Cellular control, reference and calibration using hydrogel particles

Abstract

A non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to receive a first array of data associated with a first cytometry instrument at a first time. The first data array includes a set of scattered signal data points including data representative of at least one low forward scattered signal output, at least one high forward scattered signal output, at least one low lateral scattered signal output, and at least one high lateral scattered signal output. At a second time subsequent to the first time, cytometry instrument parameters are adjusted based on the first data array, and / or cytometry instrument parameters are adjusted based on at least one of the first data array or a second data array associated with a second cytometry instrument different from the first cytometry instrument. A first cytometry measurement of the first cytometry instrument is coordinated with a second cytometry measurement of the second cytometry instrument.

Description

Cross-references to related applications

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

[0002] This disclosure relates to cell analysis techniques such as flow cytometry, and more specifically, to the conditioning and cross-calibration of cell analysis instruments based on multi-population datasets associated with hydrogel particles. Background Technology

[0003] Flow cytometry and high-throughput cell analysis (e.g., high-content imaging) are techniques that allow for the rapid isolation, counting, and characterization of individual cells and are routinely used in a variety of 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, and high-content imaging.

[0004] The varying hardware components and measurement principles of different cytometers result in differences in the scaling and sensitivity of scattering data acquisition. However, while widely adopted and commercially available multi-level reference materials exist for fluorescence, no such standard exists for simultaneous forward scattering (FSC) and side scattering (SSC), making the standardization of scattering measurements across instruments particularly challenging. To date, the mainstream approach has been to simply measure patient samples in a trial-and-error manner and manually adjust settings until consistent results are obtained. This is not only time-consuming and biohazardous, but also increases sample variability and is impractical for rare or expensive samples. Therefore, a new approach is needed. Summary of the Invention

[0005] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a processor, cause the processor to receive a first data array associated with a first cytology instrument at a first time. The first data array includes a set of scattering signal data points, which includes data representing at least one low forward scattering signal output, at least one high forward scattering signal output, at least one low side scattering signal output, and at least one high side scattering signal output. At a second time following the first time, cytology instrument parameters are adjusted based on the first data array, and / or a first cytology measurement result of the first cytology instrument is coordinated with a second cytology measurement result of the second cytology instrument based on at least one of the first data array or a second data array associated with a second cytology instrument different from the first cytology instrument.

[0006] In some embodiments, a non-transitory processor-readable medium stores instructions that, when executed by a 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 lateral scattering at a fourth signal output level, (v) data representing lateral scattering at a fifth signal output level greater than the fourth signal output level, and (vi) data representing lateral scattering at a sixth signal output level greater than the fifth signal output level. The non-transitory processor-readable medium also stores instructions that, when executed by the processor, cause the processor to perform at least one of the following: automatically adjust the control parameters of the first flow cytometer based on the first plurality of scattering signal data points, or coordinate at least one set of first-group measurements of the first flow cytometer with at least one set of second-group measurements of the second flow cytometer based on the first plurality of scattering signal data points or at least one of a second plurality of scattering signal data points associated with a second flow cytometer different from the first flow cytometer. Attached Figure Description

[0007] The patent or application documents contain at least one drawing in color. A copy of the patent or patent application publication with color drawings will be provided by the Patent Office upon request and payment of the necessary fees.

[0008] It should be understood that the accompanying drawings are primarily for illustrative purposes and are not intended to limit the scope of the subject matter described herein.

[0009] Figures 1A to 1B The flow cytometry images of forward scattering (FSC) versus side scattering (SSC) based on some implementation schemes show nine distinct signal clusters. Figure 1C The flow cytometry image showing the forward scattering area (FSC-A) versus forward scattering height (FSC-H) is shown. Figures 1A to 1B The FSC-A diagram shows nine unique signal groups (which are represented as follows) Figure 1C The three unique signal clusters shown in the FSC-A to FSC-H diagrams can be generated by a set of hydrogel particles, referred to herein as a “scatter grid” or “scatter bridge.” The unique signal clusters can be based on the specific composition and size of the hydrogel particles that produce the associated scattering. For example, while each of the scatter grid and scatter bridge generates nine unique signal clusters that can be used in the same method discussed herein, the scatter grid provides a broadly applicable signal array based on a fixed set of hydrogel particle compositions and sizes, while the scatter bridge provides a tunable signal array where the composition and size of the hydrogel particles that generate the signal array can be based on instrument gain settings and parameters and / or the cell population of interest for cytometry purposes. The scatter bridge allows for the customization of the “linear” range of the scattering spectrum for specific use cases. Figure 1A Scattering data based on scattering grids are shown in nine distinct signal groups. Figure 1B and Figure 1C Scattering data based on the scattering bridge are shown.

[0010] Figure 2 This is a diagram illustrating a system for adjusting and / or cross-calibrating a flow cytometer using scattering data based on a scattering grid (e.g., a multi-population dataset generated using hydrogel particles), according to some embodiments.

[0011] Figure 3A , Figure 4A and Figure 5A Exemplary arrays of scattering data based on scattering grids according to several embodiments are depicted, each array sequentially annotated with subsets of data generated using hydrogel particles (e.g., beads) with different average diameters (10 micrometers (µm), 20 µm, and 25 µm, respectively). In other words, Figure 3A The three gated signal groups are low forward scattering datasets generated by hydrogel particles with an average diameter of 10 μm. Figure 4A The three gated signal groups are medium (or high, when only two scattering levels are used) forward scattering datasets generated from hydrogel particles with an average diameter of 20 μm. Figure 5A The three gated signal groups are high forward scattering datasets generated by hydrogel particles with an average diameter of 25 μm.

[0012] Figures 3B to 3C , Figures 4B to 4C and Figures 5B to 5C Exemplary arrays of scattering data based on scattering bridges according to several embodiments are depicted, each array sequentially annotated with subsets of data generated using hydrogel particles (e.g., beads) with different average diameters (10 micrometers (µm), 14 µm, and 18 µm, respectively). In other words, Figures 3B to 3C The three gated signal groups are low forward scattering datasets generated by hydrogel particles with an average diameter of 10 μm. Figures 4B to 3C The three gated signal groups in the dataset are medium (or high, when only two scattering levels are used) forward scattering datasets generated from hydrogel particles with an average diameter of 14 μm. Figures 5B to 5C The three gated signal groups are high forward scattering datasets generated by hydrogel particles with an average diameter of 18 μm.

[0013] Figures 6A to 6C Exemplary scattering-grid-based scattering datasets generated by three different flow cytometers (Cytek® NL-2000 flow cytometer, Cytek® Aurora flow cytometer, and Beckman Coulter® Cytoflex 5 flow cytometer) according to some embodiments are shown, each scattering-grid-based scattering dataset comprising nine gated clusters (or gated hydrogel clusters). As used herein, a “gated cluster” can refer to a subselected set of data points (see, for example, Figures 6A to 6C The rectangle in the image represents a "gate" for a group of events.

[0014] Figures 7A to 7B This illustrates, according to some implementation schemes, the two different flow cytometers (BD® Biosciences FACSLyric) TM The exemplary scattering datasets generated by the flow cytometer and the Beckman Coulter® Cytoflex LX flow cytometer consist of nine clusters (sets of sub-selected data points).

[0015] Figures 8A to 8C The diagram illustrates a scattering dataset based on a scattering grid generated by a single Cytek® NL-2000 flow cytometer at three different times, according to one embodiment: before preventative maintenance of the Cytek® NL-2000 flow cytometer, after preventative maintenance of the Cytek® NL-2000 flow cytometer, and after preventative maintenance and adjustment of the FSC gain of the Cytek® NL-2000 flow cytometer.

[0016] Figure 9A is a diagram illustrating first data generated by a first flow cytometer according to one embodiment, the first data including scattering data based on a scattering grid, data generated using TruCyte TBNK biomarker mimics (synthetic cells), and data generated using biological cells.

[0017] Figure 9 B is a graph illustrating second data generated by a second flow cytometer (Cytek® Aurora flow cytometer) according to one embodiment. This second data includes scattering data based on a scattering grid. The graph also shows two groups of hydrogel particles intended to simulate the forward and side scattering signals of a biological sample analyzed on a first flow cytometer.

[0018] Figure 10 This is a flowchart illustrating a method, according to some embodiments, for adjusting cytology instruments and / or coordinating cytology measurement results based on scattering data based on a scattering grid or a scattering bridge.

[0019] Figure 11 This is a flowchart illustrating a method, according to some embodiments, for automatically adjusting control parameters of a flow cytometer and / or coordinating flow cytometer measurement results based on scattering data based on a scattering grid or a scattering bridge.

[0020] Figures 12A to 12C This is a graphical explanation of an implementation method for automatically adjusting the control parameters of a flow cytometer and / or coordinating flow cytometer measurement results, based on some implementation schemes.

[0021] Figure 13 This is a graphical representation of the flow cytometry scattering spectrum of scattering data based on scattering bridges, generated on a Cytek® Aurora flow cytometer with 3×3 FSC / SSC signals.

[0022] Figure 14A This is a histogram of forward scattering from selected groups of scattering data based on scattering bridges, measured on a Cytek® Aurora flow cytometer. Figure 14B This is a histogram of lateral scattering from selected groups based on scattering data measured on a Cytek® Aurora flow cytometer. (Example:) Figure 14A and Figure 14B As shown, a selected group of scattering data based on the scattering bridge generates a multi-level signal.

[0023] Figure 15A and Figure 15B It was performed on a Beckman Coulter® CytoFLEX S flow cytometer ( Figure 15A ) and BD® FACSLyric TM Flow cytometer ( Figure 15B Graphical representation of flow cytometry scattering spectra of scattering data based on scattering bridges that generate 3×3 FSC / SSC signals. Figure 15A and Figure 15B It demonstrates that inter-instrument variability can be corrected and / or controlled by using scattering data based on scattering bridges.

[0024] Figure 16 This is a graphical representation of the flow cytometry scattering spectra of CAR-T cells (1) and THP-1 cells (2) covered on a Cytek® Aurora flow cytometer based on scattering bridge-based scattering data (S).

[0025] Figure 17 This is a flowchart of a method for automatic gating to identify cell populations, based on some implementation schemes.

[0026] Figures 18A to 18D It is a graphical representation of automated gating based on some implementation schemes.

[0027] Figure 19 It is a graphical depiction showing the relative positions of biological cells with respect to the scattering bridge from Table 1. Detailed Implementation

[0028] Flow cytometry and high-throughput cytometry analysis can be used to determine beads (e.g., for biochemical measurements). In some such implementations, a beam of light is directed onto a focused liquid stream containing beads. Multiple detectors are then aligned with the point where the stream passes through the beam, with one detector aligned with the beam (e.g., to detect forward scattering (“FSC”)) and several detectors perpendicular to the beam (e.g., to detect side scattering (“SSC”)). FSC and SSC measurements are often referred to as “passive optical properties.” For particles such as cells (e.g., human cells), FSC is typically correlated with cell volume, while SSC is typically correlated with the internal complexity or size of the particle (e.g., nuclear shape, amount and type of cytoplasmic particles, or membrane roughness). As a result of these correlations, different specific cell types can exhibit different FSCs and SSCs, allowing cell types to be distinguished from each other based on their passive optical properties in flow cytometry. These measurements—FSC and SSC—form the basis of cytometry analysis in clinical and research settings. Most synthetic or polymeric products used for this type of cell analysis are made of (or substantially contain) opaque polystyrene or latex polymers, typically with fixed FSC and SSC values ​​based on the diameter of the particles themselves. Therefore, polystyrene particles of the same diameter cannot usually be distinguished from each other based solely on passive optical properties (FSC and SSC).

[0029] However, many flow cytometer vendors use quality control (QC) materials such as beads to track signal drift in their instruments over time. Typically, for forward scattering (FSC) / side scattering (SSC) calibration, a single bead cluster with a known (or “characteristic”) FSC / SSC location on the light scattering map is used as a reference material. However, this is equivalent to calibrating a linear instrument using single-point calibration. While providing some basic concepts about drift, this type of calibration often fails to reveal degradations or inconsistencies in the linearity and scaling of scattering measurements.

[0030] Furthermore, different cytometers use different measurement principles, which often leads to varying 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 used in flow cytometry provide a control for fluorescence intensity measurements and cell counts, but not for forward and side-scattering signal intensities. Therefore, cross-comparing results from different cytometers is particularly challenging, hindering scientific collaboration and increasing development time. Moreover, known methods for cross-comparing results from different cytometers involve simply using the actual sample of interest on a trial-and-error basis and manually adjusting settings until the results are consistent. This is not only time-consuming but also impractical for rare samples or low-event-rate populations.

[0031] Some embodiments of this disclosure address the aforementioned challenges using “scattering grids” or “scattering bridges,” each defined herein as a multi-group signal output map for cytological control, reference, and calibration. Scattering grids and scattering bridges can be generated using a range of reagents, including hydrogel particles with different optical properties (e.g., combinations of multiple different levels of FSCs and SSCs) due to their composition and / or size. A single scattering grid or scattering bridge can include a representation of multiple different levels (e.g., high, medium, and low) of forward-scattered signal outputs and multiple different levels (e.g., high, medium, and low) of side-scattered signal outputs within a common map.

[0032] In some implementations, the scattering grid or scattering bridge comprises multiple scattering clusters (i.e., groups of scattering-related data / data points). For example, in some implementations, the scattering grid or scattering bridge consists of nine unique equidistant signal scattering clusters generated during a single flow cytometry acquisition run. When a laboratory operator performs scattering grid or scattering bridge measurements on the flow cytometer, as a result of a single acquisition run, they record / detect the output of a signal matrix (e.g., a 3×3 signal matrix) that includes recorded low, medium, and high signal outputs from each of the forward and side scattering.

[0033] Figures 1A to 1B This is a graph showing flow cytometry data for forward scattering area (FSC-A) versus side scattering area (SSC-A), and Figure 1C This is a graph showing the flow cytometry data of FSC-A versus FSC-H. Figure 1A and Figure 1B Each of them illustrates, according to some implementation schemes, nine unique signal groups (a set of sub-selected data points) in a single scattering bridge. Figure 1C Three distinct signal groups (sets of sub-selected data points) from the scattering bridge are shown. To achieve variations in SSC, the nanoparticle loading in the formulation is altered. To achieve variations in FSC, the monomer / crosslinker ratio and the size of the hydrogel particles (and optionally, the size of the microfluidic channels) are varied. Although in Figure 1A and Figure 1B The diagram shows nine signal groups included in a 3×3 signal matrix, but various other numbers of signal groups and matrix sizes can also be envisioned (e.g., signal groups of 2, 3, 4, 5, 6, 7, 8, 10, 12, 20, 24, etc.; matrix sizes of 1×2, 1×3, 2×2, 4×1, 1×5, 2×3, 1×6, 1×7, 2×4, 1×8, 2×5, 1×10, 2×6, 3×4, 1×12, 5×4, 6×4, etc.).

[0034] In some embodiments, variations in SSC are achieved via the inclusion of nanoparticles (including proteins) in the hydrogel particles (or hydrogel particle formulations) and / or via the porosity of the hydrogel particles in the particle formulation. Alternatively or additionally, variations in FSC are achieved by adjusting the gel fraction / polymer content of the hydrogel particles. Alternatively or additionally, one or more fluorophores, nucleic acids, functional groups, and / or biomarkers may be added to the hydrogel particles (or hydrogel particle formulations) suitable for the desired application.

[0035] In some embodiments, the hydrogel particles of this disclosure comprise a material comprising a macromolecular three-dimensional network that allows the material to swell in the presence of water and shrink in the absence of water (or by reducing the amount of water), but is insoluble in water. Swelling, i.e., water absorption, is a result of the presence of hydrophilic functional groups connected to or dispersed within the macromolecular network. Crosslinking between adjacent macromolecules causes the water insolubility of these hydrogels. Crosslinking can be due to chemical (i.e., covalent) or physical (i.e., van der Waals forces, hydrogen bonds, ionic forces, etc.) bonds. Synthetically prepared hydrogels can be prepared by polymerizing monomeric materials to form a backbone and crosslinking that backbone with a crosslinking agent. A characteristic of hydrogels with specific values ​​is that the material retains its general shape, whether dehydrated or hydrated. Thus, if a hydrogel has a generally spherical shape in its dehydrated state, it will be spherical in its hydrated state.

[0036] In one embodiment, the hydrogel particles disclosed herein comprise more than about 30%, more than about 40%, more than about 50%, more than about 55%, more than about 60%, more than about 65%, more than about 70%, more than about 75%, more than about 80%, more than about 85%, more than about 90%, or more than about 95% water. In another embodiment, the water content of the hydrogel particles is about 10% by weight to about 95% by weight, or about 20% by weight to about 95% by weight, or about 30% by weight to about 95% by weight, or about 40% by weight to about 95% by weight, or about 50% by weight to about 95% by weight, or about 60% by weight to about 95% by weight, or about 70% by weight to about 95% by weight, or about 80% by weight to about 95% by weight.

[0037] In one embodiment, the hydrogel particles have one or more optical properties substantially similar to those of one or more target cells. For example, the hydrogel particles may have the optical properties of one or more peripheral blood mononuclear cells, CAR-T cells, and THP-1 cells; however, it should be understood that any biological cell type and disease can be referenced when determining the scattering grid or scattering bridge to be used. In one embodiment, the hydrogel particles have predetermined optical properties. In one embodiment, the optical property is SSC, FSC, fluorescence emission, or a combination thereof. Exemplary relationships between biological cells and scattering bridges are illustrated in... Figure 19 The table shows that the system described herein can be adjusted to accommodate a variety of biological cell types. Table 1 includes exemplary cell mimics and biological cells.

[0038] Table 1. Cell mimics and biological cells.

[0039]

[0040]

[0041] In one embodiment, the hydrogel particles are synthesized by polymerizing one or more monomers provided herein. Synthesis is carried out to form individual hydrogel particles. In one embodiment, the monomer material (monomer) is polymerized to form a homopolymer. However, in another embodiment, copolymers of different monomer units (i.e., comonomers) are synthesized and used in the methods provided herein. In one embodiment, the monomer or comonomer used in the methods and compositions described herein is a bifunctional monomer or includes a bifunctional monomer (wherein the comonomer is employed). In one embodiment, the hydrogel particles are synthesized in the presence of a crosslinking agent. In another embodiment, the hydrogel particles are synthesized in the presence of a polymerization initiator. The amount of monomer can be varied by the user to obtain, for example, specific optical properties substantially similar to those of target cells. In one embodiment, the monomer component (i.e., monomers, comonomers, bifunctional monomers or combinations thereof, such as bis / acrylamides, allylamines or other comonomers providing chemical functional groups for secondary labeling / combination, or alginates) is present in the form of about 10% by weight to about 95% by weight of the hydrogel particles. In another embodiment, the monomer component is present in the form of about 15% by weight to about 90% by weight of the hydrogel particles, or about 20% by weight to about 90% by weight of the hydrogel particles.

[0042] Examples of various monomers and crosslinking chemicals that can be used in this invention are provided in the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf, the disclosure of which is incorporated herein by reference in its entirety for all purposes). For example, hydrazine (e.g., with NHS ester compounds) or EDC coupling reactions (e.g., with maleimide compounds) can be used to construct the hydrogel particles of this invention.

[0043] In one embodiment, the monomers used for the 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, glyceryl methacrylate (GMA), ethylene glycol methacrylate, ethylene glycol, fumaric acid, their derivatives, 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, glyceryl methacrylate, hydroxypropyl methacrylate, hydroxybutyl methacrylate, or combinations thereof.

[0044] In another embodiment, one or more of the following monomers are used herein to form tunable hydrogel particles: acylated phenyl ester, phenyl methacrylate, acylated benzyl ester, benzyl methacrylate, 2-phenylethyl acrylate, 2-phenylethyl methacrylate, 2-phenoxyethyl acrylate, 2-phenoxyethyl methacrylate, phenyl thioethyl acrylate, phenyl thioethyl methacrylate, 2,4,6-tribromophenyl acrylate, 2,4,6-tribromophenyl methacrylate, pentabromophenyl acrylate, pentabromophenyl methacrylate, propylene glycol 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, 2-phenoxyethoxyethyl acrylate, 2-phenoxyethoxyethyl methacrylate, N-phenylacryloyl Amine, N-phenylmethylacrylamide, N-benzylacrylamide, N-benzylmethylacrylamide, N,N-dibenzylacrylamide, N,N-dibenzylmethylacrylamide, N-diphenylmethylacrylamide, N-(4-methylphenyl)methylacrylamide, N-1-naphthylacrylamide, N-4-nitrophenylacrylamide, N-(2-phenylethyl)acrylamide, N-triphenylmethylacrylamide, N-(4-hydroxyphenyl)acrylamide, N,N-methylphenylacrylamide, N,N-phenylbenzene Ethylacrylamide, N-diphenylmethylmethacrylamide, N-(4-methylphenyl)methylmethacrylamide, N-1-naphthylmethacrylamide, N-4-nitrophenylmethacrylamide, N-(2-phenylethyl)methylacrylamide, N-triphenylmethylmethacrylamide, N-(4-hydroxyphenyl)methylacrylamide, N,N-methylphenylmethacrylamide, N,N′-phenylphenylethylmethacrylamide, NN-vinylcarbazole, 4-vinylpyridine, and 2-vinylpyridine.

[0045] Both synthetic and biological monomers can be used in the hydrogel particles provided herein to form synthetic, bio-hydrogels, or hybrid hydrogels comprising synthetic and biological components (e.g., peptides, proteins, monosaccharides, disaccharides, polysaccharides, primary amine thiol groups, carbonyl groups, carbohydrates, and carboxylic acids present on biomolecules). For example, proteins, peptides, or carbohydrates can be used as individual monomers to form hydrogels that may or may not include synthetic monomers (or polymers) and are combined with chemically compatible comonomers and crosslinking chemicals (see, for example, Thermo Scientific Crosslinking Technical Handbook entitled “Easymolecular bonding crosslinking technology,” available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf, the entire contents of which are incorporated herein by reference for all purposes). Compatible crosslinking chemicals include, but are not limited to, amines, carboxyl groups, and other reactive chemical side groups.

[0046] Generally, polymeric chemistry / methods of any form commonly known to those skilled in the art can be employed to form polymers. In some embodiments, polymerization can be catalyzed by UV-induced group formation and reaction progression. In other embodiments, the hydrogel particles of this disclosure are produced by the polymerization of acrylamide or acrylate. For example, in one embodiment, the acrylamide is a polymerizable carbohydrate-derived acrylamide. The specific linkage of the acrylamide group to sugar readily applies to a range of monosaccharides and more advanced polysaccharides, such as synthetic polysaccharides or polysaccharides derived from natural sources, such as glycoproteins found in serum or tissues.

[0047] In some embodiments, the hydrogel particles comprise a monofunctional monomer polymerized with at least one bifunctional monomer. One example includes, but is not limited to, the formation of a polyacrylamide polymer using acrylamide and bisacrylamide (bifunctional monomers). In another embodiment, the hydrogel particles provided herein comprise a bifunctional monomer polymerized with a second bifunctional monomer. One example includes, but is not limited to, the formation of polymers having a mixed composition containing compatible chemicals such as acrylamide, bisacrylamide, and bisacrylamide structural homologues containing a wide range of additional chemicals. The range of chemically compatible monomers, bifunctional monomers, and mixed compositions will be apparent to those skilled in the art and follows principles of chemical reactivity known to those skilled in the art. (Refer to Thermo Handbook and Acrylamide Polymerization Handbook). See, for example, the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / ntent / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf) and the Polyacrylamide Emulsions Handbook (SNF Floerger, available at snf.com.au / downloads / Emulsion_Handbook E.pdf), the contents of which are hereby incorporated in their entirety by reference for all purposes.

[0048] In one embodiment, the hydrogel particles provided herein comprise a polymerizable monofunctional monomer and are monofunctional acrylic monomers. Non-limiting examples of monofunctional acrylic monomers used herein are acrylamide; methacrylamide; N-alkylacrylamides, such as N-ethylacrylamide, N-isopropylacrylamide, or N-tert-butylacrylamide; N-alkylmethylacrylamides, such as N-ethylmethylacrylamide or N-isopropylmethylacrylamide; N,N-dialkylacrylamides, such as N,N-dimethylacrylamide and N,N-diethylacrylamide; N-[(dialkylamino)alkyl]acrylamides, such as N-[3] [Dimethylamino)propyl]acrylamide or N-[3-(diethylamino)propyl]acrylamide; N-[(dialkylamino)alkyl]methacrylamide, such as N-[3-dimethylamino)propyl]methacrylamide or N-[3-(diethylamino)propyl]methacrylamide; (dialkylamino)alkyl acrylates, such as 2-(dimethylamino)ethyl acylate, 2-(dimethylamino)propyl acrylate or 2-(diethylamino)ethyl acylate; and (dialkylamino)alkyl methacrylates, such as 2-(dimethylamino)ethyl methacrylate.

[0049] A bifunctional monomer is any monomer that can be polymerized with a monofunctional monomer of the present disclosure to form a hydrogel as described herein, the monomer containing a second functional group that can participate in a second reaction (e.g., conjugation of a fluorophore or a cell surface receptor (or its domain)).

[0050] In some embodiments, the bifunctional monomer is selected from the group consisting of allylamine, allyl alcohol, allyl isothiocyanate, allyl chloride, and allyl maleimide.

[0051] The bifunctional monomer can be a bifunctional acrylic monomer. Non-limiting examples of bifunctional acrylic monomers are N,N′-methylenebisacrylamide, N,N′-methylenebismethylacrylamide, N,N′-ethylenebismethylacrylamide, N,N′-propylenebisacrylamide, and N,N′-(1,2-dihydroxyethylene)bisacrylamide.

[0052] More advanced branched and linear comonomers can be substituted in the polymer blend to modulate the refractive index while maintaining the polymer density. In some embodiments, the hydrogel particles contain molecules that modulate the optical properties of the hydrogel. Molecules capable of altering the optical properties of hydrogels are discussed further below.

[0053] In one embodiment, individual hydrogel particles or multiple hydrogel particles comprise a biodegradable polymer as a hydrogel monomer. In one embodiment, the biodegradable polymer is a poly(ester) based on polylactide (PLA), polyglycolic acid (PGA), polycaprolactone (PCL), and copolymers thereof. In one embodiment, the biodegradable polymer is a carbohydrate or 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 poly(hydroxyalkanoates) of the PHB-PHV class, additional poly(esters), and natural polymers, such as modified poly(saccharides), for example, starch, cellulose, and chitosan. In another embodiment, the biocompatible polymer is an adhesion protein, cellulose, carbohydrate, starch (e.g., maltodextrin, 2-hydroxyethyl starch, alginate), dextran, lignin, polyamino acids, amino acids, or chitin. Such biodegradable polymers are commercially available, for example, from Sigma Aldrich (St. Louis, Mo.).

[0054] 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 functionalized collagen domain), an acrylamide or acrylate-functionalized peptide, or an acrylamide or acrylate-functionalized monosaccharide, disaccharide, or polysaccharide.

[0055] Any monosaccharide, disaccharide, or polysaccharide (functionalized or otherwise) can be used as a hydrogel monomer. In one embodiment, acrylamide or acrylate-functionalized monosaccharides, disaccharides, or polysaccharides are used as polymerizable hydrogel monomers. In one embodiment, structural polysaccharides are used as polymerizable hydrogel monomers. In another embodiment, the structural polysaccharide is arabinoylxylan, cellulose, chitin, or pectin. In yet another embodiment, 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 yet another embodiment, GAGs are chondroitin sulfate, dermatan sulfate, keratin sulfate, heparin, heparin sulfate, or hyaluronic acid (also known in the art as hyaluronan or hyaluronic acid salts) used as polymerizable hydrogel monomers. Further scope of compatible biomonomers and their reactive chemicals is known to those skilled in the art and follows general principles of chemical reactivity.

[0056] Naturally occurring hydrogels include a variety of polysaccharides available from natural sources such as plants, algae, fungi, yeast, marine invertebrates, and arthropods. Non-limiting examples include agarose, dextran, chitin, cellulose-based compounds, starch, and derived starches. These typically have repeating glucose units as the main component of the polysaccharide backbone. Crosslinking chemicals for such polysaccharides are known in the art; see, for example, the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0057] In one embodiment, hyaluronic acid is used as a hydrogel monomer (as a single monomer or as a comonomer). In one embodiment, hyaluronic acid is functionalized, for example, with acrylate or acrylamide. Hyaluronic acid is a high molecular weight GAG composed of disaccharide repeating units of N-acetylglucosamine and glucuronic acid linked together by alternating β-1,4 and β-1,3 glycosidic bonds. In the human body, hyaluronic acid salts are present in several types of soft connective tissues, including skin, umbilical cord, synovial fluid, and vitreous humor. Therefore, in one embodiment, where it is desired to mimic one or more optical properties of skin cells, umbilical cord cells, or vitreous humor cells, hyaluronic acid is used as a hydrogel monomer. Depending on the desired crosslinking chemistry and other monomers used to form hydrogel particles, hyaluronic acid can be derived using a variety of reactive handles.

[0058] In yet another embodiment, a linear polysaccharide chitosan composed of randomly distributed β-(1-4)-linked D-glucosamine (deacetylation unit) and N-acetyl-D-glucosamine (acetylation unit) is used as a hydrogel monomer (as a single monomer or as a comonomer).

[0059] Other polysaccharides used as hydrogel monomers or comonomers include, but are not limited to, agar, agarose, alginic acid, alginate, α-glucan, amylopectin, amylose, arabinoxylan, β-glucan, callose, capsullan, carrageenan (e.g., κ, ι, or λ types), cellulosic acid, animal cellulose, cellulose, chitin, chitosan, leucine, gelatin, cyclodextrin, α-cyclodextrin, dextrin, sucrose, fructan, fucoidan, galactomannan, galactomannan, galactosamine galactomannan, gellan gum, dextran, glucomannan, glucuronic acid xylan, glycocalyx, glycogen, and hemi- Cellulose, homopolysaccharides, hydroxypropyl methylcellulose, icodextrin, inulin, kefiran, kelp polysaccharides, lentinan, lentinan, polysaccharides, lichen polysaccharides, mannan, mixed-linked dextran, euglena starch, pectic acid, pectin, pentameric starch, plant glycogen, pleuran, polydextrose, polysaccharide peptides, laver polysaccharides, pullulan, schizofiran, sinistrin, sizofiran, vetiver gum, xanthan gum, xylan, xyloglucan, yeast polysaccharides, or combinations thereof. As described throughout, the polysaccharides may be further functionalized depending on the desired crosslinking chemicals and / or additional comonomers used in the hydrogel. For example, in one embodiment, one or more of the polysaccharides described herein are functionalized with acrylates or acrylamide.

[0060] In one embodiment, individual hydrogel particles or multiple hydrogel particles comprise peptides, proteins, protein domains, or combinations thereof as hydrogel monomers or multiple hydrogel monomers. In another embodiment, the protein is a structural protein or its domain, such as filament, elastin, titin, or collagen or its domain. In one embodiment, the protein is an extracellular matrix (ECM) component (e.g., collagen, elastin, proteoglycan). In even further embodiments, the structural protein is collagen. In yet another embodiment, the collagen is type I collagen, type II collagen, or type III collagen or a combination thereof. In yet another embodiment, the hydrogel monomer comprises proteoglycan. In a further embodiment, the proteoglycan is a core proteoglycan, a disaccharide, a testosterone, a bikunitz inhibitor, fibrinolytic proteoglycan, a photoproteoglycan, or its domain.

[0061] In another embodiment, acrylate-functionalized structural protein hydrogel monomers are used as components of the hydrogels provided herein (e.g., acrylate-functionalized proteins or protein domains, such as filaments, elastin, titin, collagen, proteoglycans, or their functionalized domains). In yet another embodiment, the acrylate-functionalized structural protein hydrogel monomers comprise proteoglycans, such as core proteoglycans, disaccharides, testisin, bisKunitz inhibitors, fibrinolytic proteoglycans, photoproteoglycans, or their domains.

[0062] In one embodiment, the PEG monomer and oligopeptide may be mimicking extracellular matrix proteins, such as vinyl sulfone-functionalized multi-arm PEG, integrin-binding peptides, and biscysteine ​​matrix metalloproteinase peptides, used in the hydrogel provided herein. In this particular embodiment, the hydrogel is formed via a Michael-type addition reaction on PEG between the dithiolized oligopeptide and a vinyl sulfone group. A range of other compatible chemicals that may be incorporated herein will be apparent to those skilled in the art and follow general principles of chemical reactivity, see, for example, the ThermoScientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0063] Other bioactive domains from natural proteins can also be used as hydrogel monomers or portions thereof. For example, cell adhesion integrin-binding domains, controlled-release affinity-binding domains, or transglutaminase cross-linking domains can be used in the hydrogels presented herein.

[0064] In one embodiment, a recombinant DNA method is used to produce a protein designed to gel in response to changes in pH or temperature. In short, the protein consists of terminal leucine zipper domains flanked by water-soluble polyelectrolyte segments. In near-neutral aqueous solutions, the coiled-helical aggregates of these terminal domains form a three-dimensional hydrogel polymer network.

[0065] Common crosslinking agents that can 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. A range of other crosslinking chemicals that can be used will be apparent to those skilled in the art and follow general principles of chemical reactivity, see, for example, the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf).

[0066] In one embodiment, the polymerization of the hydrogel is initiated by an equivalent initiator formed from a persulfate or a catalytic group. The range of compatible initiators is known to those skilled in the art and follows general principles of chemical reactivity, see, for example, the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / sfs / brochures / 1602163-Crosslinking-Reagents-Handbook.pdf). The persulfate can be any water-soluble persulfate. Non-limiting examples of water-soluble persulfates are ammonium persulfate and alkali metal persulfates. Alkali metals include lithium, sodium, and potassium. In some embodiments, the persulfate is ammonium persulfate or potassium persulfate. In another embodiment, the polymerization of the hydrogel provided herein is initiated by ammonium persulfate.

[0067] The polymerization of hydrogels can be accelerated by an accelerator that catalyzes the formation of polymerization-unstable chemical side groups. A range of possible accelerators is known to those skilled in the art and follows general principles of chemical reactivity, see, for example, the Thermo Scientific Crosslinking Technical Handbook entitled “Easy molecular bonding crosslinking technology” (available at tools.lifetechnologies.com / content / 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, an accelerator is used in the polymerization reaction and the accelerator is N,N,N′,N′tetramethylethylenediamine, 3-dimethylamino)propionitrile, or N,N,N′,N′tetramethylethylenediamine (TEMED). In another embodiment, the accelerator is used in the polymerization reaction and is azobis(isobutyronitrile) (AIBN).

[0068] As discussed above, the hydrogels used in the compositions and methods described herein may comprise any of the monomer units and crosslinking agents as described herein, and in one aspect, are generated as hydrogel particles by polymerizing droplets (see, for example, Figure 2 Microfluidic methods for generating multiple droplets (including fluid and rigid droplets) are known to those skilled in the art. Such methods provide multiple 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 oil-based liquid substantially surrounded by an oil-based liquid).

[0069] Multiple fluid droplets (e.g., prepared using a microfluidic device) may be polydisperse (e.g., having a range of different sizes), or in some cases, the fluid droplets may be monodisperse or substantially monodisperse, for example, having a uniform diameter distribution, such that no more than about 10%, about 5%, about 3%, about 1%, about 0.03%, or about 0.01% of the droplets have an average diameter greater than about 10%, about 5%, about 3%, about 1%, about 0.03%, or about 0.01% of the average diameter. As used herein, the average diameter of a droplet population refers to the arithmetic mean of the diameters of the droplets. 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 customized, for example, by varying the flow rates of the first and second fluids within the channels of the microfluidic device or by varying the volume of the channels of the microfluidic device. Microfluidic devices incorporating microfluidic channels are particularly suitable for preparing multiple monodisperse droplets.

[0070] Therefore, this disclosure provides a group of hydrogel particles comprising a plurality of hydrogel particles, wherein the group of hydrogel particles is substantially monodisperse.

[0071] The droplet size is related to the size of the microfluidic channel. Microfluidic channels can have any size, for example, a maximum dimension perpendicular to the fluid flow that is 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.

[0072] The droplet size can be adjusted by regulating the relative flow rate. In some implementations, the droplet diameter is equal to the width of the channel, or within approximately 10%, 15%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, or 100% of the channel width.

[0073] The size of the hydrogel particles disclosed herein is substantially similar to the size of the droplets forming the hydrogel particles. Therefore, in some embodiments, the hydrogel particles have a diameter of 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 less than 1000 μm. In some embodiments, the hydrogel particles have a diameter greater 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 greater than 1000 μm. In one embodiment, the hydrogel particles have a diameter in the range of 5 μm to 100 μm.

[0074] In some embodiments, the hydrogel particles of this disclosure are spherical in shape.

[0075] In one embodiment, the hydrogel particles are supported by suspension polymerization, also known in the art as bead polymerization, granular polymerization, or particulate polymerization. In suspension polymerization, the monomer is insoluble in a continuous phase, such as an aqueous monomer solution in a continuous oil phase. In suspension polymerization, polymerization initiation occurs within monomer-rich droplets, and each droplet has more than one functional group at any given time. In one embodiment, the monomer phase comprises monomers, which may be bifunctional monomers or multiple monomer species (comonomers, which may be multiple bifunctional monomers). In one embodiment, the monomer phase comprises an initiator and / or a crosslinking agent.

[0076] Emulsion polymerization can also be used to form the hydrogel particles described herein. In emulsion polymerization, the monomers have poor solubility in the continuous phase, similar to suspension polymerization; however, polymerization initiation occurs outside the monomer droplets. In emulsion polymerization embodiments, if a surfactant is present, the initiator causes chain growth of monomers (or comonomers) dissolved in the continuous phase or monomers contained in micelles.

[0077] In another embodiment, the hydrogel particles are formed via precipitation polymerization. Precipitation polymerization is a technique that utilizes the difference in solubility between monomers and polymers to generate microparticles. Specifically, it is known that the solubility of larger polymer chains is generally lower than that of smaller polymer chains. Therefore, above a certain molecular weight, phase separation can be facilitated. Precipitation polymerization initially begins with solution polymerization in a single-phase homogeneous system. Shortly after the start of polymerization, in one embodiment, a relatively high concentration of polymer chains is present, which facilitates phase separation by nucleation. As polymerization proceeds, the concentration of polymer chains decreases, and existing particles capture the chains before the nucleation of new particles can occur. Therefore, particle nucleation occurs only shortly after the start of the reaction and lasts for a short period of time, which in one embodiment results in a narrow particle size distribution. Other methods include, but are not limited to, photolithographic particle formation, film emulsification and microchannel emulsification, and batch emulsification.

[0078] In one embodiment, hydrogel particles are formed within a microfluidic device having two oil channels focused on a central flow of an aqueous monomer solution. In this embodiment, droplets form at the interface between the two channels and the central flow to disrupt droplets in the water-in-oil emulsion. Once droplets form, in one embodiment, the droplets are stabilized prior to polymerization, for example, by adding a surfactant to the oil phase. However, in another embodiment, the droplets are unstable prior to polymerization. In one embodiment, polymerization of the monomer is triggered by adding a promoter (e.g., N,N,N′,N′tetramethylethylenediamine) to one or both oil channels after the initial droplet formation.

[0079] The aqueous monomer solutions provided above may include a single monomer species or multiple monomer species. The aqueous monomer solutions may contain comonomers, bifunctional monomers, or combinations thereof. In one embodiment, the monomers or multiple monomers may include a bifunctional monomer, such as one of the monomers described above. As described below, the comonomers can be used, for example, to modulate forward scattering or side scattering by adjusting the refractive index of the hydrogel particles.

[0080] In one embodiment, the center stream of the aqueous monomer solution contains a crosslinking agent, such as N,N′-bisacrylamide. In another embodiment, in addition to the monomer, the center stream of the aqueous monomer solution also contains a crosslinking agent and a promoter. In yet another embodiment, the aqueous monomer solution contains an initiator, such as an oxidizing agent like ammonium persulfate.

[0081] When preparing hydrogel particles with scattering grids or scattering bridges, forward scattering can be modulated by adjusting the refractive index of the gel through the addition of comonomers allyl acrylate and allyl methacrylate. Forward scattering can also be modulated using lateral scattering nanoparticles (including, but not limited to, higher-density colloidal suspensions of silica and / or PMMA particles) containing sufficient optical resolution / size / density. Lateral scattering of droplets can be modulated 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 prior to polymerization.

[0082] In one embodiment, a bead, multiple beads, a biomolecule, or multiple biomolecules are embedded (encapsulated) within a hydrogel particle. In one embodiment, the encapsulated bead or biomolecule is used to mimic one or more intracellular organelles of a target cell or a cell after phagocytosis of the particle. In one embodiment, the encapsulated or embedded bead or biomolecule is achieved during hydrogel particle formation. For example, the beads may be suspended at an appropriate concentration to allow an average of one bead to be embedded / encapsulated within a single hydrogel particle. The bead suspension may, for example, be included in an aqueous solution of the monomer. Similarly, one or more biomolecules may be incorporated into an aqueous solution of the monomer to encapsulate the one or more biomolecules.

[0083] Alternatively, once the hydrogel particles are formed, for example by the methods described above, in one embodiment, they can be further manipulated, for example by embedding beads, multiple beads, biomolecules, or multiple biomolecules within the hydrogel particles. Therefore, in one aspect of the invention, a hydrogel comprising an embedded substance is provided.

[0084] In one embodiment, the embedded material is an embedded molecule, such as a biomolecule. The biomolecule can be a single species or multiple 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 (e.g., those with different 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 nucleic acid molecules can consist of any protein or nucleic acid, as both forms of biomaterials contain unstable chemical side groups (or can be modified by commercial suppliers (e.g., chemical side group modification using integrated DNA technology)). Such side groups are compatible with the reaction chemistry commonly found in comonomer compositions (e.g., acrylate chemistry, NHS-esters, primary amines, copper-catalyzed click chemistry (Sharpless)). Those skilled in the art will understand the range of possible embedded molecules containing compatible chemicals.

[0085] In one embodiment, such as when implemented within a scattering grid or scattering bridge, different subgroups of hydrogel particles are fabricated, each subgroup containing a different concentration of biomolecules. In another embodiment, the biomolecules are nucleic acids, proteins, intracellular ions such as calcium (or other biomolecules of user choice, such as calcium). In yet another embodiment, different subgroups of hydrogel particles are prepared, each containing a different concentration of a pharmaceutical substance. In one embodiment, the pharmaceutical substance is a biomolecule (i.e., a biologic, antibody, antibody-drug conjugate, protein / enzyme, peptide, nonribosomal peptide, or related molecule) or a small molecule synthetic drug (e.g., type I / II / III polyketide compounds, nonribosomal peptides with bioactive properties, or other small molecule entities typically classified by those skilled in the art).

[0086] In one embodiment, beads with diameters 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.

[0087] In one embodiment, the disclosed hydrogel particles have a refractive index (RI) 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.

[0088] In another embodiment, the refractive index (RI) of the disclosed hydrogel particles is about 1.10 to about 3.0, or about 1.15 to about 3.0, or about 1.20 to about 3.0, or about 1.25 to about 3.0, or about 1.30 to about 3.0, or about 1.35 to about 3.0, or about 1.4 to about 3.0, or about 1.45 to about 3.0, or about 1.50 to about 3.0, or about 1.6 to about 3.0, or about 1.7 to about 3.0, or about 1.8 to about 3.0, or about 1.9 to about 3.0, or about 2.0 to about 3.0.

[0089] In some embodiments, the disclosed hydrogel particles have a refractive index (RI) of 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 about 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.

[0090] The SSC of the disclosed hydrogel particles is the most meaningful measurement compared to the SSC of the target cells. In some embodiments, the SSC of the disclosed hydrogel particles is within 30%, 25%, 20%, 15%, 10%, 5%, or 1% of the SSC of the target cells, as measured by a cytometry device.

[0091] In one embodiment, the SSC of the hydrogel particles is adjusted by incorporating high-refractive-index molecules (or more) into the hydrogel. In one embodiment, the high-refractive-index molecules are provided in the hydrogel particles, and in another embodiment, the high-refractive-index molecules are colloidal silica, alkyl acrylates, alkyl methacrylates, or combinations thereof. Thus, in some embodiments, the hydrogel particles of this disclosure comprise alkyl acrylates and / or alkyl methacrylates. In one embodiment, the concentration of the monomer is adjusted to further adjust the refractive index of the hydrogel particles.

[0092] 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 methyl, ethyl, n-propyl, isopropyl, n-butyl, isobutyl or tert-butyl, 2-ethylhexyl, heptyl or octyl. The alkyl group may be branched or straight-chain.

[0093] High refractive index molecules may also include vinyl aromatics, such as styrene and methylstyrene, which are optionally substituted on the aromatic ring with alkyl groups such as methyl, ethyl or tert-butyl, or with halogens such as chlorostyrene.

[0094] In some embodiments, the forward scattering concentration (FSC) is adjusted by regulating the percentage of monomers present in the composition, thereby altering the water content present during hydrogel particle formation. In one embodiment, when using both monomers and comonomers, the ratio of monomers to comonomers is adjusted to modify the forward scattering properties of the hydrogel particles.

[0095] The FSC of the disclosed hydrogel particles is the most meaningful measurement compared to the FSC of the target cells. In some embodiments, the FSC of the disclosed hydrogel particles is within 30%, 25%, 20%, 15%, 10%, 5%, or 1% of the FSC of the target cells, as measured by a cytometry device.

[0096] Forward scattering (FSC) is related to particle volume and can therefore be tuned by changing the particle diameter, as described herein. It has generally 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.

[0097] SSCs can be engineered by encapsulating nanoparticles within a hydrogel 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. Not wishing to be bound by theory, the ability to selectively tune both forward and side scattering of the hydrogel particles, as described herein, allows for a robust platform to mimic a wide range of cell types.

[0098] After the formation of hydrogel particles, one or more surfaces of the particles can be functionalized, for example, to mimic one or more optical properties of target cells or labeled target cells. Functionalized hydrogel particles may also include embedded beads or substances, such as 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 combinations thereof. In one embodiment, the hydrogel particles are formed by polymerizing at least one bifunctional monomer, and after formation, the hydrogel particles include one or more functional groups that can be used for further attachment of cell surface markers, epitope-binding regions of cell surface markers, fluorescent dyes, or combinations thereof. In one embodiment, the free functional group is an amine group, a carboxyl group, a hydroxyl group, or a combination thereof. Depending on the desired functionalization, it should be understood that multiple bifunctional monomers can be used, for example, to functionalize the particles using different chemicals and different molecules.

[0099] Examples of hydrogel particles (optionally implemented as beads) compatible with embodiments of this disclosure (e.g., for acquiring scattering maps via flow cytometry) can be found in U.S. Patent No. 10,753,846 entitled “HydrogelParticles with Tunable Optical Properties and Methods for Using the Same”, granted August 25, 2020, the entire contents of which are incorporated herein by reference for all purposes.

[0100] One or more embodiments of this disclosure 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 these instruments using scattering grids or scattering bridges. Although shown and described herein in relation to flow cytometry, the use of scattering grids or scattering bridges can also be applied to any other instrument capable of generating multiple levels of FSC and / or SSC. Therefore, data across multiple flow cytometers / research entities (e.g., from routine operations) can be normalized, and data acquisition can be standardized, and for multiple flow cytometers, flow cytometry performance and calibration can be monitored over time, e.g., overlapping or synchronous / simultaneous monitoring. For example, research and development teams can use scattering grid or scattering bridge data (e.g., using scattering grids, such as those referenced below) Figure 9 A to Figure 9 B (those shown and discussed) collaborate and / or coordinate their measurement results to accelerate research and development timelines. For example, as discussed below Figure 9 A to Figure 9In section B, a scattering grid is used as a common reference on the second flow cytometer. Analysis comparing the biological sample on the first flow cytometer with the scattering grid is used to generate a control hydrogel particle swarm with comparable forward and side scattering signals to the biological sample. Figure 8, discussed below, depicts the use of the scattering grid to adjust the gain settings after servicing the flow cytometer.

[0101] One or more embodiments of this disclosure offer several advantages over known flow cytometry methods. First, it should be noted that the performance of flow cytometer instruments can naturally degrade over time, for example, due to signal drift caused by hardware wear. Using one or more scattering grids or scattering bridge embodiments of this disclosure, flow cytometry laboratory operators can monitor the performance of their flow cytometers for both routine quality control purposes and non-routine quality control purposes, such as periodic preventative maintenance. For example, when flow cytometer engineers perform preventative maintenance, they may change forward scattering, side scattering, or fluorescence gain, or install new excitation sources or new detectors. Due to these changes, the nominal instrument settings may need to be significantly readjusted, thereby disrupting the routine workflow of laboratory operators. By utilizing scattering grid or scattering bridge data, laboratory operators can, for example, calibrate or adjust their instrument settings accordingly based on an assessment of reported signal acquisition data (such as forward scattering and side scattering) from the scattering grid or scattering bridge, apply the adjustments accordingly to the instrument settings, and continue their experiments using the adjusted flow cytometry settings. The aforementioned can be achieved by testing the hydrogel particle formulation on a flow cytometer, which generates signal data acquisition (e.g., a 3×3 scattering grid or scattering bridge). More specifically, the scattering grid or scattering bridge data can include low, medium, and high signal readings for both side scattering and forward scattering, for example, 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 their data acquisition measurements while reducing or eliminating the effects of noise (e.g., autofluorescence, mechanical vibration, ambient electric fields (e.g., from AC power lines), etc.) without exceeding upper limits due to saturation (e.g., multiple photons being counted as a single photon). As used herein, "hydrogel particle formulation" can refer to a series of hydrogel particles with discrete scattering signals that generate an array or matrix to optimize machine signal alignment.

[0102] Second, from an engineering perspective and regarding upstream manufacturing processes for 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 being sold as flow cytometer units. To the inventors' knowledge, there is no standardized method for assembling flow cytometer instruments from raw components, as evidenced by the wide variability in data analysis results in cell analysis. For example, there are different laser excitation sources with different intensities, wavelengths, and precisions; different lenses, mirrors, filters, and collection angles for light processing; and different detectors (e.g., photomultiplier tubes, photodiodes, and / or charge-coupled devices) with different quantum efficiencies across the electromagnetic spectrum, providing different signal processing capabilities.

[0103] Third, from the perspective of data science, machine learning, and bioinformatics, one or more embodiments of this disclosure can improve the way information is stored and processed for complex cell acquisition analysis, enabling high-confidence validation of statistical, mathematical, and probabilistic analyses across the lateral and forward scattering regions where cell analysis is typically performed on a flow cytometer.

[0104] The hydrogel particles described herein can be used in 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.

[0105] Table 2. Exemplary instruments.

[0106]

[0107]

[0108]

[0109]

[0110]

[0111] Table 3. Exemplary Instruments (continued).

[0112]

[0113]

[0114]

[0115]

[0116]

[0117] In some implementations, multiple levels of scattering can be achieved through variations in hydrogel particle modification, which may include one or more of the following: bovine serum albumin (BSA) cooking, nanoparticle loading, porosity, polymer composition, or gel fraction (e.g., polymer concentration) adjustment.

[0118] In some implementations, hydrogel particles (e.g., beads) can be conjugated to different fluorophores / biomacromolecules to clearly identify each group, which can be particularly useful when multiple groups within the same series cannot be separated by scattering due to saturation, incorrect cytometer settings, and / or insufficient detector sensitivity.

[0119] In some implementations, hydrogel particles (e.g., beads) can be conjugated to different levels of fluorophores / biomacromolecules, allowing for simultaneous calibration of fluorescence and scattering scaling of the cytometer. Various levels of fluorophores / biomacromolecules can also be quantified for use as quantitative standards.

[0120] Figure 2 This is a diagram illustrating a system 200 for adjusting and / or cross-calibrating a flow cytometer based on scattering grid or scattering bridge data (e.g., multi-population datasets generated using hydrogel particles), according to some embodiments. Figure 2 As shown, system 200 includes one or more scattering grid or scattering bridge servers 220 operatively communicating with one or more flow cytometers 210 via a network N (which may include one or more wireless and / or wired telecommunications networks). Optionally, the scattering grid or scattering bridge server 220 and / or flow cytometer 210 are operatively coupled (e.g., via network N) to one or more databases 250 or other storage media (e.g., including cloud-hosted storage media). Each of the scattering grid or scattering bridge servers 220 includes a processor 224, a transceiver 226, and an optional user interface 228 (e.g., including a graphical user interface (GUI) through which users (such as management users) can interact) operatively coupled to memory 222. Memory 222 stores processor-executable instructions 222A to perform one or more methods described herein (e.g., discussed below). Figure 10 Method 1000 or Figure 11Method 1100). Instruction 222A may include one or more algorithms 222B, which may include machine learning algorithms. Memory 222 also stores one or more of the following: scattering grid or scattering bridge data 222C (optionally including FSC data 222D and / or SSC data 222E), instrument settings 222F, instrument data 222G, conditioning data 222H, historical data 222I, or cross-calibration data 212J. Scattering grid or scattering bridge data 222C may include data generated by one or more flow cytometers using one or more hydrogel particle formulations of this disclosure. Scattering grid or scattering bridge data 222C may be stored as / arranged in one or more matrices or other two-dimensional (2D) or three-dimensional (3D) formats, for example, as shown below with reference to Figures 3 to 4. Figure 9 As shown and discussed in B. Instrument settings 222F may include settings such as FSC gain, SSC gain, fluorescence gain, and laser power associated with one or more flow cytometers or other instruments. Instrument data 222G may include identifiers (e.g., serial numbers), model numbers, manufacturer names, model names, maintenance event data, authorized user identifiers, 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 scattering grid or scattering bridge data 222C. Historical data 222I may include, for example, records associated with 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, scattering grid or scattering bridge data 222C, instrument settings 222F, instrument data 222G, adjustment data 222H, cytology measurement results, etc. The cross-calibration data 222J may include data derived from the scattering grid or scattering bridge data 222C, and may be used, for example, to reconcile measurements obtained by a first flow cytometer with measurements obtained by a second flow cytometer, or to adjust one or more settings / parameters of the first flow cytometer such that the first flow cytometer produces measurements more consistent with those obtained by the second flow cytometer, or to adjust one or more measurements obtained by the first flow cytometer such that the adjusted one or more measurements of the first flow cytometer are consistent with (or better correlated with) one or more measurements of the second flow cytometer.

[0121] Similar to the scattering grid or scattering bridge server 220, each of the flow cytometers 210 may include a processor 214, a transceiver 216, and an optional user interface 218 (e.g., including a graphical user interface (GUI) via which a user "U" (such as an operator) can interact) operatively coupled to memory 212. Memory 212 stores processor-executable instructions 212A to perform one or more methods described herein (e.g., discussed below). Figure 10 Method 1000 or Figure 11 Method 1100). Memory 212 also stores one or more of the following: scattering grid or scattering bridge data 212C (optionally including FSC data 212D and / or SSC data 212E), instrument settings 212F, instrument data 212G, adjustment data 212H, or historical data 212I. Scattering grid or scattering bridge data 212C may include data generated by the associated flow cytometer 210 using one or more hydrogel particle formulations of this disclosure. Similar to scattering grid or scattering bridge data 222C, scattering grid or scattering bridge data 212C may be stored as / arranged as one or more matrices or other two-dimensional (2D) or three-dimensional (3D) formats, such as those shown below with reference to Figures 3 to 4. Figure 9 As shown and discussed in B. 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 (e.g., serial number), model, manufacturer name, model name, maintenance event data, authorized user identifier, etc., associated with flow cytometer 210. Adjustment data 212H may include, for example, a representation of adjustments made to instrument settings 212F of flow cytometer 210 based on scattering grid or scattering bridge data 212C. Historical data 212I may include, for example, records associated with 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, scattering grid or scattering bridge data 212C, instrument settings 212F, instrument data 212G, adjustment data 212H, cytology measurement results, etc.

[0122] Figure 3A , Figure 4A and Figure 5A Exemplary scattering data based on scattering grids according to some embodiments are depicted, which are sequentially annotated with subsets of data generated using hydrogel particles (e.g., beads) with different average diameters (10 micrometers (µm), 20 µm, and 25 µm, respectively). For example, Figure 3AThe 10µm boxes shown include 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, Figure 4A The 20µm boxes shown include group "P4" (corresponding to the medium FSC signal output and the low SSC signal output), group "P5" (corresponding to the medium FSC signal output and the medium SSC signal output), and group "P6" (corresponding to the medium FSC signal output and the high SSC signal output). Similarly, Figure 5A The 25µm boxes shown 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).

[0123] Figures 3B to 3C , Figures 4B to 4C and Figures 5B to 5C Exemplary scattering data based on scattering bridges according to some embodiments are depicted, with sequential annotations of subsets of data generated using hydrogel particles (e.g., beads) with different average diameters (10 micrometers (µm), 14 µm, and 18 µm, respectively). For example, Figure 3B The 10µm boxes shown include 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, Figure 4B The 20µm boxes shown include group "P4" (corresponding to the medium FSC signal output and the low SSC signal output), group "P5" (corresponding to the medium FSC signal output and the medium SSC signal output), and group "P6" (corresponding to the medium FSC signal output and the high SSC signal output). Similarly, Figure 5B The 25µm boxes shown 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). Figure 3C , Figure 4C and Figure 5C Each of these is shown as a corresponding gating group for low FSC-A signal output and low FSC-H signal output, medium FSC-A signal output and low FSC-H signal output, and high FSC-A signal output and low FSC-H signal output, respectively.

[0124] Figures 6A to 6CExemplary scattering data based on scattering grids, generated by three different flow cytometers (Cytek® NL-2000 flow cytometer, Cytek® Aurora flow cytometer, and Beckman Coulter® Cytoflex 5 flow cytometer, respectively) according to some implementations, are shown, with each scattering grid including data for / representing nine hydrogel clusters represented by rectangular gates. Figures 6A to 6C The diagram illustrates that the same hydrogel clumps will produce different scattering signal responses (and therefore different scattering maps) on different types of flow cytometers. The scales of the signals are different, and the positions of the clumps relative to each other are different. Although Figures 6A to 6C The group numbers shown are arbitrary, but in other embodiments, the same numbering is used to indicate the same hydrogel particle group across the data points of each graph.

[0125] Figure 7A and Figure 7B This illustrates, according to some implementation schemes, the use of two different flow cytometers (BD® Biosciences FACSLyric) TM The example data generated by the flow cytometer and the Beckman Coulter® Cytoflex LX flow cytometer are based on a scattering grid, with each grid containing data for / representing nine hydrogel populations. Similarly, the same hydrogel populations produce different scattering signal responses (and thus different scattering maps) across different flow cytometers.

[0126] Figures 8A to 8C The image shows scattering data based on a scattering grid generated by a single Cytek® NL-2000 flow cytometer at three different times: before preventative maintenance of the Cytek® NL-2000 flow cytometer ( Figure 8A After preventative maintenance ( Figure 8B ), and after both preventative maintenance and adjustment of the FSC gain of the Cytek® NL-2000 flow cytometer ( Figure 8C More specifically, such as in comparison Figure 8A and Figure 8B As can be observed in the scattering plots, the scattering grid produced by the acquisition run using the hydrogel particle composition and with FSC gain setting 29 before preventative maintenance has a significantly different appearance / distribution than the scattering grid produced by the acquisition run using the same hydrogel particles and with the same FSC gain setting 29 after preventative maintenance. Furthermore, in response to... Figure 8A and Figure 8B The scattering grid was adjusted by changing the FSC gain setting of the flow cytometer from 29 to 50, thereby producing... Figure 8C The scattering pattern is basically the same as Figure 8A The scattering map was realigned.

[0127] Figure 9 A is a diagram illustrating first data generated by a first flow cytometer according to one embodiment, the first data including scattering data based on a scattering grid, data generated using TruCyte TBNK biomarker mimics (synthetic cells), and data generated using biological cells. Figure 9 B is a graph illustrating second data generated by a second flow cytometer (Cytek® Aurora flow cytometer), which includes scattering data based on a scattering grid. The graph also shows the guide position of a synthetic cell mimicry control matched relative to the scattering data based on the scattering grid and, according to one embodiment, to the scattering signal of a biological sample control from a first flow cytometer. The guide position can be identified based on its correlation with a subgroup of scattering data based on the scattering grid. Figure 9 Figure A shows the relative relationships between biological cells in a first flow cytometer and a scattering data set based on a scattering grid. This information is used to create a control for validation via a second flow cytometer. Based on the observation that the control and cells have the same relative relationships with the scattering data based on the scattering grid, it can be concluded that the control is identical to the biological cells of interest when using the first flow cytometer. The scattering data based on the scattering grid can be used in this way for both positive and negative controls (“positive” and “negative” refer to the presence or absence of a biomarker, respectively). Figure 9 A to Figure 9 The values ​​on the x and y axes of B represent arbitrary intensity signal values ​​across different flow cytometry instruments. When tracking the scattering signal of interest, the scattering data based on the scattering grid can be used as a "map" to determine / coordinate the relative scattering positions.

[0128] Figure 10 This is a flowchart illustrating a method for adjusting cytology instruments and / or coordinating cytology measurement results based on scattering grid or scattering bridge data, according to some embodiments. Figure 10 Method 1000 can, for example, use methods such as Figure 2 The system is implemented using system 200. For example... Figure 10 As shown, method 1000 includes at 1002 and at the first time ( t 1 ), receiving a first data array associated with a first cytology instrument. This first data array includes a set of scattered signal data points, which includes data representing at least one low forward scattering signal output, at least one high forward scattering signal output, at least one low side scattering signal output, and at least one high side scattering signal output. At a second time following the first time (t 2 At 1004, the cytology instrument parameters may be optionally adjusted based on the first data array, and / or at 1006, the first cytology measurement result of the first cytology instrument may be optionally coordinated with the second cytology measurement result of the second cytology instrument based on at least one of the first data array or a second data array associated with a second cytology instrument different from the first cytology instrument.

[0129] In one embodiment, at 1004, when the first data array is cytology data obtained from hydrogel particles constituting a scattering grid or scattering bridge, the obtained FSC and SSC can be compared with known standard FSC and SSC values ​​of the same hydrogel particles, and the cytology instrument can be adjusted when the comparison meets a threshold (e.g., the comparison indicates that the cytometer needs calibration). In another embodiment, the obtained FSC-A and FSC-H can be compared with known standard FSC-A and FSC-H values ​​of the same hydrogel particles, and the cytology instrument can be adjusted when the comparison meets a threshold (e.g., the comparison indicates that the cytometer needs calibration).

[0130] In one implementation, at 1006, when the first data array is cytometry data obtained from hydrogel particles by a first cytometry instrument constituting a scattering grid or scattering bridge, the obtained FSC and SSC (or FSC-A and FSC-H) values ​​can be compared with known standard FSC and SSC (or FSC-A and FSC-H) values ​​of the same hydrogel particles (i.e., the second data array associated with the second cytometry instrument), and coordination of the first data array can be performed when the comparison meets a threshold (e.g., the comparison indicates that the first cytometry instrument has drifted). To this end, at 1006, the algorithm can attempt to generate a matrix function to map the standard FSC and SSC values ​​onto the first data array. When the QC measurement result (e.g., confidence score, etc.) of the generated function is higher than a predetermined value, the algorithm continues to correct and / or normalize the data in the first data array, and the updated function can be used to correct future data. As an example, this updated function can be applied to actual samples of interest and can generate corrected and / or normalized data without hardware or software calibration of the flow cytometry instrument.

[0131] In some implementations, method 1000 (including 1004 and 1006) is performed iteratively to maintain the accuracy of the calibration and / or processed scattering data of the cytometry instrument, and / or to maintain the longitudinal performance of the cytometry instrument. For example, scattering grid or scattering bridge data can be acquired at each of multiple time points from a longitudinal study (optionally time points with predefined time intervals between them and / or according to a predefined schedule), and actual sample data can be reconciled with the scattering grid or scattering bridge data to correct and / or normalize the sample data. These corrections prevent drift or other non-ideal characteristics from affecting the readings of the actual sample data measured over multiple durations.

[0132] In another embodiment, at 1006, both the first and second cytology instruments are used to obtain information about an unknown cell population. Data from each cytology instrument based on hydrogel particles associated with a scattering grid or scattering bridge is used to ensure consistent measurement results across the instruments. A first data array corresponding to the cytology data obtained on the first cytology instrument for hydrogel particles associated with a scattering grid or scattering bridge, and a second data array corresponding to the cytology data obtained from the second cytology instrument, can each be compared with a corresponding scattering grid or scattering bridge having known FSC and SSC values. The data for each data array can be corrected and / or normalized until the two data arrays reach uniformity. For example, a function can be generated for each of the first and second data arrays based on the scattering grid or scattering bridge, which allows mapping the function to the actual sample of interest to ensure that the processed data from each of the first and second cytology instruments will be consistent and comparable, despite the different settings and potentially different raw outputs of each instrument.

[0133] In some embodiments, method 1000 further includes computationally adjusting at least one of a side-scattered signal or a forward-scattered signal based on a first data array and a second data array, the first data array being associated with a first time, and the second data array being associated with a second time occurring after the first time and after modifications to the settings of the first cytology instrument. Optionally, the plurality of scattering signal data points also include data representing at least one forward-scattered signal output and data representing at least one side-scattered signal output.

[0134] 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 a plurality of scattered signal data points.

[0135] In some embodiments, the first data array is generated using a hydrogel particle formulation via a single acquisition run of a first cytology instrument. The difference between at least one low forward scattering signal output and at least one high forward scattering signal output may be due 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 size of one or more hydrogel particles from the hydrogel particle formulation). Alternatively or additionally, the difference between at least one low side scattering signal output and at least one high side scattering signal output may be due to at least one of the following: the 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) cooking).

[0136] In some implementations, the first data array is unique to the first cytology instrument.

[0137] In some implementations, the first data array is unique to the first cytology instrument, and the second data array is unique to the second cytology instrument.

[0138] In some implementations, the multiple scattering signal data points also include data representing at least one additional level of forward scattering signal output (e.g., mid-forward scattering signal output).

[0139] In some implementations, the multiple scattering signal data points also include side scattering signal outputs representing at least one additional level (e.g., data representing the middle side scattering signal output).

[0140] In some embodiments, the plurality of scattering signal data points also include data representing forward scattering signal output at least one additional level and data representing side scattering signal output at least one additional level (e.g., mid-forward scattering signal output and mid-side scattering signal output).

[0141] Figure 11 This is a flowchart illustrating a method, according to some embodiments, for automatically adjusting control parameters of a flow cytometer and / or coordinating flow cytometer measurement results based on scattering grid or scattering bridge data. Figure 11 Method 1100 can, for example, use methods such as Figure 2 The system is implemented using system 200. For example... Figure 11As shown, method 1100 includes identifying a first plurality of scattering signal data points generated by a first flow cytometer at 1102. The first plurality of scattering 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 lateral scattering at a fourth signal output level, (v) data representing lateral scattering at a fifth signal output level greater than the fourth signal output level, and (vi) data representing lateral scattering at a sixth signal output level greater than the fifth signal output level. Method 1100 may also optionally include at least one of the following: automatically adjusting control parameters of the first flow cytometer based on a first plurality of scattering signal data points at 1104, or coordinating at least one of a first set of measurements of the first flow cytometer with at least one of a second set of measurements of the second flow cytometer based on the first plurality of scattering signal data points or at least one of a second plurality of scattering signal data points associated with a second flow cytometer different from the first flow cytometer at 1106.

[0142] As described above with reference to methods 1000 and 1100, the scattering grid or scattering bridge of this disclosure can be used as a customized reference to calibrate cytology instruments and / or for inter-instrumental (e.g., as a reference to adjust instrument settings across instruments to ensure standardized results) and / or intra-instrumental calibration. Furthermore, the scattering grid or scattering bridge of this disclosure can be used for data analysis to evaluate cytology data acquired over time in longitudinal studies, or for metadata analysis and unification of cytology results across studies. For example, the scattering grid or scattering bridge can be used for single-cell analyses based on optical properties, such as hematology, flow cytometry, microfluidic cytometry, etc. Additionally, the scattering grid or scattering bridge can be used to monitor the health and degradation of cell lines over time. Live or dead cells can be detected. In some embodiments, the method can be applied to various scattering angles (e.g., backscattering, low-angle scattering), sizes, and granularities (for image-based cytology methods), and applied to image cytology, phantom cytology, and cell sorting (e.g., FACS). Furthermore, individual hydrogel particle clusters can be conjugated to markers at various levels, such as dyes, antigens, or nucleic acids, which further extends the dimensionality of the scattering grid or scattering bridge to include other properties, such as fluorescence intensity. This enables its use in a wider range of applications, such as mass spectrometry cytometry (CyTOF), laser scanning cytometry, and single-cell sequencing.

[0143] In some implementations, a scattering grid or scattering bridge can be used to identify unknown cells within a population. For example, the relative position of the scattering grid or scattering bridge with respect to the unknown cells can be compared with known cells previously evaluated in the background of the scattering grid or scattering bridge.

[0144] In some implementations, at 1004 and based on a comparison of the subsequent data array with the first data array (or nominal value) received at 1002, it is determined that the data has undergone scaling offset, and the adjustment of the cytology instrument can be voltage and / or gain adjustment, such as... Figure 12A As shown. Figure 12A As shown, the scaling offset causes the scattered signal data points to disperse relative to the nominal value.

[0145] In some implementations, at 1004 and based on a comparison of the subsequent data array with the first data array (or nominal value) received at 1002, it is determined that a linearity shift has occurred in the data, and adjustments to the cytology instrument can include laser alignment, voltage and / or gain adjustment, and / or detector maintenance. For example... Figure 12B As shown, the linearity offset results in a uniform distribution of the scattered signal data points relative to the nominal value.

[0146] In some implementations, at 1004 and based on a comparison of the subsequent data array with the first data array (or nominal value) received at 1002, it is determined that an orthogonal shift has occurred in the data, and the adjustment of the cytology instrument can be laser alignment and / or optical alignment. For example... Figure 12C As shown, orthogonal offset causes the spatial distribution of the scattered signal data point group to rotate relative to the nominal value.

[0147] In some implementations, method 1100 further includes causing a first plurality of scattering signal data points to be displayed via a user interface, such that (i) to (vi) are depicted as clusters of data spaced apart from each other.

[0148] For example, in one implementation, multiple scattering signal data points may include nine unique clusters of data, similar to... Figure 13 The data set shown.

[0149] In some embodiments, method 1100 further includes causing the processor to display a first plurality of scattering signal data points via a user interface, such that (i) to (vi) are arranged in a two-dimensional array.

[0150] In some embodiments, the automatic adjustment of the control parameters of the first flow cytometer is based on a first plurality of scattered signal data points, and the automatic adjustment compensates for at least one of instrument degradation of the first flow cytometer, signal drift of the first flow cytometer, or noise.

[0151] In some implementations, the automatic adjustment of control parameters of the first flow cytometer is based on a first plurality of scattered signal data points, and this automatic adjustment improves the response linearity of multiple subsequent measurements across the first flow cytometer. For example, the automatic adjustment may include computationally adjusting data from additional samples based on data from the first plurality of scattered signal data points (which may, for example, be associated with one or more historical samples), such that forward scattering and / or lateral scattering can be compared between or among multiple instruments (e.g., the first flow cytometer and at least the second flow cytometer) and / or between or among instrument settings of multiple instruments.

[0152] In some embodiments, method 1100 further includes modifying sample data based on a first plurality of scattering 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.

[0153] In some implementations, the plurality of scattered signal data points further include (vii) data representing forward scattering at a seventh signal output level greater than the third signal output level, and (viii) data representing lateral scattering at an eighth signal output level greater than the sixth signal output level.

[0154] In some implementations, the first plurality of scattered signal data points are uniquely associated with the first flow cytometer.

[0155] In some embodiments, the first plurality of scattering signal data points are 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 due 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 size of one or more hydrogel particles from the hydrogel particle formulation). Alternatively or additionally, the difference between any two of the fourth, fifth, or sixth signal output levels may be due to at least one of the following: the 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.

[0156] The scattering grids or scattering bridges disclosed in this paper can be deployed for automated gating. Manual gating can be time-consuming and increases the variability of cytological data, limiting the ability to scale up data analysis. Existing automated gating strategies rely on machine learning methods to identify populations, but require large amounts of training data, which can be challenging for rare populations or limited samples. Therefore, Figure 17 This is a flowchart illustrating a method for automated gating.

[0157] Figure 17 This is a flowchart illustrating a method for generating and applying automatic gating. Figure 17 Method 1700 can, for example, use methods such as Figure 2 The system is implemented using system 200. For example... Figure 17 As shown, method 1700 includes receiving a first data array obtained by a cytometry instrument at 1702. This first data array, or scattering grid / scattering bridge, may include a set of scattering signal data points, which includes data representing at least one low forward scattering signal output, at least one high forward scattering signal output, at least one low side scattering signal output, and at least one high side scattering signal output. At 1704, a second data array including scattering signal data points corresponding to a known sample of interest can be received from the cytometry instrument. In an embodiment, the first and second data arrays are obtained at the same laser power and gain settings. The known sample of interest may contain one or more known cell populations with expected FSC and SSC values. At 1706, one or more gates can be defined for the cell populations within the sample of interest. These one or more gates can be defined relative to the detected locations of multiple scattering signal data points of the first data array. In an embodiment, the locations of clusters of multiple scattering signal data points of the first data array can be detected manually or automatically using supervised learning techniques, such as density-based spatial clustering (DBScan) or K-means clustering with a defined number of clusters. Each gate can be defined relative to the median of each detected cluster of multiple scattered signal data points in the first data array, which is the group of interest.

[0158] If gates are defined, step 1708 can be optionally performed. When a third data array including scattering signal data points corresponding to an unknown sample of interest is received almost simultaneously or shortly thereafter after one or more gates are defined at 1706, method 1700 can include mapping one or more defined gates onto the third data array to automatically gate the cell population within the unknown sample of interest. If a third data array including scattering signal data points corresponding to an unknown sample of interest is not immediately received after 1706, steps 1710 and 1712 can be optionally performed. For example, after obtaining the third data array from the cytometry instrument, a fourth data array can be obtained from the cytometry instrument. The fourth data array can include a set of scattering signal data points corresponding to the first data array and including data representing at least one low forward scattering signal output, at least one high forward scattering signal output, at least one low side scattering signal output, and at least one high side scattering signal output. The fourth data array can be considered an updated version of the first data array to account for any differences between the cytometry instrument when acquiring the first data array and when acquiring the fourth data array. At position 1712, one or more gates defined by the detected positions of clusters relative to the first data array can be mapped onto the third data array to automatically gate cell populations within unknown samples of interest. This allows any time-varying instrument performance, inter-instrumental differences, or variations in instrument settings to be taken into account. This method also mitigates any operator-to-operator variability that can occur with manual gating.

[0159] Figures 18A to 18D It provides a graphical representation of how one or more gates can be defined relative to the first data array or scattering grid / scattering bridge, allowing for extensive use of automated gating features without concern for recalibration or changes to instrument characteristics and parameters.

[0160] All publications, patents, patent applications and other documents cited in this application are incorporated herein by reference in their entirety for all purposes, as if each individual publication, patent, patent application or other document were individually indicated as incorporated by reference for all purposes.

[0161] Although various specific embodiments have been shown and described, it should be understood that various changes can be made without departing from the spirit and scope of the invention.

[0162] While various embodiments of the systems, methods, and apparatus have been described above, it should be understood that they are presented by way of example only and not limitation. Where the methods and steps described above indicate specific events occurring in a particular order, those skilled in the art who benefit from this disclosure will recognize that the order of certain steps can be modified, and such modifications are variations of the invention. Additionally, where possible, certain steps can be performed simultaneously in parallel processes, as well as sequentially as described above. While embodiments have been specifically shown and described, it should be understood that various changes in form and detail are possible. Although various embodiments have been described as having combinations of specific features and / or components, other embodiments may also have any combination of features and / or components from any of the embodiments discussed above.

[0163] As used herein, the following terms and expressions are intended to have the following meanings: The indefinite article “a / a kind” and the definite article “the / the” are intended to include both the singular and plural forms unless the context in which they are used explicitly indicates otherwise.

[0164] The terms “at least one” and “one or more” are used interchangeably to indicate that an article may include one or more of the listed elements.

[0165] Unless otherwise stated, it should be understood that all figures representing the numerical properties of quantities, ratios and components, reaction conditions, etc., are expected to be modified by the term "about" in all cases.

[0166] As used herein, the terms “about” and “approximately” generally mean the value plus or minus 10%, for example, about 250µm would include 225µm to 275µm, and about 1,000µm would include 900µm to 1,100µm.

[0167] As used in this article, the term "gating" refers to the selection of a continuous subpopulation of cells for analysis during flow cytometry.

[0168] In this disclosure, references to singular items should be understood to include plural items, and vice versa, unless otherwise expressly stated or clear from the context. Grammatical conjunctions are intended to express any and all disjunctive and conjunctive combinations connecting clauses, sentences, words, etc., unless otherwise stated or clear from the context. Therefore, the term “or” should generally be understood to mean “and / or”, etc. The use of any and all examples or exemplary language (“e.g.,” “such as,” “including,” etc.) provided herein is intended only to better illustrate embodiments and does not constitute a limitation on the scope of embodiments or claims.

Claims

1. A non-transitory processor-readable medium storing instructions, said instructions causing the processor, when executed by a processor, to: A first data array associated with a first cytology instrument is received at a first time. The first data array includes multiple scattered signal data points, which include: This represents data indicating at least one low forward scattering signal output. This represents data indicating at least one high forward-scattering signal output. Data representing at least one low side-scattering signal output, and This represents data representing at least one high side-scattering signal output; and At a second time following the first time, at least one of the following shall be performed: Adjusting the parameters of the cytology instrument based on the first data array, or Based on at least one of the first data array or a second data array associated with a second cytology instrument different from the first cytology instrument, the first cytology measurement result of the first cytology instrument is coordinated with the second cytology measurement result of the second cytology instrument.

2. The non-transitory processor-readable medium of claim 1, further storing instructions for the processor to computationally adjust at least one of a side-scattered signal or a forward-scattered signal based on the first data array and the second data array, the first data array being associated with a first time, and the second data array being associated with a second time occurring after the first time and after the settings of the first cytology instrument have been modified.

3. The non-transitory processor-readable medium of claim 1, wherein the plurality of scattered signal data points further comprises: Represents the data output of at least one forward-scattered signal, and This represents the data output of at least one mid-side scattering signal.

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

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

6. The non-transitory processor-readable medium of 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 caused by 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.

7. The non-transitory processor-readable medium of 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 caused by at least one of the following: the 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.

8. The non-transitory processor-readable medium of claim 1, wherein the first data array is unique to the first cytology instrument.

9. The non-transitory processor-readable medium of claim 1, wherein the first data array is unique to the first cytology instrument, and the second data array is unique to the second cytology instrument.

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

11. The non-transitory processor-readable medium of claim 1, wherein the plurality of scattering signal data points further include data representing the side-scattering signal output of at least one additional level.

12. The non-transitory processor-readable medium of claim 1, wherein the plurality of scattered signal data points further comprises: Data representing the output of the forward-scattered signal at least one additional level; and Data representing the output of at least one additional level of side-scattered signal.

13. A non-transitory processor-readable medium storing instructions, said instructions causing the processor, when executed by a 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 including: (i) represents the forward scattering data at the first signal output level. (ii) represents forward-scattered data at a second signal output level greater than the first signal output level. (iii) represents forward-scattered data at a third signal output level that is greater than the second signal output level. (iv) represents the data of lateral scattering at the fourth signal output level. (v) represents the data of lateral scattering at a fifth signal output level greater than the fourth signal output level, and (vi) represents the data of lateral scattering at a sixth signal output level that is greater than the fifth signal output level; and Perform at least one of the following: The control parameters of the first flow cytometer are automatically adjusted based on the first plurality of scattered signal data points, or 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 a second flow cytometer different from the first flow cytometer, at least one set of measurement results from the first flow cytometer is coordinated with at least one set of measurement results from the second flow cytometer.

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

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

16. The non-transitory processor-readable medium of claim 13, wherein: The automatic adjustment of the control parameters of the first flow cytometer is based on the first plurality 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.

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

18. The non-transitory processor-readable medium of claim 13, wherein the non-transitory processor-readable medium further stores instructions for causing the processor to modify sample data based on the first plurality of scattering signal data points.

19. The non-transitory processor-readable medium of 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 non-transitory processor-readable medium of claim 13, wherein the plurality of scattered signal data points further comprises: (vii) represents the forward-scattered data at a seventh signal output level that is greater than the third signal output level; and (viii) represents the data of lateral scattering at the eighth signal output level, which is greater than the sixth signal output level.

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

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

23. The non-transitory processor-readable medium of 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 caused by 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.

24. The non-transitory processor-readable medium of 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 caused by at least one of the following: the 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.

25. A non-transitory processor-readable medium storing instructions, said instructions causing the processor, when executed by a processor, to: The processor receives a first data array and a second data array, each associated with a cytology instrument. The first data array includes a plurality of scattered signal data points, the plurality of scattered signal data points comprising: This represents data indicating at least one low forward scattering signal output. This represents data indicating at least one high forward-scattering signal output. Data representing at least one low side-scattering signal output, and This represents data representing at least one high side-scattering signal output; and The second data array includes scattering signal data points associated with the sample of interest; as well as One or more gates are defined for the cell population within the sample of interest relative to the positions of the plurality of scattered signal data points in the first data array.

26. The non-transitory processor-readable medium of claim 25, further storing instructions for causing the processor to: The positions of the plurality of scattered signal data points in the first data array are detected by using machine learning methods to detect scattered signal clusters.

27. The non-transitory processor-readable medium of claim 26, wherein the machine learning method includes DBScan or K-means with a defined number of clusters.

28. A non-transitory processor-readable medium storing instructions, said instructions causing the processor, when executed by a processor, to: Obtain one or more gates as defined in claim 25; Receive a third data array that includes scattering signal data points corresponding to unknown samples of interest; Receive a fourth data array, the fourth data array including a plurality of scattered signal data points corresponding to the plurality of scattered signal data points of the first data array; as well as The one or more gates are mapped to the third data array based on the clusters of scattering signals detected within the plurality of scattering signal data points in the fourth data array, so as to automatically gate the cell population within the unknown sample of interest.

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