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Resistance Potential of the HLA-A2-restricted Immunodominant SARS-CoV-2 Specific CD8+ T Cell Receptor Repertoire to Antigenic Drift

GSE246491 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2026/01/15 Platform GPL16791Platform GPL15520
At a glance

RNA-seq (gene expression) · human · 30 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Samples
30 samples: 30 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Resistance potential of the HLA-A2-restricted immunodominant SARS-CoV-2-specific CD8(+) T cell receptor repertoire to antigenic driftNature communications · 2025
Summary (written by the submitters)
A major concern of COVID-19 is immune escape. While T cells are implicated in protection against severe disease, direct evaluation of their capacity to specifically target SARS-CoV-2 variants remains limited. We aim to narrow this gap by profiling the ability of CD8+ T cells to recognize SARS-CoV-2 mutations after vaccination and observe a high degree of phenotype and repertoire diversity.
Read the full summary ↓
To better understand the breadth of antigen repertoire coverage, we interrogate the ability of SARS-CoV-2 specific TCRs to bind all point mutations of the immunodominant HLA-A2:S269-277 epitope. While mutation space coverage is surprisingly vast, specific substitutions are not recognized by the vaccine-elicited repertoire. Structural analyses reveal a TCR sequence-based antigen recognition limitation intrinsic to vaccination. Further investigation reveals that this repertoire ‘hole’ is not present in the naive repertoire, suggesting future immune escape through these potential escape mutations could be relieved by modifications to vaccines.
Published in
Resistance potential of the HLA-A2-restricted immunodominant SARS-CoV-2-specific CD8(+) T cell receptor repertoire to antigenic drift
Malone MJ, Huang C, Zhang Y et al. · Nature communications 2025 · PMID 41407706 · doi:10.1038/s41467-025-67336-w
This dataset
Open GSE246491 in finder → Find more human RNA-seq datasets →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE246491_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1033336 and SRA study SRP468999. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41407706) ↗
Study design
30 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 30 sample titles: 30 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study
Datasets on a similar topic

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.