Magnitude and Dynamics of the T-Cell Response to SARS-CoV-2 Infection at Both Individual and Population Levels
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Abstract
T cells are involved in the early identification and clearance of viral infections and also support the development of antibodies by B cells. This central role for T cells makes them a desirable target for assessing the immune response to SARS-CoV-2 infection. Here, we combined two high-throughput immune profiling methods to create a quantitative picture of the T-cell response to SARS-CoV-2. First, at the individual level, we deeply characterized 3 acutely infected and 58 recovered COVID-19 subjects by experimentally mapping their CD8 T-cell response through antigen stimulation to 545 Human Leukocyte Antigen (HLA) class I presented viral peptides (class II data in a forthcoming study). Then, at the population level, we performed T-cell repertoire sequencing on 1,815 samples (from 1,521 COVID-19 subjects) as well as 3,500 controls to identify shared “public” T-cell receptors (TCRs) associated with SARS-CoV-2 infection from both CD8 and CD4 T cells. Collectively, our data reveal that CD8 T-cell responses are often driven by a few immunodominant, HLA-restricted epitopes. As expected, the T-cell response to SARS-CoV-2 peaks about one to two weeks after infection and is detectable for at least several months after recovery. As an application of these data, we trained a classifier to diagnose SARSCoV-2 infection based solely on TCR sequencing from blood samples, and observed, at 99.8% specificity, high early sensitivity soon after diagnosis (Day 3–7 = 85.1% [95% CI = 79.9-89.7]; Day 8–14 = 94.8% [90.7-98.4]) as well as lasting sensitivity after recovery (Day 29+/convalescent = 95.4% [92.1-98.3]). These results demonstrate an approach to reliably assess the adaptive immune response both soon after viral antigenic exposure (before antibodies are typically detectable) as well as at later time points. This blood-based molecular approach to characterizing the cellular immune response has applications in clinical diagnostics as well as in vaccine development and monitoring.
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SciScore for 10.1101/2020.07.31.20165647: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Viral peptide selection: Using the NCBI genome reference for SARS-CoV-2 (RefSeq accession: NC_045512.2), a list of candidate 9-10AA long peptides from across the whole viral genome was identified based on predicted affinity (<1% rank) using NetMHCpan version 4.1 (Andreatta 2016; Nielsen 2003) to common HLA-A and -B alleles as determined in the Allele Frequency Net Database (Gonzalez-Galarza 2020). RefSeqsuggested: (RefSeq, RRID:SCR_003496)BLAST searches were optimized for short sequence queries using the “-task blastp-short” argument and all full-length, exact matching TCRs were used to … SciScore for 10.1101/2020.07.31.20165647: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
Software and Algorithms Sentences Resources Viral peptide selection: Using the NCBI genome reference for SARS-CoV-2 (RefSeq accession: NC_045512.2), a list of candidate 9-10AA long peptides from across the whole viral genome was identified based on predicted affinity (<1% rank) using NetMHCpan version 4.1 (Andreatta 2016; Nielsen 2003) to common HLA-A and -B alleles as determined in the Allele Frequency Net Database (Gonzalez-Galarza 2020). RefSeqsuggested: (RefSeq, RRID:SCR_003496)BLAST searches were optimized for short sequence queries using the “-task blastp-short” argument and all full-length, exact matching TCRs were used to assess the phylogenetic placement of each candidate epitope. BLASTsuggested: (BLASTX, RRID:SCR_001653)Using the taxonomic annotations available from the NCBI taxonomy browser, the most recent common ancestor was defined as the most recent taxonomic node shared by all terminal taxa that shared an exact match to the epitope. NCBI taxonomy browsersuggested: NoneAnalysis of flow cytometry data files was performed using FlowJo (Ashland, OR). FlowJosuggested: (FlowJo, RRID:SCR_008520)Supporting Table S2: List of antigens from MIRA data where putative HLA restrictions can be attributed based on using a Mann-Whitney’s U test over the number of mapped TCRs per experiment. MIRAsuggested: (MIRA, RRID:SCR_010731)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: We found the following clinical trial numbers in your paper:
Identifier Status Title NCT04494893 Recruiting ImmuneRACE - Immune Response Action to COVID-19 Events Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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