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  3. A Sequence Knowledge-guided Deep Learning Method For Single-cell Multi-omics Translation.
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  3. A Sequence Knowledge-guided Deep Learning Method For Single-cell Multi-omics Translation.

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Reusable Single Cell for Iterative Epigenomic Analyses
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A sequence knowledge-guided deep learning method for single-cell multi-omics translation.

Mengyuan Zhao1,2, Jiawei Li3, Yanlin Jiang4

  • 1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.

Genome Biology
|April 14, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

scProTrans is a new deep learning framework that predicts protein abundance from gene expression data at single-cell resolution. This computational tool enhances multi-omics integration and overcomes limitations in proteome data acquisition.

Keywords:
Multi-omicsProteome profilingSingle-cell sequencingZero-shot translation

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Area of Science:

  • Computational Biology
  • Genomics
  • Proteomics

Background:

  • Protein analysis is crucial for understanding biological processes and diseases.
  • Proteome profiling is limited compared to RNA sequencing due to technical challenges and cost.
  • Multi-omics technologies link transcriptome and proteome, enabling computational prediction of protein abundance.

Purpose of the Study:

  • To introduce scProTrans, a deep learning framework for cross-omics translation at single-cell resolution.
  • To predict protein abundance from transcriptome data, overcoming proteomic data acquisition limitations.
  • To enhance multi-omics integration and downstream single-cell analyses.

Main Methods:

  • Developed scProTrans, a deep learning framework utilizing sequence knowledge and multi-omics integration.
  • Employed a hierarchical attention mechanism for gene/protein sequence alignment with cellular contexts.
  • Utilized a bidirectional encoder for sequence-to-embedding-to-profile learning and modality translation.
  • Incorporated cell-specific associations to capture dynamic gene-protein interplay.

    • Main Results:

    • scProTrans surpasses state-of-the-art methods in single-cell protein abundance translation across 17 datasets.
    • The framework improves protein prediction accuracy and preserves low-abundance protein signals.
    • Extended scProTrans to tri-omics (ATAC-RNA-protein) scenarios with high cross-modal prediction concordance.
    • Enhanced downstream analyses including cell clustering, subtype identification, and biomarker discovery.

    Conclusions:

    • Advanced multi-omics integration with a sequence-aware paradigm for cross-modal translation.
    • Overcame key limitations in proteome data acquisition using computational prediction.
    • Established a versatile platform for emerging multi-modal single-cell technologies with modular architecture and zero-shot capability.