Biological function is driven by the coordinated activity of cells organized into distinct tissue features, ranging from large anatomical regions to functional tissue units (FTUs), cellular neighborhoods, and ultimately individual cells. Capturing biomolecular distribution at multiple spatial scales in situ is essential for unraveling the biochemical basis of tissue organization.
A previous study explored the spatio-molecular organization of human nephrons by integrating Imaging Mass Spectrometry (IMS) data with machine learning–segmented autofluorescence microscopy images of FTUs. Building on this, an integrative framework was developed to extend spatial resolution from FTUs to the granularity of cellular neighborhoods.
In this webinar, our speaker Dr. Thai Pham will describe how this was achieved by developing an optimized marker panel to improve renal cell type classification and leveraging the high multiplexing capacity of CODEX Multiplexed Immunofluorescence (MxIF) to enhance cellular phenotyping and spatial context. Ultimately, IMS and CODEX MxIF were performed on the same tissue section, maximizing spatial fidelity across modalities and enabling accurate, comprehensive cross-modality data mining. This approach allows for the identification of lipid markers associated with specific cell types and microenvironments.
This multimodal strategy opens new avenues for spatial systems biology, providing deeper insights into tissue organization in both healthy and diseased states.
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