be any suitable device on which the processor can store and retrieve data, such as a magnetic storage device, an optical storage device, or a solid-state storage device (including magnetic or optical disks, or tapes or RAM, or any other suitable fixed or portable device). The processor may include a general-purpose digital microprocessor appropriately programmed from a computer-readable medium having the necessary program code. The program may be provided to the processor remotely via a communication channel, or it 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 the memory-related devices. For example, a magnetic or optical disk may have a program that can be read by a disk writer / reader. The system of this disclosure also includes, for example, a program in the form of a computer program product, and algorithms for use in carrying out the methods described above. The program of this disclosure may be recorded on a computer-readable medium, for example, any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as floppy disks, hard disk storage media, and magnetic tape; optical storage media such as CD-ROMs; electrical storage media such as RAM and ROM; portable flash drives; and hybrids of these categories such as magnetic / optical storage media.

[0179] The processor can also access communication channels to communicate with users in remote locations. Remote location means that the user is not in direct contact with the system and is relaying input information to the input manager from an external device such as a computer connected to a wide area network ("WAN"), telephone network, satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0180] In some embodiments, the systems according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface can be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, Wi-Fi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0181] In one embodiment, the communication interface is configured to include one or more physical ports or interfaces, such as a USB port, a USB-C port, an RS-232 port, or any other suitable electrical connection port that enables data communication between the system in question and other external devices, such as a computer terminal configured for similar complementary data communication (e.g., in a doctor's office or hospital environment).

[0182] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the system in question to communicate with computer terminals and / or networks, other devices such as communicable mobile phones, personal digital assistants, or any other communication devices that a user may use in conjunction with it.

[0183] In one embodiment, the communication interface is configured to provide a connection for data transfer using Internet Protocol (IP), Short Message Service (SMS), wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or Wi-Fi connection to the Internet via a Wi-Fi hotspot.

[0184] In one embodiment, the system in question is configured to communicate wirelessly with a server device via a communication interface using a common standard such as 802.11 or Bluetooth® RF protocol, or 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 or electrical appliance. In some embodiments, the server device has a display such as a liquid crystal display (LCD), as well as input devices such as buttons, a keyboard, a mouse, or a touchscreen.

[0185] In some embodiments, the communication interface is configured to automatically or semi-automatically communicate data stored in a target system, such as any data storage unit, with a network or server device using one or more of the above-described communication protocols and / or mechanisms.

[0186] The output controller may include a controller for any of the various known display devices for presenting information to a user, whether human or machine, local or remote. If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. The graphical user interface (GUI) controller may include any of the various known or future software programs for providing a graphical input / output interface between the system and the user and for processing user input. Functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing module to a remote user, for example, via the internet, telephone or satellite network, according to known techniques. The presentation of data by the output manager may be implemented according to various known techniques. As some examples, the data may include SQL, HTML or XML documents, email or other files, or data in other formats. The data may include an internet URL address so that the user can retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in the system under consideration may be any type of known computer platform or a type to be developed in the future, but they are typically computers of a class commonly referred to as servers. However, they may also be mainframe computers, workstations, or other types of computers. They may be connected via any known or future type of cabling or other communication systems, including wireless systems, whether or not they are networked. They may be located in the same place or physically separated.Depending on the type and / or manufacturer of the selected computer platform, various operating systems can be used on any computer platform. Suitable operating systems include Windows® NT®, Windows® XP, Windows® 7, Windows® 8, Windows® 10, iOS®, macOS®, Linux®, Ubuntu®, Fedora®, OS / 400®, i5 / OS®, IBM i®, Android®, SGI IRIX®, Oracle Solaris®, and others.

[0187] Figure 7 shows a general architecture of an exemplary computing device 700 according to a particular embodiment. The general architecture of the computing device 700 shown in Figure 7 includes the configuration of computer hardware and software components. However, it is not necessary to illustrate all of these generally conventional elements in order to provide a practicable disclosure. As shown, the computing device 700 includes a processing unit 710, a network interface 720, a computer-readable media drive 730, an input / output device interface 740, a display 750, and an input device 760, all of which may communicate with each other via a communication bus. The network interface 720 may provide connectivity to one or more networks or computing systems. Thus, the processing unit 710 may receive information and instructions from other computing systems or services via the network. The processing unit 710 may also communicate with memory 770 and further provide output information to an optional display 750 via the input / output device interface 740. For example, analysis software (such as data analysis software or programs like FlowJo®) stored as executable instructions in the non-temporary memory of the analysis system can display flow cytometry event data to the user. The input / output device interface 740 can also accept input from an optional input device 760, such as a keyboard, mouse, digital pen, microphone, touchscreen, gesture recognition system, speech recognition system, gamepad, accelerometer, gyroscope, or other input devices.

[0188] The memory 770 may contain computer program instructions (grouped as modules or components in some embodiments) that the processing unit 710 executes to implement one or more embodiments. The memory 770 generally includes RAM, ROM, and / or other persistent, auxiliary, or non-temporary computer-readable media. The memory 770 may store an operating system 772 that provides computer program instructions for use by the processing unit 710 in the general management and operation of the computing device 700. Data may be stored in a data storage device 790. The memory 770 may further include computer program instructions and other information for implementing embodiments of the present disclosure.

[0189] Non-temporary computer-readable storage medium Aspects of this disclosure further include non-temporary computer-readable storage media having instructions for performing the methods described herein, such as for performing one or more computer implementation methods described herein. The computer-readable storage media may be used in one or more computers for the full or partial automation of a system for performing the methods described herein. In certain embodiments, instructions by the methods described herein may be coded on computer-readable media in the form of “programming,” and as used herein, the term “computer-readable media” refers to any non-temporary storage medium involved in providing instructions and data to a computer for execution and processing. Examples of preferred non-temporary storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state disks, and network-attached storage (NAS), whether such devices are inside or outside a computer. A file containing information may be “stored” on computer-readable media, and “stored” means recording information so that it can be accessed and retrieved by a computer at a later date. The computer implementations described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, and many other languages.

[0190] In some embodiments, a non-temporary computer-readable storage medium includes an algorithm for irradiating particles of a sample containing fluorophores in a flow stream with a light source; an algorithm for detecting light from the irradiated particles using a photodetector system including a photodetector; an algorithm for generating a spectral data signal in response to the detected light; and an algorithm for calibrating the generated spectral data signal based on a calibration scaling coefficient and a calculated abundance of fluorophores.

[0191] In some cases, a non-temporary computer-readable storage medium includes an algorithm for detecting light by a photodetection system in multiple photodetector channels. In some embodiments, the generated spectral data signal is the sum of data signals collected across multiple photodetector channels. In some cases, the generated data signal is the data signal collected in a single photodetector channel, for example, a photodetector channel showing the maximum signal intensity of a fluorophore.

[0192] In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor based on the sum of the reference data signals and the abundance of fluorophores in the reference sample, which are generated in one or more photodetector channels in response to light detected from an irradiated reference sample. In some cases, the reference sample is a single-stained control. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating one or more calibration scaling factors and fluorophore abundances using linear regression of the total signal from the reference sample and the fluorophore abundance. In certain specific cases, the fluorophore abundance is the number of fluorophores present on a particle.

[0193] In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal before spectral decomposition of the generated spectral data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the signal intensity to be equivalent to the intensity of a single fluorophore. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the signal intensity to the intensity of a single fluorophore by dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal according to the following:

[0194]

number

[0195] In the formula, X arb is the single-stain reference spectrum, CSF is the calibration scaling factor, and f cal is a vector of fluorophore abundances, and y arb This is the detector signal vector. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for spectrally decomposing a spectral data signal to generate a decomposed data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for normalizing a reference data signal to 1. In some cases, the non-temporary computer-readable storage medium includes an algorithm for rescaling the decomposed spectral data signal. In some cases, the non-temporary computer-readable storage medium includes an algorithm for determining the occurrence rate of a single stained control. In some embodiments, the non-temporary computer-readable storage medium includes an algorithm for calibrating the generated spectral data signal according to the following:

[0196]

number

[0197] In the formula, X † is the inverse problem spectrum, CSF is the calibration scaling coefficient, and f cal is a vector of fluorophore abundances, and S is the sum of the data signals generated by multiple photodetector channels (S sum ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. max ) is selected from, y arb This is the detector signal vector.

[0198] In some embodiments, the particles have multiple different fluorophores. In some cases, the particles are multispectral beads. In some cases, the particles are cells labeled with multiple different fluorophores. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor for each of the different fluorophores. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor for each fluorophore by using the sum of the median values ​​of the fluorophore-specific signals per photodetector channel for each fluorophore. In some cases, the non-temporary computer-readable storage medium includes an algorithm for calculating a calibration scaling factor by interpolating the entire signal with a reference calibration scaling factor to determine an equivalent fluorophore abundance.

[0199] Non-temporary computer-readable storage media may be used in one or more computer systems having a display and operator input devices. Operator input devices may be, for example, a keyboard, a mouse, etc. A processing module includes a processor that can access memory containing instructions for carrying out steps of the method in question. A processing module may also 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 several other processors that are available or will become available. The processor runs an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor to coordinate and execute the functions of various computer programs that can be written in various programming languages, such as those described above, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system usually works with the processor to coordinate and execute the functions of other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all according to known technology.

[0200] kit Aspects of this disclosure further include kits, which include storage media such as magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state disks, and network-attached storage devices (NAS). Any of these programmable storage media, or others currently in use or to be developed in the future, may be included in the kits of this disclosure. In embodiments, the programmable storage media includes instructions for calibrating flow cytometer data for use in the methods described herein and in the systems described herein. In embodiments, instructions contained in a computer-readable medium provided in the kits of this disclosure or in part thereof may be implemented as software components of software for analyzing data. In these embodiments, the computer-controlled systems of this disclosure may function as software "plugins" to existing software packages (e.g., FlowJo®).

[0201] In addition to the components described above, the kit may further include instructions (in some embodiments). These instructions may be present in the kit in various forms, or one or more of them may be present in the kit. One possible form of these instructions is information printed on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, the kit's packaging, or accompanying documents. Yet another form of these instructions is a computer-readable medium on which the information is recorded, such as a diskette, compact disc (CD), or portable flash drive. Yet another possible form of these instructions is a website address that can be used via the internet to access information at a remote site.

[0202] usefulness The methods, systems, and computer systems described herein are used in a variety of applications where it is desirable to calibrate or optimize photodetection systems (e.g., those having multiple photodetectors), such as particle analyzers. The methods and systems described herein are also used in photodetection systems used to analyze and sort particulate components in samples in fluid media, such as biological samples. The disclosure also finds use in flow cytometry where it is desirable to provide a flow cytometer with improved cell sorting accuracy, enhanced particle collection, reduced energy consumption, particle charging efficiency, accurate particle charging, and improved particle deflection during cell sorting. In embodiments, the disclosure reduces the need for user input or manual adjustment during sample analysis by a flow cytometer. In certain embodiments, the methods and systems described herein provide a fully automated protocol such that little to no human input is required for the adjustment of the flow cytometer in use.

[0203] While the foregoing disclosure has been described in some detail as examples and illustrations to clarify understanding, it will be readily apparent to those skilled in the art that several changes and modifications can be made in light of the teachings of this disclosure without departing from the spirit or scope of the attached claims.

[0204] Therefore, the foregoing is merely illustrative of the principles of the present disclosure. Those skilled in the art will understand that various configurations embodying the principles of the present disclosure and that fall within its spirit and scope can be devised, although not expressly described or shown herein. Furthermore, all examples and conditional statements listed herein are intended primarily to help readers understand the principles of the present disclosure and the concepts that the disclosure has contributed to advancing the art, and should be construed as not being limited to such specifically listed examples and conditions. Furthermore, all descriptions herein listing the principles, aspects and embodiments of the present disclosure and specific examples thereof are intended to encompass both their structural and functional equivalents. Furthermore, such equivalents are intended to include both currently known equivalents and equivalents to be developed in the future, i.e., any developed elements that perform the same function regardless of their structure. Furthermore, nothing disclosed herein is intended to be made available to the public, whether such disclosure is expressly described in the claims or not.

[0205] Accordingly, the scope of this disclosure is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of this disclosure are embodied in the appended claims. In the claims, 35 U.SC § 112(f) or 35 U.SC § 112(6) are expressly defined as being invoked for limitation in the claims only if the exact phrase “means” or the exact phrase “step” is stated at the beginning of such limitation in the claims, and if such exact phrase is not used in limitation in the claims, 35 U.SC § 112(f) or 35 U.SC § 112(6) are not invoked.

Claims

1. Irradiating particles of a sample containing fluorophores in a flowstream with a light source, The light from the irradiated particles is detected by a photodetector including a photodetector. To generate a spectral data signal in response to the detected light, The generated spectral data signal is calibrated based on the calibration scaling coefficient and the calculated abundance of fluorophores. A method that includes this.

2. The method according to claim 1, wherein the generated spectral data signal includes the sum of the data signals across a plurality of photodetector channels.

3. The method according to claim 1, wherein the generated spectral data signal includes the data signal in a single photodetector channel.

4. The method according to claim 3, wherein the single photodetector channel is the photodetector channel that exhibits the maximum signal intensity for the fluorophore.

5. The method according to any one of claims 1 to 4, comprising calculating the calibration scaling coefficient.

6. In response to light detected from an irradiated reference sample, a reference data signal is generated in one or more photodetector channels, The calibration scaling coefficient is calculated based on the sum of the reference data signals and the amount of the fluorophore in the reference sample. The method according to claim 5, including the following.

7. The method according to claim 6, wherein the reference sample includes single-stained control particles.

8. The method according to any one of claims 6 to 7, wherein the calibration scaling coefficient is calculated using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel.

9. The method according to any one of claims 6 to 8, wherein one or more of the calibration scaling coefficients and the fluorophore abundances are calculated using linear regression of the total signal from the reference sample and the fluorophore abundances.

10. The method according to any one of claims 1 to 9, wherein the calculated amount of fluorophores includes the number of fluorophores present on the particles.

11. The method according to any one of claims 1 to 10, comprising calibrating the generated spectral data signal before spectral decomposition of the generated spectral data signal.

12. The method according to claim 11, comprising rescaling the signal intensity to be equivalent to the intensity of a single fluorophore.

13. The method according to claim 12, wherein rescaling the signal intensity to the intensity of a single fluorophore includes dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore.

14. The generated spectral data signal, [Math 1] (In the formula, X ａｒｂ This is the single-stain reference spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, and y ａｒｂ (This is the detector signal vector.) The method according to any one of claims 11 to 13, which is calibrated according to the following:

15. The method according to any one of claims 1 to 10, further comprising spectrally decomposing the spectral data signal to generate a decomposed data signal.

16. The method according to claim 15, wherein the reference data signal is normalized to 1.

17. The method according to any one of claims 15 to 16, comprising rescaling the decomposed spectral data signal.

18. The method according to any one of claims 15 to 17, comprising determining the occurrence rate of a single-stained control.

19. The generated spectral data signal, [Math 2] (In the formula, X † This is the inverse problem spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, S is the sum of the data signals generated by the plurality of photodetector channels (S ｓｕｍ ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. ｍａｘ ) selected from, and y ａｒｂ (This is the detector signal vector.) The method according to any one of claims 15 to 18, which is calibrated according to the following:

20. The method according to any one of claims 1 to 19, wherein the particles comprise a plurality of different fluorophores.

21. The method according to claim 20, wherein the particles are multispectral beads.

22. The method according to claim 20, wherein the particles are cells labeled with a plurality of fluorophores.

23. The method according to any one of claims 20 to 22, comprising calculating a calibration scaling factor for each of the different fluorophores.

24. The method according to claim 23, wherein the calibration scaling coefficient for each fluorophore is calculated using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel of each fluorophore.

25. The method according to any one of claims 20 to 24, wherein the calibration scaling coefficient is calculated by interpolating the entire signal with a reference calibration scaling coefficient to determine an equivalent fluorophore abundance.

26. The method according to any one of claims 1 to 25, wherein the light source includes a light-emitting diode.

27. The method according to any one of claims 1 to 25, wherein the light source includes a laser.

28. The method according to claim 26, wherein the light source includes a plurality of lasers.

29. The method according to any one of claims 1 to 28, wherein the light detection system includes a plurality of photodetectors.

30. The method according to claim 29, wherein the photodetector includes one or more photomultiplier tubes.

31. The method according to any one of claims 1 to 30, wherein the light detection system includes a photodetector array.

32. The method according to claim 31, wherein the photodetector array includes a photodiode.

33. The method according to claim 31, wherein the photodetector array includes charge-coupled elements.

34. A light source configured to irradiate particles of a sample containing fluorophores in a flowstream, A photodetection system including a photodetector for detecting light from the irradiated particles, A processor including memory, wherein the memory is operablely coupled to the processor, the memory stores instructions, and when an instruction is executed by the processor, the processor In response to the detected light, a spectral data signal is generated, and A processor and A system that includes this.

35. The system according to claim 34, wherein the generated spectral data signal includes the sum of the data signals across a plurality of photodetector channels.

36. The system according to claim 34, wherein the generated spectral data signal includes the data signal in a single photodetector channel.

37. The system according to claim 36, wherein the single photodetector channel is the photodetector channel that exhibits the maximum signal intensity for the fluorophore.

38. The system according to any one of claims 34 to 37, wherein memory stores instructions, and when an instruction is executed by the processor, the processor is instructed to calculate the calibration scaling coefficient.

39. The memory stores instructions, and when an instruction is executed by the processor, the processor: In response to the light detected from the irradiated reference sample, a reference data signal is generated in one or more photodetector channels, and The system according to claim 38, wherein the calibration scaling coefficient is calculated based on the sum of the reference data signals and the amount of the fluorophore in the reference sample.

40. The system according to claim 39, wherein the reference sample includes a single stained control particle.

41. The system according to any one of claims 38 to 39, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to calculate the calibration scaling coefficient using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel.

42. The system according to any one of claims 38 to 40, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor calculates one or more calibration scaling coefficients and fluorophore abundances using a linear regression of the total signal from the reference sample and the fluorophore abundance.

43. The system according to any one of claims 34 to 42, wherein the calculated amount of fluorophores includes the number of fluorophores present on the particles.

44. The system according to any one of claims 33 to 42, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor calibrates the generated spectral data signal before spectral decomposition of the generated spectral data signal.

45. The system according to claim 44, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to rescale the signal intensity to be equivalent to the intensity of a single fluorophore.

46. The system according to claim 45, wherein the memory stores instructions, and when the instructions are executed by the processor, the memory stores instructions to rescale the signal intensity of the raw spectral data signal to the intensity of a single fluorophore by dividing the signal intensity of the calculated abundance of the fluorophore.

47. The memory stores an instruction, and when the instruction is executed by the processor, the processor receives the generated spectral data signal. [Math 3] (In the formula, X ａｒｂ is a single staining reference spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, and y ａｒｂ (This is the detector signal vector.) The system according to any one of claims 42 to 46, which is calibrated according to the following:

48. The system according to any one of claims 34 to 47, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor spectrally decomposes the spectral data signal to generate a decomposed data signal.

49. The system according to claim 48, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to normalize the reference data signal to 1.

50. The system according to any one of claims 48 to 49, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to rescale the decomposed spectral data signal.

51. The system according to any one of claims 48 to 50, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to determine the occurrence rate of a single stained control.

52. The memory stores an instruction, and when the instruction is executed by the processor, the processor receives the generated spectral data signal. [Math 4] (In the formula, X † This is the inverse problem spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, S is the sum of the data signals generated by the plurality of photodetector channels (S ｓｕｍ ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. ｍａｘ ) selected from, and y ａｒｂ (This is the detector signal vector.) The system according to any one of claims 48 to 51, which is calibrated according to the following:

53. The system according to any one of claims 34 to 52, wherein the particles comprise a plurality of different fluorophores.

54. The system according to claim 53, wherein the particles are multispectral beads.

55. The system according to claim 53, wherein the particles are cells labeled with a plurality of fluorophores.

56. The system according to any one of claims 53 to 55, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to calculate a calibration scaling factor for each of the different fluorophores.

57. The system according to claim 56, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor calculates the calibration scaling coefficient for each fluorophore using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel for each fluorophore.

58. The system according to any one of claims 53 to 57, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor calculates the calibration scaling coefficient by interpolating all signals with a reference calibration scaling coefficient to determine the equivalent amount of fluorophores.

59. The system according to any one of claims 34 to 58, wherein the light source includes a light-emitting diode.

60. The system according to any one of claims 34 to 58, wherein the light source includes a laser.

61. The system according to claim 60, wherein the light source includes a plurality of lasers.

62. The system according to any one of claims 34 to 61, wherein the light detection system includes a plurality of photodetectors.

63. The system according to claim 62, wherein the photodetector includes one or more photomultiplier tubes.

64. The system according to any one of claims 34 to 63, wherein the light detection system includes a photodetector array.

65. The system according to claim 64, wherein the photodetector array includes a photodiode.

66. The system according to claim 64, wherein the photodetector array includes charge-coupled elements.

67. A non-temporary computer-readable storage medium that stores instructions, An algorithm for irradiating particles of a sample containing fluorophores in a flowstream with a light source, An algorithm for detecting light from the irradiated particles using a photodetector and a photodetector, An algorithm for generating a spectral data signal in response to the detected light, An algorithm for calibrating the generated spectral data signal based on the calibration scaling coefficient and the calculated abundance of the fluorophore, Non-temporary computer-readable storage media, including [specific type of storage medium].

68. The non-temporary computer-readable storage medium according to claim 67, wherein the generated spectral data signal includes the sum of the data signals across a plurality of photodetector channels.

69. The non-temporary computer-readable storage medium according to claim 67, wherein the generated spectral data signal includes the data signal in a single photodetector channel.

70. The non-temporary computer-readable storage medium according to claim 69, wherein the single photodetector channel is the photodetector channel that exhibits the maximum signal intensity for the fluorophore.

71. A non-temporary computer-readable storage medium according to any one of claims 67 to 70, comprising an algorithm for calculating the calibration scaling coefficient.

72. An algorithm for generating reference data signals for one or more photodetector channels in response to light detected from an irradiated reference sample, An algorithm for calculating the calibration scaling coefficient based on the sum of the reference data signals and the amount of the fluorophore in the reference sample, A non-temporary computer-readable storage medium according to claim 71, including the following:

73. The non-temporary computer-readable storage medium according to claim 72, wherein the reference sample comprises single-stained control particles.

74. A non-temporary computer-readable storage medium according to any one of claims 72 to 73, comprising an algorithm for calculating the calibration scaling coefficient using the sum of the median values ​​of fluorophore-specific signals for each photodetector channel.

75. A non-temporary computer-readable storage medium according to any one of claims 72 to 74, comprising an algorithm for calculating one or more calibration scaling coefficients and fluorophore abundances using a linear regression of the total signal from the reference sample and the fluorophore abundance.

76. The non-temporary computer-readable storage medium according to any one of claims 67 to 75, wherein the calculated amount of fluorophores includes the number of fluorophores present on the particles.

77. A non-temporary computer-readable storage medium according to any one of claims 67 to 75, comprising an algorithm for calibrating the generated spectral data signal before spectral decomposition of the generated spectral data signal.

78. A non-temporary computer-readable storage medium according to claim 76, comprising an algorithm for rescaling the signal intensity to be equivalent to the intensity of a single fluorophore.

79. A non-temporary computer-readable storage medium according to claim 76, comprising an algorithm for rescaling the signal intensity to the intensity of a single fluorophore by dividing the raw spectral data signal by the signal intensity of the calculated abundance of the fluorophore.

80. The generated spectral data signal, [Math 5] (In the formula, X ａｒｂ This is the single-stain reference spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, and y ａｒｂ (This is the detector signal vector.) A non-temporary computer-readable storage medium according to any one of claims 77 to 79, comprising an algorithm for calibration in accordance with the above.

81. A non-temporary computer-readable storage medium according to any one of claims 67 to 75, comprising an algorithm for spectrally decomposing the spectral data signal to generate a decomposed data signal.

82. A non-temporary computer-readable storage medium according to claim 81, comprising an algorithm for normalizing the reference data signal to 1.

83. A non-temporary computer-readable storage medium according to any one of claims 81 to 82, wherein the memory stores instructions, and when an instruction is executed by the processor, the processor causes the processor to rescale the decomposed spectral data signal.

84. A non-temporary computer-readable storage medium according to any one of claims 81 to 83, comprising an algorithm for determining the occurrence rate of a single-stained control.

85. The generated spectral data signal, [Math 6] (In the formula, X † This is the inverse problem spectrum, CSF is the calibration scaling coefficient, f ｃａｌ This is a vector of fluorophore abundances, S is the sum of the data signals generated by the plurality of photodetector channels (S ｓｕｍ ), or the data signal (S) generated in the photodetector channel that shows the maximum signal intensity for the fluorophore. ｍａｘ ) selected from, and y ａｒｂ (This is the detector signal vector.) A non-temporary computer-readable storage medium according to any one of claims 81 to 84, comprising an algorithm for calibration in accordance with the above.

86. A non-temporary computer-readable storage medium according to any one of claims 67 to 85, wherein the particles comprise a plurality of different fluorophores.

87. The non-temporary computer-readable storage medium according to claim 86, wherein the particles are multispectral beads.

88. The non-temporary computer-readable storage medium according to claim 86, wherein the particles are cells labeled with a plurality of fluorophores.

89. A non-temporary computer-readable storage medium according to any one of claims 86 to 88, comprising an algorithm for calculating the calibration scaling coefficient for each of the plurality of different fluorophores.

90. A non-temporary computer-readable storage medium according to claim 89, comprising an algorithm for calculating the calibration scaling coefficient for each fluorophore using the sum of the median values ​​of the fluorophore-specific signals for each photodetector channel for each fluorophore.

91. A non-temporary computer-readable storage medium according to any one of claims 86 to 90, comprising an algorithm for calculating the calibration scaling coefficient by interpolating the entire signal with a reference calibration scaling coefficient to determine the equivalent fluorophore abundance.

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