Spatially resolved lipidomics is pivotal for detecting and interpreting lipidomes within spatial contexts using the mass spectrometry imaging (MSI) technique. However, comprehensive and efficient lipid identification in MSI remains challenging. Herein, we introduce a high-coverage, database-driven approach combined with air-flow-assisted desorption electrospray ionization (AFADESI)-MSI to generate spatial lipid profiles across whole-body mice. Using liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS), we identified 2868 unique lipids in the serum and various organs of mice. Subsequently, we systematically evaluated the distinct ionization properties of the lipids between LC–MS and MSI and created a detailed MSI database containing 14 123 ions. This method enabled the visualization of aberrant fatty acid and phospholipid metabolism across organs in a diabetic mouse model. As a powerful extension incorporated into the MSIannotator tool, our strategy facilitates the rapid and accurate annotation of lipids, providing new research avenues for probing spatially resolved heterogeneous metabolic changes in response to diseases.

On-tissue chemical derivatization is a valuable tool for expanding compound coverage in untargeted metabolomic studies with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). Applying multiple derivatization agents in parallel increases metabolite coverage even further but results in large and more complex datasets that can be challenging to analyze. In this work, we present a pipeline to provide rigorous annotations for on-tissue derivatized MSI data using Metaspace. To test and validate the pipeline, maize roots were used as a model system to obtain MSI datasets after chemical derivatization with four different reagents, Girard’s T and P for carbonyl groups, coniferyl aldehyde for primary amines, and 2-picolylamine for carboxylic acids. Using this pipeline helped us annotate 631 unique metabolites from the CornCyc/BraChem database compared to 256 in the underivatized dataset, yet, at the same time, shortening the processing time compared to manual processing and providing robust and systematic scoring and annotation. We have also developed a method to remove false derivatized annotations, which can clean 5–25% of false derivatized annotations from the derivatized data, depending on the reagent. Taken together, our pipeline facilitates the use of broadly targeted spatial metabolomics using multiple derivatization reagents.

Level 2: metabolites identified by public/commercial libraries. Level 3: metabolites identified byMetDNA/AI database. Level 4: metabolites identified by MS fragments. (XLSX)