Abstract
The technical progress of metabolomics-the analysis of metabolite composition of cells, tissues, and biofluids-and its ability to unravel phenotypical responses of organisms to their environment was accompanied by the democratization of their use in a wide range of scientific fields. While the reliability of analytical procedures was thoroughly assessed, the effects of preanalytical processing on sample metabolic profiles (i.e., conditioning, storage, transportation, etc.) remain quite unclear. This uncertainty is especially notable for samples collected in the frame of ecological studies, which often involve field sampling and remote sample production, leading to extended transportation durations and suboptimal storage conditions. In this study, we evaluated the impact of storage duration and temperature, along with the effects of freeze-drying (or lyophilization; the process of stabilization through dehydration by sublimation), on the metabolic profiles of samples relevant to ecological studies. Specifically, we focused on the lesser mealworm (Alphitobius diaperinus), the fruit fly (Drosophila melanogaster), and the perennial ryegrass (Lolium perenne). The levels of 60 different metabolites were quantitatively analyzed using targeted metabolomics through gas chromatography-mass spectrometry (GC-MS). We report significant metabolic shifts associated with freeze-drying, resulting in both increases and decreases in the contents of more than half of the quantified metabolites across all assessed chemical families. Several amino acids exhibited more than a fourfold increase in all investigated matrices. Furthermore, while samples stored at -80°C exhibited profiles most similar to those of samples analyzed right after collection, the metabolic profiles of these samples gradually changed over the 6 months of storage. Interestingly, metabolic shifts related to sample preanalytical processing and storage were relatively consistent across the biological matrices studied, particularly between the two insect species. Based on these observations, we propose several recommendations for reliable preanalytical sample processing in ecological studies, considering logistical and economic constraints.