From Accurate Discovery to Targeted Quantitation - Peptide - IMP

From Accurate Discovery to 

Targeted Quantitation - Peptide 

Identification and Quantitation 

Based on HR/AM Data 

27-Aug-2012, IMP/IMBA Workshop, Vienna 

Dr. Yue Xuan, Dr. Michaela Scigelova 

Thermo Fisher Scientific, Bremen, Germany 

1 

The world leader in serving science

2 

Bremen Factory

Outline 

• From Discovery to Targeted Quan: A Complete Solution 

• Introduction of Q Exactive 

• Introduction of Proteome Discoverer 1.3 

• Introduction of Pinpoint 1.3 

• Walking through Discovery-to-Quantitation with an example study 

• Q Exactive recommended TopN experiment for Proteome Discovery Study 

• Guide through P.D. database search 

• Guide through Pinpoint for generating inclusion mass list 

• Q Exactive recommended targeted SIM (tSIM) experiment for Quantitation Study 

• Guide through Pinpoint for automatic data process 

• Other Quantitation Experiments on Q Exactive 

• Full scan only 

• (multiplexed) Targeted HCD MS2 experiment 

• Data Independent Acquisition (DIA) experiment 

3

From Discovery to Targeted Quan: A Complete Solution 

Biomarker Discovery 

High-resolution, informationrich 

data, Lower-throughput 

Database 

search 

LC-MS/MS 

Differential 

Expression 

Analysis 

Patient Samples 

LC-MS/MS 

Intelligent SRM 

Q Exactive 

List of candidate 

biomarkers 

Targeted Assay 

Development For 

Clinical Research 

Fast, high-throughput 

Sensitive, specific, 

targeted 

Robust, reproducible 

Cost-effective 

Final list of 

target 

proteins 

Refine 

Target 

List 

Pinpoint Software 

Refined list 

of target 

proteins 

4

Q Exactive - Discovery-to-Quantitation Flowchart 

• Target peptide ID 

• High speed 

• dd acquisition 

• Quan info 

• Proteome Discoverer 

• SIEVE 

• Target list 

• Low-level targets tSIM, tHCD 

• Other targets full MS 

• Automated quantification 

• Pinpoint 

5

Q Exactive 

6 


Q Exactive TM - Innovations and Features 

First Benchtop Quadrupole-Orbitrap Mass Spectrometer 

HCD/C-Trap Combo Cell 

QUADRUPOLE MASS FILTER 

Mass Range: 50 – 6000 amu 

Precursor Isolation: 0.4 – 10 amu 

ORBITRAP 

Resolution: 140,000 

Mass Accuracy: < 3 ppm 

ION SOURCE 

S-Lens 

7

Q Exactive: S-Lens Assembly 

• Stepped RF-level for different 

mass ranges 

• Number of S-lens levels set 

according to mass range set. 

• 2-3x lower inject times overall 

Improved ion transmission 

8

High Sensitivity with Quadrupole-based SIM Scan 

Relative Abundance 

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

195.0876 

N=248402.81 

195.0877 

N=20741.58 

Full MS 

S/N = 745 

SIM (10amu) 

S/N = 5400 

S/N (spectrum) 

6000 

5000 

4000 

3000 

2000 

1000 

0 

Gain in sensitivity (7x) 

Caffeine 

195.082195.084195.086195.088 195.09 195.092195.094 

0 

S/N (FMS) 

S/N (SIM10) 

Sensitivity Gain: 5 – 10 folds 

9

Improved Resolution – Advanced Signal Processing 

• Increases resolution by 2X at the same transient length 

160000 

Resolution 

140000 

120000 

100000 

80000 

60000 

40000 

20000 

0 

Exactive 

Q-Exactive 

0 5 10 15 

Scan Speed [Hz] 

Resolution 

at m/z 200 

Resolution 

at m/z 400 

Max. scan 

speed (Hz) 

17,500 12,500 12 

35,000 25,000 7 

70,000 50,000 3 

140,000 100,000 1.5 

10

Spectrum Multiplexing Principle 

Parallel Filling and Detection 

Spectrum Multiplexing 

Demonstration of 10-fold spectrum multiplexing 

20110503 JPH TargetSIM MSX10_110503181236 #40 RT: 0.34 AV: 1 NL: 1.35E8 

T: FTMS + ESI SIM msx ms [193.09-197.09, 260.64-264.64, 522.26-526.26, 1120.00-1124.00, 1219.99-1223.99, 1319.98-1323.98, 1419.98-1423.98, 1619.9 ... 

195.0876 

R=157606 

80 

60 

40 

20 

0 

Relative Abundance100 

262.6363 

R=133906 

524.2648 

R=93206 

1421.9758 

R=53006 

1621.9656 

R=47806 

1221.9862 

R=58406 1821.9543 

R=42104 

200 400 600 800 1000 1200 1400 1600 1800 2000 

m/z 

11 

See poster MP103 J.-P.Hauschild et al 

Multiple C-Trap Fills as a Tool for Massive Parallelization of Orbitrap Mass 

Spectrometry- a new Concept for Targeted Mass Analysis

Animation about Q Exactive 

12

http://planetorbitrap.com/ 

13

New EASY-Spray: Robust and Reproducible 

• Plug-and-spray simplicity 

• With state-of-the-art performance 

• Amazing peak capacity with coiled-up long 

columns 

• Outstanding reproducibility 

• Razor sharp peaks using nanoViper 

connections and zero dead-volume unions 

• Integrated Design 

• Complexity made simple 

• Robust performance to 1000 bar 

• Reliable spray with precision positioned glass 

emitters 

• Maximum reliability with integrated heating 

More details – Tuesday workshop 

6. Easy Nano Spray Source for Increased 

Performance nanoLC-MS with Plug-and- 

Spray Configuration 

14

From Discovery to targeted Quan 

15 


Experimental Overview 

16

Discovery 

• Strategies for discovery 

• Selecting a pathway 

• Finding differences 

• Spectral counting 

• Labeled quantitation 

− Precursor based: SILAC, di-methylation 

− Reporter based: TMT, iTRAQ 

• Label free 

17

Select a workflow 

18

Analyze data 

finding candidate proteins 

To Targeted Quantitiation 

19

20 

Proteome Discoverer 1.3

New features in Proteome Discoverer 1.3 

This release expands the workflow-based system for “deep sequencing” 

applications. 

1. Deep spectral data mining using Percolator 

2. Confident site localization for phosphopeptides with phosphoRS 

3. Improved protein and peptide grouping 

4. Biological annotation 

5. 64 bit support (Windows XP and Windows 7) 

6. Graphics 

1. Proportionally scaling Venn Diagram 

2. Volcano plots 

3. Colorful pie charts 

New purchase includes 3 years maintenance, while upgrades from PD 1.2 and 

earlier get 1 year of maintenance for free 

• All core software upgrades free during maintenance period 

• Up-to-date biological annotation through ProteinCenter access 

21

Confidence: Deep data mining using Percolator 

• Percolator uses >30 features of a 

peptide spectral match (PSM) to 

distinguish true positives from random 

matches 

• Result: more peptides and proteins 

identified with high confidence (1% FDR) 

• CID, HCD: >30% increase in PSM’s 

• ETD: Up to 80% increase in PSM’s 

ASMS 2011 Poster: MP25 

Breaking the 2000 proteins barrier in a 

standard LC run using a new benchtop 

Orbitrap instrument and multiple search 

engines. (download @ : http://portal.thermobrims.com) 

22 

Käll et al, Nature Methods 4:923-925 (2007)

PTM Localisation: Phospho-analysis Using PhosphoRS 

• Developed in collaboration with Karl Mechtler’s lab at IMP Vienna 

• Phosphorylation site confidence determination 

• is it phosphorylated? 

• site of phosphorylation? 

New protein identification details 

view compares identified PTM’s 

to those annotated by UniProt 

23

Annotation: Providing Biological Context Using ProteinCenter 

• 3 year support subscription includes ProteinCenter annotation node: 

• GO, protein family (Pfam), PTM, and ProteinCard annotation is automatically 

retrieved and stored in the PD result files 

• All can be filtered 

• Displayed in the PD Viewer 

• Previous results can be annotated or re-annotated 

Molecular Function Cellular Component Biological Process 

ASMS 2011 Poster: ThP22 401 

“Integration of a central protein repository into a standard data processing application 

for mining proteomics data” (download @ : http://portal.thermo-brims.com ) 

24

Example 1: Intelligent data analysis and reduction 

Complex data sets generated with hybrid instruments require optimized search 

strategies that are easily enabled in PD with customized data analysis workflows 

CID, PQD, 

MSA & ETD 

HCD 

Workflow for Comprehensive Identification 

Using Multiple MS/MS Modes 

ETD 

HCD 

• All MS/MS modes 

supported 

• 3 search engines 

• Powerful workflow 

editor 

• Automatic FDR 

calculation for 

measurement of peptide 

ID confidence 

25

Example 2: Robust quantification results 

Number of raw files 72 

Total file size (GB) 18.5 

Total number of MS2 spectra 893636 

• Excellent 

variability! 

• Summary of 3 replicates of 24 fractions of a 

HeLa experiment (raw files provided with 

the MaxQuant Software): 

• > 190,000 peptides at 1% FDR 

• 3560 protein groups; >12,000 proteins 

• 96.5% of peptides quantified 

26

Example 3: Graphical validation of results 

27

Example 4: Analysis of very large datasets 

Pre-filter large data sets for faster viewing of results 

Filter data before opening 

28

Example 5: Increased productivity through automation 

•Schedule Processing in 

Xcalibur 

Different workflows 

Exp 1, 5-8 Exp 2-4 

CID PQD ETD 

HCD 

LTQ Velos & 

LTQ Orbitrap 

Velos 

Daemon 

Automation 

•Transfer raw 

files and 

parameters to 

data processing 

PC 

• Maximize workflow flexibility and laboratory productivity. 

• Match scheduled data processing with high throughput data acquisition. 

• Acquire and process 24/7 

29

Example 6: Collaborate easily and share results quickly 

Viewer 

VIEWER is free! 

Viewer 

• Send files from any PC or instrument, process, and view results on any PC 

• Process batches of data from any computer 

• Analyze data as individual files or in single MudPIT file 

• Simplified data processing on centralized analysis computer 

• Handles larger, more data rich files resulting from faster scanning instruments 

30

Example 7: Publish results quickly and effortlessly 

• After extracting the content of the zip archive the index.html 

file can be opened in any web browser 

31

Thermo Scientific Proteomics Software Portal 

For more information about any of our Thermo Scientific Proteomics software 

products, please visit our new web portal, at: http://portal.thermo-brims.com/ 

32

PinPoint 

33 


Pinpoint – Main Features 

• Software package supporting targeted protein quantitation 

• Direct integration between discovery to quantitation through 

import of Sieve, Proteome Discoverer, and ProteinCenter 

output 

• Verification strategies to support all quan/qual applications 

• Robust retention time prediction approaches based on 

hydrophobicity factors and peptide retention time calibration 

kit (PRTC) 

• Automated method refinement, also across various MS 

platforms 

• ROC analysis 

34

Pinpoint - Look and Feel 

Target Method Building 

Data Analysis/Verification 

35

http://portal.thermo-brims.com/ 

36

Walking through Discovery – to - 

Quantitation based on HR/AM 

Data 

37 


Protein Kinases and Inhibitor Profiling 

Reasons to study protein kinase/drug interactions: 

•Protein kinases are commonly mutated or misregulated in 

cancer (e.g. Bcr-Abl in leukemia). 

•Numerous kinase inhibitors are approved drugs for the 

treatment of specific cancers with most binding to conserved 

ATP binding domain (e.g. Gleevec). 

•Need to evaluate kinase inhibitor IC 50 and identify off-targets 

Difficulties to contend with: 

•Over 500 protein kinases are present in the human genome. 

•Large dynamic range for endogenous protein kinase levels in a 

complex matrix (whole cell lysate). 

•Enzymatic digestion results in peptides with varying 

sequences, sizes, and missed cleavages making it difficult for 

enrichment and targeted LC-MS strategies. 

38

Desthiobiotin-ATP and -ADP Active Site Probes 

Desthiobiotin-ATP 

NH 2 

O 

H 

N 

N 

H 

O 

O 

O 

P 

O - 

O 

O 

P 

O - 

O 

O 

P 

O - 

O 

N 

N 

O 

OH OH 

N 

N 

Desthiobiotin-ADP 

NH 2 

O 

H 

N 

N 

H 

O 

O 

O 

P 

O - 

O 

O 

P 

O - 

O 

OH 

O 

N 

N 

OH 

N 

N 

Tag 

Reactive 

Group 

Selectivity 

Group 

Patricelli, M. P., et al. Functional Interrogation of the Kinome using Nucleotide Acyl Phophates Biochem. 2007, 46(2), 350-8 

39

Study: Targeted Kinase Inhibitor Profiling 

40


• ProteinID / Peptide ID 

• High speed 

• dd acquisition 

41

Methodolgy on Q Exactive for Discovery Proteomics 

- DDA + 10Hz HCD Scan 

• Parallel Filling and Detection 

• Fast Orbitrap Scanning 

• pAGC 

10 HR/AM HCD scans in 1s 

256ms 

64ms 

42

Typical Shotgun Proteomics on Q Exactive 

High Load 

>150ng on column 

Low Load 

< 150ng on column 

43

1 Full MS +10 HR/AM HCD Scans in One Second 

Ecoli_50min_newcolumn_Top10_15cm_reje... 

2/17/2011 3:39:42 PM 

RT: 15.8279 - 51.4773 


100 

80 

60 

40 

20 

16.0095 

17.9024 

25.9001 

27.4920 

22.3863 25.7411 

27.1739 

30.4806 

33.0433 

30.9771 

35.5786 

39.1805 

38.7325 

20.3567 

23.9072 28.4175 49.3818 

18.7207 34.8784 

37.5942 

41.1998 

21.9561 

25.4406 43.4203 

34.7425 41.0942 

43.5925 

46.5441 

49.1418 

41.5779 

41.6221 

47.5177 

49.6742 

0 

16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46 48 50 

Time (min) 

RT: 32.2438 - 32.2701 


0.3 

0.2 

0.1 

0.0 

0.3 

32.2447 

32.2464 32.2645 

∆ = 0.0181 min = 1.086 sec 

32.2512 32.2525 32.2538 32.2551 32.2564 32.2577 32.2590 32.2603 32.2616 32.2629 32.2694 

1 2 3 4 5 6 7 8 9 10 

0.2 

0.1 

0.0 

32.244 32.246 32.248 32.250 32.252 32.254 32.256 32.258 32.260 32.262 32.264 32.266 32.268 32.270 

Time (min) 

Ecoli_50min_newcolumn_Top10_15cm_reject_1 #12365 RT: 32.25 AV: 1 NL: 1.58E8 

T: FTMS + p NSI Full ms [300.00-2000.00] 

x2 

536.7949 

z=2 

100 

x5 


80 

60 

40 

20 

0 

4 

329.2184 

z=2 

382.9046 

z=3 

392.7555 

z=2 

6 

419.2133 

z=3 

5 

445.7585 

z=2 

484.2459 

z=3 

505.2733 

z=2 

8 

575.8452 

z=2 

3 

551.8184 

z=2 

10 

581.9907 

z=3 

630.3730 

z=2 

664.3292 

z=2 

9 

700.3623 

z=2 

711.3712 

z=3 

7 

725.8647 

z=2 

1 

350 400 450 500 550 600 650 700 750 800 850 900 950 1000 1050 1100 1150 

m/z 

784.5037 

z=? 

835.3868 

z=2 

871.9796 

z=2 

902.8879 

z=2 

950.9638 

z=2 

2 

1008.5358 

z=1 

1072.5819 

z=1 

1096.3795 

z=? 

1150.6826 

z=1 

1182.6012 

z=? 

44

Guide through P.D. database search 

1, Set up a workflow 

45


2, Open a .raw data file 

46


3, Set the search parameters (example via Mascot) 

47


4, Submit the search 

48


5, P.D. result .msf file, select “Desthiobiotin” modification 

49


• Target list 

• tSIM, tHCD 

50

Guide through Pinpoint for generating inclusion mass list 

1, Add Protein (From Spectral Library) 

51


2, Add/Edit Modifications 

SIM Quan on Precursor 

52


3, export inclusion mass list 

53


• Targeted experiments on Q Exactive 

• tSIM, tHCD 

• Automated quantification 

• Pinpoint 

54

Profiling Staurosporine Kinase Inhibition in A549 Cells 

Multiplex SIM (msxSIM) Targeted Quantification Workflow 

0.00 µM 

XICs 

IC50 Curves 

0.01 µM 

0.10 µM 

0.30 µM 

1.00µM 

3.00 µM 

10.00 µM 

The cell lysate was incubated 

with different levels of kinase 

inhibitor, Kinase- enriched, 

tryptic digested and analyzed 

using targeted msxSIM. 

Data was processed and 

statistics calculated by 

Pinpoint 1.2. 

IC50 curve for each 

kinase was generated 

based on the relative 

quantification results. 

*Thermo Scientific Pierce ActivX Probes and Enzyme Enrichment Kits 

55

Quantitation on Precursor ion – targeted multiplex SIM 

1. Selection of targeted precursor ion in the quadrupole 

2. Accumulation in C-trap 

• In multiplexing mode, step 1 and 2 are repeated to accumulate multiple precursors 

before orbitrap measurement. 

3. Transfer of ions to orbitrap for detection. In multiplexing mode, multiple 

precursors isolated separately are measured together. 

SIM → High S/N ratio 

Multiplexing SIM → High Speed 

56

Quantitation on Precursor ion – targeted multiplex SIM 

R: 35K R: 70K 

R: 140K 

492.2634 

492.2665 

492.2495 

492.2511 

492.2661 

30ppm 

m/z 

High Resolution → Selectivity 

Accurate Mass → Accuracy 

57

Targeted SIM (tSIM) Experiments on Q Exactive 

inclusion list from Pinpoint can be directly imported into Method editor! 

58

Targeted SIM (tSIM) Experiments on Q Exactive 

Example is multiplexed tSIM 

59

Examples of Targeted Kinase Peptides 

Peak definition and selectivity 

RT: 43.39 - 45.66 


100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

100 

80 

60 

40 

20 

0 

43.41 

709.1611 

43.66 

835.4882 

43.66 

843.4794 

44.22 

835.4895 

44.20 

44.27 

835.4896 

44.32 

835.4898 

835.4894 44.36 

835.4896 

44.15 

835.4898 44.41 

44.10 

835.4895 

44.62 

835.4901 

835.4901 

44.10 

843.4764 

44.34 

709.1624 

44.29 

709.1623 

44.39 

709.1622 

44.46 

709.1624 

DTVTSELAAVkIVK ( Q12851 GCK) 

45.03 

835.4913 

NISHLDLkPQNILLSSLEKPHLK (Q6PHR2 ULK3) 

44.48 

709.1622 

44.53 

709.1624 

44.25 

709.1622 44.60 

709.1623 44.81 

709.1618 

44.20 

843.4753 

44.79 

843.4759 

44.72 

843.4758 

44.86 

843.4756 

44.69 

843.4758 44.88 

843.4756 

44.65 

843.4756 

45.16 

709.1624 

44.93 

843.4756 45.16 

843.4761 

43.5 44.0 44.5 45.0 45.5 

Time (min) 

DLkPSNIFLVDTK (P19525 PKR) 

45.64 

843.4785 

Resolution> 70K is required 

60

Guide through Pinpoint for automatic data process 

1, import the .raw data files and group them 

61

Guide through Pinpoint for automatic data process 

2, automatic data processing 

62

Inhibitor Titration for IC50 Value Determination 

GCK (Q12851) 

DTVTSELAAVkIVK 

835.4927 GCK(K1) 

709.1641 ULK3(K2) 

843.4797 PKR(K2) 

44 min 45 min 

GCK 

ULK3 

PKR 

100 

ULK3 (Q6PHR2) 

NISHLDLkPQNILLSSLEKPHLK 

% Kinase Inhibition 

80 

60 

40 

20 

0 

0.01 0.1 1 10 

Staurosporine, uM 

PKR (P19525) 

DLkPSNIFLVDTK 

Kinase 

IC 50 (µM) 

GCK(K1) 0.0062 

0 0.1 0.3 1 3 10 

µMStaurosporine 

ULK3(K2) 0.035 

63 

PKR(K2) 4.21

Targeted msxSIM and Western Blot Results Agree 

100 

% Kinase Inhibition 

80 

60 

40 

20 

0 

-20 

0.01 0.1 1 10 

Staurosporine, uM 

CDK5 

AurA 

RSK2(1) 

Erk1/2 

• Available result from Western blot 

for two of the kinases, Erk1/2 and 

Rsk2, agrees very well with the 

targeted msxSIM result. 

• Staurosporine has nearly no 

inhibition to Erk1/2. 

• At 0.1 µM, the inhibition to Rsk2 

was at least 70% inhibited. 

Rsk2 

Erk1/2 

0 0.01 0.03 0.1 0.3 1 3 10 µM Staurosporine 

64

Other Quantitation 

Experiments on Q Exactive 

Yue Xuan 

65 


Other Quantitation Experiments on Q Exactive 


• (multiplexed) Targeted HCD MS2 experiment 


66

1, Full scan only (HR/AM, Quantitation on Precursor 

ion) 

• Pros 

• For middle to high 

concentrate 

analytes 

• Very easy to set up 

• Can monitoring the 

entire 

chromatographic 

eluting peaks 

• Cons 

• For low abundant 

peptides with high 

complex matrix is 

not easy 

67

2, Quantitation on Product ion - targeted HCD (SRM- 

Like) 

1. Selection of targeted precursor ion in the quadrupole. 

2. Fragmentation in collision cell. 

3. Accumulation of fragment ions. 

4. Transfer of fragment ions to Orbitrap mass analyzer for detection. 

Multiplexed 

HCD MS2 

tHCD 

→ Sensitivity 

is experimental 

and Confirmation 

available! 

Pinpoint 1.3 is able to process the data! 

High Resolution → Selectivity 

Accurate Mass → Accuracy 

68

Example of setting up msx tHCD on Q Exactive 

69

Highly Selective, Fast and Sensitive 

10 amole of heavy peptides in 250 ng human CSF digest 

RT: 14.97 - 21.09 


100 

80 

60 

40 

20 

15.72 

15.67 

15.74 

15.82 

15.85 

15.88 

15.96 

GPGEDFR 

394.193-633.3051 


0 

100 

80 

60 

40 

20 

VAHTVAYLGK 

355.877-324.2350 

18.62 

18.67 

18.60 

18.57 

18.69 

18.55 


0 

100 

80 

60 

40 

20 

YFQGYAR 

457.732-594.2989 

20.61 

20.58 

20.57 

20.54 

20.64 

20.65 

20.67 

0 

15.0 15.5 16.0 16.5 17.0 17.5 18.0 18.5 19.0 19.5 20.0 20.5 21.0 

Time (min) 

70

Low Attomole LOD, 4 Orders of Linear Dynamic 

Range 

Heavy-labeled peptides of 

eicosanoid pathway enzymes in 250 ng CSF digest. 

1E+09 

100000000 

160000000 

10000000 

140000000 1000000 

450000000 

400000000 

1E+09 

100000000 

10000000 

1000000 

Peak Area 

120000000 

100000000 

80000000 

60000000 

40000000 

100000 

10000 

1000 

1 10 100 1000 10000 100000 

y = 1372.1x + 278774 

R² = 0.9998 

VAHTVAYLGK 

Peak area 

350000000 

300000000 

250000000 

200000000 

150000000 

100000000 

100000 

10000 

1000 

1 10 100 1000 10000 100000 

y = 3820.1x + 578349 

R² = 0.9999 

YFQGYAR 

20000000 

50000000 

0 

0 20000 40000 60000 80000 100000 120000 

Sample amount (amole) 

0 

0 20000 40000 60000 80000 100000 120000 

Sample amount (amole) 

Protein Peptide LOQ (amole) LOD (amole) 

PTGDS GPGEDFR 25 8 

PTGS2 QFQYQNR 25 8 

PTGS1 LVLTVR 10 3 

HPGDS STLPFGK 25 8 

PTGES VAHTVAYLGK 30 10 

PTGIS FLNPDGSEK 50 17 

71 

TBXA1 SVADSVLFLR 100 33 

ALOX15 YTLEINVR 250 83 

ALOX12 LWEIIAR 500 167 

LTCS4 YFQGYAR 10 3

3, Data Independent Acquisition (DIA) experiment 

Multiplexed Data Independent 

Acquisition (DIA) for Comparative 

Proteomics 

• Jarrett D. Egertson1; Andreas Kuehn2; Gennifer Merrihew1; Nicholas Bateman3; Brendan MacLean1; 

Ying S. Ting1; Jesse D. Canterbury4; Markus Kellmann2; Vlad Zabrouskov4; Christine Wu3; Michael J. 

MacCoss1 

• 1University of Washington, Seattle, WA; 2Thermo Fisher Scientific, Bremen, Germany; 3University of 

Pittsburgh, Pittsburgh PA; 4Thermo Fisher Scientific, San Jose, CA 

72 

Poster: ThP26

Classical Data independent approach 

DIA lacks the specificity of DDA 

73

Multiplexed DIA 

100 4 m/z-wide windows = 400 m/z 

500 m/z 

900 

Scan 1 

74



500 m/z 

900 

Scan 1 

75



500 m/z 

900 

Scan 1 

Scan 2 

76



500 m/z 

900 

Scan 1 

Scan 2 

77



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

78



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

79



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

80



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

81



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

Scan 21 

82



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

Scan 21 

83



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

Scan 21 

84



500 m/z 

900 

Scan 1 

Scan 2 

Scan 3 

. . . 

Scan 20 

Scan 21 

85

Demultiplexing 

Intensity 

m/z 

86

Demultiplexing 

Intensity 

87 

m/z

88 

Demultiplexing

Sensitivity Similar to MS1 Quantification 

Bovine proteins spiked into S. cerevisiae lysate 

(soluble fraction) 

89

DIA experiment updates 

• DIA data can be multiplexed by mixing precursors prior to 

fragment ion analysis 

• MSX de-multiplexing and isolation list export will be included 

in Skyline v1.3 (http://skyline.maccosslab.org) 

• Plan to implement DIA into next instrument SW 

90

Summery 

• Thermo Fisher Complete Solution from ProteinID to Peptide Quan 

• Q Exactive 

• Proteome Discoverer 1.3 

• Pinpoint 1.3 

• Walking through Discovery-to-Quantitation with an example study 

• Q Exactive recommended TopN experiment for Proteome Discovery Study 

• Guide through P.D. database search 

• Guide through Pinpoint for generating inclusion mass list 

• Q Exactive recommended targeted SIM experiment for targeted Quantitation Study 

• Guide through Pinpoint for automatic data process 

• Other Quantitation Experiments on Q Exactive 


• (mulitplexed) Targeted HCD MS2 experiment 


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Acknowledgements 

• Scott Peterman (Kinase experiments and Pinpoint part) 

• Rosa Viner (Kinase experiments) 

• Ryan Bomgarden (ActivX samples and data acquisition) 

• Bernard Delarghe (Proteome Discoverer slides) 

• Markus Kellmann (Q Exactive and DIA slides) 

• Claire Dauly (Example files) 

• Zhiqi Hao (tSIM and tHCD slides) 

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From Accurate Discovery to Targeted Quantitation - Peptide - IMP

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