DeisoLAB - isotopic envelope identification by analysis of the spatial distribution of peptides in MALDI-MSI data

A new approach (DeisoLAB) is introduced to handle extensive peptide data obtained in mass spectrometry imaging experiments and detect potential isotopic envelopes. It is based on a combination of a fuzzy-inference system and analysis of spatial distribution of peptides. Potential isotopic envelope members are identified in the first step using the Mamdani-Assilan fuzzy-inference system. Then, preselected isotopic envelope members are analyzed in the second step regarding their spatial distribution. The spatial distribution of an analyte is visualized as a spatial map of molecular distribution that reflects the peak intensities registered for each m/z across the whole tissue section. When comparing the spatial maps of molecular distributions of different isotopic envelope members, it can be observed that there is a difference between those included in one isotopic envelope and those not included. To measure this difference, several image texture metrics are applied. Based on those metrics, using the Naïve Bayes classifier, peaks can be classified into two classes, envelope and non-envelope. The method was evaluated on eight MALDI-MSI datasets, including fresh-frozen and formalin-fixed paraffin-embedded tissues. The fuzzy-inference-system-based filtering reduced the number of candidate peak pairs requiring further analysis, while the subsequent spatial-distribution-based classification achieved recall values of 88.12–94.12%, precision values of 72.95–85.71%, and specificity above 99%. The overall deisotoping accuracy reached 96.98%. Currently, several tools are already available enabling analysis of peptide envelopes, however they use predominantly spectral information, whereas we propose a computational method (DeisoLAB) that additionally exploits spatial molecular distributions of species – a feature unique to MSI data. This allows for substantial broadening of the possibility of differentiation between individual isotopic envelopes, also in the case of mass spectrometry data of relatively low mass resolution.

Authors

Institutions

Publication Details

Journal
BMC Bioinformatics
Published
2026-09-21
DOI
https://doi.org/10.1186/s12859-026-06642-6
Primary Topic
Advanced Proteomics Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
article

DeisoLAB - isotopic envelope identification by analysis of the spatial distribution of peptides in MALDI-MSI data

Monika Pietrowska, Marta Gawin, Anna Glodek, Joanna Polańska
BMC Bioinformatics
Advanced Proteomics Techniques and Applications
article

DeisoLAB - isotopic envelope identification by analysis of the spatial distribution of peptides in MALDI-MSI data

Monika Pietrowska, Marta Gawin, Anna Glodek, Joanna Polańska
article en

Abstract

A new approach (DeisoLAB) is introduced to handle extensive peptide data obtained in mass spectrometry imaging experiments and detect potential isotopic envelopes. It is based on a combination of a fuzzy-inference system and analysis of spatial distribution of peptides. Potential isotopic envelope members are identified in the first step using the Mamdani-Assilan fuzzy-inference system. Then, preselected isotopic envelope members are analyzed in the second step regarding their spatial distribution. The spatial distribution of an analyte is visualized as a spatial map of molecular distribution that reflects the peak intensities registered for each m/z across the whole tissue section. When comparing the spatial maps of molecular distributions of different isotopic envelope members, it can be observed that there is a difference between those included in one isotopic envelope and those not included. To measure this difference, several image texture metrics are applied. Based on those metrics, using the Naïve Bayes classifier, peaks can be classified into two classes, envelope and non-envelope. The method was evaluated on eight MALDI-MSI datasets, including fresh-frozen and formalin-fixed paraffin-embedded tissues. The fuzzy-inference-system-based filtering reduced the number of candidate peak pairs requiring further analysis, while the subsequent spatial-distribution-based classification achieved recall values of 88.12–94.12%, precision values of 72.95–85.71%, and specificity above 99%. The overall deisotoping accuracy reached 96.98%. Currently, several tools are already available enabling analysis of peptide envelopes, however they use predominantly spectral information, whereas we propose a computational method (DeisoLAB) that additionally exploits spatial molecular distributions of species – a feature unique to MSI data. This allows for substantial broadening of the possibility of differentiation between individual isotopic envelopes, also in the case of mass spectrometry data of relatively low mass resolution.

BMC Bioinformatics
Silesian University of Technology (PL), Institute of Genetics and Animal Biotechnology of the Polish Academy of Sciences (PL), The Maria Sklodowska-Curie National Research Institute of Oncology (PL)
Silesian University of Technology
Zero hunger
Openalex Percentile: Top 23%
Advanced Proteomics Techniques and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.