Method and system for adjusting training gates to accommodate flow cytometer data

Abstract

A method is provided for adjusting a training gate prepared from a first set of flow cytometer data to accommodate a second set of flow cytometer data. In embodiments, the method includes generating an image for each of the first and second sets of flow cytometer data. In some examples, generating the image includes organizing the data into two-dimensional bins and assigning a shade to each bin such that the bins are represented by pixels. In some examples, the method includes warping the generated image of the first set of flow cytometer data with a computer-implemented algorithm to maximize similarity to the second set of flow cytometer data and applying the same transformation to the training gate. In some embodiments, the method includes overlaying the adjusted training gate on the generated image of the second set of flow cytometer data. A system and computer-readable medium for adjusting a training gate are also provided.

Description

[Background technology]

[0001] Flow cytometry is a technique used to characterize and often sort biomaterials, such as cells in blood samples or particles of interest in other types of biological or chemical samples. A flow cytometer typically includes a sample reservoir for receiving a fluid sample and a sheath reservoir containing a sheath fluid. The flow cytometer directs the sheath fluid into the flow cell while transporting particles (including cells) in the fluid sample as a cellular stream. To characterize the components of the flow stream, the flow stream is illuminated with light. Variations in the material within the flow stream, such as morphology or the presence of fluorescent labels, can cause variations in the observed light, and these variations enable characterization and separation.

[0002] For example, particles such as molecules in a fluid suspension, analyte-bound beads, or individual cells pass through a detection region, where the particles are typically exposed to excitation light from one or more lasers, and their light scattering and fluorescence properties are measured. The particles or their components are typically labeled with fluorescent dyes to facilitate detection. A number of different particles or components can be detected simultaneously by labeling them using spectrally distinct fluorescent dyes. In some implementations, the analyzer includes multiple detectors, one for each of the scattering parameters being measured and one or more for each of the distinct dyes being detected. For example, some embodiments include spectral configurations in which two or more sensors or detectors are used per dye. The resulting data includes signals measured for each of the light scattering detectors and the fluorescence emission.

[0003] Flow cytometers may further include means for recording and analyzing measured data. For example, data storage and analysis may be performed using a computer connected to the detection electronics. For example, data may be stored in tabular format, where each row corresponds to data about one particle and each column corresponds to each of the measured features. The use of a standard file format, such as the "FCS" file format, for storing data from particle analyzers facilitates the analysis of the data using separate programs and / or machines. Using current analytical methods, data is typically displayed as a one-dimensional histogram or a two-dimensional (2D) plot for ease of visualization, but multidimensional data may be visualized using other methods.

[0004] For example, parameters measured using a flow cytometer typically include light of the excitation wavelength scattered by particles at a narrow angle almost directly forward, known as forward scattering (FSC); excitation light scattered by particles perpendicular to the excitation laser, known as side scattering (SSC); and light emitted from fluorescent molecules in one or more detectors that measure signals over a range of spectral wavelengths, or from fluorescent dyes primarily detected in a particular detector or detector array. Different cell types can be identified by the light scattering properties and fluorescence emission resulting from labeling various cellular proteins or other components with fluorescent dye-labeled antibodies or other fluorescent probes.

[0005] Both flow cytometers and scanning cytometers are commercially available, for example, from BD Biosciences (San Jose, Calif.). Flow cytometry is described, for example, in Landy et al. (eds.), Clinical Flow Cytometry, Annals of the New York Academy of Sciences Volume 677 (1993); Bauer et al. (eds.), Clinical Flow Cytometry: Principles and Applications, Williams & Wilkins (1993); Ormerod (ed.), Flow Cytometry: A Practical Approach, Oxford Univ. Press (1994); Jaroszeski et al. (eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); and Practical Shapiro, Flow Cytometry, 4th ed., Wiley-Liss (2003), all of which are incorporated herein by reference. Fluorescence imaging microscopes are described, for example, in Pawley (ed.), Handbook of Biological Confocal Microscopy, 2nd Edition, Plenum Press (1989), which is incorporated herein by reference.

[0006] Data obtained from flow cytometry analysis of cells (or other particles) is often multidimensional, with each cell corresponding to a point in a multidimensional space defined by its measured parameters. A population of cells or particles can be identified as a cluster of points in the data space. Cluster identification, and thereby population identification, can be performed manually by drawing gates around populations displayed in one or more two-dimensional plots, referred to as "scatter plots" or "dot plots" of the data. Alternatively, clusters can be identified automatically, and gates defining the boundaries of the populations can be determined. Examples of methods for automated gating are described, for example, in U.S. Patents No. 4,845,653, No. 5,627,040, No. 5,739,000, No. 5,795,727, No. 5,962,238, No. 6,014,904, No. 6,944,338, and No. 8,990,047, each of which is incorporated herein by reference. Gating is used to detect large amounts of data that may be generated from a sample. Therefore, facilitating the creation and operation of gates can help improve the speed and accuracy of understanding what the results mean. [Overview of the project]

[0007] Aspects of the present invention include a method for adjusting a training gate prepared from a first set of flow cytometer data to adapt a second set of flow cytometer data. In some embodiments, the first set of flow cytometer data includes a population understood to exhibit a particular parameter and includes a training gate defined by a set of vertices drawn to distinguish the target population from the rest of the data. The method of the present disclosure includes adjusting the training gate to adapt a second set of flow cytometer data (i.e., a set of data that has not yet been gated) such that the adjusted gate fits similar populations in the second set of flow cytometer data. In embodiments, the method includes acquiring the first and second sets of flow cytometer data and generating images for each of the first and second sets of flow cytometer data (i.e., separate images for each set). In some embodiments, generating images includes organizing the flow cytometer data into two-dimensional bins based on the number of analytes present in a data plot within a given region. In some embodiments, the flow cytometer data is binned based on the state of the data with respect to one or more different parameters (e.g., forward scattering, side scattering, fluorescence). In the embodiment, each bin possesses a representative value determined by averaging the values ​​of the data contained within the bin (e.g., with respect to parameters). After the data has been binned, the method includes creating a two-dimensional histogram based on the binned data and calculating a cumulative distribution function based on the histogram. The method further includes inputting representative (e.g., mean) values ​​from each bin into the calculated cumulative distribution function to determine image generation values ​​that serve as the basis for assigning shading (i.e., brightness or darkness) so that the bins are represented by pixels. After the images have been generated, the method includes using a processor implementation algorithm to warp the generated images of a first set of flow cytometer data to maximize the similarity to the generated images of a second set of flow cytometer data.In some embodiments, the processor implementation algorithm is an image alignment algorithm that includes a mathematically modified model employing B-splines to warp the generated images of a first set of flow cytometer data. In some embodiments, the same warp transformation applied to the generated images of the first set of flow cytometer data is subsequently applied to the training gates by using B-spline coefficients to define a function for warping the training gates. In some embodiments, the training gates are then overlaid on the generated images of a second set of flow cytometer data by extracting vertices from the tuned gates and transferring them to a second set of flow cytometer data, thereby reconstructing the tuned training gates within the generated images of the second set of flow cytometer data.

[0008] Aspects of the present invention further include a system for adjusting a training gate prepared from a first set of flow cytometer data to adapt a second set of flow cytometer data. In an embodiment, the first set of flow cytometer data is obtained from an input module configured to receive and / or store flow cytometer data, and the second set of flow cytometer data is obtained from a particle analyzer. The system further includes a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to adjust a training gate prepared from the first set of flow cytometer data to adapt a second set of flow cytometer data. In an embodiment, the processor is configured to adjust the training gate to adapt to a second set of flow cytometer data (i.e., a set of data that has not yet been gated) such that the adjusted gate fits a similar population in the second set of flow cytometer data. In an embodiment, the processor is configured to generate images (i.e., separate images for each set) for each of the first and second sets of flow cytometer data. In some embodiments, generating an image involves organizing flow cytometer data into two-dimensional bins based on the number of analytes present in a data plot within a given region. In some embodiments, the flow cytometer data is binned based on the state of the data with respect to one or more different parameters (e.g., forward scattering, side scattering, fluorescence). In embodiments, each bin possesses a representative value determined by averaging the data values ​​(e.g., with respect to the parameters) contained within the bin. After the data has been binned, the processor may be configured to create a two-dimensional histogram based on the binned data and to calculate a cumulative distribution function based on the histogram. The processor may further be configured to input representative (e.g., mean) values ​​from each bin into the calculated cumulative distribution function to determine image generation values ​​that serve as a basis for assigning shading (i.e., brightness or darkness) so that the bins are represented by pixels.After the image is generated, the processor may be configured to warp the generated image of a first set of flow cytometer data using a processor implementation algorithm to maximize similarity to the generated image of a second set of flow cytometer data. In embodiments, the processor implementation algorithm is an image alignment algorithm and includes a mathematically modified model that employs B-splines to warp the generated image of the first set of flow cytometer data. In some embodiments, the same warp transformation applied to the generated image of the first set of flow cytometer data is subsequently applied to the training gates by using B-spline coefficients to define a function for warping the training gates. In embodiments, the training gates are then overlaid on the generated image of the second set of flow cytometer data by extracting vertices from the tuned gates and transferring them to the second set of flow cytometer data.

[0009] Aspects of the present invention further include a computer-controlled system and a computer-readable storage medium, each including one or more computers for fully or partially automated operation. In some embodiments, the system includes a computer having a computer-readable storage medium in which a computer program is stored, the computer program including instructions for taking a first set of flow cytometer data and a second set of flow cytometer data, including training gates, organizing the data into two-dimensional bins, creating a histogram and a cumulative distribution function based on the two-dimensional binning data, and determining image generation values ​​based on the cumulative distribution function. The computer may also include instructions for generating images for each of the first and second sets of flow cytometer data by assigning shading to each bin based on the corresponding image generation values, warping the generated images of the first set of flow cytometer data to maximize similarity with the generated images of the second set of flow cytometer data, adjusting the training gates using B-spline coefficients from warping the generated images of the first set of flow cytometer data, and overlaying the training gates onto the generated images of the second set of flow cytometer data. [Brief explanation of the drawing]

[0010] The present invention can be best understood from the following detailed description, when read in conjunction with the accompanying drawings. The drawings include the following figures.

[0011] [Figure 1] This figure shows images generated for a first set of flow cytometer data and its corresponding training gate, as well as images generated for a second set of flow cytometer data. [Figure 2] This figure shows a flowchart for automatically adjusting the training gate from a first set of flow cytometer data to adapt a second set of flow cytometer data, according to a specific embodiment. [Figure 3-1]A diagram showing adjusting a training gate to adapt a generated image of a second set of flow cytometer data. [Figure 3-2] A diagram showing adjusting a training gate to adapt a generated image of a second set of flow cytometer data. [Figure 4] A diagram showing the adjusted training gate. [Figure 5] A diagram showing examples of training gates adjusted to adapt various second sets of flow cytometer data. [Figure 6] A diagram showing additional examples of training gates adjusted to adapt various second sets of flow cytometer data. [Figure 7] A diagram showing a flow cytometer according to a particular embodiment. [Figure 8] A diagram showing a functional block diagram of an example of a processor according to a particular embodiment. [Figure 9] A diagram showing a block diagram of a computing system according to a particular embodiment. **Modes for Carrying Out the Invention**

[0012] As discussed above, a method is provided for adjusting a training gate prepared from a first set of flow cytometer data to accommodate a second set of flow cytometer data. In embodiments, the method includes generating an image for each of the first and second sets of flow cytometer data. In some examples, generating the image includes binning the data into two-dimensional bins and assigning a shade to each bin such that the bins are represented by pixels. In some examples, the method includes warping the generated image of the first set of flow cytometer data with a computer-implemented algorithm to maximize similarity to the second set of flow cytometer data and applying the same transformation to the training gate. In some embodiments, the method includes overlaying the adjusted training gate over the generated image of the second set of flow cytometer data. A system and computer-readable medium for adjusting the training gate are also provided.

[0013] Before the present invention is described in greater detail, it is to be understood that the invention is not limited to the particular embodiments described, and as such can, of course, vary. It is also to be understood that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting, since the scope of the present invention is defined solely by the appended claims.

[0014] Where a range of values is provided, unless the context clearly dictates otherwise, each intermediate value between the upper and lower limits of that range, to one-tenth of the unit of the lower limit, and any other stated value or intermediate value within the recited range, is encompassed within the invention. The upper and lower limits of these smaller ranges may independently be included in the smaller ranges and are also encompassed within the invention, subject to any specifically excluded limit within the recited range. Where the recited range includes one or both of the limits, ranges excluding either or both of those included limits are also included in the invention.

[0015] Certain ranges are presented herein with numerical values ​​preceded by the term “approximately.” The term “approximately” is used herein to provide literal confirmation of the exact number it precedes, and of the number that is close to or approximates the preceding number. When determining whether a number is close to or approximates a specifically enumerated number, that close or approximate unenumerated number may, in the context in which it is presented, provide a substantial equivalent of the specifically enumerated number.

[0016] Unless otherwise defined, all technical and scientific terms used herein have the same meanings as those commonly understood by those skilled in the art to which this invention belongs. Any methods and materials similar or equivalent to those described herein may also be used in carrying out or testing the invention, but representative exemplary methods and materials are described below.

[0017] All publications and patents cited herein are incorporated herein by reference as if each individual publication or patent were specifically and individually indicated to be incorporated by reference, and are incorporated herein by reference to disclose and describe methods and / or materials, and publications are cited in relation to those methods and / or materials. Any citation of a publication relates to its disclosure prior to the filing date, and the present invention should not be construed as acknowledging that such publication has no prior rights on the grounds of prior invention. Furthermore, the dates of publications provided may differ from the actual publication dates which may need to be independently verified.

[0018] It should be noted that, as used herein and in the appended claims, the articles “a,” “an,” and “the” refer to multiple subjects unless otherwise explicitly indicated by the context. It should also be noted that the claims may be drafted to exclude any alternative elements. Therefore, this statement is intended to function as an antecedent for the use of exclusive terms such as “solely” and “only,” or for the use of “negative” limitation, in relation to the enumeration of claim elements.

[0019] As will be apparent to those skilled in the art upon reading this disclosure, each of the distinct embodiments described and illustrated herein has distinct components and features that can be readily separated from or combined with any of the features of any of the other various embodiments without departing from the scope or spirit of the invention. Any enumerated method may be performed in the order of the enumerated events, or in any other logically possible order.

[0020] Apparatus and methods have been, or will be, described with a functional description for grammatical fluidity, but unless explicitly stated under Section 112 of the United States Patent Act, the claims should not necessarily be interpreted as being limited by an interpretation of “means” or “steps,” but should be granted the full scope of the meaning and equivalents of the definitions provided by the claims under the doctrine of legal equivalents, and if the claims are explicitly stated under Section 112 of the United States Patent Act, they should be granted the full legal equivalents under Section 112 of the United States Patent Act.

[0021] How to adapt flow cytometer data by adjusting the training gate. As discussed above, aspects of the present disclosure include a method for adapting a training gate prepared from a first set of flow cytometer data to a second set of flow cytometer data. Thus, embodiments of the present invention include obtaining first and second sets of flow cytometer data. In embodiments, the first set of flow cytometer data is characterized such that one or more populations contained therein are understood to be associated with a subtype (i.e., phenotype) of interest. In some embodiments, the first set of flow cytometer data is obtained from a previous flow cytometry experiment. Thus, the first set of flow cytometer data includes populations that, according to a particular embodiment, have been recognized or confirmed by the user as being associated with a particular characteristic of interest. Conversely, the second set of flow cytometer data according to embodiments of the present invention includes one or more populations that have not yet been defined. In other words, the populations of interest in the second set of flow cytometer data require a boundary (i.e., a gate) so that they can be formally distinguished from the rest of the data. In embodiments, the first and second sets of flow cytometer data contain data exhibiting the same parameters (e.g., the same fluorescent dye is detected in both sets).

[0022] In some embodiments, a first set of flow cytometer data includes training gates. As used herein, “gate” generally refers to a classifier boundary that identifies a subset of data of interest. In cytometry, gates can be coupled to groups of events of particular interest. In addition, “gating” generally refers to the process of classifying data using gates defined for a given dataset, where gates can be one or more regions of interest combined with Boolean logic. In some embodiments, gates define boundaries for classifying a collection of flow cytometer data. In embodiments, gates identify flow cytometer data exhibiting the same parameters. Examples of methods for gating are described, for example, in U.S. Patents 4,845,653, 5,627,040, 5,739,000, 5,795,727, 5,962,238, 6,014,904, 6,944,338, and 8,990,047, each of which is incorporated herein by reference. Thus, “training gate” refers to a gate known to define a particular subset of flow cytometer data. In other words, a training gate defines the boundaries of a set of flow cytometer data previously determined by a user or other cytometry technique to correspond to the characteristics of the subject. In embodiments, a training gate includes vertices, i.e., points on a two-dimensional plot that, when connected, form a gate. In some embodiments, the training gate is drawn by a user. In other embodiments, the training gate is pre-established by other cytometry techniques. In some embodiments, the training gate is used to define a target population within a first set of flow cytometer data. In some embodiments, the training gate has a shape that corresponds to the shape of the target population within the first set of flow cytometer data. In some embodiments, the training gate has a polygonal shape. Thus, in some embodiments, the user can define a range of data values ​​to be included within the training gate by drawing a polygon on a graph of the first set of flow cytometer data measurements.

[0023] In certain embodiments, the method includes determining the boundaries of a training gate by generating a two-dimensional data plot that includes one or more regions, e.g., two or more regions, e.g., three or more regions, e.g., four or more regions, and five or more regions, plotting a collection of particles. In some embodiments, a bitmap is generated for each region in the data plot, which, according to certain embodiments, is used as the gate for its corresponding region. In some embodiments, the boundaries of each region in the data plot are determined. In some examples, to determine the boundaries of the regions in the data plot, the method includes computing a set of vertices that form the boundary for each region in the data plot by determining the minimum and maximum values ​​along each axis of the data plot for each vertex. In these embodiments, the minimum value along the x-axis and the minimum value along the y-axis, as well as the maximum value along the x-axis and the maximum value along the y-axis, are determined for each vertex.

[0024] In some embodiments, an algorithmic transformation associated with each vertex of a region of the data plot is determined. Depending on the type of data plot employed (e.g., a bi-exponential data plot), the algorithmic transformation identified for each vertex of the data plot may vary, such as a linear numerical transformation, a logarithmic numerical transformation, or a bi-exponential numerical transformation. The transformation may be positive or negative, depending on the particle ensemble position on the data plot. For example, the transformation may be a positive linear transformation, a positive logarithmic transformation, a negative linear transformation, or a negative logarithmic transformation. In some embodiments, the identified algorithmic transformation is a positive linear transformation / positive linear transformation (i.e., a linear transformation along the positive x-axis and a linear transformation along the positive y-axis). In other embodiments, the identified algorithmic transformation is a positive logarithmic transformation / positive logarithmic transformation. In other embodiments, the algorithmic transformation is a positive linear transformation / positive logarithmic transformation. In other embodiments, the algorithmic transformation is a positive logarithmic transformation / positive linear transformation. In other embodiments, the algorithmic transformation is a negative linear transformation / negative logarithmic transformation. In other embodiments, the algorithmic transformation is a negative linear transformation / positive logarithmic transformation. In other embodiments, the algorithmic transformation is a positive logarithmic transformation / negative linear transformation.

[0025] A bitmap may be generated for each particle population region in a data plot. The term bitmap is used herein in its conventional sense to refer to a mapping index of regions in a data plot. The bitmaps described herein may be generated in data form or as a graphical representation. In some embodiments, a bitmap is formed from a single tile. In other embodiments, a bitmap is formed from multiple tiles. In some embodiments, two or more bitmap tiles are generated from a data plot, including three or more bitmap tiles, four or more bitmap tiles, five or more bitmap tiles, six or more bitmap tiles, seven or more bitmap tiles, eight or more bitmap tiles, and nine or more bitmap tiles. Each bitmap tile may include one or more vertices of the boundary from each region of the particle population of interest, for example, two or more vertices, three or more vertices, four or more vertices, and five or more vertices from each region of the particle population of interest.

[0026] In certain examples, an algorithmic transformation to be applied to each vertex in the bitmap is identified. Depending on the type of data plot employed (e.g., a biexponential data plot), the algorithmic transformation identified for each vertex in the bitmap may vary, including a linear numerical transformation, a logarithmic numerical transformation, or a biexponential numerical transformation. The transformation may be positive or negative, depending on the particle ensemble position on the data plot. In some embodiments, if the algorithmic transformation associated with a vertex in the data plot is linear, the method includes identifying a linear transformation to be applied to the corresponding vertex in the bitmap. In other embodiments, if the algorithmic transformation associated with a vertex in the data plot is logarithmic, the method includes identifying a logarithmic transformation to be applied to the corresponding vertex in the bitmap. In other embodiments, if the algorithmic transformation associated with a vertex in the data plot is biexponential, the method includes identifying a transformation to be applied to the corresponding vertex in the bitmap, including a symmetric logarithmic transformation, a linear transformation, or a combination thereof. In one example, if the algorithmic transformation associated with a vertex in the data plot is positive linear / positive linear, the method includes identifying an algorithmic transformation to be applied to the corresponding vertex in the bitmap that is positive linear / positive linear. In another example, if the algorithmic transformation associated with a vertex in a data plot is positive logarithmic / positive logarithmic, the method includes identifying a positive linear / positive linear algorithmic transformation to apply to the corresponding vertex in a bitmap.In another example, if the algorithmic transformation associated with a vertex in a data plot is negative logarithmic / positive linear, the method includes identifying an algorithmic transformation that is negative logarithmic / positive linear to apply to the corresponding vertex in a bitmap. In another example, if the algorithmic transformation associated with a vertex in a data plot is positive logarithmic / negative linear, the method includes identifying an algorithmic transformation that is positive logarithmic / negative linear to apply to the corresponding vertex in a bitmap. In another example, if the algorithmic transformation associated with a vertex in a data plot is negative linear / positive logarithmic, the method includes identifying an algorithmic transformation that is negative linear / positive logarithmic to apply to the corresponding vertex in a bitmap. In another example, if the algorithmic transformation associated with a vertex in a data plot is positive linear / negative logarithmic, the method includes identifying an algorithmic transformation that is positive linear / negative logarithmic to apply to the corresponding vertex in a bitmap. In another example, if the algorithmic transformation associated with a vertex in a data plot is negative linear / negative linear, the method includes identifying an algorithmic transformation that is negative linear / negative linear to apply to the corresponding vertex in a bitmap. In another example, if the algorithmic transformation associated with a vertex in a data plot is negative logarithmic / negative logarithmic, the method involves identifying an algorithmic transformation that is negative linear / negative linear to apply to the corresponding vertex in a bitmap.

[0027] In some embodiments, the method includes aligning algorithmic transformations applied to a bitmap. In these embodiments, the transformations are aligned so that overlaps are minimized and the transformations switch appropriately. In certain embodiments, the method includes performing an affine transformation on a bitmap and adjusting the affine transformation so that the use of the bitmap is maximized, i.e., so that the bitmap boundaries are aligned with the region bounding box. In these embodiments, if the span across the bitmap in the ADC channel is less than the bitmap resolution, a switch to a linear transformation may be performed for that section to maximize fidelity. The bitmap may be rendered by interpolating one or more line segments between two vertices. In some examples, the bitmap may be generated using a polygon drawing algorithm such as a polygon scanline fill algorithm.

[0028] In certain embodiments, a two-dimensional data plot including one or more regions having a collection of particles is generated as described in U.S. Patent No. 10,613,017, the disclosure of which is incorporated herein by reference.

[0029] "Flow cytometer data" refers to information about the parameters of a sample (e.g., cells, particles) in a flow cell, collected by any number of photodetectors in a particle analyzer. In embodiments, flow cytometer data is received from a forward scatter detector. In some examples, the forward scatter detector can provide information about the overall size of the particles. In embodiments, flow cytometer data is received from a side scatter detector. In some examples, the side scatter detector may be configured to detect refracted and reflected light from the surface and internal structure of the particles, which tends to increase with increasing particle complexity of structure. In embodiments, flow cytometer data is received from a fluorescence detector. In some examples, the fluorescence detector may be configured to detect fluorescence emission from fluorescent molecules associated with the particles in the flow cell, e.g., labeled specific binding members (such as labeled antibodies that specifically bind to a target marker). In certain embodiments, the method includes detecting fluorescence from a sample using one or more fluorescence detectors, for example, 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 twenty-five or more fluorescence detectors.

[0030] Data obtained from flow cytometry analysis of cells (or other particles) is often multidimensional, with each cell corresponding to a point in a multidimensional space defined by the measured parameters. A population of cells or particles can be identified as a cluster of points in the data space. In some embodiments, the method includes generating one or more population clusters based on determined parameters of the analytes (e.g., cells, particles) in a sample. As used herein, a “population” or “subpopulation” of analytes (e.g., cells or other particles) generally refers to a group of analytes having characteristics (e.g., optical properties, impedance properties, or temporal properties) with respect to one or more measured parameters, such that the measured parameter data forms clusters in the data space. In embodiments, the data consists of signals from multiple different parameters (e.g., including 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, and 20 or more). Thus, the populations are recognized as clusters in the data. Conversely, clusters corresponding to noise or background are also typically observed, but each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte. Clusters can be defined with respect to a subset of measured parameters that correspond to different populations only within a subset of measured parameters or features extracted from cell or particle measurements, for example, within a subset of measured parameters or features.

[0031] In embodiments, the method includes receiving flow cytometer data, calculating parameters for each analyte, and clustering the analytes together based on the calculated parameters. For example, an experiment may include particles labeled with multiple fluorophores or fluorescently labeled antibodies, and a group of particles may be defined by a population corresponding to one or more fluorescence measurements. In this example, a first group may be defined by a specific range of light scattering for a first fluorophore, and a second group may be defined by a specific range of light scattering for a second fluorophore. If the first and second fluorophores are represented on the x and y axes, respectively, and the information is displayed graphically, it may appear that two different color-coded populations define each group of particles. Any number of analytes may be assigned to a cluster, including five or more analytes, e.g., ten or more analytes, e.g., fifty or more analytes, e.g., 100 or more analytes, e.g., 500 or more analytes, e.g., 500 analytes, e.g., 1000 analytes, e.g., 1000 analytes. In certain embodiments, the method clusters together rare events detected in a sample (e.g., rare cells such as cancer cells in a sample). In these embodiments, the generated analyte cluster may contain 10 or fewer assigned analytes, for example, 9 or fewer assigned analytes, and may contain 5 or fewer assigned analytes.

[0032] After the flow cytometer data has been obtained, aspects of the present invention further include generating images for each of the first and second sets of flow cytometer data (i.e., separate images for each set of flow cytometer data). "Generating images" means converting the two-dimensional plot into an image format such that the flow cytometer data is represented by pixels that constitute an image representing the data when assembled. In embodiments, generating images for each of the first and second sets of flow cytometer data involves organizing the data into two-dimensional bins. In this disclosure, the term "bin" is used in its conventional sense to refer to a data structure in which data points that fall within a particular interval are combined. The bins are then assigned a value that represents their interval. In embodiments, the representative value of a bin is the mean value of the flow cytometer data contained within that bin. Thus, in embodiments, individual data points within a defined area of ​​the two-dimensional scatter plot are matched to a bin, and the bin is associated with a representative value that reflects the number of data points contained within it.

[0033] In some embodiments, flow cytometer data are binned according to the data state for a parameter. In such embodiments, the representative value for a given bin may be the average value of the binned data for the intensity detected for that parameter. For example, if the parameter is a fluorescence parameter, the representative value for a particular bin may be the average value of the data in that bin for the intensity of fluorescence emitted from a given fluorescent dye. In embodiments, data are binned according to the data state for one or more additional parameters. In such embodiments, the representative value for a given bin may be a composite of the average values ​​evaluated for each relevant parameter. Thus, in embodiments, combining flow cytometer data into bins may involve evaluating data for one or more additional parameters, such as two or more, three or more, four or more, five or more, ten or more, fifteen or more, and including twenty or more additional parameters.

[0034] After the flow cytometer data has been organized into bins, aspects of the present invention include determining image-generated values. As discussed herein, image-generated values ​​quantify the data point concentrations within a given bin and serve as the basis for generating an image. In certain embodiments, determining image-generated values ​​includes creating a histogram of the two-dimensionally binned data (i.e., a histogram of representative values ​​associated with each bin). In such embodiments, determining image-generated values ​​also includes calculating a cumulative distribution function based on the histogram. The cumulative distribution function is referred to in its conventional sense and determines the probability that a variable is less than or equal to a certain amount. Thus, the image-generated values ​​are obtained by substituting the representative values ​​of each bin into the cumulative distribution function calculated from the histogram. In some embodiments, the image-generated values ​​are in the range of 0 to 1. In other embodiments, the image-generated values ​​may be scaled to a range from 0 to any number selected by the user. In embodiments, higher image-generated values ​​indicate that the associated bin is associated with more data points, and lower image-generated values ​​indicate that the associated bin is associated with fewer data points.

[0035] Following the determination of the image generation value, embodiments of the present invention include assigning shading to each bin based on the image generation value corresponding to that bin. “Shading” as described herein refers to the brightness or darkness represented by a given bin in the resulting image. In some embodiments, a bin is assigned a gray shading. In such embodiments, the resulting image is a grayscale image. In other embodiments, a bin is assigned a non-gray shading. In such embodiments, the resulting image is a color image. In embodiments where the data combined in the bins is evaluated with respect to multiple parameters, the image generated from such bins may be polycolored. In such embodiments, different colors may be associated with each distinct parameter. In some embodiments, bins associated with higher image generation values ​​are assigned brighter colors, and bins associated with lower image generation values ​​are assigned darker colors. Certain embodiments of the present invention also include an image generation threshold. In such embodiments, all bins associated with image generation values ​​below the threshold are assigned black. In some embodiments, the threshold is adjustable (i.e., selected by the user from several different options). Thus, more or fewer images may be assigned black based on the magnitude of the selected threshold.

[0036] Figure 1 shows images of the first set of flow cytometer data 101 generated by the process described above, as well as the corresponding training gate 101a for defining the population of interest within the first set of flow cytometer data 101. On the other hand, the images generated for the second set of flow cytometer data 102 do not include gates that boundary similar populations of flow cytometer data.

[0037] Aspects of the present invention further include adjusting a training gate from generated images of a first set of flow cytometer data to adapt generated images of a second set of flow cytometer data. Adjusting the training gate to "adapt" the second set of flow cytometer data means changing the shape of the training gate so that the adjusted training gate explains the differences in collective morphology present in the second set of flow cytometer data compared to the first set of flow cytometer data. In certain embodiments, the adjustment of the training gate includes adjusting one or more of the constituent vertices of the training gate, up to each of them. In embodiments, the adjustment of the training gate is performed by a processor implementation algorithm. In such embodiments, the processor implementation algorithm may be configured to warp the vertices of the training gate taken from generated images of the first set of flow cytometer data to fit generated images of the second set of flow cytometer data. According to embodiments of the present disclosure, the processor implementation algorithm is an image alignment algorithm. The image alignment algorithm integrates and transforms data received from images. Therefore, the image alignment algorithm described herein analyzes the generated images of a first set of flow cytometer data, analyzes the generated images of a second set of flow cytometer data, and warps the training gates from the first set of flow cytometer data to fit the second set of flow cytometer data based on their analysis.

[0038] In one embodiment, warping training gates to fit a second set of flow cytometer data involves warping the entire generated image of the first set of flow cytometer data to maximize its similarity to the generated image of the second set of flow cytometer data. In such an embodiment, a group of subjects in the generated image of the first set of flow cytometer data is adjusted (i.e., warped) so that the contours of the group are substantially identical to the contours of similar groups in the generated image of the second set of flow cytometer data. In other words, the processor implementation algorithm may include a mathematical deformation model configured to transform / deform the first set of flow cytometer data to resemble the second set of flow cytometer data. After the generated image of the first set of flow cytometer data has been warped, the training gates contained therein may be warped accordingly. For example, Figure 2 shows a sample workflow according to a particular embodiment of the present invention. In steps 201 and 202, first and second sets of flow cytometer data are obtained. The first set of flow cytometer data includes training gate 201a. In step 203, images are generated for each of the first and second sets of flow cytometer data (for example, as described above). Subsequently, the generated images for the first set of flow cytometer data are warped so that the target population resembles the similar population in the generated images for the second set of flow cytometer data (step 204). The training gates included in the first set of flow cytometer data may then be warped in step 205 by the same transformation used in step 204. The vertices of the transformed gates in step 205 may then be applied in step 206 to the generated images for the second set of flow cytometer data. In some embodiments, the training gates are first imposed on a blank image (i.e., so that the training gates are the only elements in the image) before they are adjusted. In other words, the training gates may be separated from the rest of the generated images for the first set of flow cytometer data and imposed on a separate image.

[0039] In some embodiments, an image alignment algorithm configured to warp generated images of a first set of flow cytometer data includes B-spline warping. A B-spline (i.e., a basis spline) describes a mathematical function often used for curve fitting. B-splines are described, for example, in Gans, P., & Gill, JB (1984). Smoothing and differentiation of spectroscopic curves using spline functions, Applied spectroscopy, 38(3), 370-376, and these disclosures are incorporated herein by reference. In embodiments, B-spline warping includes calculating B-spline coefficients, which are then used to define a function for tuning a training gate. In other words, the image alignment algorithm warps generated images of a first set of flow cytometer data and outputs B-spline coefficients that numerically describe how the generated images of the first set of flow cytometer data were warped. These coefficients then serve as a basis for updating the vertices of the training gate so that a tuned training gate is generated that explains the difference between the first and second sets of flow cytometer data. The updated (i.e., tuned) vertices can then be applied to the generated image of the second set of flow cytometer data to form a tuned gate on it. For example, Figure 3 shows the process of tuning the first set of flow cytometer data and its corresponding training gate according to a particular embodiment. Image 301a shows the training gate imposed on a blank image, and image 301b shows the corresponding generated image of the first set of flow cytometer data. Image 301b is warped to image 303b, which has been tuned by the processor implementation algorithm, and a set of B-spline coefficients 302a are employed and recorded to maximize similarity to the generated image of the second set of flow cytometer data (102 shown in Figure 1). The warping of image 301b is shown by the variation 302b.Next, we define a function to warp the training gate imposed on the blank image 301a and create the adjusted training gate 303a, using the same B-spline coefficient 302a that is used to warp image 301b to image 303b.

[0040] Following the adjustment of the training gate, aspects of the present invention include overlaying the adjusted training gate onto a second set of flow cytometer data. In such embodiments, the vertices of the adjusted training gate in a blank image are applied to corresponding groups in a generated image of the second set of flow cytometer data to form a gate around those groups. For example, image 400 shown in Figure 4 shows an adjusted training gate overlaid on a second set of flow cytometer data 102. In some embodiments, adjusting the vertices of a training gate to create an adjusted training gate includes extracting vertices from the training gate (i.e., isolating and observing the position of each vertex), modifying the position of each vertex based on a B-spline coefficient (e.g., as determined above), and applying the adjusted vertices to a generated image of the second set of flow cytometer data to form an adjusted training gate. In other embodiments, the processor implementation algorithm may warp the training gate before observing the position of each vertex. In such embodiments, the processor implementation algorithm adjusts the training gate by an image alignment algorithm (e.g., as considered above), and the adjusted vertices may then be applied to the generated images of a second set of observed flow cytometer data.

[0041] In some embodiments, the adjusted training gate overlaid on a second set of flow cytometer data has a different shape from the original training gate, i.e., it adapts to the shape of the relevant population in the second set of flow cytometer data. In some examples, the relevant population in the second set of flow cytometer data is adjusted in some areas, e.g., stretched, compressed, or shifted, compared to the population in the first set of flow cytometer data. Therefore, in some examples, the adjusted training gate is shaped to adapt to these differences. In some examples, the adjusted training gate is polygonal.

[0042] Figures 5 and 6 show the training gates adjusted by the process described above. Figure 5 shows a first set of flow cytometer data 501 including the training gates. 502a to 502d show a second set of flow cytometer data, each containing a gate adjusted from image 501 by this method. Similarly, Figure 6 shows a first set of flow cytometer data 601 including the training gates. 602a and 602b show a second set of flow cytometer data, each containing a gate adjusted from image 601 by this method.

[0043] To generate flow cytometer data according to this disclosure, a sample containing particles is irradiated with a light source, light from the sample is detected, and a population of relevant particles is generated based at least in part on the measured values ​​of the detected light. In some examples, the sample is a biological sample. The term “biological sample” is used in its conventional sense to refer to a whole organism, plant, fungus, or, in some cases, a subset of the tissues, cells, or components of an animal that may be found in blood, mucus, lymph, synovial fluid, cerebrospinal fluid, saliva, bronchoalveolar lavage, amniotic fluid, amniotic umbilical cord blood, urine, vaginal fluid, and semen. Accordingly, “biological sample” refers to both a natural organism or a subset of its tissues, as well as homogenates, lysates, or extracts prepared from a subset of an organism or its tissues, including, but not limited to, plasma, serum, cerebrospinal fluid, lymph, skin sections, respiratory tract, gastrointestinal tract, cardiovascular, and urinary tract, tears, saliva, milk, blood cells, tumors, and organs. A biological sample can be any type of biological tissue, including both healthy tissue and diseased tissue (e.g., cancerous, malignant, necrotic, etc.). In certain embodiments, the biological sample is a liquid sample such as blood or its derivatives, e.g., plasma, tears, urine, semen, and in some examples, the sample is a blood sample including whole blood, such as blood obtained from a venipuncture or fingerstick (the blood may or may not be combined with any reagents such as preservatives and anticoagulants before the assay).

[0044] In certain embodiments, the source of the sample is “mammal” or “mammalian,” terms widely used to refer to organisms within the class Mammalia, including Carnivora (e.g., dogs and cats), Rodentia (e.g., mice, guinea pigs, and rats), and Primates (e.g., humans, chimpanzees, and monkeys). In some examples, the subject is human. The method may be applied to samples obtained from human subjects of both sexes at any stage of development (i.e., neonatal, infant, adolescent, or adult), and in certain embodiments, the human subject may be adolescent, adolescent, or adult. While the present invention may be applied to samples from human subjects, it should be understood that it may also be applied to samples from other animal subjects (i.e., within “non-human subjects”), such as birds, mice, rats, dogs, cats, livestock, and horses, but is not limited to the above.

[0045] In carrying out the method of the present invention, a sample having particles (e.g., in the flow stream of a flow cytometer) is irradiated with light from a light source. In some embodiments, the light source is a broadband light source that emits light having a wide range of wavelengths, for example, over 50 nm, including over 100 nm or more, over 150 nm or more, over 200 nm or more, over 250 nm or more, over 300 nm or more, over 350 nm or more, over 400 nm or more, and over 500 nm or more. For example, one suitable broadband light source emits light having wavelengths from 200 nm to 1500 nm. Another embodiment of a suitable broadband light source includes a light source that emits light having wavelengths from 400 nm to 1000 nm. If the method involves illumination with a broadband light source, the broadband light source protocol in question may include, but is not limited to, halogen lamps, deuterium arc lamps, xenon arc lamps, stabilized fiber-coupled broadband light sources, continuous spectrum broadband LEDs, superluminescent diodes, semiconductor light-emitting diodes, wide-spectrum LED white light sources, and multi-LED integrated white light sources, among other broadband light sources or any combination thereof.

[0046] In other embodiments, the method includes irradiating with a narrowband light source emitting a specific wavelength or a narrow range of wavelengths, such as a light source emitting light in a narrow wavelength range, such as a range of 50 nm or less, including a light source emitting light in a narrow wavelength range, such as a range of 40 nm or less, such as 30 nm or less, such as 25 nm or less, such as 20 nm or less, such as 15 nm or less, such as 10 nm or less, such as 5 nm or less, such as 2 nm or less, and a light source emitting a specific wavelength of light (i.e., monochromatic light). If the method includes irradiating with a narrowband light source, the narrowband light source protocol in question may include, but is not limited to, a narrow-wavelength LED, a laser diode, or a broadband light source coupled with one or more optical bandpass filters, diffraction gratings, monochromators, or any combination thereof.

[0047] Aspects of the present invention include collecting light with a detector or a combination of detectors. In some embodiments, the light is collected by one or more side-scatter detectors configured to detect side-scattered light. In further embodiments, the light is collected by one or more forward-scatter detectors configured to detect forward-scattered light. In further embodiments, the light is collected by one or more fluorescence detectors configured to detect fluorescence. In some examples, the fluorescence detectors may be configured to detect fluorescence emission from fluorescent molecules associated with particles in a flow cell, e.g., labeled specific binding members (such as labeled antibodies that specifically bind to a target marker). In certain embodiments, the method includes detecting fluorescence from a sample using one or more fluorescence detectors, e.g., two or more, e.g., three or more, e.g., four or more, e.g., five or more, e.g., six or more, e.g., seven or more, e.g., eight or more, e.g., nine or more, e.g., ten or more, e.g., fifteen or more, and twenty-five or more fluorescence detectors. In embodiments, each fluorescence detector is configured to generate a fluorescence data signal. Fluorescence from a sample can be detected independently by each fluorescence detector over one or more wavelength ranges within the 200 nm to 1200 nm range. In some examples, the method involves detecting fluorescence from a sample over wavelength ranges including 200 nm to 1200 nm, 300 nm to 1100 nm, 400 nm to 1000 nm, 500 nm to 900 nm, and 600 nm to 800 nm. In other examples, the method involves detecting fluorescence using each fluorescence detector at one or more specific wavelengths. For example, fluorescence may be detected at one or more of the following wavelengths, depending on the number of different fluorescence detectors in the photodetector system of the present invention: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, the method includes detecting the wavelength of light corresponding to the fluorescence peak wavelength of a particular fluorescent dye present in the sample.In this embodiment, flow cytometer data is received from one or more fluorescence detectors (e.g., one or more detection channels), for example, two or more, three or more, four or more, five or more, six or more, or even eight or more fluorescence detectors (e.g., eight or more detection channels).

[0048] A system for adjusting training gates to adapt flow cytometer data. Aspects of the present disclosure also further include a system for adjusting a training gate prepared from a first set of flow cytometer data to adapt a second set of flow cytometer data. In some embodiments, the system includes an input module configured to acquire a first set of flow cytometer data, a particle analyzer configured to acquire a second set of flow cytometer data, and a processor configured to analyze the flow cytometer data.

[0049] In some embodiments, the particle analyzer comprises a flow cell and a laser configured to irradiate particles within the flow cell. In some embodiments, the laser may be any convenient laser, such as a continuous-wave laser. For example, the laser may be a diode laser, such as an ultraviolet diode laser, a visible diode laser, or a near-infrared diode laser. In other embodiments, the laser may be a helium-neon (HeNe) laser. In some examples, the laser is a gas laser, such as a helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, CO laser, argon-fluorine (ArF) excimer laser, krypton-fluorine (KrF) excimer laser, xenon-chlorine (XeCl) excimer laser, or xenon-fluorine (XeF) excimer laser, or a combination thereof. In other examples, the flow cytometer includes a dye laser, such as a stilbene, coumarin, or rhodamine laser. In yet another example, the laser in question includes metal vapor lasers such as helium-cadmium (HeCd) lasers, helium-mercury (HeHg) lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof. In yet another example, the flow cytometer of the present invention includes solid-state lasers such as ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium-sapphire lasers, slim YAG lasers, ytterbium YAG lasers, Y2O3 lasers, or cerium-doped lasers, and combinations thereof.

[0050] In certain embodiments, the particle analyzer includes a light beam generator configured to produce two or more beams of frequency-shifted light. In some examples, the light beam generator includes a laser and a high-frequency generator configured to apply a high-frequency drive signal to an acousto-optical device to produce two or more angle-deflected laser beams. In these embodiments, the laser may be a pulsed laser or a continuous-wave laser. For example, the laser in the light beam generator in question may be a gas laser (e.g., helium-neon laser, argon laser, krypton laser, xenon laser, nitrogen laser, CO2 laser, CO laser, argon fluorine (ArF) excimer laser, krypton fluorine (KrF) excimer laser, xenon chlorine (XeCl) excimer laser, or xenon fluorine (XeF) excimer laser, or a combination thereof), a dye laser (e.g., stilbene, coumarin, or rhodamine laser), or a metal vapor laser (e.g., helium-cadmium (HeCd) laser, helium-mercury (HeHg) laser) The lasers may include lasers, helium-selenium (HeSe) lasers, helium-silver (HeAg) lasers, strontium lasers, neon-copper (NeCu) lasers, copper lasers, or gold lasers, and combinations thereof, as well as solid-state lasers (e.g., ruby ​​lasers, Nd:YAG lasers, NdCrYAG lasers, Er:YAG lasers, Nd:YLF lasers, Nd:YVO4 lasers, Nd:YCa4O(BO3)3 lasers, Nd:YCOB lasers, titanium-sapphire lasers, slim YAG lasers, ytterivium YAG lasers, Y2O3 lasers, or cerium-doped lasers, and combinations thereof).

[0051] The acousto-optic device can be any convenient acousto-optic protocol configured to frequency-shift laser light using applied sound waves. In certain embodiments, the acousto-optic device is an acousto-optic deflector. The acousto-optic device in this system is configured to generate an angularly deflected laser beam from light from a laser and an applied radio frequency drive signal. The radio frequency drive signal can be applied to the acousto-optic device using any suitable radio frequency drive signal source, such as a direct digital synthesizer (DDS), arbitrary waveform generator (AWG), or electric pulse generator.

[0052] In embodiments, the controller is configured to apply radio frequency drive signals to an acoustic-optical device to generate a desired number of angularly deflected laser beams within the output laser beam, and includes, for example, being configured to apply three or more radio frequency drive signals, for example four or more radio frequency drive signals, for example five or more radio frequency drive signals, for example six or more high frequency drive signals, for example seven or more high frequency drive signals, for example eight or more high frequency drive signals, for example nine or more high frequency drive signals, for example ten or more high frequency drive signals, for example fifteen or more high frequency drive signals, for example twenty-five or more high frequency drive signals, for example fifty or more high frequency drive signals, and being configured to apply one hundred or more high frequency drive signals.

[0053] In some examples, to generate an intensity profile of an angularly deflected laser beam in the output laser beam, the controller is configured to apply a high-frequency drive signal having amplitudes including approximately 0.001V to 500V, 0.005V to 400V, 0.01V to 300V, 0.05V to 200V, 0.1V to 100V, 0.5V to 75V, 1V to 50V, 2V to 40V, 3V to 30V, and approximately 5V to 25V. Each applied high-frequency drive signal has a frequency range of approximately 0.001 MHz to approximately 500 MHz, for example approximately 0.005 MHz to approximately 400 MHz, for example approximately 0.01 MHz to approximately 300 MHz, for example approximately 0.05 MHz to approximately 200 MHz, for example approximately 0.1 MHz to approximately 100 MHz, for example approximately 0.5 MHz to approximately 90 MHz, for example approximately 1 MHz to approximately 75 MHz, for example approximately 2 MHz to approximately 70 MHz, for example approximately 3 MHz to approximately 65 MHz, for example approximately 4 MHz to approximately 60 MHz, and includes approximately 5 MHz to approximately 50 MHz.

[0054] In certain embodiments, the controller has a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to generate an output laser beam with an angle-deflected laser beam having a desired intensity profile. For example, the memory may contain instructions to generate two or more angle-deflected laser beams having the same intensity, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., 25 or more, e.g., 50 or more, and the memory may contain instructions to generate 100 or more angle-deflected laser beams having the same intensity. In other embodiments, the instructions may contain instructions to generate two or more angle-deflected laser beams having different intensities, e.g., three or more, e.g., four or more, e.g., five or more, e.g., ten or more, e.g., 25 or more, e.g., 50 or more, and the memory may contain instructions to generate 100 or more angle-deflected laser beams having different intensities.

[0055] In certain embodiments, the controller has a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to generate an output laser beam having an intensity that increases from the edge to the center of the output laser beam along the horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the center of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the edge of the output laser beam along the horizontal axis, for example, 0.5% to about 95%, 1% to about 90%, about 2% to about 85%, about 3% to about 80%, about 4% to about 75%, about 5% to about 70%, about 6% to about 65%, about 7% to about 60%, about 8% to about 55%, and may include about 10% to about 50%. In other embodiments, the controller has a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to generate an output laser beam having an intensity that increases from the edge to the center of the output laser beam along the horizontal axis. In these examples, the intensity of the angularly deflected laser beam at the edge of the output beam may be in the range of 0.1% to about 99% of the intensity of the angularly deflected laser beam at the center of the output laser beam along the horizontal axis, for example, 0.5% to about 95%, 1% to about 90%, about 2% to about 85%, about 3% to about 80%, about 4% to about 75%, about 5% to about 70%, about 6% to about 65%, about 7% to about 60%, about 8% to about 55%, and may include about 10% to about 50%. In yet another embodiment, the controller has a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to generate an output laser beam having an intensity profile with a Gaussian distribution along the horizontal axis.In yet another embodiment, the controller has a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to generate an output laser beam having a top-hat intensity profile along the horizontal axis.

[0056] In some embodiments, the target light beam generator may be configured to generate an angle-deflected laser beam within a spatially separated output laser beam. Depending on the applied high-frequency drive signal and the desired irradiation profile of the output laser beam, the angle-deflected laser beams may be separated by 0.001 μm or more, e.g., 0.005 μm or more, e.g., 0.01 μm or more, e.g., 0.05 μm or more, e.g., 0.1 μm or more, e.g., 0.5 μm or more, e.g., 1 μm or more, e.g., 5 μm or more, e.g., 10 μm or more, e.g., 100 μm or more, e.g., 500 μm or more, e.g., 10 and even 5000 μm or more. In some embodiments, the system is configured to generate an angle-deflected laser beam within the output laser beam that overlaps with an adjacent angle-deflected laser beam along the horizontal axis of the output laser beam. The overlap between adjacent angle-deflected laser beams (such as beam spot overlap) may be an overlap of 0.001 μm or more, for example, an overlap of 0.005 μm or more, for example, an overlap of 0.01 μm or more, for example, an overlap of 0.05 μm or more, for example, an overlap of 0.1 μm or more, for example, an overlap of 0.5 μm or more, for example, an overlap of 1 μm or more, for example, an overlap of 5 μm or more, for example, an overlap of 10 μm or more, and includes overlaps of 100 μm or more.

[0057] In certain examples, a light beam generator configured to produce two or more beams of frequency-shifted light includes laser excitation modules such as those described in U.S. Patent Nos. 9,423,353, 9,784,661 and 10,006,852, and U.S. Patent Application Publications 2017 / 0133857 and 2017 / 0350803, the disclosures of which are incorporated herein by reference.

[0058] Aspects of the present invention also include a forward scatter detector configured to detect forward scattered light. The number of forward scatter detectors in the flow cytometer of the present invention can vary as needed. For example, the particle analyzer may include a plurality of forward scatter detectors, including one forward scatter detector, or two or more, three or more, four or more, and five or more. In certain embodiments, the flow cytometer includes one forward scatter detector. In other embodiments, the flow cytometer includes two forward scatter detectors.

[0059] Any convenient detector for detecting the collected light may be used in the forward scatter detectors described herein. The detectors of interest may include, among other detectors, active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCD), intensified charge-coupled devices (ICCD), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photovoltaic cells, photodiodes, photomultiplier tubes (PMT), phototransistors, quantum dot photoconductors, or optical sensors or detectors such as photodiodes, and combinations thereof, but are not limited thereto. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), a semiconductor charge-coupled device (CCD), an active pixel sensor (APS), a complementary metal oxide semiconductor (CMOS) image sensor, or an N-type metal oxide semiconductor (NMOS) image sensor. In certain embodiments, the detector is in the range of 0.01 cm 2 to 10 cm 2 , for example 0.05 cm 2 to 9 cm 2 , for example 0.1 cm 2 to 8 cm 2 , for example 0.5 cm 2 to 7 cm 2 and is a photomultiplier tube, such as a photomultiplier tube having an active detection surface area in each region including 1 cm [[ID=二十一]] 2 [[ID=二十二]]to 5 cm 2 .

[0060] If the particle analyzer includes multiple forward scatter detectors, each detector may be the same, or the set of detectors may be a combination of different types of detectors. For example, if the particle analyzer includes two forward scatter detectors, in some embodiments, the first forward scatter detector is a CCD type device and the second forward scatter detector (or imaging sensor) is a CMOS type device. In other embodiments, both the first and second forward scatter detectors are CCD type devices. In yet another embodiment, both the first and second forward scatter detectors are CMOS type devices. In yet another embodiment, the first forward scatter detector is a CCD type device and the second forward scatter detector is a photomultiplier tube (PMT). In yet another embodiment, the first forward scatter detector is a CMOS type device and the second forward scatter detector is a photomultiplier tube. In yet another embodiment, both the first and second forward scatter detectors are photomultiplier tubes.

[0061] In embodiments, the forward scatter detector is configured to measure light continuously or at discrete intervals. In some examples, the detector is configured to measure the collected light continuously. In other examples, the detector is configured to measure at distinct intervals, including every 0.001 milliseconds, every 0.01 milliseconds, every 0.1 milliseconds, every 1 millisecond, every 10 milliseconds, every 100 milliseconds, and every 1000 milliseconds, or at some other intervals.

[0062] Embodiments of the present invention also include an optical dispersion / separation module positioned between the flow cell and the forward scattering detector. The optical dispersion devices covered include, but are not limited to, tinted glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof, among other wavelength separation devices. In some embodiments, a bandpass filter is positioned between the flow cell and the forward scattering detector. In other embodiments, two or more bandpass filters are positioned between the flow cell and the forward scattering detector, including, for example, two or more, three or more, four or more, or five or more. In embodiments, the bandpass filters include a bandpass filter having a minimum bandwidth in the range of 2 nm to 100 nm, e.g., 3 nm to 95 nm, e.g., 5 nm to 95 nm, e.g., 10 nm to 90 nm, e.g., 12 nm to 85 nm, e.g., 15 nm to 80 nm, and a bandpass filter having a minimum bandwidth in the range of 20 nm to 50 nm, reflecting light of other wavelengths to the forward scattering detector.

[0063] In particular embodiments of the present invention, a side scattering detector is included, configured to detect the side scattering wavelengths of light (e.g., light refracted and reflected from the surface and internal structure of a particle). In other embodiments, the flow cytometer includes a plurality of side scattering detectors, e.g., two or more, e.g., three or more, e.g., four or more, and five or more.

[0064] Any convenient detector for detecting the collected light may be used in the side scattering detector described herein. The detectors in question may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), intensified charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), semiconductor charge-coupled device (CCD), active pixel sensor (APS), complementary metal-oxide-semiconductor (CMOS) image sensor, or N-type metal-oxide-semiconductor (NMOS) image sensor. In certain embodiments, the detector is 0.01 cm 2 ~10cm 2 For example, 0.05 cm 2 ~9cm 2 For example, 0.1 cm 2 ~8cm 2 For example, 0.5 cm 2 ~7cm 2 This is within the range of 1 cm 2 ~5cm 2 This is a photomultiplier tube, such as a photomultiplier tube, having an activity detection surface area in each region including the specified region.

[0065] If the particle analyzer includes multiple side-scatter detectors, each side-scatter detector may be the same, or the set of side-scatter detectors may be a combination of different types of detectors. For example, if the particle analyzer includes two side-scatter detectors, in some embodiments, the first side-scatter detector is a CCD type device and the second side-scatter detector (or imaging sensor) is a CMOS type device. In other embodiments, both the first and second side-scatter detectors are CCD type devices. In yet another embodiment, both the first and second side-scatter detectors are CMOS type devices. In yet another embodiment, the first side-scatter detector is a CCD type device and the second side-scatter detector is a photomultiplier tube (PMT). In yet another embodiment, the first side-scatter detector is a CMOS type device and the second side-scatter detector is a photomultiplier tube. In yet another embodiment, both the first and second side-scatter detectors are photomultiplier tubes.

[0066] Embodiments of the present invention also include an optical dispersion / separation module positioned between the flow cell and the side scattering detector. The optical dispersion devices covered include, but are not limited to, other wavelength separation devices, including, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof.

[0067] In some embodiments, the particle analyzer also includes a fluorescence detector configured to detect light of one or more fluorescence wavelengths. In other embodiments, the particle analyzer includes multiple fluorescence detectors, including two or more, three or more, four or more, five or more, ten or more, fifteen or more, and twenty or more.

[0068] Any convenient detector for detecting the collected light may be used in the fluorescence detector described herein. The detectors covered may include, but are not limited to, optical sensors or detectors such as active pixel sensors (APS), avalanche photodiodes, image sensors, charge-coupled devices (CCDs), sensitized charge-coupled devices (ICCDs), light-emitting diodes, photon counters, bolometers, pyroelectric detectors, photoresistors, photocells, photodiodes, photomultiplier tubes (PMTs), phototransistors, quantum dot photoconductors, or photodiodes, and combinations thereof, among other detectors. In certain embodiments, the collected light is measured using a charge-coupled device (CCD), semiconductor charge-coupled device (CCD), active pixel sensor (APS), complementary metal-oxide-semiconductor (CMOS) image sensor, or N-type metal-oxide-semiconductor (NMOS) image sensor. In certain embodiments, the detector has a depth of 0.01 cm 2 ~10cm 2 For example, 0.05 cm 2 ~9cm 2 For example, 0.1 cm 2 ~8cm 2 For example, 0.5 cm 2 ~7cm 2 This is within the range of 1 cm 2 ~5cm 2 This is a photomultiplier tube, such as a photomultiplier tube, having an activity detection surface area in each region including the specified region.

[0069] If the particle analyzer includes multiple fluorescence detectors, each fluorescence detector may be the same, or the set of fluorescence detectors may be a combination of different types of detectors. For example, if the particle analyzer includes two fluorescence detectors, in some embodiments, the first fluorescence detector is a CCD device and the second fluorescence detector (or imaging sensor) is a CMOS device. In other embodiments, both the first and second fluorescence detectors are CCD devices. In yet another embodiment, both the first and second fluorescence detectors are CMOS devices. In yet another embodiment, the first fluorescence detector is a CCD device and the second fluorescence detector is a photomultiplier tube (PMT). In yet another embodiment, the first fluorescence detector is a CMOS device and the second fluorescence detector is a photomultiplier tube. In yet another embodiment, both the first and second fluorescence detectors are photomultiplier tubes.

[0070] Embodiments of the present invention also include an optical dispersion / separation module positioned between the flow cell and the fluorescence detector. The optical dispersion devices covered include, but are not limited to, other wavelength separation devices, including, colored glass, bandpass filters, interference filters, dichroic mirrors, diffraction gratings, monochromators, and combinations thereof.

[0071] In embodiments of the present disclosure, the fluorescence detector in question is configured to measure light collected at one or more wavelengths, e.g., two or more wavelengths, e.g., five or more different wavelengths, e.g., ten or more different wavelengths, e.g., 25 or more different wavelengths, e.g., 50 or more different wavelengths, e.g., 100 or more different wavelengths, e.g., 200 or more different wavelengths, e.g., 300 or more different wavelengths, and includes measuring light emitted by a sample in a flow stream at 400 or more different wavelengths. In some embodiments, two or more detectors in a flow cytometer, as described herein, are configured to measure identical or overlapping wavelengths of collected light.

[0072] In some embodiments, the fluorescence detector in question is configured to measure light collected over a certain wavelength range (e.g., 200 nm to 1000 nm). In certain embodiments, the detector in question is configured to collect the spectrum of light over a certain range of wavelengths. For example, a particle analyzer may include one or more detectors configured to collect the spectrum of light over one or more wavelengths within the 200 nm to 1000 nm range. In yet other embodiments, the detector in question is configured to measure light emitted by a sample in a flow stream at one or more specific wavelengths. For example, a particle analyzer may include one or more detectors configured to measure light at one or more of the following wavelengths: 450 nm, 518 nm, 519 nm, 561 nm, 578 nm, 605 nm, 607 nm, 625 nm, 650 nm, 660 nm, 667 nm, 670 nm, 668 nm, 695 nm, 710 nm, 723 nm, 780 nm, 785 nm, 647 nm, 617 nm, and any combination thereof. In certain embodiments, one or more detectors may be configured to pair with specific fluorophores, such as those used with a sample in a fluorescence assay.

[0073] Suitable flow cytometry systems include: 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 Examples include, but are not limited to, those described in Syst.24(3):203-255. These disclosures are incorporated herein by reference.In specific cases, the flow cytometry systems covered include: BD Biosciences FACSCanto® flow cytometer, BD Biosciences FACSCanto® II flow cytometer, BD Accuri® flow cytometer, BD Accuri® C6 Plus flow cytometer, BD Biosciences FACSCelesta® flow cytometer, BD Biosciences FACSLyric® flow cytometer, BD Biosciences FACSVerse® flow cytometer, BD Biosciences FACSymphony® flow cytometer, BD Biosciences LSRFortessa® flow cytometer, BD Biosciences LSRFortessa® X-20 flow cytometer, BD Biosciences FACSPresto® flow cytometer, BD Biosciences FACSVia® flow cytometer, and BD Biosciences FACSCalibur® cell sorter, BD Biosciences FACSCount® cell sorter, BD Biosciences FACSLyric® cell sorter, and BD Biosciences This includes Via® cell sorter, BD Biosciences Influx® cell sorter, BD Biosciences Jazz® cell sorter, BD Biosciences Aria® cell sorter, BD Biosciences FACSAria® II cell sorter, BD Biosciences FACSAria® III cell sorter, BD Biosciences FACSAria® fusion cell sorter, and BD Biosciences FACSMelody® cell sorter, BD Biosciences FACSymphony® S6 cell sorter, etc.

[0074] In some embodiments, this system is a flow cytometry system, for example, U.S. Patent Nos. 10,663,476, 10,620,111, 10,613,017, 10,605,713, 10,585,031, 10,578,542, 10,578,469, No. 10,481,074, No. 10,302,545, No. 10,145,793, No. 10,113,967, No. 10,006,852, No. No. 9,952,076, No. 9,933,341, No. 9,726,527, No. 9,453,789, No. 9,200,334, No. 9,0 No. 97,640, No. 9,095,494, No. 9,092,034, No. 8,975,595, No. 8,753,573, No. 8,233,1 No. 46, No. 8,140,300, No. 7,544,326, No. 7,201,875, No. 7,129,505, No. 6,821,740, It is listed in the following documents: No. 6,813,017, No. 6,809,804, No. 6,372,506, No. 5,700,692, No. 5,643,796, No. 5,627,040, No. 5,620,842, No. 5,602,039, No. 4,987,086, and No. 4,498,766.

[0075] In specific examples, the flow cytometry system of the present invention is described in Diebold, et al. Nature Photonics Vol. 7(10), 806-810 (2013), as well as U.S. Patents No. 9,423,353, 9,784,661, 9,983,132, 10,006,852, 10,078,045, 10,036,699, 10,222,316, 10,288,546, 10,324,019, 10,408,758, 10,451,538, and 10,620,1 As described in Patent No. 11, and U.S. Patent Publications 2017 / 0133857, 2017 / 0328826, 2017 / 0350803, 2018 / 0275042, 2019 / 0376895, and 2019 / 0376894, the devices are configured to image particles in a flowstream by fluorescence imaging using radio frequency tagged emission (FIRE), and these disclosures are incorporated herein by reference.

[0076] In certain embodiments, the system further includes a processor having memory operably coupled to the processor, the memory containing instructions stored in the memory, which, when executed by the processor, cause the processor to adapt a second set of flow cytometer data by adjusting a training gate prepared from a first set of flow cytometer data. Thus, embodiments of the present invention include obtaining first and second sets of flow cytometer data. The first set of flow cytometer data is characterized such that one or more groups contained therein are understood to be associated with a subtype (i.e., phenotype) of subject. In embodiments, the first set of flow cytometer data is obtained from an input module. The input module may be any convenient device that communicates with the processor and is configured to provide the first set of flow cytometer data to the processor. In embodiments, the input module has memory coupled to it for storing flow cytometer data which may be invoked by the processor when needed to adjust the training gate. In some embodiments, the first set of flow cytometer data is obtained from a previous flow cytometry experiment. Therefore, the first set of flow cytometer data includes a group that, according to a particular embodiment, is recognized or confirmed by the user as being associated with a specific property of interest. Conversely, the second set of flow cytometer data according to an embodiment of the present invention includes one or more groups that have not yet been defined. In other words, the groups of interest in the second set of flow cytometer data require a boundary (i.e., a gate) so that they can be formally distinguished from the rest of the data. In an embodiment, the first and second sets of flow cytometer data include data exhibiting the same parameter (e.g., the same fluorescent dye is detected in both sets). In an embodiment, the second set of flow cytometer data is obtained from a particle analyzer such as one described above.

[0077] In embodiments, a first set of flow cytometer data acquired from an input module includes a training gate. The training gate defines the boundaries of a set of flow cytometer data previously determined by the user or other cytometry techniques to correspond to the characteristics of the subject. In embodiments, the training gate includes vertices, i.e., points on a two-dimensional plot that, when connected, form a gate. In some embodiments, the training gate is drawn by the user. In other embodiments, the training gate is pre-established by other cytometry techniques. In embodiments, the training gate is used to define a set of subjects within the first set of flow cytometer data. In embodiments, the training gate has a shape corresponding to the shape of the set of subjects within the first set of flow cytometer data. In some embodiments, the training gate has a polygonal shape. Thus, in embodiments, the user can define a range of data values ​​to be included within the training gate by drawing a polygon on a graph of the first set of flow cytometer data measurements.

[0078] Data obtained from flow cytometry analysis of cells (or other particles) is often multidimensional, with each cell corresponding to a point in a multidimensional space defined by the measured parameters. A population of cells or particles can be identified as a cluster of points in the data space. In some embodiments, the method includes generating one or more population clusters based on determined parameters of the analytes (e.g., cells, particles) in a sample. As used herein, a “population” or “subpopulation” of analytes (e.g., cells or other particles) generally refers to a group of analytes having characteristics (e.g., optical properties, impedance properties, or temporal properties) with respect to one or more measured parameters, such that the measured parameter data forms clusters in the data space. In embodiments, the data consists of signals from multiple different parameters (e.g., including 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, and 20 or more). Thus, the populations are recognized as clusters in the data. Conversely, clusters corresponding to noise or background are also typically observed, but each data cluster is generally interpreted as corresponding to a population of a particular type of cell or analyte. Clusters can be defined with respect to a subset of measured parameters that correspond to different populations only within a subset of measured parameters or features extracted from cell or particle measurements, for example, within a subset of measured parameters or features.

[0079] In the embodiments, the processor is configured to receive flow cytometer data, calculate parameters for each analyte, and cluster the analytes together based on the calculated parameters. Any number of analytes may be assigned to a cluster, including five or more analytes, e.g., ten or more analytes, e.g., fifty or more analytes, e.g., 100 or more analytes, e.g., 500 analytes, and e.g., 1000 analytes. In certain embodiments, the method clusters together rare events detected in the sample (e.g., rare cells such as cancer cells in the sample). In these embodiments, the generated analyte clusters may include ten or fewer assigned analytes, e.g., nine or fewer assigned analytes, and may include five or fewer assigned analytes.

[0080] After flow cytometer data has been acquired, the processor may be configured to generate an image (i.e., a separate image for each set of flow cytometer data) for each of the first and second sets of flow cytometer data. “Generating an image” means converting the two-dimensional plot into an image format such that the flow cytometer data is represented by pixels that constitute an image representing the data when assembled. In embodiments, generating an image for each of the first and second sets of flow cytometer data involves organizing the data into two-dimensional bins. In this disclosure, the term “bin” is used in its conventional sense to refer to a data structure in which data points that fall within a certain interval are combined. The bins are then assigned a value representing that interval. In embodiments, the representative value of a bin is the mean value of the flow cytometer data contained within that bin. Thus, in embodiments, individual data points within a defined area of ​​the two-dimensional scatter plot are matched to a bin, which is associated with a representative value that reflects the number of data points contained within it.

[0081] In some embodiments, the processor is configured to organize flow cytometer data into bins according to the status of the data for a parameter. In such embodiments, the representative value of a given bin may be the average value of the binned data for the intensity detected for that parameter. For example, if the parameter is a fluorescence parameter, the representative value of a particular bin may be the average value of the data in that bin for the intensity of fluorescence emitted from a given fluorescent dye. In embodiments, the data is binned according to the status of the data for one or more additional parameters. In such embodiments, the representative value of a given bin may be a composite of the average values ​​evaluated for each relevant parameter. Thus, in embodiments, combining flow cytometer data into bins may involve evaluating the data for one or more additional parameters, such as including two or more, three or more, four or more, five or more, ten or more, fifteen or more, and twenty or more.

[0082] After flow cytometer data has been organized into bins, the processor may be configured to create image-generated values. As considered herein, image-generated values ​​quantify the data point concentrations within a given bin and serve as the basis for generating an image. In certain embodiments, determining image-generated values ​​involves creating a histogram of the two-dimensional binned data (i.e., a histogram of representative values ​​associated with each bin). In such embodiments, determining image-generated values ​​also involves calculating a cumulative distribution function based on the histogram. The cumulative distribution function is referred to in its conventional sense and determines the probability that a variable is less than or equal to a certain amount. Thus, the image-generated values ​​are obtained by substituting the representative values ​​of each bin into the cumulative distribution function calculated from the histogram. In some embodiments, the image-generated values ​​are in the range of 0 to 1. In other embodiments, the image-generated values ​​may range from 0 to any number selected by the user. In embodiments, higher image-generated values ​​indicate that the associated bin is associated with more data points, and lower image-generated values ​​indicate that the associated bin is associated with fewer data points.

[0083] Following the determination of the image generation value, the processor may be configured to assign shading to each bin based on the image generation value corresponding to that bin. “Shading” as described herein refers to the brightness or darkness represented by a given bin in the resulting image. In some embodiments, a bin is assigned a gray shading. In such embodiments, the resulting image is a grayscale image. In other embodiments, a bin is assigned a non-gray shading. In such embodiments, the resulting image is a color image. In embodiments where the data combined in the bins is evaluated with respect to multiple parameters, the image generated from such bins may be polycolored. In such embodiments, different colors may be associated with each distinct parameter. In some embodiments, bins associated with higher image generation values ​​are assigned brighter colors, and bins associated with lower image generation values ​​are assigned darker colors. Certain embodiments of the present invention also include an image generation threshold. In such embodiments, all bins associated with image generation values ​​below the threshold are assigned black. In some embodiments, the threshold is adjustable (i.e., selected by the user from several different options). Thus, more or fewer images may be assigned black based on the magnitude of the selected threshold.

[0084] The processor may be further configured to adjust the training gate from the generated images of a first set of flow cytometer data to adapt the generated images of a second set of flow cytometer data. Adjusting the training gate to "adapt" the second set of flow cytometer data means changing the shape of the training gate so that the adjusted training gate explains the differences in collective morphology present in the second set of flow cytometer data compared to the first set of flow cytometer data. In certain embodiments, adjusting the training gate includes adjusting each of the constituent vertices of the training gate. In embodiments, the processor adjusts the training gate using a processor implementation algorithm. In such embodiments, the processor implementation algorithm is configured to adjust the vertices of the training gate taken from the generated images of the first set of flow cytometer data to fit the generated images of the second set of flow cytometer data. According to embodiments of the present disclosure, the processor implementation algorithm is an image alignment algorithm. The image alignment algorithm integrates and transforms data received from images. Therefore, the image alignment algorithm described herein analyzes the generated images of a first set of flow cytometer data, analyzes the generated images of a second set of flow cytometer data, and warps the training gates from the first set of flow cytometer data to fit the second set of flow cytometer data based on their analysis.

[0085] In some embodiments, warping training gates to fit a second set of flow cytometer data involves warping the entire generated image of the first set of flow cytometer data to maximize its similarity to the generated image of the second set of flow cytometer data. In such embodiments, a group of subjects in the generated image of the first set of flow cytometer data is adjusted (i.e., warped) so that the contours of the groups are substantially identical to the contours of similar groups in the generated image of the second set of flow cytometer data. In other words, the processor implementation algorithm may include a mathematical deformation model configured to transform / deform the first set of flow cytometer data to resemble the second set of flow cytometer data. After the generated image of the first set of flow cytometer data has been warped, the training gates contained therein may be warped accordingly. In some embodiments, before the training gates are adjusted, they are first imposed on a blank image (i.e., so that the training gates are the only elements in the image). In other words, the training gates may be separated from the rest of the generated image of the first set of flow cytometer data and imposed on a separate image.

[0086] In some embodiments, an image alignment algorithm configured to warp generated images of a first set of flow cytometer data includes B-spline warping. In embodiments, B-spline warping includes calculating B-spline coefficients, which are then used to define a function for tuning the training gate. In other words, the image alignment algorithm warps generated images of a first set of flow cytometer data and outputs B-spline coefficients that numerically describe how the generated images of the first set of flow cytometer data were warped. These coefficients then serve as a basis for updating the vertices of the training gate so that a tuned training gate is generated that explains the difference between the first and second sets of flow cytometer data. The updated (i.e., tuned) vertices can then be applied to generated images of a second set of flow cytometer data to form a tuned gate on top of it.

[0087] Following the adjustment of the training gate, aspects of the present invention include overlaying the adjusted training gate onto a second set of flow cytometer data. In such embodiments, the vertices of the adjusted training gate in a blank image are applied to corresponding groups in a generated image of the second set of flow cytometer data to form a gate around those groups. In some embodiments, adjusting the vertices of the training gate to create an adjusted training gate includes extracting vertices from the training gate (i.e., isolating and observing the position of each vertex), modifying the position of each vertex based on a B-spline coefficient (e.g., as determined above), and applying the adjusted vertices to a generated image of the second set of flow cytometer data to form an adjusted training gate. In other embodiments, the processor implementation algorithm may warp the training gate before observing the position of each vertex. In such embodiments, the processor implementation algorithm may adjust the training gate by an image alignment algorithm (e.g., as considered above), and then the adjusted vertices may be observed and applied to a generated image of the second set of flow cytometer data.

[0088] In some embodiments, the adjusted training gate overlaid on a second set of flow cytometer data has a different shape from the original training gate, i.e., it adapts to the shape of the relevant population in the second set of flow cytometer data. In some examples, the relevant population in the second set of flow cytometer data is stretched, compressed, or shifted in some areas compared to the population in the first set of flow cytometer data. Therefore, in some examples, the adjusted training gate is shaped to adapt to these differences. In some examples, the adjusted training gate is polygonal.

[0089] Figure 7 shows a system 700 for flow cytometry according to an exemplary embodiment of the present invention. The system 700 includes a flow cytometer 710, a controller / processor 790, and memory 795. The flow cytometer 710 includes one or more excitation lasers 715a-715c, a focusing lens 720, a flow chamber 725, a forward scatter detector 730, a side scatter detector 735, a fluorescence collection lens 740, one or more beam splitters 745a-745g, one or more bandpass filters 750a-750e, one or more long-pass ("LP") filters 755a-755b, and one or more fluorescence detectors 760a-760f.

[0090] The excitation lasers 715a-715c emit light in the form of laser beams. The wavelengths of the laser beams emitted from the excitation lasers 715a-715c are 488 nm, 633 nm, and 325 nm, respectively, in the exemplary system shown in Figure 7. The laser beams are first directed through one or more of the beam splitters 745a and 745b. Beam splitter 745a transmits 488 nm light and reflects 633 nm light. Beam splitter 745b transmits UV light (light with wavelengths in the range of 10-400 nm) and reflects 488 nm and 633 nm light.

[0091] Next, the laser beam is directed to a focusing lens 720, which focuses the beam into the portion of the fluid flow where the sample particles are located within the flow chamber 725. The flow chamber is part of a fluid engineering system, which directs the particles in the stream, typically one at a time, to the focused laser beam for inspection. The flow chamber may include a flow cell in a benchtop cytometer or a nozzle tip in a stream-in-air cytometer.

[0092] Light from the laser beam interacts with particles in the sample through diffraction, refraction, reflection, scattering, and absorption, and is re-emitted at various different wavelengths depending on the particle's characteristics, such as its size, internal structure, and the presence of one or more fluorescent molecules attached to, on, or within the particle. The fluorescent emission, as well as the diffracted, refracted, reflected, and scattered light, can be sent through one or more of the beam splitters 745a-745g, bandpass filters 750a-750e, longpass filters 755a-755b, and fluorescence collection lenses 740 to one or more of the forward scatter detector 730, side scatter detector 735, and one or more fluorescence detectors 760a-760f.

[0093] The fluorescence collection lens 740 collects light emitted from the particle-laser beam interaction and directs that light toward one or more beam splitters and filters. Bandpass filters, such as bandpass filters 750a-750e, allow a narrow range of wavelengths to pass through the filter. For example, bandpass filter 750a is a 510 / 20 filter. The first number represents the center of the spectral band. The second number provides the range of the spectral band. Thus, the 510 / 20 filter extends 10 nm on each side of the center of the spectral band, i.e., from 500 nm to 520 nm. Short-pass filters transmit light with wavelengths below a certain wavelength. Long-pass filters, such as long-pass filters 755a-755b, transmit light with wavelengths above a specified wavelength. For example, long-pass filter 755a, which is a 670 nm long-pass filter, transmits light above 670 nm. Filters are often selected to optimize the detector's specificity for a particular fluorescent dye. The filter can be configured so that the spectral bandwidth of the light transmitted to the detector is close to the emission peak of the fluorescent dye.

[0094] A beam splitter directs light of different wavelengths in different directions. Beam splitters can be characterized by their filtering characteristics, such as short-pass and long-pass filtering. For example, beam splitter 705g is a 620SP beam splitter, meaning that beam splitter 745g transmits light with wavelengths of 620 nm or less and reflects light with wavelengths longer than 620 nm in a different direction. In one embodiment, beam splitters 745a to 745g may be equipped with optical mirrors, such as dichroic mirrors.

[0095] The forward scatter detector 730 is positioned off-axis from the direct beam passing through the flow cell and is configured to detect diffracted light, i.e., excitation light that travels primarily forward through or around the particle. The intensity of the light detected by the forward scatter detector depends on the overall size of the particle. The forward scatter detector may include a photodiode. The side scatter detector 735 is configured to detect light refracted and reflected from the surface and internal structure of the particle and tends to increase with increasing particle complexity of structure. Fluorescence emission from fluorescent molecules associated with the particle can be detected by one or more fluorescence detectors 760a-760f. The side scatter detector 735 and the fluorescence detectors may include photomultiplier tubes. The signals detected by the forward scatter detector 730, the side scatter detector 735, and the fluorescence detectors can be converted into electronic signals (voltages) by the detectors. This data can provide information about the sample.

[0096] During operation, the cytometer operation is controlled by the controller / processor 790, and measurement data from the detector is stored in memory 795 and can be processed by the controller / processor 790. Although not explicitly shown, the controller / processor 790 may be coupled to the detector to receive output signals from it, and may also be coupled to the electromechanical components of the flow cytometer 700 to control lasers, fluid flow parameters, etc. Input / output (I / O) functions 797 may also be provided within the system. Memory 795, controller / processor 790, and I / O 797 ​​may be provided as a complete integral part of the flow cytometer 710. In such embodiments, a display may also form part of the I / O functions 797 for presenting experimental data to the user of the cytometer 700. Alternatively, some or all of the memory 795 and controller / processor 790 and I / O functions may be part of one or more external devices, such as a general-purpose computer. In some embodiments, some or all of the memory 795 and controller / processor 790 can communicate wirelessly or via wired communication with the cytometer 710. The controller / processor 790, together with the memory 795 and I / O 797, can be configured to perform various functions related to the preparation and analysis of flow cytometer experiments.

[0097] The system shown in Figure 7 includes six different detectors that detect fluorescence in six different wavelength bands (which may be referred to herein as “filter windows” for a given detector), defined by the configuration of filters and / or splitters in the beam path from the flow cell 725 to each detector. Different fluorescent molecules used in flow cytometry experiments emit light in their own characteristic wavelength bands. Specific fluorescent labels used for the experiment and their associated fluorescence emission bands may be selected to generally match the filter windows of the detectors. However, as more detectors are provided and more labels are utilized, a perfect correspondence between filter windows and fluorescence emission spectra is impossible. The fact is that while the emission spectrum peaks of a particular fluorescent molecule may be located within the filter window of one particular detector, generally, a portion of the emission spectrum of that label will also overlap with the filter windows of one or more other detectors. This may be referred to as spillover. The I / O797 can be configured to receive data on flow cytometry experiments with panels of fluorescent labels and multiple cell populations having multiple markers, each cell population having a subset of multiple markers. I / O797 may also be configured to receive biometric data for assigning one or more markers to one or more cell populations, marker density data, emission spectral data, data for assigning labels to one or more markers, and cytometer configuration data. Flow cytometer experimental data (e.g., label spectral characteristics and flow cytometer configuration data) may also be stored in memory 795. The controller / processor 790 may be configured to evaluate the assignment of one or more labels to the markers.

[0098] Those skilled in the art will recognize that the flow cytometer according to embodiments of the present invention is not limited to the flow cytometer shown in Figure 7, but may include any flow cytometer known in the art. For example, a flow cytometer may have any number of lasers, beam splitters, filters, and detectors at various wavelengths and in various different configurations.

[0099] Figure 8 shows a functional block diagram of an example of a processor 800 for analyzing and displaying data. The processor 800 may be configured to implement various processes for controlling the graphical display of biological events. The flow cytometer 802 may be configured to acquire flow cytometer data by analyzing a biological sample (e.g., as described above). The flow cytometer 802 may be configured to provide biological event data to the processor 800. A data communication channel may be included between the flow cytometer 802 and the processor 800. Data can be provided to the processor 800 via the data communication channel. The processor 800 may be configured to provide a graphical display to the display 806, including a plot (e.g., as described above). The processor 800 may be further configured to render gates around a collection of flow cytometer data shown by the display device 806, for example, overlaid on the plot. In some embodiments, the gates may be logical combinations of one or more graphical regions of interest drawn on a single-parameter histogram or a bivariate plot. In some embodiments, the display may be used to display analyte parameters or saturation detector data.

[0100] The processor 800 may further be configured to display flow cytometer data on the display device 806 inside the gate, distinct from other events in the flow cytometer data outside the gate. For example, the processor 800 may be configured to render the colors of the flow cytometer data contained inside the gate differently from the colors of the flow cytometer data outside the gate. In this way, the processor 800 may be configured to render different colors to represent each unique set of data. The display device 806 may be implemented as a monitor, a tablet computer, a smartphone, or other electronic device configured to present a graphical interface.

[0101] The processor 800 can be configured to receive gate selection signals from a first input device to identify gates. For example, the first input device can be implemented as a mouse 810. The mouse 810 can initiate gate selection signals to the processor 800 to identify a group to be displayed or manipulated via the display device 806 (for example, by clicking on or inside a desired gate when a cursor is positioned there). In some implementations, the first device may be implemented as a keyboard 808, or as other means for providing input signals to the processor 800, such as a touchscreen, stylus, photodetector, or speech recognition system. Some input devices may include multiple input functions. In such implementations, each input function may be considered an input device. For example, as shown in Figure 8, the mouse 810 may include a right mouse button and a left mouse button, each of which can generate a trigger event.

[0102] The trigger event can cause the processor 800 to change the format in which the flow cytometer data is displayed, provide input for further processing such as which portion of the data is actually displayed on the display device 806, and / or select a target population for analysis.

[0103] In some embodiments, the processor 800 may be configured to detect when gate selection is initiated by the mouse 810. The processor 800 may be further configured to automatically modify the plot visualization to facilitate the gating process. The modification may be based on a specific distribution of data received by the processor 800.

[0104] The processor 800 can be connected to a storage device 804. The storage device 804 can be configured to receive and store data from the processor 800. The storage device 804 may be further configured to allow the processor 800 to retrieve data such as flow cytometer data.

[0105] The display device 806 may be configured to receive display data from the processor 800. The display data may include plots of flow cytometer data and gates that outline sections of the plots. The display device 806 may be further configured to modify the information presented in accordance with inputs received from the processor 800, along with inputs from the device 802, storage device 804, keyboard 808, and / or mouse 810.

[0106] In some implementations, the processor 800 can generate a user interface for receiving exemplary events for sorting. For example, the user interface may include controls for receiving exemplary events or exemplary images. Exemplary events or images or exemplary gates may be provided before the collection of event data for the samples, or based on an initial set of events for a portion of the samples.

[0107] Computer control system Aspects of the present disclosure further include a computer control system, the system further including one or more computers for full or partial automation. In some embodiments, the system includes a computer having a computer-readable storage medium in which a computer program is stored, the computer program may include instructions for generating images for each of the first and second sets of flow cytometer data by, when loaded into the computer, taking a first set of flow cytometer data including training gates and a second set of flow cytometer data, organizing the data into two-dimensional bins, creating a histogram and a cumulative distribution function based on the two-dimensional binning data, determining image generation values ​​based on the cumulative distribution function, assigning shading to each bin based on the corresponding image generation values, warping the generated images of the first set of flow cytometer data to maximize similarity with the generated images of the second set of flow cytometer data, adjusting the training gates using B-spline coefficients from warping the generated images of the first set of flow cytometer data, and overlaying the training gates onto the generated images of the second set of flow cytometer data.

[0108] In embodiments, the system is configured to analyze data within software or analytical tools for analyzing flow cytometer data or nucleic acid sequence data, such as FlowJo® (Ashland, OR). FlowJo® is a software package developed by FlowJo LLC (a subsidiary of Becton Dickinson) for analyzing flow cytometer data. The software is configured to manage flow cytometer data and generate graphical reports thereon (https: / / www(dot)flowjo(dot)com / learn / flowjo-university / flowjo). Initial data can be analyzed within data analysis software or tools (e.g., FlowJo®) by appropriate means such as manual gating, cluster analysis, or other computer techniques. The system or a part thereof can be implemented as a software component of software for analyzing data, such as FlowJo®. In these embodiments, the computer-controlled system according to this disclosure can function as a software “plug-in” for existing software packages such as FlowJo®.

[0109] In the embodiment, the system includes an input module, a processing module, and an output module. The subject system may include both hardware and software components, the hardware components may take the form of one or more platforms, for example, a server, thereby allowing the functional elements, i.e., the elements of the system that perform specific tasks of the system (such as managing the input and output of information, processing information, etc.), to be executed by running software applications on and across one or more computer platforms represented by the system.

[0110] The system may include a display and an operator input device. The operator input device may be, for example, a keyboard, a mouse, etc. The processing module includes a processor that accesses memory having stored instructions to perform steps of the method of the subject. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor runs the operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of the functions of various computer programs that can be written in various programming languages ​​such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system usually works in coordination 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, according to all known technologies. The processor may be any preferred analog or digital system. In some embodiments, the processor includes analog electronics that allow the user to manually align the light source with the flow stream based on first and second optical signals. In some embodiments, the processor includes analog electronics that provide feedback control, such as negative feedback control.

[0111] System memory can be any of a variety of known or future memory storage devices. Examples include any commonly available random access memory (RAM), magnetic media such as resident hard disks or tapes, optical media such as read-write compact disks, flash memory devices, or other memory storage devices. Memory storage devices can be any of a variety of known or future devices, including compact disk drives, tape drives, removable hard disk drives, or disk drives. Such types of memory storage devices typically read from and / or write to program storage media (not shown), such as compact disks, magnetic tapes, removable hard disks, or floppy disks, respectively. Any of these program storage media, or others currently in use or to be developed in the future, can be considered computer program products. As is understood, these program storage media typically store computer software programs and / or data. Computer software programs, also called computer control logic, are typically stored in system memory and / or program storage devices used in conjunction with memory storage devices.

[0112] In some embodiments, the computer program product is described in terms of a computer-usable medium having stored control logic (a computer software program including program code). When the control logic is executed by a processor, the computer causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware, for example, using a hardware state machine. Implementations of hardware state machines for performing the functions described herein will be apparent to those skilled in the art.

[0113] Memory can be any suitable device on which the processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including magnetic or optical disks, or tapes, or RAM, or any other suitable device, whether fixed or portable). The processor may include a general-purpose digital microprocessor preferably programmed from a computer-readable medium carrying the required program code. The programming can be provided to the processor remotely via a communication channel, or it can be pre-stored in memory or any other portable or fixed computer-readable storage medium, using one of those devices together with the memory. For example, a magnetic disk or optical disk can carry programming that can be read by a disk writer / reader. The system of the present invention also includes, for example, programming in the form of a computer program product, and algorithms for use in carrying out the above methods. The programming according to the present invention can be recorded on a computer-readable medium, for example, any medium that can be directly read and accessed by a computer. Such media include, but are not limited to, floppy disks, hard disk storage media, magnetic storage media such as magnetic tape, optical storage media such as CD-ROMs, electrical storage media such as RAM and ROMs, portable flash drives, and hybrids of these categories such as magnetic / optical storage media.

[0114] The processor may also have access to a communication channel for communicating with the user at a remote location. A remote location means that the user does not have direct contact with the system, but rather relays input information to the input manager from an external device such as a computer connected to any other suitable communication channel, including a wide area network ("WAN"), telephone network, satellite network, or mobile phone (i.e., smartphone).

[0115] In some embodiments, the systems according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including, but not limited to, radio frequency (RF) communication (e.g., radio frequency identification (RFID), Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and code division multiple access (CDMA) or global system for mobile communications (GSM).

[0116] In one embodiment, the communication interface is configured to include one or more communication ports, such as a physical port or interface, such as a USB port, an RS-232 port, or any other suitable electrical connection port, to enable data communication between the subject system and other external devices, such as computer terminals (e.g., in a clinic or hospital environment), configured for similar complementary data communication.

[0117] In one embodiment, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol, enabling the System of the Subject to communicate with other devices, such as computer terminals and / or networks, communicable mobile phones, personal digital assistants, or any other communication devices that the user may use in conjunction with them.

[0118] In one embodiment, the communication interface is configured to provide a connection for data transfer using the Internet Protocol (IP) via a mobile phone network, Short Message Service (SMS), a wireless connection to a personal computer (PC) on a local area network (LAN) connected to the Internet, or a Wi-Fi connection to the Internet via a Wi-Fi hotspot.

[0119] In one embodiment, the subject system is configured to communicate wirelessly with a server device via a communication interface using a common standard such as 802.11 or Bluetooth® RF protocol, or IrDA infrared protocol. The server device may be another portable device such as a smartphone, personal digital assistant (PDA), or notebook computer, or a larger device such as a desktop computer or appliance. In some embodiments, the server device has a display such as a liquid crystal display (LCD), as well as input devices such as buttons, a keyboard, a mouse, or a touchscreen.

[0120] In some embodiments, the communication interface is configured to communicate automatically or semi-automatically with a network or server device and the system of the subject, for example, data stored in any data storage unit, using one or more of the communication protocols and / or mechanisms described above.

[0121] An output controller may include a controller for any of the various known display devices for presenting information to a user, whether human or machine, local or remote. If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. A graphical user interface (GUI) controller may include any of the various 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 a computer may communicate with each other via a system bus. Some of these communications may be achieved in alternative embodiments using a network or other type of remote communication. An output manager may also provide information generated by processing modules to a user at a remote location, for example, via the internet, telephone, or satellite network, according to known technologies. The presentation of data by the output manager may be implemented according to various known technologies. As some examples, the data may include SQL, HTML, or XML documents, email 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 within the subject system are typically of the class of computers commonly referred to as servers, but may be any type of known or future computer platform. They may also be mainframe computers, workstations, or other computer types. They may be connected via other communication systems, including any known or future type of cabling, or networked or non-networked wireless systems. They may be located in the same location or physically separated.Depending on the type and / or configuration of the selected computer platform, various operating systems may be used on any of the computer platforms. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux®, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, and others.

[0122] Figure 9 shows a general architecture of an exemplary computing device 900 according to a particular embodiment. The general architecture of the computing device 900 shown in Figure 9 includes the arrangement of computer hardware and software components. However, in order to provide a practicable disclosure, it is not necessary to show all of these generally conventional elements. As illustrated, the computing device 900 includes a processing unit 910, a network interface 920, a computer-readable media drive 930, an input / output device interface 940, a display 950, and an input device 960, all of which can communicate with each other by a communication bus. The network interface 920 can provide connectivity to one or more networks or computing systems. Thus, the processing unit 910 can receive information and instructions from other computing systems or services via the network. The processing unit 910 can also communicate with memory 970 and further provide output information to an optional display 950 via the input / output device interface 940. For example, analysis software (such as data analysis software or programs like FlowJo®) stored as executable instructions in the non-temporary memory of the analysis system can display flow cytometry event data to the user. The input / output device interface 940 can also accept input from an optional input device 960, such as a keyboard, mouse, digital pen, microphone, touchscreen, gesture recognition system, speech recognition system, gamepad, accelerometer, gyroscope, or other input device.

[0123] Memory 970 may include computer program instructions (grouped as modules or components in some embodiments) that the processing unit 910 executes to implement one or more embodiments. Memory 970 generally includes RAM, ROM, and / or other persistent, auxiliary, or non-temporary computer-readable media. Memory 970 may store an operating system 972 that provides computer program instructions used by the processing unit 910 in the general management and operation of the computing device 900. Data may be stored in a data storage device 990. Memory 970 may further include computer program instructions and other information for implementing embodiments of the present disclosure.

[0124] Computer-readable storage media Aspects of this disclosure further include a non-temporary computer-readable storage medium having instructions for carrying out the subject matter method. The computer-readable storage medium may be employed on one or more computers for fully or partially automating a system for carrying out the method described herein. In some embodiments, instructions for the method described herein may be coded on the computer-readable medium in the form of “programming,” and as used herein, the term “computer-readable medium” refers to any non-temporary storage medium involved in providing instructions and data to a computer for execution and processing. Examples of preferred non-temporary storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state disks, and network-attached storage (NAS), whether such devices are inside or outside a computer. In some examples, instructions may be provided on an integrated circuit device. The integrated circuit device in question may, in certain examples, include a reconfigurable field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or a composite programmable logic device (CPLD). A file containing information can be “stored” on a computer-readable medium, where “storage” means recording the information so that it can be accessed and searched by a computer at a later date. The computer implementations described herein can be executed using programming that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java (Sun Microsystems, Inc., Santa Clara, CA), Visual Basic (Microsoft Corp., Redmond, WA), and C++ (AT&T Corp., Bedminster, NJ), as well as many other languages.

[0125] In some embodiments, the computer-readable storage medium in question includes a computer program stored on the computer-readable storage medium, which, when loaded into a computer, may include instructions for generating images for each of the first and second sets of flow cytometer data by taking a first set of flow cytometer data and a second set of flow cytometer data including training gates, organizing the data into two-dimensional bins, creating histograms and cumulative distribution functions based on the two-dimensional binning data, determining image generation values ​​based on the cumulative distribution functions, assigning shading to each bin based on the corresponding image generation values, warping the generated images of the first set of flow cytometer data to maximize similarity with the generated images of the second set of flow cytometer data, adjusting the training gates using B-spline coefficients from warping the generated images of the first set of flow cytometer data, and overlaying the training gates onto the generated images of the second set of flow cytometer data.

[0126] In embodiments, the system is configured to analyze data within software or analysis tools for analyzing flow cytometer data or nucleic acid sequence data, such as FlowJo®. Initial data can be analyzed within data analysis software or tools (e.g., FlowJo®) by appropriate means such as manual gating, cluster analysis, or other computer techniques. The system or a part thereof can be implemented as a software component of data analysis software such as FlowJo®. In these embodiments, the computer-controlled system according to the disclosure can function as a software “plug-in” for existing software packages such as FlowJo®.

[0127] Computer-readable storage media can be used on one or more computer systems having a display and operator input devices. Operator input devices may be, for example, a keyboard, a mouse, etc. A processing module includes a processor that accesses memory having stored instructions to perform steps of the method of the subject. A processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory storage devices, and input / output controllers, cache memory, a data backup unit, and many other devices. The processor may be a commercially available processor or one of other processors that are available or will become available. The processor runs an operating system, which interfaces with firmware and hardware in a well-known manner and facilitates the processor's coordination and execution of the functions of various computer programs that can be written in various programming languages ​​such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as is known in the art. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, according to all known technologies.

[0128] Utility The apparatus, method, and computer system of the present invention are used in a variety of applications where gate control of flow cytometer data is desirable. For example, the disclosure is used when obtaining gates from a set of flow cytometer data and adjusting those gates to fit a second set of flow cytometer data. Thus, the disclosure finds use in adjusting existing gates obtained from a previously characterized set of flow cytometer data and applying them to a set of flow cytometer data that has not yet been gated. In other words, the disclosure finds use in fitting existing gates from one set of flow cytometer data to the same population in a second set of flow cytometer data, even if that population has slightly shrunk, expanded, or shifted. Thus, the method, system, and computer control system do not require the user to manually redraw gates or manipulate their vertices to draw new gates, thereby improving the efficiency of flow cytometer data analysis. In some embodiments, the method and system of the subject matter provide a fully automated protocol, resulting in adjustments to the data requiring little to no human input, if any.

[0129] This disclosure can be used to characterize many types of analytes, particularly those related to medical diagnostics or protocols for patient care, including, but not limited to, proteins (including both free proteins and proteins bound to the surface of structures such as cells), nucleic acids, viral particles, and the like. Furthermore, samples may originate from in vitro or in vivo sources, and samples may be diagnostic samples.

[0130] kit Aspects of this disclosure further include a kit, which includes storage media such as floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state disks, and network-attached storage (NAS). Any of these program storage media, or others currently in use or to be developed in the future, may be included in the kit. In embodiments, instructions contained in a computer-readable medium provided in the subject kit or a portion thereof may be implemented as software components of data analysis software such as FlowJo®. In these embodiments, the computer control system according to this disclosure can function as a software "plug-in" for existing software packages such as FlowJo®.

[0131] In addition to the components described above, the subject kit may further include (in some embodiments) instructions for installing a plug-in to an existing software package, such as FlowJo®. These instructions may be present in various forms within the subject kit, and one or more of these forms may be present within the kit. One possible form in which these instructions may be present is as printed information in the kit packaging, such as on a suitable medium or substrate, such as one or more sheets of paper on which the information is printed, or in accompanying documentation. Yet another form of these instructions may be a computer-readable medium on which the information is recorded, such as a diskette, compact disc (CD), or portable flash drive. Yet another possible form of these instructions may be a website address that can be used via the internet to access the information at a remote site.

[0132] Notwithstanding the attached claims, the disclosures described herein are also defined by the following annotations: 1. A method for adapting a second set of flow cytometer data by adjusting a training gate prepared from a first set of flow cytometer data, wherein the method is: The acquisition of a first and second set of flow cytometer data, wherein the first set of flow cytometer data includes a training gate defined by a set of vertices, To generate images for each of the first and second sets of flow cytometer data acquired, Using a processor implementation algorithm, the set of vertices defining the training gate is adjusted from the generated images of the first set of flow cytometer data, and then the generated images of the second set of flow cytometer data are adapted. Methods that include... 2. The method according to Appendix 1, wherein the processor implementation algorithm is configured to warp the generated images of a first set of flow cytometer data to maximize the similarity to the generated images of a second set of flow cytometer data. 3. The processor implementation algorithm is an image alignment algorithm that includes a mathematical deformation model, as described in Appendix 1 or 2. 4. The image alignment algorithm is the method described in Appendix 3, further including B-spline warping. 5. The method according to Appendix 4, wherein B-spline warping includes calculating a B-spline coefficient that defines a function that warps the generated images of a first set of flow cytometer data to maximize the similarity to the generated images of a second set of flow cytometer data.

[0133] 6. The processor implementation algorithm is configured to adjust the vertices of the training gates based on a function defined by the B-spline coefficients, as described in Appendix 5. 7. Adjusting the training gate is done in any one of the manner described in Appendix 1 to 6, including imposing the training gate on a blank image. 8. The processor implementation algorithm is configured to adjust the training gates imposed on blank images, as described in Appendix 7. 9. The method according to Appendix 8, further comprising overlaying a tuned training gate onto a second set of flow cytometer data by applying the tuned gate vertices from a blank image to the generated image of a second set of flow cytometer data. 10. Generating images of the first and second sets of flow cytometer data is the method according to any one of the methods described in Appendix 1 to 9, comprising organizing each of the first and second sets of flow cytometer data into two-dimensional bins and assigning shading to each bin.

[0134] 11. Generating images of the first and second sets of flow cytometer data further includes creating a two-dimensional histogram of the mean values ​​of the flow cytometer data associated with each bin, the mean values ​​of the flow cytometer data being evaluated against a parameter, as described in Appendix 10. 12. The mean value of the flow cytometer data associated with each bin is evaluated against one or more further parameters, as described in Appendix 11. 13. The method according to Appendix 12, further comprising calculating a cumulative distribution function based on a histogram and determining the image generation values ​​associated with each bin based on the cumulative distribution function. 14. The method described in Appendix 13, wherein bins associated with larger image generation values ​​are assigned brighter shading, and bins associated with smaller image generation values ​​are assigned darker shading. 15. Bins associated with image generation values ​​below a threshold are assigned black, as described in Appendix 13 or 14.

[0135] 16. The threshold is adjustable as described in Appendix 15. 17. The image is a grayscale image, as described in one of the methods described in Appendix 1 to 16. 18. The image is a color image, and is described in any one of the methods described in Appendix 1 to 16. 19. The training gate is drawn by the user in any of the following ways, as described in Appendix 1 to 18. 20. The modified training gate has a different shape from the training gate, as described in any one of the methods described in Appendix 1 to 19.

[0136] 21. A system, An input module configured to acquire a first set of flow cytometer data, which includes a training gate defined by a set of vertices, A particle analyzer component configured to acquire a second set of flow cytometer data, A processor comprising memory operably coupled to the processor, the memory containing instructions stored in the memory, and when an instruction is executed by the processor, the processor To generate images for each of the first and second sets of flow cytometer data acquired, Using a processor implementation algorithm, the set of vertices defining the training gate is adjusted from the generated images of the first set of flow cytometer data, and then the generated images of the second set of flow cytometer data are adapted. The processor and A system that includes this. 22. The system described in Appendix 21, wherein the processor implementation algorithm is configured to warp the generated images of a first set of flow cytometer data to maximize the similarity to the generated images of a second set of flow cytometer data. 23. The processor implementation algorithm is an image alignment algorithm that includes a mathematical deformation model, as described in Appendix 21 or 22. 24. The image alignment algorithm is the system described in Appendix 23, further including B-spline warping. 25. B-spline warping is a system described in Appendix 24, which includes calculating a B-spline coefficient that defines a function that warps generated images of a first set of flow cytometer data to maximize the similarity to generated images of a second set of flow cytometer data.

[0137] 26. The processor implementation algorithm is configured to adjust the vertices of the training gates based on a function defined by the B-spline coefficients, as described in Appendix 25. 27. Adjusting the training gate is a system described in any one of the appendices 21-25, which includes imposing a training gate on a blank image. 28. The processor implementation algorithm is configured to adjust the training gates imposed on blank images, as described in Appendix 27. 29. The system as described in Appendix 28, further comprising overlaying the adjusted trained gate onto a second set of flow cytometer data by applying the adjusted gate vertices from a blank image to the generated image of a second set of flow cytometer data. 30. The system described in any one of the appendices 21-29, wherein generating images of the first and second sets of flow cytometer data includes organizing each of the first and second sets of flow cytometer data into two-dimensional bins and assigning shading to each bin.

[0138] 31. Generating images of the first and second sets of flow cytometer data further includes creating a two-dimensional histogram of the mean values ​​of the flow cytometer data associated with each bin, the mean values ​​of the flow cytometer data being evaluated against the parameter, as described in Appendix 30. 32. The average value of the flow cytometer data associated with each bin is evaluated against one or more further parameters, as described in Appendix 31. 33. The system described in Appendix 32, further comprising calculating a cumulative distribution function based on a histogram and determining the image generation values ​​associated with each bin based on the cumulative distribution function. 34. The system described in Appendix 33, in which bins associated with larger image generation values ​​are assigned brighter shading, and bins associated with smaller image generation values ​​are assigned darker shading. 35. In the system described in Appendix 33 or 34, bins associated with image generation values ​​below a threshold are assigned black.

[0139] 36. The threshold is adjustable, as described in Appendix 35. 37. The image is a grayscale image, and the system is one of the systems described in any of the appendices 21-36. 38. The image is a color image and is one of the systems described in one of the appendices 21-36. 39. The training gate is a system drawn by the user, as described in any one of the appendices 21-38. 40. The adjusted training gate is a system described in any one of the appendices 21 to 39, having a different shape from the training gate.

[0140] 41. A non-temporary computer-readable storage medium, The acquisition of a first and second set of flow cytometer data, wherein the first set of flow cytometer data includes a training gate defined by a set of vertices, To generate images for each of the first and second sets of flow cytometer data acquired, A method comprising using a processor implementation algorithm to adjust the set of vertices defining the training gate from the generated images of a first set of flow cytometer data, and then adapting the generated images of a second set of flow cytometer data, A non-temporary computer-readable storage medium storing instructions for adjusting a training gate prepared from a first set of flow cytometer data to adapt a second set of flow cytometer data. 42. The non-temporary computer-readable storage medium described in Appendix 41, wherein the processor implementation algorithm is configured to warp the generated images of the first set of flow cytometer data in order to maximize the similarity of the generated images of the second set of flow cytometer data to the generated images. 43. The processor implementation algorithm is an image alignment algorithm including a mathematical deformation model, for the non-temporary computer-readable storage medium as described in Appendix 41 or 42. 44. The image alignment algorithm further includes B-spline warping, for the non-temporary computer-readable storage medium as described in Appendix 43. 45. B-spline warping, a non-temporary computer-readable storage medium as described in Appendix 44, comprising calculating a B-spline coefficient that defines a function that warps generated images of a first set of flow cytometer data to maximize the similarity to generated images of a second set of flow cytometer data.

[0141] 46. ​​A non-temporary computer-readable storage medium as described in Appendix 45, wherein the processor implementation algorithm is configured to adjust the vertices of the training gates based on a function defined by B-spline coefficients. 47. Adjusting the training gate includes imposing the training gate on a blank image, in a non-temporary computer-readable storage medium as described in any one of the appendices 41 to 45. 48. A non-temporary computer-readable storage medium as described in Appendix 47, wherein the processor implementation algorithm is configured to adjust the training gates imposed on blank images. 49. A non-temporary computer-readable storage medium as described in Appendix 48, further comprising overlaying a tuned training gate onto a second set of flow cytometer data by applying the vertices of the tuned gate from a blank image to the generated image of a second set of flow cytometer data. 50. A non-temporary computer-readable storage medium as described in any one of Appendix 41 to 49, wherein generating images of first and second sets of flow cytometer data includes organizing each of the first and second sets of flow cytometer data into two-dimensional bins and assigning shading to each bin.

[0142] 51. Generating images of the first and second sets of flow cytometer data further includes creating a two-dimensional histogram of the mean values ​​of the flow cytometer data associated with each bin, the mean values ​​of the flow cytometer data being evaluated against a parameter in a non-temporary computer-readable storage medium as described in Appendix 50. 52. The average value of the flow cytometer data associated with each bin is evaluated against one or more further parameters in a non-temporary computer-readable storage medium as described in Appendix 51. 53. A non-temporary computer-readable storage medium as described in Appendix 52, further comprising calculating a cumulative distribution function based on a histogram and determining the image generation values ​​associated with each bin based on the cumulative distribution function. 54. A non-temporary computer-readable storage medium as described in Appendix 53, in which bins associated with a larger image generation value are assigned brighter shading, and bins associated with a smaller image generation value are assigned darker shading. 55. Bins associated with image generation values ​​below a threshold are assigned black, in a non-temporary computer-readable storage medium as described in Appendix 53 or 54.

[0143] 56. The threshold is adjustable, and is a non-temporary computer-readable storage medium as described in Appendix 55. 57. The image is a grayscale image on a non-temporary computer-readable storage medium as described in any one of the appendices 41 to 56. 58. The image is a color image and is a non-temporary computer-readable storage medium as described in any one of the appendices 41 to 56. 59. The training gate is a non-temporary computer-readable storage medium described in any one of the appendices 41 to 58, which is drawn by the user. 60. The adjusted training gate is a non-temporary computer-readable storage medium described in any one of the appendices 41 to 59, having a different shape from the training gate.

[0144] Although the above invention has been described in some detail by examples and embodiments for clarity, it will be readily apparent to those skilled in the art, in light of the teachings of the present invention, that certain changes and modifications can be made to implement the invention without departing from the spirit or scope of the appended claims.

[0145] Therefore, the foregoing merely describes the principle of the present invention. Those skilled in the art will understand that various modifications embodying the principle of the present invention and falling within the spirit and scope of the invention can be devised, although these are not expressly described or shown herein. Furthermore, all examples and conditional terms listed herein are intended primarily to help the reader understand the principle of the present invention and the concepts provided by the inventors to further advance the art, and should be interpreted as not being limited to such specifically listed examples and conditions. Furthermore, all descriptions herein, as well as specific examples thereof, that enumerate the principle, aspects, and embodiments of the present invention are intended to encompass both their structural and functional equivalents. Furthermore, such equivalents are intended to include both currently known equivalents and those to be developed in the future, regardless of their structure, i.e., any elements developed to perform the same function. Furthermore, nothing disclosed herein, whether or not such disclosure is expressly listed in the claims, is intended to be publicly dedicated.

[0146] Accordingly, the scope of the present invention is not intended to be limited to the exemplary embodiments shown and described herein. Rather, the scope and spirit of the present invention are embodied in the appended claims. In the claims, § 112(f) or § 112(6) of the U.S. Patent Act is explicitly defined as evoking a limitation in a claim only when the phrase "means for" or "step for" is enumerated at the beginning of the limitation in the claim, and neither § 112(f) nor § 112(6) of the U.S. Patent Act applies if such a phrase is not used in the limitation in the claim.

[0147] Cross-reference of related applications This application relates to U.S. Provisional Patent Application No. 62 / 968,520, filed on 31 January 2020, and U.S. Provisional Patent Application No. 63 / 049,735, filed on 9 July 2020, the disclosures of which are incorporated herein by reference.

Claims

1. 1. A method of adjusting training gates prepared from a first set of flow cytometer data to accommodate a second set of flow cytometer data, comprising: acquiring first and second sets of flow cytometer data, the first set of flow cytometer data including a training gate defined by a set of vertices; generating an image for each of the acquired first and second sets of flow cytometer data; adjusting, using a processor-implemented algorithm, the set of vertices defining the training gate from the generated images of the first set of flow cytometer data to accommodate the generated images of the second set of flow cytometer data; A method comprising:

2. 2. The method of claim 1, wherein the processor-implemented algorithm is configured to warp the generated images of the first set of flow cytometer data to maximize similarity to the generated images of the second set of flow cytometer data.

3. The method of claim 1 or 2, wherein the processor-implemented algorithm is an image registration algorithm that includes a mathematical deformation model.

4. The method of claim 3 , wherein the image registration algorithm further comprises B-spline warping.

5. 5. The method of claim 4, wherein the B-spline warping comprises calculating B-spline coefficients that define a function to warp generated images of the first set of flow cytometer data to maximize similarity to generated images of the second set of flow cytometer data.

6. The method of claim 5 , wherein the processor-implemented algorithm is configured to adjust the vertices of the training gates based on the function defined by the B-spline coefficients.

7. The method of any one of claims 1 to 6, wherein adjusting the training gate comprises imposing the training gate on a blank image.

8. The method of claim 7 , wherein the processor-implemented algorithm is configured to adjust the training gate imposed on the blank image.

9. 9. The method of claim 8, further comprising overlaying the adjusted training gate onto the second set of flow cytometer data by applying the vertices of the adjusted gate from the blank image to the generated image of the second set of flow cytometer data.

10. 10. The method of claim 1, wherein generating an image of the first and second sets of flow cytometer data comprises organizing each of the first and second sets of flow cytometer data into two-dimensional bins and assigning a shade to each bin.

11. 11. The method of claim 10, wherein generating images of the first and second sets of flow cytometer data further comprises creating a two-dimensional histogram of mean values ​​of flow cytometer data associated with each bin, the mean values ​​of the flow cytometer data being evaluated against a parameter.

12. The method of claim 11 , wherein the mean value of the flow cytometer data associated with each bin is evaluated against one or more additional parameters.

13. The method of claim 12 , further comprising: calculating a cumulative distribution function based on the two-dimensional histogram; and determining an imaging value associated with each bin based on the cumulative distribution function.

14. 1. A system comprising: an input module configured to acquire a first set of flow cytometer data including a training gate defined by a set of vertices; a particle analyzer component configured to acquire a second set of flow cytometer data; 1. A processor comprising: a memory operatively coupled to the processor, the memory including instructions stored therein that, when executed by the processor, cause the processor to: generating an image for each of the acquired first and second sets of flow cytometer data; adjusting, using a processor-implemented algorithm, the set of vertices defining the training gate from the generated images of the first set of flow cytometer data to accommodate the generated images of the second set of flow cytometer data; a processor and Including, the system.

15. 1. A non-transitory computer-readable storage medium, comprising: acquiring first and second sets of flow cytometer data, the first set of flow cytometer data including a training gate defined by a set of vertices; generating an image for each of the acquired first and second sets of flow cytometer data; and adjusting, using a processor-implemented algorithm, the set of vertices defining the training gate from the generated images of the first set of flow cytometer data to accommodate the generated images of the second set of flow cytometer data. A non-transitory computer-readable storage medium having stored thereon instructions for adjusting training gates prepared from a first set of flow cytometer data to accommodate a second set of flow cytometer data.