A Protocol for Multivariate Data Visualization and Pseudotime Modeling for Analysis of Disease Trajectories Detected by Mass Spectrometry Imaging.

Abstract

Mass spectrometry imaging (MSI) has emerged as a powerful modality for spatially resolved molecular profiling of tumor and stromal compartments; however, computational frameworks for MSI data analysis lag significantly behind those developed for spatial transcriptomics, limiting its translational potential. Here, we introduce the Spatial Omics Toolkit (SPOT), an end-to-end, open-source analytical pipeline that operationalizes established statistical methods from single-cell and spatial transcriptomics into accessible workflows for MSI data. SPOT is implemented in both R and Python, uses vendor-neutral community data formats, and integrates classification modeling, dimensionality reduction, and trajectory inference to enable spatially resolved comparative analysis across disease states with minimal computational overhead. We demonstrate the utility of SPOT on stromal proteomic profiles derived from ductal carcinoma in situ (DCIS) lesion archetypes, identifying differentially expressed peptides across disease states by orthogonal statistical approaches, and reconstructing a pseudotime trajectory from DCIS to invasive breast cancer from the same patient genetics. Collectively, SPOT provides researchers with a framework for interrogating molecular pathology across diverse MSI data sets. SPOT can be found at https://github.com/angel-omics-lab.