MSIpixel: a fully automated pipeline for compound annotation and quantitation in mass spectrometry imaging experiments.
Lavinia MorosiMatteo MiottoSara TimoSara CarloniEleonora BrunoMarina MeroniElisabetta MennaSimona LodatoMaria RescignoGiuseppe MartanoPublished in: Briefings in bioinformatics (2023)
Mass spectrometry imaging (MSI) is commonly used to map the spatial distribution of small molecules within complex biological matrices. One of the major challenges in imaging MS-based spatial metabolomics is molecular identification and metabolite annotation, to address this limitation, annotation is often complemented with parallel bulk LC-MS2-based metabolomics to confirm and validate identifications. Here we applied MSI method, utilizing data-dependent acquisition, to visualize and identify unknown molecules in a single instrument run. To reach this aim we developed MSIpixel, a fully automated pipeline for compound annotation and quantitation in MSI experiments. It overcomes challenges in molecular identification, and improving reliability and comprehensiveness in MSI-based spatial metabolomics.
Keyphrases
- mass spectrometry
- high resolution
- liquid chromatography
- high performance liquid chromatography
- gas chromatography
- capillary electrophoresis
- rna seq
- ms ms
- machine learning
- tandem mass spectrometry
- deep learning
- high throughput
- multiple sclerosis
- big data
- artificial intelligence
- solid phase extraction
- bioinformatics analysis