A New Year in Proteomics

2020 was an important year for proteomics. The COVID-19 global pandemic cast a spotlight on the field's clinical utility in the fight against a public health crisis, from which there are many lessons to be drawn that will undoubtedly influence the landscape of the field for years to come.

Technology Networks spoke with Rohan Thakur, executive vice president of Life Sciences Mass Spectrometry at Bruker Daltonics, to discuss the current state-of-play in proteomics.

Upon reflection, what were some of the highlights and challenges in 2020? How did the COVID-19 pandemic demonstrate the value of clinical research proteomics? What data handling-related issues does the field carry into the New Year? And most importantly, how do we look to solve them?

Molly Campbell (MC): Let's talk about HUPO Connect 2020 which recently took place. What were some of the key highlights from this year's meeting for you?

Rohan Thakur (RT): Two specific highlights, the first of which is the launch of PaSER, our first product release from the IPA asset acquisition, which is our GPU-based search engine software. We support translational proteomics, which involves doing many runs and generating large data sets. As you generate more runs, you create large data sets and run into issues; your false positive rates etc. need to be addressed with the right software. The question becomes how can we reduce the search time so that it is less than the time taken to do the data analysis?

From a chromatography perspective we are down to 11 minute runs. The timsTOF Pro's robustness, speed and depth of coverage enabled the ability to shorten 60-90 minute runs to 11-minute runs, as in the case of Dr Roman Fischer or Dr Andrew Webb.

Bruker acquired the assets of the company on  May 8 2020, and by HUPO we could launch a product that enables the "run and done" ecosystem, which is protein and peptide IDs as fast as data acquisition.

Highlight number two was the early data that Professor Matthias Mann presented on the advancements made in single-cell proteomics on a prototype system that we're working on with him in his laboratory. The data from that system for a single cell – a true single cell, not just adding multiple cells together and calling it single-cell analysis – but really looking at proteins from a single cell, caused a bit of a stir. People were really fascinated by the data that Matthias was showing.

MC: Have there been any recent published research studies that have really excited you?

RT:  Dr Catherine Wong published a wonderful paper in Nature Communications on COVID-19 research using the timsTOF Pro. They were able to propose a two-stage mechanism of pathogenesis based on their proteomics data.

MC: There have been some impressive advancements in achieving high-speed and sensitivity in proteomics recently. Has progress in this space revealed any new challenges in other areas?

RT: The challenge comes down to handling the data, because a lot of the software was built for small data sets. If you compare proteomics with genomics, it is now becoming apparent that you need genomics, proteomics, glycomics, metabolomics and so on to give you a biological passport, or a complete view of the individual, and that's why personalized medicine was such a buzzword around five years ago. The speed that can be achieved today in proteomics, which was never the case five or so years ago, allows you to determine statistical validity for proteomics research, which is a first. Now, you can run population-wide studies and do so successfully. I think these advances, which were achieved in two-three short years, are really changing how people look at data. I think that is a challenge that we all face.

MC: With that in mind, how will you be working to support customers in developing solutions for that challenge in 2021?

RT: MetaboScape is a key software package for metabolomics. SCiLS is a great package for imaging. If you look at our imaging effort – SpatialOMx – we are collecting both proteomics and metabolomics data from one tissue sample. This was not possible until the launch of MALDI-2. You can overlay the information and have it available for the technician, pathologist or the oncologist who can look at the different molecular signatures, either in response to therapy or the progression of disease and decide how to tackle that problem on a personalized level.

This is what we are embarking on – connecting the software ecosystems to provide a seamless experience for the user and to accelerate or expand the decision-making process or actionable decisions. Right now, generating more data only poses new questions; it doesn't help you get to an actionable decision. This is where our focus will lie.  

MC: How has the COVID-19 global pandemic impacted the proteomics research field, and in parallel, how has the proteomics research field impacted efforts to fight SARS-CoV-2? Can you touch on this from Bruker’s perspective?

RT: We have two major efforts, one in Perth Australia, where Professor Jeremy Nicholson did some terrific work looking at COVID-19-related metabolism and metabolites. His study paved the way for understanding post-COVID syndrome. The second is that the timsTOF Pro is being used for COVID-19 research by the likes of the aforementioned Dr Catherine Wong who is looking at diseased vs. non-diseased patients and conducting proteomics analysis of urine samples. She was able to quantify more proteins in her study, that is increase her depth of coverage, using methods such as dia-PASEF.

I think the plus-side of the pandemic is enormous focus, almost like a "moon-shot"-type coalition effort in trying to understand the disease all over the world using proteomics and metabolomics. This has helped highlight the use of applying "omics" to solve problems that really affect humanity.

Rohan Thakur was speaking to Molly Campbell, Science Writer for Technology Networks.