Particle characterization by flow cytometry

Abstract

A label-free method of characterizing a particle by flow cytometry. The method includes illuminating the particle 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 side scatter light of the first wavelength from at least a first detector and collecting side scatter light of the second wavelength from at least a second detector. The method includes determining a particle size based on a first median side scatter light intensity at the first wavelength and a second median side scatter light intensity at the second wavelength.

Description

[0001] Cross-references to related applications

[0002] This application claims the benefit of U.S. Provisional Application No. 63 / 595,928, filed November 3, 2023; U.S. Provisional Application No. 63 / 561,405, filed March 5, 2024; and U.S. Provisional Application No. 63 / 680,717, filed August 8, 2024, the disclosures of which are incorporated herein by reference in their entirety. Background Technology

[0003] In flow cytometry, particles are arranged in a sample stream and typically pass one after another through one or more excitation beams, interacting with each beam. The light scattered or emitted by the particles during their interaction with the excitation beams is collected and analyzed to characterize and differentiate the particles. In sorting flow cytometry, particles can be extracted from the sample stream after being characterized by their interactions with one or more excitation beams, and then sorted into different groups.

[0004] Light scattered by particles is typically measured in two directions: forward-scattered light parallel to the excitation beam and side-scattered light orthogonal to the excitation beam. The side-scattered light signal is weaker than the forward-scattered light signal. Conventional flow cytometers typically include a single detector for detecting side-scattered light at a specific wavelength, which limits the use of side-scattered light signals to characterize particles via flow cytometry. Summary of the Invention

[0005] Generally, this disclosure relates to characterizing particles by flow cytometry. In one possible configuration, particles are characterized using a label-free technique that includes detecting side-scattered light at multiple wavelengths. Various aspects are described in this disclosure, including but not limited to the following.

[0006] One aspect relates to a label-free method for characterizing particles by flow cytometry, the method comprising: irradiating the particles with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength; collecting side-scattered light of a first wavelength from at least a first detector and collecting side-scattered light of a second wavelength from a second detector; determining coordinates for each particle including the intensity of the side-scattered light of the first wavelength and the intensity of the side-scattered light of the second wavelength; fitting the coordinates to a trend at least for a first population of particles; and characterizing the particles in the first population by matching the trend with a predetermined trend of values ​​representing characteristics associated with the particles.

[0007] On the other hand, a system for performing label-free characterization of particles by flow cytometry is provided, the system comprising: a light emitting unit configured to emit an excitation beam to project onto particles flowing through an interrogation region, the light emitting unit comprising: a first laser emitting a first excitation beam of a first wavelength; and a second laser emitting a second excitation beam of a second wavelength; a collection unit comprising: 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 circuit system having a memory for storing instructions, which, when executed by the processing circuit system, cause the processing circuit system to: irradiate the particles with at least the first excitation beam of the first wavelength and the second excitation beam of the second wavelength; collect the side-scattered light of the first wavelength from the first detector and collect the side-scattered light of the second wavelength from the second detector; determine coordinates including the intensity of the side-scattered light of the first wavelength and the intensity of the side-scattered light of the second wavelength; fit the coordinates to a trend at least for a first population of particles; and characterize the particles in the first population by matching the trend with a predetermined trend of values ​​representing characteristics associated with the particles.

[0008] On the other hand, a label-free method for characterizing particles by flow cytometry is involved, the method comprising: irradiating particles with multiple excitation beams having wavelengths between 300 nm and 825 nm; collecting side-scattered light generated by exciting particles with the multiple excitation beams from multiple detectors; determining coordinates for each particle including the intensity of side-scattered light of at least a first wavelength and the intensity of side-scattered light of a second wavelength; fitting the coordinates to at least a first trend and a second trend; and distinguishing a first group of particles from a second group of particles based on the first trend and the second trend.

[0009] On the other hand, a label-free method for characterizing particles by flow cytometry is involved, the method comprising: irradiating particles with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength; collecting side-scattered light of at least a first wavelength from a first detector and collecting side-scattered light of a second wavelength from a second detector; calculating a ratio of a first median side-scattered light intensity at the first wavelength to a second median side-scattered light intensity at the second wavelength; calibrating the ratio by applying a calibration equation; filtering based on a ratio of the difference between the refractive index estimated at the first wavelength and the refractive index estimated at the second wavelength; and determining the particle size based on the ratio of the first median side-scattered light intensity to the second median side-scattered light intensity filtered across the refractive index estimated at the first and second wavelengths.

[0010] On the other hand, a system for performing label-free characterization of particles by flow cytometry is provided, the system comprising: a light emitting unit configured to emit an excitation beam for projection onto particles flowing through an interrogation region, the light emitting unit comprising: a first laser emitting a first excitation beam of a first wavelength; and a second laser emitting a second excitation beam of a second wavelength; a collection unit comprising: 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 circuit system having a memory for storing instructions, the instructions being processed by the processing circuit system. When the circuit system is executed, the processing circuit system: illuminates the particle with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength; collects at least the side-scattered light of the first wavelength from a first detector and the side-scattered light of the second wavelength from a second detector; calculates the ratio of the first median side-scattered light intensity at the first wavelength to the second median side-scattered light intensity at the second wavelength; calibrates the ratio by applying a calibration equation; filters the ratio based on the difference between the refractive index estimated at the first wavelength and the refractive index estimated at the second wavelength; and determines the particle size based on the ratio of the first median side-scattered light intensity to the second median side-scattered light intensity filtered across the refractive indices estimated at the first and second wavelengths.

[0011] On the other hand, a label-free method for characterizing particles by flow cytometry is involved, the method comprising: passing particles of a biological sample through an interrogation region to irradiate them at multiple 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 spectrum, a second intensity in a blue spectrum, a third intensity in a yellow spectrum, and a fourth intensity in a red spectrum; calculating differential optical side-scattering parameters between particles of a biological sample with an unknown loading and particles of a biological sample with zero loading; calibrating the differential optical side-scattering parameters between particles of a biological sample with an unknown loading and particles of a biological sample with zero loading using a first calibration equation; calculating a refractive index difference based on the size and refractive index of the particles of the biological sample according to the differential optical side-scattering; calibrating the refractive index difference using a second calibration equation; and characterizing the unknown loading on the particles of the biological sample based on the refractive index difference calibrated by the second calibration equation.

[0012] On the other hand, a method for determining the presence of protein loading on biological particles is involved, the method comprising: receiving input parameters of biological particles for analysis by flow cytometry; extracting the mean scattering intensity from empirical data of biological particles measured by flow cytometry; calculating the percentage change between the mean scattering intensity of stained biological particles and the mean scattering intensity of unstained biological particles; calculating the scattering intensity of unstained biological particles using the input parameters and Mie scattering core-shell modeling; applying normalization to the scattering intensity calculated for unstained biological particles; calculating the scattering intensity of stained biological particles using the scattering intensity of unstained biological particles and the percentage change between the mean scattering intensity of stained biological particles and the mean scattering intensity of unstained biological particles; calculating the refractive index of the shell of stained biological particles based on the input parameters and the scattering intensity of stained particles using Mie scattering core-shell modeling; calculating the incremental refractive index by subtracting the refractive index of the sheath fluid from the refractive index of the shell of the biological particles; and characterizing the presence of protein loading on the biological particles based on the incremental refractive index.

[0013] Various additional aspects will be set forth in the following description. These aspects may involve individual features and combinations of features. It should be understood that both the above general description and the following detailed description are exemplary and illustrative only, and do not limit the broad inventive concept on which the embodiments disclosed herein are based. Attached Figure Description

[0014] The following figures, which form part of this application, are illustrations of the described technology and are not intended to limit the scope of this disclosure in any way.

[0015] Figure 1 An example of a system for performing flow cytometry is shown, which includes a flow cytometer and a workstation.

[0016] Figure 2 schematically shown Figure 1 An example of a flow cytometer.

[0017] Figure 3 An example of a method for characterizing particles by flow cytometry is illustrated schematically. This method can be... Figure 2 The flow cytometer was used for the procedure.

[0018] Figure 4 A comparison of histograms is shown, the histograms showing the results of... Figure 2 The intensity of lateral scattered light collected from plasma extracellular vesicle (EV) samples by flow cytometry is used to count particles.

[0019] Figure 5 A comparison of dot plots is shown, the dot plots being displayed based on... Figure 2The intensity of the first wavelength of side-scattered light generated by the flow cytometer from the plasma EV sample is compared with the intensity of the second wavelength of side-scattered light.

[0020] Figure 6 yes Figure 5 The detailed view of the dot plot shown in the image.

[0021] Figure 7 A dot plot is shown, which compares the intensity of side-scattered light in the violet spectrum of a sample containing a virus expressing green fluorescent protein (GfP) with the intensity of side-scattered light in the red spectrum.

[0022] Figure 8 A dot plot is shown, which compares the intensity of side-scattered light in the violet spectrum with the intensity of side-scattered light in the red spectrum for polystyrene latex (PSL) microspheres of different sizes and known refractive indices.

[0023] Figure 9 A dot plot is shown, which illustrates the overlap between the intensity of side-scattered light in the violet spectrum and the intensity of side-scattered light in the red spectrum for a sample containing GfP virus and PSL microspheres.

[0024] Figure 10 A dot plot is shown, which compares the intensity of lateral scattered light in the violet spectrum of samples containing reticuloendothelial viral (rEV) and GfP virus with the intensity of lateral scattered light in the red spectrum.

[0025] Figure 11 A dot plot of intensity data for PSL microspheres with different sizes and known refractive indices is shown at violet and red light wavelengths.

[0026] Figure 12 A dot plot of theoretical intensity data generated by a Mie scattering simulator is shown, taking into account the known refractive index and different sizes of PSL microspheres.

[0027] Figure 13 A dot plot is shown illustrating the radian sensitivity of an avalanche photodiode (APD) across different light wavelengths.

[0028] Figure 14 A dot plot is shown, which illustrates the empirical ratio of median red lateral scattering intensity to median purple lateral scattering intensity for each PSL microsphere size, compared to a first theoretical ratio that does not consider APD curvature sensitivity and a second theoretical ratio that does consider APD curvature sensitivity.

[0029] Figure 15 A dot plot of intensity data for silica microspheres with different sizes and known refractive indices at violet and red light wavelengths is shown.

[0030] Figure 16 A dot plot of theoretical intensity data generated from a Mie scattering simulator is shown, taking into account different sizes and refractive indices of silica microspheres.

[0031] Figure 17 A dot plot is shown, which illustrates the empirical ratio of median red lateral scattering intensity to median purple lateral scattering intensity for each silica microsphere size, compared to a first theoretical ratio that does not consider APD curvature sensitivity and a second theoretical ratio that does consider APD curvature sensitivity.

[0032] Figure 18 A dot plot is shown, which will Figure 14 The second theoretical ratio of the PSL microspheres shown is... Figure 17 The second theoretical ratio of the silica microspheres shown is compared.

[0033] Figure 19 A dot plot is shown, which will Figure 14 The empirical ratio of PSL microspheres shown is... Figure 17 The empirical ratios of the silica microspheres shown are compared.

[0034] Figure 20 A dot plot is shown, which compares the trend of the intensity of lateral scattered light in the violet spectrum of PSL microspheres to the intensity of lateral scattered light in the red spectrum with the trend of the intensity of lateral scattered light in the violet spectrum of silica microspheres to the intensity of lateral scattered light in the red spectrum.

[0035] Figure 21 Data collected from plasma EVs are shown. Plasma EVs include a first group with a first trend of a ratio between purple lateral scattering and red lateral scattering, and a second group with a second trend of a ratio between purple lateral scattering and red lateral scattering.

[0036] Figure 22 Data collected from samples of mouse leukemia virus (MLV) and reticuloendothelial tissue proliferating virus (rEV) are shown. The samples include a first population with a first trend of a ratio between purple and red side scattering and a second population with a second trend of a ratio between purple and red side scattering.

[0037] Figure 23 The schematic diagram illustrates the method used to implement Figure 1 Examples of various aspects of a computing system.

[0038] Figure 24 An example of generating a method that can be used to characterize particle function by flow cytometry is illustrated schematically. Figure 1 The system execution.

[0039] Figure 25 The illustration shows that generation can be performed... Figure 24 An example of a method using calibration equations in a given method.

[0040] Figure 26a It shows the execution Figure 25 The method is a point plot of empirical purple side-scattered light data captured from calibration microspheres.

[0041] Figure 26b It shows the execution Figure 25 The method is a point plot of empirical red side-scattered light data captured from calibration microspheres.

[0042] Figure 26c It shows that it can be based on Figure 25 The method generates a dot plot comparing the intensity of purple side-scattered light with that of red side-scattered light.

[0043] Figure 27 It shows that it can be based on Figure 25 A dot plot comparing the theoretical intensity of purple side-scattered light with the intensity of red side-scattered light, calculated using the method described above.

[0044] Figure 28a It shows that it includes execution Figure 25 The method captures empirical data and during execution Figure 25 The method is to calculate a point plot of theoretical data.

[0045] Figure 28b This shows the effect after applying quadratic correlation to empirical data. Figure 28a A point plot of empirical data.

[0046] Figure 29 A dot plot is shown, which includes the execution Figure 24 The method is a calibration ratio between the first wavelength scattering intensity and the second wavelength scattering intensity, calculated theoretically for each of the multiple refractive indices.

[0047] Figure 30 A chart is shown, which includes the execution Figure 24 The method involves a first set of refractive index values ​​between violet and red side-scattered light with a high refractive index (RI) difference, a second set of refractive index values ​​between violet and red side-scattered light with a medium RI difference, and a third set of refractive index values ​​between violet and red side-scattered light with a low RI difference.

[0048] Figure 31 It shows that it can be executed Figure 24A graph showing the particle size determined by the calibration ratio of the purple side-scatter intensity to the red side-scatter intensity of mouse leukemia virus (MLV) generated by the method.

[0049] Figure 32 It shows that it can be executed Figure 24 A graph showing the particle size determined by the calibration ratio of the purple side-scattered light intensity to the red side-scattered light intensity of the reticuloendothelial tissue proliferating virus (rEV) generated by the method.

[0050] Figure 33 It shows according to Figure 24 A dot plot of the calibration ratio of purple side-scattered light intensity to red side-scattered light intensity of rEV generated by the method.

[0051] Figure 34 This illustrates gating between individual groups of different size ranges. Figure 33 A dot plot comparing the intensity of purple side-scattered light with the intensity of red side-scattered light in rEV.

[0052] Figure 35 It shows according to Figure 24 The method generates a dot plot of the calibrated ratio of purple side-scattered light intensity to red side-scattered light intensity from microvesicles of the Daudi cell line.

[0053] Figure 36 It shows the relationship with Figure 35 The dot plot corresponding to the dot plot.

[0054] Figure 37 It shows according to Figure 24 A dot plot showing the calibrated ratio of the intensity of purple lateral scattered light to the intensity of red lateral scattered light from extracellular vesicles in urine generated by the method.

[0055] Figure 38 It shows the relationship with Figure 37 The dot plot corresponding to the dot plot.

[0056] Figure 39 The illustration schematically shows an example of a method for characterizing protein load on particles by flow cytometry, which can be performed by... Figure 1 The flow cytometer was used for the procedure.

[0057] Figure 40 It schematically shows that in Figure 39 Examples of sub-operations performed within the operations of a method.

[0058] Figure 41 It schematically shows that it can be made by Figure 1 This is an example of a system-executable method for determining the presence of protein loads on biological samples.

[0059] Figure 42 It shows including from according to Figure 41 The table shows the values ​​of the experiments performed using the method.

[0060] Figure 43 Another table is shown, which includes information for... Figure 42 The table shows the core-shell modeling of the V5 antibody against viruses expressing the V5 tag and monoclonal anti-V5 antibodies (designated as V5) that bind to the V5 tag to stain the virus.

[0061] Figure 44 Another table is shown, which includes information for... Figure 42 The table shows core-shell modeling of fragment antibodies (Fab), including monoclonal fragment Fab antibodies that bind to... Figure 43 The anti-V5 antibody described herein is used for staining anti-V5 antibodies bound to the virus (designated as Fab).

[0062] Figure 45 Another table is shown, which includes information for... Figure 42 The table shows core-shell modeling of immunoglobulin G (IgG) antibodies, including monoclonal IgG antibodies, which bind to... Figure 43 The anti-V5 antibody described herein is used to stain anti-V5 antibodies (designated as IgG) bound to the virus.

[0063] Figure 46 It shows including according to Figure 41 The method is used to calculate the incremental refractive index values ​​for V5 staining antibodies, Fab antibodies, and IgG antibodies.

[0064] Figure 47 A graph is shown, which indicates that according to Figure 41 The method involves calculating the shell refractive index and contrasting scattering intensity for V5 staining antibodies, Fab antibodies, and IgG antibodies.

[0065] Figure 48 This schematically illustrates another example of a method for generating particles whose function is characterized by flow cytometry, a method by... Figure 1 The system execution.

[0066] Figure 49 It shows that it can be based on Figure 48 The empirical data collection section of this method includes examples of mouse leukemia virus (MLV) virus data collected.

[0067] Figure 50 It shows including according to Figure 48 An example of a table whose values ​​are calculated using the methods described above.

[0068] Figure 51 The diagram is shown graphically. Figure 48 The calibration ratio is calculated based on particle size during the operation of the method.

[0069] Figure 52 This schematically illustrates another example of a method for generating a function of particles for characterization by flow cytometry, a method by... Figure 1 The system execution.

[0070] Figure 53 An example of a method for characterizing particles by flow cytometry is illustrated schematically. This method can be... Figure 1 The system execution.

[0071] Figure 54 It shows including according to Figure 53 Example of a table of empirical data collected during the instrument calibration phase of the method.

[0072] Figure 55 It shows including according to Figure 53 An example of a table of simulated data calculated during the instrument calibration phase of the method.

[0073] Figure 56 It shows the results based on Figure 53 The method of instrument calibration phase operation Figure 54 The table includes empirical data and Figure 55 An example of a curve plotting the correlation between simulated data in a table.

[0074] Figure 57 It shows including according to Figure 53 Example of a table of empirical data collected during the instrument calibration phase of the method.

[0075] Figure 58 It shows including according to Figure 53 An example of a table of simulated data calculated during the instrument calibration phase of the method.

[0076] Figure 59 It shows the results based on Figure 53 The method of instrument calibration phase operation Figure 57 The table includes empirical data and Figure 58 An example of a curve plotting the correlation between simulated data in a table.

[0077] Figure 60 The diagram is shown graphically according to Figure 53 The method confirms the operation of the empirical data collection phase of the first and second wavelength empirical MSI, and provides an example of a dot plot of empirical data of MLV virus.

[0078] Figure 61 It shows including targets Figure 60 The dot plot shows an example of a table of MSI for different lateral scattering channels from empirical data collected from the MLV virus.

[0079] Figure 62 It shows including according to Figure 53 This is an example of a table of simulated data processed during the processing phase of the method.

[0080] Figure 63 It shows including according to Figure 53 This is an example of a table of simulated data processed during the processing phase of the method.

[0081] Figure 64 It shows that it can be based on Figure 53 An example of a point graph generated by the method.

[0082] Figure 65 It shows that it can be based on Figure 53 Another example of a point graph generated by the method.

[0083] Figure 66 It shows that the provision is based on Figure 53 The method uses the lateral scattering intensity at the first wavelength to determine the particle size, as shown in the example table.

[0084] Figure 67 It shows that the provision is based on Figure 53 The method uses the lateral scattering intensity at a second wavelength to determine the particle size, as shown in the example table.

[0085] Figure 68 This schematically illustrates another example of a method for characterizing particles using flow cytometry, which can be performed by... Figure 1 The system execution.

[0086] Figure 69 It shows including according to Figure 68 The method is an example of a table of simulated side scattering intensity data calculated for the first wavelength.

[0087] Figure 70 It shows including according to Figure 68 The method is an example of a table of simulated side scattering intensity data calculated for the second wavelength.

[0088] Figure 71 It shows that it can be based on Figure 68 An example of a point map generated by this method.

[0089] Figure 72 It shows that it can be based on Figure 68 Another example of a point graph generated by this method.

[0090] Figure 73It shows the provision based on Figure 68 A table showing examples of methods for calculating the refractive index of particles.

[0091] Figure 74 It shows the provision based on Figure 68 The table shows another example of the method for calculating the refractive index of particles.

[0092] Figure 75 It shows including according to Figure 68 An example table of simulated ratios of lateral scattering intensity between the first and second wavelengths of an alternative method.

[0093] Figure 76 It shows that it can be based on Figure 68 An example of a point plot generated by an alternative method.

[0094] Figure 77 It shows the provision based on Figure 68 The table provides examples of alternative methods for calculating the refractive index of particles.

[0095] Figure 78 The software for post-acquisition analysis by flow cytometry (FCM) is shown. PASS A comparison with the methods described above. Detailed Implementation

[0096] Various embodiments will be described in detail with reference to the accompanying drawings, wherein the same reference numerals denote the same parts and components throughout the views. References to various embodiments do not limit the scope of the appended claims. Furthermore, any examples set forth in this specification are not intended to be limiting and merely illustrate some of the many possible embodiments of the appended claims.

[0097] This document describes an example detection system for use in flow cytometers. It should be understood that this disclosure is not limited to the detection system shown, but can be applied to flow cytometers with other types of detection systems.

[0098] Figure 1 An example of a system 10 that can be used to perform flow cytometry is shown. System 10 includes a flow cytometer 100 and a workstation 200. Typically, the flow cytometer 100 is an analytical instrument for detecting the physical and chemical properties of a sample 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 the size, concentration, and loading of nanoparticles to understand biological mechanisms of action and nanoparticle sources. The flow cytometer 100 can collect data from millions of particles or cells within minutes for display on a display monitor 204 of the workstation 200 in various formats.

[0099] The flow cytometer 100 includes a housing 101 with a sample station 104 that 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 into the sample station 104, the flow cytometer 100 can obtain a sample from the container 106 to perform flow cytometry experiments. 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.

[0100] The flow cytometer 100 may also include a sheath fluid container 107 for containing a sheath fluid mixed with the sample during flow cytometry experiments. The sheath fluid is pumped into the flow cytometer 100, thereby inducing 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 in a single file in the direction of laminar flow. The sheath fluid container 107 is connected to the flow cytometer 100 via a tube 109.

[0101] The flow cytometer 100 may also include a waste container 108 for collecting waste liquid. The waste container 108 is connected to the flow cytometer 100 via a tube 109.

[0102] Workstation 200 is connected to flow cytometer 100 via a wired or wireless connection to receive data from flow cytometer 100 for display on display monitor 204. Workstation 200 includes one or more user input devices such as mouse 206 and keyboard 208, enabling users of system 10 to input data and information, control flow cytometer 100, and change the display of data on display monitor 204.

[0103] Workstation 200 also includes computing device 202. In some examples, workstation 200 utilizes computing device 202 to process raw data received from flow cytometer 100. Alternatively or additionally, flow cytometer 100 may include computing device to process data collected from flow cytometry. In such an example, flow cytometer 100 sends processed data to workstation 200 for display on display monitor 204.

[0104] Figure 2 An example of a flow cytometer 100 is schematically illustrated. The flow cytometer 100 detects and analyzes nanoscale particles, such as particles with a diameter less than 100 nanometers (nm). Furthermore, the flow cytometer can detect and analyze particles with larger sizes, such as particles with a diameter greater than 100 nm. The flow cytometer 100 includes a light emitting unit 110 and a light collecting unit 120 that detects the characteristics of particles passing through a flow chamber 15.

[0105] The light emitting unit 110 emits one or more excitation beams for projection onto particles flowing through the interrogation zone 18 in the flow chamber 15. The light collecting unit 120 collects the light scattered or emitted from the particles flowing through the interrogation zone 18 for use by the computing device 2300 (see...). Figure 23 )analyze.

[0106] The light emitting unit 110 includes multiple light sources 111a to 111d, such as a first light source 111a, a second light source 111b, a third light source 111c, and a fourth light source 111d. The multiple light sources 111a to 111d may include more than four light sources or fewer than four light sources. The multiple light sources 111a to 111d may include lasers.

[0107] Multiple light sources 111a to 111d emit excitation beams in the range of about 300 nm to about 825 nm. As another example, light sources 111a to 111d emit excitation beams in the range of about 325 nm to about 808 nm. Each of the multiple light sources 111a to 111d emits an excitation beam of a specific wavelength. As an illustrative example, the first light source 111a emits an excitation beam in the red light spectrum (e.g., 600 nm to 850 nm), the second light source 111b emits an excitation beam in the yellow light spectrum (e.g., 560 nm to 590 nm), the third light source 111c emits an excitation beam in the blue light spectrum (e.g., 450 nm to 490 nm), and the fourth light source 111d emits an excitation beam in the violet light spectrum (e.g., 325 nm to 450 nm).

[0108] exist Figure 2 In the example shown, light sources 111a to 111d are arranged in parallel. It should be understood that the number, type, and arrangement of light sources are not limited to the examples shown and described herein and can be changed as needed. For example, the system may include three, five, six, or any other suitable number of light sources.

[0109] The light emitting unit 110 also includes a focusing lens 119. The focusing lens 119 is configured to focus the excitation beam for high scattering intensity detection of particles. For example, the excitation beam emitted by the light sources 111a to 111d passes through the focusing lens 119, which focuses the excitation beam onto the interrogation zone 18 of the flow cell 15. The interrogation zone 18 may also be referred to as the focal point, where the focused excitation beam encounters the nuclear sample stream in the flow cytometer 100.

[0110] Dichroic mirrors 117a, 117b, 117c, and 117d are arranged between the focusing lens 119 and the corresponding light sources 111a to 111d. Each of the dichroic mirrors 117a to 117d is configured to reflect the light beam from one of the corresponding light sources 111a to 111d and transmit the light beams from the other light sources. The dichroic mirrors 117a to 117d are selected and configured according to the wavelength of the light beam emitted by the corresponding light source 111a to 111d. For example, dichroic mirror 117a reflects light of wavelength emitted by light source 111a toward focusing lens 119; dichroic mirror 117b reflects and transmits light of wavelength emitted by light source 111b toward focusing lens 119; dichroic mirror 117c reflects and transmits light of wavelength emitted by light sources 111a and 111b toward focusing lens 119; and dichroic mirror 117d reflects and transmits light of wavelength emitted by light sources 111a, 111b, and 111c toward focusing lens 119.

[0111] The light beams emitted by light sources 111a to 111d are reflected or transmitted through dichroic mirrors 117a to 117d to form collinear beams. The collinear beams share an optical axis and provide a common focal point for multiple light sources by focusing on the same interrogation point. The dichroic mirrors 117a to 117d are adjustable in position or orientation, allowing them to be used to adjust the position of the focal point of the beam, particularly in a plane perpendicular to the optical axis.

[0112] Lenses 115a to 115d are arranged between the respective light sources 111a to 111d and the respective dichroic mirrors 117a to 117d. In some examples, lenses 115a to 115d are telephoto lenses. In some examples, lenses 115a to 115d are spherical lenses. In other examples, lenses 115a to 115d are aspherical lenses. Each of lenses 115a to 115d can convert the light beam into a parallel beam. Figure 2 In the example shown, each of lenses 115a to 115d is in the form of a plano-convex lens having a flat surface and a convex surface opposite each other.

[0113] Lenses 115a to 115d are adjustable in position or orientation to adjust the position of the focal point of the light beam, particularly in the plane perpendicular to the optical axis. Typically, dichroic mirrors 117a to 117d can be used for coarse adjustment of the focal point of the light beam, while lenses 115a to 115d can be used for fine adjustment of the focal point of the light beam.

[0114] It should be understood that the number, type, and arrangement of the dichroic mirrors 117a to 117d and the lenses 115a to 115d can be changed as needed and are not limited to the examples shown herein. Furthermore, the dichroic mirrors 117a to 117d and the lenses 115a to 115d can be replaced with other optical elements or optical modules with similar functions.

[0115] Beam expanders 113a to 113d are arranged between the respective light sources 111a to 111d and the respective lenses 115a to 115d. Each of the beam expanders 113a to 113d can change the cross-sectional size and divergence angle of the beam. In this way, each of the beam expanders 113a to 113d can be configured according to the desired size of the beam spot.

[0116] The light beam illuminating the particle through the focusing lens 119 has a spot size that allows for a more concentrated beam with higher power density. This increases the intensity of the light beam illuminating the particle and ultimately increases the intensity of the optical signal collected from the particle. This improves the efficiency of optical signal collection and thus provides higher resolution and higher sensitivity for nanoparticle detection.

[0117] exist Figure 2 In the example shown, the light sources 111a to 111d are in the form of lasers including corresponding laser diodes 112a to 112d. Figure 2 As further shown in the example, half-wave plates 116a to 116d are respectively placed between dichroic mirrors 117a to 117d and lenses 115a to 115d. The beam spot can be reduced by the orientation of the light sources 111a to 111d and by the use of half-wave plates 116a to 116d.

[0118] like Figure 2 As further shown, cylindrical lenses 114a to 114d are positioned between the respective beam expanders 113a to 113d and the respective lenses 115a to 115d. The horizontal size of the beam focused in the flow chamber 15 can be adjusted by replacing the cylindrical lenses 114a to 114d with alternative cylindrical lenses having different curvatures. The power of some or all of the light sources 111a to 111d can also be increased. The increased power of the light sources 111a to 111d can also improve the detection sensitivity.

[0119] Each of the beam expanders 113a to 113d is formed by a first optical section and a second optical section. Figure 2 In the example shown, each of the beam expanders 113a to 113d includes a concave lens adjacent to the corresponding light source as a first optical element, and also includes a convex lens distant from the corresponding light source as a second optical element. It should be understood that each of the beam expanders 113a to 113d is not limited to... Figure 2The example shown. Beam expanders 113a to 113d can be formed by any suitable optical lens or lens group. For example, each of the first and second optical parts can be selected from a convex lens, a convex lens group, a concave lens, and a concave lens group.

[0120] For each of the beam expanders 113a to 113d, the distance between the first optics (e.g., a concave lens) and the second optics (e.g., a convex lens) is adjustable. This allows for adjustment of the waist position (focal point) of the beam along the optical axis.

[0121] As described above, by adjusting the dichroic mirrors 117a to 117d, lenses 115a to 115d, and beam expanders 113a to 113d, individual beams can be focused at a desired interrogation point, and multiple beams can be focused at the same interrogation point. It should be understood that the position of the beam's focal point can be adjusted using any other optical element or by any other adjustment method. Adjustments to one or more of the dichroic mirrors 117a to 117d, lenses 115a to 115d, and beam expanders 113a to 113d can be performed manually or electronically using a computing device (e.g., a controller) associated with one or more actuators coupled to these components.

[0122] The light collection unit 120 includes a lateral collection unit 130 and a forward collection unit 150. The lateral collection unit 130 collects the lateral scattered light and fluorescence emitted from the particles as they pass through the flow chamber 15 and are irradiated by the excitation beam. The optical axis of the beam collected from the particles by the lateral collection unit 130 is approximately perpendicular to or at about 90 degrees to the optical axis of the beam emitted from the light sources 111a to 111d and directed toward the flow chamber 15 by the dichroic mirrors 117a to 117d.

[0123] The forward collecting unit 150 collects forward-scattered light from the particles. The optical axis of the beam collected from the particles by the forward collecting unit 150 can be approximately parallel to or at about 0 degrees to the optical axis of the beam directed toward the flow chamber 15. The lateral collecting unit 130 and the forward collecting unit 150 will be described in further detail below.

[0124] The lateral collection unit 130 includes: an optical focusing lens group comprising a concave mirror 134 and an aspherical lens 135; a collecting fiber 136; a beam splitter 133; a first wavelength division multiplexer 131; and a second wavelength division multiplexer 132. The concave mirror 134 reflects lateral scattered light and fluorescence diverging in different directions at the interrogation area 18.

[0125] Concave mirror 134 and aspherical lens 135 focus the reflected light onto the same point of collecting fiber 136 (e.g., Figure 2(As shown in the dashed box 139 in the image) the reflected light is focused onto the collecting fiber 136. The concave mirror 134 can focus the reflected light onto the fiber, while the aspherical lens 135 can make the focal point smaller (i.e., reduce aberrations).

[0126] To prevent crosstalk, beam splitter 133 is arranged to separate high-intensity scattered light from low-intensity fluorescence. Side-scattered light is directed towards first wavelength division multiplexer 131 via first optical fiber 137, while fluorescence is directed towards second wavelength division multiplexer 132 via second optical fiber 138. Optical signals with different wavelengths are separated in first wavelength division multiplexer 131 and second wavelength division multiplexer 132 for analysis.

[0127] Beam splitter 133 includes a dichroic mirror 532 and a notch filter 534. Collected light is directed through a collecting fiber 136 into beam splitter 133 toward the dichroic mirror 532. The collecting fiber 136 can be oriented such that the beam is directed toward the dichroic mirror 532 at, for example, an incident angle of 45 degrees. The dichroic mirror 532 reflects side-scattered light from the collecting fiber 136, causing the side-scattered light to enter the first wavelength division multiplexer 131 through a first fiber 137.

[0128] The fluorescence from the collecting fiber 136 passes through the dichroic mirror 532 and is incident at an angle of approximately 90 degrees onto the notch filter 534, then passes through the notch filter 534. The fluorescence then enters the second wavelength division multiplexer 132 through the second fiber 138.

[0129] Depending on the arrangement of the light sources 111a to 111d, the dichroic mirror 532 and the notch filter 534 can each have multiple frequency bands. Figure 2 In the example shown, both the dichroic mirror 532 and the notch filter 534 have four frequency bands that block four laser wavelengths. The number of frequency bands on the dichroic mirror 532 and the notch filter 534 can correspond to the number of light sources 111a to 111d.

[0130] Beam splitter 133 separates high-intensity side-scattered light from low-intensity fluorescence, which reduces or prevents crosstalk between side-scattered light and fluorescence. Additionally, by configuring the beam splitter, multiple beams can be separated and transmitted to two or more wavelength division multiplexers. The optical elements included in beam splitter 133 and their configuration can vary and are not limited to the examples shown and described herein.

[0131] In some examples, a first wavelength division multiplexer 131 receives side-scattered light from a beamsplitter 133 via a first optical fiber 137 and separates the optical signals of the side-scattered light from each other based on their wavelengths. For example, the first wavelength division multiplexer 131 separates the optical signal associated with a beam in the red light spectrum emitted by a first light source 111a, the optical signal associated with a beam in the yellow light spectrum emitted by a second light source 111b, the optical signal associated with a beam in the blue light spectrum emitted by a third light source 111c, and the optical signal associated with a beam in the violet light spectrum emitted by a fourth light source 111d. In the first wavelength division multiplexer 131, each optical signal is transmitted along an optical transmission path 510 corresponding to the optical channel of that optical signal. The side-scattered light then enters an SSC detector 515, which may include a photomultiplier tube, photodiode, or avalanche photodiode (APD) for analyzing the side-scattered light.

[0132] 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 in a non-parallel manner along the optical transmission path of the optical channel at a distance from each other. Crosstalk between side-scattered light can be reduced or prevented by setting up two filters. The first filter 511 and the second filter 512 are not arranged in parallel to avoid multiple light reflections between them and to obtain better optical density.

[0133] The second wavelength division multiplexer 132 receives the fluorescence beam from the beamsplitter 133 via the second optical fiber 138 and separates the optical signals of the fluorescence beams with 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 its optical channel. Since the fluorescence signal is weak, the second wavelength division multiplexer 132 includes a single filter 521 for each optical channel. The filtered fluorescence then enters a photodetector element 525 (e.g., a photodiode, avalanche photodiode (APD), photomultiplier tube) for further processing.

[0134] Suitable alternative configurations for wavelength division multiplexers can be used. For example, the first wavelength division multiplexer 131 and the second wavelength division multiplexer 132 may include notch filters corresponding to the respective optical channels. The notch filters can reduce or eliminate crosstalk between side-scattered light and fluorescence. In this case, the beam splitter 133 may only include a dichroic mirror 532 without the notch filter 534.

[0135] In the lateral collection unit 130, the diameter of the collecting fiber 136 may differ from the diameter of the first fiber 137 and the second fiber 138, depending on the optical transmission efficiency. Lenses in the beam splitter may cause aberrations, and therefore the output spot may be larger than the input of the beam splitter, and the fiber diameter may be selected accordingly.

[0136] The forward collection unit 150 includes a light-shielding strip 155, a concave mirror 151, a filter 157, and a forward detector 159. The light-shielding strip 155 blocks most of the light transmitted through the flow chamber 15 to reduce background noise generated by the excitation beam that directly transmits through the flow chamber 15 and allows only forward-scattered light from the particles to be collected. In some examples, most of the transmitted light is blocked so as not to saturate the forward detector 159.

[0137] Concave mirror 151 reflects the forward-scattered beam emitted from the particles. Filter 157 allows forward-scattered light with a high signal-to-noise ratio to pass through while blocking other light. Figure 2 As further shown, the forward detector 159 receives filtered forward-scattered light from the filter 157 and processes and analyzes the forward-scattered light.

[0138] Figure 3 An example of a method 300 for characterizing particles by flow cytometry is schematically illustrated, which can be performed by a flow cytometer 100. Method 300 is a label-free technique that allows particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labels. Such labels can alter the characteristics of cells upon attachment. Therefore, by being label-free, method 300 allows cells to be repeatedly used for additional tests and experiments after being interrogated by an excited beam in the flow cytometer 100. Furthermore, method 300 can be performed to characterize and / or sort both whole cells and nanoparticles, such as extracellular vesicles comprising microvesicles and exosomes.

[0139] Method 300 includes the following operation 302: irradiating the particles with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength. Figure 2 As shown, a first excitation beam of a first wavelength can be emitted by a first light source among a plurality of light sources 111a to 111d, and a second excitation beam of a second wavelength can be emitted by a second light source among a plurality of light sources 111a to 111d. In one example, the first excitation beam is emitted by the first light source 111a such that the first wavelength is in the red light spectrum, and the second excitation beam is emitted by the fourth light source 111d such that the second wavelength is in the violet light spectrum. Figure 2 As shown, the particle is simultaneously irradiated by a first excitation beam of a first wavelength and a second excitation beam of a second wavelength when it passes through the interrogation zone 18.

[0140] In some examples, operation 302 includes irradiating the particles with more than two excitation beams emitted by a plurality of light sources 111a to 111d of the flow cytometer 100. Additionally, the particles may be irradiated with different combinations of excitation beams emitted by the plurality of light sources 111a to 111d of the flow cytometer 100.

[0141] Method 300 includes the following operation 304: collecting side-scattered light of a first wavelength and collecting side-scattered light of a second wavelength. The side-scattered light of the first wavelength can be collected from a first SSC detector 515, and the side-scattered light of the second wavelength can be collected from a second SSC detector 515 (see [link to SSC detector]). Figure 2 The SSC detector 515, as described above, may include a photodiode, an avalanche photodiode (APD), a photomultiplier tube, and similar light detection elements. In operation 304, the first wavelength of side-scattered light and the second wavelength of side-scattered light are simultaneously collected by the SSC detector 515 of the flow cytometer 100. In some examples, operation 304 includes collecting additional wavelengths, such as a third wavelength, a fourth wavelength, etc., of side-scattered light when particles are irradiated by more than two excitation beams of the multiple light sources 111a to 111d of the flow cytometer 100.

[0142] Figure 4 A comparison of histograms 400a to 400d is shown, which represent particle counts (Y-axis) of the intensity of side-scattered light (X-axis) collected from a sample of plasma extracellular vesicles (EVs). Data can be collected during operation 304 of method 300. In this example, the first histogram 400a shows the intensity of side-scattered light collected in the violet spectrum, the second histogram 400b shows the intensity of side-scattered light collected in the blue spectrum, the third histogram 400c shows the intensity of side-scattered light collected in the yellow spectrum, and the fourth histogram 400d shows the intensity of side-scattered light collected in the red spectrum.

[0143] Histograms 400a to 400d can be generated, for example, by having the fourth light source 111d emit light in the violet spectrum, the third light source 111c emit light in the blue spectrum, the second light source 111b emit light in the yellow spectrum, and the first light source 111a emit light in the red spectrum, and the first SSC detector 515 collects the side-scattered light in the red spectrum, the second SSC detector 515 collects the side-scattered light in the yellow spectrum, the third SSC detector 515 collects the side-scattered light in the blue spectrum, and the fourth SSC detector 515 collects the side-scattered light in the violet spectrum.

[0144] Histograms 400a to 400d show data on particles, noise, and the overlap between particles and noise. For example... Figure 4 As shown, the first histogram 400a (violet light) provides the best sensitivity for separating EV particles from noise in a plasma sample because it has the least amount of overlap between EV particles and noise compared to the overlap shown in the second histogram 400b, the third histogram 400c, and the fourth histogram 400d.

[0145] like Figure 3 As shown, method 300 includes an operation 306 of determining coordinates for each particle, the coordinates including the intensity of side-scattered light at a first wavelength and the intensity of side-scattered light at a second wavelength. In an illustrative example, the coordinates of each particle include the intensity of side-scattered light in the violet spectrum and the intensity of side-scattered light in the red spectrum.

[0146] Figure 5 A comparison of dot plots 500a to 500c is shown, which display coordinates of the intensity of side-scattered light, including a first wavelength and a second wavelength, for each EV particle in the plasma sample. Therefore, dot plots 500a to 500c show the intensity of side-scattered light of the first wavelength relative to the intensity of side-scattered light of the second wavelength for each EV particle. In some cases, dot plots 500a to 500c are generated during operation 306 of method 300.

[0147] exist Figure 5 In the first point, Figure 500a shows the intensity (X-axis) of the side-scattered light in the violet spectrum for each particle compared to the intensity (Y-axis) of the side-scattered light in the blue spectrum; the second point, Figure 500b, shows the intensity (X-axis) of the side-scattered light in the violet spectrum for each particle compared to the intensity (Y-axis) of the side-scattered light in the yellow spectrum; and the third point, Figure 500c, shows the intensity (X-axis) of the side-scattered light in the violet spectrum for each particle compared to the intensity (Y-axis) of the side-scattered light in the red spectrum.

[0148] Return to reference Figure 3 Method 300 includes an operation 308 of fitting coordinates to one or more trends. For example, coordinates can be fitted to one or more trends by performing linear regression or other similar techniques.

[0149] like Figure 5 As shown, one or more trends can distinguish particle populations in a sample. For example, in each of dot plots 500a to 500c, two distinct particle populations are identified based on the trend of the intensity of side-scattered light at a first wavelength compared to the intensity of side-scattered light at a second wavelength. Dot plots 500a to 500c illustrate two distinct particle populations. One or more trends based on the intensity of side-scattered light at a first wavelength compared to the intensity of side-scattered light at a second wavelength can identify more than two populations or fewer than two populations (i.e., a single population) from a sample of particles. Figure 5 As shown, the third point in Figure 500c, which compares the intensity of side-scattered light in the violet spectrum (X-axis) with the intensity of side-scattered light in the red spectrum (Y-axis), shows that the two populations have the best separation.

[0150] Figure 6 yes Figure 5 The third point is a detailed view of Figure 500c. (See Figure 500c for details.) Figure 6 As shown, the two particle populations tend to exhibit diagonal lines with different slopes. The purple and red wavelengths are located on opposite sides of the spectrum, making the comparison of these two wavelengths provide the clearest contrast in separating the trends of the different particle populations.

[0151] Figure 7 Dot plot 700 is shown, which compares the intensity of side-scattered light (X-axis) in the violet spectrum of a sample containing a virus expressing green fluorescent protein (GfP) with the intensity of side-scattered light (Y-axis) in the red spectrum. The presence of the GfP virus population was verified based on fluorescence staining. Figure 7 As shown, the GfP virus population exhibits a certain trend. Furthermore, the unknown population separated from the GfP virus population shows a different trend than the GfP virus population.

[0152] Figure 8 Dot plot 800 is shown, which displays the intensity (X-axis) of lateral scattered light in the violet spectrum of polystyrene latex (PSL) microspheres of different sizes and known refractive indices compared to the intensity (Y-axis) of lateral scattered light in the red spectrum. Figure 9 Dot plot 900 shows the overlap between the intensity (X-axis) of side-scattered light in the violet spectrum and the intensity (Y-axis) of side-scattered light in the red spectrum for a sample containing GfP virus and PSL microspheres. Figure 8 and Figure 9 As shown, PSL microspheres exhibit a trend toward either a GfP virus population or an unknown population from a GfP virus sample (see [reference]). Figure 7 The trend of inconsistency. As used herein, the term "microsphere" refers to particles, such that microspheres and particles can be used interchangeably with respect to the disclosures provided herein.

[0153] Figure 10 Dot plot 1000 is shown, which compares the intensity of lateral scattered light (X-axis) in the violet spectrum of samples containing reticuloendothelial proliferating virus (rEV) and GfP virus with the intensity of lateral scattered light (Y-axis) in the red spectrum. As shown in dot plot 1000, rEV includes a first population exhibiting a first trend and a second population exhibiting poor resolution, making the trend unfittable. The first trend of the first population of rEV is compared with the trend of GfP virus or the trend of an unknown population from GfP virus samples (see also...). Figure 7 (Inconsistent)

[0154] Return to reference Figure 3Method 300 includes an operation 310 comparing the trend determined in operation 308 with a plurality of predetermined trends. The plurality of predetermined trends may include trends previously determined for known types of cells and particles. The plurality of predetermined trends may be stored in the memory of flow cytometer 100 and / or the memory of workstation 200.

[0155] Method 300 further includes operation 312 of characterizing the particle by matching the trend with a predetermined trend representing a characteristic associated with the particle. As an illustrative example, the predetermined trend may represent a value of refractive index. In such an example, operation 312 may include estimating the refractive index of the particle based on matching the trend determined in operation 308 with a predetermined trend associated with the value of refractive index. In such an example, operation 312 may also include determining the particle size based on the refractive index. For example, the particle size can be determined by equation (1) as a Rayleigh scattering approximation.

[0156] (1)

[0157] Where I0 is the light intensity before interaction with the particle. R is the scattering angle, and R is the distance between the particle and the detector. λ is the wavelength of light, n is the refractive index of the particle, and d is the diameter of the particle. The refractive index n and diameter d of the particle are sample-related variables, while the other variables are known or controllable by the flow cytometer 100. Given equation (1), once the refractive index n of the particle is estimated, the size of the particle (i.e., the diameter d) can be determined.

[0158] Alternatively, operation 312 may include estimating the particle size based on matching the trend determined in operation 308 with a predetermined trend associated with the particle diameter d. In such an example, operation 312 may also include determining the particle's refractive index n based on the diameter d using equation (1). Thus, once the particle diameter d is estimated, the particle's refractive index n can be determined.

[0159] In some cases, operation 308 may include fitting multiple trends to different particle populations within a given sample. In such an example, operation 310 includes comparing multiple trends to multiple predetermined trends, and operation 312 includes characterizing different particle populations based on the comparisons in operation 310. As an illustrative example, method 300 may include fitting a first trend to a first particle population and characterizing the first particle population by matching the first trend to predetermined trends representing values ​​(e.g., particle diameter d or refractive index n) associated with the particles, and method 300 may also include fitting a second trend to a second particle population and characterizing the second particle population by matching the second trend to predetermined trends representing another value (e.g., particle diameter d or refractive index n) associated with the particles.

[0160] In another example, operation 312 may include: when particles have the same diameter d, characterizing the protein loading or membrane composition of the particles based on the refractive index n, such as regarding Figure 21 and Figure 22 A more detailed description.

[0161] Figure 11 A dot plot 1100 shows intensity data for PSL microspheres of different sizes and known refractive indices at violet and red light wavelengths. The intensity data includes data collected from PSL microspheres with diameters d=44 nm, d=80 nm, d=100 nm, and d=144 nm. The PSL microspheres exhibit a refractive index n=1.625 at violet wavelengths (e.g., 405 nm) and a refractive index n=1.587 at red light wavelengths (e.g., 633 nm). Table 1 shows the median violet side-scattering intensity and median red side-scattering intensity for each size of PSL microsphere based on the intensity data. Table 1 also shows the ratio of median red side-scattering intensity to median violet side-scattering intensity for each PSL microsphere size.

[0162]

[0163] Table 1

[0164] Figure 12A dot plot 1200 of theoretical intensity data generated from a Mie scattering simulator is shown, taking into account the known refractive index of the PSL microspheres (n = 1.625 at violet wavelength; n = 1.587 at red wavelength) and different sizes of the PSL microspheres (e.g., 44 nm, 80 nm, 100 nm, and 144 nm). The scattering angle (e.g., 90° + / - 54°) is selected based on the configuration of the flow cytometer 100. The theoretical intensity data includes the intensity 1202 of violet side scattering and the intensity 1204 of red side scattering, which can be used to generate the theoretical ratio of red side scattering intensity to violet side scattering intensity across different scattering angles for each PSL microsphere size.

[0165] Figure 13 Dot plot 1300 shows the data, illustrating the radian sensitivity (Y-axis) of the avalanche photodiode (APD) across different light wavelengths (X-axis). The data includes the radian sensitivity of the APD at violet wavelengths (e.g., 405 nm) and red wavelengths (e.g., 633 nm). The APD radian sensitivity was considered when generating the theoretical ratio of red to violet side-scattering intensity for each PSL microsphere size. In this example, the intensities (e.g., laser power) of the light sources 111a to 111d, the beam point at interrogation region 18, and the gain settings are similar for both violet and red wavelengths.

[0166] Figure 14 Dot plot 1400 shows the empirical ratio 1402 (based on) of median red lateral scattering intensity to median violet lateral scattering intensity per PSL microsphere size, compared to a first theoretical ratio 1404 that does not consider APD radian sensitivity and a second theoretical ratio 1406 that does consider APD radian sensitivity. Figure 11 The intensity data shown in Table 1 are summarized in Table 2. Figure 14 The values ​​of the empirical ratio 1402, the first theoretical ratio 1404, and the second theoretical ratio 1406 are shown. Figure 14 As shown, the second theoretical ratio 1406 closely matches the empirical ratio 1402, which provides a basis for... Figures 6 to 10 The phenomena observed in biological samples provide credibility.

[0167]

[0168] Table 2

[0169] Figure 15A dot plot 1500 shows intensity data for silica microspheres of different sizes and known refractive indices at violet and red light wavelengths. The intensity data includes data collected from silica microspheres with diameters d=50 nm, d=55 nm, d=60 nm, d=80 nm, d=90 nm, and d=100 nm. The silica microspheres exhibit a refractive index n=1.483 at violet light wavelengths (e.g., 405 nm) and a refractive index n=1.47 at red light wavelengths (e.g., 633 nm). Table 3 shows the median violet side-scattering intensity and median red side-scattering intensity for each silica microsphere size based on the intensity data, as well as the ratio of the median red side-scattering intensity to the median violet side-scattering intensity for each silica microsphere size.

[0170]

[0171] Table 3

[0172] Figure 16 A dot plot 1600 of theoretical intensity data generated from a Mie scattering simulator is shown, taking into account the refractive index of the silica microspheres (n = 1.483 at violet wavelength; n = 1.47 at red wavelength) and different sizes of the silica microspheres (e.g., 50 nm, 55 nm, 60 nm, 80 nm, 90 nm, and 100 nm). The scattering angle (e.g., 90° + / - 54°) is selected based on the configuration of the flow cytometer 100. The theoretical intensity data includes the intensity 1202 of violet side scattering and the intensity 1204 of red side scattering, which can be used to generate a theoretical ratio of red side scattering intensity to violet side scattering intensity across different scattering angles for each silica microsphere size.

[0173] Figure 17 Dot plot 1700 shows the empirical ratio 1702 (based on) of median red side scattering intensity to median violet side scattering intensity per silica microsphere size, compared to a first theoretical ratio 1704 that does not consider APD curvature sensitivity and a second theoretical ratio 1706 that does consider APD curvature sensitivity. Figure 15 The empirical data shown in Table 3 is summarized in Table 4. Figure 17 The values ​​of the empirical ratio 1702, the first theoretical ratio 1704, and the second theoretical ratio 1706 are shown. Figure 17 As shown, except for the ratio of red side scattering intensity to violet side scattering intensity at 50 nm, the second theoretical ratio 1706 is basically matched with the empirical ratio 1702.

[0174]

[0175] Table 4

[0176] Figure 18 Point plot 1800 is shown. Point plot 1800 will... Figure 14 The second theoretical ratio 1406 of the PSL microspheres shown is... Figure 17 The second theoretical ratio 1706 of the silica microspheres shown is compared. Figure 19 Point plot 1900 is shown. Point plot 1900 will... Figure 14 The empirical ratio of PSL microspheres shown is 1402 with Figure 17 The empirical ratio 1702 of the silica microspheres shown is compared. Now refer to Figure 18 and Figure 19 PSL microspheres and silica microspheres exhibit different ratios of violet and red lateral scattering, leading to different curves. For example... Figure 18 and Figure 19 As shown, for smaller particle sizes (e.g., 40 nm to 100 nm), the curves are more dispersed, while for larger particle sizes (e.g., 100 nm to 140 nm), the curves are closer together.

[0177] Figure 20 Dot plot 2000 shows the trend of the intensity of side-scattered light in the violet spectrum of PSL microspheres compared to the intensity of side-scattered light in the red spectrum (see also 2002). Figure 8 The intensity of side-scattered light in the violet light spectrum of silica microspheres was compared with the trend of side-scattered light intensity in the red light spectrum in 2004. Trends in 2002, 2004, and... Figure 18 and Figure 19 The ratio data shown is relevant. For example, for low intensities (typically collected from smaller particle sizes), trends 2002 and 2004 are further apart, while as the lateral scattering intensity increases (typically due to larger particle sizes), trends 2002 and 2004 move closer together before starting to separate again. Therefore, trends 2002 and 2004 resemble diagonals with different slopes.

[0178] Now refer to Figures 18 to 20 The trends in the ratio of violet to red lateral scattering for PSL microspheres and those for silica microspheres indicate different trends because PSL and silica microspheres have different refractive indices (i.e., for PSL microspheres, n = 1.625 at violet wavelength and n = 1.587 at red wavelength, while for silica microspheres, n = 1.483 at violet wavelength and n = 1.47 at red wavelength). Given... Figures 11 to 20The above-mentioned phenomena exhibited by PSL microspheres and silica microspheres were discussed. Figures 4 to 10 Observations shown in biological samples of GfP virus, rEV, and plasma extracellular vesicles (EVs) indicate that the different trends in the ratio between purple and red side scattering are associated with different populations having different refractive indices. This observation was confirmed based on PSL microspheres and silica microspheres with known refractive indices and sizes. Furthermore, empirical data correlated with simulated data. Therefore, additional side scattering detectors (e.g., for purple and red side scattering) provide a technical advantage and / or improvement in identifying different populations with different refractive indices based on label-free assessment without the need for additional fluorescent staining steps. Moreover, as mentioned above, equation (1) can be used to determine particle size based on refractive index, or conversely, equation (1) can be used to determine refractive magnitude based on particle size.

[0179] Figure 21 Data 2100 collected from plasma EVs is shown. The plasma EVs include a first group A with a first trend 2102 of the ratio between violet lateral scattering and red lateral scattering, and a second group B with a second trend 2104 of the ratio between violet lateral scattering and red lateral scattering. The second trend 2104 differs from the first trend 2102 such that the refractive index of the second group B is different from that of the first group A. In this example, the first group A and the second group B of plasma EVs may have different sizes or the same size, depending on the plasma EVs under the first trend 2102 and the second trend 2104.

[0180] Figure 22 Data 2200 collected from samples of mouse leukemia virus (MLV) and reticuloendothelial cell proliferation virus (rEV) are shown. The samples include a first population A with a first trend 2202 of the ratio between purple and red side scattering and a second population B with a second trend 2204 of the ratio between purple and red side scattering. Figure 22 As shown in the example provided, the first trend 2202 and the second trend 2204 are substantially the same, such that the refractive indices of the first group A and the second group B are substantially the same, while the first group A and the second group B may have different sizes.

[0181] Figure 23 An example of a computing device 2300 is schematically shown, which is used to implement various aspects of system 10, including the functions of flow cytometer 100 and workstation 200. Examples of computing devices 2300 include server computers, desktop computers, laptop computers, tablet computers, mobile computing devices (e.g., smartphones), or other devices configured to process digital instructions.

[0182] Computing device 2300 includes one or more processing devices 2302. Examples of one or more processing devices 2302 include a central processing unit (CPU), a digital signal processor, a field-programmable gate array (FPGA), and other types of electronic computing circuitry. One or more processing devices 2302 may be part of a processing circuitry system having a memory for storing instructions that, when executed by the processing circuitry system, cause the processing circuitry system to perform the functions described herein.

[0183] The computing device 2300 also includes a system memory 2304 and a system bus 2306, the system bus 2306 coupling various system components, including the system memory 2304, to one or more processing devices 2302. The system bus 2306 is one of any number of bus architecture types, including a memory bus or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0184] System memory 2304 may include read-only memory (ROM) 2308 and random access memory (RAM) 2310. A basic input / output system (BIOS) 2312 containing basic routines may be stored in system memory 2304, which are used, for example, to transfer information within computing device 2300 during startup. RAM 2310 may be used to load and subsequently analyze waveform data (e.g., stored in a raw waveform data file, which may include digitized raw waveform data).

[0185] The computing device 2300 may also include one or more auxiliary storage devices 2314, such as hard disk drives for storing digital data. One or more auxiliary storage devices 2314 are connected to the system bus 2306 via an auxiliary storage interface 2316. One or more auxiliary storage devices 2314 and associated computer-readable media provide the computing device 2300 with non-volatile storage of computer-readable instructions (including application programs and program modules), data structures, and other data. Although the example described herein uses a hard disk drive as an auxiliary 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 ROM 2308 and / or RAM 2310. Some examples include non-transitory media. Additionally, such computer-readable storage media may include local storage devices or cloud-based storage devices.

[0186] Computing device 2300 typically includes at least some form of computer-readable medium. Computer-readable medium includes any available medium that can be accessed by computing device 2300. By way of example, computer-readable medium includes computer-readable storage media and computer-readable communication media.

[0187] Computer-readable storage media include volatile and non-volatile, 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 include, but are not limited to, random access memory, read-only memory, electrically erasable programmable read-only memory, flash memory or other memory technologies, or any other medium that can be used to store desired information and can be accessed by the computing device 2300.

[0188] Computer-readable communication media can contain computer-readable instructions, data structures, program modules, or other data in modulated data signals such as carrier waves or other transmission mechanisms, and include any information transmission medium. The term "modulated data signal" refers to a signal in which one or more features are configured to encode information in the signal. For example, computer-readable communication media include: wired media, such as wired networks or direct wired connections; and wireless media, such as acoustic, radio frequency, infrared, and other wireless media. Any combination of the foregoing is also included within the scope of computer-readable media.

[0189] Multiple program modules can be stored in secondary storage device 2314 or system memory 2304, the system memory 2304 including operating system 2318, application programs 2320, program modules 2322 (e.g., software engine), and program data 2324. Computing device 2300 can utilize any suitable operating system, such as Microsoft Windows™, Google Chrome™, Apple OS, and any other operating system suitable for computing devices.

[0190] Users provide input to computing device 2300 through one or more input devices 2326. Examples of input devices 2326 include a mouse 206, a keyboard 208, a microphone 2332, and a touch sensor 2334 (e.g., a touchpad or touch-sensitive display). Other types of input devices 2326 are considered. Input devices 2326 are typically connected to one or more processing devices 2302 via input / output interfaces 2336 coupled to system bus 2306. These input devices 2326 can be connected via any number of input / output interfaces, such as parallel ports, serial ports, game ports, or universal serial buses. Wireless communication between input devices and input / output interfaces 2336 is also possible, and in some possible implementations, includes infrared, Bluetooth® wireless technology, 802.11a / b / g / n, cellular, or other radio frequency communication systems.

[0191] Display monitor 204 may include a liquid crystal display device, a touch-sensitive display device, etc. Display monitor 204 is connected to system bus 2306 via an interface such as video adapter 2340. In addition to display monitor 204, computing device 2300 may also include various other peripheral devices, such as speakers or printers.

[0192] When used in a local area network (LAN) or wide area network (WAN) environment (e.g., the Internet), computing device 2300 is typically connected to network 2344 via network interface 2342 (e.g., an Ethernet interface). Other possible implementations use other communication devices. For example, some implementations of computing device 2300 include a modem for communication across network 2344.

[0193] The computing device 2300 is an example of a programmable electronic device, which may include one or more such computing devices. When multiple computing devices are included, such computing devices may be coupled together with a suitable data communication network to jointly perform the various functions, methods, or operations disclosed herein.

[0194] Figure 24 Another example of a method 2400 for generating a function that can be used to characterize particles by flow cytometry is illustrated schematically. Method 2400 can be executed by system 10. The function determined by method 2400 is a label-free technique that allows particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labels. Furthermore, method 2400 can be performed to characterize and / or sort both whole cells and nanoparticles, such as extracellular vesicles comprising microvesicles and exosomes. As will be further described below, this function can be used to determine particle size.

[0195] Method 2400 includes the following operation 2402: irradiating the particle with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength. As an illustrative example, the first wavelength may be in the violet wavelength range, and the second wavelength may be in the red wavelength range located at opposite ends of the spectrum.

[0196] Method 2400 includes the following operation 2404: collecting side-scattered light of a first wavelength from at least a first detector (e.g., a first SSC detector 515) and collecting side-scattered light of a second wavelength from a second detector (e.g., a second SSC detector 515). Operations 2402 and 2404 of method 2400 are similar to operations 302 and 304 of method 300 described above, such that the above description of operations 302 and 304 in method 300 is similarly applied to operations 2402 and 2404 of method 2400.

[0197] Method 2400 includes the following operation 2406: calculating the ratio of the intensity of lateral scattered light at a first wavelength collected from a first detector to the intensity of lateral scattered light at a second wavelength collected from a second detector. For example, operation 2406 may include calculating the ratio of a first median lateral scattered light intensity at a violet wavelength to a second median lateral scattered light intensity at a red wavelength. Table 5 (provided below) shows the calculation of empirical violet / red scattering intensity ratios for two populations of reticuloendothelial proliferating virus (rEV). Table 6 (provided below) shows the calculation of empirical violet / red scattering intensity ratios for three populations of microvesicles in the Daud cell line. Table 7 shows the calculation of empirical violet / red scattering intensity ratios for four populations of urinary extracellular vesicles (EVs).

[0198]

[0199] Table 5

[0200]

[0201]

[0202] Table 6

[0203]

[0204]

[0205] Table 7

[0206] Method 2400 further includes the following operation 2408: calculating the ratio of a first simulated side-scattered light intensity at a first wavelength to a second simulated side-scattered light intensity at a second wavelength. In operation 2408, the ratio between the first and second wavelengths is theoretically simulated using Mie theory or Mie core-shell modeling, using the refractive index of the target particle estimated based on different particle sizes at the first and second wavelengths. For example, operation 2408 may include simulating the ratio of a first simulated side-scattered light intensity at a violet wavelength to a second simulated side-scattered light intensity at a red wavelength.

[0207] Method 2400 includes the following operation 2410: applying a calibration equation to calibrate the analog ratio between the first wavelength and the second wavelength calculated in operation 2408. The calibration equation can be based on... Figure 25The method 2500 shown generates the calibration equation. The calibration equation can be generated, for example, as part of instrument calibration during the installation of the flow cytometer 100, because sensor alignment and other factors affecting data acquisition may differ from instrument to instrument. Therefore, the flow cytometer 100 is calibrated using the calibration equation to accommodate differences between the flow cytometer 100 and other flow cytometers. Through the calibration performed in operation 2410, all analog ratios are adjusted to approximate the empirical ratios calculated in operation 2406.

[0208] As will be described in more detail, the calibration equation is determined by correlating empirical data captured by the flow cytometer 100 with theoretical data calculated based on the known refractive index and size of the calibration microspheres. The calibration equation may be based on a linear correction factor, a quadratic correction factor, or a cubic correction factor.

[0209] Figure 25 An example of method 2500 for generating calibration equations used in method 2400 is illustrated schematically. Method 2500 includes the following operation 2502: capturing empirical data from calibration microspheres passing through interrogation zone 18 of flow cytometer 100, such that the calibration microspheres are irradiated with at least a first excitation beam of a first wavelength (e.g., violet light) and a second excitation beam of a second wavelength (e.g., red light). The calibration microspheres have known refractive indices and sizes. For example, the calibration microspheres may comprise polystyrene latex (PSL) microspheres having sizes ranging from 50 nm to 200 nm. Table 8 shows examples of empirical data captured from PSL microspheres at violet, blue, yellow, and red wavelengths.

[0210]

[0211] Table 8

[0212] Figure 26a A point plot 2600a shows empirical purple side-scattered light data captured from calibration microspheres. Figure 26b A dot plot 2600b shows empirical red side-scattered light data captured from calibration microspheres. Dot plots 2600a and 2600b can be generated in operation 2502.

[0213] Operation 2502 may include generating a dot plot of intensity at a first wavelength (e.g., violet light) compared to intensity at a second wavelength (e.g., red light). Figure 26c A dot plot 2600c is shown, which compares the intensity of purple side-scattered light with the intensity of red side-scattered light based on the empirical purple side-scattered light generated by operation 2502.

[0214] Method 2500 includes operation 2504, which calculates theoretical data based on the known refractive index and size of the calibrated microspheres. For example, operation 2504 may include calculating theoretical data based on Mie scattering theory. Table 9 shows examples of theoretical data calculated for PSL microspheres based on their known size and refractive index. Figure 27 A dot plot 2700 shows the theoretical intensity of purple side-scattered light compared to the intensity of red side-scattered light, which can be calculated based on operation 2504.

[0215]

[0216] Table 9

[0217] Method 2500 includes the following operation 2506: correlating the empirical data captured in operation 2502 with the theoretical data calculated in operation 2504. Operation 2506 may include correlating the empirical data with the theoretical data using a linear correction factor, a quadratic correction factor, or a cubic correction factor. In some cases, quadratic correlation provides the best fit between the empirical data and the theoretical data.

[0218] Figure 28a A point graph 2800a is shown, which includes empirical data captured in operation 2502 and theoretical data calculated in operation 2504. Figure 28b Plot 2800b shows the empirical data after applying quadratic correlation to the empirical data.

[0219] Return to reference Figure 25 Method 2500 includes the following operation 2508: generating a calibration equation based on the correlation between empirical and theoretical data performed in operation 2506. In this way, by understanding the correlation between theoretical and empirical data, a combined correction for the median lateral scattering intensity of purple versus the median lateral scattering intensity of red can be simulated for all refractive indices based on Mie scattering theory.

[0220] In some examples, method 2500 may also include the following operation 2510: confirming the “half-angle” of the lateral collection unit 130 of the flow cytometer 100. In flow cytometry, a “half-angle” refers to the angular range of light collected by the lateral collection unit 130, which is the cone of light gathered around the particles in the interrogation zone 18, where the half-angle represents half the cone apex angle. A larger half-angle means capturing more scattered light from a wider angular range around the particles. Modern flow cytometers typically have an SSC collection half-angle (ε) of approximately 50°, meaning that the collected SSC light passing through each of the particles in the interrogation zone 18 will be the sum of all angles from 40° to 140°. However, due to the alignment of the lateral collection unit 130 relative to the interrogation zone 18, the half-angle of the lateral collection unit 130 may vary from instrument to instrument. In some examples, the half-angle may vary by approximately + / - 10 degrees.

[0221] Operation 2510 may include comparing empirical side-scattered light intensity data with simulated side-scattered light intensity data within a range of approximately + / -10°C around a nominal half-angle of approximately 54°C to confirm the half-angle of the side-collecting unit 130. For example, the half-angle of the side-collecting unit 130 is confirmed when the empirical side-scattered light intensity for a given half-angle value is closest to the simulated side-scattered light intensity.

[0222] Method 2500 may then further include operation 2512: determining a calibration factor based on the half-angle confirmed in operation 2510. The calibration factor may then be included in the calibration equation generated in operation 2508. In some examples, the calibration factor is determined based on the difference between the empirical side-scatter intensity and the simulated side-scatter intensity for the half-angle confirmed in operation 2520. In some examples, operations 2510 and 2512 are repeated as part of routine quality control (QC) for monitoring the condition of the flow cytometer 100.

[0223] Return to reference Figure 24 Operation 2410 may include calibrating the ratio between the intensity of a first side-scattered light at a first wavelength and the intensity of a second side-scattered light at a second wavelength for a combination of relative refractive indices. For example, the intensity of the first side-scattered light at the first wavelength (e.g., violet light) may have a refractive index ranging from 1.6 to 1.38, such that each combination of refractive index values ​​in the range of 1.6 to 1.38 is calibrated to the ratio of the intensity of the second side-scattered light at the second wavelength (e.g., red light).

[0224] Table 10 provides examples of a given refractive index value (e.g., 1.47) for a first wavelength (e.g., violet light) and multiple refractive index values ​​for a second wavelength (e.g., red light). Table 10 includes the refractive index (RI) difference calculated between the refractive index value for the first wavelength (e.g., violet light) and multiple refractive index values ​​for the second wavelength (e.g., red light).

[0225]

[0226] Table 10

[0227] Figure 29 Dot plot 2900 is shown, which includes a theoretically calculated calibration ratio between the first wavelength scattering intensity and the second wavelength scattering intensity for each of the multiple refractive index values ​​shown in Table 10 for the second wavelength (e.g., red light).

[0228] Return to reference Figure 24Method 2400 may include the following operation 2412: filtering the simulated ratio calibrated in operation 2410 to narrow the range of estimated refractive indices based on particle size variations. The filtering performed in operation 2412 is done using the RI differences shown in Table 10. For example, Figure 29 The dot plot 2900 includes a portion 2902 that includes simulated second scattering intensities at a second wavelength (e.g., red light) where the RI difference is less than or equal to the maximum difference. Figure 29 In the example, the maximum difference is 0.02. Therefore, operation 2412 includes separating portion 2902 from other calibrated ratios between the intensity of the violet side-scattered light and the intensity of the red side-scattered light based on the maximum RI difference. In an alternative example, instead of filtering the ratio in operation 2412, method 2400 may include an alternative operation that receives input parameters of the refractive index at a first wavelength and a second wavelength.

[0229] Figure 30 Chart 3000 is shown, which includes a first set of refractive index values ​​3002 between violet and red side-scattered light with a high RI difference (e.g., 0.01), a second set of refractive index values ​​3004 between violet and red side-scattered light with a medium RI difference (e.g., 0.007), and a third set of refractive index values ​​3006 between violet and red side-scattered light with a low difference (e.g., 0.003). Operation 2412 may include filtering the analog ratio calibrated in operation 2410 based on a high, medium, or low RI difference.

[0230] Return to reference Figure 24 Method 2400 includes the following operation 2414: characterizing particles using a simulated ratio calibrated in operation 2410 and filtered in operation 2412. For example, the filtering ratio of the simulated light intensity from operation 2412 can be used as a function to determine the particle size based on the ratio of side-scattered light intensity determined in operation 2406 based on empirical side-scattered light data collected in operation 2404. Operation 2414 may include determining the particle size based on the filtering ratio using a predetermined RI difference (e.g., high RI difference, medium RI difference, or low RI difference).

[0231] Figure 31 Figure 3100 is shown, which includes particle sizes determined based on a calibrated ratio of the intensity of violet side-scattered light to that of red side-scattered light from mouse leukemia virus (MLV). MLV is known to have a homogeneous size population of approximately 110 nanometers (nm). Figure 3100 shows the determined particle sizes filtered through high, medium, and low RI differences. In Figure 3100, the medium RI difference provides the most accurate size determination.

[0232] Figure 32 Figure 3200 is shown, which includes particle sizes determined based on a calibrated ratio of the intensity of purple side-scattered light to the intensity of red side-scattered light of reticuloendothelial viral (rEV). rEVs are known to have heterogeneous size populations. Figure 3100b shows the determined particle sizes filtered through high, medium, and low RI differences.

[0233] Figure 33 A dot plot 3300 shows the calibrated ratio of the intensity of purple side-scattered light to the intensity of red side-scattered light of rEV generated according to the operation of method 2400. Dot plot 3300 shows a first group 3302 with a first size range (e.g., 62 nm to 200 nm) and a second group 3304 with a second size range (225 nm to 320 nm). Figure 34 Point plot 3400, corresponding to point plot 3300, is shown. Figure 34 Dot plot 3400 shows the intensity of purple side-scattered light versus red side-scattered light of rEVs gated between individual rEV populations with different size ranges (e.g., 62 nm to 200 nm vs. 225 nm to 320 nm).

[0234] Figure 35 A dot plot 3500 shows a calibrated ratio of the intensity of purple side-scattered light to the intensity of red side-scattered light of microvesicles from the Daudi cell line generated according to the operation of method 2400. Dot plot 3500 shows a first population 3502 with a first size range (e.g., 47 nm to 107 nm), a second population 3504 with a second size range (102 nm to 222 nm), and a third population 3506 with a third size range (225 nm to 310 nm).

[0235] Figure 36 Point plot 3600, corresponding to point plot 3500, is shown. For example... Figure 36 As shown, dot plot 3600 illustrates the contrast between the intensity of purple and red side-scattered light from individual groups of microvesicles with different size ranges (e.g., 47 nm to 107 nm; 102 nm to 222 nm; and 225 nm to 310 nm).

[0236] Figure 37A dot plot 3700 shows a calibrated ratio of the intensity of purple side-scattered light to the intensity of red side-scattered light of extracellular vesicles in urine generated according to the operation of method 2400. Dot plot 3700 shows a first population 3702 having a first size range (e.g., 60 nm to 90 nm), a second population 3704 having a second size range (103 nm to 369 nm), a third population 3706 having a third size range (85 nm to 162 nm), and a fourth population 3708 having a fourth size range (171 nm to 266 nm).

[0237] Figure 38 Point plot 3800, corresponding to point plot 3700, is shown. Figure 38 Dot plot 3800 shows the contrast between purple and red side-scattered light intensities of urinary extracellular vesicles gated between individual populations with different size ranges (e.g., 60 nm to 90 nm; 103 nm to 369 nm; 85 nm to 162 nm; and 171 nm to 266 nm).

[0238] Figure 39 An example of method 3900 for characterizing protein loading on particles by flow cytometry is illustrated schematically. Method 3900 can be performed by flow cytometer 100. Method 3900 is a label-free technique that allows particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labels. Furthermore, method 3900 can be performed to characterize and / or sort both whole cells and nanoparticles, such as extracellular vesicles comprising microvesicles and exosomes. Therefore, method 3900 can be used to sort and / or purify label-free biological particles based on component detection by flow cytometry. For example, method 3900 can be performed to sort and / or purify samples of extracellular vesicles, viruses, biological particles, etc.

[0239] like Figure 39 As shown, method 3900 includes the following operation 3902: passing particles of a biological sample through interrogation zone 18 to irradiate them at multiple different light wavelengths. For example, operation 3902 may include irradiating the particles of the biological sample with violet, blue, yellow, and red light spectra. The particles of the biological sample include particles with unknown loading and particles with zero loading. Particles with zero loading are unstained and naked.

[0240] Method 3900 includes the following operation 3904: capturing side-scattered light data from particles of a biological sample passing through interrogation zone 18 in operation 3902. The side-scattered light data may include a first intensity in the violet spectrum, a second intensity in the blue spectrum, a third intensity in the yellow spectrum, and a fourth intensity in the red spectrum.

[0241] Method 3900 includes the following operation 3906: calculating differential optical lateral scattering parameters between particles of a biological sample with an unknown load and particles of a biological sample with zero load. As an illustrative example, operation 3906 may include calculating the ratio of violet SSC light to blue SSC light, the ratio of violet SSC light to yellow SSC light, and the ratio of violet SSC light to red SSC light between particles of the biological sample with an unknown load and particles of the biological sample with zero load.

[0242] like Figure 39 As further shown, method 3900 includes the following operation 3908: applying a calibration equation to calibrate the differential light side scattering parameters. The calibration equation applied in operation 3908 may be... Figure 24 The same calibration applied in operation 2410 of method 2400 can be used, which can be based on Figure 25 Method 2500 operation generation, Figure 25 The operation of method 2500 is described in more detail above.

[0243] Method 3900 further includes the following operation 3910: calculating the refractive index difference based on differential light lateral scattering, according to the particle size and refractive index of the biological sample. Operation 3910 may include using Mie scattering theory to calculate the refractive index difference based on differential light lateral scattering.

[0244] Method 3900 further includes operation 3912 applying a second calibration equation that can be used to calibrate the refractive index difference calculated in operation 3910. The second calibration equation is separate from the first calibration equation applied in operation 3908, which is generated according to the operation of method 2500.

[0245] Figure 40 An example of a sub-operation performed in operation 3912 of method 3900 is illustrated schematically. Operation 3912 may include a sub-operation 4002 of passing calibration microspheres through interrogation zone 18 for irradiation at multiple different light wavelengths. Calibration microspheres include particles with a known loading, such as silica microspheres with a known loading, or bioparticles such as viruses, liposomes, etc., with a known number of biomolecules attached to the outer and / or inner surfaces of the particles via covalent and / or non-covalent bonds. Calibration microspheres may also include particles with zero loading, such that at least some of the calibration microspheres are unstained and exposed.

[0246] Operation 3912 may also include the following sub-operation 4004: capturing side-scattered light data from the calibration microsphere passing through the interrogation area 18. The side-scattered light data collected in sub-operation 4006 may include a first intensity in the violet spectrum, a second intensity in the blue spectrum, a third intensity in the yellow spectrum, and a fourth intensity in the red spectrum.

[0247] Operation 3912 may also include the following sub-operation 4006: calculating the differential side scattering parameters of the calibration microsphere. Sub-operation 4006 may include calculating the ratio of violet SSC light to blue SSC light, the ratio of violet SSC light to yellow SSC light, and the ratio of violet SSC light to red SSC light of the calibration microsphere.

[0248] Operation 3912 may further include the following sub-operation 4008: comparing the differential light side-scattering parameters calculated in sub-operation 4006 with theoretical side-scattering data calculated based on the known load, size, and refractive index of the calibration microsphere. The theoretical side-scattering data can be calculated using Mie scattering theory. A first calibration is generated based on the comparison in sub-operation 4008.

[0249] Operation 3912 may also include the following sub-operation 4010: calculating the refractive index difference based on the differential light lateral scattering calibrated by the first calibration equation and based on the size and refractive index of the calibration microsphere.

[0250] Operation 3912 may also include the following sub-operation 4012: generating a second calibration equation based on the refractive index difference of the calibration microspheres and the known loading on the calibration microspheres.

[0251] Return to reference Figure 39 Method 3900 further includes operation 3914 characterizing the unknown load on particles in a biological sample. Operation 3914 includes characterizing the unknown protein on particles of the biological sample based on the refractive index difference, using the refractive index difference calculated in operation 3910 and the second calibration equation generated in operation 3912. Therefore, based on the determined particle size, the refractive index can be calculated to characterize the particle load. The refractive index can be calculated based on differential scattering parameters, such as the ratio of violet SSC light to blue SSC light, the ratio of violet SSC light to yellow SSC light, and the ratio of violet SSC light to red SSC light.

[0252] In some examples, operation 3914 may include characterizing the interaction between a single-particle surface marker and its corresponding ligand, including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin-biotin interactions. In other examples, operation 3914 may include characterizing single-particle modifications, including molecules covalently or non-covalently attached to the particle surface. In other examples, operation 3914 may include monitoring biomolecules on the particle surface, including antigen expression levels or protein expression levels. In other examples, operation 3914 may include monitoring payload loading within the particle envelope, such as liposome particles, hydrogel particles, and viral capsids, wherein the payload loading includes deoxyribonucleic acid (DNA), messenger RNA (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes. In other examples, operation 3914 may include monitoring protein aggregation within particles of a biological sample and on the outer surface of particles of a biological sample.

[0253] Figure 41 An example of a method 4100 for determining the presence of protein load on biological sample particles is illustrated schematically. Method 4100 can be performed by system 10. Method 4100 includes the following operation 4102: receiving input parameters of a sample of biological particles for analysis by flow cytometer 100. Input parameters may include the diameter of the nucleus of the sample particle, the shell thickness of the sample particle at staining, the refractive index of the nucleus of the sample particle, and an approximate range of the refractive index of the shell of the sample particle at staining. As used herein, unstained sample particles have only a nucleus, and stained sample particles have a protein shell surrounding the nucleus. In operation 4102, the input parameters may be received via workstation 200, for example, by a user using one or more input devices (e.g., mouse 206 and keyboard 208), into designated fields displayed on display monitor 204.

[0254] Method 4100 includes the following operation 4104: extracting mean scattering intensity (MSI) data from empirical data of a sample of biological particles measured by flow cytometer 100. In operation 4104, the MSI data may include MSI data of unstained biological particles and MSI data of stained biological particles in the particle sample.

[0255] Method 4100 includes the following operation 4106: calculating the percentage change between the MSI data of stained particles and the MSI data of unstained particles. Operation 4106 may include subtracting the MSI data of unstained particles from the MSI data of stained particles, dividing by the MSI data of unstained particles, and multiplying by 100.

[0256] Method 4100 includes the following operation 4108: calculating the scattering intensity (I0.05) of the undyed particles based on the shell refractive index using the input parameters received in operation 4102 and Mie scattering core-shell modeling. scat For unstained sample particles, the refractive index of the shell is equivalent to the refractive index of the buffer in which the biological particle is suspended, such as phosphate-buffered saline (PBS) or water. Mie scattering core-shell modeling treats particles as having a solid core surrounded by a shell. Transmission electron microscopy (TEM) of extracellular biological particles shows that most of these particles have a hard shell with dense proteins surrounding the core. Mie scattering core-shell modeling is closer to simulating actual biological samples than the traditional Mie scattering theory, which assumes all particles are solid inside.

[0257] Method 4100 includes the following operation 4110: adjusting the scattering intensity (I) calculated in operation 4108... scat ) Application of normalization (I norm Therefore, the normalized scattering intensity (unstained [I]) was determined for unstained sample particles. scat *I norm ]). I norm These are instrument-related parameters, which can vary depending on the flow cytometer. norm It can be determined based on the techniques described above. For example, I norm This can be confirmed by: capturing empirical data from control particles with known scattering intensities; using the input parameters received in operation 4102 and Mie scattering theory to calculate theoretical data on the scattering intensity of the control particles; and determining I. norm This is used to compensate theoretical data to match empirical data, thereby providing calibration between simulated and experimental data.

[0258] Method 4100 includes the following operation 4112: using the unstained normalized scattering intensity [I] determined from operation 4110 scat *I norm The scattering intensity of the stained particles was calculated by the percentage change between the MSI data of the stained particles and the MSI data of the unstained particles from operation 4106, as shown in Equation 2:

[0259]

[0260] Method 4100 includes the following operation 4114: by using Mie scattering core-shell modeling, the refractive index of the shell of the dyed particle is calculated based on the scattering intensity (Iscat) determined in operation 4112 and the input parameters received in operation 4102, as shown in Equation 3:

[0261]

[0262] Here, A and B are parameters derived from simulation data.

[0263] Method 4100 includes the following step 4116: calculating an incremental refractive index by subtracting the refractive index of the buffer solution (e.g., PBS or water) in which the particle is suspended from the refractive index of the particle shell (i.e., the refractive index of the protein-loaded shell) determined in step 4114. The incremental refractive index calculated in step 4116 can determine the relationship between the presence of protein on the shell and the absence of protein on the shell (i.e., space-time). For example, when the incremental refractive index is greater than the nominal value, this indicates the presence of protein loading on the particle.

[0264] Therefore, method 4100 may include the following operation 4118: determining whether the particle has a protein load based on the incremental refractive index calculated in operation 4116. Therefore, method 4100 may include characterizing the presence of protein load on the biological particle based on the incremental refractive index.

[0265] Figure 42 Table 4200 is shown, which includes values ​​from experiments performed according to the operation of method 4100. Table 4200 includes input parameters received in operation 4102 of method 4100, such as viral diameter and refractive index of V5 antibody, fragment antibody (Fab), and immunoglobulin G (IgG) antibody.

[0266] Figure 43 Table 4300 is shown, which includes core-shell modeling for the V5 antibody in Table 4200, targeting a virus expressing the V5 tag and a monoclonal anti-V5 antibody (designated V5) that binds to the V5 tag to stain the virus. Figure 44 Table 4400 is shown, which includes core-shell modeling for Fab antibodies in Table 4200, including monoclonal fragment Fab antibodies that bind to... Figure 43 The anti-V5 antibody described herein is used for staining anti-V5 antibodies bound to the virus (designated as Fab). Figure 45 Table 4500 is shown, which includes core-shell modeling for IgG antibodies in Table 4200, including monoclonal IgG antibodies that bind to... Figure 43 The anti-V5 antibody described herein is used for staining of the anti-V5 antibody (designated as IgG) bound to the virus. In Tables 4300 to 4500, the input parameters also include nuclear radius, shell radius, nuclear refractive index, and shell refractive index. As described above, the input parameters are received in operation 4102 of method 4100.

[0267] Return to reference Figure 42Table 4200 also includes empirical parameters measured by flow cytometer 100, such as the MSI of unstained and stained V5 antibody, Fab antibody, and IgG antibody at a gain of 500. As described above, the MSI of unstained and stained V5 antibody, Fab antibody, and IgG antibody is extracted in operation 4104 of method 4100.

[0268] Table 4200 also shows the incremental MSI and percentage increments between unstained and stained V5 antibodies, Fab antibodies, and IgG antibodies. The incremental MSI between unstained and stained V5 antibodies, Fab antibodies, and IgG antibodies is calculated in operation 4106 of method 4100.

[0269] Now refer to Figures 42 to 45 Tables 4200 to 4500 also include simulation results, which include the scattering intensity (I) calculated in operation 4108 of method 4100. scat ) and the normalization of the scattering intensity calculated in operation 4110 (i.e., I scat *I norm ).

[0270] like Figure 42 As shown, Table 4200 also includes the refractive indices of the shells of unstained and stained V5 antibody, Fab antibody, and IgG antibody. As described above, these refractive indices are calculated in operation 4112 of method 4100.

[0271] Figure 46 Table 4600 is shown, which includes incremental refractive index values ​​calculated for the V5-stained antibody, Fab antibody, and IgG antibody in operation 4114 of method 4100. These incremental refractive index values ​​can be used to determine the presence of protein loading on particles passing through the interrogation zone 18 of the flow cytometer 100.

[0272] Figure 47 Plot 4700 is shown, which illustrates the shell refractive index (Y-axis) calculated according to method 4100 for V5 staining antibody, Fab antibody, and IgG antibody compared to I. scat *I norm (X-axis). In Figure 47 In the diagram, the points are from the simulation data in the tables above for V5, Fab, and Igg, and the different trends are approximated as linear. As shown in graph 4700, with I... scat *I norm As the refractive index increases, the refractive index of the shell increases essentially linearly. The refractive index of the shell is related to I calculated for the particles. scat *I norm Value-related.

[0273] Figure 48Another example of a method 4800 for generating a function that can be used to characterize particles by flow cytometry is illustrated schematically, and this method 4800 can be performed by system 10. The function determined according to method 4800 is a label-free technique that allows particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labels. Furthermore, method 4800 can be performed to characterize and / or sort both whole cells and nanoparticles, such as extracellular vesicles comprising microvesicles and exosomes. As will be further described below, this function can be used to determine particle size.

[0274] exist Figure 48 In this embodiment, method 4800 is schematically shown as having a separation between an empirical data collection section and a post-processing section. The empirical data collection section of method 4800 includes the following operation 4802: irradiating the particles with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength. As an illustrative example, the first wavelength may be in the violet wavelength range (e.g., 325 nm to 450 nm), and the second wavelength may be in the red wavelength range towards the opposite end of the spectrum (e.g., 600 nm to 850 nm).

[0275] The empirical data collection portion of method 4800 further includes the following operation 4804: collecting side-scattered light of a first wavelength from at least a first detector (e.g., a first SSC detector 515) and collecting side-scattered light of a second wavelength from a second detector (e.g., a second SSC detector 515). Operations 4802 and 4804 of method 4800 are similar to operations 2402 and 2404 of method 2400 described above.

[0276] Figure 49 An example of mouse leukemia virus (MLV) virus data 4900 collected according to the empirical data collection section of method 4800 is shown.

[0277] Return to reference Figure 48 The post-processing portion of method 4800 includes the following operation 4806: receiving a refractive index as input from a user of the flow cytometer 100. The refractive index received in operation 4806 is predetermined. In some cases, the refractive index received in operation 4806 is classified into a predetermined value range including at least one of a high value range, a medium value range, or a low value range.

[0278] The post-processing portion of method 4800 includes the following operation 4808: based on the refractive index input received in operation 4806 and using Mie scattering theory, performing a simulation of scattering intensity at both a first wavelength (e.g., within 325 nm to 450 nm) and a second wavelength (e.g., within 600 nm to 850 nm).

[0279] Figure 50 An example of Table 5000 is shown, which includes values ​​calculated according to the operation of Method 4800. In Table 5000, simulated values ​​of scattering intensity at both the first and second wavelengths are included in the second and third columns of Table 5000, as shown in the annotated figure.

[0280] Return to reference Figure 48 The post-processing portion of method 4800 includes the following operation 4810: calculating the ratio of the simulated scattering intensity at a first wavelength (e.g., violet light as shown in the second column of table 5000) to the simulated scattering intensity at a second wavelength (e.g., red light as shown in the third column of table 5000) based on different dimensions (i.e., the first column of table 5000). The ratio calculated in operation 4810 is displayed in the fourth column of table 5000.

[0281] The post-processing portion of method 4800 further includes the following operation 4812: calculating the ratio of the empirical scattering intensity at a first wavelength (e.g., violet light) to the empirical scattering intensity at a second wavelength (e.g., red light). An illustrative example of the ratio of empirical scattering intensities is provided in... Figure 49 As shown in the image.

[0282] The post-processing portion of method 4800 further includes the following operation 4814: calibrating the simulation ratio determined in operation 4810 according to different particle sizes. Operation 4814 may include applying... Figure 25 The calibration equation generated by method 2500 is shown. The calibration performed in operation 4814 tunes the simulation ratio to be close to the empirical ratio calculated in operation 4812. The calibration ratio calculated in operation 4814 is shown in the sixth column of table 5000. Figure 51 The calibration ratio calculated based on particle size in operation 4814 is shown graphically.

[0283] The post-processing portion of method 4800 further includes the following operation 4816: performing a size simulation based on Mie theory using a calibrated empirical ratio determined in operation 4814. For example, the ratio of the empirical scattering intensity at a first wavelength (e.g., violet light) to the empirical scattering intensity at a second wavelength (e.g., red light), calculated by system 10, can then be used as a label-free technique to determine particle size.

[0284] Figure 52Another example of a method 5200 for characterizing particles by flow cytometry is schematically illustrated, which can be performed by system 10. Method 5200 is similarly a label-free technique, enabling particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labeling. Furthermore, method 5200 can be performed to characterize and / or sort both whole cells and nanoparticles, such as extracellular vesicles comprising microvesicles and exosomes. As will be further described below, with Figure 48 Compared to method 4800, method 5200 includes calibrating empirical data collected by flow cytometer 100 to approximate simulated data calculated using Michaelis-Menten theory.

[0285] Method 5200 similarly separates the empirical data collection section and the post-processing section. The empirical data collection section includes: operation 5202: irradiating the particle with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength; and operation 5204: collecting at least the side-scattered light of the first wavelength from a first detector (e.g., a first SSC detector 515) and collecting the side-scattered light of the second wavelength from a second detector (e.g., a second SSC detector 515). Operations 5202 and 5204 of method 5200 are similar to... Figure 48 The operations shown are the same as those of method 4800 described above, 4802 and 4804.

[0286] The post-processing section of method 5200 includes the following operation 5206: receiving the refractive index as input from the user of flow cytometer 100. Operation 5206 of method 5200 is the same as operation 4806 of method 4800.

[0287] The post-processing portion of method 5200 further includes the following operation 5208: calculating the ratio of the empirical scattering intensity at a first wavelength (e.g., within the range of 325 nm to 450 nm) to the empirical scattering intensity at a second wavelength (e.g., within the range of 600 nm to 850 nm). Operation 5208 of method 5200 is the same as operation 4812 of method 4800.

[0288] The post-processing portion of method 5200 further includes the following operation 5210: calibrating the empirical ratio calculated in operation 5208. Operation 5208 may include applying the empirical ratio calculated in operation 5208. Figure 25 The calibration equation generated by method 2500 is shown. The calibration performed in operation 5210 tunes the empirical ratio to a near-simulation ratio.

[0289] The post-processing portion of method 5200 includes the following operation 5212: performing a size simulation based on Mie theory using a calibrated empirical ratio determined in operation 5210. For example, the ratio of empirical scattering intensity at a first wavelength to empirical scattering intensity at a second wavelength, calculated by system 10, can then be used as a label-free technique to determine particle size.

[0290] Conventional cytometers typically provide arbitrary median scattering intensity (MSI) units as output, which limits the ability to obtain useful information about sample particle characteristics, such as size and / or refractive index. As will now be discussed, a novel approach is implemented to convert the output MSI from the flow cytometer 100 into absolute units of size and refractive index to provide more meaningful information about the composition of the particle sample.

[0291] The following method can be used to replace flow cytometry post-acquisition analysis software (FCM). PASS FCM (Financial Management Center) is a software tool. PASS Allows users to obtain size calibration data based on one wavelength at a time. Looking at only one wavelength at a time can be disadvantageous because users cannot separate groups of heterogeneous extracellular vesicles due to the different refractive indices at different wavelengths.

[0292] The following method is unique in that it distinguishes populations of extracellular vesicles based on multi-wavelength scattering and provides size information for each of these populations. Furthermore, the following method can also be used to calibrate the refractive index of the population of interest.

[0293] Figure 53 An example of a method 5300 for characterizing particles by flow cytometry is schematically illustrated, which can be performed by system 10. Method 5300 is a label-free technique that enables particle characterization without the use of fluorescent agents, fluorescent dyes, or other types of labels. Method 5300 comprises three separate stages: an instrument calibration stage 5302; an empirical data collection stage 5304; and a processing stage 5306.

[0294] Each of the calibration phase 5302, the empirical data collection phase 5304, and the processing phase 5306 may share one or more operations or aspects with the methods and techniques described above. For example, calibration phase 5302 may be combined with... Figure 25 Method 2500 shares various aspects. Figure 25 Method 2500 is performed as described above to generate calibration.

[0295] like Figure 53As shown, calibration phase 5302 includes: operation 5312: irradiating particles of known size and refractive index, for example, using the light emitting unit 110 of the flow cytometer 100; operation 5314: collecting side-scattered light of a first wavelength and a second wavelength, for example, using the side-collecting unit 130 of the flow cytometer 100; operation 5316: simulating the side-scattered light of a first wavelength (e.g., violet) and the side-scattered light of a second wavelength (e.g., red), for example, using Mie theory; and operation 5318: correlating the empirical data collected in operation 5314 with the simulated data calculated in operation 5316 to determine the calibration of the flow cytometer 100. In some examples, calibration phase 5302 may also include: operation 5320: confirming the half-angle of the side-collecting unit 130 of the flow cytometer 100; and operation 5322: determining a calibration factor based on the half-angle confirmed in operation 5320. Operations 5320 and 5322 correspond to operations 2510 and 2512 of method 2500 described above.

[0296] In some examples, operations 5312 to 5318 are performed to calibrate the flow cytometer 100 based on a first wavelength (e.g., within 325 nm to 450 nm) and a second wavelength (e.g., within 600 nm to 850 nm), respectively. For example, operation 5318 may include correlating empirical side-scattering data and simulated side-scattering data for the first and second wavelengths, respectively, to define the calibration. Alternatively, operation 5318 may include correlating an empirical ratio and a simulated ratio between the first and second wavelengths to define the calibration.

[0297] Figures 54 to 59 An example is shown of calibrating a flow cytometer 100 by correlating empirical data of a first wavelength (e.g., within the range of 325 nm to 450 nm) and a second wavelength (e.g., within the range of 600 nm to 850 nm) with simulated data of the first and second wavelengths, respectively. Figure 54 An example of Table 5400 is shown, which includes empirical data collected according to operations 5312, 5314 of instrument calibration phase 5302. Table 5400 includes empirical data on side-scattered light at a first wavelength. The data shown in Table 5400 were collected from PSL microspheres of different known sizes. The empirical data in Table 5400 were collected by the side-collection unit 130 of the flow cytometer 100.

[0298] Figure 55 An example of Table 5500 is shown, which includes simulation data calculated according to operation 5316 of instrument calibration phase 5302. Table 5500 includes simulation data for side-scattered light at a first wavelength. The simulation data shown in Table 5500 can be used to calculate the theoretical scattering intensity at the wavelength of interest using Mie scattering simulations based on solid modeling.

[0299] Figure 56 The diagram illustrates operation 5318 according to instrument calibration phase 5302. Figure 54 The empirical data included in Table 5400 are... Figure 55 An example of a correlation curve for the simulated data in Table 5500. The correlation can be performed using the following equation (4):

[0300]

[0301] Where Vnorm = Inorm * the simulated first wavelength of violet.

[0302] Figure 57 An example of Table 5700 is shown, which includes empirical data collected according to operations 5312, 5314 of instrument calibration phase 5302. Table 5700 includes empirical data on side-scattered light at a second wavelength. The data shown in Table 5700 were collected from PSL microspheres of different known sizes. The empirical data in Table 5700 were collected by the side-collection unit 130 of the flow cytometer 100.

[0303] Figure 58 An example of Table 5800 is shown, which includes simulation data calculated according to operation 5316 of instrument calibration phase 5302. Table 5800 includes simulation data for side-scattered light at a second wavelength. The simulation data shown in Table 5800 can be used to calculate the theoretical scattering intensity at the wavelength of interest using Mie scattering simulations based on solid modeling.

[0304] Figure 59 An example of graph 5900 is shown, which illustrates the correlation between the empirical data included in table 5700 and the simulated data in table 5800 according to operation 5318 of instrument calibration phase 5302. The correlation can be performed using equation (4) described above.

[0305] Return to reference Figure 53 The empirical data collection phase 5304 can be combined with Figure 24 Method 2400 shares various aspects. For example, the empirical data collection phase 5304 may include: operation 5322: irradiating sample particles, for example, by using the light emitting unit 110 of the flow cytometer 100; and operation 5324: collecting the lateral scattered light from the sample particles at a first wavelength (e.g., violet) and a second wavelength (e.g., red) by using the lateral collection unit 130 of the flow cytometer 100. Operations 5322 and 5324 may be similar to Figure 24 Method 2400 operates on 2402 and 2404.

[0306] like Figure 53 As further shown, the empirical data collection phase 5304 may include the following operation 5326: confirming the average scattering intensity (MSI) of the sample particles in the first wavelength and the second wavelength. In some examples, operation 5326 may include plotting the MSI of the sample particles in the first wavelength (MSI(I1)) against the MSI of the sample particles in the second wavelength (MSI(I2)). In some examples, operation 5326 may include confirming the MSI of all data in the side scattering channels other than the MSI(I1) and MSI(I2) channels.

[0307] Figure 60 A point graph 6000 is shown graphically as an example of empirical data of MLV virus with empirical MSI of the first and second wavelengths, confirmed according to operation 5326 of empirical data collection phase 5304.

[0308] Figure 61 An example of Table 6100 is shown, which includes information for... Figure 60 The dot plot 6000 shows the MSI of different lateral scattering channels of the lateral collection unit 130 for empirical data collected from the MLV virus. Table 6100 also shows the empirical ratio between the MSI of the first wavelength and the MSI of the second wavelength.

[0309] Processing phase 5306 may include the following operation 5332: receiving refractive index as input from a user of flow cytometer 100. Processing phase 5306 also includes the following operation 5334: performing simulations of scattering intensity at a first wavelength and a second wavelength, respectively, based on the refractive index input received in operation 5332 and using Mie scattering theory. For example, operation 5334 may include: performing a simulation of scattering intensity at a first wavelength (e.g., within the range of 325 nm to 450 nm) based on Mie scattering theory and the refractive index input received in operation 5332. Operation 5334 may also include: performing a simulation of scattering intensity at a second wavelength (e.g., within the range of 600 nm to 850 nm) based on Mie scattering theory and the refractive index input received in operation 5332. Operation 5334 may be similar to Figure 48 The method 4800 operates on 4808, but the difference is that the simulation is performed on the first wavelength and the second wavelength respectively, rather than the ratio between the first wavelength and the second wavelength.

[0310] Figure 62 An example of Table 6200 is shown, which includes simulated data calculated for a first wavelength (e.g., within the range of 325 nm to 450 nm) according to operation 5334 of processing stage 5306. The simulated data for the first wavelength shown in Table 6200 is based on an input violet refractive index of 1.45037 received in operation 5332.

[0311] Figure 63 An example of Table 6300 is shown, which includes simulated data calculated for a second wavelength (e.g., within the range of 600 nm to 850 nm) according to operation 5334 of processing stage 5306. The simulated data for the second wavelength shown in Table 6200 is based on an input red refractive index of 1.4453 received in operation 5332.

[0312] Processing phase 5306 may also include the following operations 5336: calibrating the simulated side scattering data of the first wavelength and the simulated side scattering data of the second wavelength (see...). Figure 62 and Figure 63 The calibration in operation 5336 can be based on the calibration determined in operation 5318 of instrument calibration phase 5302. In operation 5336, simulated side scattering data for the first wavelength and simulated side scattering data for the second wavelength are calibrated separately for different particle sizes, such as... Figure 62 and 63 As shown.

[0313] Processing phase 5306 may also include the following operation 5338: plotting the size distribution relative to the simulated side scattering data after calibration in operation 5336.

[0314] Figure 64 An example of a dot plot 6400 that can be generated according to operation 5338 is shown. Dot plot 6400 shows the relationship between the size distribution and simulated side scattering data relative to a first wavelength (e.g., within the range of 325 nm to 450 nm) after calibration in operation 5336. Figure 64 In the table, the particle size is on the y-axis (first column of Table 6200), and the calibrated simulated side scattering data is on the x-axis (i.e., fourth column of Table 6200).

[0315] Figure 65 Another example of a dot plot 6500 that can be generated according to operation 5338 is shown. Dot plot 6500 shows the relationship between the size distribution and simulated lateral scattering data relative to a second wavelength (e.g., within the range of 600 nm to 850 nm) after calibration in operation 5336. Figure 65 In the table, the particle size is on the y-axis (first column of Table 6300), and the calibrated simulated side scattering data is on the x-axis (i.e., fourth column of Table 6300).

[0316] Processing stage 5306 includes the following operation 5340: determining the size of sample particles based on empirical side scattering data of the first and second wavelengths confirmed in operation 5326 and the calibration performed in operation 5336. In some examples, a dot plot generated in operation 5338 is used to determine the particle size.

[0317] Figure 66 Table 6600 is shown, which provides the following example in which a refractive index of 1.45037 at a first wavelength is received in operation 5332, an empirical side scattering intensity of 152556.8 at a first wavelength is detected in operation 5326, and a particle size of 121.4 nm is determined in operation 5340.

[0318] Figure 67 Table 6700 is shown, which provides the following example in which the refractive index of 1.4453 at the second wavelength is received in operation 5332, the empirical side scattering intensity of 25194.4 at the second wavelength is detected in operation 5326, and the particle size of 119.7 nm is determined in operation 5340.

[0319] 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 size based on the first wavelength and the estimated particle size based on the second wavelength are averaged to determine the size of the particles analyzed by System 10.

[0320] In alternative examples, instead of calibrating the simulated side-scattering data for the first wavelength and the simulated side-scattering data for the second wavelength, operation 5336 may include calibrating the empirical side-scattering data for the first wavelength and the empirical side-scattering data for the second wavelength confirmed in operation 5326. In such an example, operation 5318 of instrument calibration phase 5302 includes a reverse calibration in which the simulated data calculated in operation 5316 is correlated with the empirical data collected in operation 5314. Furthermore, in these alternative examples, operation 5338 may be skipped, allowing the method to proceed directly to operation 5340: determining the sample particle size based on matching the calibrated empirical side-scattering data for the first wavelength and the empirical side-scattering data for the second wavelength with the simulation performed in operation 5334.

[0321] Figure 68Another example of a method 6800 for characterizing particles by flow cytometry is illustrated schematically. Method 6800 can be performed by system 10. Method 6800 similarly includes an instrument calibration phase 6802, an empirical data collection phase 6804, and a processing phase 6806. The difference between method 6800 and method 5300 is that the processing phase 6806 determines the refractive index based on the size input received from the user.

[0322] like Figure 68 As shown, the instrument calibration phase 6802 of method 6800 includes operations 6812 to 6818 that are substantially similar to operations 5312 to 5318 of method 5300. In some examples, calibration phase 6802 may further include: operation 6820: confirming the half-angle of the lateral collection unit 130 of the flow cytometer 100; and operation 6822: determining a calibration factor based on the half-angle confirmed in operation 6820. Operations 6820 and 6822 are substantially similar to operations 2510 and 2512 of method 2500 described above.

[0323] The empirical data collection phase 6804 of method 6800 includes operations 6822 to 6826 that are substantially similar to operations 5322 to 5326 of method 5300.

[0324] Processing phase 6806 includes operation 6832 of receiving size input. Size input can be received in operation 6832 via workstation 200 connected to flow cytometer 100 of system 10. In some examples, size input is predetermined by system 10 based on open-source literature and / or user experience. Alternatively, size input can be received in operation 6832 from a user of system 10.

[0325] Processing stage 6806 includes the following operation 6834: performing a simulation of side scattering intensity at both a first wavelength and a second wavelength based on the size input received in operation 6832 and using Mie scattering theory. Operation 6834 may include: performing a simulation of side scattering intensity at a first wavelength (e.g., within the range of 325 nm to 450 nm) based on Mie scattering theory and the size input received in operation 6832. Operation 6834 may also include: performing a simulation of side scattering intensity at a second wavelength (e.g., within the range of 600 nm to 850 nm) based on Mie scattering theory and the size input received in operation 6832. Operation 6834 may be similar to operation 5334 of method 5300, except that instead of using refractive index, the particle size is used to perform the simulation.

[0326] Figure 69An example of Table 6900 is shown, which includes simulated side-scattering intensity data calculated for a first wavelength (e.g., within the range of 325 nm to 450 nm) according to operation 6834 of processing stage 6806. The simulated side-scattering intensity data shown in Table 6900 is based on an input size of 120 nm received in operation 6832.

[0327] Figure 70 An example of Table 7000 is shown, which includes simulated side-scattering intensity data calculated for a second wavelength (e.g., within the range of 600 nm to 850 nm) according to operation 6834 of processing stage 6806. The simulated side-scattering intensity data shown in Table 7000 is based on an input size of 120 nm received in operation 6832.

[0328] Processing phase 6806 includes the following operation 6836: calibrating the simulated side-scattering intensities at both the first and second wavelengths generated in operation 6834. The calibration in operation 6836 is based on the calibration determined in operation 6818 of instrument calibration phase 6802. In operation 6836, the simulated side-scattering intensity data for the first and second wavelengths can be calibrated separately for the first and second wavelengths according to different refractive indices, such as... Figure 69 and Figure 70 As shown (see the fourth column in Tables 6900 and 7000).

[0329] Processing stage 6806 may include the following operation 6838: plotting the relationship between the refractive index distribution and the simulated side scattering data after calibration in operation 6836.

[0330] Figure 71 An example of a dot plot 7100 that can be generated according to operation 6838 is shown. Dot plot 7100 shows the relationship between the refractive index distribution and simulated side scattering intensity data for a first wavelength (e.g., within the range of 325 nm to 450 nm) after calibration in operation 6836. Figure 71 In the table, the refractive index is on the y-axis (first column of Table 6900), and the calibrated simulated side scattering intensity data is on the x-axis (i.e., fourth column of Table 6900).

[0331] Figure 72 Another example of a dot plot 7200, which can be generated according to operation 6838, is shown. Dot plot 7200 shows the relationship between the refractive index distribution and simulated side-scattering intensity data at a second wavelength (e.g., within the range of 600 nm to 850 nm) after calibration in operation 6836. The refractive index is on the y-axis (first column of Table 7000), and the simulated side-scattering intensity data after calibration is on the x-axis (i.e., fourth column of Table 7000).

[0332] Processing phase 6806 includes the following operation 6840: determining the refractive index of the sample particles based on empirical side-scattering intensity data of the first wavelength and the second wavelength confirmed in operation 6826, and calibration of the simulated scattering intensity performed in operation 6836. Dot plots 7100 and 7200, showing the relationship between the refractive index distribution and the simulated side-scattering intensity data after calibration in operation 6836, can be used to determine the refractive index of the sample particles analyzed by system 10.

[0333] Figure 73 Table 7300 is shown, providing the following example: an input particle size of 120 nm is received in operation 6832, an empirical side scattering intensity of 152556.8 nm at the first wavelength is detected in operation 6826, and a particle refractive index of 1.45037 is determined in operation 684. The refractive index of 1.45037 is related to... Figure 66 The example match shown in this example is in which the refractive index is received in operation 5332 and used to estimate the particle size of 121.4 nm according to method 5300.

[0334] Figure 74 Table 7400 is shown, providing the following example: an input particle size of 120 nm is received in operation 6832, an empirical side scattering intensity of 25194.4 nm at the second wavelength is detected in operation 6826, and a particle refractive index of 1.4434 is determined in operation 6840. The refractive index of 1.4434 is... Figure 67 The example shown corresponds very well, in which the refractive index is received in operation 5332 and used to estimate the particle size of 119.7 nm according to method 5300.

[0335] In an alternative example, instead of using empirical lateral scattering intensity data for the first wavelength and empirical lateral scattering intensity data for the second wavelength alone, method 6800 may include in operation 6834 a simulation of the ratio of lateral scattering intensity between the first and second wavelengths based on Mie theory and the particle size input received in operation 6832. In such an example, operation 6836 includes calibrating the simulated ratio of lateral scattering intensity between the first and second wavelengths, operation 6838 includes plotting the relationship between the refractive index and the simulated ratio of lateral scattering intensity between the first and second wavelengths, and operation 6840 includes determining the refractive index based on the empirical ratio of the lateral scattering data between the first and second wavelengths and the calibration of the simulated ratio of lateral scattering intensity between the first and second wavelengths.

[0336] Figure 75An example of Table 7500 is shown, which includes a simulated ratio of the lateral scattering intensity between the first and second wavelengths according to the alternative of Operation 6834 (shown in the second column). Table 7500 also includes a calibration of the simulated ratio of the lateral scattering intensity between the first and second wavelengths according to the alternative of Operation 6836 (shown in the fourth column).

[0337] Figure 76 An example of a dot plot 7600 generated according to an alternative scheme of operation 6838 is shown. Dot plot 7600 shows the relationship between the refractive index distribution (y-axis) of the second wavelength and the simulated ratio (x-axis) of the lateral scattering intensity between the first and second wavelengths after calibration in operation 6836. Figure 76 In this table, the refractive index is obtained from the first column of Table 7500, and the simulated ratio of scattering intensity between the first and second wavelengths is obtained from the fourth column of Table 7500.

[0338] Figure 77 Table 7700 is shown, providing the following example: an input particle size of 120 nm is received in operation 6832, an empirical ratio of 6.055 to the lateral scattering 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 of 1.4453 determined according to an alternative to method 6800 is compared with the refractive index received in operation 5332 according to method 5300 for estimating a particle size of 119.7 nm (see...). Figure 67 )match.

[0339] In yet another alternative example, instead of calibrating the simulated side scattering data of the first wavelength and the simulated side scattering data of the second wavelength, or the ratio of simulated side scattering data between the first and second wavelengths, operation 6836 may include calibrating the empirical side scattering data of the first wavelength and the empirical side scattering data of the second wavelength, or the empirical ratio of side scattering data between the first and second wavelengths, as confirmed in operation 6826.

[0340] In such an example, operation 6818 of instrument calibration phase 6802 includes a reverse calibration in which the simulation data calculated in operation 6816 is correlated with the empirical data collected in operation 6814. Furthermore, in such an example, operation 6838 of processing phase 6806 can be skipped, allowing the method to proceed directly to operation 6840: determining the particle refractive index based on matching calibrated empirical side-scattering data of a first wavelength and a second wavelength with the simulation performed in operation 6834.

[0341] Figure 78 The software for post-acquisition analysis by flow cytometry (FCM) is shown.PASS A comparison 7800 between the gating strategies described above for determining particle size or refractive index and those described in the FCM is presented. Comparison 7800 includes a first dot plot 7802, a second dot plot 7804, and a third dot plot 7806. The first dot plot 7802 shows the gating strategies from the FCM. PASS The size calibration results for the first wavelength are shown in Figure 7804, and the second point shows the results from FCM. PASS The size calibration results for the second wavelength are shown in Figure 7806, and the third point illustrates the gating strategy for MLV virus using both the first and second wavelengths as described above in the method.

[0342] like Figure 78 As shown, the size calibration results are clearer in the first point plot 7802 at the first wavelength than in the second point plot 7804 at the second wavelength because the minor groups appear to be merging into the size calibration results in the second point plot 7804. In light of the above, by FCM... PASS Size and refractive index estimations appear to be less accurate at the second wavelength than at the first wavelength.

[0343] The third point, Figure 7806, illustrates the gating strategy for MLV viruses according to the example method described above. Due to the difference in refractive index at the first and second wavelengths, the MLV virus population is more clearly separated from the minor population in the third point, Figure 7806, which shows the average scattering intensity at the first wavelength compared to the average scattering intensity at the second wavelength. Therefore, the average scattering intensity at both the first and second wavelengths is more accurate compared to that from FCM. PASS This can improve the accuracy of size and refractive index estimation.

[0344] Furthermore, the method described above also provides the capability to identify the empirically average scattering intensity of the first wavelength group and the empirically average scattering intensity of the second wavelength group from point plots 6400, 6500, 7100, 7200, and 7600. For example... Figure 78 As shown, the method described above offers the advantage of accurately defining the average scattering intensity at each of the first and second wavelengths. This allows the user to gate specific populations, or alternatively, to provide size and refractive index calibration for all average scattering intensities displayed on a given side scattering channel of the flow cytometer 100 in system 10.

[0345] The various embodiments described above are provided for illustrative purposes only and should not be construed as limiting in any way. Various modifications may be made to the embodiments described above without departing from the true spirit and scope of this disclosure.

Claims

1. A label-free method for characterizing particles by flow cytometry, the method comprising: The particles are irradiated with at least a first excitation beam of a first wavelength and a second excitation beam of a second wavelength. The side-scattered light of the first wavelength is collected from at least the first detector, and the side-scattered light of the second wavelength is collected from the second detector; as well as The particle size is determined based on the first median lateral scattered light intensity at the first wavelength and the second median lateral scattered light intensity at the second wavelength.

2. The method according to claim 1, further comprising: Calibration is applied to the side-scattered light of the first wavelength and the side-scattered light of the second wavelength, wherein the calibration is determined by correlating empirical data with theoretical data using calibration microspheres with known refractive indices and sizes.

3. The method according to claim 1, further comprising: Calculate the ratio of the first median side-scattered light intensity at the first wavelength to the second median side-scattered light intensity at the second wavelength; as well as The particle size is determined based on the ratio of the first median side-scattered light intensity to the second median side-scattered light intensity.

4. The method according to claim 3, further comprising: The ratio of the first median lateral scattered light intensity at the first wavelength to the second median lateral scattered light intensity at the second wavelength is calibrated, wherein the calibration is determined by correlating empirical data with theoretical data using calibration microspheres having known refractive indices and sizes.

5. The method according to claim 2 or 4, wherein, The calibration is based on a linear correction factor, a quadratic correction factor, or a trigonometric correction factor.

6. The method according to any one of claims 1 to 5, wherein, The particle size is determined for particles with a size range between 30 nanometers and 400 nanometers.

7. The method according to any one of claims 1 to 6, wherein, The particle size is determined for extracellular vesicles.

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

9. The method according to any one of claims 1 to 8, further comprising: The particle refractive index is estimated based on the particle size.

10. The method according to claim 9, wherein, The ratio of the first median lateral scattered light intensity at the first wavelength to the second median lateral scattered light intensity at the second wavelength is filtered after calibration to narrow the range used to estimate the particle refractive index based on 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 an excitation beam for projection onto particles flowing through an interrogation region, the light emitting unit comprising: A first laser, which emits a first excitation beam of a first wavelength; and A second laser emits a second excitation beam of a second wavelength; Collection unit, the collection unit comprising: A first detector, configured to collect side-scattered light of the first wavelength; and A second detector, used to collect the side-scattered light of the second wavelength; and A processing circuit system having a memory for storing instructions, which, when executed by the processing circuit system, cause the processing circuit system to: The particle is irradiated with at least the first excitation beam of the first wavelength and the second excitation beam of the second wavelength; At least the side-scattered light of the first wavelength is collected from the first detector, and the side-scattered light of the second wavelength is collected from the second detector; and The particle size is determined based on the first median side-scattered light intensity and the second median side-scattered light intensity.

12. The system according to claim 11, wherein, When the instruction is executed by the processing circuit system, the processing circuit system also causes the processing circuit system to: Calibration is applied to the side-scattered light of the first wavelength and the side-scattered light of the second wavelength, wherein the calibration is determined by correlating empirical data with theoretical data using calibration microspheres with known refractive indices and sizes.

13. The system according to claim 11, wherein, When the instruction is executed by the processing circuit system, the processing circuit system also causes the processing circuit system to: Calculate the ratio of the first median lateral scattered light intensity at the first wavelength to the second median lateral scattered light intensity at the second wavelength; and The particle size is determined based on the ratio of the first median side-scattered light intensity to the second median side-scattered light intensity.

14. The system according to claim 13, wherein, When the instruction is executed by the processing circuit system, the processing circuit system also causes the processing circuit system to: The ratio of the first median lateral scattered light intensity at the first wavelength to the second median lateral scattered light intensity at the second wavelength is calibrated, wherein the calibration is determined by correlating empirical data with theoretical data using calibration microspheres having known refractive indices and sizes.

15. The system according to claim 12 or 14, wherein, The calibration is based on a linear correction factor, a quadratic correction factor, or a trigonometric correction factor.

16. The system according to any one of claims 12 to 15, wherein, The particle size is determined for particles with a size range between 30 nanometers and 400 nanometers.

17. The system according to any one of claims 12 to 16, wherein, The particle size is determined for extracellular vesicles.

18. The system according to any one of claims 12 to 17, wherein, The first wavelength is within a first wavelength spectrum, and the second wavelength is within a second wavelength spectrum, wherein the second wavelength spectrum is different from the first wavelength spectrum, and wherein the first wavelength spectrum and the second wavelength spectrum are selected from the group consisting of: a first wavelength range between 325 nm and 450 nm, a second wavelength range between 450 nm and 490 nm, a third wavelength range between 560 nm and 590 nm, and a fourth wavelength range between 600 nm and 850 nm.

19. The system according to any one of claims 11 to 18, wherein, When the instruction is executed by the processing circuit system, the processing circuit system also causes the processing circuit system to: The particle refractive index is estimated based on the particle size.

20. The system according to claim 19, wherein, The ratio of the first median lateral scattered light intensity at the first wavelength to the second median lateral scattered light intensity at the second wavelength is filtered after calibration to narrow the range used to estimate the particle refractive index based on different particle sizes.

21. A label-free method for characterizing particles by flow cytometry, the method comprising: The particles of the biological sample are passed through the interrogation zone to be irradiated at multiple different wavelengths of light; Side-scattered light data is captured from the particles of the biological sample, the side-scattered light data including a first intensity in the violet spectrum, a second intensity in the blue spectrum, a third intensity in the yellow spectrum, and a fourth intensity in the red spectrum; Calculate the differential optical lateral scattering parameters between particles of the biological sample with an unknown loading and particles of the biological sample with zero loading. The differential optical lateral scattering parameters between particles of the biological sample with the unknown loading and particles of the biological sample with the zero loading are calibrated using the first calibration equation: The refractive index difference is calculated based on the particle size and refractive index of the biological sample, according to the differential light lateral scattering. The refractive index difference is calibrated using the second calibration equation; as well as The unknown load on the particles of the biological sample is characterized based on the refractive index difference calibrated by the second calibration equation.

22. The method according to claim 21, wherein, The second calibration equation is generated by the following: The calibration microspheres are passed through the interrogation region to be irradiated at the multiple different light wavelengths, the calibration microspheres including calibration microspheres with known loading and calibration microspheres with zero loading; Lateral scattered light data is captured from the calibration microsphere passing through the interrogation area, the lateral scattered light data including the first intensity in the violet spectrum, the second intensity in the blue spectrum, the third intensity in the yellow spectrum, and the fourth intensity in the red spectrum; Calculate the differential lateral scattering parameters between the calibration microsphere with the known loading and the calibration microsphere with zero loading: The differential light side scattering parameters are compared with theoretical side scattering data to generate the first calibration equation; The refractive index difference is calculated based on the size and refractive index of the calibration microspheres and the differential light lateral scattering. as well as The second calibration equation is generated based on the refractive index difference between calibration microspheres with the known loading and calibration microspheres with zero loading.

23. The method according to claim 22, wherein, The calibration microspheres include silica microspheres with a known load or bioparticles with a known amount of biomolecules attached via covalent or non-covalent bonding.

24. The method according to any one of claims 21 to 23, wherein, Determining the size of particles in the biological sample includes: Calculate the ratio of the first intensity in the violet light spectrum to the fourth intensity in the red light spectrum; The ratio is calibrated by applying the first calibration equation; The ratio is filtered based on the difference between the refractive index estimated at the first intensity in the violet spectrum and the refractive index estimated at the fourth intensity in the red spectrum; and The size of the particle is determined based on the ratio of the first intensity in the violet light spectrum to the fourth intensity in the red light spectrum.

25. The method according to any one of claims 21 to 24, further comprising: The biological samples are sorted based on the load on the particles.

26. The method according to any one of claims 21 to 25, further comprising: Characterize the interactions between single-particle surface markers and their corresponding ligands, including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin-biotin interactions.

27. The method according to any one of claims 21 to 26, further comprising: Characterize single-particle modifications, including molecules covalently or non-covalently attached to the particle surface.

28. The method according to any one of claims 21 to 27, further comprising: Monitor biomolecules on the particle surface, including antigen expression levels or protein expression levels.

29. The method according to any one of claims 21 to 28, further comprising: Monitoring the loading of payloads inside particle envelopes such as liposomes, hydrogels, and viral capsids, wherein the payloads include deoxyribonucleic acid (DNA), messenger RNA (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes.

30. The method according to any one of claims 21 to 29, further comprising: Monitor protein aggregation inside the particles of the biological sample and on the outer surface of the particles of the biological sample.

31. A method for determining the presence of protein loading on biological particles, the method comprising: The input parameters of the biological particles are received for analysis by flow cytometry. The average scattering intensity is extracted from empirical data of the biological particles measured by the flow cytometer. Calculate the percentage change between the average scattering intensity of stained biological particles and the average scattering intensity of unstained biological particles; The scattering intensity of the unstained biological particles was calculated using the input parameters and Mie scattering core-shell modeling. Normalization is applied to the scattering intensity calculated for the unstained biological particles; The scattering intensity of the stained biological particles is calculated using the scattering intensity of the unstained biological particles and the percentage change between the average scattering intensity of the stained biological particles and the average scattering intensity of the unstained biological particles. The refractive index of the shell of the stained biological particle is calculated based on the input parameters and the scattering intensity of the stained particle using the Mie scattering core-shell modeling. The incremental refractive index is calculated by subtracting the refractive index of the sheath fluid from the refractive index of the shell of the biological particle; and The presence of the protein load on the biological particles is characterized based on the incremental refractive index.

32. The method of claim 31, further comprising: The biological particles are sorted based on the characterization of the protein load present on them.

33. The method according to claim 31 or 32, further comprising: Characterize the interactions between single-particle surface markers and their corresponding ligands, including receptor-ligand, virus-cell, protein-DNA binding, and streptavidin / avidin-biotin interactions.

34. The method according to any one of claims 31 to 33, further comprising: Characterize single-particle modifications, including molecules covalently or non-covalently attached to the particle surface.

35. The method according to any one of claims 31 to 34, further comprising: Monitor biomolecules on the particle surface, including antigen expression levels or protein expression levels.

36. The method according to any one of claims 31 to 35, further comprising: Monitoring the loading of payloads inside particle envelopes such as liposomes, hydrogels, and viral capsids, wherein the payloads include deoxyribonucleic acid (DNA), messenger RNA (mRNA), small interfering RNA (siRNA), proteins, drugs, or dyes.

37. The method according to any one of claims 31 to 36, further comprising: Monitor protein aggregation inside and on the outer surface of the bioparticles.

Images