OpenMSI Arrayed Analysis Toolkit: Analyzing Spatially Defined Samples Using Mass Spectrometry Imaging

Mass
spectrometry imaging (MSI) has primarily been applied in localizing
biomolecules within biological matrices. Although well-suited, the
application of MSI for comparing thousands of spatially defined spotted
samples has been limited. One reason for this is a lack of suitable
and accessible data processing tools for the analysis of large arrayed
MSI sample sets. The OpenMSI Arrayed Analysis Toolkit (OMAAT) is a
software package that addresses the challenges of analyzing spatially
defined samples in MSI data sets. OMAAT is written in Python and is
integrated with OpenMSI (http://openmsi.nersc.gov), a platform for storing, sharing, and analyzing MSI data. By using
a web-based python notebook (Jupyter), OMAAT is accessible to anyone
without programming experience yet allows experienced users to leverage
all features. OMAAT was evaluated by analyzing an MSI data set of
a high-throughput glycoside hydrolase activity screen comprising 384
samples arrayed onto a NIMS surface at a 450 μm spacing, decreasing
analysis time >100-fold while maintaining robust spot-finding.
The
utility of OMAAT was demonstrated for screening metabolic activities
of different sized soil particles, including hydrolysis of sugars,
revealing a pattern of size dependent activities. These results introduce
OMAAT as an effective toolkit for analyzing spatially defined samples
in MSI. OMAAT runs on all major operating systems, and the source
code can be obtained from the following GitHub repository: https://github.com/biorack/omaat.