bioRxivGenomicsPreprint
A single cell framework for multi-omic analysis of disease identifies malignant regulatory signatures in mixed phenotype acute leukemia
Jeffrey M. GranjaiD, Sandy L. KlemmiD, Lisa M. McGinnisiD, Arwa S. Kathiria, Anja MezgeriD, Benjamin E. ParksiD, Eric J. GarsiD, Michaela LiedtkeiD, Grace X. Y. ZhengiD, Howard Y. ChangiD, Ravindra MajetiiD, William James GreenleafiD
Abstract
In order to identify the molecular determinants of human diseases, such as cancer, that arise from a diverse range of tissue, it is necessary to accurately distinguish normal and pathogenic cellular programs.1-3 Here we present a novel approach for single-cell multi-omic deconvolution of healthy and pathological molecular signatures within phenotypically heterogeneous malignant cells. By first creating immunophenotypic, transcriptomic and epigenetic single-cell maps of hematopoietic development from healthy peripheral blood and bone marrow mononuclear cells, we identify cancer-specific transcriptional and chromatin signatures from single cells in a cohort of mixed phenotype acute leukemia (MPAL) clinical samples. MPALs are a high-risk subtype of acute leukemia characterized by a heterogeneous malignant cell population expressing both myeloid and lymphoid lineage-specific markers.4, 5 Our results reveal widespread heterogeneity in the pathogenetic gene regulatory and expression programs across patients, yet relatively consistent changes within patients even across malignant cells occupying diverse portions of the hematopoietic lineage. An integrative analysis of transcriptomic and epigenetic maps identifies 91,601 putative gene-regulatory interactions and classifies a number of transcription factors that regulate leukemia specific genes, including RUNX1-linked regulatory elements proximal to CD69. This work provides a template for integrative, multi-omic analysis for the interpretation of pathogenic molecular signatures in the context of developmental origin.
- organism: human
- assay: scRNA-seq
- assay: DNA methylation
- research focus: gene regulatory network inference
- approach: statistical model
- Citations
- 3
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