Mass Spectrometry-based Proteomics

Application in Human Health and Bioenergy

Wei-Jun Qian

Integrative OmicsBiological Sciences Division

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Outline

Introduction

Proteomics: How it works

Proteomic quantificationGlobal shotgunTargeted

Applications

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3 3

Measurement technologies

DNA

Genome

Info (codes) Readout Execution

RNA

Transcriptome

Proteins

Proteome

The central dogma of biology

Proteins are the workhorses of cells

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Proteomics seeks to gain a systems-level understanding of cellular functions by measuring the dynamics of proteins.

Systems biology

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Life’s complexity pyramid, Oltvai & Barabasi, Science, 2002

Question: Will genome inform you everything?

Systems Biology

Many proteins are functionally linked through allosteric or other mechanisms into biochemical 'circuits' that perform a variety of simple computational tasks including amplification, integration and information storage. (D Bray, Nature, 1995)

While long-term information is stored in the genome, the proteome is crucial for short-term information storage or learned behaviors

The cell’s large scale functional organization provides a multi-level network view of motifs, pathways, and modules, ect.

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From systems biology to synthetic biology

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Design

BuildTest

Outline

Introduction

Proteomics: How it works

Proteomic quantificationGlobal shotgunTargeted

Applications

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Proteomics(PROTEOme DynaMICS)

Proteome: the entire complement of proteinsexpressed in a cell, tissue or organism.

Proteomics deals with the dynamics of the proteome. In a simple term, proteomics primarily focuses on:

Identify Quantify

as many proteins as possible in biological samples

Proteomics is a high-throughput technology for the systems level study of proteins.

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Unique Aspects of Proteomics

Not just another version of gene expression

Quantitative proteomics can provide multi-dimensional dynamics information.

Protein expression/abundancePosttranslational modifications (phosphorylation, glycosylation, etc.)Protein isoforms (splicing variants, protein fragments)Protein-protein interactionssubcellular localization & translocation

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Protein Post-Translational Modifications (PTMs)

DNA encodes functions

mRNA transfersfunctions

Proteins fulfillfunctions

PTMs regulatefunctions

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Impact of proteomicsHuman disease

Improving diagnosticsNovel Biomarkers for diseases

Drug discoveryNew levels of understanding of biology and disease mechanisms

Bioenergy.Systems level understanding of Biomass fermentation by microbesNew engineering targets for enhanced biofuel production

Indispensable tool in systems biology

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How does proteomics work?

Protein Peptide

Enzymedigestion

Liquid chromatography-mass spectrometry

or LC-MS

How does proteomics work?

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200 600 1,000 1,400 1,800

y9

y8

b10

y7

b9b8

y6

y12

y10y11

b12b6

y5b7

b11

y13b14b13y4

200 600 1,000 1,400 1,800m/z

y9

y8

b10

y7

b9b8

y6

y12

y10y11

b12b6

y5b7

b11

y13b14b13y4

Trypsin enzyme digestion

Proteins

Peptide mixture

Peptide identifications(including possible PTMs)

LC-MS and MS/MS

LC elution time 300 1000 1700

Full MS Spectrum

m/z

l

i

MS/MS fragment Spectrum LC

Quantification

Break selected peptide

Cells, tissue, biofluids

PNNL Proteomics

Integrative Functional Proteomics

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Protein abundances

Protein activities

Multiple types of PTMs

Broad quantification

Targeted quantification

PTM crosstalk and pathways/networks

Outline

Introduction

Proteomics: How it works

Proteomic quantificationGlobal shotgunTargeted

Applications

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Relative Quantification by Spectral Counting

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Stochastic sampling

200 600 1,000 1,400 1,800

y9

y8

b10

y7

b9b8

y6

y12

y10y11

b12b6

y5b7

b11

y13b14b13y4

200 600 1,000 1,400 1,800m/z

y9

y8

b10

y7

b9b8

y6

y12

y10y11

b12b6

y5b7

b11

y13b14b13y4

MS/MS Spectrum for peptide XXXXX

Counting the number of MS/MS spectra

0

20

40

60

80

100

A B C A B C A B C

Samples

Ave

rage

spe

ctru

m c

ount 25

1

5

Qian et al., Proteomics, 2005, 5:572.

Accurate Quantification: Stable Isotope Dilution Concept

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Light (12C, 16O, 14N) and heavy isotope (13C, 18O, 15N) labeled peptide pairs have exactly identical chemical properties

Pass together through LC, ESI, In all ion transmission spaces

Sample A

Sample BHeavy

Light

Inte

nsity

m/z

HL

L/H ratio

Heavy isotope (13C, 18O, 15N) labeled peptides serve as internal standards

Quantitative Proteomics Using 16O/18O LabelingCH CR

O18OH

HO E.18OH2

H2O

CH CR

OHO18

HO E+

CH CR

O18HO18 HO E

+

CH CR

O18OH

HO E.18OH2

H2O

CH CR

OHO18

HO E+

CH CR

O18HO18 HO E

+

795.83 799.66 803.48m/z

945.01 948.65 952.29m/z

4 Da4 Da

Dry

Peptides

Resuspendin H2

18O 1:50 Trypsin:Peptide (w/w)

Boil for 10 minutes

STOP

18O-Labeling37 oC, 450 rpm

5hr

Efficient for all tryptic peptides from all samples

1,229.0

1,230.0

1,231.0

1,232.0

1,233.0

1,234.0

1,235.0

1,236.0

1,237.0

1,458 1,463 1,468 1,473 1,478 1,483

Monoisotopic mass

4.0085 Da

Scan number

0 . 0 E + 0 0

5 . 0 E + 0 6

1 . 0 E + 0 7

1 . 5 E + 0 7

1 4 6 4 1 4 6 6 1 4 6 8 1 4 7 0 1 4 7 2 1 4 7 4 1 4 7 6 1 4 7 8 1 4 8 0 1 4 8 2

Pair #372; Charge used = 2 AR = 1.03(Light ÷Heavy)

1,229.0

1,230.0

1,231.0

1,232.0

1,233.0

1,234.0

1,235.0

1,236.0

1,237.0

1,458 1,463 1,468 1,473 1,478 1,483

Monoisotopic mass

4.0085 Da4.0085 Da

Scan number

0 . 0 E + 0 0

5 . 0 E + 0 6

1 . 0 E + 0 7

1 . 5 E + 0 7

1 4 6 4 1 4 6 6 1 4 6 8 1 4 7 0 1 4 7 2 1 4 7 4 1 4 7 6 1 4 7 8 1 4 8 0 1 4 8 2

Pair #372; Charge used = 2 AR = 1.03(Light ÷Heavy)

0 . 0 E + 0 0

5 . 0 E + 0 6

1 . 0 E + 0 7

1 . 5 E + 0 7

1 4 6 4 1 4 6 6 1 4 6 8 1 4 7 0 1 4 7 2 1 4 7 4 1 4 7 6 1 4 7 8 1 4 8 0 1 4 8 2

Pair #372; Charge used = 2 AR = 1.03(Light ÷Heavy)

Petritis, et al. J. Proteome Res. 2009.

From Global Discovery to Targeted Quantification

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Limitations of global discovery: Not reproducible in terms of low overlap between large number of biological replicates or missing data.

Limited sensitivity for detecting low-abundance proteins

90 % overlap

81 % overlap

39 % overlap

10 replicates

Time (min)

Product ion chromatogram

Q1

Triple Quadrupole MS

DetectorLC-ESI

Q2 Q3

Precursor ion selection

Product ion selection

Selected SRMTransitions:

544.90

Targeted quantification and multiplexed assays

445.90601.52

Selected Reaction Monitoring (SRM) mode.50-100 proteins can be tested in a single analysis.

150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900m/z

544.90601.52

535.95

217.09

y92+

y82+

Cathepsin LMIELHNQEYRm/z = 445.90

MS/MS spectrum

Targeted mass spectrometry for quantification

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~100% reproducible detection (Only selected candidates

will be screened)

Better sensitivity and no missing data (10-50 fold more

sensitive)

High specificity and absolute quantification

Multiplexed (>100 proteins per analysis) and affinity-

reagent free

Targeted mass spectrometry can quantify almost

proteins, isoforms…

PRISM‐SRM

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PRISM: high‐pressure high‐resolution separations with intelligent selection and multiplexing

Shi et al., PNAS, 2012, 109, 15395‐400. 

3 µm, C18 column(200 µm i.d. × 50 cm)

Intelligent Selection of targeted peptides

96 well plate

Capillary LC (high pH)

#2 #6 #10

Fraction multiplexing(based on partial orthogonality between low and high pH RPLC)

On-line monitoring heavy internal standard peptides

LC-SRM

Tujin Shi

Outline

Introduction

Proteomics: How it works

Proteomic quantificationGlobal shotgunTargeted

Applications

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Post-translational modifications

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Many types of important PTMs can be measured by proteomics:

Phosphorylation Ubiquitination AcetylationProteolytic processing (N-terminal and C-terminal)Thiol-based redox modifications

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Cysteine, the archetypal redox regulatory amino acid

Ciriolo et al. Cell Death and Differentiation 2005.p1555

Thiol-based redox modifications are fundamental posttranslational regulatory systems in all organisms, playing essential role in regulating metabolism, gene expression, and many other cellular processes

Protein

SH

Protein

SNO·/GSNO

GSSG/GSHH2O2

GSH

GSH

Enzymatic Reducing Systemse.g., Thioredoxin/Glutaredoxin

ROS/RNS

O2-·/H2O2 Protein

SOH

S-sulfenylation

Protein

SNO

S-nitrosylation

Protein

SO3H

Protein

SO2H

Sulfinic acid

Protein

SSG

S-glutathionylation

H+

pH> pKa

REVERSIBLE IRREVERSIBLE

SIGNALING PATHOLOGY

Sulfonic acid

Reactive Cys Thiolate anion

OxyR: a molecular code for redox signaling

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Kim et al., Cell, 2002, 109, 383-396

Conformation changes

Redox Proteomics Approaches

Ascorbate

NEM Blocking free thiols

Glutaredoxin DTT Hydroxylamine

S-glutathionylation

S-acylation

S-nitrosylation

Selective reduction

Protein

SNO SSG Total oxidation S-acylation

LC-MS/MS

Peptides

Thiol‐affinity enrichment

On‐resin tryptic digestion

On‐resin multiplex isobaric labeling

(6-10 biological samples multiplexing)

Guo, et al.,  Nature Protocols, 2014Guo, et al.,  Mol Cell Proteomics, 2014

Application: Total thiol oxidation

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CyanobacteriumSynechocystis sp.PCC6803

Light/dark redox modulation

In collaboration with Dr. HimadriPakrasi at Washington Uni.

Jia Guo

LC-MS/MS analysis

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Proteome-wide Light/Dark Modulation of Protein Thiol Oxidation in Cyanobacteria

32Jia Guo, et al.,  Mol Cell Proteomics, 2014

oxidation

0.5 1 2

Dark DCMULight

Redox dynamics on ~2000 Cys-sites

Site stoichiometry

0

50

100

150

200

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 >90

Num

ber o

f Cys‐site

s

% Oxidation (Reversible) 

LightDarkDCMU

Redox sensitive proteins

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Sedoheptulose‐1,7‐bisP

Aspartate

Oxaloacetate Pyruvate

CO2

Glycerate 3P

1,3‐bisphosphoglycerate

Glyceraldehyde‐3P

Fructose‐1,6‐P2Fructose‐6P

Sedoheptulose‐7P

Ribulose‐5P

Ribulose‐1,5‐P2Prk

Cbbl

Gap2

Fda

Slr2094

Fda

Slr2094

AspCPykCarbon fixation

H2O

PET and phycobilisome

PSII PSICytb

Fd

ATPase

H2O⅟2O2+ H+ PC

PQ

NADP++H+

NADPH PetFPetH

PsbO AtpAAtpG

ApcBApcDApcFCpcB

FNR

0%

30%

60%

90%

120%

150%

% Oxida

tion (Reversible)

Carbon Fixation Light

Dark

DCMU

Jia Guo, et al.,  Mol Cell Proteomics, 2014

Redox sensitivity predicts enzyme functional sites

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(a) (b)

0

1

2

3

4

5

Relativ

e oxidation level  Light

DarkDCMU

(c)Gap2

WT C154S C154/C158SC292S

3.22 Å

NADP Cys154

0

2

4

6

8

10

Relativ

e Qua

ntita

tion Light

DarkDCMU

H2O H2O2 DTT DTT/H2O2

mU

/mg

prot

ein

0

200

400

600

800

1000

1200 Peroxiredoxin

(d) (e) (f)

Jia Guo, et al.,  Mol Cell Proteomics, 2014

Obesity, insulin resistance, and pancreatic islet hyperplasia

LIRKO

LiverInsulin resistance

More insulin secretion

Control

Pancreas

Blood circulatory factors

Islet hyperplasia

Liver Insulin Receptor Knockout (LIRKO) Model (mimic a level of stress)

In collaboration with Rohit Kulkarni at Joslin Diabetes Center

Evidence of Blood Circulatory Factors by Parabiosis Experiments

A LIRKO mouse and awild-type mouse weresurgically joined tocreate a commoncirculation. The beta cellreplication wasevaluated by BrdU after14 weeks.

LIRKO

wild-type

Enriched canonical pathways (mouse islets)

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0

2

4

6

8

10

12

14‐Log

 (p‐value

)Global data

Phospho data

Based on significantly altered proteins & phosphoproteins

The story of a cell under stress

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Compensatory Response of the Islet Proteome

Productivity

Efficiency

Summary

Proteomics has become an indispensable technology for biological research, especially systems biology

New technologies are continually being developed for highly sensitive quantification of proteins, isoforms, and PTMs

It would be interested to see how the technologies are being applied and integrated with other tools to achieve predictive biology and more effective synthetic biology

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