Experimental Design and Sample Preparation in Forest Tree Metabolomics

Abstract

Appropriate experimental design and sample preparation are key steps in metabolomics experiments, highly influencing the biological interpretation of the results. The sample preparation workflow for plant metabolomics studies includes several steps before metabolite extraction and analysis. These include the optimization of laboratory procedures, which should be optimized for different plants and tissues. This is particularly the case for trees, whose tissues are complex matrices to work with due to the presence of several interferents, such as oleoresins, cellulose. A good experimental design, tree tissue harvest conditions, and sample preparation are crucial to ensure consistency and reproducibility of the metadata among datasets. In this review, we discuss the main challenges when setting up a forest tree metabolomics experiment for mass spectrometry (MS)-based analysis covering all technical aspects from the biological question formulation and experimental design to sample processing and metabolite extraction and data acquisition. We also highlight the importance of forest tree metadata standardization in metabolomics studies.

Omics to address the opportunities and challenges of nanotechnology in agriculture

“…To compensate for the qualitative and quantitative variations among plant samples, biological replicates are essential, which results in more powerful statistical analysis. However, in case of high variability and limited sample availability, sample pooling is a common procedure to represent the population, which nonetheless should be reported and considered during data analysis (Rodrigues et al, 2019). Although these requirements are quintessential for any metabolomic experiment, nanotoxicity studies should also consider ENM stability in the exposure media throughout the experiment duration, environmentally or agriculturally relevant dosing, ambient fluctuations, use of appropriate positive and negative controls, and comparison with ionic and bulk-particle controls to identify nano-specific effects.…”

NMR Metabolomics Applied on the Discrimination of Variables Influencing Tomato (Solanum lycopersicum)

“…While results will almost always be obtained, their reliability and biological interpretation may be motive of doubt if the approach has not been correct [ 26 ]. The experimental design should ensure that the analytical data derived from the collected biological material would allow answering the initially proposed biological question through a reliable statistical analysis [ 27 ]. After a careful experiment design, the 1 H-NMR metabolomic process can be envisioned as four steps, including: (1) sample and extract preparation, (2) spectra acquisition, (3) spectra and data processing and analysis, and, finally, (4) data interpretation.…”

“…Improper handling of biological samples is the most likely source of bias in metabolomic studies [ 29 ]. Some important aspects concerning sample collection were vastly described by several authors, such as Rodrigues et al [ 27 ] and Barnes et al [ 28 ]. For instance, Barnes et al [ 28 ] refers that excessive variation between groups caused by, for example, a lack of control on the effect of diet or the time of day of sample collection can masquerade biologically relevant changes in metabolite levels and affect the reproducibility of the data.…”

“…For instance, Barnes et al [ 28 ] refers that excessive variation between groups caused by, for example, a lack of control on the effect of diet or the time of day of sample collection can masquerade biologically relevant changes in metabolite levels and affect the reproducibility of the data. If working under growth chambers and other controlled environmental conditions, plants should be rotated during the experiment to avoid interferences in their metabolism caused by variations in light intensity or ventilation [ 27 ]. If plants are grown in a greenhouse or field conditions, variation in environmental conditions are also expected to occur, so an usual strategy is to arrange plants in a block design, and assign randomly the treatments within each block [ 27 , 30 ].…”

“…If working under growth chambers and other controlled environmental conditions, plants should be rotated during the experiment to avoid interferences in their metabolism caused by variations in light intensity or ventilation [ 27 ]. If plants are grown in a greenhouse or field conditions, variation in environmental conditions are also expected to occur, so an usual strategy is to arrange plants in a block design, and assign randomly the treatments within each block [ 27 , 30 ]. Then, comparison between treatments are performed within blocks, since the variability within each block is lower than the variability between blocks.…”