Q: What information does mass spectrometry reveal about a protein?
A: During the development process there are a lot of tests that are applied to a product or a prototype to make sure that it’s going to perform as expected. The R&D team is going to measure aspects such as color quality, sensory quality, texture quality, and shelf life. All these components come together to determine which formulations are going to perform best under various scenarios. For example, is the product going to be refrigerated, frozen, or stored at room temperature? And what kind of packaging material gives the best shelf life and performance? Ultimately, the products that make it to the market may be far removed from what the original concept was simply because, during the process, some deficiencies are noted that need to be dealt with.
The purpose of this whole exercise is to zoom in on a product that the science says is going to be the best performer. By doing their homework before the product hits the shelves, food manufacturers can make sure that their products are going to have lasting success. By contrast, when we see people start with a great idea and then try to take shortcuts in the process to rush the product to market, they almost always end up disappointing the consumer and hurting their brand.
Q: What applications or science fields benefit most from cell protein identification?
A: Mass spectrometry-based protein identification experiments were historically a major tool for molecular biology research labs. These labs had focused experiments meant to tease out specific biological questions. As the breadth of analysis has grown for these mass spectrometry-based proteomics workflows, so has the applicability of these tools. The realization of large-scale omics (e.g., quantitative proteomics across thousands of samples and global PTM analysis) has led to much wider adoption. For example, clinical labs are now using mass spectrometers to analyze whole proteomes across large cohorts of samples. Similarly, it is far easier today for a new researcher to get started in proteomics than it was just a few years ago. Now a new researcher can rely upon mature workflows that utilize standardized LCs, columns, mass spectrometry methods, and data analysis software to simply “execute” an experiment.
Q: What are the current challenges associated with using MS or other techniques to accurately identify proteins?
A: The breadth of “typical” proteomics experiments continues to get larger and larger. Historically a proteomics experiment may have focused on just a few samples. Now hundreds or even thousands of samples per experiment is commonplace. This growth in experimental breadth was largely fueled by increasing instrument speed, sensitivity, and multiplexing capacity. While the current technologies make these “ultra-large-scale” experiments possible, the reality is these experiments still require a large investment in time and resources. Preparing thousands of samples simply requires lots of bench time. Similarly, the mass spectrometry data collection time for thousands of samples represents a large investment in capital. The mass spectrometry community recognizes that and are actively working to lower those barriers with the goal of making these very large-scale experiments become streamlined and routine.
The current limitations we are all trying to address are sample throughput, analysis breadth, and analysis depth.
Q: What developments or trends are you seeing emerge to help resolve these challenges?
A: The upstream sample preparation for these massive experiments can be quite costly. To that end, automated sample preparation tools are really starting to gain traction. Multiple instrument vendors are now offering automated sample preparation platforms that take biological samples as inputs and output digests ready to inject onto the instrument. There are other solutions and “homebrew” efforts in this space as well. By far the biggest gain from these platforms is standardization, providing consistent results across thousands of samples. At the same time, vendors are constantly advancing their mass spectrometer throughput. As I noted earlier, commercial multiplexing reagents like the TMT allow for multiplexed interrogation and quantitation of up to 18 samples in parallel. While on the forefront of this approach there are academic labs testing out concepts that allow for quantitation of up to 96 samples in parallel.
Q: How can innovations in MS instrumentation help scientists in fields like proteomics?
A: Modern mass spectrometer are now capable of analyzing ions over a mass range of three orders of magnitude, scanning from tens of m/z up to tens of thousands, with ppm mass accuracies and high sensitivity. This allows a single instrument to look at everything from small single amino acids up to large native protein complexes and proteoforms. At the same time, this wide mass range can be coupled with an immense variety of ion fragmentation and manipulation techniques (e.g., energetic activation, electron and proton transfer reactions, photon activation, and high order MSn). With all these tools in the mass spectrometer “toolbox,” the breadth of experiments possible with a modern instrument are innumerable.
Q: What other factors are important to discuss?
A: Building upon that last question, the growth in the “native-omics” space has been particularly engaging. For this experimental workflow, the researcher injects whole protein complexes into the mass spectrometer. These native complexes can be analyzed intact, torn apart, and then those fragments can be analyzed even further. In this case you see the skill set of the mass spectrometer growing to encompass what was traditionally interrogated at the lab bench. But of course, that requires a mass spectrometer capable of a very wide range of mass analysis with an equally wide range of dissociation and ion manipulation techniques.
Graeme McAlister, PhD, is a senior scientist working in the Tribrid Research and Development group at Thermo Fisher Scientific. Graeme joined Thermo Fisher Scientific 10 years ago and before that he received his PhD in Joshua Coon’s Lab at the University of Wisconsin–Madison. Following that, he held a post-doctoral position in Steven Gygi’s Lab at Harvard Medical School.