A method, system, device, and medium for constructing a multi-species cell-integrated gene expression profile

Abstract

The application discloses a kind of methods, systems, equipment and media for constructing multi-species cell-integrated gene expression profile, belong to bioinformatics technical field.The method comprises: the single-cell transcriptome sequencing data of PDX model is compared with mixed genome library, and cell-gene expression profile matrix is obtained;According to the proportion of human or mouse genes, the cell is identified as human cell, mouse cell or double cell;The single-cell transcriptome sequencing data is compared with the integrated gene sequence set of human and mouse homologous genes, and the alignment result with integrated gene is obtained;Again based on the barcode of the identified human cell and mouse cell, cell-integrated gene expression profile is obtained from the obtained alignment result, and the interaction mechanism of human and mouse cells can be known further through cell clustering analysis.

Description

[0001] Related patents

[0002] This application is a divisional application of Chinese invention patent application No. 2023100214777, filed on January 7, 2023, entitled "Method, System, Device and Medium for Single-Cell Transcriptome Data Analysis Based on PDX". Technical Field

[0003] This invention belongs to the field of biological data processing technology, specifically, it relates to a method, system, device and medium for constructing multi-species cell-integrated gene expression profiles. Background Technology

[0004] Patient-derived tumor xenograft (PDX) models are tumor models constructed by transplanting tumor tissue or primary cells derived from a patient into NSG (immunodeficial globulin) mice. Because the tumor tissue is directly transplanted into NSG mice without any artificial culture, this model retains most of the characteristics of primary tumors at the histopathological, molecular biological, and genetic levels, resulting in a higher degree of similarity to clinical tumors. PDX models are currently the closest tumor models to clinical samples, and such models are of great significance for clinical tumor assessment, treatment, and prognosis.

[0005] Using PDX models for single-cell transcriptome sequencing allows for in-depth analysis of cell types and gene expression characteristics at different stages of tumor development, thus providing guidance for tumor treatment. Currently, while the official 10× analysis software CellRanger can analyze data from multiple species pools of PDX models and obtain corresponding expression profile matrices and clustering results, the existence of numerous homologous genes between humans and mice means that even after analyzing human cells through a mixed human-mouse genome, some reads will still align with the mouse genome. This leads to inaccurate cell clustering results and downstream data mining results based directly on this expression profile. Furthermore, due to the homologous sequences between the human and mouse genomes, sequences aligned with the human genome cannot be directly used for analysis, potentially causing some mouse cells to be incorrectly identified as human cells.

[0006] In addition, in the PDX model study, in addition to the need to study the differences in gene expression changes of human-related tumor cells under different treatment schemes to find corresponding drug treatment targets, etc., it is also necessary to study how the cells in the mouse body interact with the introduced human cells. Therefore, how to separate the cell gene expression profiles of a single species from the cell sequencing data obtained from the PDX model and how to obtain the cell gene expression profiles of multiple species are crucial. However, there is currently no method for systematic analysis of single-cell transcriptome based on PDX model. SUMMARY

[0007] In order to solve at least one of the above technical problems, the technical scheme adopted by the present application is as follows:

[0008] The first aspect of the present application provides a method for analyzing single-cell transcriptome data based on PDX, comprising the following steps:

[0009] S1, aligning the obtained single-cell transcriptome sequencing data of the PDX model with a mixed genome library of human and mouse to obtain a cell-gene expression profile matrix based on the mixed genome library, wherein the mixed genome library is obtained by merging the reference genome files and gene annotation files of human and mouse;

[0010] S2, identifying the cells as human cells, mouse cells or double cells according to whether the proportion of human genes expressed in the cells or the proportion of mouse genes expressed in the cells is greater than or equal to a first preset threshold P1;

[0011] S3, based on the barcode of the cells, extracting the sequences identified as human cells and the sequences identified as mouse cells from the single-cell transcriptome sequencing data;

[0012] S4, comparing the obtained sequences of human cells with the reference genome of human and comparing the obtained sequences of mouse cells with the reference genome of mouse to obtain the corresponding cell-gene expression profile,

[0013] wherein the setting of P1 makes the difference between the double cell rate and half of the multiple cell rate not more than 5%, and the double cell rate and the multiple cell rate are calculated by the following formula:

[0014] Double cell rate = (number of double cells / (number of human cells + number of mouse cells + number of double cells)) x 100%;

[0015] Multiple cell rate = (captured cell number x 7.589 x 10 -6 + 5.272 x 10 -4 ) x 100%.

[0016] Since the multi-cell rate includes the mixture of human and human cells and mouse and mouse cells in addition to the double-cell (mixing of human and mouse cells), theoretically, the double-cell rate is equal to half of the multi-cell rate. In the present application, the double-cell rate differs from half of the multi-cell rate by no more than 5%, which means that |(double-cell rate-multi-cell rate / 2)| / (multi-cell rate / 2) x 100%≤5%. If P1 is set too high, too many cells will be judged as double cells, which is not consistent with the actual situation.

[0017] In some embodiments of the present application, P1=70%.

[0018] In some embodiments of the present application, in step S1, when the human and mouse genome files and gene annotation files are combined, in order to avoid gene and chromosome duplication, specific tags are added before the gene ID, gene name and chromosome respectively to distinguish them. For example, "human" is added before the gene ID, gene name and chromosome of human, and "mouse" is added before the gene ID, gene name and chromosome of mouse. Further, the library file that can be used for alignment is generated based on the combined genome file and gene annotation file.

[0019] In some embodiments of the present application, step S2 specifically comprises:

[0020] S21, counting the number of human genes expressed in the cell Nh and the number of mouse genes expressed in the cell Nm;

[0021] S22, calculating the proportion of human genes expressed in the cell Ph and the proportion of mouse genes expressed in the cell Pm, wherein Ph=Nh / (Nh+Nm) and Pm=Nm / (Nh+Nm);

[0022] S23, if Ph is greater than or equal to the first preset threshold P1, the cell is identified as a human cell, and if Pm is greater than or equal to the first preset threshold P1, the cell is identified as a mouse cell.

[0023] In some embodiments of the present application, for the cell that neither satisfies Ph greater than or equal to the first preset threshold P1 nor satisfies Pm greater than or equal to the first preset threshold P1, it is determined as a double cell, i.e. a cell that has both human gene expression and mouse gene expression.

[0024] barcode also called index, i.e. bar code or label, is usually used in sequencing technology to distinguish different sources of sequences. In the present application, barcode is used to distinguish different cells, i.e. sequencing sequences with the same barcode in the sequencing result mean from the same cell, so that different barcodes can represent different cells. In some descriptions of the present application, barcode and cell have the same meaning.

[0025] In some embodiments of the present application, in step S3, the sequence identified as human cell and the sequence identified as mouse cell specifically include:

[0026] S31, identifying the barcode of the sequencing sequence, comparing with the barcode of the cell, obtaining a base matching coefficient Mi, Mi=Lm / Lb, wherein Lm is the number of bases of the sequencing sequence barcode matched with the cell barcode, and Lb is the number of bases of the cell barcode;

[0027] S32, sequence extraction according to Mi and a second preset threshold P2:

[0028] If Mi=100%, the corresponding sequence is directly extracted; if P2≤Mi<100%, and the sequencing quality value of the sequencing reads corresponding to the base that is not completely matched is <10, the sequencing reads are corrected to the correct base and then the sequence is extracted; if Mi

[0029] Wherein, P2≥80%.

[0030] After the human cells and mouse cells are identified by step S2, the corresponding cell-gene expression profile of the human cells or mouse cells can also be obtained according to the alignment results of step S1. However, the alignment of step S1 is based on the alignment of the mixed genome library, and each cell's gene expression profile may not be accurate due to the influence of homologous genes. Therefore, based on step S3, the original sequencing data from human and mouse cells can be further aligned with the reference genome of human and mouse respectively to obtain the correct cell-gene expression profile matrix of human or mouse.

[0031] Different sequencing platforms usually have different barcode lengths, for example, the barcode length of Illumina 10x single cell sequencing platform is 16, and the barcode length of the MoD platform is 28. Different P2 values are selected according to different barcode lengths, and the standard for selection is usually to accept only 1-2 base mismatches. In some embodiments of the present application, the barcode length is 16, and P2 is set to 90%, allowing only 1 base to be unmatched.

[0032] In some embodiments of the present application, in step S32, the correction of the sequencing reads to the correct base means that the base with a sequencing quality value <10 in the barcode of the sequencing sequence is corrected to the base at the corresponding position of the matched cell barcode. If the sequencing quality is ≥10, it cannot be corrected, and this sequencing read does not belong to this cell and should be extracted using other barcodes or discarded.

[0033] In some embodiments of the present application, in step S4, only genome alignment and UMI correction are needed to construct the cell-gene expression profile matrix, without the need for further cell recognition process.

[0034] In some embodiments of the present application, after step S2, the following steps are performed:

[0035] S3', aligning the single-cell transcriptome sequencing data with the integrated gene sequence set of human and mouse homologous genes to obtain alignment results of the integrated genes;

[0036] S4', obtaining the cell-integrated gene expression profile from the alignment results obtained in step S3' based on the human and mouse cell barcodes identified in step S2,

[0037] wherein the integrated gene sequence set of the homologous genes is obtained based on the following steps:

[0038] (1) splicing the sequence of each homologous gene of human and mouse to obtain the integrated gene sequence of each homologous gene; 60-100 N bases are used to fill between the gene sequences of human and mouse;

[0039] (2) constructing the corresponding annotation file according to the integrated gene sequence: taking the homologous genes as an independent chromosome, and taking the homologous gene sequences from human and mouse as two transcripts.

[0040] In some embodiments of the present application, 60-100 N bases are set in the integrated gene sequence to add species information in the sorted annotation file, so as to facilitate subsequent read alignment filtering. In some specific embodiments of the present application, 80 N bases are set in the integrated gene sequence. That is, 80 N bases are inserted between the homologous gene sequence from human and the homologous gene sequence from mouse in one integrated gene sequence. On the other hand, by designing N bases, it is also possible to prevent reads from being aligned across species in the subsequent alignment process, i.e., part of a read is aligned to the homologous gene of human and part of the read is aligned to the homologous gene of mouse.

[0041] In some embodiments of the present application, in steps S3' and S4', the step of filtering the alignment results is further included:

[0042] If only one position of the last integrated gene is aligned, the corresponding alignment information is retained and one alignment information is extracted; if multiple positions of the same integrated gene are aligned, only one of the alignment information is retained; if different integrated genes are aligned, the corresponding alignment information is filtered.

[0043] The second aspect of the present application provides a single-cell transcriptome data analysis system based on PDX, comprising:

[0044] a data input module configured to obtain single-cell transcriptome sequencing data of a PDX model;

[0045] a database storage module configured to store a human reference genome, a mouse reference genome, and a mixed genome library of humans and mice, wherein the mixed genome library is obtained by merging the reference genome files and gene annotation files of humans and mice;

[0046] a first alignment module connected to the data input module and the database storage module, and configured to align the single-cell transcriptome sequencing data to the mixed genome library;

[0047] a cell recognition module connected to the first alignment module, and configured to recognize a cell as a human cell, a mouse cell, or a double cell according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to a first preset threshold P1;

[0048] a sequence acquisition module connected to the cell recognition module and the data input module, and configured to extract sequences of the human cells and sequences of the mouse cells from the single-cell transcriptome sequencing data based on the barcodes of the cells;

[0049] a cell-gene expression profile construction module connected to the sequence acquisition module and the database storage module, and configured to compare the sequences of the human cells to the human reference genome, and align the sequences of the mouse cells to the mouse reference genome, to obtain corresponding cell-gene expression profiles,

[0050] wherein the P1 is set such that the double cell rate differs from half of the multiple cell rate by no more than 5%, and the double cell rate and the multiple cell rate are calculated by the following formulae:

[0051] double cell rate = (number of double cells / (number of human cells + number of mouse cells + number of double cells)) x 100%;

[0052] multiple cell rate = (number of captured cells x 7.589 x 10 -6 + 5.272 x 10 -4 ) x 100%.

[0053] Since the multiple cell rate includes the mixture of human and human cells and mouse and mouse cells in addition to the double cells (the mixture of human and mouse cells), theoretically, the double cell rate is equal to 1 / 2 of the multiple cell rate. In the present application, the double cell rate differs from half of the multiple cell rate by no more than 5%, which means that |(double cell rate - multiple cell rate / 2)| / (multiple cell rate / 2) x 100% ≤ 5%.

[0054] In some embodiments of the present application, P1 = 70%.

[0055] In some embodiments of the present application, in order to avoid gene and chromosome duplication, specific tags are added in front of gene ID, gene name and chromosome respectively when merging the genome file and gene annotation file of human and mouse in the database storage module. For example, "human" is added in front of gene ID, gene name and chromosome of human, and "mouse" is added in front of gene ID, gene name and chromosome of mouse. Further, the library file which can be used for alignment is generated based on the merged genome file and gene annotation file.

[0056] In some embodiments of the present application, the cell recognition module recognizes cells based on and step:

[0057] The number of human genes expressed in the cell Nh and the number of mouse genes expressed in the cell Nm are counted;

[0058] The proportion of human genes expressed in the cell Ph and the proportion of mouse genes expressed in the cell Pm are calculated, wherein Ph = Nh / (Nh+Nm), and Pm = Nm / (Nh+Nm);

[0059] If Ph is greater than or equal to a first preset threshold P1, the cell is recognized as a human cell, if Pm is greater than or equal to a first preset threshold P1, the cell is recognized as a mouse cell, and the rest are double cells.

[0060] In some embodiments of the present application, for the cells that neither satisfy Ph greater than or equal to the first preset threshold P1 nor satisfy Pm greater than or equal to the first preset threshold P1, they are determined as double cells, i.e. cells that have both human gene expression and mouse gene expression.

[0061] In some embodiments of the present application, the sequence acquisition module extracts the sequences recognized as human cells and the sequences recognized as mouse cells by the following steps:

[0062] The barcode of the sequencing sequence is recognized, and compared with the barcode of the cell to obtain a base matching coefficient Mi, Mi = Lm / Lb, wherein Lm is the number of base pairs matched between the barcode of the sequencing sequence and the barcode of the cell, and Lb is the number of base pairs of the barcode of the cell;

[0063] According to Mi and a second preset threshold P2, the sequence is extracted:

[0064] If Mi = 100%, the corresponding sequence is directly extracted; if P2≤Mi<100% and the sequencing quality value of the base corresponding to the sequencing reads that do not match completely is <10, the sequence is extracted after the sequencing reads are corrected to the correct base; if Mi

[0065] wherein P2≥80%.

[0066] In some embodiments of the present application, the database storage module is further used for storing the integrated gene sequence set of human and mouse homologous genes, and the system further comprises or does not comprise the sequence acquisition module and the cell-gene expression profile construction module but comprises:

[0067] a second alignment module connected with the data input module and the database storage module respectively, used for aligning the single-cell transcriptome sequencing data with the integrated gene sequence set of human and mouse homologous genes to obtain the alignment result of the integrated gene;

[0068] a cell-integrated gene expression profile construction module connected with the cell recognition module and the second alignment module respectively, used for obtaining the cell-integrated gene expression profile from the alignment result obtained by the second alignment module based on the human cell and mouse cell barcode recognized by the cell recognition module.

[0069] wherein the integrated gene sequence set of homologous genes is obtained based on the following steps:

[0070] (1) splicing the sequence of each homologous gene of human and mouse to obtain the integrated gene sequence of each homologous gene; 60-100 N bases are used to fill between the gene sequences of human and mouse;

[0071] (2) constructing the corresponding annotation file according to the integrated gene sequence: taking the homologous gene as an independent chromosome and taking the homologous gene sequences from human and mouse as two transcripts.

[0072] The third aspect of the present application provides a computer device, comprising:

[0073] a memory for storing a computer program;

[0074] a processor for executing the computer program to realize the steps of the PDX-based single-cell transcriptome data analysis method according to any one of the first aspect of the present application.

[0075] The fourth aspect of the present application provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the PDX-based single-cell transcriptome data analysis method according to any one of the first aspect of the present application.

[0076] Advantages of the present application

[0077] Compared with the prior art, the present application has the following advantages:

[0078] The method and system of the present application can construct the human and / or mouse cell-gene expression profiles of samples before and after treatment (such as drugs or other treatment methods) of PDS mouse models, and further can perform differential gene analysis and functional enrichment analysis, etc., to obtain more data and provide technical support for clinical treatment of cancer.

[0079] The method and system of the present application can obtain the expression profiles of cells-integrated genes, and can perform downstream cell clustering and differential gene searching analysis. The clustering results obtained by this method can cluster human and mouse cells together well, and can be used to obtain the interaction mechanism of human and mouse cells. BRIEF DESCRIPTION OF DRAWINGS

[0080] Figure 1 A diagram showing the relationship between the number of captured cells and the multi-cell rate.

[0081] Figure 2 A diagram showing the single-cell transcriptome sequencing results of the PDX model in Example 1 of the present application.

[0082] Figure 3 A diagram showing the clustering results based on mixed library analysis in Example 1 of the present application.

[0083] Figure 4 A diagram showing the clustering analysis results based on extracted sequencing data of human cells in Example 1 of the present application.

[0084] Figure 5 A diagram showing the number of genes obtained based on mixed library analysis and extracted sequencing data of human cells in Example 1 of the present application.

[0085] Figure 6 A diagram showing the clustering results obtained based on the cell-gene expression profiles constructed based on mixed database.

[0086] Figure 7 A diagram showing part of the homologous gene information of human and mouse.

[0087] Figure 8 A diagram showing the sequence of mouse Cd3d gene.

[0088] Figure 9 A diagram showing the integrated gene sequence obtained by splicing the homologous Cd3d gene sequences of human and mouse.

[0089] Figure 10An annotation file of the integrated gene sequence spliced from human and mouse homologous Cd3d gene sequences is shown.

[0090] Figure 11 Results of cell-integrated gene expression profile matrix cluster analysis in Example 2 of the present application are shown.

[0091] Figure 12 A schematic diagram of a single-species cell-gene expression profile construction system in Example 3 of the present application is shown.

[0092] Figure 13 A schematic diagram of a multi-species cell-gene expression profile construction system in Example 4 of the present application is shown.

[0093] Figure 14 A schematic diagram of a single-cell transcriptome sequencing data analysis system for PDX models in Example 5 of the present application is shown. DETAILED DESCRIPTION

[0094] Unless otherwise indicated, all parts and percentages expressed herein are based upon weight, and all tests and measurements are conducted using methods contemporary with the filing date of this application. To the extent that any patent, patent application, or publication is cited in this application, the same is hereby incorporated by reference in its entirety into this application, and the equivalent thereof is intended to be expressly incorporated by reference into this application, particularly with respect to the definitions of terms used therein. If any definition contained in the prior art is inconsistent with any definition provided in this application, the definition provided in this application shall control.

[0095] In order to make the technical problems solved by the present application, technical solutions and beneficial effects clearer, the present application will be further described in detail below in combination with embodiments.

[0096] EMBODIMENTS

[0097] The following examples are presented to demonstrate preferred embodiments of the present application. Those skilled in the art will appreciate that the techniques disclosed in the following examples represent techniques that can be used to practice the present application, and thus can be considered to be preferred methods of practicing the present application. However, it is understood that those skilled in the art will be able to devise many other methods which, although not explicitly described herein, embody the principles of the invention and are thus within the spirit and scope of the present application.

[0098] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs, and the materials referred to in this disclosure are incorporated by reference in their entirety.

[0099] The experimental methods in the following examples are all conventional methods unless otherwise specified. The instruments and equipment used in the following examples are all conventional laboratory instruments and equipment unless otherwise specified. The test materials used in the following examples are all purchased from conventional biochemical reagent stores unless otherwise specified.

[0100] Example 1 Construction of cell-gene expression profile of single species based on single-cell transcriptome data of PDX model

[0101] This example provides a method for constructing a cell-gene expression profile of a single species based on single-cell transcriptome data of a PDX model, comprising the following steps:

[0102] 1. Obtain single-cell transcriptome data of PDX model

[0103] The target tissue is dissociated to obtain a single-cell suspension, then gel beads containing barcode information are combined with the mixture of cells and enzymes, and then wrapped by an oil surfactant droplet located in a microfluidic system to form GEMs (GelBeads-In-Emulsions). The gel beads in the GEMs dissolve, the cells are lysed to release mRNA, and barcoded cDNA for sequencing is generated by reverse transcription. After the liquid oil layer is destroyed, cDNA amplification, purification and library construction are performed. The completed library is sequenced to obtain single-cell transcriptome sequencing data.

[0104] 2. Obtain cell-gene expression profile based on mixed database of mice and humans

[0105] Construction of mixed genome library of humans and mice: merge the genome files (GRCh38 and GRCm39) and gene annotation files (ensemble V105 version: Homo_sapiens.GRCh38.105.chr.gtf.gz, Mus_musculus.GRCm39.105.chr.gtf.gz) of humans and mice. To avoid gene and chromosome duplication, respectively, add "human" and "mouse" before gene ID, gene name, and chromosome to distinguish. Then, based on the merged genome file and gene annotation file, use the cellranger software to generate a library file that can be used for star alignment.

[0106] For the single-cell transcriptome data of the PDX model obtained in step 1, the following processes are performed using the cellranger software:

[0107] (1) barcode extraction correction

[0108] The sequence of the first 16 bp of the R1 end of sequencing is compared with the 10x whitelist. If the sequence comes from the whitelist, the corresponding paired-end reads are retained. If the sequencing reads only have 1 base that is different from the whitelist, and the sequencing base quality value at the different position is <10, the incorrectly matched base is modified according to the whitelist and the reads are retained. Otherwise, the reads are not included in the subsequent expression matrix calculation.

[0109] (2) Sequence genome alignment

[0110] The sequenced R2 reads are aligned with the reference genome using star software to obtain alignment information, and only reads that uniquely match the positive transcript are retained and included in the barcode-gene expression matrix statistics.

[0111] (3) Cell recognition

[0112] According to the barcode-gene expression matrix, the UMI count of each barcode is counted and sorted from high to low. For high RNA content cell recognition, for example, if the UMI count of the barcode is greater than 1 / 10 of the UMI count value of the 101st barcode, it is considered to be a cell. Some low RNA content cells may not meet the above recognition conditions, and a low expression barcode is selected as a background. The gene expression of the barcode is counted, and the gene expression of the barcode corresponding to the first step is compared with the background. If the gene expression is consistent, it is considered to be background, otherwise it is considered to be a cell.

[0113] Thus, a cell-gene expression matrix based on a mixed genome library is obtained.

[0114] The specific expression profile data format is as follows:

[0115] Gene barcode 1 barcode 2 barcode 3 barcode 4 …… barcode n human_TNFRSF4 0 1 0 0 …… 2 human_CD3E 1 0 0 3 …… 0 …… …… …… …… …… …… …… mouse_Gm28417 0 0 1 0 0

[0116] 3. Identification of human and mouse cells

[0117] According to the gene name prefix, the number of human and mouse genes expressed by each cell (barcode) is counted, respectively denoted as Nh and Nm. The proportion of human and mouse genes in each barcode is calculated, respectively denoted as Ph and Pm, wherein Ph=Nh / (Nh+Nm), Pm= Nm / (Nh+Nm).

[0118] Since the alignment is performed using a mixed genome library of human and mouse, and there are a large number of homologous genes between human and mouse, even if the cell is from human, it can also express a small amount of mouse gene. If the Phof a certain barcode is greater than 70%, the barcode is from a human cell, if the Pmof a certain barcode is greater than 70%, the barcode is from a mouse cell, and the remaining barcode is considered to be a double cell. The "70%" here is the threshold set for identifying the barcode from human or mouse cells.

[0119] In the 10x single cell sequencing process, the multi-cell rate is related to the number of captured cells, as shown in Figure 1 The relationship between the multi-cell rate and the number of captured cells is fitted as follows:

[0120] Multi-cell rate = (number of captured cells x 7.589 x 10 -6 + 5.272 x 10 -4) x 100%

[0121] However, in the above multi-cell, in addition to the human and mouse cells mixed in one oil droplet (double cell), there are also human and human cells, mouse and mouse cells mixed together. Therefore, theoretically, the double cell rate should be half of the multi-cell rate, i.e. ((number of captured cells x 7.589 x 10 -6 + 5.272 x 10 -4 ) / 2). The inventors found that when the threshold is 70%, the double cell rate obtained is very close to half of the multi-cell rate.

[0122] The specific statistical table is shown in the following table (part of the display):

[0123] barcode Nh Nm Ph(%) Pm(%) Cell type AAACCTGAGCTAACAA 1871 61 96.84 3.16 Human cell AAACCTGAGTTGCAGG 645 42 93.89 6.11 Human cell AAGGCAGAGCACACAG 897 1284 41.13 58.87 Dual cell AAGGCAGGTATCTGCA 102 1053 8.83 91.17 Mouse cell ACGAGCCGTAGCCTAT 1082 192 84.93 15.07 Human cell …… …… …… …… …… …… TATGCCCCAGCATGAG 321 1673 16.10 83.90 Mouse cell TGCACCTTCAATCACG 1295 825 61.08 38.92 Dual cell

[0124] Limited by PDX models, sample sampling sites, sampling size, single cell dissociation, cell capture efficiency, etc., the actual analysis of different samples from human and mouse cells is not completely consistent. The following table shows the actual number of cells from human and mouse and the proportion from two different PDX samples:

[0125]

[0126] From the above table, the number of captured cells (number of human cells + number of mouse cells + number of double cells) of sample 1 is 8565, and the multi-cell rate is calculated as (8565 x 7.589 x 10 -6 + 5.272 x 10 -4) x 100% = 6.55%, when the threshold is set to 70%, the resulting double cell rate is 3.19%, which is only |3.19-3.28) / 3.28 x 100% = 2.7% different from half of the multi-cell rate 3.28%; the number of cells captured by sample 2 (the number of human cells + the number of mouse cells + the number of double cells) is a total of 8639, and the multi-cell rate is calculated to be (8636 x 7.589 x 10 -6 + 5.272 x 10 -4 ) x 100% = 6.61%, when the threshold is set to 70%, the resulting double cell rate is 3.25%, which is only |3.25-3.31) / 3.31 x 100% = 1.8% different from half of the multi-cell rate 3.31%. It can be seen that the above method is more accurate for identifying cells.

[0127] 4. Sequence extraction

[0128] The sequence information corresponding to the barcode from human and mouse cells determined above is extracted from the original sequencing data. According to the library structure of single-cell transcriptome, the barcode sequence comes from the first n bases of the R1 end of double-end sequencing (for example, for a 10x single-cell platform, n = 16, as shown in Figure 2 , the cell barcode of the domestic single-cell platform is longer, such as the cell barcode length of the MoBio platform is 28). The specific sequence extraction method is as follows:

[0129] 1) Calculate the base matching coefficient corresponding to each sequence: the length of the cell barcode is denoted as Lb, the length of the actually matched base is denoted as Lm, and the base matching coefficient is denoted as Mi, Mi = Lm / Lb.

[0130] 2) Considering that different single-cell platforms set different lengths of cell barcodes, the base matching coefficient threshold is set to 90%, that is, when the cell barcode is 16 bases, only 1 base is not allowed to match. When Mi = 100%, the corresponding reads pair is directly output. When 90% ≤ Mi < 100%, the sequencing quality value of the base of the sequencing reads that is not completely matched is calculated, and if the sequencing quality value of this position <10, it is considered that the mismatch is caused by sequencing error, and the sequencing reads is corrected to the correct base, that is, the corresponding reads pair is output after the sequencing reads is corrected to the barcode that has been identified as a cell.

[0131] 5. Re-obtain single-species cell-gene expression profile matrix

[0132] Although the expression profile matrix corresponding to the cells from the human can be directly obtained based on step 3, since the cell-gene expression profile is obtained based on the mixed library of the human and the mouse, and is affected by the homologous genes, the gene expression profile of each cell can not be accurate.

[0133] Therefore, based on the original data of the cells from the human and the mouse obtained in step 4, the inventors re-analyze the data by taking the reference genome of the human or the mouse as the library file, to obtain the correct cell-gene expression profile of the human and the mouse.

[0134] Since the sequences extracted in step 4 are all from the actual cells, unlike the case of normal single-cell transcriptome sequencing, in which there can be empty water-in-oil data, when generating the expression profile matrix, only genome alignment and UMI correction need to be performed, and no cell recognition needs to be performed. If cell recognition is performed, it is very likely that the same type of cells will be removed as background barcode, which will affect subsequent data mining.

[0135] By using the above method, for the PDX mouse model, the gene expression profile matrix of the cells from the human or the mouse in the sample before and after treatment can be obtained, and further data mining such as differential gene analysis and functional enrichment analysis can be performed.

[0136] Figure 3 The clustering results based on the mixed library analysis are shown, and it can be seen that the cells of the human and the mouse are divided into left and right sides.The clustering analysis results of the sequencing data corresponding to the cells from the human are shown in FIG. 4B. Figure 4

[0137] The differential gene results based on the mixed library analysis and the analysis of the original data extracted from the cells from the human are shown in the following table:

[0138]

[0139] It is found from the statistical results that there are many up-regulated differential genes based on the mixed library, and since the cells from the human and the cells from the mouse are present in the mixed library, the differential analysis based on all the cells can lead to too many differential genes. Therefore, only the 7 clusters 3, 4, 5, 8, 9, 10 and 11 from the human are subjected to differential analysis, and it is found that 5066 differential genes are found, of which 1209 differential genes are from the mouse genes, which is obviously a differential gene that does not conform to the actual situation due to the mixed library.

[0140] For the same barcode (from the cell from the human), using the mixed library analysis, due to the alignment to the mouse genome, the number of genes can be too high, as shown in the following table: Figure 5 The statistical results of the analysis of the reads extracted using the library of the human are shown in the lower part of the table.

[0141] Example 2 Multi-species cell-integrated gene expression profile construction

[0142] Example 1 is only for the analysis of cell expression profile of a single species, in practical application, it will be found that some PDX models only import tumor primary cells, if you want to know how the mouse immune cells play a role in tumor cells, and how the cells interact with each other, etc. Information, still need to generate expression profile matrix based on mixed data. Based on the cell-gene expression profile data of the mixed database of mice and humans obtained in step 1 of example 1, due to the different gene names between different species, when clustering cells, it will lead to the obvious division into two species groups (such as Figure 6 ).

[0143] For this situation, the following steps are used to obtain the corrected multi-species cell-gene expression profile matrix:

[0144] (1) Obtain the homologous gene information of human and mouse species from the Ensemble database, as shown in Figure 7 , the first two columns are human gene ID and gene name information, and the last two columns are the corresponding mouse gene ID and gene name information. There are 18973 homologous genes between human and mouse (based on human genes, there are 18973 gene IDs that can find homologous genes from mice).

[0145] (2) According to the gene chromosome position information and gene positive and negative chain information in the genome annotation file, the corresponding sequence information of each gene is extracted from the human and mouse genomes respectively. Take Cd3d gene as an example, the extracted sequence information is shown in Figure 8 .

[0146] (3) According to the homologous gene information obtained in step (1), the sequences are combined (two species genes are filled with 80 N bases, the purpose is to add species information in the sorted annotation file), and the integrated gene is obtained. Take Cd3d gene as an example, its integrated gene sequence is shown in Figure 9 .

[0147] (4) According to the integrated gene sequence, construct the corresponding annotation file (homologous genes as an independent chromosome, homologous genes from two species as two transcripts), take merge0000001 as an example, as shown in Figure 10 .

[0148] (5) Take the integrated gene sequence set as the reference genome to perform alignment analysis to obtain the alignment result of the integrated gene sequence.

[0149] (6) Alignment result filtering: for the alignment on the unique position of the previous integrated gene (and the sequence on the alignment only comes from the same species, i.e. without crossing N base alignment), the corresponding alignment information is retained, and the information of one read multiple alignment is extracted; if multiple positions of the same integrated gene are aligned (and the multiple positions on the alignment all come from the same species, i.e. without crossing N base alignment), one of the alignment information is retained; if different integrated genes are aligned, the corresponding alignment information is filtered out. In this step, about 40% of the alignment information is obtained.

[0150] (7) The barcode information of human and mouse obtained according to embodiment 1 (remove double cell information) is obtained from the filtered alignment information obtained in step (6) to obtain the cell-integrated gene expression profile matrix.

[0151] Based on the cell-integrated gene expression profile matrix, downstream cell clustering and differential gene finding analysis can be performed. Through this way, the clustering results of human and mouse cells can be well clustered together (as shown in Figure 11 ), which can be used to obtain the interaction mechanism of human and mouse cells.

[0152] Embodiment 3: Construction system of cell-gene expression profile of single species

[0153] This embodiment provides a system for constructing cell-gene expression profile of single species based on single cell transcriptome data of PDX model, as shown in Figure 12 , comprising:

[0154] a data input module for obtaining single cell transcriptome sequencing data of PDX model;

[0155] a database storage module for storing human reference genome, mouse reference genome and mixed genome library of human and mouse, wherein the mixed genome library is obtained by merging the reference genome files and gene annotation files of human and mouse, and the method is described in embodiment 1;

[0156] a first alignment module connected with the data input module and the database storage module, for aligning the single cell transcriptome sequencing data with the mixed genome library;

[0157] a cell recognition module connected with the first alignment module, for recognizing the cell as a human cell or a mouse cell according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to 70%;

[0158] a sequence acquisition module connected with the cell recognition module and the data input module, for extracting the sequence of the recognized human cell and the sequence of the recognized mouse cell from the single cell transcriptome sequencing data based on the barcode of the cell;

[0159] A cell-gene expression profile construction module connected with the sequence acquisition module and the database storage module respectively, for comparing the obtained sequence of human cells with the human reference genome, and comparing the obtained sequence of mouse cells with the mouse reference genome, to obtain the corresponding cell-gene expression profile.

[0160] Example 4 Multi-species cell-integrated gene expression profile construction system

[0161] This embodiment provides a multi-species cell-integrated gene expression profile construction system based on Example 3, as shown in Figure 13 , comprising:

[0162] A data input module for obtaining single-cell transcriptome sequencing data of a PDX model;

[0163] A database storage module for storing a human reference genome, a mouse reference genome, and a mixed genome library of human and mouse, wherein the mixed genome library is obtained by merging the reference genome files and gene annotation files of human and mouse, and the method is as described in Example 1;

[0164] A first alignment module connected with the data input module and the database storage module respectively, for aligning the single-cell transcriptome sequencing data with the mixed genome library;

[0165] A cell recognition module connected with the first alignment module, for identifying the cell as a human cell, a mouse cell, or a double cell according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to 70%;

[0166] A second alignment module connected with the data input module and the database storage module respectively, for aligning the single-cell transcriptome sequencing data with the integrated gene sequence set of human and mouse homologous genes to obtain the alignment result of the integrated gene, wherein the construction method of the integrated gene sequence set is referred to Example 2.

[0167] A cell-integrated gene expression profile construction module connected with the cell recognition module and the second alignment module, for obtaining the cell-integrated gene expression profile from the alignment result obtained from the second alignment module based on the human cell and mouse cell barcode identified by the cell recognition module.

[0168] Example 5 Single-cell transcriptome sequencing data analysis system of PDX model

[0169] This embodiment provides a single-cell transcriptome sequencing data analysis system of PDX model, which combines the systems of Examples 3 and 4, as shown in Figure 14 , comprising:

[0170] a data input module, configured to obtain single-cell transcriptome sequencing data of a PDX model;

[0171] a database storage module, configured to store a human reference genome, a mouse reference genome, and a mixed genome library of human and mouse, wherein the mixed genome library is obtained by merging the reference genome files and gene annotation files of human and mouse, and the method is described in Embodiment 1;

[0172] a first alignment module, connected with the data input module and the database storage module respectively, configured to align the single-cell transcriptome sequencing data with the mixed genome library;

[0173] a cell recognition module, connected with the first alignment module, configured to recognize a cell as a human cell, a mouse cell or a double cell according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to 70%;

[0174] a sequence acquisition module, connected with the cell recognition module and the data input module respectively, configured to extract sequences recognized as human cells and sequences recognized as mouse cells from the single-cell transcriptome sequencing data based on the barcode of the cell;

[0175] a cell-gene expression profile construction module, connected with the sequence acquisition module and the database storage module respectively, configured to compare the sequences of human cells obtained with the human reference genome, and align the sequences of mouse cells obtained with the mouse reference genome, to obtain the corresponding cell-gene expression profile,

[0176] further comprising:

[0177] a second alignment module, connected with the data input module and the database storage module respectively, configured to align the single-cell transcriptome sequencing data with the integrated gene sequence set of human and mouse homologous genes to obtain the alignment result of the integrated gene, wherein the construction method of the integrated gene sequence set is referred to Embodiment 2.

[0178] a cell-integrated gene expression profile construction module, connected with the cell recognition module and the second alignment module respectively, configured to obtain the cell-integrated gene expression profile from the alignment result obtained by the second alignment module based on the barcode of human cells and mouse cells recognized by the cell recognition module.

[0179] All the documents mentioned in the present application are cited as references in the present application, as if each document is cited as a reference individually. In addition, it should be understood that those skilled in the art can make various modifications or additions to the present application after reading the above teaching of the present application, and these equivalent forms also fall within the scope defined by the claims of the present application.

Claims

1. A method of constructing a cell-integrated gene expression profile based on PDX-based single-cell transcriptome data, characterized by, The method comprises the following steps: S1, the single cell transcriptome sequencing data of the obtained PDX model is aligned with a mixed genome library of humans and mice to obtain a cell-gene expression profile matrix based on the mixed genome library, wherein the mixed genome library is obtained by merging reference genome files and gene annotation files of humans and mice; S2, according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to a first preset threshold P1, the cell is identified as a human cell, a mouse cell or a double cell; S3, aligning the single cell transcriptome sequencing data with an integrated gene sequence set of human and mouse homologous genes to obtain an alignment result of the integrated gene; S4, based on the human cells and mouse cells barcode identified in step S2, obtaining a cell-integrated gene expression profile from the alignment result obtained in step S3, wherein the integrated gene sequence set of the homologous genes is obtained based on the following steps: (1) splicing the sequence of each homologous gene of humans and mice to obtain an integrated gene sequence of each homologous gene; 60-100 N bases are used to fill between the gene sequences of humans and mice; According to the integrated gene sequence, the corresponding annotation file is constructed: the homologous genes are regarded as an independent chromosome, and the homologous gene sequences from humans and mice are regarded as two transcripts, wherein the setting of P1 makes the double cell rate and half of the multi-cell rate differ by no more than 5%, and the double cell rate and the multi-cell rate are calculated by the following formula: Double cell rate = (double cell number / (human cell number + mouse cell number + double cell number)) × 100%; Multicellularity rate = (number of cells captured x 7.589 x 10 -6 + 5.272 x 10 -4 ) x 100%.

2. The method of claim 1, wherein the PDX-based single-cell transcriptome data is used to construct a cell-integrated gene expression profile. Step S2 specifically comprises: S21, counting the number Nh of human genes expressed in the cell and the number Nm of mouse genes expressed in the cell; S22, calculating the proportion Ph of human genes expressed in the cell and the proportion Pm of mouse genes expressed in the cell, wherein Ph = Nh / (Nh + Nm), Pm = Nm / (Nh + Nm); S23, if Ph is greater than or equal to the first preset threshold P1, the cell is identified as a human cell, if Pm is greater than or equal to the first preset threshold P1, the cell is identified as a mouse cell, and the rest is a double cell.

3. The method of claim 1, wherein the PDX-based single-cell transcriptome data is used to construct a cell-integrated gene expression profile. In steps S3 and S4, a step of filtering the alignment result is further included: If only one position of the last integrated gene is aligned, the corresponding alignment information is retained, and one alignment information is extracted; if multiple positions of the same integrated gene are aligned, only one of the alignment information is retained; if different integrated genes are aligned, the corresponding alignment information is filtered.

4. A system for constructing cell-integrated gene expression profiles based on PDX-based single-cell transcriptome data, characterized by, It comprises: a data input module for obtaining single cell transcriptome sequencing data of a PDX model; a database storage module for storing human reference genomes, mouse reference genomes and a mixed genome library of humans and mice, wherein the mixed genome library is obtained by merging reference genome files and gene annotation files of humans and mice; a first alignment module connected with the data input module and the database storage module respectively, for aligning the single cell transcriptome sequencing data with the mixed genome library; The cell recognition module is connected with the first comparison module, and is configured to recognize the cell as a human cell or a mouse cell according to whether the proportion of human genes expressed in the cell or the proportion of mouse genes expressed in the cell is greater than or equal to a first preset threshold P1. The second comparison module is connected with the data input module and the database storage module respectively, and is configured to compare the single-cell transcriptome sequencing data with the integrated gene sequence set of human and mouse homologous genes to obtain an alignment result of the integrated gene, The cell-integrated gene expression profile construction module is connected with the cell recognition module and the second comparison module respectively, and is configured to obtain the cell-integrated gene expression profile from the alignment result obtained by the second comparison module based on the human cell barcode and the mouse cell barcode recognized by the cell recognition module. The P1 is set such that the difference between the double-cell rate and half of the multi-cell rate is not more than 5%, and the double-cell rate and the multi-cell rate are calculated by the following formula: Double-cell rate = (double-cell number / (human cell number + mouse cell number + double-cell number)) × 100%; Multicellularity rate = (number of cells captured x 7.589 x 10 -6 + 5.272 x 10 -4 ) x 100%.

5. A computer device, comprising: The memory is configured to store a computer program; The processor is configured to execute the computer program to implement the steps of the method for constructing a cell-integrated gene expression profile based on PDX single-cell transcriptome data according to any one of claims 1-3.

6. A computer readable storage medium, characterized in that, The computer readable storage medium stores a computer program, and the computer program is executed by a processor to implement the steps of the method for constructing a cell-integrated gene expression profile based on PDX single-cell transcriptome data according to any one of claims 1-3. ​

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