High-throughput single-cell multi-omics three-dimensional genome detection method and system

Abstract

The invention provides a high-throughput single-cell multi-omics three-dimensional genome detection method and system. The detection method comprises the following steps: enzyme digestion reaction of fixed cells; carrying out proximity ligation treatment on the cells subjected to the enzyme digestion reaction; carrying out analysis treatment including reverse transcription on the treated cells, and respectively constructing a DNA library and an RNA library; and sequencing to obtain three-dimensional genomics information in a single cell. A high-throughput single-cell multi-omics three-dimensional genomics detection technology is designed, chromatin in cells is subjected to opening treatment through different methods, chromatin opening sites are enriched, the throughput of generated data is greatly improved, and the detection accuracy is greatly improved while the chromatin openness is detected in a single cell. The capability of detecting interaction between chromatin open sites and the gene expression capability are improved.

Description

Technical Field

[0001] The present invention relates to constructing a DNA library containing multiple molecular information, and in particular to a high-throughput single-cell multi-omics three-dimensional genome detection method and system. Background Art

[0002] Currently, most 3D genome detection technologies can only detect cell populations, cannot provide detailed information on different types of cells, and cannot show the differences between individual cells, so high-resolution single-cell analysis is not possible.

[0003] In terms of studying the three-dimensional genome structure in single cells, existing methods are mainly based on the following technical routes:

[0004] The first is to physically separate individual cells and study each cell independently. This method is a more traditional single-cell research method and has the problem of low throughput.

[0005] The second is to conduct multiple rounds of "breaking up" and "assembling" experiments on group cells, use ligase to perform multiple rounds of specific labeling for each cell, and then infer the information of the same cell by identifying the specific markers after sequencing. For example, Chinese invention patent CN116536409A discloses a three-dimensional genome detection method that is compatible with a wide range of ChIP DNA inputs, which obtains a good sequencing library by breaking, enriching, amplifying and screening the DNA of a single cell. However, this method relies on multiple rounds of ligation reactions, and there are problems of low ligation efficiency and time-consuming and labor-intensive. In addition, the current single-cell detection method can only detect two modules at most, and cannot fundamentally answer the causal relationship between the three-dimensional structure of chromatin and gene expression.

[0006] The third method is to use a microfluidic platform. For example, Chinese patent application CN117844911A discloses a spatial three-dimensional genomics sequencing method based on a microfluidic chip and its application. This method uses a microfluidic chip for barcode labeling during the overall labeling of the spatial position, thereby improving the resolution and accuracy. However, this method has no enrichment effect on DNA interactions and cannot fully study all the information of three-dimensional genomics.

[0007] Therefore, it is necessary to develop a detection system and method that can maintain high throughput while ensuring integrity and accuracy to obtain single-cell multi-omics three-dimensional genomic information. In addition, most current single-cell technologies can only capture single-omics information, making it difficult to reveal the causal relationship between chromatin structure and its transcriptional regulatory function. Summary of the invention

[0008] The technical purpose of the present invention is to provide a high-throughput single-cell multi-omics three-dimensional genome detection method and system to address the problems existing in the background technology. The chromatin in the cell is opened by different methods, and the chromatin open sites are enriched to increase the throughput of data generation, so that multi-omics information including the three-dimensional genome in a single cell can be obtained simultaneously with high throughput.

[0009] To achieve the above technical objectives, the present invention provides a high-throughput single-cell multi-omics three-dimensional genome detection method in a first aspect, the detection method comprising the following steps:

[0010] (1) Enzyme digestion reaction of fixed cells;

[0011] (2) performing proximity ligation treatment on the cells after the enzyme digestion reaction;

[0012] (3) performing analytical processing including reverse transcription on the treated cells and constructing DNA and RNA libraries respectively;

[0013] (4) Sequencing: Simultaneously obtain three-dimensional genomic information within a single cell, that is, information on open chromatin sites, chromatin interactions, and gene expression.

[0014] Preferably, the enzyme digestion reaction in step (1) specifically comprises the following steps:

[0015] (1.1) Take no less than 1×10 5 Thaw the cross-linked cells, add lysis buffer, and incubate at room temperature for at least 10 minutes;

[0016] (1.2) After the cleavage is completed, Triton X-100 is added to terminate the cleavage reaction;

[0017] (1.3) Use AluI restriction endonuclease to digest the chromatin DNA overnight.

[0018] Preferably, the proximity ligation in step (1) specifically comprises the following steps:

[0019] (1.4) After the enzyme digestion is completed, Klenow fragment and deoxyadenosine triphosphate (dATP) are added to carry out A-tailing reaction (37°C, 1 hour);

[0020] (1.5) Use T4 DNA ligase and a DNA linker with a dT terminus and biotin label to perform proximity ligation of chromatin DNA (16°C, overnight);

[0021] The DNA linker sequence is as follows:

[0022] SEQ NO: 1, DNA, 5'- / 5Phos / TAGGCGCTGG / iBIOdT / TGCGTTGT-3', Forward-76;

[0023] SEQ NO: 2, DNA, 5'- / 5Phos / CAACGCAACCAGGCCTAT-3', Reverse-76.

[0024] (1.6) After the connection is completed, wash the cells with ATAC-wash buffer and remove the supernatant.

[0025] Preferably, the analytical process of step (3) comprises combined labeling, microfluidic processing and physical separation.

[0026] As a further preference, the combined marking comprises the following process:

[0027] (2.1) Reverse transcription of adjacent treated cells;

[0028] (2.2) performing multiple rounds of mixing and splitting of the reverse transcribed cells;

[0029] (2.3) Multiple barcode labeling is used to label DNA and RNA sequences separately.

[0030] As a further preference, the microfluidic processing in step (3) includes the following process:

[0031] (3.1) Tn5 transposition reaction;

[0032] (3.2) loading the cells or cell nuclei after the Tn5 transposition reaction onto the microfluidic platform so that the single cells or cell nuclei are encapsulated in the microdroplets;

[0033] (3.3) DNA and RNA in cells are captured and labeled simultaneously through capture sequences in droplets.

[0034] As further preferred, the Tn5 transposition reaction in step (3.1) comprises the following steps:

[0035] (1.7) Resuspend the cells in 1x Nuclei buffer in the 10x Genomics Multiome kit and count the cells;

[0036] (1.8) Take no less than 1.2×10 4 The cells were subjected to Tn5 transposition reaction (37°C, 1 hour).

[0037] As further preferred, the physical separation in step (3) comprises the following process:

[0038] (4.1) performing reverse transcription on adjacent treated cells;

[0039] (4.2) performing flow cytometry sorting on the reverse transcribed cells;

[0040] (4.3) Prepare the sequencing library based on the results of flow cytometry.

[0041] Preferably, the sequencing in step (4) specifically comprises the following steps:

[0042] DNA and cDNA were purified using Dynabeads MyOne SILANE, and then separated using Dynabeads M-280 Streptavidin, and sequencing libraries were constructed for each of them to form a DNA library and an RNA library, respectively;

[0043] Illumina DNA sequencing was performed on DNA libraries and RNA libraries using the 150+8+24+150 and 28+10+10+90 sequencing strategies, respectively, to simultaneously obtain chromatin interactions, chromatin accessibility, and gene expression information of single cells.

[0044] The second aspect of the present invention provides a high-throughput single-cell multi-omics three-dimensional genome detection system, wherein the detection system is configured to implement the high-throughput single-cell multi-omics three-dimensional genome detection method described in the first aspect of the present invention, and the detection system comprises:

[0045] A pretreatment module is configured to perform pretreatment including enzyme cleavage reaction and proximity ligation on the fixed cells;

[0046] A combined labeling module is configured to implement a combined labeling operation on the pre-treated cells or cell nuclei;

[0047] A microfluidic module is configured to provide a microfluidic environment for the pre-treated cells or cell nuclei;

[0048] A physical separation module is configured to implement physical separation operations on pre-treated cells or cell nuclei; a sequencing module is configured to sequence a single cell and simultaneously obtain three-dimensional genomics information;

[0049] The preprocessing module is respectively connected to the combined labeling module, microfluidic module and physical separation module, and inputs the preprocessed cells / cell nuclei thereto; the combined labeling module, microfluidic module and physical separation module are respectively connected to the sequencing module to implement the sequencing operations after their respective processing.

[0050] The third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program is configured to, when executed by a processor, run the detection system described in the second aspect of the present invention, or implement the detection method described in the first aspect of the present invention.

[0051] The fourth aspect of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer device being configured so that when the processor executes the computer program, the detection system described in the second aspect of the present invention is run, or the detection method described in the first aspect of the present invention is implemented.

[0052] The beneficial effects of the present invention are:

[0053] 1. The present invention designs a high-throughput single-cell multi-omics three-dimensional genomics detection technology - ChAIR. The ChAIR technology uses a microfluidic platform to achieve single-cell resolution, introduces Tn5 transposase into the microfluidic system, forms droplets containing Tn5 transposase through the microfluidic system, and performs specific cell labeling on the microfluidic platform. The DNA sequence inserted by Tn5 transposase is perfectly compatible with the microfluidic platform, realizing high-throughput single-cell capture.

[0054] 2. The present invention adds a custom DNA connector to Tn5 transposase to study the accessibility and interaction of DNA, and achieves the enrichment of chromatin open sites through a microfluidic platform, which greatly improves the throughput of data generation. While detecting chromatin openness in a single cell, it also improves the ability to detect interactions between chromatin open sites and gene expression.

[0055] 3. The Tn5 transposase used in the present invention has the characteristics of completing the shearing of genomic DNA and inserting a specific DNA sequence in one step, which greatly improves the efficiency of the library construction process.

[0056] 4. The Tn5 transposase used in the present invention only binds to open chromatin, so that it can specifically enrich openness to study the interactions and openness information in chromatin, which is conducive to the precise study of the three-dimensional structure of chromatin in single cells.

[0057] In summary, the method of the present invention not only improves the efficiency of the experiment at the chemical level, but also brings innovation to the single-cell sorting platform, further improving the efficiency and accuracy of single-cell multi-omics three-dimensional genomics information research. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 Schematic diagram of the overall core concept of the detection method of the present invention, showing the simultaneous capture of gene expression, chromatin accessibility and chromatin interactions in the same cell.

[0059] Figure 2 It is an overall flow chart of the detection method in Example 1 of the present invention.

[0060] Figure 3 It is an overall structural diagram of the detection system in Example 2 of the present invention.

[0061] Figure 4 It is an overall flow chart of the detection method based on the microfluidic platform in Example 3 of the present invention.

[0062] Figure 5 It is a schematic diagram of the process of the ChAIR concept verification experiment in Example 4 of the present invention.

[0063] Figure 6 It is a 3D scatter plot of three groups of ChAIR data (RNA, ATAC, PET) obtained in Example 4 of the present invention.

[0064] Figure 7 t-SNE diagram of ChAIR-RNA, -ATAC and -PET data obtained in Example 4 of the present invention.

[0065] Figure 8 It is a visualization chart of Patski batch cell ChIATAC and RNA-seq data obtained in Example 4 of the present invention.

[0066] Fig. 9 It is a visualization chart of the whole ChAIR data of Patski cells obtained in Example 4 of the present invention.

[0067] Fig.10 This is a visualization chart of the Patski single cell trajectory obtained in Example 4 of the present invention.

[0068] Fig.11 This is a visualization chart of the ChIATAC and RNA-seq data of K562 batch cells obtained in Example 4 of the present invention.

[0069] Fig.12 It is a visualization chart of the whole ChAIR data of K562 cells obtained in Example 4 of the present invention.

[0070] Fig.13 This is a visualization chart of the trajectory of a single K562 cell obtained in Example 4 of the present invention.

[0071] Fig.14 It is a scatter plot of the expression of specific marker genes of ChAIR-RNA data of K562 and Patski cells in S and G2 / M phases in Example 5 of the present invention.

[0072] Fig.15It is a UMAP map of the ChAIR-RNA data of Patski cells in Example 5 of the present invention.

[0073] Fig.16 It is a PCA diagram of the distribution of individual K562 cells based on their cell cycle marker genes in Example 5 of the present invention.

[0074] Fig.17 It is a polymerization chamber analysis diagram of ChAIR-PET data in K562 (upper) and Patski (lower) in Example 5 of the present invention.

[0075] Fig.18 It is a graph of the aggregated TAD analysis of ChAIR-PET data in K562 (upper) and Patski (lower) in Example 5 of the present invention.

[0076] Fig.19 This is a clustered diagram of chromatin contacts between the promoter (TSS) of the phase-specific marker gene and the putative distal enhancer element (E) in Example 5 of the present invention.

[0077] Fig. 20 It is a UMAP graph of ChAIR-RNA data of 42 metacells of Patski cells sorted by cell cycle pseudo time in Example 5 of the present invention.

[0078] Fig.21 This is a distribution PCA graph of individual K562 cells further divided into meta-cells according to cell cycle pseudo-time in Example 5 of the present invention.

[0079] Fig. 22 This is an RNA score representation diagram of the S phase-specific gene (S gene) and haplotype analysis of the ChAIR-RNA data of Patski cells in Example 5 of the present invention.

[0080] Fig.23 This is an RNA score characterization diagram of G2 / M phase-specific genes (G2 / M genes) and haplotype analysis of ChAIR-RNA data of Patski cells in Example 5 of the present invention.

[0081] Fig.24 It is an example view of the 3D model of the Patski cell nuclear structure in Example 5 of the present invention, showing the open chromatin accessibility, gene expression, and haplotype-resolved nuclear localization of Xist and Flna in chrXa and chrXi in chromatin A and B regions.

[0082] Fig.25 This is a flow chart of the research design for collecting whole brain tissue samples at different days after birth in Example 6 of the present invention.

[0083] Fig.26is the reproducibility evaluation of the ChAIR data in the mouse brain in Example 6 of the present invention. Fig.26 A shows the correlation between technical replicates from ChAIR-RNA, -ATAC, and -PET data. Fig.26 B is a violin plot of the counts of UMIs, fragments, and pets captured in the ChAIR data. Fig.26 C is a schematic diagram of the percentage of mitochondrial RNA and mitochondrial DNA reads in ChAIR-RNA and -ATAC data. Fig.26 D is a schematic diagram of the percentage of intrachromosomal PET (intra-PET) in the ChAIR-PET data.

[0084] Fig. 27 It is a UMAP diagram of mouse brain cells in Example 6 of the present invention during maturation and aging combined with ChAIR-RNA data, highlighting the main brain cell categories, marked with different colors, and the reference data are marked in gray, and the cell number (n) is provided.

[0085] Fig.28 Schematic diagram of the correlation between ChAIR-RNA and reference scRNA-seq data in the quality assessment of mouse brain cell ChAIR RNA data.

[0086] Fig.29 It is a UMAP diagram of ChAIR-RNA data of mouse brain cells at five age points during maturation and aging in Example 6 of the present invention, providing the number of cells (n).

[0087] Fig.30 It is a matrix diagram of the classical marker gene expression profiles of 121 brain cell types in 7 cell categories during the maturation and aging of mouse brain cells in Example 6 of the present invention.

[0088] Fig.31 This is a UMAP graph showing typical marker gene expression signals in ChAIR RNA data in the quality assessment of mouse brain cell ChAIR RNA data in Example 6 of the present invention.

[0089] Fig.32 In Example 6 of the present invention, when evaluating the quality of ChAIR-ATAC data from mouse brain cells, the signal enrichment plots of ATAC peak sites in major brain cell classes of ChAIR-ATAC and dsciATAC data are compared side by side, and the number of ATAC peaks (n) for each class is given.

[0090] Fig.33In Example 6 of the present invention, when evaluating the quality of ChAIR-ATAC data from mouse brain cells, the signal enrichment plots of ChAIR-ATAC and dsciATAC data at TSS sites in major brain cell categories are compared side by side, and the number of ATAC peaks (n) for each class is given.

[0091] Fig.34 This is an example of a browser view of ChAIR-ATAC aggregate signals of marker gene loci between different cell types when evaluating the quality of ChAIR-ATAC data from mouse brain cells in Example 6 of the present invention.

[0092] Fig.35 In Example 6 of the present invention, in the quality assessment and identification of ChAIR-PET data of mouse brain cells, HiCRep analysis compared the cell-specific chromatin contact profiles between the existing (Tan et al., 2021) dataset and ChAIR-PET data.

[0093] Fig.36 It is a matrix diagram of specific ChAIR-ATAC peak signals across 121 cell types during maturation and aging of mouse brain cells in Example 6 of the present invention.

[0094] Fig.37 In Example 6 of the present invention, in the quality assessment and identification of the ChAIR-PET data of mouse brain cells, the ChAIR-PET data of 7 cell types were analyzed for clustered chromatin compartments (upper) and TADs (lower).

[0095] Fig.38 This is an example view of two-dimensional contact heat maps of different resolutions around specific marker genes in the quality assessment and identification of ChAIR-PET data of mouse brain cells in Example 6 of the present invention (top), and the corresponding browser views of single cell trajectories of ChAIR-RNA, -ATAC and -PET data are also shown (bottom).

[0096] Fig.39 This is a cell identity score (CIS) matrix diagram in the quality assessment and identification of mouse brain cell ChAIR-PET data in Example 6 of the present invention. Different single-modal features associated with marker genes, including transcription (T), gene body (gene activity, A), promoter (P), enhancer (E) and chromatin loop (L), and multimodal 3D epigenomic features of 23 cell groups are used. The value of the CIS group for each feature reflects its specificity in defining cell identity.

[0097] Fig.40It is a UMAP diagram of the cell identity score (CIS) calculated using transcriptome features in ChAIR data during maturation and aging of mouse brain cells in Example 6 of the present invention, which shows specificity across 23 cell populations and shows the specificity of transcriptome features in OPC and MOL cells.

[0098] Fig.41 It is a UMAP map of the cell identity score (CIS) calculated using 3D epigenomic features in ChAIR data during maturation and aging of mouse brain cells in Example 6 of the present invention, which shows specificity across 23 cell populations and shows the specificity of 3D epigenomic features in OPC and MOL cells.

[0099] Fig.42 It is a single cell ChAIR data trajectory diagram located at the Cbln1 locus during the maturation and aging of mouse brain cells in Example 6 of the present invention.

[0100] Fig.43 This is a schematic diagram of the integration of Stereo-seq (bin50) data and ChAIR-RNA data in Example 7 of the present invention when studying the region-specific 3D epigenomic features revealed by ChAIR in mouse brain cells, with the left side showing the regions in the coronal hemi-brain slices defined by Stereo-seq and the right side showing the corresponding cell types characterized by ChAIR.

[0101] Fig.44 This is a typical marker gene expression profile of ChAIR-RNA in Example 7 of the present invention.

[0102] Fig.45 It is a typical marker gene expression profile of Stereo-seq data with cell type annotation in Example 7 of the present invention.

[0103] Fig.46 It is a typical marker gene expression profile of the Stereo-seq data with anatomical region annotations in Example 7 of the present invention.

[0104] Fig.47 It is a spatial visualization expression map of cells in multiple cerebral cortical layers in Example 7 of the present invention, as well as a gene expression map of marker genes in Stereo-seq and Allen Brain Atlas in situ hybridization (ABA ISH) data.

[0105] Fig.48In Example 7 of the present invention, when studying the regional specific 3D epigenome characteristics revealed by ChAIR in mouse brain cells, the spatial distribution map of specific cell types and locations (first column), the CIS fractional signal intensity map of marker gene transcription derived from ChAIR data (second column), the marker gene-related chromatin loop map (third column) and the 3D epigenome (3Depi) feature map (fourth column) are shown.

[0106] Fig.49 In Example 7 of the present invention, when analyzing cell types across regions, a CIS matrix diagram is obtained by calculating different features.

[0107] Fig.50 It is an additional example of the spatially resolved ChAIR data region feature in Example 7 of the present invention.

[0108] Fig.51 This is an example of ChAIR data in Example 7 of the present invention when studying the region-specific 3D epigenomic features revealed by ChAIR in mouse brain cells, showing the 2D contact map of the Nel2 locus (cortical layer 2 / 3 marker gene) in layers 2 / 3, 4, and 5 / 6 and single cell trajectory maps of RNA, ATAC, and PET.

[0109] Fig.52 is another example of ChAIR data in Example 7 of the present invention, showing the characteristics of cortical layer-specific chromatin marker genes. DETAILED DESCRIPTION

[0110] In order to enable those skilled in the art to better understand and implement the technical solution of the present invention, the present invention is specifically, clearly and completely described in the following embodiments in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Moreover, based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work, any modifications, equivalent substitutions, improvements, etc., should be included in the protection scope of the present invention.

[0111] In order to enable those skilled in the art to better understand the technical solution of the present invention, some technical terms of the present invention are first explained. In the text of the present invention, these explanations are mainly to help those skilled in the art understand and implement the technical solution of the present invention, rather than to limit the present invention. In addition, when other technical interpretations of these technical terms may be made in other disclosures outside the present invention, in order to avoid technical ambiguity, the interpretations of the present invention should be based on the interpretations of the present invention when understanding the present invention.

[0112] 3D genome biology

[0113] Three-dimensional genomics is the study of how the genome is organized and interacts in three-dimensional space. It focuses not only on the genomic DNA sequence itself, but also on the spatial structure of DNA in the cell nucleus. This spatial organization has an important impact on gene expression and cell function.

[0114] Chromatin interaction

[0115] Chromatin interactions refer to the physical contacts and interactions of chromatin fibers (a complex of DNA and associated proteins) in the cell nucleus.

[0116] Chromatin accessibility

[0117] Chromatin accessibility refers to the degree of openness of DNA regions in the chromatin structure to transcription factors and other DNA binding proteins. Open chromatin regions are usually associated with regulatory regions of gene expression and are where functional elements such as gene promoters and enhancers are located. Studying chromatin accessibility can provide important information about the regulatory mechanisms of gene expression.

[0118] Gene expression

[0119] Gene expression refers to the entire process from DNA to functional proteins, including transcription (DNA to RNA), RNA processing, translation (RNA to protein), and protein modification and transport. The regulation of gene expression is the basis of cell function and organism development, and is affected by many factors, including transcription factors, chromatin state, and external signals.

[0120] Proximity ligation

[0121] Proximity ligation is a molecular biology technique used to detect interactions between protein, DNA, or RNA molecules.

[0122] Tn5 transposase

[0123] Tn5 transposase is an enzyme derived from the Tn5 transposon (a bacterial transposon) that is able to insert specific DNA sequences into genomic DNA. This enzyme is widely used in next-generation sequencing technologies because it can simultaneously shear DNA and insert sequencing adapters in one step, greatly simplifying the library preparation process. In addition, Tn5 transposase only binds to open chromatin, allowing specific studies of chromatin accessibility and interactions.

[0124] DNA linker

[0125] DNA adapters are short, synthetic double-stranded DNA fragments that are used to connect to the ends of target DNA molecules for subsequent molecular biology operations. Adapters usually contain specific sequences for sequencing primer binding, allowing the target DNA to be recognized and amplified by high-throughput sequencers. During the Tn5 transposase-mediated library construction process, the adapters are integrated into the target DNA during DNA shearing and insertion.

[0126] Microfluidics platform

[0127] Microfluidic platforms are technology platforms used to manipulate tiny volumes of liquid and conduct biological experiments. These platforms consist of tiny channels, valves, and chambers that can precisely control the flow and mixing of liquids. In single-cell analysis, microfluidic platforms are used to capture and process single cells at a high throughput, allowing genomics, transcriptomics, and other studies to be conducted at the single-cell level. When combined with Tn5 transposase, this platform can achieve high-throughput single-cell capture and analysis, improving the efficiency and accuracy of research.

[0128] Example 1, a high-throughput single-cell multi-omics three-dimensional genome detection method.

[0129] This embodiment provides an overall method for a high-throughput single-cell multi-omics three-dimensional genome detection method, which can establish DNA and RNA libraries respectively through different channels, thereby providing a library basis for sequencing.

[0130] like Figure 1-2 As shown, in this embodiment, the detection method specifically includes the following process:

[0131] (1) Performing enzyme digestion reaction on the obtained fixed cells,

[0132] (2) performing proximity ligation treatment on the cells after the enzyme digestion reaction;

[0133] (3) performing analytical processing including reverse transcription on the treated cells and constructing DNA and RNA libraries respectively;

[0134] (4) Sequencing: Simultaneously obtain three-dimensional genomic information within a single cell.

[0135] Preferably, the analytical process of step (3) comprises combined labeling, microfluidic processing and physical separation.

[0136] As a further preference, the combined marking comprises the following process:

[0137] (2.1) Reverse transcription of adjacent treated cells;

[0138] (2.2) performing multiple rounds of mixing and splitting of the reverse transcribed cells;

[0139] (2.3) Multiple barcode labeling is used to label DNA and RNA sequences separately.

[0140] As a further preference, the microfluidic processing in step (3) includes the following process:

[0141] (3.1) Tn5 transposition reaction;

[0142] (3.2) loading the cells or cell nuclei after the Tn5 transposition reaction onto the microfluidic platform so that the single cells or cell nuclei are encapsulated in the microdroplets;

[0143] (3.3) DNA and RNA in cells are captured and labeled simultaneously through capture sequences in droplets.

[0144] As further preferred, the physical separation in step (3) comprises the following process:

[0145] (4.1) performing reverse transcription on adjacent treated cells;

[0146] (4.2) performing flow cytometry sorting on the reverse transcribed cells;

[0147] (4.3) Prepare the sequencing library based on the results of flow cytometry.

[0148] The three methods above can all obtain DNA and RNA libraries prepared for sequencing, and then proceed to step (4). The sequencing in step (4) specifically includes the following process:

[0149] Illumina DNA sequencing was performed on DNA libraries and RNA libraries using different sequencing strategies to simultaneously obtain chromatin interactions, chromatin accessibility, and gene expression information of single cells.

[0150] Example 2, a high-throughput single-cell multi-omics three-dimensional genome detection system.

[0151] This embodiment provides a high-throughput single-cell multi-omics three-dimensional genome detection system, which can be used to implement the detection method described in Example 1 of the present invention.

[0152] like Figure 3 As shown, the detection system comprises:

[0153] The pretreatment module 101 is configured to perform pretreatment including enzyme cleavage reaction and proximity ligation on the fixed cells;

[0154] The combined labeling module 102 is configured to implement a combined labeling operation on the pre-processed cells or cell nuclei;

[0155] The microfluidic module 103 is configured to provide a microfluidic environment for the pre-treated cells or cell nuclei;

[0156] The physical separation module 104 is configured to implement a physical separation operation on the pre-treated cells or cell nuclei;

[0157] Sequencing module 105: configured to sequence a single cell and simultaneously obtain three-dimensional genomics information;

[0158] The pre-processing module 101 is respectively connected to the combined labeling module 102, the microfluidics module 103 and the physical separation module 104, and inputs the pre-processed cells / nuclei to them, and the combined labeling module 102, the microfluidics module 103 and the physical separation module 104 are respectively connected to the sequencing module 105 to implement the sequencing operation after each processing. The sequencing module 105 completes the final sequencing work and outputs the results.

[0159] Example 3, a high-throughput single-cell multi-omics three-dimensional genome detection method based on microfluidics technology.

[0160] This embodiment provides a specific embodiment of a high-throughput single-cell multi-omics three-dimensional genome detection method based on microfluidics technology. In the present invention, we name the high-throughput single-cell multi-omics three-dimensional genome detection method based on microfluidics technology performed according to the technology of the present invention as ChAIR method or ChAIR technology (the same below).

[0161] like Figure 4 As shown, in this embodiment, the specific process of the ChAIR method is:

[0162] S1, thaw: take 1×10 5 The cross-linked Patski cells were placed in a culture tube, taken out of the -80°C freezer and thawed on ice.

[0163] S2, lysis: After thawing, add 10 μL of 0.1% SDS lysis buffer and incubate at room temperature for 10 minutes to lyse the cells. Then, add 2.5 μL of 20% Triton X-100 to terminate the lysis reaction.

[0164] S3, enzyme digestion: Use AluI restriction endonuclease to digest the chromatin DNA overnight at 37°C.

[0165] S4, A-tailing reaction: After the enzyme digestion is completed, Klenow fragment (3'-->5'exo-) and deoxyadenosine triphosphate (dATP) are added to carry out A-tailing reaction at 37°C for 1 hour.

[0166] S5, Proximity ligation: Perform proximity ligation of chromatin DNA overnight at 16°C using T4 DNA ligase and a dT-terminated, biotinylated DNA linker.

[0167] The DNA linker sequence is as follows:

[0168] SEQ NO: 1, DNA, 5'- / 5Phos / TAGGCGCTGG / iBIOdT / TGCGTTGT-3', Forward-76;

[0169] SEQ NO: 2, DNA, 5'- / 5Phos / CAACGCAACCAGGCCTAT-3', Reverse-76.

[0170] S6, Wash: Wash cells with ATAC-wash buffer and remove the supernatant.

[0171] S7, resuspend and count: Resuspend the cells in 1x Nuclei buffer in the 10x Genomics Multiome kit and count the cells.

[0172] S8, Tn5 transposition: 1.2×10 4 The cells were incubated for 1 hour at 37°C for Tn5 transposition reaction.

[0173] S9, capture and labeling: After transposition is completed, the cells are loaded onto the 10x Genomics microfluidics platform for single cell capture and labeling.

[0174] S10, Reverse transcription: The labeled cells were subjected to reverse transcription reaction at 37°C for 45 minutes.

[0175] S11, sequencing: DNA and cDNA were purified using Dynabeads MyOne SILANE, and then separated using Dynabeads M-280Streptavidin, and sequencing libraries were constructed for each of them; Illumina DNA sequencing was performed on the DNA library and RNA library using internationally accepted sequencing strategies, such as 150+8+24+150 and 28+10+10+90 sequencing strategies.

[0176] Example 4, ChAIR concept verification experiment.

[0177] like Figure 5As shown, before the ChAIR proof-of-concept experiment, a cell population-based dual-omics method ChIATAC was first established. ChIATAC can obtain information about open chromatin sites like ATAC-seq, and can also obtain information about the interactions between these chromatin sites. By combining ChIATAC with a microfluidic system, the ChAIR method of the present invention is formed. The ChAIR method first cross-links the cells, extracts the nuclei and permeabilizes the membrane, and then performs restriction endonuclease digestion, proximity ligation, and transposition using Tn5 enzyme on the chromatin DNA in situ. The genomic DNA after transposition will be interrupted and DNA sequencing adapters will be added. Subsequently, the cell nuclei that have completed the previous reaction are put into the 10xGenomics Multiome microfluidic system. Finally, the DNA and RNA information in a single cell is revealed by sequencing the labeled DNA and RNA molecules at the same time. Each ChAIR experiment generates three data sets (tri-omics), including ChAIR-ATAC data for revealing chromatin accessibility, ChAIR-PET (paired end labeling) for parsing chromatin interactions, and ChAIR-RNA for analyzing gene expression.

[0178] In this example, a proof-of-concept experiment was performed using an equal mixture of human K562 cells (a myeloid leukemia cell line) and mouse Patski cells (a hybrid mouse fibroblast cell line) to generate a high-quality tri-omics dataset, such as Figure 6 and Figure 7 As shown, the effectiveness of the ChAIR experimental protocol in generating high-quality three-omics datasets is confirmed. As can be seen from the figure, the overall double-cell ratio is 5.2%, which also confirms the reliability of the microfluidic system in single-cell analysis.

[0179] In addition, we visualize the ChAIR data in three aspects (RNA, ATAC and PET) together with the reference genome (this can be achieved through data visualization software in the existing technology, such as Integrative Genomics Viewer or similar software), as shown in Figure 8-Figure 13 As shown in the ChAIR-PET data (shown as two points connected by a horizontal line), hierarchical clustering analysis shows that cells in the same cluster share similar chromatin folding patterns. In contrast, significant differences can be observed between cells in different clusters, highlighting the heterogeneity of cells at different cell cycle stages. Overall, ChAIR data show high efficiency in revealing the synergy between gene expression (RNA), open chromatin accessibility (ATAC), and chromatin interactions (PET) in single cells.

[0180] Example 5, experiment to verify the interaction between chromatin structure and transcription during the cell cycle.

[0181] The cell cycle is an important cell characteristic from DNA replication to cell division. The complete cell cycle includes G1 phase, S phase, G2 phase and mitosis phase. The existing literature in the prior art has detailed the characterization of specific gene transcription in each phase. At the same time, the chromatin folding structure during the cell cycle is a current active research topic. However, the exact interaction between specific transcription programs and chromatin folding dynamics during the cell cycle has not been fully characterized. This example is based on K562 and Patski cells. The three-dimensional genomics data obtained by the ChAIR method of Example 3 or Example 4 provides the above-mentioned characterization data.

[0182] The ChAIR-RNA data acquisition method in Example 4 was used to obtain the ChAIR-RNA data specific marker gene expression scatter plots of K562 and Patski cells in the S and G2 / M phases in combination with the differential expression maps of genes specifically expressed in the S phase and G2 / M phase in the prior art ( Fig.14 ) to obtain the UMAP map of Patski ChAIR-RNA data ( Fig.15 ) and the PCA plot of the distribution of individual K562 cells based on their cell cycle marker genes ( Fig.16 ). By analyzing the corresponding ChAIR-PET data and the polymerized TAD ( Fig.17 and Fig.18 ) analysis showed that compartment intensity and TAD signals were highly identifiable in G1 and S phases, but significantly reduced in G2 / M phases. In addition, promoter-enhancer interactions (TSS-E) of marker genes specifically expressed in S phase and G2 / M phase showed enhanced interaction signals in their respective phases ( Fig.19 ).

[0183] In addition, based on the position of the cell cycle on the principal component analysis (PCA) plot, we grouped the similar cells in Patski cells and K562 cells into 42 unique 'metacells', as follows: Fig. 20 and Fig.21 As shown. Cells in each metacell share highly similar cellular properties, and the chromatin contacts in each metacell serve as a robust data representative for analyzing the process of chromatin folding and gene transcription during the cell cycle and minimizing random errors in individual cells. As shown in the figure, the expression intensity (RNA) and promoter intensity (ATAC) of the S phase-specifically expressed marker genes are low in the G1 phase, increase after entering the S phase, peak toward the end of the S phase, and continue to decline in the G2 / M phase, which is closely consistent with the progression of the cell cycle pseudo-time ( Fig. 22), G2 / M-specific marker genes followed this pattern, but peaked in the G2 / M phase and declined towards the end of the phase ( Fig.23 ), showing coordinated changes in gene expression and chromatin accessibility.

[0184] In summary, by analyzing ChAIR data from two different perspectives, either ChAIR-RNA or ChAIR-PET data, we demonstrated the effectiveness of ChAIR in revealing the genomic framework supporting cell-specific transcriptional regulation and also demonstrated the ability of ChAIR to achieve efficient single-cell triplet analysis.

[0185] Example 6. Triple combinatorial landscape of transcriptome, epigenome and 3D genome in single cells of mouse brain.

[0186] In mouse brain research, existing technologies based on gene expression have extensively documented multiple cell populations and established a rich knowledge base, but the genomic mechanisms that regulate cell-specific transcriptional programs remain unclear.

[0187] This example uses the high-throughput capabilities of ChAIR to conduct a comprehensive analysis of the three-dimensional epigenome (including chromatin folding and epigenomic state) landscape and transcriptional regulation in mouse brain cells.

[0188] This example conducts a stratified study based on five key developmental time points of the brain during development and aging: day 2 (P2), day 11 (P11), day 95 (P95), day 365 (P365, 1 year), and day 730 (P730, 2 years) after birth ( Fig.25 ). Combined with the ChAIR method in Example 3 or 4, 222,698 high-quality ChAIR data were collected from these five age points, with high repeatability ( Fig.26 ). The ChAIR-RNA data were annotated using a label transfer method based on a reference single-cell RNA sequencing dataset that has been published in the prior art. This dataset analyzes cells in the central nervous system of the mouse brain. The combined ChAIR-RNA data showed high similarity to the reference data ( Fig. 27 and Fig.28 ), while showing significant age-related changes at different age points ( Fig.29 and Fig.26 A). Of the 199 well-defined mouse brain cell types known in the prior art, 121 cell types were identified using the above method, and these types were characterized by the expression of typical marker genes ( Fig.30 and Fig.31), which belong to 45 cell populations of 7 major brain cell classes: excitatory neurons (ExN), inhibitory neurons (InhN), astrocytes / ependyma (Ast), oligodendrocyte precursor cells (OPC), oligodendrocytes (Oligo), microglia (MGL), and vascular cells (VAS) ( Fig.30 and Fig.28 ). At the same time, each cell type identified by ChAIR-RNA data has corresponding high-quality ChAIR-ATAC and ChAIR-PET datasets, which show high correlation with other major cell class-related methods in terms of epigenomic state and chromatin folding features ( Fig.32 , Fig.33 , Fig.35 ) and showed specific open chromatin sites ( Fig.34 , Fig.36 ) and characterization of chromatin regions, TADs, and chromatin contacts in both ensemble and single-cell views ( Fig.37 , Fig.38 ).

[0189] To analyze the correlation between RNA, ATAC, and PET data generated by ChAIR, for 23 of the 45 cell groups, each trimodal dataset was sufficient for in-depth analysis. To quantitatively assess each data modality for cell type identification, this example measures the specificity of a given feature, including transcription (RNA data), gene activity (gene body ATAC signal), promoter strength (ATAC TSS), enhancer strength (ATAC enhancer sites), and chromatin looping (PET data), of a particular cell type over the background noise of other cells. All single-modality CISs showed clear cell type specificity, with transcription CIS and chromatin looping CIS values ​​at the same level, while ATAC-related CISs lagged ( Fig.39 ). While the CIS values ​​for transcriptional specificity and chromatin looping were not significantly different, their matrix profiles showed clear differences, suggesting that chromatin contacts may provide a perspective for cell identification distinct from gene expression. Furthermore, combining chromatin looping specificity with ATAC-based open chromatin specificity, collectively referred to as 3D epigenomic signatures or states, significantly improved the CIS values ​​for each specific cell group and for all 23 cell groups ( Fig.39 , Fig.40 , Fig.41 For example, in the case of OPCs and mature oligodendrocytes (MOLs), the composite CIS of the 3D epigenomic signature was significantly higher, with a clearer distinction between the transcriptomic signatures and background noise of OPCs and MOLs ( Fig.40 , Fig.41 ).

[0190] A single-cell browser view of ChAIR data at the Cbln1 locus further revealed extensive coordinated chromatin interactions, high levels of open accessibility, and active transcription. Fig.42 ).

[0191] This example further demonstrates that the 3D epigenomic state associated with marker genes can indicate the identity of a cell, emphasizing the close connection between the epigenomic state and 3D chromatin contacts and cell-specific transcriptional programs.

[0192] Example 7. Spatially resolved region-specific 3D epigenomic characterization.

[0193] As mentioned above, the ChAIR method of the present invention can effectively generate trimodal data including RNA and genomic information; however, unlike spatial transcriptomics, it does not provide anatomical information.

[0194] To address this issue, this example utilizes a spatial transcriptome dataset from adult mice. According to the prior art, coronal hemi-brain sections of mice can be generated by Stereo-seq, and then experiments are conducted to combine the 3D epigenomic features of ChAIR data with the brain region specificity provided by stereo-sequence data. If the ChAIR RNA data matches the stereo-sequence RNA data, then the synchronized ChAIR-ATAC and -PET data can be assigned to the corresponding spatial positions defined by Stereo-seq, thereby adding a key spatial dimension to the understanding of three-dimensional epigenomics.

[0195] The aforementioned ChAIR-RNA data were used to annotate bin 50 (50 × 50 bins) of Stereo-seq data, and the 20 anatomical regions identified by Stereo-seq were successfully linked to the 19 cell types defined by ChAIR-RNA data with high specificity ( Figure 43-Figure 46 These cell types, particularly neurons from the cortex and hippocampus, closely matched their original spatial annotations known from the prior art. The uniformity of ChAIR and Stereo-seq was validated by evaluating the spatial distribution of canonical marker genes and histological staining of adjacent tissue sections ( Fig.47 ).

[0196] Next, the ChAIR-RNA signals of various exon marker genes, including TEGLU and DGGRC, were mapped to the anatomical sections of the coronal hemisections in Stereo-seq ( Fig.48 ), these results revealed distinct expression patterns in the cerebral cortex, highlighting the outer (layer 2 / 3), middle (layer 4), inner (layer 5 / 6) regions of the neocortex and the dentate gyrus of the hippocampus ( Fig.48 , 1-2 columns), indicating that ChAIR-Stereo data integration has high accuracy.

[0197] When characterizing ChAIR data in Example 5 above, it was recognized that chromatin loops are also unique features, and the composite 3D epigenomic signature provides superior specificity for cell recognition ( Fig.39 , Fig.40 , Fig.41 ), especially for ChAIR data matching Stereo-seq data, this example focuses on 8 groups of cells with sufficient data, and 3D epigenomic data are analyzed in depth ( Fig.49 ). It can be seen that the CIS values ​​of chromatin loops, especially 3D epigenomic features, are significantly higher than those of features expressing gene expression alone ( Fig.48 2-4 columns, Fig.49 , Fig.50 ). For example, the marker gene transcription profile of TEGLU7 (specific to cortical layer 2 / 3) showed signal enrichment in both cortical layer 2 / 3 and the dentate gyrus, while chromatin loops and 3D epigenomic features showed higher signal in cortical layer 2 / 3. Similarly, marker gene transcription of DGGRC cells was enriched in the hippocampus region, with high levels of gene expression in the cortical layers. However, chromatin loops and 3D epigenomic profiles were highly specific to the hippocampus, with minimal background noise from other anatomical regions. Finally, the browser view of the single-cell resolution, particularly at the Nell2 locus, a marker gene for cortical layer 2 / 3 ( Fig.51 ) and Fig.52 Other examples shown highlight specific marker genes associated with chromatin interaction loops and ATAC signals, among which Fig.52 The ChAIR data shown in Figure A shows an overall 2D contact heatmap (left) as well as marker gene promoter-associated chromatin contacts (PET), ATAC, and single-cell RNA tracks for the locus of the cortical layer 4 marker gene Egr1 (right). Figure B shows a similar analysis for the cortical layer 5 marker gene Neto2.

[0198] Thus, this example demonstrates that RNA-based cell specificity can be better defined by integrated 3D epigenomic signatures, using ChAIR-RNA data as a bridge that can be accurately mapped to spatially resolved anatomical regions.

[0199] Embodiment 8, a computer-readable storage medium.

[0200] This embodiment provides a computer-readable storage medium on which a computer program is stored. The computer program is configured to, when executed by a processor, run the detection system described in Example 2, or implement the detection method described in Example 1, 3 or 4, or run the detection method mentioned in other embodiments.

[0201] The operation described in this embodiment refers to the completion of the relevant hardware by instructing the computer program, which can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the above-mentioned embodiments or a combination thereof. Among them, any reference to memory, storage, database or other media used in the embodiments provided by the present invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory (USB, etc.). Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0202] Embodiment 9, a computer device.

[0203] This embodiment provides a computer device, which includes but is not limited to hardware such as a memory and a processor. The memory stores a computer program that can be executed on the processor. The computer device is configured so that when the processor executes the computer program, it runs the detection system described in Example 2, or implements the detection method described in Example 1, 3 or 4, or runs the detection method mentioned in other embodiments.

[0204] The computer device may be a server, or a PC or a large computer device for experiments, and the computer device includes but is not limited to a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device may include a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The computer device may include a database for storing data. The network interface of the computer device may communicate with an external terminal via a network connection.

[0205] Although the preferred embodiments of the present invention have been described in the specification, once the basic creative concept is known to those skilled in the art, they may make additional changes and modifications to these embodiments or combinations of embodiments without creative work. Therefore, the protection scope of the present invention should be understood as the scope that the claims are intended to explain and cover, and should not be limited to the parts described in detail in the specification and embodiments of the present invention, and the contents described in the present invention, including the preferred embodiments, include all changes and modifications that fall within the scope of the present invention.

Claims

1. A high-throughput single-cell multi-omics three-dimensional genome detection method, characterized in that: The detection method simultaneously captures gene expression, chromatin openness and chromatin interaction information in a single cell, and comprises the following steps: (1) Enzyme digestion reaction of fixed cells, (2) performing proximity ligation treatment on the cells after the enzyme digestion reaction; (3) performing analytical processing including reverse transcription on the treated cells and constructing DNA and RNA libraries respectively; (4) Sequencing: Simultaneously obtain three-dimensional genomic information within a single cell.

2. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 1, characterized in that: The enzyme digestion reaction in step (1) specifically comprises the following steps: (1.1) Thaw the cross-linked cells, add lysis buffer, and incubate at room temperature for lysis. (1.2) After the cleavage is completed, Triton X-100 is added to terminate the cleavage reaction; (1.3) Use AluI restriction endonuclease to digest the chromatin DNA overnight.

3. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 1, characterized in that: The proximity ligation in step (2) specifically comprises the following steps: (1.4) After the enzyme digestion is completed, Klenow fragment and deoxyadenosine triphosphate (dATP) are added to carry out A-tailing reaction; (1.5) using T4 DNA ligase and a DNA linker with a dT terminus and a biotin label, to perform proximity ligation on the chromatin DNA, wherein the gene sequence of the DNA linker is shown in SEQ NO: 1 and SEQ NO: 2 in the sequence listing; (1.6) After the ligation is complete, wash the cells and remove the supernatant.

4. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 1, characterized in that: The analytical processing of step (3) includes combined labeling, microfluidic processing and physical separation.

5. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 4, characterized in that: The combined marking includes the following process: (2.1) Reverse transcription of adjacent treated cells; (2.2) performing multiple rounds of mixing and splitting of the reverse transcribed cells; (2.3) Multiple barcode labeling is used to label DNA and RNA sequences separately.

6. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 4, characterized in that: The microfluidic processing of step (3) includes the following process: (3.1) Tn5 transposition reaction; (3.2) loading the cells or cell nuclei after the Tn5 transposition reaction onto the microfluidic platform so that the single cells or cell nuclei are encapsulated in the microdroplets; (3.3) DNA and RNA in cells are captured and labeled simultaneously through capture sequences in droplets.

7. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 4, characterized in that: The physical separation in step (3) comprises the following process: (4.1) performing reverse transcription on adjacent treated cells; (4.2) performing flow cytometry sorting on the reverse transcribed cells; (4.3) Prepare sequencing libraries based on the results of flow cytometry.

8. A high-throughput single-cell multi-omics three-dimensional genome detection method according to claim 1, characterized in that: The sequencing in step (4) specifically includes the following process: Illumina DNA sequencing was performed on DNA libraries and RNA libraries using different sequencing strategies to simultaneously obtain chromatin interactions, chromatin accessibility, and gene expression information of single cells.

9. A high-throughput single-cell multi-omics three-dimensional genome detection system based on microfluidics technology, characterized in that: The detection system is configured to implement the detection method according to any one of claims 1 to 8, and the detection system comprises: A pretreatment module is configured to perform pretreatment including enzyme digestion reaction and proximity ligation on the fixed cells; A combined labeling module is configured to implement a combined labeling operation on the pre-treated cells or cell nuclei; A microfluidic module is configured to provide a microfluidic environment for the pre-treated cells or cell nuclei; A physical separation module, configured to implement a physical separation operation on the pre-treated cells or cell nuclei; Sequencing module: configured to sequence single cells and simultaneously obtain three-dimensional genomics information; The preprocessing module is respectively connected to the combined labeling module, microfluidic module and physical separation module, and inputs the preprocessed cells / cell nuclei thereto; the combined labeling module, microfluidic module and physical separation module are respectively connected to the sequencing module to implement the sequencing operations after their respective processing.

10. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, it runs the detection system of claim 9, or implements the detection method of any one of claims 1-8.

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