Rapid and Automatic Annotation of Multiple On-Tissue Chemical Modifications in Mass Spectrometry Imaging with Metaspace

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.