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

Glioblastoma, a highly aggressive and notoriously heterogeneous tumor of the central nervous system, has long posed formidable challenges to oncologists and neuroscientists alike. Its infiltrative growth patterns and rapid evolution thwart conventional treatments, resulting in dismal patient outcomes. Recognizing these challenges, the researchers focused on improving the quality and utility of biopsy samples, which historically have been limited by small tissue size and sampling bias. By employing investigative needle core biopsies, the team demonstrated the feasibility of acquiring robust, representative tumor samples suitable for extensive multimodal analysis.

Multimodal deep-data generation constitutes a convergence of various high-throughput and high-resolution technologies that collectively interrogate tumor biology at multiple layers, including genomic, transcriptomic, proteomic, and spatial contexts. The innovative sampling method elucidated by the study enables the extraction of precious tissue cores from live glioblastoma patients with minimal invasiveness, while preserving the architectural and molecular integrity essential for subsequent analyses. This technical refinement paves the way for integrative analyses that can decode the complex ecosystem of glioblastoma tumors.

Technically, the needle core biopsy procedure was optimized to maximize cellular yield and viability, crucial parameters that directly influence the success of downstream multi-omic platforms. The researchers meticulously evaluated the procedural parameters, such as needle gauge, penetration depth, and number of passes, to establish protocols that harmonize clinical safety with research objectives. This harmonization ensures that patients are not subjected to excessive risk while generating samples potent enough to reveal tumor heterogeneity and microenvironmental interactions at unprecedented resolution.

Once the biopsy material was obtained, the team employed a suite of advanced multiplexed analyses. Single-cell RNA sequencing allowed for the dissection of individual tumor cells and surrounding microglia populations, revealing transcriptional states and identifying rare subpopulations potentially driving invasive behavior. Concurrently, spatial transcriptomics provided a map of gene expression distribution within the biopsied tissue architecture, uncovering spatial niches that might serve as therapeutic vulnerabilities or refuges from the immune system.

Complementing transcriptomic data, proteomic profiling was integrated through mass spectrometry techniques, capturing post-translational modifications and signaling network activities that are often decoupled from mRNA expression. This proteogenomic approach offered insights into the functional consequences of genetic alterations, helping to clarify how mutational landscapes translate into phenotypic traits that affect tumor aggressiveness and treatment resistance.

Additionally, advanced imaging modalities were incorporated, including multiplex immunohistochemistry and fluorescence in situ hybridization, to anatomically validate molecular data and preserve spatial context. These imaging strategies enabled the visualization of critical cell-cell interactions, vascularization patterns, and immune infiltration dynamics that collectively shape tumor behavior and response to therapies.

One of the pivotal outcomes of this research is establishing a standardized pipeline that converts limited biopsy material into comprehensive datasets amenable to machine learning and artificial intelligence analyses. Leveraging computational biology tools, the researchers created integrative models capable of predicting tumor evolution trajectories and patient-specific therapeutic responses. This big-data paradigm, rooted in reliable sample acquisition, heralds a move towards truly individualized medicine in glioblastoma care.

The implications of this work extend beyond glioblastoma itself. Many solid tumors share the challenge of heterogeneity and sampling limitations. Thus, the methodology and technological fusion proposed could be adapted for other malignancies, addressing a universal bottleneck in cancer biology—the ability to capture detailed, multidimensional data from clinically accessible tissue.

Furthermore, this approach opens avenues for longitudinal studies. By enabling repeat biopsies with minimal risk, clinicians can monitor tumor evolution and treatment response dynamically, rather than relying on static snapshots. This temporal dimension adds critical depth to precision oncology, allowing for adaptive therapeutic strategies that respond promptly to tumor adaptations.

Despite the promise, the study acknowledges inherent challenges. The integration of multimodal datasets demands advanced bioinformatics expertise and computational infrastructure, which may not yet be widely available in all clinical settings. Additionally, standardizing tissue handling and preserving sample integrity across multiple centers require collaborative efforts and rigorous quality controls to ensure data consistency and reproducibility.

Nevertheless, the potential benefits far outweigh these hurdles. The research team highlights that the combination of minimally invasive biopsy techniques with state-of-the-art analytical technologies effectively circumvents previous constraints, producing a foundation for discovering novel biomarkers, therapeutic targets, and mechanisms of resistance.

In conclusion, the investigative needle core biopsy approach is a transformative advancement in glioblastoma research, achieving a delicate balance between clinical practicality and scientific rigor. By unlocking the ability to generate deep, multimodal datasets from limited patient-derived tissue, it empowers researchers and clinicians with richer biological insights and steers the field towards more effective, personalized treatments.

As this methodology is adopted and refined, future studies will likely elucidate even more intricate dynamics within glioblastoma ecosystems and potentially identify strategies to overcome its stubborn therapeutic resistance. This research not only exemplifies innovation at the intersection of clinical practice and molecular science but also marks a hopeful turning point for patients afflicted by this devastating disease.

Subject of Research:
Glioblastoma needle core biopsies enabling multimodal molecular and imaging data generation.

Article Title:
Investigative needle core biopsies support multimodal deep-data generation in glioblastoma.

Article References:
Yu, K.K.H., Basu, S., Baquer, G. et al. Investigative needle core biopsies support multimodal deep-data generation in glioblastoma. Nat Commun 16, 3957 (2025). https://doi.org/10.1038/s41467-025-58452-8

Image Credits:
AI Generated