CN117671676A - Method for evaluating abnormal immune cells based on space transcriptome visual image - Google Patents
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Abstract
The invention relates to a method for evaluating abnormal immune cells based on a space transcriptome visual image, belonging to the field of bioinformatics. The invention provides a method for evaluating abnormal immune cells based on a space transcriptome visual image, which comprises the steps of constructing an inflammatory infiltration core area, calculating immune cell parameters, constructing an immune typing coefficient model and the like. The invention is based on the maximum influence weight of inflammatory infiltration core region constructionImmunophenotyping coefficientCan effectively evaluate the effect of immune cells in tissue injury, and is further beneficial to combining the position information and gene expression information of cells in tissue distribution, researching abnormal signals in inflammation and related regulation and control machinesAnd (5) preparing.
Description
Technical Field
The invention relates to the technical field of bioinformatics, in particular to a method for evaluating abnormal immune cells based on a space transcriptome visual image.
Background
Spatial transcriptomics is a science that studies how gene expression changes spatially and temporally in cells. It combines knowledge of multiple disciplines of biology, genetics, biochemistry, physics, etc. Over the past several decades, scientists have developed various techniques to study changes and regulation of gene expression. Among them, the spatial transcriptomics technique is one of the most rapidly developed methods in recent years. This technique utilizes advanced imaging techniques, molecular biology techniques and computer science methods to track and analyze changes in gene expression in cells of a particular tissue or organ. The study of spatial transcriptomics is very extensive and involves determining the position of genes in space, understanding the relationship between gene expression levels and cell types, and exploring the activity and function of cells under specific conditions. Not only does these studies help us to better understand the nature and process of life, but they can also provide important information and clues for disease diagnosis and treatment.
The study of spatial transcriptomics requires support from a variety of techniques. Among the most critical techniques is the tissue slice technique. This technique allows the sample to be sliced and specific genes or proteins to be labeled using immunofluorescence or in situ hybridization. By this method we can obtain high resolution and high definition images to better understand the spatial distribution of gene expression and the change in tissue structure. Another key technique is computer image analysis. The technology can analyze and process the image by using methods such as computer algorithm, machine learning and the like. In this way we can extract features and patterns in the image and convert them into comparable data. This helps us to better understand the changes in gene expression and the regulatory mechanisms.
The study of spatial transcriptomics also involves a number of complex problems and challenges. Among the most prominent problems are the processing and analysis of data. Because spatial transcriptomics data is complex and multidimensional, specialized methods and techniques are required for processing and analysis. This includes the steps of preprocessing of data, feature extraction, cluster analysis, model building, etc. Scientists need to solve these problems using computer science, statistics, and machine learning methods. Another problem is the visualization and interpretation of data. Because spatial transcriptomics data is highly complex, specialized techniques and methods are required for visualization and interpretation. This includes various methods such as heat maps, cluster maps, three-dimensional reconstruction, and the like. By these techniques we can convert complex data into intuitive graphics and images to better understand the changes in gene expression and cellular regulatory mechanisms.
Taking the study of periodontitis and inflammatory bowel disease as an example, the current study is mainly by sequencing analysis and based on the common transcriptome (Bulk RNA sequencing), the average expression level of individual genes in a large cell population is obtained. Although single-cell transcriptome sequencing (Single cell RNA sequencing, scRNA-seq) has been reported to be applied to periodontitis and inflammatory bowel disease detection in recent years, spatial information is lost in the process of dissociating solid tissues into single cells, immune cells and abnormal immune signal molecules in the lesion areas of periodontitis and IBD cannot be visually detected, and periodontitis specific immune targets cannot be well analyzed. Although spatial transcriptomes are capable of combining positional information of cell tissue distribution with gene expression information, allowing knowledge of cell and molecule heterogeneity in a spatial context, further analysis and study of visual images of spatial transcriptomes is lacking.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for evaluating abnormal immune cells based on a space transcriptome visual image.
In order to achieve the above purpose, the technical scheme adopted by the invention is as follows:
in a first aspect, the present invention provides a method for assessing abnormal immune cells based on a spatial transcriptome visualization image, comprising the steps of:
(1) Constructing a two-dimensional coordinate system in a space transcriptome visual image, and counting the number N of inflammatory activated cells 0 Activating the coordinates of cells with a single inflammationThe inflammatory infiltration nucleus is constructed by taking R as radius as centerA heart region;
(2) Counting the number N of activated immune cells in the inflammatory infiltrate core region described in step (1) 1 CoordinatesCalculating the distance between each activated immune cell and a single inflammatory activated cell in the inflammatory infiltration core region +.>The method comprises the steps of carrying out a first treatment on the surface of the Selecting the minimum +/for each activated immunocyte>As the activated immune cell +.>A value;
(3) Activating the immune cell d according to step (2) i Value and radius R of inflammatory infiltration core region calculate maximum impact weight of each activated immune cell in inflammatory infiltration core region on individual inflammatory activation cells；
(4) The maximum impact weight according to step (3)And type probability of activated immune cells in inflammatory infiltration core region, calculating immunophenotyping coefficient of a certain type of immune cells +.>The role of such immune cells in tissue injury was assessed by the size of the immunophenotyping coefficient.
The method for evaluating the abnormal immune cells based on the space transcriptome visual image can further analyze the information in the space transcriptome visual image, and avoid the loss of sample space information caused in the detection process. At the same time, the detection of immune cells and abnormal immune signal molecules aiming at the inflammation lesion area can be visualizedAnd (3) detecting specific immune target cells caused by the locked inflammation, and further knowing the heterogeneity of the cells and molecules in a space background. The invention is based on the maximum influence weight of inflammatory infiltration core region constructionImmunotyping coefficient->Can effectively evaluate the effect of immune cells in tissue injury, and is further beneficial to combining the position information of cells in tissue distribution and gene expression information, researching abnormal signals and related regulation and control mechanisms in inflammation.
As a preferred embodiment of the method of the present invention, in step (1), the radius R of the inflammatory infiltrate core region ranges from 0 to 100 μm; preferably, the radius R of the inflammatory infiltrate core region is 40 μm. By selecting different R values, the width of the distribution in the subsequent impact weight calculation can be adjusted. Smaller R concentrates the impact weight in the region closer to the center of the circle, while larger R concentrates the weight distribution more widely.
As a preferred embodiment of the method of the present invention, in step (2), the activated immune cells are immune cells expressing inflammation-related genes; the inflammation-associated genes include, but are not limited to GRZB, INF-gamma, IL-1b.
As a preferred embodiment of the process of the present invention, in step (2), theIn microns.
As a preferred embodiment of the method of the present invention, in step (3), the maximum impact weight。
The invention realizes the distribution effect of the influence weight function by using the Gaussian distribution function, the Gaussian function is a stable distribution function, and cannot generate severe change due to small fluctuation of data, and meanwhile, the Gaussian distribution function has definite mathematical expression and parameters, becauseThis allows easy prediction of the distribution and probability density of the data and its mathematical operation is simple. According to the invention, the distribution width during the subsequent weight influence calculation can be adjusted by adjusting different R values. Smaller R concentrates the impact weight in the region closer to the center of the circle, while larger R concentrates the weight distribution more widely. When d i When=0, the impact weight wmax is 100; with d i Increasing the value, affecting the weight to decay at an exponential speed; when d i When=100, the influence weight W is 4.
As a preferred embodiment of the method of the present invention, in step (4), the immunophenotyping index of the certain kind of immune cells is. The total influence weight of a certain type of immune cells in the immune infiltration core area can be displayed by the size of the immune typing coefficient, and the method is further applied to evaluating the effect of the immune cells in corresponding tissue injury.
As a preferred embodiment of the method of the present invention, in the step (4), the method for obtaining the type probability of the activated immune cells comprises: determining a possible cell type probability pie chart of the activated immune cells through the Seurat mapping to obtain the type probability of the activated immune cells; the sum of all possible cell type probabilities is 1 for each activated immune cell. There are many single cell data in space transcriptomics, and the setup mapping enables automated, repeatable and scalable methods that can efficiently process large-scale single cell datasets while accurately assessing the probability of each cell.
As a preferred embodiment of the method of the present invention, in step (1), the step of obtaining the spatial transcriptome visualization image includes: obtaining a tissue slice of a sample to be detected through a frozen slicing technology, sequencing the tissue slice of the sample to be detected through a space transcriptome technology to obtain space transcriptome data, normalizing the space transcriptome data, and visually displaying the normalized space transcriptome data in a space staining slice to obtain a visual image of the space transcriptome.
As a preferred embodiment of the method of the invention, the spatial transcriptome visualization is of periodontal tissue and colon tissue of patients suffering from periodontitis with exacerbation of inflammatory bowel disease.
In a second aspect, the present invention provides a system for assessing abnormal immune cells based on a spatial transcriptome visualization, comprising:
the data acquisition module is used for acquiring a visual image of the space transcriptome;
the image construction module is used for constructing a two-dimensional image area by taking a single inflammation activating cell as a center in the space transcriptome visual image; the image area is an inflammation infiltration core area;
a recording module for recording immune cell parameters in the inflammatory infiltration core region, wherein the immune cell parameters comprise the number N of inflammatory activated cells 0 Number N of activated immunocytes 1 Distance of each activated immune cell from a single inflammatory activated cell in the inflammatory infiltrate core regionAnd minimal activated immune cells per activated immune cell +.>A value;
a first calculation module for calculating the maximum influence weight of each activated immune cell in the region on the single inflammatory activated cell according to the immune cell parameters in the inflammatory infiltration core region；
A second calculation module for calculating the maximum influence weightAnd type probability of activated immune cells in inflammatory infiltration core region, calculating immunophenotyping coefficient of a certain type of immune cells +.>；
And the output module is used for evaluating the effect of the immune cells in tissue injury according to the immune typing coefficient.
In a third aspect, the present invention provides a computer-readable storage medium storing a computer program; the computer program when executed by a processor performs the steps of the method of assessing abnormal immune cells based on a spatial transcriptome visualization image.
Compared with the prior art, the invention has the beneficial effects that:
1. the method for evaluating the abnormal immune cells based on the space transcriptome visual image can further analyze the information in the space transcriptome visual image, and avoid the loss of sample space information caused in the detection process. Meanwhile, the detection of immune cells and abnormal immune signal molecules in a lesion area aiming at inflammation can be visualized, specific immune target cells caused by inflammation are locked, and the heterogeneity of the cells and molecules is further known in a space background. The invention is based on the maximum influence weight of inflammatory infiltration core region constructionImmunotyping coefficient->Can effectively evaluate the effect of immune cells in tissue injury, and is further beneficial to combining the position information of cells in tissue distribution and gene expression information, researching abnormal signals and related regulation and control mechanisms in inflammation.
2. The immune analysis coefficient model can carry out in-situ detection on specific immune cells and immune molecules in periodontitis and/or inflammatory bowel disease tissues on the basis of multiple groups of study, evaluate the effect of the immune cells in tissue injury and determine key immune cells; and through detection of related gene channels, the key role of macrophages in exacerbating periodontitis is finally locked, and meanwhile, a treatment method aiming at regulating abnormal immune inflammatory response of a host is developed based on an immune regulation strategy.
Drawings
FIG. 1 is a graph of the distribution of the absolute value function of the impact weight W (di) and its derivatives: fig. 1 (a) is a graph of the functional distribution of the impact weight W (di) when r=40; fig. 1 (B) is a graph of absolute value function of W (di) derivative at r=40;
FIG. 2 is a map of transcriptomes of different types of immune cells in periodontal tissue space;
FIG. 3 is a graph showing the results of differential gene pathway enrichment analysis in periodontitis aggravated by inflammatory bowel disease and macrophage function treatment.
Detailed Description
For a better description of the objects, technical solutions and advantages of the present invention, the present invention will be further described with reference to the following specific examples.
Example 1 sample preparation of spatial transcriptome
The average of 18C 576/L mice was randomly divided into 3 groups, one group was control group, one group was simple periodontitis model group (PD group), and one group was colitis aggravated by periodontitis (PD+DSS group). Periodontal wire ligation was used to model periodontitis mice, DSS induced inflammatory enteritis, observed and recorded body weight changes, disease Activity Index (DAI), statistical colon length, and colon histological scoring to assess model establishment. Comparing the degrees of alveolar bone destruction of the mice in the PD+DSS group and the PD group through micro CT, evaluating the degrees of tissue destruction and inflammatory infiltration through tissue slice HE staining, and detecting the expression of periodontitis factors of the mice in the two groups through PCR, so as to determine the aggravation effect of colonitis on periodontitis. And (3) after obtaining a tissue sample, performing isopentane freezing and OCT embedding, and performing frozen section on the embedded tissue. The cut portions of tissue were placed on a conventional slide. Next, the tissue sections were fixed, H & E stained, and bright field photographed using a microscope with scanning capabilities. Simultaneously, 10 frozen sections with the thickness of 10 mu m are cut, RNA is extracted, and the quality of the frozen sections is evaluated by calculating RIN values. For best experimental results, 10XVisium was performed with tissue blocks having RIN values greater than 7.
Example 2 detection tissue optimization of spatial transcriptomes
All experiments of tissue optimization were done on glass slides with the aim of defining whether tissue can be used for experiments of spatial transcriptomes, while also finding the optimal tissue penetration time. The frozen section with the RIN value larger than 7 and the thickness of 10 mu m is placed in a capturing area on a tissue optimizing slide glass, the tissue section is fixed, H & E staining is carried out, the tissue section is photographed by a microscope with a scanning function, permeable treatment is carried out on the tissue section continuously after photographing is finished, mRNA in cells is released, the mRNA is captured by an oligo on the slide glass, and when a chain of cDNA is synthesized, bases with fluorescent groups are doped, so that the synthesized cDNA has fluorescence. The tissue remaining on the slide was then removed by enzymatic digestion, the cDNA with fluorescence was exposed, and the whole section was scanned with a fluorescence microscope of Cy3 channel to obtain a fluorescent stained picture.
Example 3 spatial Gene expression detection
Tissue sections were placed in the capture area on the gene expression slide. Next, the tissue section is fixed, H&E, dyeing, and photographing the tissue slice by using a microscope with a scanning function to obtain tissue morphology information. After photographing is completed, the tissue slice is continuously subjected to permeation treatment, mRNA in the cells is released, the mRNA is captured by the adjacent capture probe oligo on the glass slide, and the unique molecular identifier UMI and the special space bar code are marked. Carrying out reverse transcription on a glass slide, synthesizing a first-chain cDNA and a second-chain cDNA, carrying out denaturation treatment, recovering the synthesized second-chain cDNA into an ep tube, and completing cDNA amplification and library construction in the ep tube. The samples were loaded at a cDNA concentration of 300pM and sequenced on a NovaSeq6000 system (Illumina) using a NovaSeq200 cycle S1 flow cell to obtain spatial transcriptome data. Sequencing the following read protocol was used: reading 1:28 cycles; i7, reading an index; 10 cycles; i7 index read: 10 cycles; reading 2:90 cycles. The obtained sequencing depth was about (120-142). Times.10 6 reads/per sample.
EXAMPLE 4 normalization and visualization of spatial transcriptome data
The space transcriptome data is filtered by adopting Fastp software to obtain sequencing data which can be directly used for subsequent analysis. Counting special space bar code information and corresponding counts contained in the filtered statistical sequencing data by adopting a barcode processing algorithm, so as to judge the actually detected lattice number in the sequencing sample and obtain real space transcriptome sequencing information; comparing reads corresponding to the special space bar codes in the sequencing data with genomes corresponding to known species, analyzing the similarity and the difference between the detected unknown sequence and the known sequence, and obtaining a bam file compared with the similarity and the difference. And converting the bam file containing the various information after genome comparison, merging single-molecule tags which are compared to the same gene in the file, removing repeated UMI sequences in the single-molecule tags to obtain the number of UMIs corresponding to each gene, counting to obtain the number of genes detected by each space lattice, and visually displaying in a space dyeing sheet.
Example 5 construction of activated immunocyte analytical model based on spatial transcriptome visualization image
In the spatial transcriptome visualization of periodontal tissue in mice of the PD+DSS group, the number of inflammatory activated epithelial cells was N 0 =104, inflammation activated single epithelial cells two-dimensional coordinates were (a n , B n ). The "inflammatory core infiltrate zone" was determined centering on the inflammation activated single epithelial cells, R being the radius of the circle. Here the R value takes 40.
The immune cells expressing genes related to inflammation such as GRZB, INF-gamma, IL-1b and the like in the "inflammatory core infiltration region" are defined as "activated immune cells", and the number of activated immune cells in the "inflammatory core infiltration region" is N 1 =298, the two-dimensional coordinates of each activated immune cell were (X i , Y i ). Each activated immune cell is located at a distance from an individual epithelial cell activated by inflammation(in microns) the minimum for each activated immune cell is selectedAs the activated immune cell +.>Values. Calculate each activation exemptMaximum impact weight of epidemic cells on individual inflammatory activated epithelial cells +.>. When r=40, the distribution image of the influence weight W (di) function and its derivative absolute value function is shown in fig. 1.
By means of the setup map, a probability pie chart of the possible cell types of activated immune cells was determined, the results are shown in fig. 1. The specific method comprises the following steps: unified manifold approximation and projection (uniform manifold approximation and projection, UMAP) dimension reduction clustering is carried out on single cell sequencing data by utilizing a Seurat R package, nearest neighbor regression analysis is carried out on transcript data (HPRI) of a certain tissue obtained by a space transcriptome probe and single cell sequencing data, anchor points and anchor point scores between a single cell transcriptome and the space transcriptome are calculated, and then a probability cake map of possible cell types in the region and the most possible cell types are obtained. The sum of all possible cell type probabilities is 1 for each activated immune cell. Immunophenotyping coefficient of a type of immune cellThe method is used for evaluating the effect of the immune cells in tissue injury.
From the above mathematical model we calculated the different types of immune cells in periodontal tissue of the PS+DSS groupThe value, the mapping of the transcriptome of different types of immune cells in periodontal tissue space is shown in FIG. 2, each punctate signal in FIG. 2 is a combination of a plurality of genes, and the possible cell types can be deduced through the gene combination. In the map of fig. 2, the colored dots inside indicate the likelihood of corresponding immune cells. The immunophenotyping coefficients of different immune cells are respectively: mononuclear-macrophages 4,004, plasma cells 2,632, t cells 2,020, b cells 1,843, neutrophils 890, mast cells 403. The immunophenotyping coefficient of macrophages is greatest, thus in teeth aggravated by periodontitis or inflammatory bowel diseaseIn pericalitis, macrophages are a site of action in inflammatory tissue injury.
EXAMPLE 6 verification of the phagocytic and inflammatory regulatory function of colitis injured periodontal macrophages
The results of differential gene pathway enrichment analysis were performed on a simple periodontitis model group (PD group) and a periodontitis-aggravated colitis model group (pd+dss group) in the model mice in example 1, as shown in fig. 3 (a) - (B). FIGS. 3 (A) - (B) show that inflammatory pathway signals associated with macrophages are upregulated, while Toll-like receptor (TLRs) pathways are also significantly altered, suggesting that the associated differential genes act on macrophages primarily through TLRs and cause downstream pathway changes. Thus, macrophages are sites of inflammatory tissue injury by the above-described activated immunocytolytic model. Based on the pathway analysis, after the sorted periodontal macrophages were treated with TLR pathway inhibitors, the changes in phagocytic capacity were examined by confocal microscopy, the changes in macrophage polarization phenotype were examined by flow cytometry, and the changes in phagocytic function of periodontal macrophages were examined using a phagocytosis kit, and the results are shown in fig. 3 (C) - (D). Wherein FIG. 3 (C) is a confocal microscopy image showing increased levels of fluorescence in the TLR4-siRNA treated group, demonstrating the restoration of phagocytic capacity of macrophages. The expression of the phagocytosis related gene of periodontal macrophage after siRNA-TLRs treatment was detected by PCR, and the result is shown in FIG. 3 (E), which shows that the expression of the phagocytosis related gene CD36/MSR1/CD47 of macrophage is up-regulated after treatment. It is demonstrated that macrophages are a site of action of inflammatory tissue injury in periodontitis or in periodontitis exacerbated by inflammatory bowel disease, and their phagocytic capacity can be restored by TLR 4-siRNA.
Finally, it should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the scope of the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted equally without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. A method for assessing abnormal immune cells based on a spatial transcriptome visualization, comprising the steps of:
(1) Constructing a two-dimensional coordinate system in a space transcriptome visual image, and counting the number N of inflammatory activated cells 0 Activating the coordinates of cells with a single inflammationTaking R as a radius as a center, constructing an inflammation infiltration core region;
(2) Counting the number N of activated immune cells in the inflammatory infiltrate core region described in step (1) 1 Coordinates of activated immune cellsCalculating the distance between each activated immune cell and a single inflammatory activated cell in the inflammatory infiltration core regionThe method comprises the steps of carrying out a first treatment on the surface of the Selecting the minimum +/for each activated immunocyte>As the activated immune cell +.>A value;
(3) Activating the immune cell d according to step (2) i Value and radius R of inflammatory infiltration core region calculate maximum impact weight of each activated immune cell in inflammatory infiltration core region on individual inflammatory activation cells；
(4) The maximum impact weight according to step (3)And type probability of activated immune cells in inflammatory infiltration core region to calculate immunity of a certain type of immune cellsTyping coefficient->The role of such immune cells in tissue injury was assessed by the size of the immunophenotyping coefficient.
2. The method of claim 1, wherein in step (1), the radius R of the inflammatory infiltrate core region ranges from 0 to 100 μm.
3. The method of claim 1, wherein in step (2), the activated immune cells are immune cells expressing an inflammation-associated gene; the inflammation-related genes include GRZB, INF-gamma and IL-1b.
4. The method of claim 1, wherein in step (2), theIn microns.
5. The method of claim 1, wherein in step (3), the maximum impact weight。
6. The method of claim 1, wherein in step (4), the immunophenotyping coefficient of the certain class of immune cells is。
7. The method of claim 1, wherein in step (4), the probability of the type of the activated immune cells is obtained by: determining a possible cell type probability pie chart of the activated immune cells through the Seurat mapping to obtain the type probability of the activated immune cells; the sum of all possible cell type probabilities is 1 for each activated immune cell.
8. The method of claim 1, wherein in step (1), the step of acquiring the spatial transcriptome visualization image comprises: obtaining a tissue slice of a sample to be detected through a frozen slicing technology, sequencing the tissue slice of the sample to be detected through a space transcriptome technology to obtain space transcriptome data, normalizing the space transcriptome data, and visually displaying the normalized space transcriptome data in a space staining slice to obtain a visual image of the space transcriptome.
9. A system for assessing abnormal immune cells based on a spatial transcriptome visualization, comprising:
the data acquisition module is used for acquiring a visual image of the space transcriptome;
the image construction module is used for constructing a two-dimensional image area by taking a single inflammation activating cell as a center in the space transcriptome visual image; the image area is an inflammation infiltration core area;
a recording module for recording immune cell parameters in the inflammatory infiltration core region, wherein the immune cell parameters comprise the number N of inflammatory activated cells 0 Number N of activated immunocytes 1 Distance of each activated immune cell from a single inflammatory activated cell in the inflammatory infiltrate core regionAnd minimal activated immune cells per activated immune cell +.>A value;
a first calculation module for calculating the maximum influence weight of each activated immune cell in the region on the single inflammatory activated cell according to the immune cell parameters in the inflammatory infiltration core region；
A second calculation module for calculating the maximum influence weightAnd type probability of activated immune cells in inflammatory infiltration core region, calculating immunophenotyping coefficient of a certain type of immune cells +.>；
And the output module is used for evaluating the effect of the immune cells in tissue injury according to the immune typing coefficient.
10. A computer-readable storage medium, wherein the computer-readable storage medium stores a computer program; the computer program, when executed by a processor, implements the steps of the method of assessing abnormal immune cells based on a spatial transcriptome visualization image according to any one of claims 1-8.
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