While interesting, I think that the relevance of MassSpec based approaches will decline over the next years.
Single cell genomics already had to learn the hard lesson that pure gene quantification and big data approaches without spatial resolution or deep understanding of the biology can only get you so far. Billions have been spent on cell atlases that just deal with biology as individual cells without much to show for in terms of changes in clinical practice. Spatial omics is now finally spreading and spatial organization and interactions are being taken into account again, but the common analysis pipeline still just focus on gene level expression at the cellular level. Subcellular and extracellular information is often completely lost and gene levels are highly variant and only somewhat correlate with the phosphoproteome, the actual effect layer of biology.
Mass spec approaches share many of the same limitations, even though they deal with protein. Spatial information is blurry at best or lost at worst and equipment is expensive and requires specialized training.
Imo, the most interesting advances in the next years will come from low cost high resolution spatial proteomics with high target counts that integrate with biological modeling of processes.
I disagree. I think that proteomics is useful without spatial information because it captures a snapshot of cell state, or tissue state rather, of signaling networks via PTMs along with abundance measurements. If you don’t have spatial orientation, you at least have temporal information.
I think increasing the yield of MS2 scans to PSMs specifically by dealing with spectra containing PTMs and chimeric spectra will further enable deeper understanding of cell signaling. Additionally, the targeted analysis of specific sub-proteomes using real time search, using GoDig from the Gygi lab for example, also seems very promising.
Plus, there’s large industry efforts using proteomics as a drug screening tool, an application that doesn’t require spatial resolution of anything. Specifically groups are looking for protein expression knockdown, but it’s not too far a stretch to look for pathway perturbations using real time search and careful controls.
Does anyone know a good general audience book on proteomics? It seems to be a very interesting field with a bright future but I can never find the time to dig deep
IMHO, most fields in biology lack good on ramps when compared to CS. I think part of the reason is that it's way easier to get someone to install Python than to get access to a mass spectrometer. A big factor in me studying Biochemistry in undergrad was that it was way easier to self-teach myself the parts of CS that interested me than it was for Biochemistry, in my estimation.
I'm sorry I don't have a better answer for you, I hope someone else here does!
My experience in grad school was similar. IMO it decelerates the shift from undergrad to grad work, as you have to overcome the knowledge gulf by immersing yourself in the essentially confusing sea of primary literature. Arguably unnecessary IMO … good material at various levels of expertise makes a lot of sense.
Also, if you haven't already, maybe take a look at transcriptomics first, as there's more introductory material on this topic. For instance, there's an excellent online course at https://diytranscriptomics.com/.
There is nothing that I can think of. The tools of the trade are mass spectrometers, most dominantly right now the high end Orbitraps and the TIMS-TOF. If you’re interested in instrumentation I can send you some resources.
There’s a few good reviews I can send you if you’re interested. Let me know, I’d be happy to connect.
Thanks. I have a physics background and actually work on the medical imaging side of pharma now.
But while I might be able to understand the technicalities, I guess what I'm looking for is something that explains (without much hype) what the excitement and challenges are.
For instance, for my field, a great broad intro is Eric Topol's Deep Medicine https://a.co/d/242xgSC
Single cell genomics already had to learn the hard lesson that pure gene quantification and big data approaches without spatial resolution or deep understanding of the biology can only get you so far. Billions have been spent on cell atlases that just deal with biology as individual cells without much to show for in terms of changes in clinical practice. Spatial omics is now finally spreading and spatial organization and interactions are being taken into account again, but the common analysis pipeline still just focus on gene level expression at the cellular level. Subcellular and extracellular information is often completely lost and gene levels are highly variant and only somewhat correlate with the phosphoproteome, the actual effect layer of biology.
Mass spec approaches share many of the same limitations, even though they deal with protein. Spatial information is blurry at best or lost at worst and equipment is expensive and requires specialized training.
Imo, the most interesting advances in the next years will come from low cost high resolution spatial proteomics with high target counts that integrate with biological modeling of processes.