Imaging mass spectrometry data reduction: Automated feature identification and extraction |
| |
Authors: | Liam A McDonnell Alexandra van Remoortere Nico de Velde René J M van Zeijl André M Deeldera |
| |
Institution: | aBiomolecular Mass Spectrometry Unit, Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands;bHogeschool Leiden, Leiden, The Netherlands |
| |
Abstract: | Imaging MS now enables the parallel analysis of hundreds of biomolecules, spanning multiple molecular classes, which allows
tissues to be described by their molecular content and distribution. When combined with advanced data analysis routines, tissues
can be analyzed and classified based solely on their molecular content. Such molecular histology techniques have been used
to distinguish regions with differential molecular signatures that could not be distinguished using established histologic
tools. However, its potential to provide an independent, complementary analysis of clinical tissues has been limited by the
very large file sizes and large number of discrete variables associated with imaging MS experiments. Here we demonstrate data
reduction tools, based on automated feature identification and extraction, for peptide, protein, and lipid imaging MS, using
multiple imaging MS technologies, that reduce data loads and the number of variables by >100×, and that highlight highly-localized
features that can be missed using standard data analysis strategies. It is then demonstrated how these capabilities enable
multivariate analysis on large imaging MS datasets spanning multiple tissues. |
| |
Keywords: | |
本文献已被 ScienceDirect SpringerLink 等数据库收录! |
|