The Waters strategy for the quantification of proteins

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2 Protein quantification workshop Barcelona, 13 th November 2012 The Waters strategy for the quantification of proteins Robert Tonge Ph.D. Principal Scientist, European Omics Waters Corporation MS Technology Center Manchester, UK 2012 Waters Corporation 2

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 3

4 There are many ways to quantify proteins in complex mixtures 2D gels Discovery proteomics Validation 2012 Waters Corporation 4

5 Main principles of quantitative discovery proteomics using MS 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 Mueller LN et al. JPR (2008);7: Waters Corporation 5

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 6

7 SILAC: stable isotope labeling of amino acids in culture In-vivo label Cells grown in medium containing light (H 6 ) or heavy (D 6 ) arginine Arg incorporated into proteins Combine samples Peptide with incorporated D 6 -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 7

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 8

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: Waters Corporation 9

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 10

11 The Waters method also gives absolute quantification (1) Serendipitous discovery Protein standard development work [ADH] = x mol [BSA] = x mol [HBA] = 0.5 x mol [HBB] = 0.5 x mol Silva JC et al. MCP (2006);5: Waters Corporation 11

12 The Waters method also gives absolute quantification (2) 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 Conc = i 3 1 peptide intensity i 3 1 [Protein of interest] peptide intensity [Protein spike] 50 fmol/µl Silva JC et al. MCP (2006);5: Waters Corporation 12

13 Protein quant output example from LC-MS E 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 13

14 Protein Conentration (fmol/µg) Absolute Quant of Proteins from C.elegans. Qual AND Quant, single experiment 100 Hi3 quant method Silva JC et al. MCP (2006);5:144 Identified in 1D, 2D-3Fraction, and 2D-5Fraction Identified in 2D-3Fraction and 2D-5Fraction Identified in 2D-5Fraction only Protein 2012 Waters Corporation 14

15 Label free quant via other methods Assume more abundant proteins produce higher number of spectra 1 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 15

16 Workflow flexibility 2012 Waters Corporation 16

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 (Verify E / Skyline) 1.Dekkers DHW et al. Curr Proteomics (2010);7: Waters Corporation 17

18 A Common Workflow for Label-Free Protemics/Metabolomics/Lipidomics Integrates Waters Label-Free Data-Independent UPLC/HDMS E technology with TransOmics Informatics powered by Nonlinear Dynamics 2012 Waters Corporation 18

19 Peak detection: Improved detection and quant of co-localising peaks using IMS data Overlapping +2 species (m/z ) Prior to utilization of drift time data 2012 Waters Corporation 19

20 Peak detection: Improved detection and quant of co-localising peaks using IMS data Overlapping +2 species (m/z ) Post drift time 2012 Waters Corporation 20

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 21

22 SILAC modifications 2012 Waters Corporation 22

23 Light peptide variant 2012 Waters Corporation 23

24 Heavy peptide variant 2012 Waters Corporation 24

25 PLGS v3.0 Quantitative SILAC protein-centric output 2012 Waters Corporation 25

26 SILAC example using HDMS E : Huang et al Anal Chem Sep 15;83(18): Cultured human cells 2012 Waters Corporation 26

27 SILAC example with HDMS E : Huang et al: Conclusions The LC-HDMS E 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 27

28 UPLC/FastDDA Available on Synapt G2-S and Xevo G2 Q-TOF Replaces Survey and DDA X 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 28

29 FastDDA search results from PLGS 150msec MS/MS 2012 Waters Corporation 29

30 FastDDA increased peptide and protein identifications Replicate injections, peptide data ~15% Fast DDA Fast DDA Original DDA Original DDA 2012 Waters Corporation 30

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 31

32 Increased reporter ion accuracy for itraq Isobaric tag mode Expected TMT ratio; 6, 12, 25, 50, 100 & 200 Normal MS/MS mode 2012 Waters Corporation 32

33 Spectra recombined to give improved identification and quantification Isobaric tag-specific mode Balanced MS/MS spectrum Sequence specific ions for identification Reporter ion intensity for quantification Normal mode 2012 Waters Corporation 33

34 Analytical challenges in proteomics -Considerations for choosing a quant method Waters Corporation 34

35 Theory goes that label-free methods are associated with higher variability / lower accuracy Additive experimental variability per step The best place to introduce an internal standard is at the start of a process? Only 10-15% Peak area CV Bantscheff M et al. Anal Bioanal Chem (2007);389:1017 Kuster lab 2012 Waters Corporation 35

36 Despite this popularity of label-free approaches is increasing Data from Phillip Wright, Univ. Sheffield 2012 Waters Corporation 36

37 Its not that simple Challenges in proteomics: 1. Dynamic range 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 orthogonal methods (IMS-MS E, RP/RP, etc.) required to provide additional qualitative and quantitative information Label free (intensity) Label free (spectral counting) Stable isotope labeling (SILAC) Metabolic labeling Chemical labelling (itraq, TMT) Dynamic range Bantscheff M et al. Anal Bioanal Chem (2007);389:1017 Kuster lab 2012 Waters Corporation 37

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 38

39 Chimericy: illustrated Qualitative impact Multiplexed fragment ion spectra giving incorrect precursor/product ion assignment Incorrect protein ID Quantitative impact Stacked reporter ion intensities Incorrect quantitative results Luethy et al. J Proteome Res (2008);7: Waters Corporation 39

40 Method comparisons 2012 Waters Corporation 40

41 1. Grossmann et al (1) 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: Waters Corporation 41

42 1. Grossmann et al (2) 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 Grossman J et al. J Proteomics (2010);73: Waters Corporation 42

43 1. Grossmann et al (3) APEX empai T3PQ 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 2012 Waters Corporation 43

44 1. Grossmann et al (5) 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 2012 Waters Corporation 44

45 2. Malmstrom et al (1) 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 Malmstrom J et al. J Nature (2009);460:762 Aebersold lab 2012 Waters Corporation 45

46 no. events no. events 2. Malmstrom et al (2) log (copies/cell) log (copies/cell) r = log spectral counts r = mean = fold error mean = 1.8 Spectral counting showed poor correlation with copies/cell Hi3 correlated well with copies/cell log precursor intensity fold error Malmstrom J et al. J Nature (2009);460:762 Aebersold lab 2012 Waters Corporation 46

47 Other examples with the Waters Hi3 label free quantification method 2012 Waters Corporation 47

48 Log abundance ratio 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] 2012 Waters Corporation 48

49 Example: Levin et al (1) 2012 Waters Corporation 49

50 Example: Levin et al (6) Conclusion This is the first [independent] study which demonstrates a data-independent MS E 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 50

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 MS E /HDMS E gives perfect starting point for MRM assay design 2012 Waters Corporation 51

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53 Experimental design Ratio 1:1 ( light vs heavy ) label(s): None ( light ) label(s): K (lysine): 13 C 6 15 N 2; Δ8Da R (arginine): 13 C 6 15 N 4; Δ10Da ( heavy ) Huang X et al. Anal Chem Sep 15;83(18): Waters Corporation 53

54 Reported protein SILAC ratio values 1:1 expected for all proteins 2012 Waters Corporation 54

55 SILAC example using HDMS E, 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 55

56 SILAC example with HDMS E, 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 56

57 % Example of FastDDA 400ng E. Coli Acquisition; 500msec survey, 150msec MS/MS. Up to 5 components switched upon 400ng Ecoli DDA 0.5s survey 0.15s MSMS _003 1: TOF MS ES BPI 6.98e Time 2012 Waters Corporation 57

58 Accurate mass survey spectrum 36ms aquisition Peptide LVNELTEFAK from BSA Accurate Mass amu < 1ppm mass accuracy 2012 Waters Corporation 58

59 Increased reporter ion intensity for itraq 4x increase intensity 2012 Waters Corporation 59

60 Experimental Ratio 1. Grossmann et al (4) Ratio=2 80fmols Theoretical Ratio (vs 40fmol reference) Fetuin abundance ratio data T3PQ gave almost perfect correlation (R 2 =0.98) Two methods based on spectral counting (APEX and empai) show signal saturation above 100fmol T3PQ dynamic range 2 orders magnitude ( fmol) [data Hi3 3-orders] 2012 Waters Corporation 60

61 SILAC example with HDMS E, 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 , , and , respectively. Accurate quantification Identified peptides per protein were 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 62

62 SILAC example with HDMS E, 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 63

63 Method precision label-free, data independent LC-MS E Protein abundances in two replicate E.coli samples Most data lies on diagonal (unchanged) Protein changes lie off the diagonal 2012 Waters Corporation 64

64 Example: Levin et al (2) The first (independent) comprehensive evaluation of the MS E 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 65

65 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 [ fmol on column amounts] 2012 Waters Corporation 66

66 Example: Levin et al (4) Quant reproducibility Variability 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 2012 Waters Corporation 67

67 Example: Levin et al (5) Quant accuracy 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) 4 proteins measured alone or spiked into 2 different matrices at defined ratios Average error of % (mean SD) Accurate quantification of expression ratios ranging from 1:1.5 to 1: Waters Corporation 68

68 Accuracy test: protein complex Chaperonin-containing TCP-1 complex Two rings, each containing 8 subunits in 1:1 stoichiometry Quant on all subunits should be the same Martin-Benito J et al. EMBO J (2002);21: Waters Corporation 69

69 Accuracy of quantification of subunits 1.0 peptide x peptide y Correct result! protein Reproducibility across column loadings, and instruments 2012 Waters Corporation 70

70 Experimental vs biological variation Precision test Pancreatic β-cells, three preparations Triplicate LC-MS analyses for each Technical variability 14-17% CV (t A, t B, t C ) 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:e Waters Corporation 71

71 Accuracy test: human plasma proteins Clinically validated immunoassay quantification values vs Waters Hi3 label free quant Average protein concentration values from 20 different plasma samples Data courtesy dr. Gertjan Kramer and prof. Hans Aerts, Academic Medical Center, The Netherlands 2012 Waters Corporation 72