{
  "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.",
  "abstract_shown": true,
  "affiliations": [
    "School of Computer Science and Technology, Faculty of Electronics and Information Engineering, Xi'an Jiaotong University, No. 28 Xianning West Road, Beilin District, Xi'an, Shaanxi 710049, China."
  ],
  "article_type": "research article",
  "arxiv": null,
  "author_total": 2,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Xinyang Qian",
      "orcid": "0009-0006-0639-0240"
    },
    {
      "affs": [
        0
      ],
      "name": "Jiayin Wang",
      "orcid": "0000-0002-3862-6557"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/cnkbjeqdwre0h.bib",
  "cited_by": 0,
  "doi": "10.1093/bib/bbag586",
  "fulltext_url": "https://europepmc.org/articles/PMC13704796",
  "id": "cnkbjeqdwre0h",
  "issns": [
    "1467-5463",
    "1477-4054"
  ],
  "landing_url": "https://europepmc.org/article/MED/42932841",
  "language": "eng",
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "1312|Single-cell and spatial transcriptomics"
    ]
  ],
  "openalex": "W7223660587",
  "pdf_url": "https://europepmc.org/articles/PMC13704796?pdf=render",
  "pmcid": "PMC13704796",
  "pmid": "42932841",
  "published": "2026-09-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/cnkbjeqdwre0h.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:22:02Z",
      "native_id": "MED:42932841",
      "source": "europepmc",
      "url": "https://europepmc.org/article/MED/42932841"
    }
  ],
  "subjects": [
    {
      "field": "biochemistry",
      "origin": "openalex:1312|Single-cell and spatial transcriptomics",
      "subfield": "molecular-biology"
    }
  ],
  "title": "scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires.",
  "title_text": "scMAC: large language model-enhanced single-cell functional priors for completing functional context in bulk T cell receptor repertoires.",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:22:02Z",
  "url": "https://research.draconitic.com/paper/cnkbjeqdwre0h",
  "venue": "Briefings in bioinformatics",
  "versions": []
}