CN116825206B - Method, device and equipment for exploring FH-defect type kidney cancer key cell subgroup - Google Patents
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Abstract
The invention relates to a method, a device and equipment for exploring FH-defect type kidney cancer key cell subsets, and belongs to the technical field of bioengineering. According to the invention, sequencing detection is carried out on a plurality of tissue samples of FH-RCC diseases based on single-cell transcriptome sequencing, so that a plurality of single-cell gene expression data are obtained; according to the gene expression data of single cells, performing dimension reduction clustering and grouping on all single cells to obtain a plurality of cell subgroups; determining the cell subpopulation with increased proportion as a suspected critical cell subpopulation by comparing the proportion of different cell subpopulations between each tissue sample; based on the cell communication analysis, the cell subset with the highest interaction probability among the suspected critical cell subsets is determined as the critical cell subset of the FH-RCC disease. The single cell transcriptome sequencing is used for carrying out expression sequencing on the tissue sample of the FH-RCC disease, so that the gene expression of each cell is accurately analyzed, the heterogeneity information and the tissue state among cells are reflected, and the critical cell subgroup of the FH-RCC disease is accurately found.
Description
Technical Field
The invention belongs to the technical field of bioengineering, and particularly relates to a method, a device and equipment for exploring FH-defective kidney cancer key cell subsets.
Background
FH-deficient kidney cancer, i.e., fumarate hydratase deficient kidney cancer (Fumarate Hydratase-deficient Renal Cell Carcinoma), also known as FH-RCC, is a special type of kidney cancer characterized by a germ line or system mutation of the gene encoding fumarate hydratase.
With the development of second generation sequencing technology and molecular pathology, bulk transcriptome sequencing is commonly applied in the research of FH-RCC diseases to explore the molecular mechanism of the FH-RCC diseases. However, the average value of the expression level of one transcriptome in all cells obtained by using Bulk transcriptome sequencing cannot reflect the state of all cells or a certain group of cells in a sample. Thus, gene expression of each cell and the status of the reactive cell subpopulation cannot be analyzed more accurately using Bulk transcriptome sequencing, and thus the critical cell subpopulation of fumarate hydratase-deficient kidney cancer cannot be found accurately.
Disclosure of Invention
To this end, the present invention provides a method, apparatus and device for exploring critical cell subsets of FH-deficient kidney cancer, which helps to solve the problem of inability to accurately find critical cell subsets of fumarate hydratase deficient kidney cancer using Bulk transcriptome sequencing.
In order to achieve the above purpose, the invention adopts the following technical scheme:
in a first aspect, the invention provides a method of exploring a subpopulation of critical cells for FH-deficient kidney cancer, comprising:
sequencing and detecting tissue samples of a plurality of FH-RCC diseases based on single-cell transcriptome sequencing to obtain single-cell gene expression data in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
according to the gene expression data of the single cells, performing dimension reduction clustering on all single cells of each tissue sample to obtain a plurality of cell subgroups; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
comparing the proportion of the same cell subpopulations in the tumor surrounding normal tissue sample, the tumor primary foci tissue sample and the tumor metastasis focus tissue sample, and determining the cell subpopulations with the increased proportion as suspected critical cell subpopulations;
based on cell communication analysis, obtaining interaction probability among cell subsets, and determining the cell subset with the highest interaction probability among the suspected critical cell subsets as the critical cell subset of the FH-RCC disease.
Further, the comparing the proportion of the same cell subpopulation in the tumor surrounding normal tissue sample, the tumor primary lesion tissue sample, and the tumor metastasis lesion tissue sample, determining the cell subpopulation with the increased proportion as a suspected critical cell subpopulation comprises:
obtaining the proportion K (i, j) of the cell subgroup i in the sample j by counting the number of single cells;
wherein i=a, b, c, d, e, f; a is a T cell, B is a myeloid cell, c is an endothelial cell, d is a fibroblast, e is a B cell, and f is an epithelial cell; j=1, 2,3, j=1 is expressed as the tumor surrounding normal tissue sample, j=2 is expressed as the tumor primary lesion tissue sample, and j=3 is expressed as the tumor metastasis lesion tissue sample;
comparing the ratios K (i, 1), K (i, 2), K (i, 3) of the cell subpopulations i in the sample j, and if K (i, 2) > K (i, 1), K (i, 3) > K (i, 2), determining the cell subpopulation i as the suspected critical cell subpopulation.
Further, according to the gene expression data of the single cells, performing dimension-reducing clustering on all single cells of each tissue sample to obtain a plurality of cell subsets, including:
and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
Further, after the obtaining the plurality of cell subsets, the method comprises:
comparing the gene expression data of the cell subpopulations to obtain differential expression genes of the cell subpopulations;
and carrying out gene enrichment analysis on the differential expression genes to obtain the functional condition of the cell subpopulation.
Further, the method comprises:
and carrying out functional annotation on the cell subset according to the functional condition of the single cell subset.
Further, after determining the critical cell subpopulation of the FH-RCC disease, the method further comprises:
and interfering the key cell subset through an organoid platform and a PDX model, and determining a potential treatment target.
In a second aspect, the invention provides a device for exploring a subpopulation of critical cells for FH-deficient kidney cancer, comprising:
the transcriptome sequencing module is used for sequencing and detecting tissue samples of a plurality of FH-RCC diseases based on single-cell transcriptome sequencing to obtain single-cell gene expression data in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
the clustering and grouping module is used for performing dimension reduction clustering and grouping on all single cells of each tissue sample according to the gene expression data of the single cells to obtain a plurality of cell subgroups; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
a proportion comparison module for comparing the proportion of the same cell subsets in the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample, and determining the cell subset with the increased proportion as a suspected critical cell subset;
and the critical cell subset determining module is used for obtaining the interaction probability among the cell subsets based on cell communication analysis, and determining the cell subset with the highest interaction probability in the suspected critical cell subsets as the critical cell subset of the FH-RCC disease.
Further, the clustering module is specifically configured to: and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
Further, the apparatus comprises:
and the function annotation module is used for carrying out function annotation on the cell subset according to the function condition of the single cell subset.
In a third aspect, the invention provides a device for exploring a subpopulation of critical cells for FH-deficient kidney cancer, comprising:
one or more memories having executable programs stored thereon;
one or more processors configured to execute the executable program in the memory to implement the steps of any of the methods described above.
The invention adopts the technical proposal and has at least the following beneficial effects:
according to the invention, sequencing detection is carried out on a plurality of tissue samples of FH-RCC diseases based on single-cell transcriptome sequencing, so that single-cell gene expression data in each tissue sample is obtained; according to the gene expression data of single cells, performing dimension reduction clustering on all single cells of each tissue sample to obtain a plurality of cell subgroups; determining the cell subpopulation with increased proportion as a suspected critical cell subpopulation by comparing the proportion of the same cell subpopulation in the tumor surrounding normal tissue sample, the tumor primary foci tissue sample and the tumor metastasis focus tissue sample; then, based on cell communication analysis, the interaction probability among all cell subsets is obtained, and the cell subset with the highest interaction probability in the suspected critical cell subsets is determined as the critical cell subset of the FH-RCC disease. The single-cell transcriptome sequencing is used for carrying out expression sequencing on the tissue sample of the FH-RCC disease, so that the gene expression of each cell can be accurately analyzed and the heterogeneity information among cells is reflected, thereby being capable of more accurately reflecting the tissue state, being capable of more accurately distinguishing cell subsets with different functions, and being capable of accurately searching the critical cell subsets related to the FH-RCC disease based on the proportion of the different cell subsets among the tissue samples and the cell communication analysis.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention as claimed.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of a method of exploring a subpopulation of critical cells for FH-deficient kidney cancer in accordance with the present invention;
FIG. 2 is a schematic block diagram of an apparatus for exploring a critical cell subset for FH-deficient kidney cancer in accordance with the present invention;
FIG. 3 is a schematic block diagram of an apparatus for exploring a critical cell subset for FH-deficient kidney cancer in accordance with the present invention.
In the figure: the system comprises a 21-transcriptome sequencing module, a 22-clustering module, a 23-proportion comparison module, a 24-critical cell subset determining module, a 31-memory and a 32-processor.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the technical solutions of the present invention will be described in detail below. It will be apparent that the described embodiments are only some, but not all, embodiments of the invention. All other embodiments, based on the examples herein, which are within the scope of the invention as defined by the claims, will be within the scope of the invention as defined by the claims.
Referring to fig. 1, fig. 1 is a flowchart of a method for exploring a FH-deficient kidney cancer key cell subset according to the present invention, as shown in fig. 1, comprising the steps of:
step S11, sequencing and detecting tissue samples of a plurality of FH-RCC diseases based on single-cell transcriptome sequencing to obtain single-cell gene expression data in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
step S12, performing dimension reduction clustering grouping on all single cells of each tissue sample according to the gene expression data of the single cells to obtain a plurality of cell subgroups; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
step S13, comparing the same proportion of the cell subsets in the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample, and determining the cell subsets with the increased proportion as suspected critical cell subsets;
and S14, obtaining interaction probability among cell subsets based on cell communication analysis, and determining the cell subset with the highest interaction probability among the suspected critical cell subsets as the critical cell subset of the FH-RCC disease.
It should be noted that single-cell transcriptome sequencing is a high-throughput cell capturing technology implemented by using technologies such as microfluidic, oil drop encapsulation and Barcode labeling, and transcriptome information of each cell can be obtained. Cell transcriptome sequencing, namely, by detecting the total expression quantity of all mRNA in a single cell, a data sheet with higher resolution than that of a common transcriptome can be obtained, the gene expression condition of each cell can be accurately analyzed, cell groups can be accurately distinguished, and comparison among the cell groups can be carried out; the method can also reflect the state of the tissue more precisely and accurately, so that the key cell population related to prognosis and treatment can be searched more accurately. The specific process of single cell transcriptome sequencing is as follows: on a known microfluidic platform, gel beads with barcodes and primers and single cells are wrapped in oil drops; within each oil droplet, the gel beads were lysed and the cells lysed to release mRNA, generating barcoded cdnas for sequencing by reverse transcription; after the liquid oil layer is destroyed, the cDNA is subsequently constructed, and finally, the library is sequenced and detected by using an Illumina sequencing platform, so that a large amount of single-cell gene expression data can be obtained at one time, and expression sequencing at the single-cell level is realized.
In this example, the sample database used was a FH-RCC database based on clinical information, biological information and biological samples collected and established by cooperation of CACA-GU-based rare renal cancer cooperative group with domestic multiple units. The peri-tumor normal tissue sample is peri-tumor normal tissue, i.e., a paracancerous tissue sample; the tissue sample of the primary tumor pathogenesis is the tissue which has tumor and causes the pathological changes of the organism at first; the tumor metastasis tissue sample is a cell tissue in which tumor cells invade from a primary site to grow from other sites, forming a tumor of the same type as the primary disease.
It should be noted that, gene expression data, which reflect the abundance of mRNA of gene transcription products in cells measured directly or indirectly, can be used to analyze which genes are changed in expression, how the activities of genes are affected under different conditions, and has important applications in medical clinical diagnosis, drug efficacy judgment, revealing disease occurrence mechanisms, etc.
In single cell transcriptome sequencing, one project can yield tens of thousands of single cells, often tens of thousands of genes, so that high latitude datasets are not suitable for humans to directly read and resolve functional differences between cells. Therefore, we need to simplify the cell number by clustering and visualize the data by dimension reduction.
Dimension reduction is the simplification of complex high-dimensional data information into lower-dimensional data information which is easier to read; clustering is classification, and classification is performed according to a certain standard. After the dimension-reduced data are obtained, the cells can be clustered in groups according to a clustering algorithm, and a more visual effect is presented through a visual map, wherein the visual map can be a single-cell map.
It can be understood that by sequencing single cell transcriptome, sequencing and detecting the three tissue samples of the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample of the FH-RCC disease, the gene expression data of all single cells can be obtained; by dimension-reducing clustering of all single cells of each tissue sample based on gene expression data of the single cells, various cell subsets such as T cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells can be obtained.
It should be noted that, when comparing the ratios of each cell subset in each type of sample, the cell subset in which the ratio is increased is determined as the suspected critical cell subset; the critical cell subsets are those critical to combat FH-RCC disease, and those associated with prognosis and treatment of FH-RCC disease.
Cell communication analysis is to infer interactions between different cells by counting the expression and pairing of receptors and ligands in different cell types, and combining with a molecular information database. Cell communication analysis can help to understand the interaction relationship among cells and analyze the communication network among cells; revealing the interaction of various cells in the development process, exploring the tumor immune microenvironment and excavating the potential therapeutic targets of the diseases.
It will be appreciated that after the suspected critical cell subsets are determined, the probability of interactions between the cell subsets can be derived by cell communication analysis, such that the cell subset with the highest probability of interactions among the suspected critical cell subsets is determined to be the critical cell subset for FH-RCC disease.
By the method in this example, it was found that tumor-associated fibroblasts (CAF) were highly expressed in tumor primary foci tissue and tumor metastasis tissue, and at the same time, based on cell communication analysis, it was found that the probability of interaction between fibroblasts and various cells was significant, suggesting that fibroblasts are a critical cell subset for FH-RCC. Furthermore, in combination with clinical manifestations, FAP (fibroblast cell gene marker) -based PET/CT was found to also aid in assessing the systemic condition of FH-RCC patients.
It can be understood that the invention obtains the gene expression data of single cells in each tissue sample by sequencing and detecting the tissue samples of a plurality of FH-RCC diseases based on single cell transcriptome sequencing; according to the gene expression data of single cells, performing dimension reduction clustering on all single cells of each tissue sample to obtain a plurality of cell subgroups; determining the cell subpopulation with increased proportion as a suspected critical cell subpopulation by comparing the proportion of the same cell subpopulation in the tumor surrounding normal tissue sample, the tumor primary foci tissue sample and the tumor metastasis focus tissue sample; then, based on cell communication analysis, the interaction probability among all cell subsets is obtained, and the cell subset with the highest interaction probability in the suspected critical cell subsets is determined as the critical cell subset of the FH-RCC disease. The single-cell transcriptome sequencing is used for carrying out expression sequencing on the tissue sample of the FH-RCC disease, so that the gene expression of each cell can be accurately analyzed and the heterogeneity information among cells is reflected, thereby being capable of more accurately reflecting the tissue state, being capable of more accurately distinguishing cell subsets with different functions, and being capable of accurately searching the critical cell subsets related to the FH-RCC disease based on the proportion of the different cell subsets among the tissue samples and the cell communication analysis.
Further, the comparing the proportion of the same cell subpopulation in the tumor surrounding normal tissue sample, the tumor primary lesion tissue sample, and the tumor metastasis lesion tissue sample, determining the cell subpopulation with the increased proportion as a suspected critical cell subpopulation comprises:
obtaining the proportion K (i, j) of the cell subgroup i in the sample j by counting the number of single cells;
wherein i=a, b, c, d, e, f; a is a T cell, B is a myeloid cell, c is an endothelial cell, d is a fibroblast, e is a B cell, and f is an epithelial cell; j=1, 2,3, j=1 is expressed as the tumor surrounding normal tissue sample, j=2 is expressed as the tumor primary lesion tissue sample, and j=3 is expressed as the tumor metastasis lesion tissue sample;
comparing the ratios K (i, 1), K (i, 2), K (i, 3) of the cell subpopulations i in the sample j, and if K (i, 2) > K (i, 1), K (i, 3) > K (i, 2), determining the cell subpopulation i as the suspected critical cell subpopulation.
The cell subsets with increased proportion in the tumor surrounding normal tissue, the tumor primary focus tissue and the tumor metastasis focus tissue are searched for and determined as suspected key cell subsets by comparing the proportion of the same cell subsets in the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample.
Obtaining the proportion K (i, j) of the cell subgroup i in the sample j by counting the number of single cells; wherein i=a, b, c, d, e, f; a is a T cell, B is a myeloid cell, c is an endothelial cell, d is a fibroblast, e is a B cell, and f is an epithelial cell; j=1, 2,3, j=1 is expressed as the tumor surrounding normal tissue sample, j=2 is expressed as the tumor primary lesion tissue sample, and j=3 is expressed as the tumor metastasis lesion tissue sample.
That is, the ratio of the T cells, the myeloid cells, the endothelial cells, the fibroblasts, the B cells, and the epithelial cells in the three types of samples, respectively, is statistically obtained. The ratios of each cell subpopulation in each sample are then compared and the cell subpopulations with the increased ratio are determined as suspected critical cell subpopulations.
As exemplified below, the ratio K (d, 1) of fibroblasts d in the tumor surrounding normal tissue sample, the ratio K (d, 2) of fibroblasts d in the tumor primary lesion tissue sample, and the ratio K (d, 3) of fibroblasts d in the tumor metastasis lesion tissue sample were obtained by counting the numbers of all single cells. Comparing K (d, 1), K (d, 2), K (d, 3), if K (d, 2) > K (d, 1), K (d, 3) > K (d, 2), it is determined that the fibroblasts d are a suspected critical cell subset, as indicated by an elevated proportion of fibroblasts d in the normal tissue surrounding the tumor, in the primary tumor tissue and in the metastatic tumor tissue.
It should be noted that the suspected critical cell subset may be one or more of the six cells described above.
It should be noted that, based on differential gene analysis, it is also necessary to find high proportion of genes in these cell populations, construct gene sets, and verify the relationship between these high proportion of genes and survival and therapeutic response of FH-RCC patients.
Further, according to the gene expression data of the single cells, performing dimension-reducing clustering on all single cells of each tissue sample to obtain a plurality of cell subsets, including:
and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
It should be noted that, gene expression data, which reflect the abundance of mRNA of gene transcription products in cells measured directly or indirectly, can be used to analyze which genes are changed in expression, how the activities of genes are affected under different conditions, and has important applications in medical clinical diagnosis, drug efficacy judgment, revealing disease occurrence mechanisms, etc.
Dimension reduction is the simplification of complex high-dimensional data information into lower-dimensional data information which is easier to read; clustering is classification, and classification is performed according to a certain standard. After the dimension-reduced data is obtained, the cells can be clustered according to a clustering algorithm, and a more visual effect is presented through a visual image.
The preset cellular gene markers are the cellular gene markers of the classical cell grouping which are reported in the prior art, are genes with known functions or known sequences and play a role of specific markers.
All cell dimensionality reduction clusters are divided into different cell populations by the gene expression profile of each cell. Combining with preset cell gene markers, matching with the expression condition of each cell population gene, and dividing all cells obtained from the sample into the following six cell subsets, namely T cells, marrow cells, endothelial cells, fibroblasts, B cells and epithelial cells.
Further, after the obtaining the plurality of cell subsets, the method comprises:
comparing the gene expression data of the cell subpopulations to obtain differential expression genes of the cell subpopulations;
and carrying out gene enrichment analysis on the differential expression genes to obtain the functional condition of the cell subpopulation.
It should be noted that, the gene enrichment analysis (Gene Set Enrichment Analysis, GSEA) is usually to analyze whether a group of genes is too much present at a certain functional node compared to a random level, and to read the biological knowledge represented behind a group of genes, and reveal what role it plays in cells or outside cells. Common gene enrichment analysis includes GOterm function enrichment, KEGG pathway enrichment, MSigDB gene set enrichment and other methods.
It can be understood that the differential expression genes between different cell subsets are obtained by comparing the gene expression data of the different cell subsets, and the functional condition of each cell subset is analyzed by carrying out gene enrichment analysis on the differential expression genes, searching for GO nodes or KEGG channels and the like which are remarkably enriched in the differential expression genes. Wherein the functional conditions of the cell subsets comprise inflammatory, immune, metabolic and other related functional conditions. In this example, the functional status of the cell subsets is not particularly limited.
Further, the method comprises:
and carrying out functional annotation on the cell subset according to the functional condition of the single cell subset.
The functional annotation of the cell subset is to make high-throughput annotation of the biological functions of all genes in the genome by using bioinformatics methods and tools. According to the functional conditions of the single cell subsets obtained through gene enrichment analysis and the like, the functional annotation of the cell subsets is realized, and the functional conditions of the cell subsets can be intuitively known.
Further, after determining the critical cell subpopulation of the FH-RCC disease, the method further comprises:
and interfering the key cell subset through an organoid platform and a PDX model, and determining a potential treatment target.
Organoids (organoids) refer to micro-organs that are produced by 3D culturing stem cells in vitro, resulting in "organ-like" patterns with self-renewing and self-organizing capabilities, and that are highly similar in structure and properties to the tissue or organ from which they were derived. Organoid platforms are platforms that better mimic the tumor of a patient through organoids, and can give rise to more holes in the patient's cancer that are expected to translate into individualized anti-cancer therapies.
The PDX model (patent-Derived Tumor Xenograft) is a humanized tissue xenograft model, is a tumor model constructed by transplanting tumor tissues of a tumor Patient into a severe immunodeficiency type mouse (NSG) and enabling the tumor tissues to grow in the mouse, and can be used for rapidly measuring the treatment effect of different medicines on the tumor in vivo one by one according to the specificity and individual variability of the Patient, so that references are provided for reasonable medicines in later stages, and the success rate of treatment is improved.
The target point is the lesion part where the radioactive rays are irradiated from different directions and collected when certain radiotherapy is carried out in medicine.
It should be noted that, by using the organoid platform and the PDX model, the intervention is performed on the critical cell subset, the influence of the markers on the tumor growth is found, the treatment prospect is explored, and the potential treatment targets are provided, so that the evaluation of the treatment scheme of the patient is facilitated.
Referring to fig. 2, fig. 2 is a schematic diagram of an apparatus for exploring FH-deficient kidney cancer key cell subsets according to an embodiment of the present invention, the apparatus for exploring FH-deficient kidney cancer key cell subsets may include:
the transcriptome sequencing module 21 is configured to perform sequencing detection on a plurality of tissue samples of FH-RCC diseases based on single-cell transcriptome sequencing, so as to obtain gene expression data of single cells in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
the clustering and grouping module 22 is configured to perform dimension-reduction clustering and grouping on all single cells of each tissue sample according to the gene expression data of the single cells, so as to obtain a plurality of cell subsets; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
a proportion comparison module 23 for comparing the proportion of the same cell subsets in the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample, and determining the cell subset with the increased proportion as a suspected critical cell subset;
the critical cell subset determining module 24 is configured to obtain the interaction probability between the cell subsets based on the cell communication analysis, and determine the cell subset with the highest interaction probability of the suspected critical cell subsets as the critical cell subset of the FH-RCC disease.
Further, the cluster grouping module 22 is specifically configured to: and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
Further, the apparatus comprises:
and the function annotation module is used for carrying out function annotation on the cell subset according to the function condition of the single cell subset.
The specific manner in which each of the above embodiments of the device for exploring a subpopulation of critical cells for FH-deficient kidney cancer performs has been described in detail in the context of the above-described embodiments of the related methods, and will not be described in detail herein.
Referring to fig. 3, fig. 3 is a schematic diagram of an apparatus for exploring FH-deficient kidney cancer key cell subsets according to the present invention, comprising:
one or more memories 31 on which executable programs are stored;
one or more processors 32 for executing the executable programs in the memory 31 to implement the steps of any of the methods described above.
The specific manner in which processor 32 executes the program in memory 31 for one of the above embodiments of the apparatus for exploring a subpopulation of critical cells for FH-deficient renal cancer has been described in detail in connection with the embodiments of the method and will not be described in detail herein.
It is to be understood that the same or similar parts in the above embodiments may be referred to each other, and that in some embodiments, the same or similar parts in other embodiments may be referred to.
It should be noted that in the description of the present invention, the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. Furthermore, in the description of the present invention, unless otherwise indicated, the meaning of "plurality", "multiple" means at least two.
It will be understood that when an element is referred to as being "mounted" or "disposed" on another element, it can be directly on the other element or intervening elements may also be present; when an element is referred to as being "connected" to another element, it can be directly connected to the other element or intervening elements may be present, and further, as used herein, connection may comprise a wireless connection; the use of the term "and/or" includes any and all combinations of one or more of the associated listed items.
Any process or method description in a flowchart or otherwise described herein may be understood as: means, segments, or portions of code representing executable instructions including one or more steps for implementing specific logical functions or processes are included in the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including in a substantially simultaneous manner or in an inverse order, depending upon the function involved, as would be understood by those skilled in the art of embodiments of the present invention.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like.
Those of ordinary skill in the art will appreciate that all or a portion of the steps carried out in the method of the above-described embodiments may be implemented by a program to instruct related hardware, where the program may be stored in a computer readable storage medium, and where the program, when executed, includes one or a combination of the steps of the method embodiments.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing module, or each unit may exist alone physically, or two or more units may be integrated in one module. The integrated modules may be implemented in hardware or in software functional modules. The integrated modules may also be stored in a computer readable storage medium if implemented in the form of software functional modules and sold or used as a stand-alone product.
The above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, or the like.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (9)
1. A method of exploring a subpopulation of FH-deficient kidney cancer key cells comprising:
sequencing and detecting tissue samples of a plurality of FH-RCC diseases based on single-cell transcriptome sequencing to obtain single-cell gene expression data in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
according to the gene expression data of the single cells, performing dimension reduction clustering on all single cells of each tissue sample to obtain a plurality of cell subgroups; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
comparing the proportion of the same cell subpopulations in the tumor surrounding normal tissue sample, the tumor primary foci tissue sample and the tumor metastasis focus tissue sample, and determining the cell subpopulations with the increased proportion as suspected critical cell subpopulations;
based on cell communication analysis, obtaining interaction probability among cell subsets, and determining the cell subset with the highest interaction probability among the suspected critical cell subsets as the critical cell subset of the FH-RCC disease;
wherein said comparing the proportion of the same cell subpopulation in the tumor surrounding normal tissue sample, the tumor primary lesion tissue sample and the tumor metastasis lesion tissue sample, determining the cell subpopulation with the increased proportion as a suspected critical cell subpopulation comprises:
obtaining the proportion K (i, j) of the cell subgroup i in the sample j by counting the number of single cells;
wherein i=a, b, c, d, e, f; a is a T cell, B is a myeloid cell, c is an endothelial cell, d is a fibroblast, e is a B cell, and f is an epithelial cell; j=1, 2,3, j=1 is expressed as the tumor surrounding normal tissue sample, j=2 is expressed as the tumor primary lesion tissue sample, and j=3 is expressed as the tumor metastasis lesion tissue sample;
comparing the ratios K (i, 1), K (i, 2), K (i, 3) of the cell subpopulations i in the sample j, and if K (i, 2) > K (i, 1), K (i, 3) > K (i, 2), determining the cell subpopulation i as the suspected critical cell subpopulation.
2. The method of claim 1, wherein said clustering all single cells of each tissue sample into clusters based on said single cell gene expression data to obtain a plurality of cell subsets, comprising:
and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
3. The method of claim 2, wherein after the obtaining a plurality of cell subsets, the method comprises:
comparing the gene expression data of the cell subpopulations to obtain differential expression genes of the cell subpopulations;
and carrying out gene enrichment analysis on the differential expression genes to obtain the functional condition of the cell subpopulation.
4. A method according to claim 3, characterized in that the method comprises:
and carrying out functional annotation on the cell subpopulation according to the functional condition of the cell subpopulation.
5. The method of claim 1, wherein after determining the critical cell subpopulation of the FH-RCC disease, the method further comprises:
and interfering the key cell subset through an organoid platform and a PDX model, and determining a potential treatment target.
6. A device for exploring a subpopulation of FH-deficient kidney cancer key cells, comprising:
the transcriptome sequencing module is used for sequencing and detecting tissue samples of a plurality of FH-RCC diseases based on single-cell transcriptome sequencing to obtain single-cell gene expression data in each tissue sample; wherein, the types of tissue samples of FH-RCC disease include: a tumor surrounding normal tissue sample, a tumor primary lesion tissue sample, and a tumor metastasis lesion tissue sample;
the clustering and grouping module is used for performing dimension reduction clustering and grouping on all single cells of each tissue sample according to the gene expression data of the single cells to obtain a plurality of cell subgroups; wherein the cell subpopulation comprises at least one of the following types: t cells, myeloid cells, endothelial cells, fibroblasts, B cells and epithelial cells;
a proportion comparison module for comparing the proportion of the same cell subsets in the tumor surrounding normal tissue sample, the tumor primary focus tissue sample and the tumor metastasis focus tissue sample, and determining the cell subset with the increased proportion as a suspected critical cell subset;
the critical cell subset determining module is used for obtaining the interaction probability among cell subsets based on cell communication analysis, and determining the cell subset with the highest interaction probability in the suspected critical cell subsets as the critical cell subset of the FH-RCC disease;
the proportion comparison module is specifically used for:
obtaining the proportion K (i, j) of the cell subgroup i in the sample j by counting the number of single cells;
wherein i=a, b, c, d, e, f; a is a T cell, B is a myeloid cell, c is an endothelial cell, d is a fibroblast, e is a B cell, and f is an epithelial cell; j=1, 2,3, j=1 is expressed as the tumor surrounding normal tissue sample, j=2 is expressed as the tumor primary lesion tissue sample, and j=3 is expressed as the tumor metastasis lesion tissue sample;
comparing the ratios K (i, 1), K (i, 2), K (i, 3) of the cell subpopulations i in the sample j, and if K (i, 2) > K (i, 1), K (i, 3) > K (i, 2), determining the cell subpopulation i as the suspected critical cell subpopulation.
7. The apparatus of claim 6, wherein the cluster grouping module is specifically configured to: and performing dimension-reducing cluster analysis on all cells according to the gene expression data of the single cells, and matching with a preset cell gene marker to obtain a plurality of cell subgroups.
8. The apparatus of claim 6, wherein the apparatus comprises:
and the function annotation module is used for carrying out function annotation on the cell subset according to the function condition of the cell subset.
9. An apparatus for exploring a subpopulation of FH-deficient kidney cancer key cells, comprising:
one or more memories having executable programs stored thereon;
one or more processors configured to execute the executable program in the memory to implement the steps of the method of any one of claims 1-5.
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