Modern biology has a data problem, and it is not a shortage of measurements. The latest sequencing technologies can now read out gene expression, chromatin accessibility, protein abundance and even metabolite profiles from the same cell or the same slice of tissue, generating extraordinarily rich portraits of life at molecular resolution. But each of these measurement layers arrives drenched in noise, and the more modalities a scientist stacks together, the more that technical static threatens to drown out the biological signal. A team led by Wei Li and Yuanxiang Jiang of Nankai University, working with colleagues at BGI Research and Alibaba’s DAMO Academy, has now unveiled a computational framework designed to tackle precisely this challenge. Their tool, called DePass, is described in a study published in Nature Cell Biology, and it promises to make the integration of paired multi-omics data both more accurate and more broadly applicable than existing approaches.
The core insight behind DePass is deceptively simple: instead of treating noise removal and data integration as two separate problems to be solved in sequence, the framework couples them into a single iterative loop. Raw omics data are first organized into graphs, mathematical structures in which each cell or spatial spot is a node and edges connect measurements that are likely to be biologically related. The system then performs neighbourhood aggregation, borrowing information from connected nodes to smooth out spurious fluctuations in each modality. Crucially, the denoised data feed into an integration module that produces a unified embedding, a compact mathematical representation of each cell that fuses information across all measured layers. That embedding, in turn, is used to refine the enhancement step, and the cycle repeats. Each pass through the loop sharpens both the cleaned data and the integrated result, which is why the authors describe their architecture as dual-enhanced.
This coupled design addresses a weakness that has plagued earlier integration methods. Most existing tools were built with the assumption that the input data are reasonably clean, and they focus their mathematical machinery on aligning modalities that measure different aspects of biology. Yet multimodal experiments are inherently noisier than single-modality profiling, because each additional measurement layer introduces its own sources of dropout, batch effects and stochastic variation. When a framework ignores this elevated noise, the errors propagate into the integrated embedding, blurring the very biological distinctions the analysis is meant to reveal. By iteratively denoising while integrating, DePass prevents that degradation and, according to the authors’ benchmarks, recovers sharper and more faithful cellular structure.
The second major innovation is scope. Many current integration methods are tailored to a specific data type: some excel at single-cell experiments, where each cell carries paired measurements across modalities, while others are designed for spatial platforms, where molecular readouts are mapped onto tissue coordinates. DePass was engineered from the ground up to handle both contexts within a single framework. Whether the input is a single-cell multi-omics dataset, a spatial transcriptomics and proteomics experiment, or a spatial epigenomic profile, the same graph learning architecture applies. This generality matters for a field that is expanding explosively, with new experimental platforms appearing every year and laboratories increasingly unwilling to learn a different computational tool for each one.
To substantiate claims of generality, the team mounted an unusually comprehensive benchmarking campaign. DePass was evaluated across six modalities, nine tissue types and thirteen experimental platforms, spanning datasets from mouse brain and spleen to human tonsil, mouse embryo and human tumour tissue. The platforms ranged from established commercial systems such as 10x Genomics multiome assays to cutting-edge methods including Spatial-mux-seq, which captures four modalities simultaneously in mouse embryo tissue, and MISAR-seq, which jointly profiles gene expression and chromatin accessibility in spatial context. Across this gauntlet of tests, DePass demonstrated superior integration accuracy compared with competing methods, measured by quantitative metrics of how well cells cluster by biological identity rather than technical artifact.
Among the most striking demonstrations is the framework’s handling of spatial metabolomics. The researchers applied DePass to a mouse brain dataset combining Visium spatial transcriptomics with MALDI mass spectrometry imaging, a pairing that captures both the transcriptomic and metabolic state of tissue in register. Metabolomic data are notoriously noisy and sparse, making them a stern test of any denoising approach. DePass not only integrated the two layers coherently but also strengthened the correlations between genes and metabolites, sharpening anatomical boundaries in the enhanced data compared with the raw measurements. Similar gains appeared in a mouse spleen spatial transcriptomics and proteomics dataset, where enhancement improved the spatial coherence of marker gene and protein expression patterns.
The framework also proved its worth on clinical material. The team generated an in-house colorectal cancer dataset using Stereo-CITE-seq, a technology that profiles gene expression and surface proteins across intact tumour tissue at high resolution. When DePass was applied to this data, it uncovered substructure within immune niches that had been invisible in less carefully integrated analyses, and it resolved spatial heterogeneity within the tumour at near single-cell resolution. For cancer researchers, this kind of resolution is not a luxury. Tumours are mosaics of malignant cells, immune infiltrates, fibroblasts and vasculature, and the arrangement of these components shapes how the disease progresses and how patients respond to therapy. A tool that can faithfully integrate transcriptomic and proteomic views of that architecture could sharpen the identification of biomarkers and therapeutic targets.
Further applications underscored the breadth of the method. In hepatocellular carcinoma data combining Xenium transcriptomics with CODEX protein imaging, DePass detected a rare population of cDC1 dendritic cells, a cell type of intense interest in cancer immunology because of its role in priming antitumour immune responses, even though it comprised less than 0.15 percent of all cells. In a mouse embryo Spatial-mux-seq dataset spanning four modalities, the framework wove together transcriptomic, epigenomic and protein-level information into a coherent developmental picture. And on single-cell tri-modality datasets such as TEA-seq and DOGMA-seq, which measure transcripts, epitopes and chromatin accessibility simultaneously in individual cells, DePass preserved biologically meaningful dynamics, including cell-cycle progression reconstructed through pseudotime analysis.
Practical considerations may prove as important as raw performance. The authors report that DePass is scalable, employing efficient graph construction and training strategies that keep computational demands manageable even for large datasets, and they provide running time evaluations across their benchmark collection. The software is open source, with code deposited on GitHub and Zenodo, full documentation, and a detailed protocol published through protocols.io. Processed data from the study are publicly available through Zenodo and the China National Center for Bioinformation, and the paper’s data availability section catalogues accession numbers for every dataset analysed. This transparency lowers the barrier for other laboratories to adopt, test and extend the framework, which is often what determines whether a new method becomes a community standard or a footnote.
The arrival of DePass reflects a broader maturation of computational biology. A decade ago, the bottleneck in genomics was generating data; today, it is making sense of data whose complexity has outpaced the tools designed to analyse them. Multi-omics experiments hold the promise of capturing biology in its full dimensionality, but only if the layers can be fused without injecting artifacts. By building denoising into the integration process itself and by refusing to specialize in a single data type, DePass offers a template for what next-generation analysis frameworks may look like: unified, generalizable and honest about the noise inherent in real measurements. If the benchmarks hold up under independent scrutiny, the framework could become a standard workhorse in laboratories charting the molecular geography of tissues, from developing embryos to tumours on the operating table.
Subject of Research: A graph-based deep learning framework for integrating paired single-cell and spatial multi-omics data
Article Title: The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics
Article References: Li, W., Jiang, Y., Zhao, Q., Xu, Y., Dai, D., Rong, Y., Zhao, X., & Zhang, H. (2026). The dual-enhanced graph learning framework DePass allows paired data integration in single-cell and spatial multiomics. Nature Cell Biology. https://doi.org/10.1038/s41556-026-02067-8
Image Credits: AI Generated
DOI: 10.1038/s41556-026-02067-8
Keywords: DePass, multi-omics integration, single-cell sequencing, spatial transcriptomics, graph neural networks, deep learning, denoising, colorectal cancer, tumour microenvironment, spatial proteomics, computational biology, Nature Cell Biology
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Drew Townsend. (September 24, 2026). New AI framework DePass cleans and merges multi-omics data across cells and tissues. Scienmag. https://scienmag.com/new-ai-framework-depass-cleans-and-merges-multi-omics-data-across-cells-and-tissues/
Drew Townsend. “New AI framework DePass cleans and merges multi-omics data across cells and tissues.” Scienmag, 24 September 2026, https://scienmag.com/new-ai-framework-depass-cleans-and-merges-multi-omics-data-across-cells-and-tissues/. Accessed 24 September 2026.
Drew Townsend. “New AI framework DePass cleans and merges multi-omics data across cells and tissues.” Scienmag. September 24, 2026. https://scienmag.com/new-ai-framework-depass-cleans-and-merges-multi-omics-data-across-cells-and-tissues/
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Tags: advanced bioinformatics tools for multi-omicsbioinformatics methods for multi-layer biological dataColorectal cancercomputational biologycomputational framework for biologydata noise removal in molecular biologydeep learningdenoisingDePassDePass algorithm for data mergingGraph Neural Networkshigh-resolution tissue profiling techniquesintegrated analysis of gene expression and chromatin accessibilitymachine learning approaches in multi-omicsmulti-modal data analysis in genomicsmulti-omics data integrationmulti-omics integrationNature Cell Biologynoise reduction in single-cell sequencingsingle-cell sequencingspatial proteomicsSpatial transcriptomicstissue and cell-level omics profilingtumour microenvironment
