Lu Rahman looks at the rise of proteomics, its growing role in the quest for new treatments and some of the work being carried out in this field.
Due to the introduction of technology-enabled proteomics products, the global proteomics market is expected to reach $24.8 billion by 2024, says Grand View Research. It notes that these products find “extensive applications in drug discovery, diagnostic services, and many other research areas” and that “the increasing market penetration of these technologies, such as ESI-LC-MS (electrospray ionisation liquid chromatography mass spectrometry), is expected to serve as a driver of this market.”
Proteomics, the large-scale study of proteins, is holding increasing significance and opportunity as we look to find treatments for a range of conditions such cancer, fatty liver disease and diabetes. According to Simon Barnett, Analyst at 24/7 Wall St, “The natural next step for the ‘omics revolution, in our view, is proteomics—the analysis of proteins” .
Exciting and innovative work is being carried out in this field such as that by Systems Biologist Paola Picotti at ETH Zurich. In 2020 she won the Rössler Prize for her work in proteomics where she devised a way of measuring structural changes in thousands of proteins at the same time, which has huge ground-breaking implications for personalised therapy.
Just recently news emerged of a study that looked at how proteomics-based technology could help verify mammography screening. Thanks to work carried out by the Translational Genomics Research Institute (TGen), an affiliate of City of Hope, breast cancer, even at its initial stages, could be detected earlier and more accurately than current techniques using blood samples and a unique proteomics-based technology.
Patrick Pirrotte, PhD, an Assistant Professor and Director of TGen’s Collaborative Center for Translational Mass Spectrometry, and an international team of researchers developed a test that can detect infinitesimally small breast cancer biomarkers that are shed into the bloodstream from cells surrounding cancer known as extracellular matrix (ECM), according to the findings of their study recently published in the scientific journal Breast Cancer Research.
“Our data reinforces the idea that this release of ECM components into circulation, even at the earliest stages of malignancy, can be used to design a specific and sensitive biomarker panel to improve detection of breast cancer,” said Dr. Pirrotte, the study’s senior author. “Using a highly specific and sensitive protein signature, we devised and verified a panel of blood-based biomarkers that could identify the earliest stages of breast cancer, and with no false positives.”
Last year, scientists at Stanford generated a global map of protein expression to help explain the basis of many genetic diseases.
Stanford author Erika Hunting explained: “Unraveling the genetic basis of many human diseases is a daunting task. However, understanding where the products of disease-related genes act can provide clues into disease formation. Presently, scientists examine the RNA – the first product of a gene – to infer the tissue where genes act. Unfortunately, there is a downfall to this method: the level of protein – the active end product of a gene – often correlates poorly with RNA levels. Thus, generating a map of proteins may be a more revealing approach to understanding the cardinal foundation of disease development.”
Hunting highlighted how researchers in the lab of Michael Snyder, Stanford B. Ascherman Professor and Chair of Genetics and Director of Genomics and Personalized Medicine at Stanford University School of Medicine, generated the most comprehensive protein map to date.
“The map shows where proteins are expressed throughout the human body, providing new insights into regulation, secretome, metabolism, and human diseases. The researchers measured relative protein levels from over 12,000 genes across 32 normal human tissues. Tissue-specific proteins were identified and compared to RNA data. Information from tissue-specific proteins could lead to novel explanations of disease phenotype that could not have been deduced by RNA information alone. “
According to Lihua Jiang, a proteomics expert in the Snyder Laboratory responsible for proteomics profiling of host samples of the project, “the tissue-specific distribution of proteins can provide an in-depth view of complex biological processes that require the interplay of multiple organs. Jiang adds that “analysis of enzymes involved in amino acid metabolism revealed different roles of each organ as well as novel organs (heart, stomach, pancreas) that are important for metabolic control. We envision this kind of analysis can shed light on the understanding of many biological processes.”
Through its work, the team identified 1,329 potential drug-targeted proteins, about half of which, says Hunting, are FDA approved drug targets spanning 742 different tissues, with 368 present in more than one tissue. “For drug-targeted proteins present outside of the target organ, the drug may have unintended side effects in the off-target tissue. For example, valproic acid is an anticonvulsant drug that works through the inhibition of a protein in the brain. Snyder’s team showed that this drug-targeted protein is also enriched in the liver and pancreas, suggesting the underlying cause of reported liver and pancreas toxicity side effects,” she explains.
Companies offering innovative solutions for this area of science include Thermo Fisher Scientific, Bio-Rad, Agilent Technologies, Bruker, Merck, Danaher, Waters, PerkinElmer, Olink and Luminex Corporation.
In 2020 Olink contributed to one of the largest longitudinal COVID-19 proteomics studies to date, together with the prestigious Massachusetts General Hospital (MGH). Working with the outstanding research team from MGH, protein measurements and associated clinical data have been made available via the Olink company website.
This landmark study is part of the “COVID-19 Technology Access Framework” initiative, which was started early in the pandemic by Harvard, MIT, Stanford and the Broad institute. This aims to combine data from the most critically important technologies that may help prevent, diagnose, or treat COVID-19 infections, and to share the findings openly to accelerate research, inform the public health response and help save lives.
In collaboration with Olink, a team from MGH rapidly profiled over 1400 proteins in a cohort of COVID-19 patients using the recently launched Olink Explore 1536 platform to look for new protein biomarkers useful for preventing, diagnosing and treating COVID-19 infection. The extensive dataset, with over 1.3 million protein data points and essential clinical parameters from the study, is now freely available for everyone to access through the Olink website, to stimulate and facilitate further investigation of the pathways underlying severe disease that may be the basis for early diagnosis and clinical intervention.
The company’s expertise is well-recognised in this field with its Proteomics Olink Explore 1536 platform, utilising Next Generation Sequencing (NGS) as read-out, is earmarked for use in a project to measure plasma protein concentration in 53,000 individuals from the UK Biobank, one of the world’s largest genetic resources. The project is funded by a consortium of ten biopharmaceutical companies.
This large-scale proteomics study will further enhance previous commitments from several consortium members on genomic analyses of the 500,000 volunteers in the UK Biobank resource, with the ultimate goals of developing a better understanding of disease biology and more effective therapies.
UK Biobank is a large-scale, biomedical database and research resource containing in-depth genetic and health information from half a million UK participants. The database, which is regularly augmented with additional data, is globally accessible to approved researchers and scientists undertaking vital research into the most common and life-threatening diseases.
The Olink Explore 1536 platform combines high throughput and high-quality protein-level data from very small sample volumes. It aims to combine the specificity of paired antibody binding with attached DNA-oligos to transform a protein measuring challenge to a digital DNA counting solution. Olink Explore leverages NGS readout technology using the NovaseqTM platform from Illumina, a technology being used for whole exome and whole genome sequencing of UK Biobank.
“We are very proud to be chosen as a partner by this innovative biopharmaceutical consortium. We believe that by complementing ongoing genomics efforts with large-scale, highly validated and precise proteomic data, we will rapidly advance our understanding of real-time human biology and accelerate the development of life-saving new medicines. This is particularly pertinent given that the majority of drug targets are proteins and that the protein biomarkers are valuable tools to guide effective and efficient drug development through enhancing the understanding of disease and by identifying patients who can benefit most from therapies.” said Jon Heimer, CEO of Olink Proteomics.
Thermo Fisher Scientific
Last year, Thermo Fisher Scientific and MSAID, a software company transforming proteomics with deep learning, announced an exclusive license agreement to develop and commercialise deep learning tools for proteomics, making MSAID’s Prosit-derived framework widely accessible to proteomics laboratories. The availability of deep learning tools aimed to enable improved confidence in proteomics research results, primarily in the areas of protein profiling using label-free or tandem mass tag (TMT)-based quantification, and a variety of new applications.
Users can access deep-learning-based prediction of tandem mass spectra, allowing for the formation of entire spectral libraries on demand and facilitating the identification of peptides with up to 10 times higher confidence and the extraction of more identifications from proteomics datasets via intensity-based rescoring. In combination with Thermo Scientific Orbitrap technology, the algorithm enables emerging applications, such as immunopeptidomics and metaproteomics, for which traditional database search and statistical approaches are often ineffective.
“Increasing the confidence of protein and peptide identifications is a growing need, given that a false discovery rate of even 1% means that 1,000 out of every 100,000 peptides might be incorrectly assigned,” said Mark Sanders, Director of Life Science Mass Spectrometry Software, Thermo Fisher Scientific. “Applying deep learning tools enables data-independent analysis of proteomics samples with higher confidence and reproducibility, and, when used with Orbitrap technology, reduces the false discovery rate 10-fold, to merely 100 out of every 100,000 peptides.”
Bruker Corporation and Evosep also recently announced major progress in high-sensitivity, quantitative true single- cell proteomics, using a modified timsTOF Pro mass spectrometer connected to an Evosep One low-flow chromatography system.
These advances have been demonstrated by the Mann-group in a paper by A. Brunner et al. Key results include the first untargeted and unbiased analysis of true single cells achieving an identification depth of up to 1,500 protein groups from single FACS-sorted and individually digested HeLa cells. This sensitivity was achieved by the combined Evosep–timsTOF platform using crucial innovations.
Two new ultra low-flow methods on an otherwise unmodified Evosep One system offer 100 nL/min gradients that lead to approximately 10 times higher sensitivity than gradients at 1 μL/min, confirming theoretical expectations. These new nano-flow methods, using the WhisperTM flow technology, have the same low overhead as the other established Evosep One methods, and offer a throughput of either 20 or 40 samples per day. They make single-cell analysis robust, as shown by the analysis of 420 single cells that were analysed on the same column. The workflow retrieved known and unexpected proteins changing abundance upon chemically arresting the cell cycle at discrete points.
According to Bruker, also crucial for achieving ultra-high true single-cell sensitivity was a new modified timsTOF mass spectrometer that included a new, brighter ion source with associated modified ion optics. These precommercial innovations on the timsTOF platform were combined with the newly developed dia- PASEF1 scan mode, wherein a large percentage of all peptides are sampled in an unbiased manner. The study provided further evidence of the advantages of data-independent analysis (DIA) for short LC gradients or extremely low abundance samples, such as single cells. Even for these single cells, the quantitative reproducibility was high in this novel Evosep–timsTOF workflow.
The authors performed an in-depth comparison to cells analysed by single-cell RNA sequencing by two different technologies, allowing them to distinguish technical vs. fundamental biological differences between the proteomes and transcriptomes. This revealed that single cells have stable core proteomes with sufficient protein copy numbers to permit meaningful quantitation, whereas the transcriptome appears to behave more randomly, presumably because of low copy numbers of many mRNA transcripts per cell.
Bruker’s Vice President of Proteomics, Dr. Gary Kruppa, noted: “I am very excited about the new quantitative, true single cell study. This is another great example of what the timsTOF platform can achieve, and a further cell biology breakthrough facilitated by the dia-PASEF method. We anticipate providing the new ultra-high sensitivity timsTOF technologies to early-access collaborators in the second half of 2021 on dedicated single-cell proteomics systems, and we plan for full commercialisation in 2022.”
Dr. Nicolai Bache, a co-author on the study and Head of Applications at Evosep, added: “This type of robust single cell analysis is an important step towards enabling proteome analysis and digital pathology in everyday clinical diagnostics. We are very happy that the Evosep One stands to make a significant contribution to this promising new area.”