Data-dependent normalization strategies for untargeted metabolomics—a case study

Abstract

Despite the recent advances in the standardization of untargeted metabolomics workflows, there is still a lack of attention to specific data treatment strategies that require deep knowledge of the biological problem and need to be applied after a well-thought out process to understand the effect of the practice. One of those strategies is data normalization. Data-driven assumptions are critical especially addressing unwanted variation present in the biological model as it can be the case in heterogeneous tissues, cells with different sizes or biofluids with different concentrations. Chronic kidney disease (CKD) is a widespread disorder affecting kidney structure and function. Animal models are being developed to be able to get valuable insights into the etiopathogenesis of the condition and effect of the treatments. Moreover, diagnosis and disease staging still require defining appropriate biomarkers. Untargeted metabolomics has the potential to deal with those challenges. Renal fibrosis is one of the consequences of kidney injury which greatly affects the concentration of metabolites in the same quantity of sample. To overcome this challenge, several data normalization strategies have been applied, following a multilevel normalization method with the overall aim of focussing on the relevant biological information and reducing the influence of disturbing factors. A comprehensive evaluation of the performance of the normalization strategies, both on methods assessing the intragroup variation and on the impact on differential analysis, is provided. Finally, we present evidence of the importance of biological-model-driven guided normalization methods and discuss multiple criteria that need to be taken into consideration to obtain robust and reliable data. Special concern is transmitted on the misleading conclusions that might be the consequence of inappropriate data pre-treatment solutions applied for untargeted methods.

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Abbreviations

BCA:

Bicinchoninic acid assay

BGE:

Background electrolyte

CE-MS:

Capillary electrophoresis–mass spectrometry

CKD:

Chronic kidney disease

CTMOD:

Control genetically modified with the FAO gain-of-function group

CTWT:

Control wild-type group

ECM:

Extracellular matrix

ESI:

Electrospray ionization

FAO:

Fatty acid oxidation

FC:

Median fold change

HCA:

Hierarchical cluster analysis

IS:

Internal standard

JK:

Jack-knifing uncertainty measures

OBSMOD:

Obstruction genetically modified with FAO gain-of-function group

OBSWT:

Obstruction wild-type group

OPLS-DA:

Orthogonal partial least squares discriminant analysis

PBS:

Phosphate-buffered saline

PCA:

Principal component analysis

PLS-DA:

Partial least squares-discriminant analysis

PQN:

Probabilistic quotient normalization

QC:

Quality control

QC-SVRC:

Quality control samples and support vector regression correction

RLA:

Relative log abundance

RLE:

Relative log expression

RSD:

Relative standard deviation

TOF:

Time of flight

TUS:

Total useful signal

UUO:

Unilateral ureteral obstruction

VIP:

Variable importance in projection

WT:

Wild type

All+QC:

Complete data matrix, all samples from experimental groups, QC samples included

All-QC:

All samples from experimental groups, QC samples excluded

QC:

Matrix associated only with QC samples

2Gr:

Matrix divided into two groups, (1) control group: CTWT and CTMOD; (2) obstruction group: OBSWT and OBSMOD

4Gr:

Matrix divided into four groups: (1) CTWT; (2) CTMOD; (3) OBSWT; (4) OBSMOD

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Acknowledgements

This work was supported by Comunidad de Madrid (B-2017/BMD-3751 “NOVELREN-CM”), Ministerio de Ciencia, Innovación y Universidades (RTI 2018-095166-B-100) and Ministerio de Economía y Competitividad (MINECO) SAF2015-66107-R (SL), cofunded by the European Regional Development Fund and Instituto de Salud Carlos III REDinREN RD12/0021/0009 and RD16/0009/0016 (SL).

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Cuevas-Delgado, P., Dudzik, D., Miguel, V. et al. Data-dependent normalization strategies for untargeted metabolomics—a case study. Anal Bioanal Chem 412, 6391–6405 (2020). https://doi.org/10.1007/s00216-020-02594-9

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Keywords

  • Unwanted variation
  • Data pre-treatment
  • Normalization
  • Tissue samples
  • Capillary electrophoresis mass spectrometry
  • Biomarker discovery