An overview of technologies for MS-based proteomics-centric multi-omics

ABSTRACT

Introduction

Mass spectrometry-based proteomics reveals dynamic molecular signatures underlying phenotypes reflecting normal and perturbed conditions in living systems. Although valuable on its own, the proteome has only one level of moleclar information, with the genome, epigenome, transcriptome, and metabolome, all providing complementary information. Multi-omic analysis integrating information from one or more of these other domains with proteomic information provides a more complete picture of molecular contributors to dynamic biological systems.

Areas Covered

Here, we discuss the improvements to mass spectrometry-based technologies, focused on peptide-based, bottom-up approaches that have enabled deep, quantitative characterization of complex proteomes. These advances are facilitating the integration of proteomics data with other ‘omic information, providing a more complete picture of living systems. We also describe the current state of bioinformatics software and approaches for integrating proteomics and other ‘omics data, critical for enabling new discoveries driven by multi-omics.

Expert Commentary

Multi-omics, centered on the integration of proteomics information with other ‘omic information, has tremendous promise for biological and biomedical studies. Continued advances in approaches for generating deep, reliable proteomic data and bioinformatics tools aimed at integrating data across ‘omic domains will ensure the discoveries offered by these multi-omic studies continue to increase.

Plain Language Summary

Proteomics uses mass spectrometry to identify as many of the proteins in a system of interest as possible, making it extremely useful in biomedical research and basic biological research. Unlike next-generation DNA/genome sequencing, proteomics directly measures the changes in gene translation in response to a disease state, injury, etc. However, when proteomics data is coupled to and examined together with other forms of ‘omics’ data, such as transcriptomics, genomics, and metabolomics, a full biological picture emerges that can demonstrate the underlying regulatory networks of living systems and how they respond to positive and negative stimuli. This integration is called multi-omics and represents a powerful paradigm shift in systems biology. To be fully compatible with other ‘omics datasets, proteomics must be as complete and accurate as possible; in addition, the task of integrating multiple different kinds of datasets can be daunting to novice researchers. With this in mind, we reviewed in this manuscript the technologies that allow for the generation of the best possible proteomics for multi-omics analysis, in addition to the software tools needed to integrate proteomics data with other ‘omics data. Together, we believe this review will enable other researchers to begin applying multi-omics approaches to answer their research questions.

KEYWORDS:

• Mass spectrometry
• bottom-up proteomics
• bioinformatics
• multi-omics
• proteogenomics

Article highlights

• Improvements to bottom-up proteomic technologies, from experimental methods, sample preparation, and instrumentation, are providing improved depth and quality of proteome information

• New software is making it easier to perform multi-omic analyses on proteomics, transcriptomics, and metabolomics data

• Integration of genomic and transcriptomic sequencing data with mass spectrometry-based proteomics data has driven the emergence of proteogenomic analysis

• Data generated by bottom-up proteomics combined with other ’omics data results in more thorough molecular descriptions of dynamic biological systems

Declaration of Interests

The authors have no affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the review, including employment, consultancies, honoraria, stock ownership/options, expert testimony, grants, patents, or royalties. The funding agencies supporting this work contributed no information to or influence on the contents of the review.

Reviewer disclosures

Peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Additional information

Funding

Support for this work was provided from the National Cancer Institute’s Informatics Technology for Cancer Research (ITCR) grant 1U24CA199347 awarded to T.J.G.; A.T.R. was supported by Biotechnology Training Grant T32GM008347 from the National Institute of General Medical Sciences, an operating division of the National Institutes of Health.