Abstract
BackgroundCellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, particularly in the context of human diseases. Nevertheless, a unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. Here, we report the first multi-omic analysis of human primary naïve CD4+ T cells isolated from a single individual.ResultsIntegrating multi-omics datasets allowed us to investigate genome-wide methylation and its effect on mRNA/protein expression patterns, extent of RNA editing under normal physiological conditions and allele specific expression in naïve CD4+ T cells. In addition, we carried out a multi-omic comparative analysis of naïve with primary resting memory CD4+ T cells to identify molecular changes underlying T cell differentiation. This analysis provided mechanistic insights into how several molecules involved in T cell receptor signaling are regulated at the DNA, RNA and protein levels. Phosphoproteomics revealed downstream signaling events that regulate these two cellular states. Availability of multi-omics data from an identical genetic background also allowed us to employ novel proteogenomics approaches to identify individual-specific variants and putative novel protein coding regions in the human genome.ConclusionsWe utilized multiple high-throughput technologies to derive a comprehensive profile of two primary human cell types, naïve CD4+ T cells and memory CD4+ T cells, from a single donor. Through vertical as well as horizontal integration of whole genome sequencing, methylation arrays, RNA-Seq, miRNA-Seq, proteomics, and phosphoproteomics, we derived an integrated and comparative map of these two closely related immune cells and identified potential molecular effectors of immune cell differentiation following antigen encounter.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-015-0225-4) contains supplementary material, which is available to authorized users.
Highlights
Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins
The data was generated on peripheral blood mononuclear cells, which is a mixed population of different cell types including lymphocytes, monocytes and macrophages
Given that TSLP is one of the earliest cytokines secreted during inflammation, and CRLF2 and IL7R are co-expressed in naïve CD4+ T cells, these results suggest a likely scenario in which naïve CD4+ T cells could directly sense inflammation locally via the TSLP signaling complex
Summary
Cellular function and diversity are orchestrated by complex interactions of fundamental biomolecules including DNA, RNA and proteins. Technological advances in genomics, epigenomics, transcriptomics and proteomics have enabled massively parallel and unbiased measurements. Such high-throughput technologies have been extensively used to carry out broad, unbiased studies, in the context of human diseases. A unified analysis of the genome, epigenome, transcriptome and proteome of a single human cell type to obtain a coherent view of the complex interplay between various biomolecules has not yet been undertaken. The data was generated on peripheral blood mononuclear cells, which is a mixed population of different cell types including lymphocytes, monocytes and macrophages This cellular heterogeneity makes it difficult to gain mechanistic insights into how genome and epigenome level alterations regulate RNA and protein expression. The ability to interrogate posttranscriptional and post-translational modifications on these biomolecules and correlate changes to their genomic and epigenomic states is complicated by the heterogeneity of cell types
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