Barbara Bravi, Vinod P. Balachandran, Benjamin D. Greenbaum, Aleksandra M. Walczak , Thierry Mora , Rémi Monasson , Simona Cocco
With the increasing ability to use high-throughput next-generation sequencing to quantify the diversity of the human T cell receptor (TCR) repertoire, the ability to use TCR sequences to infer antigen-specificity could greatly aid potential diagnostics and therapeutics.
T cell receptors (TCR) are the key factors through which the adaptive immune system controls pathogens and tumors. T-cells recognize infected and malignant cells by binding antigens, short peptides that are presented on the cell surface by the Major Histocompatibility Complex (MHC) molecules.
Materials and methods
The main dataset studied consist of TRB CDR3 regions sequenced by the Adaptive Biotechnologies ImmunoSEQ platform from the T-cell assays of Ref. . We discard sequences that are generated by non-productive events, i.e., they do not have the conserved anchor residues delimiting the CDR3 region (Cysteine as the left anchor residue, Phenylalanine or Valine as right one) and they align to pseudo-genes as germline gene choices.
We have proposed probabilistic-modeling approaches (summarized in Fig 8A) to obtain sequence representations of T-cell response from RepSeq datasets. We demonstrated the inferred, probabilistic scores capture information about sequence counts and predict clonotype fold changes upon antigen-stimulation.
Citation: Bravi B, Balachandran VP, Greenbaum BD, Walczak AM, Mora T, Monasson R, et al. (2021) Probing T-cell response by sequence-based probabilistic modeling. PLoS Comput Biol 17(9): e1009297. https://doi.org/10.1371/journal.pcbi.1009297
Editor: Rustom Antia, Emory University, UNITED STATES
Received: December 28, 2020; Accepted: July 22, 2021; Published: September 2, 2021.
Copyright: © 2021 Bravi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited..
Data Availability: All relevant data are within the manuscript, its Supporting information files, and the Git repository (https://github.com/bravib/rbm_tcell).
Funding: A.M.W is the recipient of the European Research Council Consolidator Grant n.
Competing interests: I have read the journal’s policy and the authors of this manuscript have the following competing interests: B.G. has received honoraria for speaking engagements from Merck, Bristol-Meyers Squibb, and Chugai Pharmaceuticals, has received research funding from Bristol-Meyers Squibb, and has been a compensated consultant for Darwin health, PMV Pharma and Rome Therapeutics of which he is a cofounder.