The study of tissues through omics technologies has transformed the understanding of multicellular biology. However, conventional approaches lack spatial context, limiting insights into tissue heterogeneity and - in the case of cancer - the tumour microenvironment (TME). Spatial omics technologies have therefore become essential tools for elucidating cellular and molecular processes within their native context.
Recent interdisciplinary advances have made spatially resolved workflows, such as Deep Visual Proteomics (DVP), increasingly feasible. Proteomics provides a critical dimension by capturing mechanistic molecules that reflect functional cellular states. DVP has emerged as a powerful approach, integrating improvements in histology, digital pathology, and AI-driven image analysis with high-precision, contamination-free laser microdissection (LMD), and state-of-the-art mass spectrometry (MS). Together with bioinformatic pipelines capable of handling complex, high-throughput datasets, DVP enables detailed molecular characterisation of tissues. However, as a recent innovation, robust and flexible workflows for a wide range of tissue inputs and pathologies remain to be established.
Using sarcoma as a model of pronounced heterogeneity, we are implementing and optimising a spatial DVP proteomic workflow to expand the service repertoire of the Monash Proteomics and Metabolomics Platform (MPMP). This workflow, primarily focused on formalin-fixed paraffin-embedded (FFPE) samples, is designed to be scalable and adaptable across diverse tissues, diseases and research contexts. Herein, we detail the pipeline and present preliminary findings on spatially defined biomarkers and dysregulated molecular networks relevant to sarcoma biology, offering insights into the TME, treatment resistance and disease behaviour.