Reconfigurable integrated circuit for adjusting cell sort classification

Abstract

Aspects of the present disclosure include reconfigurable integrated circuits for characterizing particles of a sample in a flow stream. The reconfigurable integrated circuits according to certain embodiments are programmed to calculate a parameter of a particle in a flow stream from detected light; compare the calculated parameter of the particle to a parameter of one or more particle classifications; classify the particle based on a comparison between the parameter of the particle classification and the calculated parameter of the particle; and adjust one or more parameters of the particle classification based on the calculated parameter of the particle. Methods of characterizing particles in a flow stream utilizing the subject integrated circuits are also described. Systems and integrated circuit devices programmed for practicing the subject methods, for example on a flow cytometer, are also provided.

Description

[0001] Cross-references to related applications

[0002] This application relates to U.S. Provisional Patent Application Serial No. 62 / 872,663, filed July 10, 2019, the disclosure of which is incorporated herein by reference. Background Technology

[0003] Optical detection can be used to characterize the composition of samples (e.g., biological samples), such as when the sample is used for the diagnosis of a disease or medical condition. When a sample is illuminated, light can be scattered by the sample, transmitted through the sample, and emitted by the sample (e.g., through fluorescence). Changes in the sample composition (e.g., morphology, absorptivity, and the presence of fluorescent labels) can cause changes in the light scattered, transmitted, or emitted by the sample. These changes can be used to characterize and identify the components present in the sample. To quantify these changes, light is collected and directed onto the surface of a detector.

[0004] One technique for characterizing the components in a sample using optical detection is flow cytometry. Using data generated based on the detected light, the distribution of components can be recorded, and the desired materials can be sorted. To sort particles in a sample, a droplet charging mechanism charges droplets containing the types of particles to be sorted at the break in the flow using electrical charges. The droplets are deflected by an electrostatic field and into one or more collection containers based on the polarity and magnitude of the charge on the droplets. Uncharged droplets are not deflected by the electrostatic field. Summary of the Invention

[0005] Various aspects of this disclosure include reconfigurable integrated circuits for characterizing particles in a sample flowing stream. According to certain embodiments, the reconfigurable integrated circuit is programmed to: calculate parameters of particles in the flowing stream based on detected light; compare the calculated parameters of the particles with one or more parameters for particle classification; classify the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjust one or more parameters for particle classification based on the calculated parameters of the particles. Methods for characterizing particles in a flowing stream using the indicated integrated circuit are also described. Systems and integrated circuit devices programmed for practicing the methods of this subject matter, such as on a flow cytometer, are also provided.

[0006] In some cases, the integrated circuit device of interest may include a reconfigurable field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or a complex programmable logic device (CPLD). In some embodiments, particle classification programmed into the reconfigurable integrated circuit includes sorting classification (e.g., for sorting cells using a cell sorting device). In some cases, classifying particles includes generating particle sorting decisions. In some cases, the integrated circuit is programmed to generate particle sorting decisions based on a threshold between computational parameters of the particles and parameters for particle classification. In some embodiments, the integrated circuit is programmed to adjust one or more parameters for particle classification by changing the threshold used to generate the particle sorting decision. The threshold may be a predetermined threshold, a user-configurable threshold adjusted by user input, or a dynamic threshold adjusted based on computational parameters of the particles. In the case of a dynamic threshold adjusted based on computational parameters of the particles, the integrated circuit may be programmed to generate sorting decisions using an algorithm for updating the threshold. This algorithm may be a statically predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm updated based on computational parameters of the particles. For example, a dynamic algorithm may be a machine learning algorithm that updates the computational parameters of the particles and the parameters of particles with similar or different classification parameters.

[0007] In other embodiments, particle classification programmed into the reconfigurable integrated circuit includes one or more particle clusters. In these embodiments, the integrated circuit is programmed to classify particles by assigning them to clusters based on a comparison between classification parameters for each cluster and computational parameters of the particles. In some cases, the integrated circuit is programmed to adjust one or more parameters of particle classification by changing the classification parameters of the assigned clusters based on the computational parameters of the particles. In these cases, the integrated circuit may also include a program for plotting the computational parameters of the particles on a scatter plot. In other cases, the integrated circuit may also include a program for generating a list of computational parameters of the particles. In some embodiments, the integrated circuit includes hardware circuitry (e.g., digital circuitry) for custom operators. In some cases, the custom operators include one or more summation operators, multiplication operators, and comparison operators. In some embodiments, the integrated circuit is programmed with one or more summation operators. In other embodiments, the integrated circuit is programmed with one or more multiplication operators. In yet another embodiment, the integrated circuit is programmed with one or more comparison operators. In yet another embodiment, the integrated circuit is programmed with two or more of summation operators, multiplication operators, and comparison operators. In some cases, the integrated circuit is programmed with summation operators, multiplication operators, and comparison operators.

[0008] In some embodiments, the integrated circuit is programmed to calculate the statistical probability of a particle being assigned to a particle cluster. For example, the integrated circuit may be programmed to calculate the Mahalanobis distance between the computed parameters of the particle and each particle cluster. In other embodiments, the integrated circuit is programmed to assign a particle to a particle cluster based on a threshold between the computed parameters of the particle and the classification parameters of the particle cluster. For example, the threshold may be the overlap between the computed parameters of the particle and the classification parameters of the particle cluster. This threshold may be a predetermined threshold, a user-configurable threshold, or a dynamic threshold that changes in response to assigning a particle to a particle cluster. In some cases, the integrated circuit is programmed to adjust the threshold based on the classification parameters of the assigned particle cluster and the computed parameters of the particle. The integrated circuit may be programmed to adjust the threshold using a statically predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that updates the classification parameters of the assigned particle cluster using the computed parameters of the particle. For example, the dynamic algorithm may be a machine learning algorithm that updates the classification parameters of the particle cluster based on the computed parameters of the particle and the parameters of particles with similar or different classification parameters.

[0009] This disclosure also includes methods for characterizing particles in a sample in a flowing stream. A method according to certain embodiments includes: detecting light from a sample containing particles in the flowing stream; calculating parameters of the particles in the sample based on the detected light; comparing the calculated parameters of the particles with one or more parameters for particle classification; classifying the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjusting one or more parameters for particle classification based on the calculated parameters of the particles.

[0010] In embodiments, light is detected from a sample containing particles in a diagnostic region of a flow stream. In some embodiments, the particles are cells, such as cells in a biological sample. A method according to certain embodiments includes detecting light using one or more of the following: 1) a bright-field photodetector for generating a bright-field data signal; 2) a scattered light detector (e.g., a forward-scattering detector or a side-scattering detector) for generating a scattered light signal; and 3) a fluorescence detector for generating a fluorescence data signal. In embodiments, the light detected from particles in the sample is used to calculate parameters of the particles. For example, in one example, the method includes calculating scattered light parameters, such as forward-scattering light intensity or side-scattering light intensity. In another example, the method includes calculating fluorescence parameters, such as the fluorescence intensity of one or more fluorophores. In some embodiments, the fluorophores are associated (e.g., covalently bonded) with an analyte-binding compound (e.g., an antibody).

[0011] In some embodiments, the method includes generating a sorting classification. In these embodiments, the sorting classification may be a particle sorting decision. In some cases, the method includes generating a particle sorting decision based on a threshold between computational parameters of the particles and parameters for particle sorting. In some cases, adjusting one or more parameters for particle sorting includes changing the threshold used to generate the particle sorting decision. The threshold may be a predetermined threshold, a user-configurable threshold, or a dynamic threshold adjusted based on the computational parameters of the particles. When the threshold is a dynamic threshold, the threshold may be updated by an algorithm (e.g., an algorithm programmed into a reconfigurable integrated circuit) to generate the sorting decision. The algorithm may be a static, predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that updates the sorting decision based on the computational parameters of the particles and the parameters of the particle sorting gate. For example, the dynamic algorithm may be a machine learning algorithm that updates the sorting classification parameters based on the computational parameters of the particles and the parameters of particles previously sorted using the sorting gate.

[0012] In some embodiments, one or more calculated parameters of a particle may be plotted, for example, on a scatter plot. In other embodiments, a list of one or more calculated parameters is generated. The calculated parameters of a particle are compared with classification parameters of a group or more groups of particles (e.g., particle clusters) to identify its degree of association with the particle cluster. For example, the comparison between the calculated parameters of a particle and the classification parameters of a particle cluster may include determining that the particle has one or more properties that fall within a particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is the same as a particle within the particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is associated with the same fluorophore as a particle within the particle cluster (e.g., covalently bonded). In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is associated with an analyte-specific binding component (e.g., covalently bonded) that is the same as a particle within the particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes calculating the statistical probability that the particle is assigned to the particle cluster. In one example, the comparison between the computed parameters and the classification parameters of the particle cluster includes calculating the computed parameters of the particles and the Mahalanobis distance between the particle clusters.

[0013] The method according to embodiments includes assigning particles to particle clusters based on a comparison between classification parameters of each particle cluster and computational parameters of the particles. In some embodiments, particles are assigned to particle clusters based on a threshold between the computational parameters of the particles and the classification parameters of the particle clusters. In some cases, the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle clusters. In some cases, the threshold is a predetermined threshold. In other cases, the threshold is a user-configurable threshold. In other cases, the threshold is a dynamic threshold that changes in response to assigning particles to particle clusters. In some embodiments, the method further includes adjusting the threshold based on the classification parameters of the assigned particle clusters and the computational parameters of the particles. In some embodiments, the threshold is adjusted by a processor having a memory operatively coupled to the processor, wherein the memory has an algorithm for updating the classification parameters of the assigned particle clusters using the computational parameters of the particles. In some cases, the algorithm is a machine learning algorithm. In other cases, the algorithm is a statically predetermined algorithm. In other cases, the algorithm is a user-configurable algorithm. In other cases, the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles (e.g., particles assigned to particle clusters).

[0014] In some embodiments, the method further includes generating one or more particle clusters for the sample. The method according to some embodiments includes detecting light from multiple particles of the sample in a flowing stream, calculating parameters for each particle based on the detected light, and clustering the particles together based on the calculated parameters. In these embodiments, each particle assigned to a particle cluster can cause a change in the classification parameters of the particle cluster, such that the classification parameters are dynamic and change based on the specific calculated parameters for each assigned particle. Each particle cluster can include 5 or more assigned particles, such as 10 or more assigned particles, such as 50 or more assigned particles, such as 100 or more assigned particles, such as 500 assigned particles, and even 1000 assigned particles. In some embodiments, the particle clusters are grouped by rare events (e.g., rare cells in the sample, such as cancer cells) and can include 10 or fewer assigned particles, such as 9 or fewer, and even 5 or fewer assigned particles.

[0015] In some embodiments, the method further includes generating a sorting decision based on the particle's allocation relative to a particle cluster. In some embodiments, particles are sorted based on the statistical probability that a particle is part of a particle cluster. In other embodiments, particles are sorted based on the Mahalanobis distance between the particle and the particle cluster on a scatter plot. In some embodiments, the method includes sorting particles based on the generated sorting decision.

[0016] This disclosure also includes systems having a light detection system for characterizing particles (e.g., cells in a biological sample) in a flowing stream. A system according to certain embodiments includes a light source configured to illuminate a sample containing particles in a flowing stream; a light detection system having a photodetector; and a processor having a memory operatively coupled to a processor, wherein the memory has instructions stored thereon that, when executed by the processor, configure the processor to: calculate parameters of particles in the flowing stream based on the detected light; compare the calculated parameters of the particles with one or more parameters for particle classification; classify the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjust one or more parameters for particle classification based on the calculated parameters of the particles.

[0017] In some embodiments, particle classification programmed into the processor memory of the system of this subject includes sorting classification (e.g., for sorting cells using a cell sorting device). In some cases, classifying particles includes generating particle sorting decisions. In some cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate particle sorting decisions based on a threshold between computational parameters of the particles and parameters for particle classification. In some embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust one or more parameters for particle classification by changing the threshold used to generate the particle sorting decisions. The threshold may be a predetermined threshold, a user-configurable threshold that can be adjusted by user input, or a dynamic threshold that is adjusted based on computational parameters of the particles. In the case where the threshold is a dynamic threshold that is adjusted based on computational parameters of the particles, the memory may include an algorithm for updating the threshold used to generate particle sorting decisions. This algorithm may be a statically predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that is updated based on computational parameters of the particles. For example, a dynamic algorithm may be a machine learning algorithm that updates based on computational parameters of the particles and parameters of particles with similar or different classification parameters.

[0018] In other embodiments, particle classification programmed into the processor memory of the system of this subject includes one or more particle clusters. In these embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to classify particles by assigning particles to particle clusters based on a comparison between classification parameters for each particle cluster and computational parameters of the particles. In some cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust one or more parameters of particle classification by changing the classification parameters of the assigned particle clusters based on the computational parameters of the particles. In these cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to plot the computational parameters of the particles on a scatter plot. In other cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate a list of computational parameters of the particles. In some embodiments, the memory includes hardware circuitry (e.g., digital circuitry) for customized operators. In some cases, the customized operators include one or more summation operators, multiplication operators, and comparison operators. In some embodiments, the memory is programmed with one or more summation operators. In other embodiments, the memory is programmed with one or more multiplication operators. In some embodiments, the memory programming includes one or more comparison operators. In other embodiments, the memory programming includes two or more of a summation operator, a multiplication operator, and a comparison operator. In some cases, the memory programming includes a summation operator, a multiplication operator, and a comparison operator.

[0019] In some cases, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to calculate the statistical probability of assigning a particle to a particle cluster. For example, the system may be configured to calculate the Mahalanobis distance between the computed parameters of a particle and each particle cluster. In other embodiments, the system is configured to assign a particle to a particle cluster based on a threshold between the computed parameters of the particle and the classification parameters of the particle cluster. For example, the threshold may be the overlap between the computed parameters of the particle and the classification parameters of the particle cluster. This threshold may be a predetermined threshold, a user-configurable threshold, or a dynamic threshold that changes in response to assigning a particle to a particle cluster. In some cases, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to adjust the threshold based on the classification parameters of the assigned particle cluster and the computed parameters of the particle. The threshold may be a static, predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that updates the classification parameters of the assigned particle cluster using the computed parameters of the particle. For example, the dynamic algorithm may be a machine learning algorithm that updates the classification parameters of a particle cluster based on the computed parameters of the particle and the parameters of particles with similar or different classification parameters.

[0020] In some embodiments, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to plot the calculated parameters of the particles on a scatter plot or generate a list of the calculated parameters of the particles. In embodiments, the system is configured to compare the calculated parameters of the particles with classification parameters of one or more groups of particles (e.g., particles in a particle cluster on a scatter plot). In some cases, the system is configured to identify the degree to which a particle is associated with a particle cluster. In some embodiments, the system includes a memory storing instructions thereon that, when executed by a processor, cause the processor to determine that the particle is a particle having one or more properties that fall within a particle cluster. In other cases, the system is configured to determine that the particle is the same as a particle within a particle cluster. In still other cases, the system is configured to determine that the particle is associated with (e.g., covalently bonded) a fluorophore that is the same as a particle within a particle cluster.

[0021] In other cases, the system is configured to determine that a particle is associated with an analyte-specific binding component (e.g., covalently bonded) that is identical to the particle within the particle cluster.

[0022] In other cases, the system includes a memory storing instructions that, when executed by a processor, cause the processor to calculate the statistical probability of assigning a particle to a cluster of particles. In one example, the system includes a memory storing instructions that, when executed by a processor, cause the processor to calculate the Mahalanobis distance between the computed parameters of the particle and the cluster of particles.

[0023] In some embodiments, the system of interest includes a processor having memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to assign particles to a particle cluster based on a comparison between classification parameters of the particle cluster and computational parameters of the particles. In some embodiments, the system is configured to assign particles to a particle cluster based on a threshold between the computational parameters of the particles and the classification parameters of the particle cluster. In some cases, the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle cluster. In some cases, the threshold is a predetermined threshold. In other cases, the threshold is a user-configurable threshold. In other cases, the threshold is a dynamic threshold that changes in response to assigning particles to a particle cluster. In some embodiments, the system of interest includes a processor having memory having an algorithm for adjusting the threshold based on the classification parameters of the assigned particle cluster and the computational parameters of the particles. In these embodiments, the algorithm updates the classification parameters of the particle cluster using the computational parameters of the new particles. In some cases, the algorithm is a machine learning algorithm. In other cases, the algorithm is a statically predetermined algorithm. In other cases, the algorithm is a user-configurable algorithm. In other cases, the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles (e.g., the particles assigned to a particle cluster).

[0024] In some embodiments, the system of interest includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate one or more particle clusters for a sample (e.g., on a scatter plot). In these embodiments, the system is configured to detect light from multiple particles of a sample in a flowing stream, calculate parameters for each particle based on the detected light, and cluster the particles together based on the calculated parameters. In some embodiments, the system is configured to change the classification parameters of the particle clusters based on the calculated parameters of newly assigned particles.

[0025] In some embodiments, the system of interest may include one or more sorting decision modules configured to generate sorting decisions for particles based on the particle allocation relative to the particle cluster. In some embodiments, the system may also include a particle sorter (e.g., having a droplet deflector) for sorting particles from the flow stream based on the sorting decisions generated by the sorting decision modules. Attached Figure Description

[0026] The invention can be best understood by reading the following detailed description in conjunction with the accompanying drawings. The drawings include the following figures:

[0027] Figure 1 A flowchart is depicted according to certain embodiments for classifying particles and adjusting one or more parameters for particle classification.

[0028] Figure 2 A flowchart is depicted according to certain embodiments for assigning particles to particle swarms and adjusting one or more parameters of the assigned particle swarms.

[0029] Figure 3 A flowchart is depicted illustrating a sorting decision for generating particles and adjusting one or more parameters of a sorting gate according to certain embodiments.

[0030] Figure 4 A flow cytometer having a system for classifying particles is described according to certain embodiments. Detailed Implementation

[0031] Various aspects of this disclosure include reconfigurable integrated circuits for characterizing particles in a sample flowing stream. According to certain embodiments, the reconfigurable integrated circuit is programmed to calculate parameters of particles in the flowing stream based on detected light; compare the calculated parameters of the particles with one or more parameters for particle classification; classify the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjust one or more parameters for particle classification based on the calculated parameters of the particles. Methods for characterizing particles in a flowing stream using integrated circuits of this subject matter are also described. Systems and integrated circuit devices programmed for practicing the methods of this subject matter, such as in flow cytometry, are also provided.

[0032] Before describing the invention in more detail, it should be understood that the invention is not limited to the specific embodiments described, and therefore variations are possible. It should also be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, as the scope of the invention will be limited only by the appended claims.

[0033] Where a range of values ​​is provided, it should be understood that, unless the context explicitly specifies otherwise, every intermediate value between the upper and lower limits of the range and any other specified value or intermediate value within the range, measured in tenths of the lower limit, is included in this invention. The upper and lower limits of these smaller ranges may be independently included within the smaller ranges and also within this invention, but are subject to any specific exclusions within the range. Where the range includes one or both limitations, the range excluding those included one or both limitations is also included in this invention.

[0034] The numerical ranges given in this document are preceded by the term "approximately". The term "approximately" is used in this document to provide literal support for the exact number preceding it and for numbers that are close to or approximate to the number preceding the term. In determining whether a number is close to or approximate to a specifically cited number, the close or approximate uncited number may be a number presented in the context that provides a substantially equivalent number to the specifically cited number.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Although any methods and materials similar to or equivalent to those described herein may be used in the practice or testing of this invention, representative illustrative methods and materials are described hereafter.

[0036] All publications and patents referenced in this specification are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated as incorporated by reference, and are incorporated herein by reference to disclose and describe methods and / or materials relating to the referenced publication. Any reference to a publication is for the purpose of disclosure prior to the filing date and should not be construed as an admission that the invention is not entitled to any prior invention as described in such publication. Furthermore, the publication dates provided may differ from the actual publication dates and may require independent verification.

[0037] Note that, as used herein and in the appended claims, the singular forms “a,” “an,” and “the” include plural referents unless the context clearly specifies otherwise. Also note that claims can be drafted to exclude any optional elements. Therefore, this statement is intended as a prerequisite for using exclusive terms such as “unique,” ​​“only,” or using negative limitations when stating elements of a claim.

[0038] As will be apparent to those skilled in the art upon reading this disclosure, each individual embodiment described and illustrated herein has discrete components and features that can be readily separated from or combined with features of any of the other several embodiments without departing from the scope or spirit of the invention. Any described method may be performed in the order of the described events or in any other logically possible order.

[0039] Although the apparatus and method have been or will be described for the purposes of grammatical fluency and functional interpretation, it should be clearly understood that, unless expressly stated in 35 U.S.C., section 112, the claims should not be construed as necessarily being limited by a “means” or “step” structure, but should be given the full scope of the definition and equivalence provided by the claims based on the principle of judicial equivalence, and where the claims are expressly stated in 35 U.S.C., they should be given full statutory equivalence based on 35 U.S.C., section 112.

[0040] As described above, this disclosure provides a reconfigurable integrated circuit device for characterizing particles in a sample of a flowing stream. In further described embodiments, a reconfigurable (i.e., reprogrammable) integrated circuit (e.g., FPGA, ASIC, CPLD) is first described in more detail, having a procedure for calculating parameters of particles, comparing the calculated parameters of the particles with one or more particle classifications (e.g., sorting gate classification), classifying the particles, and adjusting one or more parameters of the particle classification. Next, a method for classifying particles (e.g., particle cluster association, sorting classification) based on calculated parameters of the particles according to detected light is described. Systems for implementing the methods of this subject matter are also provided, such as flow cytometry systems.

[0041] Reconfigurable integrated circuit devices

[0042] As described above, aspects of this disclosure include reconfigurable integrated circuit devices programmed to classify particles in a sample of a flowing stream and adjust particle classification parameters based on computational parameters of the particles. The term "reconfigurable" is used herein in its conventional sense to refer to an integrated circuit device that can be reprogrammed once or more, for example, in response to adjustments to particle classification parameters as described in more detail below. In some embodiments, the integrated circuit device is configured for user reprogramming, wherein adjustments to particle classification (e.g., sorting decision classification, cluster grouping) parameters are performed by a user. According to some embodiments, user reprogramming includes adjustments to particle classification parameters based on user input (e.g., manual programming adjustments). In other embodiments, user reprogramming includes a user-input algorithm that automatically adjusts particle classification parameters in response to computational parameters of the particles and current particle classification parameters.

[0043] In some embodiments, the integrated circuit is configured for dynamic reprogramming, such as reprogramming the integrated circuit based on machine learning. The term "machine learning" is used herein in its conventional sense to refer to the tuning of the integrated circuit's programming through computational methods that determine and implement information directly from data, without relying on predetermined equations as a model. In some embodiments, machine learning includes learning algorithms that discover patterns in a data signal (e.g., from multiple particles in a sample). In these embodiments, the learning algorithms are configured to generate better, more accurate decisions and predictions based on the quantity of data signals (i.e., the learning algorithm becomes more robust as the number of particles representing the data from the sample increases). Machine learning protocols of interest may include, but are not limited to, artificial neural networks, decision tree learning, decision tree predictive modeling, support vector machines, Bayesian networks, dynamic Bayesian networks, genetic algorithms, and other machine learning protocols.

[0044] In embodiments, the integrated circuit device of this subject is programmed to receive data signals from one or more of a bright-field photodetector, a light scattering (forward scattering, side scattering) detector, and a fluorescence photodetector; calculate parameters of particles in a flow stream based on the data signals; compare the calculated parameters of the particles with one or more particle classification parameters; classify the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjust one or more particle classification parameters based on the calculated parameters of the particles. In embodiments, the integrated circuit device is programmed to receive data signals from one or more photodetectors (e.g., one or more detection channels), such as two or more, three or more, four or more, five or more, six or more, and including eight or more photodetectors (e.g., eight or more detection channels). In some cases, the integrated circuit device of interest may include a field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or a complex programmable logic device (CPLD).

[0045] In embodiments, computational parameters for particles used to classify particles (e.g., associated with clusters, generating sorting decisions) may include one or more of the phase, intensity, or wavelength of transmitted light (determined by a bright-field photodetector), forward scattering from the particle, side scattering from the particle, and fluorescence. In some embodiments, computational parameters for particles used to classify particles according to embodiments of this disclosure include one or more defined properties of the particle, such as particle size, centroid, or eccentricity. In other embodiments, parameters for particles used to classify particles include frequency-encoded fluorescence data or computational spatial data of the particle.

[0046] In some embodiments, particle sorting programmed into a reconfigurable integrated circuit includes sorting classification (e.g., for sorting cells using a cell sorting device). 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 biomolecules), and in some cases, delivering the separated components to one or more sample collection containers. For example, particle sorting may include sorting gates for sorting two or more components of a sample, such as three or more components, such as four or more components, such as five or more components, such as ten or more components, such as fifteen or more components, and includes sorting 25 or more components of a sample.

[0047] In some cases, particle classification involves generating particle sorting decisions. In some cases, specific subgroups of particles (e.g., individual cells) are classified via “gating” based on computational parameters of the particles. In some cases, integrated circuits are programmed to generate particle sorting decisions (e.g., identifying the desired sorting gate) based on the overlap between the computational parameters of the particles and the parameters for particle classification. To select an appropriate gate, reconfigurable integrated circuits can be programmed to plot parameters (e.g., on a scatter plot) to obtain the best possible subgroup separation. In some embodiments, reconfigurable integrated circuits are programmed to plot forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC) on a two-dimensional scatter plot. In other embodiments, reconfigurable integrated circuits are programmed to plot one or more defined properties (e.g., size, centroid, eccentricity) for one or more of forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC). In other embodiments, the reconfigurable integrated circuit is programmed to gate a swarm of forward-scattering (FSC) and lateral (i.e., orthogonal) light-scattering (SSC) particles, and then gate based on one or more of the particles' defined properties (e.g., size, centroid, eccentricity). A subswarm of objects (i.e., those individual cells within the gate) is then selected and particles not within the gate are excluded. Further analysis is then performed only on those particles that fall within the classification parameters, for example, by plotting other parameters of these classified particles.

[0048] In some embodiments, the integrated circuit is programmed to adjust one or more parameters for particle classification by changing a threshold used to generate particle sorting decisions. For example, the integrated circuit can be reprogrammed to change the parameter threshold of the sorting gate used to sort particles in a sample. In some embodiments, the threshold is a predetermined threshold. In other embodiments, the threshold is a user-configurable threshold. In other embodiments, the threshold is a dynamic threshold that is adjusted based on the computational parameters of the particles.

[0049] In the case of a dynamic threshold where the threshold is adjusted based on the particle's computational parameters, the integrated circuit can be programmed with an algorithm to update the threshold used to generate sorting decisions. In some embodiments, the algorithm programmed into the integrated circuit for updating the threshold is a static, predetermined algorithm. In other embodiments, the algorithm programmed into the integrated circuit for updating the threshold is a user-configurable algorithm. In still other embodiments, the algorithm programmed into the integrated circuit for updating the threshold is a dynamic algorithm that updates the sorting decision based on the particle's computational parameters and the parameters of the particle sorting gate. For example, the dynamic algorithm could be a machine learning algorithm that updates based on the particle's computational parameters and the parameters of particles with similar or different classification parameters.

[0050] In other embodiments, particle classification programmed into a reconfigurable integrated circuit includes one or more particle clusters. In these embodiments, the integrated circuit is programmed to classify particles by assigning them to clusters based on a comparison between classification parameters for each cluster and computational parameters of the particles. In some embodiments, the integrated circuit is configured to generate one or more particle clusters based on determined parameters of particles in a sample. In these embodiments, the integrated circuit device is programmed to receive data signals generated based on detection light from multiple particles of a sample in a flowing stream, calculate parameters for each particle based on the detected light, and cluster the particles together based on the computational parameters. In these embodiments, each particle assigned to a cluster may cause a change in the classification parameters of the cluster, making the classification parameters dynamic and changing based on the specific computational parameters of each assigned particle. The integrated circuit device can be programmed to assign any number of particles (including 5 or more particles, such as 10 or more particles, such as 50 or more particles, such as 100 or more particles, such as 500 particles, and including 1000 particles) to a cluster. In some embodiments, the integrated circuit is programmed to group rare events detected in the sample (e.g., rare cells in the sample, such as cancer cells) into particle clusters. In these embodiments, the particle clusters generated by the integrated circuit may include 10 or fewer assigned particles, such as 9 or fewer and including 5 or fewer assigned particles.

[0051] In some cases, the integrated circuit is programmed to adjust one or more parameters for particle classification by changing the classification parameters of an assigned particle cluster based on particle computational parameters. In these cases, the integrated circuit may also include a program for plotting the computational parameters of the particles on a scatter plot. In other cases, the integrated circuit may also include a program for generating a list of computational parameters for the particles. In some embodiments, the integrated circuit includes hardware circuitry (e.g., digital circuitry) for customized operators. In some cases, the customized operators include one or more summation operators, multiplication operators, and comparison operators. In some embodiments, the integrated circuit is programmed with one or more summation operators. In other embodiments, the integrated circuit is programmed with one or more multiplication operators. In other embodiments, the integrated circuit is programmed with one or more comparison operators. In other embodiments, the integrated circuit is programmed with two or more of summation operators, multiplication operators, and comparison operators. In some cases, the integrated circuit is programmed with summation operators, multiplication operators, and comparison operators.

[0052] In some embodiments, the integrated circuit is programmed to calculate the statistical probability of a particle being assigned to a particle cluster. For example, the integrated circuit may be programmed to calculate the Mahalanobis distance between the computed parameters of the particle and each particle cluster. In other embodiments, the integrated circuit is programmed to assign a particle to a particle cluster based on a threshold between the computed parameters of the particle and the classification parameters of the particle cluster. For example, the threshold may be the overlap between the computed parameters of the particle and the classification parameters of the particle cluster. This threshold may be a predetermined threshold, a user-configurable threshold, or a dynamic threshold that changes in response to assigning a particle to a particle cluster. In some cases, the integrated circuit is programmed to adjust the threshold based on the classification parameters of the assigned particle cluster and the computed parameters of the particle. The integrated circuit may be programmed to adjust the threshold using a statically predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that updates the classification parameters of the assigned particle cluster using the computed parameters of the particle. For example, the dynamic algorithm may be a machine learning algorithm that updates the classification parameters of a particle cluster based on the computed parameters of the particle and the parameters of particles with similar or different classification parameters.

[0053] Figure 1 A flowchart illustrating, according to certain embodiments, is provided for classifying particles (e.g., particles in a flowing stream) and adjusting one or more parameters for particle classification. In step 100, light (light absorption, scattered light, or emission) from particles (e.g., cells) in the flowing stream is detected. In step 101, a data signal generated based on the detected light is received (e.g., via a reconfigurable integrated circuit). In step 102, parameters of the particles are calculated based on the data signal. In step 103, the calculated parameters of the particles are compared with one or more particle classification parameters, which may include, for example, particle swarm 103a or particle sorting gate 103b. In step 104, the particles are classified based on the comparison between the particle classification parameters and the calculated parameters of the particles. In step 105, the particle classification parameters (e.g., parameters for associating particles with particle swarms or including particles in sorting gates) are adjusted based on the calculated parameters of the particles to generate adjusted particle classification parameters, such as optimized particle swarm 103a1 or particle sorting gate 103b1. The adjustment of computational parameters can be performed using one or more of a static algorithm 105a, a user-input algorithm 105b, or a dynamic algorithm 105c, wherein 105c may further include a machine learning protocol 106 that optimizes (e.g., based on multiple calculated particle parameters) the algorithm used to adjust the parameters of particle classification 103a or 103b.

[0054] Figure 2A flowchart illustrating, according to certain embodiments, of assigning particles (e.g., particles in a flow stream) to particle swarms and adjusting one or more parameters of the assigned particle swarms. In step 200, light (light absorption, scattered light, or emission) from particles (e.g., cells) in the flow stream is detected. In step 201, a data signal generated based on the detected light is received (e.g., via a reconfigurable integrated circuit). In step 202, parameters of the particles are calculated based on the data signal. In step 203, the calculated parameters of the particles are compared with parameters of one or more particle swarms. In step 204, particles are assigned to particle swarms based on a comparison between particle classification parameters and the calculated parameters of the particles. In some cases, particles are classified into particle swarms based on a statistical probability 204a of the parameters associated with the particle swarm. In other cases, particles are classified into particle swarms based on a calculated distance 204b (e.g., Mahalanobis distance) based on the parameters of the particle swarms. In step 205, the parameters of the particle swarms to which the particles are assigned are adjusted based on the calculated parameters of the particles to generate adjusted particle swarm parameters. The tuning of computational parameters can be performed using one or more of a static algorithm 205a, a user-input algorithm 205b, or a dynamic algorithm 205c, wherein 205c may further include a machine learning protocol 206 that optimizes (e.g., based on multiple calculated particle parameters) the algorithm for tuning the parameters of the particle swarm.

[0055] Figure 3 A flowchart illustrating, according to certain embodiments, of generating a sorting decision for particles (e.g., particles in a flow stream) and adjusting one or more parameters of a sorting gate. In step 300, light (light absorption, scattered light, or emission) from particles (e.g., cells) in the flow stream is detected. In step 301, a data signal generated based on the detected light is received (e.g., via a reconfigurable integrated circuit). In step 302, parameters of the particles are calculated based on the data signal. In step 303, the calculated parameters of the particles are compared with parameters of one or more particle sorting gates. In step 304, a sorting decision is generated for the particles based on the comparison between the calculated parameters of the particles and the parameters of the particle sorting gates. In step 305, the parameters of the particle sorting gates used to sort the particles are adjusted based on the calculated parameters of the particles to generate an adjusted particle sorting gate. The tuning of computational parameters can be performed using one or more of a static algorithm 305a, a user-input algorithm 305b, or a dynamic algorithm 305c, wherein 305c may further include a machine learning protocol 306 for optimizing (e.g., based on multiple calculated particle parameters) the algorithm for tuning the particle sorting gate.

[0056] Methods for adjusting particle classification

[0057] This disclosure also includes methods for characterizing particles in a sample in a flowing stream. A method according to certain embodiments includes detecting light from a sample containing particles in the flowing stream; calculating parameters of the particles in the sample based on the detected light; comparing the calculated parameters of the particles with one or more parameters for particle classification; classifying the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjusting one or more parameters for particle classification based on the calculated parameters of the particles.

[0058] In implementing the methods of this subject matter, light from a light source is used to illuminate a sample containing particles in a flowing stream. In some embodiments, the light source is a broadband light source that emits light with a wide wavelength range, such as spanning 50 nm or more, for example 100 nm or more, for example 150 nm or more, for example 200 nm or more, for example 250 nm or more, for example 300 nm or more, for example 350 nm or more, for example 400 nm or more, and including spanning 500 nm or more. For example, a suitable broadband light source emits light with wavelengths from 200 nm to 1500 nm. Another example of a suitable broadband light source includes a light source that emits light with wavelengths from 400 nm to 1000 nm. When the method includes illumination using a broadband light source, the broadband light source protocols of interest may include, but are not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stable fiber-coupled broadband light sources, broadband LEDs with continuous spectra, superluminescent diodes, semiconductor light-emitting diodes, broadband LED white light sources, multi-LED integrated white light sources, and other broadband light sources or any combination thereof.

[0059] In other embodiments, the method includes illumination using a narrowband light source that emits a specific wavelength or a narrow wavelength range, such as a light source emitting light in the narrow wavelength range, like 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, and includes a light source emitting light of a specific wavelength (i.e., monochromatic light). When the method includes illumination using a narrowband light source, the narrowband light source protocol of interest may include, but is not limited to, narrow-wavelength LEDs, laser diodes, or broadband light sources coupled to one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0060] In some embodiments, the method includes irradiating the flowing stream with one or more lasers. The type and number of lasers will vary depending on the sample and the desired light collected, and may be pulsed lasers or continuous-wave lasers. For example, the lasers may be gas lasers, such as helium-neon lasers, argon lasers, krypton lasers, xenon lasers, nitrogen lasers, CO2 lasers, CO lasers, argon-fluorine (ArF) excimer lasers, krypton-fluorine (KrF) excimer lasers, xenon-chlorine (XeCl) excimer lasers, or xenon-fluorine (XeF) excimer lasers or combinations thereof; dye lasers, such as stilbene, coumarin, or rhodamine lasers; metal-vapor lasers, such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, etc. 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, Ti:sapphire lasers, thulium YAG lasers, ytterbium YAG lasers, ytterbium oxide lasers, or cerium-doped lasers and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSL), or double or triple harmonic embodiments of any of the above lasers.

[0061] The sample in the flowing stream can be illuminated using one or more of the aforementioned light sources (e.g., two or more light sources, three or more light sources, four or more light sources, five or more light sources, and including ten or more light sources). The light sources can include any combination of light sources. For example, in some embodiments, the method includes illuminating the sample in the flowing stream using a laser array (e.g., an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers).

[0062] The sample can be illuminated using wavelengths from 200 nm to 1500 nm, such as 250 nm to 1250 nm, 300 nm to 1000 nm, 350 nm to 900 nm, and including 400 nm to 800 nm. For example, in the case where the light source is a broadband light source, the sample can be illuminated using wavelengths from 200 nm to 900 nm. In other cases where the light source includes multiple narrowband light sources, the sample can be illuminated using specific wavelengths from 200 nm to 900 nm. For example, the light source can be multiple narrowband LEDs (1 nm to 25 nm), each independently emitting light with wavelengths from 200 nm to 900 nm. In other embodiments, the narrowband light source includes one or more lasers (e.g., a laser array) and illuminates the sample using specific wavelengths from 200 nm to 700 nm, for example, using a laser array having gas lasers, excimer lasers, dye lasers, metal vapor lasers, and solid-state lasers as described above.

[0063] When using more than one light source, the sample can be illuminated simultaneously, sequentially, or in combination using the light sources. For example, each of the light sources can illuminate the sample simultaneously. In other embodiments, each of the light sources illuminates the flow sequentially. When illuminating the sample sequentially using more than one light source, the duration of illumination for each light source can be independently 0.001 microseconds or more, such as 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 30 microseconds or more, and including 60 microseconds or more. For example, the method may include illuminating the sample with a light source (e.g., a laser) for a duration from 0.001 microseconds to 100 microseconds, such as 0.01 microseconds to 75 microseconds, 0.1 microseconds to 50 microseconds, 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In embodiments where the sample is illuminated sequentially using two or more light sources, the duration of illumination for each light source can be the same or different.

[0064] The time interval between each light source illumination can also vary as needed, independently spaced by delays of 0.001 microseconds or more, such as 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 15 microseconds or more, 30 microseconds or more, and including 60 microseconds or more. For example, the time interval between each light source illumination can be from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, such as 0.1 microseconds to 35 microseconds, such as 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In some embodiments, the time interval between each light source illumination is 10 microseconds. In embodiments where the sample is sequentially illuminated by more than two (i.e., three or more) light sources, the delays between each light source illumination can be the same or different.

[0065] The sample can be illuminated continuously or at discrete intervals. In some cases, the method involves continuously illuminating the sample with a light source. In other cases, the sample is illuminated with a light source at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including once every 1000 milliseconds, or some other interval.

[0066] Depending on the light source, the sample can be illuminated from varying distances, such as 0.01 mm or greater, 0.05 mm or greater, 0.1 mm or greater, 0.5 mm or greater, 1 mm or greater, 2.5 mm or greater, 5 mm or greater, 10 mm or greater, 15 mm or greater, 25 mm or greater, and including 50 mm or greater. Furthermore, the angle or illumination can vary from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and includes angles from 30° to 60°, such as at 90°.

[0067] In implementing the methods of this subject, light from an illuminated sample is measured, for example by collecting light from the sample within a certain wavelength range (e.g., 200 nm–1000 nm). In embodiments, the method may include one or more of measuring light absorption (e.g., brightfield data), measuring light scattering (e.g., forward or side-scattered light data), and measuring light emission (e.g., fluorescence data) of the sample.

[0068] A beam generator component comprising a laser and an acousto-optic device for frequency shifting the laser light can be employed. In these embodiments, the method includes irradiating the acousto-optic device with a laser. Depending on the desired wavelength of the light generated in the output laser beam (e.g., for irradiating a sample in a flowing stream), the laser can have a specific wavelength varying from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm, and including from 400 nm to 800 nm. One or more lasers can be used to irradiate the acousto-optic device, such as two or more lasers, such as three or more lasers, such as four or more lasers, such as five or more lasers, and including ten or more lasers. The lasers can include any combination of laser types. For example, in some embodiments, the method includes irradiating the acousto-optic device with an array of lasers, such as an array having one or more gas lasers, one or more dye lasers, and one or more solid-state lasers.

[0069] When using more than one laser, the lasers can be used to illuminate the acousto-optic device simultaneously, sequentially, or in combination. For example, each laser can be used to illuminate the acousto-optic device simultaneously. In other embodiments, each laser is used to illuminate the acousto-optic device sequentially. When using more than one laser to illuminate the acousto-optic device sequentially, the duration for which each laser illuminates the acousto-optic device can be independently 0.001 microseconds or more, for example, 0.01 microseconds or more, for example, 0.1 microseconds or more, for example, 1 microsecond or more, for example, 5 microseconds or more, for example, 10 microseconds or more, for example, 30 microseconds or more, and including 60 microseconds or more. For example, the method may include illuminating the acousto-optic device with lasers for durations from 0.001 microseconds to 100 microseconds, for example, 0.01 microseconds to 75 microseconds, for example, 0.1 microseconds to 50 microseconds, for example, 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In embodiments where two or more lasers are used to illuminate the acousto-optic device sequentially, the duration for which each laser illuminates the acousto-optic device can be the same or different.

[0070] The time interval between each laser irradiation can also vary as needed, independently spaced by delays of 0.001 microseconds or more, such as 0.01 microseconds or more, 0.1 microseconds or more, 1 microsecond or more, 5 microseconds or more, 10 microseconds or more, 15 microseconds or more, 30 microseconds or more, and including 60 microseconds or more. For example, the time interval between each light source irradiation can be from 0.001 microseconds to 60 microseconds, such as 0.01 microseconds to 50 microseconds, 0.1 microseconds to 35 microseconds, 1 microsecond to 25 microseconds, and including 5 microseconds to 10 microseconds. In some embodiments, the time interval between each laser irradiation is 10 microseconds. In embodiments where the acousto-optic device is sequentially irradiated by more than two (i.e., three or more) lasers, the delays between each laser irradiation can be the same or different.

[0071] The acousto-optic device can be illuminated continuously or at discrete intervals. In some cases, the method includes continuously illuminating the acousto-optic device using a laser. In other cases, the acousto-optic device is illuminated using a laser at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including once every 1000 milliseconds, or some other interval.

[0072] Depending on the laser, the acousto-optic device can be illuminated from varying distances, such as 0.01 mm or greater, 0.05 mm or greater, 0.1 mm or greater, 0.5 mm or greater, 1 mm or greater, 2.5 mm or greater, 5 mm or greater, 10 mm or greater, 15 mm or greater, 25 mm or greater, and including 50 mm or greater. Furthermore, the angle or illumination can also vary from 10° to 90°, such as from 15° to 85°, from 20° to 80°, from 25° to 75°, and includes angles from 30° to 60°, such as at 90°.

[0073] In an embodiment, the method includes applying radio frequency (RF) drive signals to an acousto-optic device to generate an angle-deflected laser beam. Two or more RF drive signals may be applied to the acousto-optic device to generate an output laser beam with a desired number of angle-deflected laser beams, such as three or more RF drive signals, four or more RF drive signals, five or more RF drive signals, six or more RF drive signals, seven or more RF drive signals, eight or more RF drive signals, nine or more RF drive signals, ten or more RF drive signals, fifteen or more RF drive signals, 25 or more RF drive signals, 50 or more RF drive signals, and including 100 or more RF drive signals.

[0074] Each angle-deflecting laser beam generated by a radio frequency (RF) drive signal has an intensity based on the amplitude of the applied RF drive signal. In some embodiments, the method includes applying an RF drive signal having an amplitude sufficient to produce an angle-deflecting laser beam with a desired intensity. In some cases, each applied RF drive signal independently has an amplitude of about 0.001V to about 500V, for example, about 0.005V to about 400V, for example, about 0.01V to about 300V, for example, about 0.05V to about 200V, for example, about 0.1V to about 100V, for example, about 0.5V to about 75V, for example, about 1V to 50V, for example, about 2V to 40V, for example, 3V to about 30V, and includes about 5V to about 25V. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, for example about 0.005 MHz to about 400 MHz, for example about 0.01 MHz to about 300 MHz, for example about 0.05 MHz to about 200 MHz, for example about 0.1 MHz to about 100 MHz, for example about 0.5 MHz to about 90 MHz, for example about 1 MHz to about 75 MHz, for example about 2 MHz to about 70 MHz, for example about 3 MHz to about 65 MHz, for example about 4 MHz to about 60 MHz, and includes about 5 MHz to about 50 MHz.

[0075] In some embodiments, a sample in a flowing stream is irradiated using an output laser beam from an acousto-optic device, which includes angle-deflected laser beams, each having an intensity based on the amplitude of an applied radio frequency drive signal. For example, the output laser beam used to irradiate particles in the flowing stream may include two or more angle-deflected laser beams, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, and even 25 or more angle-deflected laser beams. In embodiments, each angle-deflected laser beam has a different frequency offset from the frequency of the input laser beam by a predetermined radio frequency.

[0076] Each angle-deflected laser beam is also spatially offset from each other. Depending on the applied RF drive signal and the desired illumination distribution of the output laser beam, the angle-deflected laser beams can be spaced apart by 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and including 5000 μm or more. In some embodiments, the angle-deflected laser beam overlaps with, for example, an adjacent angle-deflected laser beam along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., overlap of beam points) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and includes 100 μm or more.

[0077] When a particle passes through a portion of an excitation beam formed by the superposition of two beamlets, it is exposed to the superposition of their electric fields. The fluorescence emitted by the particle is a beat-frequency encoded frequency, corresponding to the difference between the optical frequencies of the incident beamlets. For example, the frequency-encoded fluorescence emitted by a particle passing through the left horizontal edge of the excitation beam formed by the superposition of the first and second beamlets will exhibit a beat frequency corresponding to the difference between the frequencies of the second and first beamlets, i.e., f0. 第一子束 -f 第二子束 The beat frequency. In this way, the position of particles passing through the excitation beam can be encoded by the RF beat frequency associated with the radiation emitted by those particles. In some embodiments, this encoding of the particle position can be used to normalize the intensity of the detected radiation emitted by those particles relative to variations in the beam intensity (e.g., in its horizontal direction).

[0078] In some embodiments, the fluorescence emitted by the particles is frequency-encoded and corresponds to the local oscillator beam (f). LO The beat frequency is the difference between the frequency of the RF deflector beam and the frequency of the RF deflector beam. For example, frequency-coded fluorescence data includes f LO -f RF偏移子束 The beat frequency. In the case where the illumination of the flow stream includes a local oscillator spanning the width of the flow stream (e.g., the entire horizontal axis), the frequency-encoded fluorescence data includes the frequency (f) corresponding to the local oscillator beam. LOThe beat frequency is the difference between the frequency of the flow stream and the frequency of each radio frequency offset sub-beam (f1, f2, f3, f4, f5, f6, etc.). In these embodiments, the frequency-encoded fluorescence data may include multiple beat frequencies, each corresponding to a position on the horizontal axis of the flow stream.

[0079] As discussed in more detail below, in one mode of operation, particles in a flow stream can be simultaneously irradiated using multiple excitation frequencies, each of which can be obtained, for example, by shifting the center frequency of a laser beam. More specifically, multiple sample locations can be simultaneously irradiated by a laser beam generated by mixing a reference laser beam (e.g., a local oscillator) with multiple radio frequency (RF) offset laser beams, such that each sample location is irradiated by the reference beam and one of the RF offset beams to excite a fluorophore of interest (if present) at that location. In some embodiments, the reference local oscillator can be generated via an RF offset of the beam (e.g., from a laser, such as a continuous-wave laser). In these embodiments, each spatial location of a particle in the light-irradiated flow stream is "marked" with a different beat frequency corresponding to the difference between the frequency of the reference beam and the frequency of one of the RF offset beams. In these cases, the fluorescence emitted by the fluorophore spatially encodes the beat frequency.

[0080] In some cases, a beam of light with multiple radio frequency offsets is used to illuminate the flow stream, and cells in the flow stream are imaged using fluorescence imaging with radio frequency labeled emission (FIRE) to generate frequency-coded images, such as those described in Diebold et al., Nature Photonics Vol. 7(10); 806-810 (2013), and U.S. Patent Nos. 9,423,353, 9,784,661 and 10,006,852 and U.S. Patent Publications Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0081] In implementing the methods of this subject matter, a light detection system is used to detect light from the sample. As described in more detail below, the light detection system includes a bright-field photodetector and one or more fluorescence detectors. In some cases, the light detection system also includes a light scattering detector, such as a forward-scattering light detector or a side-scattering light detector, or a combination thereof. The collected light can be detected continuously or at discrete intervals. In some cases, the method involves continuous detection of light. In others, light is measured at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, and including every 1000 milliseconds, or some other interval.

[0082] During the method described herein, the detected light may be measured one or more times, for example two or more times, three or more times, five or more times, and including ten or more times. In some embodiments, the light from the sample is measured two or more times, and in some cases the data are averaged.

[0083] In some embodiments, the method includes further adjusting the light from the sample before detection. For example, light from the sample source may pass through one or more lenses, mirrors, pinholes, slits, gratings, light refractors, and any combination thereof. In some cases, the collected light passes through one or more focusing lenses, for example, to reduce light distribution. In other cases, the emitted light from the sample passes through one or more collimators to reduce beam divergence.

[0084] In embodiments, the method includes detecting light using a bright-field photodetector to generate a bright-field data signal. The bright-field photodetector can detect light from the sample at one or more wavelengths, such as at 5 or more different wavelengths, such as at 10 or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and includes detecting light at 400 or more different wavelengths. The bright-field photodetector can detect light within one or more wavelengths from 200 nm to 1200 nm. In some cases, the method includes detecting light from the sample within a certain wavelength range using a bright-field photodetector, such as 200 nm to 1200 nm, such as 300 nm to 1100 nm, such as 400 nm to 1000 nm, such as 500 nm to 900 nm, and includes 600 nm to 800 nm.

[0085] A bright-field photodetector is configured to generate one or more bright-field data signals in response to detected light, such as two or more, three or more, four or more, five or more, and including ten or more bright-field data signals in response to detected light. In cases where the bright-field photodetector is configured to detect light across multiple wavelengths (e.g., 400 nm to 800 nm), the method may include generating one or more bright-field data signals in response to each wavelength of the detected light in some cases. In other cases, a single bright-field data signal is generated in response to light detected by the bright-field photodetector across the entire wavelength range.

[0086] The method of the present invention may further include detecting fluorescence from a sample using one or more fluorescence detectors. As described in more detail below, the optical detection system may include one or more fluorescence detectors, such as two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, fifteen or more, and 25 or more fluorescence detectors. In embodiments, each fluorescence detector is configured to generate a fluorescence data signal. Each fluorescence detector may independently detect fluorescence from a sample in one or more wavelength ranges from 200 nm to 1200 nm. In some cases, the method includes detecting fluorescence from a sample within a certain wavelength range, such as 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and including 600 nm to 800 nm. In other cases, the method includes detecting fluorescence at one or more specific wavelengths using each fluorescence detector. For example, depending on the number of different fluorescence detectors in the optical detection system of this subject, fluorescence can be detected at one or more of the following locations: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In some embodiments, the method includes detecting the wavelength of light corresponding to the fluorescence peak wavelength of certain fluorophores present in the sample.

[0087] In implementing the subject method, one or more parameters of particles in the sample are calculated based on the detected light (as described above). In one example, the method includes detecting light using a bright-field photodetector configured to generate a bright-field data signal in response to the detected light. In another example, the method includes detecting light using a light scattering detector (forward scattering, side scattering) configured to generate a scattered light data signal in response to the detected light. In yet another example, the method includes detecting light using a fluorescence detector configured to generate a fluorescence data signal in response to the detected light.

[0088] In some embodiments, the method includes generating a sorting classification. In some cases, the sorting classification can be a particle sorting decision. For example, generating a sorting classification can include identifying a suitable sorting gate based on the computational parameters of the particles. The particle sorting decision can be generated based on the overlap between the computational parameters of the particles and the parameters for particle classification. To select a suitable gate, the method may also include plotting parameters (e.g., on a scatter plot) to obtain optimal separation of possible subgroups. In some embodiments, the method includes plotting forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC) on a two-dimensional scatter plot. In other embodiments, the method includes plotting one or more determined properties (e.g., size, centroid, eccentricity) for one or more of forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC). In other embodiments, the method includes gating the particle swarm for forward light scattering (FSC) and lateral (i.e., orthogonal) light scattering (SSC), and then gating based on one or more of the determined properties of the particles (e.g., size, centroid, eccentricity). Then select a subgroup of the objects (i.e., those individual cells inside the gate) and exclude particles that are not inside the gate. Then only those particles within the classification parameters are further analyzed, for example by plotting other parameters of these classification particles.

[0089] In some cases, the method includes generating a particle sorting decision based on a threshold between the particle's computational parameters and particle classification parameters. In other cases, one or more particle classification parameters are adjusted by changing the threshold used to generate the particle sorting decision. In some embodiments, the threshold is a predetermined threshold. In other embodiments, the threshold is a user-configurable threshold. In still other embodiments, the threshold is a dynamic threshold adjusted based on the particle's computational parameters.

[0090] When the threshold is dynamic, the method may further include using an algorithm (e.g., an algorithm programmed into a reconfigurable integrated circuit) to update the threshold used to generate particle sorting decisions. In some embodiments, the algorithm for updating the threshold is a statically predetermined algorithm. In other embodiments, the algorithm for updating the threshold is a user-configurable algorithm. In other embodiments, the algorithm for updating the threshold is a dynamic algorithm that updates the sorting decision based on the computational parameters of the particles and the parameters of the particle sorting gate. For example, the dynamic algorithm may be a machine learning algorithm that updates the sorting classification parameters based on the computational parameters of the particles and the parameters of particles with similar or different classification parameters (e.g., particles previously sorted using a sorting gate).

[0091] In some embodiments, one or more of the calculated parameters of a particle may be plotted, for example, on a scatter plot. In other embodiments, a list of one or more calculated parameters is generated. The calculated parameters of a particle are compared with one or more sets of classification parameters of the particle (e.g., a particle cluster) to identify its degree of association with the particle cluster. For example, the comparison between the calculated parameters of a particle and the classification parameters of a particle cluster may include determining that the particle has one or more properties that fall within the particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is the same as a particle within the particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is associated with the same fluorophore as a particle within the particle cluster (e.g., covalently bonded). In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes determining that the particle is associated with an analyte-specific binding component (e.g., covalently bonded) that is the same as a particle within the particle cluster. In other cases, the comparison between the calculated parameters and the classification parameters of a particle cluster includes calculating the statistical probability that the particle is assigned to the particle cluster. In one example, the comparison between the computed parameters and the classification parameters of the particle cluster includes calculating the computed parameters of the particles and the Mahalanobis distance between the particle clusters.

[0092] The method based on embodiments includes assigning particles to particle clusters based on a comparison between classification parameters of each particle cluster and computational parameters of the particles. In some embodiments, particles are assigned to particle clusters based on a threshold between the computational parameters of the particles and the classification parameters of the particle clusters. In some cases, the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle clusters. In some cases, the threshold is a predetermined threshold. In other cases, the threshold is a user-configurable threshold. In other cases, the threshold is a dynamic threshold that changes in response to assigning particles to particle clusters. In some embodiments, the method further includes adjusting the threshold based on the classification parameters of the assigned particle clusters and the computational parameters of the particles. In some embodiments, the threshold is adjusted by a processor having memory operatively coupled to the processor, wherein the memory has an algorithm for updating the classification parameters of the assigned particle clusters using the computational parameters of the particles. In some cases, the algorithm is a machine learning algorithm. In other cases, the algorithm is a statically predetermined algorithm. In other cases, the algorithm is a user-configurable algorithm. In other cases, the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles (e.g., the particles assigned to the particle clusters).

[0093] In some embodiments, the method further includes generating one or more particle clusters for the sample. A method based on certain embodiments includes detecting light from multiple particles of the sample in a flowing stream, calculating parameters for each particle based on the detected light, and clustering the particles together based on the calculated parameters. In these embodiments, each particle assigned to a particle cluster can cause a change in the classification parameters of the particle cluster, such that the classification parameters are dynamic and change based on the specific calculated parameters of each assigned particle. Each particle cluster can include 5 or more assigned particles, such as 10 or more assigned particles, such as 50 or more assigned particles, such as 100 or more assigned particles, such as 500 assigned particles, and may include 1000 assigned particles. In some embodiments, the particle cluster is a group of rare events (e.g., rare cells in the sample, such as cancer cells) and may include 10 or fewer assigned particles, such as 9 or fewer and may include 5 or fewer assigned particles.

[0094] In some embodiments, the method further includes generating a sorting decision based on the particle's allocation relative to a particle cluster. In some embodiments, particles are sorted based on the statistical probability that a particle is part of a particle cluster. In other embodiments, particles are classified based on the Mahalanobis distance on a scatter plot between the particle and the particle cluster. In some embodiments, the method includes sorting particles based on the generated sorting decision.

[0095] A system for adjusting particle classification

[0096] As described above, aspects of this disclosure also include systems with light detection systems for characterizing particles (e.g., cells in a biological sample) in a flowing stream. A system based on certain embodiments includes: a light source configured to illuminate a sample containing particles in a flowing stream; a light detection system with a photodetector; and a processor having a memory operatively coupled to the processor, wherein the memory has instructions stored thereon that, when executed by the processor, cause the processor to: calculate parameters of particles in the flowing stream based on the detected light; compare the calculated parameters of the particles with one or more parameters for particle classification; classify the particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and adjust one or more parameters for particle classification based on the calculated parameters of the particles.

[0097] Systems of interest include a light source configured to illuminate a sample in a flowing stream. In embodiments, the light source can be any suitable broadband or narrowband light source. Depending on the composition of the sample (e.g., cells, beads, non-cellular particles, etc.), the light source can be configured to emit varying wavelengths of light ranging from 200 nm to 1500 nm, such as from 250 nm to 1250 nm, such as from 300 nm to 1000 nm, such as from 350 nm to 900 nm, and including from 400 nm to 800 nm. For example, the light source can include a broadband light source emitting light with a wavelength range of 200 nm to 900 nm. In other cases, the light source includes a narrowband light source emitting wavelengths ranging from 200 nm to 900 nm. For example, the light source can be a narrowband LED (1 nm–25 nm) emitting light with a wavelength range of 200 nm to 900 nm.

[0098] In some embodiments, the light source is a laser. Lasers of interest may include pulsed lasers or continuous-wave lasers. For example, the laser may be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-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; a dye laser, such as a stilbene, coumarin, or rhodamine laser; or a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-mercury (HeH) laser, or a metal-vapor laser. g) Lasers, including helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulim YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, or cerium-doped lasers and combinations thereof; semiconductor diode lasers, optically pumped semiconductor lasers (OPSL), or double or triple harmonic embodiments of any of the above lasers.

[0099] In other embodiments, the light source is a non-laser light source, such as a lamp, including but not limited to halogen lamps, deuterium arc lamps, xenon arc lamps, and light-emitting diodes (LEDs), such as broadband LEDs with a continuous spectrum, superluminescent LEDs, semiconductor LEDs, broadband LED white light sources, and multi-LED integration. In some cases, the non-laser light source is a stable fiber-coupled broadband light source, a white light source, and other light sources or any combination thereof.

[0100] In some embodiments, the light source is a beam generator configured to generate two or more beams of frequency-shifted light. In some cases, the beam generator includes a laser, a radio frequency generator configured to apply a radio frequency drive signal to an acousto-optic device to generate two or more angle-deflected laser beams. In these embodiments, the laser can be a pulsed laser or a continuous-wave laser. For example, the laser in the beam generator of interest can be a gas laser, such as a helium-neon laser, an argon laser, a krypton laser, a xenon laser, a nitrogen laser, a CO2 laser, a CO laser, an argon-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; a dye laser, such as a stilbene, coumarin, or rhodamine laser; or a metal-vapor laser, such as a helium-cadmium (HeCd) laser, a helium-... Mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers and combinations thereof; solid-state lasers, such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, Ti:sapphire lasers, thulim YAG lasers, ytterbium YAG lasers, ytterbium2O3 lasers, or cerium-doped lasers and combinations thereof.

[0101] The acousto-optic device can be any convenient acousto-optic protocol configured to frequency-shift a laser using applied acoustic waves. In some embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in this subject system is configured to generate an angle-deflected laser beam based on light from a laser and an applied radio frequency (RF) drive signal. The RF drive signal can be applied to the acousto-optic device from any suitable RF drive signal source, such as a direct digital synthesizer (DDS), an arbitrary waveform generator (AWG), or an electrical pulse generator.

[0102] In an embodiment, the controller is configured to apply radio frequency drive signals to the acousto-optic device to generate a desired number of angle-deflected laser beams in the output laser beam, for example, to apply 3 or more radio frequency drive signals, such as 4 or more radio frequency drive signals, such as 5 or more radio frequency drive signals, such as 6 or more radio frequency drive signals, such as 7 or more radio frequency drive signals, such as 8 or more radio frequency drive signals, such as 9 or more radio frequency drive signals, such as 10 or more radio frequency drive signals, such as 15 or more radio frequency drive signals, such as 25 or more radio frequency drive signals, such as 50 or more radio frequency drive signals, and includes being configured to apply 100 or more radio frequency drive signals.

[0103] In some cases, in order to generate an intensity distribution of an angle-deflected laser beam in the output laser beam, the controller is configured to apply an RF drive signal with varying amplitude, such variation being, for example, from about 0.001V to about 500V, for example, from about 0.005V to about 400V, for example, from about 0.01V to about 300V, for example, from about 0.05V to about 200V, for example, from about 0.1V to about 100V, for example, from about 0.5V to about 75V, for example, from about 1V to 50V, for example, from about 2V to 40V, for example, from about 3V to about 30V, and including about 5V to about 25V. In some embodiments, each applied radio frequency drive signal has a frequency of about 0.001 MHz to about 500 MHz, for example about 0.005 MHz to about 400 MHz, for example about 0.01 MHz to about 300 MHz, for example about 0.05 MHz to about 200 MHz, for example about 0.1 MHz to about 100 MHz, for example about 0.5 MHz to about 90 MHz, for example about 1 MHz to about 75 MHz, for example about 2 MHz to about 70 MHz, for example about 3 MHz to about 65 MHz, for example about 4 MHz to about 60 MHz, and includes about 5 MHz to about 50 MHz.

[0104] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam having an angle-deflected laser beam with a desired intensity profile. For example, the memory may include instructions for generating two or more angle-deflected laser beams with the same intensity, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and including the memory may include instructions for generating 100 or more angle-deflected laser beams with the same intensity. In other embodiments, the memory may include instructions for generating two or more angle-deflected laser beams with different intensities, such as three or more, four or more, five or more, ten or more, 25 or more, or 50 or more, and including the memory may include instructions for generating 100 or more angle-deflected laser beams with different intensities.

[0105] In some embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from the edge to the center. In these cases, the intensity of the angularly deflected laser beam at the center of the output beam can be from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis, for example, from 0.5% to about 95%, for example, from 1% to about 90%, for example, from about 2% to about 85%, for example, from about 3% to about 80%, for example, from about 4% to about 75%, for example, from about 5% to about 70%, for example, from about 6% to about 65%, for example, from about 7% to about 60%, for example, from about 8% to about 55%, and includes from 10% to about 50% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis. In other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with increasing intensity along a horizontal axis from the edge to the center. In these cases, the intensity of the angularly deflected laser beam at the edge of the output beam can be from 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis, for example, from 0.5% to about 95%, from 1% to about 90%, from about 2% to about 85%, from about 3% to about 80%, from about 4% to about 75%, from about 5% to about 70%, from about 6% to about 65%, from about 7% to about 60%, from about 8% to about 55%, and includes from 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 other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with a Gaussian intensity profile along a horizontal axis. In other embodiments, the controller has a processor with a memory operatively coupled to the processor, such that the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate an output laser beam with a top-hat intensity profile along a horizontal axis.

[0106] In embodiments, the beam generator of interest can be configured to generate angle-deflected laser beams within spatially separated output laser beams. Depending on the applied radio frequency drive signal and the desired irradiance distribution of the output laser beams, the angle-deflected laser beams can be spaced 0.001 μm or more, for example, 0.005 μm or more, for example, 0.01 μm or more, for example, 0.05 μm or more, for example, 0.1 μm or more, for example, 0.5 μm or more, for example, 1 μm or more, for example, 5 μm or more, for example, 10 μm or more, for example, 100 μm or more, for example, 500 μm or more, for example, 1000 μm or more, and including 5000 μm or more. In some embodiments, the system is configured to generate angle-deflected laser beams within the output laser beams, for example, beams that overlap with adjacent angle-deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent deflection laser beams (e.g., overlap of beam points) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and includes 100 μm or more.

[0107] In some cases, beam generators configured to generate two or more frequency-shifted beams include laser excitation modules as described in U.S. Patent Nos. 9,423,353; 9,784,661 and 10,006,852, and U.S. Patent Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0108] In embodiments, the system includes a light detection system having one or more of a bright-field photodetector, a light scattering (forward scattering, side scattering) detector, and a fluorescence detector for detecting and measuring light from a sample. The bright-field detector, light scattering detector, and fluorescence detector of interest in this subject matter may include, but are not limited to, optical sensors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), enhancement-mode charge-coupled devices (ICCDs), light-emitting diodes, photon counters, calorimeters, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes, phototransistors, quantum dot photoconductors, or combinations thereof, as well as other photodetectors. In some embodiments, 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 is used to measure light from the sample. In some embodiments, the bright-field photodetector includes an avalanche photodiode (APD). In some cases, the light scattering detector is an avalanche photodiode. In some cases, one or more fluorescence detectors are avalanche photodiodes.

[0109] In some embodiments, the light detection system of interest includes a plurality of fluorescence detectors. In some cases, the light detection system includes a plurality of solid-state detectors, such as photodiodes. In some cases, the light detection system includes an array of fluorescent photodetectors, such as an array of photodiodes. In these embodiments, the photodetector array may include four or more photodetectors, such as 10 or more photodetectors, such as 25 or more photodetectors, such as 50 or more photodetectors, such as 100 or more photodetectors, such as 250 or more photodetectors, such as 50 or more photodetectors, such as 100 or more photodetectors, such as 250 or more photodetectors, such as 50 or more photodetectors, such as 100 or more photodetectors, such as 250 or more photodetectors, such as 500 or more photodetectors, such as 750 or more photodetectors, and may include 1000 or more photodetectors.

[0110] The photodetectors can be arranged in any geometric configuration as needed, including but not limited to square, rectangular, trapezoidal, triangular, hexagonal, heptagonal, octagonal, nonagonal, decagonal, dodecagonal, circular, elliptical, and irregular pattern configurations. The photodetectors in the photodetector array can be oriented at angles ranging from 10° to 180° relative to another (as referenced in the XZ plane), such as 15° to 170°, 20° to 160°, 25° to 150°, 30° to 120°, and including 45° to 90°. The photodetector array can be any suitable shape, including linear shapes (e.g., square, rectangular, trapezoidal, triangular, hexagonal, etc.), curved shapes (e.g., circular, elliptical), and irregular shapes (e.g., the bottom portion of a parabola coupled to the top portion of a plane). In some embodiments, the photodetector array has a rectangular active surface.

[0111] Each photodetector (e.g., a photodiode) in the array may have an effective surface area of ​​5 μm to 250 μm, for example 10 μm to 225 μm, 15 μm to 200 μm, 20 μm to 175 μm, 25 μm to 150 μm, 30 μm to 125 μm, and including 50 μm to 100 μm, and its length may be 5 μm to 250 μm, for example 10 μm to 225 μm, 15 μm to 200 μm, 20 μm to 175 μm, 25 μm to 150 μm, 30 μm to 125 μm, and including 50 μm to 100 μm, wherein the surface area of ​​each photodetector (e.g., a photodiode) in the array is 25 μm. 2 Up to 10000μm 2 For example, 50μm 2 Up to 9000μm 2 For example, 75μm 2 Up to 8000μm 2 For example, 100μm 2 Up to 7000μm 2 For example, 150μm 2 Up to 6000μm 2 And including 200μm 2 Up to 5000μm 2 .

[0112] The size of the photodetector array can vary based on the amount and intensity of light, the number of photodetectors, and the required sensitivity. The length can range from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, 0.1 mm to 80 mm, 0.5 mm to 70 mm, 1 mm to 60 mm, 2 mm to 50 mm, 3 mm to 40 mm, 4 mm to 30 mm, and includes 5 mm to 25 mm. The width of the photodetector array can also vary, ranging from 0.01 mm to 100 mm, for example, 0.05 mm to 90 mm, 0.1 mm to 80 mm, 0.5 mm to 70 mm, 1 mm to 60 mm, 2 mm to 50 mm, 3 mm to 40 mm, 4 mm to 30 mm, and includes 5 mm to 25 mm. Therefore, the active surface area of ​​the photodetector array ranges from 0.1 mm. 2 Up to 10000mm 2 For example, 0.5mm 2 Up to 5000mm 2 For example, 1mm 2 Up to 1000mm 2 For example, 5mm 2 Up to 500mm 2 And including 10mm 2 Up to 100mm 2 .

[0113] The photodetector of interest is configured to measure light collected at one or more wavelengths, such as at two or more wavelengths, such as at five or more different wavelengths, such as at ten or more different wavelengths, such as at 25 or more different wavelengths, such as at 50 or more different wavelengths, such as at 100 or more different wavelengths, such as at 200 or more different wavelengths, such as at 300 or more different wavelengths, and including measuring light emitted by a sample in a flowing stream at 400 or more different wavelengths.

[0114] In some embodiments, the light detection system includes a bright-field photodetector configured to generate a bright-field data signal. The bright-field photodetector can be configured to detect light from a sample at one or more wavelengths, such as five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths, and includes detecting light at 400 or more different wavelengths. The bright-field photodetector can be configured to detect light in one or more wavelength ranges from 200 nm to 1200 nm. In some cases, the method includes using a bright-field photodetector to detect light from a sample within a certain wavelength range, such as 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and including 600 nm to 800 nm.

[0115] In some embodiments, the bright-field photodetector in the light detection system of interest is configured to generate one or more bright-field data signals in response to detected light, such as two or more, three or more, four or more, five or more, and including ten or more bright-field data signals in response to detected light. Where the bright-field photodetector is configured to detect light at multiple wavelengths (e.g., 400 nm to 800 nm), in some cases the method may include generating one or more bright-field data signals in response to each detected light wavelength. In other cases, a single bright-field data signal is generated in response to light detected by the bright-field photodetector across the entire wavelength range.

[0116] In some embodiments, the light detection system includes a light scattering photodetector configured to generate a light scattering data signal. The light scattering photodetector can be configured to detect light from a sample at one or more wavelengths, such as five or more different wavelengths, ten or more different wavelengths, 25 or more different wavelengths, 50 or more different wavelengths, 100 or more different wavelengths, 200 or more different wavelengths, 300 or more different wavelengths, and includes detecting light at 400 or more different wavelengths. The light scattering photodetector can be configured to detect light in one or more wavelength ranges from 200 nm to 1200 nm. In some cases, the method includes using the light scattering photodetector to detect light from the sample within a certain wavelength range, such as 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and including 600 nm to 800 nm.

[0117] In some embodiments, the light scattering photodetector in the light detection system of interest is configured to generate one or more light scattering data signals in response to detected light, such as two or more, three or more, four or more, five or more, and including ten or more light scattering data signals in response to detected light. Where the light scattering photodetector is configured to detect light at multiple wavelengths (e.g., from 400 nm to 800 nm), in some cases, the method may include generating one or more light scattering data signals in response to light at each detected wavelength. In other cases, a single light scattering data signal is generated in response to light detected by the light scattering photodetector across the entire wavelength range.

[0118] The optical detection system includes one or more bright-field detectors, light-scattering detectors, or fluorescence detectors, such as two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, fifteen or more, and including 25 or more detectors. In embodiments, each detector is configured to generate a data signal. Light from the sample can be detected independently by each detector in one or more wavelength ranges from 200 nm to 1200 nm. In some cases, one or more detectors are configured to detect light from the sample within a certain wavelength range, such as 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and including 600 nm to 800 nm. In other cases, one or more detectors are configured to detect light at one or more specific wavelengths. For example, fluorescence can be detected at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof, depending on the number of different detectors in the optical detection system of this subject. In some embodiments, one or more detectors are configured to detect wavelengths of light corresponding to the fluorescence peak wavelengths of certain fluorophores in the sample.

[0119] The optical detection system is configured to measure light continuously or at discrete intervals. In some cases, the detector of the optical detection system is configured to continuously measure the collected light. In other cases, the optical detection system is configured to perform measurements at discrete intervals, such as every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and including every 1000 milliseconds, or other intervals.

[0120] In some embodiments, the system is configured to generate frequency-encoded fluorescence data by illuminating a sample containing particles in a flowing stream. In some embodiments, the light source includes a light generator component that generates a plurality of angle-deflected laser beams, each having an intensity based on the amplitude of an applied radio frequency drive signal (e.g., from a direct digital synthesizer coupled to an acousto-optic device). For example, the system may include a light generator component that generates two or more angle-deflected laser beams, such as three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, and including 25 or more angle-deflected laser beams. In embodiments, each of the angle-deflected laser beams has a different frequency offset from the frequency of the input laser beam by a predetermined radio frequency.

[0121] In some embodiments, the present invention system is configured to generate angle-deflected laser beams that are also spatially offset from each other. Based on the applied radio frequency drive signal and the desired irradiance distribution of the output laser beams, the present invention system can be configured to generate angle-deflected laser beams spaced 0.001 μm or more, such as 0.005 μm or more, 0.01 μm or more, 0.05 μm or more, 0.1 μm or more, 0.5 μm or more, 1 μm or more, 5 μm or more, 10 μm or more, 100 μm or more, 500 μm or more, 1000 μm or more, and including 5000 μm or more. In some embodiments, the angle-deflected laser beams overlap, for example, with adjacent angle-deflected laser beams along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (e.g., overlap of beam points) can be 0.001 μm or more, such as 0.005 μm or more, such as 0.01 μm or more, such as 0.05 μm or more, such as 0.1 μm or more, such as 0.5 μm or more, such as 1 μm or more, such as 5 μm or more, such as 10 μm or more, and includes 100 μm or more.

[0122] In some embodiments, the system includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate frequency-coded fluorescence data by calculating the difference between the light frequencies of overlapping incident beams on the flow stream. In one example, the system includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate the beat frequency at each location on the horizontal axis of the flow stream. In these embodiments, the frequency-coded fluorescence emitted by the particles corresponds to the local frequency (f). LO The beat frequency is the difference between the frequency of the RF deflector beam and the frequency of the RF offset sub-beam. For example, frequency-coded fluorescence data includes f LO -f RF偏移子束 The beat frequency. In the case where the illumination of the flow stream includes a local oscillator spanning the width of the flow stream (e.g., the entire horizontal axis), the frequency-encoded fluorescence data includes the beat frequency corresponding to the local oscillator frequency (f). LO The beat frequency is the difference between the frequency of each radio frequency offset sub-beam and the frequency of each radio frequency offset sub-beam (f1, f2, f3, f4, f5, f6, etc.). In these embodiments, the frequency-coded fluorescence data may include multiple beat frequencies, each corresponding to a position across the horizontal axis of the flow stream.

[0123] In some embodiments, particle classification programmed into the processor memory of the system of this subject includes sorting classification (e.g., for sorting cells using a cell sorting device). In some cases, classifying particles includes generating particle sorting decisions. In some cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate particle sorting decisions based on a threshold between computational parameters of the particles and parameters for particle classification. In some embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust one or more parameters of particle classification by changing the threshold used to generate the particle sorting decisions. In some embodiments, the threshold is a predetermined threshold. In other embodiments, the threshold is a user-configurable threshold. In other embodiments, the threshold is a dynamic threshold adjusted based on computational parameters of the particles. In the case where the threshold is a dynamic threshold adjusted based on computational parameters of the particles, the memory may include an algorithm for updating the threshold used to generate particle sorting decisions. In some embodiments, the algorithm for updating the threshold is a static, predetermined algorithm. In other embodiments, the algorithm for updating the threshold is a user-configurable algorithm. In other embodiments, the algorithm for updating the threshold is a dynamic algorithm that updates the sorting decisions based on computational parameters of the particles and parameters of the particle sorting gate. For example, a dynamic algorithm can be a machine learning algorithm that updates the sorting classification parameters based on the computational parameters of the particles and the parameters of particles with similar or different classification parameters (e.g., particles previously sorted using a sorting gate).

[0124] In other embodiments, particle classification programmed into the processor memory of the system of this subject includes one or more particle clusters. In these embodiments, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to classify particles by assigning them to particle clusters based on a comparison between classification parameters for each particle cluster and computational parameters of the particles. In some cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust one or more parameters of particle classification by changing the classification parameters of the assigned particle clusters based on the computational parameters of the particles. In these cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to plot the computational parameters of the particles on a scatter plot (e.g., a dot plot). In other cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate a list of computational parameters of the particles. In some cases, the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate the statistical probability of a particle being assigned to a particle cluster. For example, the system may be configured to calculate the Mahalanobis distance between the computational parameters of the particles and each particle cluster. In other embodiments, the system is configured to assign particles to particle clusters based on a threshold between the particle's computational parameters and the cluster's classification parameters. For example, the threshold could be the overlap between the particle's computational parameters and the cluster's classification parameters. This threshold could be a predetermined threshold, a user-configurable threshold, or a dynamic threshold that changes in response to assigning particles to a particle cluster. In some cases, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to adjust the threshold based on the assigned cluster's classification parameters and the particle's computational parameters. The threshold could be a static, predetermined algorithm, a user-configurable algorithm, or a dynamic algorithm that updates the assigned cluster's classification parameters using the particle's computational parameters. For example, a dynamic algorithm could be a machine learning algorithm that updates the cluster's classification parameters based on the particle's computational parameters and the parameters of particles with similar or different classification parameters.

[0125] In some embodiments, the memory includes instructions stored thereon that, when executed by a processor, cause the processor to plot calculated parameters of particles on a scatter plot or generate a list of calculated parameters of particles. In embodiments, the system is configured to compare calculated particle parameters with classification parameters of one or more groups of particles (e.g., particles in a particle cluster on a scatter plot). In some cases, the system is configured to identify the degree to which a particle is associated with a particle cluster. In some embodiments, the system includes a memory storing instructions thereon that, when executed by a processor, cause the processor to determine that a particle is a particle having one or more properties that fall within a particle cluster. In other cases, the system is configured to determine that a particle is the same as a particle within a particle cluster. In still other cases, the system is configured to determine that a particle is associated with (e.g., covalently bonded) a fluorophore that is the same as a particle within a particle cluster.

[0126] In other cases, the system is configured to determine that a particle is associated with an analyte-specific binding component (e.g., covalently bonded) that is identical to the particle within the particle cluster.

[0127] In other cases, the system includes a memory storing instructions that, when executed by a processor, cause the processor to calculate the statistical probability of a particle being assigned to a particle cluster. In one example, the system includes a memory storing instructions that, when executed by a processor, cause the processor to calculate the Mahalanobis distance between the computed parameters of a particle and the particle cluster.

[0128] In some embodiments, the system of interest includes a processor having memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to assign particles to a particle cluster based on a comparison between classification parameters of the particle cluster and computational parameters of the particles. In some embodiments, the system is configured to assign particles to a particle cluster based on a threshold between the computational parameters of the particles and the classification parameters of the particle cluster. In some cases, the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle cluster. In some cases, the threshold is a predetermined threshold. In other cases, the threshold is a user-configurable threshold. In other cases, the threshold is a dynamic threshold that changes in response to assigning particles to a particle cluster. In some embodiments, the system of interest includes a processor having memory having an algorithm for adjusting the threshold based on the classification parameters of the assigned particle cluster and the computational parameters of the particles. In these embodiments, the algorithm updates the classification parameters of the particle cluster using the computational parameters of the new particles. In some cases, the algorithm is a machine learning algorithm. In other cases, the algorithm is a statically predetermined algorithm. In other cases, the algorithm is a user-configurable algorithm. In other cases, the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles (e.g., the particles assigned to a particle cluster).

[0129] In some embodiments, the system of interest includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate one or more clusters of particles from a sample (e.g., on a scatter plot). In these embodiments, the system is configured to detect light from multiple particles of a sample in a flowing stream, calculate parameters for each particle based on the detected light, and cluster the particles together based on the calculated parameters. In some embodiments, the system is configured to change the classification parameters of the particle clusters based on the calculated parameters of the newly assigned particles.

[0130] In some embodiments, the system of interest may include one or more sorting decision modules configured to generate sorting decisions for particles based on the particle allocation relative to the particle cluster. In some embodiments, the system may also include a particle sorter (e.g., having droplet deflectors) for sorting particles from the flow stream based on the sorting decisions generated by the sorting decision modules.

[0131] Figure 4 A flow cytometer 400 employing an embodiment of the present invention is shown. The flow cytometer 400 includes a flow cell 404, a sample container 406 for supplying a fluid sample (e.g., a blood sample) to the flow cell, and a sheath fluid container 408 for supplying sheath fluid to the flow cell having a fluid subsystem 415. The flow cytometer 400 is configured to deliver a fluid sample containing particles (e.g., cells) in a flow stream along with a laminar flow of sheath fluid to the flow cell 404. The flow stream at the interrogation zone 403 is analyzed using an optical detection system 409 as described herein. The flow stream exits the flow cell 404 as a flow stream 411 through a nozzle orifice 410. The flow stream 411 can be sorted into droplets using a droplet deflector 424.

[0132] The system may also include additional sensors (e.g., imaging cameras 413 and 420) to detect sorting and whether further adjustments to the sorting decision are needed. Data signal processors 414 and 415 may be operatively coupled to the light detection system 409 to provide data signals generated based on the detected light. Processors 414 and 415 operatively communicate with hardware computing unit 450 (e.g., a reconfigurable integrated circuit such as an FPGA or a non-transitory computer-readable storage medium), which is programmed to classify particles and adjust one or more parameters of particle classification based on embodiments described herein. According to certain embodiments, the hardware computing unit includes an algorithm 450a for calculating parameters of particles in the flow based on detected light; an algorithm 450b for comparing the calculated parameters of the particles with one or more parameters for particle classification; an algorithm 450c for classifying particles based on the comparison between the particle classification parameters and the calculated parameters of the particles; and an algorithm 450d for adjusting one or more parameters of particle classification based on the calculated parameters of the particles. In some cases, the hardware computing unit also includes a machine learning protocol 450e for dynamically adjusting one or more parameters of particle classification.

[0133] The system based on some embodiments may include a display and an operator input device. For example, the operator input device may be a keyboard, mouse, etc. The processing module includes a processor that can access memory on which instructions for performing steps of the methods of this subject are stored. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices and input / output controllers, caches, data backup units, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or will be available. The processor executes the operating system and its interface with firmware and hardware in a well-known manner, facilitating the processor to coordinate and execute the functions of various computer programs written in a variety of programming languages, such as Java, Perl, C++, other high-level or low-level languages ​​and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of 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 based on known techniques. The processor may be any suitable analog or digital system. In some embodiments, the processor includes analog electronics that provide feedback control (e.g., negative feedback control).

[0134] System memory can be any of a variety of known or future memory storage devices. Examples include any commonly used random access memory (RAM), magnetic media (such as resident hard disks or magnetic tapes), optical media (such as optical discs), flash memory devices, or other storage devices. Memory storage devices can be any of a variety of known or future devices, including compact disk drives, magnetic tape drives, removable hard disk drives, or floppy disk drives. This type of memory storage device typically reads from and / or writes to program storage media (not shown), such as optical discs, magnetic tapes, removable hard disks, or floppy disks. These program storage media, or any other currently used or potentially developed in the future, can be considered computer program products. It is understood that these program storage media typically store computer software programs and / or data. Computer software programs, also known as computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0135] In some embodiments, a computer program product having a computer-usable medium having control logic (a computer software program, including program code) stored therein is described. The control logic, when executed by a processor and a computer, causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementing a hardware state machine to perform the functions described herein will be apparent to those skilled in the art.

[0136] The memory can be any suitable device in which a processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks or optical discs or magnetic tapes or RAM, or any other suitable device, fixed or portable). The processor can include a general-purpose digital microprocessor suitably programmed from a computer-readable medium carrying the necessary program code. The program can be provided to the processor remotely via a communication channel or pre-stored in a computer program product, such as a memory or some other portable or fixed computer-readable storage medium, using any of those devices connected to the memory. For example, a disk or optical disc can carry the program and can be read by a disk writer / reader. The system of the present invention also includes, for example, algorithms programmed in the form of a computer program product for practicing the methods described above. Programs based on the present invention can be recorded on computer-readable media, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0137] The processor can also access communication channels to communicate with users at remote locations. A remote location refers to a user who does not directly interact with the system but relays input information from an external device to an input manager, such as a computer connected to a wide area network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones).

[0138] In some embodiments, a system based on this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication (e.g., RFID, Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB)). Communication protocols and cellular communications, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0139] In one embodiment, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces like USB ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices, such as computer terminals (e.g., in a doctor's office or hospital environment), which are configured for similar supplemental data communication.

[0140] In one embodiment, the communication interface is configured for infrared communication. Communication or any other suitable wireless communication protocol that enables the system of this subject to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user may use in conjunction with them.

[0141] In one embodiment, the communication interface is configured to provide connectivity for data transmission using the Internet Protocol (IP) via a cellular telephone network, a short message service (SMS), a wireless connection to a personal computer (PC) connected to a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0142] In one embodiment, the system is configured to communicate wirelessly with a server device via a communication interface, for example, using 802.11 or... Common standards such as RF protocols or IrDA infrared protocols. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop; or a larger device, such as a desktop computer, device, etc. In some embodiments, the server device has a display such as a liquid crystal display (LCD) and input devices such as buttons, keyboards, mice, or touchscreens.

[0143] In some embodiments, the communication interface is configured to automatically or semi-automatically transmit data stored in the subject system, such as data stored in an optional data storage unit, to a network or server device using one or more communication protocols and / or mechanisms as described above.

[0144] The output controller may include any of a variety of known display devices for presenting information to a user (whether human or machine, local or remote). If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of image elements. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. Functional elements of the computer may communicate with each other via a system bus. Some of these communications may be implemented using networks or other types of remote communication in alternative embodiments. Based on known technologies, the output manager may also provide information generated by the processing module to a user at a remote location, such as via the Internet, telephone, or satellite networks. The presentation of data by the output manager may be based on a variety of known technologies. As some examples, the data may include SQL, HTML, or XML documents, emails, or other files, or other forms of data. The data may include Internet URL addresses so that the user can retrieve additional SQL, HTML, XML, or other documents or data from a remote source. One or more platforms present in this subject system may be any type of known computer platform or type to be developed in the future, although they generally belong to a class of computers commonly referred to as servers. However, they can also be mainframes, workstations, or other computer types. They can be connected via any known or future type of cable or other communication system (including wireless systems), whether networked or otherwise. They can be located in the same location or physically separated. Various operating systems can be used on any computer platform, depending on the type and / or brand of the chosen platform. Applicable operating systems include Windows 10, Windows... Windows XP, Windows 7, Windows8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, Ubuntu, Zorin OS, etc.

[0145] In some embodiments, the subject matter system includes one or more optical adjustment components for adjusting, for example, light incident on a sample (e.g., from a laser) or light collected from a sample (e.g., fluorescence). For example, optical adjustment may be increasing the size of the light, focusing the light, or collimating the light. In some cases, optical adjustment is an amplification scheme to increase the size of the light (e.g., beam spot), such as increasing the size by 5% or more, such as increasing it by 10% or more, such as increasing it by 25% or more, such as increasing it by 50% or more, and including increasing the size by 75% or more. In other embodiments, optical adjustment includes focusing the light to reduce the light size, such as reducing it by 5% or more, such as reducing it by 10% or more, such as reducing it by 25% or more, such as reducing it by 50% or more, and including reducing the beam spot size by 75% or more. In some embodiments, optical adjustment includes collimating the light. The term "collimating" is used in its conventional sense to refer to optically adjusting the collinearity of light propagation or reducing divergence by using light from a common propagation axis. In some cases, collimation includes narrowing the spatial cross-section of the beam (e.g., reducing the beam distribution of a laser).

[0146] In some embodiments, the optical adjustment element is a focusing lens having a magnification of 0.1 to 0.95, such as 0.2 to 0.9, 0.3 to 0.85, 0.35 to 0.8, 0.5 to 0.75, and including 0.55 to 0.7, such as from 0.6. For example, in some cases, the focusing lens is a biachromatic demagnetizing lens having a magnification of approximately 0.6. The focal length of the focusing lens can vary from 5 mm to 20 mm, such as 6 mm to 19 mm, 7 mm to 18 mm, 8 mm to 17 mm, 9 mm to 16 mm, and including 10 mm to 15 mm. In some embodiments, the focusing lens has a focal length of approximately 13 mm.

[0147] In other embodiments, the optical adjustment component is a collimator. The collimator can be any convenient collimation scheme, such as one or more mirrors or curved lenses, or combinations thereof. For example, in some cases, the collimator is a single collimating lens. In other cases, the collimator is a collimating mirror. In other cases, the collimator includes two lenses. In other cases, the collimator includes a mirror and a lens. When the collimator includes one or more lenses, the focal length of the collimating lens can vary, ranging from 5 mm to 40 mm, with focal lengths such as 6 mm to 37.5 mm, 7 mm to 35 mm, 8 mm to 32.5 mm, 9 mm to 30 mm, 10 mm to 27.5 mm, 12.5 mm to 25 mm, and including a focal length range of 15 mm to 20 mm.

[0148] In some embodiments, the present invention system includes a flow cell nozzle having a nozzle orifice configured to allow a flow stream to pass through the flow cell nozzle. The flow cell nozzle has an orifice for propagating a fluid sample to a sample diagnostic region, wherein in some embodiments, the flow cell nozzle includes a proximal cylindrical portion defining a longitudinal axis and a distal conical portion terminating in a flat surface having a nozzle orifice transverse to the longitudinal axis. The length (measured along the longitudinal axis) of the proximal cylindrical portion can vary from 1 mm to 15 mm, for example, 1.5 mm to 12.5 mm, for example, 2 mm to 10 mm, for example, 3 mm to 9 mm, and includes 4 mm to 8 mm. The length (measured along the longitudinal axis) of the distal conical portion can also vary from 1 mm to 10 mm, for example, 2 mm to 9 mm, for example, 3 mm to 8 mm, and includes 4 mm to 7 mm. In some embodiments, the diameter of the flow cell nozzle chamber can vary from 1 mm to 10 mm, for example, 2 mm to 9 mm, for example, 3 mm to 8 mm, and includes 4 mm to 7 mm.

[0149] In some cases, the nozzle chamber does not include a cylindrical portion and the entire flow pool nozzle chamber is conical. In these embodiments, the length of the conical nozzle chamber (measured along the longitudinal axis transverse to the nozzle orifice) can range from 1 mm to 15 mm, for example 1.5 mm to 12.5 mm, for example 2 mm to 10 mm, for example 3 mm to 9 mm, and includes 4 mm to 8 mm. The diameter of the proximal portion of the conical nozzle chamber can range from 1 mm to 10 mm, for example 2 mm to 9 mm, for example 3 mm to 8 mm, and includes 4 mm to 7 mm.

[0150] In an embodiment, the sample stream exits from an orifice at the distal end of the flow cell nozzle. Depending on the desired characteristics of the flow, the flow cell nozzle orifice can be of any suitable shape, wherein the cross-sectional shape of interest includes, but is not limited to: linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons; curved cross-sectional shapes such as circles and ellipses; and irregular shapes, such as the parabolic base portion connected to the top portion of a plane. In some embodiments, the flow cell nozzle of interest has a circular orifice. The size of the nozzle orifice can vary, ranging from 1 μm to 20000 μm in some embodiments, for example, 2 μm to 17500 μm, for example, 5 μm to 15000 μm, for example, 10 μm to 12500 μm, for example, 15 μm to 10000 μm, for example, 25 μm to 7500 μm, for example, 50 μm to 5000 μm, for example, 75 μm to 1000 μm, for example, 100 μm to 750 μm, and including 150 μm to 500 μm. In some embodiments, the nozzle orifice is 100 μm.

[0151] 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 sample flow to the flow cell nozzle chamber. Based on the desired characteristics of the flow flow, the rate at which the sample delivered from the sample injection port to the flow cell nozzle chamber can be 1 μL / sec or higher, for example, 2 μL / sec or higher, for example, 3 μL / sec or higher, for example, 5 μL / sec or higher, for example, 10 μL / sec or higher, for example, 15 μL / sec or higher, for example, 25 μL / sec or higher, for example, 50 μL / sec or higher, for example, 100 μL / sec or higher, for example, 150 μL / sec or higher, for example, 200 μL / sec or higher, for example, 250 μL / sec or higher, for example, 300 μL / sec or higher, for example, 350 μL / sec or higher, for example, 400 μL / sec or higher, for example, 450 μL / sec or higher, and includes 500 μL / sec or higher. For example, the sample flow rate can range from 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, such as 3 μL / sec to about 400 μL / sec, such as 4 μL / sec to about 350 μL / sec, such as 5 μL / sec to about 300 μL / sec, such as 6 μL / sec to about 250 μL / sec, such as 7 μL / sec to about 200 μL / sec, such as 8 μL / sec to about 150 μL / sec, such as 9 μL / sec to about 125 μL / sec, and includes 10 μL / sec to about 100 μL / sec.

[0152] The sample injection port can be an orifice located in the nozzle chamber wall or a conduit located near the proximal end of the nozzle chamber. When the sample injection port is an orifice located in the nozzle chamber wall, the orifice can be of any suitable shape, with cross-sectional shapes of interest including, but not limited to: linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons; curved cross-sectional shapes such as circles and ellipses; and irregular shapes, such as the bottom portion of a parabola connected to the top portion of a plane. In some embodiments, the sample injection port has a circular orifice. The size of the sample injection port orifice can vary based on its shape, and in some cases has an opening of 0.1 mm to 5.0 mm, such as 0.2 mm to 3.0 mm, 0.5 mm to 2.5 mm, 0.75 mm to 2.25 mm, 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, such as 1.5 mm.

[0153] In some cases, the sample injection port is a conduit located near the end of the flow cell nozzle chamber. For example, the sample injection port may be a conduit positioned such that its orifice aligns 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 can be any suitable shape, including but not limited to: linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons; curved cross-sectional shapes such as circles and ellipses; and irregular shapes, such as the parabolic base portion connected to the top portion of a plane. The orifice of the conduit can vary based on its shape, and in some cases has an opening of 0.1 mm to 5.0 mm, such as 0.2 mm to 3.0 mm, 0.5 mm to 2.5 mm, 0.75 mm to 2.25 mm, 1 mm to 2 mm, and including 1.25 mm to 1.75 mm, such as 1.5 mm. The shape of the sample injection port tip can 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 with a bevel angle ranging from 1° to 10°, such as 2° to 9°, such as 3° to 8°, such as 4° to 7°, and including a 5° bevel angle.

[0154] In some embodiments, the flow cell nozzle further includes a sheath fluid injection port configured to supply sheath fluid to the flow cell nozzle. In embodiments, the sheath fluid injection system is configured to supply a sheath fluid flow to the flow cell nozzle chamber, for example, in combination with a sample to generate a cascaded flow of sheath fluid around a sample flow flow. Depending on the desired characteristics of the flow flow, the rate of sheath fluid delivered to the flow cell nozzle chamber can be 25 μL / sec or higher, for example 50 μL / sec or higher, for example 75 μL / sec or higher, for example 100 μL / sec or higher, for example 250 μL / sec or higher, for example 500 μL / sec or higher, for example 750 μL / sec or higher, for example 1000 μL / sec or higher, and includes 2500 μL / sec or higher. For example, the sheath fluid flow rate can be from 1 μL / sec to about 500 μL / sec, such as 2 μL / sec to about 450 μL / sec, such as 3 μL / sec to about 400 μL / sec, such as 4 μL / sec to about 350 μL / sec, such as 5 μL / sec to about 300 μL / sec, such as 6 μL / sec to about 250 μL / sec, such as 7 μL / sec to about 200 μL / sec, such as 8 μL / sec to about 150 μL / sec, such as 9 μL / sec to about 125 μL / sec, and includes 10 μL / sec to about 100 μL / sec.

[0155] In some embodiments, the sheath injection port is an orifice located in the nozzle chamber wall. The sheath injection port orifice can be of any suitable shape, wherein the cross-sectional shape of interest includes, but is not limited to: linear cross-sectional shapes such as squares, rectangles, trapezoids, triangles, and hexagons; curved cross-sectional shapes such as circles and ellipses; and irregular shapes, such as the parabolic bottom portion connected to the top portion of a plane. The size of the sample injection port orifice can vary based on its shape, and in some cases has an opening of 0.1 mm to 5.0 mm, such as 0.2 mm to 3.0 mm, 0.5 mm to 2.5 mm, 0.75 mm to 2.25 mm, 1 mm to 2 mm, and includes 1.25 mm to 1.75 mm, such as 1.5 mm.

[0156] In some cases, the system includes a sample diagnostic region in fluid communication with the flow cell nozzle orifice. In these cases, the sample flow exits from the orifice at the distal end of the flow cell nozzle, and particles in the flow can be illuminated in the sample diagnostic region using a light source. The size of the diagnostic region can vary based on the characteristics of the flow nozzle, such as the size of the nozzle orifice and the size of the sample injection port. In embodiments, the width of the diagnostic region can be 0.01 mm or greater, for example 0.05 mm or greater, for example 0.1 mm or greater, for example 0.5 mm or greater, for example 1 mm or greater, for example 2 mm or greater, for example 3 mm or greater, for example 5 mm or greater, and includes 10 mm or greater. The length of the diagnostic region can also vary, in some cases ranging from 0.01 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 1.5 mm or more, for example 2 mm or more, for example 3 mm or more, for example 5 mm or more, for example 10 mm or more, for example 15 mm or more, for example 20 mm or more, for example 25 mm or more, and includes 50 mm or more.

[0157] The diagnostic region can be configured to facilitate illumination of a planar cross-section of the outflowing flow, or it can 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 diagnostic region includes a transparent window that facilitates illumination of a predetermined length of the outflowing flow, such as 1 mm or more, 2 mm or more, 3 mm or more, 4 mm or more, 5 mm or more, and including 10 mm or more. Depending on the light source used to illuminate the outflowing flow (described below), the diagnostic region can be configured to allow light passing through 100 nm to 1500 nm, such as 150 nm to 1400 nm, 200 nm to 1300 nm, 250 nm to 1200 nm, 300 nm to 1100 nm, 350 nm to 1000 nm, 400 nm to 900 nm, and including 500 nm to 800 nm.Therefore, the diagnostic region can be formed of any transparent material that spans the desired wavelength range, including but not limited to optical glass, borosilicate glass, Pyrex glass, ultraviolet quartz, infrared quartz, sapphire, and plastics such as polycarbonate, polyvinyl chloride (PVC), polyurethane, polyether, polyamide, polyimide, or copolymers of these thermoplastics, such as PETG (ethylene glycol-modified polyethylene terephthalate), and other polymeric plastic materials, including polyesters, wherein the polyesters of interest may include, but are not limited to, polyalkylene terephthalates, such as polyethylene terephthalate (PETG). ET), bottle-grade PET (a copolymer based on monoethylene glycol, terephthalic acid, and other comonomers such as isophthalic acid and cyclohexenedimethyl alcohol), polybutylene terephthalate (PBT) and polyhexamethylene terephthalate; polyalkylene adipates, such as polyvinyl adipate, polybutylene 1,4-adipate, and polyhexamethylene adipate; polyalkyl octanoates, such as polyethyl octanoate; polyalkyl sebacate, such as polyethylene sebacate; poly(ε-caprolactone) and poly(β-propiolactone); polyalkyl isophthalate, such as polyisophthalic acid Ethylene glycol esters; polyalkylene 2,6-naphthalene dicarboxylate esters, such as polyethylene 2,6-naphthalene dicarboxylate ester; polyalkylene sulfonyl-4,4'-dibenzoate esters, such as polyethylene sulfonyl-4,4'-dibenzoate ester; poly(p-phenylene alkylene dicarboxylate), such as poly(p-phenylene ethylene dicarboxylate); poly(trans-1,4-cyclohexanedialkylene dicarboxylate esters, such as poly(trans-1,4-cyclohexanediethylene ethylene dicarboxylate esters); poly(1,4-cyclohexane-dimethylene alkylene dicarboxylate esters, such as poly(1,4-cyclohexane-dimethylene ethylene dicarboxylate esters); poly[2.2.2] - Bicyclooctane-1,4-dimethylenealkylene dicarboxylate, such as poly[2.2.2]-bicyclooctane-1,4-dimethyleneethylene dicarboxylate; lactic acid polymers and copolymers, such as (S)-polylactide, (R,S)-polylactide, polytetramethylglycolic acid and polylactide-co-glycolic acid; polycarbonates of bisphenol A, 3,3'-dimethylbisphenol A, 3,3',5,5'-tetrachlorobisphenol A, 3,3',5,5'-tetramethylbisphenol A; polyamides, such as polyterephthalamide; polyesters, such as polyethylene terephthalate, such as Mylar. TM Polyethylene terephthalate; etc. In some embodiments, the subject matter system includes a cuvette located in the sample diagnostic region. In embodiments, the cuvette may transmit light from 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, and including 500 nm to 800 nm.

[0158] In some embodiments, the subject matter system includes a particle sorting component for sorting particles (e.g., cells) in a sample. In some cases, the particle sorting component is a particle sorting module, such as those described in U.S. Patent No. 2017 / 0299493, filed March 28, 2017, and U.S. Provisional Patent Application No. 62 / 752,793, filed October 30, 2018, the disclosures of which are incorporated herein by reference. In some embodiments, the particle sorting component includes one or more droplet deflectors, such as those described in U.S. Patent Publication No. 2018 / 0095022, filed June 14, 2017, the disclosures of which are incorporated herein by reference.

[0159] In some embodiments, the subject system is a flow cytometer system. Suitable flow cytometry systems may include, but are not limited to, those described in: Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. 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 Throm Hemost. 2004 Oct; 30(5):502-11; Alison et al. J Pathol, 2010 Dec; 222(4):335-344; and Herbig et al. (2007) Crit Rev Ther Drug Carrier Syst. 24(3):203-255; its public content is incorporated herein by reference. In some cases, flow cytometry systems of interest include BD Biosciences FACSCanto. TM II flow cytometer, BD Accuri TM Flow cytometer, BD Biosciences FACSCelesta TM Flow cytometer, BD Biosciences FACSLyric TM Flow cytometer, BDBiosciences FACSVerse TMFlow cytometer, BD Biosciences FACSymphony TM Flow cytometer, BDBiosciences LSRFortess TM Flow cytometer, BD Biosciences LSRFortess TM X-20 flow cytometer and BD Biosciences FACSCalibur TM Cell sorter, BD Biosciences FACSCount TM Cell sorter, BD Biosciences FACSLyric TM Cell sorter and BD Biosciences via TM Cell sorting instrument, BDBiosciences Influx TM Cell sorter, BD Biosciences Jazz TM Cell sorter, BD Biosciences Aria TM Cell sorting instrument and BD Biosciences FACSMelody TM Cell sorting instruments, etc.

[0160] In some embodiments, the particle sorting system of this subject is a flow cytometer system, such as U.S. Patent Nos. 10,006,852; 9,952,076; 9,933,341; 9,784,661; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8, The disclosures described in 233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; and 5,602,039 are incorporated herein by reference in their entirety.

[0161] In some cases, the system of this subject is a flow cytometer system configured to characterize and image particles in a flowing stream using fluorescence imaging with radio frequency labeled emission (FIRE), as described in, for example, Diebold, et al. Nature Photonics Vol. 7(10); 806-810 (2013), and U.S. Patent Nos. 9,423,353; 9,784,661 and 10,006,852, and U.S. Patent Publications Nos. 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0162] kit

[0163] This disclosure also includes kits, which include one or more reconfigurable integrated circuit devices described herein. In some embodiments, the kit may also include programs for the system of this subject matter, such as in the form of computer-readable media (e.g., flash drives, USB storage, optical discs, DVDs, Blu-ray discs, etc.) or instructions for downloading programs from Internet protocols or cloud servers. The kit may also include instructions for implementing the methods of this subject matter. These instructions may exist in a variety of forms within the kit of this subject matter, one or more of which may be presented in the kit. One form of these instructions may be as printed information on a suitable medium or substrate (e.g., one or more sheets of paper on which information is printed), in the packaging of the kit, in a packaging insert, etc. Another form of the instructions is a computer-readable medium, such as a disk, optical disc (CD), portable flash drive, etc., on which information has been recorded. Another form of the instructions may be as a website address that can be used via the Internet to access information on a deleted site.

[0164] practicality

[0165] The integrated circuit devices, methods, and systems of this subject matter can be used in a variety of applications requiring the analysis and sorting of particle components in samples (e.g., biological samples) within a fluid medium. In some embodiments, the integrated circuit devices, methods, and systems described herein can be used for flow cytometry characterization of biological samples (e.g., using fluorescent tags). In other embodiments, the integrated circuit devices, methods, and systems of this subject matter can be used for the spectroscopy of emitted light. Embodiments of this disclosure can be used in flow cytometers that require improved cell sorting accuracy, enhanced particle collection, particle charging efficiency, more accurate particle charging, and enhanced particle deflection during cell sorting.

[0166] Embodiments of this disclosure can also be used in applications in which cells prepared from biological samples are used for research, laboratory testing, or therapeutic purposes. In some embodiments, the methods and devices of this subject matter can facilitate the acquisition of individual cells prepared from a target fluid or tissue biological sample. For example, the methods and systems of this subject matter facilitate the acquisition of cells from fluid or tissue samples for use as samples in the research or diagnosis of a disease (e.g., cancer). Similarly, the methods and systems of this subject matter can facilitate the acquisition of cells from fluid or tissue samples for therapeutic purposes. Compared to conventional flow cytometry systems, the reconfigurable integrated circuit devices, methods, and systems of this disclosure enable the separation and collection of cells from biological samples (e.g., organs, tissues, tissue fragments, fluids) with increased efficiency and low cost.

[0167] Notwithstanding the appended claims, this disclosure is also limited by the following provisions:

[0168] 1. A reconfigurable integrated circuit, which is programmed to:

[0169] Calculate the parameters of the particles in the flowing stream based on the detected light;

[0170] Compare the calculated parameters of the particles with the parameters of one or more particle classifications;

[0171] Particles are classified based on a comparison between the parameters used for particle classification and the computational parameters of the particles; and

[0172] Adjust one or more parameters for particle classification based on the computational parameters of the particles.

[0173] 2. The reconfigurable integrated circuit according to item 1, wherein particle classification includes sorting.

[0174] 3. The reconfigurable integrated circuit according to any one of claims 1-2, wherein classifying particles includes generating particle sorting decisions.

[0175] 4. The reconfigurable integrated circuit according to claim 3, wherein the integrated circuit is programmed to generate particle sorting decisions based on a threshold between computational parameters of the particles and parameters for particle classification.

[0176] 5. The reconfigurable integrated circuit according to claim 4, wherein adjusting one or more parameters for particle sorting includes changing the threshold used to generate particle sorting decisions.

[0177] 6. The reconfigurable integrated circuit according to claim 5, wherein the threshold is a predetermined threshold.

[0178] 7. The reconfigurable integrated circuit according to claim 5, wherein the threshold is a user-configurable threshold and is adjusted by user input.

[0179] 8. The reconfigurable integrated circuit according to claim 5, wherein the threshold is a dynamic threshold adjusted based on the computational parameters of the particles.

[0180] 9. The reconfigurable integrated circuit according to claim 8, wherein the integrated circuit is programmed with an algorithm for updating the threshold for generating sorting decisions.

[0181] 10. The reconfigurable integrated circuit according to claim 9, wherein the algorithm is a statically predetermined algorithm.

[0182] 11. The reconfigurable integrated circuit according to claim 9, wherein the algorithm is a user-configurable algorithm.

[0183] 12. The reconfigurable integrated circuit according to claim 9, wherein the algorithm is a dynamic algorithm that updates based on the computational parameters of the particles.

[0184] 13. The reconfigurable integrated circuit according to claim 1, wherein the particle classification comprises one or more particle clusters.

[0185] 14. The reconfigurable integrated circuit according to claim 13, wherein classifying particles includes assigning particles to particle clusters based on a comparison between classification parameters of each particle cluster and computational parameters of the particles.

[0186] 15. The reconfigurable integrated circuit according to claim 14, wherein adjusting one or more parameters of particle classification includes changing the classification parameters of the assigned particle cluster based on particle-based computational parameters.

[0187] 16. The reconfigurable integrated circuit according to any one of claims 13-15, wherein the integrated circuit is programmed to plot the computational parameters of the particles on a scatter plot.

[0188] 17. The reconfigurable integrated circuit according to any one of claims 13-15, wherein the integrated circuit is programmed to generate a list of computational parameters for the particles.

[0189] 18. The reconfigurable integrated circuit according to any one of claims 13-17, wherein the integrated circuit is programmed to calculate the statistical probability of assigning a particle to a particle cluster.

[0190] 19. The reconfigurable integrated circuit according to any one of claims 13-17, wherein the integrated circuit is programmed to calculate the computational parameters of particles and the Mahalanobis distance between each particle cluster.

[0191] 20. The reconfigurable integrated circuit according to any one of claims 13-19, wherein the integrated circuit is programmed to assign particles to particle clusters based on a threshold between computational parameters of the particles and classification parameters of the particle clusters.

[0192] 21. The reconfigurable integrated circuit according to claim 20, wherein the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle cluster.

[0193] 22. The reconfigurable integrated circuit according to claim 20, wherein the threshold is a predetermined threshold.

[0194] 23. The reconfigurable integrated circuit according to claim 20, wherein the threshold is a user-configurable threshold.

[0195] 24. The reconfigurable integrated circuit according to claim 20, wherein the threshold is a dynamic threshold that changes in response to assigning particles to a particle cluster.

[0196] 25. The reconfigurable integrated circuit according to any one of claims 20-24, wherein the integrated circuit is programmed to adjust the threshold based on the classification parameters of the assigned particle cluster and the computational parameters of the particles.

[0197] 26. The reconfigurable integrated circuit according to claim 25, wherein the integrated circuit is programmed with an algorithm for updating the classification parameters of the allocated particle clusters.

[0198] 27. The reconfigurable integrated circuit according to claim 26, wherein the algorithm is a statically predetermined algorithm.

[0199] 28. The reconfigurable integrated circuit according to claim 26, wherein the algorithm is a user-configurable algorithm.

[0200] 29. The reconfigurable integrated circuit according to claim 26, wherein the algorithm is a dynamic algorithm that updates the classification parameters of the assigned particle cluster using the computational parameters of the particles.

[0201] 30. The reconfigurable integrated circuit according to any one of claims 1-29, wherein the reconfigurable integrated circuit comprises a field-programmable gate array (FPGA).

[0202] 31. The reconfigurable integrated circuit according to any one of claims 1-29, wherein the integrated circuit includes an application-specific integrated circuit (ASIC).

[0203] 32. A reconfigurable integrated circuit according to any one of claims 1-29, wherein the integrated circuit includes a complex programmable logic device (CPLD).

[0204] 33. A method comprising:

[0205] Detecting light from a sample containing particles in a flowing stream;

[0206] Calculate the parameters of the particles in the sample based on the detected light;

[0207] Compare the calculated parameters of the particles with the parameters of one or more particle classifications;

[0208] Particles are classified based on a comparison between the parameters used for particle classification and the computational parameters of the particles; and

[0209] Adjust one or more parameters for particle classification based on the computational parameters of the particles.

[0210] 34. According to the method of clause 33, particle classification includes sorting and classification.

[0211] 35. The method according to any one of claims 33-34, wherein classifying particles includes generating a particle sorting decision.

[0212] 36. The method according to claim 35, wherein the method includes generating a particle sorting decision based on a threshold between computational parameters of the particles and parameters for particle classification.

[0213] 37. The method according to claim 36, wherein adjusting one or more parameters for particle classification includes changing the threshold used to generate particle sorting decisions.

[0214] 38. The method according to clause 37, wherein the threshold is a predetermined threshold.

[0215] 39. The method according to clause 37, wherein the threshold is a user-configurable threshold and is adjusted by user input.

[0216] 40. The method according to claim 37, wherein the threshold is a dynamic threshold adjusted based on the computational parameters of the particles.

[0217] 41. The method according to claim 40, wherein the threshold is updated using an algorithm for generating sorting decisions.

[0218] 42. The method according to claim 41, wherein the algorithm is a statically predetermined algorithm.

[0219] 43. The method according to clause 41, wherein the algorithm is a user-configurable algorithm.

[0220] 44. The method according to claim 41, wherein the algorithm is a dynamic algorithm based on updating the computational parameters of the particles.

[0221] 45. The method according to claim 33, wherein particle classification comprises one or more particle clusters.

[0222] 46. ​​The method according to claim 45, wherein classifying particles includes assigning particles to particle clusters based on a comparison between classification parameters of each particle cluster and computational parameters of the particles.

[0223] 47. The method according to claim 46, wherein adjusting one or more parameters of particle classification includes changing the classification parameters of the assigned particle cluster based on the particle's computational parameters.

[0224] 48. The method according to any one of claims 46-47, wherein the method includes plotting the calculated parameters of the particles on a scatter plot.

[0225] 49. The method according to any one of claims 46-47, wherein the method includes a list of computational parameters for generating particles.

[0226] 50. The method according to any one of claims 46-49, wherein comparing the computational parameters of the particles with the classification parameters of each particle cluster includes calculating the statistical probability of assigning the particles to the particle clusters.

[0227] 51. The method according to any one of claims 46-49, wherein comparing the calculated parameters of the particles with the classification parameters of each particle cluster includes calculating the Mahalanobis distance between the calculated parameters of the particles and each particle cluster.

[0228] 52. The method according to any one of claims 46-51, wherein particles are assigned to particle clusters based on a threshold between computational parameters of the particles and classification parameters of the particle clusters.

[0229] 53. The method according to claim 52, wherein the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle cluster.

[0230] 54. The method according to clause 53, wherein the threshold is a predetermined threshold.

[0231] 55. The method according to clause 53, wherein the threshold is a user-configurable threshold.

[0232] 56. The method according to claim 53, wherein the threshold is a dynamic threshold that changes in response to assigning particles to a particle cluster.

[0233] 57. The method according to any one of claims 52-56 further includes adjusting the threshold based on the classification parameters of the assigned particle cluster and the computational parameters of the particles.

[0234] 58. The method according to claim 57, wherein the threshold is adjusted by a processor having a memory operatively coupled to the processor, wherein the memory includes an algorithm for updating the classification parameters of an assigned cluster of particles using computational parameters of the particles.

[0235] 59. The method according to claim 58, wherein the algorithm is a statically predetermined algorithm.

[0236] 60. The method according to clause 58, wherein the algorithm is a user-configurable algorithm.

[0237] 61. The method according to claim 58, wherein the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles.

[0238] 62. The method according to any one of claims 45-61, wherein the one or more particle clusters include computational parameters of a plurality of particles.

[0239] 63. The method according to claim 62, wherein the particle cluster is generated in the following manner:

[0240] Detecting light from multiple particles in a flowing stream;

[0241] Calculate the parameters of each particle based on the detected light; and

[0242] Particles are clustered together based on computational parameters.

[0243] 64. The method according to any one of claims 33-63, wherein detecting light from a sample in a flowing stream comprises detecting light absorption, light scattering, fluorescence, or a combination thereof.

[0244] 65. The method according to any one of claims 33-64, wherein the parameters of the particle are calculated based on the scattered light from the particle.

[0245] 66. The method according to claim 65, wherein the scattered light includes forward scattered light.

[0246] 67. The method according to claim 66, wherein the scattered light includes side-scattered light.

[0247] 68. The method according to claim 67, wherein the parameters of the particles are calculated based on the fluorescence from the particles.

[0248] 69. The method according to claim 68, wherein the parameters of the particles are calculated based on fluorescence data encoded by the frequency of the particles.

[0249] 70. The method according to any one of claims 33-69 further includes sorting the particles.

[0250] 71. The method according to any one of claims 33-70, wherein the parameters of the particles are calculated by an integrated circuit device.

[0251] 72. The method according to claim 71, wherein the integrated circuit device is a field-programmable gate array (FPGA).

[0252] 73. The method according to paragraph 71, wherein the integrated circuit device is an application-specific integrated circuit (ASIC).

[0253] 74. The method according to claim 71, wherein the integrated circuit device is a complex programmable logic device (CPLD).

[0254] 75. The method according to any one of claims 33-74 further includes irradiating the flowing stream with a light source.

[0255] 76. The method according to claim 75, wherein the flow is irradiated with a light source having a wavelength of 200 nm to 800 nm.

[0256] 77. The method according to any one of claims 75-76, wherein the method comprises irradiating the flow with a first frequency-shifted beam and a second frequency-shifted beam.

[0257] 78. The method according to claim 77, wherein the first frequency-shifted beam comprises a local oscillator (LO) beam and the second frequency-shifted beam comprises a radio frequency comb beam.

[0258] 79. The method according to any one of claims 77-78 further comprises:

[0259] Applying a radio frequency drive signal to the acousto-optic device; and

[0260] A laser is used to irradiate an acousto-optic device to generate a first frequency-shifted beam and a second frequency-shifted beam.

[0261] 80. The method according to paragraph 79, wherein the laser is a continuous wave laser.

[0262] 81. A system comprising:

[0263] A light source, configured to illuminate a sample comprising particles in a flowing stream;

[0264] An optical detection system, comprising a photodetector; and

[0265] A processor including a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, configure the processor to:

[0266] Parameters of particles in the flow are calculated based on the detected light.

[0267] Compare the calculated parameters of the particles with the parameters of one or more particle classifications;

[0268] Particles are classified based on a comparison between the parameters used for particle classification and the computational parameters of the particles; and

[0269] Adjust one or more parameters for particle classification based on the computational parameters of the particles.

[0270] 82. The system according to claim 81, wherein particle classification includes sorting and classification.

[0271] 83. The system according to any one of claims 81-82, wherein classifying particles includes generating particle sorting decisions.

[0272] 84. The system according to claim 83, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon, which, when executed by the processor, cause the processor to generate a particle sorting decision based on a threshold between computational parameters of the particles and parameters for particle classification.

[0273] 85. The system according to claim 84, wherein adjusting one or more parameters for particle classification includes changing the threshold used to generate particle sorting decisions.

[0274] 86. The system according to claim 85, wherein the threshold is a predetermined threshold.

[0275] 87. The system according to claim 85, wherein the threshold is a user-configurable threshold and is adjusted by user input.

[0276] 88. The system according to claim 85, wherein the threshold is a dynamic threshold adjusted based on the computational parameters of the particles.

[0277] 89. The system according to claim 88, wherein the memory includes an algorithm for updating thresholds used to generate sorting decisions.

[0278] 90. The system according to claim 89, wherein the algorithm is a statically predetermined algorithm.

[0279] 91. The system according to claim 89, wherein the algorithm is a user-configurable algorithm.

[0280] 92. The system according to claim 89, wherein the algorithm is a dynamic algorithm based on updating the computational parameters of the particles.

[0281] 93. The system according to claim 81, wherein particle classification comprises one or more particle clusters.

[0282] 94. The system according to claim 93, wherein classifying particles includes assigning particles to particle clusters based on a comparison between classification parameters of each particle cluster and computational parameters of the particles.

[0283] 95. The system according to claim 94, wherein adjusting one or more parameters of particle classification includes changing the classification parameters of the assigned particle cluster based on particle computational parameters.

[0284] 96. The system according to any one of claims 93-95, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to plot computational parameters of particles on a scatter plot.

[0285] 97. The system according to any one of claims 93-95, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to generate a list of computational parameters for particles.

[0286] 98. The system according to any one of claims 93-97, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate a statistical probability of assigning the particle to a particle cluster.

[0287] 99. The system according to any one of claims 93-97, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate computational parameters of particles and Mahalanobis distances between each particle cluster.

[0288] 100. The system according to any one of claims 93-99, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to assign particles to particle clusters based on a threshold between computational parameters of the particles and classification parameters of the particle clusters.

[0289] 101. The system according to claim 100, wherein the threshold is the overlap between the computational parameters of the particles and the classification parameters of the particle cluster.

[0290] 102. The system according to claim 100, wherein the threshold is a predetermined threshold.

[0291] 103. The system according to claim 100, wherein the threshold is a user-configurable threshold.

[0292] 104. The system according to claim 100, wherein the threshold is a dynamic threshold that changes in response to assigning particles to a particle cluster.

[0293] 105. The system according to any one of claims 93-104, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to adjust the threshold based on classification parameters of the assigned particle cluster and computational parameters of the particles.

[0294] 106. The system according to claim 105, wherein the memory includes an algorithm for updating the classification parameters of the assigned particle cluster using the computational parameters of the particles.

[0295] 107. The system according to claim 106, wherein the algorithm is a statically predetermined algorithm.

[0296] 108. The system according to claim 106, wherein the algorithm is a user-configurable algorithm.

[0297] 109. The system according to claim 106, wherein the algorithm is a dynamic algorithm that changes in response to the computational parameters of the particles.

[0298] 110. The system according to any one of claims 93-109, wherein the one or more particle clusters comprise computational parameters of a plurality of particles.

[0299] 111. The system according to claim 110, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon, the instructions, when executed by the processor, causing the processor to generate the particle cluster in such a way as follows:

[0300] Calculate parameters of multiple particles based on the detected light; and

[0301] Particles are clustered together based on computational parameters.

[0302] 112. The system according to any one of claims 93-111, wherein the optical detection system is configured to detect light absorption, light scattering, fluorescence, or a combination thereof.

[0303] 113. The system according to any one of claims 93-112, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate parameters of the particle based on scattered light from the particle.

[0304] 114. The system according to claim 113, wherein the scattered light includes forward scattered light.

[0305] 115. The system according to claim 114, wherein the scattered light includes side-scattered light.

[0306] 116. The system according to any one of claims 81-115, wherein the processor includes a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to calculate parameters of the particles based on fluorescence from the particles.

[0307] 117. The system according to claim 116, wherein parameters of the particles are calculated based on fluorescence data encoded from the particles at frequencies.

[0308] 118. The system according to any one of claims 81-117, wherein the light source includes a beam generator component configured to generate at least a first frequency-shifted beam and a second frequency-shifted beam.

[0309] 119. The system according to claim 118, wherein the beam generator includes an acousto-optic deflector.

[0310] 120. The system according to any one of claims 118-119, wherein the beam generator comprises a direct digital synthesizer (DDS) RF comb generator.

[0311] 121. The system according to any one of claims 118-120, wherein the beam generator component is configured to generate a frequency-shifted local oscillator beam.

[0312] 122. The system according to any one of claims 118-121, wherein the beam generator component is configured to generate a plurality of frequency-shifted comb beams.

[0313] 123. The system according to any one of claims 81-122, wherein the light source comprises a laser.

[0314] 124. The system according to claim 123, wherein the laser is a continuous wave laser.

[0315] 125. The system according to any one of claims 81-124, wherein the system is a flow cytometer.

[0316] 126. The system according to any one of claims 81-125 further includes a cell sorter.

[0317] 127. The system according to claim 126, wherein the cell sorter includes a droplet deflector.

[0318] Although the invention has been described in some detail by way of illustration and example for the purpose of clarity, it will be apparent to those skilled in the art, based on the teachings of the invention, that certain changes and modifications may be made thereto without departing from the spirit or scope of the appended claims.

[0319] Therefore, the foregoing merely illustrates the principles of the invention. It should be understood that those skilled in the art will be able to design various configurations, although not explicitly described or shown herein, that embody the principles of the invention and are included within its spirit and scope. Furthermore, all examples and conditional language referenced herein are primarily intended to aid the reader in understanding the principles of the invention and the concepts contributed by the inventors to the field, and should be construed as not being limited to these specifically referenced examples and conditions. Moreover, all statements herein recounting the principles, aspects, and embodiments of the invention and their specific examples are intended to cover their structural and functional equivalents. Furthermore, such equivalents are intended to include both currently known equivalents and those developed in the future, i.e., any elements developed that perform the same function regardless of structure. Furthermore, nothing disclosed herein, whether or not explicitly referenced in the claims, is intended for public use only.

[0320] Therefore, the scope of the invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the invention are embodied in the appended claims. In the claims, 35U.SC §112(f) or 35U.SC §112(6) is explicitly defined as being invoked to limit the claims only if the exact phrase “means for…” or the exact phrase “steps for…” is used at the beginning of such limitation in the claims; if such an exact phrase is not used in the limitation of the claims, 35U.SC §112(f) or 35U.SC §112(6) is not invoked.

Claims

1. A system comprising: A light source, used to illuminate particles in a flowing stream; A light detection system comprising a photodetector for detecting light from particles in a flowing stream; as well as A reconfigurable field-programmable gate array is programmed to: Parameters of particles in the flow are calculated based on the detected light. Compare the calculated parameters of the particles with the parameters of one or more particle classifications; Particles are classified based on a comparison between the parameters used for particle classification and the calculated parameters of the particles; and A machine learning algorithm that updates one or more parameters for particle classification using particle-based computational parameters and parameters of previously classified particles.

2. The system according to claim 1, wherein, The particle classification includes sorting and classification.

3. The system according to any one of claims 1 to 2, wherein, Classifying particles includes generating particle sorting decisions.

4. The system according to claim 3, wherein, The field-programmable gate array is programmed to generate particle sorting decisions based on a threshold between the particle's computational parameters and the particle classification parameters.

5. The system according to claim 4, wherein, Adjusting one or more parameters for particle classification includes changing the threshold used to generate particle sorting decisions.

6. The system according to claim 5, wherein, The threshold is a dynamic threshold that is adjusted based on the particle's computational parameters.

7. The system according to claim 1, wherein, The particle classification includes one or more particle clusters.

8. The system according to claim 7, wherein, Classifying particles involves assigning particles to particle clusters based on a comparison between the classification parameters of each particle cluster and the computational parameters of the particles. Adjusting one or more parameters for particle classification includes changing the classification parameters of the assigned particle clusters based on the computational parameters of the particles.

9. A method comprising: A light detection system including a photodetector is used to detect light from a sample in a flowing stream, including particles illuminated by a light source. Using a reconfigurable field-programmable gate array, the parameters of particles in the sample are calculated based on the detected light; Compare the calculated parameters of the particles with the parameters of one or more particle classifications; Particles are classified based on a comparison between the parameters used for particle classification and the computational parameters of the particles; and A machine learning algorithm is used to update the parameters of the field-programmable gate array (FPGA) for particle classification by using particle-based computational parameters and parameters of previously classified particles.

Images