Construction of the FTU lipid atlas of the human kidney. (IMAGE)
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Multimodal molecular imaging data were collected from 29 donor kidney tissues. Each tissue section was subjected to AF microscopy, MALDI IMS, and PAS-stained microscopy, in sequential order (A). Each modality is processed individually to provide AF-driven FTU segmentations, to ensure that MALDI IMS measurements are comparable and to remove potential batch effects, and to provide histopathological assessment of each tissue (B). These datasets are then integrated by spatially co-registering them onto the same spatial coordinate system and by performing a combination of unsupervised and cross-modal supervised machine learning (ML) analyses (C). Interpretable machine learning is then used to uncover spatially distinct biomarker candidates for FTUs across the overall data cohort as well as scoped to specific donor metadata such as BMI or sex.
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Farrow et. al
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