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<p>Proteins act as the terminal effectors of cellular function, encoding the phenotypic consequences of genomic and transcriptomic programmes. Although transcriptomic profiles serve as accessible proxies, they remain incomplete surrogates for the proteomic landscape that ultimately defines cellular phenotypes. Current single-cell foundation models, however, are trained exclusively on transcriptomes, resulting in biased and partial characterizations of cellular states. To address this limitation, we introduce CAPTAIN, a multimodal foundational model pretrained on over four million single cells with concurrently measured transcriptomes and a curated repertoire of 382 surface proteins across diverse human and mouse tissues. Our results show that CAPTAIN learns unified multimodal representations by modelling cross-modality dependencies and capturing the diversity of cellular states across complex biological contexts. CAPTAIN generalizes robustly across both fine-tuning and zero-shot settings, excelling in core downstream tasks such as protein imputation and expansion, cell type annotation, and batch harmonization. Beyond improved accuracy in multi-omics integration, CAPTAIN generates novel hypotheses regarding protein-driven intercellular dynamics, including potential immune interaction patterns linked to COVID-19 severity.</p>

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CAPTAIN: a multimodal foundation model pretrained on co-assayed single-cell RNA and protein

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月7日

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  • Boya Ji,
  • Tingting Hu,
  • Jiawen Wang,
  • Mengmeng Liu,
  • Liwen Xu,
  • Qinhao Zhang,
  • Siyun Zhong,
  • Libo Qiao,
  • Yan Zhang,
  • Shaoliang Peng,
  • Fulong Yu

摘要

Proteins act as the terminal effectors of cellular function, encoding the phenotypic consequences of genomic and transcriptomic programmes. Although transcriptomic profiles serve as accessible proxies, they remain incomplete surrogates for the proteomic landscape that ultimately defines cellular phenotypes. Current single-cell foundation models, however, are trained exclusively on transcriptomes, resulting in biased and partial characterizations of cellular states. To address this limitation, we introduce CAPTAIN, a multimodal foundational model pretrained on over four million single cells with concurrently measured transcriptomes and a curated repertoire of 382 surface proteins across diverse human and mouse tissues. Our results show that CAPTAIN learns unified multimodal representations by modelling cross-modality dependencies and capturing the diversity of cellular states across complex biological contexts. CAPTAIN generalizes robustly across both fine-tuning and zero-shot settings, excelling in core downstream tasks such as protein imputation and expansion, cell type annotation, and batch harmonization. Beyond improved accuracy in multi-omics integration, CAPTAIN generates novel hypotheses regarding protein-driven intercellular dynamics, including potential immune interaction patterns linked to COVID-19 severity.