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Background <p>Analysis of proteins is key to understanding biological processes, disease pathogenesis, and advancing therapeutic development. However, proteome profiling remains significantly limited when compared to the exponential growth of single-cell RNA sequencing data, owing to technical challenges and prohibitive costs associated with large-scale protein detection. Recent advancements in multi-omics technologies have established essential connections between transcriptome and proteome layers, facilitating innovative computational approaches for predicting protein abundance based on transcriptome data.</p> Results <p>Here, we present scProTrans, an interpretable deep learning framework that synergizes sequence knowledge and multi-omics integration to achieve cross-omics translation in single-cell resolution. Our framework deciphers gene-protein associations through three innovative components: Firstly, a hierarchical attention mechanism that aligns gene/protein sequences with cellular contexts using CITE-seq training data; secondly a bidirectional encoder architecture implementing sequence-to-embedding-to-profile learning for modality translation; finally cell-specific associations capturing dynamic gene-protein interplay across heterogeneous cell populations. Extensive evaluations across 17 multi-omics datasets demonstrate that scProTrans surpasses state-of-the-art methods in single-cell protein abundance translation and enhances downstream analyses, including cell clustering, subtype identification, and biomarker discovery. scProTrans improves protein prediction accuracy and preserves low-abundance protein signals, two significant aspects of single-cell protein abundance translation. Additionally, scProTrans is extended to tri-omics scenarios (ATAC-RNA-protein) via modular encoder refactoring, achieving cross-modal prediction concordance comparable to experimental replication.</p> Conclusions <p>This work advances multi-omics integration by establishing a sequence-aware paradigm for cross-modal translation, overcoming key limitations in proteome data acquisition. This modular architecture and its zero-shot capability make it a versatile platform for emerging multi-modal single-cell technologies.</p>

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A sequence knowledge-guided deep learning method for single-cell multi-omics translation

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年4月14日

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  • Mengyuan Zhao,
  • Jiawei Li,
  • Yanlin Jiang,
  • Jiahui Yan,
  • Xinyue Tang,
  • Cheng Liang,
  • Jijun Tang,
  • Fei Guo

摘要

Background

Analysis of proteins is key to understanding biological processes, disease pathogenesis, and advancing therapeutic development. However, proteome profiling remains significantly limited when compared to the exponential growth of single-cell RNA sequencing data, owing to technical challenges and prohibitive costs associated with large-scale protein detection. Recent advancements in multi-omics technologies have established essential connections between transcriptome and proteome layers, facilitating innovative computational approaches for predicting protein abundance based on transcriptome data.

Results

Here, we present scProTrans, an interpretable deep learning framework that synergizes sequence knowledge and multi-omics integration to achieve cross-omics translation in single-cell resolution. Our framework deciphers gene-protein associations through three innovative components: Firstly, a hierarchical attention mechanism that aligns gene/protein sequences with cellular contexts using CITE-seq training data; secondly a bidirectional encoder architecture implementing sequence-to-embedding-to-profile learning for modality translation; finally cell-specific associations capturing dynamic gene-protein interplay across heterogeneous cell populations. Extensive evaluations across 17 multi-omics datasets demonstrate that scProTrans surpasses state-of-the-art methods in single-cell protein abundance translation and enhances downstream analyses, including cell clustering, subtype identification, and biomarker discovery. scProTrans improves protein prediction accuracy and preserves low-abundance protein signals, two significant aspects of single-cell protein abundance translation. Additionally, scProTrans is extended to tri-omics scenarios (ATAC-RNA-protein) via modular encoder refactoring, achieving cross-modal prediction concordance comparable to experimental replication.

Conclusions

This work advances multi-omics integration by establishing a sequence-aware paradigm for cross-modal translation, overcoming key limitations in proteome data acquisition. This modular architecture and its zero-shot capability make it a versatile platform for emerging multi-modal single-cell technologies.