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A multi-omic analysis of human naïve CD4+ T cells

Overview of attention for article published in BMC Systems Biology, November 2015
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  • High Attention Score compared to outputs of the same age and source (87th percentile)

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7 X users

Citations

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36 Dimensions

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Title
A multi-omic analysis of human naïve CD4+ T cells
Published in
BMC Systems Biology, November 2015
DOI 10.1186/s12918-015-0225-4
Pubmed ID
Authors

Christopher J. Mitchell, Derese Getnet, Min-Sik Kim, Srikanth S. Manda, Praveen Kumar, Tai-Chung Huang, Sneha M. Pinto, Raja Sekhar Nirujogi, Mio Iwasaki, Patrick G. Shaw, Xinyan Wu, Jun Zhong, Raghothama Chaerkady, Arivusudar Marimuthu, Babylakshmi Muthusamy, Nandini A. Sahasrabuddhe, Rajesh Raju, Caitlyn Bowman, Ludmila Danilova, Jevon Cutler, Dhanashree S. Kelkar, Charles G. Drake, T. S. Keshava Prasad, Luigi Marchionni, Peter N. Murakami, Alan F. Scott, Leming Shi, Jean Thierry-Mieg, Danielle Thierry-Mieg, Rafael Irizarry, Leslie Cope, Yasushi Ishihama, Charles Wang, Harsha Gowda, Akhilesh Pandey

Abstract

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. 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. 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.

X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 139 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Germany 1 <1%
France 1 <1%
Ukraine 1 <1%
Sweden 1 <1%
Canada 1 <1%
Luxembourg 1 <1%
Unknown 131 94%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 34 24%
Researcher 33 24%
Student > Master 12 9%
Student > Doctoral Student 8 6%
Student > Bachelor 8 6%
Other 28 20%
Unknown 16 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 41 29%
Biochemistry, Genetics and Molecular Biology 20 14%
Immunology and Microbiology 19 14%
Medicine and Dentistry 16 12%
Computer Science 10 7%
Other 15 11%
Unknown 18 13%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 28 July 2016.
All research outputs
#7,032,436
of 25,692,343 outputs
Outputs from BMC Systems Biology
#214
of 1,131 outputs
Outputs of similar age
#81,210
of 298,027 outputs
Outputs of similar age from BMC Systems Biology
#4
of 33 outputs
Altmetric has tracked 25,692,343 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 1,131 research outputs from this source. They receive a mean Attention Score of 3.7. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 298,027 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 72% of its contemporaries.
We're also able to compare this research output to 33 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.