Haemopedia: An Expression Atlas of Murine Hematopoietic Cells

Abstract

SummaryHematopoiesis is a multistage process involving the differentiation of stem and progenitor cells into distinct mature cell lineages. Here we present Haemopedia, an atlas of murine gene-expression data containing 54 hematopoietic cell types, covering all the mature lineages in hematopoiesis. We include rare cell populations such as eosinophils, mast cells, basophils, and megakaryocytes, and a broad collection of progenitor and stem cells. We show that lineage branching and maturation during hematopoiesis can be reconstructed using the expression patterns of small sets of genes. We also have identified genes with enriched expression in each of the mature blood cell lineages, many of which show conserved lineage-enriched expression in human hematopoiesis. We have created an online web portal called Haemosphere to make analyses of Haemopedia and other blood cell transcriptional datasets easier. This resource provides simple tools to interrogate gene-expression-based relationships between hematopoietic cell types and genes of interest.

Haploinsufficiency for DNA methyltransferase 3A predisposes hematopoietic cells to myeloid malignancies

“…Further, 9 of 52 (17%) AML samples with DNMT3A mutations from the TCGA study contained NRAS or KRAS mutations (16). Finally, although tumor E did not have any somatic mutations or insertion-deletions that are known to be associated with AML, this tumor had multiple copy number alterations that may have been relevant for pathogen- Figure 8A, using a reference set of lineage-specific gene expression profiles from the Haemopedia database (32). The pooled data from the 2 samples revealed all of the expected populations.…”

“…Single nucleotide variants (SNVs) were detected using the union of 3 callers: (a) samtools version r963 (48) Manual review also resulted in the removal of all variants in the region chr1:90,100,000-151,800,000, which appeared to be artifacts. Putative variants that differed significantly from the expected homozygous or heterozygous ratios were removed using an R script (https://raw.githubusercontent.com/genome/genome/master/lib/ perl/Genome/Model/Tools/Analysis/RemoveContaminatingVar 54 murine cell types, spanning all mature hematopoietic cell lineages and several progenitor populations (32). Specifically, we constructed a reference matrix of expression profiles, where each column represents one of the 54 cell types and each row represents one of 2,978 lineage-specific genes (32).…”

“…Putative variants that differed significantly from the expected homozygous or heterozygous ratios were removed using an R script (https://raw.githubusercontent.com/genome/genome/master/lib/ perl/Genome/Model/Tools/Analysis/RemoveContaminatingVar 54 murine cell types, spanning all mature hematopoietic cell lineages and several progenitor populations (32). Specifically, we constructed a reference matrix of expression profiles, where each column represents one of the 54 cell types and each row represents one of 2,978 lineage-specific genes (32). We extracted single-cell RNA-seq data for the 2,353 lineage-specific genes that are measurably expressed in our data.…”

Transcriptional profiling of eosinophil subsets in interleukin-5 transgenic mice

“…A(ii) and B(ii)). To investigate if trajectory gene expression changes were specific to eosinophils or occurred generally during hematopoietic differentiation, we examined their expression in a broad range of hematopoietic cells (Fig. A(iii) and B(iii)).…”

Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data

“…In the case of and parameters included the number of clusters, which we set to 3 and 8 for the gold standard and silver standard datasets, respectively. Marker genes were required for the analysis with which we obtained by performing differential expression analyses on GSE86337 and an in-house dataset of isolated cell types in PBMCs 24 for the gold standard and silver standard datasets, respectively. Furthermore, we also followed upstream data handling, such as filtering of genes and normalization, as described in the documentation of the respective clustering method.…”