scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires

Abstract

Bulk T cell receptor (TCR) sequencing enables scalable immune repertoire profiling but does not measure the transcriptional states required to infer T cell functional programs. Paired single-cell RNA/TCR sequencing offers a single-cell functional prior by jointly observing TCR identity and transcriptomic states. However, transferring this prior to bulk repertoires is challenging: sparse and noisy single-cell profiles can distort the functional reference, and the probabilistic relationship between TCR identity and transcriptome-inferred functional state makes direct cell-level alignment unstable, potentially biasing repertoire-level functional composition estimation. We present scMAC, a single-cell-guided framework for functional context completion that infers TCR functional representations reflecting transcriptome-derived T-cell states. scMAC builds a denoised functional reference by integrating multi-scale expression modeling with gene-semantic priors derived from large language model encodings of gene and protein descriptions, improving gene-level functional coherence. It then learns a prototype-guided TCR-to-function mapping that combines paired cell-level latent alignment with functional prototype regularization, enabling robust repertoire-level functional composition estimation. Across real single-cell and paired RNA/TCR datasets, scMAC outperformed existing single-cell representation methods in fine-grained cell-state representation, gene functional consistency, expression recovery, and disease-relevant pathway analysis, achieving an adjusted Rand index of 0.656 on a colorectal cancer dataset with 83 cell subtypes. For repertoire-level functional composition estimation, scMAC closely matched single-cell-derived functional proportions, with an R2 of 0.979 and a Bray–Curtis distance of 0.075. These results support scMAC as a practical route for transferring single-cell-derived functional knowledge to bulk TCR repertoire analysis beyond clonotype identity and clonal abundance. scMAC is publicly available at https://github.com/Bioinformatics7181/scMAC.