University of Chile

Microgreens have emerged as a promising crop in vertical farming due to their high nutritional value and short growth cycles. Light spectrum and intensity are critical factors influencing biomass production and metabolic activity in plants, particularly in controlled environments such as vertical farming systems. This study aimed to evaluate the combined effects of light spectrum and intensity on the agronomic traits and metabolite profiles of green and purple radish (Raphanus sativus L.) microgreens. Three commercial LED spectra - NS12 (R:B = 1.9), Ph2.1 (R:B = 2.1) and AP673L (R:B = 5.5) - were tested at three intensities (100, 200, and 300 μmol m-2 s -1) in a controlled vertical farming system. Growth parameters (yield, dry matter percentage (DMP), leaf area), photosynthetic pigments, sugars, organic acids, and glucosinolates were quantified. The highest R:B ratio (5.5) under the AP673L lamp at 300 μmol m-2 s-1 significantly enhanced yield and DMP in both cultivars, while the lowest R:B ratio observed in the NS12 lamp at the same intensity maximized sugar and organic acid accumulation in green microgreens. Glucosinolate content was spectrum- and cultivar-dependent: green radish accumulated higher levels under the lowest R:B ratio (NS12), whereas purple radish responded better to the R:B = 2.1 ratio under the Ph2.1 lamp at lower intensities. These results demonstrate that optimal light strategies in vertical farming must be tailored to specific microgreen genotypes and light conditions to simultaneously maximize productivity and nutritional quality. © 2026 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

Multi-omics in combination with advanced computational methodologies synthesizes diverse omics data to provide deeper insights into molecular interactions and offers transformative potential for unravelling phenomenon behind disease complexities, improving diagnostics, disease prevention, and personalized treatments. This integrative strategy enables our understanding of gene-environment relationships, chronic disease progression, and the intricate molecular pathways involved in health. Effective multi-omics analyses require robust data sharing, accessibility, interoperability, and governance, which are critical for linking genomic elements to phenotypic traits. The Global Alliance for Genomics and Health advocates for responsible data-sharing practices, by promoting key principles such as transparency and equity. By emphasizing a collaborative approach to data utilization, our proposed framework seeks to advance improved disease prevention and treatment strategies. Multi-disciplinary collaboration, encompassing researchers, clinicians, policy makers, and patient representatives, is pivotal for driving innovation and addressing rare disease diagnostics. The success of multi-omics applications hinges on the establishment of comprehensive datasets, understanding the functional implications of multi-omic variation, adherence to findable, accessible, interoperable, reusable (FAIR) and Collective Benefit, Authority to Control, Responsibility, and Ethics (CARE) principles, and the strengthening of global genomic commons, benefiting scientific research, drug development, and broader health initiatives. Our review highlights essential components of multi-omics integration, underscoring its potential to transform the landscape of precision medicine and improved patient outcomes worldwide.