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

The research, led by Taronish Biayna, Anton Baranovskii and colleagues, provides one of the most detailed portraits yet of how cells manage intron retention, a form of RNA processing control in which an intron, a segment of RNA that is normally spliced out and discarded, is deliberately kept within a mature transcript. Rather than representing a processing error, the study shows that intron retention operates as a genuine regulatory strategy, one that allows cells to pre-manufacture messenger RNAs and hold them in reserve until the precise moment their protein products are needed.

Nuclear speckles, also called interchromatin granule clusters, are dense assemblies of splicing factors and other RNA-processing proteins that punctuate the nucleoplasm. For decades they have been viewed primarily as storage and recycling depots for the molecular machinery that removes introns. The new work adds an unexpected twist: the periphery of these speckles, the thin shell of material surrounding each cluster, functions as a dedicated holding zone for a specific and functionally coherent set of long-lived intron-retained RNAs. The RNAs are not inside the speckles themselves but accumulate in a defined rim around them, suggesting that the boundary between the speckle interior and the surrounding nucleoplasm is an active regulatory interface rather than a passive border.

To reach this conclusion, the team combined super-resolution imaging with single-molecule RNA detection methods, allowing them to see precisely where individual RNA molecules reside relative to speckle markers. They complemented these spatial maps with metabolic labeling approaches that reveal RNA age, showing that the transcripts parked at the speckle periphery are remarkably stable, persisting far longer than typical messenger RNAs, which are usually made, used and degraded within hours. This longevity is a key feature of the compartment: it enables the cell to accumulate a ready supply of transcripts whose final activation can be deferred.

What makes the discovery particularly striking is the identity of the genes whose RNAs are stored in this way. The researchers found that the pooled intron-retained transcripts are disproportionately derived from genes involved in cell cycle regulation and genome maintenance, the very processes a cell must coordinate with exquisite timing as it prepares to divide. By retaining an intron, the cell prevents these RNAs from being translated into protein while keeping them intact and available. The speckle periphery thus acts as a staging ground where the molecular instructions for division and DNA repair are held in check until the proper moment.

That moment, the study shows, arrives at mitosis. When a cell commits to division, its nuclear envelope breaks down, chromosomes condense, and the ordinary architecture of the nucleus dissolves. The researchers observed that the intron-retained RNAs accumulated at the speckle periphery undergo synchronous splicing during this transition, with the retained introns removed en masse as the cell divides. In other words, the entire stockpile is resolved at once, releasing a coordinated wave of newly matured messenger RNAs precisely when the cell reorganizes its contents and re-establishes gene expression in two daughter cells. This synchronized resolution suggests a mechanism by which cells couple RNA maturation directly to the physical events of division.

The finding reframes intron retention from a curiosity of RNA biology into a temporally precise control point. In many previous studies, intron-retained transcripts were viewed mainly as substrates for surveillance pathways that degrade defective RNAs. The new evidence indicates that at least a substantial fraction of retained transcripts are instead functional intermediates, deliberately produced and stored. Their sequestration at the speckle periphery provides a physical address that separates them from the translation machinery and from the splicing factors that would otherwise complete their maturation, while keeping them close to the very machinery they will eventually need.

The spatial logic of the system is as important as the temporal one. By concentrating related RNAs in a shared compartment, the cell may achieve coordination that would be difficult to accomplish with freely diffusing molecules. Grouping cell cycle and genome maintenance transcripts at the speckle rim could allow a single regulatory event, such as the reactivation of splicing during mitosis, to act on many genes simultaneously, ensuring that their protein products appear together rather than in a stochastic trickle. This kind of collective behavior echoes themes familiar from other membrane-less organelles, where concentrated molecules can switch states rapidly in response to cellular cues.

The work also raises new questions that the field is now poised to explore. What molecular signals mark particular intron-retained RNAs for delivery to the speckle periphery, and what prevents their premature splicing during interphase? How is the compartment disassembled and reassembled across successive rounds of the cell cycle, and does the synchronous splicing at mitosis depend on the wholesale reorganization of the nucleus or on dedicated regulatory factors? Answers to these questions could illuminate how cells safeguard the fidelity of division, and how that safeguarding fails in diseases such as cancer, where both splicing patterns and cell cycle control are frequently disrupted.

For now, the study stands as a vivid demonstration that the nucleus is organized with far more purpose than a bag of freely mixing parts. At the edge of each nuclear speckle, cells maintain a carefully curated archive of unfinished messages, genes for division and genome care held in a long-lived, spatially defined waiting room. When mitosis arrives, the doors open all at once, and the archive is converted, in a single synchronized act of splicing, into the working instructions for building two new cells.

Subject of Research: Spatial and temporal regulation of long-lived intron-retained RNAs at nuclear speckle peripheries and their synchronous splicing during mitosis

Article Title: The periphery of nuclear speckles defines a spatially and temporally regulated compartment of long-lived intron-retained RNAs that resolves during mitosis

Article References: Biayna, J., Baranovskii, A., Chaudhuri, A., Paladin, M., Erdem, B., Keller, L.-E., Barutcu, A. R., Dimmeler, S., Marsico, A., & Dumbović, G. (2026). The periphery of nuclear speckles defines a spatially and temporally regulated compartment of long-lived intron-retained RNAs that resolves during mitosis. Nature Cell Biology, 28(9), 1857-1874. https://doi.org/10.1038/s41556-026-02040-5

Image Credits: AI Generated

DOI: 10.1038/s41556-026-02040-5

Keywords: nuclear speckles, intron retention, RNA splicing, mitosis, cell cycle, genome maintenance, RNA processing, nuclear organization, long-lived RNAs, speckle periphery, gene expression, Nature Cell Biology