DOI: 10.5281/zenodo.5291737
title: Discrete regulatory modules instruct hematopoietic lineage commitment and differentiation

description: bioRxiv preprint: https://www.biorxiv.org/content/10.1101/2020.04.02.022566v4 <strong>Contact:</strong> Grigorios Georgolopoulos (ggeorgol@altius.org); Jeff Vierstra (jvierstra@altius.org) Lineage commitment and differentiation is driven by the concerted action of master transcriptional regulators at their target chromatin sites. Multiple efforts have characterized the key transcription factors (TFs) that determine the various hematopoietic lineages. However, the temporal interactions between individual TFs and their chromatin targets during differentiation and how these interactions dictate lineage commitment remains poorly understood. Here we delineate the temporal interplay between the <em>cis</em>- and the <em>trans</em>-regulatory landscape in establishing lineage commitment and differentiation in human hematopoiesis by performing a dense timecourse of chromatin accessibility (DNase I-seq), and gene expression (total and single cell RNA-seq). All data uploaded correspond to human genome build version GRCh38. <strong>Contents</strong> <strong>DNase I Hotspot (DHS) metadata: </strong>Supplementary_Data_1.txt <strong>DNase I Hotspot quantile-normalized counts:</strong> A tab-separated matrix with quantile-normalized DNase I density counts from 79,085 FDR 5% hotspots, across 12 erythroid differentiation timepoints from 3 donors, present in at least n=2 samples. Rows correspond to DHS information in Supplementary_Data_1.txt (hotspots.fdr.0.05.qnorm.counts.tsv.gz) <strong>Column information for DNase I Hotspot quantile-normalized counts: </strong>hotspots.fdr.0.05.qnorm.counts.info.tsv <strong>Developmentally regulated gene metadata (erythroid): </strong>Supplementary_Data_2.csv <strong>Gene matrix of quantile-normalized FPKM values (erythroid): </strong>A tab-separated matrix with the quantile-normalized FPKM values of all detected genes, across 13 erythroid differentiation timepoints from 3 donors. (fpkm_erythroid_qnorm.tsv.gz) <strong>Column information for the quantile-normalized FPKM gene matrix (erythroid): </strong>A tab-separated table (fpkm_erythroid_qnorm.info.tsv) <strong>CD34+ HSPC TADs at 10kb resolution: </strong>Supplementary_Data_3.bed <strong>Day 11 <em>ex vivo</em> erythroid progenitor TADs at 10kb resolution: </strong>Supplementary_Data_4.bed <strong>Transcription factor motif enrichment per DHS cluster: </strong>Supplementary_Data_5.csv <strong>Correlation information (links) between developmentally regulated DHS and target genes: </strong>Supplementary_Data_6.csv <strong>Chromatin anchor loops called from 10kb resolution Hi-C data: </strong>Supplementary_Data_7.bedgraph <strong>Developmentally regulated gene metadata (megakaryocytic): </strong>Supplementary_Data_8.csv <strong>Gene matrix of quantile-normalized FPKM values (megakaryocytic): </strong>A tab-separated matrix with the quantile-normalized FPKM values of all detected genes, across 13 megakaryocytic differentiation timepoints from 3 donors. (fpkm_megakaryocyte_qnorm.tsv.gz) <strong>Column information for the quantile-normalized FPKM gene matrix (megakaryocytic): </strong>A tab-separated table (fpkm_megakaryocyte_qnorm.info.tsv) <strong>Marker (differentially expressed) genes per single cell population: </strong>Supplementary_Data_9.csv <strong>A SCANPY h5ad Annotated DataFrame object: </strong>Annotated Data frame `anndata` in h5ad format including the gene-by-cell count matrix, Velocyto splicing kinetics (RNA velocity) information layer, along with obs, obsm, var, varm, and uns layers. (SCANPY_anndata_object.h5ad)