Multiomics Dataset on Primary Macrophages

We generated a comprehensive dataset of RNA-Seq, ChiP-Seq (H3K27me3, H3K9me2, H3K27ac, H3K16ac, H3K9ac), proteomics and metabolomics on primary monocyte-derived macrophages from several donors. Now published in Nature, we hope the community can exploit this data further.
Published in Research Data
Multiomics Dataset on Primary Macrophages
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In our latest work published in Nature we generated an extensive dataset using RNA-Seq, ChiP-Seq, metabolomics and proteomics on primary cells. We want to share this here in the Research Data Community as we think this is a very useful resource. On the whole, our study encompassed a novel mechanism of endocytosis, design of a new molecule to inhibit inflammation, the discovery that copper is implicated in the interconvertion of NAD(H), metabolomic and epigenetic characterisation and in vivo studies on inflammation. Because the paper is so rich, we think that an emphasis of the depth of the multiomics dataset is useful for people to read about.

Of course, according to Springer/ Nature policies, all the raw data and data are freely available and can be used by the scientific community.

Examples of metabolomics data used in the study. All raw data are freely available.

Importantly, we performed RNA-Seq on several donors and performed ChiP-Seq on 5 epigenetic marks: H3K27me3, H3K9me2, H3K27ac, H3K16ac, H3K9ac. This is quite unique, as often studies just look at one or two marks. However, we wanted to give a greater picture and this could now be used to look into the specific contribution of each of these activating and repressive marks and determine their exact contribution to gene expression.

Mitochondrial copper(II) regulates the epigenetic states and transcriptional programs of inflammatory macrophages. a, GO term analysis of upregulated genes in aMDM (n=10 donors). b, RNA-seq of MDM. Inflammatory gene signature highlighted in orange. Dashed line, adjusted p-value=0.05 (n=10 donors). c, RNA-seq of MDM. Iron-dependent demethylases and acetyl-transferases highlighted in blue and green, respectively. Dashed line, adjusted p-value=0.05 (n=10 donors). d, Scatter plot correlation of a representative donor of ChIP-seq read counts of histone marks in genes against RNA-seq of gene transcripts in MDM (n=10 donors). e, GO term analysis of genes in aMDM (n=10 donors) whose expression levels are downregulated upon treatment with LCC-12 (n=5 donors). f, RNA-seq of aMDM (n=10 donors) and MDM treated with LCC-12 during activation (n=5 donors). Inflammatory gene signature highlighted in orange. Dashed line, adjusted p-value=0.05. g, Scatter plot correlation of a representative donor of ChIP-seq read counts of histone marks in genes against RNA-seq of gene transcripts in aMDM (n=10 donors) and MDM treated with LCC-12 during activation (n=5 donors). h, RNA-seq of aMDM under CD44 knock out conditions. Representative of n=4 donors. Gating strategy see Supplementary Information. Inflammatory gene signature highlighted in orange. Dashed line, adjusted p-value=0.05. For a c, e and f differential gene expression was assessed with the limma/voom framework. GO enrichment was assessed with the enrichGO method from clusterProfiler. P-values were corrected for multiple testing with the Benjamini-Hochberg procedure.

We hope these datasets will be useful for the community and are happy to work with anyone who would like to take this further.

Reference: Solier et al., A druggable copper-signalling pathway that drives inflammation, Nature, 2023, 617, 386–394, doi:10.1038/s41586-023-06017-4

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