The document summarizes different methods for optimizing data-independent acquisition (DIA) workflows in a proteomics core facility. It compares the 2x green fluorescent protein (GFP) method to using a narrow chromatogram library and wide experimental window, finding the 2x GFP method works well for 2-5 samples. An example experiment profiling several proteins across conditions is described. Different permutations are tested on a subset, comparing public libraries to in-house libraries for building chromatogram libraries. The 2x GFP method using public libraries works best for depth and missing values. Challenges with ScaffoldDIA software are noted. In conclusion, DIA provides better coverage than data-dependent acquisition, especially for human samples, and can work

1. Optimizing? DIA methods
In a Academic Core facility

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 lot’s of different types of
samples

3. What our
collaborators
want
Mostly quantitative
Profiling and PTM’s (Protein
ID is mostly dead or dying)
Quick Turn around time
(quick? lol)

4. Before ScaffoldDIA
• Profiling was done by DDA
• Spectral counting
• LFQ
• TMT (10 or 11 plex)
Example from a Paper we published in
Plant Cell Last week! using Spectral
Counts and MS1 LFQ!

5. Peak picking can be an issue with MS1 LFQ

6. Typical? MS Spectra

7. Nice S/N

8. Lots of DDA looks like this

9. Random peptide KSDGIYIINLK DDA

10. Missing Values Comparison Peptide level
• DDA (maxquant 1% FDR protein and peptide)
• Fusion Lumos
• In my hands Even with MBR on, it’s difficult to get
consistent peak picking across multiple samples
Replicate Missing Values
1 16.3%
2 15.15%
3 23.47%
Missing in all 3 = 5.53%

11. ScaffoldDIA
• Peptide centric DIA issues for me
• For a long time I did not understand it
• Software difficult to use
• Protein inference was non existant
• 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!

12. Peptide Centric DIA
Ting 2015 Searle 2018

13. Random peptide KSDGIYIINLK ScaffoldDIA

14. DIA Random peptide KSDGIYIINLK

15. ScaffoldDIA random peptide (KSDGIYIINLK)

16. DDA vs DIA scatter plots Same samples
DIA DDA

17. DIA vs TMT in a core facility
DIA
• No labeling
• Faster!
• Smaller amount of “total”
protein required
• Can be more expensive
• Missing values are higher
• Data analysis can be way easier
for > 10 samples
TMT
• Labeling!
• Requires more protein
• Can be a lot cheaper!
• How could this be right?
• Missing Values are crazy low!
• Data analysis for spanning
multiple TMTplexes is
complicated

18. Recommended
Methods From
Brian

19. 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

20. 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

21. 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

22. # of runs needed by sample #

23. Scans Across the peak (determined manually!)
Scans per peak
4 Da 8 Da
2xgfp
Wide
window
24.07 20.08
24.2 20.19
24.33 20.3
24.46 20.4
24.59 20.51
24.72 20.62
24.85 20.72
24.98 20.83
25.11 20.94
25.24 21.05
25.37 21.16
25.5 21.26
25.63 21.37
25.77 21.48
25.9 21.58
26.03 21.69
21.8
21.91
22.02
sum 16 19
scans per minute Non deconvoluted 8 9.5
scans per minute deconvoluted 19

24. Scans per peak Random peptide AALEEVER
Wide Window 8 Da staggered (6 total??) 4 Da 2x gpf (7 total??)

25. Random peptide KSDGIYIINLK 2x GFP

26. ScaffoldDIA random peptide (KSDGIYIINLK)
2x GFP

27. Random peptide KSDGIYIINLK 8 Da Wide window

28. ScaffoldDIA random peptide (KSDGIYIINLK)
8 Da wide window

29. ScaffoldDIA Experimental Design

30. 2x GFP vs Narrow Chromatogram/ Wide
experimental
Pros
• Seems to work!
• Good for 2-5 samples
• Quantitation seems decent
• Scans across the peaks seem
decent
Cons
• Requires more runs / sample
• Scaffold (percolator) Crashes! with
> 10 or so DIA runs

31. Example Experiment
• Human profiling experiment
• Several proteins knocked down in a few conditions
• 5 Conditions
• 3 biological replicates per condition

32. 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

33. 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

34. Experiment seemed to work!

35. Protein expected to be knocked down
Yea!

36. Protein 2 Knockdown
Experiment Seemed
to work! Yea!

37. Original Search (Wide Window Method)
Chromatogram Library made with FASTA Only

38. Search 2 (Wide Window Method)
Chromatogram Library made with Pan Human dlib (DDA spectral) Library

39. Search 3 (Wide Window Method)
Pan Human DDA spectral Library Only, Did not use Narrow window Chromatogram Library

40. Feedback from collaborator on ScaffoldDIA data

41. Permutations Tested on a 3 sample subset (1 condition 3 bioreps)

42. Comparison of DIA methods (3 Bioreps)

43. Proteome
Tools spectral
Library

44. Measuring
the Proteome
Depth
Comparing
Pan human vs
Proteome Tools
Libraries

45. Comparison of DIA vs DDA method
Sorted by Peptides Quantified

46. Comparison of DIA vs DDA method
Sorted by Proteins Quantified

47. Proteins quantified vs MS run time 3 samples
2x GPF Public Pan human library
• No chromatogram libraries

48. missing Values peptides
Replicate Missing Values
1 8.94%
2 9.80%
3 10.49%
Maxquant LFQ
Replicate Missing Values
1 16.3%
2 15.15%
3 23.47%
Scaffold DIA 2xGFP

49. 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!

50. Another low protein ID example (infected grape Sap)

51. Wish list for ScaffoldDIA
• Calculate Scans across the peak for every peak (maybe like skyline does) + average
across entire experiment
• Peptide level exports with raw data (summed intensities) before normalization and
imputation
• Less confusing way to make chromatogram libraries when they are not staggered
and your experimental runs are
• GUI needs to be faster
• Fix percolator crashing when searching large number of files searched against
FASTA

52. Conclusions
ScaffoldDIA seems to work better than DDA especially for human
I think the 2x GFP method works okay
2x GFP Seems to even work older instruments
Using Public Libraries to make your chromatogram libraries works
way better than using a FASTA : Thus works better for human
DIA Run times can be shorter (increasing efficiency ) Than DDA
DIA Works pretty okay for undepleted Serum

53. Acknowledgements
• UC Davis Proteomics Core
• Michelle Salemi
• Tony Herren
• Justin Hocke
• Danielle Asiain