Emerging mass spectrometry-based proteomics methodologies for novel biomedical applications

Abstract

Research into the basic biology of human health and disease, as well as translational human research and clinical applications, all benefit from the growing accessibility and versatility of mass spectrometry (MS)-based proteomics. Although once limited in throughput and sensitivity, proteomic studies have quickly grown in scope and scale over the last decade due to significant advances in instrumentation, computational approaches, and bio-sample preparation. Here, we review these latest developments in MS and highlight how these techniques are used to study the mechanisms, diagnosis, and treatment of human diseases. We first describe recent groundbreaking technological advancements for MS-based proteomics, including novel data acquisition techniques and protein quantification approaches. Next, we describe innovations that enable the unprecedented depth of coverage in protein signaling and spatiotemporal protein distributions, including studies of post-translational modifications, protein turnover, and single-cell proteomics. Finally, we explore new workflows to investigate protein complexes and structures, and we present new approaches for protein-protein interaction studies and intact protein or top-down MS. While these approaches are only recently incipient, we anticipate that their use in biomedical MS proteomics research will offer actionable discoveries for the improvement of human health.

Keywords: mass spectrometry; proteomics; technology.

Figure 1.. Comparison of MS acquisition methods for proteomics.

Protein samples can be analyzed by mass spectrometry using conceptually different methodologies which each require their own unique experimental design. These workflows feature differences in the scalability of sample throughput (the number of samples in a given experiment), in the comprehensiveness of protein and peptide measurements (the number of proteins/peptides that can be detected and quantified), and finally the sensitivity of quantification (the lowest amount of protein/peptide that the method can reliably measure). Targeted mass spectrometry workflows include SRM and PRM (green box) and can measure many samples using optimized assays with deep quantitative sensitivity and accuracy. Label-free discovery mass spectrometry workflows (orange box), such as DDA and DIA may feature overall lower sensitivity for quantification, however, allow to measure proteins more comprehensively in an unbiased approach. DDA workflows are often augmented by sample preparation methods referred to as ‘isotopic labeling’ which feature good quantitative sensitivity and preserve comprehensive acquisitions but are challenged by the maximum number of samples easily achievable in a quantitative experiment.

Figure 2.. Biomedical research applications supported by recent MS-based proteomics technological advances.

(clockwise from left) Technological advances in data acquisition and throughput have improved the ability to profile the proteomes of biological samples deeply and accurately, including clinical applications. New approaches for signaling and spaciotemporal protein dynamics improve the detection and quantification of post-translational modifications, proteostasis, and even enable single-cell proteomics. Finally, methods for studying protein complexes and structure facilitate studies of protein–protein and protein–drug interactions.

Figure 3.. Overview of various comprehensive quantitative proteomics workflows.

The shared workflow for most quantitative proteomics experiments typically involve protein extraction, proteolytic digestion, and finally analysis by LC–MS using either DDA or DIA acquisitions. The most general workflow, label-free DDA or DIA, requires no additional sample processing (left). Metabolic labeling strategies like SILAC/SILAM require that the heavy isotope label is incorporated into the metabolically active cells or organisms. After harvesting and lysis cells different samples are mixed and then digested and processed together prior to DDA or in some emerging cases DIA analysis (middle). Isobaric labeling strategies, such as TMT and iTRAQ, require each sample in the multiplex to be individually digested and subsequently be reacted with the specific chemical label, prior to mixing all samples and DDA analysis (right).

Figure 4.. Strategies for assessing protein–protein interactions by mass spectrometry.

The common goal for protein–protein interaction studies involves enriching and purifying a target protein of interest (the bait protein is indicated in yellow) together with its protein network/protein complex prior to identification by LC–MS. Affinity Purification: antibodies specific to the protein of interest typically are coupled to beads and are used to enrich the protein complex directly (left). Epitope Tagging: a plasmid construct with the gene of interest tagged with a common epitope is engineered, subsequently, the tagged protein is expressed in the model system, enabling the use of common affinity enrichment systems like streptavidin, anti-HA, or anti-FLAG (middle). Proximity labeling: a plasmid construct with the gene of interest fused to a proximity labeling enzyme such as APEX or BioID is engineered allowing for the covalent biotin labeling of any protein within a predetermined vicinity of the expressed fusion construct, followed by enrichment with streptavidin beads (right).