A quest for universal anti-SARS-CoV-2 T cell assay: systematic review, meta-analysis, and experimental validation.
Akshay BinaykeAymaan ZaheerSiddhesh VishwakarmaSavita SinghPriyanka SharmaRucha ChandwaskarMudita GosainSreevatsan RaghavanDeepika Rathna MurugesanPallavi KshetrapalRamachandran ThiruvengadamShinjini BhatnagarAnil Kumar PandeyPramod Kumar GargAmit AwasthiPublished in: NPJ vaccines (2024)
Measuring SARS-CoV-2-specific T cell responses is crucial to understanding an individual's immunity to COVID-19. However, high inter- and intra-assay variability make it difficult to define T cells as a correlate of protection against COVID-19. To address this, we performed systematic review and meta-analysis of 495 datasets from 94 original articles evaluating SARS-CoV-2-specific T cell responses using three assays - Activation Induced Marker (AIM), Intracellular Cytokine Staining (ICS), and Enzyme-Linked Immunospot (ELISPOT), and defined each assay's quantitative range. We validated these ranges using samples from 193 SARS-CoV-2-exposed individuals. Although IFNγ ELISPOT was the preferred assay, our experimental validation suggested that it under-represented the SARS-CoV-2-specific T cell repertoire. Our data indicate that a combination of AIM and ICS or FluoroSpot assay would better represent the frequency, polyfunctionality, and compartmentalization of the antigen-specific T cell responses. Taken together, our results contribute to defining the ranges of antigen-specific T cell assays and propose a choice of assay that can be employed to better understand the cellular immune response against viral diseases.
Keyphrases
- sars cov
- systematic review
- high throughput
- respiratory syndrome coronavirus
- meta analyses
- immune response
- randomized controlled trial
- coronavirus disease
- single cell
- machine learning
- big data
- oxidative stress
- electronic health record
- diabetic rats
- drug induced
- artificial intelligence
- reactive oxygen species
- rna seq
- deep learning
- high throughput sequencing