1. Relative quantification of proteins by 2D DIGE and label free LC-MS/MS
6th European Summer School
19-25 August 2012
Martin Wells Brixen/Bressanone, Italy

2. Introduction Relative quantification – what do we need?
The challenges and limitations specific to proteomics
The Progenesis software brand – concept and philosophy
The relative quant products:
Progenesis SameSpots for 2D gels
Progenesis LC-MS for proteomics
Progenesis CoMet for metabolomics
Progenesis MALDI

3. Nonlinear Dynamics Ltd.
Who are we?
Nonlinear Dynamics develops proteomics and metabolomics analysis software that is different, ground-breaking and above all designed to help you generate reliable conclusions that are reproducible within and across-labs.
Founded 1989
Head Office - Newcastle Upon Tyne, UK
This slide introduces Nonlinear Dynamics and Progenesis SameSpots. We make a point to say we are helping solve the main problems with 2D analysis which is editing, matching and subjectivity. All Nonlinear marketing is based around SOS – Speed, Objectivity, Statistics.

4. Proteomics – Relative Quantification
Example applications
Discovery of protein expression changes related to disease or environment conditions
Characterisation of proteins or complexes to understand functionality and or activity

5. Relative quantification - What is the goal?
“To discover the proteins that warrant further investigation as rapidly, objectively and reliably as possible.”
Proteins x, y and z are significantly changing in their abundance as a apparent result of changes in the experimental conditions. Investigate these observation…

6. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading
Reliable measure of variance within experimental groups
Statistical tools to confidently find differences that are significant and likely to be true
Identification workflow to annotate the measured features
Confirmation that other measurements are in consensus

7. Like for like measurements
Feature detection and matching has been one of the major challenges in relative quantitative proteomics data analysis, leading to time consuming subjective user editing and non-reproducible results.
Defining a feature consistently is more important to relative quantification than individual boundaries.
Independent detection of each files leads to inconsistent interpretation of features and matching challenges
How to handle / avoid “missing” data. Ie abundance measurement in only 3 out of 5 samples

8. Aggregate co-detection
Mapping the detection to all runs avoiding missing data
Aggregate co-detection

9. Data alignment – a prerequisite to co-detection
Section prior to alignment
Section after alignment

10. Like for like measurements

11. Precision of label free quantification – ion abundance
Mean number of FTMS data points/peak
Human Lysate Digest 750ng 70 min separation mean peak width 90% Max ~21s
CVs calculated on quintruplicate analyses of 47,482 multiply charged features
Inst: Orbitrap XL and Dionex RSLCn
Duncan Smith, Paterson Institute for Cancer Research, Manchester

12. Quantitative Precision and Accuracy
Not accurate,
not precise
Accurate,
not precise
Precise,
not accurate
Accurate and precise
“The most complete dataset with accurate ratio’s and high precision was achieved by Progenesis”
Richard R. Sprenger, USD, Odense - ASMS Label-free absolute quantification of proteomes: evaluation and comparison of bioinformatics platforms and strategies

13. Benefit of high mass resolution in complex mixtures
The peptide outlines mark the boundaries of each isotope that forms part of the peptide. The peptide abundance is the sum of the peak areas within the isotope boundaries. In the example below the blue shaded areas show the peak areas that are included when calculating the abundance of that peptide. This means we can separate out the abundances of overlapping peptides, as shown in the example above.

14. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading

15. Correction for sample loading
If you loaded 20% more sample then most proteins will display a 20% increase in abundance, assuming…
Data Normalisation
Assuming like for like measurements are taken for all features you can calculate ratios for each feature. This is repeated to a common file for each sample. These ratios should on average be 1:1 assuming most proteins are not affected by the experimental condition.

16. Relative normalised abundances
5 groups with 3 replicates per group.
Graphical expression profile showing group means with error bars representing 95% confidence bounds.

17. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading
Reliable measure of variance within experimental groups

18. Reliable measure of variance
Technical noise
- e.g. experimentally introduced bias
Analytical inadequacies
- e.g. incorrect or missing quantification of experiment data
Biological variation
- how do we handle this?
These obstacles result in a loss of power and, therefore, a reduction in our ability to confidently discover significant expression changes

19. Reliable measure of variance
Determining the variance of the abundance measurement enables determination of confidence bounds. (95%)
The greater the number of replicates the smaller the confidence interval - in a well controlled environment.
Variance if often proportional to the mean.
The confidence of any reported difference or POWER is a relationship between the size of the confidence intervals and the effect size.

20. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading
Reliable measure of variance within experimental groups
Statistical tools to confidently find differences that are significant and likely to be true

21. Statistical tools
Know the assumptions and limitations of the test you apply
Student T-test and One-way ANOVA assume:
Normal distribution
Equal variance
Sample independence
To achieve the above a data transformation is typically required.
Proteomics data is typically log distributed and requires a transformation to meet the pre-requisites of many statistical tests
Log distribution
Normal distribution

22. Uni and multivariate statistics
The complete data table of quantitative information, without missing data, enables the robust and confident application of multivariate statistics to support traditional univariate tests.
Principle component analysis PCA
ANOVA p-values, typically p<0.05 – ie 5% type I error (false positive)
False discovery rate p value “correction” – q values to reduce type 1 errors
Power analysis per feature to reduce type II errors (false negative)
Correlation analysis of proteins or peptides ions abundances
See which protein abundances are highly correlated. Is there a biological cause to this effect.
The greater the biological variation the more reliable this relationship.

23. Principle component analysis
Fantastic overview of your data, enabling assessment of the major contributing factors (principle components) to variance in your experiment.
Eg. Are all the files within each group showing similar overall characteristics to each other. Are all groups different to all other groups or just one group is different to the rest.

24. False discoveries and q-values
False discoveries occur when we are observing a difference when in truth there is none, thus indicating a test of poor specificity. Q-values are a tool to reduce such false discoveries
False Discovery Rate (FDR) assumes a uniform distribution of p-values but ...
Theoretical p-value distribution
Actual p-value distribution
q-values take the into account the actual p-value distribution and can be referred to as a corrected p-value
[1] Storey,J.D. and Tibshirani,R. (2003) Statistical significance for genomewide studies. Proc. Natl Acad. Sci. USA, 100, 9440–9445.

25. Link protein expression to pathways
Find similar expression profiles
Link protein expression to pathways
Label Free LC-MS Based Proteomics for Integrated Preclinical Pharmaceutical Toxicology. Experience from the FP6 InnoMed PredTox Consortium. Scientific poster presented by Dr. Ben Collins from University College Dublin, Ireland. ASMS, 2010.

26. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading
Reliable measure of variance within experimental groups
Statistical tools to confidently find differences that are significant and likely to be true
Identification workflow to annotate the measured features

27. Identification workflow with co-detection
Peptide ion indentified obtained from any one file
No Missing data!
Identification can be confirmed by any other file(s)

28. Should MSMS acquisition simple be replicated for each sample?
22min
iGPF
DDA
A MSMS of (7.8% mz window)
B MSMS of (7.0% mz window) RT
C MSMS of (8.0% mz window)
Quintuplicate analysis gives
5 Identical LCMS experiments/sample
D MSMS of (12.3% mz window)
iGPF gives
5 completely different LCMSMS experiments/sample
E MSMS of (64.8% mz window)
92min

29. Identification workflow
Using iGPF 3.5 time more peptides were identified compared to DDA.
Many of the ‘unsuccessful’ MS2 spectra are very weak due to the low abundance of the precursor.
41,482 multiple charge ions
41,482 multiply charged ions quantified from MS1 data
FDR <1%
Further 2D-LC-MSMS annotations yielded an additional 10,100 peptide ID’s due to a reduction in complexity and increased on column concentration
iGPF 16,285
(39%)
DDA 4,522
(11%)
Duncan Smith, Paterson Institute for Cancer Research, Manchester

30. What do we need?
Experimental design that can answer the biological questions
Like for like measure of abundance across all samples to make relative comparisons
Correction for differences in sample loading
Reliable measure of variance within experimental groups
Statistical tools to confidently find differences that are significant and likely to be true
Identification workflow to annotate the measured features
Confirmation that any other measurements are in consensus

31. Protein Abundance Results
Multiple measurements identified as unique and from the same protein can be collated and the protein abundance calculated.
However you may have several reported fold changes – so which is correct?

32. Relative protein abundance calculations

33. Protein “Abundance” Results
Are all peptide measurements in agreement? Or do some peptides reflect modification differences between groups rather than abundances changes.
Activation of the same quantity of protein may result in some peptides changing significantly whilst all others remaining constant.
Are there any technical (eg miscleavage) or biological (eg PTM) outliers that need filtering out of the “abundance” measure.

34. Peptide consensus – example PTM

35. Progenesis LC-MS analytical workflow
Directly load vendor data files
Apply lock-mass correction
Apply dead time correction (if applicable)
Data
Import
RT Alignment
Wikipedia
LABEL FREE QUANTIFICATION
Automatic detection
normalisation & quantification
of LC-MS data at the peptide level
Set-up experiment groups
View Results & Peptide Ion Quantification
Multivariate Statistical
Analysis
The LC-MS data analysis components follow a linear workflow
By modularising the workflow each step can be automated as much as possible, making it fast
Fast analysis means you can run more replicates within practical time frames and quickly visualise your LC-MS data
Provides a guided approach for analysis of complex data with minimal user input
A sequential guided approach helps makes your analysis more objective
Allows you to validate and correct the analysis at each stage
This helps you understand how reliable your data is and improve the quality of your results
THEN IDENTIFY
Generate additional MSE/MS2 data to annotate quantification data
Peptide Search & Import Results
Protein View & Quantification
Report

36. Analyse fractionated samples
Analysis of Each Fraction
Reduce sample complexity but quantify more runs
Recombine Fractions
Normalise across fractions to
see global view
Protein View
Quantification & identification at the protein level
View Peptide Data
Delve into underlying
peptide information
Inclusion lists

37. Normalising pre-fractionated data
Two step process
Standard normalisation within each fraction
Use common abundance distribution characteristics to normalise between fractions

38. Precision and Accuracy – what’s needed?
Precision is more common to see reported and maybe easier to access with technical repetition Is 15%-20% Precision good – is it good enough?? Effect Size - The fold change between the groups The POWER – combination of both the above may be more suitable and relevant to differential expression results. Also takes into consideration of the sample size. Accuracy is more challenging and requires some prior knowledge . Ie the “correct” or target answer. Is 15-20% Accuracy good – is it good enough??
FDA Guidance for Industry - Bioanalytical Method Validation
The precision should not exceed 15% of the coefficient of variation (CV) except for the LLOQ, where it should not exceed 20% of the CV.
The accuracy should be within 15% of the actual value except at LLOQ, where it should not deviate by more than 20%.

39. Impact of Precision to relative quant
~15% CV
~30% CV
~50% CV

40. What analytical precision is needed?
For relative quant would it be more appropriate to report POWER than precision or even fold change?
Something that is close to the limits of detection (LOD) is likely to have poor precision (in that group) but if the effect size (fold change) is large then any short coming in precision may not be critical to the significance of the observed difference.
p<0.05
Would statistical POWER be more informative?

41. Precision and Accuracy – what’s needed?
Accuracy is more challenging and requires some prior knowledge . Ie the “correct” or target answer. Is 15-20% Accuracy good – is it good enough?? 30 50 40 20 60 10
fmol

42. Conclusion
Relative quantification of proteomics data can provide some extremely powerful biological insight.
A clear understanding of the software tools, their assumptions and limitations are vital to the correct interpretation of the data.
Knowledge of the major contributors to technical variance in your whole workflow and the control and monitoring of this can greatly impact your confidence in the results.

43. The Progenesis product range
Progenesis SameSpots for 2D gels
Progenesis LC-MS for proteomics
Progenesis CoMet for metabolomics
Progenesis MALDI

44. Thank you for your attention
Acknowledgements
Colleagues at Nonlinear Dynamics
Duncan Smith et al,
Paterson Institute for Cancer Research, Manchester
Thank you for your attention
Stats glossary