The commercial cultivation of microalgae began in the 1960s and Chlorella was one of the first target organisms. The species has long been considered a potential source of renewable energy, an alternative for phytoremediation, and more recently, as a growth and immune stimulant. However, Chlorella vulgaris, which is one of the most studied microalga, has never been comprehensively profiled chemically. In the present study, comprehensive profiling of the Chlorella vulgaris metabolome grown under normal culture conditions was carried out, employing tandem LC-MS/MS to profile the ethanolic extract and GC-MS for fatty acid analysis. The fatty acid profile of C. vulgaris was shown to be rich in omega-6, -7, -9, and -13 fatty acids, with omega-6 being the highest, representing more than sixty percent (>60%) of the total fatty acids. This is a clear indication that this species of Chlorella could serve as a good source of nutrition when incorporated in diets. The profile also showed that the main fatty acid composition was that of C₁₆-C₁₈ (>92%), suggesting that it might be a potential candidate for biodiesel production. LC-MS/MS analysis revealed carotenoid constituents comprising violaxanthin, neoxanthin, lutein, β-carotene, vulgaxanthin I, astaxanthin, and antheraxanthin, along with other pigments such as the chlorophylls. In addition to these, amino acids, vitamins, and simple sugars were also profiled, and through mass spectrometry-based molecular networking, 48 phospholipids were putatively identified.

Microalgae are promising candidate resources from marine ecology for health-improving effects. Metabolite profiling of the microalgal diatom, Chaetoceros calcitrans was conducted by using robust metabolomics tools, namely ¹H nuclear magnetic resonance (NMR) spectroscopy coupled with multivariate data analysis (MVDA). The unsupervised data analysis, using principal component analysis (PCA), resolved the five types of extracts made by solvents ranging from polar to non-polar into five different clusters. Collectively, with various extraction solvents, 11 amino acids, cholesterol, 6 fatty acids, 2 sugars, 1 osmolyte, 6 carotenoids and 2 chlorophyll pigments were identified. The fatty acids and both carotenoid pigments as well as chlorophyll, were observed in the extracts made from medium polar (acetone, chloroform) and non-polar (hexane) solvents. It is suggested that the compounds were the characteristic markers that influenced the separation between the clusters. Based on partial least square (PLS) analysis, fucoxanthin, astaxanthin, violaxanthin, zeaxanthin, canthaxanthin, and lutein displayed strong correlation to 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging and nitric oxide (NO) inhibitory activity. This metabolomics study showed that solvent extractions are one of the main bottlenecks for the maximum recovery of bioactive microalgal compounds and could be a better source of natural antioxidants due to a high value of metabolites.

This study was designed to profile the metabolites of Isochrysis galbana, an indigenous and less explored microalgae species. ¹H Nuclear Magnetic Resonance (NMR) spectroscopy and Liquid Chromatography-Mass Spectrometry (LCMS) were used to establish the metabolite profiles of five different extracts of this microalga, which are hexane (Hex), ethyl acetate (EtOAc), absolute ethanol (EtOH), EtOH:water 1:1 (AqE), and 100% water (Aq). Partial least square discriminant analysis (PLS–DA) of the generated profiles revealed that EtOAc and Aq extracts contain a diverse range of metabolites as compared to the other extracts with a total of twenty-one metabolites, comprising carotenoids, polyunsaturated fatty acids, and amino acids, that were putatively identified from the NMR spectra. Meanwhile, thirty-two metabolites were successfully annotated from the LCMS/MS data, ten of which (palmitic acid, oleic acid, α-linolenic acid, arachidic acid, cholesterol, DHA, DPA, fucoxanthin, astaxanthin, and pheophytin) were similar to those present in the NMR profile. Another eleven glycerophospholipids were discovered using MS/MS-based molecular network (MN) platform. The results of this study, besides providing a better understanding of I.galbana’s chemical make-up, will be of importance in exploring this species potential as a feed ingredient in the aquaculture industry.

Stone leaf (Tetracera scandens) is a Southeast Asian medicinal plant that has been traditionally used for the management of diabetes mellitus. The underlying mechanisms of the antidiabetic activity have not been fully explored yet. Hence, this study aimed to evaluate the α-glucosidase inhibitory potential of the hydromethanolic extracts of T. scandens leaves and to characterize the metabolites responsible for such activity through gas chromatography–mass spectrometry (GC–MS) metabolomics. Crude hydromethanolic extracts of different strengths were prepared and in vitro assayed for α-glucosidase inhibition. GC–MS analysis was further carried out and the mass spectral data were correlated to the corresponding α-glucosidase inhibitory IC₅₀ values via an orthogonal partial least squares (OPLS) model. The 100%, 80%, 60% and 40% methanol extracts displayed potent α-glucosidase inhibitory potentials. Moreover, the established model identified 16 metabolites to be responsible for the α-glucosidase inhibitory activity of T. scandens. The putative α-glucosidase inhibitory metabolites showed moderate to high affinities (binding energies of −5.9 to −9.8 kcal/mol) upon docking into the active site of Saccharomyces cerevisiae isomaltase. To sum up, an OPLS model was developed as a rapid method to characterize the α-glucosidase inhibitory metabolites existing in the hydromethanolic extracts of T. scandens leaves based on GC–MS metabolite profiling.

Watermelon, a widely commercialized fruit, is famous for its thirst-quenching property. The broad range of cultivars, which give rise to distinct color and taste, can be attributed to the differences in their chemical profile, especially that of the carotenoids and volatile compounds. In order to understand this distribution properly, water extracts of red and yellow watermelon pulps with predominantly polar metabolites were subjected to proton nuclear magnetic resonance (¹H-NMR) analysis. Deuterium oxide (D₂O) and deuterated chloroform (CDCl₃) solvents were used to capture both polar and non-polar metabolites from the same sample. Thirty-six metabolites, of which six are carotenoids, were identified from the extracts. The clustering of the compounds was determined using unsupervised principal component analysis (PCA) and further grouping was achieved using supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The presence of lycopene, β-carotene, lutein, and prolycopene in the red watermelon plays an important role in its differentiation from the yellow cultivar. A marked difference in metabolite distribution was observed between the NMR solvents used as evidenced from the PCA model. OPLS-DA and relative quantification of the metabolites, on the other hand, helped in uncovering the discriminating metabolites of the red and yellow watermelon cultivars from the same solvent system.