Use of machine learning to identify a T cell response to SARS-CoV-2

Shoukat, M Saad, Foers, Andrew D, Woodmansey, Stephen, Evans, Shelley C, Fowler, Anna and Soilleux, Elizabeth J (2021) Use of machine learning to identify a T cell response to SARS-CoV-2. CELL REPORTS MEDICINE, 2 (2). 100192-.

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Official URL: http://dx.doi.org/10.1016/j.xcrm.2021.100192

Abstract

The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease (n = 17) and SARS-CoV-2 infection-naive (control) individuals (n = 39). Applying a machine learning approach to TCR beta (TRB) repertoire data, we can classify patient/control samples with a training sensitivity, specificity, and accuracy of 88.2%, 100%, and 96.4% and a testing sensitivity, specificity, and accuracy of 82.4%, 97.4%, and 92.9%, respectively. Interestingly, the same machine learning approach cannot separate SARS-CoV-2 recovered from SARS-CoV-2 infection-naive individual samples on the basis of B cell receptor (immunoglobulin heavy chain; IGH) repertoire data, suggesting that the T cell response to SARS-CoV-2 may be more stereotyped and longer lived. Following validation in larger cohorts, our method may be useful in detecting protective immunity acquired through natural infection or in determining the longevity of vaccine-induced immunity.

Item Type:	Article
Uncontrolled Keywords:	T-Lymphocytes, Humans, Complementarity Determining Regions, Receptors, Antigen, B-Cell, Receptors, Antigen, T-Cell, Cluster Analysis, Sequence Analysis, DNA, Amino Acid Sequence, Principal Component Analysis, High-Throughput Nucleotide Sequencing, Machine Learning, COVID-19, SARS-CoV-2
Depositing User:	Symplectic Admin
Date Deposited:	03 Feb 2021 09:33
Last Modified:	18 Jan 2023 23:01
DOI:	10.1016/j.xcrm.2021.100192
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3115120