Hematopoietic stem and progenitor cells (HSPCs) can generate a diversity of blood cells throughout the human lifespan. Although these cells have been evaluated using both sorted (Corces et al. Nat. Genet. 2016) and single cell assays (Pellin et al Nat. Commun. 2019), there remains uncertainty in the degree of heterogeneity within HSPC subpopulations and their associated differentiation trajectories. The phenotypic diversity within HSPCs needs to be better characterized in order to understand the pathogenesis of blood disorders including hematologic malignancies. To address this need, we characterized healthy bone marrow mononuclear cells (BMMCs) with whole transcriptome analysis (WTA) and surface marker evaluation. We hypothesized that by utilizing concurrent RNA and surface marker analysis (n=35), we can improve HSPC clustering and characterize specific phenotypic states along unique hematopoietic differentiation trajectories. Three healthy BMMC samples were stained with antibody conjugated oligonucleotides (BD® Abseq) and analyzed using BD Rhapsody™. We filtered 8,070 high quality cells for 2,508 HSPCs, myeloid cells, and lymphocyte precursors. The antibody-derived tags (ADTs) obtained from BD® Abseq comprised 33 of the most informative features (n=2000) and resulted in more stable clustering as determined by within sum of squares (WSS = 898 verses 934 for WTA alone for 20 clusters). Additionally, we designed a targeted HSPC panel (n=500 genes) with BD® Abseq which identified similar cell clusters compared to the WTA alone and WTA plus ADT data (rand index = 0.88). HSPC clustering identified putative hematopoietic stem cell (HSC), common myeloid progenitor (CMP), and megakaryocyte-erythroid progenitor (MEP) clusters that expressed canonical surface markers. Of interest, we identified candidate novel myeloid, T-cell, and B-cell primed precursors and new surface marker expression gradients that align with specific differentiation trajectories. The results of this analysis will be presented. The HSPC clusters were converted into a signature matrix using Cibersortx (Newman et al. Nat. Biotechnol. 2019), and bulk acute myeloid leukemia (AML) and healthy samples were deconvolved into respective healthy cell clusters. We subsequently performed multivariate Cox proportional hazard analysis, and observed that high levels of healthy cluster 8 (H8; HR 3.40, 95% confidence interval 1.24-9.34), the candidate lymphoid-primed multipotent progenitor (LMPP), and low levels of healthy cluster 6 (H6; HR 0.26, 95% confidence interval 0.12-0.58), a candidate erythrocyte precursor, at diagnosis were associated with worse overall survival. Deconvolution using sorted healthy sub-populations (Corces et al. Nat. Genet. 2016) only identified erythrocyte precursors as statistically relevant (HR 0.27, 95% confidence interval 0.12-0.61). Of note, H8 had a distinct gene expression profile compared to that identified for the sorted LMPP sub-population using differential gene expression analysis. In summary, we identified novel cell type clusters and surface marker associations using combined single cell WTA and surface marker analysis (BD® Abseq). We were able to correlate cell types with both canonical and novel surface markers, and deconvolution analysis provided preliminary insights into their clinical relevance in AML.

Citation Format: Asiri Ediriwickrema, Sreejith Ramakrishnan, Margaret Nakamoto, Smita Ghanekar, Bogdan Luca, Aaron Newman, Andrew Gentles, Ravindra Majeti. Multiomic single cell analysis of normal human bone marrow identifies a unique stem and progenitor population that expands in AML [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 3779.