the waters strategy for the quantification · can be applied to any samples, including non-growing...

©2012 Waters Corporation 2

The Waters strategy for the quantification

of proteins

Robert Tonge Ph.D.

Principal Scientist, European Omics

Waters Corporation MS Technology Center

Manchester, UK

Protein quantification workshop Barcelona, 13th November 2012


Outline (09:45-10:30)

Overview of protein quantification strategies

Features of quantification using labeling reagents

Features of quantification using label-free methods

– Waters Hi3 label-free quantification

Flexibility of Waters System Solutions

Analytical challenges in proteomics (brief)

– How quantitative methods help/hinder these challenges

Comparisons of quantitative methods

Examples of Waters Hi3 label-free method

Summary

Conclusions


There are many ways to quantify proteins in complex mixtures

Discovery proteomics

Validation 2D gels


Main principles of quantitative ‘discovery proteomics’ using MS

Mueller LN et al. JPR (2008);7:51

• ‘Labelled’ methods • Compare peak areas

across peptide peak pairs separated by ‘tag’ mass

• ‘Label-free’ methods • Label-free quant

• Compare peptide peak volumes across LC-MS runs

• Spectral counting • Compare number of

MS/MS measurements for a peptide peak across LC-MS runs


‘Labelled’ vs ‘Label-free’ (1)

Labelled involves modification of one or more samples with different ‘tags’

Tends to be useful if many steps in sample prep

– In-vivo

o Cell incorporates tagged amino acid into protein

o Protein/peptide levels can be compared via tags

o Only applicable if sample is alive/growing

– In-vitro

o Cell lysate proteins are modified with reactive reagents to tag them

o Applicable to any sample

– No of treatment groups to be compared limited by number of different ‘tags’

– Reagent costs

– Methodological variability


SILAC: stable isotope labeling of amino acids in culture

• ‘In-vivo’ label • Cells grown in medium containing light (H6) or heavy (D6) arginine • Arg incorporated into proteins • Combine samples • Peptide with incorporated D6-arg will have m/z +6Da • Relative abundance of peptide, and thus protein, by comparison

Ong S et al. MCP (2002);1:376 Mann lab


iTRAQ: isobaric tag for relative and absolute quantitation

• ‘In-vitro’ labelling • PRG=Protein reactive group (NHS): N-termini and lysine labelling • MS/MS for peptide ID • MS/MS reporter ions for comparative quant Ross PL et al. MCP (2004);3:1154

Pappin lab


‘Labelled’ vs ‘Label-free’ (2)

Label-free needs no sample modification / manipulation

Can be applied to any samples, including non-growing

No constraints on experimental designs

New samples can be compared to historical data

No reagent costs (iTRAQ is $400/sample!)1

No time for sample preparation reactions

No variability introduced due to preparation reactions

1.Dekkers DHW et al. Curr Proteomics (2010);7:108


Label free protein quant via the Waters method

Relative quantitation via comparison of normalised peak volumes - only been possible following introduction of reproducible nanoUPLC


The Waters method also gives absolute quantification (1)

[ADH] = x mol

[BSA] = x mol[HBA] = 0.5 x mol[HBB] = 0.5 x mol

• Serendipitous discovery • Protein standard development work

Silva JC et al. MCP (2006);5:144


The intensity response under ESI conditions of the three most

intense peptides is a function of the molar amount infused in

the mass spectrometer

Using the ‘Hi3’ peptide intensity of a spiked internal standard as

reference, the absolute amount of every identified protein can

be calculated

The Waters method also gives absolute quantification (2)

Silva JC et al. MCP (2006);5:144

fmol/µL 50

spike] [Protein intensity peptide

interest] of [Protein intensity peptide

3

1i

3

1iConc =


Protein quant output example from LC-MSE exp.

Low energy threshold 250 counts; high energy threshold 100 counts; intensity threshold for search 1500 counts, 20-90min LC time only considered


Absolute Quant of Proteins from C.elegans. Qual AND Quant, single experiment

0.1

1

10

100

Pro

tein

Conentr

ation (

fmol/

µg)

150010005000

Protein

Identified in 1D, 2D-3Fraction, and 2D-5Fraction Identified in 2D-3Fraction and 2D-5Fraction Identified in 2D-5Fraction only

‘Hi3’ quant method Silva JC et al. MCP (2006);5:144


Label free quant via other methods

Assume more abundant proteins produce higher number of

spectra1

– more sequence coverage per protein

– increased number of MS/MS per peptide (redundant info)

Spectral counting (SC)

– Number of MS/MS spectra protein amount

emPAI score

– PAI=protein abundance index. Compares the number of peptides

observed for a protein to the maximum number that could be

observed.

– PAI protein amount

APEX

– Combines elements of SC and EMPAI

1. Liu H et al. Anal Chem (2004);76:4193 Yates lab


Workflow flexibility


Choosing the right tool for the right job

We are big fans of the label-free approach

BUT, each approach has benefits for certain applications

Flexibility is key

Waters System Solutions offer protocols for

– Label-free (commercial pioneers of this approach)

o ProteinLynx Global Server (PLGS) and NEW TransOmics

– SILAC (very new release in PLGS3.0, available Q4)

– ITRAQ/TMT(FastDDA-specific protocol)

– Rapid translation from ToF discovery to QQQ MRM

(VerifyE / Skyline)

1.Dekkers DHW et al. Curr Proteomics (2010);7:108


Integrates Waters Label-Free Data-Independent UPLC/HDMSE technology with TransOmics Informatics™ ……………………… powered by Nonlinear Dynamics

A Common Workflow …for Label-Free Protemics/Metabolomics/Lipidomics


Overlapping +2 species (m/z 952.46) Prior to utilization of drift time data

Peak detection: Improved detection and quant of co-localising peaks using IMS data


Overlapping +2 species (m/z 952.46) Post drift time

Peak detection: Improved detection and quant of co-localising peaks using IMS data


PLGS3.0 with SILAC Automation

In Demo Lab Q3, Customer release Q4

Silac samples are n-fold more complex

than regular samples so high peak

capacity analysis pipeline even more

important than with single sample

analysis


SILAC modifications


‘Light’ peptide variant


‘Heavy’ peptide variant


PLGS v3.0

Quantitative SILAC protein-centric output


SILAC example using HDMSE:

Huang et al

Anal Chem. 2011 Sep 15;83(18):6971-9.

Cultured human cells


SILAC example with HDMSE:

Huang et al: Conclusions

The LC-HDMSE technology yielded high quantitation

accuracy in the analysis of complex proteome mixtures and

is a viable alternative for SILAC-based quantitative

proteomics applications

Accurate quantitation of protein abundance is an essential

task for MS instruments and its associated data analysis

tools

Overall, the SYNAPT G2 with DIA approach showed

better quantitation accuracy and reliability than the

LTQ-Orbitrap with DDA analysis


UPLC/FastDDA

Available on Synapt G2-S and Xevo G2 Q-TOF

Replaces Survey and DDAX

Algorithms transferred to embedded PC

– Charge state recognition, lock mass correction, exact mass include/exclude

lists, collision energy settings

Fast MS survey (eg 90 msec)

– Up to 30 precursor ions may subsequently be selected for MS/MS

MS/MS spectra may be acquired at up to 30 per second

– User-definable scan rate and total time for MS/MS

Accurate mass include/exclude lists

New iTRAQ MS/MS function


FastDDA search results from PLGS

150msec MS/MS


FastDDA increased peptide and protein identifications

Fast DDA Fast DDA Original DDA Original DDA

Replicate injections, peptide data

~15%


FastDDA Isobaric tagging mode (iTRAQ)

Acquires MS/MS with two different collision energy regimes

– Fixed CE to generate reporter

– CE ramp to generate sequence information

o Varied by m/z and z

Two MS/MS spectra are combined into a single spectrum for

processing and searching with PLGS


Isobaric tag mode Expected TMT ratio; 6, 12, 25, 50, 100 & 200

Normal MS/MS mode

Increased reporter ion accuracy for iTRAQ


Spectra recombined to give improved identification and quantification

Normal mode

Isobaric tag-specific mode

Balanced MS/MS spectrum • Sequence specific ions for identification • Reporter ion intensity for quantification


Analytical challenges in proteomics

-Considerations for choosing a quant method-


Theory goes that label-free methods are associated with higher variability / lower accuracy

The best place to introduce an internal standard is at the start of a process?

Only 10-15% Peak area CV

Additiv

e

experi

menta

l vari

ability p

er

ste

p

Bantscheff M et al. Anal Bioanal Chem (2007);389:1017 Kuster lab


Despite this…popularity of label-free approaches is increasing

Data from Phillip Wright, Univ. Sheffield


Dynamic range

– The depth of the proteome data is governed by the amount of material

loaded on the LC column

– Internal standards reduce this depth / ‘dilute’ the sample

– Label-free enables the maximum dynamic range from the analytical system

Label free (intensity) Label free (spectral counting) Stable isotope labeling (SILAC) Metabolic labeling Chemical labelling (iTRAQ, TMT)

Its not that simple Challenges in proteomics: 1. Dynamic range

orthogonal methods (IMS-MSE, RP/RP, etc.) required to provide additional qualitative and quantitative information

Dynamic range

Bantscheff M et al. Anal Bioanal Chem (2007);389:1017 Kuster lab


Challenges in proteomics 2. Chimericy

Definition:

A situation in a data-dependent MS/MS experiment

Precursor ions A and B have similar m/z, and similar

chromatographic retention times ( ½ peak width)

Product ion spectra of A will be contaminated with product

ions from B

Very likely in analyte-congested areas of resolving space

More likely in internally-standardised samples (eg SILAC)


Chimericy: illustrated

Luethy et al. J Proteome Res (2008);7:4031

Qualitative impact

Multiplexed fragment ion spectra

giving incorrect

precursor/product ion

assignment

Incorrect protein ID

Quantitative impact

Stacked reporter ion intensities

Incorrect quantitative results


Method comparisons


1. Grossmann et al (1)

Grossman J et al. J Proteomics (2010);73:1740

Quant using emPAI, APEX, T3PQ (copy of Waters method)

Set 1: 4 samples (S1-S4) different combinations of 4

standard proteins

Std proteins previously quantified by AA analysis

Set 2: 1ug yeast extract with concentration range of fetuin

spike (0-300 fmol on column)



Grossman J et al. J Proteomics (2010);73:1740

Set 1

Good correlation between

the T3PQ measurement

and protein abundance

Signal response authors

machine: 1.68E6=20fmols

Black: Beta Galactosidase

Green: Fetuin

Blue: G3PDH

Red: Beta lactoglobulin



• Fetuin concs spiked into yeast and concentration of fetuin+7 different yeast proteins plotted

• Only fetuin quant line should increase • emPAI and APEX quant saturates, and signals are variable cf. T3PQ

APEX

emPAI

T3PQ


“Our evaluation shows that the currently

publicly available label-free quantification

methods are limited in terms of dynamic

range, variance, and accuracy of protein

abundance calculation”


“Precursor signal intensity based methods (T3PQ)

turn out to be more robust”

i.e a copy of the Waters method


2. Malmstrom et al (1)

Malmstrom J et al. J Nature (2009);460:762 Aebersold lab

19 proteins from leptospira

absolutely quantified by LC-

MRM using stable-isotope

labelled internal standard

peptides (32 peptides for 19

proteins)

Baseline quant figs for 19

proteins spanning 40-

15,000 copies per cell

abundance levels

Compared this baseline with

several label free MS methods


log

(co

pie

s/c

ell)

log

(co

pie

s/c

ell)

10

9

8

7

6

5

4

3

10

9

8

7

6

5

4

3

log spectral counts

log precursor intensity

3 4 5 6 7 8

15 16 17 18 19 20 21 22

r = 0.75141

r = 0.93761

no

. events

no

. events

mean = 2.9

mean = 1.8

fold error

3 4 5 6 71 2

50

40

30

20

10

0

fold error

2.0 2.5 3.0 3.5 4.01.0 1.5

350

300

250

200

150

100

50

0

Malmstrom J et al. J Nature (2009);460:762 Aebersold lab

2. Malmstrom et al (2)

Hi3 correlated well with copies/cell

Spectral counting showed poor correlation with copies/cell


Other examples with the Waters Hi3 label

free quantification method


Accuracy test: 4 protein mixture differentially spiked into E. coli

E.coli protein levels are unchanged

Protein standard spike ratios theoretically 8, 2 and 0.5-fold

Protein, ratio (log ratio log ratio 95% confidence interval) [probability of up

or down regulation at stated ratio; 1.00=very certain up reg]

Log a

bundance r

atio


Example: Levin et al (1)


Example: Levin et al (6) Conclusion

This is the first [independent] study which demonstrates a data-independent MSE approach is capable of producing reliable and accurate quantification of proteins in various background matrices and across dozens of samples

This method also produced reliable identification with high peptide and sequence coverage


Conclusions

Many methods to quantify proteins in complex samples

– Flexibility is key

Label-free methods are gaining in popularity

– Applicable to any sample

– No reagent costs

– No compromise on dynamic range or chimeracy

– Limitless flexibility with experimental designs

– Not as variable as many people think (10-15% CVs)

– A match for any other protein quant method?

The Waters Hi3 label-free quantification method gives protein identification, plus absolute quantification ‘for free’

Absolute quantification can be very beneficial for a fuller understanding of biological systems

MSE/HDMSE gives perfect starting point for MRM assay design


Experimental design

Ratio 1:1 (‘light’ vs ’heavy’)

label(s): None

(‘light’)

label(s): K (lysine): 13C6

15N2; Δ8Da

R (arginine): 13C615N4; Δ10Da

(‘heavy’)

Huang X et al. Anal Chem. 2011 Sep 15;83(18):6971-9


Reported protein SILAC ratio values

1:1 expected for all proteins


SILAC example using HDMSE, Huang:

Data quality (1)

Distributions of mass accuracy for the precursor (A) and product ions (B)

The high accuracy of the TOF-MS detector results in that mass errors of less than ±10 ppm for more than 92.0% of the precursor ions (70% <

3ppm) and more than 91.9% of the product ions (60% <3ppm)

Accurate mass for precursor and product ions for confidence in identification

High resolution MS and MS/MS data makes quantitation possible from both precursor and product ions


SILAC example with HDMSE, Huang:

Data quality (2)

High reproducibility

Difference in RT for 97.8% of

the pairs was less than

±0.05 min (A)

Difference in mass error for

92.3% of the pairs was less

than ±5 ppm (B)

Difference in ion mobility

(IM) for 96.4% of the pairs

are less than ±0.5 bins (C)


Example of FastDDA – 400ng E. Coli

400ng Ecoli DDA 0.5s survey 0.15s MSMS

Time15.00 20.00 25.00 30.00 35.00 40.00 45.00 50.00 55.00 60.00 65.00 70.00 75.00 80.00

%

0

100

200111_003 1: TOF MS ES+ BPI

6.98e5

514.3

485.3

562.3

401.2

601.3

426.7

452.7

488.5

406.2

468.3

460.3

478.8

517.8

688.8

447.7

445.6

609.8

575.3

528.3

551.8

995.2

672.4

901.5

750.4

799.9

843.1

983.5

598.8

799.4

801.8759.4811.4

1104.3

964.1766.4

Acquisition; 500msec survey, 150msec MS/MS.

Up to 5 components switched upon


Accurate mass survey spectrum 36ms aquisition

Peptide LVNELTEFAK from BSA

Accurate Mass 582.3195amu

< 1ppm mass accuracy


Increased reporter ion intensity for iTRAQ

4x increase intensity



Theoretical Ratio (vs 40fmol reference)

Experim

enta

l Ratio

Ratio=2 80fmols

Fetuin abundance ratio data

T3PQ gave almost perfect correlation (R2=0.98)

Two methods based on spectral counting (APEX and emPAI) show signal

saturation above 100fmol

T3PQ dynamic range 2 orders magnitude (0.7-135fmol) [data Hi3 3-orders]



Quant accuracy

Heavy- and light-labelled cells combined at ratios 1:1(A), 1:5(B), 1:10(C)

Correlations between the heavy versus light proteins were 0.9759, 0.9556, and 0.9711, respectively. Accurate quantification

Identified peptides per protein were 10.24-10.68 on average demonstrating a relatively high degree of sequence coverage by the LC-MSE analysis

Such high sequence coverage benefits not only protein identification but also the accuracy of protein quantitation



Quant comparison with LTQ-Orbi

With the Orbitrap the ratios of

quantified peptides show a

comet-like distribution

With SYNAPT-G2 there is a

more uniform distribution

Synapt shows more

accurate quant at lower

peptide intensities

True value of ratio is indicated by dashed line


Method precision label-free, data independent LC-MSE

Protein abundances in

two replicate E.coli

samples

Most data lies on

diagonal (unchanged)

Protein changes lie off

the diagonal


Example: Levin et al (2)

The first (independent) comprehensive evaluation of the MSE approach for proteomic analysis

Technique was assessed for reproducibility, linear response, quantification accuracy, and protein identification power

Used typical samples used in proteomic analysis

(low, medium, and high complexity)

Protein abundances were calculated by summing the volumes of the three most intense peptides for each proteins in a sample (‘Hi3’ method)


Example: Levin et al (3) Linearity

Linear dynamic range of quantification of three orders of magnitude

Limit of quantification of 61 amol/uL in low-complexity samples and 488 amol/uL in high-complexity samples [0.3-2.4fmol on column amounts]


Example: Levin et al (4) Quant reproducibility

Example data for myoglobin measured alone or in two matrices

2 concentrations, 10 replicates of each

8-26% CV for label free quantification – highly reproducible quantification

Variability


Example: Levin et al (5) Quant accuracy

4 proteins measured alone or spiked into 2 different matrices at defined ratios

Average error of 26.3 12.6% (mean SD)

Accurate quantification of expression ratios ranging from

1:1.5 to 1:6

Expected 1:4 Log (-2.00)

Expected 1:1.5 Log (-0.85)

Expected 2:1 Log (1.00)

Expected 6:1 Log (2.58)


Chaperonin-containing TCP-1 complex

Two rings, each containing 8 subunits in 1:1 stoichiometry

– Quant on all subunits should be the same

Accuracy test: protein complex

Martin-Benito J et al. EMBO J (2002);21:6377


protein

peptide x peptide y

Accuracy of quantification of subunits

1.0

Correct result! Reproducibility across column loadings, and instruments


Experimental vs biological variation Precision test

Pancreatic β-cells, three preparations

Triplicate LC-MS analyses for each

Technical variability 14-17% CV (tA, tB, tC)

– care is needed to achieve this

Technical variability + Biological variability seen in

b1 and b2: ~20%

– depends on normalisation method

Technical variability < biological variability

Differences in molar abundance of proteins between

cell preparations of 45% could be discerned

Black=median

White=average

Grey=95% CI

Range, upper quartile, lower quartile

Martens GA et al. Plos One (2010);5:e14214


Accuracy test: human plasma proteins

Data courtesy dr. Gertjan Kramer and prof. Hans Aerts, Academic Medical Center, The Netherlands

Clinically validated immunoassay quantification values vs Waters Hi3 label free quant

Average protein concentration values from 20 different plasma samples

the waters strategy for the quantification · can be applied to any samples, including non-growing...

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