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. 2015 Nov 6;9:75.
doi: 10.1186/s12918-015-0225-4.

A Multi-Omic Analysis of Human Naïve CD4+ T Cells

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Free PMC article

A Multi-Omic Analysis of Human Naïve CD4+ T Cells

Christopher J Mitchell et al. BMC Syst Biol. .
Free PMC article

Abstract

Background: 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, 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.

Results: Integrating 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.

Conclusions: We 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.

Figures

Fig. 1
Fig. 1
Summary of large-scale omics datasets acquired and the corresponding technologies that were used in this study
Fig. 2
Fig. 2
Transcriptome of naïve CD4+ T cells. a Pie chart representing the number of diverse classes of transcripts identified in naïve CD4+ T cells. b Agarose gel depicting PCR amplified products from RNA isolated from various hematopoietic cell types. Labels at the top show surface markers based on which the cells were purified and labels on the side show cufflinks identifier for each transcript
Fig. 3
Fig. 3
Distribution of proteins expressed in naïve CD4+ T cells based on their relative abundance. a Distribution of proteins identified in naïve CD4+ T cells and their corresponding abundance based on normalized iBAQ (log2) values. Genes shown in red indicate those that have been removed in NCBI RefSeq 62, but were detected in our proteomics study. b Predicted domain architecture of a subset of RefSeq annotated hypothetical proteins identified in our study
Fig. 4
Fig. 4
Workflow and summary of RNA editing events observed in naïve CD4+ T cells. a The workflow that was used to identify putative sites of RNA editing in naïve CD4+ T cell transcriptome. The number of putative RNA editing sites is shown on the y-axis and reasons for disqualifying them are represented on the x-axis. b Matrix showing all the nucleotide substitutions observed in the confirmed RNA editing sites. The numbers highlighted in red indicate canonical changes (A-to-G and C-to-T). G-to-A and T-to-C are also highlighted as those changes are likely canonical edits with incorrect strand assignment of RNA-Seq data. Numbers highlighted in green are non-canonical events. c Distribution of RNA editing sites based on their location within transcripts
Fig. 5
Fig. 5
Proteogenomics approach to identify novel protein coding regions. A peptide identified by proteogenomics that mapped to an intronic region of FXR1 gene. MS/MS spectra of the peptide and read density from RNA-Seq data that supports the new isoform are depicted
Fig. 6
Fig. 6
Differential methylation, gene/protein expression and phosphorylation pattern between naïve CD4+ T cells and memory T cells. a Differential protein expression and phosphorylation pattern between naïve and memory CD4+ T cells. The black data points represent relative protein expression levels between naïve and memory T cells while the red and blue bars indicate relative phosphorylation levels in naïve and memory T cells, respectively. b Examples of kinases, phosphatases, proteases, transcription factors and signaling molecules that showed significant differences in their expression level and/or phosphorylation levels between naïve and memory T cells. Vertical and horizontal ovals represent proteins and circles represent phosphorylation sites. Higher levels in memory cells are indicated in blue, lower levels are indicated in red while unchanged abundance is indicated by grey color
Fig. 7
Fig. 7
T cell receptor signaling pathway showing downstream mediators regulated by DNA methylation, gene/protein expression and/or protein phosphorylation. T cell receptor signaling pathway depicting genes with their corresponding promoter methylation status, gene and protein expression levels and phosphorylation status between naïve and memory T cells. Promoter methylation status, transcriptomic, proteomic and phosphoproteomic measurements were imported into GeneSpring and overlaid onto the TCR signaling pathway to identify molecular signatures underlying the transition from naïve CD4+ T cells to memory CD4+ T cells. Different shapes are used to denote promoter methylation, mRNA, protein and phosphorylation status. Red indicates high in naïve CD4+ T cells, blue indicates high in memory CD4+ T cells and the unchanged are depicted in white. Different colored arrows are used to depict various modes of regulation of pathway components

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