PNNL

The Science

Unambiguous identification of all molecules in a biological or environmental sample is the “holy grail” of metabolomics. Making this level of identification requires separation and measurement of the molecules using advanced instrumentation: liquid (LC) or gas chromatography, ion mobility spectrometry (IMS), high resolution and/or tandem mass spectrometry (MS/MS), or infrared (IR) spectroscopy, among others; more importantly, combining these instruments sequentially is a necessity. However, measurements of this type, for example LC-IMS-MS/MS, will produce highly complex multidimensional measurements that are difficult to analyze because typical software is tailored to a particular instrument configuration. The new software, DEIMoS, or Data Extraction for Integrated Multidimensional Spectrometry, operates on data of any dimension.

The Impact

As technology advances and instrumentation evolves, researchers need accompanying software to gain meaning from data. DEIMoS enables researchers to analyze LC-IMS-MS/MS data—the current state-of-the-art in untargeted metabolomics research. Even more importantly, though, researchers can build upon the DEIMoS architecture for future data processing needs rather than starting from scratch. Researchers are already developing the next iteration that supports structures for lossless ion manipulation coupled with infrared spectroscopy and tandem mass spectrometry (SLIM-IR-MS/MS). The latter instrument platform, and the associated data analysis fulfilled by DEIMoS, represents the next step towards unambiguous compound identification in metabolomics.

Summary

DEIMoS, a Python application programming interface and command-line tool for high-dimensional MS data analysis workflows, offers ease of development and access to efficient algorithmic implementations. Functionalities include feature detection, feature alignment, collision cross section calibration, isotope detection, and MS/MS spectral deconvolution, with the output comprising detected features aligned across study samples and characterized by mass, collision cross section, tandem mass spectra, and isotopic signature. Notably, DEIMoS operates on N-dimensional data and not limited to specific acquisition instrumentation; algorithm implementations simultaneously utilize all dimensions to improve detection sensitivity, increase alignment/feature matching confidence among datasets, and mitigate convolution artifacts in tandem mass spectra. DEIMoS was evaluated on LC-IMS-MS/MS data to illustrate the advantages of a multidimensional approach in each data processing step.

Funding

This research was supported by the National Institutes of Health, National Institute of Environmental Health Sciences grant U2CES030170. Additional support was provided by the Pacific Northwest National Laboratory, Laboratory Directed Research and Development Program, and is a contribution of the Biomedical Resilience & Readiness in Adverse Operating Environments (BRAVE) Agile project.