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case_images: Case presentation and general overview of single cell transcriptome sequencing results in a refractory DLBCL sample (A) The treatment process of a 78-year-old woman and the representative CT images at the time of diagnosis (2021.02) and at two points of disease progression (2021.06 and 2021.08) (B) Uniform Manifold Approximation and Projection (UMAP) representation of twelve clusters and five identified cell types. (C) Heatmap of the relative expression level of marker genes across cells, sorted by cell type. Marker genes included CD19, CD79A and MS4A1 for B cells, CD2, CD3D and CD3E for T cells, GNLY and NCAM1 for NK cells, CD14 and LYZ for Myeloid cells, COL1A1 for fibroblasts. The expression was measured as the z-score normalized log2 (count+1). (D) Expression levels of typical marker genes across 9044 cells illustrated as UMAP plots.
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case_images: Figure 1 ). Five major cell types were identified using canonical marker genes: B cells (marker genes: CD19, MS4A1 and CD79A), T cells (marker genes: CD3D, CD3E, CD2),
case_images: Malignant identification of B cells extracted from the refractory DLBCL patient's tissue and integration analysis of benign B cells from external data (A) UMAP plot of 4361 B cells from the refractory DLBCL tissue, showing the formation of the 4 main clusters. The IGKC fraction, IGKC / (IGKC + IGLC2), was calculated for each B cell. If the IGKC fraction was >0.5, we classified a B cell as kappa+, and if this ratio was below 0.5, we classified the B cell as lambda+. The percentage of B cells expressing either kappa or lambda was calculated per ranscriptionally distinct B-cell cluster. The non-malignant healthy B-cell cluster contained approximately 50% kappa- and 50% lambda-expressing B cells, whereas the malignant clusters contained B cells homogeneously expressing the lambda or kappa light chain. All the four B cell clusers (cluster 1, 2, 3 and 8) identified in this refractory DLBCL sample expressed the lambda light chain homogeneously and were therefore identified as malignant B cells. (B) After integrating the scRNA-Seq data from the study of Roider et al., three clusters were identified after re-clustering. Cluster 0,1 from external single-cell data was identified as benign B cell population, Cluster 1 from our refractory DLBCL sample was identified as malignant. (C) Heat map showing the top 10 differential expressed genes in the malignant B cell subpopulation and non-malignant B cell subpopulations (Wilcoxon test). (D) Several significant pathways had higher or lower activities in malignant B cell subpopulation than non-malignant B cell subpopulations by Gene Set Enrichment Analysis (GSEA). (E) Bubble map of significant pathways enriched in the malignant B cells subpopulation by GSEA.
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case_images: To investigate the transcriptomic heterogeneity of malignant B cells in DLBCL tissues, we re-clustered the B cells and identified four cell subpopulations ( Figure 2A ). IGKC fraction (IGKC/IGKC + IGLC2) method was used to distinguish malignant B cells.  Figure 2A  shows the IGKC fraction of the four B cell clusters 1, 2, 3, 8. It can be seen that the IGKC fraction of almost all B cells in these four clusters was lower than 0.25, indicating that they were B cells with uniform expression of lambda+. The Uniform Manifold Approximation and Projection (UMAP) showed that all B cells of the four samples were clustered into three clusters (0, 1, 2), and one malignant cluster (cluster 1) identified by the IGKC scoring method was well distinguished from the other two non-malignant clusters (cluster 0, 2) ( Figure 2B ). We constructed a heat map showing that the top 10 genes expressed in malignant B cells were related to ATP synthesis (ATP5F1E, ATP5MG, and ATP5MC2) and transcription regulation (ATF5, EEF1G, and ELOB), as well as LNCRNA (GAS5 and SNHG5) and ribosomal protein (RACK1) ( Figure 2C ). Furthermore, GSEA analysis showed that antigen processing and presentation enrichment were significantly downregulated in malignant B cells compared with normal B cells ( Figures 2D, E ).
case_images: Heterogeneity analysis among two malignant B cell subpopulations (cluster2_8 vs cluster1_3) (A) The volcano map shows the differentially expressed genes between cluster2_8 and cluster1_3. (B) Several significant pathways with higher activities in cluster2_8 than cluster1_3 by GSEA. (C) Heatmap of the relative expression fold change (log2) of genes in essential pathogenic signaling pathways and HLA molecules in cluster2_8 and cluster1_3. (D) UMAP plots of selected genes (MYC, BCL2) expression level of different subsets of B cells. (E) UMAP plots of two immune checkpoint related genes (CD274, CD47) expression.
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case_images: Figure 3A ). Further GSEA analysis showed that hallmarks of TNFA signaling via NFKB, IL6/JAK/STAT3 signaling, inflammatory response signaling, and interferon-gamma signaling were highly enriched in cluster2_8 compared with cluster1_3 ( Figure 3B ). In addition, we compared the expression levels of different pathogenic signaling pathways in B-cell lymphoma in two subpopulations and the results showed that expression of BCR signaling, CD40 signaling, and NFKB signaling was generally higher in cluster2_8 vs. cluster1_3. In addition, cluster1_3 showed low expression of HLA I and II molecules, which may be beneficial for evasion of immune surveillance ( Figure 3C ). Therefore, we analyzed MYC and BCL2 gene expression in B cell clusters and found that the expression of both two genes was significantly higher in cluster2_8 than in cluster1_3 ( Figure 3D ). In addition, the expression of CD274 and CD47, two immunosuppressive immune checkpoints, were higher in cluster2_8 than in cluster1_3 ( Figure 3E ).
case_images: Subclustering analysis of T cells. (A) UMAP plots of expression of marker genes (CD2, CD3D and CD3E) in identified T cell clusters. (B) Identification and annotation of T cell subsets. Cells were colored according to their cluster or type. (C) UMAP plots plot showing the CD8 positive T cells clusters and exhausted-related signature genes (LAG3, PDCD1, TIGIT, HAVCR2, CTLA4, and TOX) expression in CD8 positive T cells clusters. (D) UMAP plots plot showing the expression of marker genes (CD4, IL2RA, FOXP3) in CD4 Treg cells. (E) Dot plot of predicted ligand-receptor interactions between different cell subsets in the microenvironments.
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case_images: T lymphocytes in the LME, are highly heterogeneous and play a crucial role in tumor immune evasion and immunotherapeutic efficacy. To study the intrinsic transcriptome characteristics of infiltrating T cells in DLBCL, particularly those with immunosuppressive properties, we re-clustered T cells and identified 10 CD4+ or CD8+ T cell subsets ( Figures 4A, B ). T cells. In addition, based on increased expression of exhausted markers (LAG3, PDCD1, TIGIT, HAVCR2, CTLA4, and TOX), we identified two typical exhausted CD8+ T cell clusters (C3 and C6,  Figures 4B, C ). Tregs cells and CD8+ exhausted T cells were significantly enriched in this relapsed/refractory patient, highlighting the microenvironment's immunosuppressive nature. This finding suggests that this patient may benefit from immune checkpoint blockade therapies. T cells ( Figure 4E ). TNF-TNFR2 and TNF-ICOS interactions were more enriched between MYC+BCL2+ B cells and T cells, suggesting that MYC+BCL2+ B cells may contribute to the maintenance of the tumor-friendly immune microenvironment. This is because TNF-TNFR2 and TNF-ICOS signals have been shown to recruit immunosuppressant immune cells.