Inter-patient variability and the similarity of healthy and leukemic stem cells (LSCs) have impeded the characterization of LSCs in acute myeloid leukemia (AML) and their differentiation landscape. Here, we introduce CloneTracer, a novel method that adds clonal resolution to single-cell RNA-seq datasets. Applied to samples from 19 AML patients, CloneTracer revealed routes of leukemic differentiation. Although residual healthy and preleukemic cells dominated the dormant stem cell compartment, active LSCs resembled their healthy counterpart and retained erythroid capacity. By contrast, downstream myeloid progenitors constituted a highly aberrant, disease-defining compartment: their gene expression and differentiation state affected both the chemotherapy response and leukemia's ability to differentiate into transcriptomically normal monocytes. Finally, we demonstrated the potential of CloneTracer to identify surface markers misregulated specifically in leukemic cells. Taken together, CloneTracer reveals a differentiation landscape that mimics its healthy counterpart and may determine biology and therapy response in AML.
Keywords: AML; CSC; HSC; LSC; acute myeloid leukemia; cancer stem cells; cellular differentiation; computational biology; computational method; hematopoietic stem cells; leukemic stem cells; single-cell RNA-seq; single-cell genomics; single-cell transcriptomics.
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