A Machine Learning-Driven Comparison of Ion Images Obtained by MALDI and MALDI‑2 Mass Spectrometry Imaging

Matrix-assisted laser desorption ionization mass spectrometry
imaging
(MALDI-MSI) enables label-free imaging of biomolecules in biological
tissues. However, many species remain undetected due to their poor
ionization efficiencies. MALDI-2 (laser-induced post-ionization) is
the most widely used post-ionization method for improving analyte
ionization efficiencies. Mass spectra acquired using MALDI-2 constitute
a combination of ions generated by both MALDI and MALDI-2 processes.
Until now, no studies have focused on a detailed comparison between
the ion images (as opposed to the generated m/z values)
produced by MALDI and MALDI-2 for mass spectrometry imaging (MSI)
experiments. Herein, we investigated the ion images produced by both
MALDI and MALDI-2 on the same tissue section using correlation analysis
(to explore similarities in ion images for ions common to both MALDI
and MALDI-2) and a deep learning approach. For the latter, we used
an analytical workflow based on the Xception convolutional neural
network, which was originally trained for human-like natural image
classification but which we adapted to elucidate similarities and
differences in ion images obtained using the two MSI techniques. Correlation
analysis demonstrated that common ions yielded similar spatial distributions
with low-correlation species explained by either poor signal intensity
in MALDI or the generation of additional unresolved signals using
MALDI-2. Using the Xception-based method, we identified many regions
in the t-SNE space of spatially similar ion images containing MALDI
and MALDI-2-related signals. More notably, the method revealed distinct
regions containing only MALDI-2 ion images with unique spatial distributions
that were not observed using MALDI. These data explicitly demonstrate
the ability of MALDI-2 to reveal molecular features and patterns as
well as histological regions of interest that are not visible when
using conventional MALDI.