Single-Cell Analysis Reveals Characterization of Infiltrating T Cells in Moderately Differentiated Colorectal Cancer

Objective This study aimed to characterize the tumor-infiltrating T cells in moderately differentiated colorectal cancer. Methods Using single-cell RNA sequencing data of isolated 1632 T cells from tumor tissue and 1252 T cells from the peripheral blood of CRC patients, unsupervised clustering analysis was performed to identify functionally distinct T cell populations, followed by correlations and ligand-receptor interactions across cell types. Finally, differential analysis of the tumor-infiltrating T cells between colon cancer and rectal cancer were carried out. Results A total of eight distinct T cell populations were identified from tumor tissue. Tumor-Treg showed a strong correlation with Th17 cells. CD8+TRM was positively correlated with CD8+IEL. Seven distinct T cell populations were identified from peripheral blood. There was a strong correlation between CD4+TN and CD4+blood-TCM. Colon cancer and rectal cancer showed differences in the composition of tumor-infiltrating T cell populations. Tumor-infiltrating CD8+IEL cells were found in rectal cancer but not in colon cancer, while CD8+ TN cells were found in the peripheral blood of colon cancer but not in that of rectal cancer. A larger number of tumor-infiltrating CD8+ Tex (88.94%) cells were found in the colon cancer than in the rectal cancer (11.06%). The T cells of the colon and rectal cancers showed changes in gene expression pattern. Conclusions We characterized the T cell populations in the CRC tumor tissue and peripheral blood.


INTRODUCTION
Colorectal cancer (CRC) is the fourth most fatal disease in the world with approximately 900 000 deaths per year (1). Colon and rectal cancers are considered as a single tumor entity, called CRC, because both develop in the large bowel, which is regarded as a single organ (2). However, there are differences in their etiology, histology, drug response, and prognosis, based on tumor location within the colon (approximately 150 cm) and rectum (approximately 16 cm) (3). Based on the CRC statistics of 2017, the proximal colon is the most common tumor location, accounting for 41% of CRC cases, while distal colon and rectum account for 21% and 25%, respectively, and the distribution of tumor location differs with age and gender (4). The risk of malignant transformation in the rectal mucosa is at least four times higher than that in the colon mucosa. In formal carcinogenesis, 85-90% of cancers occur mostly in the form of adenomas (5). Compared with that for colon cancer (total mesorectal excision), the surgery for rectal cancer presents more challenges and risks due to its anatomy and topographic position (6).
The immune system influences the development and progression of CRC, and the great advances in understanding tumor immune microenvironment provide novel therapeutic strategies for CRC (7). Some immune cell subsets have been regarded as promising targets for immunotherapy and in the study of clinical biomarkers, including T cells and natural killer (NK) cells (8,9). Fujimoto et al. found that patients with adenocarcinoma showed significantly elevated CD4/CD8 ratio and regulatory T cells (Tregs), based on the paired analyses with normal mucosa, and the accumulation frequency of Tregs was related to the tumor's local infiltration and extension (10). De Simone et al. reported that infiltrating Treg cells in tumors showed high suppressive activity, and up-regulation of several immune checkpoints and their ligands were found in tumorinfiltrating Treg cells, which provide potential targets in immunotherapy (11). The tumor-infiltrating T lymphocytes (TILs) are essential for effective anti-tumor immune response, and high level of TILs has been reported to be related to better prognostic survival in CRC (12)(13)(14).
Intratumoral heterogeneity (phenotypic diversity, such as (epi)genetic abnormality and cell surface markers) continues to be a challenge in cancer treatment (15). The developed single-cell RNA sequencing (scRNA-seq) technologies recently showed incomparable superiority in evaluating tumor cellular heterogeneity (16). Based on the immune atlas constructed by scRNA-seq of isolated immune cells from tissue (normal and breast cancer), peripheral blood, and lymph node, Azizi et al. found that there was enormous diversity in immune cells. They suggested that T cells and myeloid cells were the two major cellular targets in tumor immunotherapy, and the T cells in peripheral blood and lymph node showed diverse phenotypes in comparison with that of tumor tissue (17). Using scRNA-seq, Chung et al. characterized the heterogeneous tumor cells and the surrounding immune and stromal cells, and they found that the majority of non-cancer cells were mainly macrophages, T and B lymphocytes, in which both macrophages (M2 phenotype) and lymphocytes (regulatory/exhausted phenotype) were immunosuppressive phenotypes (18).
Based on scRNA-seq of T cells and T cell receptor tracking, Zhang et al. identified 20 T cell subsets in CRC and comprehensively dissected the T cell properties (functions and clonalities), uncovering the development and migration of T cells within tumors (19). Azizi et al. had reported that T cells in the peripheral blood and lymph node showed diverse phenotypes compared to those of the T cells in the breast cancer tissue (17). Therefore, using the scRNA-seq data of Zhang et al., we further investigated the characteristics of isolated T cells from the tumor tissue and peripheral blood in this study. In the study by Zhang et al., the 12 CRC patients showed different pathological types (one patient was well-differentiated, five patients were moderately differentiated, two patients were poorly or moderately differentiated, and four patients were poorly differentiated), which could have been a confounding factor for the results. Therefore, only the moderately differentiated samples, accounting for the maximum proportion, were selected and used in our analysis. In addition, considering the similarities and differences between colon and rectal cancers, the differences in tumor-infiltrating T cells and peripheral blood T cells between colon cancer and rectal cancer, which have not yet been reported in CRC, were determined in this study. The findings of this study provide novel insights and potential targets in the immunotherapy of CRC.

Data Acquisition
Following the method of Zhang et al. (19), the normalized gene expression matrix files for each cell were acquired. A total of 12 patients with rectum adenocarcinoma or colon adenocarcinoma (one well differentiated, five moderately differentiated, two poorly or moderately differentiated, and four poorly differentiated) were included in the data set, of which five patients (moderately differentiated) were enrolled in our current study (Table S1). The original data set contained scRNA-seq data of different subtypes of T cells collected from tumor tissue, adjacent normal mucosa, and peripheral blood of the patients. The data from the tumor tissue and peripheral blood were used in the current analysis. After data screening, the gene expression matrix files for each tumor tissue cell of the five moderately differentiated patients were obtained, including 1632 cells and 12,547 genes. Similarly, the gene expression matrix files for each cell from the peripheral blood of the five moderately differentiated patients were obtained, including 1,252 cells and 12,547 genes. All the analyses on the tumor tissue and peripheral blood data were performed independently.

Unsupervised Cluster Analysis and Cell Type Identification
The cells were divided into two major categories, based on previous flow cell sorting, including CD8+ T cells and CD4+ T cells. To reach a well-clustered result, clustering analysis was firstly performed for CD8+ T cells and CD4+ T cells and then the obtained cell clusters were merged. In brief, the top 1500 highly variable genes were selected using the sscClust package (20) (https://github.com/Japrin/sscClust), followed by the analysis of dimension reduction based on the Spearman correlation coefficient between cells. K-means was used for the clustering analysis. A total of 2-20 cell clusters were pre-set. The normalized mutual information (NMI) index was selected to determine the number of final cell clusters, and a larger NMI index represents a more accurate clustering result. The clustering results were visualized using t-distributed stochastic neighbor embedding (tSNE). The marker genes of each cell cluster in the study of Zhang et al. were used to define the obtained cell clusters. The heatmap and violin plot for each gene across cell clusters showed their expression in different cell clusters, and the cell cluster with significantly high expression of marker genes was defined as the cell type that corresponded with the marker genes.

Identification of Correlation and Cell-Cell Crosstalk Between Cell Clusters
Based on the average expression of each gene in cell clusters, the Pearson correlation coefficient between cell clusters was calculated using the Cor function of the R language, and the Corrplot (21) (https://mirrors.tuna.tsinghua.edu.cn/CRAN/web/ packages/corrplot) of the R package was used to plot the correlation heatmap. The iTALK (22) (https://github.com/ Coolgenome/iTALK) of the R package was used to investigate the cell-cell crosstalk between cell clusters. Briefly, the top 50% highly expressed genes of each cell cluster were selected and matched to the 2,648 non-redundant ligand-receptor pairs included in the iTALK package. There are four categories of ligand-receptor pairs, according to the ligand types, including growth factor, cytokine, checkpoint, and other ligand-receptor pairs. The top 20 ligand-receptor pairs for each type were presented by the ligand-receptor interaction network.

Differential Expression Analysis for Infiltrating T Cells Between Colon Cancer and Rectal Cancer
To analyze the differences between colon cancer and rectal cancer at the cellular level, the differential analysis of colon cancer vs. rectal cancer was performed using the classical Bayesian method of the Limma package (23). For each cell cluster, the differentially expressed genes (DEGs) in colon cancer vs. those in rectal cancer were screened with an adjusted P value < 0.05 and |logFC|>1 after multiple tests correction, using the Benjamini & Hochberg method.

Enrichment Analysis and Protein-Protein Interactions for DEGs
For the DEGs in each cell cluster, enrichment analysis was performed using the online tool, Metascape (24) (http:// metascape.org) to investigate the involved functional terms, including biological process terms in gene ontology, KEGG pathways, and Reactome pathways. The parameters were set as: Min Overlap = 3, P value cutoff = 0.05, and Min Enrichment = 1.5. The protein-protein interactions (PPI) for DEGs were retrieved from the BioGrid (25), InWeb_IM (26), and OmniPath (27) databases using the online tool Metascape, with default parameters (Min Network Size=3 and Max Network Size=500). Based on the obtained interactions, the PPI network was visualized using Cytoscape software (28) (version 3.4.0, http://chianti.ucsd.edu/cytoscape-3.4.0/). In addition, the Molecular Complex Detection (MCODE) algorithm (29) of Metascape was used to identify the modules of the PPI network. Enrichment analysis was also performed for the genes in modules.
In total, eight cell clusters were identified ( Figure 1A). The marker genes of the eight cell clusters were used to further validate the defined cell clusters ( Figures 1B and 2). High expression of CD4_C12-CTLA4 (tumor regulatory T cell, Tumor-Treg) markers, such as CCR8, RTKN2, and FOXP3, was observed in C1. High expression of CD8_C07-LAYN (CD8 + intraepithelial lymphocyte, CD8 + IEL) markers, such as  Figure  2, the gene on the left represents the marker genes of tumor-Treg. Horizontal axis represents the 8 identified cell clusters (C1 to C8). It could be seen that the marker genes of tumor-Treg were significant highly expressed in C1. Therefore, C1 cells were defined as tumor-Treg. CD160, KLRC3, and KLRC2, was observed in C8, suggesting that the definitions of cell clusters were accurate. The specific functional annotations of cell clusters were shown in http://crc. cancer-pku.cn/. Among the eight cell clusters, there was a strong correlation between C1 (Tumor-Treg) and C4 (T help 17 cell, Th17), while C1 and C4 showed a weak correlation with other cell clusters. C5 (CD8 + effector memory T cell, CD8 + T EM ) showed a strong correlation with different cell clusters, including C3 (CD4 + effector memory T cell, CD4 + T EM ), C8 (CD8 + IEL), C7 (CD8 + Tissue-resident memory T cell, CD8 + T RM ), and C6 (CD8 + exhausted T cell, CD8 + T EX ) ( Figure 1C).

Differences in Tumor-Infiltrating T Cells Between Colon Cancer and Rectal Cancer
The infiltrating T cells in colon cancer and rectal cancer were counted (Table 1A). It could be seen that C1 included 547 infiltrating Treg cells, of which 339 were in colon cancer, and 208 were in rectal cancer. C6 included 199 infiltrating CD8 + T EX cells, and the majority of the cells were in colon cancer (177 cells, 88.94%) compared to those in rectal cancer (22 cells, 11.06%). C8 included 54 infiltrating CD8 + IEL cells, which were all in rectal cancer. Therefore, C8 was excluded from the differential expression analysis. The screened DEGs are shown in Table  1B. C1 (Tumor-Treg) showed 112 DEGs between colon cancer  and rectal cancer, such as TNF and CXCR3, which were upregulated, while CXCR6 and CCR6 were down-regulated in colon cancer in comparison to that of rectal cancer. For C4 (Th17), only nine DEGs, including TNFRSF9 and CXCL13 were screened between colon cancer and rectal cancer ( Figure 4A and Figure S2). Enrichment analysis showed that there were similarities and differences between the enriched functions and pathways for those DEGs ( Figure 4B). For example, the DEGs in C7 (CD8 + T RM ) were enriched in T cell differentiation, T cell activation, lymphocyte activation, and regulation of lymphocyte activation, of which T cell activation and lymphocyte activation were similar to that of C1 (Tumor-Treg). We further investigated the interactions between those DEGs and constructed the PPI network ( Figure 5). Besides, the modules were identified from the PPI network, and enrichment analysis was also performed for the genes in modules ( Table 2). For example, for the DEGs in C1 (Tumor-Treg), the PPI network contained 51 genes and 80 interactions, and two modules were identified from the PPI network. The genes in module 1, including DNAJA1, EEF2, and TUBB4B, were enriched in cellular responses to stress and cellular responses to external stimuli pathways, while the genes in module 2 (CCL5, CCR6, CXCR3, and CXCR6) were implicated in pathways, including chemokine receptors bind chemokines, chemokine-mediated signaling pathway, and cellular response to chemokines.
In total, seven cell clusters were identified ( Figure 6A). The marker genes of the seven cell clusters were used to further validate the defined cell clusters (Figures 6B and 7). High expression of CD4_C10-FOXP3 (Blood regulatory T cell, Blood-Treg) markers, such as RTKN2, IKZF2, and FOXP3, were observed in C4, suggesting that the definition of cell clusters was accurate. The specific functional annotations of cell clusters were shown in http://crc.cancer-pku.cn/. C1 was the cluster of CD4 + Naive T cells (CD4 + T N ), C2 was the cluster of CD4 + central memory T cells (CD4 + blood-T CM ), C3 was the cluster of CD4 + recently activated effector memory T cells (CD4 + T EMRA /T EFF ), C5 was the cluster of CD8 + T N cells, C6 was the cluster of CD8 + T CM cells, and C7 was the cluster of CD8 + T EMRA /T EFF cells.
Among the seven cell clusters, there was a strong correlation between C3 (CD4 + T EMRA /T EFF ) and C7 (CD8 + T EMRA /T EFF ), while C3 and C7 showed a weak correlation with the other cell clusters. Besides, C1(CD4 + T N ) and C2 (CD4 + blood-T CM ) showed a strong correlation ( Figure 6C).

The Ligand-Receptor Pairs in Blood T Cells Crosstalk
In total, 4546 ligand-receptor pairs were screened among the seven cell clusters, including 263 growth factors, 448 cytokines, 168 checkpoints, and 3,667 other ligand-receptor pairs. The top 20 ligand-receptor pairs for each type were presented by the ligand-receptor interaction network ( Figure 8). C3 (CD4 + T EMRA /T EFF ) showed more crosstalk with other cell clusters. For example, a cytokine ligand-receptor pair (CCL5-CXCR3/ CCR3) was found between C3 (CD4 + T EMRA /T EFF ) and C4 (Blood-Treg), and other ligand-receptor pair (CFH-SELL) was found between C3 (CD4 + T EMRA /T EFF ) and C5 (CD8 + T N ).

Differences for Blood T Cells Between Colon Cancer and Rectal Cancer
The number of blood T cells in colon cancer and rectal cancer were counted (Table 3A). It could be seen that C1 included 344 blood CD4 + Naive T cells, of which 208 were in colon cancer, and 136 were in rectal cancer. C4 included 142 Blood-Treg cells, of which colon cancer (65 cells) and rectal cancer (77 cells) shared a similar number of Blood-Treg cells. C5 included 29 CD8 + Naive T cells, which were all in colon cancer. Therefore, C5 was excluded from the differential expression analysis. The screened DEGs are shown in Table 3B. For C7 (CD8 + T EMRA /T EFF ), there were 359 DEGs between colon cancer and rectal cancer, such as HSPA1L and IL7R were up- regulated, while CD8A and CTSW were down-regulated in colon cancer compared to that in rectal cancer. For C3 (CD4 + T EMRA /T EFF ), only three DEGs were screened between colon cancer and rectal cancer, and no DEGs were found for C6 (CD8 + T CM ) ( Figure 9A and Figure S4). Enrichment analysis showed that the DEGs in different cell clusters were enriched in different functions and pathways ( Figure 9B). For example, DEGs in C7 (CD8 + T EMRA /T EFF ) were enriched in the adaptive immune system, antigen processing and presentation pathways, as well as in lymphocyte activation, T cell activation, and other biological processes. The interactions between those DEGs were further investigated to construct the PPI network and its modules ( Figure 10). In addition, enrichment analysis was also A B

FIGURE 4 | Differentially expressed genes in tumor-infiltrating T cells between colon cancer and rectal cancer. (A)
Volcano plot shows the up-regulated and downregulated genes in the T cell clusters between colon cancer and rectal cancer; (B) Enrichment analysis for the differentially expressed genes in tumor-infiltrating T cells between colon cancer and rectal cancer. Bubble diagram shows the top 5 gene ontology biological processes, KEGG pathways, and Reactome pathways for differentially expressed genes in tumor-infiltrating T cells between colon cancer and rectal cancer. Vertical axis shows terms of biological processes and pathways, and horizontal axis represents the genes in different cell clusters. Larger node size represents the larger ratio of enriched genes/total genes. The color from blue to red represents P value from large to small. C1, tumor-Treg; C2, CD4+ tissue-resident memory T cells; C3, CD4+ effector memory T cells; C5, CD8+ effector memory T cells; C6, CD8+ exhausted T cell; C7, CD8+ tissue-resident memory T cells. For C8 (CD8+ intraepithelial lymphocyte), differential analysis was not performed for that all the cells were from rectal cancer.   performed for the genes in modules ( Table 4). For example, for the DEGs in C7 (CD8 + T EMRA /T EFF ), the PPI network contained 265 genes and 1,205 interactions, and five modules were identified from the PPI network. The genes in different modules were enriched in different biological processes and pathways. The genes in module 4 included chemokine CCL5 and chemokine receptors CCR6, CCR7, and CXCR3, and were enriched in the chemokine signaling pathway, in response to chemokines. The genes in module 5 (TNFAIP3, MALT1, CASP8, and RBCK1) were implicated in signaling, including TNF signaling and I-kappaB kinase/NF-kappaB signaling, while the genes in module 3, including HLA-DRB1, HLA-DPB1, and CD247, were involved in PD-1 signaling, translocation of ZAP-70 to immunological synapse, and costimulation by the CD28 family.

DISCUSSION
In spite of the great advances in tumor immunotherapy, the heterogeneity of immune cells within tumors confounded the understanding of underlying mechanisms and the predictive performance on outcomes (17). T cells are important cellular targets in tumor immunotherapy, and TILs can be used to predict clinical responses and survival of patients (30). The scRNA-seq technologies show incomparable superiority in the assessment of tumor cellular heterogeneity, which represents an ongoing challenge in cancer treatment (16). In this study, using the scRNA-seq data of isolated T cells from the tumor tissues and peripheral blood of CRC patients which were moderately differentiated, we found that the T cells in the peripheral blood (CD4 + /CD8 + Naive T cells, CD4 + /CD8 + central memory T cell, CD4 + /CD8 + recently activated effector memory T cells and Blood-Treg) showed diverse phenotypes compared to those of the T cells in the tumor tissue (tumor-Treg, CD4 + /CD8 + T RM T cell, CD4 + /CD8 + effector memory T cells, Th17 cells, CD8 + exhausted T cell, and CD8 + intraepithelial lymphocytes), and this finding is consistent with that of Azizi et al. (17). Using unsupervised cluster analysis, we identified eight T cell types from tumor tissue, including tumor-Treg, CD4 + /CD8 + T RM T cell, CD4 + /CD8 + effector memory T cells, Th17 cells, CD8 + exhausted T cells, and CD8 + intraepithelial lymphocytes. Those T cell types may represent the major tumor-infiltrating T cell subsets in moderately differentiated CRC, and could be more specific cellular targets for the clinical immunotherapy of moderately differentiated CRC patients. T RM T cells are a recently found lymphocyte lineage specialized by some memory T cells for life and function within tissues (not present within peripheral blood) (31). T RM cells function in the enhancement of protective immunity, and its characteristics in tumors always are related to favorable outcomes (32). We found that CD8 + T EM showed a strong correlation with different cell clusters, including CD4 + T EM , CD8 + IEL, CD8 + T RM , and CD8 + T EX . Hence, we speculated that CD8 + T EM may be a more important tumor-infiltrating T cell type in moderately differentiated CRC. The function of CD8 + T EM and its prognostic value in CRC, especially moderately differentiated CRC, should be further investigated. T cell exhaustion represents a state of T cell function deterioration. The strong effector functions are lost and various inhibitory receptors are expressed in exhausted T cells (T EX ) (33). Fu et al. suggested that the tumor tissue showed high percentages of T EX and Treg cells compared to those in the peripheral blood (34), suggesting that tumor tissue showed relatively more immunosuppressive phenotypes. Consistently, CD8 + T EX cells were identified from the tumor tissue but not from the peripheral blood, in our study. On the contrary, tumor microenvironments consist of various cell types that communicate by ligand-receptor pairs. Targeted ligandreceptor pairs will provide promising targets in tumor immunotherapy, such as immune checkpoint inhibitors. We found that tumor-infiltrating CD8 + T EX showed more crosstalk with other cell clusters. For example, CD8 + T EX showed crosstalk with tumor-infiltrating Treg by a CCL4-CCR8 cytokine ligandreceptor pair. The scRNA-seq approach is useful for the study of the interactions across cell types in tumor microenvironments (35). In our study, a total of 7,852 ligand-receptor pairs among eight T cell types were identified from the tumor tissue, and 4,546 ligand-receptor pairs among the seven T cell clusters were identified from the peripheral blood. For example, checkpoint ligand-receptor pairs, such as tumor Treg CD80-Th17 cell CTLA4 and tumor Treg CD274-Th17 cell PDCD1, were identified in our study.
We also found some similarities and differences in the infiltrating T cells between colon cancer and rectal cancer. Tumor-infiltrating CD8 + IEL was found in rectal cancer but not in colon cancer, while CD8 + T N cell was found in the peripheral blood of colon cancer patients but not in that of rectal cancer patients. A larger number of tumor-infiltrating CD8 + Tex (88.94%) were found in colon cancer than in rectal cancer (11.06%). In the peripheral blood, 18.09% of CD4 + T EMRA /T EFF cells were found in colon cancer and 81.91% in rectal cancer, while 64.06% of CD8 + T EMRA /T EFF cells were found in colon cancer and 35.96% in rectal cancer. These findings suggest a heterogeneity for tumor-infiltrating T cells between colon cancer and rectal cancer. In spite of these differences, there are also similarities in the tumorinfiltrating T cells between colon cancer and rectal cancer. For example, the number of tumor-infiltrating CD8 + T RM , Th17, CD8 + T EM , and CD8 + T RM in colon cancer was not significantly different from that of rectal cancer. In peripheral blood, the number of CD4 + blood-T CM , Blood-Treg, and CD8 + T CM cells in colon cancer had no significant difference with that of rectal cancer. Besides, we further analyzed the differences in molecular expression and found that tumor-Treg cells showed more DEGs, while Th17 cells showed few DEGs between colon cancer and rectal cancer. For tumor-Treg, a total of 112 genes differentially expressed (43 up-regulated and 69 down-regulated) between colon cancer and rectal cancer. The PPI network that was constructed by those genes contained two significant modules. CCR6, CXCR3, CXCR6, and CCL5 were the genes in one significant module, and they were enriched in chemokine receptors bind chemokines, chemokine-mediated signaling pathway, etc. In peripheral blood, CD4 + T EMRA /T EFF and CD8 + T CM showed few DEGs, while CD4 + T N and CD8 + T EMRA /T EFF showed more DEGs between colon cancer and rectal cancer. For CD8 + T EMRA /T EFF , a total of 359 DEGs (69 up-regulated and 259 down-regulated) between colon cancer and rectal cancer. Those genes were mainly enriched in the adaptive immune system, T cell activation, and lymphocyte activation. Furthermore, the PPI network contained five significant modules, and the genes in different modules were implicated in different functions and pathways. For example, HLA-DRB1, HLA-DPB1, HLA-DPA1, HLA-DRB5, CD247, LCP2, PTPN11, and CD3E were genes in one significant module, and were involved in PD-1 signaling, generation of second messenger molecules, costimulation by the CD28 family, etc. Naive T cells are resting cells and are important for normal T cell homeostasis (36). IELs are a larger population of lymphocytes in intestinal epithelium constituting a part of the intestinal mucosal barrier (37). Memory T cell populations include T CM and T EM populations, which have different homing capacity and effector function. T EM cells mediate protective memory with immediate effector function, while T CM cells mediate reactive memory with less or without effector function (38).
The differences in tumor-infiltrating T cells between colon cancer and rectal cancer provide insights into the study of the  Figure 7, the gene on the left represents the marker genes of blood-Treg. Horizontal axis represents the seven identified cell clusters (C1 to C8). It could be seen that the marker genes of blood-Treg were significantly highly expressed in C4. Therefore, C4 was defined as tumor-Treg.  characteristics of tumor-infiltrating immune cells of colon and rectal cancers, and provide potential cellular targets and molecular targets in the immunotherapy of colon cancer and rectal cancer. Although only five samples were included in our study, we profiled 1632 T cells from the tumor tissue and 1252 cells from the peripheral blood using scRNA-seq. Based on 644 isolated glomerular cells, Fu et al. revealed the dynamic changes in diabetic kidney disease using scRNA-seqanalysis (39). Therefore, the sample size is enough to characterize the tumor-infiltrating T cells. In addition, only the five CRC patients with moderate differentiation were selected, which eliminated the confounding factors from different differentiation degree. Besides, multiple infiltrating T cells, including CD8 + T cells (CD3 + and CD8 + ), T helper cells (CD3 + , CD4 + , and CD25 − ), and regulatory T cells (CD3 + , CD4 + , and CD25 high) were collected, suggesting that our characterization of tumor-infiltrating T cells was relatively comprehensive. After scRNA-seq, clustering analysis was performed, which contributed to the identification of more functionally distinct T cell populations.

CONCLUSIONS
Based on scRNA-seq analysis, we characterized the T cell subsets in CRC tumor tissue and peripheral blood. The T cells in peripheral blood showed diverse phenotypes in comparison A B FIGURE 9 | Differentially expressed genes in blood T cells between colon cancer and rectal cancer. (A) Volcano plot shows the up-regulated and down-regulated genes in the T cell clusters between colon cancer and rectal cancer; (B) Enrichment analysis for the differentially expressed genes in blood T cells between colon cancer and rectal cancer. Bubble diagram shows the top 5 Gene Ontology biological processes, KEGG pathways and Reactome pathways for differentially expressed genes in the peripheral blood T cells between colon cancer and rectal cancer. Vertical axis shows terms of biological processes and pathways, and horizontal axis represents the genes in different cell clusters. Larger node size represents the larger ratio of enriched genes/total genes. The color from blue to red represents P value from large to small. C1, CD4+ Naive T cell. C2, CD4+ central memory T cell; C3, CD4+ recently activated effector memory T cells; C4, blood-Treg; C7, CD8+ recently activated effector memory T cells.
with that of tumor tissue, and exhausted T cells were identified in tumor tissue. Various ligand-receptor interactions were identified across cell types. Colon cancer and rectal cancer also showed differences in the composition of tumor-infiltrating T cell populations. These findings can promote a better understanding of the mechanisms underlying tumorinfiltrating T cells in the progression of CRC and provide potential targets in the immunotherapy of CRC.
FIGURE 10 | PPI network and modules for differentially expressed genes in blood T cells between colon cancer and rectal cancer. PPI network shows the interactions for differentially expressed genes in blood T cells between colon cancer and rectal cancer. Triangle nodes represent the up-regulated genes and Vshaped nodes represent the down-regulated genes; node size represent the degree of the node in network; different color represent different modules; C1, CD4+ Naive T cell. C2, CD4+ central memory T cell; C7, CD8+ recently activated effector memory T cells.

DATA AVAILABILITY STATEMENT
Publicly available datasets were analyzed in this study. This data can be found here: EGA (accession number EGAS00001002791); Gene Expression Omnibus (GEO) (accession number GSE108989).