LC–HRMS-Driven Computational Toolbox to Assess Extraction Protocols Dedicated to Untargeted Analysis: How to Ease Analyzing Pesticide-Contaminated Soils?

Metabolomics
is a powerful approach that allows for high
throughput
analysis and the acquisition of large biochemical data. Nonetheless,
it still faces several challenging requirements, such as the development
of optimal extraction and analytical methods able to respond to its
high qualitative and quantitative requisites. Hence, the objective
of the present article is to suggest a LC–HRMS-based untargeted
profiling approach aiming to provide performant tools that help assess
the performance and the quality of extraction methods. It is applied
in a herbicide-contaminated soil metabolomics context. The trifactorial
experimental design consists of 150 samples issued from five different
extraction protocols, two types of soils, and three contamination
conditions (contaminated soils with two different formulated herbicides
against uncontaminated soils). Four performance and quality criteria
are investigated using adapted LC–HRMS-driven computational
tools. First, 861 metabolic features are annotated, and then the width
of metabolome coverage and quantitative performance of the five different
extraction protocols are assessed in all samples using various optimized
configurations of heatmaps as well as van Krevelen diagrams. Then,
the reproducibility of LC–HRMS profiles issued from the five
extractions is studied by two different approaches: Euclidean distances
and relative standard deviations. The two methods are examined and
compared. Their advantages and limitations are thus discussed. After,
the capacity of the different extractions to discriminate between
contaminated and uncontaminated soils will be evaluated using orthogonal
projections to latent structures-discriminant analysis. Different
data scaling parameters are tested, and the results are explored and
discussed. All of the suggested computational and visualization tools
are performed using public-access platforms or open-source software.
They can be readapted by metabolomics developers and users according
to their study contexts and fields of application.