Presented July 17th 2019 Genomeweb ABRF Core facility seminar

1. Developing more “universal”
methods in 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
Part of 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 we do 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 get samples
like this!

7. Suspension Trapping
• Dissolve sample 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 = commercial source

9. Classic Bind, Wash, Elute Biochemistry

10. Why I like the 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
with the s-trapExample from internet

12. Proteomic Profiling
Almost everything is quantitative now!

13. Before ScaffoldDIA
Profiling was done 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 can be an issue with MS1 LFQ

15. Lots of DDA looks like this

16. Missing Values Comparison Peptide
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. Peptide
Centric DIA
Acquisition

18. Random peptide KSDGIYIINLK ScaffoldDIAExample Peptide Centric detection

19. DIA Random peptide KSDGIYIINLK

20. ScaffoldDIA/
Encyclopedia
Peptide centric DIA issues 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 8 Da
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 Size 4 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
Issues we 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 Learning to
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
Window Size 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 Size 4 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 to work!

29. Protein expected
to be knocked
down
Yea!

30. Protein 2
Knockdown
1
1
Experiment Seemed
to work! Yea!

31. Comparison of DIA methods vs DDA
Sorted by Proteins Quantified

32. Feedback from collaborator
on ScaffoldDIA data

33. Example serum
data (Rat Non
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. infected
grape Sap
Example

35. infected
grape Sap
Example
using Prosit
ScaffoldDIA Prosit vs FASTA
Pathogens Quantified
Prosit = 90
Using FASTA Only= 18
Grape Proteins
Quantified
Prosit = 450
Using FASTA Only= 165

36. Conclusions
S-traps work great in 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 Proteomics Core
◦ Michelle Salemi
◦ Tony Herren
◦ Gabriela Grigorean
◦ Justin Hocke
◦ Danielle Asiain
Workshop (https://registration.genomecenter.ucdavis.edu/events/Proteomics_2019/).