Identification method of stemness maintenance gene of hematopoietic stem cell and application thereof

Abstract

The invention provides an identification method of a stemness maintenance gene of hematopoietic stem cells and application of the identification method. The method for identifying the stemness maintenance gene of the hematopoietic stem cell comprises the following steps: performing scRNA sequencing on hematopoietic progenitor cells with different cell sources and different culture times; and analyzing scRNA sequencing data to obtain the stemness maintenance gene of the hematopoietic stem cells. The method fills the theoretical blank that the stemness maintenance gene of the hematopoietic stem cell cannot be researched in the prior art, and further provides theoretical support for clinical application of the stemness maintenance gene.

Description

Technical Field

[0001] The present invention relates to the technical field of gene identification, and in particular to a method for identifying a stemness-maintaining gene of a hematopoietic stem cell and its application. Background Art

[0002] Hematopoietic stem cells (HSCs) are a type of cell in the body that has the potential for self-renewal and multidirectional differentiation. HSCs can differentiate into all types of mature blood cells and are the original progenitor cells of blood cells in adult vertebrates. Their number and functional homeostasis are essential for maintaining the body's normal hematopoietic system and physiological functions. The generation, maintenance of stemness, differentiation, and self-renewal of hematopoietic stem cells are complexly and precisely regulated by numerous signaling pathways and regulatory factors, and cell fate selection is usually closely related to changes in gene expression.

[0003] Most of the research on the maintenance of hematopoietic stem cells in vitro is based on cell experiments, which consumes a lot of time, manpower and economic costs. The rapid development of single-cell sequencing technology in recent years has achieved the separation and labeling of single cells, filling the gaps of bulk RNA-seq and other research technologies, allowing us to analyze gene expression at single-cell resolution, thereby more accurately analyzing the transcriptome of tens of thousands of cells and deepening our understanding of the temporal and spatial differential expression of tissues. However, there is still a lack of systematic and comprehensive single-cell research on HSC stemness maintenance genes. Summary of the invention

[0004] The main purpose of the present invention is to provide a method for identifying stemness maintenance genes of hematopoietic stem cells and its application, so as to solve the problem of lack of methods for identifying stemness maintenance genes of hematopoietic stem cells in the prior art.

[0005] In order to achieve the above-mentioned purpose, according to the first aspect of the present invention, a method for identifying stemness-maintaining genes of hematopoietic stem cells is provided, characterized in that it includes: performing scRNA sequencing on hematopoietic progenitor cells of different cell sources and different culture times; analyzing the scRNA sequencing data to obtain the stemness-maintaining genes of hematopoietic stem cells.

[0006] Furthermore, the source of hematopoietic progenitor cells includes umbilical cord blood, peripheral blood or bone marrow; preferably, the hematopoietic progenitor cells include CD34+ cells; preferably, the CD34+ cells include CD34+CD38- cells; preferably, the different culture times refer to 0h and one or more culture time points in 0-96h; preferably, 0h and 48h, 0h and 72h or 0h and 96h.

[0007] Furthermore, scRNA sequencing includes: constructing single-cell transcriptome sequencing libraries of hematopoietic progenitor cells of different cell sources and different culture times, and sequencing each single-cell transcriptome sequencing library, preferably double-end sequencing, to obtain raw sequencing data; performing quality control on the raw data to obtain scRNA sequencing data that can be used for subsequent analysis. Preferably, the quality control includes: using cell barcode information and UMI information to remove sequencing data corresponding to cells below the first quality standard from the raw data of scRNA double-end sequencing to obtain a cell expression matrix; removing sequencing data corresponding to cells below the second quality standard from the cell expression matrix to obtain scRNA sequencing data; preferably, the first quality standard is that the number of genes is equal to (first quartile-interquartile range), or equal to (third quartile+interquartile range), and the average number of UMIs per gene is greater than or equal to 3; preferably, the second quality standard is selected from one or more of the following: 1) cells with abnormal gene numbers; 2) cells with a mitochondrial mRNA ratio higher than 5%; further preferably, cells with abnormal gene numbers are defined according to the following method: i) using Tukey's test method; ii) calculating the number of genes for each cell, and removing cells with gene numbers lower than (first quartile-interquartile range) or higher than (third quartile+interquartile range).

[0008] Furthermore, the scRNA sequencing data are analyzed to obtain stemness maintenance genes of hematopoietic stem cells, including: cell clustering of the scRNA sequencing data to obtain different cell clusters, including HSC cell clusters; analyzing the differentially expressed genes in different cell clusters to obtain genes differentially expressed in hematopoietic stem cells; among the differentially expressed genes in hematopoietic stem cells, differentially expressed gene analysis is performed according to different cell sources and different culture times to obtain stemness maintenance genes.

[0009] Furthermore, differentially expressed genes and functional enrichment analysis are performed on hematopoietic stem cells of different cell sources and different culture times, so as to obtain stemness maintenance genes related to cell sources and stemness maintenance genes related to culture time, respectively; among the genes differentially expressed in hematopoietic stem cells, the genes involved in the differentially expressed gene analysis according to different cell sources and different culture times need to meet the following conditions: 1) expressed in at least 20% of the cells and P<0.05; 2) the expression level fold change is more than 1.28 times; preferably, hematopoietic stem cells of different cell sources and different culture times include the following groups: 1) umbilical cord blood-derived: CD34+ uncultured and CD34+ in vitro cultured: 2) peripheral blood-derived: CD34+ uncultured and CD34+ in vitro cultured: 3) umbilical cord blood-derived CD34+ uncultured and peripheral blood-derived CD34+ uncultured.

[0010] Furthermore, obtaining stemness-maintaining genes associated with cell sources includes: extracting scRNA sequencing data corresponding to each hematopoietic stem cell in group 3); performing differentially expressed gene analysis according to different cell sources using the corresponding scRNA sequencing data; recording genes that are significantly highly expressed in hematopoietic stem cells derived from umbilical cord blood as stemness-maintaining genes associated with cell sources; wherein the expression level in hematopoietic stem cells derived from umbilical cord blood is recorded as E1, and the expression level in hematopoietic stem cells derived from peripheral blood is recorded as E2, and when E1 / E2≥1.28, it is defined as significantly highly expressed in hematopoietic stem cells derived from umbilical cord blood.

[0011] Furthermore, obtaining stemness-maintaining genes associated with culture time includes: extracting scRNA sequencing data corresponding to the hematopoietic stem cells of group 1) and group 2); using the corresponding scRNA sequencing data to perform differentially expressed gene analysis according to whether they are cultured; screening out genes that are significantly highly expressed in uncultured hematopoietic stem cells to obtain candidate genes; screening out genes that exist in both umbilical cord blood and peripheral blood from the candidate genes, and recording them as stemness-maintaining genes associated with culture time; wherein the expression level in uncultured hematopoietic stem cells is recorded as E3, and the expression level in hematopoietic stem cells from peripheral blood is recorded as E4, and when E3 / E4≥1.28, it is defined as significantly highly expressed in uncultured hematopoietic stem cells.

[0012] Furthermore, using the scRNA sequencing data of cells that meet the analysis process, all cells are subjected to unsupervised clustering to obtain different cell clusters; before performing unsupervised clustering, the method further includes normalizing the scRNA sequencing data and / or eliminating batch effects; preferably, after obtaining different cell clusters, the method further includes visualizing the different cell clusters; the visualization includes performing principal component analysis on the data after unsupervised clustering, and performing unified manifold approximation and dimensionality reduction projection processing; preferably, the data after unsupervised clustering is the cell-gene expression matrix data of scRNA-seq after quality control, normalization and / or elimination of batch effects; preferably, the data subjected to unified manifold approximation and dimensionality reduction projection processing is the data with the top 35-40 values ​​ranked in the principal component analysis.

[0013] Furthermore, while analyzing the differentially expressed genes in different cell clusters and obtaining the genes differentially expressed in hematopoietic stem cells, the method also includes at least one of the following: 1) constructing a cell development and differentiation trajectory; 2) characterizing the global transcriptome characteristics of hematopoietic stem cells; preferably, the differentially highly expressed marker genes of all cell types are used as input for the software for constructing the cell development trajectory, and all cells are sorted in pseudo time to construct the cell development and differentiation trajectory, wherein the expression level fold change of more than 1.28 times is defined as differentially high expression; preferably, the global transcriptome characteristics of hematopoietic stem cells are characterized from three aspects: cell cycle, gene function and transcription factor network regulation.

[0014] In order to achieve the above-mentioned purpose, according to the second aspect of the present invention, a method for evaluating the stemness characterization ability of stemness maintenance genes of hematopoietic stem cells is provided, the method comprising: scoring the relative expression levels of the stemness maintenance genes obtained by the above-mentioned method and the marker genes differentially expressed in hematopoietic stem cells obtained by the above-mentioned method, respectively, with the relative expression levels of the hematopoietic stem cell marker genes, and deriving the stemness characterization ability of the stemness maintenance genes according to the scores.

[0015] In order to achieve the above purpose, according to the third aspect of the present invention, there is provided a method for identifying the stemness maintenance gene of hematopoietic stem cells, and its application in screening culture reagents for in vitro culture of hematopoietic stem cells is provided.

[0016] By applying the technical scheme of the present invention and the technical scheme of the present application, scRNA sequencing is performed using hematopoietic progenitor cells of different cell sources and different culture times, and the scRNA sequencing data is analyzed, it is possible to identify the stemness maintenance genes of hematopoietic stem cells, filling the theoretical gap in the prior art that the stemness maintenance genes of hematopoietic stem cells cannot be studied, and further provides theoretical support for the clinical application of stemness maintenance genes. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0018] Figure 1 The single-cell transcriptional profiles of human HSC and downstream progenitor cells in the embodiments of the present invention are shown ( Figure 1 A and B) and cell type ratio ( Figure 1 C in the above formula. Figure 1 The "integrated" in the A means integration, and the figure shown is an integrated single-cell transcriptional profile of human HSCs and downstream progenitor cells; Figure 1The upper left of B is CB CD34 + Initial state Single cell transcriptional profiles, with CB CD34 on the upper right + Stimulated single-cell transcriptional profile, mPB CD34 at the bottom left + Single cell transcriptional profile of the initial state, mPB CD34 in the lower right + Single-cell transcriptional profiling of excited states; Figure 1 The C in the figure shows the proportion of each cell type in the sample, and the horizontal axis of the coordinates from left to right are CB CD34 + Initial state_1, CB CD34 + Initial state_2, CB CD34 + Excited state_1, CB CD34 + Excited state_2, mPB CD34 + Initial state_1, mPB CD 34 + Initial state_2, mPB CD34 + Excited state_1, mPB CD34 + Excited state_2. Among them, CB CD34 + Initial state_1, CB CD34 + Initial state_2 is two parallel samples. The legend on the right shows HSC for hematopoietic stem cells, MLP for multipotent lymphoid progenitor cells, CMP for common myeloid progenitor cells, MEP for megakaryocyte-erythroid progenitor cells, MPP for multipotent progenitor cells, GMP for granulocyte-macrophage progenitor cells, LMPP for lymphoid-myeloid progenitor cells, MEMP for megakaryocyte-erythroid-mast cell progenitor cells, and ProB for B progenitor cells. Among them, MEP-1, MEP-2, and MEP-3 are different cell subtypes of MEP.

[0019] Figure 2The GO enrichment results of differentially expressed genes of human HSC and downstream progenitor cells in the embodiment of the present invention are shown. The Chinese meanings of the biological functions from top to bottom on the right side of the heat map are response to glucocorticoids, response to corticosteroids, response to steroid hormones, response to reactive oxygen, response to purine-containing compounds, regulation of angiotensin levels in the blood, angiotensin maturation, leukocyte aggregation or rolling, cytoplasmic sequestration of transcription factors, response to interferon-α, leukocyte proliferation, regulation of lymphocyte activation, DNA replication, DNA-dependent DNA replication, DNA replication of the cell cycle, nuclear division, organelle division, mitotic nuclear fission, mitochondrial inner membrane organization, positive regulation of cell activation, MT ATP synthesis coupled proton transport, regulation of hematopoiesis, bone marrow cell differentiation, megakaryocyte differentiation, embryonic hematopoiesis, regulation of erythroid differentiation, bone marrow cell homeostasis, processing of exogenous peptide antigens by MHCⅡ, processing of peptide antigens by MHCⅡ, and processing of polysaccharide antigens by MHCⅡ.

[0020] Figure 3 The present invention shows the HSC gene function ( Figure 3 A in), cell cycle ( Figure 3 B) and transcription factor regulatory network ( Figure 3 C and D) in the feature representation diagram. Among them, Figure 3 A in the figure is the GO enrichment result of up-regulated genes in HSCs. The horizontal axis in the figure indicates the P value, and the vertical axis from top to bottom is the response to glucocorticoids, the response to corticosteroids, the response to steroid hormones, the response to reactive oxygen species, the cellular response to calcium ions, the negative regulation of multicellular growth, the regulation of spindle checkpoints, the regulation of spindle assembly checkpoints in the mitotic cell cycle, the regulation of mitotic spindle checkpoints, and the arrest of the mitotic cell cycle; Figure 3 B in the figure is the proportion of HSCs and other types of cells in the G0 / G1 phase of the cell cycle; Figure 3 C in the figure represents the enrichment of regulators in different types of cells. The Chinese meaning of Scaled AUC Score is the AUC score range. The right side lists the types of regulators enriched in HSCs in detail. TEAD4_extended (20g) means a gene regulatory network composed of transcription factor TEAD4 and all target genes, and 20g represents the number of target genes included is 20. Figure 3 D in Figure 3 is the single-cell transcriptional profile of the four most significantly enriched regulators in HSCs in Figure 3, where the grayscale indicates the size of their AUC values.

[0021] Figure 4 The culture time-dependent stemness maintenance genes ( Figure 4A, B and C in the cell origin and stemness maintenance genes ( Figure 4 D) in the identification result. Among them, Figure 4 A in the figure shows CB CD34 in HSCs cells in the initial state + Compared with cells in the excited state, the up-regulated genes, down-regulated genes and genes with no significant difference; Figure 4 B in the figure shows mPB CD34 in HSCs cells in the initial state + Compared with cells in the excited state, the up-regulated genes, down-regulated genes and genes with no significant difference; Figure 4 C in the figure is a collection of 716 genes upregulated in the initial state compared with the stimulated state in CB HSCs, 1164 genes upregulated in the initial state compared with the stimulated state in mPB HSCs, and 247 CT-SRGs genes whose ranges overlap; Figure 4 D in the figure represents CD34 derived from CB in HSCs cells + Initial cells and mPB CD34 + Compared with the initial state cells, the genes expressed in the respective cells are upregulated.

[0022] Figure 5 The GO enrichment results of stemness-maintaining genes related to cell sources and culture time in the embodiments of the present invention are shown. Among them, Figure 5 A in the figure is the GO enrichment result of the specifically expressed genes in CT-SRGs. The vertical axes from top to bottom are the response to glucocorticoids, the response to corticosteroids, the response to reactive oxygen species, the response to steroid hormones, and the mitotic DNA integrity checkpoint; Figure 5 B in the figure is the GO enrichment result of the specifically expressed genes in CS-SRGs. The vertical axes from top to bottom are ATP metabolism process, purine nucleoside triphosphate metabolism process, response to oxygen level, assembly of NA DH dehydrogenase complex, and assembly of mitochondrial respiratory chain complex; Figure 5 The C in the figure is the GO enrichment result of the common specifically expressed genes in CT-SRGs and CS-SR Gs. The vertical axis from top to bottom is the co-translational proteins targeting membrane, proteins targeting ER, translation initiation, mRNA decomposition and metabolism process, and ribonucleoprotein biogenesis.

[0023] Figure 6 The results of evaluating the stemness maintenance genes and the HSC marker genes obtained by unsupervised clustering in the embodiment of the present invention are shown. The expression levels of the HSC marker genes ( Figure 6 A and B in the figure) and the ability to reconstruct differentiation trajectories ( Figure 6 C in: unsupervised HSC marker genes, Figure 6 D in: stemness maintenance gene). Figure 6 A in the figure is the significance of LT-HSC (Long term HSC, the HSC at the most upstream of the developmental differentiation trajectory and with the strongest stemness) in Novershternet et al., the ordinate is the GSVA enrichment score, and the abscissa is the HSC marker genes, CT-SRGs and CS-SRGs; Figure 6 B in the figure is the HSC significance in Milyavsky et al., with the ordinate being the GSVA enrichment score and the abscissa being the HSC marker genes, CT-SRGs and CS-SRGs; Figure 6 The C in it is a cell differentiation trajectory constructed based on the differential genes of all cell types, where Component means composition, HSC is hematopoietic stem cell, MLP is multipotent lymphoid progenitor cell, CMP is common myeloid progenitor cell, MEP is megakaryocyte-erythroid progenitor cell, MPP is multipotent progenitor cell, GMP is granulocyte-macrophage progenitor cell, LMPP is lymphoid-myeloid progenitor cell, MEMP is megakaryocyte-erythroid-mast cell progenitor cell, Pro B is B progenitor cell, Myeloid is bone marrow cell, Erythoid is red blood cell; Figure 6 The two figures on the left in D are cell differentiation trajectories constructed using the screened CT-SRGs, where Pseudotime means pseudo time sequence (developmental time sequence predicted by monocle2), the darker the color, the more advanced the differentiation position, State means cell state, and Lymphoid means lymphoid; Figure 6 The figure on the right side of D in the figure colors each cell according to its state, and shows the GO enrichment results of key genes for lineage branch differentiation with the corresponding cell type lineages (lymphoid, myeloid, and erythroid). Figure 6 The table contents in D in the figure are the GO enrichment results of pattern 2 genes (lymph), pattern 1 genes (HSC / MPPs), and pattern 3 genes (bone marrow cells and erythrocytes). Among them, the table contents of the GO enrichment results of pattern 2 genes (lymph) have Chinese meanings from top to bottom: histone modification, covalent chromatin modification, B cell activation, lymphocyte activation regulation, the table contents of the GO enrichment results of pattern 1 genes (HSC / MPPs) have Chinese meanings from top to bottom: SRP-dependent membrane-targeted co-translational protein, membrane-targeted co-translational protein, ER-targeted protein, nuclear transcription mRNA decomposition metabolic process, protein localization to the endoplasmic reticulum establishment, and the table contents of the GO enrichment results of pattern 3 genes (bone marrow cells and erythrocytes) have Chinese meanings from top to bottom: purine ribonucleoside monophosphate metabolic process, purine nucleoside monophosphate metabolic process, ATP metabolic process, neutrophil activation, platelet degranulation. DETAILED DESCRIPTION

[0024] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present invention will be described in detail below in conjunction with the embodiments.

[0025] As mentioned in the background technology, existing studies have found that the stemness of hematopoietic stem cells from different sources and different culture times is different, but the understanding of the key genes that affect stemness is still unclear. Therefore, in order to better apply hematopoietic stem cells in clinical practice, it is necessary to identify the stemness-maintaining genes in hematopoietic stem cells. This application uses single-cell sequencing to obtain a transcriptome library of hematopoietic progenitor cells at single-cell resolution, and analyzes the cell information in the library to obtain relevant information on the stemness-maintaining genes of hematopoietic stem cells in single cells. A series of small molecules that can amplify hematopoietic stem cells in vitro have been analyzed and identified, providing important data support for the subsequent clinical application of hematopoietic stem cells.

[0026] In a first typical embodiment of the present application, a method for identifying stemness-maintaining genes of hematopoietic stem cells is provided, comprising: performing scRNA (single-cell RNA) sequencing on hematopoietic progenitor cells of different cell sources and different culture times; analyzing the scRNA sequencing data to obtain the stemness-maintaining genes of hematopoietic stem cells.

[0027] Hematopoietic stem cells belong to hematopoietic progenitor cells. Hematopoietic progenitor cells are a general term that includes the most upstream and stem-like progenitor cells (hematopoietic stem cells, LT-HSC) and downstream hematopoietic progenitor cells. Downstream hematopoietic progenitor cells include lymphoid progenitor cells (LMPP, MLP, ProB, etc.), myeloid progenitor cells (MEMP, CMP, GMP) and erythroid progenitor cells (MEP).

[0028] Through single-cell RNA sequencing, RNA libraries are constructed at single-cell resolution for hematopoietic progenitor cell samples, such as CD34+ or CD34+CD38-, and the RNA library results obtained by sequencing are analyzed to identify stemness-maintaining genes in hematopoietic stem cells. Traditional RNA sequencing of population cells, namely the Bulk RNA-seq method, sequences all different types of cells in the sample, which can reflect the average gene expression level of all cells in the sample, but cannot obtain the expression changes of genes in each cell in the sample, that is, heterogeneity at the single-cell level. Single-cell RNA sequencing can obtain the gene expression characteristics of different cell subtypes in the sample.

[0029] The method of the present application can identify the stemness maintenance genes of hematopoietic stem cells in multi-dimensional cell types. In a preferred embodiment, the above-mentioned hematopoietic progenitor cell sources include umbilical cord blood, peripheral blood or bone marrow; hematopoietic progenitor cells include CD34+ cells; preferably, CD34+ cells include CD34+CD38- cells; different culture times refer to 0h and one or more culture time points in 0-96h (for example, 0h and 48h, 0h and 72h or 0h and 96h). The present application can analyze the expression differences of stemness maintenance genes between different types of cells, such as stemness maintenance gene information specifically expressed in hematopoietic stem cells from different sources.

[0030] At present, the main cell sources for human hematopoietic stem cell research are cord blood (CB), bone marrow (BM) and mobilized peripheral blood (mPB). More and more studies have found that there are differences in the stemness of HSC cells from different sources, which are mainly manifested in the following aspects. From the perspective of cell immunophenotype, the abundance of CD34 antigen expression on the surface of CB-derived HSC cells is about 2 to 3 times that of mPB-derived HSC cells. From the perspective of the ability to differentiate and form monoclonal clones (CFU) in vitro, the total number of CFU formed by CB-derived HSC is higher than that of mPB-derived HSC. In addition, under different culture systems, CB-derived HSCs all showed higher cell division and proliferation abilities, indicating that the in vitro expansion ability of CB-derived HSCs is better than that of mPB-derived HSCs. More importantly, some scientists have found that after immunodeficient mice received highly purified CB and BM-derived HSC transplants, stem cell-like cells expressing albumin appeared in their livers, but there are few cases of successful in vivo transplantation of mPB-derived HSCs to reconstruct the hematopoietic system. In summary, the ability of CB-derived HSCs to maintain stemness and reconstruct the hematopoietic system in vivo is stronger than that of mPB-derived HSCs. Therefore, it is very important to analyze the stemness maintenance gene information of hematopoietic stem cells from different sources for the theoretical update of stemness maintenance genes.

[0031] Among them, in some embodiments of the present application, hematopoietic progenitor cells (CBCD 34+) derived from umbilical cord blood and cultured in vitro for 0 hours are involved. ), hematopoietic progenitor cells derived from umbilical cord blood and cultured in vitro for 48 hours (CB CD34+stimulated), hematopoietic progenitor cells derived from peripheral blood and cultured in vitro for 0 hours (mPB CD34+ ), hematopoietic progenitor cells from peripheral blood and cultured in vitro for 48h (mPB CD34+stimulated).

[0032] In addition, any method that can analyze hematopoietic progenitor cells from a blood sample is applicable to the present application. In a preferred embodiment, a density gradient centrifugation method is used to separate monocytes in a blood sample, and then a magnetic bead separation method is used to purify the hematopoietic progenitor cells in the separated monocyte sample, and then the hematopoietic progenitor cells are cultured accordingly according to the culture time.

[0033] The purification and culture of hematopoietic progenitor cells can be performed using a kit. In a preferred embodiment, human umbilical cord blood hematopoietic progenitor cells are purified using Kit II (StemCell Technologies, 17896) and cultured in SFEM II medium (StemCell Technologies, 09605) supplemented with CD34+ expander (St emCell Technologies, 02691); human peripheral blood hematopoietic progenitor cells are mobilized and purified using the ClinMACS CD34 MicroBead Kit (Miltenyi, 130-046-702), and cultured in the presence of 100 ng / ml recombinant human cytokine thrombopoietin (TPO) (Prime Gene, 102-06), 100 ng / ml fms-related tyrosine kinase 3 ligand (FltL) (PrimeGene, 103-05), 100 ng / ml stem cell factor (SCF) (PrimeGene, 102-01) and 25 ng / ml The cells were cultured in SCGM medium supplemented with IL3 (PrimeGene, 101-03).

[0034] Any scRNA sequencing method capable of constructing a single-cell RNA library is applicable to the present application. In a preferred embodiment, the above-mentioned scRNA sequencing includes: constructing single-cell transcriptome sequencing libraries of hematopoietic progenitor cells of different cell sources and different culture times, and sequencing each single-cell transcriptome sequencing library.

[0035] Wherein, the construction of scRNA library includes breaking the cell, and using magnetic beads to capture the RNA released therein, obtaining cDNA by reverse transcription, and amplifying it. Amplification is mainly divided into single-end sequencing and double-end sequencing. In order to obtain a sequencing library with higher accuracy and good coverage depth, in a preferred embodiment, sequencing is double-end sequencing to obtain original sequencing data. In the embodiment of the present application, DNBelab C series high-throughput single-cell RNA library preparation kit (MGI, 100001082) is used to amplify the cDNA obtained by reverse transcription in CB34+ cells to generate a barcode library. And the index library is constructed according to the manufacturer's protocol. The sequencing library is quantified by Qubit ssDNA detection kit (Thermo Fisher Scientific). Sequencing is carried out by the DIPSEQ T7 sequencer of the National Gene Bank according to the following sequencing strategy: the reading length of read 1 is 41bp, and the reading length of read 2 is 100bp.

[0036] In a preferred embodiment, the original data is quality controlled to obtain scRNA sequencing data that can be used for subsequent analysis. Further quality control of the obtained single-cell transcriptome information can ensure that the subsequent gene analysis is more reliable.

[0037] Any method capable of high-quality screening of library construction quality is applicable to this application, such as removing abnormal values ​​of the total number of transcript molecules, the total number of genes, and the proportion of transcripts derived from mitochondrial genes in the original data.

[0038] In a preferred embodiment, the above-mentioned quality control includes: using cell barcode information and UMI information to remove the sequencing data corresponding to cells below the first quality standard from the raw data of scRNA double-end sequencing to obtain a cell expression matrix; preferably, the first quality standard is that the number of genes is equal to (first quartile-interquartile range), or equal to (third quartile+interquartile range), and the average number of UMIs per gene is greater than or equal to 3. In the sequencing process, different types of cells are marked with barcode information, and the total number of transcript molecules is indicated by UMI information. After the obtained raw data is classified according to the cell barcode information, the cells with abnormal data are removed according to the UMI information to obtain a cell expression matrix that can be further analyzed for gene data.

[0039] In a preferred embodiment, the specific implementation method of quality control of cell barcode information and UMI information is as follows: use STAR (https: / / github.com / alexdobin / STAR) to align the human GRCh38 genome, use Sambamba (https: / / github.com / biod / sambamba / releases) software to sort the original bam files obtained by the alignment, sort the cells according to the number of UMIs in the reads fragments corresponding to each CB, estimate the gene expression of the cells, filter out cells with poor quality, and extract the cell expression matrix.

[0040] In addition, the original FASTQ data can be processed using PISA (v.0.7) (https: / / github.com / shi quan / PISA) to initially filter out low-quality data.

[0041] After filtering the number of transcript molecules in the original data according to the first quality standard using the cell barcode information and the UMI information, further quality control can be performed on the total number of genes and other data in the filtered data. In a preferred embodiment, the sequencing data corresponding to the cells below the second quality standard are removed from the cell expression matrix to obtain scRNA sequencing data.

[0042] In a preferred embodiment, the second quality standard is selected from one or more of the following: 1) cells with abnormal gene quantity; 2) cells with a mitochondrial mRNA ratio higher than 5%.

[0043] Cells with abnormal gene numbers are mainly defined by different outlier analysis methods. In a preferred embodiment, cells with abnormal gene numbers are defined as follows: i) using Tukey's test method; ii) calculating the number of genes in each cell, and removing cells with gene numbers lower than (first quartile - interquartile range) or higher than (third quartile + interquartile range).

[0044] Among them, Tukey's test is a statistical method for multiple comparisons. It compares the mean differences between each group of data to obtain two or more significantly different groups in the data set. Specific analysis process: 1. Calculate the mean, variance and standard error of each sample; 2. Use ANOVA (analysis of variance) to test whether there is a significant difference; 3. If the ANOVA test results show a significant difference, Tukey's test can be used for further multiple comparisons; 4. Tukey's test sets the confidence level and determines the critical value as required. If the difference between the means of a group exceeds the threshold specified by this confidence level, these groups are considered to be meaningfully different. 5. Count which groups have significant differences, and give their p-value and other parameters for reference.

[0045] In addition, outlier analysis of gene numbers by quartiles is also a common method of statistical analysis. The first quartile is the 25% number after the gene numbers are arranged from small to large, the third quartile is the 75% number after the gene numbers are arranged from small to large, and the interquartile range is the difference between the third quartile and the first quartile.

[0046] In addition, the R package DoubleFinder can be used to remove abnormal data containing two or more cells, namely doublets.

[0047] In a preferred embodiment, analyzing the scRNA sequencing data to obtain the stemness maintenance genes of hematopoietic stem cells includes: performing cell clustering on the scRNA sequencing data to obtain different cell clusters, wherein the different cell clusters include HSC cell clusters; analyzing the differentially expressed genes in different cell clusters to obtain the genes differentially expressed in hematopoietic stem cells; among the differentially expressed genes in hematopoietic stem cells, performing differentially expressed gene analysis according to different cell sources and different culture times to obtain the stemness maintenance genes.

[0048] Cell clustering is the classification process of classifying cells expressing similar genes in sequencing data into the same category. It mainly goes through two stages, feature selection and clustering algorithm. Feature selection can be performed through dimensionality reduction technology, and data visualization and information aggregation can be performed through principal component analysis, t-SNE, UMAP and other methods. Among them, principal component analysis (PCA) uses orthogonal transformation to convert a series of possibly linearly related variables into a set of linearly unrelated new variables, which have become principal components, so as to use new variables to display the characteristics of data in a smaller dimension. Clustering algorithms include K-means clustering, that is, according to the pre-set number of classifications, a corresponding number of points are randomly selected in the sample space as the starting cluster center points, and the points in the space closest to each starting center point are taken as a set to complete the first clustering; the average value of all points in the first clustering set is obtained as the new center point, and the second clustering is performed. Until the center point no longer changes.

[0049] The differentially expressed gene analysis is performed based on different types of cells. In a preferred embodiment, differentially expressed genes and functional enrichment analysis are performed on hematopoietic stem cells of different cell origins and different culture times, so as to obtain stemness maintenance genes related to cell origin and stemness maintenance genes related to culture time, respectively; among the genes differentially expressed in hematopoietic stem cells, the genes involved in the differentially expressed gene analysis according to different cell origins and different culture times need to meet the following conditions: 1) expressed in at least 20% of the cells and P<0.05; 2) the expression level fold change is more than 1.28 times.

[0050] The analysis and identification of differentially expressed genes depends on different judgment criteria to obtain differentially expressed genes of different degrees. The specific analysis method in the embodiment of the present application is: using the "FindMarkers" function of the Seurat software package in R package to perform differentially expressed gene analysis of all cell types respectively, and applying the R language package clusterProfiler to perform Gene Ontology (GO) functional enrichment analysis on the screened high-expression differentially expressed genes. Only GO entries with a q value less than 0.05 after FDR correction are considered to be statistically significant.

[0051] In order to obtain differentially expressed genes under different circumstances, it is necessary to group related variables. In a preferred embodiment, hematopoietic stem cells of different cell sources and different culture times include the following groups: 1) cord blood-derived: CD34+ uncultured and CD34+ in vitro cultured: 2) peripheral blood-derived: CD34+ uncultured and CD34+ in vitro cultured: 3) cord blood-derived CD34+ uncultured and peripheral blood-derived CD34+ uncultured. Among them, the information of stemness maintenance genes of hematopoietic stem cells of different culture times can be obtained through the differential analysis of group 1) and group 2); the information of stemness maintenance genes of hematopoietic stem cells of different sources can be obtained through the differential analysis of group 3).

[0052] In a preferred embodiment, obtaining stemness-maintaining genes associated with cell sources includes: extracting scRNA sequencing data corresponding to each hematopoietic stem cell in group 3); performing differentially expressed gene analysis according to different cell sources using the corresponding scRNA sequencing data; recording genes that are significantly highly expressed in hematopoietic stem cells derived from umbilical cord blood as stemness-maintaining genes associated with cell sources; wherein the expression level in hematopoietic stem cells derived from umbilical cord blood is recorded as E1, and the expression level in hematopoietic stem cells derived from peripheral blood is recorded as E2, and when E1 / E2≥1.28, it is defined as significantly highly expressed in hematopoietic stem cells derived from umbilical cord blood.

[0053] In a preferred embodiment, obtaining stemness-maintaining genes associated with culture time includes: extracting scRNA sequencing data corresponding to each of group 1) and group 2) of hematopoietic stem cells; using the corresponding scRNA sequencing data to perform differentially expressed gene analysis based on whether they are cultured; screening out genes that are significantly highly expressed in uncultured hematopoietic stem cells to obtain candidate genes; screening out genes that exist in both umbilical cord blood and peripheral blood from the candidate genes, and recording them as stemness-maintaining genes associated with culture time; wherein the expression level in uncultured hematopoietic stem cells is recorded as E3, and the expression level in hematopoietic stem cells from peripheral blood is recorded as E4, and when E3 / E4≥1.28, it is defined as significantly highly expressed in uncultured hematopoietic stem cells.

[0054] Before performing differential gene analysis, the sequencing data needs to be further standardized. In a preferred embodiment, the scRNA sequencing data of cells that meet the analysis process are used to perform unsupervised clustering on all cells to obtain different cell clusters; before performing unsupervised clustering, the method also includes normalizing the scRNA sequencing data and / or eliminating batch effects.

[0055] Among them, unsupervised clustering is a machine learning method that does not require prior knowledge or known classification of data sets. It groups and classifies data based on similarity to discover potential patterns and structures. Normalization refers to a gene subtracting its mean expression in all samples and then dividing it by its standard deviation of expression values ​​in all samples. The information of normalized data expression abundance is indicated by the standard deviation.

[0056] In the specific embodiment of the present application, the "NormalizeData" function is used to normalize the data with a scaling factor of 10,000, and then the data is log transformed; and the FindIntegrationAnchors and IntegratedData functions are used to perform the CCA algorithm on the data with batch effects (CB CD34+ CB CD34+stimulated, mPB CD34+ e, mPB CD34+stimulated) were processed to eliminate batch effects.

[0057] Cell clustering includes dimensionality reduction processing for visualizing data. In a preferred embodiment, after obtaining different cell clusters, the method also includes visualizing the different cell clusters; the visualization processing includes principal component analysis of the data after unsupervised clustering, and unified manifold approximation and dimensionality reduction projection processing. The data after unsupervised clustering is the cell-gene expression matrix data of scRNA-seq after quality control, normalization and / or elimination of batch effects.

[0058] Common unsupervised clustering algorithms include K-means clustering, hierarchical clustering, etc. In addition to the K-means clustering mentioned above, hierarchical clustering is a clustering method that divides samples into multiple levels for classification. In this method, each sample forms a unit set in the initial state, and builds larger and larger subsets by iteratively merging the most similar units to form a tree structure. As mentioned above, principal component analysis is a method of using new variables to display the characteristics of data in a smaller dimension. Unified manifold approximation and dimensionality reduction projection processing is a nonlinear dimensionality reduction technology that maps high-dimensional data to low-dimensional space and retains the local and global structures of the data, so that similar data points are closer in low-dimensional space, and dissimilar points are farther away. In a preferred embodiment, the data subjected to unified manifold approximation and dimensionality reduction projection processing are the top 35-40 data in the principal component analysis value ranking.

[0059] The specific visualization implementation method of cell clustering of the present application is: 4000 highly variable genes (HVGs) are detected using the "FindVariableFeatures" function in combination with the "vs t" method. The features are scaled by the "ScaleData" function, and PCA (principal component analysis) is performed on the previously determined variable features by the "RunPCA" function, and the first 40 important PCs representing the data set are used. A shared nearest neighbor graph is constructed between each cell for a given data set by the "FindNeighbors" function, and the modular function is optimized. UMAP (Uniform Manifold Approximation and Dimensionality Reduction Projection) is then performed using the first 40 principal components. The data of all samples are analyzed using the same method. Finally, the function FindClusters is used to determine the optimal resolution for calculating cell clustering.

[0060] Comprehensive analysis and annotation of gene information is particularly important for subsequent use of genes or for better understanding of the regulatory mechanisms within organisms. Therefore, while conducting differential gene expression analysis, it is also possible to study aspects such as cell development or gene regulation. In a preferred embodiment, while analyzing differentially expressed genes in different cell clusters and obtaining genes differentially expressed in hematopoietic stem cells, the method also includes at least one of the following: 1) constructing a cell development and differentiation trajectory; 2) characterizing the global transcriptome characteristics of hematopoietic stem cells.

[0061] Constructing the cell development and differentiation trajectory is from the perspective of trajectory analysis, that is, analyzing the dynamic process of continuous cell changes. In a preferred embodiment, constructing the cell development and differentiation trajectory includes taking the differentially highly expressed marker genes of all cell types as the input of the software for constructing the cell development trajectory, sorting all cells in pseudo time, and constructing the cell development and differentiation trajectory, wherein the expression level change of more than 1.28 times is defined as differentially highly expressed, and the numerical definition of high expression will affect the accuracy of the identification and analysis results.

[0062] In an embodiment of the present application, a specific method for constructing a cell development and differentiation trajectory includes: using differentially highly expressed marker genes of all cell types as input to Monocle2 (v.2.14.0), sorting all cells in pseudo time, and constructing a cell differentiation trajectory. Set the parameter "expressionFamily = negbinomial.", and use the "reduceDimension" function and DDRTree method. Set the parameter "residualModelFormulaStr" to remove batch effects, and use "reduction_method" to achieve dimensionality reduction. Use the BEAM function to detect genes that separate cells into considered cell branches. We use the "plot_multiple_branches_heatmap" function to separate branch-related gene sets with q values ​​less than or equal to 10e-4.

[0063] The global transcriptome features include analysis of the expression level, enhancer structure, post-translational modification and other aspects of the genes under different conditions to obtain comprehensive gene interactions and regulation mechanisms. In a preferred embodiment, the global transcriptome features of hematopoietic stem cells are characterized from three aspects: cell cycle, gene function and transcription factor network regulation. The platform for the characterization of the global transcriptome features of cells mainly includes Scater, Seurat and Scanpy, wherein Scanpy is a platform for data analysis based on Python, and the other two are based on R language to complete data analysis. There are multiple analysis tools in these three platforms, which can be used for comparison according to different needs.

[0064] In a preferred embodiment, the method used for the cell cycle analysis of the hematopoietic stem cell transcriptome is to score the cell cycle stage of each single cell using the "CellCycleScoring" function in Seurat (v.3.2.1) based on marker genes directly related to the S phase and G2M phase in human cells.

[0065] In a preferred embodiment, the above-mentioned analysis method for gene function can be performed by GO and KEGG enrichment. The specific analysis method in the embodiment of the present application is to use the functions enrichGO and enrichKEGG in clusterProfiler to perform GO and KEGG enrichment on the culture time-related stemness maintenance genes (CT-SRGs) and cell source-related stemness maintenance genes (CS-SRGs), respectively, with the parameters of "pvalueCutoff=0.05, qValueCutoff=0.05, OrgDb=org.Hs.eg.db, ont=BP".

[0066] Gene regulatory network refers to the collection of regulatory relationships between transcription factors (TF) and TF binding sites of specific mRNAs. In a preferred embodiment, the method for analyzing the transcription factor network regulation of hematopoietic stem cells is to identify the key transcription factors and their networks of each cell type through the default parameters of the R package SCE NIC and the cisTarget database (https: / / resources.aertslab.org / cistarget / ).

[0067] In a second typical embodiment of the present application, a method for evaluating the stemness characterization ability of stemness maintenance genes of hematopoietic stem cells is provided, the method comprising: scoring the relative expression levels of the stemness maintenance genes obtained by the above method and the marker genes differentially expressed in hematopoietic stem cells obtained by the above method, respectively, with the relative expression levels of the hematopoietic stem cell marker genes, and deriving the stemness characterization ability of the stemness maintenance genes according to the scores.

[0068] The accuracy of the stemness-maintaining gene obtained by the method of the present application can be confirmed by evaluation, thereby providing a guarantee for the quality of future applications. The above-mentioned hematopoietic stem cell marker gene is a gene known to be specifically highly expressed in hematopoietic stem cells. In a preferred embodiment, the hematopoietic stem cell marker gene is a gene obtained by collecting and collating the top 250 highly expressed genes highly expressed in hematopoietic stem cells in published studies. Among them, specifically highly expressed means that the expression level in hematopoietic stem cells is at least 1.28 times the expression level in other cells.

[0069] Methods for analyzing the above comparison results include: AUCell scoring or GSVA analysis, etc. In a preferred embodiment, GSVA analysis is used for scoring. The relative expressions of the stemness maintenance genes (SRGs) and the marker genes differentially expressed in hematopoietic stem cells and the hematopoietic stem cell marker genes obtained by the above method are scored by GSVA analysis (https: / / www.bioconductor.org / packages / de vel / bioc / vignettes / GSVA / inst / doc / GSVA.html) to evaluate whether SRGs have better stemness characterization ability than the marker genes differentially expressed in hematopoietic stem cells obtained by unsupervised clustering.

[0070] In a third typical embodiment of the present application, a method for identifying stemness-maintaining genes of hematopoietic stem cells is provided, and the use of stemness-maintaining genes of hematopoietic stem cells identified in screening culture reagents for in vitro culture of hematopoietic stem cells is provided.

[0071] The culture reagents for screening in vitro culture of hematopoietic stem cells include cytokines, matrix proteins and amino acid nutrients that can be used to culture hematopoietic stem cells. In a preferred embodiment, the culture reagents include small molecule compounds, such as STAT3 / JAK4 signaling pathway inhibitors, small molecule Cucurbitacin I with JAK inhibitor function, small molecule Fenretinide with apoptosis promoter, retinoid receptor promoter, and RAR promoter small molecule function, etc., which can provide support for the future in vitro culture of hematopoietic stem cells.

[0072] In a preferred embodiment, the screening method for the above-mentioned small molecule compounds is to perform gene-small molecule compound association analysis. The analysis platform used is CLUE (https: / / clue.io / query), a cloud analysis platform in CMap. CMap is a database containing expression changes of more than 1,500 genes in various cell types treated with about 5,000 small molecule compounds and 3,000 genetic agents. The cloud analysis platform CLUE is used to analyze perturbation (perturbagens) data sets generated using gene expression (L1000) and proteomic (P100 and GCP) measurements.

[0073] To understand the relationship between the expression changes of stemness-maintaining genes and small molecules, we used the query tool gene expression (L1000) of CLUE with the following default query parameters: (1) select the “Touchstone”, “Latest and select”, and “Individual Query” parameters; (2) paste the gene into the “Upregulated Genes” dialog box and check the correct gene name; (3) submit the query and download the “DETAILED LIST” and “HEAT MAP” results for advanced screening analysis to obtain the results of the small molecule species associated with it.

[0074] The present application is further described in detail below in conjunction with specific embodiments. These embodiments should not be construed as limiting the scope of protection claimed in the present application.

[0075] (1) Establishment of a standard system for the purification and culture of human umbilical cord blood and peripheral blood cells

[0076] Eight samples of different cell sources and culture times were obtained from the hospital, as shown in Table 1, and divided into four groups: cord blood without in vitro culture (CB CD34+ ), umbilical cord blood with in vitro culture (CB CD34+stimulated), peripheral blood without in vitro culture (mPB CD34+ ), peripheral blood source and in vitro culture (mPB CD34+stimulated).

[0077] Table 1:

[0078]

[0079]

[0080] (2) Construction of single-cell transcriptome profiles of human HSCs and downstream progenitor cells from different cell sources and at different culture times

[0081] We constructed single-cell transcriptome maps of human HSCs and downstream progenitor cells from different cell sources and at different culture times ( Figure 1 A), set up four groups (CB CD34+ CB CD34+stimulated, mPB CD34+ mPB CD34+stimulated). We analyzed 16,196 cells and identified 12 cell clusters, which were visualized using unified manifold approximation and projection (UMAP) ( Figure 1 B) We found that the cell ratios of samples from different cell sources and culture times were different. The samples (CB: 13.9%, mPB: 13.8%) had a higher proportion of HSC cells than stimulated (CB: 2.76%, mPB: 3.29%). Figure 1 C), confirming that HSCs will gradually lose their stemness after in vitro culture. The cell ratios of samples from different sources also vary greatly. We performed functional enrichment analysis on the differentially expressed genes in 12 cell types and displayed them in a heat map ( Figure 2 ), characterizing the functions of all hematopoietic progenitor cells including HSCs.

[0082] We explained the global transcriptome characteristics of HSC from three aspects: cell cycle, gene function, and transcription factor network regulation. We found that the genes upregulated in HSC were also enriched in pathways related to cell cycle and mitosis, such as the regulation of spindle checkpoints, the regulation of cell cycle spindle assembly checkpoints, and mitotic cell cycle arrest ( Figure 3 A), indicating that most HSC cells may be in a quiescent state, which is also related to HSC self-renewal and maintenance of stemness. Accordingly, we examined the cell cycle of HSC compared with other cell types and found that 96.4% of HSC cells were in the G0|G1 phase ( Figure 3 B). We also found that some TFs and their co-expressed genes were significantly enriched in HSCs ( Figure 3C and D), in addition to HLF as an HSC marker gene, some unreported TFs related to HSC stemness maintenance that are worth exploring were also found, such as FOS, MECOM, MEF2C, TEAD4, ELK3 and HOXA9.

[0083] (3) A method for identifying and evaluating human HSC stemness-maintaining genes was proposed

[0084] Based on this approach, culture time-related (CT-SRGs) were identified ( Figure 4 A, B, and C) and cell-of-origin related (CS-SRGs) ( Figure 4 D) stemness maintenance genes. We found that CT-SRGs and CS-SRGs have similarities and differences in function. Among them, CT-SRGs, which are SRGs related to culture time, are enriched in some HSC-related pathways, such as those related to glucocorticoids, steroid corticosteroid response, cell cycle, etc. ( Figure 5 A), while CS-SRGs were enriched in pathways related to energy metabolism, purine metabolism, etc. ( Figure 5 B), indicating the accuracy of the identification method. In addition, they were jointly enriched in pathways related to translation initiation, RNA metabolism, protein targeting, etc. ( Figure 5 C), suggesting the widespread existence of key biological processes such as protein synthesis and ribosome-encoded gene transcription.

[0085] By combining the HSC signature gene expression levels and differentiation trajectory reconstruction capabilities of stemness maintenance genes and HSC marker genes obtained by unsupervised clustering, we found that stemness maintenance genes can better characterize HSCs ( Figure 6 A and B in Figure 1), and can better separate the lymphoid and myeloid lineages in terms of differentiation trajectories ( Figure 6 C and D) to further verify the accuracy and applicability of the method

[0086] (4) Molecular marker screening

[0087] Through the analysis of gene-small molecule association, we further explored the correlation between stemness-maintaining genes and in vitro culture small molecules. We not only discovered the small molecule Fenretinide related to stemness-maintaining genes reported in published studies, but also discovered new small molecules such as CucurbitacinI that are expected to be used to help HSCs proliferate longer in vitro, providing a broader new perspective for future exploration of HSC stemness maintenance and in vitro culture research (Table 2).

[0088] Table 2:

[0089] Small molecules Gene function Correlation (%) N mTOR inhibitors, PI3K inhibitors, protein kinase inhibitors 98.44 C1 STAT3 / JAK4 signaling pathway inhibitor, JAK inhibitor 96.69 F Apoptosis promoter, retinoid receptor promoter, RAR promoter 96 C2 Calcium channel blockers, calmodulin antagonists 95.74

[0090] From the above description, it can be seen that the above embodiments of the present invention achieve the following technical effects: the technical solution of the present application uses hematopoietic progenitor cells (CD 34+ cells) of different cell sources and culture time as models to perform integrated analysis of single-cell transcriptomes, systematically analyzes a large number of single cells in 4 groups with different cell sources and culture time, and establishes the first single-cell transcriptome map of human HSC and downstream progenitor cells focusing on different cell sources and different culture time, and comprehensively and systematically characterizes the global transcriptome characteristics of HSC from aspects such as gene function, cell cycle and core regulatory transcription factors. A method for identifying and evaluating human HSC stemness maintenance genes is proposed, and compared with HSC marker genes obtained by unsupervised clustering, new small molecules that are expected to be used to help HSC expand longer in vitro are discovered through gene-small molecule compound association analysis, providing a new perspective for future exploration of HSC stemness maintenance and in vitro culture research.

[0091] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for identifying stemness-maintaining genes of hematopoietic stem cells, characterized in that: include: scRNA sequencing of hematopoietic progenitor cells from different cell sources and at different culture times; The scRNA sequencing data is analyzed to obtain the stemness maintenance gene of the hematopoietic stem cell.

2. The method according to claim 1, characterized in that: The source of the hematopoietic progenitor cells includes umbilical cord blood, peripheral blood or bone marrow; Preferably, the hematopoietic progenitor cells include CD34+ cells; Preferably, the CD34+ cells include CD34+CD38- cells; Preferably, the different culture times refer to 0h and one or more culture time points among 0-96h; preferably 0h and 48h, 0h and 72h or 0h and 96h.

3. The method according to claim 1, characterized in that The scRNA sequencing comprises: Respectively constructing single-cell transcriptome sequencing libraries of the hematopoietic progenitor cells of different cell sources and different culture times, and sequencing each of the single-cell transcriptome sequencing libraries, preferably double-end sequencing, to obtain original sequencing data; Performing quality control on the raw data to obtain the scRNA sequencing data that can be used for subsequent analysis; Preferably, the quality control includes: Using the cell barcode information and the UMI information, the sequencing data corresponding to the cells below the first quality standard are removed from the raw data of the scRNA double-end sequencing to obtain a cell expression matrix; Removing sequencing data corresponding to cells below the second quality standard from the cell expression matrix to obtain the scRNA sequencing data; Preferably, the first quality standard is that the number of genes is equal to (first quartile - interquartile range), or equal to (third quartile + interquartile range), and the average number of UMIs per gene is greater than or equal to 3; Preferably, the second quality standard is selected from one or more of the following: 1) Cells with abnormal gene numbers; 2) except cells with a mitochondrial mRNA ratio higher than 5%; Further preferably, the cells with abnormal gene number are defined as follows: i) Adopt Tukey's test method; ii) Calculate the number of genes in each cell, and remove cells with the number of genes lower than (first quartile - interquartile range) or higher than (third quartile + interquartile range).

4. The method according to claim 1, characterized in that: The scRNA sequencing data is analyzed to obtain the stemness maintenance genes of the hematopoietic stem cells including: Performing cell clustering on the scRNA sequencing data to obtain different cell clusters, wherein the different cell clusters include HSC cell clusters; Analyzing the differentially expressed genes in the different cell clusters to obtain genes differentially expressed in the hematopoietic stem cells; Among the genes differentially expressed in the hematopoietic stem cells, differentially expressed gene analysis is performed according to different cell sources and different culture times to obtain the stemness maintenance gene.

5. The method according to claim 4, characterized in that Performing differentially expressed genes and functional enrichment analysis on the hematopoietic stem cells of different cell sources and different culture times, thereby obtaining the stemness maintenance genes associated with the cell sources and the stemness maintenance genes associated with the culture time, respectively; Among the genes differentially expressed in the hematopoietic stem cells, the genes involved in the differentially expressed gene analysis according to different cell sources and different culture times need to meet the following conditions: 1) expressed in at least 20% of cells and P < 0.05; 2) the fold change of expression is more than 1.28 times; Preferably, the hematopoietic stem cells of different cell sources and different culture times include the following groups: 1) Umbilical cord blood: CD34+ uncultured and CD34+ in vitro cultured: 2) From peripheral blood: CD34+ uncultured and CD34+ in vitro cultured: 3) Uncultured CD34+ cells from umbilical cord blood and uncultured CD34+ cells from peripheral blood.

6. The method according to claim 5, characterized in that Obtaining the stemness-maintaining gene associated with the cell source includes: Extracting the scRNA sequencing data corresponding to each of the hematopoietic stem cells in group 3); Using the corresponding scRNA sequencing data, differentially expressed genes were analyzed according to different cell sources; The genes that are significantly highly expressed in the hematopoietic stem cells derived from the umbilical cord blood are recorded as the stemness maintenance genes associated with the cell origin; Among them, the expression level in the hematopoietic stem cells derived from the umbilical cord blood is recorded as E1, and the expression level in the hematopoietic stem cells derived from the peripheral blood is recorded as E2. When E1 / E2≥1.28, it is defined as significantly high expression in the hematopoietic stem cells derived from the umbilical cord blood.

7. The method according to claim 5, characterized in that Obtaining the stemness-maintaining gene associated with culture time includes: Extracting scRNA sequencing data corresponding to the hematopoietic stem cells of group 1) and group 2); Using the corresponding scRNA sequencing data, differentially expressed genes were analyzed according to whether they were cultured or not; Screening out genes that are significantly highly expressed in uncultured hematopoietic stem cells to obtain candidate genes; Screening out genes that exist in both the umbilical cord blood source and the peripheral blood source from the candidate genes, and recording them as the stemness maintenance genes related to the culture time; Among them, the expression level in uncultured hematopoietic stem cells is recorded as E3, and the expression level in hematopoietic stem cells derived from peripheral blood is recorded as E4. When E3 / E4≥1.28, it is defined as significantly high expression in the uncultured hematopoietic stem cells.

8. The method according to claim 4, characterized in that Using the scRNA sequencing data of cells that meet the analysis process, all cells are clustered unsupervisedly to obtain different cell clusters; Before performing the unsupervised clustering, the method further includes normalizing the scRNA sequencing data and / or eliminating batch effects; Preferably, after obtaining the different cell clusters, the method further comprises visualizing the different cell clusters; The visualization processing includes performing principal component analysis on the data after the unsupervised clustering, and performing unified manifold approximation and dimensionality reduction projection processing; Preferably, the data after the unsupervised clustering is the cell-gene expression matrix data of scRNA-seq after sequentially undergoing quality control, the normalization processing and / or the batch effect elimination processing; Preferably, the data subjected to the unified manifold approximation and dimensionality reduction projection processing are the data with the top 35-40 values ​​ranked by principal component analysis.

9. The method according to any one of claims 4 to 8, characterized in that: While analyzing the differentially expressed genes in the different cell clusters to obtain the genes differentially expressed in the hematopoietic stem cells, the method further includes at least one of the following: 1) Construct cell development and differentiation trajectory; 2) characterizing the global transcriptome characteristics of the hematopoietic stem cells; Preferably, differentially highly expressed marker genes of all cell types are used as inputs of software for constructing cell development trajectories, and all cells are sorted in pseudo-time to construct the cell development and differentiation trajectory, wherein a fold change of expression of more than 1.28 times is defined as differentially highly expressed; Preferably, the global transcriptome characteristics of the hematopoietic stem cells are characterized from three aspects: cell cycle, gene function and transcription factor network regulation.

10. A method for evaluating the stemness characterization ability of stemness maintenance genes of hematopoietic stem cells, characterized in that: The method comprises: The relative expression levels of the stemness maintenance gene obtained by the method of any one of claims 1 to 9 and the marker gene differentially expressed in hematopoietic stem cells obtained by the method of claim 4 are scored respectively with the relative expression levels of the hematopoietic stem cell marker gene, The stemness characterization ability of the stemness maintenance gene is derived according to the score.

11. Use of the hematopoietic stem cell stemness maintenance gene identified by the method for identifying the hematopoietic stem cell stemness maintenance gene according to any one of claims 1 to 9 in screening culture reagents for in vitro culture of hematopoietic stem cells.

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