T cells play a dual role in various physiopathological states, capable of eliminating tumors and infected cells, while also playing a pathogenic role when activated by autoantigens, causing self-tissue damage. The regulation of T cell-peptide/major histocompatibility complex (TCR-pMHC) recognition is crucial for maintaining disease balance and treating cancer, infections, and autoimmune diseases. Despite efforts, predictive models of TCR-pMHC specificity are still in the early stages. Inspired by advances in protein structure prediction via deep neural networks, we evaluated AlphaFold 3 (AF3)-based AI computation as a method to predict TCR epitope specificity. We demonstrate that AlphaFold can model TCR-pMHC interactions, distinguishing valid epitopes from invalid ones with increasing accuracy. Immunogenic epitopes can be identified for vaccine development through in silico high-throughput processes. Additionally, higher-affinity and specific T cells can be designed to enhance therapy efficacy and safety. An accurate TCR-pMHC prediction model is expected to greatly benefit T-cell-mediated immunotherapy and aid drug design. Overall, precise prediction of T-cell immunogenicity holds significant therapeutic potential, allowing the identification of peptide epitopes linked to tumors, infections, and autoimmune diseases. Although there is much work to be done before these predictions achieve widespread practical use, we are optimistic that deep learning-based structural modeling is a promising pathway for the generalizable prediction of TCR-pMHC interactions.

Antigen-specific T cell populations are of great value for studying immune recognition but tedious to generate by limiting dilution or cloning. Here, we develop a streamlined approach to generate antigen-specific T cell clones directly from peripheral blood using the cloneXplorer, a live-cell analysis and clone isolation platform based on conical microwell arrays. This platform continuously monitors cell proliferation, cytokine secretion, and surface markers in up to 100,000 single cell co-cultures, enabling the identification of rare, functionally defined T cells, which can be recovered for clonal expansion or sequence analysis. We benchmark the platform by performing several key demonstrations. First, we show that this platform can efficiently generate monoclonal cell populations from cell lines and human T cells. Next, we demonstrate that antigen-specificity can be identified at single cell resolution using a co-culture of Jurkat cells expressing NFAT-GFP, CD8, and a T cell receptor and K562 antigen presenting cells (APC) expressing a peptide library. Thereafter, we show that immune activation in mouse and human primary samples can be monitored by time lapse analysis of Interferon gamma (IFN-γ) secretion in individual microwell co-cultures using a fluorescent sandwich assay. Finally, we combine these capabilities in a proof-of-concept demonstration, which uses IFN-γ secretion and the presence of CD8 surface markers as hierarchical gates to isolate and expand antigen-specific T cells from human peripheral blood, and we verify their specificity by tetramer staining. Together, these results showcase potential applications of the cloneXplorer platform in cell line development, and in screening and validating immune receptor interactions with specific antigens.

Renal cell carcinoma (RCC) accounts for 90% of adult renal cancer cases and is characterized by significant heterogeneity within its tumor microenvironment. This study tests the hypothesis that tumor-associated macrophages (TAMs) influence RCC progression and patient response to treatment by investigating the prognostic implications of TAM signatures. Utilizing independent single-cell RNA sequencing data from RCC patients, we developed eight distinct TAM signatures reflective of TAM presence. A LASSO Cox regression model was constructed to predict survival outcomes, evaluated using the TCGA dataset, and validated across independent RCC cohorts. Model performance was assessed through Kaplan–Meier survival plots, receiver operating characteristic (ROC) curves, and principal component analysis. Survival analysis demonstrated that specific TAM signature gene expressions serve as significant prognostic markers, identifying TAM signatures positively correlated with patient survival and macrophage infiltration. A 27-gene TAM risk model was established, successfully stratifying patients into risk categories, with low-risk patients showing improved overall survival. These findings provide insights into the role of TAMs in modulating the RCC tumor immune microenvironment and their impact on patient prognosis, suggesting that TAM-based signatures may serve as useful prognostic markers and potential targets to enhance RCC treatment strategies.

Renal cell carcinoma (RCC) accounts for 90% of adult renal cancer cases and is characterized by significant heterogeneity within its tumor microenvironment. This study tests the hypothesis that tumor-associated macrophages (TAMs) influence RCC progression and patient response to treatment by investigating the prognostic implications of TAM signatures. Utilizing independent single-cell RNA sequencing data from RCC patients, we developed eight distinct TAM signatures reflective of TAM presence. A LASSO Cox regression model was constructed to predict survival outcomes, evaluated using the TCGA dataset, and independently validated across multiple RCC cohorts. Model performance was assessed through Kaplan-Meier survival plots, receiver operating characteristic (ROC) curves, principal component analysis. Survival analysis demonstrated that specific TAM signature gene expressions serve as significant prognostic markers, identifying TAM signatures positively correlated with patient survival and macrophage infiltration. A 27-gene TAM risk model was established, successfully stratifying patients into risk categories, with low-risk patients showing improved overall survival. These findings provide insights into the role of TAMs in modulating the RCC tumor immune microenvironment and their impact on patient prognosis, suggesting that TAM-based signatures may serve as useful prognostic markers and potential targets to enhance RCC treatment strategies.