scRNA-seq of total bone marrow mononuclear cells and CD3+ T cells of multiple myeloma patients and healthy donors

The study includes 50 scRNA-seq samples of bone marrow aspirates of 11 multiple myeloma patients and 3 healthy donors generated with the 10x Genomics platform (Chromium Single Cell 3’ Solution v2). For the multiple myeloma patients, paired longitudinal samples are available from initial diagnosis and long-term surivor state. For each patient and timepoint, total bone marrow mononuclear cells and CD3+ T cells have been sequenced separately.

Type: Other
Archiver: European Genome-Phenome Archive (EGA)

1 Dataset 2 Publications

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Dataset ID	Description	Technology	Samples
EGAD00001010025	scRNA This dataset contains 50 scRNA-seq samples from bone marrow aspirates of 11 multiple myeloma patients experiencing long-term survival and 3 healthy donors. For each donor, total bone marrow and CD3+ T cells were sequenced. For multiple myeloma patients, paired samples were collected at initial diagnosis and between 7-17 years after first-line therapy. Bone marrow mononuclear cells were isolated by Ficoll density gradient centrifugation. For sorting of total bone marrow cells singlet, live cells were gated and sorted, for sorting of T cells CD45+, CD3+ cells were gated and sorted on either FACSAria Fusion or FACSAria II. Single-cell RNA sequencing were generated using 10x Genomics single-cell RNAseq technology (Chromium Single Cell 3’ Solution v2) according to the manufacturer’s protocol and sequenced on an Illumina HiSeq4000 (paired end, 26 and 74 bp). Bulk RNA Singlet, live CD3+CD4- CXCR3+CD8+ and CD3+CD4- CXCR3-CD8+ cells were sorted from 7 bone marrow and 3 peripheral blood samples of 7 multiple myeloma patients using a FACSAria Fusion machine. Bulk-RNA sequencing libraries were generated using the SMART Seq Stranded Total RNA-Seq kit (Takara) and sequenced using the Illumina NovaSeq 6000 platform (2 x 100 bp).	Illumina HiSeq 4000 Illumina NovaSeq 6000	82

Publications	Citations
Proteomic signatures improve risk prediction for common and rare diseases. Carrasco-Zanini J, Pietzner M, Davitte J, Surendran P, Croteau-Chonka DC, Robins C, Torralbo A, Tomlinson C, Grünschläger F, Fitzpatrick N, Ytsma C, Kanno T, Gade S, Freitag D, Ziebell F, Haas S, Denaxas S, Betts JC, Wareham NJ, Hemingway H, Scott RA, Langenberg C. Nat Med 30: 2024 2489-2498	4
Multiple myeloma long-term survivors exhibit sustained immune alterations decades after first-line therapy. Lutz R, Grünschläger F, Simon M, Awwad MHS, Bauer M, Yousefian S, Beumer N, Jopp-Saile L, Sedlmeier A, Solé-Boldo L, Avanesyan B, Vonficht D, Stelmach P, Steinbuss G, Boch T, Steiger S, Baertsch MA, Prokoph N, Rippe K, Durie BGM, Wickenhauser C, Trumpp A, Müller-Tidow C, Hübschmann D, Weinhold N, Raab MS, Brors B, Goldschmidt H, Imbusch CD, Hundemer M, Haas S. Nat Commun 15: 2024 10396	0

Publications

Citations

Proteomic signatures improve risk prediction for common and rare diseases.
Carrasco-Zanini J, Pietzner M, Davitte J, Surendran P, Croteau-Chonka DC, Robins C, Torralbo A, Tomlinson C, Grünschläger F, Fitzpatrick N, Ytsma C, Kanno T, Gade S, Freitag D, Ziebell F, Haas S, Denaxas S, Betts JC, Wareham NJ, Hemingway H, Scott RA, Langenberg C. Nat Med 30: 2024 2489-2498

Multiple myeloma long-term survivors exhibit sustained immune alterations decades after first-line therapy.
Lutz R, Grünschläger F, Simon M, Awwad MHS, Bauer M, Yousefian S, Beumer N, Jopp-Saile L, Sedlmeier A, Solé-Boldo L, Avanesyan B, Vonficht D, Stelmach P, Steinbuss G, Boch T, Steiger S, Baertsch MA, Prokoph N, Rippe K, Durie BGM, Wickenhauser C, Trumpp A, Müller-Tidow C, Hübschmann D, Weinhold N, Raab MS, Brors B, Goldschmidt H, Imbusch CD, Hundemer M, Haas S. Nat Commun 15: 2024 10396