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Genome web july 2019 presentation phinney
This document describes the work done at the UC Davis Proteomics Core Facility, which uses suspension trapping and peptide-centric DIA with deep learning to develop more universal proteomics methods. The core analyzes diverse sample types from various species using techniques like AP-MS, TMT, DIA, and PTM analysis. Peptide-centric DIA with ScaffoldDIA software provides better quantification than DDA, especially for human samples. Using Prosit deep learning to generate in silico libraries improves pathogen and protein identification from samples like infected grape sap. The core finds that DIA with gas phase fractionation works well for smaller sample sets and provides advantages over DDA for applications like undepleted serum proteomics.
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Genome web july 2019 presentation phinney
- 1. Developing more “universal” methodsin a core facility using Suspension trapping and Peptide Centric DIA (+ deep Learning) UC DAVIS GENOME CENTER PROTEOMICS CORE PROTEOMICS.UCDAVIS.EDU GENOME WEB 2019
- 2. UC Davis Core Facility Partof the UC Davis Genome Center ◦ Core Facilities ◦ Genomics, Metabolomics, Proteomics UC Davis pretty unique, lot’s of Ag and Medical research. We run lots of different types of samples
- 3. •Human Hair &Skin & Fingermarks & 11K year old human teeth! •Grape Sap •Lettuce •Yeast •Fish livers •Walnut Bark and Pellicle (walnut skin) •Tardigrades •Horse Lung Lavage (Horse Snot) •Dog tissue •Sea Otter hearts (diseased cardiomyopathy, very fibrotic) •Brain inclusion bodies •Bovine uterine fluid •Isolated HDL particles from serum •Wheat •Human Milk •Cow Milk Species/Tissue analyzed in our Core Facility in the last year
- 4. •BioID •AP-MS •Proteomic Profiling • TMT •DIA •PTM’s • Phospho • Ubiq • Glyco-peptides • SILAC (I hate these) Types of the samples analyzed
- 5. All samples wedo require them to be digested Types of Protease Digestions • In-gel • Direct digestion within polyacrylamide gels • In-solution • “Standard” in-solution (non-denaturing) • Urea in-solution (denaturing) • On-bead (ex. immunoprecipitation, pulldowns) • Column assisted and filter based • FASP (Filter Aided Sample Prep) • S-Trap (Suspension Trapping) Ludwig et al. JPR. 2018
- 6. And sometimes we getsamples like this!
- 7. Suspension Trapping • Dissolvesample in 5% SDS • Creates a fine protein particulate suspension • Acidified protein SDS solution Trapped in a quartz filter • SDS is washed away • Protein aggregates digested on the filter • Peptides eluted
- 8. Protifi = commercialsource
- 9. Classic Bind, Wash,Elute Biochemistry
- 10. Why I likethe S-Traps PROS • Allows use of high SDS (strongly anionic denaturing surfactant) • SDS = perfect solubilizer • Historically, would have to separate out into gel (SDS-PAGE) and do in-gel digests, which are inefficient and have poor peptide recoveries • S-trap allows to keep in solution • Does not require additional desalting/cleanup post-digest • This results in better peptide recoveries as C18 clean-up results in 25-40% losses • Compatible with a broad range of salts, detergents, and small molecules that typically require pre-digest cleanup (triton, tween, PEG, glycerol, SDS, high salt) • Note: not compatible with GuHCl (charge interaction) • Scalable • Larger or small columns allow for broad range of protein loads CONS • w/o optimization, missed cleavage rates may suffer • Some iterations of the columns can be prone to leaking/drying out which can affect reproducibility • Throughput can be a problem when tackling large sample sizes (this is improving) • Does not work w/o ≥ 5% SDS
- 11. Samples that have worked withthe s-trapExample from internet
- 12. Proteomic Profiling Almost everythingis quantitative now!
- 13. Before ScaffoldDIA Profiling wasdone by DDA ◦ Spectral counting ◦ LFQ ◦ TMT (10 or 11 plex) Example from a Paper we published in Plant Cell using Spectral Counts and MS1 LFQ!
- 14. Peak picking canbe an issue with MS1 LFQ
- 15. Lots of DDAlooks like this
- 16. Missing Values ComparisonPeptide level In my hands Even with MBR on, it’s difficult to get consistent peak picking across multiple samples • DDA (maxquant 1% FDR protein and peptide) • Fusion Lumos Replicate Missing Values 1 16.3% 2 15.15% 3 23.47% Missing in all 3 = 5.53%
- 17.
- 18. Random peptide KSDGIYIINLKScaffoldDIAExample Peptide Centric detection
- 19. DIA Random peptideKSDGIYIINLK
- 20. ScaffoldDIA/ Encyclopedia Peptide centric DIAissues for me ◦ For a long time I did not understand it ◦ Older Software difficult to use before ScaffoldDIA / Encyclopedia ◦ Protein inference was non existent ◦ Seems to be 1 million plus ways to ◦ Acquire the data ◦ Window Size? ◦ Staggered or overlapping? ◦ Libraries ◦ Chromatogram DIA, DDA, public libraries, no libraries ◦ Analyze the data ◦ Using Libraries or FASTA sequences make a big difference ◦ How you make your chromatogram library can make a big difference!
- 21. Methods Wide Window Size 8Da Mass Range 400-1000 # windows 151 Staggered Y Collission energy 30 Max Inject time 20 ms Resolution MS2 15K AGC target 400,000
- 22. Chromatogram Library Window Size4 Da Mass Range 100 Da x 6 (400-1000) # windows 25 Staggered Yes Collission energy 30 Max Inject time 60 ms Resolution MS2 30K AGC target 400,000 Chromatogram Library setup
- 23. 2x GPF method Issueswe found using traditional method with makes chromatogram libraries (narrow window) and Sample acquisition (Wide Window) • Lots of Runs if you only have 2-3 samples! • Hard to run DIA Pilot Projects Fusion Lumos
- 24. Using Deep Learningto Predict DDA libraries One issue with peptide DIA is that it seems to works a lot better with a DDA Library ◦ You need to generate one yourself ◦ Or use a public one (Pan Human) ◦ But now, with Prosit, you can use deep learning to predict one without generating one using your mass spec! ◦ Introduces at ASMS this year
- 25. Example Human Experiment Human profiling experiment ◦Several proteins knocked down in a few conditions ◦ 5 Conditions ◦ 3 biological replicates per condition
- 26. Methods Wide 2x GFP WindowSize 8 Da 4Da Mass Range 400-1000 360-758,760-1150 # windows 151 100 x2 Staggered Y N Collission energy 30 30 Max Inject time 20 ms 54 Resolution MS2 15K 30K AGC target 400,000 50,000
- 27. Chromatogram Library Window Size4 Da Mass Range 100 Da x 6 (400-1000) # windows 25 Staggered No Collission energy 30 Max Inject time 60 ms Resolution MS2 30K AGC target 400,000
- 28. Experiment seemed towork!
- 29. Protein expected to beknocked down Yea!
- 30.
- 31. Comparison of DIAmethods vs DDA Sorted by Proteins Quantified
- 32. Feedback from collaborator onScaffoldDIA data
- 33. Example serum data (RatNon Depleted) • You Don’t have to ID a lot of proteins to have really useful data! • First I thought this data sucked • Collaborator was ecstatic and loved it! • Not crippled by serum albumin like DDA is!
- 34.
- 35. infected grape Sap Example using Prosit ScaffoldDIAProsit vs FASTA Pathogens Quantified Prosit = 90 Using FASTA Only= 18 Grape Proteins Quantified Prosit = 450 Using FASTA Only= 165
- 36. Conclusions S-traps work greatin a core facility Peptide Centric DIA seems to work better than DDA especially for human Peptide Centric DIA- Prosit seems to work for organisms without a DDA library DIA 2x Gas phase fractionation method works well, especially with < 6 samples DIA Works pretty okay for undepleted Serum Lot’s of advantages using DIA in a core (shorter runs, less susceptible to contamination, much better quant, even better ID compared to DDA)
- 37. Acknowledgements UC Davis ProteomicsCore ◦ Michelle Salemi ◦ Tony Herren ◦ Gabriela Grigorean ◦ Justin Hocke ◦ Danielle Asiain Workshop (https://registration.genomecenter.ucdavis.edu/events/Proteomics_2019/).