bioRxivCancer BiologyPreprint
Single cell transcriptional evolution of myeloid leukaemia of Down syndrome
Mi K. TrinhiD, K SchuscheliD, Hasan IssaiD, Rebecca Thomas, Conor ParksiD, Ágnes OszláncziiD, Toochi OgbonnahiD, Di ZhouiD, Lira MamanovaiD, Elena PrigmoreiD, Emilia Robertson, Angus HodderiD, Anna WengeriD, Nathaniel D. AndersoniD, Holly J. WhitfieldiD, Taryn D. TregeriD, José Gonçalves-DiasiD, Karin C. M. StraathofiD, David O’ConnoriD, Matthew D. YoungiD, Laura JardineiD, Stuart Paul AdamsiD, Jan‐Henning KlusmanniD, Jack Luke BartramiD, Sam BehjatiiD
Abstract
Children with Down syndrome have a 150-fold increased risk of developing myeloid leukaemia (ML-DS). Unusually for a childhood leukaemia, ML-DS arises from a preleukaemic state, termed transient abnormal myelopoiesis (TAM), via a conserved sequence of mutations. Here, we examined the relationship between the genetic and transcriptional evolution of ML-DS from natural variation; a rich collection of primary patient samples and fetal tissues with a range of constitutional karyotypes. We distilled transcriptional consequences of each genetic step in ML-DS evolution, utilising single cell mRNA sequencing, complemented by phylogenetic analyses in progressive disease. We found that transcriptional changes induced by the TAM-defining GATA1 mutations are retained in, and account for most of the ML-DS transcriptome. The GATA1 transcriptome pervaded all stages of ML-DS, including progressive disease that had undergone genetic evolution. Our approach delineates the transcriptional evolution of ML-DS and provides an analytical blueprint for distilling consequences of mutations within their pathophysiological context.
- organism: human
- assay: scRNA-seq
- research focus: cell type annotation
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: deep learning
- approach: statistical model
- Citations
- 2
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