Proteomic analysis involves fractionating and enriching cells or tissue to isolate proteins, then further breaking the proteins into peptides. The peptides are separated using chromatography and introduced into a mass spectrometer to determine their mass-to-charge ratios. Data-dependent acquisition is used to automatically select peptides for fragmentation and sequencing to identify the proteins present. Proteomics provides information about protein expression levels, post-translational modifications, interactions, and dynamics that complement genomics and transcriptomics data.

1. Proteomic Analysis:
Proteomic Analysis:
Basics and Applications
Basics and Applications
Ignasi Forné
February 14, 2013

2. Introduction
Genomics

3. Introduction
Transcriptomics

4. Introduction
Proteomics

5. Introduction
Genomics Transcriptomics Proteomics
Location, Time, Interaction partners, PTMs, Dynamics, Turnover
Adapted from Hein et al, Handbook of System biology 2013

6. Proteomics: Basics
Fractionation Fractionation
Enrichment Enrichment
Cells/Tissue Proteins Peptides

7. Proteomics: Basics
Fractionation Fractionation
Enrichment Enrichment
Cells/Tissue Proteins Peptides
nESI
C18‐HPLC
Mass Spectrometer (m/z)
Time

8. Proteomics: Basics
Fractionation Fractionation
Enrichment Enrichment
Cells/Tissue Proteins Peptides
nESI
C18‐HPLC
Mass Spectrometer (m/z)
Time

9. Proteomics: Basics
Fractionation Fractionation
Enrichment Enrichment
Cells/Tissue Proteins Peptides
nESI
C18‐HPLC
Mass Spectrometer (m/z)
Time

10. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
Full MS
p [ ] MS2 MS2 MS2 MS2
T: FTMS + p NSI Full ms [300.00-2000.00] 472.7700
472.7700 100 _ _ _ top 1 top 2 top 3 top n
100
F: ITMS + c NSI d w Full ms2 472.77@cid35.00
90
575.30
90 100
80 428.25
90
ce
80 70
Relative Abundanc
60 80
70
473.2710
Relative Abundance
50 70
Relativ Abundance
60
40 60
50 944.5325
513.2991 30 50
ve
769.4453
769 4453
40 691.3953 20 40
473.7723
10 472.3233 30 703.36
30
471.8120 474.2738 517.17
0 20 242.05 343.35
20 472 473 474
1046.2464 232.18
/ 10
0 686 31 713.30
686.31
10 1106.5581 1381.7826
1844.3618
873.53
1306.7396 1537.8872
0
1890.9115
0 200 400 600 800
400 600 800 1000 1200 1400 1600 1800 2000 m/z
m/z
Time

11. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
Full MS
MS2 MS2 MS2 MS2
T: FTMS + p NSI Full ms [300.00-2000.00]
472.7700 944.5324 top 1 top 2 top 3 top n
100 100
F: ITMS + c NSI d w Full ms2 944.53@cid35.00
90 1108.53
90
100
80
80 90
Relative Abundanc e
70
945.0824
810.08
70 60 80
945.5353
Relative Abundance
50 70
Relativ Abundance
60
761.14
40 60
50 944.5325 944.2667
513.2991 30 50
ve
A
769.4453
769 4453
945.7967
40 691.3953 20 621.24
946.5384 40
10
30 9 30 1266.51
943.5265
0
20 944 945 946 947 20 353.26
1046.2464 m/z 493.13
493 13 915.90
1381.7826 10 1469.38
1469 38
10 1106.5581
1844.3618 1714.74
1306.7396 1537.8872 1890.9115 0
0 500 1000 1500
400 600 800 1000 1200 1400 1600 1800 2000
m/z m/z
Time

12. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
Full MS
MS2 MS2 MS2 MS2
513.2990
T: FTMS + p NSI Full ms [300.00-2000.00] 100
472.7700 top 1 top 2 top 3 top n
100
90 F: ITMS + c NSI d w Full ms2 513.30@cid35.00
513.6329
80 534.53
90 100
598.58
dance
70
80 90
60
Relative Abund
80
70 50
Relative Abundance
70
Relativ Abundance
40 513.9668
60
30 60
50 944.5325
513.2991 20 50 655.13
655 13
ve
769.4453
769 4453 514.3010
40 691.3953 10 513.1269
512.7917 514.6352 40
0
30 513.0 513.5 514.0 514.5 30 449.42
m/z
20
20
230.02 717.35
1046.2464 10 343.04
343 04
10 1106.5581 1381.7826
1844.3618
825.52 926.25 1050.54
1306.7396 1537.8872
0
1890.9115
0 200 400 600 800 1000
400 600 800 1000 1200 1400 1600 1800 2000
m/z
m/z
Time

13. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
DDA Video

14. Proteomics: Basics
Peptide charge and mass
p g
(M+2H)2+ [ The charge
p ]
100
472.7700 (m2-m1)/z= 0.5  z=2
90
[m2-m1 = 1]
80
z=2  m= M+2H+
ance
70
Relativ Abunda
60
473.2710
50 The mass
ve
40
0 m/z= (M+2H+)/2
30 m/z=472.7700
20 473.7723 H+ =1 0073
=1.0073
10 472.3233
471.8120 474.2738
0 M 943.5254
M= 943 5254
472 473 474
m/z /

15. Proteomics: Basics
_ _ _
F: ITMS + c NSI d w Full ms2 472.77@cid35.00
575.30
100
428.25
90
80
undance
70
60
lative Abu
50
40
Rel
30 703.36
517.17
20 242.05 343.35
232.18
10 686.31 713.30
873.53
0
200 400 600 800
m/z

16. Proteomics: Basics
y5
dance
y4
Rellative Abund
y3 y7
b3
y2 b4 y6
b2 b5
b7
b6
m/z

17. Proteomics: Basics
Ser Ile Val Lys
Gly Val Met Glu
y5
dance
y4
Rellative Abund
y3 y7
b3
y2 b4 y6
b2 b5
b7
b6
m/z

18. Proteomics: Basics
Ser Ile Val Lys
Gly Val Met Glu
Ser Val Ile Gly Ser Val Ile Met Val Glu
Gly
dance
Ser Val Ile Met Val
Rellative Abund
Gly
Gly Ser Val
b3
Gly Ser Val Ile Met
Ser
Gly b4
b2 b5
b7
b6
m/z

19. Proteomics: Basics
Ser Ile Val Lys
Gly Val Met Glu
Ile Met Val Glu Lys
y5
Met Val Glu Lys Ser Val Ile Met Val Glu Lys
dance
y4
Rellative Abund
Val Glu
Gl Lys
L Val Ile Met Val Glu Lys
Glu Lys
y3 y7
y2 y6
m/z

20. Proteomics: Basics
Ser Ile Val Lys
Gly Val Met Glu
y5
Ile
dance
y4
Rellative Abund
Met
y3 Val y7
b3 Ser
y2 Val
b4 y6
Ile
b2
Met b5
Val
Ser Glu
b7
b6
m/z

21. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
The Charge
The Mass of the Peptide
The Masses of the Fragments

22. Proteomics: Basics
nESI DDA
C18‐HPLC
Full MS
top 1
MS2
top 2
MS2
top 3
p
MS2
....
MS2
top n-1
MS2
Mass Spectrometer (m/z)
top n
MS2
Time Time
Peptide identification Protein identification
Relative Abundance
Sequence
Database
Search
R
m/z
Score Calculation
Target-Decoy-based
Peptide to protein mapping
FDR thresholding
th h ldi
FDR thresholding
Adapted from Hein et al, Handbook of System biology 2013

23. Proteomics: Basics
nESI
Peptide Quantification
Peptide Quantification C18‐HPLC
p [ ]
472.7700
100
90
80
ance
70
Relative Abunda
60
473.2710
50
40
30
20 473.7723
10 472.3233
Intensity 0
471.8120
472 473 474
474.2738
Mass Spectrometer (m/z)
Mass Spectrometer (m/z)
/
Peptide 1 L Sample A
Time
m/z

24. Proteomics: Basics
Peptide Quantification
Peptide Quantification
Intensity
Peptide 1 L Sample A
Time
m/z

25. Proteomics: Basics
Peptide Quantification
Peptide Quantification
Label Free
(across runs)
( )
Peptide 1 L Sample B
Intensity
Peptide 1 L Sample A
Time
Peptide 1 H Sample B
run 2
run 2
MS2 Reporter Ions
run 1
Isotopic Labelling
(SILAC, Dimethyl,
(SILAC Dimethyl
Heavy peptides) m/z

26. Proteomics: Basics
Ong et al, Nat Chem Biol 2005

27. Proteomics: Basics
de Godoy et al, Nature 2008

28. Proteomics: Basics
In depth analysis
In depth analysis
Cox et al, Ann Rev Biochemistry 2011 Michalski et al, J Proteome Res 2011

29. Proteomics: Applications
Expression Proteomics
WT WT+KO
Lys Protein
Identification
PINK1 KO
KO +
Lys +8
In‐gel
digestion Quantification
LC‐MSMS
SILAC
2
BSA
1.5
WT > KO
Keratin 1
se
log 10 Ratio H/L Revers
0.5
Serie1
0
-2 -1.5 -1 -0.5 0 0.5 1 1.5
g
-0.5
-1
KO> WT -1.5
log 10 Ratio H/L Forw ard
Schreiner et al, Mol Biol Cell 2012

30. Proteomics: Applications
Interactomics
‐/+P
Arg, Lys
Protein
Protein
Extract 1
Identification
Arg +10, Lys +8 P +
Protein
Extract 2 In‐gel Quantification
digestion LC‐MSMS
SILAC
2
1.5
INTERACTORS
Proteins
confirmed in
erse (SILAC 6)
both SILAC
1 1 and 6
lo 10 Ratio H/L Reve
0.5
05
0 Serie1
-1.5 -1.25 -1 -0.75 -0.5 -0.25 0 0.25 0.5 0.75 1 1.25 1.5
-0.5
og
-1
-1.5
-2
log 10 Ratio H/L Forward (SILAC 1)

31. Proteomics: Applications
PTM Analysis
Ub Ligase
GG GG‐K
Identification
In‐gel
GG +
digestion LC‐MSMS
DCC complex Quantification
GG
Villa et al, Mol Cell 2012

32. Proteomics: Applications
PTM Analysis
Control
P
Protein P‐site
Extract P Identification
In‐solution P
Phosphopeptide
digestion LC‐MSMS
enrichment
+
P
Protein
Extract P Quantification
P Labelfree
P
Stimulus
l
Ac
Ac Ac‐K
Protein
Extract
Extract Ac Identification
In‐solution Ac
digestion Acetylpeptide enrichment
LC‐MSMS +
Protein
Extract
Extract
Quantification
Ac Labelfree
Ac

33. Proteomics: Missing values
Proteome
run 1 run 2 run 3 run 4
Time Course
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1
5
Protein
10
...

34. Proteomics
Nature Methods: Method of the Year 2012

35. Proteomics: Targeted Analysis
Proteome
run 1 run 2 run 3 run 4
Time Course
Ti C
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
1
5
Protein
10
...

36. Proteomics: Targeted Analysis
Ser Ile Val Lys
Gly Val Met Glu
y5
dance
y4
Rellative Abund
y3 y7
b3
y2 b4 y6
b2 b5
b7
b6
m/z

37. Proteomics: Targeted Analysis
Ser Ile Val Lys
Gly Val Met Glu
Ile Met Val Glu Lys
y5
Met Val Glu Lys
dance
y4
Rellative Abund
Val Glu
Gl Lys
L
y3
m/z

38. Proteomics: Targeted Analysis
p [ ]
472.7700
472 7700
100 Ile Met Val Glu Lys
90 y5
Met Val Glu Lys
80
ce
Relativ Abundanc
Relativ Abundance
70
60 Val Glu Lys y4
473.2710
50 y3
ve
ve
40
30
20 473.7723
10 472.3233
472 3233
471.8120 474.2738
0
472 473 474
/
m/z
SRM Video Picotti et al, Nat Methods 2012

39. Proteomics: Targeted Analysis
Gallien et al, J Mass Spectrometry 2011

40. Proteomics: Targeted Analysis
Picotti et al, Cell 2009

41. Proteomics: Targeted Analysis
Picotti et al, Cell 2009

42. Structural Proteomics
Hein et al, Handbook of System biology 2013

43. Structural Proteomics
Examples
Herzog et al Science 2012

44. Summary
• MS‐based Proteomics:
• Identification
•Q
Quantification
ifi i
• Shotgun/Discovery
• Targeted
• Structural Proteomics and Interactomics

45. Thanks!!
Prof. Axel Imhof
And the Colleagues at the Imhof‘s and the ZfP
(Molecular Biology Dept. Biomedical Center‐LMU)

46. Questions??

47. Proteomic Analysis:
Proteomic Analysis:
Basics and Applications
Basics and Applications
Ignasi Forné
February 14, 2013