Cited by 45Multiple Myeloma Research and TreatmentsSingle-cell and spatial transcriptomicsMonoclonal and Polyclonal Antibodies Research

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

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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.

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Research works printed but not linked: 20

  1. Liu, 2021, Co-evolution of tumor and immune cells during progression of multiple myeloma, Nat Commun, 12, 2559, 10.1038/s41467-021-22804-x · DOI
  2. Adams, 2019, High-parameter mass cytometry evaluation of relapsed/refractory multiple myeloma patients treated with daratumumab demonstrates immune modulation as a novel mechanism of action, Cytometry A, 95, 279, 10.1002/cyto.a.23693 · DOI
  3. Redoglia, 1990, Multiple myeloma: altered CD4/CD8 ratio in bone marrow, Haematologica, 75, 129
  4. Koike, 2002, Relationship between CD4(+)/CD8(+) T cell ratio and T cell activation in multiple myeloma: reference to IL-16, Leuk Res, 26, 705, 10.1016/S0145-2126(01)00192-8 · DOI
  5. Zelle-Rieser, 2016, T cells in multiple myeloma display features of exhaustion and senescence at the tumor site, J Hematol Oncol, 9, 116, 10.1186/s13045-016-0345-3 · DOI
  6. Butler, 2018, Integrating single-cell transcriptomic data across different conditions, technologies, and species, Nat Biotechnol, 36, 411, 10.1038/nbt.4096 · DOI · Paper page
  7. Dietz, 2006, A novel source of viable peripheral blood mononuclear cells from leukoreduction system chambers, Transfusion, 46, 2083, 10.1111/j.1537-2995.2006.01033.x · DOI
  8. Van Gassen, 2015, FlowSOM: using self-organizing maps for visualization and interpretation of cytometry data, Cytometry A, 87, 636, 10.1002/cyto.a.22625 · DOI
  9. Kotecha, 2010, Web-based analysis and publication of flow cytometry experiments, Curr Protoc Cytom, 10.1002/0471142956.cy1017s53 · DOI
  10. Vogel, 2012, Insights into the regulation of protein abundance from proteomic and transcriptomic analyses, Nat Rev Genet, 13, 227, 10.1038/nrg3185 · DOI · Paper page
  11. Kim, 2020, CiteFuse enables multi-modal analysis of CITE-seq data, Bioinformatics, 36, 4137, 10.1093/bioinformatics/btaa282 · DOI
  12. Ding, 2020, Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth, Sci Rep, 10, 19825, 10.1038/s41598-020-76972-9 · DOI
  13. Ntranos, 2019, A discriminative learning approach to differential expression analysis for single-cell RNA-seq, Nat Methods, 16, 163, 10.1038/s41592-018-0303-9 · DOI
  14. Krebs, 2020, Pathogen-induced tissue-resident memory TH17 (TRM17) cells amplify autoimmune kidney disease, Sci Immunol, 5, eaba4163, 10.1126/sciimmunol.aba4163 · DOI
  15. Calame, 2001, Plasma cells: finding new light at the end of B cell development, Nat Immunol, 2, 1103, 10.1038/ni1201-1103 · DOI
  16. Qiu, 2020, Embracing the dropouts in single-cell RNA-seq analysis, Nat Commun, 11, 1169, 10.1038/s41467-020-14976-9 · DOI
  17. Xiong, 2018, The expression of actin-related protein 2/3 complex subunit 5 (ARPC5) expression in multiple myeloma and its prognostic significance, Med Sci Monit, 24, 6340, 10.12659/MSM.908944 · DOI
  18. Liu, 2017, Network-based analysis of the molecular mechanisms of multiple myeloma and monoclonal gammopathy of undetermined significance, Oncol Lett, 14, 4167, 10.3892/ol.2017.6723 · DOI
  19. Jang, 2019, Molecular signatures of multiple myeloma progression through single cell RNA-Seq, Blood Cancer J, 9, 2, 10.1038/s41408-018-0160-x · DOI
  20. Tang, 2019, The single-cell sequencing: new developments and medical applications, Cell Biosci, 9, 53, 10.1186/s13578-019-0314-y · DOI

Metadata

Authors
Yao, L., Jayasinghe, R. G., Lee, B. H., Bhasin, S. S., Pilcher, W., Doxie, D. B., Gonzalez-Kozlova, E., Dasari, S., Fiala, M. A., Pita-Juarez, Y., Strausbauch, M., Kelly, G., Thomas, B. E., Kumar, S. K., Cho, H. J., Anderson, E., Wendl, M. C., Dawson, T., D'souza, D., Oh, S. T., Cheloni, G., Li, Y., DiPersio, J. F., Rahman, A. H., Dhodapkar, K. M., Kim-Schulze, S., Vij, R., Vlachos, I. S., Mehr, S., Hamilton, M., Auclair, D., Kourelis, T., Avigan, D., Dhodapkar, M. V., Gnjatic, S., Bhasin, M. K., & Ding, L.
Affiliations
  • Washington University in St. Louis (US)
  • Icahn School of Medicine at Mount Sinai (US)
  • Emory University (US)
  • 2Icahn School of Medicine at Mt. Sinai, New York, New York.
  • Mayo Clinic (US)
  • 5Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts.
  • Multiple Myeloma Research Foundation (US)
  • Beth Israel Deaconess Medical Center (US)
Corresponding author
Ding, Li
Venue
Cancer Research Communications
Published
2022-10-25
Volume
2
Issue
10
Pages
1255-1265
ISSN
2767-9764 (ISSN-L)
OpenAlex
W4293757902
Cited by
45
References
5
Citations by year
2026: 10 · 2025: 19 · 2024: 5 · 2023: 11
FWCI
6.36
Type
journal article
Language
en
Open access
Locations
  • Cancer Research Communications (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • Digital Commons@Becker (Washington University School of Medicine) (submittedVersion, cc-by)
Funders
Multiple Myeloma Research Foundation
Topics
  • Health Sciences › Medicine › Hematology › Multiple Myeloma Research and Treatments
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Immunology and Microbiology › Immunology › Monoclonal and Polyclonal Antibodies Research
Keywords
multiple myeloma, tumor immune microenvironment, bone marrow, single-cell RNA sequencing, mass cytometry, CITE-seq, multi-omics integration, assay concordance, cell type markers, CD4/CD8 ratio, natural killer cells, Rac2, PSMB9, disease progression, macrophages, monocytes
MeSH terms
Single-Cell Gene Expression Analysis, Humans, Multiple Myeloma, Pilot Projects, CD8-Positive T-Lymphocytes, Tumor Microenvironment, Transcriptome

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