Innovations in mass spectrometry (MS) technology have enabled researchers to uncover previously unexplored parts of the proteome over the years, assisting the discovery of new drugs and pushing the boundaries of personalized medicine. Despite such developments, various challenges—such as post-translational modifications (PTMs), complex biological matrices, and genetic/environmental influences over protein expression—hinder deep coverage of the proteome.
These challenges can be overcome, however, if MS-based analyses incorporate orthogonal approaches and thereby sharpen their focus on accurate, sensitive, high-throughput protein identification and quantification. Ion mobility spectrometry (IMS) is a powerful analytical technique that has seen widespread growth over the last five decades and has considerable potential in proteomics applications to increase peak capacity, widen dynamic range, and improve the signal-to-noise (S/N) ratio.
A fourth dimension of separation
Trapped ion mobility spectrometry (TIMS) is an IMS method that can be easily integrated into a mass spectrometer without a noticeable loss in ion transmission or sensitivity,1 increasing confidence in compound characterization. The ability to separate ions by mobility boosts sensitivity and provides additional selectivity, because the ions are separated by a fourth parameter—their collisional cross section (CCS).
TIMS, when coupled with the parallel accumulation–serial fragmentation (PASEF) method, adds this additional dimension of separation to proteomics workflows and combines it with up to a 10-fold increase in sequencing speed, while improving sensitivity and maintaining mass spectral resolution and accuracy.2 This is crucial for delving deeper into complex proteomes to obtain quantitative data in a short amount of time. The combined power of TIMS and PASEF allows for greater proteome or subproteome coverage from small sample volumes taken, for example, from tissue biopsies.
Increasing throughput with dia-PASEF
Data independent acquisition (DIA) workflows have gained in popularity in recent years as they overcome the issue of stochastic selection of peptide precursors encountered in typical data dependent acquisition (DDA) approaches, and thereby promise reproducible and accurate protein identification and quantification across large sample cohorts. The PASEF principle has now been combined with DIA in a new acquisition method known as dia-PASEF, which sets new selectivity and sensitivity standards for DIA and achieves unprecedented peptide identification rates with reproducible quantification (Figure 1).3–5
The future of proteomics
Combining the advantages of DIA with the inherent efficiency of PASEF, to create the new acquisition mode dia-PASEF, realizes the concept of identifying thousands of proteins from minimal sample volumes. Researchers are now applying deep learning to proteomics to mine dia-PASEF data more efficiently, making the most of the method’s boosted throughput, efficient ion utilization, rapid peptide identification and quantification, and unparalleled sensitivity. These advanced technologies are set to continue the evolution of MS-based proteomics.
For more information on dia-PASEF, please visit www.bruker.com/en/products-and-solutions/mass-spectrometry/timstof/timstof-pro-2.html
Gary Kruppa, PhD, is vice president of proteomics at Bruker Daltonics.
1. Wormwood KL, Deng L, Hamid AM, DeBord D, Maxon L. The Potential for Ion Mobility in Pharmaceutical and Clinical Analyses. In Advancements of Mass Spectrometry in Biomedical Research. Woods A, Darie C, eds.; Springer: Cham, 2019; 1140: 299–316.
2. Meier F, Brunner AD, Koch S, Koch H, Lubeck M, Krause M, Goedecke N, Decker J, Kosinski T, Park MA, Bache N, Hoerning O, Cox J, Räther O, Mann M. Online Parallel Accumulation-Serial Fragmentation (PASEF) with a Novel Trapped Ion Mobility Mass Spectrometer. Mol. Cell. Proteomics 2018; 17(12): 2534–2545.
3. Kaspar-Schoenefeld S, Meier F, Brunner AD, Frank M, Ha A, Lubeck M, Raether O, Collins B, Aebersold R, Rost H, Mann M. Parallel accumulation–serial fragmentation combined with data-independent acquisition (diaPASEF). AppNote LC-MS 157.
4. Kaspar-Schoenefeld S, Marx K, Gandhi T, Reiter L, Meier F, Brunner AD, Frank M, Ha A, Lubeck M, Raether O, Distler U, Tenzer S, Aebersold R, Collins B, Rost H, Mann M. diaPASEF: label-free quantification of highly complex proteomes. AppNote LC-MS 160.
5. Meier F, Brunner AD, Frank M, Ha A, Bludau I, Voytik E, Kaspar-Schoenefeld S, Lubeck M, Raether O, Bache N, Aebersold R, Collins BC, Röst HL, Mann M. diaPASEF: parallel accumulation–serial fragmentation combined with data-independent acquisition. Nat. Methods 2020; 17(12): 1229–1236. DOI: 10.1038/s41592-020-00998-0.