by Gary Kruppa, Vice President of Proteomics at Bruker Daltonics, and Roman Fischer, Associate Professor in Clinical Proteomics, Target Discovery Institute, University of Oxford
The proteomics market is expected to grow 16.6% by 2026.1 Significant advances in technology and new methodologies over the last two decades have made proteomics an extremely powerful tool for protein scientists, biologists, and clinical researchers.2 The discovery of diagnostic and prognostic markers are the driving force behind finding new cures for diseases.
Mass spectrometry (MS) can help researchers to discover critical “molecular windows” within complex disease processes. The ongoing developments in MS technology have enabled researchers to start visualizing the proteome – the set of proteins expressed at a particular time – of a cell or organism, with the goal of comprehensively identifying all proteins and their associated biological activities. This article will discuss the accelerated use of proteomics in biomedical and clinical research laboratories, as well as research at Oxford University’s Target Discovery Institute (TDI) and the work the team has undertaken in response to the COVID-19 pandemic.
The Challenges for Clinical Proteomics
Clinical proteomics applies the science of proteomics to patient care. However, the analysis of proteomics data from patients requires special strategies. Challenges include how to extract meaningful protein expression signatures from data with high individual variability, how to integrate the genomic background of the patients into the analysis of proteomics data, and how to determine biomarkers and properly estimate their predictive power.
The growth of MS-based proteomics represents both challenges and solutions. Traditionally, proteomics has not been the driving force of clinical projects, as sample sets were typically extremely small. Moreover, the coverage of complete proteomes was difficult to achieve due to the limited speed, sensitivity, and resolution of mass spectrometers.
In the past, in clinical proteomic research, scientists would struggle to analyze blood samples due to their high dynamic range in proteomic abundance and it was not always possible to run multiple samples at one time. This meant that laboratories were often faced with the challenge of which machine to prioritize. Where high-throughput proteomics in the past has been addressed by simply increasing the number of mass spectrometers in laboratories, analyzing more samples at a low throughput, new technologies have now been developed to improve analysis times and robustness, making high-throughput proteomics possible.
The Evolution of Clinical Proteomics
Proteomics is dependent on the ability to detect ions. Trapped ion mobility spectrometry (TIMS) is a separation technique in gas phase, which resolves sample complexity with an added dimension of separation in addition to high-performance liquid chromatography (HPLC) and MS, increasing peak capacity and confidence in compound characterization. Some cutting-edge mass spectrometers use a process called parallel accumulation serial fragmentation process (PASEF), which provides high speed and sensitivity to reach new depths in shotgun proteomics.
PASEF technology can achieve > 100 Hz sequencing speed without losing sensitivity or resolution. This is achieved by synchronizing the quadrupole isolation mass window with the elution time of the specific peptide packages from the TIMS funnel. Superior results can be obtained from less than 200 ng sample load, therefore reducing both sample preparation costs and MS maintenance frequency. Using a 90 min gradient length, more than 5400 protein groups can be identified from a typical human cell line lysate.
Clinical Proteomics Enabling Studies for COVID-19
Research at Oxford University’s TDI focuses on linking recent advances in genetics, genomics and cell and chemical biology to address the need for accurately defined drug targets to accelerate drug development. The group recently investigated the use of proteomics for deep phenotyping of the host immune response in COVID-19 infection.
Figure 1: Principal Component analysis of COVID-19 disease severity groups using shotgun high throughput proteomics data.
The COVID-19 Multi-omics Blood Atlas (COMBAT) consortium is part of a large multidepartment collaboration at the University of Oxford that is characterizing the immunological response to SARS-CoV-2 infections. Specifically, COMBAT aims to perform deep phenotyping of the peripheral blood response in SARS-CoV-2 infection to understand why some patients with COVID-19 develop severe disease, with the goal of identifying these patients early and treating them in a targeted manner. To do this, large-scale datasets derived from a core set of patient samples taken at different time points of infection are being generated using a wide array of molecular techniques (multi-omic and immunological) at different laboratories in Oxford. The project analyzed roughly 500 samples and looked at proteins that correlated with disease severity.4
This study involved 200-300 patients, some with samples taken at multiple time points, which were analyzed using high-throughput proteomics technology. The study took a new approach by using subgroups of these samples to analyze with other -omics techniques, such as site analysis of certain blood cells. The plasma proteome enabled sub-phenotyping into patient clusters, which were predictive of severity and outcome (Figures 1 and 2).
Figure 2: Unsupervised hierarchical clustering of quantified proteins reveals plasma protein clusters correlating with disease severity.
Among the eight types of -omics technologies that were used in this large study on the same set of samples, proteomics was the best suited to stratify the COVID-19 patients into different disease severity groups, while other technologies provided data to help understand the underlying biology. Additionally, the study identified prognostic indicators and allows prediction of outcomes, with the eventual aim of intervention and tailored treatment for these patients.
This proteomic analysis identified specific plasma acute phase protein levels as indicators of severe disease, with evidence for hallmarks of acute phase inflammation, complement activation/attack, fibrin clots, proteases, serum amyloid, tissue necrosis, receptor mediated endocytosis and cholesterol transport. The Consortium discovered plasma protein signatures that can be used to stratify acute hospitalized COVID-19 cases into disease sub-phenotypes, with cluster membership informative for response state and associated with differential 28-day mortality.
Studies such as this multi-omics blood atlas will provide essential insights to inform future drug development, clinical trial design and personalized medicine approaches for COVID-19.
Looking Towards the Future with Proteomics
As we look towards future drug discovery and development and a more personalized approach to medicine, high-throughput proteomics will play an increasingly important role. Ultimately, we hope to see a progression where this method is used in a clinical setting, with instruments installed in hospitals and being used for the day-to-day screening of patient samples. With MS enabling the measurement of multiple analytes at the same time, scientists could potentially use proteomics to analyze a whole pathway or assembly of proteins from a patient sample. Subsequently, this could lead to improved diagnoses for diseases such as cancerous tumors. Another benefit of using high-throughput proteomics is that instead of being tested for one disease, most patients have comorbidities that could be identified with MS in a systems biology-based approach.
Personalized medicine is within touching distance of scientists and clinical researchers today and with the advancements we are seeing in different technologies and methodologies, the way diseases are understood and treated in the future looks to be changed drastically by the power of proteomics.
References
- Allied Market Research. (2021) Proteomics Market Size and Industry Growth 2027. [online] Available at: https://www.alliedmarketresearch.com/proteomics-market [Accessed 24 May 2021].
- Cox J and Mann M (2011) Quantitative, High-Resolution Proteomics for Data-Driven Systems Biology, Annu. Rev. Biochem. 80: 273-299.
- Covid-19 Multi-omics Blood ATlas (COMBAT) Consortium, Ahern DJ, Ai Z, et al. (2021) A blood atlas of COVID-19 defines hallmarks of disease severity and specificity, medRxiv, https://doi.org/10.1101/2021.05.11.21256877.
About the Authors: Roman Fischer is an Associate Professor and Senior Group Leader in Clinical Proteomics at the University of Oxford. He leads the Discovery Proteomics Facility at the Target Discovery Institute. RF studied Biotechnology at the Technical University Braunschweig and obtained his PhD at the Helmholtz Centre for Infection Research for the analysis of host-pathogen interactions of Listeria monocytogenes using proteomic methods (2007). After postdoctoral studies on Il-1 signalling in the laboratory of Professor Sir Philip Cohen in Dundee (Scotland), Roman started to develop clinical proteomics at the University of Oxford in 2009. Since 2013 RF leads the Discovery Proteomics Facility and applies proteomic methods to a multitude of scientific questions. Currently he focusses his research on the development of high-throughput and spatial proteomics methods and their application to large clinical cohorts and specimen.
Gary Kruppa, PhD, Vice President of Proteomics, Bruker Daltonics Inc., has over 30 years of experience in the field of mass spectrometry, having served as a Vice President at Bruker Daltonics for over 20 years. Kruppa received his PhD in chemical physics from the California Institute of Technology, and his BS from the University of Delaware. Kruppa oversees market and applications development management for Bruker's innovative solutions for research in proteomics.