1. Introduction to the Proteomics
Yet-Ran Chen, 陳逸然
ABRC, Academia Sinica
National Taiwan Ocean University
National Taiwan University

2. Analysis of Proteome
為何蛋白體的研究會皆在基因體之後？

3. Y The Proteomics Study Genomics Transcriptomics Proteomics
DNA
RNA
Protein
Post Translational
Modifications Y
5x ~ 50x
Functional Links
Per Protein
~3.3 billion BPs
22,165 Genes
(Aug 2009)
> Proteins
Lab of MS-Based OMICS Research, ABRC, Academia Sinica

5. DNA Microarray
Animation

6. Genes Proteins
Coded by 4 building blocks at least 20 building blocks
Amplification Possible (PCR) Not possible
Modifications
Chemical Methylation Several – acetylation,
sulphation, nitration,
phosphorylation
glycosylation, oxidation
Structural frame shift, changes in both
deletions, primary and tertiary
point mutation structure
Machinery no translocation after synthesis have to be
translocated and proteins
function in a network fashion and are very dynamic in structure and abundance
mRNA = Protein

7. DNA Microarray

8. Y Complexity of the Proteome DNA RNA Proteins Modified Proteins
Genome
Transcriptome
Proteome
DNA
RNA
Proteins
Modified
Proteins
Biological
Function Y
Transcription
Translation
Post-Translation
Modification
為何蛋白體的研究會皆在基因體之後？
80,000 Genes
> 1,000,000 Proteins
x 5 to 50
functional
links per protein

9. Proteomics in the Context of the Cell
The proteomics was from the word proteome which combines the protein and genome
Patterson SD & Aebersold RH, Nature Genetics 33, 311 (2003)

10. Analysis of Proteome
為何蛋白體的研究會皆在基因體之後？

11. Analysis of Proteome
為何蛋白體的研究會皆在基因體之後？

12. Analysis of Proteome
為何蛋白體的研究會皆在基因體之後？

13. Proteins Measured Clinically in Plasma Span > 10 Orders of Magnitude in Abundance (199 proteins, literature values)
Copyright

14. 1010 Really Is Wide Dynamic Range (Here on a linear scale)
Copyright

15. Human Plasma Proteome 0-90% 90-99% Ref:
Fig. Pie chart representing the relative contribution of proteins within plasma.
Twenty-two proteins constitute 99% of the protein content of plasma
0-90%
90-99%
Of great interest
Molecular & Cellular Proteomics 2:1096–1103, 2003
Ref:

16. Biomarker Discovery

17. Workflow for the development of Novel Protein Biomarker Candidates
Nature Biotechnology 2006,24:972

18. Biomarker Publication over Past Decade
Joseph A. Ludwig*‡ and John N. Weinstein* Nature Rev. Cancer 2005

19. Proteomics in the Context of the Cell
The proteomics was from the word proteome which combines the protein and genome
Patterson SD & Aebersold RH, Nature Genetics 33, 311 (2003)

20. Analysis of Proteome
為何蛋白體的研究會皆在基因體之後？

21. A more complex biological problem
Diverse solution

22. Commonly Used Proteomics Workflow in MS
為何蛋白體的研究會皆在基因體之後？
Erol E. Gulciek, National Institutes of Health

23. Proteomic Workflow Biological Question Acquire Biological Sample
Specific Physiological Status
Collect the Specific Cells or Tissue etc..
Extract the (sub)Proteome of Interest
Experimental Control
Protein
Protein Purification/Fractionation
…………
Isotopic Labeling
ICAT/SILAC/PhIAT
1D PAGE
2D PAGE LC
IEF
Affinity
Digestion
Digestion
Digestion
Peptide
Peptide Purification/Fractionation
…………
Isotopic Labeling
iTRAQ/18O…….
LC (SCX/RP)
IMAC/TiO2 LC
IEF
Affinity
Qualitative
LC-MS/MS
Isotope Assisted Quantitative
LC-MS/MS
Labeling Free Quantitative
LC-MS/MS
MS Anal.
MS Signal Processing
Peptide/Protein Identification
Peptide/Protein Quantitation
Result Interpretation
Data Anal.
Lab of MS-Based OMICS Research, ABRC, Academia Sinica

24. Proteomic Workflow Sample Preparation Biological Question Acquire
Specific Physiological Status
Collect the Specific Cells or Tissue etc..
Extract the (sub)Proteome of Interest
Experimental Control
Protein
Protein Purification/Fractionation
…………
Isotopic Labeling
ICAT/SILAC/PhIAT
1D PAGE
2D PAGE LC
IEF
Affinity
Digestion
Digestion
Digestion
Peptide
Peptide Purification/Fractionation
…………
Isotopic Labeling
iTRAQ/18O…….
LC (SCX/RP)
IMAC/TiO2 LC
IEF
Affinity
Mass Spectrometry Analysis
Qualitative
LC-MS/MS
Isotope Assisted Quantitative
LC-MS/MS
Labeling Free Quantitative
LC-MS/MS
MS Anal.
Data Handling and Analysis
MS Signal Processing
Peptide/Protein Identification
Peptide/Protein Quantitation
Result Interpretation
Data Anal.
Lab of MS-Based OMICS Research, ABRC, Academia Sinica

25. Biological Pre-fractionation of sample
Current approaches
Biological Pre-fractionation of sample
(organs, tissues, cell types, subcellular components)
Protein Extract
Chromatographic Fractionation
Label with amino acid
targeted affinity reagents
such as ICATTM
Enzymatic or chemical Digest
Multidimensional LC
1-D Gel
Electrophoresis
2-D Gel
Electrophoresis
Digest
Digest
Digest
Mass spectrometry
Electrospray &/or MALDI
Chromatography

26. A B Proteome analysis using two-dimensional gel electrophoresis
Visualized by 2-DE + gel staining B A
為何蛋白體的研究會皆在基因體之後？
Protein
Identification

28. ? A B Proteome analysis using two-dimensional gel electrophoresis
Visualized by 2-DE + gel staining B A
為何蛋白體的研究會皆在基因體之後？ ?
Protein
Identification

29. Insulin consists of two polypeptide chains, A and B, held together by two disulfide bonds. The A chain has 21 residues and the B chain has 30 residues.
The sequence shown is that of bovine insulin.
The hormone insulin consists of two polypeptide chains, A and B, held together by two disulfide cross-bridges (S–S). The A chain has 21 amino acid residues and an intrachain disulfide; the B polypeptide contains 30 amino acids. The sequence shown is for bovine insulin.

30. Figure 5.18 Summary of the sequence analysis of catrocollastatin-C, a 23.6-kD protein found in the venom of the western diamondback rattlesnake Crotalus atrox. Sequences shown are given in the one-letter amino acid code. (Adapted from Shimokawa, K., et al., Archives of Biochemistry and Biophysics 343:35–43.)  

31. Amino Acid Sequence Can Be Determined by Mass Spectrometry
Mass spectrometry separates particles on the basis of mass-to-charge ratio
Fragments of proteins can be generated in various ways
MS can also separate these fragments

32. What Mass Spectrometry Can Do in Proteomics ?
Protein Identification
Identify Protein Modifications
Study Protein-Protein or Protein-Small Molecule Interaction
Protein Conformation Study
Protein Quantification
Quantification of Protein Modification
Etc…..

33. Protein Identification by Mass Spectrometry
Peptide Mass
Fingerprinting
(PMF) MS
Analysis
Calculation of
Peptide Mass
MS Spectrum
Specific Protein
Fragmentation
Chemical
Enzymatic
High Energy CID
Protein
Sample
Peptides
MS/MS Analysis of
Peptide Fragments
MS/MS
Spectrum
Calculation of
Peptide
Fragment
Mass MS
Analysis
MS/MS Ion Search

34. Protein Identification by Mass Spectrometry
Peptide Mass
Fingerprinting
(PMF) MS
Analysis
Calculation of
Peptide Mass
MS Spectrum
Specific Protein
Fragmentation
Chemical
Enzymatic
High Energy CID
Protein
Sample
Peptides
MS/MS Analysis of
Peptide Fragments
MS/MS
Spectrum
Calculation of
Peptide
Fragment
Mass MS
Analysis
MS/MS Ion Search

35. Mass Spectrometry Analysis
Peptide Mass Fingerprinting (PMF)
Phosphorylation
Glycosylation
Peptide Mixtures
Post -
translational
Modification % %
Intensity
Intensity
Extraction, clean up
Mass Spectrometry Analysis
Mass Spectrum
600
600
m/z
m/z
3000
3000

36. Fenyo D Curr Opin Biotechnol 11:391 (2000)
The Peptide Mass Fingerprinting
Fenyo D Curr Opin Biotechnol 11:391 (2000)

37. Complexity of Protein Mixture
MALDI
Peptide Mass Fingerprinting (PMF) Good for 1― few proteins
Complexity of Protein Mixture
Tandem MS (MS/MS)
Direct LC-MS/MS
Poor salt tolerance
Ion suppression

38. When there is simply not enough peptide match !
Phosphorylation
Glycosylation
Peptide Mixtures
Post -
translational
Modification % %
Intensity
Intensity
Extraction, clean up
Mass Spectrometry Analysis
No Match
Mass Spectrum
600
600
m/z
m/z
3000
3000

39. Protein Identification by Mass Spectrometry
Peptide Mass
Fingerprinting
(PMF) MS
Analysis
Calculation of
Peptide Mass
MS Spectrum
Specific Protein
Fragmentation
Chemical
Enzymatic
High Energy CID
Protein
Sample
Peptides
MS/MS Analysis of
Peptide Fragments
MS/MS
Spectrum
Calculation of
Peptide
Fragment
Mass MS
Analysis
MS/MS Ion Search

40. Protein Identification by Mass Spectrometry
Peptide Mass
Fingerprinting
(PMF) MS
Analysis
Calculation of
Peptide Mass
MS Spectrum
Specific Protein
Fragmentation
Chemical
Enzymatic
High Energy CID
Protein
Sample
Peptides
MS/MS Analysis of
Peptide Fragments MS
Analysis
MS/MS
Spectrum
Calculation of
Peptide
Fragment
Mass
MS/MS Ion Search

41. Tandem Mass Spectrometry (MS/MS)
Ionization Source
MALDI Electrospray
CID
Molecular Ion
Molecular Ions
Fragment Ions
m/z
m/z

42. Peptide Fragmentation
a,b,c – N-terminal side
x,y,z – C-terminal side

43. * * Protein ID Using MS/MS I II III Peptides12 14 16
Protein
mixture
Time (min)
1D, 2D, 3D peptide separation * II
200
400
600
800
1000
1200
m/z Q1 Q2
Collision Cell Q3
Tandem mass spectrum
Correlative sequence database searching
III
Find Peptide
Partial Sequence
200
400
600
800
1000
1200
200
400
600
800
1000
1200
m/z
m/z
Theoretical
Acquired
Protein identification
Protein identification

44. A B Proteome analysis using two-dimensional gel electrophoresis
Visualized by 2-DE + gel staining B A
為何蛋白體的研究會皆在基因體之後？
Protein
Identification

45. Two-dimensional gel electrophoresis

46. Two-dimensional differential gel electrophoresis (DIGE)
The workflow for 2-D DIGE consists of the following steps: 1. Labeling 2. Electrophoresis 3. Image acquisition 4. Image analysis
Workflow come from GE Healthcare website

47. Drawbacks of 2-DE
Limited mass and pH ranges
Low abundant proteins are poorly detected
Reproducibility
Solubility problems

48. Disadvantages 2D gels
Poor ability to handle certain classes of proteins
membrane, basic, acidic, high and low molecular weight proteins
Multiple spots correspond to the same protein
Multiple protein products co-migrate to the same spot
Cannot visualize low abundant proteins
(cannot visualize proteins present at less than 1000 copies per cell*)
Time consuming and difficult to automate
Limited recovery of analyte for further analysis
(faint spots on 2-D gels are not analyzed)
*Reference – Marc R. Wilkins et al, Electrophoresis, 1998, 19,

49. Biological Pre-fractionation of sample
Current approaches
Biological Pre-fractionation of sample
(organs, tissues, cell types, subcellular components)
Protein Extract
Chromatographic Fractionation
Label with amino acid
targeted affinity reagents
such as ICATTM
Enzymatic or chemical Digest
Multidimensional LC
1-D Gel
Electrophoresis
2-D Gel
Electrophoresis
Digest
Digest
Digest
Mass spectrometry
Electrospray &/or MALDI
Chromatography

50. separation of peptides
Proteome Analysis Using MudPIT
(Multidimensional Protein Identification Technology)
SCX RP
Denatured
Protein Complex
Digested
Peptides
2D chromatographic
separation of peptides
Tandem
Mass Spectrometry
Identify proteins
in complex
Link et al. Nature Biotech. 1999, 14, 957

51. * * Peptide ID Using MS/MS I II III Peptides12 14 16
Protein
mixture
Time (min)
1D, 2D, 3D peptide separation * II
200
400
600
800
1000
1200
m/z Q1 Q2
Collision Cell Q3
Tandem mass spectrum
Correlative sequence database searching
III
Find Peptide
Partial Sequence
200
400
600
800
1000
1200
200
400
600
800
1000
1200
m/z
m/z
Theoretical
Acquired
Protein identification
Protein identification

52. * * Peptide ID Using MS/MS I II III Peptides12 14 16
Protein
mixture
Time (min)
1D, 2D, 3D peptide separation * II
200
400
600
800
1000
1200
m/z Q1 Q2
Collision Cell Q3
Tandem mass spectrum
Correlative sequence database searching
III
Find Peptide
Partial Sequence
200
400
600
800
1000
1200
200
400
600
800
1000
1200
m/z
m/z
Theoretical
Acquired
Protein identification
Protein identification

53. Isotopic labeling: a another strategy for differential display proteomics

54. In vitro labeling (proteolytic labeling)
18O water
Cells A
extraction
reduction
alkylation
digestion
Cells B
separation
MS IDENTIFICATION and QUANTITATION
Stewart T. et al. R.ap. Comm.. Mass Spectrom. 15(24) (2001)
Yao XD et al. Anal. Chem.73(13):

55. Protein Identification
Analysis of Proteome Using Isotopic Labeling
Isotope-coded affinity tags (ICAT) :
Nature biotechnology (1999)17:
Reagent :
Light reagent : D0-ICAT (X=H)
Heavy reagent : D8-ICAT (X=D)
Cysteine
Biotin
Linker (light or heavy)
Thiol-specific
reactive group
Process :
Cell
Low Copper
Concentration
Label
ICAT-L
ICAT-H MS
Protein Quantitative
Combination
trypsinization
Affinity
isolation MS
Analysis
We use the ICAT-Light to lalbe the protein expressed in low copper connection
And ICAT-heavy to label the protein expressed inHigh copper concentration
MS/MS
Protein Identification
Cell
High Copper
Concentration

56. How 18O incorporated H2O H2 (18)O R-C NH-R’ R-C NH-R’ O O R-C OH
enzyme
H2O
enzyme
H2 (18)O
R-C OH
R-C (18)OH O O + +
H2N-R’
H2N-R’

57. In vivo labeling mix Cells A Cells B
extraction
reduction
alkylation
digestion
Cells B
separation
MS IDENTIFICATION and QUANTITATION
Enriched Medium (13C, 15N or amino acid with deuterium)
Oda Y, et al. P Natl. Acad. Sci. USA (1999)
Pasa-Tolic L, et al. J Am Chem. Soc (1999)

58. Protein Identification
Analysis of M. Capsulatus Proteome Using Isotopic Labeling
Isotope-coded affinity tags (ICAT) :
Nature biotechnology (1999)17:
Reagent :
Light reagent : D0-ICAT (X=H)
Heavy reagent : D8-ICAT (X=D)
Cysteine
Biotin
Linker (light or heavy)
Thiol-specific
reactive group
Process :
Cell
Low Copper
Concentration
Label
ICAT-L
ICAT-H MS
Protein Quantitative
Combination
trypsinization
Affinity
isolation MS
Analysis
We use the ICAT-Light to lalbe the protein expressed in low copper connection
And ICAT-heavy to label the protein expressed inHigh copper concentration
MS/MS
Protein Identification
Cell
High Copper
Concentration

59. Problems with ICAT Expensive (~ $ 500/sample)
Selectivity for cysteine may be a problem (~8% peptides contain Cys)
Isotopic effect in high resolution chromatography. Zhang RJ et al. Anal. Chem (2001)
Quantitation accuracy (?) ...+/- 10%

60. Multiplexed Protein Quantitation
Molecular and Cellular Proteomics, 3.12 (2004) 1154

61. Workflow of Multiplexed Protein Quantitation

62. Why Isotopic Labeling …..
Reduce the variation
Ionization Efficiency
LC Recovery
Enzymatic Digestion
Protein Purification and Fractionation
Protein Extraction / Isolation
Labeling Efficiency
Cost $$
Time Course
PTM
Sample Size

63. Common Quantitative MS Workflow
Anal Bioanal Chem (2007) 389:1017–1031

64. Quantitative Approaches in LC-MS Based Proteomics
Label-Free Proteomics
Measure abundances of tryptic peptides from the proteome of interest
Compare abundances of the same peptide across LC-MS analyses
Journal of Proteome Research 2008, 7, 51–61

65. BRIEFINGS IN BIOINFORMATICS, September 28, 2007

66. Label-Free Proteomics Approach
- Extraction Ion Chromatogram Intensity (XIC)
Linear correlation between MS signal intensities and the relative quantity of peptides
Quantification of proteins and metabolites by mass spectrometry without isotopic labeling or spiked standards. (2003) Anal. Chem. 75, 4818–4826
Quantitative proteomic analysis by accurate mass retention time pairs. (2005) Anal. Chem. 77, 2187–2200

67. Label-Free Proteomics Approach
- Extraction Ion Chromatogram Intensity (XIC)
Sample A
Sample A A
A’’
TIC
TIC
XIC
of M/Z=Y
XIC
of M/Z=Y’’
X Time
X’’ Time
Expression Ratio = A/A’’
X Time
X’’ Time
MS Survey
MS Survey
MS/MS
MS/MS
Protein ID
P-E-P-T-I-D-E
RT=X M/Z=Y
Protein ID
P-E-P-T-I-D-E
RT=X’’ M/Z=Y’’

69. Label-Free Proteomics Approach
- Extraction Ion Chromatogram Intensity (XIC)
Sample A
Sample A A
A’’
TIC
TIC
XIC
of M/Z=Y
XIC
of M/Z=Y’’
X Time
X’’ Time
Expression Ratio = A/A’’
X Time
X’’ Time
MS Survey
MS Survey
RT Reproducibility
X  X’’
MS Accuracy
Y  Y’’
Coeluted Peptide
A  A’’
Ionization Efficiency
Insufficient Data Points
MS/MS
MS/MS
Protein ID
P-E-P-T-I-D-E
RT=X M/Z=Y
Protein ID
P-E-P-T-I-D-E
RT=X’’ M/Z=Y’’

70. Label-Free Proteomics XIC
Analytical Chemistry, Vol. 80, No. 4, February 15, 2008

71. A fixed integration window for that peptide is then defined by the mean centroid
retention time across all samples in the group ± a fixed retention time window
w/o Aligned Aligned

72. Label-Free Proteomics XIC
DDA Method and Quantitation Accuracy MS MS
MS/MS MS
MS/MS
MS/MS MS MS MS

73. Label-Free Proteomics XIC
MS Only + DDA analysis
MS High / Low (MSE)
Mass Spectrom. 2006; 20: 1989–1994

74. DDA and MSE
DDA (Data-dependent analysis) results both in a loss of data in the MS mode when MS/MS data are being acquired and poor duty cycles, thus making it less than ideal for fast analysis and narrow, rapidly eluting, peaks.
Here the application of a new form of MS and MS/MS data acquisition, that maximizes the instrument duty cycle and collects both precursor and fragment ion information in exact mass mode, is described. This new method of LC/MS data acquisition, called MSE.
MSE whereby both precursor and fragment mass spectra are simultaneously acquired by alternating between high and low collision energy during a single chromatographic run.
Journal of Proteome Research Vol. 8, No. 7,
J Am Soc Mass Spectrom 2002, 13, 792–803

75. Spectral Deconvolution by Chromatographic Retention Time
In practise the selection of time resolved sets of exact masses (spectra) for dB searching is more sophisticated.
In the LC-MS analysis of complex tryptic digests there is a high probability that many peptides will co-elute resulting in MS spectra containing more than one molecular ion. The corresponding MSE spectra will therefore contain a mixture of fragments from all of the coeluting molecular ions. Waters’ proprietary Expression informatics tools can deconvolute these composite peptide spectra using ion retention time and chromatographic peak profile as the key parameters to associate only related molecular and fragment ions.
Spectral Deconvolution by Chromatographic Retention Time:
STEP 1: The algorithm plots “molecular ion chromatograms” for each ion detected in an MS spectrum at a given time.
STEP 2: The algorithm plots “fragment ion chromatograms” for each ion detected in the corresponding MSE spectrum.
STEP 3: The algorithm extracts deconvoluted spectra by matching the exact retention times of molecular & fragment ions. Each deconvoluted spectrum contains only molecular & fragment ions that maximise at exactly the same retention time.

76. Absolute quantification – condition signatures
peptide must replicate across all injections
quantify relative to a protein of know mola concentration
n = 3
Signature principle
State 2log ratio [protein]x of interest over [protein]y fmol (protein ratio within a given condition)
Molecular & Cellular Proteomics 5:144–156, 2006

77. Targeted Label-Free Approach Using LC-MS
Journal of Proteome Research 2007, 6,

78. Label-Free Proteomics Spectral Counting
Analytical Chemistry, Vol. 76, No. 14, July 15, 2004

79. Label-Free Proteomics Spectral Counting
“If protein relative abundance increases in a proteomics
sample more peptides will be identified.”
Counting and comparing the number of fragment spectra identifying peptides of a given protein
Protein Abundance Index (PAI)
Exclude Hydrophobic Peptide
Genome Res :

80. SILAC vs. Spectra Counting vs. MS/MS TIC
Proteomics 2008, 8, 994–999

85. Anal Bioanal Chem (2007) 389:1017–1031

86. Performance….. ICAT Label Free XIC Spectra Count
Mol. Cell. Proteomics (2002) 1, 323–333
Proteomics (2005) 5, 1204–1208
J. Proteome Res. (2002) 1, 47–54
Detect ~1.5 fold change in protein abundance over a dynamic ranfe from 10- to 100-fold
Label Free XIC
Anal. Chem. (2002)74, 4741–4749
J. Proteome Res. (2002) 1, 317–323
SD < 11% with Dynamic Range from fmol (spike single protein in serum sample)
Anal. Chem. (2002) 75, 4818–4826
SD < 26% (spiked several protein in serum sample)
Spectra Count
Anal. Chem. (2004) 76, 4193–4201
Linearity over 2 orders of magnitude with high correlation to the relative protein concentration
Label-Free (XIC and Spectra Count)
Molecular & Cellular Proteomics (2005) 4,1487–1502
Detect ~2.5 fold with high confident

87. Common Quantitative MS Workflow
Anal Bioanal Chem (2007) 389:1017–1031

88. Anal Bioanal Chem (2007) 389:1017–1031

89. Label-Free Quantitation Softwares
Etc……..
Journal of Proteome Research 2008, 7, 51–61

90. Publication of the Confidant Proteomics Result
Use common search algorithm(s) and database to search
Employ method (s) for evaluation of the FP rate
Plan to integrate data for searching to find weak associations not evident in single datasets
Employ common/consistent annotation of results
Store data in original instrument vendor format in as minimally processed form as possible (exchange formats in flux)
Files contain all the interesting info in unprocessed form
parent peak intensities for quantitation
resolution, peak spacing (charge states)
acquisition parameters
Etc……..
Guideline Draft for Proteomics Data Publication
Molecular Cellular Proteomics, July 14, 2005