Method for extracting immune signal of binary aggregate

Abstract

The invention relates to the technical field of biomedical engineering, and particularly discloses a binary aggregate immune signal extraction method which comprises the following steps: screening out disease-related high-expression genes by utilizing a disease model, a normal model and a drug treatment model; an important protein molecule is screened based on a high-expression gene by using a nucleic acid-protein affinity analysis method; constructing a fusion vector based on cytoskeleton protein, important protein molecules and green fluorescent protein tracing; on the basis of fusion vectors of a disease model, a normal model and a drug treatment model, risk genes and possible downstream proteins associated with the agglomerates are identified through space group imaging, and meanwhile, the agglomeration process of the agglomerates is quantified. According to the method, the influence of the problems of state damage of the agglomerates, loss of spatial distribution information and the like is reduced, and immune microenvironment in-situ spatial organizational imaging and analysis of the evolution process of complex diseases are accurately realized.

Description

Technical Field

[0001] The invention relates to the technical field of biomedical engineering, and in particular to a method for extracting immune signals from binary coagulation bodies. Background Art

[0002] In the evolution of complex diseases, nucleic acids and proteins in important immune cells can play an immunomodulatory function by forming binary condensates, and their observation and analysis are of great significance for understanding the common pathological basis of autoimmune diseases. For example, a long-chain terminal repeat sequence is an endogenous nucleic acid sequence in the genome, which is usually silent under physiological conditions; but under pathological conditions, its transcription and translation products, such as RNA and polypeptides, can form immune complexes or binary condensates with protein molecules and participate in immune responses. Most of the current research focuses on the assembly and regulation of condensates with known components. However, the immune signaling pathways downstream of condensates are very complex and difficult to measure. To address this problem, the commonly used methods currently used are simply nucleic acid molecule sequencing and protein spectrum analysis. The physical solidification and nucleic acid and protein purification processes of such methods will cause problems such as the destruction of the condensate state and the loss of spatial distribution information. Therefore, the understanding of how condensates play an immunomodulatory role is limited, and it is difficult to achieve the goal of in situ spatial omics imaging and analysis of the immune microenvironment in the evolution of complex diseases. Summary of the invention

[0003] Based on this, the present invention proposes a method for extracting immune signals from binary aggregates, which is suitable for discovering important transposons in autoimmune diseases and studying their functions, and promoting the transformation process of candidate drugs.

[0004] According to a first aspect of the present invention, a method for extracting immune signals from binary coacervates is provided, comprising:

[0005] Using disease models, normal models, and drug treatment models, we screen out disease-related highly expressed genes;

[0006] Using nucleic acid-protein affinity analysis methods, based on the highly expressed genes, important protein molecules are screened out;

[0007] Constructing a fusion vector based on cytoskeleton protein, the important protein molecule and green fluorescent protein tracing;

[0008] Based on the fusion vectors of the disease model, normal model and drug treatment model, the risk genes and possible downstream proteins associated with the aggregates are identified through spatial omics imaging, and the aggregation process of the aggregates is quantified.

[0009] According to an embodiment of the present invention, the use of a disease model, a normal model, and a drug treatment model to screen out disease-related highly expressed genes includes:

[0010] preparing disease model cell suspension and normal model cell suspension from peripheral immune tissues or peripheral blood of the disease model and normal model;

[0011] The cells in the disease model cell suspension and the normal model cell suspension are lysed using a nucleic acid extraction reagent, and the solution is subjected to organic phase separation, and then centrifuged after standing.

[0012] Separating and extracting nucleic acids from the disease model cells and the normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins as well as information on normal model expression genes and normal model proteins;

[0013] By comparing the genes expressed in the disease model with the genes expressed in the normal model, highly expressed genes are screened out.

[0014] According to an embodiment of the present invention, the fusion vector based on the disease model, the normal model and the drug treatment model identifies the risk genes and possible downstream proteins associated with the aggregates through spatial omics imaging, and quantifies the aggregation process of the aggregates, including:

[0015] Detection and analysis of aggregates are achieved using techniques such as fluorescence imaging and fluorescence photobleaching recovery experiments;

[0016] Sample important peripheral immune tissues for disease and drug treatment models;

[0017] Using fusion proteins to capture and identify immune-related RNA nucleic acid molecules;

[0018] Spatial omics imaging was performed using confocal microscopy to conduct fluorescence localization, quantification, and differential analysis.

[0019] According to an embodiment of the present invention, the separation and extraction of nucleic acids from the disease model cells and the normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins and information on normal model expression genes and normal model proteins includes:

[0020] Add 1 ml of nucleic acid extraction reagent to the disease model cell suspension and / or normal model cell suspension at a ratio of 5 million cells to 1 ml of nucleic acid extraction reagent, and mix well;

[0021] Adding chloroform to the disease model cell suspension and / or the normal model cell suspension and mixing, wherein the volume ratio of the nucleic acid extraction reagent to the chloroform is 10:1-10:3, to obtain a disease model lysate and / or a normal model lysate;

[0022] After standing and centrifuging, the disease model lysate and / or the normal model lysate undergoes organic phase separation to obtain an aqueous phase containing RNA, an organic phase containing DNA, and an intermediate phase containing proteins and other impurities.

[0023] According to an embodiment of the present invention, the separating and extracting nucleic acids from the disease model cells and the normal model cells for sequencing to obtain disease model expression gene and disease model protein information and normal model expression gene and normal model protein information further comprises:

[0024] Adding isopropanol to the aqueous phase, wherein the volume ratio of the nucleic acid extraction reagent to the isopropanol is 5:2-5:3, incubating at 4° C., and centrifuging to precipitate the RNA in the aqueous phase;

[0025] Add an equal volume of anhydrous ethanol to the organic phase, mix well, and centrifuge to precipitate the DNA;

[0026] A 10% ethanol solution containing 0.1 molar sodium citrate is added to the DNA, the precipitate is resuspended, incubated, centrifuged, dried and washed to obtain a DNA precipitate.

[0027] According to an embodiment of the present invention, screening out highly expressed genes by comparing the disease model expressed genes with the normal model expressed genes comprises:

[0028] Performing high-throughput sequencing analysis on the separated DNA precipitate and RNA precipitate to obtain analysis results of the disease model and / or normal model;

[0029] By comparing the analysis results of the disease model with the analysis results of the normal model, the differentially expressed genes are functionally annotated, thereby screening out highly expressed immune-related genes.

[0030] According to an embodiment of the present invention, it also includes:

[0031] The highly expressed genes include one or more of Camk4, Ddx4, Fas, Nox2, and Nlrp6.

[0032] According to an embodiment of the present invention, the construction of a fusion vector based on cytoskeleton protein, the important protein molecule and green fluorescent protein tracing includes:

[0033] The cytoskeleton protein includes the cytoskeleton protein TPPP fragment in SEQ ID No.1;

[0034] The highly expressed gene CAMK4 includes the nucleic acid sequence in SEQ ID No. 2;

[0035] The highly expressed gene DDX4 includes the nucleic acid sequence in SEQ ID No. 3;

[0036] The highly expressed gene FAS includes the nucleic acid sequence in SEQ ID No.4;

[0037] The highly expressed gene NLRP6 includes the nucleic acid sequence in SEQ ID No. 5;

[0038] The important protein molecule includes the CAMK4 protein sequence in SEQ ID No.6;

[0039] The important protein molecule includes the DDX4 protein sequence in SEQ ID No.7;

[0040] The important protein molecule includes the FAS protein sequence in SEQ ID No.8;

[0041] The important protein molecule includes the NLRP6 protein sequence in SEQ ID No.9.

[0042] According to an embodiment of the present invention, the method of capturing and identifying immune-related RNA nucleic acid molecules using fusion proteins includes:

[0043] Transcribing the fusion vector to obtain a vector RNA nucleic acid molecule;

[0044] The carrier RNA nucleic acid molecule is intravenously injected into mice by lipid nanoparticle delivery, comprising:

[0045] Dissolving the carrier RNA nucleic acid molecule in a sodium citrate buffer having a concentration of 5-10 mmol / L;

[0046] The carrier RNA nucleic acid molecule and the lipid nanoparticle are mixed, and the mixture of the carrier RNA nucleic acid molecule and the lipid nanoparticle is injected intravenously into a mouse.

[0047] The weight ratio of the total lipids in the lipid nanoparticles to the carrier RNA nucleic acid molecules is in the range of 20:1-50:1.

[0048] According to an embodiment of the present invention, the drug treatment model is a result model of the disease model after four weeks of drug treatment.

[0049] It can be seen from the above technical solution that the method for extracting immune signals from binary coacervates provided by the present invention has the following beneficial effects:

[0050] The present invention proposes that compared with conventional methods, the present invention reduces the impact of problems such as the destruction of the state of aggregates and the loss of spatial distribution information, and accurately realizes the in situ spatial omics imaging and analysis of the immune microenvironment of the evolution process of complex diseases; the present invention can comprehensively analyze the condensation process of binary aggregates and draw a regional map of nucleic acid-protein interactions; the present invention utilizes the properties of aggregates and spatial omics imaging technology to realize the in situ precise screening of immune-related genes; the present invention is suitable for discovering important transposons and their functions in autoimmune diseases, and promoting the transformation of related candidate drugs. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Schematic diagram of the system framework of an embodiment of the present invention.

[0052] Figure 2 Schematic diagram of the gene expression analysis process according to an embodiment of the present invention.

[0053] Figure 3 Schematic diagram of the screening of important protein molecules according to an embodiment of the present invention.

[0054] Figure 4 Schematic diagram of agglomerate detection and change monitoring according to an embodiment of the present invention, wherein step a is a schematic diagram of the agglomerate detection process, and step b is a schematic diagram of the lipid nanoparticle delivery imaging process. DETAILED DESCRIPTION

[0055] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0056] According to a first aspect of the present invention, a method for extracting immune signals from binary coacervates is provided, comprising:

[0057] Using disease models, normal models, and drug treatment models, we screen out disease-related highly expressed genes;

[0058] Using nucleic acid-protein affinity analysis methods, we screen out important protein molecules based on highly expressed genes;

[0059] Constructed a fusion vector based on cytoskeletal proteins, important protein molecules and green fluorescent protein tracing;

[0060] Based on the fusion vectors of disease models, normal models and drug treatment models, spatial omics imaging is used to identify risk genes and possible downstream proteins associated with aggregates, and the aggregation process of aggregates is quantified.

[0061] like Figure 1As shown, the present invention uses important peripheral immune tissues or peripheral blood of disease models and normal models to extract nucleic acid molecules and proteins through organic phase separation, and conducts nucleic acid sequencing analysis. Preliminary screening of highly expressed immune-related genes and protein information to construct an immune gene library.

[0062] The present invention uses the third-generation AlphaFold tool and the nucleic acid-protein affinity analysis method to screen out a protein molecule with molecular structural characteristics such as low-complexity domain, shortest amino acid sequence, and strongest nucleic acid molecule affinity. A green fluorescent protein fusion expression vector is constructed, and lipid nanoparticles are used to deliver it for expression, and the formation and changes of aggregates are detected and analyzed.

[0063] Based on the screening results, the present invention uses disease models and drug treatment models for comparison, and uses fluorescent probes and antibodies to perform in situ spatial omics imaging in important immune cells. Fusion proteins are used to capture nucleic acid molecules and observe signals in and adjacent to the condensate to identify important nucleic acids and protein molecules that play an immunomodulatory function. At the same time, the fluorescence microscopy results are quantified for differential analysis. The condensate is associated with the immune response, thereby effectively and accurately extracting the binary condensate immune signal in situ and promoting the transformation of candidate drugs.

[0064] According to an embodiment of the present invention, the constructed cytoskeleton protein is fused with important protein molecule fragments, referring to Figure 4 a. The protein retains the core function of the cytoskeleton protein TPPP, which is stably expressed in cells and has a large capture area. It also has an important protein molecule fragment IDR sequence that drives the formation of condensates, thereby capturing nucleic acid molecules.

[0065] According to an embodiment of the present invention, Figure 2 As shown in the figure, using disease models, normal models and drug treatment models, the disease-related high-expression genes screened include:

[0066] preparing disease model cell suspension and normal model cell suspension from peripheral immune tissues or peripheral blood of the disease model and normal model;

[0067] Using a nucleic acid extraction reagent to lyse cells in the disease model cell suspension and the normal model cell suspension and to separate the solution into an organic phase, and then centrifuging after standing to obtain the disease model cells and the normal model cells;

[0068] Isolating and extracting nucleic acids from disease model cells and normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins as well as information on normal model expression genes and normal model proteins;

[0069] By comparing the genes expressed in the disease model with those expressed in the normal model, highly expressed genes were screened out.

[0070] According to an embodiment of the present invention, the peripheral immune tissues or peripheral blood of the disease model and the normal model are made into the disease model cell suspension and the normal model cell suspension, specifically comprising: taking fresh spleen tissues of lupus erythematosus disease mice and normal mice, grinding them, filtering them through a cell sieve with a pore size of 70 microns, and making cell suspensions, which are placed on ice. The peripheral blood is lysed by red blood cell lysis solution, centrifuged, discarded the supernatant, and resuspended in pre-cooled phosphate buffer.

[0071] According to an embodiment of the present invention, nucleic acids in disease model cells and normal model cells are separated and extracted for sequencing to obtain information on disease model expression genes and disease model proteins and information on normal model expression genes and normal model proteins, including:

[0072] Add 1 ml of nucleic acid extraction reagent to the disease model cell suspension and / or normal model cell suspension at a ratio of 5 million cells to 1 ml of nucleic acid extraction reagent, and mix well;

[0073] Adding chloroform to the disease model cell suspension and / or the normal model cell suspension and mixing, wherein the volume ratio of the nucleic acid extraction reagent to the chloroform is 10:1-10:3, to obtain a disease model lysate and / or a normal model lysate;

[0074] After standing and centrifuging, the disease model lysate and / or the normal model lysate undergoes organic phase separation to obtain an aqueous phase containing RNA, an organic phase containing DNA, and an intermediate phase containing proteins and other impurities.

[0075] According to an embodiment of the present invention, the nucleic acid extraction reagent may be TRIzol RNA extraction reagent, ThermoFisher Scientific.

[0076] According to an embodiment of the present invention, the main components of the nucleic acid extraction reagent are phenol, guanidine isothiocyanate and sodium dodecyl sulfate.

[0077] According to an embodiment of the present invention, separating and extracting nucleic acids from disease model cells and normal model cells for sequencing to obtain disease model expression gene and disease model protein information and normal model expression gene and normal model protein information also includes:

[0078] Adding isopropanol to the aqueous phase, wherein the volume ratio of the nucleic acid extraction reagent to the isopropanol is 5:2-5:3, incubating at 4°C, and centrifuging to precipitate the RNA in the aqueous phase;

[0079] Add an equal volume of anhydrous ethanol to the organic phase and mix well, then centrifuge to precipitate DNA;

[0080] A 10% ethanol solution containing 0.1 molar sodium citrate was added to the DNA, the precipitate was resuspended, incubated, centrifuged, dried and washed to obtain a DNA precipitate.

[0081] According to an embodiment of the present invention, nucleic acids in disease model cells and normal model cells are separated and extracted for sequencing to obtain information on disease model expression genes and disease model proteins as well as information on normal model expression genes and normal model proteins, specifically including: adding 1 ml of nucleic acid extraction reagent to the suspension at a ratio of 5 million cells to 1 ml of nucleic acid extraction reagent and aspirating to mix. After standing for 5 minutes, 0.2 ml of chloroform is added to 1 ml of reagent and mixed. After standing for 3 minutes, centrifuge at 12,000 × g at 4°C for 15 minutes to separate the lysate into an organic phase.

[0082] Transfer the upper aqueous phase to a new tube, add 0.5 ml of isopropanol per 1 ml of nucleic acid extraction reagent, incubate at 4°C for 10 minutes, and centrifuge at 12,000 × g for 10 minutes to precipitate RNA. Discard the supernatant and resuspend the precipitate in 75% ethanol at a 1:1 ratio. Centrifuge at 7,500 × g for 5 minutes, discard the supernatant, and dry for 5-10 minutes. Finally, re-dissolve the precipitate with 50 μl of RNase-free water.

[0083] Separate the organic phase and add an equal volume of anhydrous ethanol to mix, centrifuge at 2000×g for 5 minutes to precipitate DNA. Resuspend the precipitate with 10% ethanol solution containing 0.1 mol sodium citrate in a ratio of 1:1 extraction reagent. After incubation for 30 minutes, centrifuge at 2000×g for 5 minutes at 4°C, discard the supernatant, and air dry for 5-10 minutes. Wash twice with 8 mmol / l sodium hydroxide solution to obtain DNA.

[0084] According to an embodiment of the present invention, by comparing the disease model expression genes with the normal model expression genes, the highly expressed genes screened out include:

[0085] Performing high-throughput sequencing analysis on the separated DNA precipitates and RNA precipitates to obtain analysis results of the disease model and / or normal model;

[0086] By comparing the analysis results of the disease model with those of the normal model, the differentially expressed genes were functionally annotated to screen out highly expressed immune-related genes.

[0087] According to an embodiment of the present invention, it also includes:

[0088] The highly expressed genes include one or more of Camk4, Ddx4, Fas, Nox2, and Nlrp6.

[0089] According to an embodiment of the present invention, Figure 3 As shown, the nucleic acid-protein affinity analysis method is used to screen out important protein molecules based on highly expressed genes, including:

[0090] Based on the highly expressed genes screened out, the amino acid sequence of the protein product molecule is obtained. The properties of the protein molecule are evaluated using the AlphaFold third-generation tool and the nucleic acid-protein affinity analysis method. The specific screening criteria are: whether it has structural features such as low-complexity domains, amino acid sequence length, and nucleic acid molecule affinity.

[0091] The low-complexity domains of related protein molecules are calculated using the model, and the important protein sequences are screened using the tool. The sequences have the following characteristics: low-complexity domains, amino acid sequence length of 20, and strong affinity for nucleic acid molecules. The selected amino acid fragments are referred to by their names as TARGET, such as Figure 3 As shown. Proteins with the same sequence were searched in the protein database to further screen for possible immune-related proteins.

[0092] According to an embodiment of the present invention, a fusion vector based on cytoskeleton proteins, important protein molecules and green fluorescent protein tracing is constructed, including:

[0093] The cytoskeleton protein includes the cytoskeleton protein TPPP fragment in SEQ ID No. 1;

[0094] The highly expressed gene CAMK4 includes the nucleic acid sequence in SEQ ID No. 2;

[0095] The highly expressed gene DDX4 includes the nucleic acid sequence in SEQ ID No. 3;

[0096] The highly expressed gene FAS includes the nucleic acid sequence in SEQ ID No. 4;

[0097] The highly expressed gene NLRP6 includes the nucleic acid sequence in SEQ ID No. 5;

[0098] The important protein molecule includes the CAMK4 protein sequence in SEQ ID No.6;

[0099] The important protein molecule includes the DDX4 protein sequence in SEQ ID No.7;

[0100] The important protein molecule includes the FAS protein sequence in SEQ ID No.8;

[0101] The important protein molecule includes the NLRP6 protein sequence in SEQ ID No.9.

[0102] According to an embodiment of the present invention, a fusion vector based on cytoskeleton proteins, important protein molecules and green fluorescent protein tracing is constructed, including:

[0103] like Figure 4As shown in step a, green fluorescent protein is used for fluorescent tracing, and the disordered sequences at both ends of the cytoskeletal protein fragment (aa.45-166) are removed to retain its core function. The fusion carrier protein is used as a tool for capturing target RNA nucleic acid molecules after in vivo expression.

[0104] Primers were designed based on the sequence fragments screened, and the corresponding gene fragments were amplified using PCR. Then, the gene fragments and vectors obtained by digestion with restriction endonucleases were digested, and the gene fragments after digestion were connected to the vector using T4 DNA ligase. The constructed plasmids were mixed in proportion using Lipofectamine reagent (Thermo Fisher Scientific) and transfected into cells and amplified on the culture medium.

[0105] According to an embodiment of the present invention, based on the fusion vector of the disease model, the normal model and the drug treatment model, the risk genes and possible downstream proteins associated with the aggregates are identified through spatial omics imaging, and the aggregation process of the aggregates is quantified, including:

[0106] Detection and analysis of aggregates are achieved using techniques such as fluorescence imaging and fluorescence photobleaching recovery experiments;

[0107] Sample important peripheral immune tissues for disease and drug treatment models;

[0108] Using fusion proteins to capture and identify immune-related RNA nucleic acid molecules;

[0109] Spatial omics imaging was performed using confocal microscopy to conduct fluorescence localization, quantification, and differential analysis.

[0110] According to an embodiment of the present invention, the detection and analysis of condensates are achieved by using technologies such as fluorescence imaging and fluorescence photobleaching recovery experiments, including:

[0111] 48 hours after transfection, the cells were observed under a spinning disk confocal microscope with a 63x oil immersion objective. The specific indicators for condensate detection are: observe whether the green fluorescence in the cells forms condensates of sufficient size under a microscope; secondly, count the fluorescence intensity and compare it with the untransfected group to analyze whether there is a significant difference; use small molecule reagents such as 1,6-hexanediol to treat cells that can dissolve condensate droplets to observe the morphological changes of condensates; and perform fluorescence bleaching recovery experiments. Specifically, a 5×5 square micrometer area was bleached with a 488-nanometer laser, and the fluorescence intensity was recorded every 5 seconds after bleaching for a total of 5 minutes. The recovery of fluorescence intensity was recorded and normalized. Continuously observe whether the fluorescence intensity of the condensate recovers over time and measure the recovery time to quantify the droplet dynamics. The condensation process of the condensate was analyzed from multiple aspects.

[0112] Through the above steps, the coagulation ability of the selected coagulation components is verified, and quantitative information of the coagulation process of the immune-related gene library and the coagulation is obtained.

[0113] According to an embodiment of the present invention, the transfection reagent is Lipofectamine 3000 (Thermo Fisher Scientific), and the transfection process includes the following steps:

[0114] The cells were seeded into 24-well plates, with 5-10×10^4 cells per well, and cultured overnight in a cell culture incubator at 37°C and 5% CO2.

[0115] In a sterile microcentrifuge tube A, dissolve 1-3 μg of plasmid DNA in 100 μL Opti-MEM I Reduced Serum Medium and mix gently. In another sterile microcentrifuge tube B, add 20 μL Lipofectamine 3000 to 100 μL Opti-MEM I Reduced Serum Medium and mix gently. Incubate at room temperature for 5 minutes.

[0116] Add the diluted DNA solution in tube A to the diluted Lipofectamine 3000 solution in tube B, mix gently, and incubate at room temperature for 15-20 minutes to form a DNA-Lipofectamine complex.

[0117] Wash three times with 1 mL PBS, then remove the PBS. Add 100 μL DNA-Lipofectamine complex to each well and gently shake the 24-well plate to evenly distribute the complex. Place the 24-well plate in an incubator and culture for 4-6 hours.

[0118] After 4-6 hours, gently aspirate the culture medium containing DNA-Lipofectamine complexes. Add 1 mL of fresh cell culture medium containing 10% FBS and 1% penicillin-streptomycin to each well. Continue culturing for 24-48 hours.

[0119] According to an embodiment of the present invention, the drug treatment model is a model of the results of the disease model after four weeks of drug treatment.

[0120] According to an embodiment of the present invention, the autoimmune disease animal model is a systemic lupus erythematosus mouse, and the treatment model mouse is treated by administering the candidate drug mycophenolate mofetil (MCE) for 4 weeks. The important peripheral immune tissue is the spleen, and the slice thickness is 50 microns.

[0121] According to an embodiment of the present invention, the fusion protein is used to capture and identify immune-related RNA nucleic acid molecules, including:

[0122] The fusion vector is transcribed to obtain a vector RNA nucleic acid molecule;

[0123] The carrier RNA nucleic acid molecules were intravenously injected into mice via lipid nanoparticle delivery, including:

[0124] Dissolve the carrier RNA nucleic acid molecule in a sodium citrate buffer at a concentration of 5-10 mmol / L;

[0125] Mixing carrier RNA nucleic acid molecules and lipid nanoparticles, and injecting the mixture of carrier RNA nucleic acid molecules and lipid nanoparticles into mice intravenously,

[0126] The weight ratio of total lipid to carrier RNA nucleic acid molecules in the lipid nanoparticles ranges from 20:1 to 50:1.

[0127] According to an embodiment of the present invention, Figure 4 The RNA nucleic acid molecules transcribed by the above fusion vector were intravenously injected into mice by lipid nanoparticle delivery. Figure 4 As shown in step b, tissue samples were taken 4 hours after injection.

[0128] The RNA delivery steps are as follows: the molar mass of the sequence is calculated to determine the required molecular weight, and the RNA is dissolved in a sodium citrate buffer at a concentration of 5-10 mmol per liter. The RNA is mixed with lipid nanoparticles, the weight ratio of total lipid to mRNA is 40:1, and the amount of RNA injected per mouse is initially set to 2.5 micrograms (based on body weight ratio 0.1 micrograms / gram). Each mouse is intravenously injected with 50 microliters of a mixture of lipids and RNA. According to the needs, the gradient concentration is set to further quantify the indicators of the coacervate formation.

[0129] Fluorescence co-localization is used to confirm the components of the condensate and the adjacent space. The presence of the screened highly expressed genes or protein analysis in the condensate space is observed to detect immune relevance. At the same time, the condensate components are specifically extracted and separated for analysis to identify new RNA nucleic acid molecules with immune regulatory functions.

[0130] The RNA nucleic acid sequence captured by the cytoskeletal protein was identified by immunocrosslinking coprecipitation and sequencing. The cell suspension was placed on ice and irradiated with ultraviolet light at a wavelength of 365 nanometers for 30 seconds to crosslink the protein-nucleic acid complex. The complex was subsequently extracted by conventional immunoprecipitation, purified, and sequenced to extract nucleic acid molecule information.

[0131] According to an embodiment of the present invention, spatial omics imaging is performed by confocal microscopy to perform fluorescence localization, quantification and difference analysis, specifically:

[0132] After four weeks of drug treatment, the therapeutic effect of the drug was confirmed by combining ELISA test with serum. The sample sections were observed under a spinning disk confocal microscope with a 100x oil immersion objective to observe the distribution of aggregates in cells.

[0133] Antibodies are used to label immune cell types. Specifically, the cell labeling antibody is CD68-PE, and the specifically labeled immune cells are macrophages. Fluorescent probes are used to specifically label the identified nucleic acid sequences.

[0134] Important immune cells were identified through the FITC channel, the average fluorescence intensity, number of droplets, and fluorescence area of ​​aggregates in macrophages and other cells were calculated, and the molecular weight of each component in the aggregates was quantified.

[0135] The imaging and analysis steps are as follows: when shooting, samples are taken at intervals of 1 micron along the Z axis, and the entire longitudinal length of the tissue is 50 microns, and 50 continuous fluorescent image files with spatial information are obtained for each tissue sample.

[0136] The image processing script was written in Python to identify and quantify the fluorescent spots in each channel and calculate their area, number and average fluorescence intensity; the above detection indicators were statistically analyzed with the control group. The data were subjected to non-parametric t-test using statistical software to obtain more information. The comparisons were between the disease model and the drug treatment model, between macrophages and other tissue cells, and between intracellular and extracellular.

[0137] By integrating the difference information and analysis results and visualizing them, we discovered new nucleic acid and protein molecules with immune regulatory functions, and systematically realized the imaging and analysis of in situ spatial omics of the immune microenvironment.

[0138] The specific embodiments described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for extracting immune signals from binary coacervates, characterized in that: include: Using disease models, normal models, and drug treatment models, we screen out disease-related highly expressed genes; Using nucleic acid-protein affinity analysis methods, based on the highly expressed genes, important protein molecules are screened out; Constructing a fusion vector based on cytoskeleton protein, the important protein molecule and green fluorescent protein tracing; Based on the fusion vectors of the disease model, normal model and drug treatment model, the risk genes and possible downstream proteins associated with the aggregates are identified through spatial omics imaging, and the aggregation process of the aggregates is quantified.

2. The method for extracting immune signals from binary coacervates according to claim 1, characterized in that: The above-mentioned screening of disease-related highly expressed genes using disease models, normal models and drug treatment models includes: preparing disease model cell suspension and normal model cell suspension from peripheral immune tissues or peripheral blood of the disease model and normal model; Using a nucleic acid extraction reagent to lyse cells in the disease model cell suspension and the normal model cell suspension and separate the solution into an organic phase, standing the solution and then centrifuging to obtain the disease model cells and the normal model cells; Separating and extracting nucleic acids from the disease model cells and the normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins as well as information on normal model expression genes and normal model proteins; By comparing the genes expressed in the disease model with the genes expressed in the normal model, highly expressed genes are screened out.

3. The method for extracting immune signals from binary coacervates according to claim 1, characterized in that: The fusion vector based on the disease model, normal model and drug treatment model identifies the risk genes and possible downstream proteins associated with the aggregates through spatial omics imaging, and quantifies the aggregation process of the aggregates, including: Detection and analysis of aggregates are achieved using techniques such as fluorescence imaging and fluorescence photobleaching recovery experiments; Sample important peripheral immune tissues for disease and drug treatment models; Using fusion proteins to capture and identify immune-related RNA nucleic acid molecules; Spatial omics imaging was performed using confocal microscopy to conduct fluorescence localization, quantification, and differential analysis.

4. The method for extracting immune signals from binary coacervates according to claim 2, characterized in that: The separating and extracting of nucleic acids from the disease model cells and the normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins and information on normal model expression genes and normal model proteins includes: Add 1 ml of nucleic acid extraction reagent to the disease model cell suspension and / or normal model cell suspension at a ratio of 5 million cells to 1 ml of nucleic acid extraction reagent, and mix well; Adding chloroform to the disease model cell suspension and / or the normal model cell suspension and mixing, wherein the volume ratio of the nucleic acid extraction reagent to the chloroform is 10:1-10:3, to obtain a disease model lysate and / or a normal model lysate; After standing and centrifuging, the disease model lysate and / or the normal model lysate undergoes organic phase separation to obtain an aqueous phase containing RNA, an organic phase containing DNA, and an intermediate phase containing proteins and other impurities.

5. The method for extracting immune signals from binary coacervates according to claim 2, characterized in that: The separating and extracting of nucleic acids from the disease model cells and the normal model cells for sequencing to obtain information on disease model expression genes and disease model proteins and information on normal model expression genes and normal model proteins also includes: Adding isopropanol to the aqueous phase, wherein the volume ratio of the nucleic acid extraction reagent to the isopropanol is 5:2-5:3, incubating at 4° C., and centrifuging to precipitate the RNA in the aqueous phase; Add an equal volume of anhydrous ethanol to the organic phase, mix well, and centrifuge to precipitate the DNA; A 10% ethanol solution containing 0.1 molar sodium citrate is added to the DNA, the precipitate is resuspended, incubated, centrifuged, dried and washed to obtain a DNA precipitate.

6. The method for extracting immune signals from binary coacervates according to claim 2, characterized in that: The screening of highly expressed genes by comparing the disease model expressed genes with the normal model expressed genes includes: Performing high-throughput sequencing analysis on the separated DNA precipitate and RNA precipitate to obtain analysis results of the disease model and / or normal model; By comparing the analysis results of the disease model with the analysis results of the normal model, the differentially expressed genes are functionally annotated, thereby screening out highly expressed immune-related genes.

7. The method for extracting immune signals from binary coacervates according to claim 1, characterized in that: Also includes: The highly expressed genes include one or more of Camk4, Ddx4, Fas, Nox2, and Nlrp6.

8. The method for extracting immune signals from binary coacervates according to claim 7, characterized in that: The construction of a fusion vector based on cytoskeleton protein, the important protein molecule and green fluorescent protein tracing includes: The cytoskeleton protein includes the cytoskeleton protein TPPP fragment in SEQ ID No.1; The highly expressed gene CAMK4 includes the nucleic acid sequence in SEQ ID No. 2; The highly expressed gene DDX4 includes the nucleic acid sequence in SEQ ID No. 3; The highly expressed gene FAS includes the nucleic acid sequence in SEQ ID No.4; The highly expressed gene NLRP6 includes the nucleic acid sequence in SEQ ID No. 5; The important protein molecule includes the CAMK4 protein sequence in SEQ ID No.6; The important protein molecule includes the DDX4 protein sequence in SEQ ID No.7; The important protein molecule includes the FAS protein sequence in SEQ ID No.8; The important protein molecule includes the NLRP6 protein sequence in SEQ ID No.

9.

9. The method for extracting immune signals from binary coacervates according to claim 5, characterized in that: The method of using fusion protein to capture and identify immune-related RNA nucleic acid molecules includes: Transcribing the fusion vector to obtain a vector RNA nucleic acid molecule; The carrier RNA nucleic acid molecule is intravenously injected into mice by lipid nanoparticle delivery, comprising: Dissolving the carrier RNA nucleic acid molecule in a sodium citrate buffer having a concentration of 5-10 mmol / L; The carrier RNA nucleic acid molecule and the lipid nanoparticle are mixed, and the mixture of the carrier RNA nucleic acid molecule and the lipid nanoparticle is injected intravenously into a mouse. The weight ratio of the total lipids in the lipid nanoparticles to the carrier RNA nucleic acid molecules is in the range of 20:1-50:

1.

10. The method for extracting immune signals from binary coacervates according to claim 7, characterized in that: The drug treatment model is a result model after four weeks of drug treatment of the disease model.

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