Biology subjects

Nastase, A.-M.

Publications and source records attributed to Nastase, A.-M..

2 recordsLinked to original sources

bioRxiv2025

A computational workflow for microscopy-guided ion identification in clinical mass spectrometry imaging datasets

Matrix-assisted laser desorption/ionisation mass spectrometry imaging (MALDI-MSI) datasets were acquired from brain tissue samples obtained from living traumatic brain injury (TBI) patients. This is a proof-of-concept study which presents a computational workflow for identifying TBI-specific ions in MSI datasets through integrated pathological annotation and mass spectrometry imaging analysis. Pathological annotations of TBI regions were obtained from haematoxylin and eosin-stained microscopy images of the same sections. The microscopy images were then registered with the MSI datasets to enable precise delineation of TBI areas within the MSI data. Binary masks of the registered TBI regions were then used to perform cosine similarity searches, identifying ions potentially associated with TBI pathology. In order to relatively quantify the difference magnitude between TBI regions and surrounding tissue, bootstrap sampling was applied to non-TBI tissue areas, generating mean intensity values for comparison with average TBI region intensities. Additionally, a synthetic MSI dataset was generated to validate and optimize this analytical approach. Using this integrated computational workflow, TBI specific ions were identified in clinical MSI datasets.

bioinformatics↗

bioRxiv2021

Alignment of multiple metabolomics LC-MS datasets from disparate diseases to reveal fever-associated metabolites

Acute febrile illnesses are still a major cause of mortality and morbidity globally, particularly in low to middle income countries. The aim of this study was to determine any possible metabolic commonalities of patients infected with disparate pathogens that cause fever. Three liquid chromatography-mass spectrometry (LC-MS) datasets investigating the metabolic effects of malaria, leishmaniasis and Zika virus infection were used. The retention time (RT) drift between the datasets was determined using landmarks obtained from the standard reference mixtures generally used in the quality control of the LC-MS experiments. We used fitted Gaussian Process models (GPs) to perform a high level correction of the RT drift between the experiments, followed by standard peakset alignment between the samples with corrected RTs of the three LC-MS datasets. Statistical analysis, annotation and pathway analysis of the integrated peaksets were subsequently performed. Metabolic dysregulation patterns common across the datasets were identified, with kynurenine pathway being the most affected pathway between all three fever-associated datasets.

bioinformatics↗