Mass spectrometry imaging has become one of the most powerful tools in modern biology because it can map hundreds of molecules directly within a tissue slice, revealing where lipids, metabolites, proteins and other biomolecules reside without labels or stains. Yet the technique has long been constrained by its spatial resolution. Conventional instruments raster a laser across a tissue section with a defined step size, and features smaller than that step blur together. Boosting resolution usually demands costly instrumentation such as specialized lasers, transmission-mode optics or next-generation ion optics, placing subcellular molecular imaging out of reach for many laboratories.

TEMI takes a different path. Instead of refining the instrument, the team enlarges the tissue itself. Drawing on principles from expansion microscopy, a technique introduced more than a decade ago, the researchers embed tissue in a water-rich hydrogel polymer network. When the gel swells, it carries the tissue along with it, spreading the molecules apart and effectively magnifying the sample before any mass spectrometry takes place. A laser raster that would have been too coarse for the original tissue becomes fine enough to resolve individual cells in the expanded version, yielding more than a 3.5-fold improvement in effective imaging resolution with standard equipment.

The critical challenge was chemistry. Traditional expansion protocols often rely on harsh denaturation conditions, including high heat and strong detergents, to homogenize tissue and allow gels to stretch uniformly. Those conditions would destroy the very molecules mass spectrometry aims to detect. The TEMI workflow solves this by using a re-embedding strategy that expands tissue under mild, harsh-condition-free conditions, preserving the chemical integrity of lipids, metabolites, N-glycans, peptides and proteins. The result is a sample that is both physically enlarged and chemically faithful to its original molecular composition.

The published protocol walks readers through every stage of the process. It begins with constructing a gelation chamber, then describes hydrogel-based tissue gelation and expansion, followed by cryosectioning of the expanded tissue-hydrogel composite. Because expanded samples are soft and hydrated, cutting them into thin sections requires careful handling, and the protocol provides the specific parameters that make reproducible sectioning possible. Detailed guidance covers matrix application, data acquisition and visualization pipelines, along with troubleshooting tips accumulated through the team’s extensive experimentation.

One of the most striking capabilities of TEMI is multiomics mapping on a single tissue section. The protocol describes a sequential workflow in which N-glycans are released in situ by treatment with the enzyme PNGase F, after which proteins are digested with trypsin and imaged, all following lipid and metabolite analysis on the same expanded section. This means a single slice of brain tissue can yield spatially resolved maps of multiple molecular classes, each anchored to the same anatomical context, an efficiency that conventional workflows struggle to match.

Quality control receives particular attention. The protocol includes a dedicated workflow for measuring deformation maps and expansion factors, which quantify how uniformly the gel has expanded. Uneven expansion would distort molecular maps, so the researchers provide computational tools, released through a public code repository, that quantify tissue expansion non-uniformity. They also demonstrate that analyte delocalization during sample preparation is minimal: lipid and small-metabolite signals were detected exclusively in tissue regions, with no corresponding signals in adjacent blank hydrogel areas, confirming that molecules stay where they belong during the swelling process.

The demonstration experiments showcase the method’s power on the mouse cerebellum, a tissue with exquisitely organized layers of cells. Comparing an unexpanded control cerebellum imaged with a 50-micrometer laser raster against a double-embedded, expanded sample at the same step size reveals a dramatic difference in molecular detail. Pushing further, the team performed TEMI after three cycles of gel embedding and expansion with a 10-micrometer raster step, resolving biomolecular heterogeneity that remains invisible in unexpanded tissue. The protocol also demonstrates mapping of small metabolites, N-glycans and proteins across cerebellar structures, illustrating the breadth of the multiomics capability.

The work builds on the team’s primary research paper published in Nature Methods, which introduced TEMI and established its foundational performance, and on a rich history of expansion microscopy methods developed by collaborators at the Howard Hughes Medical Institute’s Janelia Research Campus, including protein-retention expansion microscopy and ten-fold robust expansion. Raw datasets from the protocol’s figures are publicly available in the MassIVE repository, and the deformation-measurement code is hosted on GitHub, lowering barriers for laboratories that want to adopt or adapt the approach.

The implications extend across basic biology and medicine. Single-cell spatial resolution for untargeted molecular imaging could illuminate how metabolic gradients shape tissue function, how lipid compositions vary between neighboring cells in the nervous system, and how disease states such as cancer or neurodegeneration alter molecular architecture at scales previously accessible only to antibody-based imaging. Because TEMI requires no instrument modifications, it promises broad accessibility: laboratories with standard mass spectrometry imaging systems can now reach a resolution regime once reserved for a handful of specialized facilities. The protocol’s authors, spanning the University of Wisconsin-Madison and HHMI Janelia, and supported in part by the National Institutes of Health, position the method as a practical bridge between the spatial-omics revolution and the everyday mass spectrometry lab, inviting a wide research community to stretch its view of tissue, quite literally, into sharper focus.

Subject of Research: Tissue expansion combined with mass spectrometry imaging for high-spatial-resolution multiomics molecular mapping

Article Title: Tissue expansion mass spectrometry imaging (TEMI) for high-spatial-resolution multiomics molecular mapping

Article References: Tissue expansion mass spectrometry imaging (TEMI) for high-spatial-resolution multiomics molecular mapping. (n.d.). https://doi.org/10.1038/s41596-026-01427-w

Image Credits: AI Generated

DOI: 10.1038/s41596-026-01427-w

Keywords: mass spectrometry imaging, tissue expansion, TEMI, spatial omics, single-cell imaging, lipidomics, metabolomics, N-glycans, proteomics, hydrogel expansion, Nature Protocols, multiomics

The central insight driving the review is that the tumor microenvironment is not a passive backdrop but an active determinant of both treatment response and clinical outcomes. In colorectal cancer, the composition of immune cells infiltrating a tumor, the ratio of tumor cells to stroma, the presence of tertiary lymphoid structures and the burden of tumor-infiltrating lymphocytes all carry prognostic and predictive weight. Yet these features are traditionally measured on tissue samples obtained through biopsy or surgery, which capture only a fragment of the tumor and cannot be repeated freely over time. Radiomics offers an alternative. By extracting hundreds of quantitative features from routine medical images, such as computed tomography and magnetic resonance imaging, radiomics aims to characterize the whole tumor, including its internal heterogeneity, without touching the patient.

Technically, radiomics pipelines convert standard acquisitions such as T2-weighted imaging, contrast-enhanced T1-weighted imaging and diffusion-weighted imaging into high-dimensional feature sets. First-pass texture statistics capture local gray-level variation, while higher-order features extracted through filtered images and deep learning networks probe patterns that the human eye cannot resolve. Apparent diffusion coefficient maps derived from diffusion-weighted imaging, for example, reflect tissue cellularity and can serve as indirect surrogates of tumor density and stromal content. The review organizes the colorectal cancer radiomics literature into three thematic clusters: features that correlate directly with microenvironment components, features associated with vascular-invasion-related phenotypes, and features tied to tumor-intrinsic properties that are themselves shaped by the microenvironment. Each cluster, the authors argue, contributes a different piece of the puzzle linking what radiologists see on a screen to what pathologists see under a microscope and what molecular biologists sequence in the lab.

The vascular-invasion theme is particularly consequential clinically. Extramural venous invasion and microvascular invasion are established markers of poor prognosis in colorectal cancer, signaling that tumor cells have entered the circulatory system and raised the risk of metastasis. Radiomic models trained on CT and MRI can flag these phenotypes before surgery, potentially informing decisions about neoadjuvant therapy and surgical planning. Meanwhile, radiomic signatures predicting microsatellite instability and deficient mismatch repair status offer a non-invasive proxy for the single most important biomarker in modern colorectal cancer immunotherapy, since patients with dMMR or MSI-high tumors are the ones most likely to benefit from immune checkpoint inhibitors. In locally advanced rectal cancer, radiomic models have also been used to predict pathological complete response after chemoradiotherapy, a finding that could eventually help identify patients for organ-preserving strategies.

Pathomics extends the same quantitative logic to the microscopic scale. Whole-slide imaging digitizes histopathology slides, and computational methods then profile tumor architecture and the spatial distribution of immune cells at high throughput. Where a pathologist might estimate tumor-infiltrating lymphocyte density visually, pathomic pipelines can quantify it precisely, map the spatial relationships between tumor nests and stromal compartments, and compute the tumor-stroma ratio automatically. Deep learning models trained on whole-slide images can even predict molecular alterations, such as microsatellite instability, directly from hematoxylin and eosin-stained tissue. Because pathomics operates on resected or biopsied tissue, it provides the microscopic ground truth that radiomics lacks, and the two approaches are natural partners: radiomics sees the whole tumor in vivo, while pathomics resolves the cellular detail of the sampled regions.

The most ambitious portion of the review describes cross-scale integration, in which radiomic and pathomic features are fused with genomics and transcriptomics to trace a continuous chain from macroscopic phenotype to molecular mechanism. Radiomic features that predict microsatellite instability, for instance, can be connected to the immune-inflamed transcriptional programs that accompany deficient mismatch repair, including upregulated checkpoint molecules and enriched cytotoxic T-cell signatures. Consensus molecular subtypes of colorectal cancer, which stratify tumors by their gene-expression patterns, also leave imaging fingerprints, and studies reviewed by the authors show that radiomic models can distinguish between these molecular classes with useful accuracy. At the single-cell and spatial-omics frontier, technologies such as single-cell RNA sequencing and spatially resolved transcriptomics reveal the precise cellular neighborhoods within the microenvironment, including interactions between tumor-associated macrophages, myeloid-derived suppressor cells, cancer-associated fibroblasts and lymphocytes, offering mechanistic explanations for the imaging features that models detect.

Multimodal fusion is the methodological glue holding this framework together. Rather than treating imaging, pathology and molecular data as parallel silos, fusion approaches combine them within a single predictive model, allowing each modality to compensate for the blind spots of the others. Deep learning architectures can ingest radiomic features from CT or MRI, pathomic features from whole-slide images, and genomic or transcriptomic profiles from the same patient, learning joint representations that outperform any single data type. The review highlights early studies demonstrating that such combined models improve prediction of prognosis, immunotherapy response and treatment-related outcomes in colorectal cancer compared with unimodal baselines, suggesting that the cross-scale framework is more than the sum of its parts.

The authors are explicit, however, about the caveats. Much of the evidence reviewed is retrospective, derived from single-center cohorts with limited sample sizes, and few radiomic models have been validated prospectively or across diverse populations. Standardization of image acquisition, feature definitions and model reporting remains inconsistent across the field, raising concerns about reproducibility. The relationship between imaging features and microenvironment biology is often correlational rather than mechanistically established, and the review repeatedly emphasizes that clinical utility, and any role in therapeutic decision-making, remain to be established through prospective validation. These are not trivial hurdles; they are the same obstacles that have slowed the translation of radiomics in other cancer types.

Even so, the trajectory described in the review is striking. What began as an effort to squeeze extra information out of images that radiologists already acquire routinely has matured into a multi-scale program that connects the radiology suite to the pathology lab and the sequencing core. If prospective studies bear out the promise of imaging-driven multi-omics integration, clinicians could one day profile a patient’s tumor microenvironment repeatedly, cheaply and non-invasively, tracking its evolution under therapy and selecting patients for immunotherapy with far greater precision than today’s single-timepoint biomarkers allow. For a disease that kills hundreds of thousands of people each year and frustrates clinicians with its heterogeneity, that would represent a genuinely transformative shift, one that this review maps out with unusual technical clarity and admirable restraint about what has, and has not, yet been proven.

Subject of Research: Decoding the tumor microenvironment in colorectal cancer through radiomics and multi-omics integration

Article Title: From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer

Article References: Gong, L., Wu, X., Zhang, W., Lai, B., Yuan, J., Gu, Y., Shen, H., Liu, X., Xiong, Y., Zheng, J., Wang, L., Han, X., Zhang, B., & Zhang, S. (2026). From imaging to multi-omics: decoding the tumor microenvironment in colorectal cancer. Journal of Translational Medicine. https://doi.org/10.1186/s12967-026-08958-6

Image Credits: AI Generated

DOI: 10.1186/s12967-026-08958-6

Keywords: colorectal cancer, tumor microenvironment, radiomics, pathomics, multi-omics, immunotherapy, microsatellite instability, deep learning, spatial omics, tumor heterogeneity, precision oncology, biomarkers

The framework, developed by Seowon Chang, Alexander Fleischmann and Ying Ma, addresses a problem that has grown acute as spatial assays have diversified. Transcriptome-plus-protein platforms such as SPOTS and spatial CITE-seq, epigenome-transcriptome methods that jointly capture chromatin accessibility or histone marks alongside RNA, metabolomic imaging and multiplexed approaches such as spatial-Mux-seq each carry modality-specific signals that generic integration tools tend to wash out. Earlier software could typically merge only two modalities, struggled with the massive spot counts of Visium HD, and returned a single consensus answer with no indication of where the model was guessing. SCIGMA’s designers set out to build a system that was scalable, generalizable across platforms and honest about its own uncertainty.

Architecturally, SCIGMA combines two ideas that have proven powerful in machine learning but had not been married in quite this way for spatial biology. The first is a multiview graph neural network built on graph attention layers, which represents each spatial location as a node connected to its tissue neighbors, letting the model learn from both molecular measurements and the physical arrangement of the tissue. Each modality is encoded through its own branch, so transcriptomic, proteomic, epigenomic and metabolic information is transformed into a shared latent space without being forced into a single flattened feature matrix. The second idea is an uncertainty-aware contrastive learning objective: by treating temperature as a learnable, uncertainty-linked quantity, the model learns to pull together representations of the same location seen through different molecular lenses while pushing apart mismatched views, and it modulates how strongly it aligns views depending on how reliable the data at that location appear to be.

This uncertainty machinery is more than a statistical nicety. After training, SCIGMA produces spatially resolved uncertainty maps that highlight regions of biological or technical heterogeneity — tumor margins, regions with mixed cell populations, or spots where assay quality degrades. The authors show that uncertainty estimates flag locations where feature reconstruction error is highest, meaning researchers can see precisely where the integrated representation is least trustworthy rather than accepting a smoothed-over consensus. Interpretability was a design goal throughout: the framework preserves modality-specific signals within its joint embedding, so users can trace which molecular layer drives a given spatial domain and recover regulatory programs that are visible only in, say, the chromatin channel and not the transcriptome.

The evaluation was unusually broad. The team benchmarked SCIGMA across 19 datasets spanning eight modalities, ten tissue types and nine technological platforms, ranging from spatial epigenome-transcriptome profiling of the postnatal mouse brain to protein-plus-RNA measurements of the mouse spleen, single-cell-resolution Xenium Prime datasets from human ovarian and cervical cancers, and enormous Visium HD sections of mouse intestine and human colorectal cancer with more than one million spots each. Where ground truth allowed comparison, SCIGMA outperformed existing methods on spatial domain detection, preservation of modality-specific information, feature reconstruction and reproducibility across repeated runs — a metric that matters given growing concern about the fragility of machine learning models in biomedical data science.

The biological case studies illustrate what multimodal integration buys that single-modality analysis cannot. In the mouse brain, combining chromatin accessibility or histone modification data with transcriptomics let SCIGMA refine cortical layers and white matter boundaries beyond what RNA alone revealed, and to link transcriptional identities with the regulatory elements that govern them. In the spleen, joint transcriptome-proteome analysis sharpened the demarcation of follicles, marginal zones and T cell zones, tying protein-level markers such as CD19 and CD3 components to their local transcriptional contexts. Analyses of Xenium Prime tumor sections demonstrated that the framework resolves intratumoral heterogeneity, distinguishing tumor epithelial niches, stromal compartments and immune infiltrates while its uncertainty layer highlights the unstable boundary regions where tumor biology is most contested.

Scalability was addressed head-on. Naive graph neural networks choke when every spot in a Visium HD section becomes a node in a graph with millions of vertices. SCIGMA incorporates efficient sampling strategies and nearest-neighbor retrieval, borrowing ideas from the Faiss library, to keep training tractable, and the authors demonstrate full-length analysis of Visium HD datasets exceeding one million spatial locations. Equally important is extensibility: the framework is modular, so when a future technology profiles six, seven or more molecular layers simultaneously, additional modality encoders can be attached without redesigning the core model. The team demonstrated this flexibility on spatial-Mux-seq mouse brain data, simultaneously integrating five modalities into a coherent joint representation.

Software accessibility rounded out the release. The SCIGMA package, along with scripts reproducing every published analysis and a suite of tutorials, is openly available through GitHub and archived on Zenodo, supporting the reproducibility standards the paper explicitly engages with. Funding came from the National Science Foundation and the National Institutes of Health, and the work was carried out at Brown’s Center for Computational Molecular Biology with computational support from the university’s Center for Computation and Visualization. The authors report no competing interests, and the article underwent peer review by researchers including Mengjie Chen, Zhaoheng Li and Wei Sun.

For the field, SCIGMA arrives at a moment when the volume and variety of spatial data are outpacing analysis tools. High-resolution platforms such as Visium HD and Xenium Prime are making million-spot, subcellular-resolution datasets routine, and multiplexed assays are stacking molecular layers that earlier software simply could not combine. A framework that handles five modalities, scales to the largest current datasets, runs across platforms without platform-specific tuning, and quantifies its own confidence offers a kind of common analytical ground that spatial biology has lacked. If it holds up in broad community use, the practical effect will be that researchers spend less time wrestling incompatible data formats into fragile pipelines and more time asking biological questions — about how tumors orchestrate their microenvironments, how cortical architecture is patterned, and how genomic regulation plays out across the geography of living tissue.

Subject of Research: Development of SCIGMA, a scalable uncertainty-aware deep learning framework for integrating spatial multiomics data across modalities and platforms

Article Title: Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA

Article References: Scalable, generalizable and uncertainty-aware integration of spatial multiomics across diverse modalities and platforms with SCIGMA. (n.d.). https://doi.org/10.1038/s41588-026-02706-8

Image Credits: AI Generated

DOI: 10.1038/s41588-026-02706-8

Keywords: spatial omics, multiomics integration, deep learning, graph neural networks, contrastive learning, uncertainty estimation, spatial transcriptomics, tumor heterogeneity, spatial domain detection, Visium HD, Xenium Prime, computational biology

A team of Austrian researchers believes they have a practical answer. In a study published in BMC Bioinformatics, Markus Steiner, Stephan Drothler, Jan P. Höpner, Roland Geisberger and Nadja Zaborsky, based at Paracelsus Medical University, the Salzburg Cancer Research Institute and Paris-Lodron University Salzburg, introduce Spatialgater, an open, web-based tool built in R Shiny that lets scientists select individual cells directly on a zoomable image of their tissue. Rather than forcing analysts to write custom scripts every time they want to isolate, say, a cluster of immune cells sitting at the edge of a tumor nest, Spatialgater allows them to draw polygon gates around cells of interest with a few clicks of a mouse — in situ, exactly where the cells actually live.

The problem the tool addresses is rooted in how spatial omics data are currently processed. The dominant data structure in the R ecosystem for this kind of work is the SpatialExperiment class, a container that stores molecular expression measurements alongside the spatial coordinates of each cell. Standard analysis workflows built on this framework typically proceed by clustering cells according to their expression of biomolecules — proteins, transcripts or other markers — and then subsetting or annotating cells based on those molecular groupings. What these workflows largely ignore is geography. A tumor cell and its lookalike elsewhere on the slide may share nearly identical expression profiles, yet occupy radically different microenvironments, one nestled against a blood vessel, the other buried in dense stroma. Clustering alone cannot tell them apart by location.

Computational biologists have developed patch-detection and neighborhood-analysis methods that identify recurring spatial patterns, such as regions where particular cell types consistently co-occur. These approaches are powerful for systematic surveys of tissue architecture, but they are fundamentally automated and population-level. None of them gives a researcher the simple, immediate ability to say: show me these cells, right here, and let me select exactly the ones I mean. That gap matters in practice. Pathologists and immunologists often spot something visually striking — an unusual accumulation of T cells at an invasive margin, a suspicious ring of macrophages around a necrotic core — and want to interrogate those specific cells without writing bespoke code or approximating their region of interest with crude coordinate filters.

Spatialgater fills that gap with a deliberately simple interface. The tool renders cells as dots overlaid on a zoomable image of the tissue, so users can navigate the sample much as they would navigate a digital slide under a microscope. Drawing a polygon directly on the image selects every cell whose coordinates fall inside the drawn boundary. The selection is fully interactive: researchers can zoom in to single-cell resolution, refine a gate, add additional polygons and inspect the molecular profiles of the cells they have captured. Because the tool operates on SpatialExperiment objects, it slots naturally into existing R-based spatial omics pipelines rather than requiring researchers to export their data into an unfamiliar format.

Two features elevate the tool beyond a simple lasso. The first is an integrated k-nearest-neighbor function that can automatically extend a manually drawn gate across spatially similar microenvironments elsewhere in the tissue. If a researcher delineates a distinctive cellular neighborhood in one region, the algorithm can propagate that selection to comparable regions, dramatically reducing the manual labor of annotating large tissue sections while keeping the human judgment that defined the original gate. The second is traceability. Every polygon a user draws is recorded in a log file, creating an auditable record of exactly how each selection was made — a small but significant safeguard for reproducibility in a field where manual choices have traditionally gone undocumented.

Export and integration are equally considered. Selected cell identifiers can be written out as a standard CSV file for use in any downstream software, or saved directly back into the original SpatialExperiment object as a new logical column, meaning manual selections become first-class citizens in subsequent statistical analyses. This design choice reflects the authors’ core motivation: boosting interactivity while reducing the programming burden of image analysis. A researcher no longer needs to be a fluent R programmer to translate a visual observation into a computable cell set; the web interface handles the translation.

To demonstrate the tool in action, the team applied Spatialgater to a publicly available imaging mass cytometry dataset of breast cancer tissue, using it to characterize and compare T cells according to their spatial location within the tumor microenvironment. The demonstration speaks directly to one of the hottest questions in cancer immunology: how the precise positioning of immune cells — whether T cells are excluded from tumor nests, patrolling the invasive margin or dispersed through stroma — shapes antitumor immunity and predicts response to immunotherapy. A tool that makes spatially targeted cell selection fast and intuitive could accelerate exactly this kind of location-dependent immune analysis. The team also validated the package’s gating functionality using test data derived from a wild-type mouse, with the animal work approved by the Austrian Federal Ministry of Education, Science, and Research under approval number BMBWF 2023-0.644.528.

The significance of the work may lie less in any single algorithm than in what it signals about the maturing of spatial omics. The field’s first generation of tools focused on generating and processing data; the next generation is about making those data genuinely explorable by working biologists. By lowering the barrier between seeing something interesting in a tissue image and acting on it computationally, Spatialgater embodies a broader shift toward interactive, human-in-the-loop bioinformatics. The software is open access, published under a Creative Commons Attribution license, and funded by the Austrian Science Fund, WISS 2025 through the Cancer Cluster Salzburg, and the Province of Salzburg. For laboratories drowning in multiplexed images and struggling to connect visual insight with computational rigor, a free browser-based gate-drawing tool may prove to be one of those deceptively simple additions that changes daily practice.

Subject of Research: An interactive R Shiny web tool for spatially selecting and gating individual cells in spatial omics datasets.

Article Title: Spatialgater: an R Shiny webtool for in situ gating of cells in spatial omics experiments

Article References: Steiner, M., Drothler, S., Höpner, J. P., Geisberger, R., & Zaborsky, N. (2026). Spatialgater: an R Shiny webtool for in situ gating of cells in spatial omics experiments. BMC Bioinformatics. https://doi.org/10.1186/s12859-026-06620-y

Image Credits: AI Generated

DOI: 10.1186/s12859-026-06620-y

Keywords: Spatialgater, R Shiny, spatial omics, single cell gating, imaging mass cytometry, SpatialExperiment, multiplexed imaging, breast cancer, T cells, tumor microenvironment, bioinformatics software, Shiny