Exploring the metabolic differences directly on tissues is essential for the comprehensive understanding of how multicellular organisms function. Mass spectrometry imaging (MSI) is an attractive technique toward this goal; however, MSI in metabolomics scale has been hindered by multiple limitations. This is most notable for single cell level high-spatial resolution imaging because of the limited number of molecules in small sampling size and the low ionization yields of many metabolites. Several on-tissue chemical derivatization approaches have been reported to increase MSI signals of targeted compounds, especially in matrix-assisted laser desorption/ionization (MALDI)-MSI. Herein, we adopt a combination of chemical derivatization reactions, to selectively enhance metabolite signals of a specific functional group for each consecutive tissue section. Three well-known on-tissue derivatization methods were used as a proof of concept experiment: coniferyl aldehyde for primary amines, Girard’s reagent T for carbonyl groups, and 2-picolylamine for carboxylic acids. This strategy was applied to the cross-sections of leaves and roots from two different maize genotypes (B73 and Mo17), and enabled the detection of over six hundred new unique metabolite features compared to without modification. Statistical analysis indicated quantitative variation between metabolites in the tissue sections, while MS images revealed differences in localization of these metabolites. Combined, this untargeted approach facilitated the visualization of various classes of compounds, demonstrating the potential for untargeted MSI in the metabolomics scale.
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.
Dopant-assisted atmospheric pressure chemical ionization (dAPCI) is a soft ionization method rarely used for gas chromatography-mass spectrometry (GC-MS). The current study combines GC-dAPCI with tandem mass spectrometry (MS/MS) for analysis of a complex mixture such as lignin pyrolysis analysis. To identify the structures of volatile lignin pyrolysis products, collision-induced dissociation (CID) MS/MS using a quadrupole time-of-flight mass spectrometer (QTOFMS) and pseudo MS/MS through in-source collision-induced dissociation (ISCID) using a single stage TOFMS are utilized. To overcome the lack of MS/MS database, Compound Structure Identification (CSI):FingerID is used to interpret CID spectra and predict best matched structures from PubChem library. With this approach, a total of 59 compounds were positively identified in comparison to only 22 in NIST database search of GC-EI-MS dataset. This study demonstrates the effectiveness of GC-dAPCI-MS/MS to overcome the limitations of traditional GC-EI-MS analysis when EI-MS database is not sufficient. Graphical Abstract ᅟ.
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