Motivation Spatial lipidomics enables the study of how lipids are distributed within tissues, providing insights into tissue structure and function. However, analyzing complex mass spectrometry (MS) imaging (MSI) data remains challenging due to limited tools for high-confidence annotation, especially for integrating MSI, MS, and MS/MS pipelines. Results We developed LipidLocator, an open-source, interactive Shiny web application as a unified spatial lipidomics pipeline. LipidLocator integrates MSI data analysis from normalization, spatial clustering, and differential abundance analysis to MS and MS/MS-based lipid annotation. We utilized LipidLocator to analyze DESI-MSI and AP-SMALDI data from adult zebrafish sections, human renal carcinoma, and mouse whole brain sections, to demonstrate its ability to segment distinct anatomical structures and tissue sub-regions and to generate high-confidence lipid profiles using integrated MS and MS/MS annotation. LipidLocator is an end-to-end open-source spatial lipidomics pipeline, facilitating lipid imaging studies in various organisms and covering different lipid detection technologies, providing a valuable and user-friendly resource for investigating lipid metabolism. Availability And Implementation The LipidLocator application is freely available as a Docker image on Docker Hub at pratarora/lipidlocator. Installation instructions and code are available at https://github.com/MercaderLabAnatomy/LipidLocator.
Arora et al. (Thu,) studied this question.