Unknown,Transcriptomics,Genomics,Proteomics

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Single-cell chromatin accessibility data using scATAC-seq

ABSTRACT: Cell-to-cell variation is a universal feature of life that impacts a wide range of biological phenomena, from developmental plasticity to tumor heterogeneity. While recent advances have improved our ability to document cellular phenotypic variation the fundamental mechanisms that generate variability from identical DNA sequences remain elusive. Here we reveal the landscape and principles of cellular DNA regulatory variation by developing a robust method for mapping the accessible genome of individual cells via assay of transposase accessible chromatin sequencing (ATAC-seq). Single-cell ATAC-seq (scATAC-seq) maps from hundreds of single-cells in aggregate closely resemble accessibility profiles from tens of millions of cells and provides insights into cell-to-cell variation. Accessibility variance is systematically associated with specific trans-factors and cis-elements, and we discover combinations of trans-factors associated with either induction or suppression of cell-to-cell variability. We further identify sets of trans-factors associated with cell-type specific accessibility variance across 6 cell types. Targeted perturbations of cell cycle or transcription factor signaling evoke stimulus-specific changes in this observed variability. The pattern of accessibility variation in cis across the genome recapitulates chromosome topological domains de novo, linking single-cell accessibility variation to three-dimensional genome organization. All together, single-cell analysis of DNA accessibility provides new insight into cellular variation of the “regulome.” Profiles of single cell epigenomes, assayed using scATAC-seq, across 8 cell types and 4 targeted cell manipulations. The complete data set contains a total of 1,632 assayed wells.

ORGANISM(S): Mus musculus

SUBMITTER: Jason Buenrostro

PROVIDER: E-GEOD-65360 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:Gene regulatory networks that govern hematopoietic stem cells (HSC) and leukemia–initiating cells (L-IC) are deeply entangled. Thus, the discovery of compounds that target L-IC while sparing HSC is an attractive but difficult endeavor. Presently, most drug discovery approaches fail to counter-screen compounds against normal hematopoietic stem/progenitor cells (HSPC) to assess therapeutic index. Here, we present a combined in vitro and in vivo strategy to identify compounds specific to L-IC in acute myeloid leukemia (AML). A high-throughput screen of 4000 compounds on novel leukemia cell lines derived from human experimental leukemogenesis models yielded 80 hits, of which most were toxic to normal HSPC. Of the 10 compounds that passed this initial filter, we chose to characterize a single compound, kinetic riboside (KR), on AML L-IC and HSPC. KR demonstrated comparable efficacy to standard therapies against 63 primary AMLs. In vitro, KR effectively targeted the L-IC-enriched CD34+CD38- AML fraction, while sparing normal HSPC enriched fractions, although these effects were mitigated on HSC assayed in vivo, and highlights the importance of in vivo L-IC and HSC assays to measure function. Overall, we provide a novel approach to screen large drug libraries for the discovery of anti-L-IC compounds for human leukemias. The gene expression profile of TEX and M9-ENL1 cells were compared to HL-60 (Series GSE16160, GSE28185) and THP1 (Series GSE28185), to determine if TEX and M9-ENL1 cells were more enriched in stem cell (embryonic, adult, leukemia, and cancer) gene sets using Gene Set Enrichment Analysis (GSEA). TEX and M9-ENL1 cells were treated with DMSO in triplicate. Total RNA was harvested and analyzed with the Affymetrix Human Genome U133A 2.0 Array. Raw data was background corrected with RMA, normalized using quantiles method, corrected using only PM values, and median polished to generate log2 values. Published HL-60 and THP-1 raw data using the same Affymetrix array were processed identically. To account for batch variation, the log2 values were adjusted using ComBat in GenePattern with a parametric Empirical Bayes priors distribution estimation method and without covariate analysis.

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Single-cell chromatin accessibility data using scATAC-seq

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