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Mass spectrometry imaging (MSI)‐based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography‐tandem mass spectrometry (LC–MS)‐based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI‐driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation.Highlights MSI‐driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.
Mass spectrometry imaging (MSI)‐based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography‐tandem mass spectrometry (LC–MS)‐based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI‐driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation.Highlights MSI‐driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.
RationaleSilver doping of electrospray is known to increase the abundance of olefinic compounds detected by mass spectrometry. While demonstrated in targeted experiments, this has yet to be investigated in an untargeted study. Utilizing infrared matrix‐assisted laser desorption electrospray ionization mass spectrometry imaging (IR‐MALDESI‐MSI), an untargeted lipidomics experiment on mouse liver was performed to evaluate the advantages of silver‐doped electrospray.Methods10 ppm silver nitrate was doped into the IR‐MALDESI solvent consisting of 60% acetonitrile and 0.2% formic acid. Using an Orbitrap mass spectrometer in positive ionization mode, MSI was performed, analyzing from m/z 150 to m/z 2000 to capture all lipids with potential silver adducts. The lipids detected in the control and silver‐doped electrosprays were compared by annotating using the LIPID MAPS Structural Database and eliminating false positives using the metabolite annotation confidence score.ResultsSilver‐doped electrospray allowed for the detection of such ions of lipid molecules as [M + H]+ or [M + NH4]+ and as [M + Ag]+. Among the ions seen as [M + H]+ or [M + NH4]+, the signal was comparable between the control and silver‐doped electrosprays. The silver‐doped electrospray led to a 10% increase in the number of detected lipids, all of which contained a bay region increasing the interaction between silver and alkenes. Silver preferentially interacted with lipids that did not contain hard bases such as phosphates.ConclusionsSilver‐doped electrospray enabled detection of 10% more olefinic lipids, all containing bay regions in their putative structures. This technique is valuable for detecting previously unobserved lipids that have the potential to form bay regions, namely fatty acyls, glycerolipids, prenol lipids, and polyketides.
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