Evaluation of Data Analysis Platforms and Compatibility with MALDI-TOF Imaging Mass Spectrometry Data Sets

Imaging
mass spectrometry (IMS) has proven to be a useful tool
when investigating the spatial distributions of metabolites and proteins
in a biological system. One of the biggest advantages of IMS is the
ability to maintain the 3D chemical composition of a sample and analyze
it in a label-free manner. However, acquiring the spatial information
leads to an increase in data size. Due to the increased availability
of commercial mass spectrometers capable of IMS, there has been an
exciting development of different statistical tools that can help
decipher the spatial relevance of an analyte in a biological sample.
To address this need, software packages like SCiLS and the open source
R package Cardinal have been designed to perform unbiased spectral
grouping based on the similarity of spectra in an IMS data set. In
this note, we evaluate SCiLS and Cardinal compatibility with MALDI-TOF
IMS data sets of the Gram-negative pathogen Pseudomonas aeruginosa PA14. Both software were able to perform unsupervised segmentation
with similar performance. There were a few notable differences which
are discussed related to the identification of statistically significant
features which required optimization of preprocessing steps, region
of interest, and manual analysis.