↓ Skip to main content

Exploring the pre-immune landscape of antigen-specific T cells

Overview of attention for article published in Genome Medicine, August 2018
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)

Mentioned by

twitter
34 tweeters

Citations

dimensions_citation
32 Dimensions

Readers on

mendeley
92 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Exploring the pre-immune landscape of antigen-specific T cells
Published in
Genome Medicine, August 2018
DOI 10.1186/s13073-018-0577-7
Pubmed ID
Authors

Mikhail V. Pogorelyy, Alla D. Fedorova, James E. McLaren, Kristin Ladell, Dmitri V. Bagaev, Alexey V. Eliseev, Artem I. Mikelov, Anna E. Koneva, Ivan V. Zvyagin, David A. Price, Dmitry M. Chudakov, Mikhail Shugay

Abstract

Adaptive immune responses to newly encountered pathogens depend on the mobilization of antigen-specific clonotypes from a vastly diverse pool of naive T cells. Using recent advances in immune repertoire sequencing technologies, models of the immune receptor rearrangement process, and a database of annotated T cell receptor (TCR) sequences with known specificities, we explored the baseline frequencies of T cells specific for defined human leukocyte antigen (HLA) class I-restricted epitopes in healthy individuals. We used a database of TCR sequences with known antigen specificities and a probabilistic TCR rearrangement model to estimate the baseline frequencies of TCRs specific to distinct antigens epitopespecificT-cells. We verified our estimates using a publicly available collection of TCR repertoires from healthy individuals. We also interrogated a database of immunogenic and non-immunogenic peptides is used to link baseline T-cell frequencies with epitope immunogenicity. Our findings revealed a high degree of variability in the prevalence of T cells specific for different antigens that could be explained by the physicochemical properties of the corresponding HLA class I-bound peptides. The occurrence of certain rearrangements was influenced by ancestry and HLA class I restriction, and umbilical cord blood samples contained higher frequencies of common pathogen-specific TCRs. We also identified a quantitative link between specific T cell frequencies and the immunogenicity of cognate epitopes presented by defined HLA class I molecules. Our results suggest that the population frequencies of specific T cells are strikingly non-uniform across epitopes that are known to elicit immune responses. This inference leads to a new definition of epitope immunogenicity based on specific TCR frequencies, which can be estimated with a high degree of accuracy in silico, thereby providing a novel framework to integrate computational and experimental genomics with basic and translational research efforts in the field of T cell immunology.

Twitter Demographics

The data shown below were collected from the profiles of 34 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 92 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 27 29%
Student > Ph. D. Student 21 23%
Student > Master 16 17%
Student > Bachelor 8 9%
Other 6 7%
Other 10 11%
Unknown 4 4%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 23 25%
Agricultural and Biological Sciences 18 20%
Immunology and Microbiology 18 20%
Medicine and Dentistry 10 11%
Chemistry 3 3%
Other 13 14%
Unknown 7 8%

Attention Score in Context

This research output has an Altmetric Attention Score of 20. 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 19 December 2018.
All research outputs
#1,334,140
of 19,844,828 outputs
Outputs from Genome Medicine
#305
of 1,295 outputs
Outputs of similar age
#31,623
of 292,799 outputs
Outputs of similar age from Genome Medicine
#1
of 1 outputs
Altmetric has tracked 19,844,828 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,295 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.6. This one has done well, scoring higher than 76% 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 292,799 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them