Integrated decoding hematopoiesis and leukemogenesis using single-cell sequencing and its medical implication

Abstract Single-cell RNA sequencing provides exciting opportunities to unbiasedly study hematopoiesis. However, our understanding of leukemogenesis was limited due the high individual differences. Integrated analyses hematopoiesis and potentially new insights. Here we analyzed ~200,000 single-cell transcriptomes bone marrow mononuclear cells (BMMCs) its subsets from 23 clinical samples. We constructed a comprehensive cell atlas as hematopoietic reference. developed counterpart composite index (CCI; available at GitHub: https://github.com/pengfeeei/cci) search for healthy each leukemia subpopulation, by integrating multiple statistics map onto reference cells. Interestingly, found subpopulations patient had different counterparts. Analysis showed trajectories were similar that their counterparts, indicating developmental termination initiating phases leads thus explained origin heterogeneity. CCI further predicts subtypes, cellular heterogeneity, stemness patient. Analyses diagnosis, refractory, remission relapse vividly presented dynamics population during treatment. counterparts relapsed closer root tree than other cells, although transcriptomic genetic variants haplotype tracing derived an early minor population. In summary, this unified framework with reference, which provided novel biological medical implication.