CellCell BiologyPeer-reviewed journal article
Mapping the Cellular Biogeography of Human Bone Marrow Niches Using Single-Cell Transcriptomics and Proteomic Imaging
Shovik BandyopadhyayiD, Michael P. DuffyiD, Kyung Jin AhniD, Jonathan SussmaniD, Minxing PangiD, David W. SmithiD, Gwendolyn Duncan, Iris Zhang, Jeffrey Huang, Yulieh Lin, Barbara XiongiD, Tamjid ImtiaziD, Chia-Hui Chen, Anusha ThadiiD, Changya CheniD, Jason Z. XuiD, Mélissa Reichart, Zachary MartineziD, Caroline Diorio, Chider CheniD, Vinodh PillaiiD, Oraine Snaith, Derek A. OldridgeiD, Siddharth Bhattacharyya, Ivan P. MaillardiD, , Kai TaniD
Abstract
The bone marrow is the organ responsible for blood production. Diverse non-hematopoietic cells contribute essentially to hematopoiesis. However, these cells and their spatial organization remain largely uncharacterized as they have been technically challenging to study in humans. Here, we used fresh femoral head samples and performed single-cell RNA sequencing (scRNA-Seq) to profile 29,325 enriched non-hematopoietic bone marrow cells and discover nine transcriptionally distinct subtypes. We next employed CO-detection by inDEXing (CODEX) multiplexed imaging of 18 individuals, including both healthy and acute myeloid leukemia (AML) samples, to spatially profile over one million single cells with a novel 53-antibody panel. We discovered a relatively hyperoxygenated arterio-endosteal niche for early myelopoiesis, and an adipocytic, but not endosteal or perivascular, niche for early hematopoietic stem and progenitor cells. We used our atlas to predict cell type labels in new bone marrow images and used these predictions to uncover mesenchymal stromal cell (MSC) expansion and leukemic blast/MSC-enriched spatial neighborhoods in AML patient samples. Our work represents the first comprehensive, spatially-resolved multiomic atlas of human bone marrow and will serve as a reference for future investigation of cellular interactions that drive hematopoiesis.
- organism: human
- assay: scRNA-seq
- assay: proteomics
- research focus: cell type annotation
- research focus: spatial domain detection
- approach: deep learning
- approach: statistical model
- Citations
- 331
- FWCI
- 36.70
- Field percentile
- top 1%
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