key: cord-0282784-6wf0oofn authors: Ding, Renpeng; Liu, Shang; Wang, Shanshan; Chen, Huanyi; Wang, Fei; Xu, Qumiao; Zhu, Linnan; Dong, Xuan; Gu, Ying; Chao, Cheng-Chi; Gao, Qianqian title: Single-cell transcriptome analysis of the immunosuppressive effect of differential expression of tumor PD-L1 on responding TCR-T cells date: 2020-07-24 journal: bioRxiv DOI: 10.1101/2020.07.23.217059 sha: 15cf1b3da14f67c4fdcb4bc8349e3705fdbf9aee doc_id: 282784 cord_uid: 6wf0oofn PD-L1 expression levels in tumors do not consistently predict cancer patients’ response to PD-(L)1 inhibitors. We therefore evaluated how tumor PD-L1 levels affect the anti-PD-(L)1 efficacy and T cell function. We used MART-1-specific TCR-T cells (TCR-TMART-1) stimulated with MART-127-35 peptide-loaded MEL-526 tumor cells with different proportions of them expressing PD-L1 to perform cellular assays and high-throughput single-cell RNA sequencing. Compared to control T cells, TCR-TMART-1 were more sensitive to exhaustion and secreted lower pro-inflammatory but higher anti-inflammatory cytokines with increasing proportions of PD-L1+ tumor cells. The colocalization of T cells and tumor cells in gene clusters correlated negatively with the proportion of PD-L1+ tumor cells and positively with immune cell cytotoxicity. Moreover, elevated proportion of PD-L1+ tumor cells increased PD-L1 expression and decreased PD-1 expression on T cells and enhanced T cell death. The expression of PD-1 and PD-L1 in T cells and macrophages also correlated positively with COVID-19 severity. For T cell clusters, CXCR4 and early activation marker CD69 were upregulated in cluster 1. Cluster 3 was more similar to cluster 5 and the expression of SELL, IL7R, and TCF7 was Afterwards, the co-culture was subjected to scRNA-seq. Unstimulated TCR-T cells (5×10^5 4 5 2 cells/ml) were incubated for 6 h alone before subjected to scRNA-seq. The expression matrix obtained in the above steps was used as input to Seurat v. 3 to perform 4 9 5 batch effect correction, standardization, dimensionality reduction, and clustering. First, the 4 9 6 "LogNormalize" function was applied to normalize the data. Second, the "vst" method in the 4 9 7 "FindVariableFeatures" function was used to detect variable genes, and the top 3000 variable 4 9 8 genes were selected for downstream analysis. Third, the "FindIntegrationAnchors" and 4 9 9 "IntegrateData" functions were used to correct batch effects. Fourth, the top 3000 variable genes We applied the FindMarkers to differential gene expression analysis. For each cluster of T cells The Monocle (version 2) algorithm with the signature genes of different functional clusters was 5 1 5 applied to order CD8 + T cells excluding clusters expressing proliferating or mitochondrial genes in 5 1 6 pseudo time. UMI value was first converted into normalized mRNA counts by the "relative2abs" 5 1 7 function in monocle and created an object with parameter "expressionFamily = negbinomial.size" Gene Ontology (GO) enrichment analysis was performed on the differential genes of each cluster, 5 2 3 and the results were used for cell type definition. The "enrichGO" function in the "clusterProfiler" 5 2 4 package to perform GO analysis using the corresponding default parameters. Pathways with the q 5 2 5 value <0.05 corrected by FDR were used for analysis. GSVA was used to identify the molecular phenotype of each cluster with the normalized UMI data. The average normalized expression across T cell clusters was first obtained. Then, GSVA scores of 5 3 0 gene sets for different clusters were calculated. GSVA values were plotted as a heatmap using R 5 3 1 package "pheatmap". The data that support the findings of this study have been deposited into CNGB Sequence Archive 5 3 5 . t e p h e n , C h i a r i o n -S i l e n i V a n n a , G o n z a l e z R e n e , G r o b J e a n -J a c q u e s , 6 2 3 R u t k o w s k i P i o t r , C o w e y C h a r l e s L a n c e , e t a l . , 6 6 7 u p r e g u l a t i o n o f P D -1 , L A G -3 , a n d C T L A -4 l i m i t s t h e e f f i c a c y o f s i n g l e -a g e 8 13 17 18 1 3 5 9 12 14 6 7 10 11 15 16 19 S100A6 TXN MT2A ROMO1 S100A2 FDCSP AL358472.6 RAB13 CXCL10 GABBR1 SOD2 IFITM3 FTH1 CXCL11 UBD IDO1 CCL2 MAGEA4 IGFBP3 CD274 ICAM1 FN1 SERPINE2 DCBLD2 MMP9 INHBA TNFRSF11B TNC JUN 7SK RN7SK HSPB1 CSKMT AP001160.1 FOS ZFAS1 CXCL8 SOX4 KRT18 HES1 CXCL3 CXCL2 YBX1 CNN3 RPL35 TPM1 PCLAF YWHAE CAVIN1 CAV1 MAL CXCR4 STK17B SYTL3 CD69 PTGER2 IL4R CD7 SELL TRIB2 TTN IL7R FYB1 TCF7 RIPOR2 LRRN3 GIMAP7 ATM SORL1 SYNE2 EVL MALAT1 CD52 TXNIP PLEK GZMK SYNE1 TRGC2 RCBTB2 RBL2 CCL5 AC015849.1 NKG7 CST7 GNLY CCR5 GZMA HLA−DRB1 HLA−DQA1 GZMH CCL4 STMN1 HMGN2 ASPM CENPF RRM2 HMGB2 HIST1H4C MKI67 NUSAP1 TOP2A ODC1 MCM6 HSPD1 TYMS PCNA CSF2 DUSP4 IL2RA CCND2 AC006064.4 CCL3 IL5 TNFRSF9 XCL2 XCL1 RGS16 REL FABP5 CRTAM IFNG RGCC GZMB CCL1 IL2 IL3 SNHG3 PTPN7 ANKRD36C Cluster 10 Cluster 7 Cluster 11 Cluster 15 Cluster 12 Cluster 1 Cluster 5 Cluster 9 Cluster 3 Cluster 9 Cluster 12 GBP5 CD2 PBXIP1 GNLY ITGA4 ARL4C GZMK GZMA RIPOR2 LTB GIMAP5 IFITM2 IFITM1 PDE3B NEAT1 BTG1 ITM2B LPAR6 RCBTB2 GZMH CORO1A CYBA CCL5 LIMD2 ITGB2 RAC2 LGALS1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 TCR-T MART-1 T null int low Ctrl PD-L1 high PD-L1 int PD-L1 low B TNFRSF14 IL6R IRAK2 IL12RB1 IL10RB IFNAR1 IL17RA TNFRSF13C IL2RG TNFRSF25 IL7R SIGIRR IL10RA IL27RA IFNAR2 TNFRSF18 TNFRSF9 TNFRSF1B IL12RB2 IL18R1 IL18RAP IL1RAP IFNGR1 IL15RA IL2RA RELT TNFRSF1A IL21R IRAK1 IFNLR1 TIRAP TNFRSF12A IL22RA1 IL1RL1 TNFRSF21 TNFRSF19 IL17RD IL1R1 IL1RAPL2 IL1R2 IL4R IFNGR2 IL23R TNFRSF8 TNFRSF10B TNFRSF11A CSF2RB IL2RB IL17RC IL17RE IL31RA TNFRSF4 IL17RB TNFRSF11B IL1RAPL1 IL13RA2 IL13RA1 IL24 IL16 TNFSF12 IL7 TNFSF13B TNFSF4 IFNG TNFSF9 TNFSF14 TGFB1 CSF2 TNFSF11 IL2 IL3 IL31 IL17D IL11 IL15 IL6 CSF1 TNF IL34 TNFSF8 TNFSF15 TGFB3 IL23A IL12A IL5 IL13 IL19 IL1A IL1B TNFSF10 IL4 IL10 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Ctrl PD-L1 high PD-L1 int PD-L1 low Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART-1 Tnull TCR-TMART TP53 BAX BAK1 BCL2 BCL2L1 CYCS APAF1 CASP9 CASP3 TNFRSF1A TRADD FADD BID FAS FASLG CASP8 CASP10 RIPK1 RIPK3 MLKL SQSTM1 MAP1LC3A MAP1LC3B ATG5 ATG7 BECN1 GSDMD GSDME IL18 IL1B PD-L1 high PD-L1 int PD-L1 low PD-L1 high PD-L1 low PD-L1 int 4 7 3 5 7 3 6 7 3 7 7 3 8