Kudos to @dtadros.bsky.social for developing this website

In our hands, the TCR Specificity Profile framework (www.biorxiv.org/content/10.1101/2025.11.17.688817v1) is not only central for understanding the key determinants of TCR-epitope recognition, but also to assess quality and reproducibility in new data.

Interested in understanding how epitope recognition specificity is encoded in your TCR sequences? Try our new interactive page for building TCR specificity profiles on the TCR Motif Atlas: tcrmotifatlas.unil.ch/Building_mot....

GitHub - GfellerLab/SuperCell at supercell-2.0 Coarse-graining of large single-cell RNA-seq data into metacells - GitHub - GfellerLab/SuperCell at supercell-2.0

SuperCell2.0 is available at github.com/GfellerLab/S...

Thanks to precious help from @benoitduc.bsky.social and @johannajoyce.bsky.social, the proposed markers for this population could be validated, thereby paving the way to further functional characterizations.

Applying this approach to large multimodal atlases from blood and tumor samples identifies interferon-primed monocytes and macrophages in the circulation and in the tumor microenvironment

With SuperCell2.0, Leonard Herault shows how multimodal metacells can help reduce the size and sparsity of single-cell multiomic datasets, while preserving, and even sometimes enhancing, the biological information.

Single-cell multiomic datasets are getting larger and larger in numbers of cells, but the measurements for each cell remain as sparse as 10 years ago...

Happy to share the beautiful manuscript of Leonard Herault: www.biorxiv.org/content/10.6...

Key determinants of T cell epitope recognition revealed by TCR specificity profiles Interactions between T-Cell Receptors (TCRs) and antigenic peptides presented on Major Histocompatibility Complex (MHC) molecules are central to the immune recognition of infected and malignant cells....

The fact that most CDR3 residues are determined by V/J choices (including many that directly contact the epitope in crystal structures) is fully consistent with our recent observation that epitope recognition specificity is primarily encoded in V/J usage (www.biorxiv.org/content/10.1...).

We then leveraged these results for quality assessment of TCR repertoire data by monitoring inconsistencies between CDR3 and V/J gene annotation. This analysis revealed different sources of noise in repertoires of TCRs of both undetermined and known specificity.

Similarly, batch effects resulting in different V/J gene usage across TCR repertoires will lead to apparent enrichment in specific CDR3 motifs or k-mers.

This has important consequences for interpreting CDR3 sequence patterns. For instance, any constrain in CDR1/CDR2 residues (e.g., recognition of a specific epitope) will impact multiple residues in CDR3 sequences.

Our results demonstrate that CDR3 length is strongly influenced by the number of germline-encoded CDR3 residues in V and J genes, and that on average 80% of CDR3α and 65% of CDR3β residues are determined by V and J gene usage.

Here we precisely quantify the impact of V/J choices on CDR3 length and amino acid composition.

CDR3 loops are known to mediate important interactions with the epitope. For this reason, many (most?) studies have focused on CDR3 sequences when analyzing TCR repertoires.

TCR repertoires are characterized by a very high sequence diversity resulting from different choices of V (D) and J genes and rearrangements taking place at the V(D)J junction within the CDR3 loops.

Statistical modelling of CDR3 sequences provides robust quality control for TCR repertoire datasets T-Cell Receptors (TCRs) show extensive sequence diversity across T cells. This diversity arises from different choices of V and J genes and from insertions and deletions at the V(D)J junction within t...

Very proud of the work of Dana Moreno about statistical modelling of CDR3 sequences in TCR repertoires: www.biorxiv.org/content/10.6....

Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation Scientific Reports - Assessing data size requirements for training generalizable sequence-based TCR specificity models via pan-allelic MHC-I point-mutation ligandome evaluation

Nice results: rdcu.be/eTNVr. Looks like the decision to develop specific ML models of TCR-epitope interactions for each epitope was a reasonable choice.

For those who followed the IMMREP25 competition and are interested in understanding how specificity was encoded for each epitope, check our TCR Motif Atlas IMMREP25 page: tcrmotifatlas.unil.ch/Browse_epito...

A huge thank you to all collaborators, including Yan Liu, Giancarlo Croce, Dana Moreno, Daniel Tadros, Julien Racle, Anne-Christine Thierry, Petra Baumgartner, Alexandra Michel, Vincent Zoete and Alexandre Harari

GitHub - GfellerLab/TEMPO: TCR-epitope motif-based interaction predictor TCR-epitope motif-based interaction predictor. Contribute to GfellerLab/TEMPO development by creating an account on GitHub.

The associated TCR-Epitope Motif-based interaction Predictor (TEMPO) can be accessed at github.com/GfellerLab/T... and can score a million TCRs in one minute on a standard CPU.

The TSP for hundreds of epitopes can be explored at the TCR Motif Atlas (tcrmotifatlas.unil.ch/home), including many sequence, length and MHC restriction variants of an immunodominant epitope.

Most of this work is based on experimentally identified TCRs, but we also we demonstrate that TSPs can be approximated with AlphaFold3 by scoring baseline TCR repertoires and focusing on the best scoring TCRs, thereby paving the way for in silico sorting of epitope-specific TCRs.

This does not mean that CDR3 sequences are not important for epitope recognition. Instead it reflects that a large fraction of CDR3 residues (including many directly contacting the epitope in X-ray structures) are determined by V/J usage.

In particular, our results demonstrate that much of the specificity in TCR-epitope recognition is encoded in V/J usage, with many epitopes showing up to ten-fold enrichment in specific V or J genes.

We show that TSPs unravel key determinants of TCR-epitope recognition specificity, predict cross-reactivity and reveal how TCR specificity evolves with epitope sequence, binding mode and MHC restriction.

Here we introduce a fully interpretable framework, called TCR Specificity Profiles or TSPs, to model TCR-epitope recognition.

Recent developments in machine learning and structure-based approaches (e.g., AF3) are revolutionizing TCR-epitope interaction predictions. However, interpreting such predictions to understand how specificity is encoded in TCR sequences remains challenging.

Very happy to share our latest manuscript about TCR-epitope recognition specificity: www.biorxiv.org/content/10.1...