Matrix Selection for the Visualization of Small Molecules and Lipids in Brain Tumors Using Untargeted MALDI-TOF Mass Spectrometry Imaging Article Swipe
Tianyao Lu
,
Lutz Freytag
,
Vinod K. Narayana
,
Zachery Moore
,
Shannon J. Oliver
,
Adam Valkovic
,
Brunda Nijagal
,
Amanda L. Peterson
,
David P. De Souza
,
Malcolm J. McConville
,
James R. Whittle
,
Sarah A. Best
,
Saskia Freytag
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/metabo13111139
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/metabo13111139
Matrix-assisted laser desorption/ionization mass spectrometry imaging allows for the study of metabolic activity in the tumor microenvironment of brain cancers. The detectable metabolites within these tumors are contingent upon the choice of matrix, deposition technique, and polarity setting. In this study, we compared the performance of three different matrices, two deposition techniques, and the use of positive and negative polarity in two different brain cancer types and across two species. Optimal combinations were confirmed by a comparative analysis of lipid and small-molecule abundance by using liquid chromatography–mass spectrometry and RNA sequencing to assess differential metabolites and enzymes between normal and tumor regions. Our findings indicate that in the tumor-bearing brain, the recrystallized α-cyano-4-hydroxycinnamic acid matrix with positive polarity offered superior performance for both detected metabolites and consistency with other techniques. Beyond these implications for brain cancer, our work establishes a workflow to identify optimal matrices for spatial metabolomics studies.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/metabo13111139
- https://www.mdpi.com/2218-1989/13/11/1139/pdf?version=1699533219
- OA Status
- gold
- Cited By
- 12
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4388540723
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4388540723Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/metabo13111139Digital Object Identifier
- Title
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Matrix Selection for the Visualization of Small Molecules and Lipids in Brain Tumors Using Untargeted MALDI-TOF Mass Spectrometry ImagingWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-09Full publication date if available
- Authors
-
Tianyao Lu, Lutz Freytag, Vinod K. Narayana, Zachery Moore, Shannon J. Oliver, Adam Valkovic, Brunda Nijagal, Amanda L. Peterson, David P. De Souza, Malcolm J. McConville, James R. Whittle, Sarah A. Best, Saskia FreytagList of authors in order
- Landing page
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https://doi.org/10.3390/metabo13111139Publisher landing page
- PDF URL
-
https://www.mdpi.com/2218-1989/13/11/1139/pdf?version=1699533219Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://www.mdpi.com/2218-1989/13/11/1139/pdf?version=1699533219Direct OA link when available
- Concepts
-
Mass spectrometry, Mass spectrometry imaging, Metabolomics, Small molecule, Computational biology, Chemistry, Matrix (chemical analysis), Metabolic pathway, Chromatography, Biochemistry, Biology, EnzymeTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
12Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 6, 2024: 6Per-year citation counts (last 5 years)
- References (count)
-
48Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| corresponding_institution_ids | https://openalex.org/I165779595, https://openalex.org/I196021976 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/15 |
| sustainable_development_goals[0].score | 0.46000000834465027 |
| sustainable_development_goals[0].display_name | Life in Land |
| citation_normalized_percentile.value | 0.87023944 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |