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Integration of Mass Spectrometry Imaging and Machine Learning Visualizes Region-Specific Age-Induced and Drug-Target Metabolic Perturbations in the Brain.

ABSTRACT: Detailed metabolic imaging of specific brain regions in early aging may expose pathophysiological mechanisms and indicate effective neuropharmacological targets in the onset of cognitive decline. Comprehensive imaging of brain aging and drug-target effects is restricted using conventional methodology. We simultaneously visualized multiple metabolic alterations induced by normal aging in specific regions of mouse brains by integrating Fourier-transform ion cyclotron resonance mass spectrometry imaging and combined supervised and unsupervised machine learning models. We examined the interplay between aging and the response to tacrine-induced acetylcholinesterase inhibition, a well-characterized therapeutic treatment against dementia. The dipeptide carnosine (β-alanyl-l-histidine) and the vitamin α-tocopherol were significantly elevated by aging in different brain regions. l-Carnitine and acetylcholine metabolism were found to be major pathways affected by aging and tacrine administration in a brain region-specific manner, indicating altered mitochondrial function and neurotransmission. The highly interconnected hippocampus and retrosplenial cortex displayed different age-induced alterations in lipids and acylcarnitines, reflecting diverse region-specific metabolic effects. The subregional differences observed in the hippocampal formation of several lipid metabolites demonstrate the unique potential of the technique compared to standard mass spectrometry approaches. An age-induced increase of endogenous antioxidants, such as α-tocopherol, in the hippocampus was detected, suggesting an augmentation of neuroprotective mechanisms in early aging. Our comprehensive imaging approach visualized heterogeneous age-induced metabolic perturbations in mitochondrial function, neurotransmission, and lipid signaling, not always attenuated by acetylcholinesterase inhibition.

SUBMITTER: Vallianatou T

PROVIDER: S-EPMC8291481 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Advances in the field of targeted proteomics and mass spectrometry have significantly improved assay sensitivity and multiplexing capacity. The high-throughput nature of targeted proteomics experiments has increased the rate of data production, which requires development of novel analytical tools to keep up with data processing demand. Currently, development and validation of targeted mass spectrometry assays require manual inspection of chromatographic peaks from large datasets to ensure quality, a process that is time consuming, prone to inter- and intra-operator variability and limits the efficiency of data interpretation from targeted proteomics analyses. To address this challenge, we have developed TargetedMSQC, an R package that facilitates quality control and verification of chromatographic peaks from targeted proteomics datasets. This tool calculates metrics to quantify several quality aspects of a chromatographic peak, e.g. symmetry, jaggedness and modality, co-elution and shape similarity of monitored transitions in a peak group, as well as the consistency of transitions' ratios between endogenous analytes and isotopically labeled internal standards and consistency of retention time across multiple runs. The algorithm takes advantage of supervised machine learning to identify peaks with interference or poor chromatography based on a set of peaks that have been annotated by an expert analyst. Using TargetedMSQC to analyze targeted proteomics data reduces the time spent on manual inspection of peaks and improves both speed and accuracy of interference detection. Additionally, by allowing the analysts to customize the tool for application on different datasets, TargetedMSQC gives the users the flexibility to define the acceptable quality for specific datasets. Furthermore, automated and quantitative assessment of peak quality offers a more objective and systematic framework for high throughput analysis of targeted mass spectrometry assay datasets and is a step towards more robust and faster assay implementation.

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Integration of Mass Spectrometry Imaging and Machine Learning Visualizes Region-Specific Age-Induced and Drug-Target Metabolic Perturbations in the Brain.

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