Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace.
Evan A LarsonTrevor T ForsmanLachlan StuartTheodore AlexandrovYoung-Jin LeePublished in: Analytical chemistry (2022)
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.
Keyphrases
- mass spectrometry
- liquid chromatography
- high performance liquid chromatography
- gas chromatography
- high resolution
- ms ms
- tandem mass spectrometry
- high resolution mass spectrometry
- gas chromatography mass spectrometry
- simultaneous determination
- capillary electrophoresis
- liquid chromatography tandem mass spectrometry
- solid phase extraction
- rna seq
- ultra high performance liquid chromatography
- electronic health record
- big data
- machine learning
- healthcare
- cancer therapy
- drug delivery
- affordable care act
- deep learning
- single cell
- data analysis
- case control