Abstract
Neural rosettes are hallmarks of the neural progenitor cell stage that is a necessary pre-condition for manufacturing central nervous system lineages. Characterization of early changes during differentiation through positional arrangement and metabolic shifts that occur in a multi-day protocol would facilitate cell culture quality monitoring and optimization of batch culture yield. We describe an analytical framework for identifying neural rosettes from confocal microscopy within a colony of differentiating stem cells and translating co-registered, cell-resolved MALDI imaging data into interpretable readouts that are compatible with cell manufacturing needs. Rather than evaluating hundreds of ion images sequentially, the pipeline converts each region of interest into a single-cell feature matrix and summarizes whole-spectrum variation using PCA, graph-based Leiden clustering, and UMAP visualization. The resultant metabolic neighborhoods provide quantification of molecular heterogeneity within colonies and - when mapped back to x-y space - form coherent spatial domains. Together, these outputs create a practical bridge between multimodal MALDI capabilities and process-relevant interpretation: neighborhoods can be compared across conditions, ranked markers can be prioritized as putative critical quality attributes, and spatial organization can be quantified without manual, feature-by-feature screening.