Real-time adaptive method for specific resolution of fluorophores in a sample and system therefor - Patent Application 20070122997

Abstract

Aspects of the present disclosure include methods for spectrally resolving light from fluorophores with overlapping fluorescence spectra in a sample using adaptive measurement variance. The method, according to certain embodiments, includes detecting light from particles in the sample containing multiple fluorophores with overlapping fluorescence spectra using a light detection system, determining the measurement variance of the detected light for each particle, and spectrally resolving the light from each fluorophore in the sample using a weighted least-squares algorithm that uses the measurement variance determined for each particle. In some embodiments, the spectral unmixing matrix is ​​calculated by the weighted least-squares algorithm using an adaptive measurement variance model. Systems and integrated circuit devices (e.g., field-programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

Description

[Background technology]

[0001] A flow-type particle sorting system, such as a sorting flow cytometer, is used to sort particles in a fluid sample based on at least one measured characteristic of the particles.

[0002] In a flow-type particle sorting system, particles such as molecules, analyte-bound beads, or individual cells in a fluid suspension are passed in a stream through a detection region where a sensor detects particles contained in the stream of the type to be sorted. Upon detecting particles of the type to be sorted, the sensor triggers a sorting mechanism that selectively isolates the particles of interest.

[0003] Particle detection is typically performed by passing a fluid stream through a detection region where particles are exposed to illumination from one or more lasers, and the particles' light scattering and fluorescence properties are measured. Particles or their components can be labeled with fluorescent dyes to facilitate detection, and by labeling different particles or components with spectrally distinct fluorescent dyes, multiple different particles or components can be detected simultaneously. Detection is performed using one or more photosensors to facilitate independent measurement of the fluorescence of each distinct fluorescent dye.

[0004] In a flow cytometer, background noise and variations in system settings can result in changes in light detection. Data analysis in flow cytometry considers noise and changes to light illumination and detector settings as static and constant during an experiment. Summary of the Invention

[0005] Aspects of the present disclosure include methods for spectrally resolving light from fluorophores with overlapping fluorescence spectra in a sample using adaptive measurement variance. The method, according to certain embodiments, includes detecting light from particles in the sample containing multiple fluorophores with overlapping fluorescence spectra using a light detection system, determining the measurement variance of the detected light for each particle, and spectrally resolving the light from each fluorophore in the sample using a weighted least-squares algorithm that uses the measurement variance determined for each particle. In some embodiments, the spectral unmixing matrix is ​​calculated by the weighted least-squares algorithm using an adaptive measurement variance model. Systems and integrated circuit devices (e.g., field-programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0006] In embodiments, the measurement variance is determined for each particle. In some embodiments, the measurement variance is determined for each photodetector of the photodetection system, such as when the photodetection system includes 16 or more photodetectors or 32 or more photodetectors. In some cases, the measurement variance is a change in a photodetector gain parameter for one or more of the photodetectors. In some cases, the measurement variance is a change in a trigger threshold parameter for one or more of the photodetectors of the photodetection system. In some cases, the measurement variance is a change in a photodetection duration parameter (e.g., a measurement window gate) of each photodetector. In some cases, the measurement variance is a change in a photonic shot noise parameter detected by each photodetector.

[0007] In some embodiments, the measurement variance is calculated for each particle according to: V = (L × T) + (Q × Y)

[0008] where L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the photoelectron scaling factor, and Y is the photodetector signal intensity. In some cases, the method includes adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors. In some cases, the method includes adjusting the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors. In some cases, the method includes adjusting the baseline sampling variance in response to changes in trigger threshold of one or more of the photodetectors. For example, the baseline sampling variance can be adjusted at predetermined time intervals, such as every 6 hours, 12 hours, 18 hours, or 24 hours. In certain examples, the baseline sampling variance is adjusted whenever system conditions (e.g., temperature, background illumination) or settings (e.g., photodetector gain, light source intensity) are changed. In certain examples, the baseline sampling variance is adjusted when light is detected from a new sample (e.g., when a new sample is run in the particle analyzer, as described in more detail below). In certain embodiments, the method includes adjusting the scaling factor in response to changes in photodetector gain for one or more of the photodetectors. In some cases, the method includes generating a scaling factor calibration curve based on the photodetector gain for one or more of the photodetectors. In particular examples, the scaling factors are calculated from the scaling factor versus detector gain calibration curve using the gain setting of each photodetector at the time of measurement. In particular embodiments, the scaling factor versus detector gain calibration curve does not change over an extended period of time, e.g., over 6 hours or more, e.g., over 12 hours or more, including over 1 day or more.

[0009] In some embodiments, the sampling pulse duration is different for one or more of the photodetectors of the optical detection system. In some embodiments, the sampling pulse duration is the same for two or more of the photodetectors of the optical detection system. In certain embodiments, the sampling pulse duration is the same for each of the photodetectors of the optical detection system. In certain embodiments, a measurement variance is determined for each particle on each photodetector in real time.

[0010] In some embodiments, the sample of interest contains multiple fluorophores, each of which has a fluorescence spectrum that overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. In certain instances, the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 10 nm or more, for example, 25 nm or more, and 50 nm or more. In some cases, the fluorescence spectrum of one or more fluorophores in the sample overlaps with the fluorescence spectrum of two different fluorophores in the sample by, for example, 10 nm or more, for example, 25 nm or more, and 50 nm or more. In other embodiments, the sample of interest contains multiple fluorophores with non-overlapping fluorescence spectra. In these embodiments, the fluorescence spectrum of each fluorophore is adjacent to at least one other fluorophore by 10 nm or less, for example, 9 nm or less, for example, 8 nm or less, for example, 7 nm or less, for example, 6 nm or less, for example, 5 nm or less, for example, 4 nm or less, for example, 3 nm or less, for example, 2 nm or less, and 1 nm or less.

[0011] In embodiments, the method includes calculating a spectral unmixing matrix of the fluorescence spectrum of each fluorophore in the sample and using a weighted least squares algorithm and the measurement variance determined for each particle. In some cases, calculating the spectral unmixing matrix includes using a weighted least squares algorithm. In some cases, the weighted least squares algorithm is calculated according to:

[0012]

number

[0013] where y is the measured detector value from multiple photodetectors of the photodetection system for each particle (e.g., cell), a is the estimated fluorophore abundance, X is spillover, and W is:

[0014]

number

[0015] In some embodiments, each W ii is calculated as follows:

[0016]

number

[0017] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0018] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0019] In some embodiments, the weighted least squares algorithm is calculated using a Cholesky decomposition. In some cases, the weighted least squares algorithm is calculated using a Cholesky decomposition according to: X T WXa=XT Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0020] In certain embodiments, the method for spectrally resolving the light from each fluorophore comprises, for example, a weighted least squares algorithm (X) of each particle detected by the light detection system to sort particles in a sample in real time. T In some embodiments, the inverse of (X T WX) can be inverted using an iterative Newton-Raphson calculation according to T WX) -1 This includes approximating

[0021]

number

[0022] W G is a predetermined approximation of W determined from the baseline variance of each photodetector in the photodetection system (i.e., in the absence of particle-induced fluorescence). In some embodiments, the method further includes estimating the baseline variance of each photodetector. In some cases, the method includes determining the photodetector noise component (e.g., electronic noise, background light, etc.) using a single stained control sample. In certain examples, the method includes determining the variance of each photodetector before illuminating the sample with the light source. In other examples, the method includes estimating W, W before illuminating the sample with the light source. G In certain embodiments, the method includes determining a predetermined approximation of W G A0 -1 In these embodiments, the pre-calculated A0 -1 is the A of each particle detected by the optical detection system-1 can be used as a first approximation of

[0023] In other embodiments, the method for spectrally resolving light from each fluorophore includes using a Sherman-Morrison iterative inverse updater. In some cases, the method includes calculating A using the Sherman-Morrison equation.

[0024]

number

[0025] In a particular example, the method uses the Sherman-Morrison formula to calculate the inverse of the perturbation of A0. -1 In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the method includes calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0026]

number

[0027] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0028]

number

[0029] In these embodiments, each w iThe change to can be used to recalculate each A from A with each new weight matrix W (i.e., a different value than W). In some embodiments, the method includes executing a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0030]

number

[0031] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0032] In some embodiments, the method for spectrally decomposing light from each fluorophore comprises calculating a weighted least squares algorithm by matrix decomposition (i.e., factorization). In some cases, the method comprises LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In certain examples, the LU decomposition comprises Gaussian elimination. In other examples, the LU decomposition comprises a modified Cholesky decomposition, LDL decomposition, where D is a diagonal matrix. In certain embodiments, the weighted least squares algorithm (a) is calculated using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0033] In other embodiments, the method for spectrally decomposing light from each fluorophore includes calculating a weighted least squares algorithm by QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the weighted least squares algorithm (a) is calculated using QR factorization according to the following:

[0034]

number

[0035] In yet other embodiments, the method for spectrally decomposing the light from each fluorophore includes computing a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is performed by computing the product X - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, the weighted least squares algorithm (a) is calculated using singular value decomposition according to: z=U T y Σw=z a=Vw

[0036] In some embodiments, the abundance of one or more fluorophores in a sample (e.g., on particles) is determined by spectrally decomposing the light from each fluorophore in the sample based on the calculated spectral unmixing matrix of the fluorescence spectrum and the measurement variance determined for each particle. In some cases, the method includes estimating the abundance of one or more fluorophores on particles and identifying particles in the sample based on the estimated abundance of each fluorophore. In certain examples, the identified particles in the sample (e.g., cells in a biological sample) are sorted.

[0037] In some embodiments, the method includes illuminating the sample with a light source. In some cases, the sample is illuminated by the light source in the flow stream. In some cases, the light source includes one or more lasers. In some cases, the light is detected with a light detection system having multiple light detectors. In some embodiments, one or more of the light detectors are photomultiplier tubes. In some embodiments, one or more of the light detectors are photodiodes (e.g., avalanche photodiodes, APDs). In certain embodiments, the light detection system includes a light detector array, such as a light detector array having multiple photodiodes or a charge-coupled device (CCD).

[0038] Aspects of the present disclosure also include systems for carrying out the subject methods. The systems, according to certain embodiments, include a light source configured to illuminate particles of a sample having multiple fluorophores with overlapping fluorescence spectra, a light detection system having multiple photodetectors, and a processor including a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to determine a measured variance of the detected light for each particle and spectrally resolve the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measured variance determined for each particle.

[0039] In some embodiments, the memory includes instructions for determining a measurement variance of each photodetector for each particle. In some cases, the memory includes instructions for determining the measurement variance based on a change in a photodetector gain parameter. In some cases, the memory includes instructions for determining the measurement variance based on a change in a trigger threshold parameter for one or more of the photodetectors. In particular examples, the memory includes instructions for determining the measurement variance based on a change in a photodetection duration parameter of each photodetector for each particle. In particular examples, the memory includes instructions for determining the measurement variance based on a change in a photonic shot noise parameter detected by each photodetector.

[0040] In some embodiments, the system includes a processor having a memory operably coupled to the processor, the memory including stored instructions for calculating a measurement variance for each particle according to: V = (L × T) + (Q × Y)

[0041] where L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is a photoelectron scaling factor, and Y is the photodetector signal intensity. In some cases, the memory includes instructions for adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors. In some cases, the memory includes instructions for adjusting the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the photodetection system. In some cases, the memory includes instructions for adjusting the baseline sampling variance in response to changes in trigger threshold of one or more of the photodetectors of the photodetection system. In some cases, the memory includes instructions for adjusting the baseline sampling variance at predetermined time intervals, such as every 6 hours, 12 hours, or 24 hours.

[0042] In some embodiments, the memory includes instructions for adjusting the scaling factors in response to changes in photodetector gain for one or more of the photodetectors. In some cases, the memory includes instructions for generating a scaling factor calibration curve based on the photodetector gain for one or more of the photodetectors. In particular examples, the memory includes instructions for determining the scaling factors from the scaling factor versus detector gain calibration curve using the gain setting of each photodetector at the time of measurement. In particular embodiments, the scaling factor versus detector gain calibration curve remains unchanged over an extended period of time, e.g., over 6 hours or more, e.g., over 12 hours or more, including over 1 day or more.

[0043] In some embodiments, the memory includes instructions for generating different sampling pulse durations for one or more of the photodetectors of the optical detection system. In some embodiments, the sampling pulse duration is the same for two or more of the photodetectors of the optical detection system. In certain embodiments, the sampling pulse duration is the same for each of the photodetectors of the optical detection system. In certain embodiments, the memory includes instructions for determining the measured variance of each particle for each photodetector in real time.

[0044] In some embodiments, the memory includes instructions for spectrally resolving light from each fluorophore based on the calculated spectral unmixing matrix of the fluorescence spectrum and the measured variance determined for each particle. In some embodiments, the memory includes instructions for calculating the spectral unmixing matrix using a weighted least squares algorithm. In some embodiments, the memory includes instructions for calculating a weighted least squares algorithm according to:

[0045]

number

[0046] where y is the measured detector value from multiple photodetectors of the optical detection system for a particle (e.g., a cell), a is the estimated fluorophore abundance, X is spillover, and W is:

[0047]

number

[0048] In some embodiments, each W ii is calculated as follows:

[0049]

number

[0050] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0051] In some embodiments, the memory includes instructions for computing a weighted least squares algorithm using a Cholesky decomposition. In some cases, the memory includes instructions for computing a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0052] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: TWX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0053] In certain embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory being configured to calculate a weighted least squares (X) fit in a weighted least squares algorithm for each particle detected by the optical detection system to separate particles in a sample in real time. T In a particular example, the processor may include a processor including stored instructions that cause the processor to approximate the inverse of (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0054]

number

[0055] W G is a predetermined approximation of W determined from the variance of each photodetector in the light detection system. The variance, in some embodiments, has a photodetector noise component. For example, the photodetector noise component can include one or more of electronic noise and optical background light. In some embodiments, the variance of the photodetector is proportional to the measured intensity of light by the photodetector. In some embodiments, the photodetector noise component is estimated using a single stained control sample. In certain embodiments, the memory includes stored instructions that, when executed by the processor, cause the processor to automatically determine the variance of each photodetector before illuminating the sample with the light source. In other embodiments, the memory, when executed by the processor, causes the processor to automatically determine W before illuminating the sample with the light source. G In a particular embodiment, the memory includes stored instructions that, when executed by the processor, cause the processor to determine a predetermined W. G Using A0-1 In these embodiments, the pre-calculated A0 -1 is stored in memory and is the A of each particle detected by the optical detection system. -1 may be used by the processor as a first approximation of

[0056] In another embodiment, the system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to approximate a weighted least squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the memory includes instructions for calculating A using the Sherman-Morrison formula.

[0057]

number

[0058] In the specific example, the memory uses the Sherman-Morrison formula to calculate the inverse of the perturbation of A0. -1 In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T Optionally, the memory includes instructions for calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0059]

number

[0060] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i Tand A can be expressed as follows:

[0061]

number

[0062] In these embodiments, each w i A change to W can be used to recalculate each A from A with each new weight matrix W (i.e., a different value from W). In some embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to perform a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0063]

number

[0064] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0065] In another embodiment, a system includes a processor having a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm for each particle through matrix decomposition. In some cases, the memory includes instructions for LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, a system includes a processor having a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm (a) using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0066] In another embodiment, a system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm using QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the memory includes instructions for calculating a weighted least squares algorithm (a) using QR factorization according to:

[0067]

number

[0068] In yet another embodiment, a system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is a product X - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, the memory includes instructions for computing a weighted least squares algorithm (a) using singular value decomposition according to: z=U T y Σw=z a=Vw

[0069] In some embodiments, the system includes a processor having a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to estimate the abundance of one or more of the fluorophores in the sample with a weighted least squares algorithm using the measurement variance determined for each particle. In some embodiments, the memory includes instructions for identifying particles in the sample based on the estimated abundance of each fluorophore on the particle. In certain examples, the system includes a particle sorter for sorting the identified particles in the sample.

[0070] Also provided is an integrated circuit device programmed to spectrally resolve light from multiple fluorophores in a sample. According to certain embodiments, the integrated circuit is programmed to determine a measurement variance of light detected by a light detection system for each particle of a sample including multiple fluorophores with overlapping fluorescence spectra, and to spectrally resolve light from each fluorophore in the sample based on a calculated spectral unmixing matrix of the fluorescence spectra and the measurement variance determined for each particle. In some cases, the integrated circuit is programmed to determine the measurement variance for each photodetector of the light detection system for each particle. In some cases, the integrated circuit is programmed to determine the measurement variance based on a change in a photodetector gain parameter. In some cases, the integrated circuit is programmed to determine the measurement variance based on a change in a trigger threshold parameter for one or more of the photodetectors. In certain examples, the integrated circuit is programmed to determine the measurement variance based on a change in a light detection duration parameter for each photodetector for each particle. In certain examples, the integrated circuit is programmed to determine the measurement variance based on a change in a photonic shot noise parameter detected by each photodetector.

[0071] In some embodiments, the integrated circuit is programmed to calculate the measured variance of each particle according to: V = (L × T) + (Q × Y)

[0072] where L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is a photoelectron scaling factor, and Y is the photodetector signal intensity. In some cases, the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors. In some cases, the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the photodetection system. In some cases, the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in trigger threshold of one or more of the photodetectors of the photodetection system. In some cases, the integrated circuit is programmed to adjust the baseline sampling variance at predetermined time intervals, such as every 6 hours, 12 hours, or 24 hours.

[0073] In some embodiments, the integrated circuit is programmed to adjust the scaling factor in response to changes in photodetector gain for one or more of the photodetectors. In some cases, the integrated circuit is programmed to generate a scaling factor calibration curve based on the photodetector gain for one or more of the photodetectors. In particular examples, the integrated circuit is programmed to determine the scaling factor from the scaling factor vs. detector gain calibration curve using the gain setting of each photodetector at the time of measurement. In particular embodiments, the scaling factor vs. detector gain calibration curve remains unchanged over an extended period of time, for example, over 6 hours or more, for example, over 12 hours or more, including over one day or more.

[0074] In some embodiments, the integrated circuit is programmed to generate different sampling pulse durations for one or more of the photodetectors of the optical detection system. In some embodiments, the sampling pulse duration is the same for two or more of the photodetectors of the optical detection system. In certain embodiments, the sampling pulse duration is the same for each of the photodetectors of the optical detection system. In certain embodiments, the integrated circuit is programmed to determine a measured variance of each particle for each photodetector in real time.

[0075] In some embodiments, the integrated circuit is programmed to spectrally resolve the light from each fluorophore based on the calculated spectral unmixing matrix of the fluorescence spectrum and the measured variance determined for each particle. In some embodiments, the integrated circuit is programmed to calculate the spectral unmixing matrix using a weighted least squares algorithm. In some embodiments, the integrated circuit is programmed to calculate a weighted least squares algorithm according to:

[0076]

number

[0077] where y is the measured detector value from multiple photodetectors of the photodetection system for each particle (e.g., cell), a is the estimated fluorophore abundance, X is spillover, and W is:

[0078]

number

[0079] In some embodiments, each W ii is calculated as follows:

[0080]

number

[0081] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0082] In some embodiments, the integrated circuit is programmed to calculate a weighted least squares algorithm using a Cholesky decomposition. In some cases, the integrated circuit is programmed to calculate a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0083] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0084] In certain embodiments, an integrated circuit device performs a weighted least squares algorithm (X) analysis of each particle detected by the optical detection system to sort particles in a sample in real time. T WX). In the specific example, (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0085]

number

[0086] W G is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system. In some embodiments, the integrated circuit device is programmed to further estimate the variance of each photodetector. In particular examples, the integrated circuit device is programmed with an estimate of the photodetector noise component based on a single stained control sample. In other examples, the variance of each photodetector is programmed into the integrated circuit device before the sample is illuminated with the light source. In still other examples, an iterative Newton-Raphson calculation, W, is performed before the sample is illuminated with the light source. G A predetermined approximation of W at G Using A0 -1 In these embodiments, the pre-calculated A0 -1 is programmed into the integrated circuit device and is the A of each particle detected by the optical detection system. -1 can be used as a first approximation of

[0087] In other embodiments, the subject integrated circuit device is programmed to approximate a weighted least squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the integrated circuit device is programmed to calculate A using the Sherman-Morrison formula.

[0088]

number

[0089] In a particular example, the integrated circuit device calculates the inverse of the perturbation of A0 using the Sherman-Morrison formula. -1In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the integrated circuit is programmed to calculate ΔA (i.e., A-A) as the product of a column vector with each iteration W according to:

[0090]

number

[0091] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0092]

number

[0093] In these embodiments, each w i A change to W can be used to recalculate each A from A with each new weight matrix W (i.e., a value different from W). In some embodiments, the integrated circuit device is programmed to implement a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0094]

number

[0095] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A-1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0096] In other embodiments, the subject integrated circuit device is programmed to calculate a weighted least squares algorithm for each particle by matrix decomposition. In some cases, the integrated circuit device is programmed with instructions for LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm (a) using a modified Cholesky decomposition according to the following: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0097] In other embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm using QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm (a) using QR factorization according to:

[0098]

number

[0099] In yet another embodiment, the integrated circuit device is programmed to calculate a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is a product X - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, an integrated circuit device is programmed to compute a weighted least squares algorithm (a) using singular value decomposition according to: z=U T y Σw=z a=Vw

[0100] In some embodiments, the integrated circuit is programmed to estimate the abundance of one or more fluorophores in the sample based on the calculated spectral unmixing matrix and the measurement variance determined for each particle. In some embodiments, the integrated circuit is programmed to identify particles in the sample based on the estimated abundance of each fluorophore on the particle. In certain examples, the integrated circuit is programmed to generate a sorting decision based on the identified particles in the sample.

[0101] A non-transitory computer-readable storage medium having instructions with an algorithm for spectrally resolving light from multiple fluorophores in a sample is also described. According to certain embodiments, the non-transitory computer-readable storage medium has an algorithm for determining a measurement variance of light detected by a light detection system for each particle of a sample including multiple fluorophores with overlapping fluorescence spectra, and an algorithm for spectrally resolving light from each fluorophore in the sample using a weighted least squares algorithm using the measurement variance determined for each particle. In some cases, the non-transitory computer-readable storage medium includes instructions with an algorithm for determining a measurement variance for each photodetector of the light detection system for each particle. In some cases, the non-transitory computer-readable storage medium includes instructions with an algorithm for determining a measurement variance based on a change in a photodetector gain parameter. In some cases, the non-transitory computer-readable storage medium includes instructions with an algorithm for determining a measurement variance based on a change in a trigger threshold parameter for one or more of the photodetectors. In certain examples, the non-transitory computer-readable storage medium includes instructions with an algorithm for determining a measurement variance based on a change in a light detection duration parameter for each photodetector for each particle. In a particular example, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance based on a change in a photonic shot noise parameter detected by each photodetector.

[0102] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for calculating the measured variance of each particle according to: V = (L × T) + (Q × Y)

[0103] where L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is an optoelectronic scaling factor, and Y is the photodetector signal intensity. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in photonic shot noise parameters detected by one or more photodetectors of the photodetection system. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in trigger thresholds for one or more of the photodetectors of the photodetection system. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance at predetermined time intervals, such as every 6 hours, 12 hours, or 24 hours.

[0104] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the scaling factors in response to changes in photodetector gain in one or more of the photodetectors. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating a scaling factor calibration curve based on the photodetector gain for one or more of the photodetectors. In particular examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining the scaling factors from the scaling factor versus detector gain calibration curve using the gain setting of each photodetector during measurement. In particular embodiments, the scaling factor versus detector gain calibration curve does not change over an extended period of time, e.g., over 6 hours or more, e.g., over 12 hours or more, including over 1 day or more.

[0105] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating different sampling pulse durations for one or more of the photodetectors of the optical detection system. In some embodiments, the sampling pulse duration is the same for two or more of the photodetectors of the optical detection system. In certain embodiments, the sampling pulse duration is the same for each of the photodetectors of the optical detection system. In certain embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining the measured variance of each particle for each photodetector in real time.

[0106] In some embodiments, a non-transitory computer-readable storage medium comprises instructions having an algorithm for spectrally resolving light from each fluorophore based on a calculated spectral unmixing matrix of the fluorescence spectrum and the measured variance determined for each particle. In some embodiments, the non-transitory computer-readable storage medium comprises instructions having an algorithm for calculating the spectral unmixing matrix using a weighted least squares algorithm. In some embodiments, the non-transitory computer-readable storage medium comprises instructions having an algorithm for calculating a weighted least squares algorithm according to:

[0107]

number

[0108] where y is the measured detector value from multiple photodetectors of the photodetection system for each particle (e.g., cell), a is the estimated fluorophore abundance, X is spillover, and W is:

[0109]

number

[0110] In some embodiments, each W ii is calculated as follows:

[0111]

number

[0112] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0113] In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm using a Cholesky decomposition. In some cases, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0114] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0115] In certain embodiments, the non-transitory computer-readable storage medium includes a memory for storing a weighted least squares algorithm (X) of each particle detected by the optical detection system to sort particles in a sample in real time. TWX). In particular, we include an algorithm for approximating the inverse of (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0116]

number

[0117] W G is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm that further estimates the variance of each photodetector. In particular examples, the non-transitory computer-readable storage medium includes an algorithm having an estimate of the photodetector noise component based on a single stained control sample. In other examples, the variance of each photodetector is programmed into the non-transitory computer-readable storage medium before the sample is illuminated with the light source. In yet other examples, an iterative Newton-Raphson calculation, W, is performed before the sample is illuminated with the light source. G In a particular embodiment, the non-transitory computer-readable storage medium is programmed with a predetermined approximation of W in G A0 -1 In these embodiments, the algorithm for pre-calculating A0 -1 is programmed into a non-transitory computer-readable storage medium and is used to calculate the A of each particle detected by the optical detection system. -1 can be used as a first approximation of

[0118] In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for approximating a weighted least-squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating A using the Sherman-Morrison formula.

[0119]

number

[0120] In particular examples, the non-transitory computer-readable storage medium includes an algorithm for calculating the inverse of a perturbation of A using the Sherman-Morrison formula. In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0121]

number

[0122] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0123]

number

[0124] In these embodiments, each w i A change to W may be used to recalculate each A from A with each new weight matrix W (i.e., a different value than W). In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for performing a Sherman-Morrison iterative inverse updater to approximate a spectral unmixing matrix according to:

[0125]

number

[0126] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0127] In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a weighted least squares algorithm for each particle through matrix decomposition. In some cases, the non-transitory computer-readable storage medium includes an algorithm for LU matrix decomposition, in which a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a weighted least squares algorithm (a) using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0128] In other embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm with QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm (a) using QR factorization according to:

[0129]

number

[0130] In yet another embodiment, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm (a) using singular value decomposition according to: z=U T y Σw=z a=Vw

[0131] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for estimating the abundance of one or more of the fluorophores in the sample based on the calculated spectral unmixing matrix and the measurement variance determined for each particle. In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for identifying particles in the sample based on the estimated abundance of each fluorophore on the particle. In certain examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating a sorting decision based on the identified particles in the sample. [Brief explanation of the drawings]

[0132] The invention may be best understood from the following detailed description when read in conjunction with the accompanying drawings, in which:

[0133] [Figure 1A] 1 illustrates a flow diagram for spectrally decomposing light using adaptive measurement dispersion, according to certain embodiments. [Figure 1B] 1 shows a flow diagram for spectrally resolving light from fluorophores in a sample using an adaptive spectral unmixing algorithm, according to certain embodiments. [Figure 2A-1] 10 illustrates a comparison of the spread of unmixed data across photodetectors within a range of photodetector gain settings, according to certain embodiments. [Figure 2A-2] 10 illustrates a comparison of the spread of unmixed data across photodetectors within a range of photodetector gain settings, according to certain embodiments. [Figure 2A-3] 10 illustrates a comparison of the spread of unmixed data across photodetectors within a range of photodetector gain settings, according to certain embodiments. [Figure 2A-4] 10 illustrates a comparison of the spread of unmixed data across photodetectors within a range of photodetector gain settings, according to certain embodiments. [Figure 2B-1] 10A-10C show diagrams illustrating a comparison of the spread of unmixed data when calculated using an ordinary least squares algorithm and an adaptive weighted least squares algorithm, in accordance with certain embodiments. [Figure 2B-2] 10A-10C show diagrams illustrating a comparison of the spread of unmixed data when calculated using an ordinary least squares algorithm and an adaptive weighted least squares algorithm, in accordance with certain embodiments. [Figure 3A-1] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3A-2] 1 illustrates an image-enabled particle sorter in accordance with certain embodiments. [Figure 3B] 1 illustrates image-enabled particle sorting data processing in accordance with certain embodiments. [Figure 4A] FIG. 1 illustrates a functional block diagram of a particle analysis system in accordance with certain embodiments. [Figure 4B] 1 illustrates a flow cytometer according to certain embodiments. [Figure 5] FIG. 1 illustrates a functional block diagram of an example particle analyzer control system in accordance with certain embodiments. [Figure 6A] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 6B] 1 illustrates a schematic diagram of a particle sorter system in accordance with certain embodiments. [Figure 7] 1 illustrates a block diagram of a computing system in accordance with certain embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0134] Aspects of the present disclosure include methods for spectrally resolving light from fluorophores with overlapping fluorescence spectra in a sample using adaptive measurement variance. The method, according to certain embodiments, includes detecting light from particles in the sample containing multiple fluorophores with overlapping fluorescence spectra using a light detection system, determining the measurement variance of the detected light for each particle, and spectrally resolving the light from each fluorophore in the sample using a weighted least-squares algorithm that uses the measurement variance determined for each particle. In some embodiments, the spectral unmixing matrix is ​​calculated by the weighted least-squares algorithm using an adaptive measurement variance model. Systems and integrated circuit devices (e.g., field-programmable gate arrays) for implementing the subject methods are also described. Non-transitory computer-readable storage media are also provided.

[0135] Before describing the present invention in more detail, it is to be understood that this invention is not limited to particular embodiments described, as such may, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting, since the scope of the present invention will be limited only by the appended claims.

[0136] Where a range of values ​​is provided, unless the context clearly dictates otherwise, it is understood that each intervening value, to the tenth of the unit of the lower limit, between the upper and lower limits of that range, and any other stated or intervening value in that stated range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit in the stated range. Where the stated range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0137] Certain ranges are described herein by numerical values ​​preceded by the term "about." The term "about" is used herein to literally support the exact number it precedes, as well as a number that is near or approximately the number preceded by the term. In determining whether a number is near or approximately a specifically stated number, a number not stated to be near or approximately may be a number that, in the context in which it is presented, represents a substantial equivalent to the specifically stated number.

[0138] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. Although any methods and materials similar or equivalent to those described herein can also be used in the practice or testing of the present invention, representative exemplary methods and materials are now described.

[0139] All publications and patents cited herein are incorporated by reference to disclose and describe the methods and / or materials for which the publications are cited, as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. The citation of any publication is for its disclosure prior to the filing date and should not be construed as an admission that the present invention is not entitled to antedate such publication by virtue of prior invention. Further, the publication dates provided may be different from the actual publication dates, which may need to be independently confirmed.

[0140] It should be noted that, as used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. It should be further noted that the claims may be drafted to exclude any optional element. Accordingly, this statement is intended to serve as a predicate for use of exclusive terminology, such as "solely," "only," and the like, in connection with the recitation of claim elements or the use of a "negative" limitation.

[0141] As will be apparent to those skilled in the art upon reading this disclosure, each of the individual embodiments described and illustrated herein has individual components and features which may be readily separated or combined with the features of any of the other several embodiments without departing from the scope or spirit of the invention. Any recited method can be carried out in the order of events recited or in any other order which is logically possible.

[0142] Although the apparatus and methods are described for grammatical fluidity with functional descriptions, it is to be clearly understood that the claims should not be construed as necessarily limited by "means" or "step" limitation constructions unless expressly formulated under 35 U.S.C. § 112, but rather should be given the full scope of meaning and equivalents of the definitions provided by the claims under the doctrine of equivalents, and that if a claim is expressly formulated under 35 U.S.C. § 112, the full statutory equivalents under 35 U.S.C. § 112 should be given.

[0143] As summarized above, the present disclosure provides a method for spectrally resolving fluorophores in a sample. In further describing embodiments of the present disclosure, the method for spectrally resolving fluorophores in a sample is first described in more detail, including estimating the abundance of each fluorophore in the sample (e.g., on particles in the sample) and identifying and sorting particles based on the estimated abundance of each fluorophore. Next, a system and integrated circuit device programmed to implement the subject method by calculating a spectral unmixing matrix using adaptive measurement variance are described. A non-transitory computer-readable storage medium is also provided.

[0144] How to spectrally resolve light from fluorophores using adaptive measurement dispersion Aspects of the present disclosure include methods for spectrally resolving light from fluorophores with overlapping fluorescence spectra in a sample using an adaptive measurement dispersion approach. As described in more detail below, the subject methods provide for greater accuracy in spectral resolution of overlapping fluorescence in a sample by accounting (e.g., in real time) for variations in measurement noise associated with sample type, instrument settings, and overall experimental conditions. In certain examples, the methods described herein provide for explicitly calculating weighting components of a spectral unmixing algorithm for each particle whose light is detected (e.g., passing through an interrogation region of a flow stream in a flow cytometer). The adaptive measurement dispersion approach takes into account particle intensities across multiple fluorescence channels, current instrument settings (e.g., detector gain, trigger threshold, measurement event duration), as well as sample characteristics (which may have various noise characteristics, such as increased optical background noise due to unbound fluorophores in solution). In some embodiments, the method includes a weighted least-squares algorithm for spectrally resolving light from each fluorophore in a sample. In some cases, weighted least squares algorithms spectrally unmix input measurements (e.g., fluorescence signals across multiple photodetectors) known to exhibit complex resilience (i.e., have different variances). FIG. 1A shows a flow diagram for spectrally unmixing light using adaptive measurement variance, according to certain embodiments. As described in more detail below, data signals are generated from detected light from illuminated particles in a flow stream (101), and an adaptive noise model is determined for each event (i.e., measurement variance is calculated for each particle) (102). The data signals are spectrally unmixed using the calculated measurement variance (103), such as a weighted least squares spectral unmixing algorithm that applies the calculated measurement variance.

[0145] The term "spectrally resolved" is used herein in its conventional sense to refer to spectrally distinguishing each fluorophore in a sample by assigning or attributing overlapping wavelengths of light to each contributing fluorophore. In embodiments, the overlapping spectral components of fluorescence attributable to each fluorophore are determined by calculating a spectral unmixing matrix (as described in more detail below). In some embodiments, the sample of interest has multiple fluorophores, the fluorescence spectrum of each fluorophore overlapping with the fluorescence spectrum of at least one other fluorophore in the sample. In some cases, the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 5 nm or more, e.g., 10 nm or more, e.g., 25 nm or more, and including 50 nm or more. In certain examples, the fluorescence spectrum of one or more fluorophores in the sample overlaps with the fluorescence spectra of two or more different fluorophores in the sample, e.g., each overlap of the fluorescence spectra is 5 nm or more, e.g., 10 nm or more, e.g., 25 nm or more, including 50 nm or more. In other embodiments, the sample of interest includes multiple fluorophores with non-overlapping fluorescence spectra. In these embodiments, the fluorescence spectrum of each fluorophore is adjacent to at least one other fluorophore by 10 nm or less, such as 9 nm or less, for example 8 nm or less, for example 7 nm or less, such as 6 nm or less, for example 5 nm or less, such as 4 nm or less, for example 3 nm or less, such as 2 nm or less, and including 1 nm or less.

[0146] In carrying out the subject methods, a sample is illuminated with a light source, and light from the sample is detected by a light detection system having multiple photodetectors. In some embodiments, the sample is a biological sample. The term "biological sample" is used in its conventional sense, and refers to a whole organism, whole plant, whole fungus, or a subset of animal tissues, cells, or component parts, as may be found in, for example, blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage fluid, amniotic fluid, umbilical cord blood, urine, vaginal fluid, and semen. Thus, a "biological sample" refers to both intact organisms or subsets of their tissues, as well as homogenates made from organisms or subsets of their tissues, including, but not limited to, lysates or extracts, such as plasma, serum, cerebrospinal fluid, lymph, skin, respiratory, gastrointestinal, cardiovascular, and genitourinary tract sections, tears, saliva, milk, blood cells, tumors, and organs. A biological sample may be any type of biological tissue, including both healthy and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or a derivative thereof, e.g., plasma, tears, urine, semen, etc., and in some cases the sample is a blood sample, including whole blood, such as blood obtained from venipuncture or finger stick (which may or may not be combined with any reagents, such as preservatives, anticoagulants, etc., prior to assay).

[0147] In certain embodiments, the source of the sample is a "mammal," a term used broadly to describe organisms belonging to the class Mammalia, including the orders Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some cases, the subject is human. The present methods may be applied to samples obtained from human subjects of both genders and at any stage of development (i.e., newborn, infant, juvenile, adolescent, adult), and in certain embodiments, the human subject is a juvenile, adolescent, or adult. While the present invention may be applied to samples from human subjects, it should be understood that the present methods may also be performed on samples from other animal subjects (i.e., "non-human subjects"), such as, but not limited to, birds, mice, rats, dogs, cats, livestock, and horses.

[0148] In performing the subject methods, a sample (e.g., in a flow stream of a flow cytometer) is illuminated with light from a light source. In some embodiments, the light source is a broadband light source, emitting light having a wide range of wavelengths, including, for example, those ranging from 50 nm or greater, 100 nm or greater, 150 nm or greater, 200 nm or greater, 250 nm or greater, 300 nm or greater, 350 nm or greater, 400 nm or greater, and 500 nm or greater. For example, any suitable broadband light source emits light having a wavelength between 200 nm and 1500 nm. Another example of a suitable broadband light source includes a light source that emits light having a wavelength between 400 nm and 1000 nm. Where the method includes irradiating with a broadband light source, the broadband light source protocol of interest can include, but is not limited to, a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a stabilized fiber-coupled broadband light source, a broadband LED with a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integrated white light source, or any combination thereof, among other broadband light sources.

[0149] In other embodiments, the method comprises irradiating a narrowband light source that emits a specific wavelength or narrow range of wavelengths, for example, a light source that emits light in a narrow range, such as a range of 50 nm or less, for example, 40 nm or less, for example, 30 nm or less, for example, 25 nm or less, for example, 20 nm or less, for example, 15 nm or less, for example, 10 nm or less, for example, 5 nm or less, for example, 2 nm or less (including light sources that emit specific wavelengths of light (i.e., monochromatic light)). When the method comprises irradiating 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.

[0150] In certain embodiments, the method includes irradiating the sample with one or more lasers. As noted above, the type and number of lasers will depend on the sample and the desired light to be collected, and may be gas lasers such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers, or combinations thereof. In other examples, the method includes irradiating the flowstream with a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the method includes irradiating the flowstream with a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof. In yet another example, the method includes irradiating the flowstream with a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:yCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0151] The sample may be illuminated with one or more of the above-mentioned light sources, including two or more light sources, three or more light sources, four or more light sources, five or more light sources, etc., including ten or more light sources. The light source may include any combination of light source types. For example, in some embodiments, the method includes illuminating the sample of the flow stream with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0152] The sample may be irradiated with a wavelength in the range of 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm (including 400 nm to 800 nm). For example, if the light source is a broadband light source, the sample may be irradiated with a wavelength in the range of 200 nm to 900 nm. In other cases, where the light source includes multiple narrowband light sources, the sample may be irradiated with a specific wavelength in the range of 200 nm to 900 nm. For example, the light source may be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light having a wavelength range of 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array), and the sample is irradiated with a specific wavelength in the range of 200 nm to 700 nm, such as a laser array including the gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers described above.

[0153] When two or more light sources are used, the sample can be illuminated by the light sources simultaneously, sequentially, or a combination thereof. For example, each light source can illuminate the sample simultaneously. In other embodiments, the flow stream is illuminated sequentially by each of the light sources. When two or more light sources are used to sequentially illuminate the sample, the time for which each light source illuminates the sample can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include irradiating the sample with a light source (e.g., a laser) for a period ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, inclusive. In embodiments in which the sample is illuminated sequentially with two or more light sources, the duration for which the sample is illuminated by each light source can be the same or different.

[0154] The time period between illumination by each light source can also be independently variable, optionally separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and 60 microseconds or more. For example, the time period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time period between illumination by each light source is 10 microseconds. In embodiments in which the sample is illuminated sequentially by more than two (i.e., three or more) light sources, the delay between illumination by each light source can be the same or different.

[0155] The sample can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the sample in the sample with a light source. In other examples, the sample is illuminated by the light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0156] Depending on the light source, the sample may be illuminated from a variety of distances, including 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, such as 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more. Also, the angle or illumination may be variable between 10° and 90°, for example 15° and 85°, for example 20° and 80°, for example 25° and 75°, for example 30° and 60°, for example 90°.

[0157] In certain embodiments, the method includes irradiating the sample with two or more frequency-shifted light beams. As described above, a light beam generator component having a laser and an acousto-optical device for frequency-shifting the laser light may be used. In these embodiments, the method includes irradiating the acousto-optical device with a laser. Depending on the desired wavelength of light produced in the output laser beam (e.g., for use in irradiating the sample in the flow stream), the laser may have a specific wavelength between 200 nm and 1500 nm, e.g., between 250 nm and 1250 nm, e.g., between 300 nm and 1000 nm, e.g., between 350 nm and 900 nm (including 400 nm and 800 nm). The acousto-optical device may be irradiated with one or more lasers, e.g., two or more lasers, e.g., three or more lasers, e.g., four or more lasers, e.g., five or more lasers, or may include ten or more lasers. The lasers may include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optical device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0158] When two or more lasers are used, the acousto-optical device can be illuminated by the lasers simultaneously, sequentially, or a combination thereof. For example, the acousto-optical device can be illuminated by each of the lasers simultaneously. In other embodiments, the acousto-optical device is illuminated sequentially by each of the lasers. When two or more lasers are used to sequentially illuminate the acousto-optical device, the time for which each laser illuminates the acousto-optical device can independently be 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 30 microseconds or more, including 60 microseconds or more. For example, the method can include illuminating the acousto-optical device with the laser for a period ranging from 0.001 microseconds to 100 microseconds, e.g., 0.01 microseconds to 75 microseconds, e.g., 0.1 microseconds to 50 microseconds, e.g., 1 microsecond to 25 microseconds, inclusive. In embodiments in which the acousto-optic device is illuminated sequentially with two or more lasers, the duration for which the acousto-optic device is illuminated by each laser may be the same or different.

[0159] The time between illumination by each laser can also be independently variable, optionally separated by a delay of 0.001 microseconds or more, e.g., 0.01 microseconds or more, e.g., 0.1 microseconds or more, e.g., 1 microsecond or more, e.g., 5 microseconds or more, e.g., 10 microseconds or more, e.g., 15 microseconds or more, e.g., 30 microseconds or more, and 60 microseconds or more. For example, the time period between illumination by each light source can range from 0.001 microseconds to 60 microseconds, e.g., 0.01 microseconds to 50 microseconds, e.g., 0.1 microseconds to 35 microseconds, e.g., 1 microsecond to 25 microseconds, and e.g., 5 microseconds to 10 microseconds. In certain embodiments, the time between illumination by each laser is 10 microseconds. In embodiments in which the acousto-optic device is illuminated sequentially by more than two (i.e., three or more) lasers, the delay between illumination by each laser can be the same or different.

[0160] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the acousto-optic device with a laser. In other examples, the acousto-optic device is illuminated with a laser at discrete intervals, such as every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or some other interval.

[0161] Depending on the laser, the acousto-optic device may be illuminated from a variety of distances, including 0.01 mm or more, such as 0.05 mm or more, for example 0.1 mm or more, for example 0.5 mm or more, for example 1 mm or more, for example 2.5 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 25 mm or more, and 50 mm or more, and the angle or illumination may be variable in the range of an angle between 10° and 90°, for example 15° and 85°, for example 20° and 80°, for example 25° and 75°, for example 30° and 60°, for example 90°.

[0162] In an embodiment, the method includes applying a high frequency drive signal to an acousto-optic device to generate an angularly deflected laser beam. Two or more high frequency drive signals may be applied to the acousto-optic device to generate an output laser beam having a desired number of angularly deflected laser beams (including, for example, 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 one hundred or more high frequency drive signals).

[0163] The angularly deflected laser beams produced by the high frequency drive signals each have an intensity based on the amplitude of the applied high frequency drive signal. In some embodiments, the method includes applying high frequency drive signals having an amplitude sufficient to produce an angularly deflected laser beam having a desired intensity. In some cases, each of the applied high frequency drive signals independently has an amplitude of about 0.001 V to about 500 V, e.g., about 0.005 V to about 400 V, e.g., about 0.01 V to about 300 V, e.g., about 0.05 V to about 200 V, e.g., about 0.1 V to about 100 V, e.g., about 0.5 V to about 75 V, e.g., about 1 V to about 50 V, e.g., about 2 V to about 40 V, e.g., about 3 V to about 30 V, including about 5 V to about 25 V. In some embodiments, each of the applied high frequency drive signals has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0164] In these embodiments, the angularly deflected laser beams within the output laser beam are spatially separated. Depending on the applied high frequency drive signal and the desired illumination profile of the output laser beam, the angularly deflected laser beams may be spaced apart by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the angularly deflected laser beams overlap with adjacent angularly deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0165] In certain examples, the flow stream may be a photocatalytic device such as those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as those described in U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, 10,620,111, and the like. and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference, and cells in the flow stream are illuminated with multiple beams of frequency-shifted light and imaged by fluorescence imaging using radio frequency tagged emission (FIRE) to generate frequency-encoded images as described in U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0166] As described above, in embodiments, light from the illuminated sample is conveyed to a light detection system and measured by multiple light detectors, as described in more detail below. In some embodiments, the method includes measuring the collected light over a wavelength range (e.g., 200 nm to 1000 nm). For example, the method may include collecting a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the method includes measuring the collected light at one or more specific wavelengths. For example, the collected light may be measured at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method comprises measuring a wavelength of light corresponding to the fluorescence peak wavelength of the fluorophore, hi some embodiments, the method comprises measuring light collected across the fluorescence spectrum of each fluorophore in the sample.

[0167] The collected light may be measured continuously or at discrete intervals. In some cases, the method includes measuring the light continuously. In other examples, the light is measured at discrete intervals, including measuring the light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and 1000 milliseconds, or at some other interval.

[0168] Measurements of the collected light may be made one or more times during the subject method, e.g., two or more times, e.g., three or more times, e.g., five or more times, and ten or more times. In certain embodiments, light propagation is measured two or more times, and the data for a particular instance is averaged.

[0169] Light from the sample may be measured at one or more wavelengths, for example 5 or more different wavelengths, for example 10 or more different wavelengths, for example 25 or more different wavelengths, for example 50 or more different wavelengths, for example 100 or more different wavelengths, for example 200 or more different wavelengths, for example 300 or more different wavelengths, including measuring light collected at 400 or more different wavelengths.

[0170] In embodiments, the method includes spectrally resolving light from each fluorophore in the sample. 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, spectrally resolving light from each fluorophore includes calculating a spectral unmixing matrix of the fluorescence spectra for each of a plurality of fluorophores having overlapping fluorescence in the sample detected by the light detection system. As described in more detail below, spectrally resolving light from each fluorophore and calculating the spectral unmixing matrix for each fluorophore can be used to estimate the abundance of each fluorophore in the sample. In certain embodiments, the abundance of each fluorophore associated with a target particle can be determined. The abundance of each fluorophore associated with a target particle can be used in identifying and classifying the particle. In some cases, the identified or classified particles can be used to sort target particles (e.g., cells) in the sample. In certain embodiments, spectrally resolving fluorophores in the sample, such as by calculating spectral unmixing, is performed fast enough to sort particles in real time after detection by the light detection system.

[0171] In some embodiments, measurement variance is determined for each particle. The term "measurement variance" is used herein in its conventional sense to refer to variation (e.g., noise) that occurs during the data acquisition process, such as variations in light detection, data signal generation, or illumination of the sample. In some cases, measurement variance is the variance of a statistical distribution measured when a particle is repeatedly measured. In some cases, measurement variance is estimated (e.g., predicted) using a statistical model that includes different parameters of the light detection system. In some cases, measurement variance includes changes in photodetector gain in one or more of the light detectors of the light detection system. In some cases, measurement variance includes changes in trigger thresholds of one or more of the light detectors of the light detection system. In some cases, measurement variance includes changes in light detection duration of each light detector for each particle. In some cases, measurement variance includes changes in photonic shot noise detected by each light detector for each particle. In certain embodiments, methods of the present disclosure use an adaptive noise model in which spectrally resolving light from a sample includes calculating measurement noise associated with sample type, light detection and illumination system settings, and experimental conditions in real time for each particle in the sample. For example, in a flow cytometer, measurement variance can include baseline noise, photonic (Poisson) shot noise, or random fluctuations in the excitation and collection efficiencies of the light detection system.

[0172] In embodiments, the measured variance of the detected light is determined for each particle in the sample. In some cases, the optical detection system (described in more detail below) includes multiple optical detectors, and the measured variance is determined for each particle in one or more of the optical detectors, such as two or more, such as three or more, such as four or more, such as five or more, such as ten or more, such as fifteen or more, such as twenty-five or more, including determining the measured variance for each particle in fifty or more of the optical detectors. Depending on the number of optical detectors in the optical detection system, the measured variance can be determined for each particle in the sample, including 5% or more, such as 10% or more, such as 20% or more, such as 30% or more, such as 40% or more, such as 50% or more, such as 60% or more, such as 70% or more, such as 80% or more, such as 90% or more of the optical detectors. In certain examples, the measured variance of the detected light is determined for each particle in all (i.e., 100%) of the optical detectors in the optical detection system.

[0173] In some embodiments, the measured variance is the variance of the photodetector gain in one or more of the photodetectors. In some cases, the determined variance may be a change in photodetector gain including 0.0001 mV or more, such as 0.0005 mV or more, for example 0.001 mV or more, for example 0.005 mV or more, for example 0.01 mV or more, for example 0.05 mV or more, for example 0.1 mV or more, for example 0.5 mV or more, such as 1 mV or more, for example 2 mV or more, for example 3 mV or more, for example 4 mV or more, for example 5 mV or more, for example 10 mV or more, for example 50 mV or more, for example 100 mV or more, for example 500 mV or more, and 1000 mV or more. For example, the measurement variance may be a change in photodetector gain of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, including when the measurement variance is a change in photodetector gain of 2% or more. In particular examples, the determined measurement variance is an increase in photodetector gain of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, such as 1% or more, including 2% or more. In certain examples, the determined measurement variance is a reduction in photodetector gain of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including 2% or more.

[0174] In some embodiments, the measurement variance is the variance of the trigger threshold of one or more of the photodetectors of the photodetection system. For example, the measurement variance may be a change in the trigger threshold of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including when the measurement variance is a change in the trigger threshold of 2% or more. In certain examples, the determined measurement variance is an increase in the trigger threshold of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, including 2% or more. In certain examples, the determined measurement variance is a decrease in the trigger threshold of 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, including 2% or more.

[0175] In some embodiments, the measurement variance is the variance of the light detection duration (e.g., measurement window gate) of one or more of the light detectors of the light detection system. In some cases, the determined variance can be a change in light detection duration of 0.0001 μs or more, such as 0.0005 μs or more, for example 0.001 μs or more, for example 0.005 μs or more, such as 0.01 μs or more, for example 0.05 μs or more, such as 0.1 μs or more, for example 0.5 μs or more, such as 1 μs or more, for example 2 μs or more, for example 3 μs or more, such as 4 μs or more, for example 5 μs or more, for example 10 μs or more, such as 50 μs or more, for example 100 μs or more, for example 500 μs or more, and including 1000 μs or more. For example, the measurement variance can be a change in light detection duration of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, including when the measurement variance is a change in light detection duration of 2% or more. In certain examples, the determined measurement variance is an increase in light detection duration of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, including 2% or more. In certain examples, the determined measurement variance is a reduction in the light detection period of 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, including 2% or more.

[0176] In some embodiments, the measurement variance is the variance of the photonic shot noise detected by each photodetector. For example, the measurement variance may be a change in photonic shot noise of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, for example 1% or more, including when the measurement variance is a change in photonic shot noise of 2% or more. In certain examples, the determined measurement variance is an increase in photonic shot noise of 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, for example 1% or more, including 2% or more. In certain examples, the determined measurement variance is a reduction in photonic shot noise of 0.0001% or more, such as 0.0005% or more, for example 0.001% or more, such as 0.005% or more, for example 0.01% or more, such as 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, including 2% or more.

[0177] In some embodiments, the measurement variance depends on the intensity of the data signal generated from the detected light. In some cases, the measurement variance depends linearly on the data signal intensity. In other examples, the measurement variance depends quadratically on the data signal intensity. In particular examples, the measurement variance is independent of (i.e., does not depend on) the intensity of the data signal generated from the detected light.

[0178] In some embodiments, the method includes calculating the measured variance of each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the optoelectronic scaling factor, Y is the photodetector signal intensity.

[0179] In some embodiments, the baseline sampling variance is adjusted in response to a change in photodetector gain in one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance increases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more, in response to a change in photodetector gain in one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance decreases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to changes in photodetector gain in one or more photodetectors of the photodetection system.

[0180] In some embodiments, the baseline sampling variance is adjusted in response to a change in the photonic shot noise detected by one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance increases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, for example 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, for example 10% or more, for example 25% or more, including 50% or more, in response to a change in the photonic shot noise of one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance decreases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to changes in photonic shot noise in one or more photodetectors of the photodetection system.

[0181] In some embodiments, the baseline sampling variance is adjusted in response to a change in the trigger threshold of one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance increases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, such as 2% or more, for example 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in the trigger threshold of one or more photodetectors of the optical detection system. In some cases, the baseline sampling variance decreases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in trigger threshold in one or more photodetectors of the photodetection system.

[0182] In some embodiments, the baseline sampling variance can be adjusted when a change in one or more of the photodetector gain, trigger threshold, and photonic shot noise is detected, or a predetermined period thereafter. In some cases, the baseline sampling variance can be adjusted immediately after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected. In other cases, the baseline sampling variance is adjusted 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more, after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected, including adjusting the baseline sampling variance 60 minutes or more after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected.

[0183] In certain embodiments, the baseline sampling variance is adjusted at predetermined time intervals, including, for example, every 0.01 minute or more, for example, every 0.05 minutes or more, for example, every 0.1 minute or more, for example, every 0.5 minutes or more, for example, every 1 minute or more, for example, every 5 minutes or more, for example, every 10 minutes or more, for example, every 15 minutes or more, for example, every 30 minutes or more, for example, every 60 minutes or more, for example, every 2 hours or more, for example, every 4 hours or more, for example, every 8 hours or more, for example, every 12 hours or more, for example, every 16 hours or more, for example, every 20 hours or more, and every 24 hours or more.

[0184] In some embodiments, the scaling factor is adjusted in response to a change in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the scaling factor increases by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more in response to a change in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the scaling factor decreases by 0.00001% or more, for example, 0.0001% or more, for example, 0.0005% or more, for example, 0.001% or more, for example, 0.005% or more, for example, 0.01% or more, for example, 0.05% or more, for example, 0.1% or more, for example, 0.5% or more, for example, 1% or more, for example, 2% or more, for example, 5% or more, for example, 10% or more, for example, 25% or more, including 50% or more, in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In certain embodiments, a calibration curve is generated for the scaling factor based on the photodetector gain of one or more of the photodetectors of the photodetection system. In certain examples, the scaling factor is calculated from a scaling factor vs. detector gain calibration curve using the gain setting of each photodetector at the time of measurement. In certain embodiments, the scaling factor vs. detector gain calibration curve does not change over an extended period of time, for example, over 6 hours or more, for example, over 12 hours or more, including over 1 day.

[0185] In some embodiments, the measurement duration of each sampling pulse is the same for each of the photodetectors of the optical detection system. In some embodiments, the measurement duration of each sampling pulse is different for one or more of the photodetectors of the optical detection system. Depending on the number of photodetectors in the optical detection system, the measurement duration may be different for one or more of the photodetectors, for example, two or more, for example, three or more, for example, four or more, for example, five or more, for example, ten or more, for example, fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifty-six or more, including ... In a particular example, the measurement duration of each sampling pulse is different for all (ie, 100%) of the photodetectors in the photodetection system.

[0186] The measurement variance can be calculated according to embodiments of the present disclosure immediately after a data signal is generated from the detected light for each particle. In other examples, the measurement variance is calculated 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more after a data signal is generated from the detected light for each particle, including adjusting the baseline sampling variance 60 minutes or more after a data signal is generated from the detected light. In certain embodiments, the measurement variance is determined for each particle for each photodetector in real time.

[0187] In embodiments, spectrally resolving the light from each fluorophore having overlapping fluorescence includes calculating a spectral unmixing matrix using the measurement variance determined for each particle in the sample. In some cases, the method includes calculating the spectral unmixing matrix using a weighted least squares algorithm. In some cases, the weighted least squares algorithm is calculated according to:

[0188]

number

[0189] where y is the measured detector value from multiple photodetectors of the photodetection system for each cell, a is the estimated fluorophore abundance, X is the spillover, and W is:

[0190]

number

[0191] In some embodiments, each W ii is calculated as follows:

[0192]

number

[0193] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0194] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: Optionally, the method includes calculating (X x ) for each cell detected by the light detection system to calculate the spectral unmixing matrix. TWX). (X T WX) -1 X T W

[0195] In some cases, the method includes determining the photodetector noise component (e.g., electronic noise, background light, etc.) using a single stained control sample. In certain examples, the method includes determining the variance of each photodetector before illuminating the sample with the light source. In other examples, the method includes determining the variance of W, W before illuminating the sample with the light source. G determining a predetermined approximation of

[0196] In some embodiments, the weighted least squares algorithm is calculated using a Cholesky decomposition. In some cases, the weighted least squares algorithm is calculated using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0197] In certain embodiments, the method for spectrally resolving the light from each fluorophore comprises, for example, a weighted least squares algorithm (X) of each particle detected by the light detection system to sort particles in a sample in real time. T In some embodiments, the inverse of (X T WX) can be inverted using an iterative Newton-Raphson calculation according to T WX) -1 This includes approximating

[0198]

number

[0199] W G is a predetermined approximation of W determined from the baseline variance of each photodetector in the photodetection system (i.e., in the absence of particle-induced fluorescence). In some embodiments, the method further includes estimating the baseline variance of each photodetector. In some cases, the method includes determining the photodetector noise component (e.g., electronic noise, background light, etc.) using a single stained control sample. In certain examples, the method includes determining the variance of each photodetector before illuminating the sample with the light source. In other examples, the method includes estimating W, W before illuminating the sample with the light source. G In certain embodiments, the method includes determining a predetermined approximation of W G A0 -1 In these embodiments, the pre-calculated A0 -1 is the A of each particle detected by the optical detection system -1 can be used as a first approximation of

[0200] In other embodiments, the method for spectrally resolving light from each fluorophore includes using a Sherman-Morrison iterative inverse updater. In some cases, the method includes calculating A using the Sherman-Morrison equation.

[0201]

number

[0202] In a particular example, the method uses the Sherman-Morrison formula to calculate the inverse of the perturbation of A0. -1 In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the method includes calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0203]

number

[0204] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0205]

number

[0206] In these embodiments, each w i The change to can be used to recalculate each A from A with each new weight matrix W (i.e., a different value than W). In some embodiments, the method includes executing a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0207]

number

[0208] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0209] In some embodiments, the method for spectrally decomposing light from each fluorophore comprises calculating a weighted least squares algorithm by matrix decomposition (i.e., factorization). In some cases, the method comprises LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In certain examples, the LU decomposition comprises Gaussian elimination. In other examples, the LU decomposition comprises a modified Cholesky decomposition, LDL decomposition, where D is a diagonal matrix. In certain embodiments, the weighted least squares algorithm (a) is calculated using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0210] In other embodiments, the method for spectrally decomposing light from each fluorophore includes calculating a weighted least squares algorithm by QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the weighted least squares algorithm (a) is calculated using QR factorization according to the following:

[0211]

number

[0212] In yet other embodiments, the method for spectrally decomposing the light from each fluorophore includes computing a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is performed by computing the product X - =UΣV T where U and V are orthogonal matrices and Σ is X -In a particular example, the weighted least squares algorithm (a) is calculated using singular value decomposition according to: z=U T y Σw=z a=Vw

[0213] 1B shows a flow diagram for spectrally resolving light from fluorophores in a sample using an adaptive spectral unmixing algorithm, according to certain embodiments. Spectrally resolving light from fluorophores in a sample is performed using a weighted least squares algorithm, which can be implemented in hardware (e.g., an integrated circuit such as an FPGA) or software stored in particle analysis system memory. The weighted least squares algorithm involves calculating W as described above using an optoelectronic scaling factor Q, a measurement duration T, the detector signal for each particle Y, and a noise constant λ at each detector. The unmixed spectral data F is then calculated using M T WMF=M T The spectral spillover matrix M is determined according to WY. As shown in Figure 1B, the spectral spillover matrix M, photoelectron scaling factor Q, and measurement variance λ, sampled at the beginning of data acquisition for each photodetector, are generated in software (e.g., stored in memory within the system processor), which can be updated once per data acquisition to reflect the active photodetector gain settings and the type of sample in the flow stream. The data signal Y and measurement duration T are obtained from the particle analysis system and determined for each particle in the sample (i.e., updated once per event).

[0214] FIG. 2A shows a diagram illustrating a comparison of unmixed data spread across photodetectors within a range of photodetector gain settings, according to certain embodiments. Using an ordinary least squares algorithm, varying the photodetector gain can significantly increase the spread of spillover across different measurement parameters, such as photodetector gain. As shown in FIG. 2A, a weighted least squares algorithm using an adaptive measure of variance manages the spread of spillover across a wide range of photodetector gain settings. FIG. 2B shows a comparison of unmixed data spread when calculated using an ordinary least squares algorithm and an adaptive weighted least squares algorithm, according to certain embodiments. Many factors can contribute to unmixed data spread, which can lead to a loss of biological resolution in samples containing biological components of interest (e.g., cells, biological macromolecules, intracellular vesicles).

[0215] In some embodiments, the method includes calculating the abundance of one or more fluorophores in the sample from the resolved light in the spectrum from each fluorophore. In certain examples, the abundance of fluorophores associated with the target particle (e.g., chemically associated (i.e., covalently, ionically) or physically associated) is calculated from the resolved light in the spectrum from each fluorophore associated with the particle. For example, in one example, the relative abundance of each fluorophore associated with the target particle is calculated from the resolved light in the spectrum from each fluorophore. In another example, the absolute abundance of each fluorophore associated with the target particle is calculated from the resolved light in the spectrum from each fluorophore. In certain embodiments, particles can be identified or classified based on the relative abundance of each fluorophore determined to be associated with the particle. In these embodiments, particles can be identified or classified by any convenient protocol, such as comparing the relative or absolute abundance of each fluorophore associated with the particle to a control sample having particles of known identity, or by performing spectroscopic or other assay analysis of a population of particles (e.g., cells) having calculated relative or absolute abundances of associated fluorophores.

[0216] In certain embodiments, the method includes sorting one or more particles (e.g., cells) of a sample identified based on the estimated abundance of a fluorophore associated with the particle. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., droplets containing cells, droplets containing non-cellular particles such as biological macromolecules) and, in some instances, delivering the separated components to one or more sample collection vessels. For example, the method may include sorting two or more components of a sample, e.g., three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, including sorting 25 or more components of a sample.

[0217] In sorting particles identified based on the abundance of a fluorophore associated with the particle, the method includes data acquisition, analysis, and recording, such as by a computer, where multiple data channels record data from each detector used to obtain overlapping spectra of the multiple fluorophores associated with the particle. In these embodiments, the analysis includes spectrally resolving light from the multiple fluorophores having overlapping spectra associated with the particle (e.g., by calculating a spectral unmixing matrix) and identifying the particle based on the estimated abundance of each fluorophore associated with the particle. This analysis can be conveyed to a sorting system configured to generate a set of digitized parameters based on the particle classification.

[0218] In some embodiments, a method for sorting components of a sample includes sorting particles (e.g., cells in a biological sample) with a particle sorting module having deflector plates, such as described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module having deflector plates, such as described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0219] System for spectrally resolving light from fluorophores using adaptive measurement dispersion As summarized above, aspects of the present disclosure include a system for spectrally resolving light from fluorophores with overlapping fluorescence spectra in a sample. The system, according to certain embodiments, includes a light source configured to illuminate particles of the sample having multiple fluorophores with overlapping fluorescence spectra; a light detection system having multiple photodetectors; and a processor including a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to determine a measured variance of the detected light for each particle and spectrally resolve the light from each fluorophore in the sample using a weighted least-squares algorithm using the measured variance determined for each particle. As noted above, the term "spectrally resolving" is used herein in its conventional sense to refer to spectrally distinguishing each fluorophore in the sample by assigning or attributing light of overlapping wavelengths to each contributing fluorophore. In embodiments, the overlapping spectral components of the fluorescence due to each fluorophore are determined by calculating a spectral unmixing matrix. In embodiments, the subject system is used to characterize a sample having multiple fluorophores, and the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. In some cases, the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 5 nm or more, for example, 10 nm or more, for example, 25 nm or more, and including 50 nm or more. In certain examples, the fluorescence spectrum of one or more fluorophores in the sample overlaps with the fluorescence spectrum of two or more different fluorophores in the sample, for example, each overlap of the fluorescence spectra is 5 nm or more, for example, 10 nm or more, for example, 25 nm or more, including 50 nm or more.

[0220] In embodiments, the system includes a light source configured to illuminate a sample having multiple fluorophores, each fluorophore having a fluorescence spectrum that overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. In embodiments, the light source may be any suitable broadband or narrowband light source. Depending on the components in the sample (e.g., cells, beads, non-cellular particles, etc.), the light source may be configured to emit light at various wavelengths, ranging from 200 nm to 1500 nm, e.g., 250 nm to 1250 nm, e.g., 300 nm to 1000 nm, e.g., 350 nm to 900 nm, and including 400 nm to 800 nm. For example, the light source may include a broadband light source emitting light having a wavelength between 200 nm and 900 nm. In other examples, the light source includes a narrowband light source emitting a wavelength between 200 nm and 900 nm. For example, the light source may be a narrowband LED (1 nm to 25 nm) emitting light having a wavelength between 200 nm and 900 nm. In certain embodiments, the light source is a laser. In some cases, the target system includes a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, or a xenon-fluorine (XeF) excimer laser, or a combination thereof. In other examples, the target system includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In still other examples, the target laser includes a metal vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof.In yet another example, the subject system includes a solid-state laser, such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:yCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, or a cerium-doped laser, and combinations thereof.

[0221] In other embodiments, the light source is a non-laser light source such as a lamp, including but not limited to a halogen lamp, a deuterium arc lamp, a xenon arc lamp, a light emitting diode such as a broadband LED having a continuous spectrum, a superluminescent light emitting diode, a semiconductor light emitting diode, a broadband LED white light source, a multi-LED integration, etc. In some cases, the non-laser light source is a stabilized fiber coupled broadband light source, a white light source, or any combination thereof, among other light sources.

[0222] The light source may be positioned at any suitable distance from the sample (e.g., the flow stream in a flow cytometer), for example, at a distance of 0.001 mm or more from the flow stream, for example, 0.005 mm or more, for example, 0.01 mm or more, for example, 0.05 mm or more, for example, 0.1 mm or more, for example, 0.5 mm or more, for example, 1 mm or more, for example, 5 mm or more, for example, 10 mm or more, for example, 25 mm or more, including 100 mm or more. Further, the light source may illuminate the sample at any suitable angle (e.g., relative to the perpendicular axis of the flow stream), for example, at an angle in the range of 10° to 90°, for example, 15° to 85°, for example, 20° to 80°, for example, 25° to 75°, including an angle of 30° to 60°, for example, 90°.

[0223] The light source can be configured to illuminate the sample continuously or at discrete intervals. In some cases, the system includes a light source configured to continuously illuminate the sample, such as with a continuous wave laser that continuously illuminates the flow stream at an interrogation point within the flow cytometer. In other examples, the system of interest includes a light source configured to illuminate the sample at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or at some other interval. When the light source is configured to illuminate the sample at discrete intervals, the system may include one or more additional components to provide intermittent illumination 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 and exposing the sample to the light source.

[0224] In some embodiments, the light source is a laser. Lasers of interest may include pulsed or continuous wave lasers. For example, the laser may be a gas laser such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (HeSe) laser, a helium-silver (HeAg) laser, a strontium laser, or a fluorine laser. metal vapor lasers, such as neon-copper (NeCu) 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, thorium YAG lasers, ytterbium YAG lasers, Yb2O3 lasers, and cerium-doped lasers, and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSLs), or frequency-doubled or frequency-tripled implementations of any of the above lasers.

[0225] In certain embodiments, the light source is an optical beam generator configured to generate two or more frequency-shifted optical beams. In some cases, the optical beam generator includes a laser and a radio-frequency generator configured to apply a radio-frequency drive signal to an acousto-optic device to generate two or more angularly deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous-wave laser. For example, the laser in the optical beam generator of interest may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO laser, a CO laser, an argon-fluorine (ArF) excimer laser, a krypton-fluorine (KrF) excimer laser, a xenon-chlorine (XeCl) excimer laser, a xenon-fluorine (XeF) excimer laser, or a combination thereof; a dye laser, such as a stilbene, coumarin, or rhodamine laser; a helium-cadmium (HeCd) laser, a helium-mercury (HeHg) laser, a helium-selenium (H metal vapor lasers such as a helium-silver (HeAg) laser, a strontium laser, a neon-copper (NeCu) laser, a copper laser, or a gold laser, and combinations thereof; solid-state lasers such as a ruby ​​laser, a Nd:YAG laser, a NdCrYAG laser, an Er:YAG laser, a Nd:YLF laser, a Nd:YVO4 laser, a Nd:yCa4O(BO3)3 laser, a Nd:YCOB laser, a titanium sapphire laser, a thulium YAG laser, a ytterbium YAG laser, a Yb2O3 laser, a cerium-doped laser, and combinations thereof.

[0226] The acousto-optical device may be any convenient acousto-optical protocol configured to frequency-shift laser light using applied acoustic waves. In a specific embodiment, the acousto-optical device is an acousto-optical deflector. The acousto-optical device in the target system is configured to generate an angularly deflected laser beam from light from a laser and an applied high-frequency drive signal. The high-frequency drive signal may be applied to the acousto-optical device using any suitable high-frequency drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0227] In an embodiment, the controller is configured to apply high frequency drive signals to the acousto-optic device to produce a desired number of angularly deflected laser beams in the output laser beam, including being configured to apply three or more high frequency drive signals, for example four or more high frequency drive signals, for example five or more high frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals, including being configured to apply one hundred or more high frequency drive signals.

[0228] In some cases, to produce 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 0.001 V to about 500 V, for example, from about 0.005 V to about 400 V, for example, from about 0.01 V to about 300 V, for example, from about 0.05 V to about 200 V, for example, from about 0.1 V to about 100 V, for example, from about 0.5 V to about 75 V, for example, from about 1 V to about 50 V, for example, from about 2 V to about 40 V, for example, from 3 V to about 30 V, including from about 5 V to about 25 V. In some embodiments, each of the applied high frequency drive signals has a frequency of about 0.001 MHz to about 500 MHz, for example, about 0.005 MHz to about 400 MHz, for example, about 0.01 MHz to about 300 MHz, for example, about 0.05 MHz to about 200 MHz, for example, about 0.1 MHz to about 100 MHz, for example, about 0.5 MHz to about 90 MHz, for example, about 1 MHz to about 75 MHz, for example, about 2 MHz to about 70 MHz, for example, about 3 MHz to about 65 MHz, for example, about 4 MHz to about 60 MHz, including about 5 MHz to about 50 MHz.

[0229] In certain embodiments, the controller includes a processor having a memory operably coupled to the processor, the memory having instructions stored therein that, when executed by the processor, cause the processor to generate an output laser beam having an angularly deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angularly deflected laser beams having the same intensity (e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more), or the including memory may include instructions for generating one hundred or more angularly deflected laser beams having the same intensity. In other embodiments, the controller may include instructions for generating two or more angularly deflected laser beams having different intensities (e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., twenty-five or more, e.g., fifty or more), or the including memory may include instructions for generating one hundred or more angularly deflected laser beams having different intensities.

[0230] In certain embodiments, the controller includes a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In another embodiment, the controller includes a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam that increases in intensity from the edge to the center of the output laser beam along a horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam may be in a range of 0.1% to about 99%, e.g., 0.5% to about 95%, e.g., 1% to about 90%, e.g., about 2% to about 85%, e.g., about 3% to about 80%, e.g., about 4% to about 75%, e.g., about 5% to about 70%, e.g., about 6% to about 65%, e.g., about 7% to about 60%, e.g., about 8% to about 55%, including about 10% to about 50% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis. In yet another embodiment, the controller comprises a processor having a memory operably coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam having an intensity profile with a Gaussian distribution along a horizontal axis.In yet another embodiment, the controller comprises a processor having a memory operatively coupled to the processor, the memory storing instructions that, when executed by the processor, cause the processor to produce an output laser beam having a top-hat intensity profile along a horizontal axis.

[0231] In embodiments, the objective optical beam generator may be configured to produce angularly polarized laser beams within the spatially separated output laser beam. Depending on the applied high frequency drive signal and the desired irradiance profile of the output laser beam, the angularly polarized laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 1000 μm or more, including 5000 μm or more. In some embodiments, the system is configured to produce angularly polarized laser beams within the output laser beam that overlap with adjacent angularly polarized laser beams along the horizontal axis of the output laser beam, such as 5000 μm or more. The overlap between adjacent angularly deflected laser beams (e.g., beam spot overlap) may be an overlap of 0.001 μm or more, such as an overlap of 0.005 μm or more, for example an overlap of 0.01 μm or more, for example an overlap of 0.05 μm or more, for example an overlap of 0.1 μm or more, for example an overlap of 0.5 μm or more, for example an overlap of 1 μm or more, for example an overlap of 5 μm or more, for example an overlap of 10 μm or more, including an overlap of 100 μm or more.

[0232] In certain examples, an optical beam generator configured to generate two or more frequency-shifted optical beams may be used, for example, as disclosed in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013) and U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,410, 10,423,353, 10,784,661, 10 ... Nos. 10,451,538, 10,620,111, and U.S. Patent Publication Nos. 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0233] In embodiments, the system includes a light detection system having a plurality of light detectors. The light detectors of interest may include optical sensors such as, but not limited to, active pixel sensors (APS), avalanche photodiodes (APDs), image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other light detectors. In certain embodiments, light from the sample is measured with a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal-oxide semiconductor (CMOS) image sensor, or an N-type metal-oxide semiconductor (NMOS) image sensor.

[0234] In some embodiments, the subject light detection system includes multiple light detectors. In some cases, the light detection system includes multiple solid-state detectors, such as photodiodes. In certain examples, the light detection system includes a light detector array, such as an array of photodiodes. In these embodiments, the light detector array may include four or more light detectors, e.g., ten or more light detectors, e.g., twenty-five or more light detectors, e.g., fifty or more light detectors, e.g., one hundred or more light detectors, e.g., two hundred or more light detectors, e.g., five hundred or more light detectors, e.g., seven hundred or more light detectors, e.g., seven hundred or more light detectors. For example, the detector may be a photodiode array having four or more photodiodes, e.g., ten or more photodiodes, e.g., twenty-five or more photodiodes, e.g., fifty or more photodiodes, e.g., one hundred or more photodiodes, e.g., two hundred or more photodiodes, e.g., five hundred or more photodiodes, e.g., seven hundred or more photodiodes, e.g., one thousand or more photodiodes.

[0235] The photodetectors can be arranged in any geometric configuration as desired, including, but not limited to, square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, non-angular, decagonal, dodecagonal, circular, elliptical, and irregularly patterned configurations. The photodetectors within the photodetector array may be oriented relative to one another at angles (referenced in the XZ plane), including angles between 10° and 180°, such as between 15° and 170°, such as between 20° and 160°, such as between 25° and 150°, such as between 30° and 120°, and such as between 45° and 90°. The photodetector array may be of any suitable shape, including rectilinear shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear shapes such as circular and elliptical, and irregular shapes such as a parabolic base coupled to a flat top. In certain embodiments, the photodetector array has a rectangular active surface.

[0236] Each photodetector (e.g., photodiode) in the array may have an active surface with a width ranging from 5 μm to 250 μm, such as 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and a length ranging from 5 μm to 250 μm, for example 10 μm to 225 μm, for example 15 μm to 200 μm, for example 20 μm to 175 μm, for example 25 μm to 150 μm, for example 30 μm to 125 μm, including 50 μm to 100 μm, and 2 ~10,000 μm 2 , e.g., 50 μm 2 ~9000μm 2 , e.g., 75 μm 2 ~8000μm 2 , e.g., 100 μm 2 ~7000μm 2 , e.g., 150 μm 2 ~6000μm 2 range of 200 μm 2 ~5000μm 2 Includes:

[0237] The size of the photodetector array may vary depending on the amount and intensity of light, the number of photodetectors, and the desired sensitivity, and may have a length ranging from 0.01 mm to 100 mm, such as 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, including 5 mm to 25 mm. The width of the photodetector array may also vary from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, for example, 0.1 mm to 80 mm, for example, 0.5 mm to 70 mm, for example, 1 mm to 60 mm, for example, 2 mm to 50 mm, for example, 3 mm to 40 mm, for example, 4 mm to 30 mm, including 5 mm to 25 mm. Thus, the active surface of the photodetector array may be 0.1 mm 2 ~10,000mm 2 , e.g. 0.5 mm 2 ~5000mm 2 , e.g. 1 mm2 ~1000mm 2 , e.g. 5mm 2 ~500mm 2 may be in the range of 10mm 2 ~100mm 2 Includes:

[0238] The optical detector of interest is configured 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 twenty-five or more different wavelengths, for example fifty or more different wavelengths, for example one hundred or more different wavelengths, for example two hundred or more different wavelengths, for example three hundred or more different wavelengths, including measuring light emitted by the sample in the flow stream at four hundred or more different wavelengths.

[0239] In some embodiments, the photodetector is configured to measure light collected over a wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the photodetector of interest is configured to collect a spectrum of light over a wavelength range. For example, the system may include one or more detectors configured to collect a spectrum of light over one or more wavelength ranges from 200 nm to 1000 nm. In still other embodiments, the detector of interest is configured to measure light from a sample in the flowstream at one or more specific wavelengths. For example, the system may include one or more detectors configured to measure light at one or more of 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the photodetector may be configured to pair with a particular fluorophore, such as one used with the sample in a fluorescence assay, hi some embodiments, the photodetector is configured to measure the collected light across the fluorescence spectrum of each fluorophore in the sample.

[0240] The light detection system may be configured to measure light continuously or at discrete intervals. In some cases, the target light detector is configured to continuously obtain measurements of collected light. In other examples, the light detection system may be configured to perform measurements at discrete intervals, such as measuring light every 0.001 milliseconds, 0.01 milliseconds, 0.1 milliseconds, 1 millisecond, 10 milliseconds, 100 milliseconds, and every 1000 milliseconds, or some other interval.

[0241] In embodiments, the system is configured to analyze light from an illuminated sample and spectrally resolve light from each fluorophore in the sample. In some embodiments, the system includes a memory having stored thereon instructions for determining overlap between each different fluorophore in the sample and calculating the contribution of each fluorophore to the overlapping fluorescence. In certain embodiments, the system is configured to calculate a spectral unmixing matrix of the fluorescence spectra of multiple fluorophores having overlapping fluorescence in the sample detected by the light detection system. As described in more detail below, the system may also be configured to estimate the abundance of each fluorophore in the sample. In certain embodiments, the abundance of each fluorophore associated with a target particle can be determined. The system may be configured to identify and classify target particles based on the abundance of each fluorophore associated with the target particle. In some cases, the system is configured to sort the identified or classified particles. In these embodiments, the system may include a computer control system further including one or more computers for full or partial automation of the system for performing the methods described herein. In some embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program when loaded into the computer comprising instructions for illuminating a flow cell having a sample in a flow stream with a light source; detecting light from the flow cell with a light detection system having a plurality of light detectors; calculating a spectral unmixing matrix for the fluorescence spectra of the plurality of fluorophores for each particle detected by the light detection system; estimating the abundance of each fluorophore using the spectral unmixing matrix; and sorting particles in the sample based on the estimated fluorophore abundances.

[0242] In some embodiments, the system includes a computer having a computer-readable storage medium having a computer program stored thereon, the computer program further including instructions, when loaded into the computer, for determining a measured variance of light detected for each particle and spectrally resolving light from each fluorophore in the sample based on the calculated spectral unmixing matrix of the fluorescence spectrum and the measured variance determined for each particle. In some cases, the memory includes instructions for determining the measured variance of each photodetector for each particle. In some cases, the memory includes instructions for determining the measured variance based on a change in photodetector gain. In some cases, the memory includes instructions for determining the measured variance based on a change in trigger threshold for one or more of the photodetectors. In certain examples, the memory includes instructions for determining the measured variance based on a change in light detection duration for each photodetector for each particle. In certain examples, the memory includes instructions for determining the measured variance based on a change in photonic shot noise detected by each photodetector.

[0243] In some cases, the system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions for calculating the measurement variance for each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the photoelectron scaling factor, Y is the photodetector signal intensity.

[0244] In some embodiments, the memory includes instructions for adjusting the baseline sampling variance in response to a change in photodetector gain in one or more photodetectors of the optical detection system. In some cases, the memory includes instructions for increasing the baseline sampling variance in response to a change in photodetector gain in one or more photodetectors of the optical detection system by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more. In some cases, the memory includes instructions for reducing the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in photodetector gain in one or more photodetectors of the photodetection system.

[0245] In some embodiments, the memory comprises instructions for adjusting the baseline sampling variance in response to a change in photonic shot noise detected by one or more photodetectors of the optical detection system. In some cases, the memory comprises instructions for increasing the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more, in response to a change in photonic shot noise in one or more photodetectors of the optical detection system. In some cases, the memory includes instructions for reducing the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in photonic shot noise detected by one or more photodetectors of the photodetection system.

[0246] In some embodiments, the memory includes instructions for adjusting the baseline sampling variance in response to a change in a trigger threshold of one or more photodetectors of the optical detection system. In some cases, the memory includes instructions for increasing the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, such as 0.0005% or more, such as 0.001% or more, such as 0.005% or more, such as 0.01% or more, such as 0.05% or more, such as 0.1% or more, such as 0.5% or more, such as 1% or more, such as 2% or more, such as 5% or more, such as 10% or more, such as 25% or more, including 50% or more, in response to a change in a trigger threshold of one or more photodetectors of the optical detection system. In some cases, the memory includes instructions for reducing the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in the trigger threshold of one or more photodetectors of the photodetection system.

[0247] In some embodiments, the memory includes instructions for adjusting the baseline sampling variance when a change in one or more of the photodetector gain, trigger threshold, and photonic shot noise is detected, or a predetermined period thereafter. In some cases, the memory includes instructions for adjusting the baseline sampling variance immediately after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected. In other cases, the baseline sampling variance is adjusted 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more, after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected, including adjusting the baseline sampling variance 60 minutes or more after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected.

[0248] In certain embodiments, the memory comprises instructions for adjusting the baseline sampling variance at predetermined time intervals, including, for example, every 0.01 minute or more, for example, every 0.05 minutes or more, for example, every 0.1 minute or more, for example, every 0.5 minutes or more, for example, every 1 minute or more, for example, every 5 minutes or more, for example, every 10 minutes or more, for example, every 15 minutes or more, for example, every 30 minutes or more, for example, every 60 minutes or more, for example, every 2 hours or more, for example, every 4 hours or more, for example, every 8 hours or more, for example, every 12 hours or more, for example, every 16 hours or more, for example, every 20 hours or more, and every 24 hours or more.

[0249] In some embodiments, the memory comprises instructions for adjusting the scaling factor in response to a change in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the memory comprises instructions for increasing the scaling factor in response to a change in photodetector gain in one or more photodetectors of the photodetection system by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more. In some cases, the memory includes instructions for decreasing the scaling factor by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more, in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In certain embodiments, the memory includes instructions for generating a calibration curve of the scaling factor based on the photodetector gain of one or more of the photodetectors of the photodetection system.

[0250] In some embodiments, the memory includes instructions for having the measurement duration of each sampling pulse be the same for each of the photodetectors of the optical detection system. In some embodiments, the memory includes instructions for having the measurement duration of each sampling pulse differ for one or more of the photodetectors of the optical detection system. Depending on the number of photodetectors in the optical detection system, the measurement duration may differ for one or more of the photodetectors, for example, two or more, for example, three or more, for example, four or more, for example, five or more, for example, ten or more, for example, fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifty-eight or more, including ... In a particular example, the measurement duration of each sampling pulse is different for all (ie, 100%) of the photodetectors in the photodetection system.

[0251] In some embodiments, the memory includes instructions for calculating the measurement variance immediately after a data signal is generated from the detected light for each particle. In other examples, the memory includes instructions for calculating the measurement variance 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more after a data signal is generated from the detected light for each particle, including adjusting the baseline sampling variance 60 minutes or more after a data signal is generated from the detected light. In certain embodiments, the memory includes instructions for calculating the measurement variance for each particle for each photodetector in real time.

[0252] In embodiments, the memory includes instructions for spectrally resolving light from each fluorophore having overlapping fluorescence and for calculating a spectral unmixing matrix having the determined measurement variance for each particle in the sample. In some cases, the memory includes instructions for calculating the spectral unmixing matrix using a weighted least squares algorithm. In some cases, the memory includes instructions for calculating a weighted least squares algorithm according to:

[0253]

number

[0254] where y is the measured detector value from multiple photodetectors of the photodetection system for each cell, a is the estimated fluorophore abundance, X is the spillover, and W is:

[0255]

number

[0256] In some embodiments, the memory stores data for each W according to: ii It includes instructions for calculating

[0257]

number

[0258] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i. In certain embodiments, the spectral unmixing matrix is ​​calculated according to: In some cases, the memory stores (X TWX). (X T WX) -1 X T W

[0259] In some embodiments, the memory includes instructions for computing a weighted least squares algorithm using a Cholesky decomposition. In some cases, the memory includes instructions for computing a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0260] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0261] In certain embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory being configured to calculate a weighted least squares (X) fit in a weighted least squares algorithm for each particle detected by the optical detection system to separate particles in a sample in real time. T In a particular example, the processor may include a processor including stored instructions that cause the processor to approximate the inverse of (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0262]

number

[0263] W G is a predetermined approximation of W determined from the variance of each photodetector in the light detection system. The variance, in some embodiments, has a photodetector noise component. For example, the photodetector noise component can include one or more of electronic noise and optical background light. In some embodiments, the variance of the photodetector is proportional to the measured intensity of light by the photodetector. In some embodiments, the photodetector noise component is estimated using a single stained control sample. In certain embodiments, the memory includes stored instructions that, when executed by the processor, cause the processor to automatically determine the variance of each photodetector before illuminating the sample with the light source. In other embodiments, the memory, when executed by the processor, causes the processor to automatically determine W before illuminating the sample with the light source. G In a particular embodiment, the memory includes stored instructions that, when executed by the processor, cause the processor to determine a predetermined W. G Using A0 -1 In these embodiments, the pre-calculated A0 -1 is stored in memory and is the A of each particle detected by the optical detection system. -1 may be used by the processor as a first approximation of

[0264] In another embodiment, the system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to approximate a weighted least squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the memory includes instructions for calculating A using the Sherman-Morrison formula.

[0265]

number

[0266] In the specific example, the memory uses the Sherman-Morrison formula to calculate the inverse of the perturbation of A0. -1 In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T Optionally, the memory includes instructions for calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0267]

number

[0268] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0269]

number

[0270] In these embodiments, each w i A change to W can be used to recalculate each A from A with each new weight matrix W (i.e., a different value from W). In some embodiments, the system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to perform a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0271]

number

[0272] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0273] In another embodiment, a system includes a processor having a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm for each particle through matrix decomposition. In some cases, the memory includes instructions for LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, a system includes a processor having a memory operably coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm (a) using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0274] In another embodiment, a system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm using QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the memory includes instructions for calculating a weighted least squares algorithm (a) using QR factorization according to:

[0275]

number

[0276] In yet another embodiment, a system includes a processor having a memory operatively coupled to the processor, the memory including stored instructions that, when executed by the processor, cause the processor to calculate a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is a product X - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, the memory includes instructions for computing a weighted least squares algorithm (a) using singular value decomposition according to: z=U T y Σw=z a=Vw

[0277] In some cases, the memory includes instructions for determining photodetector noise components (e.g., electronic noise, background light, etc.) using a single stained control sample. In particular examples, the memory includes instructions for determining the variance of each photodetector before illuminating the sample with the light source. In other examples, the memory includes instructions for determining W, W before illuminating the sample with the light source. G The method includes instructions for determining a predetermined approximation of

[0278] In some embodiments, the system includes a computer having a computer-readable storage medium storing a computer program, the computer program further comprising instructions for, when loaded into the computer, calculating the abundance of one or more fluorophores in the sample from the resolved light in the spectrum from each fluorophore. In certain examples, the abundance of fluorophores associated with (e.g., chemically associated (i.e., covalently, ionically) or physically associated with) a target particle is calculated from the resolved light in the spectrum from each fluorophore associated with the particle. For example, in one example, the relative abundance of each fluorophore associated with the target particle is calculated from the resolved light in the spectrum from each fluorophore. In another example, the absolute abundance of each fluorophore associated with the target particle is calculated from the resolved light in the spectrum from each fluorophore.

[0279] In certain embodiments, the system is configured to identify or classify particles based on the relative abundance of each fluorophore determined to be associated with the particle. In these embodiments, the subject systems can be configured to identify or classify particles by any convenient protocol, such as by comparing the relative or absolute abundance of each fluorophore associated with the particle to a control sample having particles of known identity, or by performing spectroscopic or other assay analysis of a population of particles (e.g., cells) having calculated relative or absolute abundances of associated fluorophores.

[0280] Systems according to some embodiments may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses memory storing instructions for executing the steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known ways and facilitates the processor's coordination and execution of functions of various computer programs, which may be written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0281] System memory may be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as a resident hard disk or tape, optical media such as a read-and-write compact disk, flash memory devices, or other memory storage devices. The memory storage device may be any of a variety of known or future devices, including a compact disk drive, tape drive, removable hard disk drive, or diskette drive. Such types of memory storage devices typically read from and / or write to a program storage medium (not shown), such as a compact disk, magnetic tape, removable hard disk, or magnetic diskette, respectively. Any of these program storage media, or others now in use or that may later be developed, may be considered a computer program product. As will be appreciated, these program storage media typically store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with the memory storage devices.

[0282] In some embodiments, a computer program product is described that includes a computer usable medium having stored thereon control logic (a computer software program including program code). The control logic, when executed by a processor of a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using hardware state machines. Implementing a hardware state machine to perform the functions described herein will be apparent to one skilled in the art.

[0283] The memory may be any suitable device from which the processor can store and retrieve data, such as a magnetic, optical, or solid-state storage device (including a magnetic or optical disk, or tape, or RAM, or any other suitable device, fixed or portable). The processor may include a general-purpose digital microprocessor that is appropriately programmed from a computer-readable medium carrying the necessary program code. The programming may be provided to the processor remotely through a communications channel or may be pre-stored in a computer program product, such as memory or some other portable or fixed computer-readable storage medium using any of these devices in conjunction with the memory. For example, a magnetic or optical disk may carry the program and be read by a disk writer / reader. The system of the present invention also includes programming, e.g., in the form of a computer program product, algorithms for use in implementing the above-described methods. The programming according to the present invention may be recorded on a computer-readable medium, e.g., any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, magnetic storage media such as magnetic disks, hard disk storage media, and magnetic tape, optical storage media such as CD-ROM, storage media such as RAM, ROM, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0284] The processor may also have access to a communication channel for communicating with a user at a remote location, meaning that the user is not in direct contact with the system but relays input information to the input manager from an external device, such as a computer connected to a wide area network ("WAN"), a telephone network, a satellite network, or any other suitable communication channel, including a mobile phone (i.e., a smartphone).

[0285] In some embodiments, a system according to the present disclosure may be configured to include a communications interface. In some embodiments, the communications interface includes a receiver and / or a transmitter for communicating with a network and / or another device. The communications interface may be configured for wired or wireless communications, including, but not limited to, radio frequency (RF) communications (e.g., radio frequency identification (RFID), Zigbee communications protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communications protocol, and cellular communications such as code division multiple access (CDMA) or global system for mobile communications (GSM).

[0286] In one embodiment, the communications interface is configured to include one or more communications ports, e.g., a physical port or interface such as a USB port, an RS-232 port, or any other suitable electrical connection port that enables data communication between the subject system and other external devices, such as a computer terminal (e.g., in a doctor's office or hospital environment) configured for similar complementary data communication.

[0287] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject system to communicate with computer terminals and / or other devices such as networks, communication-enabled mobile phones, personal digital assistants, or any other communication device that a user may use in conjunction with.

[0288] In one embodiment, the communication interface is configured to provide connectivity for data transfer using Internet Protocol (IP) over a cellular network, Short Message Service (SMS), a wireless connection to a personal computer (PC) in a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0289] In one embodiment, the target system is configured to wirelessly communicate with a server device via a communications interface using common standards such as, for example, 802.11 or Bluetooth® RF protocols, or the IrDA infrared protocol. The server device may be another portable device, such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device, such as a desktop computer, appliance, etc. In some embodiments, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, a keyboard, a mouse, or a touchscreen.

[0290] In some embodiments, the communications interface is configured to automatically or semi-automatically communicate data stored in the subject system, e.g., the optional data storage unit, with a network or server device using one or more of the communications protocols and / or mechanisms described above.

[0291] The output controller may include a controller for any of a variety of known display devices for presenting information to a user, whether human or machine, local or remote. When one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of pixels. A graphical user interface (GUI) controller provides a graphical input / output interface between the system and the user and may include any of a variety of known or future software programs for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. The output manager may also provide information generated by the processing modules to a remote user, for example, via the Internet, telephone, or satellite network, according to known techniques. Presentation of data by the output manager may be performed according to various known techniques. As some examples, the data may include SQL, HTML, or XML documents, emails, or other files, or data in other formats. The data may also include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from remote sources. The platform or platforms present in the subject system may be of any type of known or future-developed computer platform, but they are typically of a class of computers commonly referred to as servers. However, they may also be mainframe computers, workstations, or other computer types. They may be connected via any known or future type of cabling or other communication system, including wireless systems, and may or may not be networked. They may be co-located or physically separated.In some cases, various operating systems may be employed on any computer platform depending on the type and / or manufacturer of the computer platform selected. Suitable operating systems include Windows 10, Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, etc.

[0292] In certain embodiments, the subject systems include one or more optical conditioning components for conditioning light, such as light irradiated onto a sample (e.g., from a laser) or light collected from a sample (e.g., scattering, fluorescence). For example, the optical conditioning may be increasing the dimensions of the light, the focus of the light, or collimating the light. In some cases, the optical conditioning is an expansion protocol that increases the dimensions of the light (e.g., beam spot), including increasing the dimensions by 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, and including increasing the dimensions by 75% or more. In other embodiments, the optical conditioning includes focusing the light to reduce the dimensions of the light, e.g., 5% or more, e.g., 10% or more, e.g., 25% or more, e.g., 50% or more, including reducing the dimensions of the beam spot by 75% or more. In certain embodiments, the optical conditioning includes collimating the light. The term "collimate" is used in its conventional sense to refer to optically adjusting the collinearity of light propagation or reducing the divergence of light from a common propagation axis. In some cases, collimating involves narrowing the spatial cross-section of the light beam (eg, reducing the beam profile of a laser).

[0293] In some embodiments, the optical conditioning component is a focusing lens having a magnification ratio of 0.1 to 0.95, e.g., a magnification ratio of 0.2 to 0.9, e.g., a magnification ratio of 0.3 to 0.85, e.g., a magnification ratio of 0.35 to 0.8, e.g., a magnification ratio of 0.5 to 0.75, e.g., a magnification ratio of 0.55 to 0.7, e.g., a magnification ratio of 0.6. For example, the focusing lens is a dual achromatic demagnifying lens, in a particular example, having a magnification ratio of approximately 0.6. The focal length of the focusing lens may vary in the range of 5 mm to 20 mm, e.g., 6 mm to 19 mm, e.g., 7 mm to 18 mm, e.g., 8 mm to 17 mm, e.g., 9 mm to 16 mm, including focal lengths in the range of 10 mm to 15 mm. In a particular embodiment, the focusing lens has a focal length of approximately 13 mm.

[0294] In other embodiments, the optical conditioning component is a collimator. The collimator may be any convenient collimation protocol, such as one or more mirrors or curved lenses, or a combination thereof. For example, the collimator is a single collimating lens in certain instances. In other instances, the collimator is a collimating mirror. In yet other instances, the collimator includes two lenses. In yet other instances, the collimator includes a mirror and a lens. When the collimator includes one or more lenses, the focal length of the collimating lens may vary in a range of 5 mm to 40 mm, e.g., 6 mm to 37.5 mm, e.g., 7 mm to 35 mm, e.g., 8 mm to 32.5 mm, e.g., 9 mm to 30 mm, e.g., 10 mm to 27.5 mm, e.g., 12.5 mm to 25 mm, including focal lengths in a range of 15 mm to 20 mm.

[0295] In some embodiments, the subject systems include a flow cell nozzle having a nozzle orifice configured to direct a flow stream through the flow cell nozzle. The subject flow cell nozzle has an orifice that propagates a fluid sample to a sample interrogation region, and in some embodiments, the flow cell nozzle includes a proximal cylindrical portion defining a longitudinal axis and a distal frusto-conical portion terminating in a flat surface having a nozzle orifice transverse to the longitudinal axis. The length of the proximal cylindrical portion (as measured along the longitudinal axis) may vary from 1 mm to 15 mm, e.g., 1.5 mm to 12.5 mm, e.g., 2 mm to 10 mm, e.g., 3 mm to 9 mm, including 4 mm to 8 mm. The length of the distal frusto-conical portion (as measured along the longitudinal axis) may also vary from 1 mm to 10 mm, e.g., 2 mm to 9 mm, e.g., 3 mm to 8 mm, including 4 mm to 7 mm. The diameter of the flow cell nozzle chamber may in some embodiments vary in the range of 1 mm to 10 mm, such as 2 mm to 9 mm, such as 3 mm to 8 mm, including 4 mm to 7 mm.

[0296] In certain examples, the nozzle chamber does not include a cylindrical portion, and the entire flow cell nozzle chamber is frustoconical in shape. In these embodiments, the length of the frustoconical nozzle chamber (as measured along a longitudinal axis transverse to the nozzle orifice) may range from 1 mm to 15 mm, such as 1.5 mm to 12.5 mm, such as 2 mm to 10 mm, for example, 3 mm to 9 mm, including 4 mm to 8 mm. The diameter of the proximal portion of the frustoconical nozzle chamber may range from 1 mm to 10 mm, such as 2 mm to 9 mm, for example, 3 mm to 8 mm, including 4 mm to 7 mm.

[0297] In some embodiments, the sample flow stream diverges from an orifice at the distal end of the flow cell nozzle. Depending on the desired characteristics of the flow stream, the flow cell nozzle orifice can be of any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, hexagonal, etc., curved cross-sectional shapes such as circular, elliptical, and irregular shapes such as a parabolic bottom coupled to a flat top. In certain embodiments, the flow cell nozzle of interest has a circular orifice. The size of the nozzle orifice may vary in some embodiments from 1 μm to 20,000 μm, such as from 2 μm to 17,500 μm, for example, from 5 μm to 15,000 μm, for example, from 10 μm to 12,500 μm, for example, from 15 μm to 10,000 μm, for example, from 25 μm to 7,500 μm, for example, from 50 μm to 5,000 μm, for example, from 75 μm to 1,000 μm, for example, from 100 μm to 750 μm, including from 150 μm to 500 μm. In a particular embodiment, the nozzle orifice is 100 μm.

[0298] In some embodiments, the flow cell nozzle includes a sample injection port configured to provide a sample to the flow cell nozzle. In embodiments, the sample injection system is configured to provide a suitable flow of sample to the flow cell nozzle chamber. Depending on the desired characteristics of the flow stream, the rate of sample delivered by the sample injection port to the flow cell nozzle chamber may be 1 μL / sec or more, for example, 2 μL / sec or more, for example, 3 μL / sec or more, for example, 5 μL / sec or more, for example, 10 μL / sec or more, for example, 15 μL / sec or more, for example, 25 μL / sec or more, for example, 50 μL / sec or more, for example, 100 μL / sec or more, for example, 150 μL / sec or more, for example, 200 μL / sec or more, for example, 250 μL / sec or more, for example, 300 μL / sec or more, for example, 350 μL / sec or more, for example, 400 μL / sec or more, for example, 450 μL / sec or more, including 500 μL / sec or more. For example, the sample flow rate may be in the range of 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, including 10 μL / sec to about 100 μL / sec.

[0299] The sample injection port may be an orifice disposed in the wall of the nozzle chamber or a conduit disposed at the proximal end of the nozzle chamber. When the sample injection port is an orifice disposed in the wall of the nozzle chamber, the orifice may have any desired cross-sectional shape, including, but not limited to, straight cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, and irregular shapes such as a parabolic bottom coupled to a flat top. In certain embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice may vary depending on the shape, with an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm).

[0300] In certain examples, the sample injection port is a conduit located at the proximal end of the flow cell nozzle chamber. For example, the sample injection port may be a conduit positioned so that the orifice of the sample injection port is aligned with the flow cell nozzle orifice. When the sample injection port is a conduit aligned with the flow cell nozzle orifice, the cross-sectional shape of the sample injection tube may be any suitable shape, including, but not limited to, linear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curved cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom coupled to a flat top. In certain examples, the orifice of the conduit may have an opening ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm), and may vary depending on the shape. The shape of the tip of the sample injection port may be the same as or different from the cross-sectional shape of the sample injection tube. For example, the orifice of the sample injection port may include a beveled tip having a bevel angle in the range of 1° to 10° (e.g., 2° to 9°, e.g., 3° ​​to 8°, e.g., 4° to 7°), including a bevel angle of 5°.

[0301] In some embodiments, the flow cell nozzle also includes a sheath fluid injection port configured to provide sheath fluid to the flow cell nozzle. In embodiments, the sheath fluid injection system is configured to provide a flow of sheath fluid to the flow cell nozzle chamber, e.g., in conjunction with the sample, to produce a stacked flow stream of sheath fluid surrounding the sample flow stream. Depending on the desired characteristics of the flow stream, the velocity of the sheath fluid delivered to the flow cell nozzle chamber can be 25 μL / sec or more, e.g., 50 μL / sec or more, e.g., 75 μL / sec or more, e.g., 100 μL / sec or more, e.g., 250 μL / sec or more, e.g., 500 μL / sec or more, e.g., 750 μL / sec or more, e.g., 1000 μL / sec or more, including 2500 μL / sec or more. For example, the sheath fluid flow rate may be in the range of 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, for example, 3 μL / sec to about 400 μL / sec, for example, 4 μL / sec to about 350 μL / sec, for example, 5 μL / sec to about 300 μL / sec, for example, 6 μL / sec to about 250 μL / sec, for example, 7 μL / sec to about 200 μL / sec, for example, 8 μL / sec to about 150 μL / sec, for example, 9 μL / sec to about 125 μL / sec, including 10 μL / sec to about 100 μL / sec.

[0302] In some embodiments, the sheath fluid injection port is an orifice disposed in the wall of the nozzle chamber. The sheath fluid injection port orifice may have any suitable cross-sectional shape, including, but not limited to, rectilinear cross-sectional shapes such as square, rectangular, trapezoidal, triangular, and hexagonal, curvilinear cross-sectional shapes such as circular and elliptical, as well as irregular shapes such as a parabolic bottom coupled to a flat top. The size of the sample injection port orifice may vary depending on the shape, with specific examples having openings ranging from 0.1 mm to 5.0 mm, e.g., 0.2 mm to 3.0 mm, e.g., 0.5 mm to 2.5 mm, e.g., 0.75 mm to 2.25 mm, e.g., 1 mm to 2 mm (including 1.25 mm to 1.75 mm, e.g., 1.5 mm).

[0303] The subject systems, in certain examples, include a sample interrogation region in fluid communication with the flow cell nozzle orifice. In these examples, a sample flow stream diverges from an orifice at the distal end of the flow cell nozzle, and particles in the flow stream can be illuminated with a light source in the sample interrogation region. The size of the interrogation region can vary depending on characteristics of the flow nozzle, such as the size of the nozzle orifice and the size of the sample injection port. In embodiments, the interrogation region can have a width of 0.01 mm or more, e.g., 0.05 mm or more, e.g., 0.1 mm or more, e.g., 0.5 mm or more, e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 5 mm or more, including 10 mm or more. The length of the interrogation region can also vary in some examples along a length of 0.01 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., 1.5 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 5 mm or more, e.g., 10 mm or more, e.g., 15 mm or more, e.g., 20 mm or more, e.g., 25 mm or more, including 50 mm or more.

[0304] The investigation region may be configured to facilitate illumination of a planar cross-section of the diverging flow stream, or may be configured to facilitate illumination of a diffuse field of a predetermined length (e.g., using a diffuse laser or lamp). In some embodiments, the investigation region includes a transparent window to facilitate illumination of a diverging flow stream of a predetermined length (e.g., 1 mm or more, e.g., 2 mm or more, e.g., 3 mm or more, e.g., 4 mm or more, e.g., 5 mm or more, including 10 mm or more). Depending on the light source used to illuminate the diverging flow stream (as described below), the investigation region may be configured to pass light in the range of 100 nm to 1500 nm, e.g., 150 nm to 1400 nm, e.g., 200 nm to 1300 nm, e.g., 250 nm to 1200 nm, e.g., 300 nm to 1100 nm, e.g., 350 nm to 1000 nm, e.g., 400 nm to 900 nm, including 500 nm to 800 nm. Thus, the research area includes optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and other polymeric plastic materials including plastics, such as polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (glycol-modified polyethylene terephthalate), polyesters, among which polyesters of interest include poly(ethylene terephthalate) (PET), bottle-grade PET (monoethylene glycol, terephthalic acid, and isophthalic acid, cyclohexene dimethanol, and other polymeric materials). Poly(alkylene terephthalates) such as poly(ethylene adipate), poly(1,4-butylene adipate), poly(hexamethylene adipate) and the like; poly(alkylene suberates) such as poly(ethylene suberate); poly(alkylene sebacates) such as poly(ethylene sebacate); poly(ε-caprolactone) and poly(β-propiolactone); poly(alkylene isophthalates) such as poly(ethylene isophthalate);Poly(alkylene 2,6-naphthalenedicarboxylates) such as poly(ethylene 2,6-naphthalenedicarboxylate); poly(alkylenesulfonyl-4,4'-dibenzoates) such as poly(ethylenesulfonyl-4,4'-dibenzoate); poly(p-phenylene alkylene dicarboxylates) such as poly(p-phenylene ethylene dicarboxylate); poly(trans-1,4-cyclohexanediyl alkylene dicarboxylates) such as poly(trans-1,4-cyclohexanediyl ethylene dicarboxylate); poly(1,4-cyclohexanedimethylene alkylene dicarboxylates) such as poly(1,4-cyclohexanedimethylene ethylene dicarboxylate); poly([2.2.2]-bicyclooctane-1,4-dimethylene ethylene dicarboxylate) The optical filter can be formed from any transparent material that transmits the desired wavelength range, including, but not limited to, poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylate), such as poly([2.2.2]-bicyclooctane-1,4-dimethylene alkylene dicarboxylate); lactic acid polymers and copolymers, such as (S)-polylactide, (R,S)-polylactide, poly(tetramethylglycolide), and poly(lactide-co-glycolide); and polycarbonates of bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, and 3,3',5,5'-tetramethylbisphenol A; polyamides, such as poly(p-phenylene terephthalamide); polyesters, such as polyethylene terephthalate, e.g., Mylar™ polyethylene terephthalate, and the like. In some embodiments, the system of interest includes a cuvette positioned in the sample interrogation region. In some embodiments, the cuvette may transmit light in the range of 100 nm to 1500 nm, such as 150 nm to 1400 nm, such as 200 nm to 1300 nm, such as 250 nm to 1200 nm, such as 300 nm to 1100 nm, such as 350 nm to 1000 nm, such as 400 nm to 900 nm, including 500 nm to 800 nm;

[0305] In certain embodiments, a light detection system having a plurality of light detectors as described above is part of or disposed within a particle analyzer, such as a particle sorter, hi certain embodiments, the system of interest is a flow cytometry system that includes a photodiode and amplifier component as part of the light detection system for detecting light emitted by a sample in a flow stream. Suitable flow cytometry systems include those described in Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford University Press (1997); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry, 3rd ed., Wiley-Liss (1995); Virgo, et al. (2012) Ann Clin Biochem. Jan; 49(pt 1):17-28; Linden, et al., Semin Thromb Hemost. 2004 Oct; 30(5):502-11; Alison, et al. J Pathol, 2010 Dec; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255, the disclosures of which are incorporated herein by reference.In particular examples, flow cytometry systems of interest include a BD Biosciences FACSCanto™ flow cytometer, a BD Biosciences FACSCanto™ II flow cytometer, a BD Accuri™ flow cytometer, a BD Accuri™ C6 Plus flow cytometer, a BD Biosciences FACSCelesta™ flow cytometer, a BD Biosciences FACSLyric™ flow cytometer, a BD Biosciences FACSVerse™ flow cytometer, a BD Biosciences FACSymphony™ flow cytometer, a BD Biosciences LSRFortessa™ flow cytometer, a BD Biosciences LSRFortessa™ X-20 flow cytometer, a BD Biosciences FACSPresto™ flow cytometer, a BD Biosciences FACSVia™ flow cytometer, and a BD Biosciences FACSCalibur™ cell sorter, a BD Biosciences FACSCount™ cell sorter, a BD Biosciences These include the FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, BD Biosciences FACSAria™ II cell sorter, BD Biosciences FACSAria™ III cell sorter, BD Biosciences FACSAria™ Fusion cell sorter, and BD Biosciences FACSMelody™ cell sorter, BD Biosciences FACSymphony™ S6 cell sorter, etc.

[0306] In some embodiments, the subject systems may be implemented using the same or similar technology as disclosed in U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, 10,478,479, and 10,478,479, the disclosures of which are incorporated herein by reference in their entireties. Specification No. 81,074, Specification No. 10,302,545, Specification No. 10,145,793, Specification No. 10,113,967, Specification No. 10,006,852, Specification No. 9,952, Specification No. 076, Specification No. 9,933,341, Specification No. 9,726,527, Specification No. 9,453,789, Specification No. 9,200,334, Specification No. 9,097,640 , Specification No. 9,095,494, Specification No. 9,092,034, Specification No. 8,975,595, Specification No. 8,753,573, Specification No. 8,233,146, Specification No. 8,14 Specification No. 0,300, Specification No. 7,544,326, Specification No. 7,201,875, Specification No. 7,129,505, Specification No. 6,821,740, Specification No. 6,813,017 and flow cytometry systems such as those described in the following patents: US Pat. Nos. 6,809,804, 6,372,506, 5,700,692, 5,643,796, 5,627,040, 5,620,842, 5,602,039, 4,987,086, and 4,498,766.

[0307] In some embodiments, the subject system is a particle sorting system configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module with deflection plates, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0308] In certain examples, the flow cytometry system of the present invention may be any of those described in Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), as well as U.S. Patent Nos. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408, and 2019 / 0376895, and 2019 / 0376894, the disclosures of which are incorporated herein by reference.

[0309] In certain embodiments, the system is configured to sort one or more particles (e.g., cells) of a sample identified based on the estimated abundance of a fluorophore associated with the particle, as described above. The term "sorting" is used herein in its conventional sense to refer to separating components of a sample (e.g., cells, non-cellular particles such as biological macromolecules, etc.) and, in some instances, delivering the separated components to one or more sample collection vessels. For example, the subject systems may be configured to sort samples having more than two components, e.g., three or more components, e.g., four or more components, e.g., five or more components, e.g., ten or more components, e.g., fifteen or more components, including sorting samples having twenty-five or more components. One or more of the sample components may be separated from the sample and delivered to a sample collection vessel, e.g., two or more sample components, e.g., three or more sample components, e.g., four or more sample components, e.g., five or more sample components, e.g., ten or more sample components (including fifteen or more sample components).

[0310] In some embodiments, the subject particle sorting system is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference. In certain embodiments, particles (e.g., cells) of a sample are sorted using a sorting determination module having multiple sorting determination units, such as that described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference. In some embodiments, the subject system includes a particle sorting module with deflection plates, such as that described in U.S. Patent Publication No. 2017 / 0299493, filed March 28, 2017, the disclosure of which is incorporated herein by reference.

[0311] In a specific embodiment, the system is a fluorescence imaging system using a radio frequency tagged luminescence imaging enabled particle sorter as shown in FIG. 3A. Particle sorter 300 includes an optical illumination component 300a including a light source 301 (e.g., a 488 nm laser) that generates an output beam of light 301a, which is split into beams 302a and 302b by a beam splitter 302. Light beam 302a propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 303 to generate output beam 303a having one or more angularly deflected light beams. In some cases, output beam 303a generated from acousto-optic device 303 includes a local oscillator beam and multiple radio frequency comb beams. Light beam 302b propagates through an acousto-optic device (e.g., an acousto-optic deflector, AOD) 304 to generate output beam 304a having one or more angularly deflected light beams. In some cases, output beam 304a generated from acousto-optic device 304 includes a local oscillator beam and multiple radio frequency comb beams. Output beams 303a and 304a generated from acousto-optical devices 303 and 304, respectively, are combined with beam splitter 305 to generate output beam 305a, which is conveyed through optical component 306 (e.g., an objective lens) to illuminate particles in flow cell 307. In certain embodiments, acousto-optical 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 overlapped with the array of beamlets at beam combiner 305. In certain embodiments, the light illumination system having a light source and acousto-optical device may also include those described in Schraivogel et al. (“High-speed fluorescence image-enabled cell sorting,” Science (2022), 375(6578):315-320) and U.S. Patent Publication No. 2021 / 0404943, the disclosures of which are incorporated herein by reference.

[0312] Output beam 305a illuminates sample particles 308 propagating through flow cell 307 (e.g., with sheath fluid 309) in illumination region 310. As shown in illumination region 310, multiple beams (e.g., angularly deflected, high-frequency shifted optical beams shown as dots across illumination region 310) overlap with a reference local oscillator beam (shown as hatched across illumination region 310). Due to their different optical frequencies, the overlapping beams exhibit beating behavior, whereby each beamlet emits a distinct frequency f 1~n carries a sinusoidal modulation.

[0313] Light from the illuminated sample is conveyed to a light detection system 300b, which includes multiple light detectors. Light detection system 300b includes a forward scatter light detector 311 for generating a forward scatter image 311a and a side scatter light detector 312 for generating a side scatter image 312a. Light detection system 300b also includes a bright-field light detector 313 for generating a light loss image 313a. In some embodiments, forward scatter detector 311 and side scatter detector 312 are photodiodes (e.g., avalanche photodiodes, APDs). In some cases, bright-field light detector 313 is a photomultiplier tube (PMT). Fluorescence from the illuminated sample is also detected by fluorescence light detectors 314-317. In some cases, light detectors 314-317 are photomultiplier tubes. Light from the illuminated sample is directed through a beam splitter 320 to the side scatter detection channel 312 and the fluorescence detection channels 314-317. Light detection system 300b includes bandpass optical components 321, 322, 323, and 324 (e.g., dichroic mirrors) for transmitting light of predetermined wavelengths to photodetectors 314-317. In some cases, optical component 321 is a 534 nm / 40 nm bandpass. In some cases, optical component 322 is a 586 nm / 42 nm bandpass. In some cases, optical component 323 is a 700 nm / 54 nm bandpass. In some cases, optical component 324 is a 783 nm / 56 nm bandpass. The first number represents the center of the spectral band. The second number indicates the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, i.e., from 500 nm to 520 nm.

[0314] Data signals generated in response to light detected in scattered light detection channels 311 and 312, bright-field light detection channel 313, and fluorescence detection channels 314-317 are processed by real-time digital processing by processors 350 and 351. Images 311a-317a can be generated in each light detection channel based on the data signals generated by processors 350 and 351. Image-enabled sorting is performed in response to a sorting signal generated by sorting trigger 352. Sorting component 300c includes deflection plates 331 for deflecting particles into a sample container 332 or to a waste stream 333. In some cases, sorting component 300c is configured to sort particles using an enclosed particle sorting module, such as that described in U.S. Patent Publication No. 2017 / 0299493 (filed March 28, 2017, the disclosure of which is incorporated herein by reference). In certain embodiments, the sorting component 300c includes a sorting determination module having multiple sorting determination units, such as those described in U.S. Patent Publication No. 2020 / 0256781, the disclosure of which is incorporated herein by reference.

[0315] FIG. 3B illustrates image-enabled particle sorting data processing according to certain embodiments. In some cases, the image-enabled particle sorting data processing is a low-latency data processing pipeline. Each photodetector generates pulses with high-frequency modulation that encodes an image (waveform). Fourier analysis is performed to reconstruct the image from the modulated pulses. The image processing pipeline generates a set of image features (image analysis) that are combined with features derived from the pulse processing pipeline (event packets). Real-time sorting electronics then classify particles based on the image features and generate sort decisions that are used to selectively charge droplets.

[0316] In some embodiments, the system is a particle analyzer, and particle analysis system 401 (FIG. 4A) can be used to analyze and characterize particles, with or without physically sorting the particles into a collection vessel. FIG. 4A shows a functional block diagram of a particle analysis system for computation-based sample analysis and particle characterization. In some embodiments, particle analysis system 401 is a flow system. Particle analysis system 401 shown in FIG. 4A can be configured, in whole or in part, to perform methods, such as those described herein. Particle analysis system 401 includes a fluidic system 402. Fluidic system 402 can include or be coupled to a sample tube 405 and a moving fluid column within the sample tube through which particles 403 (e.g., cells) of the sample move along a common sample path 409.

[0317] The particle analysis system 401 includes a detection system 404 configured to collect a signal from each particle as it passes through one or more detection stations along a common sample path. A detection station 408 generally refers to a monitoring area 407 of the common sample path. Detection, in some implementations, may include detecting light or one or more other characteristics of the particle 403 as it passes through the monitoring area 407. In FIG. 4A , one detection station 408 is shown with one monitoring area 407. Some implementations of the particle analysis system 401 may include multiple detection stations. Additionally, some detection stations may monitor more than one area.

[0318] Each signal is assigned a signal value to form a data point for each particle. This data may be referred to as event data, as described above. The data points may be multidimensional data points that include values ​​for each property measured for the particle. The detection system 404 is configured to collect such data points continuously over a first time interval.

[0319] The particle analysis system 401 may also include a control system 306. The control system 406 may include one or more processors, amplitude control circuitry, and / or frequency control circuitry. The illustrated control system may be operatively associated with the fluid system 402. The control system may be configured to generate a calculated signal frequency for at least a portion of the first time interval based on the Poisson distribution and the number of data points collected by the detection system 404 during the first time interval. The control system 406 may be further configured to generate an experimental signal frequency based on the number of data points in the portion of the first time interval. The control system 406 may further compare the experimental signal frequency to the calculated signal frequency or a predetermined signal frequency.

[0320] 4B shows a system 400 for flow cytometry according to an exemplary embodiment of the invention. System 400 includes a flow cytometer 410, a controller / processor 490, and a memory 495. Flow cytometer 410 includes one or more excitation lasers 415a-415c, a focusing lens 420, a flow chamber 425, a forward scatter detector 430, a side scatter detector 435, a fluorescence collection lens 440, one or more beam splitters 445a-445g, one or more bandpass filters 450a-450e, one or more longpass ("LP") filters 455a-455b, and one or more fluorescence detectors 460a-460f.

[0321] Pump lasers 115a-115c emit light in the form of laser beams. In the exemplary system of FIG. 4B, the wavelengths of the laser beams emitted from pump lasers 415a-415c are 488 nm, 633 nm, and 325 nm, respectively. The laser beams are first directed through one or more of beam splitters 445a and 445b. Beam splitter 445a transmits 488 nm light and reflects 633 nm light. Beam splitter 445b transmits ultraviolet light (light with wavelengths ranging from 10 nm to 400 nm) and reflects 488 nm and 633 nm light.

[0322] The laser beam is then directed onto a focusing lens 420, which focuses the beam onto a portion of the flow stream where the sample particles are located, within a flow chamber 425. The flow chamber is the part of a fluidic system that directs particles, typically one at a time, in a stream towards the focused laser beam for interrogation. A flow chamber can include a flow cell in a benchtop cytometer or a nozzle tip in a stream-in air cytometer.

[0323] Light from the laser beam interacts with the particles of the sample by diffraction, refraction, reflection, scattering, and absorption by re-emission at a variety of different wavelengths, depending on particle characteristics such as particle size, internal structure, and the presence of one or more fluorescent molecules attached to or naturally present on or within the particle. The fluorescent emission and diffracted, refracted, reflected, and scattered light may be sent via one or more of beam splitters 445a-g, bandpass filters 450a-e, longpass filters 455a-b, and fluorescence collection lens 440 to one or more of forward scatter detector 430, side scatter detector 435, and one or more fluorescence detectors 460a-f.

[0324] The fluorescence collection lens 440 collects light emitted from particle-laser beam interactions and routes it toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 450a-450e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 450a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number indicates the range of the spectral band. Thus, a 510 / 20 filter extends 10 nm on either side of the center of the spectral band, from 500 nm to 520 nm. Shortpass filters transmit light equal to or shorter than a specific wavelength. Longpass filters, such as longpass filters 455a-455b, transmit wavelengths equal to or longer than a specific wavelength. For example, longpass filter 455a, a 670 nm longpass filter, transmits light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorochrome. The filter can be configured so that the spectral band of light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0325] Beam splitters direct light of different wavelengths in different directions. Beam splitters can be characterized by filter properties such as short-pass and long-pass. For example, beam splitter 445g is a 620SP beam splitter, meaning that beam splitter 445g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in different directions. In one embodiment, beam splitters 445a-445g may include optical mirrors such as dichroic mirrors.

[0326] The forward scatter detector 430 is positioned slightly off-axis from the direct beam through the flow cell and is configured to detect diffracted light, or excitation light traveling primarily forward through or around the particle. The intensity of light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 435 is configured to detect refracted and reflected light from the particle's surface and internal structure, which tends to increase as the particle's structure becomes more complex. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 460a-460f. The side scatter detector 435 and the fluorescence detector may include photomultiplier tubes. The signals detected by the forward scatter detector 430, side scatter detector 435, and fluorescence detector can be converted to electronic signals (voltage) by the detectors. This data can provide information about the sample.

[0327] Those skilled in the art will recognize that flow cytometers according to embodiments of the present invention are not limited to the flow cytometer shown in Figure 4B, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in a variety of different configurations.

[0328] During operation, the operation of the cytometer is controlled by the controller / processor 490, and measurement data from the detectors may be stored in memory 495 and processed by the controller / processor 490. Although not explicitly shown, the controller / processor 490 may be coupled to the detectors to receive output signals from the detectors, and may also be coupled to the electrical and electromechanical components of the flow cytometer 400 to control lasers, fluid flow parameters, etc. Input / output (I / O) functionality 497 may also be provided in the system. The memory 495, controller / processor 490, and I / O 497 may be provided entirely as an integral part of the flow cytometer 410. In such embodiments, a display may also form part of the I / O functionality 497 for presenting experimental data to a user of the cytometer 400. Alternatively, some or all of the memory 495 and controller / processor 490 and I / O functionality may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 495 and controller / processor 490 may be in wireless or wired communication with the cytometer 410. Controller / processor 490, together with memory 495 and I / O 497, can be configured to perform a variety of functions associated with the preparation and analysis of flow cytometer experiments.

[0329] The system shown in FIG. 4B includes six different detectors that detect fluorescence in six different wavelength bands (sometimes referred to herein as the "filter windows" of a given detector), as defined by the configuration of filters and / or splitters in the beam path from the flow cell 425 to each detector. Different fluorescent molecules used in a flow cytometer experiment emit light in their own characteristic wavelength bands. The particular fluorescent labels used in the experiment and their associated fluorescence emission bands may be selected to approximately match the filter windows of the detectors. However, as many detectors are provided and many labels are utilized, perfect correspondence between filter windows and fluorescence emission spectra is not possible. While the peak of the emission spectrum of a particular fluorescent molecule may fall within the filter window of one particular detector, it is generally true that a portion of that label's emission spectrum also overlaps with the filter windows of one or more other detectors. This is sometimes referred to as spillover. The I / O 497 can be configured to receive data for a flow cytometer experiment involving a panel of fluorescent labels and multiple cell populations with multiple markers, each cell population having a subset of the multiple markers. I / O 497 may also be configured to receive biological data assigning one or more markers to one or more cell populations, marker density data, emission spectrum data, data assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data, such as label spectral characteristics and flow cytometer configuration data, may also be stored in memory 495. Controller / processor 490 may be configured to evaluate one or more assignments of labels to markers.

[0330] 5 shows a functional block diagram of an example particle analyzer control system for analyzing and displaying biological events, such as an analysis controller 500. The analysis controller 500 can be configured to implement various processes for controlling the graphical display of biological events.

[0331] 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.

[0332] The analysis controller 500 can be configured to receive biological event data from the particle analyzer or sorting system 502. The biological event data received from the particle analyzer or sorting system 502 can include flow cytometry event data. The analysis controller 500 can be configured to provide a graphical display including a first plot of the biological event data on the display device 506. The analysis controller 500 can be further configured to render a region of interest, for example, as a gate around a population of the biological event data shown by the display device 506, overlaid on the first plot. In some embodiments, the gate can be a logical combination of one or more graphical regions of interest depicted on a histogram or bivariate plot of a single parameter. In some embodiments, the display can be used to display particle parameters or saturation detector data.

[0333] Analysis controller 500 can be further configured to display biological event data within the gate on display device 506 differently from other events within the biological event data outside the gate. For example, analysis controller 500 can be configured to render the color of biological event data contained within the gate differently from the color of biological event data outside the gate. Display device 506 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface.

[0334] The analysis controller 500 may be configured to receive a gate selection signal identifying a gate from a first input device. For example, the first input device may be implemented as a mouse 510. The mouse 510 may initiate a gate selection signal to the analysis controller 500 identifying a gate to be displayed on or manipulated via the display device 506 (e.g., by clicking the desired gate when a cursor is positioned there). In some implementations, the first device may be implemented as a keyboard 508 or other means for providing input signals to the analysis controller 500, such as a touchscreen, a stylus, a photodetector, or a voice recognition system. Some input devices may include multiple input functions. In such implementations, each input function can be considered an input device. For example, as shown in FIG. 5, the mouse 510 may include a right mouse button and a left mouse button, each capable of generating a trigger event.

[0335] The trigger event can cause the analysis controller 500 to change how the data is displayed, what portions of the data are actually displayed on the display device 506, and / or provide input for further processing, such as selecting a population for particle sorting purposes.

[0336] In some embodiments, the analysis controller 500 can be configured to detect when a gate selection is initiated by the mouse 510. The analysis controller 500 can be further configured to automatically modify the visualization of the plot to facilitate the gating process. The modification can be based on a particular distribution of the biological event data received by the analysis controller 500.

[0337] The analysis controller 500 can be connected to a storage device 504. The storage device 504 can be configured to receive and store biological event data from the analysis controller 500. The storage device 504 can also be configured to receive and store flow cytometry event data from the analysis controller 500. The storage device 504 can be further configured to enable retrieval of biological event data, such as flow cytometry event data, by the analysis controller 500.

[0338] The display device 506 can be configured to receive display data from the analysis controller 500. The display data can include plots of the biological event data and gates that delineate sections of the plot. The display device 506 can be further configured to modify the information presented according to input received from the analysis controller 500, along with input from the particle analyzer 502, the storage device 504, the keyboard 508, and / or the mouse 510.

[0339] In some implementations, the analysis controller 500 can generate a user interface for receiving exemplary events for sorting. For example, the user interface can include controls for receiving exemplary events or exemplary images. The exemplary events or images or exemplary gates can be provided prior to collection of event data for the sample or based on an initial set of events for a portion of the sample.

[0340] FIG. 6A is a schematic diagram of a particle sorter system 600 (e.g., particle analyzer or sorting system 502) according to one embodiment presented herein. In some embodiments, the particle sorter system 600 is a cell sorter system. As shown in FIG. 6A, a droplet-forming transducer 602 (e.g., a piezoelectric oscillator) is coupled to a fluid conduit 601, which may be coupled to, include, or be a nozzle 603. Within the fluid conduit 601, a sheath fluid 604 hydrodynamically focuses a sample fluid 606 containing particles 609 into a moving fluid column 608 (e.g., a stream). Within the moving fluid column 608, the particles 609 (e.g., cells) are aligned in single file across a monitoring area 611 (e.g., where a laser stream intersects) illuminated by an illumination source 612 (e.g., a laser). Vibration of droplet-forming transducer 602 causes moving fluid column 608 to break up into multiple droplets 610 , some of which contain particles 609 .

[0341] During operation, the 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 is fed to a timing circuit 628, which in turn feeds a flash charge circuit 630. At a drop breakoff point, signaled by a timed drop delay (Δt), a flash charge can be applied to the moving fluid column 608 so that the droplets of interest carry a charge. The droplets of interest may contain one or more particles or cells to be sorted. The charged droplets can then be sorted by activating a deflection plate (not shown) to deflect the droplets into a collection tube or a container, such as a multi-well or microwell sample plate, and a well or microwell can be associated with the particular droplet of interest. As shown in FIG. 6A, the droplets can be collected in a waste receptacle 638.

[0342] Detection system 616 (e.g., a droplet boundary detector) helps automatically determine the phase of the droplet drive signal when a particle of interest passes through 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. Detection system 616 allows the instrument to accurately calculate the location of each detected particle in the droplet. Detection system 616 can provide amplitude signal 620 and / or phase 618 signals, which then (via amplifier 622) provide to amplitude control circuit 626 and / or frequency control circuit 624. Amplitude control circuit 626 and / or frequency control circuit 624 then control droplet forming transducer 602. Amplitude control circuit 626 and / or frequency control circuit 624 can be included in a control system.

[0343] In some implementations, the sorting electronics (e.g., detection system 616, detection station 614, and processor 640) can be coupled with a memory configured to store the detected events and sorting decisions based thereon. The sorting decisions can be included in the particle's event data. In some implementations, the detection system 616 and detection station 614 can be implemented as a single detection unit or can be communicatively coupled such that event measurements can be collected by either the detection system 616 or the detection station 614 and provided to a non-collecting element.

[0344] FIG. 6B is a schematic diagram of a particle sorter system according to one embodiment presented herein. The particle sorter system 600 shown in FIG. 6B includes deflection plates 652 and 654. An electric charge can be applied via stream charging wires within the barbs. This creates a stream of droplets 610 containing particles 610 for analysis. The particles can be illuminated with one or more light sources (e.g., lasers) to generate light scattering and fluorescence information. The information about the particles is analyzed, such as by sorting electronics or other detection systems (not shown in FIG. 6B). Deflection plates 652 and 654 can be independently controlled to attract or repel charged droplets and guide them toward a destination collection vessel (e.g., any of 672, 674, 676, or 678). 6B, deflector plates 652 and 654 can be controlled to direct particles along a first path 662 toward a receptacle 674 or along a second path 668 toward a receptacle 678. If the particle is not of interest (e.g., does not exhibit scattering or illumination information within a specified sorting range), the deflector plates can allow the particle to continue along flow path 664. Such uncharged droplets can enter a waste receptacle, such as via an aspirator 670.

[0345] Sorting electronics may be included to initiate the collection of measurements, receive fluorescent signals about the particles, and determine how to adjust the deflection plates to cause particle sorting. An exemplary implementation of the embodiment shown in Figure 6B includes the BD FACSAria™ line of flow cytometers commercially offered by Becton, Dickinson and Company (Franklin Lakes, NJ).

[0346] Integrated Circuit Devices Aspects of the present disclosure also include integrated circuit devices programmed to spectrally resolve light according to the methods described herein, such as from each fluorophore in a sample having multiple fluorophores with overlapping fluorescence spectra. In some embodiments, the subject integrated circuit devices comprise field programmable gate arrays (FPGAs). In other embodiments, the integrated circuit devices comprise application specific integrated circuits (ASICs). In still other embodiments, the integrated circuit devices comprise complex programmable logic devices (CPLDs). In some embodiments, the subject integrated circuit devices are programmed to determine the overlap between each different fluorophore in the sample and calculate the contribution of each fluorophore to the overlapping fluorescence.

[0347] In certain embodiments, the integrated circuit is programmed to calculate a spectral unmixing matrix of fluorescence spectra of multiple fluorophores with overlapping fluorescence in a sample detected by a light detection system having multiple light detectors. In certain embodiments, an integrated circuit device according to certain embodiments is programmed to calculate a spectral unmixing matrix of fluorescence spectra of multiple fluorophores for each cell in the sample. As described in more detail below, the integrated circuit device can be programmed to estimate the abundance of each fluorophore in the sample. In certain embodiments, the abundance of each fluorophore associated with a target particle can be determined. The integrated circuit can be programmed to identify and classify target particles based on the abundance of each fluorophore associated with the target particle. In some cases, the integrated circuit is configured to sort the identified or classified particles.

[0348] In certain embodiments, the integrated circuit is programmed to determine a measurement variance of the detected light for each particle and spectrally resolve the light from each fluorophore in the sample based on the calculated spectral unmixing matrix of the fluorescence spectrum and the measurement variance determined for each particle. In some cases, the integrated circuit is programmed to determine the measurement variance for each photodetector for each particle. In some cases, the integrated circuit is programmed to determine the measurement variance based on a change in photodetector gain. In some cases, the integrated circuit is programmed to determine the measurement variance based on a change in trigger threshold for one or more of the photodetectors. In certain examples, the integrated circuit is programmed to determine the measurement variance based on a change in the light detection duration of each photodetector for each particle. In certain examples, the integrated circuit is programmed to determine the measurement variance based on a change in the photonic shot noise detected by each photodetector.

[0349] In some cases, the integrated circuit is programmed to calculate the measured variance of each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the optoelectronic scaling factor, Y is the photodetector signal intensity.

[0350] In some embodiments, the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the integrated circuit is programmed to increase the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more, in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the integrated circuit is programmed to reduce the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, such as 2% or more, for example 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in photodetector gain in one or more photodetectors of the photodetection system.

[0351] In some embodiments, the integrated circuit is programmed to adjust the baseline sampling variance in response to a change in photonic shot noise detected by one or more photodetectors of the optical detection system. In some cases, the integrated circuit is programmed to increase the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, for example 0.01% or more, for example 0.05% or more, for example 0.1% or more, for example 0.5% or more, for example 1% or more, for example 2% or more, for example 5% or more, for example 10% or more, for example 25% or more, including 50% or more, in response to a change in photodetector gain in the photonic shot noise detected by one or more photodetectors of the optical detection system. In some cases, the integrated circuit is programmed to reduce the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, such as 2% or more, for example 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to a change in photodetector gain in the photonic shot noise detected by one or more photodetectors of the photodetection system.

[0352] In some embodiments, the integrated circuit is programmed to adjust the baseline sampling variance in response to a change in the trigger threshold of one or more photodetectors of the optical detection system. In some cases, the integrated circuit is programmed to increase the baseline sampling variance by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, for example 0.01% or more, for example 0.05% or more, for example 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, for example 10% or more, for example 25% or more, including 50% or more, in response to a change in the photodetector gain in the trigger threshold of one or more photodetectors of the optical detection system. In some cases, the reduction in baseline sampling variance may be by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, such as 5% or more, for example 10% or more, such as 25% or more, including 50% or more, depending on the change in trigger threshold in one or more photodetectors of the photodetection system.

[0353] In some embodiments, the integrated circuit is programmed to adjust the baseline sampling variance when or a predetermined period of time after a change in one or more of the photodetector gain, trigger threshold, and photonic shot noise is detected. In some cases, the integrated circuit is programmed to adjust the baseline sampling variance immediately after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected. In other cases, the integrated circuit is programmed to adjust the baseline sampling variance 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected, including adjusting the baseline sampling variance 60 minutes or more after a change in the photodetector gain, trigger threshold, and photonic shot noise is detected.

[0354] In certain embodiments, the integrated circuit is programmed to adjust the baseline sampling variance at predetermined time intervals, including, for example, every 0.01 minute or more, for example, every 0.05 minutes or more, for example, every 0.1 minute or more, for example, every 0.5 minutes or more, for example, every 1 minute or more, for example, every 5 minutes or more, for example, every 10 minutes or more, for example, every 15 minutes or more, for example, every 30 minutes or more, for example, every 60 minutes or more, for example, every 2 hours or more, for example, every 4 hours or more, for example, every 8 hours or more, for example, every 12 hours or more, for example, every 16 hours or more, for example, every 20 hours or more, and every 24 hours or more.

[0355] In some embodiments, the integrated circuit is programmed to adjust the scaling factor in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In some cases, the integrated circuit is programmed to increase the scaling factor in response to changes in photodetector gain in one or more photodetectors of the photodetection system by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, such as 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, such as 0.1% or more, for example 0.5% or more, such as 1% or more, for example 2% or more, for example 5% or more, such as 10% or more, for example 25% or more, including 50% or more. In some cases, the integrated circuit is programmed to decrease the scaling factor by 0.00001% or more, such as 0.0001% or more, for example 0.0005% or more, for example 0.001% or more, for example 0.005% or more, such as 0.01% or more, for example 0.05% or more, for example 0.1% or more, such as 0.5% or more, for example 1% or more, such as 2% or more, for example 5% or more, for example 10% or more, such as 25% or more, including 50% or more, in response to changes in photodetector gain in one or more photodetectors of the photodetection system. In certain embodiments, the integrated circuit is programmed to generate a calibration curve of the scaling factor based on the photodetector gain of one or more of the photodetectors of the photodetection system.

[0356] In some embodiments, the integrated circuit is programmed to use a measurement duration of each sampling pulse that is the same for each of the photodetectors of the photodetection system. In some embodiments, the integrated circuit is programmed to use a measurement duration of each sampling pulse that is different for one or more of the photodetectors of the photodetection system. Depending on the number of photodetectors in the photodetection system, the measurement duration may be different for one or more of the photodetectors, for example, two or more, for example, three or more, for example, four or more, for example, five or more, for example, ten or more, for example, fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifteen or more, for example, sixteen or more, for example, thirty-two or more, for example, sixty-four or more, for example, one hundred and twenty-eight or more, for example, two hundred and fifty-eight or more, including ... In a particular example, the measurement duration of each sampling pulse is different for all (ie, 100%) of the photodetectors in the photodetection system.

[0357] In some embodiments, the integrated circuit is programmed to calculate the measurement variance immediately after a data signal is generated from the detected light for each particle. In other examples, the integrated circuit is programmed to calculate the measurement variance 0.01 seconds or more, e.g., 0.05 seconds or more, e.g., 0.1 seconds or more, e.g., 0.5 seconds or more, e.g., 1 second or more, e.g., 5 seconds or more, e.g., 10 seconds or more, e.g., 15 seconds or more, e.g., 30 seconds or more, e.g., 60 seconds or more, e.g., 5 minutes or more, e.g., 10 minutes or more, e.g., 15 minutes or more, e.g., 30 minutes or more after a data signal is generated from the detected light for each particle, including adjusting the baseline sampling variance 60 minutes or more after a data signal is generated from the detected light. In certain embodiments, the integrated circuit is programmed to calculate the measurement variance for each particle for each photodetector in real time.

[0358] In embodiments, the integrated circuit is programmed to spectrally resolve light from each fluorophore having overlapping fluorescence, and instructions for calculating a spectral unmixing matrix using the measurement variance determined for each particle in the sample. In some cases, the integrated circuit is programmed to calculate the spectral unmixing matrix using a weighted least squares algorithm. In some cases, the integrated circuit is programmed to calculate a weighted least squares algorithm according to:

[0359]

number

[0360] where y is the measured detector value from multiple photodetectors of the photodetection system for each cell, a is the estimated fluorophore abundance, X is the spillover, and W is:

[0361]

number

[0362] In some embodiments, the integrated circuit may include a plurality of W ii It is programmed to calculate

[0363]

number

[0364] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ iis the noise constant at detector i. In certain embodiments, the spectral unmixing matrix is ​​calculated according to: In some cases, the integrated circuit calculates (X T WX). (X T WX) -1 X T W

[0365] In some cases, the integrated circuit is programmed to determine the photodetector noise components (e.g., electronic noise, background light, etc.) using a single stained control sample. In certain examples, the integrated circuit is programmed to determine the variance of each photodetector before illuminating the sample with the light source. In other examples, the integrated circuit determines W, W before illuminating the sample with the light source. G The system is programmed to determine a predetermined approximation of

[0366] In some embodiments, the integrated circuit is programmed to calculate a weighted least squares algorithm using a Cholesky decomposition. In some cases, the integrated circuit is programmed to calculate a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0367] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 XT W

[0368] In certain embodiments, an integrated circuit device performs a weighted least squares algorithm (X) analysis of each particle detected by the optical detection system to sort particles in a sample in real time. T WX). In the specific example, (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0369]

number

[0370] W G is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system. In some embodiments, the integrated circuit device is programmed to further estimate the variance of each photodetector. In particular examples, the integrated circuit device is programmed with an estimate of the photodetector noise component based on a single stained control sample. In other examples, the variance of each photodetector is programmed into the integrated circuit device before the sample is illuminated with the light source. In still other examples, an iterative Newton-Raphson calculation, W, is performed before the sample is illuminated with the light source. G A predetermined approximation of W at G Using A0 -1 In these embodiments, the pre-calculated A0 -1 is programmed into the integrated circuit device and is the A of each particle detected by the optical detection system. -1 can be used as a first approximation of

[0371] In other embodiments, the subject integrated circuit device is programmed to approximate a weighted least squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the integrated circuit is programmed to calculate A using the Sherman-Morrison formula.

[0372]

number

[0373] In a particular example, the integrated circuit uses the Sherman-Morrison formula to calculate the inverse of the perturbation of A0. -1 In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the integrated circuit device is programmed to calculate ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0374]

number

[0375] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0376]

number

[0377] In these embodiments, each w iA change to W can be used to recalculate each A from A with each new weight matrix W (i.e., a value different from W). In some embodiments, the integrated circuit device is programmed to implement a Sherman-Morrison iterative inverse updater to approximate the spectral unmixing matrix according to:

[0378]

number

[0379] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0380] In other embodiments, the subject integrated circuit device is programmed to calculate a weighted least squares algorithm for each particle by matrix decomposition. In some cases, the integrated circuit device is programmed with instructions for LU matrix decomposition, for example, where a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm (a) using a modified Cholesky decomposition according to the following: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0381] In other embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm using QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, the integrated circuit device is programmed to calculate a weighted least squares algorithm (a) using QR factorization according to:

[0382]

number

[0383] In yet another embodiment, the integrated circuit device is programmed to calculate a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is a product X - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, an integrated circuit device is programmed to compute a weighted least squares algorithm (a) using singular value decomposition according to:

[0384] z=U T y Σw=z a=Vw

[0385] In some embodiments, the integrated circuit of interest is programmed to calculate the abundance of one or more fluorophores in a sample from the decomposed light in the spectrum from each fluorophore. In certain examples, the abundance of fluorophores associated with a target particle (e.g., chemically associated (i.e., covalently, ionically) or physically associated) is calculated from the decomposed light in the spectrum from each fluorophore associated with the particle. For example, in one example, the integrated circuit is programmed to calculate the relative abundance of each fluorophore associated with the target particle from the decomposed light in the spectrum from each fluorophore. In another example, the integrated circuit is programmed to calculate the absolute abundance of each fluorophore associated with the target particle from the decomposed light in the spectrum from each fluorophore.

[0386] In certain embodiments, the integrated circuit is programmed to identify or classify particles based on the relative abundance of each fluorophore determined to be associated with the particle. In these embodiments, the integrated circuit can be programmed to identify or classify particles by any convenient protocol, such as by comparing the relative or absolute abundance of each fluorophore associated with the particle to a control sample having particles of known identity, or by performing spectroscopic or other assay analysis of a population of particles (e.g., cells) having calculated relative or absolute abundances of associated fluorophores.

[0387] Non-transitory computer-readable storage medium Aspects of the present disclosure further include non-transitory computer-readable storage media having instructions for implementing the subject methods. The computer-readable storage media may be used on one or more computers for fully or partially automating systems for implementing the methods described herein. In certain embodiments, instructions according to the methods described herein may be encoded on a computer-readable medium in the form of "programming," and the term "computer-readable medium" as used herein refers to any non-transitory storage medium involved in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include magnetic disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray disks, solid-state disks, and network-attached storage (NAS), whether such devices are internal or external to the computer. Files containing information may be "stored" on a computer-readable medium, where "storing" refers to recording information so that it can be accessed and retrieved at a later date by a computer. The computer-implemented methods described herein may be executed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Python, Java, JavaScript, C, C#, C++, Go, R, Swift, PHP, as well as many others.

[0388] Also described is a non-transitory computer-readable storage medium having instructions with an algorithm for spectrally resolving light from multiple fluorophores in a sample. The non-transitory computer-readable storage medium according to certain embodiments has an algorithm for determining a measured variance of light detected by a light detection system for each particle of a sample including multiple fluorophores with overlapping fluorescence spectra, and an algorithm for spectrally resolving light from each fluorophore in the sample based on a calculated spectral unmixing matrix of the fluorescence spectra and the measured variance determined for each particle.

[0389] In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance for each photodetector of the photodetection system for each particle. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance based on a change in photodetector gain. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance based on a change in trigger threshold for one or more of the photodetectors. In certain examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance based on a change in light detection duration of each photodetector for each particle. In certain examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining a measurement variance based on a change in photonic shot noise detected by each photodetector.

[0390] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for calculating the measured variance of each particle according to: V = (L × T) + (Q × Y) where L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is an optoelectronic scaling factor, and Y is the photodetector signal intensity. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in photonic shot noise parameters detected by one or more photodetectors of the photodetection system. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance in response to changes in trigger thresholds for one or more of the photodetectors of the photodetection system. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the baseline sampling variance at predetermined time intervals, such as every 6 hours, 12 hours, or 24 hours.

[0391] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for adjusting the scaling factors in response to changes in photodetector gain in one or more of the photodetectors. In some cases, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating a scaling factor calibration curve based on the photodetector gain for one or more of the photodetectors. In particular examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining the scaling factors from the scaling factor versus detector gain calibration curve using the gain setting of each photodetector during measurement. In particular embodiments, the scaling factor versus detector gain calibration curve does not change over an extended period of time, e.g., over 6 hours or more, e.g., over 12 hours or more, including over 1 day or more.

[0392] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating different sampling pulse durations for one or more of the photodetectors of the optical detection system. In some embodiments, the sampling pulse duration is the same for two or more of the photodetectors of the optical detection system. In certain embodiments, the sampling pulse duration is the same for each of the photodetectors of the optical detection system. In certain embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for determining the measured variance of each particle for each photodetector in real time.

[0393] In some embodiments, a non-transitory computer-readable storage medium comprises instructions having an algorithm for spectrally resolving light from each fluorophore based on a calculated spectral unmixing matrix of the fluorescence spectrum and the measured variance determined for each particle. In some embodiments, the non-transitory computer-readable storage medium comprises instructions having an algorithm for calculating the spectral unmixing matrix using a weighted least squares algorithm. In some embodiments, the non-transitory computer-readable storage medium comprises instructions having an algorithm for calculating a weighted least squares algorithm according to:

[0394]

number

[0395] where y is the measured detector value from multiple photodetectors of the photodetection system for each particle (e.g., cell), a is the estimated fluorophore abundance, X is spillover, and W is:

[0396]

number

[0397] In some embodiments, each W ii is calculated as follows:

[0398]

number

[0399] Q is a scaling factor, T is the measurement duration of each sampling pulse, and σ i 2 is the variance at detector i, and y i is the signal at detector i, and λ i is the noise constant at detector i.

[0400] In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm using a Cholesky decomposition. In some cases, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x

[0401] In certain embodiments, the spectral unmixing matrix is ​​calculated according to: T WX) is inverted for each particle detected by the light detection system to calculate the spectral unmixing matrix. (X T WX) -1 X T W

[0402] In certain embodiments, the non-transitory computer-readable storage medium includes a memory for storing a weighted least squares algorithm (X) of each particle detected by the optical detection system to sort particles in a sample in real time. TWX). In particular, we include an algorithm for approximating the inverse of (X T The inversion of WX) is approximated using an iterative Newton-Raphson calculation according to:

[0403]

number

[0404] W G is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system. In some embodiments, the non-transitory computer-readable storage medium includes an algorithm that further estimates the variance of each photodetector. In particular examples, the non-transitory computer-readable storage medium includes an algorithm having an estimate of the photodetector noise component based on a single stained control sample. In other examples, the variance of each photodetector is programmed into the non-transitory computer-readable storage medium before the sample is illuminated with the light source. In yet other examples, an iterative Newton-Raphson calculation, W, is performed before the sample is illuminated with the light source. G In a particular embodiment, the non-transitory computer-readable storage medium is programmed with a predetermined approximation of W in G A0 -1 In these embodiments, the algorithm for pre-calculating A0 -1 is programmed into a non-transitory computer-readable storage medium and is used to calculate the A of each particle detected by the optical detection system. -1 can be used as a first approximation of

[0405] In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for approximating a weighted least-squares algorithm for each particle using a Sherman-Morrison iterative inverse updater. In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating A using the Sherman-Morrison formula.

[0406]

number

[0407] In particular examples, the non-transitory computer-readable storage medium includes an algorithm for calculating the inverse of a perturbation of A using the Sherman-Morrison formula. In some embodiments, the inverse of A is calculated using the formula X T W0X, and the inverse matrix of A is calculated by the formula X T In some cases, the non-transitory computer-readable storage medium includes an algorithm for calculating ΔA (i.e., A−A) as a column vector product with each iteration W according to:

[0408]

number

[0409] According to an embodiment, ΔA i =X T ΔW i X=α i m i m i T and A can be expressed as follows:

[0410]

number

[0411] In these embodiments, each w i A change to W may be used to recalculate each A from A with each new weight matrix W (i.e., a different value than W). In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for performing a Sherman-Morrison iterative inverse updater to approximate a spectral unmixing matrix according to:

[0412]

number

[0413] In some embodiments, A0 -1 The pre-calculated value of A1 -1 is used to calculate the pre-calculated A1 -1 Using A2 -1 Calculate the value A -1 is from i=1 to N D Each (A i-1 ) -1 Using A i -1 can be calculated by repeatedly calculating

[0414] In other embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a weighted least squares algorithm for each particle through matrix decomposition. In some cases, the non-transitory computer-readable storage medium includes an algorithm for LU matrix decomposition, in which a matrix is ​​decomposed into a product of a lower triangular (L) matrix and an upper triangular (U) matrix. In particular examples, the LU decomposition includes Gaussian elimination. In other examples, the LU decomposition includes a modified Cholesky decomposition, an LDL decomposition, where D is a diagonal matrix. In particular embodiments, the non-transitory computer-readable storage medium includes an algorithm for calculating a weighted least squares algorithm (a) using a modified Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B LDL degradation Lz=B, where z=DL T a Lower triangular matrix solution Dx=z, where x=L T a Diagonal matrix solution L T a=x upper triangular matrix solution

[0415] In other embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm with QR factorization. In some cases, the QR factorization is a matrix that is the product of an orthogonal (Q) matrix and an upper triangular (R) matrix. In some embodiments, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm (a) using QR factorization according to:

[0416]

number

[0417] In yet another embodiment, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm by singular value decomposition (SVD). In some cases, the singular value decomposition is - =UΣV T where U and V are orthogonal matrices and Σ is X - In a particular example, a non-transitory computer-readable storage medium includes an algorithm for computing a weighted least squares algorithm (a) using singular value decomposition according to: z=U T y Σw=z a=Vw

[0418] In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for estimating the abundance of one or more of the fluorophores in the sample based on the calculated spectral unmixing matrix and the measurement variance determined for each particle. In some embodiments, the non-transitory computer-readable storage medium includes instructions having an algorithm for identifying particles in the sample based on the estimated abundance of each fluorophore on the particle. In certain examples, the non-transitory computer-readable storage medium includes instructions having an algorithm for generating a sorting decision based on the identified particles in the sample.

[0419] The non-transitory computer-readable storage medium may be used in one or more computer systems having a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses a memory in which instructions for executing the steps of the subject method are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, a system memory, a memory storage device, an input / output controller, a cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are or become available. The processor executes an operating system, which interfaces with firmware and hardware in well-known ways to facilitate the processor's coordination and execution of the functions of various computer programs, which may be written in various programming languages, such as those mentioned above, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of the other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all in accordance with known techniques.

[0420] kit Aspects of the present disclosure further include kits, which include one or more of the integrated circuits described herein. In some embodiments, the kits may further include programming for the subject systems, such as in the form of a computer-readable medium (e.g., a flash drive, USB storage, a compact disc, a DVD, a Blu-ray disc, etc.), or instructions for downloading the programming from an Internet web protocol or cloud server. The kits may further include instructions for implementing the subject methods. These instructions may be present in the subject kits in a variety of forms, one or more of which may be present in the kit. One form in which these instructions may be present is information printed on a suitable medium or substrate, such as one or more pieces of paper with the information printed on them, kit packaging, a package insert, etc. Another form in which these instructions may be present is a computer-readable medium on which the information is recorded, such as a diskette, a compact disc (CD), a portable flash drive, etc. Another form in which these instructions may be present is a website address that can be used via the Internet to access the information at the removed site.

[0421] Utilities The subject systems, methods, and computer systems find use in a variety of applications in which it is desirable to analyze and sort particle components in samples in fluid media, such as biological samples. In some embodiments, the systems and methods described herein find use in flow cytometry characterization of biological samples labeled with fluorescent tags. In other embodiments, the systems and methods find use in spectroscopy of emitted light. Additionally, the subject systems and methods find use in increasing the signal obtained from light collected from a sample (e.g., within a flow stream). In particular examples, the present disclosure finds use in improving the measurement of light collected from a sample illuminated within a flow stream in a flow cytometer. Embodiments of the present disclosure are used where it is desirable to provide a flow cytometer with improved cell sorting accuracy, improved particle collection, particle charging efficiency, more accurate particle charging, and improved particle deflection during cell sorting.

[0422] Embodiments of the present disclosure are also useful in applications where cells prepared from biological samples may be desired for research, laboratory testing, or therapeutic use. In some embodiments, the subject methods and devices may facilitate obtaining individual cells prepared from target fluid or tissue biological samples. For example, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used as research or diagnostic specimens for diseases such as cancer. Similarly, the subject methods and systems may facilitate obtaining cells from fluid or tissue samples used for therapeutic purposes. The disclosed methods and devices enable the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, bodily fluids) with improved efficiency and lower cost compared to conventional flow cytometry systems.

[0423] The subject matter described herein, including embodiments, may be useful alone or in combination with one or more other embodiments or embodiments. Without limiting the description, certain non-limiting aspects of the present disclosure, numbered 1 to 193, are provided below. As will be apparent to those skilled in the art upon reading this disclosure, each individually numbered aspect can be used or combined with any of the preceding or subsequent individually numbered aspects. This is intended to support all such combinations of aspects, and is not limited to the combinations of aspects explicitly provided below.

[0424] 1. detecting light with a light detection system from particles of a sample that include multiple fluorophores with overlapping fluorescence spectra; determining a measured variance of the detected light for each particle; and Spectrally resolving the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measurement variance determined for each particle. A method comprising: 2. The method of embodiment 1, wherein the light detection system comprises a plurality of light detectors, and the measured dispersion of each particle is determined for each light detector. 3. The method of aspect 1 or 2, wherein the measurement variance comprises a change in a photodetector gain parameter in one or more of the photodetectors of the photodetection system. 4. The method of any one of aspects 1-3, wherein the measurement variance comprises a change in a trigger threshold parameter for one or more of the photodetectors of the photodetection system. 5. The method of any one of aspects 1-4, wherein the measurement variance comprises a change in a light detection duration parameter for each light detector for each particle.

[0425] 6. The method of any one of aspects 1-5, wherein the measurement variance comprises a change in a photonic shot noise parameter detected by each photodetector for each particle. 7. The measurement variance is calculated for each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the optoelectronic scaling factor, Aspect 7. The method of any one of aspects 1 to 6, wherein Y is the photodetector signal intensity. 8. The method of embodiment 7, wherein the method includes adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors of the photodetection system. 9. The method of aspect 7 or 8, wherein the method comprises adjusting the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the optical detection system. 10. The method of any one of aspects 7-9, wherein the method comprises adjusting the baseline sampling variance in response to changes in the trigger threshold of one or more of the photodetectors of the optical detection system.

[0426] 11. The method of any one of aspects 7 to 10, wherein the baseline sampling variance is adjusted at predetermined time intervals. 12. The method of embodiment 11, wherein the method comprises adjusting for baseline sampling variance every 24 hours. 13. The method of any one of aspects 7-12, wherein the method includes adjusting a scaling factor in response to a change in photodetector gain for one or more of the photodetectors of the photodetection system. 14. The method of any one of aspects 7-13, wherein the method further comprises generating a calibration curve of the scaling factor based on photodetector gains for one or more of the photodetectors of the photodetection system. 15. The method of any one of aspects 7-14, wherein the measurement duration of each sampling pulse is different for one or more of the photodetectors of the photodetection system.

[0427] 16. The method of any one of aspects 7-14, wherein the measurement duration of each sampling pulse is the same for each of the photodetectors of the photodetection system. 17. The method of any one of aspects 1-16, wherein the measurement variance is determined for each particle at each photodetector in real time. 18. The method of any one of aspects 1-17, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. 19. The method of embodiment 18, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 10 nm or more. 20. The method of embodiment 18, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 25 nm or more.

[0428] 21. The method of any one of aspects 1-20, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample. 22. The method of embodiment 21, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 10 nm or more. 23. The method of embodiment 21, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 25 nm or more. 24. The method of any one of aspects 1-23, comprising calculating a spectral unmixing matrix for the fluorescence spectrum of each fluorophore in the sample using a weighted least squares algorithm. 25. A weighted least squares algorithm is calculated according to:

[0429]

number

[0430] During the ceremony, y is the measured detector value from the multiple photodetectors of the photodetection system for each cell, where a is the estimated fluorophore abundance, X is the spillover, 25. The method of any one of aspects 1 to 24, wherein W is:

[0431]

number

[0432] 26.Each W ii is calculated according to

[0433]

number

[0434] During the ceremony, Q is a scaling factor, T is the measurement duration of each sampling pulse, σ i 2 is the variance at detector i, y i is the signal at detector i, λ i 26. The method of embodiment 25, wherein: i is a noise constant at detector i. 27. The method of any one of aspects 1-24, wherein the weighted least squares algorithm is calculated using a Cholesky decomposition. 28. The method of aspect 27, wherein the weighted least squares algorithm is calculated using a Cholesky decomposition according to: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L Ta L T a=x 29. The method of any one of aspects 1-24, wherein the weighted least squares algorithm is calculated by QR factorization. 30. The method of embodiment 29, wherein the weighted least squares algorithm is calculated using QR factorization according to:

[0435]

number

[0436] 31. The method of any one of aspects 1-24, wherein the weighted least squares algorithm is calculated using singular value decomposition. 32. The weighted least squares algorithm is calculated using singular value decomposition according to z=U T y Σw=z a=Vw where U and V are orthogonal matrices and Σ is the singular value of X - 32. The method of embodiment 31, wherein the matrix is ​​a diagonal matrix comprising: 33. The method of any one of aspects 1-24, wherein the method comprises calculating a spectral unmixing matrix according to: (X T WX) -1 X T W 34. The method calculates the spectral unmixing matrix (X T 25. The method of any one of embodiments 1-24, comprising inverting (WX). 35.(X T WX) can be inverted using an iterative Newton-Raphson calculation as follows: T WX),

[0437]

number

[0438] In the formula, WG 35. The method of embodiment 34, wherein W is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system.

[0439] 36. Before irradiating the sample with the light source, G 36. The method of embodiment 35, further comprising determining: 37. Calculated W G A0 in value -1 37. The method of embodiment 36, further comprising pre-calculating: 38. The method uses a Sherman-Morrison iterative inverse updater according to T 38. The method of any one of embodiments 25 to 37, comprising approximating WX).

[0440]

number

[0441] 39.ΔA i =X T ΔW i X=α i m i m i T wherein:

[0442]

number

[0443] 39. The method of embodiment 38, wherein 40. The method of embodiment 39, further comprising approximating a spectral unmixing matrix according to:

[0444]

number

[0445] 41.A0 -1 Pre-computed values ​​for A1 -1 is used to calculate A i-1 But from i=1 to N D Each (A i-1 ) -1 A using i -1 The method of embodiment 40, wherein the value is calculated by repeated calculation of 42. The method of any one of aspects 1-24, wherein the weighted least squares algorithm is calculated using matrix decomposition. 43. The method of embodiment 42, wherein the matrix decomposition comprises LU decomposition. 44. The method of any one of aspects 1-43, wherein the weighted least squares algorithm using measurement variance is computed on a field programmable gate array. 45. The method of any one of aspects 1-44, further comprising illuminating the sample with a light source.

[0446] 46. ​​The method of embodiment 45, wherein the light source comprises a laser. 47. The method of embodiment 46, wherein the light source comprises a plurality of lasers. 48. The method of any one of aspects 1-47, wherein the optical detection system comprises a plurality of optical detectors. 49. The method of embodiment 48, wherein the photodetector comprises one or more photomultiplier tubes. 50. The method of any one of embodiments 1-49, wherein the light detection system comprises a light detector array.

[0447] 51. The method of embodiment 50, wherein the photodetector array comprises photodiodes. 52. The method of embodiment 51, wherein the photodetector array comprises a charge-coupled device.

[0448] 53. A light source configured to illuminate particles of a sample comprising a plurality of fluorophores having overlapping fluorescence spectra; a light detection system including a plurality of light detectors; and 1. A processor including a memory operatively coupled to the processor, the memory, when executed by the processor, causing the processor to: determining a measured dispersion of the detected light for each particle; Spectrally resolving the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measurement variance determined for each particle. a processor including stored instructions for causing Including, the system. 54. The system of embodiment 53, wherein the memory includes instructions for determining the measured variance of each photodetector for each particle. 55. The system of aspect 53 or 54, wherein the measurement variance comprises a change in a photodetector gain parameter in one or more of the photodetectors of the optical detection system. 56. The system of any one of aspects 53-55, wherein the measurement variance comprises a change in a trigger threshold parameter for one or more of the photodetectors of the optical detection system. 57. The system of any one of embodiments 53-56, wherein the measurement variance comprises a change in a light detection duration parameter for each light detector for each particle.

[0449] 58. The system of any one of aspects 53-57, wherein the measurement variance comprises a change in a photonic shot noise parameter detected by each photodetector for each particle. 59. The memory includes instructions for calculating the measurement variance for each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the photoelectron scaling factor, 59. The system of any one of aspects 53 to 58, wherein Y is the photodetector signal intensity. 60. The system of embodiment 59, wherein the memory includes instructions for adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors of the photodetection system. 61. The system of aspect 59 or 60, wherein the memory comprises instructions for adjusting the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the optical detection system. 62. The system of any one of aspects 59-61, wherein the memory includes instructions for adjusting the baseline sampling variance in response to changes in trigger thresholds for one or more of the photodetectors of the photodetection system.

[0450] 63. The system of any one of aspects 59-62, wherein the memory comprises instructions for adjusting the baseline sampling variance at predetermined time intervals. 64. The system of aspect 63, wherein the memory comprises instructions for adjusting the baseline sampling variance every 24 hours. 65. The system of any one of aspects 59-64, wherein the memory includes instructions for adjusting a scaling factor in response to changes in photodetector gain for one or more of the photodetectors of the photodetection system. 66. The system of any one of aspects 59-65, wherein the memory includes instructions for generating a calibration curve of scaling factors based on photodetector gains for one or more of the photodetectors of the photodetection system. 67. The system of any one of aspects 59-66, wherein the measurement duration of each sampling pulse is different for one or more of the photodetectors of the photodetection system.

[0451] 68. The system of any one of aspects 59-66, wherein the measurement duration of each sampling pulse is the same for each of the photodetectors of the photodetection system. 69. The system of any one of aspects 53-68, wherein the memory includes instructions for determining the measured dispersion of each particle for each photodetector in real time. 70. The system of any one of aspects 53-69, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. 71. The system of aspect 70, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 10 nm or more. 72. The system of embodiment 70, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 25 nm or more.

[0452] 73. The system of any one of aspects 53-72, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample. 74. The system of embodiment 73, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 10 nm or more. 75. The system of embodiment 73, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 25 nm or more. 76. The system of any one of aspects 53-75, wherein the memory comprises instructions for calculating a spectral unmixing matrix for the fluorescence spectrum of each fluorophore in the sample using a weighted least squares algorithm. 77. A memory includes instructions for computing a weighted least squares algorithm according to:

[0453]

number

[0454] During the ceremony, y is the measured detector value from the multiple photodetectors of the photodetection system for each cell, where a is the estimated fluorophore abundance, X is the spillover, The system of any one of aspects 53 to 76, wherein W is:

[0455]

number

[0456] 78.Each W ii is calculated according to

[0457]

number

[0458] During the ceremony, Q is a scaling factor, T is the measurement duration of each sampling pulse, σ i 2 is the variance at detector i, y i is the signal at detector i, λ i 78. The system of embodiment 77, wherein: 79. The system of any one of aspects 53-76, wherein the memory comprises instructions for computing a weighted least squares algorithm using Cholesky decomposition. 80. The system of aspect 79, wherein the memory comprises instructions for calculating a weighted least squares algorithm using Cholesky decomposition according to the following: X T WXa=X T Wy Aa=B LDL T a=B Lz=B, where z=DL T a Dx=z, where x=L T a L T a=x 81. The system of any one of aspects 53-76, wherein the memory comprises instructions for computing a weighted least squares algorithm using QR factorization. 82. The system of aspect 81, wherein the memory comprises instructions for calculating a weighted least squares algorithm using QR factorization according to the following:

[0459]

number

[0460] 83. The system of any one of aspects 53-76, wherein the memory comprises instructions for computing a weighted least squares algorithm using singular value decomposition. 84. A memory includes instructions for computing a weighted least squares algorithm using singular value decomposition according to: z=U T y Σw=z a=Vw where U and V are orthogonal matrices and Σ is the singular value of X - 84. The system of embodiment 83, wherein the matrix is ​​a diagonal matrix comprising: 85. The system of any one of aspects 53-76, wherein the memory comprises instructions for calculating a spectral unmixing matrix according to the following: (X T WX) -1 X T W 86. The memory calculates the spectral unmixing matrix (X T 77. The system of any one of embodiments 53-76, comprising instructions for inverting (WX). 87.(X T WX) can be inverted using an iterative Newton-Raphson calculation as follows: T WX),

[0461]

number

[0462] In the formula, W G87. The system of embodiment 86, wherein W is a predetermined approximation of W determined from the variance of each photodetector in the photodetection system.

[0463] 88. The memory is set to W before irradiating the sample with the light source. G 88. The system of embodiment 87, further comprising instructions for determining: 89. Memory is calculated W G A0 in value -1 88. The system of embodiment 87, further comprising pre-calculating: 90. The memory is updated using the Sherman-Morrison iterative inverse updater according to T 90. The system of any one of embodiments 77-89, comprising instructions for approximating WX.

[0464]

number

[0465] 91.ΔA i =X T ΔW i X=α i m i m i T where:

[0466]

number

[0467] 91. The system of embodiment 90, wherein 92. The system of aspect 91, wherein the memory includes instructions for approximating a spectral unmixing matrix according to:

[0468]

number

[0469] 93.A0 -1 The pre-computed values ​​for A1 -1is used to calculate A i -1 But from i=1 to N D Each (A i-1 ) -1 A using i -1 The system of embodiment 92, wherein the calculation is performed by repeating the calculation of 94. The system of any one of aspects 53-93, wherein the memory comprises instructions for computing a weighted least squares algorithm by matrix decomposition. 95. The system of aspect 94, wherein the matrix decomposition includes LU decomposition. 96. The system of any one of aspects 53-95, wherein the system comprises a field programmable gate array including programming for calculating a weighted least squares algorithm using the measurement variance. 97. The system of any one of aspects 53-96, wherein the light source comprises a laser.

[0470] 98. The system of embodiment 97, wherein the light source comprises multiple lasers. 99. The system of any one of aspects 53-98, wherein the photodetector comprises one or more photomultiplier tubes. 100. The system of any one of embodiments 53-99, wherein the optical detection system comprises a photodetector array. 101. The system of embodiment 100, wherein the photodetector array comprises a photodiode. 102. The system of embodiment 101, wherein the photodetector array comprises a charge-coupled device.

[0471] 103. An integrated circuit comprising: determining a measured variance of light detected by the light detection system for each particle of the sample containing multiple fluorophores with overlapping fluorescence spectra; Spectrally resolve the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measurement variance determined for each particle An integrated circuit that is programmed to 104. The integrated circuit of aspect 103, wherein the integrated circuit is programmed to determine a measurement variance for each photodetector of the photodetection system for each particle. 105. The integrated circuit of aspect 103 or 104, wherein the measurement variance comprises a change in a photodetector gain parameter in one or more of the photodetectors of the photodetection system. 106. The integrated circuit of any one of aspects 103-105, wherein the measurement variance comprises a change in a trigger threshold parameter for one or more of the photodetectors of the photodetection system. 107. The integrated circuit of any one of aspects 103-106, wherein the measurement variance comprises a variation in a light detection duration parameter for each photodetector for each particle.

[0472] 108. The integrated circuit of any one of aspects 103-107, wherein the measurement variance comprises a change in a photonic shot noise parameter detected by each photodetector for each particle. 109. An integrated circuit is programmed to calculate the measured variance of each particle according to: V = (L × T) + (Q × Y) During the ceremony, L is the baseline sampling variance of each photodetector, T is the measurement duration of each sampling pulse, Q is the optoelectronic scaling factor, 109. The integrated circuit according to any one of aspects 103 to 108, wherein Y is a photodetector signal intensity. 110. The integrated circuit of aspect 109, wherein the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors of the photodetection system. 111. The integrated circuit of aspect 109 or 110, wherein the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the optical detection system. 112. The integrated circuit of any one of aspects 109-111, wherein the integrated circuit is programmed to adjust the baseline sampling variance in response to changes in the trigger threshold of one or more of the photodetectors of the photodetection system.

[0473] 113. The integrated circuit of any one of aspects 109-112, wherein the integrated circuit is programmed to adjust the baseline sampling variance at predetermined time intervals. 114. The integrated circuit of aspect 113, wherein the integrated circuit is programmed to adjust the baseline sampling variance every 24 hours. 115. The integrated circuit of any one of aspects 109-114, wherein the integrated circuit is programmed to adjust the scaling factor in response to changes in photodetector gain for one or more of the photodetectors of the photodetection system. 116. The integrated circuit of any one of aspects 109-114, wherein the integrated circuit is programmed to generate a calibration curve of the scaling factor based on the photodetector gain of one or more of the photodetectors of the photodetection system. 117. The integrated circuit of any one of aspects 109-114, wherein the measurement duration of each sampling pulse is different for one or more of the photodetectors of the photodetection system.

[0474] 118. The integrated circuit of any one of aspects 109-114, wherein the measurement duration of each sampling pulse is the same for each of the photodetectors of the photodetection system. 119. The integrated circuit of any one of aspects 109-114, wherein the measurement duration of each sampling pulse is the same for each of the photodetectors of the photodetection system. 120. The integrated circuit of any one of aspects 103-119, wherein the integrated circuit is programmed to determine the measured variance of each particle for each photodetector in real time. 121. The integrated circuit of any one of aspects 103-120, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample. 122. The integrated circuit of embodiment 121, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 10 nm or more.

[0475] 123. The integrated circuit of embodiment 121, wherein the fluorescence spectrum of each fluorophore overlaps with the fluorescence spectrum of at least one other fluorophore in the sample by 25 nm or more. 124. The integrated circuit of any one of aspects 103-123, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample. 125. The integrated circuit of embodiment 124, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 10 nm or more. 126. The integrated circuit of embodiment 124, wherein the fluorescence spectrum of at least one fluorophore in the sample overlaps with the fluorescence spectra of two different fluorophores in the sample by 25 nm or more. 127. The integrated circuit of any one of aspects 103-126, wherein the integrated circuit is programmed to calculate a spectral unmixing matrix of the fluorescence spectra of each fluorophore in the sample using a weighted least squares algorithm.

[0476] 128. An integrated circuit is programmed to calculate a weighted least squares algorithm according to:

[0477]

number

[0478] During the ceremony, y is the measured detector value from the multiple photodetectors of the photodetection system for each cell, where a is the estimated fluorophore abundance, X is the spillover, 128. The integrated circuit according to any one of aspects 103 to 127, wherein W is:

[0479]

number

[0480] 129.Each W ii is calculated according to

[0481]

number

Claims

1. detecting light with a light detection system from particles of a sample that include a plurality of fluorophores having overlapping fluorescence spectra; determining a measured dispersion of the detected light for each particle; and spectrally resolving the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measurement variance determined for each particle. A method comprising:

2. The method of claim 1 , wherein the light detection system includes a plurality of light detectors, and the measurement variance for each particle is determined for each light detector.

3. The measured variance is, for each particle, a change in a photodetector gain parameter in one or more of the photodetectors of the photodetection system; a change in a trigger threshold parameter for one or more of the photodetectors of the photodetection system; The change in the photodetection duration parameter for each photodetector for each particle, and The change in the photonic shot noise parameters detected by each photodetector 3. The method of claim 1 or 2, comprising one or more of:

4. The measurement variance is calculated for each particle according to: V=(L×T)+(Q×Y) During the ceremony, L is the baseline sampling variance of each photodetector; T is the measured duration of each sampling pulse; Q is the optoelectronic scaling factor; The method of any one of claims 1 to 3, wherein Y is the photodetector signal intensity.

5. The method comprises: adjusting the baseline sampling variance in response to changes in photodetector gain in one or more photodetectors of the photodetection system; adjusting the baseline sampling variance in response to changes in photonic shot noise detected by one or more photodetectors of the photodetection system; adjusting the baseline sampling variance in response to changes in trigger thresholds of one or more of the photodetectors of the photodetection system; adjusting the scaling factor in response to changes in photodetector gain for one or more of the photodetectors of the photodetection system; and generating a calibration curve of the scaling factor based on the photodetector gain of one or more of the photodetectors of the photodetection system; The method of claim 4, comprising one or more of:

6. The method of any one of claims 1 to 5, wherein the measurement variance is determined for each particle at each photodetector in real time.

7. The method of any one of claims 1 to 6, wherein the weighted least squares algorithm is calculated using a Cholesky decomposition.

8. The weighted least squares algorithm is calculated using a Cholesky decomposition according to: X Ｔ WAa=A Ｔ Wy Aa = B LDL Ｔ a=B Lz = B, where z = DL Ｔ a Dx = z, where x = L Ｔ a L Ｔ a=x The method of claim 7.

9. a light source configured to illuminate particles of a sample comprising a plurality of fluorophores having overlapping fluorescence spectra; a light detection system including a plurality of light detectors; and A processor including a memory operatively coupled thereto, the memory, when executed by the processor, causing the processor to: determining a measured dispersion of the detected light for each particle; spectrally resolving the light from each fluorophore in the sample using a weighted least squares algorithm that uses the measurement variance determined for each particle. a processor including stored instructions for causing A system comprising:

10. The measured variance is, for each particle, a change in a photodetector gain parameter in one or more of the photodetectors of the photodetection system; a change in a trigger threshold parameter for one or more of the photodetectors of the photodetection system; The change in the photodetection duration parameter for each photodetector for each particle, and The change in the photonic shot noise parameters detected by each photodetector The system of claim 9 , comprising one or more of:

11. the memory includes instructions for calculating the measurement variance of each particle according to: V=(L×T)+(Q×Y) During the ceremony, L is the baseline sampling variance of each photodetector; T is the measured duration of each sampling pulse; Q is the optoelectronic scaling factor, and 11. The system of claim 9 or 10, wherein Y is the photodetector signal intensity.

12. A system according to any one of claims 9 to 11, wherein the memory includes instructions for computing the weighted least squares algorithm using a Cholesky decomposition.

13. The memory includes instructions for computing the weighted least squares algorithm using a Cholesky decomposition according to: X Ｔ WAa=A Ｔ Wy Aa = B LDL Ｔ a=B Lz = B, where z = DL Ｔ a Dx = z, where x = L Ｔ a L Ｔ a=x The system of claim 12.

14. 1. An integrated circuit comprising: determining a measured variance of light detected by the light detection system for each particle of the sample containing multiple fluorophores with overlapping fluorescence spectra; an integrated circuit programmed to spectrally resolve light from each fluorophore in said sample using a weighted least squares algorithm that uses the measurement variance determined for each particle.

15. 1. A non-transitory computer-readable storage medium, comprising: determining a measured variance of light detected by the light detection system for each particle of a sample including multiple fluorophores with overlapping fluorescence spectra; and A non-transitory computer-readable storage medium comprising stored instructions for spectrally resolving light from each fluorophore in a sample using a weighted least squares algorithm that uses the measurement variance determined for each particle.

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