Lecture 3 Differential Expression Proteomics · Lecture 3 Differential Expression Proteomics Arthur...

Lecture 3 Differential Expression Proteomics

Arthur Moseley arthurmoseleydukeedu

Genome Academy April 2013

Quantitative Mass Spectrometry of Peptides and Proteins

ndash Quantitative MS is easy to try hard to do right ndash Sets of ldquoLight and Heavyrdquo reagents can be used for relative

quantitation

ndash Quantitative MS often relies on use of isotopically labeled authentic standards

ndash Spiking authentic stable-labeled molecules (peptides drugs pesticides etc) into samples provides for molar quantitation

bull THE Gold standard approach for quantitative mass spectrometry

ndash Label-free quantitation is often very useful bull Used for relative quantitation and ldquoTop-3rdquo Mole Quantitation bull Ultimate flexibility in experimental design

ldquoOld-Schoolrdquo Differential Expression Proteomics

(ICAT) developed by Reudi Aebersold (Nature Biotechnology 17 994 1999)

The First Mass Spec Based Differential Expression Proteomics

ICAT Reagent and Strategy

Goshe and Smith Curr Op in Biotech (2003) 14101

Stable Isotope Labeling for Quantitative Proteomics - Lots of Options

Analytical Challenges Associated with Performing Quantitative Proteomics Using Chemical Isotopic Labeling

bull Bypassing gels avoids problems with membrane proteins other special cases

bull Sample loading issues contributing to poor dynamic range are reduced

bull Not all proteins contain targeted amino acid (tag dependent consideration)

bull Post-translational modifications can be missed (tag dependent)

bull Quantitation from LCMS relative intensities of isotope clusters

bull Qualitative Identification from LCMSMS peptide sequencing (MSMS)

bull Analytical challenge - very complex mixtures (30000+ peptidessample) are made more complex by isotope labeling (doubles number of analytes)

ndash pre-fractionate samples ndash Multidimensional analytical HPLC (capillary LCLCMSMS)

httpdocsappliedbiosystemscompebiodocs00113379pdf

Applied Biosystems iTRAQ reagents use isobaric tags

Multiple tags present with the same nominal mass in survey spectra Quantitation is done during the MSMS step simultaneously with peptide identification Only quantify peptides sequenced by MSMS - A subset of all peptides present

Label-free methods quantitate all species regardless of identification


Metabolic Stable Isotope Coding

SILAC generates a lot of data regarding 2 samples - Be aware of statistical limitations

Even when quantitative methods are used most of the time the focus is on function There is little attention to the details of quantitation

Such an approach is fundamentally flawed

Forget not the basic principals of quantitative analyses

ndash Replication QCs Validation

Rigorously use Quantitatively Reproducible Analytical Methods Forget not the basics of analytical chemistry

bull Highly reproducible chromatography is required

bull A high sampling rate across the chromatographic peak is required for accurate quantitation bullIdeally want 15-20 sampling points across chromatographic profile bullHighly reproducible chromatography is required for sample-to-sample comparisons

bull High resolution accurate mass (precursor amp products) tandem mass spectrometry technology needed

bull For quantitative selectivity (near isobaric cross-talk)

bull For accurate qualitative identifications 1 FPR at peptide level (Decoy DB Peptide Prophet)

bull No QCs = No Quantifiably Reliable Data

bull No Replication = No Quantifiably Reliable Data

bull No Common Standard = No Meaningful Comparison across Projects

Overview of Label Free Quantitation

LC Separation

Acquisition of MS Data

Import Raw Data Data Alignment amp Feature Extraction

Import Raw MSMS Data

Annotation amp PeptideProtein

Analysis

Statistical Analysis of Differences

Acquisition of Selected MSMS

Data Via Targeted Analysis

Peptide Identification

(Database Search Engine)

(courtesy Rosetta Biosoftware)

LC retention time

mas

s-to

-cha

rge

(mz

) rat

io

Gel-Free Label Free Proteomics High Resolution Accurate Mass 3D Peptide Mass Map

X and Y coordinates identify the peptide Y coordinate (mass-to charge ratio) is fixed to lt5 ppm error

X coordinate (LC Retention Time) has more variability (typically lt 6 seconds)

An isotope group of a peptide

bullIntensity (AUC) of SIC of peptide is the quantitative measure

bullMust be accurately measured across statistically significant sample cohort

Results of Data Alignment based on Accurate Mass and Retention Time

Raw Data

Aligned Data

111015 Features Aligned across 16 LCMS Analyses

of Cell Lines

How to QC this vast Amount of Data

Aligned Data Combined by

Biological Condition

QC of Individual Isotope Groups pairwise t-tests of significance of peak area

measurement

Column Condition QC1 Sample 1 Sample 2 Sample 3 Sample 4 Sample 5 Sample 6 Sample 7 Sample 8 Sample 9 Sample 10 QC 2 Sample 11 Sample 12 Sample 13

Rigorously use Quantitatively Reproducible Analytical Methods Daily QC Checks of Data Acquisition Precision and Reproducibility

Instrument Performance Checks Day 1(+) QCs Column Conditioning Preliminary database searches

Day 2 Data Collection Day 3 Data Collection

QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1

Sample X QC X helliphelliphellip

Day X Data Collection

bull Want to maximize biological powering - analyzing as many samples as possible

bull Must use robust LC-MS platform and singlicate analysis of each sample

bull Data QC is performed by daily injections of a ldquostandardrdquo of the same biological sample (pool)

bull Aliquots of same pool used in all projects ndash QC tracking across projects

Quantitatively Reproducible Analytical Methods Forget not the basics of analytical chemistry

Assessing Quantitative Reproducibility with Daily QCs

bull Analytical Variability ~ 35000 peptides

bull Daily QC Sample (pool of QC plasma sample)

bull Analytical + Biological Variability bull Patient Samples

25 CV Plasma Peptides

Note X- Axis Scale Differences QC Samples 0 to 170 CV Biological Samples 0 to 500 CV


~ 40 peptides CV lt 10



~ 2 peptides

CV lt 25

QC Metric 1 = CV (Anal + Biol Variability) - CV (Anal Variability)

- Alternating cycles (1 sec each) of precursor product scans provides high reproducibility via a high sampling rate across chromatographic peak

- Major attribute of MSE

VVGLSTLPEYIEK 128 CV across all samples

Rigorously use Quantitatively Reproducible Analytical Methods Assessing Quantitative Reproducibility at the Peptide Level with QCs

Reproducibility of Internal Standard Spiked into Each Sample

ADH1_YEAST (50fmolug) Peptide Abundance across 60 patient clinical cohort

DDA Data Qual only

Label Free Intensity Plots differential expression visualization

Cluster Analysis of Label Free Quantitation Datasets

bull Cluster Analyses ndash Examine large data sets

and determine if items behave similarly

ndash Data belonging to the same cluster are similar at some level

ndash Data sets in different clusters are less similar at some level

ndash Make a preliminary assessment of possible relationships between clusters and identify data sets for further investigation

Proteins

Trea

tmen

t Gro

ups

Differential Protein Expression

bull Differential protein expression studies are key for ndash Identifying biomarkers of disease and treatment response ndash Elucidating biological pathways ndash Identifying and validating protein drug targets

bull Essentially all differential proteomics studies have

studied relative protein expression ndash Isotope labeling methods ndash Label free methods

bull Differential proteomic expression studies based on

ldquoabsoluterdquo quantitation have yet to be fully exploited

Relative Protein Expression

bull Provides data on protein expression changes between two or more samples within the same experiment

bull Requires direct comparison of proteolytic peptides or marker ions from proteolytic peptides ndash Provides relative abundance ratios of the same

protein between different samples ndash Data does not easily extrapolate beyond the

experiment bull Experiments are isolated ldquoislands of informationrdquo

One Exemplar Biomarker Discovery amp Verification Project

Biomarkers to Predict Outcomes of Hepatitis C Patient Treatment in Serum of Treatment Naive Patients

Jeanette McCarthy Keyur Patel Joe Lucas and John McHutchison

Spontaneous clearance (~25)

20 cirrhosis

3-5 cancer

Chronic infection

Eligible for Treatment

Responders Non-responders (gt50)

Hepatic Fibrosis Steatosis Insulin resistance Dyslipidemia

Increased risk of diabetes

Unknown consequences

Cohort Selection and Placement in the Pipeline (Guided by an ldquoUnmet Clinical Needrdquo US HUPO 2009)

First Discover in Matched Cohorts to Focus on the Clinical Variable of Interest

Second Verify in All-Comers Trials to Test Robustness

Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s

Biomarker Validation

Biomarker Discovery

Biomarker Verification

Duke Hepatology Biorepository - 3169 patients Discovery Cohort - small discovery experiment - well matched cohort from Biorepository - n = 55 patients - lsquoomic LCMSMS

Biomarker Discovery Paradigm Challenge Hepatitis C Cohorts ndash all by UPLCQ-Tof

Open Platform LCMS LCMSMS (MRM) LCMSMS (MRM)

Verification Cohort 2 - pediatric patients - ldquoall-comersrdquo trial - N = 50 patients

Verification Cohort 1 - well matched cohort from Biorepository - n = 41 patients

Verification Validation Cohort 3 - ldquoall-comersrdquo trial (Australia) - N = 243 patients

Insure Professional Use of Statistical Tools Suitable for High Dimensional Data Analyses

Sparse Latent Factor Regression - Bayesian Factor Regression Modeling

35000 Isotope Groups Predictive Factor ldquoMetaproteinsrdquo

Factor Score ldquoExpression Valuerdquo

Statistical Analysis Joe Lucas PhD

Duke IGSP

bull Regression - Leads directly to prediction bull Sparsity ndash Most peptides are irrelevant for prediction bull Latent Factors ndash let data determine important relationships bull Resulting model for prediction

bull Initial Metaprotein Model - 650 Isotope Groups

Pastor Thomas Bayes

Key Features of Metaprotein Expression Modeling Bayesian Factor Regression Model

bull Allows correction of large scale correlational structure between proteins arising from technical rather than biological variability

bull Casts a ldquowide netrdquo initially for predictive peptides

bull Models both identified and unidentified peptides

bull Utilizes identifications while allowing for incorrect identifications

bull Recognizes that some peptides from a protein may be post-translationally modified and the expression of these peptides may not be representative of the protein as a whole

bull Can be used in the creation of predictive models based on multiple proteins

capturing ldquopathwayrdquo expression

Joe Lucas et al ldquoMetaprotein Expression Modeling for Label-Free Quantitative Proteomicsrdquo J Proteome Res in review Oral presentation 2011 RECOMB Satellite Conference on Computational Proteomics

Remember A Metaprotein Modelhellip bull A Metaprotein is a group of peptides which exhibit

a similar expression pattern across the cohort(s)

bull A Metaprotein may contain ndash All peptides from one protein ndash A subset of peptides from a protein ndash A collection of peptides from multiple proteins

bull Model constructed with intensity measurements

aggregated at the isotope group level ndash Identified or unidentified peptides

Metaprotein expression modeling for label-free quantitative proteomics Lucas JE Thompson JW Dubois LG McCarthy J Tillmann H Thompson A Shire N Hendrickson R Dieguez F Goldman P Schwarz K Patel K McHutchison J Moseley MA BMC Bioinformatics 2012 May 413(1)74

Discovery and Initial Verification of SVR-Prediction Using Unbiased Data

Discovery Data Build Model

Use Model to Predict SVR (Blinded)

Patel et al Hepatology 2011 Jun53(6)1809-1818

Reproducibility of Metaprotein Biosignatures

bull Build predictive model with first three cohorts bull Predict NR SVR in ldquoBig Pharmardquo measured data

ndash different LCMSMS (LTQ-Orbi) system in different lab ndash Metaprotein model maintained consistent results

Discovery Cohort N = 55

Matched

Discovery Cohort Measured by

Big Pharma Lab

Verification Cohort 1

N = 41 Matched


N = 50 All-Comers




protein between different samples ndash Data does not extrapolate beyond the experiment

bull Experiments are isolated ldquoislands of informationrdquo

Absolute Protein Expression ndash lsquoomic scale

bull Calculation of the absolute amount of the proteins present (ng or fm) in a sample ndash Permits determination of stoichiometry of

proteins in macromolecular complexes ndash Permits extrapolation of results to different

experiments in different labs

bull These workers made the notable and unexpected observation ndash ldquothe average MS signal response for the three

most abundant peptides per mole of protein is constant within a coefficient of variation of less than 10rdquo

ndash ldquoGiven an internal standard this relationship is used to calculate a universal response factor (countsmole)rdquo

NOTE ndash ldquoabsoluterdquo is a controversial description

Intensity Distribution of Peptides from One Protein Response Per fmol for a Six Protein Mixture

Biological ldquoValidationrdquo by Determining Stoichometric Ratios

Absolute Quantification of Proteins by LCMSE

A Virtue of Parrallel MS Acquistion

Jeffrey C Silva Marc V Gorenstein Guo-Zhong Li Johannes PC Vissers Scott Geromanos Molecular amp Cellular Proteomics 5144-156 2006

Absolute Quantitation at the Protein Level - E coli lysate spiked with 4 exogenous proteins

Absolute Quantitation for Measurement of Fold-Changes E coli spiking Experiment

Quantitative proteomics reveals metabolic and pathogenic properties of Chlamydia trachomatis developmental forms Hector A Saka1 J Will Thompson2 Yi-Shan Chen1 Yadunanda Kumar13 Laura G Dubois2 M Arthur Moseley2 andRaphael H Valdivia1

Mol Microbiol 2011 December 82(5) 1185ndash1203 PMCID PMC3225693

Quantitative proteomics reveals metabolic and pathogenic properties of Chlamydia trachomatis developmental forms

Hector A Saka1 J Will Thompson2 Yi-Shan Chen1 Yadunanda Kumar13 Laura G Dubois2 M Arthur Moseley2 and Raphael H Valdivia1

C trachomatis is the most common bacterial STD and exhibits a biphasic development cycle ndash EB infectious RB non-infectious

Protein Identification Metrics (Swissprot Human NCBI C trachomatis 1 FDR)

EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)

Mass spectrometry allows us to distinctly isolate the signal from Chlamydia versus Human gt54 of C trachomatis proteome

Global Strategy for Using Mass Spectrometry to Deal with Mixed Proteomes using UPLCUPLCMSMS

Calculate sum ng of Chlamydia proteins use this to normalize fmol values

EB CT842 80 plusmn 3 fmolug RB CT842 84 plusmn 19 fmolug

Calculate fmol of Each Protein Using Method of Silva and Geromanos

EB 339 Chlamydia Proteins CT842 200 plusmn 25 fmol

RB 181 Chlamydia Proteins CT842 47 plusmn 9 fmol

Remove Peptide Matches Shared Between Species

EB 14 Homologous Peptides RB 8 Homologous Peptides

Search Against Both Human and Chlamydia DB

EB 754 Human 3916 Chlamydia Peptides RB 4025 Human 1274 Chlamydia Peptides

Protein NameSample 1

(uncorrected)Sample 2


(corrected)Sample 2

(corrected)Measured Ratio (uncorrected)

Measured Ratio (corrected) Theoretical Ratio

ALBU_BOVIN 217plusmn4 118plusmn35 561plusmn9 132plusmn39 18 43 40ADH1_YEAST 234plusmn6 260plusmn7 604plusmn7 291plusmn7 090 21 20ENO1_YEAST 50plusmn3 112plusmn7 129plusmn5 125plusmn7 050 10 20PYGM_RABIT 277plusmn05 105plusmn5 69plusmn3 117plusmn5 026 060 050E Coli Proteins (average) 109plusmn6 263plusmn17 281plusmn12 294plusmn19 041 10 10

RatiosQuantitation (fmolug)

Validation of Species-Specific Quantitation using a Model System

Sample 1 Sample 2Spiked E Coli Lysate 025 ug 05 ugMouse Brain Lysate 025 ug 0 ugTotal Column Load 05 ug 05 ug

Reproducibility of Protein Quantitation

Protein CV Distribution EB Protein CV Distribution RB

Species-Specific Correction Applied to Chlamydia Protein Quantitation

EB vs RB Quantitation (with Species-Specific Scaling) Select Proteins with Verification

Protein Classes and Relative Abundance in the Developmental Forms

- EBs are enriched in T3S-effectors and chaperones as well as in enzymes involved in glucose catabolism - RBs are enriched for protein synthesis and assembly components ATP generation and transport and nutrient import

Molecular Evidence of the Different Metabolic Properties of the two Developmental Stages

Proteomic results show the EB and RB proteomes are streamlined for their function - maximum infectivity for EB replicative capacity for RBs

Figure 1Fundamentals of isotope-dilution mass spectrometry for quantification (A) Amount of the native or endogenous peptide in the sample is quantified using the ratio of the mass spectrometric response to the endogenous peptide and the SIS peptide and the initial amount of the SIS peptide spiked into the sample (B) In SRM only specific product ions from collision-induced dissociation events are recorded The top panel illustrates the operations of an ion-trap mass spectrometer whereas the bottom panel illustrates the operations of a triple quadrupole mass spectrometer for SRM Note that operations in an ion-trap are sequential in time for a given population of injected ions whereas in a triple quadrupole each quadrupole specializes in carrying the three operations simultaneously on the ions that are continuously conveyed Parent ion mz product ion mz and elution-time criteria from SRM enable selectivity and sensitivity for the detection of specific peptides in complex mixtures from biological sources Recording multiple product ion trasitions as in multiple reaction monitoring can further increase the selectivity SIS Stable isotope-labeled standard SRM Selected reaction monitoring

A Simple Explanation of Selected Reaction Monitoring for Quantitative Analysis Mayya and Han Expert Rev Proteomics 3(6) 597-610 (2006)

(A) In the regular MRM mode of acquisition the mass spectrometer records product ion transitions intended from the SIS peptide and the endogenous peptide in alternate scans (B) The mass spectrometer can be instructed to record multiple product ion transitions from multiple SIS and endogenous peptide pairs and continue to do so in each acquisition cycle for the entire duration of chromatography This allows multiplexed quantification However the reduced sampling frequency can compromise sensitivity reproducibility and accuracy of quantification (C) The chromatographic duration can be subdivided into time-segments or slices wherein different endogenous peptides are quantified using corresponding acquisition cycles However the method is limited by the peak capacity of the online chromatographic method and requires highly reproducible elution times (D) It is practically difficult to achieve consistent elution times of peptides in complex mixtures on a routine basis A hybrid staggered multiplexingrsquo is an optimum strategy as it attempts to maximize the elution time-window for the peptides and also to minimize the number of MRMs in each acquisition cycle The peptide-pairs in each acquisition cycle are indicated for illustrating the staggeredrsquo nature of acquisition

Skyline Open Source Software for Targeted Method Development and Data Analysis

Fig 2 Calibration curves for quantifying heavy-labeled pure AAC and TNFα peptides The ion signals for different amounts of pure synthetic heavy peptides were measured using LCndashMS and used to determine the linear range of quantification on the linear ion trap instrument Duplicate analyses were performed for each amount of peptide injected Error bars show the range for each measurement

A major bottleneck for validation of new clinical diagnostics is the development of highly sensitive and specific assays for quantifying proteins We previously described a method stable isotope standards with capture by antipeptide antibodies wherein a specific tryptic peptide is selected as a stoichiometric representative of the protein from which it is cleaved is enriched from biological samples using immobilized antibodies and is quantitated using mass spectrometry against a spiked internal standard to yield a measure of protein concentration In this study we optimized a magnetic-bead-based platform amenable to high-throughput peptide capture and demonstrated that antibody capture followed by mass spectrometry can achieve ion signal enhancements on the order of 10(3) with precision (CVs lt10) and accuracy (relative error approximately 20) sufficient for quantifying biomarkers in the physiologically relevant ngmL range These methods are generally applicable to any protein or biological fluid of interest and hold great potential for providing a desperately needed bridging technology between biomarker discovery and clinical application

Anal Biochem 2007 Mar 1362(1)44-54 Epub 2006 Dec 20 Antibody-based enrichment of peptides on magnetic beads for mass-spectrometry-based quantification of serum biomarkers Whiteaker JR Zhao L Zhang HY Feng LC Piening BD Anderson L Paulovich AG Source Fred Hutchinson Cancer Research Center 1100 Fairview Avenue N PO Box 19024 Seattle WA 98109-1024 USA

Acknowledgments Duke University Proteomics Core Facility

httpwwwgenomedukeeducoresproteomics

Biostatistics Dr Joseph Lucas Funding NIH S10 grant Duke School of Medicine CTSA grant UL1RR024128

Lecture 3Differential Expression Proteomics




Slide Number 5

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Slide Number 10

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Slide Number 15

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Slide Number 18

Slide Number 19

Slide Number 20





One Exemplar Biomarker Discovery amp Verification ProjectBiomarkers to Predict Outcomes of Hepatitis C Patient Treatment in Serum of Treatment Naive PatientsJeanette McCarthy Keyur Patel Joe Lucas and John McHutchison

Cohort Selection and Placement in the Pipeline(Guided by an ldquoUnmet Clinical Needrdquo US HUPO 2009)

Slide Number 27

Slide Number 28


Remember A Metaprotein Modelhellip


Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39

Quantitative proteomics reveals metabolic and pathogenicproperties of Chlamydia trachomatis developmental formsHector A Saka1 J Will Thompson2 Yi-Shan Chen1 Yadunanda Kumar13 Laura GDubois2 M Arthur Moseley2 and Raphael H Valdivia1

Protein Identification Metrics(Swissprot Human NCBI C trachomatis 1 FDR)


Validation of Species-Specific Quantitationusing a Model System





Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55

AcknowledgmentsDuke University Proteomics Core Facilityhttpwwwgenomedukeeducoresproteomics


ndash Quantitative MS is easy to try hard to do right ndash Sets of ldquoLight and Heavyrdquo reagents can be used for relative

quantitation

ndash Quantitative MS often relies on use of isotopically labeled authentic standards

ndash Spiking authentic stable-labeled molecules (peptides drugs pesticides etc) into samples provides for molar quantitation

bull THE Gold standard approach for quantitative mass spectrometry

ndash Label-free quantitation is often very useful bull Used for relative quantitation and ldquoTop-3rdquo Mole Quantitation bull Ultimate flexibility in experimental design





































LC Separation





Analysis







LC retention time

mas

s-to

-cha

rge

(mz

) rat

io








Raw Data

Aligned Data


of Cell Lines





measurement





QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

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Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55






































LC Separation





Analysis







LC retention time

mas

s-to

-cha

rge

(mz

) rat

io








Raw Data

Aligned Data


of Cell Lines





measurement





QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

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Slide Number 19

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Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55


LC retention time

mas

s-to

-cha

rge

(mz

) rat

io








Raw Data

Aligned Data


of Cell Lines





measurement





QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55



Raw Data

Aligned Data


of Cell Lines





measurement





QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55



measurement





QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55






QC X-1 Sample X-5

Sample X-4

Sample X-3

Sample X-2

Sample X-1


















~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55













~ 2 peptides

CV lt 25








DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55






DDA Data Qual only








Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55









Proteins

Trea

tmen

t Gro

ups
















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55

















20 cirrhosis

3-5 cancer

Chronic infection









Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55





Number of Analytes

Number of Samples

10000s

10s

100-1000

100 -1000

10s

1000s


Biomarker Discovery













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55













Duke IGSP



Pastor Thomas Bayes
























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55

























Matched


Big Pharma Lab


N = 41 Matched


N = 50 All-Comers



























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55




























EB (775)

RB (1120)

420 355 700

HUMAN (990 ~5)

CHLAMYDIA (485 55)

EB (349)

RB (851)

210 139 641 210 216 59

EB (426)

RB (269)















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55















(corrected)Sample 2





























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55
























Slide Number 5

Slide Number 6


Slide Number 8


Slide Number 10

Slide Number 11

Slide Number 12

Slide Number 13


Slide Number 15

Slide Number 16

Slide Number 17

Slide Number 18

Slide Number 19

Slide Number 20







Slide Number 27

Slide Number 28




Slide Number 32



Slide Number 35

Slide Number 36



Slide Number 39









Slide Number 48

Slide Number 49

Slide Number 50


Slide Number 52

Slide Number 53

Slide Number 54

Slide Number 55


Lecture 3 Differential Expression Proteomics · Lecture 3 Differential Expression Proteomics Arthur...

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Transcript of Lecture 3 Differential Expression Proteomics · Lecture 3 Differential Expression Proteomics Arthur...