Formalin-fixed paraffin-embedded (FFPE) samples are valuable but underutilized in single-cell omics research due to their low RNA quality. In this study, leveraging a recent advance in single-cell genomic technology, we introduce snPATHO-seq, a versatile method to derive high-quality single-nucleus transcriptomic data from FFPE samples. We benchmarked the performance of the snPATHO-seq workflow against existing 10x 3’ and Flex assays designed for frozen or fresh samples and highlighted the consistency in snRNA-seq data produced by all workflows. The snPATHO-seq workflow also demonstrated high robustness when tested across a wide range of healthy and diseased FFPE tissue samples. When combined with FFPE spatial transcriptomic technologies such as FFPE Visium, the snPATHO-seq provides a multi-modal sampling approach for FFPE samples, allowing more comprehensive transcriptomic characterization.

Recent developments in spatially resolved omics have expanded studies linking gene expression, epigenetic alterations, protein levels, and metabolite intensity to tissue histology. The integration of multiple spatial measurements can offer new insights into alterations propagating across modalities, however, it also presents experimental and computational challenges. 

To set the multimodal data into a shared coordinate system for enhanced integration and analysis, we propose MAGPIE, a framework for co-registering spatially resolved transcriptomics and spatial metabolomics measurements on the same or consecutive tissue sections, present within their existing histological context. Further, we showcase the utility of the MAGPIE framework on spatial multi-omics data from lung tissue, an inherently heterogeneous tissue type with integrity challenges and for which we developed an experimental sampling strategy to allow multimodal data generation. In these case studies, we were able to link pharmaceutical co-detection with endogenous responses in rat lung tissue following inhalation of a small molecule, which had previously been stopped during preclinical development with findings of lung irritation, and to characterise the metabolic and transcriptomic landscape in a mouse model of drug-induced pulmonary fibrosis in conjunction with histopathology annotations.

The generalisability and scalability of the MAGPIE framework were further benchmarked on public datasets from multiple species and tissue types, demonstrating applicability to both DESI and MALDI mass spectrometry imaging together with Visium-enabled transcriptomic assessment. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in a pharmacological context, and offers a modular, accessible computational workflow for data integration.