JUN 20, 2019 06:00 AM PDT
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Connecting Genetic and Transcriptional Variation in Acute Myeloid Leukemia at Single-Cell Resolution

C.E. CREDITS: P.A.C.E. CE | Florida CE
Speakers
  • Senior Scientist, Computational Biology, 10X Genomics
    Biography
      Dr. Williams is a Senior Computational Biologist at 10x Genomics in Pleasanton, CA. He received his PhD from Virginia Commonwealth University in 2010 and continued in academia until 2015 pursuing postdoctoral fellowships at both the Salk Institute for Biological Studies and University of Virginia. Dr. Williams has published many manuscripts in high impact journals which range in focus from genetics and genomics to cardiovascular disease and cancer. In 2016 Dr. Williams joined 10x Genomics in the Applications group. His current research focuses on developing and applying novel single-cell and spatial technologies to unravel complex biological systems.

    Abstract:

    Virtually all tumors are genetically heterogeneous, containing subclonal populations of cells that are defined by distinct mutations. Subclones can have unique phenotypes that influence disease progression, but these phenotypes are difficult to characterize: subclones usually cannot be physically purified, and bulk gene expression measurements obscure interclonal differences. Single-cell RNA-sequencing has revealed transcriptional heterogeneity within a variety of tumor types, but it is unclear how this expression heterogeneity relates to subclonal genetic events – for example, whether particular expression clusters correspond to mutationally defined subclones. To address this question, we developed an approach that integrates enhanced whole genome sequencing (eWGS) with the 10x Genomics Chromium Single Cell 5’ Gene Expression workflow (scRNA-seq) to directly link expressed mutations with transcriptional profiles at single cell resolution. Using bone marrow samples from five cases of primary human Acute Myeloid Leukemia (AML), we generated WGS and scRNA-seq data for each case. Duplicate single cell libraries representing a median of 20,474 cells per case were generated from the bone marrow of each patient. Although the libraries were 5’ biased, we detected expressed mutations in cDNAs at distances up to 10 kbp from the 5’ ends of well-expressed genes, allowing us to identify hundreds to thousands of cells with AML-specific somatic mutations in every case. This data made it possible to distinguish AML cells (including normal-karyotype AML cells) from surrounding normal cells, to study tumor differentiation and intratumoral expression heterogeneity, to identify expression signatures associated with subclonal mutations, and to find cell surface markers that could be used to purify subclones for further study. The data also revealed transcriptional heterogeneity that occurred independently of subclonal mutations, suggesting that additional factors drive epigenetic heterogeneity. This integrative approach for connecting genotype to phenotype in AML cells is broadly applicable for analysis of any sample that is phenotypically and genetically heterogeneous.

    Learning Objectives: 

    1. Detection of SNVs from RNA-seq in single-cells is possible 
    2. SNVs drive subclonal expression signatures in AML


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