Mass Spectrometry Imaging Combined with Sparse Autoencoder Method Reveals Altered Phosphorylcholine Distribution in Imipramine Treated Wild-Type Mice Brains.
Md Foyzur RahmanAriful IslamMd Monirul IslamMd Al MamunLili XuTakumi SakamotoTomohito SatoYutaka TakahashiTomoaki KahyoSatoka AoyagiKozo KaibuchiMitsutoshi SetouPublished in: International journal of molecular sciences (2024)
Mass spectrometry imaging (MSI) is essential for visualizing drug distribution, metabolites, and significant biomolecules in pharmacokinetic studies. This study mainly focuses on imipramine, a tricyclic antidepressant that affects endogenous metabolite concentrations. The aim was to use atmospheric pressure matrix-assisted laser desorption/ionization (AP-MALDI)-MSI combined with different dimensionality reduction methods to examine the distribution and impact of imipramine on endogenous metabolites in the brains of treated wild-type mice. Brain sections from both control and imipramine-treated mice underwent AP-MALDI-MSI. Dimensionality reduction methods, including principal component analysis, multivariate curve resolution, and sparse autoencoder (SAE), were employed to extract valuable information from the MSI data. Only the SAE method identified phosphorylcholine (ChoP) as a potential marker distinguishing between the control and treated mice brains. Additionally, a significant decrease in ChoP accumulation was observed in the cerebellum, hypothalamus, thalamus, midbrain, caudate putamen, and striatum ventral regions of the treated mice brains. The application of dimensionality reduction methods, particularly the SAE method, to the AP-MALDI-MSI data is a novel approach for peak selection in AP-MALDI-MSI data analysis. This study revealed a significant decrease in ChoP in imipramine-treated mice brains.
Keyphrases
- wild type
- mass spectrometry
- high fat diet induced
- data analysis
- high resolution
- liquid chromatography
- transcription factor
- ms ms
- healthcare
- electronic health record
- spinal cord
- deep brain stimulation
- newly diagnosed
- capillary electrophoresis
- big data
- machine learning
- metabolic syndrome
- single cell
- photodynamic therapy
- oxidative stress
- particulate matter
- gas chromatography
- air pollution
- neural network
- white matter
- adverse drug
- artificial intelligence
- deep learning
- cerebral ischemia