NTDR, 2012

Basic principles of NMR-based metabolomics

Nils NybergNPR, Department of Drug Design and Pharmacology

NTDR, 2012

Outline

NMR as universal detector

Top-down approach in science

Metabolomics and metabonomics

Case story: starved ewes and fat lambs

Step-by-step procedure in data handling• Processing• Export/import• Calibration• Baseline adjustment• Projection of data to a common axis• Integration and merging of buckets• PCA

NTDR, 2012

Basic principles of NMR-based metabolomics

Metabolome• The complete set of small-molecule metabolites in a

biological system• Metabolomics, proteomics, transcriptomics, …

Metabolic profile, metabolic fingerprint• A quantitative determination of the metabolome in a

sample or an individual

Metabonomics• Quantitative measurement of metabolic response to

stimuli or genetic modification…• Toxicology• Disease diagnostics• Functional genomics (determination of phenotypes)• Nutrigenomics (human diet, drugs and microflora)

Metabonomics ~ metabolomics ~ metabolic profiles

NTDR, 2012

The universal detector

NMR: the most universal detector for small metabolites• No physical separation of analytes!• Robust => reproducible results• Directly quantitative• Simple sample preparation

• Information rich

• Not as sensitive as mass spectrometry• Expensive

NMR is good for a top-down approach• Study the whole system first, before breaking it into

smaller pieces

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Starved ewes and fat lambs

Undernutrition during fetal development is associated with increased risk of metabolic diseases later in life.• Dutch winter famine 1944

• Obesity at the age of 50 y in men and women exposed to famine prenatally, Am J Clin Nutr 1999;70:811–16.

• Coronary heart disease, hypertension, and type 2 diabetes.

• Consequences of “programming,” whereby a stimulus or insult at a critical, sensitive period of early life has permanent effects on structure, physiology, and metabolism.

Metabolic programming• Phenotypic alterations by fetal adaption• Higher risk of obesity and diabetes if mismatched diet

(programmed to cope with famine, exposed to hypernutrition)

NTDR, 2012

Starved ewes and fat lambs

Hypothesis: • Metabolic programming by feed restriction leads to changed

metabolic pathways• The changes can be studied by acquiring NMR spectra of urine

Sheep as animal model system• Before birth: Ewes well fed or starved (50% of energy)• After birth: Normal diet or “High fat, high carbohydrate”

diet

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Starved ewes and fat lambs: results

164 NMR spectra• Repeats at 2 and 6 months

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Starved ewes and fat lambs: results

Principal Component Analysis (PCA)• Data reduction, keep the variance• Display the relationships between samples

NTDR, 2012

Starved ewes and fat lambs: results

Age: 2 months, adopting to ruminant digestion• Separation depending on diet, / • Some samples ahead, separated from

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Data handling: procedures and terms

From FID’s to one table

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Data handling: procedures and terms

Keep track of your samples and data!• Enter title or label for each sample

FID’s to spectra:• Window function, Fourier transform,

phasing, base line adjustmentMake spectra comparable

• Calibration of ppm-scale• Project data on a common axis• Normalize

Compress data/simplify spectra• Integrate (binning, buckets)

Simplify calculations/interpretation of models• Mean center• Scaling

NTDR, 2012

FID’s to spectra

Use the same processing parameters for all spectra!• Window function with parameters

• Exponential Multiplication with a line broadening factor of 1 Hz

• Number of data points in the final spectrum• 32768 data points/20 ppm/600 MHz = 2.7 data

points/Hz• Make sure the peaks are properly defined.

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FID’s to spectra

Adjust each spectrum individually• Phasing: Adjust only zeroth-order phase constant if

possible

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FID’s to spectra

Base line adjustment• Make sure the base line is represented in the spectrum (large SW)• Use a simple function (2nd or 3rd order polynomial)

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Calibration of ppm-scale

Select a reference peak• In all spectra• TMS, DSS or Residual solvent signal• Sharp, well resolved

• Global shift (error in lock position)• Local shift (day to day variation in lock)

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Project data on a common axis

Discrete data points in different spectra are not necessarily aligned• Normally a very small effect

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Project data on a common axis

4.624.644.66 1H [ppm]

Serum, CPMG, -Glc H-1 after calibration

NTDR, 2012

Project data on a common axis

4.64 1H [ppm]

Serum, CPMG, -Glc H-1 after calibration

NTDR, 2012

Project data on a common axis

4.64 1H [ppm]

Serum, CPMG, -Glc H-1 after calibration

NTDR, 2012

Normalization

Make data directly comparable with each other• by removing known variation• by reducing unknown variation• row-wise operation (for each sample)

Variation caused by• different amounts/concentrations/volumes• instrument settings (tuning/matching, gain)

Variation expressed as• additive effects (base line)• multiplicative effects

Context dependent processing!• urine, serum, juice, …• depending on the type of samples, sampling schemes

and sample preprocessing

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Some Normalization schemes

Normalize to • constant sum• constant squared sum• highest signal

Find a common constant feature in the spectra• internal standard• invariant metabolite

• e.g. urinary creatinine/body weight

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Normalization

0 1 2 3 4 5 6 7 8 9 100

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1.5Before normalization to constant sum

0 1 2 3 4 5 6 7 8 9 100

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0.04After normalization to constant sum

Be pragmatic – if it works, it’s probably ok!• But make sure the sampling and analysis parameters are

kept constantSome normalization schemes will introduce new correlations

• Normalize to constant sum = if one signal increases, others are decreased

Binning

Binning = Bucketing = Integration of spectral ranges

Reduce data set• typical spectra: 65536 data points (64k)• binned data ~200 data points

Remove variability of chemical shifts• temperature• pH• concentration• overall composition of samples (salt, proteins,…)

Reduce effects of differences in shimming• the area of a peak is a more robust measure than

intensity value of each point

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Binning

Integration into smaller ranges• Bucketing or binning• Start with equidistant ranges, ~0.01-0.05 ppm• Combine vicinal buckets with a high degree of co-variation

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Mean/median centering

Removes (subtracts) the mean/median value of each variable

Operates on the columns of the data matrix (for each variable/bucket)

Centering of the data gives more stable numerical solutions for the PCA (and other transformations). • If not used – the first pc will be the mean spectrum…

Use median centering for a more robust centering• less sensitive to outliers

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Centering

Raw data, before centering

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Centering

Mean centered

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Centering

Median centered

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Scaling

Scaling sets the weighting (importance) of each variable in the models

For NMR-spectroscopic data• the largest signals have the highest variance• small signals have low variance• noise have lowest variance

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1H [ppm]

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28 Serum CPMG-spectra (AFB)

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Auto scaling

Auto scaling (variables divided by standard deviation, variance set to 1).

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Pareto scaling

Pareto scaling (variables divided by the square root of the standard deviation).

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

-0.1

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1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Log transform

The data range is ’compressed’ by calculation of the logarithmic values before centering.

1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

-3

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1.16 ppm 1.12 ppm 1.08 ppm 1.04 ppm 1.01 ppm 0.98 ppm 0.94 ppm

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Scaling

Centering• Removes the offset in the

data• Highlights the differences

within each variableAuto scaling

• Sets the variance of each variable to unity.

• Inflates the noise.• All signals equally

important.Pareto scaling

• Reduce relative importance of large values.

• Scaling effect between no scaling (only centering) and auto scaling.

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PCA

Principal Component Analysis (PCA)• Calculate scores and loadings• Data reduction (from 65000 data points to two…)• Keep the variance, don’t show the noise• Display the relationships between samples

X (systematic + random variation)S

Loadings

NTDR, 2012

PCA

Principal Component Analysis (PCA)• Calculate scores and loadings• Data reduction (from 65000 data points to two…)• Keep the variance, don’t show the noise• Display the relationships between samples

X (systematic variation)S

Loadings

E (random variation)

NTDR, 2012

PCA

Principal Component Analysis (PCA)• Calculate scores and loadings• Data reduction (from 65000 data points to two…)• Keep the variance, don’t show the noise• Display the relationships between samples