Method and system for evaluating treatment effect of traditional Chinese medicine based on single cell and space transcriptome data

Abstract

The invention discloses a method and system for evaluating the treatment effect of traditional Chinese medicine based on single cell and spatial transcriptome data, and the method comprises the following steps: respectively obtaining single cell data and spatial transcriptome data of a tissue sample before and after administration, and carrying out the preprocessing; classifying the cells and identifying cell types; the expressed ligand and receptor genes are paired to obtain ligand-receptor pairs, and the ligand-receptor pairs which are differentially expressed before and after administration are screened out; acquiring space coordinate information of a single cell, and constructing a cell interaction network and a differential gene network according to the cell type, the ligand-receptor pair and the space coordinate information; weighting processing is conducted on the cell interaction network and the differential gene network, and comprehensive indexes for evaluating the effect of the traditional Chinese medicine are obtained.The brand-new method for evaluating the disease treatment effect of the traditional Chinese medicine is provided, the method is scientific and reliable, and the treatment effect of the traditional Chinese medicine on complex diseases can be accurately reflected.

Description

Technical Field

[0001] The present invention belongs to the technical field of single-cell data analysis, and more specifically, relates to a method and system for evaluating the therapeutic effects of traditional Chinese medicine based on single-cell and spatial transcriptome data. Background Art

[0002] At the intersection of modern medicine and traditional Chinese medicine, accurately evaluating the therapeutic and preventive effects of traditional Chinese medicine on complex diseases has always been a highly concerned and challenging topic.

[0003] 1. Challenges in the Treatment and Prevention of Complex Diseases

[0004] Complex diseases, such as cardiovascular disease, cancer, and neurodegenerative disorders, often involve multiple genes, multiple cell types, complex intercellular interactions, and alterations in the body's internal environment. These diseases are often characterized by chronic progression, high heterogeneity, and difficulty in curing. Traditional medical research methods have numerous limitations in deeply understanding the development and progression of complex diseases and evaluating the effectiveness of treatments.

[0005] 2. Characteristics and Evaluation Dilemmas of Traditional Chinese Medicine Treatment and Prevention

[0006] Traditional Chinese medicine (TCM), a treasure of traditional Chinese medicine, has a long history and rich practical experience in the treatment and prevention of complex diseases. TCM is often used in the form of compound prescriptions, with complex and diverse ingredients, including multiple chemical components, exerting their effects through multiple targets and pathways. However, precisely because of the complexity of its composition and mechanism of action, accurately evaluating the therapeutic and preventive effects of TCM on complex diseases faces enormous difficulties. Traditional methods for evaluating the efficacy of TCM are mainly based on macro-level methods such as clinical symptom observation and biochemical marker testing. For example, by observing the improvement of patients' symptoms after taking TCM, such as whether pain is relieved or sleep is improved, and by testing changes in relevant biochemical indicators in body fluids such as blood sugar, blood lipids, and liver function indicators. However, these methods have obvious shortcomings: it is difficult to accurately locate the specific targets and pathways of TCM: due to the multi-target characteristics of TCM, it is difficult to determine which cells, genes and molecular pathways it acts on based solely on macroscopic symptoms and changes in routine biochemical indicators; it is unable to reflect heterogeneity at the cellular level: the responses of cells from different individuals to TCM may vary greatly, and traditional evaluation methods cannot delve into the individual cell level to reveal this heterogeneity, which may lead to overestimation or underestimation of the efficacy of TCM.

[0007] With the continuous development of biotechnology, single-cell transcriptome technology has provided a powerful tool for in-depth research on gene expression at the cellular level. Through this technology, the transcriptome of a single cell can be sequenced at high throughput to obtain detailed information on gene expression within the cell, thereby revealing the heterogeneity of gene expression between cells. However, single-cell transcriptome technology also has certain limitations in its application, mainly because it loses the spatial location information of cells in the tissue. For complex diseases, the spatial location of cells is closely related to their function and response to drugs (including traditional Chinese medicine). For example, in tumor tissue, the spatial distribution and interaction between tumor cells and surrounding immune cells, stromal cells, etc. will affect the sensitivity of tumor cells to therapeutic drugs, and single-cell transcriptome technology cannot take this spatial factor into account when used alone.

[0008] In recent years, the rise of single-cell spatial transcriptomics technology has brought new hope for solving the above problems. This technology can not only obtain the transcriptome information of a single cell like single-cell transcriptomics technology, but also simultaneously retain the spatial position coordinates of the cell in the tissue, so that it can more comprehensively and accurately depict the gene expression status of different cells in the tissue and their spatial distribution relationship. Using single-cell spatial transcriptomics technology, we can deeply explore the spatial distribution characteristics of cells in complex disease tissues, the interactions between cells, and the spatial heterogeneity of gene expression, which is of great significance for understanding the pathogenesis of complex diseases and evaluating the effects of treatment methods (such as traditional Chinese medicine).

[0009] Based on the above-mentioned needs for the treatment and prevention of complex diseases, the difficulties in evaluating the effects of traditional Chinese medicine, the limitations of single-cell transcriptome technology, and the advantages of single-cell spatial transcriptome technology, there is an urgent need for a method that can fully utilize single-cell and spatial transcriptome data to accurately and comprehensively evaluate the effects of traditional Chinese medicine in treating / preventing complex diseases. Summary of the Invention

[0010] In response to at least one defect or improvement need in the prior art, the present invention provides a method and system for evaluating the efficacy of traditional Chinese medicine in treating diseases based on single-cell and spatial transcriptome data. The purpose is to make full use of single-cell spatial transcriptome data to accurately and comprehensively evaluate the effects of traditional Chinese medicine in treating / preventing complex diseases, and to provide a more reliable and effective approach for the scientific evaluation of traditional Chinese medicine in the field of complex disease treatment and prevention.

[0011] To achieve the above objectives, according to one aspect of the present invention, a method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data is provided, comprising the following steps:

[0012] Single-cell data and spatial transcriptome data of tissue samples before and after administration were obtained and preprocessed;

[0013] Classify cells and identify cell types;

[0014] Pairing the expressed ligand and receptor genes to obtain ligand-receptor pairs, and screening out ligand-receptor pairs that are differentially expressed before and after administration;

[0015] Obtaining spatial coordinate information of individual cells, and constructing a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair, and spatial coordinate information;

[0016] The cell interaction network and gene interaction network are weighted to obtain a comprehensive index for evaluating the effect of traditional Chinese medicine.

[0017] Furthermore, the acquisition of single cell data and spatial transcriptome data of tissue samples before and after administration and pre-processing includes:

[0018] Obtain single-cell suspensions from tissue samples before and after administration, perform reverse transcription, amplify cDNA, and then construct and sequence libraries to obtain single-cell data. Software is used to clean and standardize the single-cell data and annotate the cell types.

[0019] Spatial transcriptome data of tissue samples before and after drug administration were obtained, and differentially expressed genes between groups were analyzed and pathway enrichment analysis was performed.

[0020] Furthermore, the cell classification and cell type identification includes:

[0021] Cells were preliminarily classified into cell types based on gene expression characteristics;

[0022] The preliminarily classified cells are verified and adjusted in combination with known cell type marker genes.

[0023] Furthermore, constructing a cell interaction network includes:

[0024] Obtain the tissue location distribution of individual cells based on spatial coordinate information;

[0025] The cell interaction network callback index is used to quantitatively describe the spatial interaction strength between cells. The calculation formula of the cell interaction network callback index is:

[0026]

[0027] Among them, RRODN C,μ is the callback index of the cell interaction network; μ is the cell type; ψ is the ligand-receptor pair; V μ,ψ is the average betweenness centrality of the ligand-receptor pair ψ in the gene interaction network of the cell type combination μ; EoRC μ,ψ Represents whether the ligand-receptor ψ of the cell type combination μ is effectively recalled.

[0028] Furthermore, the spatially weighted Pearson correlation coefficient was used to calculate whether the ligand-receptor pair was effectively recalled.

[0029] Furthermore, the construction of the gene interaction network includes:

[0030] The cell type network callback index, the importance of up-regulated differentially genes, and down-regulated differentially genes were used to quantitatively describe the gene interaction strength; the calculation formula is:

[0031]

[0032] RRODN D,τ is the network callback index of the τth cell type, W τ,γ is the network centrality of gene γ in the τth cell type, EoRD τ,γ Whether the gene γ representing the τth cell type is effectively called back is calculated as follows:

[0033]

[0034] RL τ,γ is the callback degree of the γth gene of the τth cell type, and the calculation formula is:

[0035]

[0036] log2E τ,γ is the logarithmic value of the average expression level of the γth gene in the τth cell type under given experimental conditions, and the calculation formula is:

[0037]

[0038] (log2E τ,γ ) model -(log2E τ,γ ) sham >0.5

[0039] Furthermore, the cell interaction network and the differential gene network are weighted to obtain a comprehensive evaluation index of the effect of traditional Chinese medicine, including the following calculation formula:

[0040]

[0041] Among them, F represents the comprehensive evaluation index, α and β are the weights of the cell interaction network and the differential gene network, respectively, and α+β=1; RRODN D,τ is the network callback index of the τth cell type; RRODN up,τ and RRRODN down,τ The importance of up-regulated differentially genes and down-regulated differentially genes, respectively.

[0042] According to a second aspect of the present invention, a system for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data is also provided, comprising:

[0043] A data acquisition module, which is used to obtain single-cell data and spatial transcriptome data of tissue samples before and after administration, and perform preprocessing;

[0044] a classification module for classifying cells and identifying cell types;

[0045] A pairing module is used to pair the expressed ligand and receptor genes to obtain ligand-receptor pairs, and to screen out ligand-receptor pairs that are differentially expressed before and after administration;

[0046] A construction module, which is used to obtain the spatial coordinate information of a single cell and construct a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair and spatial coordinate information;

[0047] The processing module is used to perform weighted processing on the cell interaction network and the gene interaction network to obtain a comprehensive index for evaluating the effect of traditional Chinese medicine.

[0048] According to the third aspect of the present invention, an electronic device is also provided, comprising at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit performs the steps of any one of the above methods.

[0049] According to a fourth aspect of the present invention, a computer-readable medium is provided, which stores a computer program executable by an electronic device, and when the computer program runs on the electronic device, the electronic device executes the steps of any one of the above methods.

[0050] In general, the above technical solutions conceived by the present invention can achieve the following beneficial effects compared with the prior art:

[0051] (1) The present invention provides a method and system for evaluating the efficacy of traditional Chinese medicine in treating diseases based on single-cell and spatial transcriptome data. By integrating multi-dimensional data such as gene expression levels, cell types, ligand-receptor pair information, and spatial distribution information, it provides a new method for evaluating the therapeutic effects of traditional Chinese medicine, which can comprehensively reflect the effects of traditional Chinese medicine on diseases, especially complex diseases. This helps to discover the potential targets and mechanisms of action of traditional Chinese medicine. For example, when evaluating the preventive effects of traditional Chinese medicine on neurodegenerative diseases, it can reveal the effects of traditional Chinese medicine on nerve cells and their surrounding supporting cells from multiple aspects, providing richer data support for accurately evaluating the efficacy of traditional Chinese medicine.

[0052] (2) The present invention provides a method and system for evaluating the efficacy of traditional Chinese medicine in treating diseases based on single-cell and spatial transcriptome data. By using single-cell spatial transcriptome data, it is possible to analyze the therapeutic effects of traditional Chinese medicine on diseases at the single-cell level, thereby accurately analyzing the therapeutic effects of drugs at the cellular level.

[0053] (3) The present invention provides a method and system for evaluating the efficacy of traditional Chinese medicine in treating diseases based on single-cell and spatial transcriptome data, which fully considers the spatial distribution of cells in tissues and their interactions, thereby comprehensively evaluating the effects of traditional Chinese medicine in the disease microenvironment and providing a more comprehensive efficacy evaluation than traditional methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a flow chart of a method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data provided by an embodiment of the present invention;

[0055] Figure 2 This is the spatial transcriptome atlas of single-cell hearts from rats with acute myocardial infarction, provided in an embodiment of the present invention; (A) UMAP dimensionality reduction cell annotation results for single-cells in the sham group. (B) UMAP dimensionality reduction cell annotation results for single-cells in the model group. (C) UMAP dimensionality reduction cell annotation results for single-cells in the SM-H group. (D) Highly expressed genes in the single-cell transcriptome of the sham group. (E) Highly expressed genes in the single-cell transcriptome of the model group. (F) Highly expressed genes in the single-cell transcriptome of the SM-H group. (G) Spatial-seq technical process used in this study. (H) Spatially differentially expressed genes based on the spatial transcriptome atlas.

[0056] Figure 3 is the analysis result of the comprehensive evaluation index F of each test group provided by the embodiment of the present invention;

[0057] Figure 4 1. A set of graphs showing the dose-effect relationship of Shenmai Injection provided in an embodiment of the present invention in improving cardiac function in rats with acute myocardial infarction;

[0058] Figure 5 This is a histopathological staining diagram of the treatment of acute myocardial infarction rats with the Shenmai Injection provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0059] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.

[0060] Example 1

[0061] Figure 1 FIG2 is a flow chart of a method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data disclosed in the present invention. As shown in the figure, this embodiment discloses a method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data, comprising the following steps:

[0062] S1, data acquisition: obtain single-cell data and spatial transcriptome data of tissue samples before and after drug administration, and perform preprocessing;

[0063] Specifically, they include:

[0064] S101, acquiring single-cell data

[0065] Key disease tissue sites were collected before and after administration, and single-cell suspensions were obtained by filtration after dissociation. Chips and reagents were prepared according to the 10xGenomics operating manual. The single-cell suspensions were connected to the chip, RNA was captured and labeled, and cDNA was reverse transcribed and amplified before library construction and sequencing to obtain single-cell data of animal tissues.

[0066] S102, obtain spatial transcriptome data

[0067] The spatial transcriptome data was analyzed using the Spatial-seq technology developed by the research team: Liao J, Qian J, Fang Y, et al. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution[J]. Nat Commun, 2022, 13(1): 6498. In one example, the following steps are included:

[0068] 2.1 Barcode Magnetic Bead Synthesis: First, dissolve the Barcode X sequence primer and react it with carboxyl magnetic beads. Then, add EDC solution and repeat the reaction. After repeating the reaction, wash the beads and set aside. Next, perform PCR extension on the PCR solution containing Barcode Y and the magnetic beads attached to Barcode X. After the reaction, wash and transfer the beads. Then, add the PCR solution containing Barcode Z and continue PCR extension. Finally, wash the beads and incubate with the exonuclease mixture. After the reaction in a water bath, discard the supernatant and resuspend the beads for storage.

[0069] 2.2 Single cell cutting and collection method based on laser capture microdissection (LCM): animal tissue was dissected, frozen in liquid nitrogen and embedded in OCT glue, the slice thickness was 14 μm, and then treated with different concentrations of ethanol, tar violet stained, and then dehydrated with different concentrations of ethanol and dried; the stained slices were scanned under an LCM microscope, and the images were imported into the workstation. The CellOutliner software was used to identify the outline of single cells, generate pre-coded vector graphics documents and assign collection wells; the LCM system was used to cut single cells in the specified order so that they fell into the collection wells of a 384-well plate containing lysis solution and different barcoded magnetic beads, and the magnetic beads were collected after lysis; finally, the magnetic beads were transferred to an EP tube, and reverse transcription reaction was performed after multiple washing and resuspension. After the reaction was completed, the magnetic beads were washed again, and the magnetic beads were resuspended with the Pre-AmpPCR system for PCR amplification. After the library was built, Xten platform paired-end sequencing.

[0070] S103 preprocesses single-cell data and spatial transcriptome data

[0071] 3.1 Single-cell transcriptome data analysis: Single-cell data were acquired using CellRanger software, and expression matrices were obtained using the STAR sequence alignment algorithm. Abnormal cell data were removed, and multi-group data were integrated and standardized. Dimensionality reduction analysis was performed using methods such as t-SNE and UMAP. Cell types were annotated, and cell proportions were calculated for downstream analysis.

[0072] 3.2 Spatial transcriptome data analysis: The original Fastq files were split and sequenced to obtain spatial barcodes. The spatial expression matrix was obtained through STAR alignment. The Spots expression matrix was matched from the spatial, dose-effect, and temporal dimensions to analyze differentially expressed genes between groups and perform pathway enrichment analysis.

[0073] S2, classify cells and identify cell types;

[0074] Specifically, it includes: preliminary classification of cells into cell types based on gene expression characteristics;

[0075] The preliminarily classified cells are verified and adjusted in combination with known cell type marker genes.

[0076] In this embodiment, existing single-cell spatial transcriptome data are used to perform a preliminary classification of cells using a clustering analysis algorithm (such as a graph theory-based clustering method or a density-based clustering method). Cells are divided into different types based on gene expression characteristics. For example, when studying complex tumor-related diseases, tumor cells, immune cells, stromal cells, and other types can be distinguished.

[0077] The preliminary cell classification is verified and adjusted by combining known cell type marker genes. These marker genes are usually highly expressed in specific cell types. By comparing the expression of these genes, cell types can be identified more accurately.

[0078] For cell types that are difficult to distinguish based on gene expression signatures, spatial information can be used to assist in identification. For example, the regularity with which certain cells are distributed at specific locations within a tissue can be used to further confirm the cell type.

[0079] S3, pairing the expressed ligand and receptor genes to obtain ligand-receptor pairs, and screening out ligand-receptor pairs that are differentially expressed before and after drug administration;

[0080] Extract gene expression information from single-cell spatial transcriptome data and screen all expressed ligand and receptor genes. Build a database of ligand-receptor interactions using bioinformatics databases (such as KEGG and Reactome) and existing literature.

[0081] By comparing and analyzing the gene expression data before and after drug administration, we focused on the ligand-receptor pairs whose expression changed significantly after TCM treatment.

[0082] Combined with cell type information, the distribution of ligand-receptor pairs among different cell types is analyzed to determine which cell types may interact with each other, and to speculate on the changes in these interactions during traditional Chinese medicine treatment and their potential impact on the disease.

[0083] S4, obtaining spatial coordinate information of individual cells, and constructing a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair, and spatial coordinate information;

[0084] Based on the spatial coordinate information in the single-cell spatial transcriptome data, the position of each cell in the tissue is accurately mapped to obtain the spatial coordinate information of a single cell. By constructing a spatial map of the tissue, the distribution of different cell types in the tissue can be intuitively displayed.

[0085] Analyze the spatial distribution patterns of key ligand-receptor pairs found in ligand-receptor pair recognition. For example, observe whether the cells containing the ligand and receptor are spatially adjacent, and whether this proximity changes before and after traditional Chinese medicine treatment.

[0086] Specifically, the cell interaction network callback index is used to quantitatively describe the strength of spatial interactions between cells.

[0087] Assume that the cell type data is

[0088] T={T1,T2,…T t}

[0089] The total number of each cell type in a sample under a given experimental condition is defined as:

[0090] C={C1,C2,…,C t},C τ ∈N

[0091] The expression profile data of the τth cell type is defined as:

[0092] G τ ={E τ,1 ,E τ,2 ,…,E τ,g}

[0093]

[0094] The ligand-receptor pair is defined as:

[0095] L={(L1,R1),(L2,R2),…,(L p ,R p )},

[0096] (L ψ ,R ψ )∈{(G1,G1),(G1,G2),…,(G1,G g ),(G2,G1),…,(G g ,G g )},G γ ∈G,

[0097] Spatial distribution information data is

[0098] S={(X1,Y1),(X2,Y2),…,(X n ,Y n )},X η ∈Q

[0099] Cell type pairs are defined as:

[0100]

[0101] where g is the total number of genes, n is the total number of mapped cells, p is the total number of different ligand-receptor pairs, t is the total number of cell types, N is the set of nonnegative integers, and Q is the set of rational numbers. T × T is the Cartesian product.

[0102] The calculation formula of the cell interaction network callback index is:

[0103]

[0104] Among them, RRODN C,μis the callback index of the cell interaction network; μ is the cell type; ψ is the ligand-receptor pair; V μ,ψ is the average betweenness centrality of the ligand-receptor pair ψ in the gene interaction network of the cell type combination μ; EoRC μ,ψ Represents whether the ligand-receptor ψ of the cell type combination μ is effectively recalled.

[0105] In one example, the spatially weighted Pearson correlation coefficient is used to calculate whether the ligand-receptor pair is effectively recalled. The calculation formula is as follows:

[0106]

[0107] RL μ,ψ is the callback degree of the ligand receptor of cell type μ to γ, and the calculation formula is:

[0108]

[0109] I μ,ψ is the spatially weighted Pearson correlation coefficient of the expression of μ and ligand receptor pairs for a given cell type combination under given experimental conditions.

[0110] Randomly select a donor cell a and its closest receptor cell b on the two-dimensional plane, and calculate the spatial distance weight:

[0111]

[0112] d a,b is the Euclidean distance and h is the bandwidth.

[0113] Then, the spatial weighted value of the expression level of the ligand-receptor pair is calculated:

[0114]

[0115] is the spatial weighted value of ligand expression, is the spatial weighted value of receptor expression, x and y are the expression levels of ligand gene and receptor gene respectively. Then the covariance is calculated:

[0116]

[0117] And the weighted standard deviation:

[0118]

[0119] Finally, the spatial weighted Pearson correlation coefficient is calculated:

[0120]

[0121] Specifically, constructing a gene interaction network includes:

[0122] The cell type network callback index, the importance of up-regulated differentially genes, and down-regulated differentially genes were used to quantitatively describe the gene interaction strength; the calculation formula is:

[0123]

[0124] RRODN D,τ is the network callback index of the τth cell type, W τ,γ is the network centrality of gene γ in the τth cell type, EoRD τ,γ Whether the gene γ representing the τth cell type is effectively called back is calculated as follows:

[0125]

[0126] RL τ,γ is the callback degree of the γth gene of the τth cell type, and the calculation formula is:

[0127]

[0128] log2E τ,γ is the logarithmic value of the average expression level of the γth gene in the τth cell type under given experimental conditions, and the calculation formula is:

[0129]

[0130] (log2E τ,γ ) model -(log2E τ,γ ) sham >0.5

[0131] S5, performing weighted processing on the cell interaction network and the differential gene network to obtain a comprehensive evaluation index for the effect of traditional Chinese medicine.

[0132] Specifically, the calculation formula is as follows:

[0133]

[0134] Where F represents the comprehensive evaluation index; α and β are the weights of the cell interaction network and the differential gene network, respectively, and α + β = 1; RRODN D,τ is the network callback index of the τth cell type; RRODN up,τ and RRRODN down,τ The importance of up-regulated differentially genes and down-regulated differentially genes, respectively.

[0135] The comprehensive evaluation index F is obtained through the above calculations. The larger the F value, the more significant the therapeutic / preventive effect of traditional Chinese medicine on complex diseases may be.

[0136] Example 2

[0137] Taking the treatment of acute myocardial infarction with Shenmai injection as an example, this embodiment provides a method for evaluating the effect of Shenmai injection in treating acute myocardial infarction.

[0138] Shenmai injection is derived from the ancient prescription "Shengmai San" and is composed of ginseng and ophiopogon. It is mostly used clinically to improve the immune function of cancer patients. When used in combination with chemotherapy drugs, it has a certain synergistic effect and can reduce the toxic and side effects caused by chemotherapy drugs. Coronary heart disease is the second most deadly disease in my country, and qi deficiency and blood stasis are its common syndromes. The main drug ginseng in Shenmai injection has the effects of greatly replenishing vital energy, restoring the pulse and strengthening the body, replenishing qi and blood, and strengthening the mind and calming the mind. The auxiliary drug ophiopogon has the effects of nourishing yin and moistening the lungs, benefiting the stomach and promoting fluid production, and clearing the heart and eliminating vexation. The combination of these drugs has the effects of replenishing qi and strengthening the body, nourishing yin and promoting fluid production, and promoting pulse production. Although some studies on Shenmai injection have been conducted for the treatment of coronary heart disease, the actual treatment data, scientific connotation and network regulatory mechanism are still unclear. The present invention aims to use a method for evaluating the effect of traditional Chinese medicine in treating / preventing complex diseases based on single-cell spatial transcriptome data to evaluate the time-space mechanism of Shenmai injection in resisting acute myocardial infarction, and to study its clinical application.

[0139] 1. Experimental Materials

[0140] 1.1 Experimental Animals

[0141] Healthy SPF male Sprague-Dawley rats, weighing 180-200 g, were obtained from Shanghai SLAC Laboratory Animal Co., Ltd. The rats were housed in a 12-hour light / dark cycle (8:30 AM–8:30 PM), with a light intensity of 15-20 lux, an ambient temperature of 22-26°C, and a humidity of 40%-70%. They had free access to food and water. Animal experiments were conducted in accordance with the guidelines of the Zhejiang University Animal Ethics Committee and the Guide for the Care and Use of Laboratory Animals of the U.S. National Institutes of Health, and were approved by animal ethics committees. Every effort was made to minimize pain or discomfort to the animals and to minimize the number of animals used.

[0142] 1.2 Experimental drugs and reagents

[0143] Table 1 Experimental drugs and reagents

[0144]

[0145]

[0146] 1.3 Experimental reagent formula

[0147] 0.1M MES:

[0148] MES 491mg

[0149] Adjust the pH of ddH2O NaOH solution to 5.0 and dilute to 25 mL

[0150] 0.2M MES:

[0151] MES 982mg

[0152] Adjust the pH of ddH2O NaOH solution to 5.0 and dilute to 25 mL

[0153] TE-TW solution:

[0154] 5 μL of 10% Tween-20 solution

[0155] TE (pH 8.0) dilute to 50 mL

[0156] TE-SDS solution:

[0157] 500 μL of 10% SDS solution

[0158] TE (pH 8.0) dilute to 10 mL

[0159] EDC solution:

[0160] EDC 300mg

[0161] 0.1M MES dilute to 5mL

[0162] Lysis buffer:

[0163]

[0164] Tar violet dye:

[0165] Tar violet powder 100mg

[0166] 75% ethanol solution is diluted to 10 mL and filtered through Watman No.1 filter paper. 1.4 Experimental equipment

[0167] Table 2 Experimental instruments

[0168]

[0169] 2. Experimental methods

[0170] 2.1 Preparation of acute myocardial infarction rat model

[0171] After one week of pre-adaptation, rats were subjected to left anterior descending coronary artery (LAD) ligation surgery to establish a model. Preoperatively, the rats were fasted for 12 hours and anesthetized with intraperitoneal injection of sodium pentobarbital. The rats were fixed and a tracheal tube was inserted to connect to a ventilator. The chest skin was disinfected, the skin was cut open, the muscle layer was separated to expose the ribs, the intercostal muscles were spread open to find the heart, the pericardium was torn, the heart was pulled out, the coronary artery was ligated 3-4 mm from the beginning of the LAD between the pulmonary artery cone and the left atrial appendage, and the chest wound was sutured. After surgery, the rats were placed on an electric blanket for care, their heartbeat and breathing were closely monitored, and abnormal rats were rescued. The sham operation group underwent thoracotomy but the LAD was not ligated.

[0172] 2.2 Experimental animal grouping and drug administration method

[0173] Rats were divided into a model group (MI), a blank injection group (Blank), a drug-treated group (low-, medium-, and high-dose Shenmai injection groups, SM-L, SM-M, and SM-H, respectively), and a sham-operated group (Sham). The drug-treated groups were pre-treated for 7 days with low, medium, and high doses of 0.21 mL / kg, 0.42 mL / kg, and 0.84 mL / kg, respectively. The blank injection group was injected with 0.9% saline. Drug-treated groups continued for 7 days after surgery.

[0174] 2.3 Methods for evaluating cardiac function in rats

[0175] Seven days after surgery, rats were anesthetized with an intraperitoneal injection of 1% sodium pentobarbital. After hair removal, cardiac function was assessed using small animal ultrasound. B-mode and M-mode ultrasound images were obtained, and relevant cardiac parameters (such as IVsd, LVIDd, and LVPWd) were measured. Left ventricular ejection fraction (EF%) and left ventricular fractional shortening (FS%) were calculated. Differences in cardiac function were then compared among the groups.

[0176] 2.4 Rat heart tissue sampling method

[0177] After anesthetizing the rats, blood was collected from the abdominal aorta, centrifuged at room temperature, and serum was collected for storage. The chest cavity was opened and the heart was removed. The heart was rinsed in ice-cold saline, blotted dry, weighed, embedded in OCT frozen section embedding medium, and stored at -80°C until needed.

[0178] 2.5 Histopathological H&E staining method

[0179] Place the embedded heart tissue in the embedding area of the microtome and freeze. Attach the cryostat to the microtome to make a rough cut. Trim the cut surface and cut several fine slices. Use a brush to remove any tissue debris from the knife. Place the anti-roll plate in place and begin sectioning at a thickness of 5 μm. Keep a record of the sections. Fix the slices in a 95:5 solution of methanol and acetic acid for 5 minutes. Rinse the fixed sections with water and stain them in a preheated 50°C hematoxylin solution for 1.0 minute. Remove and rinse with water for several seconds. Differentiate with 1% hydrochloric acid and ethanol. After washing, bluing is performed in 40-60°C water. After washing, stain with 5% eosin solution for 10 seconds. Wash with water, dehydrate rapidly with graded ethanol, clear with xylene, and mount with neutral gum.

[0180] 2.6 Rat Heart Tissue scRNA-seq Sequencing

[0181] 10x Genomics-based scRNA-seq method: Cardiac cells were collected from three rats each in the sham, model, blank, SM-L, SM-M, and SM-H groups. After dissociation, single-cell suspensions were obtained by filtration. Microarrays and reagents were prepared according to the 10x Genomics manual. The single-cell suspensions were connected to the microarray, RNA was captured and labeled, and cDNA was reverse transcribed and amplified for library construction and sequencing.

[0182] 2.7 Spatial-seq sequencing of rat heart tissue

[0183] Methods refer to the article published on Spatial-seq technology: Liao J, Qian J, Fang Y, et al. De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution[J]. Nat Commun, 2022, 13(1): 6498.

[0184] 2.7.1 Barcode Magnetic Bead Synthesis: First, dissolve the primer containing Barcode X and react it with carboxyl magnetic beads. Then, add EDC solution and repeat the reaction. After repeating the reaction, wash the beads and set aside. Next, perform PCR extension on the magnetic beads linked to Barcode X using the PCR solution containing Barcode Y. After the reaction, wash and transfer the beads. Then, add the PCR solution containing Barcode Z and continue PCR extension. Finally, wash the beads and incubate with the exonuclease mixture. After the reaction in a water bath, discard the supernatant and resuspend the beads for storage.

[0185] 2.7.2 Single-cell cutting and collection method based on laser capture microdissection (LCM): The rat heart was dissected and frozen in liquid nitrogen and then embedded in OCT glue. The slices were 14 μm thick and sequentially treated with different concentrations of ethanol, stained with tar violet, dehydrated with different concentrations of ethanol and dried; the stained slices were scanned under an LCM microscope, and the images were imported into the workstation. The outlines of single cells were identified using CellOutliner software, and pre-coded vector graphics documents were generated and collection wells were assigned; the LCM system was used to cut single cells in a specified order so that they fell into the collection wells of a 384-well plate containing lysis solution and different barcoded magnetic beads. The magnetic beads were collected after lysis; finally, the magnetic beads were transferred to an EP tube, and reverse transcription reaction was performed after multiple washing and resuspension. After the reaction, the magnetic beads were washed again and the magnetic beads were resuspended using the Pre-Amp PCR system for PCR amplification. After the library was built, Xten platform paired-end sequencing was performed. Three rats were selected from each of the sham, model, and Shenmai treatment groups. Twenty-four spots were selected from each of the infarct core, risk, and normal areas. These spots were separated and collected in 96-well plates using liquid chromatography-mass spectrometry (LCM), labeled with spatial barcodes, and subsequently processed as described above for single-cell sequencing.

[0186] 2.8 Single-cell and spatial transcriptome data acquisition and analysis

[0187] The original Fastq files of spatial transcriptome sequencing were split to obtain spatial barcodes, and the spatial expression matrix was obtained through STAR alignment. The Spots expression matrix was matched from the spatial, dose-effect, and temporal dimensions. CellRanger software was used to obtain single-cell data, and the expression matrix was obtained through the STAR sequence alignment algorithm. Abnormal cell data were removed, and multi-group data were integrated and standardized. After dimensionality reduction using t-SNE, post-editing analysis was performed.

[0188] 2.9 Cell type identification

[0189] An unsupervised clustering algorithm (such as a clustering algorithm based on graph theory) is used to preliminarily cluster cells based on the similarity of gene expression. Referring to the known cardiac cell type-specific marker gene database and combining with literature reports, the markers of the preliminarily clustered cell groups are verified. For example, cardiomyocytes usually highly express troponin (cTn)-related genes, and endothelial cells express vascular endothelial growth factor receptor (VEGFR)-related genes. Based on the expression of marker genes, the cell types in the cardiac tissue are accurately identified, including cardiomyocytes, endothelial cells, fibroblasts, and immune cells (such as macrophages, T cells, etc.). The proportions and gene expression differences of each cell type were compared between the Shenmai injection treatment group and the untreated acute myocardial infarction group and the healthy control group. Observe whether Shenmai injection causes changes in the number of specific cell types and changes in gene expression within these cell types.

[0190] 2.10 Ligand-receptor pair recognition

[0191] Gene expression information of all cells was extracted from single-cell spatial transcriptome data. The focus was on genes related to intercellular communication, including ligand and receptor genes. The Shenmai injection treatment group, the acute myocardial infarction untreated group, and the healthy control group were screened separately to identify ligand and receptor genes that may be differentially expressed between the different groups. Based on biological databases (such as KEGG, BioGRID, etc.) and existing literature knowledge, a ligand-receptor interaction network was constructed. The screened genes were matched to identify potential ligand-receptor pairs. The expression differences of ligand-receptor pairs between the Shenmai injection treatment group and the acute myocardial infarction untreated group were compared. Statistical analysis was used to identify ligand-receptor pairs that changed significantly after treatment.

[0192] 2.11 Spatial Information Deconstruction

[0193] Using spatial coordinate information from single-cell spatial transcriptome data, the spatial position of each cell in cardiac tissue was precisely mapped. A spatial distribution map of cardiac cells was constructed to visually display the positional relationships between different cell types in cardiac tissue. Key ligand-receptor pairs identified in ligand-receptor pair identification were analyzed for their spatial distribution patterns in cardiac tissue. Spatial statistical methods were used to quantitatively describe the strength of spatial interactions between cells. The effects of Shenmai Injection on spatial interactions between cells in cardiac tissue were evaluated.

[0194] 2.12 Comprehensive evaluation of spatial information

[0195] The multi-dimensional data, including gene expression, cell type, ligand-receptor pair information, and cell spatial distribution, were integrated. The data obtained from the above analyses were used to calculate the comprehensive evaluation index F, which reflects the therapeutic effect of Shenmai Injection on acute myocardial infarction.

[0196] like Figure 2As shown, the spatial transcriptome atlas of single cells in the heart of rats with acute myocardial infarction in this example. The Sham group had a total of 12,170 single cells, and the identified cell subpopulations included smooth muscle cells (SMC), endothelial cells (EC), fibroblasts (Fib), macrophages (Macrophages), neutrophils (Neu), pericytes (ALPC), natural killer T cells (NKT) and B cells (B); the Model group had a total of 14,512 single cells, and the cell subpopulations included SMC, EC, Fib, Macrophages, ALPC, NKT, and plasma cells (Pla); the SM-H group had a total of 13,315 single cells, and the cell subpopulations included SMC, EC, Fib, Macrophages, Neu, ALPC, NKT, and B. The distribution of highly expressed genes varied across different groups. Compared to the Sham group, a large number of mitochondrial family genes related to energy metabolism (Mt-co2, Mt-co1, Mt-co3, Mt-atp6, and Mt-cyb) were highly expressed in the Model group. Following administration of the Shenmai formula, a large number of COX family genes related to immune regulation (COX2, COX1, and COX3) were highly expressed among the highly expressed genes. Furthermore, the spatial transcriptome map of the rat heart was collected using Spatial-seq technology, containing 360 spots, which revealed spatially differentially expressed genes such as Ugt2a1, Clec2g, Nbea, and Acsl3.

[0197] The comprehensive evaluation index F was calculated using "Sham (sham operation group), Model (model group), Blank (blank injection group), SM-L (low-dose Shenmai injection treatment group), SM-M (medium-dose Shenmai injection treatment group), and SM-H (high-dose Shenmai injection treatment group)". Figure 3 It is a statistical chart of the comprehensive evaluation index F analysis results of each test group, such as Figure 3 As shown, the comprehensive evaluation index of the Blank group was 9, indicating that only a few genes showed significant differences in expression between the Blank injection group and the model group, failing to significantly modify the expression profile changes induced by acute myocardial infarction. Compared with the Blank group, the Comprehensive Evaluation Index of the SM-L group was 589.07, a significant improvement compared to the Blank injection group, but the magnitude of the increase was relatively small, indicating that low-dose Shenmai Injection has some therapeutic effect on acute myocardial infarction, but the effect is limited. The Comprehensive Evaluation Index of the SM-M group further increased to 1047.2, indicating that medium-dose Shenmai Injection is more effective than low-dose in treating acute myocardial infarction and can more effectively improve the condition of cardiac tissue. The Comprehensive Evaluation Index of the SM-H group was 1710.76, reaching a relatively high level, indicating that high-dose Shenmai Injection has the most significant therapeutic effect on acute myocardial infarction, significantly improving various cardiac tissue indicators to levels approaching or reaching those of the sham surgery group.

[0198] 2.13 Evaluation of cardiac function in rats with acute myocardial infarction

[0199] Figure 4 As shown, 7 days after LAD surgery, the cardiac function of rats further declined. Compared with the Sham group, the EF% of rats in the Model group was 45.69% (**p<0.01, vs 7 days after Sham). Shenmai injection intervention significantly improved cardiac function in the low, medium, and high dose groups. The EF% of rats in the low dose group was 61.33% (*p<0.05, vs 7 days after Model surgery), the EF% of rats in the medium dose group was 61.86% (*p<0.05, vs 7 days after Model surgery), and the EF% of rats in the high dose group was 67.75% (*p<0.05, vs 7 days after Model surgery).

[0200] 2.14 Study on cardiac histopathology in rats with acute myocardial infarction

[0201] The myocardium of each group of rats was stained with H&E. Figure 5 As shown: Sham group: the whole heart section was normal, the cardiomyocytes were arranged in bundles with clear boundaries, the cell nucleus morphology and size were normal, the cytoplasm was evenly stained, and there was basically no inflammatory cell infiltration in the myocardial tissue. Model group: a large number of inflammatory cells infiltrated the infarct core area and its surrounding myocardial tissue, which expanded radially, the cardiomyocytes were disorderly arranged, there was edema, the cell boundaries were unclear, the nuclei were condensed or dissolved, and the cytoplasm was lightly stained. Shenmai administration group: inflammatory cell infiltration was reduced, the cardiomyocytes were arranged more closely, and there was mild edema. Among them, the drug intervention groups for 3 days and 1 day can better improve the inflammatory cell infiltration and edema. In the pathological sections of the drug intervention group for 7 days, the inflammatory cell infiltration was significantly reduced, the cardiomyocytes were neatly arranged, the edema was not obvious, and the cell boundaries were clear.

[0202] In summary, the comprehensive evaluation index F analysis results of Shenmai Injection obtained by the method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data provided by the present invention are consistent with the phenotypic data results of rats with acute myocardial infarction, including cardiac function indicators and cardiac tissue pathology results, that is, Shenmai Injection has a significant therapeutic effect on acute myocardial infarction. Therefore, it can be concluded that the method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data provided by the present invention can be used as a scientific and reasonable method for evaluating the therapeutic effect of traditional Chinese medicine.

[0203] Example 3

[0204] The purpose of this embodiment is to provide a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data provided by the present invention are implemented.

[0205] Example 4

[0206] The purpose of this embodiment is to provide a computer-readable storage medium.

[0207] A computer-readable storage medium stores a computer program, which, when executed by a processor, performs the steps of the method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell spatial transcriptome data provided by the present invention.

[0208] Example 5

[0209] The purpose of this embodiment is to provide a system for evaluating the therapeutic effects of traditional Chinese medicine based on single-cell and spatial transcriptome data, including:

[0210] A data acquisition module, which is used to obtain single-cell data and spatial transcriptome data of tissue samples before and after administration, and perform preprocessing;

[0211] a classification module for classifying cells and identifying cell types;

[0212] A pairing module is used to pair the expressed ligand and receptor genes to obtain ligand-receptor pairs, and to screen out ligand-receptor pairs that are differentially expressed before and after administration;

[0213] A construction module, which is used to obtain the spatial coordinate information of a single cell and construct a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair and spatial coordinate information;

[0214] The processing module is used to perform weighted processing on the cell interaction network and the gene interaction network to obtain a comprehensive index for evaluating the effect of traditional Chinese medicine.

[0215] The steps involved in the devices or systems of the above embodiments 3, 4, and 5 correspond to those of the method embodiment 1. For detailed implementation, please refer to the relevant description of embodiment 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media that includes one or more instruction sets; it should also be understood to include any medium that can store, encode, or carry an instruction set for execution by a processor and cause the processor to perform any method of the present invention.

[0216] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0217] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

[0218] It will be easily understood by those skilled in the art that the above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data, characterized in that: The steps include: Single-cell data and spatial transcriptome data of tissue samples before and after administration were obtained and preprocessed; Classify cells and identify cell types; Pairing the expressed ligand and receptor genes to obtain ligand-receptor pairs, and screening out ligand-receptor pairs that are differentially expressed before and after drug administration; Obtaining spatial coordinate information of individual cells, and constructing a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair, and spatial coordinate information; The cell interaction network and gene interaction network are weighted to obtain a comprehensive evaluation index for the effect of traditional Chinese medicine.

2. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 1, characterized in that: The obtaining of single cell data and spatial transcriptome data of tissue samples before and after administration and pre-processing includes: Obtain single-cell suspensions from tissue samples before and after administration, perform reverse transcription, amplify cDNA, and then construct and sequence libraries to obtain single-cell data. Software is used to clean and standardize the single-cell data and annotate the cell types. Spatial transcriptome data of tissue samples before and after drug administration were obtained, and differentially expressed genes between groups were analyzed and pathway enrichment analysis was performed.

3. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 1, characterized in that: The cell classification and cell type identification comprises: Cells were preliminarily classified into cell types based on gene expression characteristics; The preliminarily classified cells are verified and adjusted in combination with known cell type marker genes.

4. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 1, wherein: Building a cell interaction network includes: Obtain the tissue location distribution of individual cells based on spatial coordinate information; The cell interaction network callback index is used to quantitatively describe the spatial interaction strength between cells. The calculation formula of the cell interaction network callback index is: Among them, RRODN C,μ is the callback index of the cell interaction network; μ is the cell type; ψ is the ligand-receptor pair; V μ,ψ is the average betweenness centrality of the ligand-receptor pair ψ in the gene interaction network of the cell type combination μ; EoRC μ,ψ Represents whether the ligand-receptor ψ of the cell type combination μ is effectively recalled.

5. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 4, characterized in that: The spatially weighted Pearson correlation coefficient was used to calculate whether the ligand-receptor pair was effectively recalled.

6. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 1, wherein: The constructing of the gene interaction network comprises: The cell type network callback index, the importance of up-regulated differentially genes, and down-regulated differentially genes were used to quantitatively describe the gene interaction strength; the calculation formula is: RRODN D,τ is the network callback index of the τth cell type, W τ,γ is the network centrality of gene γ in the τth cell type, EoRD τ,γ Whether the gene γ representing the τth cell type is effectively called back is calculated as follows: RL τ,γ is the callback degree of the γth gene of the τth cell type, and the calculation formula is: log2E τ,γ is the logarithmic value of the average expression level of the γth gene in the τth cell type under given experimental conditions, and the calculation formula is: (log2E τ,γ ) model -(log2E τ,γ ) sham >0.5 7. The method for evaluating the therapeutic effect of traditional Chinese medicine based on single-cell and spatial transcriptome data according to claim 1, wherein: The cell interaction network and the differential gene network are weighted to obtain a comprehensive evaluation index of the effect of traditional Chinese medicine, including the following calculation formula: Among them, F represents the comprehensive evaluation index, α and β are the weights of the cell interaction network and the differential gene network, respectively, and α+β=1; RRODN D,τ is the network callback index of the τth cell type; RRODN up,τ and RRRODN down,τ The importance of up-regulated differentially genes and down-regulated differentially genes, respectively.

8. A system for evaluating the therapeutic effects of traditional Chinese medicine based on single-cell and spatial transcriptome data, characterized in that: include: A data acquisition module, which is used to obtain single-cell data and spatial transcriptome data of tissue samples before and after administration, and perform preprocessing; a classification module for classifying cells and identifying cell types; A pairing module is used to pair the expressed ligand and receptor genes to obtain ligand-receptor pairs, and to screen out ligand-receptor pairs that are differentially expressed before and after administration; A construction module, which is used to obtain the spatial coordinate information of a single cell and construct a cell interaction network and a gene interaction network based on the cell type, ligand-receptor pair and spatial coordinate information; The processing module is used to perform weighted processing on the cell interaction network and the gene interaction network to obtain a comprehensive index for evaluating the effect of traditional Chinese medicine.

9. An electronic device, characterized in that: The method comprises at least one processing unit and at least one storage unit, wherein the storage unit stores a computer program, and when the computer program is executed by the processing unit, the processing unit executes the steps of the method according to any one of claims 1 to 7.

10. A computer-readable medium, characterized in that It stores a computer program that can be executed by an electronic device. When the computer program is run on the electronic device, the electronic device executes the steps of the method according to any one of claims 1 to 7.

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