Dataset Information

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis.

ABSTRACT: As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are observed in T cells, macrophages, and monocytes. Concordance and correlation analysis of cell type marker gene expression across different modalities highlighted the importance of choosing cell type marker genes best suited to particular modalities. By integrating data from these three assays, we found International Staging System stage 3 patients exhibited decreased CD4⁺ T/CD8⁺ T cells ratio. Moreover, we observed upregulation of RAC2 and PSMB9, in natural killer cells of fast progressors compared with those of nonprogressors, as revealed by both scRNA-seq and CITE-seq RNA measurement. This detailed examination of the immune microenvironment in multiple myeloma using multiple single-cell technologies revealed markers associated with multiple myeloma rapid progression which will be further characterized by the full-scale immune atlas project.

Significance

scRNA-seq, CyTOF, and CITE-seq are increasingly used for evaluating cellular heterogeneity. Understanding their concordances is of great interest. To date, this study is the most comprehensive examination of the measurement of the immune microenvironment in multiple myeloma using the three techniques. Moreover, we identified markers predicted to be significantly associated with multiple myeloma rapid progression.

SUBMITTER: Yao L

PROVIDER: S-EPMC10035369 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis.

Yao Lijun L Jayasinghe Reyka G RG Lee Brian H BH Bhasin Swati S SS Pilcher William W Doxie Deon Bryant DB Gonzalez-Kozlova Edgar E Dasari Surendra S Fiala Mark A MA Pita-Juarez Yered Y Strausbauch Michael M Kelly Geoffrey G Thomas Beena E BE Kumar Shaji K SK Cho Hearn Jay HJ Anderson Emilie E Wendl Michael C MC Dawson Travis T D'souza Darwin D Oh Stephen T ST Cheloni Giulia G Li Ying Y DiPersio John F JF Rahman Adeeb H AH Dhodapkar Kavita M KM Kim-Schulze Seunghee S Vij Ravi R Vlachos Ioannis S IS Mehr Shaadi S Hamilton Mark M Auclair Daniel D Kourelis Taxiarchis T Avigan David D Dhodapkar Madhav V MV Gnjatic Sacha S Bhasin Manoj K MK Ding Li L

Cancer research communications 20221025 10

As part of the Multiple Myeloma Research Foundation (MMRF) immune atlas pilot project, we compared immune cells of multiple myeloma bone marrow samples from 18 patients assessed by single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), and cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) to understand the concordance of measurements among single-cell techniques. Cell type abundances are relatively consistent across the three approaches, while variations are obse ...[more]

PMID: 36969740

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Project description:BackgroundThe prognostic management of bladder cancer (BLCA) remains a great challenge for clinicians. Recently, bulk RNA-seq sequencing data have been used as a prognostic marker for many cancers but do not accurately detect core cellular and molecular functions in tumor cells. In the current study, bulk RNA-seq and single-cell RNA sequencing (scRNA-seq) data were combined to construct a prognostic model of BLCA.MethodsBLCA scRNA-seq data were downloaded from Gene Expression Omnibus (GEO) database. Bulk RNA-seq data were obtained from the UCSC Xena. The R package "Seurat" was used for scRNA-seq data processing, and the uniform manifold approximation and projection (UMAP) were utilized for downscaling and cluster identification. The FindAllMarkers function was used to identify marker genes for each cluster. The limma package was used to obtain differentially expressed genes (DEGs) affecting overall survival (OS) in BLCA patients. Weighted gene correlation network analysis (WGCNA) was used to identify BLCA key modules. The intersection of marker genes of core cells and genes of BLCA key modules and DEGs was used to construct a prognostic model by univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) analyses. Differences in clinicopathological characteristics, immune microenvironment, immune checkpoints, and chemotherapeutic drug sensitivity between the high and low-risk groups were also investigated.ResultsscRNA-seq data were analyzed to identify 19 cell subpopulations and 7 core cell types. The ssGSEA showed that all 7 core cell types were significantly downregulated in tumor samples of BLCA. We identified 474 marker genes from the scRNA-seq dataset, 1556 DEGs from the Bulk RNA-seq dataset, and 2334 genes associated with a key module identified by WGCNA. After performing intersection, univariate Cox, and LASSO analysis, we obtained a prognostic model based on the expression levels of 3 signature genes, namely MAP1B, PCOLCE2, and ELN. The feasibility of the model was validated by an internal training set and two external validation sets. Moreover, patients with high-risk scores are predisposed to experience poor OS, a larger prevalence of stage III-IV, a greater TMB, a higher infiltration of immune cells, and a lesser likelihood of responding favorably to immunotherapy.ConclusionBy integrating scRNA-seq and bulk RNA-seq data, we constructed a novel prognostic model to predict the survival of BLCA patients. The risk score is a promising independent prognostic factor that is closely correlated with the immune microenvironment and clinicopathological characteristics.

Dataset Information

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis.

Significance

Publications

Comprehensive Characterization of the Multiple Myeloma Immune Microenvironment Using Integrated scRNA-seq, CyTOF, and CITE-seq Analysis.

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