Spatial analysis method for samples

Abstract

The present application relates to the technical field of biological medicine, and particularly relates to a spatial analysis method of a sample, a construction method of a non-invasive identification model of a tertiary lymphoid organ and a method for non-invasive identification of a tertiary lymphoid organ. The method comprises: processing spatial transcriptome data from different regions of a sample from a tissue of interest to identify a tertiary lymphoid organ region and a non-tertiary lymphoid organ region of the sample, so as to better explore the formation mechanism thereof, complete non-invasive identification of the tertiary lymphoid organ, and clarify the role of the tertiary lymphoid organ in atherosclerosis.

Description

Technical Field

[0001] This invention relates to the field of biomedical technology, specifically to a spatial analysis method for samples that utilizes spatiotemporal omics technology combined with bioinformatics visualization analysis, a method for constructing a non-invasive identification model of tertiary lymphoid organs, and a method for non-invasive identification of tertiary lymphoid organs. Background Technology

[0002] Atherosclerosis is a widespread vascular disease with significant impacts on human health. It is characterized by plaque buildup in arteries, leading to reduced blood flow and potentially causing serious complications such as heart attacks and strokes. Tertiary lymphoid organs (TLOs) are encapsulated groups of lymphocytes present in various vascular diseases, including abdominal aortic aneurysms and atherosclerosis. Recent studies have shown that arterial TLOs form on the adventitia near atherosclerotic lesions, indicating that inflammation spreads from the intima to the adventitia, triggering an immune response.

[0003] As the primary site of the immune response in atherosclerosis, regulating TLO formation and thus controlling the immune response in atherosclerosis holds promise as a novel therapeutic target. However, the exact location and impact of this immune response remain unclear. Furthermore, it is currently unknown whether the location of different TLOs will have varying effects on atherosclerosis. Current identification of tertiary lymphoid structures primarily relies on the characteristics of 12 cytokines or on HE staining results, which has limited analytical breadth and poor spatial resolution, restricting comprehensive multi-perspective characterization of TLOs.

[0004] Therefore, there is an urgent need for a spatial analysis method for samples to better explore their formation mechanism, complete the non-invasive identification of tertiary lymphoid organs, and clarify their role in atherosclerosis. Summary of the Invention

[0005] This invention aims to at least partially solve one of the technical problems in related technologies. To this end, embodiments of this invention propose a spatial analysis method for samples, a method for constructing a non-invasive identification model of tertiary lymphoid organs, and a method for non-invasive identification of tertiary lymphoid organs.

[0006] An embodiment of the first aspect of the present invention provides a spatial analysis method for a sample, comprising: processing spatial transcriptomic data from different regions of a sample from a tissue of interest to identify tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample.

[0007] In some embodiments, the spatial transcriptome data includes a first gene expression matrix carrying spatial coordinates and a tissue ssDNA map, wherein processing spatial transcriptome data from different regions of a sample from the tissue of interest includes:

[0008] Single-cell resolution tissue images are obtained based on the tissue ssDNA map obtained by staining the tissue image; and

[0009] The tissue image at single-cell resolution is registered with the first gene expression matrix carrying spatial coordinates to obtain the first single-cell gene expression matrix.

[0010] In some embodiments, obtaining a single-cell resolution tissue image includes:

[0011] The ssDNA map is segmented into tissue sample regions using a deep learning model.

[0012] The location of each cell nucleus in the tissue sample region was determined using a Gaussian weighted model.

[0013] Multiple cell regions were determined by segmenting the overlapping cell regions using the Euclidean distance between the cell nuclei; and

[0014] The multiple cell regions are segmented using a watershed algorithm to obtain a tissue image at single-cell resolution.

[0015] In some embodiments, obtaining the first single-cell gene expression matrix includes:

[0016] Convert the plurality of cell regions into the spatial coordinates;

[0017] The tissue image at single-cell resolution is registered based on the spatial coordinates; and

[0018] The gene expression data in the first gene expression matrix are divided into the corresponding cell regions to obtain the first single-cell gene expression matrix.

[0019] In some embodiments, identifying the tertiary lymphoid organ region and non-tertiary lymphoid organ region of the sample includes: performing a first cell clustering and a first cell type annotation on the cell region based on the first single-cell gene expression matrix; and identifying immune cell-rich regions as the tertiary lymphoid organ region and other regions as non-tertiary lymphoid organ regions based on the first cell type and the spatial coordinates.

[0020] In some embodiments, the single-cell transcriptome sequencing data includes a second single-cell gene expression matrix, wherein before identifying the tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample, the method further includes: performing a second cell clustering based on the second single-cell gene expression matrix; and annotating the second cell clusters with a second cell type based on reference markers.

[0021] In some embodiments, identifying the tertiary lymphoid organ region and non-tertiary lymphoid organ region of the sample includes: integrating the second cell type annotation into the first cell type annotation; and identifying immune cell-rich regions as the tertiary lymphoid organ regions and other regions as non-tertiary lymphoid organ regions based on the first cell type and the spatial coordinates.

[0022] In some embodiments, the method further includes: obtaining genes specifically expressed in the tertiary lymphoid organ regions as biomarkers of the tertiary lymphoid organs based on the gene expression matrices of the identified tertiary lymphoid organ regions and the non-tertiary lymphoid organ regions.

[0023] In some embodiments, the biomarker is selected from at least one of the following genes: IGLL5, ACTA2, RPL41, RPS27, FAU, RPL26, B2M, RPS16, RPS11, RPS20, TOMM7, RPS28, RPS14, RPS23, ADIRF, IGKC, RPL8, RPS6, RPS2, RPL13, RPL13A, IGHA2, RPL30, RPL9, AP001324.1, RPS12, RPS8, RPL32, RPL34, RPL37A, RPL18, RPS10, RPS15A, RPLLP0, RPL31, RPS3A, MALAT1, RPS27A, RPS19, TAGLN, RPL3, IGLC 2. RPL11, AL122020.1, AC073861.1, SSR4, TXNDC5, RPL35A, RPL10A, RPS5, RPL35, TMEM258, AEBP1, RPL4, HLA-DRB1, RPL10, RPS15, RPLP2, RPL37, JCHAIN, RPL17-C18orf32, IGLC3, RPL12, RPL21, RPL7, RPL17, RPL29, AC135178.2, RPL13AP25, CALD1, HSP90B1, MZB1, RPS7, RPS4X, XBP1, CCDC80, VCAN, DSTN, IGLV3-25, RPSA, RPL24, RPL23A, and CXCL12.

[0024] A second aspect of the present invention provides a method for constructing a non-invasive identification model for tertiary lymphoid organs, comprising: obtaining training images of a target region of a sample using imaging methods; extracting image features based on the training images of the target region; obtaining phenotypic data of the sample based on a spatial analysis method of the sample as described in any embodiment of the first aspect of the present invention, wherein the phenotypic data includes the identification results of the tertiary lymphoid structures within the sample and / or the detection information of biomarkers within the sample; and training a non-invasive identification model for the tertiary lymphoid organs by combining the image features and the phenotypic data.

[0025] A third aspect of the present invention provides a method for non-invasive identification of tertiary lymphoid organs, comprising: obtaining an image of a target region of a sample through imaging methods; and inputting the image of the target region into a non-invasive identification model of tertiary lymphoid organs as described in a second aspect of the present invention to determine the presence of the tertiary lymphoid organs.

[0026] The embodiments of the present invention achieve the following beneficial effects:

[0027] (1) For the first time, a method combining spatiotemporal transcriptomics and single-cell sequencing technology was provided to identify tertiary lymphoid organs in samples (especially in human atherosclerotic plaques). This method can solve the problems of limited analysis scope and poor spatial resolution of traditional methods, and provide a comprehensive multi-perspective characterization of tertiary lymphoid organs, providing more comprehensive information to better explore the formation mechanism of tertiary lymphoid organs and clarify the role of tertiary lymphoid organs in diseases (such as atherosclerosis).

[0028] (2) For the first time, a set of characteristic genes of tertiary lymphoid organs (i.e. plaque tertiary lymphoid organs) existing in human atherosclerotic plaques were provided as biomarkers, which can serve as potential targets for the diagnosis, prevention or treatment of vascular diseases.

[0029] (3) For the first time, a non-invasive identification model and a non-invasive identification method for tertiary lymphoid organs are provided, so that image information can be used before surgery to determine whether plaques have tertiary lymphoid structures (e.g., as a marker of carotid plaque stability), which can be used to assist in the diagnosis of vascular disease risk with high accuracy, and achieve the effect of early intervention and early diagnosis. Attached Figure Description

[0030] To more clearly illustrate the technical solutions in this invention or related technologies, the drawings used in the description of the embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are only embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0031] Figure 1 It is a single-cell atlas of the second cell cluster obtained in human atherosclerotic plaques according to the method for identifying the third-level lymphatic organs described in the embodiments of this application.

[0032] Figure 2 It is a spatial transcriptome single-cell atlas of the first cell cluster obtained in human atherosclerotic plaques according to the identification method of the three-level lymphoid organs described in the embodiments of this application.

[0033] Figure 3 This illustrates an immune cell-specific enrichment region obtained in a human atherosclerotic plaque according to the method for identifying tertiary lymphoid organs as described in the embodiments of this application.

[0034] Figure 4 HE staining validation results of tertiary lymphoid organs identified in human atherosclerotic plaques are shown.

[0035] Figure 5 The results of multiplex immunofluorescence validation of tertiary lymphoid organs identified in human atherosclerotic plaques are shown.

[0036] Figure 6 The results of multiplex immunofluorescence validation of tertiary lymphoid organ biomarkers identified in human atherosclerotic plaques are shown.

[0037] Figure 7 A columnar distribution of tertiary lymphoid organ cell composition identified in human atherosclerotic plaques is shown. Detailed Implementation

[0038] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0039] Atherosclerosis is a widespread vascular disease with significant impacts on human health. It is characterized by plaque buildup in arteries, leading to reduced blood flow and potentially causing serious complications such as heart attacks and strokes. Tertiary lymphoid organs (TLOs) are encapsulated groups of lymphocytes present in various vascular diseases, including abdominal aortic aneurysms and atherosclerosis. Recent studies have shown that arterial TLOs form on the adventitia near atherosclerotic lesions, indicating that inflammation spreads from the intima to the adventitia, triggering an immune response.

[0040] As the primary site of the immune response in atherosclerosis, regulating TLO formation and thus controlling the immune response in atherosclerosis holds promise as a novel therapeutic target. However, the exact location and impact of this immune response remain unclear. Furthermore, it is currently unknown whether the location of different TLOs will have varying effects on atherosclerosis. Current identification of tertiary lymphoid structures primarily relies on the characteristics of 12 cytokines or on HE staining results, which has limited analytical breadth and poor spatial resolution, restricting comprehensive multi-perspective characterization of TLOs.

[0041] An embodiment of the first aspect of this invention proposes a spatial analysis method for samples, comprising: processing spatial transcriptomic data from different regions of a sample from a tissue of interest to identify tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample. This invention provides, for the first time, a method combining spatiotemporal transcriptomics and single-cell sequencing technologies to identify tertiary lymphoid organs in samples (particularly in human atherosclerotic plaques). This method overcomes the limitations of traditional methods in terms of analytical breadth and spatial resolution, providing a comprehensive, multi-perspective characterization of tertiary lymphoid organs and offering more complete information to better explore the formation mechanism of tertiary lymphoid organs and clarify their role in diseases (e.g., atherosclerosis).

[0042] In some embodiments, the spatial transcriptome data includes a first gene expression matrix carrying spatial coordinates and a tissue ssDNA map, wherein processing spatial transcriptome data from different regions of a sample from the tissue of interest includes:

[0043] Single-cell resolution tissue images are obtained based on the tissue ssDNA map obtained by staining the tissue image; and

[0044] The tissue image at single-cell resolution is registered with the first gene expression matrix carrying spatial coordinates to obtain the first single-cell gene expression matrix.

[0045] In some embodiments, obtaining a single-cell resolution tissue image includes:

[0046] The ssDNA map is segmented into tissue sample regions using a deep learning model.

[0047] The location of each cell nucleus in the tissue sample region was determined using a Gaussian weighted model.

[0048] Multiple cell regions were determined by segmenting the overlapping cell regions using the Euclidean distance between the cell nuclei; and

[0049] The multiple cell regions are segmented using a watershed algorithm to obtain a tissue image at single-cell resolution.

[0050] In some embodiments, obtaining the first single-cell gene expression matrix includes:

[0051] Convert the plurality of cell regions into the spatial coordinates;

[0052] The tissue image at single-cell resolution is registered based on the spatial coordinates; and

[0053] The gene expression data in the first gene expression matrix are divided into the corresponding cell regions to obtain the first single-cell gene expression matrix.

[0054] In other words, through the above image-based cell segmentation and gene allocation, a single-cell-level gene expression matrix can be obtained, which includes comprehensive information such as single cell, gene, gene expression data, and spatial coordinates.

[0055] In some embodiments, identifying the tertiary lymphoid organ region and non-tertiary lymphoid organ region of the sample includes: performing a first cell clustering and a first cell type annotation on the cell region based on the first single-cell gene expression matrix; and identifying immune cell-rich regions as the tertiary lymphoid organ region and other regions as non-tertiary lymphoid organ regions based on the first cell type and the spatial coordinates.

[0056] It is understandable that by performing cell clustering and cell type annotation on the above matrix, the information included can be further expanded to include more comprehensive information such as single cell type, gene expression data, and spatial coordinates. Tertiary lymphoid organs are aggregates of lymphocytes that form after birth, mainly including T cell regions (such as CD4+, CD8+ effector T cells and memory T cells, as well as Treg cells that suppress the immune response), B cell follicles, and conventional dendritic cells, monocyte-derived dendritic cells, lymphoid dendritic cells, and plasmacytoid dendritic cells, etc. Therefore, by identifying regions rich in immune cells, tertiary lymphoid organs can be effectively identified. The comprehensive information with spatial resolution obtained through the above methods can provide a foundation for exploring the formation mechanism of TLO, clarifying the role of TLO in atherosclerosis, and conducting in-depth research on plaque-related tertiary lymphoid organs.

[0057] In some embodiments, the single-cell transcriptome sequencing data includes a second single-cell gene expression matrix, further comprising, before identifying tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample: performing a second cell clustering based on the second single-cell gene expression matrix; and performing a second cell type annotation on the second cell clusters based on reference markers. The reference markers include marker genes from published studies.

[0058] In some embodiments, identifying the tertiary lymphoid organ region and non-tertiary lymphoid organ region of the sample includes: integrating the second cell type annotation into the first cell type annotation; and identifying immune cell-rich regions as the tertiary lymphoid organ regions and other regions as non-tertiary lymphoid organ regions based on the first cell type and the spatial coordinates.

[0059] In some embodiments, the method further includes: obtaining genes specifically expressed in the tertiary lymphoid organ regions as biomarkers of the tertiary lymphoid organs based on the gene expression matrices of the identified tertiary lymphoid organ regions and the non-tertiary lymphoid organ regions.

[0060] In some embodiments, the biomarker is selected from at least one of the following genes: IGLL5, ACTA2, RPL41, RPS27, FAU, RPL26, B2M, RPS16, RPS11, RPS20, TOMM7, RPS28, RPS14, RPS23, ADIRF, IGKC, RPL8, RPS6, RPS2, RPL13, RPL13A, IGHA2, RPL30, RPL9, AP001324.1, RPS12, RPS8, RPL32, RPL34, RPL37A, RPL18, RPS10, RPS15A, RPLLP0, RPL31, RPS3A, MALAT1, RPS27A, RPS19, TAGLN, RPL3, IGLC 2. RPL11, AL122020.1, AC073861.1, SSR4, TXNDC5, RPL35A, RPL10A, RPS5, RPL35, TMEM258, AEBP1, RPL4, HLA-DRB1, RPL10, RPS15, RPLP2, RPL37, JCHAIN, RPL17-C18orf32, IGLC3, RPL12, RPL21, RPL7, RPL17, RPL29, AC135178.2, RPL13AP25, CALD1, HSP90B1, MZB1, RPS7, RPS4X, XBP1, CCDC80, VCAN, DSTN, IGLV3-25, RPSA, RPL24, RPL23A, and CXCL12.

[0061] This invention defines, for the first time, tertiary lymphoid organs in arterial plaques and provides a gene set characteristic of these tertiary lymphoid organs in atherosclerotic plaques. This gene set has been validated as stable and reliable through multiplex immunofluorescence assays and can serve as a biomarker for tertiary lymphoid organs in arterial plaques.

[0062] In some embodiments, the biomarkers further include CTSD and / or CD68.

[0063] In some embodiments, the biomarker is specifically expressed in tertiary lymphoid organ regions; preferably, the biomarker is specifically highly expressed in arterial tertiary lymphoid organ regions.

[0064] A second aspect of this invention proposes a method for constructing a non-invasive identification model of tertiary lymphoid organs. This method enables the preoperative determination of the presence of tertiary lymphoid structures in plaques using image information and biomarker information under non-invasive conditions, thereby providing high-precision auxiliary diagnosis of vascular disease risk and achieving early intervention and early diagnosis. The method includes: obtaining training images of a target region of a sample using imaging methods; extracting image features based on the training images of the target region; obtaining phenotypic data of the sample using a spatial analysis method as described in any embodiment of the first aspect of this invention, wherein the phenotypic data includes the identification results of the tertiary lymphoid structures within the sample and / or the detection information of biomarkers within the sample; and training a non-invasive identification model of the tertiary lymphoid organs by combining the image features and the phenotypic data.

[0065] In some embodiments, the phenotypic data includes disease phenotypic data of a population.

[0066] In some embodiments, the biomarker information includes the expression level of the biomarker, the SNP site located on the biomarker, and its genotype.

[0067] It is understood that samples used for model building include, but are not limited to, blood, arterial plaque tissue samples, and carotid artery tissue. In some embodiments, the sample is an arterial plaque tissue sample.

[0068] Optionally, the imaging methods include vascular ultrasound, angiography, CT angiography, and magnetic resonance angiography.

[0069] A third aspect of the present invention provides a method for non-invasive identification of tertiary lymphoid organs, comprising: obtaining an image of a target region of a sample through imaging methods; and inputting the image of the target region into a non-invasive identification model of tertiary lymphoid organs as described in a second aspect of the present invention to determine the presence of the tertiary lymphoid organs.

[0070] In some embodiments, the method of training the model may use a ScanNet-based 3D segmentation model, including Spherical Mask, OneFromer3D, QueryFormer, Mask3D, SPFormer, ISBNet, SoftGroup, TD3D, PBNet, DKNet, etc.

[0071] Unless otherwise specified, the experimental methods used in the following examples are conventional methods, performed according to the techniques or conditions described in the literature in this field or according to the product instructions. Unless otherwise specified, the materials and reagents used in the following examples are commercially available.

[0072] Unless otherwise specified, the quantitative experiments in the following examples are all repeated three times, and the results are averaged.

[0073] Example 1: Identification of Tertiary Lymphoid Organs

[0074] 1.1 Sample Preparation

[0075] This example illustrates the sample source and the detailed preparation process.

[0076] (1) Sample source

[0077] This study included 143 patients with symptomatic carotid artery stenosis ranging from 50% to 99% or asymptomatic carotid artery stenosis ranging from 70% to 99% (according to the North American Symptomatic Carotid Endarterectomy (NASCET) criteria). These patients underwent standard carotid endarterectomy (CEA) or eversion CEA between May 2021 and May 2023. The study was approved by the Ethics Review Committee of Peking Union Medical College Hospital (approval numbers: JS-2966 and I-22PJ019). All eligible participants submitted informed consent forms before enrolling in this study.

[0078] The carotid intima tissue samples obtained through the above surgical resection were used for the preparation of cryosections for stereo-seq and for the preparation of single-cell suspensions for scRNA-seq.

[0079] (2) Preparation of frozen sections

[0080] A portion of the carotid intima tissue sample obtained in (1), which was stored at -80°C, was placed in a cryostat (Leica CM1950) to thaw at a temperature of -20°C.

[0081] Thawed sample blocks were sectioned into 10 μm sections according to the required tissue location for Stereo-seq, with two consecutive 10 μm sections used for hematoxylin-eosin staining (HE).

[0082] Using a soft brush and tweezers, gently flatten the thin Stereo-seq slice on the low-temperature metal surface (-20°C) inside the cryomicroscopy machine, then carefully place it on a pre-cooled Stereo-seq chip (-20°C) to complete the mounting for Stereo-seq sequencing.

[0083] (3) Preparation of single-cell suspension

[0084] Fresh tissue plaque samples were rinsed with PBS buffer (Solarbio, catalog number: P1020) and mechanically dissociated into small fragments. These fragments were then appropriately mixed in 10 ml of DMEM-PBS buffer (collagenase type IV (Sigma, C5138) at a final concentration of 1 mg / ml, and DNase (Sigma, DN25), hyaluronidase (Sigma, H3506), collagenase type XI (Sigma, C7657), and collagenase type II (Sigma, C6885), and digested at 37°C for 30 min.

[0085] Add 3 ml of FBS to stop digestion, and filter the mixture through 70 μm and 40 μm cell filters in sequence. Then centrifuge at 300 g and 4 °C for 8 min and retain the cell pellet.

[0086] The cell pellet was lysed with red blood cell lysis buffer (Solarbio, catalog number: R1010), and the cells were then resuspended at a concentration of 1,250 cells / µL in cell resuspension buffer (Sigma, catalog number: A1933) to obtain a single-cell suspension.

[0087] 1.2 Stereo-seq Sequencing Data Processing

[0088] This embodiment utilizes the StereoCell pipeline, including image stitching, registration, tissue segmentation, cell nucleus segmentation, and molecular markers, to obtain a high-confidence spatially resolved single-cell gene expression matrix. Stereo-seq library construction and sequencing were performed according to the methods described in the article (Chen A, Liao S, Cheng M, et al. Spatiotemporal transcriptomic atlas of mouse organogenesis using DNA nanoball-patterned arrays. Cell. 2022; 185(10):1777-1792.e21. doi:10.1016 / j.cell.2022.04.003).

[0089] Specifically, using BGI's self-developed Cellbin Studio tool, the gene expression matrix with spatial coordinates obtained from Stereo-seq sequencing and the ssDNA map are registered according to the track line. The deep learning model in the tool is used to segment the tissue region of the ssDNA map, the Gaussian weighted model is used to determine the cell location, the Euclidean distance is used to segment the overlapping cell regions, and finally the watershed algorithm is used to convert each cell region into the spatial coordinates corresponding to DNB, thereby assigning each gene to the corresponding cell and obtaining a high-confidence spatially resolved single-cell gene expression matrix.

[0090] 1.3 scRNA-seq sequencing data processing

[0091] This embodiment, based on single-cell sequencing data, performed data alignment, hypervariable gene analysis, cell clustering, and cell annotation to obtain a second single-cell gene expression matrix that can be reliably optimized for single-cell gene expression matrices based on spatial transcriptomics.

[0092] (1) scRNA-seq sequencing: Library preparation followed the DNBelab C series single-cell library preparation kit (MGI, 1000021082). The sequencing library was quantified using the Qubits sDNA detection kit (purchased from Yisheng Biotechnology, catalog number: 12645-A). Sequencing was performed using the DIPSEQT7 sequencer from Beijing BGI Genomics Co., Ltd., with the following sequencing strategy: read 1 length 41 bp, read 2 length 100 bp.

[0093] (2) Sequencing data alignment: PISA (v0.12b; https: / / github.com / shiquan / PISA) was used to filter and demultiplex the raw data from DNBSEQ-T1 or DNBSEQ-T7 sequencing. Then, STAR (v2.7.1a) and sambamba (v0.7.0) were used to align and sort the clean reads against the reference genome (GRCh38). Finally, single-cell expression matrices were obtained using PISA based on the reads aligned with mRNA exons.

[0094] (3) Hypervariable gene analysis: First, low-quality cells with fewer than 500 genes or a mitochondrial gene ratio greater than 20% were filtered out. Furthermore, only genes expressed in at least three cells were retained. Then, DoubletFinder (v2.0.3) was used to remove twins in each library using default parameters, and the top 5% of cells similar to pseudo-twins were excluded. Finally, the UMI count data were normalized using the SCTransform function via regularized negative binomial regression, retaining the top 3000 hypervariable genes.

[0095] (4) Cell clustering and annotation

[0096] Cell clustering for scRNA-seq was performed using Seurat (v4.3.0.1) in R (v4.3.1). First, the PrepSCTIntegration, FindIntegrationAnchors, and IntegrateData functions were applied to identify anchors to integrate gene expression matrices from multiple libraries. UMAP dimensionality reduction was performed using the first 30 principal components, and cell clustering was performed at a resolution of 1.2. Finally, the FindAllMarkers function with default parameters was used to identify characteristic genes for each clustered cell subpopulation, and cell annotation was performed using marker genes from previously published studies.

[0097] The results of the above dimensionality reduction cell clustering are as follows: Figure 1 As shown, Figure 1 It is a single-cell atlas of the second cell cluster obtained in human atherosclerotic plaques according to the method for identifying the third-level lymphatic organs described in the embodiments of this application.

[0098] 1.4 Data Integration of Stereo-seq and scRNA-seq

[0099] Seurat was used to integrate and annotate single-cell data with spatial transcriptome (Stereo-seq) data. First, the SCTransform function was used to normalize both the single-cell and spatial transcriptome data. Based on the normalized data, the FindTransferAnchor function was used to identify anchor genes (genes shared by significantly differentially expressed genes in single cells and spatial transcriptomes) between the two datasets. Finally, the TransferData function was used to transform the annotation results from the single-cell data into each cell of the spatial transcriptome data, selecting the cell type with the highest probability as the annotation for that cell.

[0100] The integrated spatial transcriptome single-cell atlas is as follows: Figure 2 As shown, Figure 2 It is a spatial transcriptome single-cell atlas of the first cell cluster obtained in human atherosclerotic plaques according to the identification method of the three-level lymphoid organs described in the embodiments of this application.

[0101] 1.5 Identification of Tertiary Lymphoid Organs

[0102] Based on cell annotation information and its spatial location, observe whether there are special regions where immune cells (such as T cells, B cells, etc.) are significantly enriched, and identify them as tertiary lymphoid organ regions.

[0103] The results of the identification of the tertiary lymphoid organ region are as follows: Figure 3 As shown. Figure 3 This illustrates an immune cell-specific enrichment region obtained in a human atherosclerotic plaque according to the method for identifying tertiary lymphoid organs as described in the embodiments of this application.

[0104] 1.6 Validation of the identified tertiary lymphoid organs

[0105] This embodiment verifies the reliability of the identified tertiary lymphoid organ regions through H&E staining images and multiplex immunofluorescence experiments using classic tertiary lymphoid organ markers (CD19, CD3, CD11C).

[0106] (1) H&E staining verification: HE (hematoxylin and eosin staining) staining was performed according to the standard protocol.

[0107] (2) Verification by multiplex immunofluorescence assay

[0108] Frozen and paraffin sections with a thickness of 4 μm were incubated with specific primary antibodies (CD3 (GB12014, Servicebio), CD11c (GB11059, Servicebio), CD19 (GB11061-1, Servicebio)) at 4 °C for 12 h. Then, slides containing the sections were incubated with HRP-binding antibodies (purchased from Servicebio, catalog numbers: GB21303, GB22403) at room temperature for 1.5 h. The stained tissues were scanned using a confocal microscope (TCS-SP8 gSTED 3X, Leica) and observed using Leica Application Suite X (version 3.7.4, Leica). The positive staining area of ​​the patch cross-sections was quantified using ImageProPlus (Ver. 6.0, MediaCybernetics, USA).

[0109] Figure 4 HE staining results were used to verify the identification of tertiary lymphoid organs in human atherosclerotic plaques. L represents the luminal portion and N represents the necrotic core. The HE staining image of the carotid plaque shows the presence of a necrotic core and a large number of lymphocyte aggregation areas.

[0110] Figure 5 This figure shows the results of multiplex immunofluorescence validation of tertiary lymphoid organs identified in human atherosclerotic plaques. The image illustrates that in the regions identified as tertiary lymphoid organs, the classic markers CD19, CD3, and CD11C are clearly clustered in these regions, thus validating the reliability of the identification method described in this embodiment.

[0111] Example 2: Construction of a set of characteristic genes for tertiary lymphoid organs

[0112] Based on the tertiary lymphoid organ regions and non-tertiary lymphoid organ regions identified in Example 1, spatial transcriptome data were grouped, and genes highly expressed in the tertiary lymphoid organ regions were screened using the FindMarks function in Seurat (screening thresholds were log2 Fold Change > 0.5 and P value < 0.05) as the characteristic gene set of tertiary lymphoid organs. Furthermore, the characteristic genes in human atherosclerotic plaques were verified using multiplex immunofluorescence.

[0113] The screening results are shown below. The gene set of tertiary lymphoid organ characteristic genes includes the following genes:

[0114] IGLL5, ACTA2, RPL41, RPS27, FAU, RPL26, B2M, RPS16, RPS11, RPS20, TOMM7, RPS28, RPS14, RPS23, ADIRF, IGKC, RPL8, RPS6, RPS2, RPL13, RPL13A, IGHA2, RPL30, RP L9, AP001324.1, RPS12, RPS8, RPL32, RPL34, RPL37A, RPL18, RPS10, RPS15A, RPLP0, RPL31, RPS3A, MALAT1, RPS27A, RPS19, TAGLN, RPL3, IGLC2, RPL11, AL122020. 1. AC073861.1, SSR4, TXNDC5, RPL35A, RPL10A, RPS5, RPL35, TMEM258, AEBP1, RPL4, HLA-DRB1, RPL10, RPS15, RPLP2, RPL37, JCHAIN, RPL17-C18orf32, IGLC3, RPL 12. RPL21, RPL7, RPL17, RPL29, AC135178.2, RPL13AP25, CALD1, HSP90B1, MZB1, RPS7, RPS4X, XBP1, CCDC80, VCAN, DSTN, IGLV3-25, RPSA, RPL24, RPL23A and CXCL12.

[0115] Example 3: Cellular composition of tertiary lymphoid organs

[0116] Based on the tertiary lymphoid organ regions and non-tertiary lymphoid organ regions identified in Example 1, distribution statistics were performed based on the cell annotation data corresponding to each region.

[0117] The results are as follows Figure 7As shown, the tertiary lymphoid region is dominated by plasma cells, macrophages (MPs), and smooth muscle cells (SMCs), with a small number of endothelial cells, B cells, and T cells. This result indicates that this method can help elucidate the formation and function of tertiary lymphoid structures by understanding their cellular composition, thus providing a theoretical basis for the study of atherosclerotic plaques.

[0118] Example 4: Construction and Application of a Non-invasive Identification Model for Tertiary Lymphoid Organs

[0119] This embodiment utilizes the biomarkers obtained in the above embodiments to detect biomarker-related information and corresponding phenotypic data (such as whether or not a person has vascular disease, vascular disease severity assessment indicators, etc.) in a population, and combines them with target area images obtained through imaging methods to train a non-invasive recognition model for tertiary lymphoid organs using machine learning methods.

[0120] In this embodiment, the imaging method can be CTA. CTA is a non-invasive angiography that uses computer-aided three-dimensional reconstruction to obtain a good understanding of the condition of blood vessels in the body without trauma. Therefore, CTA can be used to observe the overall picture of aortic plaques from multiple angles before the operation.

[0121] Specifically, when constructing model training data, angiographic images (target area images) of human arterial plaques are obtained through CTA scanning, and spatial genome sequencing is performed on the extracted plaques. The presence or absence of tertiary lymphoid structures is identified using the biomarkers obtained in the above embodiments, thereby obtaining corresponding phenotypic data. The training samples for the model include CTA angiographic images and phenotypic data describing whether tertiary lymphoid structures are present, which are then used for supervised training of the model.

[0122] For model training, a non-invasive identification model for tertiary lymphoid organs is trained using the above training samples, so that the presence of tertiary lymphoid structures in plaques can be determined preoperatively using the image features of CTA images.

[0123] The model training can be implemented using a 3D segmentation model based on ScanNet, including but not limited to Spherical Mask, OneFromer3D, QueryFormer, Mask3D, SPFormer, ISBNet, SoftGroup, TD3D, PBNet, or DKNet.

[0124] Furthermore, if the plaque is determined to have a grade 3 lymphatic structure, it can be surgically removed during subsequent treatment. If there is no grade 3 lymphatic structure, it can be treated with methods such as vascular stents. No specific limitations are made here.

[0125] Practical application results show that this non-invasive identification model can accurately and effectively use image information before surgery under non-invasive conditions to determine whether plaques have tertiary lymphoid structures, so as to assist in the diagnosis of vascular disease risk with high accuracy and achieve the effect of early intervention and early diagnosis.

[0126] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," and "circumferential" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing this invention and simplifying the description, and are not intended to indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0127] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0128] In this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," "linking," and "fixing," etc., should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection, an electrical connection, or a connection that allows communication between them; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components, unless otherwise explicitly limited. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0129] In this invention, unless otherwise explicitly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "over," and "on top" of the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0130] In this invention, the terms "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to a specific feature, structure, material, or characteristic described in connection with that embodiment or example, which is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0131] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A spatial analysis method for samples, characterized in that, include: Spatial transcriptomic data from different regions of a sample from the tissue of interest are processed to identify tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample.

2. The spatial analysis method for samples according to claim 1, characterized in that, The spatial transcriptome data includes a first gene expression matrix carrying spatial coordinates and a tissue ssDNA map. The processing of spatial transcriptome data from different regions of samples from the tissue of interest includes: Single-cell resolution tissue images are obtained based on the tissue ssDNA map obtained by staining the tissue image; and The tissue image at single-cell resolution is registered with the first gene expression matrix carrying spatial coordinates to obtain the first single-cell gene expression matrix.

3. The spatial analysis method for samples according to claim 2, characterized in that, The process of obtaining tissue images at single-cell resolution includes: The ssDNA map is segmented into tissue sample regions using a deep learning model. The location of each cell nucleus in the tissue sample region was determined using a Gaussian weighted model. Multiple cell regions were determined by segmenting the overlapping cell regions using the Euclidean distance between the cell nuclei; and The multiple cell regions are segmented using a watershed algorithm to obtain a tissue image at single-cell resolution.

4. The spatial analysis method for samples according to claim 3, characterized in that, The process of obtaining the first single-cell gene expression matrix includes: Convert the plurality of cell regions into the spatial coordinates; The tissue image at single-cell resolution is registered based on the spatial coordinates; and The gene expression data in the first gene expression matrix are divided into the corresponding cell regions to obtain the first single-cell gene expression matrix.

5. The spatial analysis method for samples according to claim 2, characterized in that, The identification of the tertiary lymphoid organ region and the non-tertiary lymphoid organ region of the sample includes: Based on the first single-cell gene expression matrix, the cell regions are subjected to first cell clustering and first cell type annotation; and Based on the first cell type and the spatial coordinates, the immune cell-rich area is identified as the tertiary lymphoid organ region, and other areas are identified as non-tertiary lymphoid organ regions.

6. The spatial analysis method for samples according to claim 1, characterized in that, The single-cell transcriptome sequencing data includes a second single-cell gene expression matrix, which further includes the following before identifying the tertiary lymphoid organ regions and non-tertiary lymphoid organ regions of the sample: Second cell clustering was performed based on the second single-cell gene expression matrix; and The second cell cluster is annotated with the second cell type based on the reference markers.

7. The spatial analysis method for samples according to claim 6, characterized in that, The identification of the tertiary lymphoid organ region and the non-tertiary lymphoid organ region of the sample includes: Integrate the second cell type annotation into the first cell type annotation; Based on the first cell type and the spatial coordinates, the immune cell-rich area is identified as the tertiary lymphoid organ region, and other areas are non-tertiary lymphoid organ regions.

8. The spatial analysis method for samples according to claim 7, characterized in that, Also includes: Based on the gene expression matrices of the identified tertiary lymphoid organ regions and the non-tertiary lymphoid organ regions, genes specifically expressed in the tertiary lymphoid organ regions are obtained as biomarkers of the tertiary lymphoid organs.

9. The spatial analysis method for samples according to claim 8, characterized in that, The biomarker is selected from at least one of the following gene groups: IGLL5, ACTA2, RPL41, RPS27, FAU, RPL26, B2M, RPS16, RPS11, RPS20, TOMM7, RPS28, RPS14, RPS23, ADIRF, IGKC, RPL8, RPS6, RPS2, RPL13, RPL13A, IGHA2, RPL30, RPL9, AP001324.1, RPS12, RPS8, RPL32, RPL34, RPL37A, RPL18, RPS10, RPS15A, RPLP0, RPL31, RPS3A, MALAT1, RPS27A, RPS19, TAGLN, RPL3, IGLC2, RP L11, AL122020.1, AC073861.1, SSR4, TXNDC5, RPL35A, RPL10A, RPS5, RPL35, TMEM258, AEBP1, RPL4, HLA-DRB1, RPL10, RPS15, RPLP2, RPL37, JCHAIN, RPL17-C18orf32, IG LC3, RPL12, RPL21, RPL7, RPL17, RPL29, AC135178.2, RPL13AP25, CALD1, HSP90B1, MZB1, RPS7, RPS4X, XBP1, CCDC80, VCAN, DSTN, IGLV3-25, RPSA, RPL24, RPL23A, and CXCL12.

10. A method for constructing a non-invasive identification model of tertiary lymphoid organs, characterized in that, include: Training images of the target region of the sample are obtained through imaging methods; Image features are extracted from the training images based on the target region; Phenotypic data of the sample is obtained based on the spatial analysis method of any one of claims 1 to 9, wherein the phenotypic data includes the identification results of the tertiary lymphoid structures within the sample and / or the detection information of biomarkers within the sample; and The non-invasive identification model of the tertiary lymphoid organs is trained by combining the image features and the phenotypic data.

11. A non-invasive method for identifying tertiary lymphoid organs, characterized in that, include: The target region of the sample is obtained using imaging methods; The image of the target region is input into the non-invasive identification model of the tertiary lymphoid organ as described in claim 10 to determine the presence of the tertiary lymphoid organ.

Images