Calibration in Proteomics. Proteomics 202 :: Practical Proteomics Using the Skyline Software Ecosystem Lindsay K. Pino Monday, Jan 22

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1 Calibration in Proteomics Proteomics 202 :: Practical Proteomics Using the Skyline Software Ecosystem Lindsay K. Pino Monday, Jan 22

2 objectives Define calibration for proteomics List common methods for MS calibration Review general practices for constructing calibration curves for MS proteomics 2

3 agenda Review calibration within the framework of assay validation Assess impacts of figures of merit on a proteomics assay Discuss current and future methods for calibration curve construction and analysis 3

4 Recall: Establishing knowledge in a targeted assay What peptides are (1) detectable, (2) chromatograph well, (3) are unique to the protein, and (4) are stable and robust over time? What transitions provide a sensitive and unique measure of that peptide? 4

5 asur ut ut ua tity MS signal is not inherently calibrated S = k c + b S = signal (MS intensity) k = (ionization) coefficient c = concentration* of analyte *influenced by many factors, including sample preparation, degradation, autosampler accuracy, ESI stability, b = background signal i ut ua tity 5

6 asur ut ut ua tity Ideally, MS signal correlates linearly with input S = k c + b Coming up! Us S yli s calibrati curv functionality to calibrate peak areas to known input quantities i ut ua tity 6

7 Primary standards define units of weights and measures 7

8 Meaningful, reproducible quantitative MS proteomics 8

9 Defining method validation Method validation is the process used to confirm that the analytical procedure employed for a specific test is suitable for its intended use. Results from method validation can be used to judge the quality, reliability and consistency of analytical results; it is an integral part of any good analytical practice. 9

10 Validation Recommendation 1: Fit for purpose tiers of validation Carr

11 Validation Recommendation 2: Minimal set of experiments for quantification Grant & Hoofnagle

12 Validation Recommendation 3: CPTAC guidelines for assay characterization 12

13 Review check 1 Why is it necessary to calibrate an assay? What determines the validation experiments required for an assay? 13

14 agenda Review calibration within the framework of assay validation Assess impacts of figures of merit on a proteomics assay Discuss current and future methods for calibration curve construction and analysis 14

15 Accuracy and precision are two well-known figures of merit High Precision Low Precision High Accuracy Low Accuracy 15

16 Reproducibility, linearity, and limit of detection / quantitation 16

17 Reproducibility, linearity, and limit of detection / quantitation 17

18 Reproducibility, linearity, and limit of detection / quantitation 18

19 Reproducibility, linearity, and limit of detection / quantitation 19

20 Reproducibility, linearity, and limit of detection / quantitation 20

21 Reproducibility, linearity, and limit of detection / quantitation 21

22 Highlight: Measurement by a novel LC-MS/MS methodology reveals similar serum concentrations of Vitamin D-binding protein in blacks and whites Henderson

23 Assay development and validation can and should eliminate candidate targets Henderson

24 Review check 2 How would you set up a validation experiment for reproducibility? If you update your sample preparation protocol, do you have to repeat validations? 24

25 agenda Review calibration within the framework of assay validation Assess impacts of figures of merit on a proteomics assay Discuss current and future methods for calibration curve construction and analysis 25

26 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 26

27 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 2. Formulate serial dilutions of analyte peptides 27

28 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 2. Formulate serial dilutions of analyte peptides Types of standards Internal vs external Peptides (specific or general), xt ti s, proteins, SILAC 28

29 Effect of standard choice on quantification su rt th us of recombinant [stable isotope labeled (SIL)] proteins as [internal standards] rather than [tryptic SIL peptides] or [enzymatically cleavable SIL surrogates] Shuford

30 Effect of standard choice on quantification [Quantitative protein epitope signature tags] are a valuable alternative to [stable-isotope labeled (SIL) proteins] in terms of precision and accuracy, although SIL and [winged] SIL peptides can yield accurate results as well when peptides are selected c sci usly Oeckl

31 Effect of standard choice on quantification Shuford 2017, Oeckl

32 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 2. Formulate serial dilutions of analyte peptides 32

33 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 2. Formulate serial dilutions of analyte peptides 33

34 RECALL: How would you design a curve for <50 peptides? >1,000 peptides? Targeted MS Scale: 10 s s of peptides Method: Synthesize labeled isotope standards DIA-MS Scale: 1,000 10,000 s of peptides Method: Background proteome Synthesized heavyisotope standard Endogenous peptides 34

35 Constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix 2. Formulate serial dilutions of analyte peptides 3. Acquire calibration curve samples Technical replicate 1 blank increasing [analyte] Technical replicate 2 Technical replicate 3 35

36 Identifying common shapes of calibration curves 1 fmol/ul AGQSAYPEFSR++ 10 fmol/ul 100 fmol/ul 1000 fmol/ul Pino LK, unpublished 36

37 Determining the most quantitative proxy for a protein of interest Pino LK, unpublished 37

38 Approaches to modeling LC-MS calibration curve data Method Data Model LOD/LOQ definition Citation Blank Sample Blank + sample LOD = 3.29 σ B LOQ = 3 LOD = 10 σ B Currie 1968 Blank and Low Concentration Sample Calibration curve LOD = μ B +t (1-β) (σ B + σ S )/ LOQ = 3 LOD Addona 2009 Relative standard deviation (RSD) Limit, aka %CV method Calibration curve RSD = level p 1 (1 - p 2 log(level)) (RSD: abs value of CV) Vial 2003 Calibration Curve Calibration curve Regression (linear, logistic) LOD = 3 s y x / slope LOQ = 3 LOD Anderson 1989 QuaSAR Calibration curve with labeled standards Weighted linear regression (least median square) LOD = 3 s y x / slope LOQ = 3 LOD Mani 2012 MSstats Calibration curve Weighted linear OR logistic regression LOB = intersection of noise and linear regime LOD = lower bound of linear regime CI and noise Hahn (under review) 38

39 ut ut Measured signal Consider assumptions in your curve data when choosing a model linear regime noise regime i ut Known quantity Example Label-free quantification Extremely sparse curve data 30% zero overall 50% zero in blanks Highly reproducible measurements even in noise regime 39

40 ut ut Measured signal A proposed piecewise linear model to fit sparse, label-free LC-MS calibration curves linear regime assume m noise = 0 noise regime i ut Known quantity Pino LK, unpublished 40

41 Utilizing the linear regime for calibrating measured signal to a known Coming up! Us S yli s calibrati curv functionality to calibrate peak areas to known values. 41

42 Review check 3 How would you determine whether an analyte was linear? What are some assumptions in calibration curve data? 42

43 summary Reproducibility, stability, selectivity, linearity, and LOD/LOQ are commonly recommended figures of merit used to judge the quality, reliability and consistency of MS proteomics results Calibration curves are useful for validating measurements and calibrated quantification. Important considerations in calibration curve construction and analysis include appropriate choice of matrix, appropriate choice of analyte, and assessment of data assumptions. 43

44 For more information LITERATURE: Gra t & H f agl 2014 ara is f u : Carr 2014 fit f r ur s : VIDEO: On serum protein quantification (Andy Hoofnagle, MD PhD) TUTORIAL: Setting up peak area calibration for absolute quant in Skyline solutequant-3_5.pdf RESOURCE: CPTAC Assay Portal 44

45 More details on constructing a calibration curve for MS proteomics 1. Prepare a representative sample matrix a. Use a complex mixture of protein that approximates as closely as possible the actual experiments b. Scaled-up bulk digests may not reflect digestion conditions of actual experimental volumes 2. Formulate serial dilutions of analyte peptides a. Cover a range of concentrations expected for unknowns, including a blank b. Make at least six points in addition to the blank c. Generate enough of each point for 3 injections 3. Acquire calibration curve samples a. Samples should be run unrandomized in technical triplicate b. Samples should be run beginning with the blank, increasing with analyte concentration c. Additional solvent blanks may be used between technical replicates to account for possible column carry-over Adapted from 45