Control, reference, and calibration of cytometry using hydrogel particles

Abstract

Non-transient processor-readable media, when executed by the processor, store instructions causing the processor to receive a first data array associated with a first site-metric instrument at a first time point. The first data array includes a set of scattered signal data points, each containing data representing at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low side scattered signal output, and at least one high side scattered signal output. At a second time point following the first, the site-metric instrument parameters are adjusted based on the first data array, and / or the first site-metric measurement of the first site-metric instrument is compared with a second site-metric measurement of a different site-metric instrument, based on at least one of the first data array or a second data array associated with a second site-metric instrument.

Description

Cross - reference to related applications

[0001] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 496,017, filed on April 13, 2023, and U.S. Provisional Patent Application No. 63 / 539,496, filed on September 20, 2023, the contents of each of which are hereby incorporated by reference in their entirety. This application relates to U.S. Patent No. 10,753,846, issued on August 25, 2020, entitled "Hydrogel Particles with Tunable Optical Properties and Methods for Using the Same", the entire content of which is hereby incorporated by reference in its entirety for all purposes.

Technical Field

[0002] The present disclosure relates to cell analysis techniques such as flow cytometry, and more specifically, to the adjustment and cross - calibration of cell analysis instruments based on multi - population datasets related to hydrogel particles.

Background Art

[0003] Flow cytometry, and high - throughput cell measurement analysis (e.g., high - content imaging) are techniques that enable the rapid separation, counting, and characterization of individual cells and are routinely used in various applications in clinical and laboratory settings. Cytometry devices are known in the art and include commercially available devices for performing flow cytometry, and FACS, hematology, as well as high - content imaging.

[0004] Cytometers differ in hardware components and measurement principles, resulting in different scaling and sensitivity for acquiring scattering data. However, while there are widely adopted and commercially available multi-level reference materials for fluorescence, no such references exist for simultaneous forward scattering (FSC) and side scattering (SSC), making it particularly difficult to standardize scattering measurements between instruments. To date, the mainstream method is simply trial and error, measuring patient samples and manually adjusting settings until the results match. This is not only time-consuming but also biohazardous, increases sample variability, and is impractical for rare or expensive samples. Therefore, a new approach is needed. [Overview of the Initiative]

[0005] In some embodiments, non-transient processor-readable media stores instructions that, when executed by the processor, cause the processor to receive a first data array associated with a first site-metric instrument at a first time point. The first data array includes a set of scattered signal data points, each containing data representing at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low side scattered signal output, and at least one high side scattered signal output. At a second time point following the first time point, site-metric instrument parameters are adjusted based on the first data array, and / or a first site-metric measurement of the first site-metric instrument is compared with a second site-metric measurement of a different site-metric instrument, based on at least one of the first data array or a second data array associated with the second site-metric instrument.

[0006] In some embodiments, non-transient processor-readable media store instructions that, when executed by the processor, cause the processor to identify a first plurality of scattered signal data points generated by a first flow cytometer. The first plurality of scattered signal data points include: (i) data representing forward scattering at a first signal output level; (ii) data representing forward scattering at a second signal output level greater than the first signal output level; (iii) data representing forward scattering at a third signal output level greater than the second signal output level; (iv) data representing side scattering at a fourth signal output level; (v) data representing side scattering at a fifth signal output level greater than the fourth signal output level; and (vi) data representing side scattering at a sixth signal output level greater than the fifth signal output level. The non-temporary processor-readable media, when executed by the processor, also causes the processor to automatically adjust the control parameters of a first flow cytometer based on a first plurality of scattered signal data points, or to match a first set of at least one measurement of the first flow cytometer with a second set of at least one measurement of a second flow cytometer different from the first flow cytometer, and it stores instructions based on at least one of the first plurality of scattered signal data points or the second plurality of scattered signal data points associated with the second flow cytometer. [Brief explanation of the drawing]

[0007] [Figure 1A] Figures 1A-1B are flow cytometer images plotting forward scattering (FSC) versus side scattering (SSC) in several embodiments, showing nine unique signal clusters. [Figure 1B] Figures 1A-1B are flow cytometer images plotting forward scattering (FSC) versus side scattering (SSC) in several embodiments, showing nine unique signal clusters. [Figure 1C] Figure 1C shows a flow cytometer image plotting the forward scattering region (FSC-A) against the forward scattering height (FSC-H). [Figure 2] Figure 2 shows a system for tuning and / or cross-calibrating a flow cytometer using scatter grid-based scattering data (e.g., a multi-population dataset generated using hydrogel particles), according to several embodiments. [Figure 3A] Figures 3A, 4A, and 5A show exemplary arrays of ScatterGrid-based scattering data according to several embodiments. [Figure 3B] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 3C] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 4A] Figures 3A, 4A, and 5A show exemplary arrays of ScatterGrid-based scattering data according to several embodiments. [Figure 4B] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 4C] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 5A] Figures 3A, 4A, and 5A show exemplary arrays of ScatterGrid-based scattering data according to several embodiments. [Figure 5B] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 5C] Figures 3B-3C, 4B-4C, and 5B-5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments. [Figure 6A]Figures 6A–6C show exemplary scatter grid-based scattering datasets generated by three different flow cytometers (a Cytek® NL-2000 flow cytometer, a Cytek® Aurora flow cytometer, and a Beckman Coulter® Cytoflex 5 flow cytometer, respectively) in several embodiments. [Figure 6B] Figures 6A–6C show exemplary scatter grid-based scattering datasets generated by three different flow cytometers (a Cytek® NL-2000 flow cytometer, a Cytek® Aurora flow cytometer, and a Beckman Coulter® Cytoflex 5 flow cytometer, respectively) in several embodiments. [Figure 6C] Figures 6A–6C show exemplary scatter grid-based scattering datasets generated by three different flow cytometers (a Cytek® NL-2000 flow cytometer, a Cytek® Aurora flow cytometer, and a Beckman Coulter® Cytoflex 5 flow cytometer, respectively) in several embodiments. [Figure 7A] Figures 7A–7B show exemplary scatter grid-based scattering datasets generated by two different flow cytometers (BD® Biosciences FAkmyric® flow cytometer and Beckman Coulter® Cytoflex LX flow cytometer, respectively) in several embodiments. [Figure 7B]Figures 7A–7B show exemplary scatter grid-based scattering datasets generated by two different flow cytometers (BD® Biosciences FAkmyric® flow cytometer and Beckman Coulter® Cytoflex LX flow cytometer, respectively) in several embodiments. [Figure 8A] Figures 8A-8C show ScatterGrid-based scattering data generated by a single Cytek® NL-2000 flow cytometer at three different time points in one embodiment: before preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, after preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, and after adjustment of the FSC gain and preventative maintenance are performed on the Cytek® NL-2000 flow cytometer. [Figure 8B] Figures 8A-8C show ScatterGrid-based scattering data generated by a single Cytek® NL-2000 flow cytometer at three different time points in one embodiment: before preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, after preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, and after adjustment of the FSC gain and preventative maintenance are performed on the Cytek® NL-2000 flow cytometer. [Figure 8C] Figures 8A-8C show ScatterGrid-based scattering data generated by a single Cytek® NL-2000 flow cytometer at three different time points in one embodiment: before preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, after preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, and after adjustment of the FSC gain and preventative maintenance are performed on the Cytek® NL-2000 flow cytometer. [Figure 9]Figure 9A is a plot showing first data generated by a first flow cytometer according to one embodiment, the first data including scatter grid-based scattering data, data generated using TruCyte TBNK biomarker mimetic (synthetic cell), and data generated using biological cells. Figure 9B is a plot showing second data generated by a second flow cytometer (Cytek® Aurora flow cytometer) according to one embodiment, the second data including scatter grid-based scattering data, and the plot also shows two populations of hydrogel particles, meaning they mimic the forward and side scattering signals of a biological sample analyzed on the first flow cytometer. [Figure 10] Figure 10 is a flowchart illustrating a method for tuning a site metric instrument and / or correlating site metric measurements based on scatter grid-based or scatter bridge-based scattering data, according to several embodiments. [Figure 11] Figure 11 is a flowchart illustrating a method, according to several embodiments, for automatically adjusting the control parameters of a flow cytometer based on scatter grid-based or scatter bridge-based scattering data, and / or for correlating flow cytometer measurements. [Figure 12A] Figures 12A to 12C show graphical interpretations of implementations of automatic adjustment of flow cytometer control parameters and / or matching of flow cytometer measurements according to several embodiments. [Figure 12B] Figures 12A to 12C show graphical interpretations of implementations of automatic adjustment of flow cytometer control parameters and / or matching of flow cytometer measurements according to several embodiments. [Figure 12C] Figures 12A to 12C show graphical interpretations of implementations of automatic adjustment of flow cytometer control parameters and / or matching of flow cytometer measurements according to several embodiments. [Figure 13]Figure 13 is a graphical display of a flow cytometry scatter profile of ScatterBridge-based scatter data that generates 3×3 FSC / SSC signals on a Cytek® Aurora flow cytometer. [Figure 14A] Figure 14A is a histogram of forward scatter of a selected population of ScatterBridge-based scatter data measured on a Cytek® Aurora flow cytometer. [Figure 14B] Figure 14B is a histogram of side scatter of a selected population of ScatterBridge-based scatter data measured on a Cytek® Aurora flow cytometer. [Figure 15A] Figures 15A and 15B are graphical displays of flow cytometry scatter profiles of ScatterBridge-based scatter data. [Figure 15B] Figures 15A and 15B are graphical displays of flow cytometry scatter profiles of ScatterBridge-based scatter data. [Figure 16] Figure 16 is a graphical display of a flow cytometry scatter profile of ScatterBridge-based scatter data (S) overlaid with CAR-T cells (1) and THP-1 cells (2) on a Cytek® Aurora flow cytometer. [Figure 17] Figure 17 is a flowchart of an automated gating method for identification of cell populations according to some embodiments. [Figure 18A] Figures 18A - 18D are graphical displays of automated gating according to some embodiments. [Figure 18B] Figures 18A - 18D are graphical displays of automated gating according to some embodiments. [Figure 18C] Figures 18A - 18D are graphical displays of automated gating according to some embodiments. [Figure 18D]Figures 18A to 18D show graphic representations of automatic gating according to several embodiments. [Figure 19] Figure 19 is a graph showing the relative positions of the biological cells forming Table 1 with respect to the ScatterBridge. [Modes for carrying out the invention]

[0008] A patent or application file must include at least one drawing in color. A copy of this patent or patent application publication, including the color drawing, will be provided by the Patent Office upon request and payment of the required fees.

[0009] It should be understood that the drawings are for illustrative purposes only and are not intended to limit the scope of the subject matter described herein.

[0010] Figures 1A–1B are flow cytometer images plotting forward scattering (FSC) versus side scattering (SSC) in several embodiments, showing nine unique signal clusters. Figure 1C shows a flow cytometer image plotting forward scattering region (FSC-A) versus forward scattering height (FSC-H). The nine unique signal clusters shown in the FSC-A versus FSC-H plots of Figures 1A–1B, which appear as three unique signal clusters in the FSC-A versus FSC-H plots of Figure 1C, can be generated by a set of hydrogel particles referred to herein as a “ScatterGrid” or “ScatterBridge”. The unique signal clusters may be based on the specific composition and size of the hydrogel particles that generate the associated scattering. For example, while ScatterGrid and ScatterBridge each generate nine unique signal populations that can be used in the same manner discussed herein, ScatterGrid generally provides an applicable signal array based on the composition and size of a fixed set of hydrogel particles, while ScatterBridge provides an adaptable signal array, and the composition and size of the hydrogel particles resulting in the signal array may be based on the instrument's gain settings and parameters, as well as / or the target cell population, to facilitate its cytometry. ScatterBridge allows for tuning the “linear” range of the scattering profile to suit a particular use case. Figure 1A shows ScatterGrid-based scattering data in a unique signal population. Figures 1B and 1C show ScatterBridge-based scattering data.

[0011] Figure 2 shows a system for tuning and / or cross-calibrating a flow cytometer using scatter grid-based scattering data (e.g., a multi-population dataset generated using hydrogel particles), according to several embodiments.

[0012] Figures 3A, 4A, and 5A show exemplary arrays of ScatterGrid-based scattering data according to several embodiments, each sequentially annotating subsets of data generated using hydrogel particles (e.g., beads) of varying average diameters (10 micrometers (μm), 20 μm, and 25 μm, respectively). In other words, the three gated signal sets in Figure 3A are low forward scattering datasets generated by hydrogel particles with an average diameter of 10 μm. The three gated signal sets in Figure 4A are medium (or higher, if only two scattering levels are available) forward scattering datasets generated by hydrogel particles with an average diameter of 20 μm. The three gated signal sets in Figure 5A are high forward scattering datasets generated by hydrogel particles with an average diameter of 25 μm.

[0013] Figures 3B–3C, 4B–4C, and 5B–5C show exemplary arrays of scatterbridge-based scattering data according to several embodiments, each sequentially annotating subsets of data generated using hydrogel particles (e.g., beads) of varying average diameters (10 micrometers (μm), 14 μm, and 18 μm, respectively). In other words, the three gated signal sets in Figures 3B–3C are low forward scattering datasets generated by hydrogel particles with an average diameter of 10 μm. The three gated signal sets in Figures 4B–4C are medium (or higher, if only two scattering levels are available) forward scattering datasets generated by hydrogel particles with an average diameter of 14 μm. Figures 5B–5C show three gated signal sets, high forward scattering datasets generated by hydrogel particles with an average diameter of 18 μm.

[0014] Figures 6A–6C show exemplary scatterGrid-based scattering datasets generated by three different flow cytometers (a Cytek® NL-2000 flow cytometer, a Cytek® Aurora flow cytometer, and a Beckman Coulter® Cytoflex 5 flow cytometer, respectively) according to several embodiments, each scatterGrid-based scattering dataset containing nine gate populations (or gated hydrogel populations). As used herein, “gated population” may refer to a sub-selected set of data points (for example, the rectangular boxes in Figures 6A–6C are “gates” representing populations of events).

[0015] Figures 7A–7B show exemplary scatter grid-based scattering datasets generated by two different flow cytometers (a BD® Biosciences FAkmyric® flow cytometer and a Beckman Coulter® Cytoflex LX flow cytometer, respectively) in several embodiments, each containing nine sets (sub-selected sets of data points).

[0016] Figures 8A-8C show ScatterGrid-based scattering data generated by a single Cytek® NL-2000 flow cytometer at three different time points in one embodiment: before preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, after preventative maintenance is performed on the Cytek® NL-2000 flow cytometer, and after adjustment of the FSC gain and preventative maintenance are performed on the Cytek® NL-2000 flow cytometer.

[0017] Figure 9A is a plot showing first data generated by a first flow cytometer according to one embodiment, the first data including ScatterGrid-based scattering data, data generated using TruCyte TBNK biomarker mimetic (synthetic cell), and data generated using biological cells.

[0018] Figure 9B is a plot showing second data generated by a second flow cytometer (Cytek® Aurora flow cytometer) according to one embodiment, the second data including scatter grid-based scattering data, and the plot also shows two populations of hydrogel particles, which means they mimic the forward and side scattering signals of a biological sample analyzed on the first flow cytometer.

[0019] Figure 10 is a flowchart illustrating a method for tuning a site metric instrument and / or correlating site metric measurements based on scatter grid-based or scatter bridge-based scattering data, according to several embodiments.

[0020] Figure 11 is a flowchart illustrating a method, according to several embodiments, for automatically adjusting the control parameters of a flow cytometer based on scatter grid-based or scatter bridge-based scattering data, and / or for correlating flow cytometer measurements.

[0021] Figures 12A to 12C show graphical interpretations of implementations of automatic adjustment of flow cytometer control parameters and / or matching of flow cytometer measurements according to several embodiments.

[0022] Figure 13 is a graphical representation of the flow cytometry scattering profile for scatterbridge-based scattering data generating a 3x3 FSC / SSC signal on a Cytek® Aurora flow cytometer.

[0023] Figure 14A is a histogram of forward scattering for a selected population of scatterbridge-based scattering data measured on a Cytek® Aurora flow cytometer, and Figure 14B is a histogram of side scattering for a selected population of scatterbridge-based scattering data measured on a Cytek® Aurora flow cytometer. As shown in Figures 14A and 14B, the selected population of scatterbridge-based scattering data generates a multilevel signal.

[0024] Figures 15A and 15B are graphical representations of flow cytometry scattering profiles of scatterbridge-based scattering data, generating 3x3 FSC / SSC signals on a Beckman Coulter® CytoFLEX S (Figure 15A) and a BD® FACSLyric® flow cytometer (Figure 15B). Figures 15A and 15B demonstrate inter-instrument variability that can be corrected and / or controlled by using scatterbridge-based scattering data.

[0025] Figure 16 is a graphical representation of flow cytometry scattering profiles of ScatterBridge-based scattering data (S) overlaid with CAR-T cells (1) and THP-1 cells (2) on a Cytek® Aurora flow cytometer.

[0026] Figure 17 is a flowchart of an automated gating method for identifying cell populations, according to several embodiments.

[0027] Figures 18A to 18D show graphic representations of automatic gating according to several embodiments.

[0028] Figure 19 is a graph showing the relative positions of the biological cells forming Table 1 with respect to the ScatterBridge.

[0029] Detailed description of the invention Beads can be assayed using flow cytometry and high-throughput cytometric analysis (e.g., for biochemical measurements). In some such implementations, a beam of light is directed onto a focused flow of liquid containing the beads. Multiple detectors are then directed to the point where the flow passes through the light ray, with one detector coinciding with the light beam (e.g., detecting forward scattering (FSC)) and several detectors perpendicular to the light beam (e.g., detecting side scattering (SSC)). FSC and SSC measurements are typically referred to as “passive optical properties.” For particles such as cells (e.g., human cells), FSC typically correlates with cell volume, and SSC typically correlates with the internal complexity of the particle, or particle size (e.g., nuclear shape, amount and type of cytoplasmic granules, or membrane roughness). As a result of these correlations, different specific cell types may exhibit different FSC and SSC, and consequently, cell types can be distinguished from one another based on passive optical properties in flow cytometry. These measurements—FSC and SSC—form the basis of cytometric analysis in clinical and research settings. Most synthetic or polymer products used in such cell analysis are made from (or substantially contain) polystyrene or latex opaque polymers, generally having fixed FSC and SSC values ​​based on the particle diameter itself. Thus, polystyrene particles of the same diameter are generally indistinguishable from one another based solely on passive optical properties (FSC and SSC).

[0030] Nevertheless, many flow cytometer vendors use quality control (QC) materials, such as beads, to track the signal drift of their instruments over time. Typically, for forward scattering (FSC) / side scattering (SSC) calibration, a single population of beads with known (or "characteristic") FSC / SSC locations on a light scattering plot is used as the reference material. However, this is equivalent to calibrating a linear instrument using a single-point calibration. While it provides some basic ideas about drift, this type of calibration typically fails to reveal degradation or inconsistencies in the linearity and scaling of scattering measurements.

[0031] Furthermore, different cytometers use different measurement principles, which typically result in different scaling and sensitivity. While some known multilevel reference materials (such as Spherotech Rainbow beads) exist for fluorescence, no such standards exist for forward and side scattering. In other words, known calibration beads for use in flow cytometry provide control over fluorescence intensity measurement and cell number, but not for forward and side scattering signal intensities. Therefore, cross-comparing results from different cytometers is particularly difficult, hindering scientific collaboration and increasing development time. Moreover, known methods for cross-comparing results from different cytometers simply involve testing and trial-and-error, using actual target samples and manually adjusting settings until the results match. This is not only time-consuming but also impractical for rare samples or populations with low event rates.

[0032] Several embodiments of this disclosure address the aforementioned challenges using a “ScatterGrid” or “ScatterBridge,” each defined herein as a multi-population signal output plot for cytometric control, reference, and calibration. ScatterGrids and ScatterBridges can be generated using a reagent set comprising hydrogel particles having distinct optical properties (e.g., combinations of multiple different levels of FSC and SSC) due to their composition and / or size. A single ScatterGrid or ScatterBridge may include representations of multiple different levels (e.g., high, medium, and low) of forward scattering signal output and multiple different levels (e.g., high, medium, and low) of side scattering signal output within a common plot.

[0033] In some embodiments, a ScatterGrid, or ScatterBridge, includes multiple scattering populations (i.e., scattering-related data / groupings of data points). For example, in some implementations, a ScatterGrid, or ScatterBridge, consists of nine unique, evenly spaced signal scattering populations generated in a single flow cytometer acquisition run. When a laboratory operator performs a ScatterGrid, or ScatterBridge measurement on a flow cytometer, they register / detect a signal matrix (e.g., a 3x3 signal matrix) output as a result of a single acquisition run, including recorded low-signal, medium-signal, and high-signal outputs for forward scattering and side scattering, respectively.

[0034] Figures 1A and 1B are plots of flow cytometry data showing forward scattering area (FSC-A) versus side scattering area (SSC-A), and Figure 1C is a plot of flow cytometry data showing FSC-A versus FSC-H. Figures 1A and 1B show nine unique signal sets (sub-selected sets of data points) in a single ScatterBridge according to several embodiments. Figure 1C shows three unique signal sets (sub-selected sets of data points) from the ScatterBridge. To achieve SSC variation, the nanoparticle loading in the formulation was varied. To achieve FSC variation, the monomer / crosslinker ratio and the hydrogel particle size (and optionally, the microfluidic channel size) were varied. As shown in Figures 1A and 1B, a 3x3 signal matrix containing nine signal groups is also intended, along with various other numbers of signal groups and matrix dimensions (e.g., signal groups such as 2, 3, 4, 5, 6, 7, 8, 10, 12, 24, and matrix dimensions such as 1x2, 1x3, 2x2, 4x1, 1x5, 2x3, 1x6, 1x7, 2x4, 1x8, 2x5, 1x10, 2x6, 3x4, 1x12, 5x4, 6x4).

[0035] In some embodiments, variations in SSC are achieved through the inclusion of nanoparticles in the hydrogel particles (or hydrogel particle formulation), including proteins in the hydrogel particles (or hydrogel particle formulation), and / or through the porosity of the hydrogel particles in the particle formulation. Alternatively, variations in FSC are achieved through adjustment of the gel fraction / polymer content of the hydrogel particles. Alternatively, one or more fluorophores, nucleic acids, functional groups, and / or biomarkers may be added to the hydrogel particles (or hydrogel particle formulation) to make them applicable to the desired application.

[0036] In some embodiments, the hydrogel particles of this disclosure include a material comprising a polymeric three-dimensional network that allows it to swell in the presence of water and contract in the absence of water (or due to a decrease in its amount), but not to dissolve in water. Swelling, i.e., absorption of water, is a result of the presence of hydrophilic functional groups attached to or dispersed in the polymeric network. Crosslinking between adjacent polymers results in the water-insoluble nature of these hydrogels. Crosslinking may result from chemical bonding (i.e., covalent bonding) or physical bonding (i.e., VanDer-Waal forces, hydrogen bonding, ionic forces, etc.). Synthetically prepared hydrogels may be prepared by polymerizing monomer materials to form a framework and crosslinking the framework with a crosslinking agent. A particular property of hydrogels is that the material retains its general shape whether dehydrated or hydrated. Thus, if a hydrogel has a nearly spherical shape in the dehydrated state, that hydrogel will be spherical in the hydrated state.

[0037] In one embodiment, the hydrogel particles disclosed herein contain about 30%, about 40%, about 50%, about 55%, about 60%, about 65%, about 70%, about 75%, about 80%, about 85%, about 90%, or about 95% water. In another embodiment, the hydrogel particles have a water content of about 10% to about 95%, or about 20% to about 95%, or about 30% to about 95%, or about 40% to about 95%, or about 50% to about 95%, or about 60% to about 95%, or about 70% to about 95%, or about 80% to about 95%.

[0038] In one embodiment, the hydrogel particles have one or more optical properties that are substantially similar to the optical properties of one or more target cells. For example, the hydrogel particles may have one or more optical properties of peripheral blood mononuclear cells, CAR-T cells, and THP-1 cells, but of course, any biological cell type and disease may be referenced when determining the ScatterGrid or ScatterBridge to be used. In one embodiment, the hydrogel particles have a predetermined optical property. In one embodiment, the optical property is SSC, FSC, fluorescence emission, or a combination thereof. The relationship between exemplary biological cells and the ScatterBridge is shown in Figure 19, demonstrating that the system described herein is adaptable to various biological cell types. Table 1 includes exemplary cell mimes and biological cells.

[0039] [Table 1]

[0040] In one embodiment, hydrogel particles are synthesized by polymerizing one or more monomers provided herein. The synthesis is carried out to form individual hydrogel particles. In one embodiment, monomer materials (monomers) are polymerized to form homopolymers. However, in another embodiment, copolymers of different monomer units (i.e., comonomers) are synthesized and used in the methods provided herein. The monomers or comonomers used in the methods and compositions described herein are, in one embodiment, difunctional monomers or contain difunctional monomers (comonomers are used). In one embodiment, hydrogel particles are synthesized in the presence of a crosslinking agent. In further embodiments, the hydrogel particles are synthesized in the presence of a polymerization initiator. The amount of monomer can be modified by the user of the present invention to obtain specific optical properties, for example, substantially similar to the optical properties of a target cell. In one embodiment, monomer components (or more) (i.e., monomers, comonomers, difunctional monomers, or combinations thereof, e.g., bis / acrylamide, allylamine, or secondary label / conjugate, or other comonomers providing chemical functionality for alginates at various crosslinking ratios) are present in about 10% to about 95% by weight of the hydrogel particles. In further embodiments, monomer components (or more) are present in about 15% to about 90% by weight of the hydrogel particles, or about 20% to about 90% by weight of the hydrogel particles.

[0041] Examples of various monomers and crosslinking chemicals available for use with the present invention are provided in the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf, the disclosures of which are incorporated by reference in their entirety for all purposes). For example, hydrogel particles of the present invention can be constructed using hydrazine (e.g., with NHS ester compounds) or EDC coupling reactions (e.g., with maleimide compounds).

[0042] In one embodiment, monomers for use with hydrogel particles provided herein are lactic acid, glycolic acid, acrylic acid, 1-hydroxyethyl methacrylate, ethyl methacrylate, 2-hydroxyethyl methacrylate (HEMA), propylene glycol methacrylate, acrylamide, N-vinylpyrrolidone (NVP), methyl methacrylate, glycidyl methacrylate, glycerol methacrylate (GMA), glycol methacrylate, ethylene glycol, fumaric acid, their derivatized versions, or combinations thereof. In one embodiment, one or more of the following monomers are used herein to form the hydrogel particles of the present disclosure: 2-hydroxyethyl methacrylate, hydroxyethoxyethyl methacrylate, hydroxydiethoxyethyl methacrylate, methoxyethyl methacrylate, methoxyethoxyethyl methacrylate, methoxydiethoxyethyl methacrylate, poly(ethylene glycol) methacrylate, methoxy-poly(ethylene glycol) methacrylate, methacrylic acid, sodium methacrylate, glycerol methacrylate, hydroxypropyl methacrylate, hydroxybutyl methacrylate, or combinations thereof.

[0043] In another embodiment, one or more of the following monomers are used herein to form adjustable hydrogel particles: phenyl acrylate, phenyl methacrylate, benzyl acrylate, benzyl methacrylate, 2-phenylethyl acrylate, 2-phenylethyl methacrylate, 2-phenoxyethyl acrylate, 2-phenoxyethyl methacrylate, phenylthioethyl acrylate, phenylthioethyl methacrylate, 2,4,6-tribromophenyl acrylate, 2,4,6-tribromophenyl methacrylate Pentabromophenyl acrylate, pentabromophenyl methacrylate, pentachlorophenyl acrylate, pentachlorophenyl methacrylate, 2,3-dibromopropyl acrylate, 2,3-dibromopropyl methacrylate, 2-naphthyl acrylate, 2-naphthyl methacrylate, 4-methoxybenzyl acrylate, 4-methoxybenzyl methacrylate, 2-benzyloxyethyl acrylate, 2-benzyloxyethyl methacrylate, 4-chlorophenoxyethyl acrylate, 4-chlorophenoxyethyl methacrylate acrylate, 2-phenoxyethoxyethyl acrylate, 2-phenoxyethoxyethyl methacrylate, N-phenylacrylamide, N-phenylmethacrylamide, N-benzylacrylamide, N-benzylmethacrylamide, N,N-dibenzylacrylamide, N,N-dibenzylacrylamide, N-diphenylmethylacrylamide, N-(4-methylphenyl)methylacrylamide, N-1-naphthylacrylamide, N-4-nitrophenylacrylamide, N-(2-phenylethyl)acrylamide, N-triphenyl Nylmethylacrylamide, N-(4-hydroxyphenyl)acrylamide, N,N-methylphenylacrylamide, N,N-phenylphenylethylacrylamide, N-diphenylmethylmethacrylamide, N-(4-methylphenyl)methylmethacrylamide, N-1-naphthylmethacrylamide, N-4-nitrophenylmethacrylamide, N-(2-phenylethyl)methacrylamide, N-triphenylmethylmethacrylamide, N-(4-hydroxyphenyl)methacrylamide, N,N-methylphenylmethacrylamide, N,N'-Phenylphenylethyl methacrylamide, N-vinylcarbazole, 4-vinylpyridine, 2-vinylpyridine.

[0044] Both synthetic monomers and biomonomers may be used in hydrogel particles provided herein to form synthetic hydrogels, biohydrogels, or hybrid hydrogels containing synthetic and biocomponents (e.g., peptides, proteins, monosaccharides, disaccharides, polysaccharides, primary amine sulfhydryls, carbonyls, carbohydrates, and carboxylic acids present on biomolecules). For example, proteins, peptides, or carbohydrates may be used as individual monomers to form hydrogels with or without synthetic monomers (or polymers), and in combination with chemically compatible comonomers and crosslinking chemicals (e.g., provided in the Thermo Scientific Crosslinking Technical Handbook titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf, the disclosure of which is incorporated in whole by reference for all purposes)). Suitable crosslinking chemicals include, but are not limited to, amines, carboxyls, and other reactive chemical side groups.

[0045] In general, polymers can be formed using any form of polymerization chemistry / method commonly known to those skilled in the art. In some embodiments, polymerization can be catalyzed by ultraviolet light-induced radical formation and reaction progression. In other embodiments, the hydrogel particles of this disclosure are produced by polymerization of acrylamide or acrylate. For example, the acrylamide in one embodiment is a polymerizable carbohydrate derivatized acrylamide. The specific binding of the acrylamide group to sugars readily accommodates a variety of monosaccharides and higher polysaccharides, such as naturally occurring synthetic polysaccharides or polysaccharides, including glycoproteins found in serum or tissues.

[0046] In some embodiments, the hydrogel particles comprise a monofunctional monomer polymerized with at least one difunctional monomer. Examples include, but are not limited to, the formation of polyacrylamide polymers using acrylamide and bisacrylamide (difunctional monomers). In other embodiments, the hydrogel particles provided herein comprise a difunctional monomer polymerized with a second difunctional monomer. Examples include, but are not limited to, the formation of polymers using mixed compositions containing compatible chemicals, such as acrylamide, bisacrylamide, and bisacrylamide structural homologs, which may contain a wide range of additional chemicals. The range of chemically compatible monomers, difunctional monomers, and mixed compositions will be apparent to those skilled in the art and will follow chemical reactivity principles known to those skilled in the art (see Thermo Handbook and Acrylamide Polymerization Handbook). For example, please refer to the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf), and the Polyacrylamide Emulsions Handbook (available at SNF Floerger, snf.com.au / downloads / Emulsion_Handbook E.pdf), where the disclosures of each are incorporated by reference in their entirety for all purposes.

[0047] In one embodiment, the hydrogel particles provided herein comprise a polymerizable monofunctional monomer, which is a monofunctional acrylic monomer. Non-limiting examples of monofunctional acrylic monomers for use herein include acrylamide; methacrylamide; N-alkylacrylamide, e.g., N-ethylacrylamide, N-isopropylacrylamide, or N-tert-butylacrylamide; N-alkylmethacrylamide, e.g., N-ethylmethacrylamide, or N-isopropylmethacrylamide; N,N-dialkylacrylamide, e.g., N,N-dimethylacrylamide, and N,N-diethylacrylamide; N-[(dialkylamino)alkyl]acrylamide, e.g., N -[3-dimethylamino)propyl]acrylamide, or N-[3-(diethylamino)propyl]acrylamide; N-[(dialkylamino)alkyl]methacrylamide, e.g., N-[3-dimethylamino)propyl]methacrylamide; (dialkylamino)alkyl acrylate, e.g., 2-(dimethylamino)ethyl acrylate, 2-(dimethylamino)propyl acrylate, or 2-(diethylamino)ethyl acrylate; and (dialkylamino)alkyl methacrylate, e.g., 2-(dimethylamino)ethyl methacrylate.

[0048] A bifunctional monomer is any monomer that can polymerize with a monofunctional monomer of the Disclosure to form a hydrogel, such as those described herein, further comprising a second functional group that may be involved in a second reaction, such as a fluorophore or a conjugation of a cell surface receptor (or its domain).

[0049] In some embodiments, the bifunctional monomer is selected from the group consisting of allylamine, allyl alcohol, allyl isothiocyanate, allyl chloride, and allyl maleimide.

[0050] Difunctional monomers can be difunctional acrylic monomers. Non-limiting examples of difunctional acrylic monomers include N,N'-methylenebisacrylamide, N,N'-methylenebismethacrylamide, N,N'-ethylenebisacrylamide, N,N'-ethylenebismethacrylamide, N,N'-propylenebisacrylamide, and N,N'-(1,2-dihydroxyethylene)bisacrylamide.

[0051] To adjust the refractive index while maintaining polymer density, higher-order branched and linear comonomers can be substituted in the polymer mixture. In some embodiments, the hydrogel particles contain molecules that modulate the optical properties of the hydrogel. Molecules that can alter the optical properties of the hydrogel are discussed further below.

[0052] In one embodiment, individual hydrogel particles, or a group thereof, contain a biodegradable polymer as a hydrogel monomer. In one embodiment, the biodegradable polymer is a poly(ester) based on polylactide (PLA), polyglycolide (PGA), polycaprolactone (PCL), and copolymers thereof. In one embodiment, the biodegradable polymer is a carbohydrate, or a protein, or a combination thereof. For example, in one embodiment, monosaccharides, disaccharides, or polysaccharides (e.g., glucose, sucrose, or maltodextrin), peptides, proteins (or their domains) are used as hydrogel monomers. Other biodegradable polymers include PHB-PHV class poly(hydroxyalkanoates), additional poly(esters), and natural polymers, such as starch, cellulose, and modified poly(saccharides) like chitosan. In another embodiment, the biocompatible polymer is an adhesive protein, cellulose, carbohydrate, starch (e.g., maltodextrin, 2-hydroxyethyl starch, alginic acid), dextran, lignin, polyamino acids, amino acids, or chitin. Such biodegradable polymers are commercially available, for example, from Sigma Aldrich (St. Louis, Missouri).

[0053] In one embodiment, the biomonomer is functionalized with acrylamide or acrylate. For example, in one embodiment, the polymerizable acrylamide-functionalized biomolecule is an acrylamide or acrylate-functionalized protein (e.g., acrylamide-functionalized collagen or a functionalized collagen domain), an acrylamide or acrylate-functionalized peptide, or an acrylamide or acrylate-functionalized monosaccharide, disaccharide, or polysaccharide.

[0054] Any monosaccharide, disaccharide, or polysaccharide (functionalized or otherwise) can be used as a hydrogel monomer. In one embodiment, acrylamide, or an acrylate-functionalized monosaccharide, disaccharide, or polysaccharide is used as a polymerizable hydrogel monomer. In one embodiment, a structural polysaccharide is used as a polymerizable hydrogel monomer. In further embodiments, the structural polysaccharide is arabinoxylan, cellulose, chitin, or pectin. In another embodiment, alginic acid (alginate) is used as a polymerizable hydrogel monomer. In yet another embodiment, glycosaminoglycans (GAGs) are used as polymerizable monomers in the hydrogels provided herein. In further embodiments, the GAG ​​is chondroitin sulfate, dermatan sulfate, keratin sulfate, heparin, heparin sulfate, or hyaluronic acid (also known in the art as hyaluron or hyaluronic acid) and is used as a polymerizable hydrogel monomer. The range of suitable biomonomers and their reactive chemicals is known to those skilled in the art and follows general chemical reactivity principles.

[0055] Examples of natural hydrogels include a variety of polysaccharides available from natural sources such as plants, algae, fungi, yeasts, marine invertebrates, and arthropods. Non-exclusive examples include agarose, dextran, chitin, cellulosic compounds, starch, and derivatized starch. These generally have repeating glucose units as the majority of their polysaccharide backbone. Crosslinking chemicals for such polysaccharides are well known in the art; see, for example, the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0056] In one embodiment, hyaluronan is used as a hydrogel monomer (either as a single monomer or as a comonomer). In one embodiment, hyaluronan is functionalized with, for example, acrylate or acrylamide. Hyaluronan is a high molecular weight GAG composed of disaccharide repeating units of N-acetylglucosamine and glucuronic acid, linked together via alternating β-1,4 and β-1,3 glycosidic bonds. In the human body, hyaluronic acid is found in several soft connective tissues, including skin, umbilical cord, synovial fluid, and vitreous fluid. Therefore, in one embodiment, hyaluronan is used as a hydrogel monomer when it is desirable to mimic one or more optical properties of skin cells, umbilical cord cells, or vitreous fluid cells. Hyaluronan can be derivatized with various reactive handlings depending on the desired crosslinking chemistry and other monomers used to form the hydrogel particles.

[0057] In yet another embodiment, chitosan, a linear polysaccharide composed of randomly distributed β-(1-4)-linked D-glucosamine (deacetylation units) and N-acetyl-D-glucosamine (acetylation units), is used as a hydrogel monomer (either as a single monomer or a comonomer).

[0058] Other polysaccharides for use as hydrogel monomers or comonomers include agar, agarose, alginic acid, alguluronic acid, alpha-glucan, amylopectin, amylose, arabinoxylan, beta-glucan, callose, capslan, carrageenan polysaccharides (e.g., kappa, yota, or lambdaclase), cellodextrin, cerulin, cellulose, chitin, chitosan, chrysolaminalin, curdlan, cyclodextrin, alpha-cyclodextrin, dextrin, ficol, fructan, fucoidan, galactoglucomannan, galactomannan, galactosaminogalactan, and gellan gum. Examples of polysaccharides include, but are not limited to, glucans, glucomannan, glucuronoxylan, glycocalyx, glycogen, hemicellulose, homopolysaccharides, hypromellose, icodextrin, inulin, kefiran, laminarin, lentinan, levan polysaccharides, lysenin, mannan, mixed-bonded glucans, paramylon, pectic acid, pectin, pentastarch, phytoglycogen, pleuran, polydextrose, polysaccharide peptides, porphyran, pullulan, schizophyllan, sinistrin, schizophyllan, welan gum, xanthan gum, xylan, xyloglucan, zymosan, or combinations thereof. As described throughout, polysaccharides may be further functionalized depending on the desired crosslinking chemistry used in the hydrogel and / or additional comonomers. For example, in one embodiment, one or more of the polysaccharides described herein are functionalized with acrylates or acrylamides.

[0059] In one embodiment, individual hydrogel particles, or a plurality thereof, include peptides, proteins, protein domains, or combinations thereof as hydrogel monomers, or a plurality thereof. In a further embodiment, the protein is a structural protein, or a domain thereof, e.g., silk, elastin, titin, or collagen, or a domain thereof. In one embodiment, the protein is an extracellular matrix (ECM) component (e.g., collagen, elastin, proteoglycan). In a further embodiment, the structural protein is collagen. In a further embodiment, the collagen is type I collagen, type II collagen, or type III collagen, or a combination thereof. In another embodiment, the hydrogel monomer includes a proteoglycan. In a further embodiment, the proteoglycan is decorin, biglycan, testican, bicinin, fibromodulin, lumican, or a domain thereof.

[0060] In another embodiment, the acrylate-functionalized structural protein hydrogel monomer is used as a component of the hydrogel provided herein (e.g., acrylate-functionalized proteins or protein domains, e.g., silk, elastin, titin, collagen, proteoglycan, or their functionalized domains). In a further embodiment, the acrylate-functionalized structural protein hydrogel monomer is a proteoglycan, e.g., decorin, biglycan, testican, bicinine, fibromodulin, lumican, or their domains.

[0061] In one embodiment, PEG monomers and oligopeptides that mimic extracellular matrix proteins may be used in the hydrogels provided herein, for example, vinyl sulfone-functionalized multi-armed PEG, integrin-binding peptides, and bis-cysteine ​​matrix metalloproteinase peptides. In this particular embodiment, the hydrogel is formed by a Michael-type addition reaction between a di-thiolated oligopeptide and a vinyl sulfone group on the PEG. A range of additional suitable chemicals that can be incorporated herein will be apparent to those skilled in the art and follows general chemical reactivity principles. See, for example, the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0062] Other bioactive domains in natural proteins can also be used as hydrogel monomers or as parts thereof. For example, cell adhesion integrin-binding domains, regulatory-release affinity-binding domains, or transglutaminase-crosslinking domains can be used in the hydrogels provided herein.

[0063] In one embodiment, a recombinant DNA method is used to generate a protein designed for gels in response to changes in pH or temperature. Briefly, the protein consists of terminal leucine zipper domains adjacent to a water-soluble polyelectrolyte segment. In a nearly neutral aqueous solution, the multiple coil aggregates of the terminal domains form a three-dimensional hydrogel polymer network.

[0064] Common crosslinking agents that may be used to crosslink the hydrogels provided herein include, but are not limited to, ethylene glycol dimethacrylate (EGDMA), tetraethylene glycol dimethacrylate, and N,N'-15 methylenebisacrylamide. The range of additional crosslinking chemicals that may be used will be apparent to those skilled in the art and will follow general principles of chemical reactivity. See, for example, the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0065] In one embodiment, the polymerization of the hydrogel is initiated by a persulfate or an equivalent initiator that catalyzes radical formation. The range of suitable initiators is known to those skilled in the art and follows general chemical reactivity principles. See, for example, the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf). The persulfate can be any water-soluble persulfate. Non-limiting examples of water-soluble persulfates include ammonium persulfate and alkali metal persulfates. Examples of alkali metals include lithium, sodium, and potassium. In some embodiments, the persulfate is ammonium persulfate or potassium persulfate. In further embodiments, the polymerization of the hydrogels provided herein is initiated by ammonium persulfate.

[0066] The polymerization of hydrogels can be accelerated by accelerators that can catalyze the formation of chemically unstable lateral groups. A range of possible accelerators is known to those skilled in the art and follows general chemical reaction principles. See, for example, the Thermo Scientific Crosslinking Technical Handbook, titled "Easy molecular bonding crosslinking technology" (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf). In one embodiment, the accelerator is a tertiary amine. The tertiary amine can be any water-soluble tertiary amine. In one embodiment, the accelerator used in the polymerization reaction is N,N,N',N'tetramethylethylenediamine, 3-dimethylamino)propionitrile, or N,N,N',N'tetramethylethylenediamine (TEMED). In another embodiment, the accelerator used in the polymerization reaction is azobis(isobutyronitrile) (AIBN).

[0067] As described above, hydrogels for use in the compositions and methods described herein may comprise either monomer units and crosslinking agents, as described herein, and in one embodiment are produced as hydrogel particles by polymerization of droplets (see, for example, Figure 2). Microfluidic methods for producing a plurality of droplets, including fluid droplets and rigid droplets, are known to those skilled in the art. Such methods provide a plurality of droplets containing a first fluid and substantially surrounded by a second fluid, wherein the first and second fluids are substantially immiscible (e.g., droplets containing an aqueous liquid substantially surrounded by an oily liquid).

[0068] Multiple fluid droplets (e.g., prepared using a microfluidic apparatus) may be polydisperse (e.g., having a range of different particle sizes), or in some cases, the fluid droplets may be monodisperse or substantially monodisperse, for example, having a homogeneous distribution of diameter, such that about 10% or less, about 5%, about 3%, about 1%, about 0.03%, or about 0.01% of the droplets have an average particle size greater than about 10%, about 5%, about 3%, about 1%, about 0.03%, or about 0.01% of the average diameter. The average diameter of the droplet population refers to the arithmetic mean of the droplet diameters, as used herein. The average diameter of the particles can be measured, for example, by light scattering techniques. In one embodiment, the average diameter of the hydrogel particles is adjusted, for example, by varying the flow rates of the first and second fluids in the channel(s) of the microfluidic apparatus, or by varying the volume of the channel(s) of the microfluidic apparatus. Microfluidic devices equipped with microfluidic channels are particularly suitable for preparing multiple monodisperse droplets.

[0069] Accordingly, this disclosure provides a collection of hydrogel particles comprising multiple hydrogel particles, the collection of hydrogel particles being substantially monodisperse.

[0070] The droplet size is related to the microfluidic channel size. The microfluidic channel may be of any size, for example, less than about 5 mm or 2 mm, or less than about 1 mm, or less than about 500 μm, less than about 200 μm, less than about 100 μm, less than about 60 μm, less than about 50 μm, less than about 40 μm, less than about 30 μm, less than about 25 μm, less than about 10 μm, less than about 3 μm, less than about 1 μm, less than about 300 nm, less than about 100 nm, less than about 30 nm, or less than about 10 nm, having a maximum dimension perpendicular to the fluid flow.

[0071] The droplet size can be adjusted by adjusting the relative flow rate. In some embodiments, the droplet diameter is equal to or within approximately 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the channel width.

[0072] The dimensions of the hydrogel particles of this disclosure are substantially similar to those of the droplets on which they are formed. Thus, in some embodiments, the hydrogel particles have diameters less than about 1 μm, 2, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, 300, 350, 400, 450, 500, 600, 800, or 1000 μm. In some embodiments, the hydrogel particles have diameters greater than approximately 1 μm, or greater than 2, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90, 100, 120, 150, 200, 250, 300, 350, 400, 450, 500, 600, 800, or 1000 μm. In one embodiment, the hydrogel particles have diameters in the range of 5 μm to 100 μm.

[0073] In some embodiments, the hydrogel particles of the present disclosure are spherical in shape.

[0074] In one embodiment, hydrogel particles are supported by suspension polymerization, also known in the art as pearl, bead, or granule polymerization. In suspension polymerization, the monomer is insoluble in the continuous phase, for example, an aqueous monomer solution in a continuous oil phase. In suspension polymerization, polymerization initiators are generated in a timely manner in monomer-rich droplets, with more than one radical per droplet at a time. In one embodiment, the monomer phase may be a bifunctional monomer or a monomer that can be multiple monomer species (comonomer, multiple bifunctional monomers). In one embodiment, the monomer phase includes an initiator and / or a crosslinking agent.

[0075] Emulsion polymerization can be used to form the hydrogel particles described herein. In emulsion polymerization, the monomers have low solubility in the continuous phase, similar to suspension polymerization; however, polymerization initiation occurs outside the monomer droplets. In embodiments of emulsion polymerization, the initiator, if a surfactant is present, causes chain growth of the monomer (or comonomer) dissolved in the continuous phase, or of the monomer contained in the micelles.

[0076] In another embodiment, hydrogel particles are formed by precipitation polymerization. Precipitation polymerization is a technique that utilizes the difference in solubility of monomers and polymers to generate microparticles. Specifically, it is known that larger polymer chains generally have lower solubility than smaller polymer chains. Therefore, beyond a certain molecular weight, phase separation may be preferable. Precipitation polymerization initially begins as solution polymerization in a single-phase, homogeneous system. In one embodiment, a relatively high concentration of polymer chains is present immediately after the start of polymerization, which is preferable for phase separation by nucleation. As polymerization progresses, the concentration of polymer chains decreases, and existing particles capture chains before nucleation of new particles can occur. Therefore, particle nucleation occurs only for a short period immediately after the start of the reaction, resulting in a narrow particle size distribution in one embodiment. Additional methods include, but are not limited to, lithographic particle formation, membrane emulsification, and microchannel emulsification, as well as bulk emulsification.

[0077] In one embodiment, hydrogel particles are formed in a microfluidic device having two oil channels focused on a central flow of aqueous monomer solution. In this embodiment, droplets are formed at the interface between the two channels and the central flow, separating the droplets in the water-in-oil emulsion. In one embodiment, once the droplets are formed, they are stabilized before polymerization, for example, by adding a surfactant to the oil phase. However, in another embodiment, the droplets are not stabilized before polymerization. In one embodiment, polymerization of the monomer is induced after initial droplet formation by adding an accelerator (e.g., N,N,N′,N′-tetramethylethylenediamine) to one or both of the oil channels.

[0078] As provided above, the aqueous monomer solution may contain a single monomer species or multiple monomer species. The aqueous monomer solution may contain a comonomer, a difunctional monomer, or a combination thereof. In one embodiment, the monomer, or multiple monomers, may include a difunctional monomer, for example, one of the monomers described above. As described below, the comonomer may be used to adjust forward scattering or side scattering, for example, by adjusting the refractive index of the hydrogel particles.

[0079] In one embodiment, the central stream of the aqueous monomer solution contains a crosslinking agent, such as N,N'-bisacrylamide. In a further embodiment, the central stream of the aqueous monomer solution contains a crosslinking agent and an accelerator in addition to the monomer. In yet another embodiment, the aqueous monomer solution contains an initiator, such as an oxidizing agent such as ammonium persulfate.

[0080] In the preparation of hydrogel particles for ScatterGrid or ScatterBridge, forward scattering can be regulated by adjusting the refractive index of the gel by adding comonomer allyl acrylate and allyl methacrylate. Forward scattering can also be regulated with side-scattering nanoparticles containing sufficient optical resolution / size / density, including but not limited to high-density colloidal suspensions of silica and / or PMMA particles. Side scattering of droplets can be regulated before polymerization by adding a colloidal suspension of silica nanoparticles and / or PMMA (poly(methyl methacrylate)) particles (e.g., approximately 100 nm in diameter) to the central aqueous phase.

[0081] In one embodiment, beads, multiple beads, biomolecules, or multiple biomolecules are embedded (encapsulated) within a hydrogel particle. In one embodiment, the encapsulated beads or biomolecules are used to mimic one or more intracellular organelles of a target cell, or a cell after ingestion of the particles. In one embodiment, the encapsulation or embedding of beads or biomolecules is achieved during hydrogel particle formation. For example, beads can be suspended at an appropriate concentration to embed / encapsulate an average of one bead in a single hydrogel particle. The bead suspension can be contained, for example, in an aqueous solution of monomers. Similarly, biomolecules, or mixtures of biomolecules, can be incorporated into an aqueous solution of monomers to encapsulate the biomolecules, or multiple biomolecules.

[0082] Alternatively, once the hydrogel particles are formed, for example, by the method described above, in one embodiment, the hydrogel particles may be further manipulated by embedding, for example, beads, a plurality of beads, a biomolecule, or a plurality of biomolecules within the hydrogel particles. Thus, in one aspect of the present invention, a hydrogel containing an embedding material is provided.

[0083] In one embodiment, the embedded material is an embedded molecule, such as a biomolecule. The biomolecule may be a single species or several different species. For example, proteins, peptides, carbohydrates, nucleic acids, or combinations thereof can be encapsulated within the hydrogel particles of the present invention. Furthermore, different nucleic acid molecules (various sequences or nucleic acid types, such as genomic DNA, messenger RNA, or DNA-RNA hybrids) can be encapsulated by the hydrogel particles of the present invention. These may consist of any protein or nucleic acid, as both forms of the biomaterial contain unstable chemical side groups (or are commercially available from vendors (e.g., Integrated DNA Technology chemical side group modification)). Such side groups are compatible with reactive chemicals commonly found in comonomer compositions (e.g., acrylate chemistry, NHS esters, primary amines, copper-catalyzed click chemistry (Sharpless)). The range of embedded molecules that may contain compatible chemicals will be understood by those skilled in the art.

[0084] In one embodiment, different subpopulations of hydrogel particles are prepared, each having a different concentration of biomolecules, which may be the case when implemented within a ScatterGrid or ScatterBridge. In a further embodiment, the biomolecules are intracellular ions such as nucleic acids, proteins, calcium (or other biomolecules of user choice, e.g., calcium). In another embodiment, different subpopulations of hydrogel particles are prepared, each having a different concentration of drug substance. In one embodiment, the drug substance is a biomolecule (i.e., a biocompound, antibody, antibody-drug conjugate, protein / enzyme, peptide, non-ribosomal peptide, or related molecule) or a small molecule synthetic drug (e.g., type I / II / III polyketides, non-ribosomal peptides with bioactive properties, or other small molecule entities commonly classified by those skilled in the art).

[0085] In one embodiment, beads having a diameter of about 1 μm to about 3 μm, about 2 μm to about 4 μm, or about 3 μm to about 7 μm are embedded in the hydrogel provided herein. For example, in one embodiment, the beads have a diameter of about 3 μm to about 3.5 μm.

[0086] In one embodiment, the refractive index (RI) of the disclosed hydrogel particles is greater than about 1.10, greater than about 1.15, greater than about 1.20, greater than about 1.25, greater than about 1.30, greater than about 1.35, greater than about 1.40, greater than about 1.45, greater than about 1.50, greater than about 1.55, greater than about 1.60, greater than about 1.65, greater than about 1.70, greater than about 1.75, greater than about 1.80, greater than about 1.85, greater than about 1.90, greater than about 1.95, greater than about 2.00, greater than about 2.10, greater than about 2.20, greater than about 2.30, greater than about 2.40, greater than about 2.50, greater than about 2.60, greater than about 2.70, greater than about 2.80, or greater than about 2.90.

[0087] In another embodiment, the refractive index (RI) of the disclosed hydrogel particles is approximately 1.10 to approximately 3.0, or approximately 1.15 to approximately 3.0, or approximately 1.20 to approximately 3.0, or approximately 1.25 to approximately 3.0, or approximately 1.30 to approximately 3.0, or approximately 1.35 to approximately 3.0, or approximately 1.4 to approximately 3.0, or approximately 1.45 to approximately 3.0, or approximately 1.50 to approximately 3.0, or approximately 1.6 to approximately 3.0, or approximately 1.7 to approximately 3.0, or approximately 1.8 to approximately 3.0, or approximately 1.9 to approximately 3.0, or approximately 2.0 to approximately 3.0.

[0088] In some embodiments, the refractive index (RI) of the disclosed hydrogel particles is less than about 1.10, less than about 1.15, less than about 1.20, less than about 1.25, less than about 1.30, less than about 1.35, less than about 1.40, less than about 1.45, less than about 1.50, less than about 1.55, less than about 1.60, less than about 1.65, less than about 1.70, less than about 1.75, less than about 1.80, less than about 1.85, less than about 1.90, less than 1.95, less than about 2.00, less than about 2.10, less than about 2.20, less than about 2.30, less than about 2.40, less than about 2.50, less than about 2.60, less than about 2.70, less than about 2.80, or less than about 2.90.

[0089] The SSC of the disclosed hydrogel particles is most meaningfully measured in comparison to that of target cells. In some embodiments, the disclosed hydrogel particles, when measured by a cytometry device, have an SSC of 30%, 25%, 20%, 15%, 10%, 5%, or 1% of that of target cells.

[0090] In one embodiment, the SSC of the hydrogel particles is adjusted by incorporating high refractive index molecules (or more thereof) into the hydrogel. In one embodiment, the high refractive index molecules are provided in the hydrogel particles, and in further embodiments, the high refractive index molecules are colloidal silica, alkyl acrylates, alkyl methacrylates, or a combination thereof. Thus, in some embodiments, the hydrogel particles of the present disclosure contain alkyl acrylates and / or alkyl methacrylates. In one embodiment, the refractive index of the hydrogel particles is further adjusted by adjusting the monomer concentration.

[0091] Alkyl acrylates or alkyl methacrylates may contain 1 to 18, 1 to 8, or 2 to 8 carbon atoms in the alkyl group, such as a methyl group, ethyl group, n-propyl group, isopropyl group, n-butyl group, isobutyl group or tert-butyl group, 2-ethylhexyl group, heptyl group, or octyl group. The alkyl group may be branched or linear.

[0092] High refractive index molecules also include vinylarenes, such as styrene and methylstyrene, which may be optionally substituted on the aromatic ring with alkyl groups, such as methyl, ethyl, or tert-butyl, or halogens, such as chlorostyrene.

[0093] In some embodiments, the FSC is adjusted by adjusting the percentage of monomers present in the composition, thereby changing the water content present during hydrogel formation. In one embodiment, monomers and comonomers are used, and the ratio of monomers and comonomers is adjusted to change the forward scattering properties of the hydrogel particles.

[0094] The FSC of the disclosed hydrogel particles is most meaningfully measured in comparison to the FSC of target cells. In some embodiments, the disclosed hydrogel particles, when measured by a cytometry device, have FSCs of 30%, 25%, 20%, 15%, 10%, 5%, or 1% or less of that of target cells.

[0095] FSC is related to particle volume and can therefore be tuned by changing the particle diameter, as described herein. Generally, it has been observed that larger objects refract more light than smaller objects, resulting in a higher forward scattering signal (and vice versa). Therefore, in one embodiment, the particle diameter is varied to tune the FSC properties of the hydrogel particles. For example, in one embodiment, the hydrogel particle diameter is varied by utilizing larger microfluidic channels during particle formation.

[0096] SSCs can be manipulated by encapsulating nanoparticles within hydrogel particles to mimic organelles in target cells. In some embodiments, the hydrogel particles of this disclosure comprise one or more types of nanoparticles selected from the group consisting of polymethyl methacrylate (PMMA) nanoparticles, polystyrene (PS) nanoparticles, and silica nanoparticles. While we do not wish to be constrained by theory, the ability of hydrogels to selectively tune both forward and side scattering, as described herein, enables robust platforms that mimic a wide range of cell types.

[0097] After the hydrogel particles are formed, one or more surfaces of the particles may be functionalized, for example, to mimic one or more optical properties of a target cell or a labeled target cell. The functionalized hydrogel particles may also contain materials such as embedded beads or biomolecules, as described above. In one embodiment, one or more hydrogel particles are functionalized with one or more fluorescent dyes, one or more cell surface markers (or their epitope-binding regions), or a combination thereof. In one embodiment, the hydrogel particles are formed by polymerizing at least one difunctional monomer, and after formation, the hydrogel particles contain one or more functional groups that can be used for further binding of cell surface markers, epitope-binding regions of cell surface markers, fluorescent dyes, or a combination thereof. In one embodiment, the free functional groups are amine groups, carboxyl groups, hydroxyl groups, or a combination thereof. It should be understood that, depending on the desired functionality, multiple difunctional monomers may be used to functionalize the particles, for example, using different chemicals and different molecules.

[0098] Examples of hydrogel particles (optionally implemented as beads) that conform to embodiments of the present disclosure (e.g., for use in generating scattering plots via flow cytometer acquisition runs) can be found in U.S. Patent No. 10,753,846, issued on 25 August 2020, titled "Hydrogel Particles with Tunable Optical Properties and Methods for Using the Same," the entirety of which is incorporated herein by reference for all purposes.

[0099] One or more embodiments of this disclosure use ScatterGrid or ScatterBridge to facilitate product-based testing of multiple different flow cytometry instrument models and manufacturers with different instrument settings to calibrate the relative FSC and SSC signal responses between those instruments. While shown and described herein in relation to flow cytometers, the use of ScatterGrid or ScatterBridge can also be applied to any other instrument that can produce multiple levels of FSC and / or SSC. Thus, data (e.g., from daily operations) can be normalized, data acquisition can be standardized across multiple flow cytometer / research entities, and the performance and calibration of flow cytometers can be monitored over time, for example, temporally or concurrently / simultaneously across multiple flow cytometers. For example, research and development teams may manually collaborate and / or reconcile their measurements using ScatterGrid or ScatterBridge data (e.g., using ScatterGrid, as shown and discussed below, see Figures 9A–9B), thereby accelerating the research and development timeline. For example, in Figures 9A–9B discussed below, a control population of hydrogel particles with forward and side-scatter signals corresponding to the biological sample was generated using ScatterGrid as a common reference on a second flow cytometer, compared with the analysis of a biological sample using ScatterGrid on a first flow cytometer. Figure 8 shows the use of ScatterGrid to adjust gain settings after the flow cytometer has been serviced, as discussed below.

[0100] One or more embodiments of the present disclosure offer several advantages over known flow cytometry processes. First, it should be noted that the performance of flow cytometer instruments can naturally degrade over time due to, for example, hardware wear and signal drift resulting from breakage. Using one or more embodiments of the ScatterGrid or ScatterBridge of the present disclosure, flow cytometry laboratory operators can monitor the performance of their flow cytometers for periodic quality control purposes, as well as for non-periodic quality control purposes such as periodic preventive maintenance. For example, when flow cytometer engineers perform preventive maintenance, they may change the forward scattering, side scattering, or fluorescence gain, or install a new excitation source or a new detector. As a result of these changes, it may be necessary to significantly readjust the nominal instrument settings, thereby disrupting the laboratory operator's daily workflow. By utilizing ScatterGrid or ScatterBridge data, laboratory operators can appropriately calibrate or adjust instrument settings based on an evaluation of reported signal acquisition data, such as forward and side scattering, from the ScatterGrid or ScatterBridge, apply the adjustments to the instrument settings as needed, and continue experiments with the adjusted flow cytometry settings. The above can be achieved by testing hydrogel particle formulations on a flow cytometer that generate signal data acquisition (e.g., 3×3 ScatterGrid or ScatterBridge). More specifically, ScatterGrid or ScatterBridge data may include both side and forward scattering, e.g., low, medium, and high signal readouts for instrument settings designed for routine biological cell analysis. Thus, laboratory operators in the field of biological cell analysis can ensure a linear response to data acquisition measurements while reducing or eliminating the effects of noise (e.g., autofluorescence, mechanical vibration, ambient electric field (e.g., from AC power lines)) without exceeding the upper limit due to saturation (e.g., multiple photons represented as a single photon).As used herein, “hydrogel particle formulation” may refer to an assembly of hydrogel particles having discrete scattering signals that generate an array or matrix for optimizing mechanical signal alignment.

[0101] Secondly, from an engineering standpoint and with respect to the upstream manufacturing processes of flow cytometers, one or more embodiments of this disclosure offer several advantages. For example, the manner in which individual hardware, firmware, and software components are manufactured and assembled can be standardized before they are sold as flow cytometer units. To the best of the inventors' knowledge, as evidenced by the wide variation in data analysis results in cell analysis, there is no standardized method for assembling flow cytometer instruments from raw components. For example, there are different laser excitation sources with varying intensities, wavelengths, and accuracies, different lenses, mirrors, filters, and collection angles for photoprocessing, and different detectors (e.g., photomultiplier tubes, photodiodes, and / or charge-coupled devices) with different quantum efficiencies across the electromagnetic spectrum that provide different signal processing capabilities.

[0102] Thirdly, from the perspective of data science, machine learning, and bioinformatics, one or more embodiments of the present disclosure improve methods for storing and processing information for complex cell acquisition analysis, thereby enabling highly reliable validation of statistical, mathematical, and probabilistic analyses across the lateral and forward scattering regions, where cell analysis is typically performed on a flow cytometer.

[0103] The hydrogel particles described herein can be used with any flow cytometer known to those skilled in the art. For example, one or more flow cytometers provided in Tables 2 and 3 below are suitable for use with the hydrogels and assays described herein.

[0104] [Table 2-1] [Table 2-2] [Table 2-3] [Table 2-4] [Table 2-5]

[0105] [Table 3-1] [Table 3-2] [Table 3-3] [Table 3-4] [Table 3-5]

[0106] In some embodiments, multiple levels of scattering can be achieved through changes in hydrogel particle modification, which may include one or more of the following: bovine serum albumin (BSA) preparations, nanoparticle loading, porosity, polymer composition, or gel fraction (e.g., polymer concentration) adjustments.

[0107] In some embodiments, hydrogel particles (e.g., beads) can be conjugated with different fluorophores / biomacromolecules for clear identification of each population, which can be particularly useful when multiple populations within the same collection cannot be separated by scattering due to saturation, incorrect cytometer settings, and / or insufficient detector sensitivity.

[0108] In some embodiments, hydrogel particles (e.g., beads) can be conjugated with different levels of fluorophores / biomacromolecules to simultaneously calibrate the fluorescence and scattering scaling of the cytometer. The various levels of fluorophores / biomacromolecules can also be quantified to serve as quantitative standards.

[0109] Figure 2 shows a system 200 for tuning and / or cross-calibrating a flow cytometer based on ScatterGrid or ScatterBridge data (e.g., a multi-encompassing dataset generated using hydrogel particles), according to several embodiments. As shown in Figure 2, the system 200 includes one or more ScatterGrid or ScatterBridge servers 220 that operably communicate with one or more flow cytometers 210 via a network N (which may include one or more wireless and / or wired communication networks). Optionally, the ScatterGrid or ScatterBridge server(s) 220 and / or the flow cytometer(s) 210 are operably coupled to one or more databases 250 or other storage media (e.g., including cloud-hosted storage media) (e.g., via network N). Each of the ScatterGrid or ScatterBridge servers 220 includes a processor 224 operably coupled to memory 222, a transceiver 226, and optionally a user interface 228 (including a graphical user interface (GUI) through which users, such as an administrator user, can interact). Memory 222 stores processor-executable instructions 222A to implement one or more methods described herein (e.g., method 1000 in Figure 10, or method 1100 in Figure 11, discussed hereafter). Instructions 222A may include one or more algorithms 222B, which may include machine learning algorithms. Memory 222 also stores one or more of the following: ScatterGrid or ScatterBridge data 222C (optionally including FSC data 222D, and / or SSC data 222E), instrument settings 222F, instrument data 222G, adjustment data 222H, history data 222I, or cross-calibration data 212J. ScatterGrid, or ScatterBridge data 222C, may include data generated by one or more flow cytometers using one or more hydrogel particle formulations of the present disclosure.ScatterGrid or ScatterBridge data 222C may be stored / arranged as one or more matrices or other two-dimensional (2D) or three-dimensional (3D) formats, as shown and discussed below with reference to, for example, Figures 3-9B. Instrument settings 222F may include settings such as FSC gain, SSC gain, fluorescence gain, and laser output associated with one or more flow cytometers or other instruments. Instrument data 222G may include identifiers (e.g., serial number), model number, manufacturer name, model name, maintenance event data, authorized user identifier, etc., associated with one or more flow cytometers or other instruments. Adjustment data 222H may include, for example, a representation of adjustments made to the instrument settings 222F of one or more flow cytometers or other instruments based on ScatterGrid or ScatterBridge data 222C. The historical data 222I may include records related to one or more of the following: past adjustments made to one or more flow cytometers or other instruments, maintenance events associated with one or more flow cytometers or other instruments, ScatterGrid or ScatterBridge data 222C, instrument settings 222F, instrument data 222G, adjustment data 222H, cytometry measurements, etc. The cross-calibration data 222J may include data derived from ScatterGrid or ScatterBridge data 222C, which can be used, for example, to match measurements made by a first flow cytometer with measurements made by a second flow cytometer, or to adjust one or more settings / parameters of the first flow cytometer so that the first flow cytometer produces measurements that better match those made by the second flow cytometer, or to adjust one or more measurements made by the first flow cytometer so that one or more adjusted measurements of the first flow cytometer match (or better correlate) with one or more measurements of the second flow cytometer.

[0110] Each flow cytometer(s) 210, like the ScatterGrid or ScatterBridge server 220, may include a processor 214, transceivers 216, and optionally a user interface 218 (e.g., a graphical user interface (GUI) on which a user "U", such as an operator, can interact) operably coupled to memory 212. Memory 212 stores processor-executable instructions 212A to perform one or more of the methods described herein (e.g., method 1000 in Figure 10, or method 1100 in Figure 11, discussed hereafter). Memory 212 also stores one or more of the following: ScatterGrid or ScatterBridge data 212C (optionally including FSC data 212D, and / or SSC data 212E), instrument settings 212F, instrument data 212G, adjustment data 212H, or history data 212I. ScatterGrid or ScatterBridge data 212C may include data generated by the associated flow cytometer 210 using one or more hydrogel particle formulations of the present disclosure. Similar to ScatterGrid or ScatterBridge data 222C, ScatterGrid or ScatterBridge data 212C may be stored / arranged as one or more matrices or other two-dimensional (2D) or three-dimensional (3D) formats, as shown and discussed below with reference to, for example, Figures 3-9B. Instrument settings 212F may include settings such as FSC gain, SSC gain, and fluorescence gain associated with one or more flow cytometers or other instruments. Instrument data 212G may include identifiers associated with the flow cytometer 210 (e.g., serial number), model number, manufacturer name, model name, maintenance event data, authenticated user identifier, etc. The adjustment data 212H may include, for example, a representation of adjustments made to the instrument settings 212F of the flow cytometer 210 based on ScatterGrid or ScatterBridge data 212C.The historical data 212I may include records related to one or more of the following: past adjustments made to one or more flow cytometers or other instruments, maintenance events associated with one or more flow cytometers or other instruments, ScatterGrid or ScatterBridge data 212C, instrument settings 212F, instrument data 212G, adjustment data 212H, cytometry measurements, etc.

[0111] Figures 3A, 4A, and 5A show exemplary scatter grid-based scattering data sequentially annotating subsets of data generated using hydrogel particles (e.g., beads) of varying average diameters (10 micrometers (μm), 20 μm, and 25 μm, respectively) according to several embodiments. For example, the 10 μm box shown in Figure 3A includes group "P1" (corresponding to low FSC signal output and low SSC signal output), group "P2" (corresponding to low FSC signal output and medium SSC signal output), and group "P3" (corresponding to low FSC signal output and high SSC signal output). Similarly, the 20 μm box shown in Figure 4A includes group "P4" (corresponding to medium SC signal output and low SSC signal output), group "P5" (corresponding to medium FSC signal output and medium SSC signal output), and group "P6" (corresponding to medium FSC signal output and high SSC signal output). Similarly, the 25 μm box shown in Figure 5A includes group "P7" (corresponding to high FSC signal output and low SSC signal output), group "P8" (corresponding to high FSC signal output and medium SSC signal output), and group "P9" (corresponding to high FSC signal output and high SSC signal output).

[0112] Figures 3B–3C, 4B–4C, and 5B–5C illustrate exemplary scatterbridge-based scattering data sequentially annotating subsets of data generated using hydrogel particles (e.g., beads) of varying average diameters (10 micrometers (μm), 14 μm, and 18 μm, respectively) according to several embodiments. For example, the 10 μm box shown in Figure 3B includes group "P1" (corresponding to low FSC signal output and low SSC signal output), group "P2" (corresponding to low FSC signal output and medium SSC signal output), and group "P3" (corresponding to low FSC signal output and high SSC signal output). Similarly, the 20 μm box shown in Figure 4B includes group "P4" (corresponding to medium FSC signal output and low SSC signal output), group "P5" (corresponding to medium FSC signal output and medium SSC signal output), and group "P6" (corresponding to medium FSC signal output and high SSC signal output). Similarly, the 25 μm boxes shown in Figure 5B include group "P7" (corresponding to high FSC signal output and low SSC signal output), group "P8" (corresponding to high FSC signal output and medium SSC signal output), and group "P9" (corresponding to high FSC signal output and high SSC signal output). Figures 3C, 4C, and 5C show the respective gate groups corresponding to low FSC-A and low FSC-H signal output, medium FSC-A and low FSC-H signal output, and high FSC-A and low FSC-H signal output, respectively.

[0113] Figures 6A–6C show exemplary scatter grid-based scattering data generated by three different flow cytometers (a Cytek® NL-2000 flow cytometer, a Cytek® Aurora flow cytometer, and a Beckman Coulter® Cytoflex 5 flow cytometer, respectively), where each scatter grid contains data for / representing nine hydrogel populations, indicated by rectangular gates according to several embodiments. Figures 6A–6C demonstrate that the same population of hydrogels produces different scattering signal responses (and therefore different scattering plots) on different types of flow cytometers. The signal scales are distinct, and the population positions are distinct from each other. The population numbers shown in Figures 6A–6C are arbitrary, and in other implementations, data points representing the same population of hydrogel particles across the plot are annotated with the same number.

[0114] Figures 7A and 7B show exemplary scatter grid-based scattering data generated by two different flow cytometers (a BD® Biosciences FALabyric® flow cytometer and a Beckman Coulter® Cytoflex LX flow cytometer, respectively) according to several embodiments, where each scatter grid contains / represents data from nine hydrogel populations. Again, the same population of hydrogels generates different scattering signal responses (and therefore different scattering plots) across different flow cytometers.

[0115] Figures 8A–8C show ScatterGrid-based scattering data generated by a single Cytek® NL-2000 flow cytometer at three different time points according to one embodiment: before preventive maintenance was performed on the Cytek® NL-2000 flow cytometer (Figure 8A), after preventive maintenance was performed (Figure 8B), and after both preventive maintenance and adjustment of the FSC gain on the Cytek® NL-2000 flow cytometer (Figure 8C). More specifically, as can be observed when comparing the scattering plots in Figures 8A and 8B, an acquisition run using the hydrogel particle composition before preventive maintenance and with an FSC gain setting of 29 produced a ScatterGrid with a significantly different appearance / distribution from the ScatterGrid produced in an acquisition run using the same hydrogel particles and with the same FSC gain setting of 29 after preventive maintenance. Furthermore, in response to the ScatterGrid in Figures 8A and 8B, the FSC gain setting of the flow cytometer was adjusted from 29 to 50 to obtain the scattering plot in Figure 8C, which is substantially realigned with the scattering plot in Figure 8A.

[0116] Figure 9A is a plot showing first data generated by a first flow cytometer according to one embodiment, the first data including ScatterGrid-based scattering data, data generated using TruCyte TBNK biomarker mimetic (synthetic cell), and data generated using biological cells. Figure 9B is a plot showing second data generated by a second flow cytometer (Cytek® Aurora flow cytometer), the second data including ScatterGrid-based scattering data, and the plot also shows the guide position of the synthetic cell mimetic control, which matches the scattering signal of the biological sample control from the first flow cytometer, relative to the ScatterGrid-based scattering data, and according to one embodiment. The guide position can be identified based on the relevant position compared to a subpopulation of ScatterGrid-based scattering data. Based on the plot in Figure 9A, the relative relationship between biological cells in the first flow cytometer and the ScatterGrid-based scattering dataset was determined. This information was used to generate a control for validation via the second flow cytometer. Based on the observation that the ScatterGrid-based scattering data for the control has the same relative relationships as the cells, it can be concluded that the control is identical to the biological cells of interest when using the first flow cytometer. ScatterGrid-based scattering data can be used in this manner for both positive and negative controls ("positive" and "negative" controls, referring to the presence or absence of the biomarker, respectively). In Figures 9A-9B, the numbers on the x and y axes are arbitrary intensity signal values ​​across different flow cytometry instruments. ScatterGrid-based scattering data can be used as a map to determine / adjust relative scattering positions when tracking the scattering signals of interest.

[0117] Figure 10 is a flowchart illustrating a method for tuning a site metric instrument and / or matching site metric measurements based on ScatterGrid or ScatterBridge data, according to several embodiments. Method 1000 in Figure 10 may be implemented using a system, such as system 200 in Figure 2. As shown in Figure 10, Method 1000 includes receiving a first data array associated with a first site metric instrument at 1002 and at a first time point (t1). The first data array includes a set of scattered signal data points, each containing data representing at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low side scattered signal output, and at least one high side scattered signal output. At a second time point (t2) following the first time point, the site metric instrument parameters are optionally adjusted in 1004 based on the first data array, and / or the first site metric measurement of the first site metric instrument is optionally matched in 1006 with a second site metric measurement of a different site metric instrument, based on at least one of the first data array or a second data array associated with the second site metric instrument.

[0118] In one embodiment, if the first data array in 1004 is cytometric data obtained from hydrogel particles constituting a ScatterGrid or ScatterBridge, the resulting FSC and SSC can be compared to known standard FSC and SSC values ​​for the same hydrogel particles, and if the comparison meets a threshold (for example, the comparison indicates that the cytometer needs to be calibrated), adjustments can be made to the cytometric instrument. In the embodiment, the resulting FSC-A and FSC-H can be compared to known standard FSC-A and FSC-H values ​​for the same hydrogel particles, and if the comparison meets a threshold (for example, the comparison indicates that the cytometer needs to be calibrated), adjustments can be made to the cytometric instrument.

[0119] In one embodiment, if the first data array in 1006 is cytometric data acquired from hydrogel particles by a first cytometric instrument, it constitutes a ScatterGrid or ScatterBridge, and the obtained FSC and SSC (or FSC-A and FSC-H) can be compared with known reference FSC and SSC (or FSC-A and FSC-H) values ​​for the same hydrogel particles (i.e., a second data array associated with a second cytometric instrument), and if the comparison meets a threshold (e.g., the comparison indicates that the first cytometer has drifted), a match to the first data array may be made. For this purpose, in 1006, the algorithm may attempt to generate a matrix function for mapping standard FSC and SSC values ​​onto the first data array. If the QC measurement of the generated function (e.g., a reliability score) is higher than a predetermined value, the algorithm then proceeds to correct and / or standardize the data in the first data array, and this updated function may be used to correct future data. For example, the updated function may be applied to the actual sample in question, and the corrected and / or standardized data may be generated without hardware or software calibration of the flow cytometry instrument.

[0120] In some implementations, methods 1000, including 1004 and 1006, are performed iteratively to ensure that the accuracy of the cytometric instrument and / or the processed scattering data is maintained, and / or the longitudinal performance of the cytometric instrument is maintained. For example, ScatterGrid or ScatterBridge data can be acquired at each point in time from multiple points in the longitudinal test (of which the points have a predetermined time interval between them, and / or follow a predetermined schedule), and the actual sample data can be compared with the ScatterGrid or ScatterBridge data to correct and / or standardize the sample data. These corrections prevent drift or other suboptimal characteristics from affecting readouts from the actual sample data measured over multiple durations.

[0121] In another embodiment, in 1006, information about an unknown cell population is obtained using both a first cytometric instrument and a second cytometric instrument. Data for each cytometric instrument based on hydrogel particles, associated with a ScatterGrid or ScatterBridge, are used to ensure consistent measurements across instruments. A first data array, corresponding to the cytometric data obtained on the first cytometric instrument for hydrogel particles associated with a ScatterGrid or ScatterBridge, and a second data array, corresponding to the cytometric data obtained from the second cytometric instrument, can each be compared to the corresponding ScatterGrid or ScatterBridge with known FSC and SSC values. Data correction and / or standardization for each data array may be performed until both data arrays are harmonized. For example, a function can be generated for each of the first and second data arrays based on ScatterGrid or ScatterBridge, which allows for the mapping of that function to the actual sample of interest and ensures that the processed data from each of the first and second cytometric instruments is consistent and equivalent, even though each instrument has different settings and, in some cases, different raw outputs.

[0122] In some implementations, method 1000 also includes computationally adjusting at least one of the side-scattered signals or forward-scattered signals based on a first data array and a second data array, the first data array being associated with a first time point, and the second data array being associated with a second time point occurring after the first time point and after modifications to the settings of the first site-metric instrument. Optionally, the multiple scattered signal data points also include data representing at least one mid-forward-scattered signal output and data representing at least one mid-side-scattered signal output.

[0123] In some implementations, the first data array includes a two-dimensional arrangement (e.g., a square or rectangular arrangement) of scattered signal data points from multiple scattered signal data points.

[0124] In some implementations, the first data array is generated using a hydrogel particle formulation via a single acquisition run of a first cytometric instrument. The difference between at least one low forward scattering signal output and at least one high forward scattering signal output may be at least one of the following: the monomer / crosslinker ratio of the hydrogel particle formulation, the gel fraction of the hydrogel particle formulation, or the particle size associated with the hydrogel particle formulation (e.g., the particle size of one or more hydrogel particles from the hydrogel particle formulation). Alternatively, the difference between at least one low side scattering signal output and at least one high side scattering signal output may be at least one of the following: nanoparticle loading of the hydrogel particle formulation, the hydrogel porosity of the hydrogel particle formulation, or the protein conjugation associated with the hydrogel particle formulation (e.g., thermal crosslinking of proteins such as bovine serum albumin (BSA) preparations).

[0125] In some implementations, the first data array is unique to the first site metric instrument.

[0126] In some implementations, the first data array is unique to the first site-metric instrument, and the second data array is unique to the second site-metric instrument.

[0127] In some implementations, multiple scattered signal data points further include data representing at least one additional level of the forward scattered signal output (e.g., mid-forward scattered signal output).

[0128] In some implementations, multiple scattered signal data points further include data representing at least one additional level of the side-scattered signal output (e.g., the middle-scattered signal output).

[0129] In some implementations, multiple scattering signal data points also include data representing at least one additional level of the forward scattering signal output, and data representing at least one additional level of the side scattering signal output (e.g., mid-forward scattering signal output and mid-side scattering signal output).

[0130] Figure 11 is a flowchart illustrating a method for automatically adjusting the control parameters of a flow cytometer and / or correlating flow cytometer measurements based on ScatterGrid or ScatterBridge data, according to several embodiments. Method 1100 in Figure 11 may be implemented using a system, for example, system 200 in Figure 2. As shown in Figure 11, Method 1100 includes identifying a first set of scattered signal data points generated by a first flow cytometer in 1102. The first set of scattered signal data points includes (i) data representing forward scattering at a first signal output level, (ii) data representing forward scattering at a second signal output level greater than the first signal output level, (iii) data representing forward scattering at a third signal output level greater than the second signal output level, (iv) data representing side scattering at a fourth signal output level, (v) data representing side scattering at a fifth signal output level greater than the fourth signal output level, and (vi) data representing side scattering at a sixth signal output level greater than the fifth signal output level. Method 1100 also optionally includes, in 1104, automatically adjusting the control parameters of the first flow cytometer based on a first plurality of scattered signal data points, and, in 1106, comparing a first set of at least one measurement of the first flow cytometer with a second set of at least one measurement of a second flow cytometer different from the first flow cytometer, based on at least one of the first plurality of scattered signal data points or a second plurality of scattered signal data points associated with the second flow cytometer.

[0131] As described above with reference to Methods 1000 and 1100, the ScatterGrid or ScatterBridge of this disclosure may be used as a custom reference for calibrating cytometric instruments, and / or for inter-instrument (e.g., as a reference for adjusting instrument settings between instruments to ensure standardized results), and / or for intra-instrument calibration. Furthermore, the ScatterGrid or ScatterBridge of this disclosure may be used to evaluate cytometric data acquired over time in longitudinal studies, or for metadata analysis and harmonization of cytometric results across studies. For example, the ScatterGrid or ScatterBridge may be used in optically-based single-cell analyses such as hematology, flow cytometry, and microfluidic cytometry. Furthermore, the ScatterGrid or ScatterBridge may be used to monitor the health and degradation of cell lines over time. Live or dead cells may be detected. In some embodiments, the method can be applied to various scattering angles (e.g., backscattering, low-angle scattering), particle size, and particle size (used in image-based cytometry methods), as well as to image cytometry, ghost cytometry, and cell sorting (e.g., FACS). Furthermore, individual populations of hydrogel particles may be conjugated with markers of varying levels, such as dyes, antigens, or nucleic acids, which further extends the dimensional properties of the ScatterGrid or ScatterBridge to include other properties (such as fluorescence intensity). This enables use in a wider range of applications, such as mass cytometry (CyTOF), laser scanning cytometry, and single-cell sequencing.

[0132] In some embodiments, a ScatterGrid or ScatterBridge can be used to identify unknown cells within a population. For example, the relative position of an unknown cell in the ScatterGrid or ScatterBridge can be compared to a known cell previously evaluated in the context of the ScatterGrid or ScatterBridge.

[0133] In some implementations, a scaling shift is determined to have occurred with respect to the data based on a comparison between the first data array (or nominal value) received at 1004 and 1002 and the subsequent data array, and adjustments to the cytometric instrument may be voltage and / or gain adjustments, as shown in Figure 12A. As shown in Figure 12A, the scaling shift causes dispersion in the collection of scattered signal data points relative to the nominal value.

[0134] In some implementations, a linearity shift in the data is determined based on a comparison between the first data array (or nominal value) received at 1004 and 1002 and the subsequent data array, and adjustments to the cytometric instrument may include laser alignment, voltage, and / or gain adjustment, as well as / or detector maintenance. As shown in Figure 12B, the linearity shift results in a uniform distribution of the scattered signal data points relative to the nominal value.

[0135] In some implementations, an orthogonality shift is determined to have occurred in the data based on a comparison between the first data array (or nominal value) received at 1004 and 1002 and the subsequent data array, and adjustments to the cytometric instrument may be laser alignment and / or optical alignment. As shown in Figure 12C, the orthogonality shift causes a rotation of the spatial distribution of the collection of scattered signal data points relative to the nominal value.

[0136] In some implementations, method 1100 also includes displaying a first set of scattered signal data points via a user interface, such that (i) to (vi) are shown as clustered data sets spaced apart from each other.

[0137] For example, in this embodiment, the multiple scattered signal data points may include nine unique clustered data sets, similar to those shown in Figure 13.

[0138] In some implementations, method 1100 also includes causing the processor to display a first set of scattered signal data points via a user interface, such that (i) to (vi) are arranged in a two-dimensional array.

[0139] In some implementations, the automatic adjustment of the control parameters of the first flow cytometer is based on a first set of scattered signal data points, and the automatic adjustment compensates for at least one of the following: instrument degradation of the first flow cytometer, signal drift of the first flow cytometer, or noise.

[0140] In some implementations, the automatic adjustment of the control parameters of the first flow cytometer is based on a first set of scattered signal data points, and the automatic adjustment improves the response linearity across multiple subsequent measurements of the first flow cytometer. The automatic adjustment may include, for example, computer-based adjustments for data from additional samples, which are based on the first set of scattered signal data points (which may be associated with, for example, one or more past samples), thereby allowing comparison of forward scattering and / or side scattering between multiple instruments (e.g., a first flow cytometer and at least a second flow cytometer) and / or between instrument settings of multiple instruments.

[0141] In some implementations, method 1100 also includes modifying sample data based on a first set of scattered signal data points. The sample data may include sample data generated by a first flow cytometer and sample data generated by a second flow cytometer.

[0142] In some implementations, multiple scattered signal data points also include (vii) data representing forward scattering at a seventh signal output level greater than the third signal output level, and (viii) data representing side scattering at an eighth signal output level greater than the sixth signal output level.

[0143] In some implementations, the first set of scattered signal data points is uniquely associated with the first flow cytometer.

[0144] In some implementations, the first set of scattered signal data points is generated using a hydrogel particle formulation via a single acquisition run of a first flow cytometer. The difference between any two of the first, second, or third signal output levels may be attributable to at least one of the following: the monomer / crosslinker ratio of the hydrogel particle formulation, the gel fraction of the hydrogel particle formulation, or the particle size associated with the hydrogel particle formulation (e.g., the diameter of one or more hydrogel particles from the hydrogel particle formulation). Alternatively, the difference between any two of the fourth, fifth, or sixth signal output levels may be attributable to at least one of the following: nanoparticle loading of the hydrogel particle formulation, the hydrogel porosity of the hydrogel particle formulation, or the protein conjugate associated with the hydrogel particle formulation.

[0145] The ScatterGrid, or ScatterBridge, disclosed herein, can be deployed for automated gating. Manual gating is time-consuming, adds variability to site metric data, and can limit the ability to scale up data analysis. Existing automated gating strategies rely on machine learning approaches to identify populations, but require extensive training data, which can be difficult for rare populations or limited samples. Therefore, Figure 17 is a flowchart illustrating an automated gating method.

[0146] Figure 17 is a flowchart illustrating a method for generating and applying automated gating. Method 1700 in Figure 17 can be carried out using a system such as system 200 in Figure 2, for example. As shown in Figure 17, Method 1700 includes, in 1702, receiving a first data array acquired by a cytometric instrument. The first data array, or ScatterGrid / ScatterBridge, may include a set of scattered signal data points, each containing data representing at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low side scattered signal output, and at least one high side scattered signal output. In 1704, a second data array may be received from the cytometric instrument, containing scattered signal data points corresponding to a known sample of interest. In embodiments, the first and second data arrays are acquired under the same laser power and gain settings. The known sample of interest may include one or more known cell populations having expected FSC and SSC values. In 1706, one or more gates may be defined for cell populations within the sample of interest. One or more gates can be defined for the detection locations of multiple scattered signal data points in a first data array. In embodiments, the locations of clusters of multiple scattered signal data points in a first data array can be detected manually or automatically using supervised learning techniques (e.g., density-based spatial clustering (DBScan) or K-means clustering with a defined number of clusters). Each gate can be defined for a target population relative to the median of each detected cluster of multiple scattered signal data points in a first data array.

[0147] With the gates defined, step 1708 may be performed optionally. If a third data array containing scattered signal data points corresponding to the unknown sample of interest is received approximately simultaneously with or immediately after defining one or more gates in step 1706, method 1700 may include mapping one or more defined gates onto the third data array to automatically gate the cell populations in the unknown sample of interest. If a third data array containing scattered signal data points corresponding to the unknown sample of interest is not received immediately after step 1706, then steps 1710 and 1712 may be performed optionally. For example, after acquiring the third data array from the cytometric instrument, a fourth data array may be acquired from the cytometric instrument. The fourth data array may include a set of scattered signal data points corresponding to the first data array, containing data representing at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low side scattered signal output, and at least one high side scattered signal output. The fourth data array can be considered an updated version of the first data array, taking into account any differences between cytometric instruments when the first data array was acquired and when the fourth data array was acquired. In 1712, one or more gates defined for the detected locations of clusters in the first data array are mapped onto the third data array, allowing for automatic gating of cell populations within the unknown sample of interest. In this way, time-dependent shifts in instrument performance, differences between instruments, or changes in instrument settings are accounted for. This method also mitigates the variability between any operators seen in manual gating.

[0148] Figures 18A–18D provide a graphical representation of how defining one or more gates to the first data array, or ScatterGrid / ScatterBridge, enables the widespread use of automated gating features without concerns about recalibration or shifts in instrument characteristics and parameters.

[0149] All publications, patents, patent applications, and other documents referenced herein are incorporated herein by reference in whole for all purposes to the same extent that each individual publication, patent, patent application, or other document is individually indicated to be incorporated herein by reference for all purposes.

[0150] While various specific embodiments have been illustrated and described, it will be understood that various modifications can be made without departing from the spirit and scope of the invention.

[0151] While various embodiments of the systems, methods, and apparatus have been described above, it should be understood that they are presented merely as examples and not as limitations. Where the above-described methods and steps describe specific events occurring in a particular order, a person skilled in the art who is interested in this disclosure will recognize that the order of certain steps can be modified, and such modifications constitute variations of the invention. Furthermore, certain steps may be performed simultaneously in parallel processes as far as possible, or sequentially as described above. While embodiments have been specifically shown and described, it will be understood that various modifications of form and detail are possible. While embodiments have been specifically shown and described, it will be understood that various modifications of form and detail are possible. Various embodiments have been described as having certain features and / or combinations of components, but other embodiments are possible having any features and / or combinations of components from any of the above-described embodiments.

[0152] As used herein, the following terms and expressions are intended to have the following meanings:

[0153] The indefinite articles "a" and "an," as well as the definite article "the," are intended to include both singular and plural forms unless the context in which they are used explicitly indicates otherwise.

[0154] "At least one" and "one or more" are used interchangeably because the article can indicate that the listed elements may include one or more.

[0155] Unless otherwise indicated, it should be understood that all numbers used in this specification and in the claims to express quantities, ratios, and numerical properties of components, reaction conditions, etc., are intended to be modified in all instances by the term “approximately.”

[0156] As used herein, the terms “about” and “approximately” generally mean plus or minus 10% of the stated value; for example, about 250 μm includes 225 μm to 275 μm, and about 1,000 μm includes 900 μm to 1,100 μm.

[0157] As used herein, the term "gating" refers to the selection of a contiguous subpopulation of cells for analysis in flow cytometry.

[0158] In this disclosure, references to singular items should be understood to include plural items unless otherwise explicitly stated or evident from the context, and vice versa. Grammatical confoundings are intended to represent any and all auxiliary and auxiliary combinations of combined clauses, sentences, words, etc., unless otherwise stated or evident from the context. Thus, the term “or” should generally be understood to mean “and / or.” Any or all embodiments provided herein, or any use of exemplary language (such as “for example,” “etc,” “including,” etc.) is intended merely to better illustrate the embodiments and does not impose any limitation on the embodiments or claims.

Claims

1. A non-temporary processor-readable medium for storing instructions, wherein when the instructions are executed by the processor, the processor... At a first point in time, receiving a first data array associated with a first site metric device, wherein the first data array is Data representing at least one low forward scattering signal output, Data representing at least one high forward scattering signal output, Data representing at least one low side-scatter signal output, Receiving, and including multiple scattered signal data points, including data representing at least one high side scattering signal output, and At the second point in time following the first point in time, Based on the first data array, adjust the site metric instrument parameters, or A non-temporary processor-readable medium that causes the first site metric measurement of the first site metric device to be compared with a second site metric measurement of a second site metric device different from the first site metric device, based on at least one of the first data array or a second data array associated with the second site metric device.

2. The non-temporary processor-readable medium according to claim 1, further storing instructions causing the processor to computationally adjust at least one of a side-scatter signal or a forward-scatter signal based on the first data array and the second data array, wherein the first data array is associated with a first time point in time, and the second data array is associated with a second time point in time occurring after the first time point and after modifications to the settings of the first site-metric device.

3. The aforementioned plurality of scattered signal data points Data representing at least one mid-forward scattering signal output, and The non-temporary processor-readable medium according to claim 1, further comprising data representing at least one intermediate side-scatter signal output.

4. The non-transient processor-readable medium according to claim 1, wherein the first data array includes a two-dimensional arrangement of the scattered signal data points from the plurality of scattered signal data points.

5. The non-transient processor-readable medium according to claim 1, wherein the first data array is generated using a hydrogel particle formulation via a single acquisition run of the first cytometric instrument.

6. The non-transient processor-readable medium according to claim 5, wherein the difference between the at least one low forward scattering signal output and the at least one high forward scattering signal output is due to at least one of the monomer / crosslinker ratio of the hydrogel particle formulation, the gel fraction of the hydrogel particle formulation, or the particle size associated with the hydrogel particle formulation.

7. The non-transient processor-readable medium according to claim 5, wherein the difference between the at least one low side scattering signal output and the at least one high side scattering signal output is due to at least one of the nanoparticle loading of the hydrogel particle formulation, the hydrogel porosity of the hydrogel particle formulation, or the protein conjugate associated with the hydrogel particle formulation.

8. The non-transient processor-readable medium according to claim 1, wherein the first data array is unique to the first site-metric device.

9. The non-transient processor-readable medium according to claim 1, wherein the first data array is unique to the first site-metric device, and the second data array is unique to the second site-metric device.

10. The non-transient processor-readable medium according to claim 1, wherein the plurality of scattered signal data points further include data representing at least one additional level of forward scattered signal output.

11. The non-transient processor-readable medium according to claim 1, wherein the plurality of scattered signal data points further include data representing at least one additional level of lateral scattered signal output.

12. The aforementioned plurality of scattered signal data points The data represented by at least one additional level of forward scattering signal output, The non-transient processor-readable medium according to claim 1, further comprising data representing at least one additional level of side-scatter signal output.

13. A non-temporary processor-readable medium for storing instructions, wherein when the instructions are executed by the processor, the processor... Identifying a first plurality of scattered signal data points generated by a first flow cytometer, wherein the first plurality of scattered signal data points are (i) Data representing forward scattering at the first signal output level, (ii) Data representing forward scattering with a second signal output level greater than the first signal output level, (iii) Data representing forward scattering at a third signal output level greater than the second signal output level, (iv) Data representing lateral scattering at the fourth signal output level, (v) Data representing lateral scattering at a fifth signal output level greater than the fourth signal output level, (vi) Identifying data that represents lateral scattering at a sixth signal output level greater than the fifth signal output level, and Automatically adjusting the control parameters of the first flow cytometer based on the first plurality of scattered signal data points, or A non-temporary processor-readable medium that causes a first set of at least one measurement of the first flow cytometer to be compared with a second set of at least one measurement of a second flow cytometer different from the first flow cytometer, based on at least one of the first plurality of scattered signal data points or a second plurality of scattered signal data points associated with the second flow cytometer.

14. The non-temporary processor-readable medium according to claim 13, further storing instructions for causing the processor to display the first plurality of scattered signal data points such that (i) to (vi) are shown as a clustered set of data points spaced apart from each other via a user interface.

15. The non-temporary processor-readable medium according to claim 13, further storing instructions for causing the processor to display the first plurality of scattered signal data points via a user interface, such that (i) to (vi) are arranged in a two-dimensional array.

16. The automatic adjustment of the control parameters of the first flow cytometer is based on the first plurality of scattered signal data points. The non-transient processor-readable medium according to claim 13, wherein the automatic adjustment compensates for at least one of the following: deterioration of the equipment of the first flow cytometer, signal drift of the first flow cytometer, or noise.

17. The automatic adjustment of the control parameters of the first flow cytometer is based on the first plurality of scattered signal data points. The non-transient processor-readable medium according to claim 13, wherein the automatic adjustment improves the response linearity across a plurality of subsequent measurements of the first flow cytometer.

18. The non-temporary processor-readable medium according to claim 13, further storing instructions for the processor to modify sample data based on the first plurality of scattered signal data points.

19. The non-temporary processor-readable medium according to claim 18, wherein the sample data includes sample data generated by the first flow cytometer and sample data generated by the second flow cytometer.

20. The aforementioned plurality of scattered signal data points (vii) Data representing forward scattering at a seventh signal output level greater than the third signal output level, (viiii) data representing lateral scattering at an eighth signal output level greater than the sixth signal output level, further comprising the non-temporary processor-readable medium according to claim 13.

21. The non-transient processor-readable medium according to claim 13, wherein the first plurality of scattered signal data points are uniquely associated with the first flow cytometer.

22. The non-transient processor-readable medium according to claim 13, wherein the first plurality of scattered signal data points are generated using a hydrogel particle formulation via a single acquisition run of the first flow cytometer.

23. The non-transient processor-readable medium according to claim 22, wherein the difference between any two of the first signal output level, the second signal output level, or the third signal output level is due to at least one of the monomer / crosslinking agent ratio of the hydrogel particle formulation, the gel fraction of the hydrogel particle formulation, or the particle size associated with the hydrogel particle formulation.

24. The non-transient processor-readable medium according to claim 22, wherein the difference between any two of the fourth signal output level, the fifth signal output level, or the sixth signal output level is due to at least one of the nanoparticle loading of the hydrogel particle formulation, the hydrogel porosity of the hydrogel particle formulation, or the protein conjugate associated with the hydrogel particle formulation.

25. A non-temporary processor-readable medium for storing instructions, wherein when the instructions are executed by the processor, the processor... The processor receives a first data array and a second data array, each associated with a site metric device, wherein the first data array is Data representing at least one low forward scattering signal output, Data representing at least one high forward scattering signal output, Data representing at least one low side-scatter signal output, Includes a plurality of scattering signal data points, including data representing at least one high side scattering signal output, The second data array includes scattered signal data points associated with the target sample, and is received. A non-temporary processor-readable medium that causes to define one or more gates for a cell population in the target sample with respect to the positions of the plurality of scattered signal data points of the first data array.

26. The aforementioned processor, A non-temporary processor-readable medium for further storing instructions that cause the location of the plurality of scattered signal data points in the first data array to be detected by detecting clusters of scattered signals using a machine learning method.

27. The non-temporary processor-readable media according to claim 26, wherein the machine learning method includes a DBScan or K-means having a defined number of clusters.

28. A non-temporary processor-readable medium for storing instructions, wherein when the instructions are executed by the processor, the processor... Obtaining one or more gates defined according to claim 25, Receiving a third data array containing scattered signal data points corresponding to the unknown sample of the target, Receiving a fourth data array which includes a plurality of scattered signal data points corresponding to the plurality of scattered signal data points of the first data array, A non-transient processor-readable medium that causes one or more gates to be mapped to the third data array based on clusters of scattered signals detected within the plurality of scattered signal data points of the fourth data array, thereby automatically gate a population of cells in the unknown sample of the target.

Images