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The photon flux density (PFD) and spectrum regulate the growth, quality attributes, and postharvest physiology of leafy vegetables grown indoors. However, limited information is available on how a photon spectrum enriched with a broad range of different wavebands regulates these factors. To determine this, we grew baby-leaf lettuce ‘Rouxai’ under a PFD of 200 µmol m−2 s−1 provided by warm-white (WW; control) light-emitting diodes (LEDs) supplemented with either 30 µmol m−2 s−1 of ultraviolet-A (+UV30) or 50 µmol m−2 s−1 of blue (+B50), green (+G50), red (+R50), or WW (+WW50) light. We then quantified growth attributes and accumulated secondary metabolites at harvest and during storage in darkness at 5 °C. Additional +G50 light increased shoot fresh and dry weight by 53% and 59% compared to the control. Relative chlorophyll concentration increased under +UV30, +G50, and especially +B50. At harvest, +B50 increased total phenolic content (TPC) by 25% and anthocyanin content (TAC) by 2.0-fold. Additionally, +G50 increased antiradical activity (DPPH) by 29%. After each day of storage, TPC decreased by 2.9 to 7.1% and DPPH by 3.0 to 6.2%, while TAC degradation was less pronounced. Principal component analysis indicated a distinct effect of +G50 on the lettuce at harvest. However, concentrations of metabolites before and during storage were usually greatest under the +B50 and +R50 treatments.

Since they emerged ~125 million years ago, flowering plants have evolved to dominate the terrestrial landscape and survive in the most inhospitable environments on earth. At their core, these adaptations have been shaped by changes in numerous, interconnected pathways and genes that collectively give rise to emergent biological phenomena. Linking gene expression to morphological outcomes remains a grand challenge in biology, and new approaches are needed to begin to address this gap. Here, we implemented topological data analysis (TDA) to summarize the high dimensionality and noisiness of gene expression data using lens functions that delineate plant tissue and stress responses. Using this framework, we created a topological representation of the shape of gene expression across plant evolution, development, and environment for the phylogenetically diverse flowering plants. The TDA-based Mapper graphs form a well-defined gradient of tissues from leaves to seeds, or from healthy to stressed samples, depending on the lens function. This suggests there are distinct and conserved expression patterns across angiosperms that delineate different tissue types or responses to biotic and abiotic stresses. Genes that correlate with the tissue lens function are enriched in central processes such as photosynthetic, growth and development, housekeeping, or stress responses. Together, our results highlight the power of TDA for analyzing complex biological data and reveal a core expression backbone that defines plant form and function.

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