Every tissue in the human body is a mosaic. Tumor biopsies, blood samples, and frozen sections of diseased organs all contain swirling mixtures of immune cells, fibroblasts, epithelial cells, and blood vessel lining, each carrying its own molecular signature. For years, biologists have struggled with a deceptively simple question: given a bulk measurement of a tissue, can we work out exactly how many of each cell type are hiding inside it? A new computational tool called CrossBranch, described in BMC Genomics by Qianbei Yi, Jiaqi Yuan, Peng Xu, and Wenbin Liu of Guangzhou University, offers a fresh and technically ambitious answer to that question, and its implications stretch from basic tissue biology to the way oncologists read the architecture of tumors.

The problem the researchers set out to solve is known as cell-type deconvolution. Modern sequencing technologies allow scientists to profile gene expression in individual cells, building reference atlases that describe the molecular fingerprints of dozens of cell types. Bulk RNA sequencing, by contrast, measures the average expression of millions of cells at once, producing a single blended signal. Deconvolution methods attempt to reverse this blending, using single-cell references to estimate the proportions of each cell type in the mixed sample. The catch is that the reference data and the target data rarely come from the same world. Differences in tissue processing, sequencing platforms, donor populations, and even the fundamental physics of the measurement—counting RNA transcripts versus quantifying proteins—create distribution discrepancies that can quietly corrupt the estimates. Existing statistical and deep learning approaches, the authors note, are often undermined by exactly these cross-domain gaps.

CrossBranch attacks the problem with a dual-branch representation learning architecture, a design that processes the same data through two complementary channels before merging them. The first branch operates at the level of individual genes, capturing fine-grained expression patterns that distinguish one cell type from another. The second branch is pathway-informed, meaning it encodes the data through the lens of curated biological pathways—coordinated groups of genes that work together in processes such as immune signaling, metabolism, or cell division. By combining gene-level and pathway-level information, the framework aims to learn representations that are both precise and biologically meaningful, less likely to be thrown off by noise in any single gene and more likely to capture the coordinated programs that actually define cellular identity.

One of the most clever aspects of the method is how it sidesteps the chronic shortage of ground truth. In real tissues, nobody knows the exact proportions of every cell type, so there is nothing to train a supervised model on directly. CrossBranch solves this by generating labeled simulated mixtures from single-cell reference data. The software takes known single-cell profiles and artificially blends them in controlled proportions, producing training examples where the correct answer—the true cell-type composition—is known by construction. A prediction head, the final layer of the neural network, is then trained on these simulated mixtures to estimate cell-type proportions from mixed expression profiles.

But training on simulated data alone would recreate the very problem CrossBranch was designed to fix: the simulated mixtures would live in a slightly different statistical universe than the real target samples. The framework therefore adds a latent-space alignment strategy. Both the simulated mixtures and the real target data are encoded into a shared latent space, a compressed mathematical representation learned by the network, and the training process actively reduces the distribution discrepancies between them in that space. In effect, the model learns to view simulated and real data as if they came from the same domain, allowing knowledge gained from the labeled simulations to transfer accurately to real bulk RNA-seq, proteomic, and spatial measurements.

Spatial transcriptomics, a technology that measures gene expression while preserving the physical layout of a tissue slice, receives special treatment in the framework. Because neighboring spots on a spatial slide are likely to share similar cellular environments—cells do not arrange themselves randomly—CrossBranch incorporates a neighboring-spot-based spatial consistency loss. This additional term encourages the model to produce proportion estimates that vary smoothly across adjacent locations, suppressing implausible spatial flicker while still allowing genuine biological boundaries, such as the edge of a tumor nest, to show through. Ablation analyses, in which individual components of the model are removed one at a time, confirmed that the pathway-level representation, the cross-domain alignment, and the spatial neighborhood modeling each contribute measurably to the overall performance.

How well does it actually work? Across benchmark datasets spanning bulk RNA sequencing, proteomics, and spatial transcriptomics, CrossBranch consistently achieved competitive deconvolution performance compared with existing statistical and deep learning methods. That breadth matters. Most deconvolution tools are built for one modality and stumble when handed data from another, particularly proteomics, where the measured molecules are proteins rather than RNA transcripts and the correspondence between reference and target is even more tenuous. A single unified framework that performs well across all three modalities simplifies the analytical pipeline for research groups that routinely juggle multiple data types from the same patient samples.

The most striking results, however, come from the applications to cancer. The researchers applied CrossBranch to prostate, colorectal, and pancreatic tumor datasets, and the framework succeeded in identifying tumor-associated cellular changes—shifts in the cellular makeup of diseased tissue compared with healthy tissue—as well as cell-type patterns associated with patient survival. In the spatial analyses, CrossBranch pinpointed the localization of malignant epithelial cells within tissue sections, detected the co-localization of fibroblasts and endothelial cells, a pairing widely studied in tumor biology because cancer-associated fibroblasts and blood vessel cells cooperate to shape the tumor microenvironment, and revealed compartment-specific spatial organization within tumors. These are exactly the kinds of findings that turn a deconvolution tool from a mathematical curiosity into a biological instrument.

The significance of this work lies in what it says about the future of computational biology. Tissue heterogeneity is central to understanding disease mechanisms: a tumor that is 40 percent cancer-associated fibroblasts may respond very differently to immunotherapy than one dominated by cytotoxic T cells, even if the malignant cells themselves look identical. Tools like CrossBranch make it possible to extract that compositional information from the cheap, routine bulk measurements that hospitals already generate, rather than requiring expensive single-cell or spatial assays for every sample. By explicitly modeling the domain gap between reference and target data, the method addresses what many in the field consider the central weakness of deconvolution approaches, and the open availability of the source code on GitHub for academic and noncommercial use should accelerate adoption and independent testing.

There are, of course, the usual caveats that accompany any new machine learning method in biology. The framework depends on the quality and relevance of the single-cell reference atlases used to generate its simulated training mixtures, and performance on a given tissue will reflect how well those references capture the true cellular diversity of the sample. The published benchmarks and cancer applications are encouraging, but as with any computational tool, the broader community will need to stress-test it across additional tissues, diseases, and platforms. Still, the combination of pathway-informed representations, cross-domain alignment, and spatial consistency represents a thoughtful synthesis of biological knowledge and deep learning design. As spatial omics technologies mature and reference atlases grow, frameworks like CrossBranch point toward a future in which the cellular composition of any tissue—healthy or diseased—can be read out reliably from whatever measurement happens to be available, bringing the hidden mosaic of human tissue into sharper focus than ever before.

Subject of Research: Cross-domain cell-type deconvolution using dual-branch representation learning for omics data

Article Title: CrossBranch: cross-domain cell-type deconvolution with dual-branch representation learning

Article References: Yi, Q., Yuan, J., Xu, P., & Liu, W. (2026). CrossBranch: cross-domain cell-type deconvolution with dual-branch representation learning. BMC Genomics. https://doi.org/10.1186/s12864-026-13360-z

Image Credits: AI Generated

DOI: 10.1186/s12864-026-13360-z

Keywords: cell-type deconvolution, cross-domain learning, representation learning, single-cell RNA sequencing, bulk RNA-seq, proteomics, spatial transcriptomics, tumor microenvironment, pathway-level representation, deep learning, cancer biology, BMC Genomics

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Tags: bioinformatics for tissue analysisBMC Genomicsbulk RNA sequencing analysisbulk RNA-seqcancer biologycancer tissue architecturecell-type deconvolutioncell-type mapping algorithmscomputational biology toolscross-domain learningcross-omics data analysisdeep learningmulti-omics data integrationpathway-level representationProteomicsrepresentation learningsingle-cell reference atlasesSingle-Cell RNA SequencingSpatial transcriptomicstissue composition estimationtissue heterogeneity mappingtumor microenvironmenttumor microenvironment profiling