The Importance of Experimental Design, Quality Assurance, and Control in Plant Metabolomics Experiments

Abstract

The output of metabolomics relies to a great extent upon the methods and instrumentation to identify, quantify, and access spatial information on as many metabolites as possible. However, the most modern machines and sophisticated tools for data analysis cannot compensate for inappropriate harvesting and/or sample preparation procedures that modify metabolic composition and can lead to erroneous interpretation of results. In addition, plant metabolism has a remarkable degree of complexity, and the number of identified compounds easily surpasses the number of samples in metabolomics analyses, increasing false discovery risk. These aspects pose a large challenge when carrying out plant metabolomics experiments. In this chapter, we address the importance of a proper experimental design taking into consideration preventable complications and unavoidable factors to achieve success in metabolomics analysis. We also focus on quality control and standardized procedures during the metabolomics workflow.

Experimental Design and Sample Preparation in Forest Tree Metabolomics

“…It is important to highlight that frequently (and wrongly) experiments are designed for other "omics" technologies (i.e., transcriptomics, proteomics), and the leftover samples are later used for metabolomics analysis. This can extensively compromise the entire metabolomics analysis because the objective of the study might be different, the number of replicates may not be sufficient, or the sample storage conditions were not ideal, thereby affecting the stability of metabolites within the sample [9].…”

“…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. Therefore, the experimental design ( Figure 1) typically includes all variables of the experiment, from the plant growth and treatments (e.g., plant growth conditions, randomization, replicates, controls), sample preparation conditions (e.g., harvested tissue, quenching method, pool material or not, metabolite extraction protocol), and analytical platform (e.g., GC-MS, LC-MS, mass spectrometry imaging, targeted or untargeted approach) to statistical treatments [7,9,34]. Added to these factors, all sources of additional variation (e.g., genotype, sample size, tissue selection, developmental stage, environmental conditions, batch/block effect) should be investigated and minimized to avoid misleading conclusions [7,9,35].…”

“…The experimental design should also take into account the time frame of the metabolomics experiment. Because metabolites are highly dynamic (in time and space), a metabolomics study can reflect the steady state (or instant snap-shot) of the metabolism or its dynamic time-course evaluation [9,[36][37][38]. In plant metabolomics, due to the destructive nature of the sampling procedure, most conducted studies are transversal (i.e., cross-sectional), where different samples are used for each time point, whereas in human metabolomics, longitudinal studies are fairly common [39,40].…”

“…LC-MS, mass spectrometry imaging, targeted or untargeted approach) to statistical treatments [7,9,34]. Added to these factors, all sources of additional variation (e.g., genotype, sample size, tissue selection, developmental stage, environmental conditions, batch/block effect) should be investigated and minimized to avoid misleading conclusions [7,9,35].…”

“…However, in the field, plants are subjected to uncontrolled variations in the environment (e.g., variations in light intensity, temperature, water availability). Despite the plant's metabolism degree of plasticity acting as a buffer against sudden fluctuations in the environment, this complex set of variables deeply impact the plant's physiology and metabolism [9], which is often the case of forest tree long-term research. Hence, care must be taken when making comparisons amongst field-grown individuals or even when extrapolating results or establishing correlations between trees grown under controlled and uncontrolled (i.e., field) environmental conditions [43].…”