Particle characterization by flow cytometry

Abstract

A label-free method of characterizing particles by flow cytometry. The method includes illuminating the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength. The method includes collecting from at least a first detector side scattered light of the first wavelength and collecting from a second detector side scattered light of the second wavelength. The method includes determining particle sizes based on the first median side scatter light intensities under the first wavelength and second median side scatter light intensities under the second wavelength.

Description

PARTICLE CHARACTERIZATION BY FLOW CYTOMETRYCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Application Nos. 63 / 595,928, filed November 3, 2023; 63 / 561,405, filed March 5, 2024; and 63 / 680,717, filed August 8. 2024; the disclosures of which are hereby incorporated by reference in their entireties.BACKGROUND

[0002] In flow cytometry, particles are arranged in a sample stream, and ty pically pass one-by-one through one or more excitation light beams with which the particles interact. Light scattered or fluoresced by' the particles upon interaction with the one or more excitation beams is collected and analyzed to characterize and differentiate the particles. In a sorting flow cytometer, particles may be extracted out of the sample stream after having been characterized by their interaction with the one or more excitation beams, and thereby sorted into different groups.

[0003] The light scattered by the particles is typically measured in two directions: forward scattered light which is parallel with the excitation light beams, and side scattered light which is orthogonal to the excitation light beams. Relative to forward scattered light signals, the side scattered light signals are weak. Conventional flow cytometers typically include a single detector for detecting the side scattered light under a particular wavelength which limits the usage of the side scattered light signals for characterizing the particles by flow cytometry'.SUMMARY

[0004] In general terms, the present disclosure relates to characterizing particles by flow cytometry'. In one possible configuration, the particles are characterized in a label- free technique that includes detecting side scattered light under multiple wavelengths. Various aspects are described in this disclosure, which include, but are not limited to, the following aspects.

[0005] One aspect relates to a label-free method of characterizing particles by flow cytometry, the method comprising: illuminating the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength; collecting from at least a first detector side scattered light of the first wavelength and collecting from a second detector side scattered light of the secondwavelength; determining for each particle coordinates including an intensity of the side scattered light of the first wavelength and an intensity of the side scattered light of the second wavelength; fitting the coordinates to a trend for at least a first population of the particles; and characterizing the particles in the first population by matching the trend to a predetermined trend representing a value of a characteristic associated with the particles.

[0006] Another aspect relates to a system for performing label-free characterization of particles by flow cytometry, the system comprising: a light emitting unit configured to emit excitation light beams for projection onto the particles flowing through an interrogation zone, the light emitting unit including: a first laser emitting a first excitation light beam of a first wavelength; and a second laser emitting a second excitation light beam of a second wavelength; a collection unit including: a first detector for collecting side scattered light of the first wavelength; a second detector for collecting side scattered light of the second wavelength; and a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to: illuminate the particles with at least the first excitation light beam of the first wavelength and the second excitation light beam of the second wavelength; collect from the first detector side scattered light of the first wavelength and collect from the second detector side scattered light of the second wavelength; determine coordinates including an intensity of the side scattered light of the first wavelength and an intensity of the side scattered light of the second wavelength; fit the coordinates to a trend for at least a first population of the particles; and characterizing the particles in the first population by matching the trend to a predetermined trend representing a value of a characteristic associated with the particles.

[0007] Another aspect relates to a label-free method of characterizing particles by flow cytometry, the method comprising: illuminating the particles with a plurality of excitation light beams, the plurality of excitation light beams having wavelengths between 300 nm and 825 nm; collecting from a plurality of detectors side scattered light resulting from excitation of the particles with the plurality of excitation light beams; determining for each particle coordinates including at least an intensity of side scattered light of a first wavelength and an intensity of side scattered light of a second wavelength; fitting the coordinates to at least a first trend and a second trend; anddistinguishing a first population of the particles from a second population of the particles based on the first and second trends.

[0008] Another aspect relates to a label-free method of characterizing particles by flow cytometry, the method comprising: illuminating the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength; collecting from at least a first detector side scattered light of the first wavelength and collecting from a second detector side scattered light of the second wavelength; calculating ratios of first median side scatter light intensities under the first wavelength to second median side scatter light intensities under the second wavelength; applying a calibration equation to calibrate the ratios; filtering the ratios based on a difference between refractive indices estimated under the first wavelength and refractive indices estimated under the second wavelength; and determining particle sizes based on the ratios of first median side scatter light intensities to second median side scatter light intensities filtered across the refractive indices estimated under the first and second wavelengths.

[0009] Another aspect relates to a system for performing label-free characterization of particles by flow cytometry, the system comprising: a light emitting unit configured to emit excitation light beams for projection onto the particles flowing through an interrogation zone, the light emitting unit including: a first laser emitting a first excitation light beam of a first wavelength; and a second laser emitting a second excitation light beam of a second wavelength; a collection unit including: a first detector for collecting side scattered light of the first wavelength; a second detector for collecting side scattered light of the second wavelength; and a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to: illuminate the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength; collect from at least a first detector side scattered light of the first wavelength and collecting from a second detector side scattered light of the second wavelength; calculate ratios of first median side scatter light intensities under the first wavelength to second median side scatter light intensities under the second wavelength; apply a calibration equation to calibrate the ratios; filter the ratios based on a difference between refractive indices estimated under the first wavelength and refractive indices estimated under the second wavelength; and determine particle sizes based on the ratiosof first median side scatter light intensities to second median side scatter light intensities filtered across the refractive indices estimated under the first and second wavelengths.

[0010] Another aspect relates to a label-free method of characterizing particles by flow cytometry, the method comprising: passing particles of a biological sample through an interrogation zone for illumination under a plurality of different light wavelengths; capturing side scattered light data from the particles of the biological sample, the side scattered light data including a first intensity in a violet light spectrum, a second intensity in a blue light spectrum, a third intensity in a yellow light spectrum, and a fourth intensity in a red light spectrum; calculating differential light side scatter parameters between the particles of the biological sample having an unknown amount of loading and particles of the biological sample having zero loading; applying a first calibration equation to calibrate the differential light side scatter parameters between the particles of the biological sample having the unknown amount of loading and the particles of the biological sample having the zero loading; calculating refractive index differentials as a function of the differential light side scatter based on size and refractive index of the particles of the biological sample; applying a second calibration equation to calibrate the refractive index differentials; and characterizing the unknown loading on the particles of the biological sample based on the refractive index differentials calibrated by the second calibration equation.

[0011] Another aspects relates to a method of determining protein loading presence on biological particles, the method comprising: receiving input parameters of the biological particles for analysis by a flow cytometer; extracting mean scatter intensity from empirical data of the biological particles measured by the flow cytometer; calculating a percent change between the mean scatter intensity of stained biological particles and the mean scatter intensity of unstained biological particles; calculating scatter intensity for the unstained biological particles by using the input parameters and Mie scattering core-shell modeling; applying a normalization to the scatter intensity calculated for the unstained biological particles; calculating scatter intensity of the stained biological particles using the scatter intensity of the unstained biological particles and the percent change betw een the mean scatter intensity of the stained biological particles and the mean scatter intensity of the unstained biological particles; calculating a refractive index of a shell of the stained biological particles based on theinput parameters and the scatter intensity of the stained particles by using the Mie scattering core-shell modeling; calculating a delta refractive index by subtracting a refractive index of a sheath fluid from the refractive index of the shell of the biological particles; and characterizing the protein loading presence on the biological particles based on the delta refractive index.

[0012] A variety of additional aspects will be set forth in the description that follows. The aspects can relate to individual features and to combination of features. It is to be understood that both the foregoing general description and the following detailed description are exemplary' and explanatory' only and are not restrictive of the broad inventive concepts upon which the embodiments disclosed herein are based.DESCRIPTION OF THE FIGURES

[0013] The following drawing figures, which form a part of this application, are illustrative of the described technology and are not meant to limit the scope of the disclosure in any manner.

[0014] FIG. 1 illustrates an example of a system for performing flow cytometry, the system including a flow cytometer and a workstation.

[0015] FIG. 2 schematically illustrates an example of the flow cytometer of FIG. 1.

[0016] FIG. 3 schematically illustrates an example of a method of characterizing particles by flow cytometry which can be performed by the flow cytometer of FIG. 2.

[0017] FIG. 4 illustrates a comparison of histograms illustrating particle counts for intensities of side scattered light collected from a sample of plasma extracellular vesicles (EVs) by the flow cytometers of FIG. 2.

[0018] FIG. 5 illustrates a comparison of plots displaying intensities of side scattered light of a first wavelength versus intensities of side scattered light of a second wavelength generated based on the data collected from the sample of plasma EVs by the flow cytometer of FIG. 2.

[0019] FIG. 6 is a detailed view of a plot show n in FIG. 5.

[0020] FIG. 7 illustrates a plot displaying intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red light spectrum for a sample containing a virus expressing green fluorescent protein (GfP).

[0021] FIG. 8 illustrates a plot displaying intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red light spectrum for polystyrene latex (PSL) beads of different sizes and known refractive index.

[0022] FIG. 9 illustrates a plot showing an overlap of the intensities of side scattered light in the violet light spectrum versus the intensities of side scattered light in the red light spectrum for the sample containing the GfP virus and the PSL beads.

[0023] FIG. 10 illustrates a plot displaying intensities of side scattered light in the violet light spectrum versus intensities of the side scattered light in the red light spectrum for a sample containing reticuloendotheliosis virus (rEV) and GfP virus.

[0024] FIG. 11 illustrates a plot of intensity data for PSL beads having different sizes and known refractive indices in violet and red light wavelengths.

[0025] FIG. 12 illustrates a plot of theoretical intensity data produced from a Mie scattering simulator that considers known refractive indices and different sizes of the PSL beads.

[0026] FIG. 13 illustrates a plot of data showing avalanche photodiode (APD) radian sensitivities across different wavelengths of light.

[0027] FIG. 14 illustrates a plot showing an empirical ratio of median red side scatter intensity to median violet side scatter intensity for each PSL bead size compared to a first theoretical ratio that does not consider APD radian sensitivities, and a second theoretical ratio that does consider the APD radian sensitivities.

[0028] FIG. 15 illustrates a plot of intensity data for silica beads having different sizes and known refractive indices in violet and red light wavelengths.

[0029] FIG. 16 illustrates a plot of theoretical intensity data generated from the Mie scattering simulator that considers the refractive indices and different sizes of the silica beads.

[0030] FIG. 17 illustrates a plot showing an empirical ratio of the median red side scatter intensity to the median violet side scatter intensity for each silica bead size compared to a first theoretical ratio that does not consider the APD radian sensitivities, and a second theoretical ratio that does consider the APD radian sensitivities.

[0031] FIG. 18 illustrates a plot comparing the second theoretical ratio of the PSL beads shown in FIG. 14 to the second theoretical ratio of the silica beads shown in FIG. 17.

[0032] FIG. 19 illustrates a plot comparing the empirical ratio of the PSL beads show n in FIG. 14 to the empirical ratio of the silica beads show n in FIG. 17.

[0033] FIG. 20 illustrates a plot comparing a trend of intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red lightspectrum of PSL beads to a trend of intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red light spectrum of silica beads.

[0034] FIG. 21 illustrates data collected from plasma EVs that include a first population having a first trend of ratios between violet side scatter and red side scatter, and a second population having a second trend of ratios between violet side scatter and red side scatter.

[0035] FIG. 22 illustrates data collected from a sample of murine leukemia virus (MLV) and reticuloendotheliosis virus (rEV) that includes a first population having a first trend of ratios between violet side scatter and red side scatter, and a second population having a second trend of ratios between violet side scatter and red side scatter.

[0036] FIG. 23 schematically illustrates an example of a computing system for implementing aspects of the system of FIG. 1.

[0037] FIG. 24 schematically illustrates an example of a method of generating a function that can be used to characterize particles by flow cytometry performed by the system of FIG. 1.

[0038] FIG. 25 schematically illustrates an example of a method of generating a calibration equation that can be used in the method of FIG. 24.

[0039] FIG. 26a shows a plot of empirical violet side scattered light data captured from calibration beads in performance of the method of FIG. 25.

[0040] FIG. 26b shows a plot of empirical red side scattered light data captured from the calibration beads in performance of the method of FIG. 25.

[0041] FIG. 26c shows a plot of empirical violet side scattered light intensity versus red side scattered light intensity that can be generated in accordance with the method of FIG. 25.

[0042] FIG. 27 shows a plot of theoretical violet side scattered light intensity versus red side scattered light intensity that can be calculated in accordance with the method of FIG. 25.

[0043] FIG. 28a shows a plot that includes empirical data captured in performance of the method of FIG. 25 and theoretical data calculated in performance of the method of FIG. 25.

[0044] FIG. 28b shows a plot of the empirical data of FIG. 28a after a quadratic correlation is applied to the empirical data.

[0045] FIG. 29 shows a plot that includes calibrated ratios between theoretically calculated first wavelength and second wavelength scatter intensities for each plurality of refractive indices in performance of the method of FIG. 24.

[0046] FIG. 30 show s a chart that includes a first set of refractive index values between violet and red side scattered light having a high refractive index (RI) differential value, a second set of refractive index values between violet and red side scattered light having a medium RI differential value, and a third set of refractive index values between violet and red side scattered light having a low differential value in performance of the method of FIG. 24.

[0047] FIG. 31 show's a chart of particle sizes determined from the calibrated ratios of violet side scatter light intensity to red side scatter light intensity for murine leukemia virus (MLV) that can be generated by performance of the method of FIG. 24.

[0048] FIG. 32 shows a chart of particle sizes determined from the calibrated ratios of violet side scatter light intensity' to red side scatter light intensity for reticuloendotheliosis viruses (rEVs) that can be generated by performance of the method of FIG. 24.

[0049] FIG. 33 shows a plot of calibrated ratios of violet side scatter light intensity to red side scatter light intensity for rEVs generated in accordance with the method of FIG 24

[0050] FIG. 34 show s a plot of violet side scatter light intensity' versus red side scatter light intensity for the rEVs of FIG. 33 gated between separate populations of different size ranges.

[0051] FIG. 35 show's a plot of calibrated ratios of violet side scatter light intensity to red side scatter light intensity for microvesicles from the Daudi cell line generated in accordance w ith the operations of the method of FIG. 24.

[0052] FIG. 36 show s a plot that corresponds to the plot of FIG. 35.

[0053] FIG. 37 show s a plot of calibrated ratios of violet side scatter light intensity to red side scatter light intensity for urinary extracellular vesicles generated in accordance w'ith the operations of the method of FIG. 24.

[0054] FIG. 38 show s a plot that corresponds to the plot of FIG. 37.

[0055] FIG. 39 schematically illustrates an example of a method of characterizing protein loading on particles by flow cytometry which can be performed by the flow cytometer of FIG. 1.

[0056] FIG. 40 schematically illustrates an example of sub-operations performed in an operation of the method of FIG. 39.

[0057] FIG. 41 schematically illustrates an example of a method of determining protein loading presence on biological samples that can be performed by the system of FIG 1.

[0058] FIG. 42 illustrates a table that includes values from an experiment performed in accordance with the operations of the method of FIG. 41.

[0059] FIG. 43 illustrates another table that includes core-shell modeling for V5 antibodies in the table of FIG. 42 for virus expressing V5 tag and monoclonal anti -V 5 antibodies that bind to the V5 tag to stain the virus (designated as V5).

[0060] FIG. 44 illustrates another table that includes core-shell modeling for fragment antibodies (Fab) in the table of FIG. 42 including monoclonal fragment Fab antibodies that bind to the anti-V5 antibodies described in FIG. 43 for staining the anti- V5 antibodies bound on the virus (designated as Fab).

[0061] FIG. 45 illustrates another table that includes core-shell modeling for Immunoglobulin G (IgG) antibodies in the table of FIG. 42 including monoclonal IgG antibodies that bind to the anti-V5 antibodies described in FIG. 43 for staining the anti- V5 antibodies bound on the virus (designated as IgG).

[0062] FIG. 46 illustrates a table that includes delta refractive index values calculated in accordance with the method of FIG. 41 for the V5 stained, Fab, and IgG antibodies.

[0063] FIG. 47 illustrates a graph showing shell refractive index versus scatter intensity calculated for the V5 stained, Fab, and IgG antibodies in accordance with the operations of the method of FIG. 41.

[0064] FIG. 48 schematically illustrates another example of a method of generating a function to characterize particles by flow cytometry performed by the system of FIG. 1.

[0065] FIG. 49 illustrates an example of murine leukemia virus (MLV) virus data that can be collected in accordance with an empirical data collection portion of the method of FIG. 48.

[0066] FIG. 50 illustrates an example of a table that includes values calculated in accordance with operations of the method of FIG. 48.

[0067] FIG. 51 graphically illustrates calibrated ratios calculated as a function of particle size in an operation of the method of FIG. 48.

[0068] FIG. 52 schematically illustrates another example of a method of generating a function to characterize particles by flow cytometry performed by the system of FIG. 1.

[0069] FIG. 53 schematically illustrates an example of a method of characterizing particles by flow cytometry that can be performed by the system of FIG. 1.

[0070] FIG. 54 illustrates an example of a table that includes empirical data collected in accordance with operations of an instrument calibration phase of the method of FIG. 53.

[0071] FIG. 55 illustrates an example of a table that includes simulated data calculated in accordance with an operation of the instrument calibration phase of the method of FIG. 53.

[0072] FIG. 56 illustrates an example of a graph illustrating a correlation of the empirical data included in the table of FIG. 54 with the simulated data in the table of FIG. 55 in accordance with an operation of the instrument calibration phase of the method of FIG. 53.

[0073] FIG. 57 illustrates an example of a table that includes empirical data collected in accordance with operations of the instrument calibration phase of the method of FIG. 53.

[0074] FIG. 58 illustrates an example of a table that includes simulated data calculated in accordance with an operation of the instrument calibration phase of the method of FIG. 53.

[0075] FIG. 59 illustrates an example of a graph illustrating a correlation of the empirical data included in the table of FIG. 57 with the simulated data in the table of FIG. 58 in accordance with an operation of the instrument calibration phase of the method of FIG. 53.

[0076] FIG. 60 graphically illustrates an example of a plot of empirical data for MLV vims that identifies empirical MSI for the first and second wavelengths in accordance with an operation of an empirical data collection phase of the method of FIG. 53.

[0077] FIG. 61 illustrates an example of a table that includes MSI of the different side scatter channels for the empirical data collected from the MLV virus shown in in the plot of FIG. 60.

[0078] FIG. 62 illustrates an example of a table that includes simulated data calculated in accordance with an operation of a processing phase of the method of FIG. 53.

[0079] FIG. 63 illustrates an example of a table that includes simulated data calculated in accordance with an operation of the processing phase of the method of FIG. 53.

[0080] FIG. 64 shows an example of a plot that can be generated in accordance with an operation of the method of FIG. 53.

[0081] FIG. 65 shows another example of a plot that can be generated in accordance with an operation of the method of FIG. 53.

[0082] FIG. 66 illustrates a table providing an example where a particle size is determined using side scatter intensity under a first wavelength in accordance with the method of FIG. 53.

[0083] FIG. 67 illustrates a table providing an example where a particle size is determined using side scatter intensity' under a second w avelength in accordance with the method of FIG. 53.

[0084] FIG. 68 schematically illustrates another example of a method of characterizing particles by flow cytometry that can be performed by the system of FIG. 1.

[0085] FIG. 69 illustrates an example of a table that includes simulated side scatter intensity data calculated for the first wavelength in accordance with the method of FIG. 68.

[0086] FIG. 70 illustrates an example of a table that includes simulated side scatter intensity data calculated for the second wavelength in accordance with the method of FIG. 68.

[0087] FIG. 71 shows an example of a plot that can be generated in accordance with the method of FIG. 68.

[0088] FIG. 72 shows another example of a plot that can be generated in accordance with the method of FIG. 68.

[0089] FIG. 73 illustrates a table that provides an example of calculating a particle refractive index in accordance with the method of FIG. 68.

[0090] FIG. 74 illustrates a table that provides another example of calculating a particle refractive index in accordance with the method of FIG. 68.

[0091] FIG. 75 illustrates an example of a table that includes simulated ratios of side scatter intensities between the first and second wavelengths in accordance with an alternative of the method of FIG. 68.

[0092] FIG. 76 shows an example of a plot that can be generated in accordance with the alternative of the method of FIG. 68.

[0093] FIG. 77 illustrates a table that provides an example of calculating a particle refractive index in accordance w ith the alternative of the method of FIG. 68.

[0094] FIG. 78 illustrates a comparison of a flow cytometer post-acquisition analysis software (FCMPASS) to the methods described above.DETAILED DESCRIPTION

[0095] Various embodiments will be described in detail with reference to the drawings, wherein like reference numerals represent like parts and assemblies throughout the several views. Reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples set forth in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.

[0096] An example detection system is described herein for use in a flow' cytometer. It should be understood that the present disclosure is not limited to the illustrated detection system, but may be applied to flow cytometers with other types of detection systems.

[0097] FIG. 1 illustrates an example of a system 10 that can be used to perform flow cytometry. The system 10 includes a flow cytometer 100 and a workstation 200. In general, the flow' cytometer 100 is an analytical instrument that detects physical and chemical properties of samples of cells or particles. In some examples, the flow cytometer 100 is designed to capture robust and high quality data for characterizing biologically relevant nanoparticles. The flow cytometer 100 is a single instrument that provides simultaneous assessment of nanoparticle size, concentration, and cargo to understand biological mechanisms of action and nanoparticle origins. The flow'cytometer 100 can collect data from millions of particles or cells in a matter of minutes for display in a variety of formats on a display monitor 204 of the workstation 200.

[0098] The flow cytometer 100 includes a housing 101 having a sample station 104 which receives a container 106 containing a sample of cells and / or particles. In some examples, the container 106 contains a sample of nanoparticles such as extracellular vesicles (EVs). A user of the system 10 can manually load the container 106 into the sample station 104. Once loaded in the sample station 104, the flow cytometer 100 can acquire the sample from the container 106 to perform the flow cytometry experiment. In some examples, the container 106 is a sample tube such as a 1.5-mL or 2-mL microtube, and / or has a diameter of 12 mm and a height of 75 mm.

[0099] The flow cytometer 100 can further include a sheath fluid container 107 for holding sheath fluid that is mixed with the sample during the flow cytometry experiment. The sheath fluid is pumped into the flow cytometer 100 causing a laminar flow. The sample is injected at a higher pressure into the center of the laminar flow of the sheath fluid. Hydrodynamic focusing causes the particles to align, single file in the direction of the laminar flow. The sheath fluid container 107 is connected to the flow cytometer 100 via tubing 109.

[0100] The flow cytometer 100 can further include a waste container 108 for collecting waste fluid. The waste container 108 is connected to the flow cytometer 100 via tubing 109.

[0101] The workstation 200 connects to the flow cytometer 100 via a wired or wireless connection to receive data from the flow cytometer 100 for display on the display monitor 204. The workstation 200 includes one or more user input devices such as a mouse 206 and a keyboard 208 allowing a user of the system 10 to enter data and information, control the flow cytometer 100, and alter the display of the data on the display monitor 204.

[0102] The w orkstation 200 further includes a computing device 202. In some examples, the workstation 200 utilizes the computing device 202 to process raw data received from the flow cytometer 100. Alternatively, or additionally, the flow cytometer 100 can include a computing device to process data collected from flow cytometry. In such examples, the flow7cytometer 100 sends processed data to the workstation 200 for display on the display monitor 204.

[0103] FIG. 2 schematically illustrates an example of the flow cytometer 100. The flow cytometer 100 detects and analyzes particles on the nanoscopic scale such as particles having a diameter less than 100 nanometers (nm). Also, the flow cytometer can detect and analyze particles having a larger size such as particles having a diameter greater than 100 nm. The flow cytometer 100 includes a light emitting unit 110, and a light collection unit 120 that detects characteristics of particles passing through a flow chamber 15.

[0104] The light emitting unit 110 emits one or more excitation light beams for projection onto particles flowing through an interrogation zone 18 in the flow chamber 15. The light collection unit 120 collects light scattered or emitted from the particles that flow through the interrogation zone 18 for analysis by a computing device 2300 (see FIG. 23).

[0105] The light emitting unit 110 includes a plurality' of light sources 11 la-11 Id such as a first light source I l la, a second light source 111b, a third light source 111c, and a fourth light source 11 Id. The plurality of light sources 11 la- 11 Id can include more than four light sources, or fewer than four light sources. The plurality of light sources 11 la-1 l id can include lasers.

[0106] The plurality7of light sources 11 la-11 Id emit excitation light beams within a range of about 300 nm to about 825 nm. As a further example, the light sources 11 la- 11 Id emit excitation light beams within a range of about 325 nm to about 808 nm. Each of the plurality of light sources 1 1 1 a-1 1 1 d emits an excitation light beam of a particular wavelength. As an illustrative example, the first light source 11 la emits an excitation light beam in the red light spectrum (e.g., 600-850 nm), the second light source 111b emits an excitation light beam in the yellow light spectrum (e.g., 560-590 nm). the third light source 111c emits an excitation light beam in the blue light spectrum (e.g., 450- 490 nm), and the fourth light source 1 1 Id emits an excitation light beam in the violet light spectrum (e.g., 325-450 nm).

[0107] In the example shown in FIG. 2, the light sources 11 la-11 Id are arranged in parallel. It should be understood that the number, the type, and the arrangement of the light sources are not limited to the example shown and described herein, and may be changed as needed. For example, the system may include three, five, six, or any other suitable number of light sources.

[0108] The light emitting unit 110 further includes a focal lens 119. The focal lens 119 is configured to focus the excitation light beams for high scatter intensity- detection of particles. For example, the excitation light beams emitted by the light sources 11 la- 11 Id pass through the focal lens 119, which focuses the excitation light beams to the interrogation zone 18 of the flow chamber 15. The interrogation zone 18 may also be referred to as a focus point where the focused excitation light beams meet the core sample stream in the flow cytometer 100.

[0109] Dichroic mirrors 117a, 117b, 117c, and 117d are arranged between the focal lens 119 and the respective light sources 11 la-11 Id. Each of the dichroic mirrors 117a- 117d is configured to reflect a light beam of a corresponding one of the light sources 11 la- 11 Id and transmit the light beams of the other light sources. The dichroic mirrors 117a-l 17d are selected and configured according to the wavelengths of the light beams emitted by the respective light sources 11 la-11 Id. For example, the dichroic mirror 117a reflects light of the wavelength emitted by the light source I l la toward the focal lens 119. the dichroic mirror 117b reflects light of the wavelength emitted by the light source 111b toward the focal lens 119 and transmits light of the wavelength emitted by the light source I l la, the dichroic mirror 117c reflects light of the wavelength emitted by the light source 111c toward the focal lens 119 and transmits light of the wavelengths emitted by the light sources I l la and 111b, and the dichroic mirror 117d reflects light of the wavelength emitted by the light source 11 Id toward the focal lens 1 19 and transmits light of the wavelengths emitted by the light sources 1 11 a, 1 11 b, and 111c.

[0110] The light beams emitted by the light sources 111 a- 111 d are reflected by or transmitted through the dichroic mirrors 117a- 117d to form collinear beams. The collinear beams share an optical axis, and provide a confocal point of multiple light sources by focusing on the same interrogation point. The dichroic mirrors 117a- 117d are adjustable in their positions or orientations, such that they can be used to adjust the position of the focus point of the light beams, especially, the position on a plane perpendicular to the optical axis.[OHl] Lenses 115a-l 15d are arranged between the respective light sources 11 la- 11 Id and the respective dichroic mirrors 117a-117d. In some examples, the lenses 115a-l 15d are long-focus lens. In some examples, the lenses 115a-l 15d are spherical lenses. In other examples, the lenses 115a-l 15d are aspheric lenses. Each of the lenses115a- 115d can convert light beams into parallel beams. In the example shown in FIG. 2, each of the lenses 115a- 115d is in the form of planoconvex lens with a flat surface and a convex surface opposite to each other.

[0112] The lenses 115a-l 15d are adjustable in their positions or orientations, so as to adjust the position of the focus point of the light beams, especially, the position on the plane perpendicular to the optical axis. Generally, the dichroic mirrors 117a-117d can be used to roughly adjust the position of the focus point of the light beams, whereas the lenses 115a- 115d can be used to finely adjust the position of the focus point of the light beams.

[0113] It should be understood that the number, the type, and the arrangement of the dichroic mirrors 117a-117d and the lenses 115a-l 15d may be changed as needed, and are not limited to the example illustrated herein. Also, the dichroic mirrors 117a- 117d and the lenses 115a-l 15d can be replaced with other optical elements or optical modules with similar functions.

[0114] Beam expanders 113a-l 13d are arranged between the respective light sources 11 la-1 l id and the respective lenses 115a- 115d. Each of the beam expanders 113a- 113d can change a sectional dimension and a divergence angle of a light beam. As such, each of the beam expanders 113a- 113d are configurable according to a desired size of a spot of a light beam.

[0115] The light beams irradiated on the particles by the focal lens 119 have a spot size that allows for more concentrated light beams with a higher power density. This can increase intensity of the light beams irradiated on the particles, and ultimately the intensity of the optical signals collected from the particles. This can improve the efficiency of collecting the optical signals, and thereby provide higher resolution and higher sensitivity for nanoparticle detection.

[0116] In the example shown in FIG. 2, the light sources 111 a- 111 d are in the form of lasers that include respective laser diodes 112a-112d. As further shown in the example of FIG. 2. half-wave plates 116a-l 16d are provided between the dichroic mirrors 117a- 117d and the lenses 115a- 115d, respectively. The spot of the light beam can be reduced by orientation of the light sources 11 la-11 Id and by use of the halfwave plates 116a- 116d.

[0117] As further shown in FIG. 2, cylindrical lenses 114a-l 14d are provided between the respective beam expanders 113a- 113d and the respective lenses 115a-115d. The horizontal size of the spot of the light beam focused in the flow chamber 15 can be adjusted by replacing the cylindrical lenses 114a-l 14d with replacement cylindrical lenses having different curvatures. The power of some or all of the light sources 11 la-11 Id can also be increased. The increased power of the light sources ll la-l l ld can also improve detection sensitivity.

[0118] Each of the beam expanders 113a-l 13d is formed of a first optical part and a second optical part. In the example shown in FIG. 2, each of the beam expanders 1 Bal l 3d includes a concave lens adjacent to the corresponding light source as the first optical part, and further includes a convex lens away from the corresponding light source as the second optical part. It should be understood that each of the beam expanders 113a- 113d is not limited to the example shown in FIG. 2. The beam expanders 113a- 113d may be formed of any suitable optical lens or lens group. For example, each of the first optical part and the second optical part can be selected from one of a convex lens, a convex lens group, a concave lens, and a concave lens group.

[0119] For each of the beam expanders 113a-l 13d, the distance between the first optical part (e.g., the concave lens) and the second optical part (e.g., the convex lens) is adjustable. This allows for adjustment of a waist position (the focus point) of the light beam on the optical axis.

[0120] As described above, by adjusting the dichroic mirrors 117a-l 17d, the lenses 115a-l 15d, and the beam expanders 113a-l 13d. the individual light beams can be focused at the desired interrogation point, and multiple light beams can be focused at the same interrogation point. It should be understood that the position of the focus point of the light beams may be adjusted by adopting any other optical element or in any other adjustment manner. One or more adjustments to the dichroic mirrors 117a-l 17d, the lenses 115a-l 15d, and the beam expanders 113a-l 13d may be made manually, or may be made electronically using a computing device (e.g., a controller) that is associated with one or more actuators coupled to these components.

[0121] The light collection unit 120 includes a side collection unit 130 and a forward collection unit 150. The side collection unit 130 collects side scattered light and fluorescent light emitted from the particles in the sample as they are irradiated by the excitation light beams while passing through the flow chamber 15. The optical axis of light beams collected from the particles by the side collection unit 130 is approximately perpendicular to, or about 90 degrees, from the optical axis of the lightbeams emitted from the light sources 111 a- 111 d and directed by the dichroic mirrors 117a- 117d toward the flow chamber 15.

[0122] The forward collection unit 150 collects forward scattered light from the particles. The optical axis of light beams collected from the particles by the forward collection unit 150 may be approximately parallel to, or about 0 degrees from, the optical axis of the light beams that are directed toward the flow chamber 15. The side collection unit 130 and the forward collection unit 150 are described in further detail below.

[0123] The side collection unit 130 includes an optical focusing lens group including a concave mirror 134 and an aspheric lens 135, a collection fiber 136, a beam splitter 133, a first wavelength division multiplexer 131, and a second wavelength division multiplexer 132. The concave mirror 134 reflects the side scattered light and the fluorescent light that diverge in various directions at the interrogation zone 18.

[0124] The concave mirror 134 and the aspheric lens 135 focus the reflected light onto the collection fiber 136 by focusing on the same point of the collection fiber 136 as shown in the dotted block 139 in FIG. 2. The concave minor 134 can focus the reflected light on the fiber, while the aspheric lens 135 can make the focal point smaller (i.e., reduce the aberration).

[0125] To prevent crosstalk, the beam splitter 133 is arranged to separate the scattered light that has high intensity from the fluorescent light that has low intensity. The side scattered light is directed toward the first wavelength division multiplexer 131 via a first fiber 137, and the fluoresced light is directed toward the second wavelength division multiplexer 132 via a second fiber 138. Optical signals with different wavelengths are separated in the first wavelength division multiplexer 131 and the second wavelength division multiplexer 132 for analysis.

[0126] The beam splitter 133 includes a dichroic mirror 532 and a notch filter 534. Collected light is directed into the beam splitter 133 toward the dichroic mirror 532 by the collection fiber 136, which may be oriented such that the light beam is directed toward the dichroic mirror 532 at an incident angle of, for example, 45 degrees. The dichroic mirror 532 reflects the side scattered light coming out of the collection fiber 136 such that the side scattered light enters the first wavelength division multiplexer 131 through the first fiber 137.

[0127] The fluorescent light coming out of the collection fiber 136 passes through dichroic mirror 532, and is incident to the notch filter 534 at an incident angle of about 90 degrees and then passes through the notch filter 534. The fluorescent light enters the second wavelength division multiplexer 132 through the second fiber 138.

[0128] The dichroic mirror 532 and the notch filter 534 can each have multiple bands according to the arrangement of the light sources 11 la-11 Id. In the example shown in FIG. 2. the dichroic mirror 532 and the notch filter 534 both have four bands that block four laser wavelengths. The number of bands on the dichroic mirror 532 and the notch filter 534 can correspond to the number of the light sources ll la-l l ld.

[0129] The beam splitter 133 separates the side scattered light having high intensity from the fluorescent light having low intensity, which reduces or prevents crosstalk of the side scattered light to the fluorescent light. In addition, by providing the beam splitter, it is possible to separate and transmit multiple light beams into two or more wavelength division multiplexers. The optical elements included in the beam splitter 133 and their configuration may be changed, and are not limited to the example shown and described herein.

[0130] In some examples, the first wavelength division multiplexer 131 receives the side scattered light from the beam splitter 133 via the first fiber 137, and separates from each other the optical signals of the side scattered light based on their wavelengths. For example, the optical signals associated with the light beam in the red light spectrum emitted by the first light source I l la, the optical signals associated with the light beam in the yellow light spectrum emitted by the second light source 11 lb, the optical signals associated with the light beam in the blue light spectrum emitted by the third light source 111c, and the optical signals associated with the light beam in the violet light spectrum emitted by the fourth light source 11 Id are separated in the first wavelength division multiplexer 131. In the first wavelength division multiplexer 131, each optical signal is transmitted along an optical transmission path 510 corresponding to an optical channel of the optical signal. Thereafter, the side scattered light enters an SSC detector 515 which can include a photodiode, an avalanche photodiode (APD), or a photomultiplier tube for analyzing the side scattered light.

[0131] The first wavelength division multiplexer 131 includes a first filter 511 and a second filter 512 for each optical channel. The first filter 511 and the second filter 512 are arranged at a certain distance from each other along the optical transmissionpath of the optical channel in a non-parallel manner. Crosstalk between side scattered light can be reduced or prevented by providing the two filters. The first and second filters 511 and 512 are not arranged in parallel so as to avoid multiple reflections of light between them and achieve a better optical density.

[0132] The second wavelength division multiplexer 132 receives a fluorescent beam from the beam splitter 133 via the second fiber 138, and divides the optical signals of the fluorescent beam having different wavelengths from each other. In the second wavelength division multiplexer 132, each optical signal is transmitted along an optical transmission path 520 corresponding to an optical channel of the optical signal. Since the fluorescent signal is weak, the second wavelength division multiplexer 132 includes a single filter 521 for each optical channel. Thereafter, the filtered fluorescent light enters a light detection element 525 (e.g., a photodiode, an avalanche photodiode (APD), a photomultiplier tube) for further processing.

[0133] Alternative suitable configurations for the wavelength division multiplexers may be used. For example, the first and second wavelength division multiplexers 131, 132 can include notch filters corresponding to the respective optical channels. The notch filters can reduce or eliminate the crosstalk of the side scattered light to the fluorescence light. In this case, the beam splitter 133 may only include the dichroic mirror 532 with no notch filter 534.

[0134] In the side collection unit 130. a diameter of the collection fiber 136 may be different from diameters of the first fiber 137 and the second fiber 138 according to the light transmission efficiency. Lenses in the beam splitter may cause aberration, and thus the output light spots may be larger than input of the beam splitter, and the fiber diameters may be selected accordingly.

[0135] The forward collection unit 150 includes an obscuration bar 155, a concave mirror 151, a filter 157, and a forward detector 159. The obscuration bar 155 blocks a large portion of the light transmitted through the flow chamber 15 to reduce background noise created by the excitation light beams transmitting directly through the flow chamber 15, and to allow collection of only forw ard scattered light from the particles. In some examples, the majority of the transmitted light is blocked so as not to saturate the forw ard detector 159.

[0136] The concave mirror 151 reflects a forw ard scattered beam emitted from the particles. The filter 157 allows forward scattered light with a high signal-to-noise ratioto pass, and block other light. As further shown in FIG. 2, the forward detector 159 receives the filtered forward scattered light from the filter 157, and processes and analyzes the forward scattered light.

[0137] FIG. 3 schematically illustrates an example of a method 300 of characterizing particles by flow cytometry which can be performed by the flow cytometer 100. The method 300 is a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. Such labels can alter characteristics of cells when attached thereto. Thus, by being label-free, the method 300 allows the cells to be reusable for additional tests and experiments after being interrogated by the excitation light beams in the flow cytometer 100. Further, the method 300 can be performed to characterize and / or sort both entire cells, and nanoparticles such as extracellular vesicles including microvesicles and exosomes.

[0138] The method 300 includes an operation 302 of illuminating the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength. As shown in FIG. 2, the first excitation light beam of the first wavelength can be emitted by a first light source of the plurality of light sources ll la-ll ld and the second excitation light beam of the second wavelength can be emitted by a second light source of the plurality of light sources 11 la-11 Id. In one example, the first excitation light beam is emitted by the first light source I l la such that the first wavelength is in the red light spectrum, and the second excitation light beam is emitted by the fourth light source 11 Id such that the second wavelength is in the violet light spectrum. As shown in FIG. 2, a particle when passing through the interrogation zone 18 is simultaneously illuminated by the first excitation light beam of the first wavelength and the second excitation light beam of the second wavelength.

[0139] In some examples, operation 302 includes illuminating the particles by more than two excitation light beams emitted by the plurality of light sources 11 la-11 Id of the flow cytometer 100. Additionally, the particles can be illuminated by different combinations of excitation light beams emitted by the plurality of light sources 11 la- 11 Id of the flow cytometer 100.

[0140] The method 300 includes an operation 304 of collecting side scattered light of the first wavelength and collecting side scattered light of the second wavelength. The side scattered light of the first wavelength can be collected from a first SSC detector515, and the side scattered light of the second wavelength can be collected from a second SSC detector 515 (see FIG. 2). As described above, the SSC detectors 515 can include photodiodes, avalanche photodiodes (APDs), photomultiplier tubes, and similar types of light detection elements. In operation 304, the side scattered light of the first wavelength and the side scattered light of the second wavelength are collected simultaneously by the SSC detectors 515 of the flow cytometer 100. In some examples, operation 304 includes collecting side scattered light of additional wavelengths such as a third wavelength, a fourth wavelength, and so on when the particles are illuminated by more than two excitation light beams by the plurality of light sources l l la-l l ld of the flow cytometer 100.

[0141] FIG. 4 illustrates a comparison of histograms 400a-400d illustrating particle counts (Y axis) for intensities of side scattered light (X axis) collected from a sample of plasma extracellular vesicles (EVs). The data can be collected during operation 304 of the method 300. In this example, a first histogram 400a illustrates intensities of side scattered light collected in the violet light spectrum, a second histogram 400b illustrates intensities of side scattered light collected in the blue light spectrum, a third histogram 400c illustrates intensities of side scattered light collected in the yellow light spectrum, and a fourth histogram 400d illustrates intensities of side scattered light collected in the red light spectrum.

[0142] The histograms 400a-400d can be generated such as by having the fourth light source 11 1 d emit the light in the violet light spectrum, the third light source 1 1 1c emit the light in the blue light spectrum, the second light source 111b emit the light in the yellow light spectrum, the first light source 11 la emit the light in the red light spectrum, and a first SSC detector 515 collects the side scattered light in the red light spectrum, a second SSC detector 515 collects the side scattered light in the yellow light spectrum, a third SSC detector 515 collects the side scattered light in the blue light spectrum, and a fourth SSC detector 515 collects the side scattered light in the violet light spectrum.

[0143] The histograms 400a-400d illustrate data for particles, noise, and overlap between the particles and noise. As shown in FIG. 4, the first histogram 400a (violet light) provides the best sensitivity to separate the EV particles in the plasma sample from the noise because it has the least amount of overlap between the EV particles andthe noise than the overlap shown in the second histogram 400b. the third histogram 400c, and the fourth histogram 400d.

[0144] As shown in FIG. 3, the method 300 includes an operation 306 of determining for each particle coordinates including an intensity of the side scattered light of the first wavelength and an intensity of the side scattered light of the second wavelength. In one illustrative example, the coordinates for each particle include an intensity of the side scattered light in the violet light spectrum and an intensity of the side scattered light in the red light spectrum.

[0145] FIG. 5 illustrates a comparison of plots 500a-500c displaying coordinates including an intensity of the side scattered light of a first wavelength and an intensity of a side scattered light of the second wavelength for each EV particle in the sample of plasma. Thus, the plots 500a-500c illustrate for the EV particles intensities of the side scattered light of the first wavelength versus intensities of the side scattered light of the second wavelength. In some instances, the plots 500a-500c are generated during operation 306 of the method 300.

[0146] In FIG. 5, a first plot 500a displays for each particle an intensity of the side scattered light in the violet light spectrum (X axis) versus an intensity of the side scattered light in the blue light spectrum (Y axis); a second plot 500b displays for each particle an intensity of the side scattered light in the violet light spectrum (X axis) versus an intensity of the side scattered light in the yellow light spectrum (Y axis), and a third plot 500c displays for each particle an intensity of the side scattered light in the violet light spectrum (X axis) versus an intensity of the side scattered light in the red light spectrum (Y axis).

[0147] Referring back to FIG. 3, the method 300 includes an operation 308 of fitting the coordinates to one or more trends. For example, the coordinates can be fit to one or more trends by performing linear regression or other similar techniques.

[0148] As show n in FIG. 5, the one or more trends can distinguish populations of particles in the sample. For example, in each of the plots 500a-500c, two different populations of particles are identified based on the trend of intensity of the side scattered light of the first wavelength versus intensity of the side scattered light of the second wavelength. While two different populations of particles are shown in the plots 500a-500c. more than tw o populations can be identified from a sample of particles or fewer than two populations (i.e.. a single population) can be identified from the sampleof particles based on one or more trends of intensity of the side scattered light of the first wavelength versus intensity of the side scattered light of the second wavelength. As shown in FIG. 5, the third plot 500c displaying the intensity of the side scattered light in the violet light spectrum (X axis) versus the intensity of the side scattered light in the red light spectrum (Y axis) has the best separation between the two populations.

[0149] FIG. 6 is a detailed view of the third plot 500c of FIG. 5. As show n in FIG. 6, the two particle populations have trends that appear as diagonals having different slopes. Violet and red are on opposite sides of the light spectrum such that comparing these two wavelengths provides the clearest contrast for separating the trends of the different particle populations.

[0150] FIG. 7 illustrates a plot 700 displaying intensities of side scattered light in the violet light spectrum (X axis) versus intensities of side scattered light in the red light spectrum (Y axis) for a sample containing a virus expressing green fluorescent protein (GfP). Based on fluorescent staining, presence of the GfP virus population is verified. As shown in FIG. 7, the GfP virus population exhibits a trend. Also, an unknown population separate from the GfP virus population exhibits a trend that is different from the trend of the GfP virus population.

[0151] FIG. 8 illustrates a plot 800 displaying intensities of side scattered light in the violet light spectrum (X axis) versus intensities of side scattered light in the red light spectrum (Y axis) for polysty rene latex (PSL) beads of different sizes and known refractive index. FIG. 9 illustrates a plot 900 showing an overlap of the intensities of side scattered light in the violet light spectrum (X axis) versus the intensities of side scattered light in the red light spectrum (Y axis) for the sample containing the GfP virus and the PSL beads. As shown in FIGS. 8 and 9, the PSL beads exhibit a trend not coinciding with the trend of the GfP virus population or the trend of the unknown population from GfP virus sample (see FIG. 7). As used herein, the term “bead” refers to a particles such that beads and particles can be used interchangeably with respect to the disclosure provided herein.

[0152] FIG. 10 illustrates a plot 1000 displaying intensities of side scattered light in the violet light spectrum (X axis) versus intensities of the side scattered light in the red light spectrum (Y axis) for a sample containing reticuloendotheliosis virus (rEV) and GfP virus. As shown in the plot 1000, the rEV includes a first population exhibiting a first trend and a second population exhibiting poor resolution such that no trend can befitted. The first trend of the first population of the rEV does not coincide with the trend of the GfP virus or the trend of the unknown population from the GfP virus sample (see also FIG. 7).

[0153] Referring back to FIG. 3, the method 300 includes an operation 310 of comparing the trend determined in operation 308 to a plurality of predetermined trends. The plurality of predetermined trends can include trends that are previously determined for known types of cells and particles. The plurality of predetermined trends can be stored in a memory of the flow cytometer 100 and / or a memory of the workstation 200.

[0154] The method 300 further includes an operation 312 of characterizing the particles by matching the trend to a predetermined trend representing a value of a characteristic associated with the particles. As an illustrative example, a predetermined trend can represent a value of refractive index. In such example, operation 312 can include estimating a refractive index of the particles based on the trend determined in operation 308 matching a predetermined trend associated with a value of the refractive index. In such example, operation 312 can further include determining sizes of the particles based on the refractive index. For example, the size of the particles can be determined by Equation (1), which is the Rayleigh scattering approximation,where Io is the light intensity’ before interaction with the particle, 0 is scattering angle, R is distance between the particle and detector, / . is wavelength of the light, n is refractive index of the particle, and d is diameter of particle. The refractive index n and the diameter d of the particle are sample dependable variables, while the remaining variables are either known or are controllable by the flow cytometer 100. Given Equation (1), once the refractive index n of the particles is estimated, the size (i.e., diameter d) of the particles can be determined.

[0155] Alternatively, operation 312 can include estimating a size of the particles based on the trend determined in operation 308 matching a predetermined trend associated with a diameter d of the particles. In such example, operation 312 can further include determining the refractive index n of the particles based on the diameter d by using Equation (1). Thus, once the diameter d of the particles is estimated, the refractive index n of the particles can be detennined.

[0156] In some instances, operation 308 can include fitting multiple trends for different populations of particles within a given sample. In such examples, operation 310 includes comparing the multiple trends to a plurality of predetermined trends, and operation 312 includes characterizing the different populations of particles based on the comparison in operation 310. As an illustrative example, the method 300 can include fitting a first trend for a first population of the particles, and characterizing the first population of the particles by matching the first trend to a predetermined trend representing a value of a characteristic associated with the particles (e g., particle diameter d or refractive index n), and the method 300 can further include fitting a second trend for a second population of the particles, and characterizing the second population of the particles by matching the second trend to a predetermined trend representing another value of the characteristic associated with the particles (e.g., particle diameter d or refractive index ri).

[0157] In further examples, operation 312 can include characterizing a protein loading or a membrane composition of the particles based on the refractive index n when the particles have the same diameter d, as will be described in more detail with respect to FIGS. 21 and 22.

[0158] FIG. 11 illustrates a plot 1100 of intensity data for PSL beads having different sizes and known refractive indices in violet and red light wavelengths. The intensity data includes data collected from PSL beads having a diameter d of 44 nm, a diameter d of 80 nm, a diameter d of 100 nm, and a diameter of 144 nm. The PSL beads exhibit a refractive index n of 1.625 in the violet light wavelength (e.g., 405 nm) and exhibit a refractive index n of 1.587 in the red light wavelength (e.g., 633 nm). Table 1 shows median violet side scatter intensities and median red side scatter intensities for each size of the PSL beads based on the intensity data. Table 1 further shows a ratio of the median red side scatter intensity to the median violet side scatter intensity for each PSL bead size.Table 1.

[0159] FIG. 12 illustrates a plot 1200 of theoretical intensity data produced from a Mie scattering simulator that considers known refractive indices of the PSL beads (n = 1.625 in the violet light wavelength; n = 1.587 in the red light wavelength), and different sizes of the PSL beads (e.g., 44 nm, 80 nm, 100 nm. and 144 nm). Scattering angles were chosen based on the configuration of the flow cytometer 100 (e.g., 90° + / - 54°). The theoretical intensity data includes intensities of violet side scatter 1202 and intensities of red side scatter 1204 that can be used to generate a theoretical ratio of red side scatter intensity to violet side scatter intensity across different scattering angles for each PSL bead size.

[0160] FIG. 13 illustrates a plot 1300 of data showing avalanche photodiode (APD) radian sensitivities (Y axis) across different wavelengths of light (X axis). The data includes APD radian sensitivity at the violet wavelength (e.g., 405 nm) and at the red wavelength (e.g., 633 nm). The APD radian sensitivities are considered when generating the theoretical ratio of red side scatter intensity to violet side scatter intensity for each PSL bead size. In this example, the intensity of the light sources 11 la-11 Id (e.g., laser power) and beam spot at the interrogation zone 18 and gain setting are similar for both the violet and red wavelengths.

[0161] FIG. 14 illustrates a plot 1400 showing an empirical ratio 1402 of the median red side scatter intensity to the median violet side scatter intensity for each PSL bead size (based on the intensity data shown in FIG. 11 and summarized in Table 1) compared to a first theoretical ratio 1404 that does not consider the APD radian sensitivities, and a second theoretical ratio 1406 that does consider the APD radian sensitivities. Table 2 summarizes values of the empirical ratio 1402, the first theoretical ratio 1404, and the second theoretical ratio 1406 shown in FIG. 14. As shown in FIG. 14, the second theoretical ratio 1406 closely matches the empirical ratio 1402, which provides confidence in the phenomena observed in biological samples of FIGS. 6-10.Table 2.

[0162] FIG. 15 illustrates a plot 1500 of intensity data for silica beads having different sizes and known refractive indices in violet and red light w avelengths. The intensity data includes data collected from silica beads having a diameter d of 50 nm, a diameter d of 55 nm. a diameter d of 60 nm. a diameter d of 80 nm. a diameter d of 90 nm, and a diameter d of 100 nm. The silica beads exhibit a refractive index n of 1.483 in the violet light wavelength (e.g., 405 nm) and exhibit a refractive index n of 1.47 in the red light wavelength (e.g., 633 nm). Table 3 shows median violet side scatter intensities and median red side scatter intensities for each size of the silica beads based on the intensify data, and a ratio of the median red side scatter intensify to the median violet side scatter intensify' for each silica bead size.Table 3.

[0163] FIG. 16 illustrates a plot 1600 of theoretical intensity data generated from a Mie scattering simulator that considers the refractive indices of the silica beads (n = 1.483 in the violet light wavelength; n = 1.47 in the red light wavelength), and the different sizes of the silica beads (e.g., 50 nm. 55 nm. 60 nm, 80 nm, 90 nm, and 100 nm). Scattering angles were chosen based on the configuration of the flow cytometer 100 (e.g., 90° + / -54°). The theoretical intensity data includes intensities of violet side scatter 1202 and intensities of red side scatter 1204 that can be used to generate a theoretical ratio of red side scatter intensity to violet side scatter intensity across different scattering angles for each silica bead size.

[0164] FIG. 17 illustrates a plot 1700 showing an empirical ratio 1702 of the median red side scatter intensity to the median violet side scatter intensity for each silica bead size (based on the empirical data shown in FIG. 15 and summarized in Table 3) compared to a first theoretical ratio 1704 that does not consider the APD radian sensitivities, and a second theoretical ratio 1706 that does consider the APD radian sensitivities. Table 4 summarizes values of the empirical ratio 1702, the first theoretical ratio 1704, and the second theoretical ratio 1706 shown in FIG. 17. As shown in FIG. 17, the second theoretical ratio 1706 largely matches the empirical ratio 1702, except for the ratio of red side scatter intensity to violet side scatter intensity at 50 nm.Table 4.

[0165] FIG. 18 illustrates a plot 1800 comparing the second theoretical ratio 1406 of the PSL beads show n in FIG. 14 to the second theoretical ratio 1706 of the silica beads shown in FIG. 17. FIG. 19 illustrates a plot 1900 comparing the empirical ratio 1402 of the PSL beads shown in FIG. 14 to the empirical ratio 1702 of the silica beads shown in FIG. 17. Referring now to FIGS. 18 and 19. the PSL beads and the silica beads have ratios that trend differently between violet side scatter and red side scatter, which results in different curves. As shown in FIGS. 18 and 19, the curves are more spread apart for smaller particle sizes (e g., 40 nm - 100 nm) and the curves come closer together at larger particle sizes (e.g., 100 nm - 140 nm).

[0166] FIG. 20 illustrates a plot 2000 comparing a trend 2002 of intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red light spectrum of PSL beads (see also FIG. 8) to a trend 2004 of intensities of side scattered light in the violet light spectrum versus intensities of side scattered light in the red light spectrum of silica beads. The trends 2002. 2004 correlate to the ratio data displayed in FIGS. 18 and 19. For example, trends 2002, 2004 are further apart for low intensities (typically collected from smaller particle sizes), and the trends 2002, 2004 move closer together as the side scatter intensity increases (typically due to larger particle sizes) before the trends 2002, 2004 begin to separate again. Thus, the trends 2002, 2004 resemble diagonals having different slopes.

[0167] Referring now to FIGS. 18-20, the trends of the ratios between violet side scatter and red side scatter of the PSL and silica beads show s that the trends differ because the PSL beads and the silica beads have different refractive indices (i.e., n = 1.625 in the violet light wavelength and n = 1.587 in the red light wavelength for PSL beads versus n = 1.483 in the violet light wavelength and n = 1.47 in the red light w avelength for silica beads). Given the foregoing phenomena demonstrated for the PSL and silica beads in FIGS. 11-20, the observations shown in biological samples of GfP virus, rEVs, and plasma extracellular vesicles (EVs) in FIGS. 4-10 suggests that different trends of ratios between violet side scatter and red side scatter are correlated to different populations with different refractive indices. This observation is proven basedon the PSL and silica beads which have known refractive indices and sizes. Also, empirical data was correlated with simulated data. As a result, additional side scatter detectors such as for violet side scatter and red side scatter provide a technical advantage and / or improvement of identifying different populations having different refractive indices based on a label free assessment with no need of an additional step of fluorescence staining. Also, as described above. Equation (1) can be used to determine particle size based on refractive index, or inversely. Equation (1) can be used to determine refractive size based on particle size.

[0168] FIG. 21 illustrates data 2100 collected from plasma EVs that include a first population A having a first trend 2102 of ratios between violet side scatter and red side scatter, and a second population B having a second trend 2104 of ratios between violet side scatter and red side scatter. The second trend 2104 differs from the first trend 2102 such that the refractive index of the second population B differs from the refractive index of the first population A. In this example, the first and second populations A, B of plasma EVs can have different sizes or the same size depending where the plasma EVs are on the first and second trends 2102, 2104.

[0169] FIG. 22 illustrates data 2200 collected from a sample of murine leukemia virus (MLV) and reticuloendotheliosis virus (rEV) that includes a first population A having a first trend 2202 of ratios between violet side scatter and red side scatter, and a second population B having a second trend 2204 of ratios between violet side scatter and red side scatter. As shown in the example provided in FIG. 22, the first and second trends 2202, 2204 are substantially the same such that the refractive index of the first and second populations A, B are substantially the same, while the first and second populations A. B can have different sizes.

[0170] FIG. 23 schematically illustrates an example of a computing device 2300 for implementing aspects of the system 10, including functions of the flow- cytometer 100 and the w orkstation 200. Examples of the computing device 2300 include a server computer, a desktop computer, a laptop computer, a tablet computer, a mobile computing device (such as a smartphone), or other devices configured to process digital instructions.

[0171] The computing device 2300 includes one or more processing devices 2302.Examples of the one or more processing devices 2302 include central processing units (CPUs), digital signal processors, field-programmable gate arrays, and other types ofelectronic computing circuits. The one or more processing devices 2302 can be part of a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry, cause the processing circuitry to perform the functionalities described herein.

[0172] The computing device 2300 further includes a system memory 2304, and a system bus 2306 that couples various system components including the system memory 2304 to the one or more processing devices 2302. The system bus 2306 is one of any number of types of bus structures including a memory bus, or a memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0173] The system memory 2304 can include a read only memory' (ROM) 2308 and a random access memory (RAM) 2310. A basic input / output system (BIOS) 2312 containing the basic routines that act to transfer information within computing device 2300, such as during start up, can be stored in the system memory 2304. The RAM 2310 can be used for loading and subsequently analyzing the waveform data (e.g., stored in a raw waveform data file, which can include digitalized raw waveform data).

[0174] The computing device 2300 can also include one or more secondary storage devices 2314 such as a hard disk drive for storing digital data. The one or more secondary' storage devices 2314 are connected to the system bus 2306 by a secondary' storage interface 2316. The one or more secondary storage devices 2314 and associated computer readable media provide nonvolatile storage of computer readable instructions (including application programs and program modules), data structures, and other data for the computing device 2300. Although the example described herein employs a hard disk drive as a secondary storage device, other types of computer readable storage media are used in other embodiments. Examples of these other types of computer readable storage media include the ROM 2308 and / or the RAM 2310. Some examples include non-transitory media. Additionally, such computer readable storage media can include local storage or cloud-based storage.

[0175] The computing device 2300 typically includes at least some form of computer readable media. Computer readable media includes any available media that can be accessed by the computing device 2300. By way' of example, computer readable media include computer readable storage media and computer readable communication media.

[0176] Computer readable storage media includes volatile and nonvolatile, removable and non-removable media implemented in any device configured to store information such as computer readable instructions, data structures, program modules or other data. Computer readable storage media includes, but is not limited to, random access memory. read only memory', electrically erasable programmable read only memory, flash memory’ or other memory’ technology, or any other medium that can be used to store the desired information and that can be accessed by the computing device 2300.

[0177] Computer readable communication media can embody computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term ‘'modulated data signal’’ refers to a signal that has one or more characteristics set in a manner as to encode information in the signal. For example, computer readable communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency, infrared, and other wireless media. Combinations of any of the above are also included within the scope of computer readable media.

[0178] A number of program modules can be stored in secondary’ storage device 2314 or the system memory 2304, including an operating system 2318. application programs 2320, program modules 2322 (such as the software engines), and program data 2324. The computing device 2300 can utilize any suitable operating system, such as Microsoft Windows™, Google Chrome™, Apple OS, and any other operating system suitable for a computing device.

[0179] A user provides inputs to the computing device 2300 through one or more input devices 2326. Examples of input devices 2326 include the mouse 206, the keyboard 208, a microphone 2332, and a touch sensor 2334 (such as a touchpad or touch sensitive display). Additional types of the input devices 2326 are contemplated. The input devices 2326 are often connected to the one or more processing devices 2302 through an input / output interface 2336 that is coupled to the system bus 2306. These input devices 2326 can be connected by any number of input / output interfaces, such as a parallel port, serial port, game port, or a universal serial bus. Wireless communication between input devices and the input / output interface 2336 is possible as well, andincludes infrared, BLUETOOTH® wireless technology, 802.11a / b / g / n. cellular, or other radio frequency communication systems in some possible embodiments.

[0180] The display monitor 204 can include a liquid crystal display device, a touch sensitive display device, and the like. The display monitor 204 connects to the system bus 2306 via an interface, such as a video adapter 2340. In addition to the display monitor 204. the computing device 2300 can include various other peripheral devices, such as speakers or a printer.

[0181] When used in a local area networking environment or a wide area networking environment (such as the Internet), the computing device 2300 is typically connected to a network 2344 through a network interface 2342. such as an Ethernet interface. Other possible embodiments use other communication devices. For example, some embodiments of the computing device 2300 include a modem for communicating across the network 2344.

[0182] The computing device 2300 is an example of programmable electronics, which may include one or more such computing devices. When multiple computing devices are included, such computing devices can be coupled together with a suitable data communication network so as to collectively perform the various functions, methods, or operations disclosed herein.

[0183] FIG. 24 schematically illustrates another example of a method 2400 of generating a function that can be used to characterize particles by flow cytometry which can be performed by the system 10. The function determined from the method 2400 is a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. Further, the method 2400 can be performed to characterize and / or sort both entire cells, and nanoparticles such as extracellular vesicles including microvesicles and exosomes. As will be described further below, the function can be used to determine particle sizes.

[0184] The method 2400 includes an operation 2402 of illuminating the particles with at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength. As an illustrative examples, the first wavelength can be in the violet light wavelength range and the second wavelength can be in the red light wavelength range, which is at the opposite end of the light spectrum.

[0185] The method 2400 includes an operation 2404 of collecting from at least a first detector (e.g., a first SSC detector 515) side scattered light of the first wavelengthand collecting from a second detector (e.g., a second SSC detector 515) side scattered light of the second wavelength. The operations 2402, 2404 of the method 2400 are similar to the operations 302, 304 of the method 300 described above such that the description above of operations 302, 304 in the method 300 similarly applies to the operations 2402, 2404 of the method 2400.

[0186] The method 2400 includes an operation 2406 of calculating ratios of the side scattered light intensity collected from the first detector under the first wavelength to the side scattered light intensity collected from the second detector under the second wavelength. For example, operation 2406 can include calculating ratios of a first median side scatter light intensity under the violet light wavelength to a second median side scatter light intensity under the red light wavelength. Table 5 (provided below) shows a calculation of empirical violet / red scatter intensity ratios for two populations of reticuloendotheliosis virus (rEV). Table 6 (provided below) shows a calculation of empirical violet / red scatter intensity ratios for three populations of microvesicles from the Daud cell line. Table 7 shows a calculation of empirical violet / red scatter intensity ratios for four populations of urinary extracellular vesicles (EVs).Table 5.Table 6.Table 7.

[0187] The method 2400 further includes an operation 2408 of calculating ratios of a first simulated side scatter light intensity under the first wavelength to a second simulated side scatter light intensity under the second wavelength. In operation 2408, the ratios between the first and second wavelengths are theoretically simulated using Mie theory or Mie core-shell modeling using estimated refractive indices of targeted particles under the first and second wavelengths as a function of different particle sizes. For example, operation 2408 can include simulating ratios of a first simulated side scatter light intensity under the violet light wavelength to a second simulated side scatter light intensity under the red light wavelength.

[0188] The method 2400 includes an operation 2410 of applying a calibration equation to calibrate the simulated ratios between the first and second wavelengths calculated in operation 2408. The calibration equation can be generated in accordance with a method 2500 shown in FIG. 25. The calibration equation can be generated as part of an instrument calibration such as during installation of the flow cytometer 100 since sensor alignments and other factors affecting the capture of data may vary from instrument to instrument. Accordingly, the flow' cytometer 100 is calibrated by using the calibration equation to accommodate differences between the flow cytometer 100 and other flow cytometers. By the calibration performed in operation 2410, all simulated ratios are tuned to be close to the empirical ratios calculated in operation 2406.

[0189] As will be described in more detail, the calibration equation is determined by correlating empirical data captured by the flow' cytometer 100 to theoretical data calculated based on known refractive indices and sizes of calibration beads. The calibration equation can be based on a linear correction factor, a quadratic correction factor, or a cubic correction factor.

[0190] FIG. 25 schematically illustrates an example of a method 2500 of generating the calibration equation for use in the method 2400. The method 2500 includes an operation 2502 of capturing empirical data from calibration beads passing through the interrogation zone 18 of the flow cytometer 100 such that the calibration beads are illuminated by at least the first excitation light beam of the first wavelength (e.g., violet light) and the second excitation light beam of the second wavelength (e.g., red light). The calibration beads have known refractive indices and sizes. For example, the calibration beads can include polystyrene latex (PSL) beads having sizes ranging from 50 nanometers to 200 nanometers. Table 8 shows an example of empirical data captured from PSL beads under violet, blue, yellow, and red wavelengths.Table 8.

[0191] FIG. 26a shows a plot 2600a of empirical violet side scattered light data captured from the calibration beads. FIG. 26b shows a plot 2600b of empirical red side scattered light data captured from the calibration beads. The plots 2600a, 2600b can be generated in operation 2502.

[0192] Operation 2502 can include generating a plot of the intensities under the first wavelength (e.g., violet light) versus the intensities under the second wavelength (e.g., red light). FIG. 26c shows a plot 2600c of empirical violet side scattered light intensity versus red side scattered light intensity that can be generated in accordance with operation 2502.

[0193] The method 2500 includes an operation 2504 of calculating theoretical data based on the known refractive indices and sizes of the calibration beads. For example, operation 2504 can include calculating the theoretical data based on the Mie scatteringtheory. Table 9 shows an example of theoretical data calculated for the PSL beads based on their known sizes and refractive indices. FIG. 27 shows a plot 2700 of theoretical violet side scattered light intensity versus red side scattered light intensity that can be calculated in accordance with operation 2504.Table 9.

[0194] The method 2500 includes an operation 2506 of correlating the empirical data captured in operation 2502 to the theoretical data calculated in operation 2504. Operation 2506 can include correlating the empirical data to the theoretical data using a linear correction factor, a quadratic correction factor, or a cubic correction factor. In some instances, quadratic correlation provides the best fit of the empirical data to the theoretical data.

[0195] FIG. 28a shows a plot 2800a that includes the empirical data captured in operation 2502 and the theoretical data calculated in operation 2504. FIG. 28b shows a plot 2800b of the empirical data after a quadratic correlation is applied to the empirical data.

[0196] Referring back to FIG. 25, the method 2500 includes an operation 2508 of generating the calibration equation based on the correlation of the empirical data to the theoretical data performed in operation 2506. In this manner, by knowing the correlation between the theoretical data and the empirical data, violet verses red median side scatter intensity can be corrected for all combination of refractive indices based on Mie scattering theory simulations.

[0197] In some examples, the method 2500 can further include an operation 2510 of identifying a "‘half angle” of the side collection unit 130 of the flow cytometer 100. In flow cytometry, the "half angle" refers to the angular range of light collected by the side collection unit 130, which is the cone of light gathered around the particle passing through the interrogation zone 18, with the half angle representing half of the cone's apex angle. A larger half angle means more scattered light is captured from a wider range of angles around the particle. Modem flow cytometers typically have an SSC collection half-angle (e) of about 50° which means the collected SSC light of each of the particles passing through the interrogation zone 18 would be the sum of all angles from 40° to 140°. However, the half angle of the side collection unit 130 may vary from instrument to instrument due to alignment of the side collection unit 130 relative to the interrogation zone 18. In some examples, the half angle may vary by about + / - 10 degrees.

[0198] Operation 2510 can include comparing empirical side scatter light intensitydata to simulated side scatter light intensity data over a range of about + / - 10°C around a nominal half angle of about 54°C to identify the half angle of the side collection unit 130. For example, the half angle of the side collection unit 130 is identified when the empirical side scatter light intensify is closest to the simulated side scatter light intensity for a given half angle value.

[0199] The method 2500 can then further include an operation 2512 of determining a calibration factor based on the half angle identified in operation 2510. The calibration factor can then be included in the calibration equation that is generated in operation 2508. In some examples, the calibration factor is determined based on a difference between the empirical side scatter light intensify and the simulated side scatter light intensify for the half angle identified in operation 2520. In some examples, operations 2510 and 2512 are repeated as part of daily qualify control (QC) for monitoring the condition of the flow cytometer 100.

[0200] Referring back to FIG. 24, operation 2410 can include calibrating the ratios for combinations of relative refractive indices between the first side scatter light intensities under the first wavelength and the second side scatter light intensities under the second wavelength. For example, the first side scatter light intensities under the first wavelength (e.g., violet light) can have refractive indices that range between 1.6 and 1.38 such that the ratios with the second side scatter light intensities under the secondwavelength (e.g., red light) are calibrated for each combination of refractive index value in the range between 1.6 and 1.38.

[0201] Table 10 provides an example for a given refractive index value (e.g., 1.47) of the first wavelength (e.g., violet light), and a plurality of refractive index values of the second wavelength (e.g., red light). Table 10 includes refractive index (RI) differential values calculated between the refractive index value of the first wavelength (e.g.. violet light) and the plurality of refractive index values of the second wavelength (e.g., red light).Table 10.

[0202] FIG. 29 shows a plot 2900 that includes calibrated ratios between theoretically calculated first and second wavelength scatter intensities for each of the plurality of refractive index values under the second wavelength (e.g., red light) shown in Table 10.

[0203] Referring back to FIG. 24, the method 2400 can include an operation 2412 of filtering the simulated ratios that are calibrated in operation 2410 to narrow down a range for estimated refractive indices as a function of particle size. The filtering performed in operation 2412 is by way of the RI differential values shown in Table 10. For example, the plot 2900 in FIG. 29 includes a portion 2902 that includes simulated second scatter intensities under the second wavelength (e.g., red light) having RI differential values less than or equal to a maximum differential. In the example of FIG. 29, the maximum differential is 0.02. Accordingly, operation 2412 includes segmenting the portion 2902 from the other calibrated ratios between the intensities for violet side scattered light and the intensities for red side scattered light based on the maximum RI differential value. In alternative examples, instead of filtering the ratios in operation2412, the method 2400 can include an alternative operation of receiving input parameters of refractive indices on the first and second wavelengths.

[0204] FIG. 30 shows a chart 3000 that includes a first set of refractive index values 3002 between violet and red side scattered light having a high RI differential value (e.g., 0.01), a second set of refractive index values 3004 between violet and red side scattered light having a medium RI differential value (e.g.. 0.007). and a third set of refractive index values 3006 between violet and red side scattered light having a low differential value (e.g., 0.003). Operation 2412 can include filtering the simulated ratios that are calibrated in operation 2410 based on the high RI differential value, the medium RI differential value, or the low RI differential value.

[0205] Referring back to FIG. 24, the method 2400 includes an operation 2414 of characterizing the particles using the simulated ratios that are calibrated in operation 2410 and that are filtered in operation 2412. For example, the filtered ratios of the simulated light intensity from operation 2412 can be used as a function to determine particle size based on the ratios of the side scattered light intensity determined in operation 2406 from the empirical side scattered light data collected in operation 2404. Operation 2414 can include determining particle sizes based on the filtered ratios using a predetermined RI differential value such as the high RI differential value, the medium RI differential value, or the low RI differential value.

[0206] FIG. 31 shows a chart 3100 that includes particle sizes determined from the calibrated ratios of violet side scatter light intensity to red side scatter light intensity for murine leukemia virus (MLV). MLV is known to have a homogenous size population of around 110 nanometers (nm). The chart 3100 show s the determined particle sizes filtered by the high RI differential value, the medium RI differential value, and the low RI differential value. In the chart 3100, the medium RI differential value provides the most accurate size determination.

[0207] FIG. 32 show s a chart 3200 that includes particle sizes determined from the calibrated ratios of violet side scatter light intensity to red side scatter light intensity for reticuloendotheliosis viruses (rEVs). The rEVs are known to have a heterogenous size population. The chart 3100b shows the determined particle sizes filtered by the high RI differential value, the medium RI differential value, and the low RI differential value.

[0208] FIG. 33 show s a plot 3300 of calibrated ratios of violet side scatter light intensity to red side scatter light intensity for rEVs generated in accordance with theoperations of the method 2400. The plot 3300 shows a first population 3302 having a first range of sizes (e.g., 62-200 nm) and a second population 3304 having a second range of sizes (225-320 nm). FIG. 34 shows a plot 3400 that corresponds to the plot 3300. FIG. 34 shows a plot 3400 of the violet side scatter light intensity versus red side scatter light intensity for rEVs gated between the separate rEV populations having different size ranges (e.g., 62-200 nm vs. 225-320 nm).

[0209] FIG. 35 shows a plot 3500 of calibrated ratios of violet side scatter light intensity to red side scatter light intensity for microvesicles from the Daudi cell line generated in accordance with the operations of the method 2400. The plot 3500 shows a first population 3502 having a first range of sizes (e.g., 47-107 nm), a second population 3504 having a second range of sizes (102-222 nm), and a third population 3506 having a third range of sizes (225-310 nm).

[0210] FIG. 36 shows a plot 3600 that corresponds to the plot 3500. As shown in FIG. 36, the plot 3600 shows the violet side scatter light intensity versus red side scatter light intensity for microvesicles that is gated between the separate populations having different sizes ranges (e.g., 47-107 nm; 102-222 nm and 225-310 nm).

[0211] FIG. 37 shows a plot 3700 of calibrated ratios of violet side scatter light intensity7to red side scatter light intensity7for urinary7extracellular vesicles generated in accordance with the operations of the method 2400. The plot 3700 shows a first population 3702 having a first range of sizes (e.g., 60-90 nm), a second population 3704 having a second range of sizes (103-369 nm), a third population 3706 having a third range of sizes (85-162 nm), and a fourth population 3708 having a fourth range of sizes (171-266 nm).

[0212] FIG. 38 shows a plot 3800 that corresponds to the plot 3700. The plot 3800 of FIG. 38 shows the violet side scatter light intensity versus red side scatter light intensity for the urinary extracellular vesicles gated between the separate populations having different sizes ranges (e.g., 60-90 nm; 103-369 nm; 85-162 nm; and 171-266 nm).

[0213] FIG. 39 schematically illustrates an example of a method 3900 of characterizing protein loading on particles by flow cytometry. The method 3900 can be performed by the flow cytometer 100. The method 3900 is a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. Further, the method 3900 can be performed to characterize and / orsort both entire cells, and nanoparticles such as extracellular vesicles including microvesicles and exosomes. Accordingly, the method 3900 can be used to sort and / or purify label-free biological particles based on composition detection by flow cytometry. For example, the method 3900 can be performed to sort and / or purify samples of extracellular vesicles, viruses, biological particles, and the like.

[0214] As shown in FIG. 39, the method 3900 includes an operation 3902 of passing particles of a biological sample through the interrogation zone 18 for illumination under a plurality of different light wavelengths. For example, operation 3902 can include illuminating the particles of the biological sample in the violet light spectrum, the blue light spectrum, the yellow light spectrum, and the red light spectrum. The particles of the biological sample include particles having an unknown amount of loading and particles having zero loading. The particles having zero loading are unstained and naked.

[0215] The method 3900 includes an operation 3904 of capturing side scattered light data from the particles of the biological sample that pass through the interrogation zone 18 in operation 3902. The side scattered light data can include a first intensify in a violet light spectrum, a second intensify in a blue light spectrum, a third intensify7in a yellow light spectrum, and a fourth intensify' in a red light spectrum.

[0216] The method 3900 includes an operation 3906 of calculating differential light side scatter parameters between the particles of the biological sample having the unknown amount of loading and the particles of the biological sample having the zero loading. As an illustrative example, operation 3906 can include calculating ratios of violet SSC light versus blue SSC light, ratios of violet SSC light versus yellow SSC light, and ratios of violet SSC light versus red SSC light between the particles of the biological sample having the unknown loading and the particles of the biological sample having the zero loading.

[0217] As further shown in FIG. 39, the method 3900 includes an operation 3908 of applying a calibration equation to calibrate the differential light side scatter parameters. The calibration equation applied in operation 3908 can be the same calibration applied in operation 2410 of the method 2400 of FIG. 24, which can be generated in accordance with the operations of the method 2500 of FIG. 25, which are described above in more detail.

[0218] The method 3900 further includes an operation 3910 of calculating refractive index differentials as a function of differential light side scatter based on size and refractive index of the particles of the biological sample. Operation 3910 can include using the Mie scattering theory to calculate the refractive index differentials as a function of differential light side scatter.

[0219] The method 3900 further includes an operation 3912 of applying a second calibration equation that can be used to calibrate the refractive index differentials calculated in operation 3910. The second calibration equation is separate from the first calibration equation applied in operation 3908, which is generated in accordance with the operations of the method 2500.

[0220] FIG. 40 schematically illustrates an example of sub-operations performed in the operation 3912 of the method 3900. Operation 3912 can include a sub-operation 4002 of passing calibration beads through the interrogation zone 18 for illumination under the plurality of different light wavelengths. The calibration beads include particles having a known amount of loading such as silica beads having a known loading, or biological particles such as viruses, liposomes, and the like having a known quantity of biomolecules attached via covalent and / or non-covalent bonding on exterior and / or interior surfaces of the particles. The calibration beads further include particles having zero loading such that at least some of the calibration beads are unstained and naked.

[0221] Operation 3912 can further include a sub-operation 4004 of capturing side scattered light data from the calibration beads passing through the interrogation zone 18. The side scattered light data collected in sub-operation 4006 can include first intensities in the violet light spectrum, second intensities in the blue light spectrum, third intensities in the yellow light spectrum, and fourth intensities in the red light spectrum.

[0222] Operation 3912 can further include a sub-operation 4006 of calculating differential light side scatter parameters for the calibration beads. Sub-operation 4006 can include calculating ratios of violet SSC light versus blue SSC light, ratios of violet SSC light versus yellow SSC light, and ratios of violet SSC light versus red SSC light for the calibration beads.

[0223] Operation 3912 can further include a sub-operation 4008 of comparing the differential light side scatter parameters calculated in sub-operation 4006 withtheoretical side scattered light data that is calculated based on the known loading, size, and refractive of the calibration beads. The theoretical side scattered light data can be calculated using the Mie scattering theory. A first calibration is generated based on the comparison in sub-operation 4008.

[0224] Operation 3912 can further include a sub-operation 4010 of calculating refractive index differentials as a function of the differential light side scatter calibrated by the first calibration equation and based on size and refractive index of the calibration beads.

[0225] Operation 3912 can further include a sub-operation 4012 of generating the second calibration equation based on the refractive index differentials of the calibration beads and the known amount of loading on the calibration beads.

[0226] Referring back to FIG. 39, the method 3900 further includes an operation 3914 of characterizing the unknown loading on the particles in the biological sample. Operation 3914 includes using the refractive index differentials calculated in operation 3910 and the second calibration equation generated in operation 3912 to characterize the unknown proteins on the particles of the biological sample as a function of the refractive index differentials. Accordingly, based on the determined sizes of the particles, refractive indices can be calculated to characterize the loading of the particles. The refractive indices can be calculated as a function of the differential scatter parameters such as the ratios of violet SSC light versus blue SSC light, ratios of violet SSC light versus yellow SSC light, and ratios of violet SSC light versus red SSC light.

[0227] In some examples, operation 3914 can include characterizing single particle surface marker interactions with corresponding ligands including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin -biotin interactions. In some further examples, operation 3914 can include characterizing single particle modification including covalently or non-covalently attached molecules on particle surfaces. In some further examples, operation 3914 can include monitoring biomolecules on particle surfaces including antigen expression level or protein expression level. In some further examples, operation 3914 can include monitoring cargo loading inside particle envelop such as liposome particles, hydrogel particles, and virus capsids, wherein the cargo loading includes deoxyribonucleic acid (DNA), messenger ribonucleic acid (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes. In some further examples, operation 3914 can include monitoring proteinaggregation inside the particles of the biological sample and on an outer surface of the particles of the biological sample.

[0228] FIG. 41 schematically illustrates an example of a method 4100 of determining presence of protein loading on biological sample particles. The method 4100 can be performed by the system 10. The method 4100 includes an operation 4102 of receiving input parameters of a sample of the biological particles for analysis by the flow cytometer 100. The input parameters can include a diameter of the core of the sample particles, a shell thickness of the sample particles when stained, a refractive index of the core of the sample particles, and an approximate range of refractive index of the shell of the sample particles when stained. As used herein, unstained sample particles have a core only, and stained sample particles have a shell of proteins around the core. In operation 4102, the input parameters can be received via the workstation 200 such as by a user using one or more input devices such as the mouse 206 and the keyboard 208 to enter the input parameters into designated fields displayed on the display monitor 204.

[0229] The method 4100 includes an operation 4104 of extracting mean scatter intensity (MSI) data from empirical data of the sample of biological particles measured by the flow cytometer 100. In operation 4104, the MSI data can include MSI data of unstained biological particles and MSI data of stained biological particles in the sample of particles.

[0230] The method 4100 includes an operation 4106 of calculating a percent change between the MSI data of the stained particles and the MSI data of the unstained particles. Operation 4106 can include subtracting the MSI data of the unstained particles from the MSI data of the stained particles, dividing by the MSI data of unstained particles, and multiplying by 100.

[0231] The method 4100 includes an operation 4108 of calculating scatter intensity (Iscat) of the unstained particles by using the input parameters received in operation 4102 and Mie scattering core-shell modeling as a function of shell refractive index. For unstained sample particles, the refractive index of the shell is equivalent to the refractive index of the buffer in which the biological particles are suspended such as phosphate-buffered saline (PBS) or water. The Mie scattering core-shell modeling considers particles as having a solid core with a shell around the solid core. Transmission electron microscopy (TEM) of biological extracellular particles showsthat most of these particles have a hard shell with dense proteins around a core. The Mie scattering core-shell modeling more closely mimics actual biological samples than traditional Mie scattering theory which considers all particles to be solid inside.

[0232] The method 4100 includes an operation 4110 of applying a normalization (Inorm) to the scatter intensity (Iscat) calculated in operation 4108. As a result, a normalized scatter intensity is determined for the unstained sample particles (unstained [Iscat* Inorm]). The Inorm is an instrument dependable parameter that may vary from flow cytometer to flow cytometer. The Inorm can be determined in accordance with the techniques described above. For example, the Inorm can be identified by capturing empirical data from control particles having a known scatter intensity, calculating theoretical data for the scatter intensity of the control particles using the input parameters received in operation 4102 and the Mie scattering theory, and determining the Inorm to compensate the theoretical data to match the empirical data and thereby provide calibration between simulation data and experiment data.

[0233] The method 4100 includes an operation 4112 of calculating scatter intensity of the stained particles using the unstained normalized scatter intensity [ Iscat Inorm] determined from operation 4110 and a percent change between the MSI data of the stained particles and the MSI data of the unstained particles from operation 4106, as noted by Equation 2:(2) Stainedf Iscat*Inorm] = Unstained) Iscat*Inorm]*(100+%delta MSI) / 100

[0234] The method 4100 includes an operation 4114 of calculating refractive index of the shell of the stained particles, as a function of scatter intensity (Iscat) determined in operation 4112 and the input parameters received in operation 4102. by using Mie scattering core-shell modeling, as noted by Equation 3:(3) Shell RI= A* Stained [Iscat * Inorm] + B where A and B are parameters derived from simulation data.

[0235] The method 4100 includes an operation 4116 of calculating a delta refractive index by subtracting the refractive index of a buffer (e.g., PBS or water) in which the particles are suspended from the refractive index of the shell of the particles (i.e., refractive index of loaded protein shell) determined in operation 4114. The delta refractive index calculated in operation 4116 can determine the presence of protein on the shell versus when the shell does not have the presence of the protein (i.e., whenempty). For example, when the delta refractive index is larger than a nominal value, this indicates a presence of protein loading on the particles.

[0236] Accordingly, the method 4100 can include an operation 4118 of determining whether particles have protein loading or not based on the delta refractive index calculated in operation 4116. Thus, the method 4100 can include characterizing the protein loading presence on the biological particles based on the delta refractive index.

[0237] FIG. 42 illustrates a table 4200 that includes values from an experiment performed in accordance with the operations of the method 4100. The table 4200 includes the input parameters received in operation 4102 of the method 4100 such as virus diameter and refractive index for V5 antibodies, fragment antibodies (Fab), and Immunoglobulin G (IgG) antibodies.

[0238] FIG. 43 illustrates a table 4300 that includes core-shell modeling for the V5 antibodies in the table 4200 for virus expressing V5 tag and monoclonal anti-V5 antibodies that bind to the V5 tag to stain the virus (designated as V5). FIG. 44 illustrates a table 4400 that includes core-shell modeling for Fab antibodies in the table 4200 including monoclonal fragment Fab antibodies that bind to the anti-V5 antibodies described in FIG. 43 for staining the anti-V5 antibodies bound on the virus (designated as Fab). FIG. 45 illustrates a table 4500 that includes core-shell modeling for IgG antibodies in the table 4200 including monoclonal IgG antibodies that bind to the anti- V5 antibodies described in FIG. 43 for staining the anti-V5 antibodies bound on the virus (designated as IgG). In tables 4300-4500, the input parameters further include core radius, shell radius, core refractive index, and shell refractive index. As described above the input parameters are received in operation 4102 of the method 4100.

[0239] Referring back to FIG. 42, the table 4200 further includes empirical parameters that are measured by the flow cytometer 100 such as the MSI for unstained and stained V5, Fab, and IgG antibodies at 500 gain. As described above, the MSI for the unstained and stained V5, Fab, and IgG antibodies is extracted in operation 4104 of the method 4100.

[0240] The table 4200 further shows the delta MSI between the unstained and stained V5, Fab, and IgG antibodies, and the percentage delta. The delta MSI between the unstained and stained V5, Fab, and IgG antibodies is calculated in operation 4106 of the method 4100.

[0241] Referring now to FIGS. 42-45, the tables 4200-4500 further include simulation results that include the scatter intensity (Lcat) calculated in operation 4108, and the normalization of the scatter intensity (i.e., Iscat * Inorm) calculated in operation 4110 of the method 4100.

[0242] As shown in FIG. 42, the table 4200 further includes the refractive indices of the shells of the unstained and stained V5, Fab. and IgG antibodies. As described above, these refractive indices are calculated in operation 4112 of the method 4100.

[0243] FIG. 46 illustrates a table 4600 that includes the delta refractive index values calculated in operation 4114 of the method 4100 for the V5 stained, Fab, and IgG antibodies. The delta refractive index values can be used to determine whether there is protein loading or not on the particles passing through the interrogation zone 18 of the flow cytometer 100.

[0244] FIG. 47 illustrates a graph 4700 showing shell refractive index (Y-axis) versus Iscat*Inorm (X-axis) calculated for the V5 stained, Fab, and IgG antibodies in accordance with the operations of the method 4100. In FIG. 47, the dots are simulated data from the tables above for V5, Fab and Igg, and the different trends are linear approximations. As shown in the graph 4700, the refractive index of the shell increases substantially linearly as the IScat*Inonn increases. The refractive index of the shell correlates with the Iscat*Inmm values calculated for the particles.

[0245] FIG. 48 schematically illustrates another example of a method 4800 of generating a function that can be used to characterize particles by flow cytometry which can be performed by the system 10. The function determined from the method 4800 is a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. Further, the method 4800 can be performed to characterize and / or sort both entire cells, and nanoparticles such as extracellular vesicles including microvesicles and exosomes. As will be described further below; the function can be used to determine particle sizes.

[0246] In FIG. 48, the method 4800 is schematically illustrated as split between an empirical data collection portion and a post processing portion. The empirical data collection portion of the method 4800 includes an operation 4802 of illuminating the particles with at least a first excitation light beam of a first w avelength and a second excitation light beam of a second wavelength. As an illustrative examples, the first wavelength can be in the violet light wavelength range (e.g., 325 nm - 450 nm) and thesecond wavelength can be in the red light wavelength range (e.g.. 600 nm - 850 nm), which is towards the opposite end of the light spectrum.

[0247] The empirical data collection portion of the method 4800 further includes an operation 4804 of collecting from at least a first detector (e.g., a first SSC detector 515) side scattered light of the first wavelength and collecting from a second detector (e.g., a second SSC detector 515) side scattered light of the second wavelength. The operations 4802, 4804 of the method 4800 are similar to the operations 2402, 2404 of the method 2400 described above.

[0248] FIG. 49 illustrates an example of murine leukemia virus (MLV) virus data 4900 that can be collected in accordance with the empirical data collection portion of the method 4800.

[0249] Referring back to FIG. 48, the post processing portion of the method 4800 includes an operation 4806 of receiving refractive index as an input from a user of the flow cytometer 100. The refractive index received in operation 4806 is predetermined. In some instances, the refractive index received in operation 4806 is categorized into a predetermined range of values including at least one of a high, medium, or low range of values.

[0250] The post processing portion of the method 4800 includes an operation 4808 of performing a simulation of scatter intensity under both the first wavelength (e g., within 325 nm - 450 nm) and the second wavelength (e.g., within 600 nm - 850 nm) based on the refractive index input received in operation 4806 and using the Mie scattering theory7.

[0251] FIG. 50 illustrates an example of a table 5000 that includes values calculated in accordance with the operations of the method 4800. In the table 5000, values from the simulation of the scatter intensity under both the first wavelength and the second wavelength are included in the second and third columns of the table 5000, as shown in the annotated figure.

[0252] Referring back to FIG. 48, the post processing portion of the method 4800 includes an operation 4810 of calculating ratios of the simulated scatter intensity under the first wavelength (e.g., violet light shown in the second column of the table 5000) to the simulated scatter intensity under the second wavelength (e.g., red light shown in the third column of the table 5000) as a function of different sizes (i.e., first column of thetable 5000). The ratios calculated in operation 4810 are shown in the fourth column of the table 5000.

[0253] The post processing portion of the method 4800 also includes an operation 4812 of calculating a ratio of the empirical scatter intensity under the first wavelength (e.g., violet light) to the empirical scatter intensity under the second wavelength (e.g., red light). An illustrative example of the ratio of the empirical scatter intensities is shown in FIG. 49.

[0254] The post processing portion of the method 4800 further includes an operation 4814 of calibrating the simulated ratios determined in operation 4810 as a function of different particle sizes. Operation 4814 can include applying the calibration equation generated by the method 2500 shown in FIG. 25. The calibration performed in operation 4814 tunes the simulated ratios to be close to the empirical ratios calculated in operation 4812. The calibrated ratios calculated in operation 4814 are shown in the sixth column of the table 5000. FIG. 51 graphically illustrates the calibrated ratios calculated as a function of particle size in operation 4814.

[0255] The post processing portion of the method 4800 further includes an operation 4816 of performing simulation of size based on the Mie theory using the calibrated empirical ratios determined in operation 4814. For example, ratios of empirical scatter intensity under the first wavelength (e.g., violet light) to the empirical scatter intensity under the second wavelength (e.g., red light) calculated by the system 10 can thereafter be used to determine particle sizes as a label-free technique.

[0256] FIG. 52 schematically illustrates another example of a method 5200 of characterizing particles by flow cytometry which can be performed by the system 10. The method 5200 is similarly a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. Further, the method 5200 can be performed to characterize and / or sort both entire cells, and nanoparticles such as extracellular vesicles including microvesicles and exosomes. As will be described further below, in contrast to the method 4800 of FIG. 48, the method 5200 includes calibrating empirical data collected by the flow cytometer 100 to be close to the simulated data calculated using the Mie theory.

[0257] The method 5200 is similarly split between an empirical data collection portion and a post processing portion. The empirical data collection portion includes an operation 5202 of illuminating the particles with at least a first excitation light beam ofa first wavelength and a second excitation light beam of a second wavelength, and an operation 5204 of collecting from at least a first detector (e.g., a first SSC detector 515) side scattered light of the first wavelength and collecting from a second detector (e.g., a second SSC detector 515) side scattered light of the second wavelength. The operations 5202, 5204 of the method 5200 are the same as the operations 4802, 4804 of the method 4800 shown in FIG. 48 and described above.

[0258] The post processing portion of the method 5200 includes an operation 5206 of receiving refractive index as an input from a user of the flow cytometer 100. Operation 5206 of the method 5200 is the same as operation 4806 of the method 4800.

[0259] The post processing portion of the method 5200 further includes an operation 5208 of calculating a ratio of the empirical scatter intensity under the first wavelength (e.g., within 325 nm - 450 nm) to the empirical scatter intensity under the second wavelength (e.g., within 600 nm - 850 nm). Operation 5208 of the method 5200 is the same as operation 4812 of the method 4800.

[0260] The post processing portion of the method 5200 further includes an operation 5210 of calibrating the empirical ratios calculated in operation 5208. Operation 5208 can include applying the calibration equation generated by the method 2500 shown in FIG. 25. The calibration performed in operation 5210 tunes the empirical ratios to be close to simulated ratios.

[0261] The post processing portion of the method 5200 includes an operation 5212 of performing simulation of size based on the Mie theory using the calibrated empirical ratios determined in operation 5210. For example, ratios of empirical scatter intensity7under the first wavelength to the empirical scatter intensity under the second wavelength calculated by the system 10 can thereafter be used to determine particle sizes as a label-free technique.

[0262] Conventional cytometers typically provide arbitrary- medium scatter intensity (MSI) units as an output which limits the ability to get useful information about sample particle characteristics such as size and / or refractive index. As will noyv be discussed, new methods are performed to translate output MSI from the flow cytometer 100 to absolute units of size and refractive index to provide more meaningful information about particle sample composition.

[0263] The following methods can be implemented as a software tool that can replace flow cytometer post-acquisition analysis software (FCMP SS). FCMPASS allowsuser to obtain size calibration data based on one wavelength at a time. Looking only at one wavelength at a time can be disadvantageous because users are unable to split populations of heterogeneous structures of extracellular vesicles due to different refractive indices at different wavelengths.

[0264] The following methods have a unique feature to differentiate populations of extracellular vesicles based on multiple wavelength scatters and to provide size information for each of these populations. In addition, the following methods can also be used to calibrate refractive indices of populations of interest.

[0265] FIG. 53 schematically illustrates an example of a method 5300 of characterizing particles by flow cytometry that can be performed by the system 10. The method 5300 is a label-free technique such that the particles are characterized without using fluorochromes, fluorescent dyes, or other types of labels. The method 5300 includes three separate phases: an instrument calibration phase 5302; an empirical data collection phase 5304; and a processing phase 5306.

[0266] Each of the calibration phase 5302, the empirical data collection phase 5304, and the processing phase 5306 can share one or more operations or aspects with the methods and techniques described above. For example, the calibration phase 5302 can share aspects with the method 2500 of FIG. 25, which as described above, is performed to generate a calibration.

[0267] As shown in FIG. 53, the calibration phase 5302 includes an operation 5312 of illuminating particles having a known size and refractive index such as by using the light emitting unit 110 of the flow cytometer 100; an operation 5314 of collecting side scattered light of the first wavelength and side scattered light of the second wavelength such as by using the side collection unit 130 of the flow cytometer 100; an operation 5316 of simulating the side scattered light of the first wavelength (e.g., violet) and side scattered light of the second wavelength (e.g., red) such as by using the Mie theory; and an operation 5318 of correlating the empirical data collected in operation 5314 to the simulated data calculated in operation 5316 to determine a calibration for the flow cytometer 100. In some examples, the calibration phase 5302 can further include an operation 5320 of identifying the half angle of the side collection unit 130 of the flow cytometer 100, and an operation 5322 of determining a calibration factor based on the half angle identified in operation 5320. Operations 5320, 5322 correspond to operations 2510, 2512 of the method 2500, which are described above.

[0268] In some examples, operation 5312-5318 are performed for calibrating the flow cytometer 100 based on the first wavelength (e.g., within 325 nm - 450 nm) and the second wavelength (e.g., within 600 nm - 850 nm), separately. For example, operation 5318 can include correlating the empirical and simulated side scattered data for the first and second wavelengths separately to define the calibration. Alternatively, operation 5318 can include correlating empirical and simulated ratios between the first and second wavelengths to define the calibration.

[0269] FIGS. 54-59 illustrate examples of calibrating the flow cytometer 100 based on separately correlating the empirical data of the first wavelength (e.g., within 325 nm - 450 nm) and the second wavelength (e.g., within 600 nm - 850 nm) with the simulated data of the first and second wavelengths. FIG. 54 illustrates an example of a table 5400 that includes empirical data collected in accordance with operations 5312, 5314 of the instrument calibration phase 5302. The table 5400 includes empirical data of side scattered light of the first wavelength. The data show n in the table 5400 is collected from PSL beads having different known sizes. The side collection unit 130 of the flow cytometer 100 collected the empirical data of table 5400.

[0270] FIG. 55 illustrates an example of a table 5500 that includes simulated data calculated in accordance w ith operation 5316 of the instrument calibration phase 5302. The table 5500 includes simulated data of side scattered light of the first wavelength. The simulated data shown in the table 5500 can be calculated using Mie scattering simulations based on solid modeling to identify theoretical scatter intensity for the wavelength of interest.

[0271] FIG. 56 illustrates an example of a graph illustrating a correlation of the empirical data included in the table 5400 of FIG. 54 with the simulated data in the table 5500 of FIG. 55 in accordance with operation 5318 of the instrument calibration phase 5302. The correlation can be performed by using the following equation (4):where Vnorm=Inorm*Simulated Violet for the first wavelength.

[0272] FIG. 57 illustrates an example of a table 5700 that includes empirical data collected in accordance with operations 5312, 5314 of the instrument calibration phase 5302. The table 5700 includes empirical data of side scattered light of the second wavelength. The data shown in the table 5700 is collected from PSL beads havingdifferent known sizes. The side collection unit 130 of the flow cytometer 100 collected the empirical data of table 5700.

[0273] FIG. 58 illustrates an example of a table 5800 that includes simulated data calculated in accordance with operation 5316 of the instrument calibration phase 5302. The table 5800 includes simulated data of side scattered light of the second wavelength. The simulated data shown in the table 5800 can be calculated using Mie scattering simulations based on solid modeling to identify theoretical scatter intensity for the wavelength of interest.

[0274] FIG. 59 illustrates an example of a graph 5900 illustrating a correlation of the empirical data included in the table 5700 with the simulated data in the table 5800 in accordance with operation 5318 of the instrument calibration phase 5302. The correlation can be performed by using equation (4), which is described above.

[0275] Referring back to FIG. 53, the empirical data collection phase 5304 can share aspects with the method 2400 of FIG. 24. For example, the empirical data collection phase 5304 can include an operation 5322 of illuminating the sample particles such as by using the light emitting unit 110 of the flow cytometer 100 and an operation 5324 of collecting side scattered light of the sample particles under the first wavelength (e.g., violet) and under the second wavelength (e.g., red) such as by using the side collection unit 130 of the flow cytometer 100. Operations 5322, 524 can be similar to operations 2402 and 2404 of the method 2400 of FIG. 24.

[0276] As further shown in FIG. 53, the empirical data collection phase 5304 can include an operation 5326 of identifying mean scatter intensify (MSI) of the sample particles in the first and second wavelengths. In some examples, operation 5326 can include plotting the MSI of the sample particles in the first wavelength (MSI(Il))) versus the MSI of the sample particles in the second wavelength (MSI(I2)). In some examples, operation 5326 can include identifying MSI of all data in the side scatter channels in additional to the MSI(Il) and MSI(I2) channels.

[0277] FIG. 60 graphically illustrates an example of a plot 6000 of empirical data for MLV virus that identifies empirical MSI for the first and second wavelengths in accordance with operation 5326 of the empirical data collection phase 5304.

[0278] FIG. 61 illustrates an example of a table 6100 that includes MSI of the different side scatter channels of the side collection unit 130 for the empirical data collected from the MLV virus shown in in the plot 6000 of FIG. 60. The table 6100further shows an empirical ratio between the MSI of the first wavelength versus the MSI of the second wavelength.

[0279] The processing phase 5306 can include an operation 5332 of receiving refractive index as an input from a user of the flow cytometer 100. The processing phase 5306 further includes an operation 5334 of performing a simulation of scatter intensity separately under both the first wavelength and the second wavelength based on the refractive index input received in operation 5332 and using the Mie scattering theory. For example, operation 5334 can include performing the simulation of scatter intensity7on the first wavelength (e.g., within 325 nm - 450 nm) based on the Mie scattering theory and the refractive index input received in operation 5332. Operation 5334 can further include performing the simulation of scatter intensity on the second wavelength (e.g., within 600 nm - 850 nm) based on the Mie scattering theory and the refractive index input received in operation 5332. Operation 5334 can be similar to operation 4808 of the method 4800 of FIG. 48 except that the simulation is performed on the first and second wavelengths separately instead of a ratio between the first and second wavelengths.

[0280] FIG. 62 illustrates an example of a table 6200 that includes simulated data calculated for the first wavelength (e.g., within 325 nm - 450 nm) in accordance with operation 5334 of the processing phase 5306. The simulated data for the first wavelength shown in the table 6200 is based on an input violet refractive index of 1.45037 received in operation 5332.

[0281] FIG. 63 illustrates an example of a table 6300 that includes simulated data calculated for the second wavelength (e.g., within 600 nm - 850 nm) in accordance with operation 5334 of the processing phase 5306. The simulated data for the second wavelength shown in the table 6200 is based on an input red refractive index of 1.4453 received in operation 5332.

[0282] The processing phase 5306 can further include an operation 5336 of calibrating the simulated side scattered data for the first and second wavelengths (see FIGS. 62 and 63). The calibration in operation 5336 can be based on the calibration determined in operation 5318 of the instrument calibration phase 5302. In operation 5336, the simulated side scattered data for the first and second wavelengths is calibrated separately for the first and second w avelengths as a function of different particle sizes, as is shown in FIGS. 62 and 63.

[0283] The processing phase 5306 can further include an operation 5338 of plotting a size distribution versus simulated side scattered data after calibration in operation 5336.

[0284] FIG. 64 shows an example of a plot 6400 that can be generated in accordance with operation 5338. The plot 6400 shows size distribution versus simulated side scattered data of the first wavelength (e.g.. within 325 nm - 450 nm) after calibration in operation 5336. In FIG. 64, particle size is on the y-axis (first column of the table 6200) and simulated side scattered data after calibration is on the x- axis (i.e., fourth column of the table 6200).

[0285] FIG. 65 shows another example of a plot 6500 that can be generated in accordance with operation 5338. The plot 6500 shows size distribution versus simulated side scattered data of the second wavelength (e.g., within 600 nm - 850 nm) after calibration in operation 5336. In FIG. 65, particle size is on the y-axis (first column of the table 6300) and simulated side scattered data after calibration is on the x- axis (i.e.. fourth column of the table 6300).

[0286] The processing phase 5306 includes an operation 5340 of determining the size of the sample particles based on the empirical side scattered data of the first and second wavelengths identified in operation 5326 and the calibration performed in operation 5336. In some examples, the plot generated in operation 5338 is used to determine the particle size.

[0287] FIG. 66 illustrates a table 6600 providing an example where a refractive index of 1.45037 for the first wavelength is received in operation 5332, an empirical side scatter intensity of 152556.8 under the first wavelength is detected in operation 5326, and a particle size of 121.4 nm is determined in operation 5340.

[0288] FIG. 67 illustrates a table 6700 providing an example where a refractive index of 1.4453 for the second wavelength is received in operation 5332, an empirical side scatter intensity of 25194.4 under the second wavelength is detected in operation 5326, and a particle size of 119.7 nm is determined in operation 5340.

[0289] As shown in tables 6600 and 6700, the estimated particle size based on the second wavelength (119.7 nm) is substantially close to the estimated particle size based on the first wavelength (121.4 nm). In some examples, the estimated particle sizes based on the first and second wavelengths are averaged to determine sizes for particles analyzed by the system 10.

[0290] In alternative examples, instead of calibrating the simulated side scattered data for the first and second wavelengths, operation 5336 can include calibrating the empirical side scattered data of the first and second wavelengths identified in operation 5326. In such examples, operation 5318 of the instrument calibration phase 5302 includes a reversed calibration where the simulated data calculated in operation 5316 is correlated to the empirical data collected in operation 5314. Further, in these alternative examples, operation 5338 can be skipped such that the method proceeds directly to operation 5340 of determining the size of the sample particles based on matching the calibrated empirical side scattered data of the first and second wavelengths to the simulation performed in operation 5334.

[0291] FIG. 68 schematically illustrates another example of a method 6800 of characterizing particles by flow cytometry that can be performed by the system 10. The method 6800 similarly includes an instrument calibration phase 6802, an empirical data collection phase 6804, and a processing phase 6806. The method 6800 differs from the method 5300 in that the processing phase 6806 determines refractive index based on size inputs received from the user.

[0292] As shown in FIG. 68, the instrument calibration phase 6802 of the method 6800 includes operations 6812-6818 which are substantially similar to the operations 5312-5318 of the method 5300. In some examples, the calibration phase 6802 can further include an operation 6820 of identifying the half angle of the side collection unit 130 of the flow cytometer 100, and an operation 6822 of determining a calibration factor based on the half angle identified in operation 6820. Operations 6820, 6822 are substantially similar to operations 2510, 2512 of the method 2500, which are described above.

[0293] The empirical data collection phase 6804 of the method 6800 includes operations 6822-6826 which are substantially similar to operations 5322-5326 of the method 5300.

[0294] The processing phase 6806 includes an operation 6832 of receiving a size input. The size input can be received in operation 6832 via the workstation 200 that is connected to the flow cytometer 100 of the system 10. In some examples, the size input is predetermined by the system 10 based on open-source literature and / or based on user experience. Alternatively, the size input can be received in operation 6832 from a user of the system 10.

[0295] The processing phase 6806 includes an operation 6834 of performing a simulation of side scater intensity under both the first wavelength and the second wavelength based on the size input received in operation 6832 and using the Mie scatering theory. Operation 6834 can include performing the simulation of side scatter intensity on the first wavelength (e.g., within 325 nm - 450 nm) based on the Mie scatering theory and the size input received in operation 6832. Operation 6834 can further include performing the simulation of side scater intensity on the second wavelength (e.g., within 600 nm - 850 nm) based on the Mie scatering theory and the size input received in operation 6832. Operation 6834 can be similar to operation 5334 of the method 5300 except that instead of using refractive index, the simulation is performed using particle size.

[0296] FIG. 69 illustrates an example of a table 6900 that includes simulated side scater intensity data calculated for the first wavelength (e.g., within 325 nm - 450 nm) in accordance with operation 6834 of the processing phase 6806. The simulated side scater intensity data shown in the table 6900 is based on an input size of 120 nm received in operation 6832.

[0297] FIG. 70 illustrates an example of a table 7000 that includes simulated side scater intensity7data calculated for the second wavelength (e.g., within 600 nm - 850 nm) in accordance with operation 6834 of the processing phase 6806. The simulated side scater intensity data shown in the table 7000 is based on an input size of 120 nm received in operation 6832.

[0298] The processing phase 6806 includes an operation 6836 of calibrating the simulated side scater intensities under both the first w avelength and the second wavelengths that are generated in operation 6834. The calibration in operation 6836 is based on the calibration determined in operation 6818 of the instrument calibration phase 6802. In operation 6836, the simulated side scater intensity data for the first and second wavelengths can be calibrated for the first and second wavelengths separately as a function of different refractive indices, as shown in FIGS. 69 and 70 (see fourth columns in tables 6900 and 7000).

[0299] The processing phase 6806 can include an operation 6838 of ploting refractive index distribution versus simulated side scattered data after calibration in operation 6836.

[0300] FIG. 71 shows an example of a plot 7100 that can be generated in accordance with operation 6838. The plot 7100 shows refractive index distribution versus simulated side scatter intensity data of the first wavelength (e.g., within 325 nm - 450 nm) after calibration in operation 6836. In FIG. 71, refractive index is on the y- axis (first column of the table 6900) and simulated side scatter intensity data after calibration is on the x-axis (i.e. , fourth column of the table 6900).

[0301] FIG. 72 shows another example of a plot 7200 that can be generated in accordance with operation 6838. The plot 7200 shows refractive index distribution versus simulated side scatter intensity data of the second wavelength (e.g., within 600 nm - 850 nm) after calibration in operation 6836. The refractive index is on the y-axis (first column of table 7000) and simulated side scatter intensity data after calibration is on the x-axis (i.e., fourth column of table 7000).

[0302] The processing phase 6806 includes an operation 6840 of determining the refractive index of the sample particles based on the empirical side scatter intensity data of the first and second wavelengths identified in operation 6826 and the calibration of the simulated scatter intensities performed in operation 6836. The plots 7100, 7200 showing refractive index distribution versus simulated side scatter intensity data after the calibration in operation 6836 can be used for determining the refractive index of the sample particles analyzed by the system 10.

[0303] FIG. 73 illustrates a table 7300 that provides an example where an input particle size of 120 nm is received in operation 6832, an empirical side scatter intensity of 152556.8 for the first wavelength is detected in operation 6826, and a particle refractive index of 1.45037 is determined in operation 6840. The refractive index 1.45037 matches the example shown in FIG. 66 where the refractive index is received in operation 5332 and is used for estimating the particle size of 121.4 nm in accordance with the method 5300.

[0304] FIG. 74 illustrates a table 7400 that provides an example where an input particle size of 120 nm is received in operation 6832, an empirical side scatter intensity of 25194.4 for the second wavelength is detected in operation 6826, and a particle refractive index of 1.4434 is determined in operation 6840. The refractive index 1.4434 closely corresponds to the example shown in FIG. 67 where the refractive index is received in operation 5332 and is used for estimating the particle size of 119.7 nm in accordance with the method 5300.

[0305] In an alternative example, instead of separately using the empirical side scattered intensity data of the first and second wavelengths, the method 6800 can include in operation 6834 simulating ratios of side scatter intensities between the first and second wavelengths based on the Mie theory and the particle size input received in operation 6832. In such examples, operation 6836 includes calibrating the simulated ratios of side scatter intensities between the first and second wavelengths, operation 6838 includes plotting refractive indices versus the simulated ratios of side scatter intensities between the first and second wavelengths, and operation 6840 includes determining refractive index based on the empirical ratios of the side scatter data between the first and second wavelengths and the calibration of the simulated ratios of side scatter intensities between the first and second wavelengths.

[0306] FIG. 75 illustrates an example of a table 7500 that includes simulated ratios of side scatter intensities between the first and second wavelengths in accordance with the alternative of operation 6834 (shown in second column). The table 7500 further includes calibration of the simulated ratios of side scatter intensities between the first and second wavelengths in accordance with the alternative of operation 6836 (shown in fourth column).

[0307] FIG. 76 shows an example of a plot 7600 that can be generated in accordance with the alternative of operation 6838. The plot 7600 shows refractive index distribution of the second wavelength (y-axis) versus simulated ratios of side scatter intensities between the first and second wavelengths after calibration in operation 6836 (x-axis). In FIG. 76, the refractive indices are obtained from the first column of the table 7500, and the simulated ratios of scatter intensities between the first and second wavelengths are obtained from the fourth column of the table 7500.

[0308] FIG. 77 illustrates a table 7700 that provides an example where an input particle size of 120 nm is received in operation 6832, an empirical ratio of 6.055 for the side scatter intensity data between the first and second wavelengths is determined, and a refractive index of 1.4453 is determined in operation 6840. The refractive index 1.4453 determined in accordance with the alternative of the method 6800 matches the refractive index received in operation 5332 for estimating the particle size of 119.7 nm in accordance with the method 5300 (see FIG. 67).

[0309] In yet another alternative example, instead of calibrating the simulated side scattered data for the first and second wavelengths or the ratio of the simulated sidescatered data between the first and second wavelengths, operation 6836 can include calibrating the empirical side scatered data of the first and second wavelengths identified in operation 6826 or the empirical ratio of the side scatered data between the first and second wavelengths.

[0310] In such examples, operation 6818 of the instrument calibration phase 6802 includes a reversed calibration where the simulated data calculated in operation 6816 is correlated to the empirical data collected in operation 6814. Further, in such examples, operation 6838 of the processing phase 6806 can be skipped such that the method proceeds directly to operation 6840 of determining the particle refractive index based on matching the calibrated empirical side scatered data of the first and second wavelengths to the simulation performed in operation 6834.

[0311] FIG. 78 illustrates a comparison 7800 of gating strategies between the flow cytometer post-acquisition analysis software (FCMPASS) and the methods described above for determining particle size or refractive index. The comparison 7800 includes a first plot 7802 showing size calibration results from the FCMPASS for the first wavelength by itself, a second plot 7804 showing size calibration results from the FCMPASS for the second wavelength by itself, and a third plot 7806 showing a gating strategy7for the MLV virus using both the first and second wavelengths, as in the methods described above.

[0312] As shown in FIG. 78, the size calibration results are clearer in the first plot 7802 under the first wavelength than in the second plot 7804 under the second wavelength because a secondary' population appears to be merging into the size calibration results in the second plot 7804. In view of the foregoing, size and refractive index estimates by FCMPASS appears to be less accurate under the second wavelength than under the first wavelength.

[0313] The third plot 7806 shows a gating strategy of the MLV virus in accordance wi th the example methods described above. Due to difference in refractive indices on the first and second wavelengths, the population of the MLV virus is more clearly separated from the secondary population in the third plot 7806 which shows mean scater intensities under the first wavelength versus mean scater intensities under the second wavelength. As a result, the mean scater intensities on both the first and second wavelengths is more accurate, which can improve the accuracy of the size and refractive index estimates over the results from the FCMPASS.

[0314] In addition, the methods described above further provide a functionality to identify empirical mean scatter intensity of populations for the first and second wavelengths from the plots 6400, 6500, 7100, 7200, and 7600. As shown in FIG. 78, the methods described above provide an advantage of accurately defined mean scatter intensity on each of the first and second wavelengths. This allows users to gate specific populations, or alternatively, size and refractive index calibrations can be provided for all mean scatter intensities displayed on a given side scatter channel of the flow cytometer 100 in the system 10.

[0315] The various embodiments described above are provided by way of illustration only and should not be construed to be limiting in any way. Various modifications can be made to the embodiments described above without departing from the true spirit and scope of the disclosure.

Claims

What is claimed is:

1. A label-free method of characterizing particles by flow cytometry, the method comprising: illuminating the particles wi th at least a first excitation light beam of a first wavelength and a second excitation light beam of a second wavelength; collecting from at least a first detector side scattered light of the first wavelength and collecting from a second detector side scattered light of the second wavelength; and determining particle sizes based on the first median side scatter light intensities under the first wavelength and second median side scatter light intensities under the second wavelength.

2. The method of claim 1, further comprising: applying a calibration to the side scattered light of the first wavelength and the side scattered light of the second wavelength, wherein the calibration is determined from correlating empirical data to theoretical data for calibration beads having know n refractive indices and sizes.

3. The method of claim 1, further comprising: calculating ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength; and determining the particle sizes based on the ratios of the first median side scatter light intensities to the second median side scatter light intensities.

4. The method of claim 3, further comprising: applying a calibration to the ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength, wherein the calibration is determined from correlating empirical data to theoretical data for calibration beads having known refractive indices and sizes.

5. The method of claims 2 or 4. wherein the calibration is based on a linear correction factor, a quadratic correction factor, or a cubic correction factor.

6. The method of any of claims 1-5, wherein the particle sizes are determined for particles ranging in size between 30 nanometers and 400 nanometers.

7. The method of any of claims 1-6, wherein the particle sizes are determined for extracellular vesicles.

8. The method of any of claims 1-7, wherein the first wavelength is within a first spectrum of wavelengths and the second wavelength is within a second spectrum of wavelengths, wherein the second spectrum of wavelengths is different from the first spectrum of wavelengths, and wherein the first and second spectrums of wavelengths are selected from a group consisting of a first range of wavelengths between 325-450 nanometers, a second range of wavelengths between 450-490 nanometers, a third range of wavelengths between 560-590 nanometers, and a fourth range of wavelengths between 600-850 nanometers.

9. The method of any of claims 1-8, further comprising: estimating particle refractive indices based on the particle sizes.

10. The method of claim 9, wherein ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength are filtered after calibration to narrow down a range for estimating the particle refractive indices as a function of different particle sizes.

11. A system for performing label-free characterization of particles by flow cytometry, the system comprising: a light emitting unit configured to emit excitation light beams for projection onto the particles flowing through an interrogation zone, the light emitting unit including: a first laser emitting a first excitation light beam of a first wavelength; anda second laser emitting a second excitation light beam of a second wavelength; a collection unit including: a first detector for collecting side scattered light of the first wavelength; and a second detector for collecting side scattered light of the second wavelength; and a processing circuitry having a memory for storing instructions which, when executed by the processing circuitry', cause the processing circuitry' to: illuminate the particles with at least the first excitation light beam of the first wavelength and the second excitation light beam of the second wavelength; collect from at least the first detector side scattered light of the first wavelength and collect from the second detector side scattered light of the second wavelength; and determine particle sizes based on first median side scatter light intensities and second median side scatter light intensities.

12. The system of claim 11, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to: apply a calibration to the side scattered light of the first wavelength and the side scattered light of the second wavelength, wherein the calibration is determined from correlating empirical data to theoretical data for calibration beads having known refractive indices and sizes.

13. The system of claim 11, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to: calculate ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength; and determine the particle sizes based on the ratios of the first median side scatter light intensities to the second median side scatter light intensities.

14. The system of claim 13, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry’ to: apply a calibration to the ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength, wherein the calibration is determined from correlating empirical data to theoretical data for calibration beads having known refractive indices and sizes.

15. The system of claim 12 or 14, wherein the calibration is based on a linear correction factor, a quadratic correction factor, or a cubic correction factor.

16. The system of any of claims 12-15, wherein the particle sizes are determined for particles ranging in size between 30 nanometers and 400 nanometers.

17. The system of any of claims 12-16, wherein the particle sizes are determined for extracellular vesicles.

18. The system of any of claims 12-17, wherein the first wavelength is within a first spectrum of wavelengths and the second wavelength is within a second spectrum of wavelengths, wherein the second spectrum of wavelengths is different from the first spectrum of wavelengths, and wherein the first and second spectrums of wavelengths are selected from a group consisting of a first range of wavelengths between 325-450 nanometers, a second range of wavelengths between 450-490 nanometers, a third range of wavelengths between 560-590 nanometers, and a fourth range of w avelengths between 600-850 nanometers.

19. The system of any of claims 11-18, wherein the instructions, when executed by the processing circuitry, further cause the processing circuitry to: estimate particle refractive indices based on the particle sizes.

20. The system of claim 19, wherein ratios of the first median side scatter light intensities under the first wavelength to the second median side scatter light intensities under the second wavelength are filtered after calibration to narrow down a range for estimating the particle refractive indices as a function of different particle sizes.

21. A label-free method of characterizing particles by flow cytometry, the method comprising: passing particles of a biological sample through an interrogation zone for illumination under a plurality of different light wavelengths; capturing side scattered light data from the particles of the biological sample, the side scattered light data including a first intensity in a violet light spectrum, a second intensity in a blue light spectrum, a third intensity in a yellow light spectrum, and a fourth intensity in a red light spectrum; calculating differential light side scatter parameters between the particles of the biological sample having an unknown amount of loading and particles of the biological sample having zero loading; applying a first calibration equation to calibrate the differential light side scatter parameters between the particles of the biological sample having the unknown amount of loading and the particles of the biological sample having the zero loading; calculating refractive index differentials as a function of the differential light side scatter based on size and refractive index of the particles of the biological sample; applying a second calibration equation to calibrate the refractive index differentials; and characterizing the unknown loading on the particles of the biological sample based on the refractive index differentials calibrated by the second calibration equation.

22. The method of claim 21, wherein the second calibration equation is generated by: passing calibration beads through the interrogation zone for illumination under the plurality of different light wavelengths, the calibration beads including calibration beads having a known amount of loading and calibration beads having zero loading; capturing side scattered light data from the calibration beads passing through the interrogation zone, the side scattered light data including the first intensity in the violet light spectrum, the second intensity in the blue light spectrum, the third intensity in the yellow light spectrum, and the fourth intensity in the red light spectrum;calculating differential light side scatter parameters between the calibration beads having the known amount of loading and the calibration beads having the zero loading; comparing the differential light side scatter parameters with theoretical side scattered light data to generate the first calibration equation; calculating refractive index differentials as a function of the differential light side scatter based on size and refractive index of the calibration beads; and generating the second calibration equation based on the refractive index differentials of the calibration beads having the know n amount of loading and the calibration beads having the zero loading.

23. The method of claim 22, w herein the calibration beads include silica beads having a known loading, or biological particles having a known quantity of biomolecules attached via covalent or non-covalent bonding.

24. The method of any of claims 21-23, wherein determining the sizes of the particles in the biological sample includes: calculating ratios of the first intensities in the violet light spectrum to the fourth intensities in the red light spectrum; calibrating the ratios by applying the first calibration equation; filtering the ratios based on a difference between refractive indices estimated under the first intensities in the violet light spectrum and refractive indices estimated under the fourth intensities in the red light spectrum; and determining the sizes of the particles based on the ratios of the first intensities in the violet light spectrum to the fourth intensities in the red light spectrum.

25. The method of any of claims 21-24, further comprising: sorting the biological sample based on the loading on the particles.

26. The method of any of claims 21-25, further comprising: characterizing single particle surface marker interactions with corresponding ligands including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin-biotin interactions .

27. The method of any of claims 21-26, further comprising: characterizing single particle modification including covalently or non- covalently attached molecules on particle surface.

28. The method of any of claims 21-27, further comprising: monitoring biomolecules on particle surfaces including antigen expression level or protein expression level.

29. The method of any of claims 21-28, further comprising: monitoring cargo loading inside particle envelop such as liposome particles, hydrogel particles, and virus capsids, wherein the cargo loading includes deoxyribonucleic acid (DNA), messenger ribonucleic acid (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes.

30. The method of any of claims 21-29, further comprising: monitoring protein aggregation inside the particles of the biological sample and on an outer surface of the particles of the biological sample.

31. A method of determining protein loading presence on biological particles, the method comprising: receiving input parameters of the biological particles for analysis by a flow cytometer; extracting mean scatter intensity from empirical data of the biological particles measured by the flow cytometer; calculating a percent change between the mean scatter intensity of stained biological particles and the mean scatter intensity of unstained biological particles; calculating scatter intensity for the unstained biological particles by using the input parameters and Mie scattering core-shell modeling; applying a normalization to the scatter intensity calculated for the unstained biological particles; calculating scatter intensity of the stained biological particles using the scatter intensity of the unstained biological particles and the percent change between the meanscater intensity of the stained biological particles and the mean scater intensity of the unstained biological particles; calculating a refractive index of a shell of the stained biological particles based on the input parameters and the scater intensity of the stained particles by using the Mie scatering core-shell modeling; calculating a delta refractive index by subtracting a refractive index of a sheath fluid from the refractive index of the shell of the biological particles; and characterizing the protein loading presence on the biological particles based on the delta refractive index.

32. The method of claim 31. further comprising: sorting the biological particles based on characterization of the protein loading presence on the biological particles.

33. The method of claim 31 or 32, further comprising: characterizing single particle surface marker interactions with corresponding ligands including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin-biotin interactions.

34. The method of any of claims 31-33, further comprising: characterizing single particle modification including covalently or non- covalently atached molecules on particle surface.

35. The method of any of claims 31-34, further comprising: monitoring biomolecules on particle surfaces including antigen expression level or protein expression level.

36. The method of any of claims 31-35, further comprising: monitoring cargo loading inside particle envelop such as liposome particles, hydrogel particles, and virus capsids, wherein the cargo loading includes deoxyribonucleic acid (DNA), messenger ribonucleic acid (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes.

37. The method of any of claims 31-36, further comprising: monitoring protein aggregation inside the biological particles and on an outer surface of the biological particles.

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