A T cell receptor sequencing-based assay identifies cross-reactive recall CD8+ T cell clonotypes against autologous HIV-1 epitope variants

Chan, HY; Zhang, J; Garliss, CC; Kwaa, AK; Blankson, JN; Smith, K. N.

doi:10.21417/hyc2020fi

Cited by 3 publications

(10 citation statements)

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“…We present DeepTCR, a platform of both unsupervised and supervised deep learning that is able to be applied at the level of individual T cell receptor sequences as well as at the level of whole T cell repertoires, which can learn patterns in the data that may be used for both descriptive and predictive purposes. In order to demonstrate the utility of these algorithms, we collected a variety of TCR-Seq datasets including samples sorted by antigen specificity [20][21][22] , samples collected from single-cell RNA-seq experiments (10x_Genomics), and samples collected from a novel experimental assay used in detecting functional expansion of T cells 31 (full dataset details in Supplementary Fig. 1).…”

Section: Resultsmentioning

confidence: 99%

“…Arguably, methods such as the ones shown in this paper may be uniquely able to discover these changes for the first time. Additionally, while the data that exist to train these models comes through high-throughput methods such as tetramer sorting 21,22 or T cell stimulation assays 31 , these methods often introduce some level of noise due to non-specific binding or stimulation. Unfortunately, gold standard methods that would require isolation and cloning of T cell receptors to specifically interrogate the specificity of a given TCR are highly laborious and low throughput.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, when applying this trained repertoire classifier for TCR-level inference, we noted that 17/18 (Fisher's exact test: p < 1e−10) of the originally reported experimentally validated TCR-peptide pairs were correctly predicted to be cognate binders ( Supplementary Fig. 14) 31 .…”

Section: Supervised Approaches Improve Antigen-specific Classificationmentioning

confidence: 93%

“…experimental exposures, therapies, clinical outcomes) apply to an entire repertoire of TCR sequences and not to any individual sequence. To test the utility of this approach, we collected data from published TCR-Seq data of an assay where T cells from an elite suppressor (ES8) of HIV were cultured with autologous HIV-1 Gag and Nef epitope variants and sequenced after culture to determine the immune repertoire against each epitope 31,[44][45][46][47][48] . In the original work, TCR sequences were deemed to be antigen specific if they met certain statistical requirements based on the read count of a given sequence, a proxy for clonal expansion.…”

Section: Supervised Approaches Improve Antigen-specific Classificationmentioning

confidence: 99%

“…All data analyzed in this manuscript can be found at https://github.com/sidhomj/ DeepTCR as well as in the original publications that generated the data [20][21][22]31 . The 10X Genomics dataset used to train the supervised sequence regression can be found at https://www.10xgenomics.com/resources/application-notes/a-new-way-of-exploringimmunity-linking-highly-multiplexed-antigen-recognition-to-immune-repertoire-andphenotype/.…”

Section: Data Availabilitymentioning

confidence: 99%

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DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

et al. 2021

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Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks. The ability to learn complex patterns in data has tremendous implications in immunogenomics. T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system and allows for modeling its sequence determinants of antigenicity. We present DeepTCR, a suite of unsupervised and supervised deep learning methods able to model highly complex TCR sequencing data by learning a joint representation of a TCR by its CDR3 sequences and V/D/J gene usage. We demonstrate the utility of deep learning to provide an improved ‘featurization’ of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCRs and extraction of antigen-specific TCRs from noisy single-cell RNA-Seq and T-cell culture-based assays. Our results highlight the flexibility and capacity for deep neural networks to extract meaningful information from complex immunogenomic data for both descriptive and predictive purposes.

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Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Supervised Approaches Improve Antigen-specific Classificationmentioning

confidence: 93%

Section: Supervised Approaches Improve Antigen-specific Classificationmentioning

confidence: 99%

Section: Data Availabilitymentioning

confidence: 99%

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DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

et al. 2021

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Functional characterization of CD4+ T cell receptors crossreactive for SARS-CoV-2 and endemic coronaviruses

Dykema

Zhang²,

Woldemeskel³

et al. 2021

Journal of Clinical Investigation

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CONFLICTS OF INTERESTDMP and KNS have filed for patent protection on a subset of the technologies described herein (US provisional patent application no. 62/407,820). AD, ALC, FD, DMP, JB, and KNS have filed for patent protection on a subset of the technologies described herein (US provisional patent application no. 63/135,534). SZ is a founder of, holds equity in, and serves as a consultant to Personal Genome Diagnostics. SZ holds equity in Thrive Earlier

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CD4+ T cells from COVID-19 mRNA vaccine recipients recognize a conserved epitope present in diverse coronaviruses

Woldemeskel¹,

Dykema²,

Garliss³

et al. 2022

Journal of Clinical Investigation

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Recent studies have shown that vaccinated individuals harbor T cells that can cross-recognize SARS-CoV-2 and endemic human common cold coronaviruses (HCoVs). However, it is still unknown whether CD4+ T cells from vaccinated individuals recognize peptides from bat coronaviruses that may have the potential of causing future pandemics. In this study, we identified a SARS-CoV-2 spike protein epitope (S815-827) that is conserved in coronaviruses from different genera and subgenera including SARS-CoV, MERS-CoV, multiple bat coronaviruses and a feline coronavirus. Our results showed that S815-827 is recognized by 42% of vaccinated participants in our study who received the Pfizer-BioNTech (BNT162b2) or Moderna (mRNA-1273) COVID-19 vaccines. Using T cell expansion and T cell receptor sequencing assays, we demonstrated that S815-827-reactive CD4+ T cells from the majority of responders cross-recognize homologous peptides from at least 6 other diverse coronaviruses. Our results support the hypothesis that the current mRNA vaccines elicit T cell responses that can cross-recognize bat coronaviruses, and thus might induce some protection against potential zoonotic outbreaks. Furthermore, our data provide important insights that inform the development of T cell-based pan-coronavirus vaccine strategies.

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A T cell receptor sequencing-based assay identifies cross-reactive recall CD8+ T cell clonotypes against autologous HIV-1 epitope variants

Cited by 3 publications

References 0 publications

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

DeepTCR is a deep learning framework for revealing sequence concepts within T-cell repertoires

Functional characterization of CD4+ T cell receptors crossreactive for SARS-CoV-2 and endemic coronaviruses

CD4+ T cells from COVID-19 mRNA vaccine recipients recognize a conserved epitope present in diverse coronaviruses

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