Normalization strategies of urine samples for metabolomics

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1 Normalization strategies of urine samples for metabolomics Individual dilution factors and chemometrics evaluation Yoric Gagnebin CHIMIOMETRIE XVII January 20 th, 2016

2 Metabolomics & Health Biofluids

UHPLC-QTOF-MS Metabolomic Workflow Samples Sample preparation Data acquisition Raw data Multivariate analysis Features Patient stratification mz 587.917771 565.872737 227.088748 980.230573 788.

3 UHPLC-QTOF-MS Metabolomic Workflow Samples Sample preparation Data acquisition Raw data Multivariate analysis Features Patient stratification mz rt QC QC QC QC QC QC QC QC QC QC QCD10A QCD10B QCD10C QCD25A QCD25B QCD25C A1A A1B A1C A2A A2B A2C A3A A3B A3C E1A E1B E1C Preprocessing data

4 Urine issue + Non-invasively collected + Large quantity availability + Simple sample preparation ± Provides a complete metabolic profile unlike blood which provides a snapshot - Variability of samples concentration (up to 15 fold for healthy people) How to have the better metabolic profile comparison? Normalization strategies Evaluation of normalization by multivariate analysis 19 samples from 8 people (4 women, 4 men, n=3) Quality Control (QCs) : Pool of all samples Analytical variability assessment : Multiple injections of QCs and samples Concentration variability assessment : Injections of QCs, diluted QCs and samples

5 UHPLC-QTOF-MS Metabolomic Workflow Samples Sample preparation Pre-acquisition normalization Data acquisition Raw data Multivariate analysis Patient stratification Features Post-acquisition mz rt QC normalization QC QC QC QC QC QC QC QC QC QCD10A QCD10B QCD10C QCD25A QCD25B QCD25C A1A A1B A1C A2A A2B A2C A3A A3B A3C E1A E1B E1C Preprocessing data

6 RSD Data filtration according to data quality According two criteria (using QC information) : 1. Response to dilution 3871 to 2923 features 2. RSD of diluted QCs / QCs ratio 2923 to 2500 features 100,00% 2923 features 3871 features 90,00% 80,00% 70,00% 60,00% 50,00% 2500 features 3072 features 40,00% 30,00% 20,00% 10,00% 0,00% 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 0,9 1 1,1 1,2 1,3 1,4 1,5 1,6 1,7 1,8 1,9 2 Diluted QCs / QCs

7 Normalization strategies Step 1 : Sample preparation Pre-acquisition Common dilution factor Individual dilution factor Step 2 : Analytical normalization Post-acquisition No normalization QC-RLSC Step 3 : Concentration normalization Post-acquisition No normalization Osmolality Creatinine MSTUS PQN

8 Dilution factor Sample preparation : individual dilution Concentration estimator : NMR integration methods Creatinine Osmolality 3,50 3,00 2,50 2,00 1,50 NMR methods 1/2 Osmolality Creatinine 1,00 0,50 0, Sample number

9 Sample preparation : individual dilution Dilution factor Peak area ratio Concentration estimator : NMR integration methods Creatinine Osmolality : Fast Low volume Whole sample representative Gold standard Leveling sample Reduce concentration variability 3,50 3,00 2,50 2,00 1,50 1,00 0,50 0,00 NMR methods 1/2 Osmolality Creatinine Sample number 1 Analytical conditions : Reduce analytical drift No loss of information Same analytical conditions 0,8 0,6 0,4 0,2 0 QCs after dilution by osmolality QCs after dilution 1:1 in water QCs after dilution 1:4 in water Less than 15% of the signal intensity is lost after 24h of analysis for the same dilution factor average Processing order

10 Dilute & Shoot, ESI pos dilution comparison PCA Pareto Scaling Principal Component Analysis Biplot of common dilution factor and specific dilution factor urine data : Specific dilution W1 W2 W3 W4 M1 M2 M3 M4 QCs Feature Reduce analytical variability Reduce concentration variability Residual variability

11 Normalization strategies Step 1 : Sample preparation Pre-acquisition Common factor Individual factor Step 2 : Analytical normalization Post-acquisition No normalization QC-RLSC Step 3 : Concentration normalization Post-acquisition No normalization Osmolality Creatinine MSTUS PQN

12 Peak area (x1000) Peak area (x1000) Quality control based robust LOESS signal correction (QC-RLSC) Locally weighted scatterplot smoothing (LOESS) curve is fitted to the QC samples, the correction curve interpolated, to which the total data set for that peak is corrected. Etiocholanolone glucuronide Raw data Samples QCs QC-RLSC Samples QCs Processing order Processing order

13 Dilute & Shoot, ESI pos QC-RLSC PCA Pareto Scaling Principal Component Analysis Biplot before and after QC-RLSC normalization : QC-RLSC W1 W2 W3 W4 M1 M2 M3 M4 QCs Feature Eliminate analytical variability Concentration variability

14 Normalization strategies Step 1 : Sample preparation Pre-acquisition Common factor Individual factor Step 2 : Analytical normalization Post-acquisition No normalization QC-RLSC Step 3 : Concentration normalization Post-acquisition No normalization Osmolality Creatinine MSTUS PQN

15 Post-acquisition normalization External measurement as correction factor: Osmolality Creatinine NMR integration methods Data treatment : MS Total Useful Signal (MSTUS) Probabilistic Quotient Normalization (PQN)

16 Dilute & Shoot, ESI pos Osmolality PCA Pareto Scaling Principal Component Analysis Biplot of common dilution factor and osmolality as correction factor : Osmolality W1 W2 W3 W4 M1 M2 M3 M4 QCs Feature Correction factor is not the best concentration normalization

Post-acquisition normalization External measurement : Osmolality Creatinine NMR integration methods Data treatment : MS Total Useful Signal (MSTUS) ǀ correction with the sum of all features present

17 Post-acquisition normalization External measurement : Osmolality Creatinine NMR integration methods Data treatment : MS Total Useful Signal (MSTUS) ǀ correction with the sum of all features present in all samples Probabilistic Quotient Normalization (PQN) ǀ Initial data set : Data set after MSTUS : An increase in the concentration of one component requires a decrease in the concentration of remaining components P. Filzmoser, B. Walczak, What can go wrong at the data normalization step for identification of biomarkers?, J. Chromatography A, 1362 (2014)

18 Dilute & Shoot, ESI pos PQN PCA Pareto Scaling Principal Component Analysis Biplot before and after PQN normalization : W1 W2 W3 W4 M1 M2 M3 M4 QCs Feature PQN Best concentration normalization

19 Sequential normalization strategies Step 1 : Sample preparation Pre-acquisition Common factor Individual factor Step 2 : Analytical normalization Post-acquisition No normalization QC-RLSC Step 3 : Concentration normalization Post-acquisition No normalization Osmolality Creatinine MSTUS PQN

20 Metabolomic study : Chronic Kidney Disease Orthogonal Partial Least Squares of urine samples of 41 patients (P) and 40 controls (CTRL) : CTRL vs. P QC-RLSC PQN Pareto scaling Pareto scaling Normalization Components R 2 X R 2 Y Q QC-RLSC + PQN Goal : Patient stratification, biomarkers identification, personalized medicine

21 Conclusion Samples dilution by osmolality or creatinine improves analytical conditions and allows levelling sample and biological normalization QC-RLSC normalizes the residual analytical drift If samples dilution by osmolality or creatinine does not provide sufficient concentration normalization : Add PQN step to normalize the samples concentration The sequential normalization strategy allows the metabolic profile comparison of every sample and assessing phenotype modifications.

22 Serge Rudaz Boccard Julien Sophie De Seigneux Schappler Julie Tonoli David Belen Ponte Marco Randazzo Laurence Marcourt Pierre Lescuyer Thank you for your attention

Low-level environmental phthalate exposure associates with urine metabolome. alteration in a Chinese male cohort

Supporting Information Low-level environmental phthalate exposure associates with urine metabolome alteration in a Chinese male cohort Jie Zhang*, Liangpo Liu, Xiaofei Wang, Qingyu Huang, Meiping Tian,