Daria Buszta

Spatial transcriptomics enables high-resolution gene expression mapping in intact tissues. Xenium is widely adopted for its reliability, accessibility and data quality, yet the properties and limitations of Xenium-derived data remain poorly characterized. Here we present one of the most comprehensive Xenium datasets so far, encompassing over 40 breast and lung tumor sections profiled using diverse gene panels. Leveraging this resource, we systematically dissect technical noise—including transcript spillover—along with assay specificity, panel performance and segmentation strategies. We demonstrate that single-nucleus RNA sequencing enables precise quantification of transcript contamination. Building on these insights, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a method that improves signal purity by resolving mixed transcriptomic signals. SPLIT enhances background correction and cell-type resolution and enables the revelation of T-cell exhaustion signatures associated with malignant cell colocalization—signals that would otherwise remain obscured. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement.

Spatial metabolomics, measured through mass spectrometry imaging (MSI), provides high-throughput, spatially resolved information on metabolite distributions within tissues, including endogenous metabolites and exogenous compounds. This offers a direct readout of cellular biochemical activity and phenotypes, not fully captured by transcriptomics or proteomic profiling. However, inferring biologically meaningful patterns from noisy, high-dimensional MSI data, particularly across multiple samples and complex experimental designs, remains challenging, and often requires substantial programming expertise. Here we introduce SMEW ( S patial M etabolomics E nhanced W orkflow), a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data. SMEW provides a unified environment for hierarchical analysis across bulk-, region- and pixel-level resolutions, allowing comparisons between experimental conditions like disease or treatment groups while highlighting coherent metabolic patterns and linking these patterns to biological pathways. The workflow leverages local spatial covariation to robustly summarise MSI data through dimensionality reduction, clustering and identification of spatially variable metabolites. In addition, metabolite co-localisation and covariation network analysis, together with spatially resolved pathway enrichment facilitate the biological interpretation of cross-condition datasets within a single integrated interface. SMEW is applicable across MSI technologies and mass resolutions, as illustrated through case studies on DESI and MALDI-ToF datasets from lung, liver, and kidney. By complementing existing MSI processing and visualisation tools with an accessible, multi-sample, and biologically interpretable analysis framework, SMEW enables functional, flexible, rigorous and intuitive exploration of spatial metabolomics datasets. Graphical Key Points: SMEW provides a flexible, interactive and shareable Shiny-based platform designed to enable code-free downstream analysis of spatial metabolomics MSI data The SMEW framework enables hierarchical analysis at bulk-, region- and pixel levels within a unified framework without relying on extensive programming expertise The pipeline integrates spatially aware clustering, pathway analysis and identification of metabolite co-localisation modules The workflow facilitates flexible comparison of multi-sample experimental conditions through multivariate modelling, differential testing and covariation networks to study treatment- and disease-associated metabolite dynamics SMEW has been applied to interrogate diverse biological questions, including characterising disease-associated remodelling in a mouse bleomycin model of pulmonary fibrosis, exploring the therapeutic index of antisense oligonucleotides in the liver and assessing metabolic heterogeneity in a small molecule-treated mouse renal tumour model

Recent advancements in probe-based, full-transcriptome technologies for FFPE tissues, such as Visium CytAssist, Chromium Flex, and GeoMx DSP, enable analysis of archival samples, facilitating the generation of data from extensive cohorts. However, these methods can be labor-intensive and costly, requiring informed selection based on research objectives. We compare these methods on FFPE tumor samples in Breast, NSCLC and DLBCL showing 1) good-quality, highly reproducible data from all methods; 2) GeoMx data containing cell mixtures despite marker-based preselection; 3) Visium and Chromium outperform GeoMx in discovering tumor heterogeneity and potential drug targets. We recommend the use of Visium and Chromium for high-throughput and discovery projects, while the manually more challenging GeoMx platform with targeted regions remains valuable for specialized questions.

Spatial transcriptomics has transformed our ability to map gene expression within intact tissues at cellular and subcellular resolution. Among current platforms, Xenium is widely adopted for its reliability, accessibility, and high data quality. Yet, the properties and limitations of Xenium-derived data remain poorly characterized. Here, we present one of the most comprehensive Xenium datasets to date, encompassing over 40 breast and lung tumor sections profiled using a diverse set of gene panels. Leveraging this resource, we systematically dissect technical noise—including transcript diffusion—alongside assay specificity, panel performance, and segmentation strategies. Our comparison of targeted panels with the newer 5K panel reveals that although the latter captures more transcripts overall, it suffers from reduced per-gene sensitivity and persistent diffusion, even with enhanced chemistry. We demonstrate that single-nucleus RNA-seq (snRNA-seq) markedly improves cell type annotation and enables more precise quantification of diffusion. Building on this, we introduce SPLIT (Spatial Purification of Layered Intracellular Transcripts), a novel method that integrates snRNA-seq with RCTD deconvolution to enhance signal purity. SPLIT effectively resolves mixed transcriptomic signals, improving background correction and cell-type resolution. Together, our findings provide a critical benchmark for Xenium performance and introduce a scalable strategy for signal refinement—advancing the accuracy and utility of spatial transcriptomics.

Recent advancements in probe-based, full-transcriptome, high-resolution technologies for Formalin-Fixed Paraffin-Embedded (FFPE) tissues, such as Visium CytAssist, Chromium Flex (10X Genomics), and GeoMx DSP (Nanostring), have opened new opportunities for studying decades-old archival samples in biobanks, facilitating the generation of data from extensive cohorts. However, the experimental protocols can be labor-intensive and costly; therefore, it is thus essential for researchers to carefully evaluate the strengths and limitations of each technology in relation to their specific research objectives. Here, we report the results of a comparative analysis of the three methods mentioned above on FFPE archival tumor samples from four non-small cell lung cancer, four breast cancer and six diffuse large B-cell lymphoma. We highlight some relative advantages and disadvantages of each method in the context of operational challenges, bioinformatic analysis and biological discovery. Our results show that: 1) all three methods yielded good-quality, highly reproducible transcriptomic data from serial sections of the same FFPE block; 2) GeoMx data contained mixtures of cell types, even when pre-selecting areas with cell type-specific markers; 3) high-throughput spot-level (Visium) or cell-level (Chromium) data enabled the identification of tumor heterogeneity within and between patients, which could be used to identify targeted therapies. Our data support the use of Visium and Chromium for high-throughput and discovery-driven projects, while the GeoMx platform could be suited for addressing specialized questions on targeted regions. All data generated from this study, including GeoMx, Visium, Chromium, H&E, and expert annotations are publicly available.