Abstract
Precise mapping of leukemic cells onto the known hematopoietic hierarchy is important for understanding the cell-of-origin and mechanisms underlying disease initiation and development. However, this task remains challenging because of the high interpatient and intrapatient heterogeneity of leukemia cell clones as well as the differences that exist between leukemic and normal hematopoietic cells. Using single-cell RNA sequencing (scRNA-seq) data with a curated clustering approach, we constructed a comprehensive reference hierarchy of normal hematopoiesis. This reference hierarchy was accomplished through multistep clustering and annotating over 100,000 bone marrow mononuclear cells derived from 25 healthy donors. We further employed the cosine distance algorithm to develop a likelihood score to determine the similarities of leukemic cells to their putative normal counterparts. Using our scoring strategies, we mapped the cells of acute myeloid leukemia (AML) and B cell precursor acute lymphoblastic leukemia (BCP-ALL) samples to their corresponding counterparts. The reference hierarchy also facilitated bulk RNA sequencing (RNA-seq) analysis, enabling the development of a least absolute shrinkage and selection operator (LASSO) score model to reveal subtle differences in lineage aberrancy within AML or BCP-ALL patients. To facilitate interpretation and application, we established an R-based package (HematoMap) that offers a fast, convenient, and user-friendly tool for identifying and visualizing lineage aberrations in leukemia from scRNA-seq and bulk RNA-seq data. Our tool provides curated resources and data analytics for understanding leukemogenesis, with the potential to enhance leukemia risk stratification and personalized treatments. The HematoMap is available at https://github.com/NRCTM-bioinfo/HematoMap.