ArticleGigaScience2020
A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data.
Article in GigaScience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- An organoid-guided roadmap for precision delivery of epigallocatechin gallate in oral submucous fibrosis.Frontiers in bioengineering and biotechnology · 2026Review
- Mass spectrometry imaging: new eyes on natural products for drug research and development.Acta pharmacologica Sinica · 2022Review
- Imaging Technologies for Cerebral Pharmacokinetic Studies: Progress and Perspectives.Biomedicines · 2022Review
- Evaluation and comparison of unsupervised methods for the extraction of spatial patterns from mass spectrometry imaging data (MSI).Scientific reports · 2022Article
- A methodological approach to correlate tumor heterogeneity with drug distribution profile in mass spectrometry imaging data.GigaScience · 2020Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDrug mass spectrometry imaging (MSI) data contain knowledge about drug and several other molecular ions present in a biological sample. However, a proper approach to fully explore the potential of such type of data is still missing. Therefore, a computational pipeline that combines different spatial and non-spatial methods is proposed to link the observed drug distribution profile with tumor heterogeneity in solid tumor. Our data analysis steps include pre-processing of MSI data, cluster analysis, drug local indicators of spatial association (LISA) map, and ions selection.
resultsThe number of clusters identified from different tumor tissues. The spatial homogeneity of the individual cluster was measured using a modified version of our drug homogeneity method. The clustered image and drug LISA map were simultaneously analyzed to link identified clusters with observed drug distribution profile. Finally, ions selection was performed using the spatially aware method.
conclusionsIn this paper, we have shown an approach to correlate the drug distribution with spatial heterogeneity in untargeted MSI data. Our approach is freely available in an R package 'CorrDrugTumorMSI'.
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