Development of an object-based image analysis tool for mass spectrometry imaging ion classification.

Autor: Eisenberg SM; FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA., Knizner KT; FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA., Muddiman DC; FTMS Laboratory for Human Health Research, Department of Chemistry, North Carolina State University, Raleigh, NC, 27695, USA. dcmuddim@ncsu.edu.
Jazyk: angličtina
Zdroj: Analytical and bioanalytical chemistry [Anal Bioanal Chem] 2023 Aug; Vol. 415 (19), pp. 4725-4730. Date of Electronic Publication: 2023 May 24.
DOI: 10.1007/s00216-023-04764-x
Abstrakt: Mass spectrometry imaging (MSI) is an analytical technique that can detect and visualize thousands of m/z values resolved in two- and three-dimensional space. These m/z values lead to hundreds of molecular annotations, including on-tissue and background ions. Discrimination of sample-related analytes from ambient ions conventionally involves manual investigation of each ion heatmap, which requires significant researcher time and effort (for a single tissue image, it can take an hour to determine on-tissue and off-tissue species). Moreover, manual investigation lends itself to subjectivity. Herein, we present the utility of an ion classification tool (ICT) developed using object-based image analysis in MATLAB. The ICT functions by segmenting ion heatmap images into on-tissue and off-tissue objects through binary conversion. The binary images are analyzed and within seconds used to classify the ions as on-tissue or background using a binning approach based on the number of detected objects. In a representative dataset with 50 randomly selected annotations, the ICT was able to accurately classify 45/50 ions as on-tissue or background.
(© 2023. Springer-Verlag GmbH Germany, part of Springer Nature.)
Databáze: MEDLINE
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