The research team employed imaging mass cytometry (IMC), a technology that uses metal-conjugated antibodies and mass spectrometry to detect dozens of proteins simultaneously on a single tissue section. Where conventional immunofluorescence is limited to roughly four or five markers because of spectral overlap, IMC allowed the researchers to build a panel of 32 antibodies covering 19 cell population markers, 3 activation markers and 10 ECM components. Critically, the team went beyond standard cellular analysis. Because matrix molecules sit outside cells, traditional cell-mask-based approaches fail to capture them. To solve this, the researchers developed DeepThresh, a novel deep-learning thresholding algorithm modelled on a U-Net architecture with a ResNet152 encoder. Trained on expert-annotated images, DeepThresh generated robust binary masks of ECM staining across all tissue regions, overcoming the variability that plagued classical thresholding methods such as Otsu’s algorithm.

With matrix staining segmented, the pipeline calculated Euclidean distances from every cell to each ECM component, and these distance profiles were clustered to define twelve distinct lung matrix environments. This is the study’s central methodological innovation: for the first time, every cell in the image carries both its canonical identity and a quantitative description of its immediate matrix surroundings. The approach was applied to lung sections from BALB/c and C57BL/6 mice, two widely used inbred strains, following chronic twice-weekly intranasal exposure to a cocktail of house dust mite, ragweed and Aspergillus extracts, a model known as DRA that reproduces key features of human allergic airway pathology, including collagen deposition, hyaluronan accumulation and steroid-resistant recruitment of eosinophils and neutrophils.

The resulting atlas revealed that the healthy lung is divided into two major matrix compartments. The alveolar parenchyma, the delicate gas-exchange region, is characterised by proximity to heparan sulphate, laminin gamma-1, type-IV collagen and fibrinogen. In contrast, the adventitial cuff, the connective tissue sheath surrounding airways and blood vessels, is enriched for type-I, type-III and type-VI collagens together with hyaluronan. During allergic inflammation, these spatially constrained environments changed in strikingly region-specific ways, and the two mouse strains responded differently, underscoring how genetic background shapes not just the magnitude of inflammation but its anatomical architecture.

One of the most visually dramatic findings concerned the adventitial cuff. In allergic animals, infiltrating immune cells accumulated in discrete, contiguous patches around the pulmonary artery and airway-adjacent vessels, and these patches were significantly more numerous and larger in BALB/c mice than in C57BL/6 mice. Three-dimensional precision-cut lung slices stained with fluorescent antibodies confirmed the IMC findings and revealed internal structure within the cuff: the region adjacent to the blood vessel was dominated by CD11b-positive CD64-positive monocyte-derived macrophages, while a more distal, densely nucleated zone resembled induced bronchus-associated lymphoid tissue (iBALT), rich in B cells and largely excluding macrophages. These iBALT-like structures formed only in BALB/c animals, suggesting that strain-specific type-2 immune skewing may provide the signals needed to seed organised lymphoid tissue within the inflamed lung.

The matrix itself shifted around these inflammatory patches in a coordinated fashion. Allergic BALB/c mice showed expansion of sparse ‘inflammatory zone’ matrix environments with increased distances to type-I, type-III and type-VI collagens, but reduced distances to laminin gamma-1 and hyaluronan. This represents a shift from a densely collagenous matrix to one dominated by laminin and hyaluronan, molecules known to regulate cell migration through tissue. Because hyaluronan-rich matrices can facilitate or direct immune cell infiltration, this remodelling may actively sculpt the localisation of B cell activation and myeloid accumulation within the cuff, offering a mechanistic hypothesis for why inflammation aggregates in these specific niches.

The alveolar parenchyma told a different story. Following allergen challenge, the ratio of alveolar type-I (ATI) to alveolar type-II (ATII) epithelial cells fell from roughly 3:1 in healthy controls to approximately 1:1 in allergic animals of both strains, a signature of injury and repair, since ATII cells are known to proliferate and replace damaged ATI cells. Immunofluorescent validation using RAGE as an ATI marker and surfactant protein C as an ATII marker confirmed the relative loss of the ATI compartment. Spatially, the alveolar region subdivided into resting alveoli enriched for ATI cells and basement membrane components, and activated alveolar regions enriched for ATII cells, alveolar macrophages and proximity to hyaluronan and chondroitin sulphate. These glycosaminoglycans are known regulators of immune cell migration, and chondroitinase treatment in fibrosis models reduces macrophage numbers, suggesting that this matrix shift may directly control macrophage retention in the damaged alveolus.

Perhaps the most translational insight concerns the airway subepithelial space, the region beneath the airway epithelium that undergoes extensive remodelling in human asthma. The study found that this region expanded significantly during allergic challenge in both strains and became a hub of immune-stromal interaction. Two fibroblast populations were identified there: alpha-smooth muscle actin-positive stromal cells, present at steady state, and S100a4-positive fibroblasts, which expanded dramatically during allergy. Neighbourhood analysis showed that S100a4-positive fibroblasts and CD11b-positive immune cells, including MerTK-positive macrophages, came into close contact specifically during allergic inflammation, and both stromal populations overlayed type-I and type-III collagen deposition. Because macrophages and fibroblasts are known to regulate each other reciprocally, and because type-VI collagen in this niche can promote fibroblast survival and migration, the authors propose that this immune-stromal network constitutes a regulatory circuit controlling ECM deposition during allergic airway pathology.

The study’s design choices also matter for future work. The entire pipeline was developed on formalin-fixed paraffin-embedded tissue, the predominant preservation method in human biobanks, meaning the approach can be translated directly to archived patient samples. All datasets and code, including the DeepThresh and MatrixIMC pipelines, have been made publicly available. By integrating cellular and matrix analysis within a single tissue section, the framework captures a dimension of tissue biology that single-cell RNA sequencing alone cannot, since transcriptomic profiles of matrix genes are often poor surrogates for the post-translationally modified proteins actually present in tissue. The authors caution that future work must clarify the temporal sequence of these changes and establish causality, but the atlas already generates testable hypotheses about how targeting specific matrix components or the macrophage-fibroblast interactions within remodelling niches could intervene in allergic airway disease. For a field that has long catalogued inflammation cell by cell, this study makes a compelling case that where a cell sits, and what it sits on, may matter as much as what the cell is.

Subject of Research: Spatial mapping of extracellular matrix and cellular environments during allergic airway inflammation using imaging mass cytometry in mouse models

Article Title: Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation

Article References: Parkinson, J. E., Bryant, M., Ghafoor, M., Dodd, R. J., Tompkins, H. E., Fergie, M., Burgess, M. O., Rattray, M., & Sutherland, T. E. (2026). Extracellular matrix phenotyping by imaging mass cytometry defines distinct cellular matrix environments associated with allergic airway inflammation. Molecular Systems Biology. https://doi.org/10.1038/s44320-026-00234-5

Image Credits: AI Generated

DOI: 10.1038/s44320-026-00234-5

Keywords: imaging mass cytometry, extracellular matrix, allergic airway inflammation, asthma, airway remodelling, lung immunology, hyaluronan, collagen, fibroblasts, macrophages, spatial biology, mouse models

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