Isotopic envelope identification by analysis of the spatial distribution of components in MALDI-MSI data

TL;DR

This paper introduces a novel fuzzy system-based method for identifying isotopic envelopes in MALDI-MSI data, improving detection of overlapping envelopes and handling large datasets effectively.

Contribution

The paper presents a new algorithm using Mamdani-Assilan fuzzy logic and spatial distribution analysis for isotope envelope identification in MALDI-MSI data, addressing limitations of existing methods.

Findings

Effective detection of overlapping isotopic envelopes.

Suitable for large MALDI-MSI datasets.

Outperforms three existing deisotoping algorithms.

Abstract

One of the significant steps in the process leading to the identification of proteins is mass spectrometry, which allows for obtaining information about the structure of proteins. Removing isotope peaks from the mass spectrum is vital and it is done in a process called deisotoping. There are different algorithms for deisotoping, but they have their limitations, they are dedicated to different methods of mass spectrometry. Data from experiments performed with the MALDI-ToF technique are characterized by high dimensionality. This paper presents a method for identifying isotope envelopes in MALDI-ToF molecular imaging data based on the Mamdani-Assilan fuzzy system and spatial maps of the molecular distribution of peaks included in the isotopic envelope. Several image texture measures were used to evaluate spatial molecular distribution maps. The algorithm was tested on eight datasets obtained from the MALDI-ToF experiment on samples from the National Institute of Oncology in Gliwice from patients with cancer of the head and neck region. The data were subjected to pre-processing and feature extraction. The results were collected and compared with three existing deisotoping algorithms. The analysis of the obtained results showed that the method for identifying isotopic envelopes proposed in this paper enables the detection of overlapping envelopes by using the approach oriented to study peak pairs. Moreover, the proposed algorithm enables the analysis of large data sets.