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. 2021 Nov 29;12:6960. doi: 10.1038/s41467-021-26951-z

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© The Author(s) 2021

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Fig. 1 — a Typical disease course of RRMM patients and sampling time-points of the study. b Workflow for scRNA-seq including CD138 sorting of myeloma cells. c Overview of scRNA-seq data analysis. Left: clustering and cell type identification was based on scRNA-seq of 212,404 cells from primary mononuclear bone marrow samples of RRMM patients (n = 20) as shown by an UMAP embedding colored by sample without batch-effect correction. Middle: myeloma cells subclones according to genetic aberrations were identified while changes of BME cells were evaluated against the HCA data set of healthy donors. Right: by integrating these data, subclone-specific interactions of myeloma cells with BME cells were revealed. d UMAP embedding as shown in panel (c) but colored according to expression of the plasma cell marker TNFRSF17. e Same as panel (d) but colored for plasma cells according to the annotation with SingleR.