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©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
©2012 Waters Corporation 3
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
©2012 Waters Corporation 4
There are many ways to quantify proteins in complex mixtures
Discovery proteomics
Validation 2D gels
©2012 Waters Corporation 5
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
©2012 Waters Corporation 6
‘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
©2012 Waters Corporation 7
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
©2012 Waters Corporation 8
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
©2012 Waters Corporation 9
‘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
©2012 Waters Corporation 10
Label free protein quant via the Waters method
Relative quantitation via comparison of normalised peak volumes - only been possible following introduction of reproducible nanoUPLC
©2012 Waters Corporation 11
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
©2012 Waters Corporation 12
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 =
©2012 Waters Corporation 13
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
©2012 Waters Corporation 14
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
©2012 Waters Corporation 15
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
©2012 Waters Corporation 16
Workflow flexibility
©2012 Waters Corporation 17
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
©2012 Waters Corporation 18
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
©2012 Waters Corporation 19
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
©2012 Waters Corporation 20
Overlapping +2 species (m/z 952.46) Post drift time
Peak detection: Improved detection and quant of co-localising peaks using IMS data
©2012 Waters Corporation 21
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
©2012 Waters Corporation 22
SILAC modifications
©2012 Waters Corporation 23
‘Light’ peptide variant
©2012 Waters Corporation 24
‘Heavy’ peptide variant
©2012 Waters Corporation 25
PLGS v3.0
Quantitative SILAC protein-centric output
©2012 Waters Corporation 26
SILAC example using HDMSE:
Huang et al
Anal Chem. 2011 Sep 15;83(18):6971-9.
Cultured human cells
©2012 Waters Corporation 27
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
©2012 Waters Corporation 28
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
©2012 Waters Corporation 29
FastDDA search results from PLGS
150msec MS/MS
©2012 Waters Corporation 30
FastDDA increased peptide and protein identifications
Fast DDA Fast DDA Original DDA Original DDA
Replicate injections, peptide data
~15%
©2012 Waters Corporation 31
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
©2012 Waters Corporation 32
Isobaric tag mode Expected TMT ratio; 6, 12, 25, 50, 100 & 200
Normal MS/MS mode
Increased reporter ion accuracy for iTRAQ
©2012 Waters Corporation 33
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
©2012 Waters Corporation 34
Analytical challenges in proteomics
-Considerations for choosing a quant method-
©2012 Waters Corporation 35
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
©2012 Waters Corporation 36
Despite this…popularity of label-free approaches is increasing
Data from Phillip Wright, Univ. Sheffield
©2012 Waters Corporation 37
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
©2012 Waters Corporation 38
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)
©2012 Waters Corporation 39
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
©2012 Waters Corporation 40
Method comparisons
©2012 Waters Corporation 41
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)
©2012 Waters Corporation 42
1. Grossmann et al (2)
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
©2012 Waters Corporation 43
1. Grossmann et al (3)
• 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
©2012 Waters Corporation 44
“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”
1. Grossmann et al (5)
“Precursor signal intensity based methods (T3PQ)
turn out to be more robust”
i.e a copy of the Waters method
©2012 Waters Corporation 45
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
©2012 Waters Corporation 46
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
©2012 Waters Corporation 47
Other examples with the Waters Hi3 label
free quantification method
©2012 Waters Corporation 48
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
©2012 Waters Corporation 49
Example: Levin et al (1)
©2012 Waters Corporation 50
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
©2012 Waters Corporation 51
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
©2012 Waters Corporation 53
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
©2012 Waters Corporation 54
Reported protein SILAC ratio values
1:1 expected for all proteins
©2012 Waters Corporation 55
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
©2012 Waters Corporation 56
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)
©2012 Waters Corporation 57
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
©2012 Waters Corporation 58
Accurate mass survey spectrum 36ms aquisition
Peptide LVNELTEFAK from BSA
Accurate Mass 582.3195amu
< 1ppm mass accuracy
©2012 Waters Corporation 59
Increased reporter ion intensity for iTRAQ
4x increase intensity
©2012 Waters Corporation 60
1. Grossmann et al (4)
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]
©2012 Waters Corporation 62
SILAC example with HDMSE, Huang:
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
©2012 Waters Corporation 63
SILAC example with HDMSE, Huang:
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
©2012 Waters Corporation 64
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
©2012 Waters Corporation 65
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)
©2012 Waters Corporation 66
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]
©2012 Waters Corporation 67
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
©2012 Waters Corporation 68
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)
©2012 Waters Corporation 69
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
©2012 Waters Corporation 70
protein
peptide x peptide y
Accuracy of quantification of subunits
1.0
Correct result! Reproducibility across column loadings, and instruments
©2012 Waters Corporation 71
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
©2012 Waters Corporation 72
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