Abstract
Mass spectrometry imaging (MSI) allows for the correlation of spatial localization and chemical information directly from biological surfaces. A data set can contain thousands of ion signals with varying degrees of co-localisation. Ion mobility separation based on travelling wave technology can be utilized to add specificity to the MSI experiment. This leads to highly complex data sets that necessitate the need for advanced automated computerized processing. Here, we investigate the use of different Hierarchal Clustering Analysis (HCA) methods to aid the analysis of digested tissue sections by clustering ion images based on correlation. Four proteins digests from BSA, Phosphorylase B, ADH and Enolase were spotted forming a 6x6 array comprising four 4x4 overlapping squares. Mouse fibrosarcoma model tissue sections were washed and on-tissue tryptic digested overnight. Matrix was applied evenly in several coats. Data were acquired using MALDI SYNAPT G1 and G2-S instruments in MS mode with tri-wave ion guide optics to separate ions according to their ionic mobility. The acquisition mass range was from 700–3,000 Da. Data were processed and visualized using High Definition Imaging MALDI software. Data reduction is initially achieved by peak picking using multidimensional (m/z and drift time) detection algorithms. A second step aims at generating ion distributions images comprising x,y coordinates and a third step correlates all processed ion distributions using Pearson product-moment algorithms. Different HCA methods were assessed in terms of their ability to cluster peaks from the tryptic digest imaging data set into related peptide groups, which can be used for PMF protein identification. The methods were also evaluated in terms of the time required to complete the analysis and number of hierarchal levels created. Top-down K-medoid HCA was applied to the fibrosarcoma tissue data. It successfully clustered tryptic peptides with multiple protein identifications from a complex digested tissue section.