Particle characterization by flow cytometry

Abstract

A method of characterizing a biological nanoparticle by flow cytometry is provided. The method includes illuminating the nanoparticle with one or more excitation light beams as the nanoparticle passes individually through an interrogation zone. The method further includes collecting light from the nanoparticle passing through the interrogation zone in a plurality of channels. The method includes characterizing the nanoparticle based on the light collected in the plurality of channels.

Description

[0001] Cross-references to related applications

[0002] This application is filed as a PCT international patent application on March 10, 2025, and claims the benefit of U.S. Provisional Application No. 63 / 563,091, filed March 8, 2024, and U.S. Provisional Application No. 63 / 566,884, filed March 18, 2024, each of which is incorporated in its entirety by reference to the appropriate extent. Background Technology

[0003] In flow cytometry, particles are arranged in a sample stream and typically pass one by one through one excitation beams with which they interact. The light scattered or emitted by the particles upon interaction with one or more excitation beams is collected and analyzed to characterize and differentiate the particles. In sorted flow cytometry, particles can be extracted from the sample stream after being characterized by their interaction with one or more excitation beams and thus 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 from particles for characterization by flow cytometry.

[0005] CytoFLEX nano is a flow cytometer from Beckman Coulter, developed as a nanoparticle analyzer with sufficient sensitivity to characterize small particles in suspensions, such as extracellular vesicles (EVs) or lipid nanoparticles (LNPs). CytoFLEX nano facilitates the detection, counting, and immunophenotyping of individual bioparticles, such as EVs, viral particles, virus-like particles, and LNPs.

[0006] Lipid nanoparticles (LNPs) have become increasingly valuable research and therapeutic tools. LNPs consist of a capsid filled with a drug-grade compound such as modified mRNA. Several methods exist for determining LNP quality, such as electron microscopy, dynamic light scattering and tracking, and AUC. However, most of these techniques require complex sample preparation, expensive instrumentation, and sometimes complex data analysis. Dynamic light scattering, nanoparticle tracking, and AUC provide assay characterization of samples but lack specificity. In one embodiment of this disclosure, an alternative to current methods for characterizing LNPs using nanoflow cytometry is disclosed. This technique is a significant improvement over existing methods because, for example, it requires minimal sample preparation and provides rapid results that can be correlated with functional cell transduction assays, high-throughput single-particle characterization up to 5K events per second, and accurate counting. In one embodiment of this disclosure, a flow cytometer capable of characterizing particles smaller than 100 nm, such as the CytoFLEX nano, is used to characterize LNPs of different sizes and / or different concentrations.

[0007] Extracellular vesicles (EVs) are a heterogeneous family of membrane particles released by all cells, providing crucial insights into a wide range of fields and diseases. To better understand EVs, researchers must characterize them. However, analyzing EVs is challenging due to their heterogeneity, size, refractive index, and complex properties. For example, the overall size range of EVs can be as small as 20 nm (recently discovered supermeres and exomeres) to as large as 5 μm (larger apoptotic bodies). Conventional flow cytometry struggles to characterize biological samples smaller than approximately 100–150 nm or beads / particles smaller than approximately 80–100 nm. CytoFLEX nano is a highly sensitive flow cytometer capable of detecting nanoparticles via violet side scattering down to 20 nm and offering improved sensitivity and resolution for all six fluorescence channels. In one embodiment of this disclosure, a method for immunophenotypic characterization using a designed 5-color scheme is disclosed to identify different types of EVs in anemic platelet-rich plasma (PPP) samples. In one embodiment of this disclosure, a flow cytometer capable of characterizing particles smaller than 100 nm, such as CytoFLEX nano, is used to characterize EVs of different sizes and / or with different contents.

[0008] The ability of CytoFLEX nano to characterize small particles in suspensions, such as extracellular vesicles (EVs) or lipid nanoparticles (LNPs), makes it attractive to combine with other laboratory techniques, such as ultracentrifugation, column chromatography, ELISA, ELISPOT, and / or electron microscopy. In one embodiment of this disclosure, a flow cytometer capable of characterizing particles smaller than 100 nm, such as CytoFLEX nano, can be used before, after, simultaneously with, or in any combination of other laboratory techniques to confirm the characterization of small particles by the other laboratory technique and / or to further characterize the small particles. In one embodiment, the other laboratory techniques are ultracentrifugation, column chromatography, electron microscopy, ELISA, ELISPOT, or any combination thereof.

[0009] CytoFLEX nano's ability to characterize small particles in suspension, such as extracellular vesicles (EVs) or lipid nanoparticles (LNPs), also makes it attractive to use it to detect the degree of modification or insertion of surface markers, targeting ligands, etc., on the surface of small particles, including EVs and LNPs. Summary of the Invention

[0010] Generally, this disclosure relates to characterizing particles by flow cytometry. Various aspects are described in this disclosure, including but not limited to the following.

[0011] One aspect relates to a method for characterizing particles by flow cytometry, the method comprising: illuminating the particle with one or more excitation beams as the particle individually passes through an interrogation zone; collecting light from the particle passing through the interrogation zone in a plurality of channels; and characterizing the particle based on the light collected in the plurality of channels.

[0012] On the other hand, a method for improving centrifugation using flow cytometry is provided, the method comprising: performing centrifugation on a sample; performing flow cytometry analysis on the sample after centrifugation; and adjusting one or more parameters of the centrifugation based on the flow cytometry analysis.

[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 the foregoing 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.

[0014] This disclosure provides a method for characterizing particles in a flow cytometer, the method comprising: irradiating the particle with one or more excitation beams at one or more wavelengths as the particle individually passes through an interrogation zone; collecting side-scattered light from the particle passing through the interrogation zone at multiple wavelengths in multiple channels; identifying the average intensity of the side-scattered light in each of the multiple channels; calculating an average calibration factor determined in the same flow cytometer by multiple standard particles having known refractive indices and sizes; characterizing the particle based on the side-scattered light collected in the multiple channels; inputting one or more known parameters; inputting initial guessed parameters and parameter limits for the particle, and optionally parameter constraints; and calculating unknown parameters that minimize a loss function and satisfy the limits and constraints.

[0015] In some cases, the particles are biological particles. In some cases, the particles are nanoparticles. In some cases, the nanoparticles are biological nanoparticles.

[0016] In some cases, determining the average calibration factor involves irradiating calibration beads of known size and refractive index at one or more wavelengths; collecting side-scattered light from all wavelengths of interest; calculating the forward Mie scattering profile for each input calibration bead diameter; integrating the Mie scattering profile for each calibration bead diameter; calculating the calibration factor for each calibration bead size; calculating the goodness of fit for the current half-angle; reporting the half-angle with the highest goodness of fit; calculating the Mie scattering profile at the half-angle identified in the previous step; and calculating the average calibration factor and CV for all calibration bead sizes for the half-collection angles of the previous step.

[0017] In some cases, the integration of the Mie scattering distribution is performed in 0.1-degree steps over 90 degrees ± the current collection half-angle or between 0 and 90 degrees.

[0018] In some cases, the calculation of the forward Mie scattering distribution includes input parameters selected from the group consisting of: bead diameter, bead refractive index at a given wavelength, medium refractive index, wavelength, incident light polarization, and detector polarization sensitivity; calculation of the distribution in the range of 0-180 degrees, optionally in 0.1-degree steps; output of the Mie scattering intensity as a function of the polar angle; and numerical integration of the forward Mie scattering intensity over an angular range defined by the currently proposed collection half-angle.

[0019] In some cases, the method further includes calculating a calibration factor, which involves determining the ratio of the scattering intensity measurement to the integrated forward Mie intensity to obtain the calibration factor.

[0020] In some cases, the goodness of fit is calculated as 1-CV of all calibration factors.

[0021] In some cases, the CV is calculated based on a set of calibration factors:

[0022] CV = Standard deviation (all calibration factors) / Mean (all calibration factors).

[0023] In some cases, the calculation of the unknown parameters involves employing a minimization algorithm and initial guesses of parameters, parameter bounds, and optional parameter constraints.

[0024] In some cases, the minimization algorithm is Powell's algorithm or a constrained trust region minimization algorithm.

[0025] In some cases, the loss function is: Loss function = [(integrated Mie signal calculation value) × (calibration factor) – signal measurement value].

[0026] In some cases, the calculated value of the Mie signal varies with... Figure 60 The particle parameters listed vary.

[0027] In some cases, the particles, optionally nanoparticles, are characterized as extracellular vesicles, lipid nanoparticles, viruses, virus-like particles, or protein aggregates.

[0028] In some cases, the particles, optionally nanoparticles, are characterized based on the amount of protein loading on the surface of the nanoparticles or the amount of therapeutic payload within the nanoparticles.

[0029] In some cases, the particles, optionally nanoparticles, are characterized based on the detection of empty therapeutic payload, partial therapeutic payload, or complete therapeutic payload.

[0030] In some cases, the plurality of channels includes a plurality of side-scattering channels and a plurality of fluorescence channels.

[0031] In some cases, the particles are characterized based on one or more properties identified in the plurality of side scattering channels and optionally the plurality of fluorescence channels.

[0032] In some cases, the method further includes sorting the particles based on their characterization.

[0033] In some cases, the particles are sorted to increase the purity or yield of the nanoparticles.

[0034] In some cases, the particles are sorted based on their secretions.

[0035] In some cases, empty and intact particles can be distinguished based on a detectable increase in scattering.

[0036] In some cases, the excitation light is selected from one or more, two or more of the following: VSSC1, VSSC2, BSSC, YSSC and RSSC.

[0037] In some cases, the increase in scattering is directly related to the ability of the particles to transduce cells.

[0038] In some cases, the cells are selected from the group consisting of patient cells and production cell lines, optionally wherein the production cell line is a CHO cell line.

[0039] In some cases, the patient is a human patient.

[0040] In some cases, the characterization further includes determining unknown parameters selected from the group consisting of: the solid particle size, the solid particle refractive index, the core-shell particle shell thickness, the core diameter, the core-shell particle shell refractive index, and / or the core refractive index.

[0041] In some cases, the characterization further includes inputting one or more known parameters, which are selected from the group consisting of: the solid particle size, the solid particle refractive index, the core-shell particle shell thickness, the core diameter, the core-shell particle shell refractive index, and / or the core refractive index.

[0042] In some cases, the method further includes using flow cytometry to improve particle sample preparation prior to particle characterization. The method comprises: performing a sample preparation technique on the biological sample; performing flow cytometry analysis on the sample after preparation; and adjusting one or more parameters of the sample preparation technique based on the flow cytometry analysis. In some cases, this method can be used to perform quality control on particles used for manufacturing / preparing particles such as EVs, LNPs, viruses, or virus-like particles.

[0043] In some cases, the sample preparation technique is selected from the group consisting of centrifugation, membrane filtration, precipitation, and chromatographic purification.

[0044] In some cases, one or more parameters of the centrifugation are selected from the group consisting of: centrifugation speed, duration, and temperature.

[0045] In some cases, the purity or yield of one or more target particles in the sample is increased, and optionally the membrane filtration is selected from the group consisting of ultrafiltration, tangential flow filtration, dialysis and tangential flow filtration (TFF).

[0046] In some cases, the chromatographic purification method is selected from the group consisting of: size exclusion chromatography (SEC), immunoaffinity capture, affinity chromatography, and ion exchange chromatography.

[0047] In some cases, the particles are nanoparticles.

[0048] In some cases, the particles are biological particles.

[0049] In some cases, nanoparticles are biological nanoparticles.

[0050] In some cases, there are systems that use the methods described in this paper. Attached Figure Description

[0051] The following drawings, which form part of this application, are illustrations of the described techniques and are not intended to limit the scope of this disclosure in any way.

[0052] Figure 1 Examples of systems that can be used to perform flow cytometry are shown, including a flow cytometer and a workstation.

[0053] Figure 2 schematically shown Figure 1 Examples of flow cytometry detection systems.

[0054] Figure 3 It schematically demonstrates the use of... Figure 1 Examples of methods to improve sample preparation by using flow cytometry performed by a flow cytometer.

[0055] Figure 4 It schematically demonstrates that it can be done through Figure 1 Examples of methods for characterizing nanoparticles performed using flow cytometry.

[0056] Figure 5 It schematically demonstrates that it can be done through Figure 1 Examples of methods for characterizing lipid nanoparticles performed using flow cytometry.

[0057] Figure 6 The illustration shows the implementation. Figure 1 Examples of various aspects of computing systems.

[0058] Figure 7 The histograms of PBS buffer at gain 200 are shown, with noise identified as the baseline (left inset); the histogram of an empty LNP sample (middle inset); and the histogram of an NLP sample loaded with eGFP-encoded mRNA (right inset).

[0059] Figure 8AFour types of LNPs loaded with mRNA, with no, low, medium, and high levels of monoclonal antibody (mAb) modification on the LNP surface, and their corresponding histograms (bottom row) are shown. The upper right figure depicts a model of an LNP loaded with RNA without mAb on the LNP surface (left) and a model of an LNP loaded with RNA modified with mAb on the LNP surface (right).

[0060] Figure 8B A table is shown listing LNPs loaded with mRNA with no, low, medium, and high levels of monoclonal antibody (mAb) modification on their surfaces (top of the table) and their corresponding 2D plots from left to right using RSSC-H relative to VSSC1-H: LNPs loaded with mRNA (naked LNPs, no mAb), LNPs loaded with mRNA with low mAb on their surfaces, LNPs loaded with mRNA with medium mAb on their surfaces, and LNPs loaded with mRNA with high mAb on their surfaces.

[0061] Figure 9 An embodiment of this disclosure is disclosed, wherein the sample flow events per second (EPS) characterization of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 9 HEK293T microvesicles were disclosed, and the number of events per minute was analyzed as a percentage of the number of events in the first minute.

[0062] Figure 10 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 10 Time plots of HEK293T microvesicles running three times at 1 µl / min, 3 µl / min and 6 µl / min (column) are disclosed, where p = particle.

[0063] Figure 11 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 11 It was made public. Figure 10 The data and bar graphs disclosed are from three runs of HEK293T microvesicles at 1 µl / min, 3 µl / min and 6 µl / min (column) with a dilution of 1:1000 (5.3 × 10^5 p / mL = 530 p / µl), where p = particle.

[0064] Figure 12An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 12 Time plots of HEK293T microvesicles running three times at 1 µl / min, 3 µl / min and 6 µl / min (column) are disclosed, where p = particle.

[0065] Figure 13 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 13 It was made public. Figure 12 The data and bar graphs disclosed are from three runs of HEK293T microvesicles at 1 µl / min, 3 µl / min and 6 µl / min (column), with a dilution of 1:500 (1.06 × 10^6 p / mL = 1060 p / µl), where p = particle.

[0066] Figure 14 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 14 Time plots of HEK293T microvesicles running three times at 1 µl / min, 3 µl / min and 6 µl / min (column) with a dilution of 1:250 (2.12 × 10^6 p / mL = 2120 p / µl) are disclosed, where p = particle.

[0067] Figure 15 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 15 It was made public. Figure 14 The data and bar graphs disclosed are from three runs of HEK293T microvesicles at 1 µl / min, 3 µl / min and 6 µl / min (column), with a dilution of 1:250 (2.12 × 10^6 p / mL = 2120 p / µl), where p = particle.

[0068] Figure 16 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 16 The sfGFp viral particle assays at 1 μl / min and 3 μl / min were disclosed as sample controls. Data analysis was based on all events and GFP-positive (B531-H) events. Gating strategies were implemented. Figure 16 The information is publicly available, where p = particle.

[0069] Figure 17 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 17 Time plots of sfGFp (where p = particle) viral particle assays at 1 μl / min and 3 μl / min, serving as sample controls, are published. Data analysis is based on all events and GFP-positive (B531-H) events.

[0070] Figure 18 An embodiment of this disclosure is disclosed, wherein the characterization of sample flows (EPS) of biological samples of different concentrations is obtained at different sample flow rates. More specifically, Figure 18 It was made public. Figure 17 The data and bar graphs published herein are for sfGFp (where p = particle) viral particle testing at 1 μl / min and 3 μl / min, serving as sample controls. Data analysis is based on all events and GFP-positive (B531-H) events.

[0071] Figure 19 An embodiment of this disclosure is disclosed, demonstrating the volume counting of biological particles using flow cytometry. More specifically, Figure 19 The V5 virus gating strategy has been made public.

[0072] Figure 20 An embodiment of this disclosure is disclosed, demonstrating the volume counting of biological particles using flow cytometry. More specifically, Figure 20 The MV gating strategy has been made public.

[0073] Figure 21 An embodiment of this disclosure is disclosed, demonstrating the volume counting of biological particles using flow cytometry. More specifically, Figure 21 The distribution of event counts under different conditions has been disclosed.

[0074] Figure 22 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 22A graph showing the PBS control is presented to illustrate the baseline and commercially available rEV-GFP (B531) without staining but with GFP labeling. On the left, the acquisition settings for gain include FSC = 100, VSSC1 = 50, VSSC2 = 200, BSSC = 100, YSSC = 100, RSSC = 100, V447 = 1000, B531 = 1000, Y595 = 1000, R670 = 1000, R710 = 1000 and R792 = 1000, Y595 = 1000, R670 = 1000, R710 = 1000 and R792 = 1000. The first two rows are from PBS (sample buffer = blank). The bottom two rows are from rEV diluted 1:1000, which is the optimal dilution chosen after titration. rEV is green and endogenous, so the right-hand image shows the B531 channel of both PBS and the sample.

[0075] Figure 23 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 23 We have disclosed that rEVs were successfully stained using single staining with CD81 PB, PE, APC and vFRed, and detected on a CytoFLEX nano instrument.

[0076] Figure 24 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 24 Successful staining of rEVs using single staining with CD81 PB, PE, APC, and vFRed was disclosed, and the results were detected on a CytoFLEX nano instrument.

[0077] Figure 25 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 25 We have disclosed that rEVs were successfully stained using a combination of vFRed lipid dye and CD81-PB and detected on a CytoFLEX nano instrument.

[0078] Figure 26 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 26 We have disclosed that rEVs were successfully stained using a combination of vFRed lipid dye and CD81-APC, and detected on a CytoFLEX nano instrument.

[0079] Figure 27An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 27 Characterization of antibodies at the same dilutions as in the previous figure is disclosed to assess the contribution of antibody aggregates to sample staining assessment.

[0080] Figure 28 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 28 Characterization of antibodies and vFRed at the same dilutions as in the previous figure is disclosed to assess the contribution of antibody aggregates and vFRed aggregates to the overall analysis.

[0081] Figure 29 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 29 Examples of different PPP EV fractions separated from a single sample using a qEV 70 nm SEC column are disclosed.

[0082] Figure 30 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 30 The instrument setup and compensation matrix for multicolor staining are disclosed.

[0083] Figure 31 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 31 Single staining using CD81 PB, CD9 FITC, CD63 APC, CD61 PC7, and CD235a PE is disclosed.

[0084] Figure 32 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 32 Single staining using CD81 PB, CD9 FITC, CD63 APC, CD61 PC7, and CD235a PE is disclosed.

[0085] Figure 33 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 33 The staining of the same sample with an intact antibody group, which was diluted at the same dilution in a mixture, is disclosed.

[0086] Figure 34An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 34 Antibody background was disclosed between PBS buffer, Ab only, unstained samples, and single-stained samples.

[0087] Figure 35 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 35 Antibody background was disclosed between PBS buffer, Ab only, unstained samples, and multiple-stained (Ab mixture) samples.

[0088] Figure 36 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 36 Data from unstained and stained samples were disclosed, with fraction 6 selected from qEV 70nm SEC columns.

[0089] Figure 37 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 37 Data from unstained and stained samples were disclosed, with fraction 6 selected from qEV 70nm SEC columns.

[0090] Figure 38 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 38 Disclosed application Figure 36 and Figure 37 CytoFLEX nano settings and compensation for data in the database.

[0091] Figure 39 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 39 The same samples were evaluated using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting the CD61-PC7 positive population.

[0092] Figure 40 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 40 The same samples were evaluated using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting the CD61-PC7 positive population.

[0093] Figure 41An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 41 Data obtained using fraction 6 collected from a qEV 35 nm SEC column are disclosed. Monochromatic staining with CD9 FITC, CD81 PB, CD63 APC, CD61 PC7, and CD235a PE is shown, as well as staining of the same samples with an antibody (mixture) group.

[0094] Figure 42 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 42 Data obtained using fraction 6 collected from a qEV 35 nm SEC column are disclosed. Monochromatic staining with CD9 FITC, CD81 PB, CD63 APC, CD61 PC7, and CD235a PE is shown, as well as staining of the same samples with an antibody (mixture) group.

[0095] Figure 43 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 43 The setup and compensation applied to fraction 6 collected from qEV 35 nm SEC columns are disclosed.

[0096] Figure 44 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 44 The same assessment was disclosed using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting CD61-PC7 positive individuals.

[0097] Figure 45 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize anemic platelet-rich plasma samples. More specifically, Figure 45 The same assessment was disclosed using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting CD61-PC7 positive individuals.

[0098] Figure 46 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 46 Unstained GFP-labeled samples were disclosed.

[0099] Figure 47 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 47 It was made public. Figure 46 The CytoFLEX nano settings used.

[0100] Figure 48 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 48 Single staining with CD81 antibody targeting the same epitope of the tetraspan membrane protein in three different colors is disclosed: PE (first row), APC (second row), and PB (third row), as well as single staining with vFRed lipid dye (fourth row).

[0101] Figure 49 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 49 Combinations of CD81APC and vFRed, along with GFP markers, were disclosed and evaluated using different triggers and compared with slight MFI changes.

[0102] Figure 50 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 50 Combinations of CD81APC and vFRed, along with GFP markers, were disclosed and evaluated using different triggers and compared with slight MFI changes.

[0103] Figure 51 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 51 Combinations of CD81PB and vFRed, along with GFP markers, were disclosed and evaluated using different triggers and compared with slight MFI changes.

[0104] Figure 52 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 52 Combinations of CD81PB and vFRed, along with GFP markers, were disclosed and evaluated using different triggers and compared with slight MFI changes.

[0105] Figure 53 An embodiment of this disclosure is disclosed, which demonstrates the characterization of heterogeneous EVs using flow cytometry.

[0106] Figure 54 An embodiment of this disclosure is disclosed, which demonstrates the use of flow cytometry to characterize biological samples based on protein loading on the surface of biological samples.

[0107] Figure 55 An embodiment of this disclosure is disclosed, which demonstrates the use of flow cytometry to characterize drug loading in liposomes.

[0108] Figure 56 An embodiment of this disclosure is disclosed, which demonstrates the use of flow cytometry to characterize the expression levels of biomolecules.

[0109] Figure 57 An embodiment of this disclosure is disclosed, which demonstrates the use of flow cytometry to characterize single-particle surface marker interactions.

[0110] Figure 58 An embodiment of this disclosure is disclosed, which demonstrates the use of flow cytometry to characterize low-abundance biomarkers.

[0111] Figure 59 A method for determining flow cytometer instrument calibration factors is disclosed, comprising instrument calibration (step 1), empirical data collection (step 2), and post-processing steps (step 3) to calculate output parameters that minimize the loss function and satisfy bounds and constraints.

[0112] Figure 60 Ten different use cases of solid nanoparticles and core-shell nanoparticles are disclosed, along with input and output parameters that can be determined according to the methods of this disclosure.

[0113] Figure 61 The algorithm parameters for ten different use cases are disclosed, including nanoparticle constraint parameters, initial guess parameters, and parameter bounds.

[0114] Figure 62 Two use case scenarios for solid particles are disclosed. In use case 1: the refractive index is a known input parameter, and the size is an unknown output parameter. In use case 2: the size is a known input parameter, and the refractive index is an unknown output parameter.

[0115] Figure 63 Four use case scenarios for core-shell modeling are disclosed. Use Case 3: Core refractive index (RI), shell RI, and shell thickness are known input parameters, while core size is an unknown output parameter. Use Case 4: Shell RI, core RI, and core size are known input parameters, and shell thickness is an unknown output parameter. Use Case 5: Shell RI, shell thickness, and core size are known input parameters, and core RI is an unknown output parameter. Use Case 6: Shell thickness, core RI, and core size are known input parameters, and shell thickness is an unknown output parameter.

[0116] Figure 64Four use case scenarios for core-shell modeling are disclosed. Use Case 7: Shell RI and shell thickness are known input parameters; core RI and core size are unknown output parameters. Use Case 8: Core RI and core size are known input parameters; core RI and core size are unknown output parameters. Use Case 9: Core RI and shell RI are known input parameters; shell thickness and core size are unknown output parameters. Use Case 10: Core size and shell thickness are known input parameters; core RI and shell RI are unknown output parameters.

[0117] Figure 65 A method for instrument calibration is disclosed to define calibration factors based on identified half-angles. Empirical data collection was obtained from multiple polystyrene-sized reference beads with known sizes and RIs. The top row plot shows the purple VSSC1 and VSSC2 histograms for the two sets of polystyrene-sized reference beads. Several tables in the upper right show the nominal reference bead size, CV%, and RI at four wavelengths: 405, 488, 561, and 633 nm. Next, for three instruments, MP03, MP05, and MP07, the half-angle is defined for each instrument. The plot in the middle row shows the fit confidence % (goodness of fit) as a function of scattering angle; the half-collection angle is defined with the highest goodness of fit. Next, calibration factors are defined based on the identified half-angles. The bottom table shows the average calibration factors for all bead sizes at different scattering wavelengths for the three flow cytometers MP05, MP07, and MP03, including VSSC1 (purple), BSSC (blue), YSSC (yellow), and RSSC (red).

[0118] Figure 66 The following examples of empirical data collection in Step 2 are disclosed: As a first example of a biological control for commercially available MLV viral particles, the top row shows histograms of VSSC1 (a), BSSC (b), YSSC (c), and RSSC (d), and a dot plot (e) showing the RSSC gating strategy of VSSC relative to MLV; and as a second example of a biological control for commercially available recombinant EVs, the bottom row shows dot plot (a) showing VSSC1 relative to B531 FL, with P2 gating based on unstained and P1 GFP positive. Dot plot (b) shows BSSC relative to B531 FL. Dot plot (c) shows YSSC relative to B531 FL. Dot plot (d) shows RSSC relative to B531 FL.

[0119] Figure 67Examples of empirical data collection in Step 2 are disclosed below: a third example of a biological control for lipid nanoparticles (LNPs), and histograms of 20 nm VSSC1 for a) buffer, b) empty LNPs and c) loaded LNPs; and a fourth example of a biological control for LNPs, and histogram overlays of four LNPs with polyA-substituted mRNA payloads, approximate mRNA concentrations (ug / mL), and low, medium, and high mAb concentrations (mg / mL) during click-reaction loading of LNPs, as shown in a table.

[0120] Figure 68 A table of post-processing parameters for step 3 of use case 1 with known RI and unknown diameter is disclosed. The table identifies the instrument, particle type (MLV), channel (VSSC1, BSSC, YSSC, or RSSC), expected nucleus diameter (nm) from cryogenic TEM, input parameters for expected nucleus RI, empirical data, output parameters for predicted nucleus diameter (nm), and prediction size error (prediction-expectation).

[0121] Figure 69 A table of post-processing parameters for step 3 of use case 2 with known diameter and unknown RI is disclosed. The table identifies the input parameters of instrument, particle type (MLV), gain, channel (VSSC1, BSSC, YSSC, or RSSC), expected nucleus diameter (nm), expected nucleus RI, empirical data, output parameters of predicted nucleus RI, and predicted RI error (predicted-expected).

[0122] Figure 70 A table of post-processing parameters for step 3 of use case 5 is disclosed, with known shell RI, shell size, core size, and unknown core RI. The table identifies the input parameters: instrument, particle type (rEV), gain, channel (VSSC1, BSSC, YSSC, or RSSC), (i) expected core diameter (nm), (ii) expected shell thickness (nm), and (iii) expected shell RI. The output parameter is the predicted core RI. Other parameters include the expected core RI, empirical data, and the predicted core RI error (predicted core RI - expected core RI).

[0123] Figure 71A table of post-processing parameters for step 3 of use case 10 is disclosed, including known shell thickness and known core size, as well as unknown shell RI and unknown core RI. The table identifies the input parameters for instrument, particle type (rEV), gain, channel (VSSC1, BSSC, YSSC, or RSSC), expected core diameter (nm), expected shell thickness (nm); expected core RI, expected shell RI, empirical data, constraint (shell RI > core RI); and output parameters for predicted core RI, predicted core RI error (predicted core RI – expected core RI), and predicted shell RI error (predicted shell RI – expected shell RI).

[0124] Figure 72 An example of the post-processing step 3 of use case 2 is disclosed, wherein step A is in the algorithm for nanoparticles with known diameter and unknown RI (left), step B is inputting known parameters of the size of empty LNP nanoparticles and RNA-loaded LNP nanoparticles, step C calculates RI, and step D compares the RI between loaded and empty (ΔRI = RI_loaded – RI_empty). This illustrates that the change in refractive index from loaded to empty may be related to the amount of mRNA payload.

[0125] Figure 73An example of the post-processing step (step 3) for step A of either use case 10 or use case 6 in the algorithm is disclosed. In the left inset, for use case 10, the shell thickness and core size are known, and the shell RI and core RI are unknown (left side); for use case 6, the shell thickness and core size are known, and the shell RI and core RI are unknown. Step B shows the input of known parameters for the core size and shell thickness of the loaded nanoparticles with and without mAb on the surface and the loaded nanoparticles with mAb on the surface, in the left center inset. Step C illustrates the calculation of the shell RI and core RI for the loaded LNP particles [instrument, particle type of the loaded LNP, gain 200 for all side scattering channels, channel (VSSC1, BSSC, YSSC, or RSSC), input parameters core size, shell thickness, empirical data on VSSC1 medium scattering intensity, and output parameters for shell RI and core RI], and for loaded LNP particles with mAbs on their surface [instrument, particle type of the loaded LNP with mAbs, gain 200 for all side scattering channels, channel (VSSC1, BSSC, YSSC, or RSSC), input parameters core size, shell thickness, empirical data on VSSC1 medium scattering intensity, and output parameters for shell RI and core RI]. Step D illustrates the steps for comparing the RI of loaded LNPs with those of loaded LNPs with different amounts of mAbs on their surface, where (Δshell RI = shell RI load + mAb – shell RI load), right inset. Variations in shell RI can be correlated with the amount of mAbs on the nanoparticle surface. (Δshell RI = shell RI load + mAb – shell RI load). Changes in nuclear refractive index can be correlated with the amount of mAb on the surface of loaded nanoparticles.

[0126] Figure 74 The algorithmic workflow for instrument calibration is disclosed: Calculate the forward Mie scattering distribution 5915 for each input bead diameter; integrate the Mie scattering distribution over 90 degrees ± the current collection half-angle for each bead diameter; calculate the calibration factor 5925 for each bead size; calculate the goodness of fit 5930 (1 - CV of all calibration factors) for the current collection half-angle; and report the collection half-angle with the highest goodness of fit. A loop iterating through the considered collection half-angles from 0 degrees to 90 degrees is performed in 0.1-degree steps between steps 5920, 5925, and 5930. Calibration is determined as calibration factor = signal measurement / integrated signal calculation. A plot of the normalized calibration factor versus bead diameter (nm) is shown at the bottom. A plot of the goodness of fit versus collection half-angle is shown in the upper right, showing the best fit at collection half-angle = 50.8 degrees.

[0127] Figure 75Step 1, step 5915, for calculating the forward Mie scattering distribution is disclosed. The pySCATMECH library is used. The distribution is calculated in 0.1-degree steps within the range of 0-180 degrees. The distribution is independent of the collection half-angle and is therefore calculated only once. Inputs include bead diameter, bead refractive index at a given wavelength, medium refractive index at a given wavelength, wavelength, incident light polarization, and detector polarization sensitivity. Output is the Mie scattering intensity as a function of the polar angle. The right side is a graph showing the calculated Mie scattering intensity relative to the angle (degrees) for polystyrene beads with diameters of 44 nm, 80 nm, 141 nm, 304 nm, 600 nm, and 1000 nm.

[0128] Figure 76 Step 2 of the integral of the scattering intensity calculation is disclosed. The Mie scattering distribution is numerically integrated over the angular range defined by the currently proposed collection angle using Simpson's rule. The integrand needs to be weighted by sin θ. Example outputs are shown in the four figures on the right as the Mie scattering intensity relative to the angle (degrees) at collection half-angles of 20 degrees (top left), 40 degrees (top right), 60 degrees (bottom left), and 80 degrees (bottom right).

[0129] Figure 77 Step 3, which calculates the calibration factor for each bead size 5925, is disclosed. The calibration factor is defined as the ratio of the scattering intensity measurement to the integrated forward Mie intensity calculated in step 2. The calibration factor is calculated for each bead diameter. The signal measurement of the 44 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 44 nm bead to obtain the calibration factor for the 44 nm bead. The signal measurement of the 80 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 80 nm bead to obtain the calibration factor for the 80 nm bead. The signal measurement of the 100 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 100 nm bead to obtain the calibration factor for the 100 nm bead. The signal measurement of the 144 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 144 nm bead to obtain the calibration factor for the 144 nm bead.

[0130] Figure 78 Step 4, Goodness-of-Fit, is demonstrated. The output of Step 3 is a set of calibration factors for the diameter of each input bead at a single proposed collection half-angle. First, the coefficient of variation (CV) is calculated based on the set of calibration factors:

[0131] CV = Standard deviation (all calibration factors) / Mean (all calibration factors).

[0132] Next, calculate the "goodness of fit" for the proposed half-angle collection: Goodness of fit = 1 – CV. Since CV 2:0, the goodness of fit ≤ 1.

[0133] Figure 79 Step 5 is shown: Identifying the most likely collection angle 5935. Step 4 is repeated for each collection half-angle considered. The output is the "goodness of fit" for each potential collection half-angle. The calibration factor is an instrument parameter and should not depend on the bead size, so ideally, the CV calculated based on multiple bead sizes would be 0. Due to measurement uncertainty, there is some variation between beads, and therefore the CV is not zero. The most likely collection half-angle is identified as the angle with the smallest CV, as this is closest to the ideal. To calculate the goodness of fit, the collection half-angle with the largest goodness of fit is selected, because the definition of 1-CV means that this is the angle whose CV is closest to 0.

[0134] Figure 80 Three plots are shown showing the fitted confidence % versus scattering angle for three flow cytometry instruments, MP03, MP05, and MP07, excluding data at 44 nm and 103 nm. The half-angles at maximum confidence are 50.7 degrees, 49.4 degrees, and 52.1 degrees, as shown in the table. The half-angles are instrument-specific but gain-independent.

[0135] Figure 81 The top, middle, and bottom figures show a comparison of half-angle calculations by three instruments, MP03, MP05, and MP07, excluding 44 nm and 103 nm data (left figure) and including 44 nm and 103 nm data (right figure). By excluding 103 nm, the confidence level of matching empirical data with theoretical simulations is improved.

[0136] Figure 82 The algorithmic workflow for calculating the average calibration factor and CV for all bead sizes is demonstrated. The calibration factor shows almost no change when the correct collection angle is identified; that is, the calibration factor will not depend on the bead size, as it is an instrument parameter. Example outputs of the normalized calibration factor versus bead diameter are shown in four graphs for collection half-angles of 20°, 40°, 60°, and 80°. The normalized calibration factor exhibits minimal change at a collection half-angle of 60°.

[0137] Figure 83 A general algorithm for all use cases is shown, with some variations in the algorithm's parameters. The same loss function is used for all use cases:

[0138] Loss function = [(integrated Mie signal calculation value) × (calibration factor) – signal measurement value]. The calculated Mie signal value varies with... Figure 60 The particle parameters listed vary.

[0139] Figure 84 Michaelis calculations for LNP1 solid modeling are shown for empty and mRNA-loaded LNPs. The histogram on the left shows LNP1-empty. The histogram on the right shows LNP1-eGFP. The table shows the results for the median solid with RI values ​​of 1.470, 1.450, 1.435, 1.430, and 1.420 (from left to right), as well as the population results for LNP1-empty and LNP1-eGFP where the size of the loaded LNP is greater than the size of the empty LNP.

[0140] Scene #1

[0141] 1) Empty: RI is known -> calculate size.

[0142] 2) If the load size remains unchanged -> calculate RI,

[0143] 3) ΔRI = RI (load) - RI (empty).

[0144] Scene #2

[0145] 1) Empty: Size is known -> Calculate RI.

[0146] 2) If the load RI remains unchanged -> calculate the size.

[0147] 3) Δsize = Size (load) - Size (empty).

[0148] Scenario #3: Both size and RI are changed.

[0149] Figure 85 A flowchart illustrating examples of using loaded LNPs and loaded LNPs with different amounts of antibody is provided. The steps include irradiation with one or more excitation beams, collection of light from multiple scattered and FL channels, extraction of MSI data for the loaded LNP and loaded LNPs with different amounts of mAb, obtaining unknown parameters using an algorithm with known input parameters, and calculating the changes in unknown parameters between the loaded LNP and the loaded LNP with mAb. In some cases, the load RI is calculated using the algorithm and size as inputs. In other cases, the load size is calculated using the algorithm and RI as inputs.

[0150] In some cases, the shell RI is calculated using an algorithm and inputs such as the stained sample load size, RI, and shell thickness. The calculation is ΔRI = RI(shell) - RI(buffer) = RI(shell) - RI(buffer) - the instrument determines the sensitivity of the protein load.

[0151] In some cases, the algorithm, along with the inputs of the load size and RI, calculates the shell thickness and shell RI. The calculation ΔRI = RI(shell) - RI(buffer) - shell thickness determines the sensitivity of the instrument in determining the protein load.

[0152] In some cases, algorithms are used along with inputs of the protein loading RI and shell RI of the stained sample to calculate shell thickness and instrument sensitivity to determine protein loading.

[0153] In some cases, algorithms are used along with the input of protein load size and RI to calculate shell thickness and shell RI. Calculate ΔRI - RI(shell) - RI(buffer), shell thickness, and instrument sensitivity for determining protein load.

[0154] Figure 86 A flowchart illustrating examples using unstained and stained samples is shown. The steps include illumination with one or more excitation beams, collection of light from multiple scattered and FL channels, and extraction of MSI data from unstained and stained samples. In some cases, an algorithm is used along with inputs of load size and RI to calculate shell thickness and shell RI. ΔRI is calculated as ΔRI = RI(shell) - RI(buffer) - shell thickness, determining the instrument's sensitivity to protein load. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate unstained size and RI. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate both shell RI and unstained RI. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate both shell thickness and unstained size. Detailed Implementation

[0155] Various embodiments will be described in detail with reference to the accompanying drawings, wherein in several views, the same reference numerals denote the same parts and assemblies. Reference to the various embodiments does 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 embodiments of the many possible implementations of the appended claims.

[0156] definition

[0157] Unless the context clearly indicates otherwise, the singular forms “a / an” and “the” are intended to include the plural forms as well.

[0158] The term “and / or” refers to and covers any and all possible combinations of one or more of the related listed items.

[0159] The term “about” when referring to measurable values ​​such as the amount of a compound, dosage, time, temperature, etc., means covering a variation of + / - 10%, 5%, 1%, 0.5%, or even 0.1% of the specified amount.

[0160] The term "particle size" refers to the overall measurement of particle size, expressed as the diameter of a sphere having the same volume or surface area as the particle. Unless otherwise stated, the term "particle size" refers to the overall measurement of particle size, expressed as the diameter of a sphere having the same volume as the particle.

[0161] The term "particle size" refers to the straight-line distance across particles.

[0162] As used herein, the term "nanoparticle" refers to nanoparticles with a size of 1 nm to 1,000 nm. In some cases, the size of the nanoparticle is 10 nm to 1,000 nm. In some cases, the size of the nanoparticle is 5 nm to 500 nm. In some cases, the size of the nanoparticle is 1 nm to 100 nm. In some cases, the size of the nanoparticle is 10 nm to 100 nm. In some cases, the nanoparticle is a bioparticle. The term "bioparticle" refers to naturally occurring nanoparticles or engineered particles. In some cases, the size of the bioparticle is 10 nm to 1,000 nm. In some cases, the size of the bioparticle is 5 nm to 500 nm. In some cases, the size of the bioparticle is 1 nm to 100 nm. In some cases, the size of the bioparticle is 10 nm to 100 nm. Nanoparticles can be solid nanoparticles. The term "solid nanoparticle" is a nanoparticle that contains a solid material. Nanoparticles can be "core-shell nanoparticles" having a core and a shell surrounding the core. In some cases, core-shell particles refer to composite particles comprising a core and a shell, wherein the core is formed of a first material and the shell is formed of a second material that completely or partially surrounds the core. In some cases, a "hollow core" within a core-shell structure means that the core is hollow and the interior of the shell is essentially empty space. In some cases, the core has a different composition from the shell. In some cases, nanoparticles are bioparticles that can be isolated, for example, from cells, cultures, tissues, or subjects. In some cases, nanoparticles can be synthesized. In some cases, nanoparticles are commercially available. The choice of core and shell materials can be tailored to impart specific physical, chemical, or biological properties to core-shell particles, such as enhanced stability, targeting function, or controlled release properties. For example, when a lipid nanoparticle is internally filled with mRNA, the lipid layer is the shell, and the mRNA inside the lipid layer is the core. For example, when a lipid nanoparticle is internally filled with mRNA and its surface is modified with an antibody, the entire lipid nanoparticle filled with mRNA is the core, and the antibody on the surface of the lipid nanoparticle is the shell. In some cases, the core can be an "empty core" within a core-shell structure, meaning the core is hollow and the shell essentially contains empty space. For example, lipid nanoparticles do not have any cargo loaded inside; the lipid layer is the shell, and the core is considered empty because it is only filled with water or a diluent buffer.

[0163] In some cases, the particles (e.g., nanoparticles) are spherical or predominantly spherical in shape.

[0164] In some cases, particles (e.g., nanoparticles) are solid particles (e.g., solid nanoparticles).

[0165] In some cases, particles (e.g., nanoparticles) are core-shell particles (e.g., core-shell nanoparticles).

[0166] In some cases, particles (e.g., nanoparticles) can be used to carry "therapeutic payloads." In some cases, therapeutic payloads can be selected from the group consisting of: nucleic acids, such as RNA, mRNA, and DNA; proteins; antibodies; antigen-binding fragments of antibodies; vaccines; biomolecules; and drugs. In some cases, drugs can be nucleic acids, proteins, isolated natural products, synthetic molecules, semi-synthetic molecules, or combinations thereof. In some cases, therapeutic payloads are chemotherapeutic agents, antibiotics, or nucleic acids. Antibodies or antigen-binding fragments of antibodies can be selected based on their affinity and specificity for binding to the target antigen. The target antigen can be any suitable therapeutic target.

[0167] As used herein, the term “extracellular vesicle” (EV) refers to membrane-encapsulated biological particles secreted by cells and present in all biological fluids. EVs are heterogeneous in size. The three main types of EVs are exosomes, microvesicles (MVs), and apoptotic bodies. EVs can be broadly classified into small EVs, 30–200 nm or 30–150 nm, commonly referred to as exosomes, and larger EVs—30–1,000 nm, 100–1,000 nm, or 250 nm to 1,000 nm or larger, also known as microvesicles (MVs). MVs can be formed through direct outward budding or extrusion of the cell’s plasma membrane. Apoptotic bodies typically range in size from 50 nm to 5000 nm in diameter. Dead cells release apoptotic bodies into the extracellular space. In some cases, EVs are recombinant EVs (rEVs). EVs can carry nucleic acids, such as miRNAs, mRNAs, and proteins, and play an important role in cell communication in both normal and disease states. In some cases, exosomes (EVs) are shed from cells and carry markers of cellular origin, and are studied as potential biomarkers for various diseases and therapies. EVs can be isolated using any known technique, such as size exclusion chromatography (SEC), ultrafiltration, or differential ultracentrifugation. Recombinant EVs are commercially available. In some cases, EVs are lipid-bound vesicles secreted by cells into the extracellular space. The content of EVs can include lipids, nucleic acids, and proteins, such as proteins associated with the plasma membrane, cytosol, and lipid metabolism. (Doyle et al., 2019, Cells, 8, 727). Exosomes can serve as carriers of biomarkers for diseases. EV analysis can include any suitable technique known in the art, such as nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), electron microscopy such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM), tunable resistive pulse sensing (tRPS), immunoassay methods such as flow cytometry, Western blotting, immuno-based microfluidic separation, thermoelectrophoresis, and / or mass spectrometry (MS)-based proteomics analysis.

[0168] As used herein, the term "lipid nanoparticle" (LNP) refers to extremely small, typically spherical particles with an average diameter of about 10 nm to about 1000 nm, primarily composed of lipids. Lipid nanoparticles are described, for example, by Mehta et al., 2023, *ACS Materials*, 3, 600-619. Lipid nanoparticles can include any suitable lipid material. In some cases, lipid nanoparticles may comprise one or more of phospholipids, cholesterol, cationic lipids, ionizable lipids, and PEGylated lipids. In some cases, LNPs are selected from liposomes, nanoemulsions, solid lipid nanoparticles, nanostructured lipid carriers, and lipid polymer hybrid nanoparticles. In some cases, liposomes have a phospholipid bilayer shell and a water core. In some cases, the nanoparticles are solid lipid nanoparticles (SLNs). SLNs are prepared from lipids that are solid at room temperature and 37°C. In some cases, the solid lipids used in SLN are selected from, for example, tripalmitoyl glyceride, cetyl alcohol, glyceryl monostearate, trimyristyl glyceride, tristearin, stearic acid, etc.

[0169] In some cases, nanoemulsions have a surfactant outer shell and an oil core (o / w) or a water core (w / o). In some cases, solid lipid nanoparticles have a surfactant outer shell and a solid lipid core. In some cases, nanostructured lipid carriers include a surfactant shell and a core containing both solid and liquid lipids. In some cases, lipid-polymer hybrid nanoparticles include a lipid shell and a polymer core. In some cases, lipid nanoparticles can be used to carry "therapeutic payloads" (as described above). In some cases, the surface of lipid nanoparticles can be modified with targeting components such as proteins, antibodies, peptides, DNA, small targeting molecules, etc., for delivery to specific tissues and cells.

[0170] The term "virus-like particle" (VLP) refers to a nanostructure assembled from viral capsid proteins. VLPs are virus-derived structures composed of one or more different molecules that can self-assemble, mimicking the form and size of viruses, but lack genetic material, preventing them from infecting host cells. The three main categories of VLPs are encapsulated, unencapsulated, and chimeric. VLPs can be used, for example, in vaccine development and the intracellular delivery of biomolecules and compounds. VLPs can be used to encapsulate, for example, "therapeutic payloads" (as described above), inorganic nanoparticles, polymers, and fluorescent dye molecules. The surface of VLPs can be modified with targeting portions such as proteins, antibodies, peptides, DNA, small targeting molecules, etc., for delivery to specific tissues and cells. VLPs are described, for example, in Nooraei et al., *J Nanobiotechnol* (2021) 19:59; and Travossos et al., *International Journal of Molecular Science* 2024, 25, 6699.

[0171] The term "forward scattering" (FSC) refers to a physical parameter measured in flow cytometry that detects scattering along the path of a laser and indicates the relative size of the cells. FSC intensity is proportional to the diameter of the cell and is primarily due to diffraction around the cell.

[0172] The term “side scattering” (SSC) refers to a measurement in flow cytometry that detects scattering at a 90-degree angle relative to a laser and can help provide information about the internal complexity of a cell (i.e., granularity), which is proportional to the number of organelles and other components inside the cell cytoplasm.

[0173] The term "violet side scattering" (VSSC) refers to the side scattering signal generated by a violet laser at 405 nm. "VSSC1" and "VSSC2" refer to the violet side scattering used to detect the side scattering signal produced by small particles being analyzed when illuminated by a violet laser (405 nm). The difference between VSSC1 and VSSC2 is the amount of scattered light collected. The VSSC1 channel captures 95% of the side scattering light generated by the analyzed particles when illuminated by a violet laser (405 nm), providing information about the particle size and complexity. VSSC1 is typically used to detect smaller particles with low refractive index or low scattering intensity. Compared to VSSC1, the VSSC2 channel captures 5% of the side scattering light, providing additional particle information and potentially helping to distinguish different particle subgroups based on their light scattering characteristics. VSSC2 is typically used to detect signals from slightly larger particles and / or particles with higher refractive indices.

[0174] The term "blue side scattering" (BSSC) refers to SSC using a blue laser at 488 nm.

[0175] The term "yellow side scattering" (YSSC) refers to SSC using a yellow laser at 561 nm.

[0176] The term "red side scattering" (RSSC) refers to SSC using a red laser at 633 nm.

[0177] The term "polar angle" refers to the angle at which scattered light is collected and measured. In some cases, forward scattering (FSC) is collected along the laser path (0 degrees). In other cases, side scattering (SSC) is collected at 90 degrees.

[0178] The term "fluorescent dye" refers to any dye, molecule, complex, or particle that can be excited by electromagnetic radiation of a given wavelength and emit photons of another wavelength. The terms "fluorescent dye," "fluorescent probe," "fluorescent molecule," "fluorophore," "chromophore," "fluorescent substance," "fluorescent nanocrystal," and their grammatical equivalents are used interchangeably herein to refer to fluorescent dyes.

[0179] The term "optical standard particle" or "optical particle" refers to particles that can be used to calibrate and measure the size and number of particles in a sample. As used herein, particles and beads are used interchangeably to refer to the same thing and refer to any solid particle of virtually any shape suitable for measurement by a fluorescence instrument. Various types of particles can be used, such as silica beads, polystyrene beads, magnetic beads, latex beads, bioparticles, protein particles, virus-like particles, liposome particles, gold nanoparticles, lipid nanoparticles (LNP), hydrogel particles, and poly(methyl methacrylate) (PMMA) particles. In some cases, the beads are silica beads. In other cases, the beads are polystyrene beads. Depending on the application, particle size can range from a few nanometers to micrometers. In some cases, the average size of the optical particles or beads ranges from about 10 nm to about 2 μm, from about 10 nm to about 900 nm, from about 20 nm to about 2 μm, from about 900 nm to about 2 μm, from about 20 nm to about 900 nm, from about 30 nm to about 800 nm, from about 40 nm to about 500 nm, from about 50 nm to about 400 nm, or from about 80 nm to about 300 nm.

[0180] In some cases, the term "size reference bead" or "calibration bead" refers to a non-fluorescent microsphere suspension used as a size reference in flow cytometry or particle analyzers. For example, size reference beads can be unstained microspheres of known diameter, such as unstained polystyrene microspheres, as determined by transmission electron microscopy, for example. In some cases, size reference beads are polystyrene beads. In some cases, size reference beads, also known as calibration beads, are commercially available, for example, the nanoViS nanoscale size standard (Beckman Coulter). In some cases, size reference beads comprise multiple sizes. In some cases, calibration beads comprise multiple calibration beads with two or more diameters. In some cases, calibration beads comprise multiple bead sizes with diameters of 40 nm to 1000 nm, 80–1000 nm, 100–1000 nm, 40–600 nm, 40–300 nm, and 40–150 nm. In some cases, calibration beads comprise a variety of sizes selected from 44 nm, 80 nm, 105 nm, 144 nm, 300 nm, 600 nm, and 1000 nm ± 10%. In some cases, size reference beads, also referred to as calibration beads, comprise one, two, or more of 44 nm, 80 nm, 105 nm, and 144 nm. In some cases, size reference beads, also referred to as calibration beads, comprise one, two, or more of 144 nm, 300 nm, 600 nm, and 1000 nm. In some cases, the size of cells in an experimental sample can be estimated by comparing the side-scattered (SSC) signal with the signal from size reference beads, also referred to as calibration beads. Size reference beads, also referred to as calibration beads, can be mixed with or used in parallel with the experimental sample.

[0181] The term "Simpson's rule" refers to a numerical integration method that can be used to approximate the definite integral of functions, as seen in the context of Mie scattering calculations. (Cartwright, 2017, *Journal of Mathematical Sciences* and *Mathematics Education*, Vol. 12, No. 2, pp. 1-9.)

[0182] The term "Powell's algorithm," also known as the Powell method, refers to a minimization algorithm used to find local minima of a function. Vassiliadis, VS, Conejeros, R. (2001). Powell's method. Included in: Floudas, CA, Pardalos, PM (edited). *Encyclopedia of Optimization*. Springer, Boston, MA. https: / / doi.org / 10.1007 / 0-306-48332-7_393.

[0183] The term "constrained trust region" refers to a minimization algorithm optimization technique that finds the minimum of a function while respecting constraints, iteratively refining the approximation within a dynamically adjusted trust region. (Byrd et al., 1987, *SIAM Journal on Numerical Analysis*, pp. 1152-1170).

[0184] The term "pySCATMECH library" refers to the Python interface for SCATMECH: a C++ class library for polarized light scattering. SCATMECH is an object-oriented C++ class library developed to distribute models for light scattering applications. For information on SCATMECH applications, see https: / / pages.nist.gov / SCATMECH / index.htm. The pySCATMECH package provides a Python interface to many of the library's functionalities. pages.nist.gov / pySCATMECH / index.html. The current version 0.1.9 (December 19, 2024) requires Python 3.6 or higher. In some cases, the scatterer module is used for Mie scattering or Mie scattering with coatings.

[0185] The term "room temperature" (RT) refers to 23°C ± 2°C.

[0186] In some cases, the hydrophilic polymer can be, for example, PEG, protein, peptide sequence, peptide-like substance, DNA sequence, RNA, carbohydrate, oxazoline, polyol, dendrite, dendritic polyglycerol, cellulose, chitosan or derivatives thereof.

[0187] In flow cytometry, a "half-angle" refers to the angular range of light collected by the lateral collection unit 130, which is a cone of light gathered around a particle passing through the interrogation zone 18, where the half-angle represents half the apex angle of the cone. A larger half-angle means capturing more scattered light from a wider angular range around the particle. Modern flow cytometers typically have an SSC collection half-angle (ε) of approximately 50°, meaning that the collected SSC light passing through each particle 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 can vary from instrument to instrument. In some instances, the half-angle can vary by approximately + / - 10 degrees. In some cases, the half-angle is calculated for each instrument.

[0188] In some cases, half-angle identification may include comparing empirical side-scattered light intensity data with simulated side-scattered light intensity data in the range of approximately + / -10°C near the nominal half-angle of approximately 54°C to identify the half-angle of the side-collecting unit 130. For example, the half-angle of the side-collecting unit 130 is identified when the empirical side-scattered light intensity is closest to the simulated side-scattered light intensity for a given half-angle value.

[0189] The term "Mie scattering" refers to the scattering of plane waves of light by a spherical particle. Mie scattering can be applied to particles of any size. In some cases, the particles are smaller than the wavelength of light. In other cases, the scattering pattern depends on the particle size, refractive index, polarization of the incident light, refractive index of the medium, and wavelength of the incident light. Mie scattering is not strongly dependent on wavelength. Mie scattering is applicable to a wide range of wavelengths. For calculation purposes, Mie scattering depends on certain dependencies, such as... Figure 59 The input and output parameters in step 3. In some cases, input parameters include one or more particle sizes, particle refractive indices, shell thickness, shell refractive index, wavelength, buffer material of the medium, refractive index of the buffer material or medium, half-collection angle, and calibration factor. In some cases, input parameters include one or more particle sizes, particle refractive indices, shell thickness, shell refractive index, core size, core refractive index, wavelength, buffer material of the medium, refractive index of the buffer material or medium, half-collection angle, and calibration factor.

[0190] The term "calibration factor" refers to the ratio of the measured scattering intensity value to the actual scattering intensity value. Figure 59 The ratio of the calculated Mie scattering intensity values ​​in step 2 of the algorithm shown.

[0191] The term "goodness of fit" refers to 1 minus the coefficient of variation (CV) of the calibration factor calculated for each bead.

[0192] The term "particle refractive index" is a measure of how much light bends as it passes through a particle; essentially, it indicates how much the speed of light changes as it enters the particle compared to the surrounding medium.

[0193] The term "nuclear refractive index" refers to the refractive index of a particle near its center.

[0194] The term "nucleus size" refers to the diameter of a particle near its center.

[0195] The term "shell thickness" refers to the thickness of the outer layer surrounding the core of a core-shell particle (e.g., a nanoparticle).

[0196] In the context of the core-shell model, the term "shell size" refers to the thickness or size of the outer layer or shell surrounding the core of the particle (e.g., nanoparticles).

[0197] In the context of the core-shell model, the term "shell thickness" refers to a measure of the distance between the outer surface of the core and the outer surface of the shell of a particle (e.g., a nanoparticle).

[0198] The term "shell refractive index" refers to the refractive index of the outer layer of the core of a core-shell particle (e.g., a nanoparticle).

[0199] The term "excitation wavelength" refers to the wavelength used to scatter light from the particles.

[0200] The term "buffer refractive index" refers to the refractive index of the diluent used to dilute a sample.

[0201] The term "output parameter" refers to... Figure 59 The parameters obtained after calculation in steps 1-3 of the algorithm shown. In some cases, the output parameters are one or more of the following: particle size, particle refractive index, core diameter, shell thickness, core refractive index, shell refractive index, core diameter and core refractive index, shell thickness and shell refractive index, core diameter and shell thickness, or core refractive index and shell refractive index.

[0202] The term "initial guess parameters" refers to the parameters used as a starting point for calculating unknown parameters, employing minimization algorithms such as Powell's algorithm or constrained trust region minimization to avoid local minima and find a single solution close to its true value. Clients may know the initial guess parameters based on their own prior data, or they may be based on data created for particles such as viruses, EVs, and LNPs. Figure 61The data shown uses initial guess parameters. In some cases, the initial guess parameter for the solid particle RI is 1.4. In some cases, the initial guess parameter for the solid particle size is 100 nm. In some cases, the initial guess parameter for the core size of core-shell particles is 100 nm. In some cases, the initial guess parameter for the shell size is 10 nm. In some cases, the initial guess parameter for the core RI is 1.4. In some cases, the initial guess parameter for the shell RI is 1.4. In some cases, the initial guess parameter for the core RI is 1.4, and the core diameter is 50 nm. In some cases, the initial guess parameter for the shell RI is 1.4, and the shell thickness is 10 nm. In some cases, the initial guess parameter for the core diameter is 50 nm, and the shell thickness is 75 nm. In some cases, the initial guess parameter for the core diameter is 100 nm, and the shell thickness is 10 nm. In some cases, the initial guess parameter for the core RI is 1.4, and the shell RI is 1.45. In some cases, the initial guess parameter for the core RI is 1.45, and the shell RI is 1.4.

[0203] The term "loss function" is used to measure how closely a predicted value matches the actual value. The loss function can be calculated as follows:

[0204] Loss function = [(integrated Mie signal calculation value) × (calibration factor) – signal measurement value].

[0205] The terms "limits and constraints" refer to the limiting conditions for each parameter; for example, the limit for refractive index can be between 1.33 and 2.5. In some cases, the constraint includes shell thickness > core diameter. In some cases, the constraint includes shell thickness < core diameter. In some cases, the constraint includes shell RI > core RI. In some cases, the constraint includes shell RI < core RI. In some cases, the limit for solid particle RI is [1.33, 2.5]. In some cases, the limit for solid particle size is [10 nm, 1500 nm]. In some cases, the limit for core size is [10 nm, 1500 nm]. In some cases, the limit for shell size is [0 nm, 1500 nm]. In some cases, the limit for core RI is [1.33, 2.5]. In some cases, the limit for shell RI is [1.33, 2.5].

[0206] The following abbreviations are used in this disclosure.

[0207] AA700 refers to Alexa Fluor 700. AA750 refers to Alexa Fluor 750. Ab refers to antibody. ALS refers to amyotrophic lateral sclerosis (ALS). APC refers to allophycocyanin. AUC refers to analytical ultracentrifuge. B531 refers to a specific bandpass filter designed to isolate and detect light at approximately 531 nm wavelength when excited by a blue laser at 488 nm in the CytoFlex nano. BIOS refers to Basic Input / Output System. BFF refers to Reciprocating Flushing. BV refers to Brilliant Violet™ (Thermo Fisher Scientific Inc.). CHO refers to Chinese hamster ovary. CPU refers to Central Processing Unit. CV refers to Coefficient of Variation. EDTA refers to ethylenediaminetetraacetic acid (EDTA). ELISA refers to Enzyme-Linked Immunosorbent Assay. ELISPOT refers to Enzyme-Linked Immunosorbent Spot. EPS refers to Events per Second. EV refers to Extracellular Vesicles. rEV refers to Recombinant Extracellular Vesicles. FACS refers to Fluorescence-Activated Cell Sorting. FITC refers to Fluorescein Isothiocyanate. FL stands for fluorescence. vFRed™ refers to a fluorescent membrane dye used to measure extracellular vesicles (Cellarcus, vFC™ kit). GFP stands for green fluorescent protein. HEK refers to human embryonic kidney cells. IFU refers to instructions for use. IL refers to interleukin. LNP refers to lipid nanoparticles. MFI refers to mean fluorescence intensity. MV refers to microvesicles. PB refers to Pacific Blue™ (Thermo Fisher Scientific). PBS refers to phosphate-buffered saline solution. PC7 refers to R-phycoerythrin-cyanine 7 dye. PE refers to R-phycoerythrin. PPP refers to anemic platelet plasma. qEV® refers to size exclusion chromatography, which can be used for example to separate exosomes and other extracellular vesicles (Izon Science Ltd.). R670 refers to a specific bandpass filter designed to isolate and detect light at a wavelength of approximately 670 nm when excited by a red laser at 638 nm in the CytoFlex nano. RAM refers to random access memory. RT refers to room temperature. SEC refers to size exclusion chromatography. sfGFP refers to superfolded GFP. VEGF refers to vascular endothelial growth factor. VSSC refers to violet side scattering. VSV refers to vesicular stomatitis virus. V refers to the V5 tag expressed on the surface of MLV virus. V447 refers to a specific bandpass filter designed to isolate and detect light at approximately 447 nm when excited by a 405 nm violet laser in the CytoFlex nano. WBC refers to white blood cells. Y595 refers to a specific bandpass filter designed to isolate and detect light at approximately 595 nm when excited by a 561 nm yellow laser in the CytoFlex nano.

[0208] All patents, patent applications, and publications mentioned in this article are incorporated herein by reference in their entirety.

[0209] The embodiments described in one aspect of this disclosure are not limited to the described aspects. Embodiments may also be applied to different aspects of this disclosure as long as they do not prevent these aspects of this disclosure from operating for their intended purpose.

[0210] Figure 1 Examples of a system 10 that can be used to perform flow cytometry are shown. System 10 includes a flow cytometer 100 and a workstation 200. In some cases, the flow cytometer 100 is incorporated into aspects described in PCT International Patent Application No. PCT / CN2022 / 099413, filed June 17, 2022; U.S. Provisional Patent Application No. 63 / 595,928, filed November 3, 2023; and U.S. Provisional Patent Application No. 63 / 561,405, filed March 5, 2024, the disclosures of which are incorporated herein by reference in their entirety.

[0211] Generally, a flow cytometer 100 is an analytical instrument used to detect the physical and chemical properties of cellular or particle samples. The flow cytometer can be any type of flow cytometer. In some cases, the flow cytometer is a CytoFlex flow cytometer or a CytoFlex nano flow cytometer (both available from Beckman Coulter). The flow cytometer 100 is designed to capture robust, high-quality data for characterizing biologically relevant particles, such as nanoparticles. The flow cytometer 100 is a single instrument that provides simultaneous evaluation of particle (e.g., nanoparticle) size, concentration, and cargo to understand biological mechanisms of action and nanoparticle origin. The flow cytometer 100 can collect data from millions of particles or cells within minutes for display on a display monitor 204 of workstation 200 in various formats.

[0212] The flow cytometer 100 includes a housing 101 with a sample station 104 that houses a container 106 containing cell and / or particle samples. In some instances, the container 106 contains particle (e.g., nanoparticle) samples such as extracellular vesicles (EVs), viruses, and LNPs. 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 collect samples from the container 106 to perform flow cytometry experiments. In some instances, 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.

[0213] The flow cytometer 100 may further include a sheath fluid container 107 for retaining the 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 aligns the particles in a single file in the direction of laminar flow. The sheath fluid container 107 is connected to the flow cytometer 100 via conduit 109.

[0214] The flow cytometer 100 may further include a waste container 108 for collecting waste fluid. The waste container 108 is connected to the flow cytometer 100 via a conduit 109.

[0215] 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, which allow the user of system 10 to input data and information, control flow cytometer 100, and change the display of data on display monitor 204.

[0216] Workstation 200 further includes computing device 202. In some instances, 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 instances, flow cytometer 100 transmits processed data to workstation 200 for display on display monitor 204.

[0217] like Figure 1As further shown, system 10 further includes a sample preparation component 150, which can be used to prepare and purify biological particles (e.g., nanoparticles), such as extracellular vesicles (EVs), lipid nanoparticles (LNPs), and viruses or virus-like particles, from collected samples. The sample preparation component 150 can be a centrifuge, a membrane filtration system, a chromatography system, or a precipitation device. In some cases, sample preparation techniques can be used to prepare and purify biological particles (e.g., nanoparticles), for example, the sample preparation techniques are selected from the group consisting of centrifugation, membrane filtration, precipitation, and chromatographic purification. Sample preparation involves using the sample preparation component 150, such as centrifugation, e.g., differential centrifugation, density gradient centrifugation; membrane filtration systems, e.g., ultrafiltration or tangential flow filtration; chromatography systems, e.g., size exclusion chromatography (SEC), immunoaffinity capture, affinity chromatography, and / or ion exchange chromatography; and / or a precipitation device. Extracellular vesicles (EVs) can be prepared and purified using differential centrifugation: a series of centrifugation steps at increasing speeds to remove cells, debris, and larger particles, followed by ultracentrifugation to precipitate the EVs. Extracellular vesicles (EVs) can be prepared and purified using density gradient centrifugation: EVs are separated using a sucrose or iodixanol gradient based on their buoyant density. EVs can also be prepared and purified using ultrafiltration: filtration through membranes with specific pore sizes concentrates EVs and removes contaminants. EVs can be prepared and purified using size exclusion chromatography (SEC): EVs are separated from other components based on size exclusion principles. EVs can be prepared and purified using immunoaffinity capture: EVs on a solid matrix are captured using antibodies targeting specific EV surface markers. EVs can be prepared and purified using precipitation: EVs are precipitated from solution using polymers such as polyethylene glycol (PEG). Lipid nanoparticles (LNPs) can be prepared and purified using dialysis: small molecules and solvents are removed from LNPs by dialysis against a large buffer solution. Lipid nanoparticles (LNPs) can also be prepared and purified using ultracentrifugation: high-speed centrifugation precipitates LNPs while removing free lipids and other impurities. Lipid nanoparticles (LNPs) can be prepared and purified using size exclusion chromatography (SEC): particle size separation is used to obtain a homogeneous population of LNPs. Lipid nanoparticles (LNPs) can also be prepared and purified using tangential flow filtration (TFF): a filtration technique used to concentrate and percolate LNPs, improving purity and homogeneity. Lipid nanoparticles (LNPs) can be prepared and purified using gradient centrifugation: a density gradient is used to purify LNPs based on their buoyant density. Virus-like particles can be prepared and purified using differential centrifugation: continuous centrifugation removes cells and debris, followed by ultracentrifugation to precipitate the virus. Virus-like particles can be prepared and purified using density gradient centrifugation: a sucrose or cesium chloride gradient is used to purify the virus based on its buoyant density. Virus-like particles can also be prepared and purified using PEG precipitation: polyethylene glycol-mediated precipitation concentrates the virus particles.Virus-like particles can be prepared and purified using ultrafiltration: concentrating viral particles using a membrane with a specific molecular weight cutoff. Virus-like particles can also be prepared and purified using affinity chromatography: capturing and purifying viruses based on their surface proteins using specific ligands or antibodies. Finally, virus-like particles can be prepared and purified using anion exchange chromatography: separating them from contaminants by utilizing the charge properties of the viral particles.

[0218] In some instances, the sample preparation assembly 150 is a centrifuge configured to perform analytical ultracentrifugation. As will be described in more detail, in at least some instances, the centrifuge 150 centrifuges a container 106 containing a sample, and then analyzes the sample via a flow cytometer 100 after centrifugation. In such instances, the analysis performed by the flow cytometer 100 is used to adjust one or more operations of the centrifuge 150 to maximize the purity, yield, or enrichment of the target particles collected from the sample by the centrifugation performed by the centrifuge 150. Thus, the flow cytometer 100 can be used as an analytical tool to improve the centrifugation performed by the centrifuge 150 for the collection of target particles.

[0219] Workstation 200 can be communicatively coupled to flow cytometer 100 and centrifuge 150 via network 160, which connects and exchanges data between workstation 200 and flow cytometer 100 and centrifuge 150. Network 160 can include any type of wired or wireless connection, or any combination thereof. In some instances, wireless connectivity can be achieved using Wi-Fi, ultra-wideband (UWB), Bluetooth, etc. In some instances, network 160 is an Internet of Things (IoT) network.

[0220] In another embodiment of system 10 including centrifuge 150, the centrifuge is used to characterize the sample after analysis by flow cytometer 100, and the centrifuge analysis is used to adjust one or more operations of the flow cytometer to maximize the characterization of particles in the sample. Therefore, centrifuge 150 can be used as a tool to improve the characterization of particles in a sample by flow cytometer 100. In another embodiment of system 10 including centrifuge 150, flow cytometer 100 is used simultaneously with centrifuge 150 to characterize particles and / or maximize the purity, yield, or enrichment of target particles.

[0221] The above discussion is presented in the context of centrifugation, but this disclosure contemplates the use of flow cytometry in conjunction with any laboratory technique / instrument to improve particle characterization and / or maximize the purity, yield, or enrichment of target particles. This includes, but is not limited to, electron microscopy, column chromatography, ELISA, ELISPOT, and other laboratory techniques for characterizing and / or purifying particles. While this considers particles of any size, it is particularly relevant to particles larger than about 20 nm, larger than about 40 nm, larger than about 60 nm, larger than about 70 nm, larger than about 80 nm, smaller than about 100 nm, smaller than about 90 nm, smaller than about 80 nm, smaller than about 70 nm, smaller than about 60 nm, smaller than about 50 nm, or any combination thereof. In the examples, smaller than 100 nm, or smaller than 80 nm, or smaller than 70 nm, or smaller than 60 nm, or any combination thereof.

[0222] Extracellular vesicles (EVs) are a heterogeneous family of membrane particles released by all cells, providing crucial insights into a wide range of fields and diseases. To better understand EVs, researchers must characterize them. However, analyzing EVs is challenging due to their heterogeneity, size, refractive index, and complex properties. For example, the overall size of EVs can range from 20 nm to 5 μm for larger apoptotic bodies.

[0223] In some cases, the flow cytometer 100 has high sensitivity to detect nanoparticles of about 40 nm via purple side scattering, and also provides better sensitivity and resolution through six fluorescence channels.

[0224] Figure 2 An example of the detection system 12 of the flow cytometer 100 is illustrated schematically. The detection system 12 detects and analyzes nanoscale particles, such as particles with a diameter less than 100 nanometers (nm). Additionally, the detection system 12 can detect and analyze particles of larger size, such as particles with a diameter greater than 100 nm. The detection system 12 includes a light-emitting unit 110 and a light-collecting unit 120 that detects the characteristics of particles passing through the flow chamber 15.

[0225] The light-emitting unit 110 emits one or more excitation beams to project 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 analysis by the computing device 600 (see [link to relevant documentation]). Figure 6 ).

[0226] The light-emitting unit 110 includes multiple light sources 111a-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-111d may include more than four light sources or fewer than four light sources. The multiple light sources 111a-111d may include lasers.

[0227] The plurality of light sources 111a-111d emit excitation beams in the range of about 300 nm to about 825 nm. As another example, light sources 111a-111d emit excitation beams in the range of about 325 nm to about 808 nm. Each of the plurality of light sources 111a-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 spectrum (e.g., 625-825 nm), the second light source 111b emits an excitation beam in the yellow spectrum (e.g., 560-590 nm), the third light source 111c emits an excitation beam in the blue spectrum (e.g., 450-490 nm), and the fourth light source 111d emits an excitation beam in the violet spectrum (e.g., 300-450 nm).

[0228] exist Figure 2 In the example shown, light sources 111a-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.

[0229] The light-emitting unit 110 further includes a focusing lens 119. The focusing lens 119 is configured to focus an excitation beam for high-intensity particle scattering detection. For example, an excitation beam emitted by light sources 111a-111d passes through the focusing lens 119, which focuses the excitation beam onto the interrogation zone 18 of the flow chamber 15. The interrogation zone 18 may also be referred to as the focal point, where the focused excitation beam is in contact with the nuclear sample stream in the flow cytometer 100.

[0230] Dichroic mirrors 117a, 117b, 117c, and 117d are arranged between the focusing lens 119 and the corresponding light sources 111a-111d. Each of the dichroic mirrors 117a-117d is configured to reflect the light beam from one of the corresponding light sources 111a-111d and transmit the light beams from the other light sources. The dichroic mirrors 117a-117d are selected and configured according to the wavelength of the light beam emitted by the corresponding light sources 111a-111d. For example, dichroic mirror 117a reflects light of wavelength emitted by light source 111a toward focusing lens 119; dichroic mirror 117b reflects light of wavelength emitted by light source 111b toward focusing lens 119 and transmits light of wavelength emitted by light source 111a; dichroic mirror 117c reflects light of wavelength emitted by light source 111c toward focusing lens 119 and transmits light of wavelengths emitted by light sources 111a and 111b; and dichroic mirror 117d reflects light of wavelength emitted by light source 111d toward focusing lens 119 and transmits light of wavelengths emitted by light sources 111a, 111b, and 111c.

[0231] The light beams emitted by light sources 111a-111d are reflected or transmitted by dichroic mirrors 117a-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-117d are adjustable in position or orientation, allowing them to be used to adjust the position of the focal point of the beam, particularly its position perpendicular to the optical axis in a plane.

[0232] Lenses 115a-115d are arranged between the corresponding light sources 111a-111d and the corresponding dichroic mirrors 117a-117d. In some examples, lenses 115a-115d are telephoto lenses. In some examples, lenses 115a-115d are spherical lenses. In other examples, lenses 115a-115d are aspherical lenses. Each of lenses 115a-115d can convert a light beam into a parallel light beam. Figure 2 In the examples shown, each of lenses 115a-115d is in the form of a plano-convex lens, having a flat surface and convex surfaces opposite each other.

[0233] Lenses 115a-115d are adjustable in position or orientation to adjust the focal point of the light beam, particularly its position perpendicular to the optical axis on a plane. Typically, dichroic mirrors 117a-117d are used to roughly adjust the focal point of the light beam, while lenses 115a-115d are used for fine-tuning the focal point.

[0234] It should be understood that the number, type, and arrangement of the dichroic mirrors 117a-117d and lenses 115a-115d can be changed as needed and are not limited to the examples shown herein. Furthermore, the dichroic mirrors 117a-117d and lenses 115a-115d can be replaced with other optical elements or modules with similar functions.

[0235] Beam expanders 113a-113d are arranged between the respective light sources 111a-111d and the respective lenses 115a-115d. Each of the beam expanders 113a-113d can change the cross-sectional size and divergence angle of the beam. Therefore, each of the beam expanders 113a-113d can be configured according to the desired size of the beam spot.

[0236] The light beam illuminating the particle by 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.

[0237] exist Figure 2 In the example shown, the light sources 111a-111d are in the form of lasers including corresponding laser diodes 112a-112d. Figure 2 As further shown in the example, half-wave plates 116a-116d are respectively disposed between dichroic mirrors 117a-117d and lenses 115a-115d. The light spot of the beam can be reduced by the orientation of the light source 111a-111d and by using half-wave plates 116a-116d.

[0238] like Figure 2 As further shown, cylindrical lenses 114a-114d are positioned between the corresponding beam expanders 113a-113d and the corresponding lenses 115a-115d. The horizontal size of the beam focused in the flow chamber 15 can be adjusted by replacing cylindrical lenses 114a-114d with alternative cylindrical lenses having different curvatures. The power of some or all of the light sources 111a-111d can also be increased. Increasing the power of the light sources 111a-111d can also improve the detection sensitivity.

[0239] Each of the beam expanders 113a-113d is formed by a first optical portion and a second optical portion. Figure 2 In the examples shown, each of the beam expanders 113a-113d includes a concave lens adjacent to the corresponding light source as a first optical portion, and further includes a convex lens distant from the corresponding light source as a second optical portion. It should be understood that each of the beam expanders 113a-113d is not limited to... Figure 2The example shown. Beam expanders 113a-113d can be formed by any suitable optical lens or lens group. For example, each of the first optical part and the second optical part can be selected from one of a convex lens, a convex lens group, a concave lens, and a concave lens group.

[0240] For each of the beam expanders 113a-113d, the distance between the first optical element (e.g., a concave lens) and the second optical element (e.g., a convex lens) is adjustable. This allows adjustment of the waist position (focus) of the beam on the optical axis.

[0241] As described above, by adjusting the dichroic mirrors 117a-117d, lenses 115a-115d, and beam expanders 113a-113d, a single beam can be focused at a desired point of inquiry, and multiple beams can be focused at the same point of inquiry. It should be understood that the position of the beam's focal point can be adjusted by employing any other optical element or by any other adjustment method. One or more adjustments to the dichroic mirrors 117a-117d, lenses 115a-115d, and beam expanders 113a-113d can be made manually or electronically using a computing device (e.g., a controller) associated with one or more actuators coupled to these components.

[0242] The light collection unit 120 includes a lateral collection unit 130 and a forward collection unit 152. The lateral collection unit 130 collects laterally scattered light and fluorescence emitted from particles in the sample, as they are irradiated by the excitation beam as they pass through the flow chamber 15. 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-111d and guided toward the flow chamber 15 by the dichroic mirrors 117a-117d.

[0243] Forward collecting unit 152 collects forward-scattered light from the particles. The optical axis of the beam collected from the particles by forward collecting unit 152 can be approximately parallel to or at approximately 0 degrees to the optical axis of the beam guided toward flow chamber 15. Lateral collecting unit 130 and forward collecting unit 152 are described in further detail below.

[0244] 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 various directions at the interrogation area 18.

[0245] The concave mirror 134 and the aspherical lens 135 focus the reflected light onto the collecting fiber 136 by focusing on the same point on the collecting fiber 136, such as... Figure 2As shown in the dashed box 139. The concave mirror 134 can focus the reflected light onto the optical fiber, while the aspherical lens 135 can make the focal point smaller (i.e., reduce distortion).

[0246] To prevent crosstalk, beam splitter 133 is arranged to separate high-intensity scattered light from low-intensity fluorescence. Side-scattered light is guided through first optical fiber 137 toward first wavelength division multiplexer 131, and fluorescence is guided through second optical fiber 138 toward second wavelength division multiplexer 132. Optical signals with different wavelengths are separated in first wavelength division multiplexer 131 and second wavelength division multiplexer 132 for analysis.

[0247] Beam splitter 133 includes a dichroic mirror 532 and a notch filter 534. Collected light is guided into beam splitter 133 via a collecting fiber 136 toward the dichroic mirror 532, the collecting fiber being oriented such that the beam is guided toward the dichroic mirror 532 at, for example, an angle of incidence of 45 degrees. The dichroic mirror 532 reflects side-scattered light from the collecting fiber 136, causing the side-scattered light to enter first wavelength division multiplexer 131 via first fiber 137.

[0248] The fluorescence from the collecting fiber 136 passes through the dichroic mirror 532 and is incident on the notch filter 534 at an angle of approximately 90 degrees, then passes through the notch filter 534. The fluorescence then enters the second wavelength division multiplexer 132 through the second fiber 138.

[0249] Depending on the arrangement of the light sources 111a-111d, the dichroic mirror 532 and the notch filter 534 can each have multiple bands. Figure 2 In the example shown, both the dichroic mirror 532 and the notch filter 534 have four bands that block four laser wavelengths. The number of bands on the dichroic mirror 532 and the notch filter 534 can correspond to the number of light sources 111a-111d.

[0250] Beam splitter 133 separates high-intensity side-scattered light from low-intensity fluorescence, thereby reducing or preventing crosstalk between side-scattered light and fluorescence. Additionally, by providing a beam splitter, multiple beams can be separated and transmitted into two or more wavelength division multiplexers. The optical elements included in beam splitter 133 and their configuration can be varied and are not limited to the examples shown and described herein.

[0251] In some instances, 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 the wavelength of the side-scattered light. For example, the optical signal associated with a beam in the red spectrum emitted by a first light source 111a, the optical signal associated with a beam in the yellow spectrum emitted by a second light source 111b, the optical signal associated with a beam in the blue spectrum emitted by a third light source 111c, and the optical signal associated with a beam in the violet spectrum emitted by a fourth light source 111d are separated in the first wavelength division multiplexer 131. In the first wavelength division multiplexer 131, each optical signal is transmitted along an optical transmission path 510 corresponding to an optical channel of the optical signal. The side-scattered light then enters an SSC detector 515, which may include a photodiode, an avalanche photodiode (APD), or a photomultiplier tube for analyzing the side-scattered light. Figure 6 In the example shown, there are six separate lateral scattering channels.

[0252] 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, spaced apart from each other along the optical transmission path of the optical channel. By providing two filters, crosstalk between side-scattered light can be reduced or prevented. The first filter 511 and the second filter 512 are not arranged in parallel to avoid multiple reflections of light between them and to achieve better optical density.

[0253] The second wavelength division multiplexer 132 receives a fluorescence beam from the beamsplitter 133 via a second optical fiber 138 and separates the optical signals of the fluorescence beams, which have different wavelengths from each other. In the second wavelength division multiplexer 132, each optical signal is transmitted along an optical transmission path 520 corresponding to an optical channel. Because the fluorescence signal is relatively 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. Figure 6 In the example shown, there are six separate fluorescence channels.

[0254] 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. Notch filters can reduce or eliminate crosstalk between side-scattered light and fluorescence. In this case, beam splitter 133 may consist only of a dichroic mirror 532 without the notch filter 534.

[0255] In the lateral collection unit 130, the diameter of the collecting fiber 136 can differ from the diameters of the first fiber 137 and the second fiber 138, depending on the light transmission efficiency. Lenses in the beam splitter may cause distortion, therefore the output spot can be larger than the input of the beam splitter, and the fiber diameter can be selected accordingly.

[0256] The forward collection unit 152 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 to be collected from the particles. In some instances, most of the transmitted light is blocked so as not to saturate the forward detector 159.

[0257] Concave mirror 151 reflects the forward-scattered light 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.

[0258] Figure 3 An example is schematically illustrated of a method 300 that improves centrifugation performed by centrifuge 150 using flow cytometry performed by flow cytometer 100. Method 300 can be performed by system 10, such as... Figure 1 As shown.

[0259] Method 300 includes an operation 302 of receiving a sample inside the rotor chamber of a centrifuge 150. Operation 302 may include receiving at least one of containers 106 containing the sample. In some instances, operation 302 includes receiving multiple containers 106, each container containing a sample. Operation 302 includes receiving one or more containers 106. As an illustrative example, the sample includes a blood sample.

[0260] Method 300 includes operation 304 performing centrifugation on the sample received in operation 302. Operation 304 may include performing ultracentrifugation by high-g force separation through high-speed rotation of the rotor chamber. For example, operation 304 may include rotating the sample from 100,000 × g to 1,000,000 × g to purify and / or characterize one or more target particles in the sample. Thus, operation 304 includes rotating the vortex chamber of centrifuge 150 at a predetermined speed, duration, and temperature to isolate and / or purify target particles, such as extracellular vesicles (EVs), within the sample.

[0261] Method 300 includes an operation 306 performing flow cytometry analysis on a sample after centrifugation is completed in operation 304. Operation 306 may include receiving a container 106 containing the sample in sample station 104 after centrifugation is complete. Flow cytometry analysis can characterize particles in the sample, such as by determining their size and refractive index. By characterizing the particles, the purity or yield of the target particles in the sample can be determined. In some instances, operation 306 includes comparing the purity or yield of the target particles with a threshold to determine whether centrifugation meets quality control requirements. In some cases, operation 306 includes sorting the particles in the sample using flow cytometry to increase their purity.

[0262] The analysis performed in Operation 306 differs from that typically performed during ultracentrifugation because the flow cytometry analysis in Operation 306 analyzes each particle individually, rather than in batch analyses typically performed during ultracentrifugation. Therefore, the flow cytometry analysis provides a higher level of granularity.

[0263] Method 300 includes sample optimization operation 308, such as adjusting one or more parameters of centrifugation based on the characterization of particles determined in operation 306. As described above, particles are characterized to determine the purity or yield of one or more target particles. Sample optimization operation 308 may include adjusting one or more of centrifugation speed, duration, and temperature to increase the purity or yield of target particles. Therefore, method 300 includes using a flow cytometer 100 as an analytical tool that provides a direct feedback loop for improving centrifugation performed by a centrifuge 150. This method is described in the context of using a centrifuge in conjunction with a flow cytometer, but this disclosure contemplates the use of other laboratory instruments besides centrifuges, such as electron microscopy ELISA, ELISPOT, or column chromatography, or any combination of centrifugation, electron microscopy, ELISA, ELISPOT, and column chromatography. The ability of CytoFLEX nano to effectively characterize nanoparticles (e.g., down to about 20 nm in size) means it can be conveniently used as a rapid analytical tool to aid in the identification, discovery, and / or characterization of particles in a sample in conjunction with other laboratory techniques such as electron microscopy, ultracentrifugation, ELISA, ELISPOT, and / or column chromatography. In the embodiments, flow cytometry is used to provide feedback to the conditions used for electron microscopy, ultracentrifugation, ELISA, ELISPOT, and / or column chromatography in order to better separate and / or characterize the particles of interest.

[0264] Multiple side-scattering channels and multiple fluorescence channels in the flow cytometer 100 can be used to characterize nanoparticles such as lipid nanoparticles and distinguish them from contaminants such as extracellular debris smaller than 100 nm. In some instances, scores can be calculated based on one or more properties from the multiple side-scattering channels and multiple fluorescence channels. These scores can be used as part of a radar chart method to identify and differentiate nanoparticles. In some instances, comparing data collected from one or more side-scattering channels with data collected from one or more fluorescence channels can be used to identify patterns used to characterize nanoparticles.

[0265] As an example, multiple side-scattering channel data and multiple fluorescence channel data can be collected from nanoparticles with a known refractive index. The data can be fed into a database to train a machine learning algorithm to detect properties and / or patterns in the side-scattering and fluorescence channels. Subsequently, the machine learning algorithm can be used to characterize or distinguish other types of nanoparticles with unknown refractive indices that exhibit similar properties or patterns in the side-scattering and fluorescence channels.

[0266] Figure 4 An example of a method 400 for characterizing nanoparticles, which can be performed by a flow cytometer 100, is illustrated. Method 400 uses a multicolor panel to identify different types of target particles. As an illustrative example, method 400 can identify different types of EVs in anemic platelet plasma (PPP) samples.

[0267] Method 400 includes operation 402 of preparing a sample, such as an LNP or PPP sample, from a blood sample collected from a subject. Operation 402 may include centrifuging the sample. As an illustrative example, the blood sample may be centrifuged at 200 × g for 5 minutes. Operation 402 may further include removing the sample from the top of the container and filtering it through a 200 nm syringe. As an illustrative example, approximately 1 mL of PPP may be removed from the container. Operation 402 may include further purification of the removed PPP using a separation column that separates PPP based on size (e.g., 70 nm) by size exclusion chromatography.

[0268] Step 402 may further include screening selected fractions of PPP for immunophenotypic analysis using single-color and multicolor staining. Step 402 may further include staining the fractions of PPP with antibodies. For example, the fractions of PPP may be stained with CD81 PB, CD9 FITC, CD63 APC, CD61 PC7, and CD235a PE.

[0269] Method 400 includes operation 404, which involves collecting flow cytometry data from a sample prepared in operation 402 using a flow cytometer 100. Operation 404 may include irradiating particles in the sample that individually pass through interrogation region 18 with one or more excitation beams. Operation 404 may include collecting lateral scattering data from the particles, the lateral scattering data being collected by multiple lateral scattering channels in a first wavelength division multiplexer 131 of the flow cytometer 100. Operation 404 may further include collecting fluorescence data from the particles, the fluorescence data being collected by multiple fluorescence channels in a second wavelength division multiplexer 132.

[0270] Method 400 includes an operation 406 to characterize the nanoparticles based on the analysis performed in operation 404. For example, the fraction containing the EV can be identified based on the size distribution and fluorescence staining of the fractions collected from the PPP sample. Flow cytometer 100 is capable of detecting and distinguishing more than one fluorescence (FL) signal on individual EV particles, including PB, FITC, PE, PC7, and APC. Antibody aggregates and antibody background interference can be avoided when appropriate antibody concentrations are used and appropriate controls are employed. Furthermore, flow cytometer 100 can compensate for spillover between different fluorescent dyes, thereby allowing for simultaneous analysis of multicolor staining.

[0271] In embodiments, method 400 is performed or modified prior to implantation to characterize EV, LNP, or viral particles with different contents, different surface markers, different targeting ligands, or any combination thereof. In embodiments, the degree of modification or insertion of the surface market, the targeting ligand, or the combination thereof is characterized.

[0272] Figure 5 This illustration schematically demonstrates an example of a method 500 for characterizing lipid nanoparticles that can be performed using a flow cytometer 100. Lipid nanoparticles (LNPs) have become increasingly valuable research and therapeutic tools. LNPs consist of a capsid filled with a compound possessing pharmaceutical properties, such as modified mRNA. Method 500 utilizes a flow cytometer 100 to characterize LNPs. Method 500 minimizes sample preparation and provides rapid results that can be correlated with functional cell transduction assays. Method 500 can be performed as part of a flow cytometry workflow for characterizing LNPs using a flow cytometer 100. Method 500 can also be performed as a rapid method for measuring LNP quality.

[0273] Method 500 includes step 502 of preparing a first sample for analysis by flow cytometer 100. As an illustrative example, step 502 may include collecting commercially available empty and intact LNPs, such as encapsulated green fluorescent protein (GFP) encoding mRNA. Step 502 may further include diluting the LNPs. For example, the LNPs may be diluted 1:200 in filtered phosphate-buffered saline (PBS).

[0274] Method 500 includes step 504 of preparing a second sample for flow cytometry analysis. The second sample is a functional assay. Step 504 may include diluting a suspension of Chinese hamster ovary (CHO) cells in an appropriate culture medium and seeding the diluted CHO cells in multiple wells. As an illustrative example, 200 μL of diluted CHO cells may be seeded into a U-bottom 96-well plate (250,000–1,000,000 cells / mL). Step 504 may further include adding commercially available LNPs in the range of 1–10 μL / well (100–30,000 ng mRNA / well). After a period of time (e.g., 24–48 hours), step 504 may include removing the CHO cells from the wells.

[0275] Method 500 includes step 506, which involves analyzing the first and second samples prepared by steps 502 and 504 to quantify the percentage of GFP-positive cells. Step 506 includes analyzing the first and second samples using a flow cytometer 100. In some instances, a PBS control is used to understand and reduce background interference. Step 506 may further include using fluorescence microscopy to confirm the presence of GFP-positive cells.

[0276] Method 500 includes operations 508 for characterizing LNPs. For example, empty and intact LNPs can be distinguished based on a detectable increase in scattering. This increase in scattering for positive / intact LNPs can be quantified. Therefore, there is a correlation between LNP scattering levels and the ability of LNPs to transduce CHO cells.

[0277] Multiple side-scatter channels in the flow cytometer 100 can further extend the dynamic range to capture side-scattered light in the violet spectrum to identify protein aggregates, such as amyloid plaques associated with diseases such as ALS, Alzheimer's disease, and Parkinson's disease. For example, the dynamic range of side-scattered light in the violet spectrum can be from 40 nm to 1000 nm. In some cases, the flow cytometer 100 is incorporated in respect of aspects described in U.S. Provisional Patent Application No. 63 / 608,615, filed December 11, 2023, the disclosure of which is incorporated herein by reference in its entirety.

[0278] The flow cytometer 100 has sensitivity in the violet spectrum for detecting side-scattered light, which can be used to detect protein aggregates whose size can vary between 100 nm and 1 μm. Furthermore, once detected, the flow cytometer 100 can characterize and distinguish different types of protein aggregates. In some instances, the flow cytometer 100 uses attenuated scattering to preferentially detect protein aggregates. In some instances, dyes, such as proteostat, can be used to specifically label aggregates, and the sensitivity of the fluorescence channels in the flow cytometer 100 allows for the detection of labeled aggregates.

[0279] The flow cytometer 100 can be further used to detect and sort particles based on particle secretion. This is advantageous given the current inability to detect single-cell secretion. For example, assays for detecting proteins typically average the protein secretion of large groups of cells, thus neglecting the microenvironment of individual cells. The flow cytometer 100 provides an improvement over these methods by identifying specific cells in a mixture of cells that secrete the desired protein. The flow cytometer 100 can sort particles based on detected attachment to heterofunctionalized particles used for particle secretion separation, as described in PCT International Patent Application No. PCT / US2022 / 072754, filed June 3, 2022, the disclosure of which is incorporated herein by reference in its entirety.

[0280] The flow cytometer 100 can be further used to detect and sort particles based on complete and incomplete assembly. For example, viral load count can be detected by the flow cytometer 100. As an illustrative example, vesicular stomatitis virus (VSV) has been used in the development of therapeutics. During the development process, VSV is known to undergo incomplete assembly. For example, the ratio of internal to external components changes as the particle grows / matures. The flow cytometer 100 can be used to detect the internal and external components of VSV to determine particle maturity. In some instances, the flow cytometer 100 can be used to sort VSV based on particle maturity.

[0281] As another example, the flow cytometer 100 can be used to detect the degree of vector assembly in lentiviruses and other retroviruses. For example, lentiviruses are multilayered particles, and therefore the layers are easily peeled off and / or damaged during processing. The flow cytometer 100 can detect the presence or absence of specific envelope proteins, which will indicate particle maturity and integrity, as they are related to function. The flow cytometer 100 can be considered for assessing the function-related physical properties of particles, which would be significantly faster and less costly than complex cell-based assays, and would take at least a full day to perform. The flow cytometer 100 can also be used to distinguish wild-type viruses (i.e., naturally occurring, non-mutated viral strains) from one another.

[0282] The flow cytometer 100 can be further used to determine the amount of protein on or inside the surface of LNPs, EVs, lentiviral vectors, etc. For example, the flow cytometer 100 can detect empty, partially loaded, or fully loaded lentiviral particles, as well as the loading of other particles with a diameter of approximately 90 nm. The flow cytometer 100 can further characterize the drug loading in liposomes by providing label-free single-particle characterization of liposomes, including characterization of receptor / ligand binding.

[0283] The flow cytometer 100 can also use a variety of fluorescent dyes to detect properties from combinations of data sources such as LNPs or viral vectors to assess encapsulation efficiency or quantify cargo packaged in particles. The flow cytometer 100 can further detect the degree of modification or insertion of surface markers or targeting ligands, such as partially adding targeting lipids to the surface of LNPs or engineering adenoviruses to express and present specific surface markers, which can be used in the development of mRNA vaccines. Furthermore, the flow cytometer 100 is sensitive to biomarkers with low abundance and small sample sizes, making it more effective for detecting the presence of biomarkers in samples collected from subjects.

[0284] Figure 6 Examples of computing devices 600 used to implement various aspects of system 10 are illustrated schematically, including the functionality of flow cytometer 100 and workstation 200. Examples of computing devices 600 include server computers, desktop computers, laptop computers, tablet computers, mobile computing devices (such as smartphones), or other devices configured to process digital instructions.

[0285] The computing device 600 includes one or more processing devices 602. Examples of the one or more processing devices 602 include a central processing unit (CPU), a digital signal processor, a field-programmable gate array (FPGA), and other types of electronic computing circuitry. The one or more processing devices 602 may be part of a processing circuitry system having a memory for storing instructions, which, when executed by the processing circuitry system, causes the processing circuitry system to perform the functions described herein.

[0286] The computing device 600 further includes a system memory 604 and a system bus 606 that couples various system components including the system memory 604 to one or more processing devices 602. The system bus 606 is one of any number of types of bus structures, including a memory bus or memory controller; a peripheral bus; and a local bus using any of a variety of bus architectures.

[0287] System memory 604 may include read-only memory (ROM) 608 and random access memory (RAM) 610. A basic input / output system (BIOS) 612 may be stored in system memory 604, the BIOS containing basic routines for transferring information within computing device 600, such as during startup. RAM 610 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).

[0288] The computing device 600 may also include one or more secondary storage devices 614, such as hard disk drives for storing digital data. One or more secondary storage devices 614 are connected to the system bus 606 via a secondary storage interface 616. The one or more secondary storage devices 614 and associated computer-readable media provide the computing device 600 with non-volatile storage of computer-readable instructions (including application programs and program modules), data structures, and other data. Although the examples described herein use hard disk drives as secondary storage devices, other types of computer-readable storage media are used in other embodiments. Examples of these other types of computer-readable storage media include ROM 608 and / or RAM 610. Some examples include non-transitory media. Additionally, such computer-readable storage media may include local storage devices or cloud-based storage devices.

[0289] Computing device 600 typically includes at least some form of computer-readable medium. Computer-readable medium includes any available medium that can be accessed by computing device 600. For example, computer-readable medium includes computer-readable storage media and computer-readable communication media.

[0290] 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 computing device 600.

[0291] Computer-readable communication media can embody 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 delivery medium. The term "modulated data signal" refers to a signal having one or more characteristics set up in a manner that encodes information in a 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.

[0292] Multiple program modules can be stored in secondary storage device 614 or system memory 604, including operating system 618, application program 620, program module 622 (such as software engine), and program data 624. Computing device 600 can utilize any suitable operating system, such as Microsoft Windows™, Google Chrome™, Apple OS, and any other operating system suitable for the computing device.

[0293] Users provide input to computing device 600 through one or more input devices 626. Examples of input devices 626 include a mouse 206, a keyboard 208, a microphone 632, and a touch sensor 634 (such as a touchpad or touch-sensitive display). Additional types of input devices 626 are envisioned. Input devices 626 are typically connected to one or more processing devices 602 via input / output interfaces 636 coupled to system bus 606. These input devices 626 can be connected by any number of input / output interfaces, such as parallel ports, serial ports, game ports, or universal serial buses. In some possible embodiments, wireless communication between the input devices and the input / output interfaces 636 is also possible and includes infrared, BLUETOOTH® wireless technology, 802.11a / b / g / n, cellular, or other radio frequency communication systems.

[0294] Display monitor 204 may include a liquid crystal display device, a touch-sensitive display device, etc. Display monitor 204 is connected to system bus 606 via an interface, such as video adapter 640. In addition to display monitor 204, computing device 600 may include various other peripheral devices, such as speakers or printers.

[0295] When used in a local area network (LAN) or wide area network (WAN) environment (such as the Internet), computing device 600 is connected to network 160 via network interface 642, such as an Ethernet interface. Other possible embodiments use other communication devices. For example, some embodiments of computing device 600 include a modem for communication across network 160.

[0296] The computing device 600 is an example of a programmable electronic device that may include one or more such computing devices. When multiple computing devices are included, they may be coupled to a suitable data communication network to jointly perform the various functions, methods, or operations disclosed herein.

[0297] Flow cytometers can be used to determine the amount of therapeutic payload in nanoparticles, the amount of antibody binding to nanoparticles, and / or predict cell transduction efficiency using the methods disclosed herein. The methods may include setting instrument configuration parameters for calculations in subsequent operations, determining the collection angle, determining calibration factors, and analyzing measurement results.

[0298] Figure 59 Step 1 illustrates a method for determining the calibration factor of a flow cytometer instrument. In some cases, the flow cytometer instrument calibration method shown in Step 1 includes irradiating multiple standard particles (each with a known size and refractive index) 5905, collecting side scattering from all wavelengths of interest 5910, calculating the Mie scattering distribution for each standard particle 5915, integrating the Mie scattering distribution over a collection half-angle of 0–90 degrees for each standard particle size 5920, calculating the goodness of fit for the current half-angle 5935, reporting the collection half-angle with the highest goodness of fit 5940, and calculating the average calibration factor and CV for all standard particle sizes for the collection half-angles from the previous steps 5945. Figure 59 Step 2 illustrates a flow cytometry empirical data collection method. In some cases, the method for collecting empirical data on flow cytometry particles (e.g., nanoparticles) includes irradiating the particle 5950 at one or more wavelengths; collecting lateral scattering 5960 from all wavelengths of interest; and identifying the average scattering intensity 5965 and 5970 at each of the wavelengths of interest. In some cases, comparing the average lateral scattering intensity 5960 at each of the wavelengths of interest with a calibration factor 5945 determined in the same flow cytometer involves correlating the particle empirical data with theoretical data from multiple standard particles having known refractive indices and sizes. If the gain and wavelength from the empirical data are the same as the theoretical data found in the instrument calibration in Step 1, the method proceeds to post-processing step 3. If the gain and wavelength from the empirical data are different from the theoretical data found in the instrument calibration factor in Step 1, the method includes rerunning the instrument calibration method of Step 1 with standard particles of known size and refractive index to obtain the calibration factor of Step 2. Figure 59Step 3 illustrates the post-processing steps. In some cases, the post-processing steps include inputting known particle parameters 5975; initial guesses of the input particle parameters, bounds of the particle parameters, and constraints on the particle parameters 5980; and calculating the output parameters 5985 that minimize the loss function and satisfy the bounds and constraints. In some cases, the particle input parameters are selected from the following group: Figure 59 The particle size, particle refractive index, shell thickness, shell refractive index, wavelength, buffer material, buffer refractive index, half collection angle of step 1, and the calibration factor of step 1 or step 2, 5945 or 5970.

[0299] In some cases, post-processing steps include, for example: Figure 60 The diagram shows particle (e.g., nanoparticles) input and output parameters for ten different use cases, including solid nanoparticles and core-shell nanoparticles. In some cases, the post-processing step particle input parameters are selected from the group consisting of: solid particle diameter, solid particle refractive index, core-shell particle shell thickness, core-shell particle shell refractive index, core-shell particle core refractive index, and core-shell particle core diameter. In some cases, the post-processing step particle output parameters are selected from the group consisting of: solid particle diameter, solid particle refractive index, core-shell particle shell thickness, core-shell particle shell refractive index, core-shell particle core refractive index, and core-shell particle core diameter. In some cases, the post-processing step includes an input parameter for solid particle diameter and an output for solid particle refractive index. In some cases, the post-processing step includes an input parameter for solid particle refractive index and an output for solid particle diameter. In some cases, the post-processing step includes input parameters for shell thickness, shell refractive index, and core refractive index, and an output for core diameter. In some cases, the post-processing step includes input parameters for core diameter, shell refractive index, and core refractive index, and an output for shell thickness. In some cases, the post-processing step includes input parameters for shell refractive index, shell thickness, and core diameter, and output for core refractive index.

[0300] In some cases, post-processing steps include initial guesses, limits, and constraints on the particles (e.g., nanoparticles) as input, such as... Figure 61As shown. In some cases, the initial guess, limits, and constraints for the refractive index of the solid particles are 1.4, 1.33–2.5, and none, respectively. In some cases, the post-processing step includes input initial guesses, limits, and constraints for the solid particle size, which are 100 nm, 10 nm–1500 nm, and none, respectively. In some cases, the post-processing step includes input initial guesses, limits, and constraints for the core-shell particle size, which are 100 nm, 10 nm–1500 nm, and none, respectively. In some cases, the post-processing step includes input initial guesses, limits, and constraints for the shell thickness of the core-shell particles, which are 10 nm, 0 nm–1500 nm, and none, respectively. In some cases, the post-processing step includes input initial guesses, limits, and constraints for the core refractive index of the core-shell particles, which are 1.4, 1.33–2.5, and none, respectively. In some cases, the post-processing step includes input initial guesses, limits, and constraints for the shell refractive index of the core-shell particles, which are 1.4, 1.33–2.5, and none, respectively. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the refractive index of the core-shell nanoparticles, which are 1.4, 1.33–2.5, and none, respectively; and initial input guesses, limits, and constraints for the core diameter, which are 50 nm, 10 nm–1500 nm, and none, respectively. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the shell refractive index of the core-shell nanoparticles, which are 1.4, 1.33–2.5, and none, respectively; and initial input guesses, limits, and constraints for the shell thickness, which are 10 nm, 0 nm–1500 nm, and none, respectively. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the core diameter, which are 50 nm, 10 nm–1500 nm, and shell thickness > core diameter, respectively; and initial input guesses, limits, and constraints for the shell thickness, which are 75 nm, 0 nm–1500 nm, and shell thickness > core diameter, respectively. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the core diameter of the particle, which are 100 nm, 10 nm–1500 nm, and shell thickness < core diameter; and initial input guesses, limits, and constraints for the shell thickness, which are 10 nm, 0 nm–1500 nm, and shell thickness < core diameter. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the core diameter of the particle, which are 100 nm, 10 nm–1500 nm, and none; and initial input guesses, limits, and constraints for the shell thickness, which are 10 nm, 0 nm–1500 nm, and none. In some cases, the post-processing steps include initial input guesses, limits, and constraints for the core refractive index of the particle, which are 1.4, 1.33–2.5, and shell RI > core RI; and initial input guesses, limits, and constraints for the shell refractive index, which are 1.45, 1.33–2.5, and shell RI > core RI.In some cases, the post-processing steps include input initial guesses, limits, and constraints for the nucleus refractive index, which are 1.45, 1.33–2.5, and shell RI < nucleus RI, respectively, and input initial guesses, limits, and constraints for the shell refractive index, which are 1.4, 1.33–2.5, and shell RI < nucleus RI, respectively. In other cases, the post-processing steps include input initial guesses, limits, and constraints for the nucleus refractive index, which are 1.4, 1.33–2.5, and none, respectively, and input initial guesses, limits, and constraints for the shell refractive index, which are 1.45, 1.33–2.5, and none, respectively.

[0301] like Figure 60 and 61 As shown, ten use case scenarios with various input and output parameters are provided. These are... Figures 62-64 It is presented in a graphical format. Figure 62 Two use case scenarios for solid particles are disclosed. In use case 1: the refractive index is a known input parameter, and the size is an unknown output parameter. In use case 2: the size is a known input parameter, and the refractive index is an unknown output parameter. Figure 63 Four use case scenarios for core-shell modeling are disclosed. Use Case 3: Core refractive index (RI), shell RI, and shell thickness are known input parameters, while core size is an unknown output parameter. Use Case 4: Shell RI, core RI, and core size are known input parameters, and shell thickness is an unknown output parameter. Use Case 5: Shell RI, shell thickness, and core size are known input parameters, and core RI is an unknown output parameter. Use Case 6: Shell thickness, core RI, and core size are known input parameters, and shell thickness is an unknown output parameter. Figure 64 Four use case scenarios for core-shell modeling are disclosed. Use Case 7: Shell RI and shell thickness are known input parameters; core RI and core thickness are unknown output parameters. Use Case 8: Core RI and core size are known input parameters; core RI and core size are unknown output parameters. Use Case 9: Core RI and shell RI are known input parameters; shell thickness and core size are unknown output parameters. Use Case 10: Core size and shell RI are known input parameters; core RI and core size are unknown output parameters.

[0302] Figure 65An instrument calibration method based on the identified half-angle-limited average calibration factor is demonstrated (step 1). Empirical data collection is obtained from multiple polystyrene-sized reference beads with known sizes and RIs. The top row plot shows the purple VSSC1 and VSSC2 histograms for two groups of polystyrene-sized reference beads: the first group of polystyrene reference beads (low VIs) with nominal sizes of 40 nm, 89 nm, 103 nm, and 141 nm, and the second group of polystyrene reference beads (high VIs) with nominal sizes of 141 nm, 304 nm, 600 nm, and 1020 nm, respectively. The table in the upper right shows the nominal size (nm), CV%, and RI of the polystyrene beads at four illumination wavelengths: 405 nm (RI = 1.6253), 488 nm (RI = 1.6039), 561 nm (RI = 1.5931), and 633 nm (RI = 1.586). Next, for the three instruments MP03, MP05, and MP07, the half-angle is limited for each instrument. The three graphs in the middle row show the fit confidence percentage (“goodness of fit”) as a function of scattering angle. Gain, wavelength, and particle size are the independent variables. The half-collection angle for each instrument is defined by the highest goodness of fit. Next, the average calibration factor for each instrument is defined based on the identified half-angle. Gain, wavelength, and particle size are the dependent variables. The table at the bottom shows the average calibration factor for all bead sizes at different scattering wavelengths for the three flow cytometers MP05, MP07, and MP03, targeting VSSC1 (purple), BSSC (blue), YSSC (yellow), and RSSC (red) illumination wavelengths.

[0303] Figure 66 The top row shows a first example of empirical data collection (step 2) for a biological control of commercially available MLV virus-like particles. The top row also shows histograms of VSSC1 (a), BSSC (b), YSSC (c), and RSSC (d), and a dot plot (e) showing the MLV gating strategy (RSSC-H relative to VSSC1-H). The MLV signal is indicated by arrows in the histograms and circled in the dot plot.

[0304] Figure 66 The bottom row shows a second example of empirical data collection (step 2) for biological controls of commercially available recombinant EVs. The bottom row shows a scatter plot (a) showing VSSC1 relative to B531 FL, and P2 (36.56%) gated on unstained and P1 (23.10%) GFP-positive rEVs. Scatter plot (b) shows BSSC relative to B531 FL. Scatter plot (c) shows YSSC relative to B531 FL. Scatter plot (d) shows RSSC relative to B531 FL.

[0305] Figure 67The top row shows a third example of empirical data collection (step 2) for biological controls used in lipid nanoparticles (LNPs). The top row shows histograms of VSSC1 at 20 nm for a) buffer, b) empty LNPs, and c) LNPs loaded with LNPs.

[0306] Figure 67 The bottom row shows a fourth example of empirical data collection for LNPs as biological controls (step 2), and a histogram overlay of VSSC1 for four LNPs with polyA substitution mRNA payloads and different amounts of mAb on their surface, as shown in the table.

[0307] Figure 68 The post-processing parameter table for use case 1 (step 3) is shown for MLV particles with known parameter RI and unknown particle size parameter. The table identifies, from left to right, the instrument, particle type (MLV), gain (200), irradiation channel (VSSC1, BSSC, YSSC, or RSSC), expected nucleus diameter (nm) from the cryogenic TEM, input parameters for expected nucleus RI, empirical data, output parameters for predicted nucleus diameter (nm), and predicted nucleus diameter error (predicted-expected).

[0308] Figure 69 The post-processing parameter table for use case 2 (step 3) is shown for MLV particles with known diameter and unknown RI. The table identifies, from left to right, the instrument, particle type (MLV), gain, irradiation channel (VSSC1, BSSC, YSSC, or RSSC), input parameters for expected nucleus diameter (nm), expected nucleus RI, empirical data, output parameters for predicted nucleus RI, and predicted RI error (predicted - expected).

[0309] Figure 70 The post-processing parameter table for use case 5 (step 3) is shown for rEV particles with known shell RI, shell thickness, core size, and unknown core RI. The table identifies the input parameters: instrument, particle type (rEV), gain, channel (VSSC1, BSSC, YSSC, or RSSC), (i) expected core diameter (nm), (ii) expected shell thickness (nm), and (iii) expected shell RI; expected core RI, empirical data; and output parameters: predicted core RI and predicted core RI error (predicted core RI - expected core RI).

[0310] Figure 71The post-processing parameter table for use case 10 (step 3) is shown for rEV particles with known shell size, known core size, unknown shell RI, and unknown core RI. The table identifies, from left to right, the input parameters for instrument, particle type (rEV), gain, channel (VSSC1, BSSC, YSSC, or RSSC), expected core diameter (nm), and expected shell thickness (nm); the output parameters for expected core RI, expected shell RI, empirical data, constraint (shell RI > core RI), predicted core RI, predicted core RI error (predicted core RI – expected core RI), predicted shell RI, and predicted shell RI error (predicted shell RI – expected shell RI).

[0311] Figure 72 An example of the post-processing step (step 3) of use case 2 in the algorithm is shown. Step A shows an LNP nanoparticle with a known diameter and an unknown RI (left) in the left inset. Step B shows the known input size parameters of an empty LNP nanoparticle, and a loaded LNP nanoparticle shown in the left middle inset. Step C shows the calculation of the output RI of an empty LNP (particle type = empty, input size 64 nm, empirical data 4962, output RI = 1.435) and a loaded LNP (particle type = loaded, input size 64 nm, empirical data 12625, output RI = 1.47) in the middle right inset. Step D shows the calculation of ΔRI for loaded and empty LNPs. (ΔRI = RI loaded – RI empty) or 1.47 - 1.435 = 0.035. This illustrates that the change in refractive index from loaded to empty may be related to the amount of mRNA payload.

[0312] Figure 73The following diagram illustrates an example of step 3, the post-processing step, in use case 10, or step A, in use case 6. In the left inset, for use case 10, the shell thickness and core size are known, and the shell RI and core RI are unknown (left side); for use case 6, the shell size, core thickness, and core size are known, and the shell RI is unknown. Step B shows the input of known parameters for the core size and shell thickness of loaded nanoparticles with and without mAb on the surface, in the left center inset. Step C illustrates the calculation of the shell RI and core RI for the loaded LNP particles [instrument, particle type of the loaded LNP, gain 200 for all side scattering channels, channel (VSSC1, BSSC, YSSC, or RSSC), input parameters core size, shell thickness, empirical data on VSSC1 medium scattering intensity, and output parameters for shell RI and core RI], and for loaded LNP particles with mAbs on their surface [instrument, particle type of the loaded LNP with mAbs, gain 200 for all side scattering channels, channel (VSSC1, BSSC, YSSC, or RSSC), input parameters core size, shell thickness, empirical data on VSSC1 medium scattering intensity, and output parameters for shell RI and core RI]. Step D illustrates the steps for comparing the RI of loaded LNPs with those of loaded LNPs with different amounts of mAbs on their surface, where (Δshell RI = shell RI load + mAb – shell RI load), right inset. Variations in shell RI can be correlated with the amount of mAbs on the nanoparticle surface. (Δshell RI = shell RI load + mAb – shell RI load). Changes in nuclear refractive index can be correlated with the amount of mAb on the surface of loaded nanoparticles.

[0313] Figure 74 The algorithmic workflow for instrument calibration is shown, comprising (i) calculating the forward Mie scattering distribution for each input bead diameter 5915; (ii) integrating the Mie scattering distribution for each bead diameter at 90 degrees ± the current collection half-angle 5920; (iii) calculating the calibration factor for each bead size 5925; (iv) calculating the goodness of fit for the current collection half-angle (1-CV of all calibration factors) 5930; and (v) reporting the collection half-angle with the highest goodness of fit 5935. A loop traversing the considered collection half-angles from 0 degrees to 90 degrees is performed between steps 5920, 5925, and 5930 in 0.1-degree increments. Calibration is determined as calibration factor = signal measurement / integrated signal calculation. A plot of the normalized calibration factor versus bead diameter (nm) is shown at the bottom. A plot of the goodness of fit versus collection half-angle is shown in the upper right, illustrating the best fit at collection half-angle = 50.8 degrees.

[0314] Figure 75A method for calculating the forward Mie scattering distribution is shown 5915. The pySCATMECH library is used. The distribution is calculated in 0.1-degree steps within the range of 0–180 degrees. The distribution is independent of the collection half-angle and is therefore calculated only once. Inputs include the bead diameter, the bead refractive index at a given wavelength, the medium refractive index at a given wavelength, the wavelength, the incident light polarization, and the detector polarization sensitivity. The output is the Mie scattering intensity as a function of the polar angle. The right side is a graph showing the calculated Mie scattering intensity relative to the angle (degrees) for polystyrene beads with diameters of 44 nm (A), 80 nm (B), 141 nm (C), 304 nm (D), 600 nm (E), and 1000 nm (F).

[0315] Figure 76 A method for integrating the calculated scattering intensity is shown 5920. The Mie scattering distribution is numerically integrated over an angular range defined by the currently proposed collection angle using Simpson's rule. The integrand needs to be weighted by sin θ. Example outputs are shown in the four figures on the right as the Mie scattering intensity relative to the angle (degrees) at collection half-angles of 20 degrees (top left), 40 degrees (top right), 60 degrees (bottom left), and 80 degrees (bottom right).

[0316] Figure 77 A method 5925 for calculating the calibration factor for each bead size is shown. The calibration factor is defined as the ratio of the scattering intensity measurement to the integrated forward Mie intensity calculated in step 2. The calibration factor is calculated for each bead diameter. The signal measurement of the 44 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 44 nm bead to obtain the calibration factor for the 44 nm bead. The signal measurement of the 80 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 80 nm bead to obtain the calibration factor for the 80 nm bead. The signal measurement of the 100 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 100 nm bead to obtain the calibration factor for the 100 nm bead. The signal measurement of the 144 nm bead is divided by the integrated forward Mie scattering model over the entire collection angle of the 144 nm bead to obtain the calibration factor for the 144 nm bead.

[0317] Figure 78 Method 5930 for calculating goodness of fit is shown. The output of step 3 is a set of calibration factors for the diameter of each input bead at a single proposed collection half-angle. First, the coefficient of variation (CV) is calculated based on the set of calibration factors:

[0318] CV = Standard deviation (all calibration factors) / Mean (all calibration factors). Next, calculate the "goodness of fit" for the currently proposed collection half-angle: Goodness of fit = 1 – CV. Since CV ≥ 0, goodness of fit ≤ 1.

[0319] Figure 79 The diagram shows the identification of the most likely collection angle 5935. Method 5930 is repeated for each collection half-angle considered. The output is the "goodness of fit" for each potential collection half-angle. The calibration factor is an instrument parameter and should not depend on bead size; therefore, ideally, the calculated CV for multiple bead sizes would be 0. However, due to measurement uncertainties, there are some variations between beads, and therefore the CV is not zero. The most likely collection half-angle is identified as the angle with the smallest CV, as this is closest to the ideal. To calculate the goodness of fit, the collection half-angle with the largest goodness of fit is selected, since the definition of 1-CV means that this is the angle whose CV is closest to 0. See the graph of goodness of fit versus collection half-angle, which shows the best-fit collection half-angle at 49.5 degrees.

[0320] Figure 80 Examples of half-angle calculations for three flow cytometer instruments are shown. Three plots are presented for the fitted confidence % versus scattering angle for the three flow cytometer instruments MP03, MP05, and MP07, excluding 44 nm and 103 nm bead data. For the MP03, MP07, and MP05 instruments, the half-angles at maximum confidence are 50.7 degrees, 49.4 degrees, and 52.1 degrees, respectively, as shown in the table. The half-angle is instrument-specific but gain-independent.

[0321] Figure 81 The top, middle, and bottom figures show a comparison of half-angle calculations by three instruments, MP03, MP05, and MP07, excluding 44 nm and 103 nm data (left figure) and including 44 nm and 103 nm data (right figure). By excluding 103 nm, the confidence level of matching empirical data with theoretical simulations is improved.

[0322] Figure 82 The algorithmic workflow for calculating the average calibration factor and CV for all bead sizes is shown. When the correct collection angle is identified, the calibration factor changes almost nothing; that is, the calibration factor will not depend on the bead size, as it is an instrument parameter. Example outputs of the normalized calibration factor versus bead diameter are shown in four graphs for collection half-angles of 20°, 40°, 60°, and 80°. At a collection half-angle of 60°, the normalized calibration factor exhibits minimal change.

[0323] Figure 83 A general algorithm for all use cases is shown, with some variations in the algorithm's parameters. All use cases use the same loss function:

[0324] Loss function = [(integrated Mie signal calculation value) × (calibration factor) – signal measurement value]. The calculated Mie signal value varies with... Figure 60The particle parameters vary depending on the listed parameters. In some cases, the particle parameters are selected from the following group: particle size, particle refractive index, shell thickness, shell refractive index, core diameter, and core refractive index. The general algorithm involves calculating the unknown parameters that minimize the loss function while satisfying the bounds and constraints. Inputs include the selection of the minimization algorithm, initial guesses, bounds, and optional constraints.

[0325] Figure 84 Michaelis calculations for LNP1 solid modeling are shown for empty and mRNA-loaded LNPs. The histogram on the left shows LNP1-empty. The histogram on the right shows LNP1-eGFP. The table shows the results for the median solid with RI values ​​of 1.470, 1.450, 1.435, 1.430, and 1.42 (from left to right), as well as the population results for LNP1-empty and LNP1-eGFP where the size of the loaded LNP is greater than the size of the empty LNP.

[0326] In scenario #1,

[0327] 1) Empty: RI is known -> calculate size.

[0328] 2) If the load size remains unchanged -> calculate RI,

[0329] 3) ΔRI = RI (load) - RI (empty).

[0330] In scenario #2,

[0331] 1) Empty: Size is known -> Calculate RI.

[0332] 2) If the load RI remains unchanged -> calculate the size.

[0333] 3) Δsize = Size (load) - Size (empty).

[0334] Scenario #3: Both size and RI are changed.

[0335] Figure 85A flowchart of "Example 2" using loaded LNPs and loaded LNPs with different amounts of antibody is shown. The steps include (i) irradiation with one or more excitation beams, (ii) collecting light from multiple scattering and FL channels, (iii) extracting MFI data for the loaded LNPs and loaded LNPs with different amounts of mAb, obtaining unknown parameters using an algorithm with known input parameters, and (iv) calculating the changes in unknown parameters between the loaded LNPs and the loaded LNPs with mAb. In some cases, the loading RI is calculated using the algorithm and size inputs. In some cases, the loading size is calculated using the algorithm and RI inputs. In some cases, the algorithm is calculated along with the loading size, RI, shell thickness of the stained sample, and shell RI inputs. ΔRI = RI(shell) - RI(buffer) is calculated, and the instrument determines the sensitivity of the protein loading.

[0336] In some cases, the algorithm, along with the inputs of the load size and RI, calculates the shell thickness and shell RI. The calculation ΔRI = RI(shell) - RI(buffer) - shell thickness determines the sensitivity of the instrument in determining the protein load.

[0337] In some cases, algorithms are used along with inputs of the protein loading RI and shell RI of the stained sample to calculate shell thickness and instrument sensitivity to determine protein loading.

[0338] In some cases, algorithms are used along with the input of protein load size and RI to calculate shell thickness and shell RI. Calculate ΔRI - RI(shell) - RI(buffer), shell thickness, and instrument sensitivity for determining protein load.

[0339] Figure 86 A flowchart illustrating examples of using LNPs loaded with protein-rich polymeric substances (LNPs) and LNPs loaded with different amounts of antibody is shown. The steps include (i) irradiation with one or more excitation beams of selected wavelengths, (ii) collection of light from multiple scattering and FL channels, and (iii) extraction of MFI data from unstained and stained samples. In some cases, an algorithm is used along with inputs of load size and RI to calculate shell thickness and shell RI, including the calculation of ΔRI = RI(shell) - RI(buffer), and the shell thickness is used to determine the instrument's sensitivity in determining protein load. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate unstained size and RI. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate both shell RI and unstained RI. In some cases, an algorithm is used along with inputs of stained shell thickness and shell RI to calculate both shell thickness and unstained size.

[0340] Figure 7Histograms of PBS buffer at gain 200 are shown, with noise identified as baseline (left inset, noise 90.68%, P2 (5.44%)); histogram of empty LNP sample (middle inset, noise 8.42%, P2 (88.66%)); and histogram of NLP sample loaded with eGFP-encoded mRNA (right inset, noise 4.47%, P2 (94.7%)).

[0341] Figure 8A From left to right, four histograms are shown of LNPs loaded with mRNA modified with no, low, medium, and high levels of monoclonal antibodies (mAb) on the LNP surface. The upper right figure depicts models of LNPs loaded with RNA (left) and LNPs loaded with RNA modified with mAb (right).

[0342] Figure 8B Raw data point plots of mRNA-loaded LNPs modified with no, low, medium, and high levels of monoclonal antibodies (mAb) are shown. From left to right, 2D plots using RSSC-H versus VSSC1-H are shown: mRNA-loaded LNPs (surface-naked LNPs), mRNA-loaded LNPs with low mAb, mRNA-loaded LNPs with medium mAb, and mRNA-loaded LNPs with high mAb.

[0343] The various embodiments described above are provided by way of illustration only and should not be construed as limiting in any way. Various modifications may be made to the above embodiments without departing from the true spirit and scope of this disclosure.

[0344] Example

[0345] Example 1: Using flow cytometry to characterize lipid nanoparticles.

[0346] The following examples describe a flow cytometry workflow for characterizing LNPs using the Beckman Coulter CytoFLEX nano flow cytometer. In these examples, this technique, combined with orthogonal techniques, can be used in academic and biopharmaceutical laboratories as a rapid method for measuring LNP quality.

[0347] LNP1-empty and LNP1-eGFP (LNPs loaded with encapsulated mRNA encoding E. coli green fluorescent protein (eGFP)) (available from Cytiva) were diluted 1:200 in filtered phosphate-buffered saline (PBS). These samples were analyzed using a CytoFLEX nano flow cytometer (available from Beckman Coulter). LNPs in PBS alone were used as controls. Data were collected and further analyzed using Kaluza software (available from Beckman Coulter).

[0348] Add LNP1-empty and LNP1-eGFP to 96-well plates at doses ranging from 1 to 10 μL / well (100–30,000 ng mRNA / well). Reference Figure 7 LNP1-empty cells were analyzed using CytoFLEX nano flow cytometry under the conditions disclosed herein. (See also: [reference needed]) Figure 7 We analyzed complete LNPs containing mRNA encoding eGFP using CytoFLEX nano flow cytometry under the conditions disclosed herein. Figure 7 The results show that CytoFLEX nano can rapidly distinguish between empty LNPs, intact LNPs, and LNPs loaded with mRNA. LNPs with higher scattering have higher transduction percentages. Figure 7 The volume of LNP in each well shown is 0 µl, 3 µl, and 10 µl.

[0349] Under the conditions disclosed herein, flow cytometry was used to analyze low-level polynucleotides (LNPs) loaded with mRNA modified with different amounts of monoclonal antibodies (mAbs) on their surfaces. Reference Figure 8A The data is presented as a histogram in the bottom small chart. (Reference) Figure 8B The data is presented as a scatter plot. Two Figure 8A and 8B The following samples are depicted from left to right: LNPs loaded with mRNA, LNPs loaded with mRNA with a small amount of mAb modification, LNPs loaded with mRNA with a moderate amount of mAb modification, and LNPs loaded with mRNA with a large amount of mAb modification. The top inset shows models of the RNA-loaded LNPs and the mAb-modified RNA-loaded LNPs. The results clearly demonstrate the differences in light scattering properties among these LNP samples with varying amounts of mAb modification on their surfaces. Lateral scattering measurements of the proposed model were performed using CytoFLEX nano flow cytometry to fit the data to the Michaelis-Menten theoretical model, enabling the calculation of the refractive index for each sample. This process is crucial for understanding the optical properties and behavior of LNPs under different conditions, thus contributing to the optimization of their design and function for therapeutic applications.

[0350] Example 2: Characterization of events per second (EPS) stabilization under different sample flow rates

[0351] The CytoFLEX nano flow cytometer allows selection from three different sample flow rates (1 µl / min, 3 µl / min, and 6 µl / min) and also offers the option to customize the flow rate in 1 µl / min increments between the lowest (1 µl / min) and the highest (6 µl / min). The adjustable sample flow rate allows for a range of bio-nanoparticle concentrations to be accommodated in the sample.

[0352] When collecting samples at different flow rates, it is recommended to characterize the sample flow rate (EPS) for different concentrations of biological samples to identify the 3 µL / min sample flow rate for biological sample collection.

[0353] HEK293T fluorescent microvesicles and superfolded green fluorescent protein (sfGFP) samples were used for analysis.

[0354] HEK293T fluorescent microvesicles were prepared using phosphate-buffered saline (PBS) filtered through 20 nm at dilutions of 1:250 (2.12 × 10^3 p / ul), 1:500 (1.06 × 10^3 p / ul), and 1:1000 (0.5 × 10^3 p / ul). Samples were acquired on a CytoFLEX nano at three flow rates: 1 ul / min, 3 ul / min, and 6 ul / min.

[0355] sfGFP was rehydrated in 100 μl of 30 nm W-8 water (Thermo Fisher Scientific) and diluted in PBS filtered through a 20 nm filter. sfGFP was tested at a concentration of 3.8 × 10^4 p / µl and a flow rate of 3 µl / min.

[0356] The CytoFLEX nano was set to VSSC1 with a gain of 100 and a threshold (TH) of 360. Data was collected using the following steps: Step 1: Onboard cleaning with 10 pre- and post-washes. Step 2: Baseline check with a baseline monitor using the sample setup. Step 3: Run the sample at the desired flow rate and defined concentration for 10 minutes. Steps 1 through 3 were repeated for each combination (sample flow rate and concentration). The number of events per minute acquired during each 10-minute run was gated and segmented for analysis.

[0357] Each visual assessment was gated on a 10-minute timemap in 1-minute increments. Gating of the GFP-positive population was plotted based on the B531H versus VSSC1H plot. GFP-positive events were shown in a different color than all events within each 1-minute increment. For example, Figure 9 HEK293T microvesicles were disclosed, and the number of events per minute was analyzed as a percentage of the number of events in the first minute.

[0358] Different dilutions of HansaBioMed fluorescent microvesicles were run at different sample flow rates: 1 μL / min, 3 μL / min, and 6 μL / min. The percentage of event recovery at each minute of the 10-minute acquisition compared to the first minute was calculated, and the averages from three replicates were summed. Figures 10 to 17 middle.

[0359] Figure 10 and Figure 11 Timeplots of HEK293T microvesicles at a 1:1000 dilution (5.3 × 10^5 p / mL = 530 p / ul) at 1 μL / min, 3 μL / min, and 6 μL / min (column) are presented. The bar graphs and data tables represent the average values ​​from the three replicates. Reference Figure 10 and Figure 11 At 1 μL / min, it took approximately 3 minutes to reach stable sample flow. 25% of total events and 50% of GFP-positive events were lost. At 3 μL / min, it took approximately 1 minute and 20 seconds to reach stable sample flow. 15% of total events and 30% of GFP-positive events were lost. At 6 μL / min, the sample flow showed an inconsistent pattern between replicates and never reached a true steady state: replicates 2 and 3 stabilized immediately after intensification, while replicate 1 showed an initial decrease followed by an increase in the first 40 seconds before reaching stable flow. All replicates showed a significant decrease approximately 5 minutes and 20 seconds after initiation. (Reference) Figure 10 In the timeline (top band), gray corresponds to all events, green (bottom band) represents GFP-positive events, and blue (middle band) represents noise peaks. GFP-positive particles follow the same EPS decreasing trend. 25% of total events and 50% of GFP-positive events are lost. 3 μL / min shows better event recovery and more desirable sample flow behavior.

[0360] Figure 12 and Figure 13 Time plots of HEK293T microvesicles diluted 1:500 at 1 μL / min, 3 μL / min and 6 μL / min were published. Figure 12 and Figure 13 This is the average of three repeated experiments. (Reference) Figure 12 and Figure 13Running HEK293T fluorescent microvesicles at a 1:500 dilution (1.06 × 10^6 p / mL = 1060 p / ul) resulted in sample flow behavior similar to that at a 1:1000 dilution (except at 6 uL / min). 25% of total events and 50% of GFP-positive events were lost. At 1 uL / min and 6 uL / min, approximately 50% of total events and GFP-positive events were lost, with approximately 10–13% of total events and 30% of GFP-positive events lost at 3 uL / min. 3 uL / min showed better event recovery and more desirable sample flow behavior.

[0361] Figure 14 and Figure 15 Time plots of HEK293T microvesicles diluted 1:250 (2.12 × 10^6 p / mL = 2120 p / ul) at 1 μL / min, 3 μL / min and 6 μL / min were published. Figure 14 and Figure 15 This is the average of three repeated experiments. (Reference) Figure 14 and Figure 15 It disclosed its relationship with Figures 10 to 13 Similar sample flow behavior was observed at 1:1000 and 1:500 dilutions. At 1 μL / min, approximately 30% of the sample was lost, with approximately 8-10% at 3 μL / min and approximately 23% at 6 μL / min. For GFP-positive particles (B531-H plot and data), approximately 50% of the sample was lost at 1 μL / min, with approximately 30% at 3 μL / min and approximately 26% at 6 μL / min. 3 μL / min showed better event recovery and more desirable sample flow behavior.

[0362] Figure 16 , Figure 17 and Figure 18 The assay of sfGFp viral particles at 1 μL / min and 3 μL / min, used as sample controls, is disclosed. (Reference) Figure 16 and Figure 17 Data analysis was based on all events and GFP-positive (B531-H) events. Gating strategies were implemented in... Figure 16 Publicly available in China. (Reference) Figure 17 and Figure 19 At 1 μL / min, sfGFP 1:500 did not achieve sample flow stability until almost 6 minutes after recording began. At 3 μL / min, the sample flow rate reached stability approximately 2 minutes after recording began. GFP-positive particles followed the same EPS decreasing trend.

[0363] Compared to the HEK293T microvesicles mentioned above, sfGFP sample flow rate stability may require a longer time. Users are advised to evaluate and select an appropriate sample rate based on their sample requirements.

[0364] In this example, the flow rates were run at 1 ul / min (slow), 3 ul / min (medium), and 6 ul / min (fast), and the data indicates that the flow rates are adjustable.

[0365] Conclusion: Sample flow rates of 1 µl / min, 3 µl / min, and 6 µl / min were tested using biological samples of different concentrations. 3 µl / min is a recommended sample flow rate for biological samples based on event recovery, EPS stabilization time, and consistency of sample flow over time.

[0366] The EPS decrease was greatest at a sample flow rate of 1 µl / min, and became more pronounced with increasing concentration, as the percentage of EPS under stability was lower than at lower concentrations. The EPS decrease was smallest at 3 µl / min. Increasing the sample concentration showed the smallest decrease, with a stable and sustained time-plot. EPS began to decrease after 5 minutes at 6 µl / min, exhibiting a similar trend across different sample concentrations.

[0367] Example 3: Volume counting of biological particles using flow cytometry

[0368] The CytoFLEX nano flow cytometer can count nanoscale particles, including biological nanoscale samples. This test case aims to evaluate the accuracy, repeatability, and reproducibility when testing biological nanoscale samples. Preliminary acceptance criteria are proposed based on the actual accuracy performance evaluation.

[0369] This example first runs for 5 days with all aborts enabled using the V5 virus and Daudi MV samples, then repeats for 3 days with the V5 virus sample and with 2 aborts disabled.

[0370] The following samples were freshly prepared for the AM run (morning run) and then prepared again for the PM run (afternoon run). The morning and afternoon runs were at least 2 hours apart. v5 virus was stained with anti-v5 PE and diluted to 1:1000 with PBS buffer filtered through 20 nm after staining. Daudi MV was stained with CD81 PE and diluted to 1:500 with PBS buffer filtered through 20 nm after staining.

[0371] CytoFLEX nano was set to the following settings. (1) For Virus v5: VSSC1 at gain 50; VSSC1-H at th 250; and Y535 at gain 2000. (2) For DAUDI MV: VSSC1 at gain 100; VSSC1-H at th 350; and Y535 at gain 1000.

[0372] Data was collected in two acquisition / load modes / states: Mode 1 - load was applied once for each of the four acquisitions; and Mode 2 - load and unload were applied for each repeated experiment, repeated four times.

[0373] This example is designed to account for variability in the number of days, daily runs, instruments, load status, and repeatability (within a run). In this example, the test is repeated for 5 days (with abort enabled), run twice a day (with an interval > 2 hours between morning and afternoon), with 4 replicate experiments per run on 2 instruments each day, as follows: 1. Start-up - cleaning and QC instruments. 2. Check baseline with baseline monitor; run 4 replicate experiments with DAUDI MV stained with CD81 PE (under load and unload) – 10 minutes each. Run 4 replicate experiments with v5 virus stained with v5 PE. These are morning runs. 3. Onboard cleaning 1xBFF. 4. Check baseline with baseline monitor. 5. Wait at least 2 hours and run 4 replicate experiments with DAUDI MV stained with CD81 PE (under load and unload) – 10 minutes each. Run 4 replicate experiments with v5 virus stained with v5 PE. These are afternoon runs. 6. Onboard cleaning 1xBFF. 7. Check the baseline using the baseline monitor. 8. Turn off the instrument.

[0374] Repeat the test using the v5 virus sample for 3 days (with 2 items disabled to stop), run twice a day (with an interval of > 2 hours between morning and afternoon), and run 4 times each time on 2 instruments, as shown above for the virus.

[0375] Gating strategies and data analysis. V5 virus gating strategy: P1 is used for all non-noise particles of each VSSC1-H; V5PE is gated on V5 positive virus particles, such as... Figure 19 The disclosed MV gating strategy: P1 is used for all non-noise particles of each VSSC1-H; MVPE is gating on CD81-PE positive MV particles, such as... Figure 20 What has been made public.

[0376] First, the data collected under the enabled stop-loss scenario was analyzed: Variation components from all sources of variability were estimated based on a nested structure. These sources of variability included inter-day, inter-operation, inter-instrument, inter-load, and inter-repeatability (within-operation). The standard deviation (SD) and coefficient of variation (CV%) were calculated based on the variance component estimates.

[0377] Tables 1A and 1B summarize the results of the analysis of variance for virus samples stained with V5: Particle count in P1 gating / μl; Particle count in peak 1 / μl; Particle count in peak 2 / μl; Particle count in V5-positive virus / μl. Reproducibility for all populations was approximately 10%, and reproducibility (total variability) < 20% met the preliminary specifications. All factors affected total variability except for load status relating only to P1 events.

[0378] Table 1A: Results of differential analysis of virus samples stained with V5.

[0379]

[0380] Table 1B: Results of differential analysis of virus samples stained with V5.

[0381]

[0382] Tables 2A and 2B summarize the results of the analysis of variance for MV samples stained with CD81-PE. Particle count in P1 / μl. Particle count of CD81+ MV particles / μl.

[0383] Table 2A: Results of differential analysis of MV samples stained with CD81-PE.

[0384]

[0385] Table 2B: Results of differential analysis of MV samples stained with CD81-PE.

[0386]

[0387] The reproducibility between the two populations was >10%, and for positive MV, the reproducibility (total variability) was >20%, up to 40%. All factors affected the total variability, particularly within runs (reproducibility), across days, runs, and instruments. Analysis revealed the following reasons: 1. Low MV-CD81-PE+ particle count in the sample. Lower particle counts may indicate higher variability. 2. This sample was not as stable as viral samples. Losses occurred during sample processing and analysis due to stability issues, such as degradation over time, event loss due to absorption, dilution, etc.

[0388] Perform a second test (retest) if the overlap and excessively long abort functions are disabled. Use V5 virus particles to rule out the influence of sample instability. Stain the sample with anti-V5 in PE for 3 days following the same protocol as above.

[0389] Figure 21 Examples of different fractions obtained by size exclusion chromatography (SEC) of blood samples (after centrifugation and filtration) are shown. Figure 21 The data analyzed in Table 3 are used to visualize the distribution of event counts under different conditions. Figure 21 ), and calculate the corresponding CV% to quantify the variability among days, operation, instrument, load state and repeated experiments (Table 3).

[0390] Table 3: Corresponding CV%.

[0391]

[0392]

[0393] No unloading showed higher variability than load / unloading, as in previous tests, and this was also observed in other samples; this is a characteristic of the loading pattern. Afternoon tests showed higher variability in particle recovery across all days compared to the morning. The investigation found that using the same stained samples in the morning and afternoon, without requiring a wash step to pause staining after staining, and with the reaction continuing until the afternoon time point, caused the higher variability. Reproducibility based on morning data was all <15%; reproducibility at all levels was within 30%.

[0394] Final accuracy specifications for volume counting based on biological particle test data: repeatability: 20%; repeatability for total variability: 30%.

[0395] Example 4: Using flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs)

[0396] This example demonstrates the use of CytoFLEX nano for immunophenotypic analysis of extracellular vesicles. More specifically, CytoFLEX nano is used to detect biological particles when stained with antibodies, triggered on the VSSC1 or FL channel, wherein the antibodies are labeled with BV421 / PB, FITC, PE, APC, AA700, or AA750.

[0397] For Examples 4 and 5, recombinant extracellular sigmata (rEV sigmata, on which green fluorescent protein (GFP) is expressed on its membrane surface), anemic platelet plasma extracellular vesicles (PPP EV), HansaBioMed HEK293 exosomes, and HansaBioMed HEK293 microvesicles were stained with the following antibodies and / or lipid dyes as disclosed in Table 4.

[0398] Table 4: Biological samples and staining combinations used in Examples 4 and / or 5.

[0399]

[0400] In the first round of characterization, two different samples were characterized. The first sample was Sigma rEV stained with CD81 TAPA clones in three different colors (PB, PE, and APC), and also stained alone with vFRed lipid dyes or in combination with CD81 PB or CD81 APC. The second sample was PPP EV prepared from fresh human blood and then stained with an antibody group disclosed in: Brittany, GC, Chen, YQ, Martinez, E. et al. A Novel Semiconductor-Based Flow Cytometer with Enhanced Light-Scatter Sensitivity for the Analysis of Biological Nanoparticles. Scientific Reports 9, 16039 (2019). https: / / doi.org / 10.1038 / s41598-019-52366-4.

[0401] Figure 22 This is a graph of the PBS control to show the baseline and unstained rEV-GFP (B531) with GFP label. Figure 22 An embodiment of this disclosure is disclosed, demonstrating the use of flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs). More specifically, Figure 22A graph showing the PBS control is presented to illustrate the baseline and commercially available rEV-GFP (B531) without staining but with GFP labeling. On the left, the acquisition settings for gain include FSC = 100, VSSC1 = 50, VSSC2 = 200, BSSC = 100, YSSC = 100, RSSC = 100, V447 = 1000, B531 = 1000, Y595 = 1000, R670 = 1000, R710 = 1000, and R792 = 1000, Y595 = 1000, R670 = 1000, R710 = 1000, and R792 = 1000. The first two rows are from PBS (sample buffer = blank). The bottom two rows are from rEV diluted 1:1000, which is the optimal dilution chosen after titration. rEV is green and endogenous, so the right-hand image shows the B531 channel of both PBS and the sample.

[0402] rEVs were successfully stained using single staining with CD81 PB, PE, APC, and vFRed, and detected on a CytoFLEX nano instrument. Figure 23 In the diagram, the first row shows unstained rEVs, the second row shows rEVs stained with the vFRed lipid dye, and the third row shows the gating, where Y595-H has been plotted against VSSC1-H to show the darker and lighter populations of vFRed-positive EVs. The second and third rows show single staining with vFRed (a lipid dye) from Cellarcus, assuming all EVs are bound. The darker populations consist of smaller EVs.

[0403] exist Figure 24 The image shows single staining of rEVs. The first row shows unstained rEVs, indicating the presence of only the B531 population. The second row shows rEVs stained with CD81 PB in the Pacific Blue channel (V447). The third row shows single staining of CD81 PE in the PE channel (Y595). The fourth row shows single staining of CD81 APC in the APC channel (R670). All these stainings are compensated and show both darker and brighter populations that are CD81 positive. vFRed staining produces a positive signal in the Y595 and R670 channels. CD81 PB staining produces a positive signal in the V447 channel. CD81 PE staining produces a positive signal in the Y595 channel. CD81 APC staining produces a positive signal in the R670 channel. The three CD81 antibodies are from the same clone (TAPA-1), and their sensitivity varies slightly in different channels (also taking into account their different F / P ratios and steric hindrance).

[0404] exist Figure 25In the diagram, recurrent EVs were successfully stained using a combination of vFRed lipid dye and CD81-PB. Unstained rEVs are shown in the top row. Serial rEV staining with vFRed and CD81-PB is shown in the second row. CD81-PB staining was a positive event in the V447 channel, while vFRed positivity was observed in the Y595 and R670 channels. As a monochrome staining control, rEVs stained with vFRed are shown in the third row. vFRed positivity was observed in the Y595 channel. A vFRed-positive event was also observed in the R670 channel (data not shown). As a monochrome staining control, rEVs stained with CD81-PB are shown in the fourth row. CD81-PB positivity was observed in the V447 channel.

[0405] exist Figure 26 In the diagram, rEVs were successfully stained using a combination of vFRed lipid dye and CD81-APC. Unstained rEVs are shown in the top row. Serial rEV staining with vFRed and CD81 APC is shown in the second row. CD81 APC staining should be present in positive events in the R670 channel, but vFRed positivity was also observed in the Y595 and R670 channels. Dual CD81 APC and vFRed staining does not show distinct CD81 APC populations above the vFRed positive population. This is due to the faintness of the CD81 APC positive signal, which cannot be distinguished from the vFRed positive signal. Additionally, no compensation was performed to account for vFRed signal overflow into the R670 channel.

[0406] exist Figure 27 In the previous section, antibodies were run at the same dilutions to assess the potential contribution of antibody aggregates to sample staining assessment. PBS, antibody-only (Ab) columns, and unstained sample columns provided information on the gating strategy for monochromatic positivity on the Y-axis: V447 channel for CD81 PB, Y595 channel for CD81 PE, R670 channel for CD81 APC, and Y595 channel for vFRed. The numbers shown in each figure represent events within the gating and indicate that these events did not significantly contribute to the positive population in the assessment. The antibody aggregate with the highest number was CD81 PE antibody, but it accounted for less than 2% of the total positive event count (222 / 11531 = 1.9%). As a monochromatic staining control, rEV stained with vFRed is shown in row 3. vFRed positivity was observed in the Y595 and R670 channels. As a monochromatic staining control, rEV stained with CD81 APC is shown in row 4. CD81 PB positivity was observed in the R670 channel.

[0407] Figure 28Antibody background controls are shown: antibodies are treated only as samples and, if present, diluted in the same manner to identify antibody aggregates. The first row shows combinations of CD81 PB and vFRed, and the second row shows CD81 APC and vFRed. The first two columns are the channels for those fluorescent dyes, the third and fourth columns are comparisons with unstained samples in those channels, and the last two columns are the stained samples and results in those channels. Figure 28 In this study, antibodies and vFRed were run at the same dilutions as described above to assess the contribution of antibody and vFRed aggregates to the overall analysis. Only the columns for 2 Abs (PB or APC in the left column, vFRed in the right column) and unstained samples provide information on the gating strategy for two-color positivity on the Y-axis: V447 channel for CD81 PB, Y595 channel for vFRed, and R670 channel for both CD81 APC and vFRed. The numbers shown in each figure represent events within the gating and indicate that these events did not significantly contribute to the positive population being assessed. The largest number of aggregates in CD81 APC + vFRed staining was from vFRed. The contribution of vFRed aggregates was approximately 4.2% of the total positive event count (515 / 12270 = 4.2%). Table 5 shows the compensation matrix generated for two-color staining. This compensation matrix was applied to all two-color stainings.

[0408] Table 5: Compensation matrix for double staining.

[0409]

[0410] Conclusions of rEV characterization in Example 4. The CD81 marker labeled the positive populations in PB, PE, and APC. The percentages in PE and APC were comparable, at 37.23% and 37.7%, respectively, with PB slightly lower at 15.94%. The vFRed lipid dye showed a positivity rate of 54.55% in this sample. Sequential staining allowed for the combination of antibody and lipid dye staining, even with some spillover from the dye in the APC channels. From a single staining perspective, vFRed contributed 4.2% to CD81 APC staining. Therefore, for a total of three colors, the combination of double staining with the present green label can be characterized using CytoFLEX nano.

[0411] Example 5: Using flow cytometry to detect and characterize extracellular vesicles (EVs) from anemic platelet plasma samples.

[0412] Similar to Example 4, this example demonstrates the use of CytoFLEX nano for immunophenotypic analysis of extracellular vesicles. More specifically, CytoFLEX nano is used to detect biological particles when stained with antibodies, triggered on the VSSC1 or FL channel, wherein the antibodies are labeled with BV421 / PB, FITC, PE, APC, AA700, or AA750.

[0413] Platelet-Potential Anemia Plasma (PPP) EV, prepared from fresh human blood, was obtained as follows: Blood was centrifuged at 200 × g for 5 minutes. 1 mL of PPP was collected from the top and filtered through a 200 nm syringe. PPP was further purified using an Izon size exclusion column (qEV 70 nm SEC column). Selected fractions were screened and used for single-color and multicolor staining for immunophenotypic analysis. PPP was then stained with the following antibodies: CD81 PB, CD9 FITC, CD63 APC, CD61 PC7, and CD235a PE. Samples were collected on a CytoFLEX nano flow cytometer and analyzed in a CytExpert nano.

[0414] Figure 29 Examples of different PPP EV fractions separated from a single sample using a qEV 70 nm SEC column are disclosed. Fraction 12 was used for both monochrome and multicolor staining for immunophenotypic analysis. Figure 30 The instrument setup and compensation matrix for multicolor staining are shown.

[0415] Figure 31 and Figure 32 Single staining using CD81 PB, CD9 FITC, CD63 APC, CD61 PC7, and CD235a PE is disclosed. Figure 31 The diagram shows single staining of fraction 12. The top row is unstained fraction 12. The second row is fraction 12 stained with CD81 PB (CD81 is not present in every donor; donors with high CD81 are selected here to show positivity). The third row is fraction 12 stained with CD9 FITC. The fourth row is fraction 12 stained with CD63 APC (usually negative or very low). For each of these, a graph involving all combinations is shown as a control for crosstalk and appropriate compensation. PPP EV is usually positive for CD9, negative or slightly positive for CD63, and some donors are positive for CD81 (e.g., ...). Figure 31 (The donors in the sample), and some donors were completely negative. Some fractions were positive for CD61 PC7, the number decreased as the SEC fraction increased, and CD235a PE was negative if platelet and red blood cell clearance was effective. Figure 32The diagram shows single staining continuing from fraction 12. The top row shows fraction 12 stained with CD61PC7. The bottom row shows fraction 12 stained with CD235a PE (decreasing as fraction # increases). A bivariate plot relating antibody dye color to their expected detection channels is outlined in red. Compensation Figure 31 and Figure 32 The data is publicly available in China.

[0416] Figure 33 Multiple staining of 12 samples from the same fraction using an intact antibody group diluted at the same dilution in a mixture is disclosed. The top row shows monochromatic staining for the mixture samples. The bottom row shows a combination of markers indicating biological significance.

[0417] It shows results similar to those obtained with monochrome, where antibody aggregates or other background contribute less.

[0418] Figure 34 Antibody backgrounds were disclosed between PBS buffer, Ab only, unstained samples, and single-antibody samples. This is a single-antibody control only. Row 1: CD9 FITC, Row 2: CD81 PB, Row 3: CD63 APC, and Row 4: CD61 PC7. Column 1: PBS in those channels as controls, Column 2: Samples treated only with antibodies and diluted in the same manner to identify antibody aggregates (if present), Column 3: Unstained samples as controls, and the last column: Samples fully stained for comparison. PBS, Ab only, and unstained samples provide information for gating strategies to gate positive populations. The numbers in each figure represent events within the gating. Events in PBS, Ab only, and unstained samples contribute little to the total event count for fully stained EV samples.

[0419] Figure 35 Antibody backgrounds were disclosed for PBS-buffered, Ab-only, unstained, and multiplex-stained (Ab mixture) samples. PBS, Ab-only, and unstained samples provide information for gating strategies to gate positive populations. The numbers in each figure represent events within the gating mechanism. Events in PBS, Ab-only, and unstained samples contribute minimally to the total event count for fully stained EV samples. First row: For CD235a PE, compared to... Figure 34 The same, therefore monochromatic. The last three lines are a mixture of all antibodies together in each channel of interest: V447, B531, Y595, R670, and R792: Line 1: defines the contamination of each antibody (if present) in each channel; Line 2: appropriate compensation shows that unstained samples are not positive; Line 3: stained real samples and results.

[0420] Based on the size distribution and fluorescence staining of fractions collected from qEV 70 nm and 35 nm SEC columns, the fractions in which EVs reside can be identified. The CytoFLEX nano flow cytometer can detect and distinguish more than one FL signal on a single PPP EV particle, including PB, FITC, PE, PC7, and APC. Data indicate that antibody aggregates and antibody background interference can be avoided when appropriate antibody concentrations are used and suitable controls are employed. Data also show that the CytoFLEX nano can compensate for spillover between different fluorescent dyes, allowing for simultaneous analysis of five-color staining.

[0421] Example 6: Using flow cytometry to detect and characterize extracellular vesicles (EVs) from anemic platelet plasma samples.

[0422] Repeat the characterization of PPP EV in Example 5 and evaluate different triggering options. Follow the same protocol. For size exclusion chromatography (SEC), evaluate both 70 nm (qEV 70) and 35 nm (qEV 35) cutoff columns.

[0423] Figure 36 and Figure 37 Data from both unstained and stained samples are disclosed, with fraction 6 selected from a qEV 70 nm SEC column. Monochromatic staining with CD9 FITC, CD81 PB, CD63 APC, CD61 PC7, and CD235a PE is shown, as well as staining of the same samples with an antibody (mixture) group. A histogram of VSSC1-H for fraction 6, stained to show the EV size distribution, is also shown. This fraction shows small and large EVs. Figure 38 Disclosed application Figure 36 and Figure 37 CytoFLEX nano settings and compensation for data in the database.

[0424] Figure 39 and Figure 40 The same samples were evaluated using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting the CD61-PC7 positive population.

[0425] Figure 41 and Figure 42 Data obtained from fraction 6 collected from a qEV 35 nm SEC column are disclosed. Monochromatic staining with CD9FITC, CD81 PB, CD63 APC, CD61 PC7, and CD235a PE is shown, as well as staining of the same sample with an antibody (mixture) group. A histogram of VSSC1-H for fraction 6, stained to show the EV size distribution, is also shown. This fraction shows small and large EVs. Figure 42The lower right corner shows the superposition of the grades from two different SEC columns for comparison. Figure 43 The settings and compensations obtained using qEV35nm for this sample are disclosed.

[0426] Figure 44 and Figure 45 The same assessment was disclosed using different triggers: B531 trigger, targeting CD9 positive individuals, and R670 trigger, targeting CD61-PC7 positive individuals.

[0427] Conclusions from the characterization of PPP EV: CytoFLEX nano can characterize up to five colors with appropriate control, without interference from antibody aggregates. Through compensation, CytoFLEX nano can manage crosstalk from collinear systems. Fluorescence triggering facilitates focusing on specific populations.

[0428] Similar to Example 5, the fractions containing EVs can be identified based on the size distribution and fluorescence staining of fractions collected from qEV 70 nm and 35 nm SEC columns. The CytoFLEX nano flow cytometer can detect and distinguish more than one FL signal on a single PPP EV particle, including PB, FITC, PE, PC7, and APC. Data indicate that antibody aggregates and antibody background interference can be avoided when appropriate antibody concentrations are used and suitable controls are employed. Data also show that the CytoFLEX nano can compensate for spillover between different fluorescent dyes, allowing for simultaneous analysis of five-color staining.

[0429] Example 7: Using flow cytometry to detect and characterize recombinant extracellular vesicles (rEVs)

[0430] Repeat the above characterization of rEV in Example 4 using the same scheme. Figure 46 Unstained GFP-labeled samples were disclosed, and Figure 47 The CytoFLEX nano settings used in this example are disclosed.

[0431] Figure 48 Single staining with CD81 antibody targeting the same epitope of the tetraspan membrane protein in three different colors is disclosed: PE (first row), APC (second row), and PB (third row), as well as single staining with vFRed lipid dye (fourth row).

[0432] Figure 49 and Figure 50 Combinations of CD81APC and vFRed with GFP markers, evaluated with different triggers and compared to slight changes in MFI, are disclosed. The following events were triggered due to compensation of the trigger channel by other dyes.

[0433] Figure 51 and Figure 52 Combinations of CD81PB and vFRed with GFP markers, evaluated with different triggers and compared to slight MFI changes, are disclosed. The following events were triggered due to compensation of the trigger channel by other dyes.

[0434] CytoFLEX nano offers a variety of triggering options, enabling analysis of samples and separation of specific staining populations in multiple ways. This allows for the acquisition of more information from the same expression markers. CytoFLEX nano can detect and distinguish more than one FL signal on a single particle, including PB, FITC, PE, and APC, and can simultaneously detect and distinguish up to five color stains without being affected by antibody background noise.

[0435] Example 8: Characterizing heterogeneous EVs using flow cytometry or protein loading on biological sample surfaces

[0436] refer to Figure 53 Flow cytometry (e.g., CytoFLEX nano) is used to characterize unstained EVs composed of heterogeneous populations. For example, unstained EV populations are loaded into CytoFLEX nano for characterization using multi-wavelength side scattering. CytoFLEX nano successfully distinguishes heterogeneous populations of unstained EVs, such as population #1 (64–99 nm) and population #2 (59–205 nm).

[0437] Example 9: Characterizing protein loading on biological sample surfaces

[0438] refer to Figure 54 Flow cytometry (e.g., CytoFLEX nano) was used to characterize heterogeneous EV populations based on varying amounts of protein on the EV surface within the population. For example, heterogeneous EV populations were loaded into CytoFLEX nano for characterization using multi-wavelength side scattering. CytoFLEX nano successfully distinguished heterogeneous EV populations based on varying amounts of protein on each EV surface.

[0439] Example 10: Using flow cytometry to characterize drug loading in liposomes

[0440] refer to Figure 55 Flow cytometry (e.g., CytoFLEX nano) is used to characterize liposomes based on the amount of drug loaded into each liposome. For example, due to different refractive indices, flow cytometry characterizes liposomes based on the loaded biomolecules in each liposome.

[0441] Example 11: Using flow cytometry to characterize the expression levels of biomolecules

[0442] refer to Figure 56 Flow cytometry (e.g., CytoFLEX nano) is used to monitor the expression levels of biomolecules on the surface of biological particles, such as antigen expression levels in vaccine development applications.

[0443] Example 12: Using flow cytometry to characterize single-particle surface marker interactions

[0444] refer to Figure 57 Flow cytometry (e.g., CytoFLEX nano) is used to characterize single-particle surface marker interactions with their corresponding ligands: receptor-ligand, protein-DNA binding, etc.

[0445] Example 13: Biomarkers for characterizing low abundance

[0446] refer to Figure 58 Flow cytometry (e.g., CytoFLEX nano) is used to identify biomarkers with low abundance, such as for the early detection of cancer. Figure 58 As shown.

[0447] Example 14: Isolation of single cells using heterofunctional granules based on single-cell secretion

[0448] Flow cytometry (e.g., CytoFLEX nano) is used in vaccine response studies to isolate H1 influenza virus (H1IV) antibody-secreting cells. Blood is collected from vaccinated patients. B cells from the patient's blood are enriched and stained with anti-IgD, anti-CD3, anti-CD19, anti-CD38, and anti-CD14. Antibody-secreting cells are captured using beads and separated from other B cells. The isolated individual H1IV antibody-secreting cells are separated from other antibody-secreting cells by flow cytometry (e.g., CytoFLEX nano) using FACS. The sorted H1IV antibody-secreting cells are incubated and then validated for secretion using ELISA.

[0449] Example 15: Isolation of single cells using heterofunctional granules based on single-cell secretion

[0450] Flow cytometry (e.g., CytoFLEX nano) is used to quantify secreted proteins. Janus particles are used to capture target-cell secretion proteins on beads; for example, Janus particles are used to target Jurkat cells via anti-CD3 / 38. Targeting Jurkat cells with anti-CD3 / 38 on the Janus particles triggers Jurkat cell activation, causing Jurkat cells to secrete IL-2. The IL-2 secreted by Jurkat cells binds to anti-IL-2 on the surface of the Janus particles, and then to fluorescent anti-IL-2. Secretory cells are isolated at various secretion levels, e.g., low-secreting, medium-secreting, and high-secreting cells, using flow cytometry (e.g., CytoFLEX nano) via FACS. The isolated cell population is expanded, and secretion is confirmed by ELISA.

[0451] In another embodiment of Example 15, both IL-2 and VEGF secretion rates were used to isolate Jurkat cells.

[0452] Table 6 lists information on the biological materials used in the examples, unless otherwise stated herein.

[0453] Table 6: Biomaterials.

[0454]

[0455] Unless otherwise specified, the following staining scheme is used in the examples.

[0456] V5 virus staining:

[0457] ● 6.8 × 10^8 p / vial

[0458] ● Resuspended in 100 μL of W8 water = 6.8 × 10^6 p / μL

[0459] ● Stain with anti-v5 PE (5 μL virus + 5 μL PBS)

[0460] ● Rb pAb ab72480 batch GR3304043-4

[0461] ● 1 μL stock solution (0.1 mg / mL) + 9 μL PBS = 10 μg / mL

[0462] ● 4 μL of 10 μg / mL + 6 μL of PBS = Final Ab concentration 2 μg / mL

[0463] ● Mix ab and sample (final sample concentration: 1.7 × 10^6 p / ul)

[0464] ● Incubate at room temperature for 1 hour and dilute at a ratio of 1:1000 (finally 1:5000).

[0465] MV DAUDI MV dyeing:

[0466] ● 3.9 × 10^10 p / mL = 3.9 × 10^7 p / ul

[0467] ● Test titrations were performed at 1:100 (3.9 × 10^5 p / ul), 1:1000 (3.9 × 10^4 p / ul), 1:5000 (7.8 × 10^3 p / ul), and 1:10000 (3.9 × 10^3 p / ul). The 1:5000 titration was selected.

[0468] ● Start with a 1:5 dilution: 2 μL + 8 μL PBS = 10 μL sample

[0469] ● 1:2, where CD81 PE (2 ug / mL) = 10 μL sample + 9 μL PBS + 1 μL CD81 PE, 20 μG / mL

[0470] ● CD81 PE is 120 ug / mL, therefore 1.7 μL Ab + 8.3 μL PBS = 20 ug / mL CD81 PE

[0471] ● Stain in the dark at room temperature for 1 hour.

[0472] ● Dilute at a ratio of 1:500 to read

[0473] rEV preparation and staining:

[0474] ● Restore one bottle of rEV with 100 uL of ice-cold W-8 water.

[0475] ● Mix by moving the liquid up and down (without vortex).

[0476] ● Divide 10 μL into low-bonding Eppendorf tubes and store at -80°C.

[0477] ● For double staining:

[0478] - Prepare rEV at a 1:1000 ratio (concentration fixed once a batch number is obtained).

[0479] - Preparation of vFRed: The stock solution is 100× in DMSO. Dilute 1 μL to a total of 10 μL with vFRed diluent buffer to obtain a 10× working solution.

[0480] - Prepare 2× vFred solution: 2 μL 10× vFRed working solution + 8 μL PBS filtered through 20 nm.

[0481] - Mix 2× vFRed with 10 ul rEV

[0482] - Stain in the dark at room temperature for 1 hour.

[0483] Add CD81 APC or CD81 PB at a final concentration of 2 ug / mL.

[0484] ● For single staining:

[0485] - Prepare rEV at a 1:1000 ratio (concentration fixed once a batch number is obtained).

[0486] - CD81 APC, CD81 PB, and CD81 PE were prepared in a single tube at 4 μg / mL in PBS filtered through a 20 nm filter, for a total of 10 μl.

[0487] Mix each tube with 10 ul rEV

[0488] - Stain in the dark at room temperature for 1 hour.

[0489] - Dilute to a certain concentration and run

[0490] - Run at 3 ul / min for 2 minutes

[0491] PPP EV staining:

[0492] ● Rotate at maximum speed for 10 minutes at room temperature, using the antibody requirement (+2).

[0493] ● Prepare antibodies at a concentration of 2 μg / ml in a total of 25 μL of PBS filtered through a 20 nm filter.

[0494] ● Incubate with the sample: 25 μL PPP EV and 25 μL antibody

[0495] ● Incubate all samples at room temperature for 1 hour

[0496] ● Dilute at a ratio of 1:500 in PBS filtered through a 20 nm filter (if the final result is 1:1000).

[0497] ● Run until the time graph is a table, then record for 1 minute.

[0498] Unless otherwise stated, the following PPP EV preparation protocols are used with fresh blood.

[0499] ● Centrifuge whole blood (human whole blood into a K3-EDTA vacuum container) at 160 g for 5 minutes at room temperature.

[0500] ● Collect the supernatant and label it "PPP"; avoid collecting platelet-rich plasma near the WBC layer.

[0501] ● Use a 5 ml syringe to filter the PPP fraction using a 0.2 μm filter to remove residual platelets and large particles.

[0502] ● After IFU fabrication, the filtered PPP is passed through an Izon qEV single SEC column for retention, thereby removing a large number of proteins and lipoproteins smaller than 70 nm.

[0503] ● Rinse the column with 2 mL of 1X PBS

[0504] ● Apply 150-200 μl PPP

[0505] ● Collect 200 μl fractions for subsequent analysis.

[0506] ● Test fractions 4-9 by running fractionation on CytoFLEX nano; this step will determine the fractions to be stained, and this will be the final dilution run on the instrument.

[0507] ● Based on the following selection criteria:

[0508] - High EV content. Minimum dilution of 1:200 after staining is recommended to minimize antibody background noise and aggregation; a good post-staining dilution range is 1:200 to 1:1000.

[0509] - Indicates different EV sizes, including small and large EVs.

[0510] Example 15: Flow cytometer instrument calibration to define calibration factors

[0511] Figure 65An instrument calibration method based on an identified half-angle-limited calibration factor is demonstrated. Empirical data were collected from multiple polystyrene-sized reference beads with known sizes and RIs. The top row plot shows the purple VSSC1 and VSSC2 histograms for two sets of polystyrene-sized reference beads, comprising a low-size group with nominal sizes of 40 nm, 80 nm, 103 nm, and 141 nm; and a high-size group with nominal sizes of 141 nm, 304 nm, 600 nm, and 1020 nm. (nanoViS nanoscale size standard, D03231, Beckman Coulter) Several tables in the upper right show the nominal reference bead size, CV%, and RI at four wavelengths of 405, 488, 561, and 633 nm. Next, half-angles were determined for the three instruments MP03, MP05, and MP07. Gain was independent, wavelength was independent, and particle size was the independent variable. The graph in the middle row shows the fit confidence % (goodness of fit) as a function of scattering angle; the half-collection angle is limited to the highest goodness of fit. Next, calibration factors are defined based on the identified half-angles. Gain is dependent, wavelength is dependent, and particle size is dependent. The table at the bottom shows the average calibration factors for all bead sizes at different scattering wavelengths for the three flow cytometers MP05, MP07, and MP03: VSSC1 (purple), BSSC (blue), YSSC (yellow), and RSSC (red). (Table 7)

[0512] Table 7: Calibration factors for three flow cytometers at four scattering wavelengths

[0513]

[0514] All compositions and methods disclosed and claimed herein can be prepared and performed according to this disclosure without excessive experimentation. While the compositions and methods of this disclosure have been described in conjunction with the foregoing illustrative embodiments, it will be apparent to those skilled in the art that changes, alterations, modifications, and variations can be made to the compositions, methods, and steps or the order of steps in the methods described herein without departing from the true concept, spirit, and scope of this disclosure. More specifically, it will be apparent that certain agents, additives, and ingredients similar in their physical, chemical, physiological, and / or site-occupancy properties can replace the agents, additives, and ingredients described herein while achieving the same or similar results. It will be apparent to those skilled in the art that all such similar substitutions and modifications are considered to be within the spirit, scope, and concept of this disclosure as defined by the claims appended below.

[0515] The following numbered clauses define other exemplary aspects and features of this disclosure:

[0516] Clause 1. A method for characterizing particles in a flow cytometer, the method comprising: irradiating the particle with one or more excitation beams at one or more wavelengths as the particle individually passes through an interrogation zone; collecting side-scattered light from the particle passing through the interrogation zone at multiple wavelengths in multiple channels; identifying the average intensity of the side-scattered light in each of the multiple channels; comparing the average intensity of the side-scattered light in each of the multiple channels with an average calibration factor determined in the same flow cytometer by a plurality of standard particles having known refractive indices and sizes; characterizing the particle based on the side-scattered light collected in the multiple channels; inputting one or more known parameters; inputting initial speculative parameters and parameter limits for the particle, and optionally parameter constraints; and calculating unknown parameters that minimize a loss function and satisfy the limits and constraints.

[0517] Clause 2. The method according to Clause 1, wherein determining the average calibration factor comprises irradiating calibration beads having known size and refractive index at one or more wavelengths; collecting side-scattered light from all wavelengths of interest; calculating the forward Mie scattering distribution for each input calibration bead diameter; integrating the Mie scattering distribution for each calibration bead diameter; calculating the calibration factor for each calibration bead size; calculating the goodness of fit for the current half-angle; reporting the collection half-angle with the highest goodness of fit; calculating the Mie scattering distribution at the half-angle identified in the previous step; and calculating the average calibration factor and CV for all calibration bead sizes for the half-collection angle of the previous step.

[0518] Clause 3. The method according to Clause 2, wherein the integration of the Mie scattering distribution is performed in 0.1-degree steps over 90 degrees ± the current collection half-angle or from 0 degrees to 90 degrees.

[0519] Clause 4. The method according to Clause 2 or 3, wherein the calculation of the forward Mie scattering distribution comprises input parameters selected from the group consisting of: bead diameter, bead refractive index at a given wavelength, medium refractive index, wavelength, incident light polarization, and detector polarization sensitivity; calculation of the distribution in the range of 0 degrees to 180 degrees, optionally in 0.1-degree steps; and output of the Mie scattering intensity as a function of the polar angle; and numerical integration of the forward Mie scattering intensity over an angular range defined by the presently proposed collection half-angle.

[0520] Clause 5. The method according to Clause 4 further comprises calculating a calibration factor, said calibration factor comprising determining the ratio of the scattering intensity measurement to the integrated forward Mie intensity to obtain the calibration factor.

[0521] Clause 6. The method according to any one of Clauses 2 to 5, wherein the goodness of fit is calculated as 1-CV of all calibration factors, wherein the CV is calculated based on a set of calibration factors: CV = Standard deviation (all calibration factors) / Average (all calibration factors).

[0522] Clause 7. The method according to any one of Clauses 1 to 6, wherein the calculation of the unknown parameter includes employing a minimization algorithm and initial guesses of parameters, parameter bounds, and optionally parameter constraints.

[0523] Clause 8. The method described in Clause 7, wherein the minimization algorithm is the Powell algorithm or the constrained trust zone minimization algorithm.

[0524] Clause 9. The method according to any one of Clauses 6 to 8, wherein the loss function is: Loss function = [(integrated Mie signal calculation value) × (calibration factor) – signal measurement value].

[0525] Clause 10. The method according to any one of Clauses 6 to 9, wherein the calculated value of the Mie signal follows... Figure 60 The particle parameters listed vary.

[0526] Clause 11. The method according to any one of Clauses 1 to 10, wherein the particles, optionally nanoparticles, are characterized as extracellular vesicles, lipid nanoparticles, viral particles, virus-like particles, or protein aggregates.

[0527] Clause 12. The method according to Clause 11, wherein the particles are characterized as lipid nanoparticles.

[0528] Clause 13. The method according to any one of Clauses 1 to 12, wherein the particles, optionally nanoparticles, are characterized based on the amount of protein load on the surface of the particles or the amount of therapeutic payload within the particles.

[0529] Clause 14. The method according to any one of Clauses 1 to 13, wherein the particles, optionally nanoparticles, are characterized based on the detection of empty therapeutic payload, partial therapeutic payload, or complete therapeutic payload.

[0530] Clause 15. The method according to any one of Clauses 1 to 14, wherein the plurality of channels comprises a plurality of side-scattering channels and optionally a plurality of fluorescence channels.

[0531] Clause 16. The method according to any one of Clauses 1 to 15, wherein the particles are characterized based on one or more properties identified in the plurality of side scattering channels and optionally the plurality of fluorescence channels.

[0532] Clause 17. The method according to any one of Clauses 1 to 16, further comprising: sorting the particles based on the characterization of the particles, optionally wherein the particles are sorted to increase the purity or yield of the particles after centrifugation, optionally wherein the particles are sorted based on their secretion.

[0533] Clause 18. The method according to any one of Clauses 8 to 17, wherein the empty and intact particles can be distinguished based on a detectable increase in scattering.

[0534] Clause 19. The method according to any one of Clauses 1 to 18, wherein the excitation light is selected from one or more, two or more of the following: VSSC1, VSSC2, BSSC, YSSC and RSSC.

[0535] Clause 20. The method according to Clause 18 or 19, wherein the increase in scattering is directly related to the ability of the particle to transduce cells.

[0536] Clause 21. The method according to Clause 20, wherein the cells are selected from the group consisting of patient cells and production cell lines, optionally wherein the production cell line is a CHO cell line.

[0537] Clause 22. The method according to any one of Clauses 1 to 21, wherein the unknown parameter is selected from the group consisting of: the solid particle size of the particle, the refractive index of the solid particle, the shell thickness of the core-shell particle, the core diameter of the core-shell particle, the shell refractive index of the core-shell particle, and / or the core refractive index of the core-shell particle.

[0538] Clause 23. The method according to any one of Clauses 1 to 22, further comprising, prior to particle characterization, using a flow cytometry method to improve particle sample preparation, the method comprising: performing a sample preparation technique on a biological sample; performing flow cytometry analysis on the sample after sample preparation; and adjusting one or more parameters of the sample preparation technique based on the flow cytometry analysis.

[0539] Clause 24. The method according to Clause 23, wherein the sample preparation technique is selected from the group consisting of centrifugation, membrane filtration, precipitation and chromatographic purification.

[0540] Clause 25. The method according to Clause 23 or 24, wherein one or more parameters of the centrifugation are selected from the group consisting of centrifugation speed, duration and temperature, optionally for increasing the purity or yield of one or more target particles in the sample.

[0541] Clause 26. The method according to Clause 23 or 24, wherein the membrane filtration is selected from the group consisting of: ultrafiltration, tangential flow filtration, dialysis, and tangential flow filtration (TFF), and

[0542] Clause 27. The method described in Clause 23 or 24, wherein the chromatographic purification is selected from the group consisting of: size exclusion chromatography (SEC), immunoaffinity capture, affinity chromatography and ion exchange chromatography.

[0543] Clause 28. The method according to any one of Clauses 1 to 27, wherein the particles are nanoparticles.

[0544] Clause 29. The method according to any one of Clauses 1 to 28, wherein the particles or nanoparticles are biological particles or bionanoparticles.

[0545] Clause 30. The method according to any one of Clauses 1 to 29, wherein the known parameters are selected from the group consisting of: the solid particle size of the nanoparticles, the refractive index of the solid particles, the shell thickness of the core-shell particles, the core diameter of the core-shell particles, the shell refractive index of the core-shell particles, and / or the core refractive index of the core-shell particles.

[0546] Clause 31. The method according to any one of Clauses 1 to 30, wherein the unknown parameter is selected from the group consisting of: the solid particle size of the nanoparticle, the refractive index of the solid particle, the shell thickness of the core-shell particle, the core diameter of the core-shell particle, the shell refractive index of the core-shell particle, and / or the core refractive index of the core-shell particle.

[0547] Clause 32. The method according to any one of Clauses 1 to 31, wherein the known parameter and the unknown parameter are different.

[0548] Clause 33. A system for performing a method of characterizing particles in a flow cytometer, the method comprising:

[0549] As the particle individually passes through the interrogation zone, the particle is irradiated with one or more excitation beams at one or more wavelengths; side-scattered light from the particle passing through the interrogation zone at multiple wavelengths is collected in multiple channels; the average side-scattered light intensity in each of the multiple channels is identified; the average side-scattered light intensity in each of the multiple channels is compared with an average calibration factor determined in the same flow cytometer by multiple standard particles having known refractive indices and sizes; the particle is characterized based on the side-scattered light collected in the multiple channels; one or more known parameters are input;

[0550] Input initial speculative parameters and parameter bounds for the particles, as well as optional parameter constraints; and calculate the unknown parameters that minimize the loss function and satisfy the bounds and constraints.

[0551] Clause 34. The system according to Clause 33, wherein the method comprises the method according to any one of Clauses 1 to 32.

Claims

1. A method for characterizing particles in a flow cytometer, the method comprising: When the particle passes through the interrogation zone alone, the particle is irradiated with one or more excitation beams at one or more wavelengths; Lateral scattered light from the particles passing through the interrogation zone at multiple wavelengths is collected in multiple channels; Identify the average lateral scattered light intensity in each of the plurality of channels; Calculate the average calibration factor determined by multiple standard particles with known refractive indices and sizes in the same flow cytometer; The particles are characterized based on the side-scattered light collected in the plurality of channels; Input one or more known parameters; Input the initial guess parameters and parameter limits of the particle, and optionally parameter constraints; and Calculate the unknown parameters that minimize the loss function and satisfy the bounds and constraints, optionally. The known parameters are selected from the group consisting of: solid particle size of nanoparticles, refractive index of solid particles, shell thickness of core-shell particles, core diameter of core-shell particles, shell refractive index of core-shell particles, and / or core refractive index of core-shell particles.

2. The method of claim 1, wherein determining the average calibration factor comprises Irradiate a calibration bead with a known size and refractive index at one or more wavelengths; Collect side-scattered light from all wavelengths of interest; Calculate the forward Mie scattering profile for each input calibration bead diameter; Integrate the Mie scattering distribution over the diameter of each calibration bead; Calculate the calibration factor for each calibration bead size; Calculate the goodness of fit for the current half-angle; The report shows the collection half-angle with the highest goodness of fit; Calculate the Mie scattering distribution at the half-angle identified in the previous step; as well as For the half-collection angle of the previous step, calculate the average calibration factor and CV for all calibration bead sizes.

3. The method according to claim 2, wherein the integration of the Mie scattering distribution is performed in 0.1-degree steps over a range of 90 degrees ± the current collection half-angle or 0 degrees to 90 degrees.

4. The method according to claim 2 or 3, wherein the calculation of the forward Mie scattering distribution comprises Input parameters, which are selected from the following group: bead diameter, bead refractive index at a certain wavelength, medium refractive index, wavelength, incident light polarization, and detector polarization sensitivity; Calculate the distribution in the range of 0 degrees to 180 degrees, optionally in steps of 0.1 degrees; as well as Output the Mie scattering intensity as a function of polar angle; as well as The forward Mie scattering intensity is numerically integrated over the angular range defined by the currently proposed collection half-angle. The method optionally further includes calculating a calibration factor, the calculated calibration factor comprising... The ratio of the measured scattering intensity to the integrated forward Mie intensity is determined to obtain the calibration factor.

5. The method according to any one of claims 2 to 4, wherein The goodness of fit is calculated as the 1-CV of all calibration factors, and The CV is calculated based on a set of calibration factors: CV = Standard deviation (all calibration factors) / Mean (all calibration factors).

6. The method according to any one of claims 1 to 5, wherein calculating the unknown parameter comprises The algorithm employs a minimization approach, along with initial parameter guesses, parameter bounds, and optional parameter constraints. Optionally, the minimization algorithm is Powell's algorithm or a constrained trust region minimization algorithm, and Further, optionally, the loss function is: Loss function = [(integrated Mie signal calculation) × (calibration factor) – signal measurement], wherein the calculated Mie signal varies with the particle parameters listed in Figure 60.

7. The method according to any one of claims 1 to 6, wherein the particles, optionally the nanoparticles, are characterized as extracellular vesicles, lipid nanoparticles, viral particles, virus-like particles, or protein aggregates. Optionally, the particles are characterized based on the amount of protein loading on the surface of the nanoparticles or the amount of therapeutic payload within the nanoparticles.

8. The method according to any one of claims 1 to 7, wherein the particles, optionally nanoparticles, are characterized based on the detection of empty therapeutic payload, partial therapeutic payload, or complete therapeutic payload.

9. The method according to any one of claims 1 to 8, wherein the plurality of channels comprises a plurality of side-scattering channels and a plurality of fluorescence channels, optionally wherein the particles are characterized based on one or more properties identified in the plurality of side-scattering channels and optionally the plurality of fluorescence channels.

10. The method according to any one of claims 1 to 9, further comprising: The particles are sorted based on their characterization, optionally to increase the purity or yield of the particles after centrifugation, optionally wherein the particles are sorted based on their secretion.

11. The method according to any one of claims 1 to 10, wherein the particles are distinguishable based on a detectable increase in scattering. Optionally, the excitation light is selected from one or more, two or more, or two or more of the following: VSSC1, VSSC2, BSSC, YSSC, and RSSC. Optionally, the increase in scattering is directly related to the ability of the particle to transduce cells. Optionally, the cells are selected from the group consisting of patient cells and production cell lines, and optionally, the production cell line is a CHO cell line.

12. The method according to any one of claims 1 to 11, wherein the unknown parameter is selected from the group consisting of: the solid particle size of the nanoparticle, the refractive index of the solid particle, the shell thickness of the core-shell particle, the core diameter of the core-shell particle, the shell refractive index of the core-shell particle, and / or the core refractive index of the core-shell particle.

13. The method according to any one of claims 1 to 12, further comprising, prior to particle characterization, using flow cytometry to improve particle sample preparation, said method comprising: Perform sample preparation techniques on biological samples; After sample preparation, the samples were analyzed by flow cytometry; and One or more parameters of the sample preparation technique are adjusted based on the flow cytometry analysis, wherein the sample preparation technique is optionally selected from the group consisting of: centrifugation, membrane filtration, precipitation, and chromatographic purification. Optionally, one or more parameters of the centrifugation are selected from the group consisting of: centrifugation speed, duration, and temperature. Optionally, to increase the purity or yield of one or more target particles in the sample, further optionally, the membrane filtration is selected from the group consisting of: ultrafiltration, tangential flow filtration, dialysis, and tangential flow filtration (TFF), and Further optionally, the chromatographic purification method described herein is selected from the group consisting of: size exclusion chromatography (SEC), immunoaffinity capture, affinity chromatography, and ion exchange chromatography.

14. The method according to any one of claims 1 to 13, wherein the particles are nanoparticles, and optionally the nanoparticles are bio-nanoparticles.

15. A system for performing the method according to any one of claims 1 to 14.

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