Microalgal swimming signatures and neutral lipids production across growth phases

Abstract

Microalgae have been shown as a potential bioresource for food, biofuel, and pharmaceutical products. During the growth phases with corresponding environmental conditions, microalgae accumulate different amounts of various metabolites. We quantified the neutral lipids accumulation and analyzed the swimming signatures (speed and trajectories) of the motile green alga, Dunaliella primolecta, during the lag–exponential–stationary growth cycle at different nutrient concentrations. We discovered significant changes in the neutral lipid content and swimming signatures of microalgae across growth phases. The timing of the maximum swimming speed coincided with the maximum neutral lipid content and both maxima occurred under nutrient stress at the stationary growth phase. Furthermore, the swimming trajectories suggested statistically significant changes in swimming modes at the stationary growth phase when the maximum intracellular neutral lipid content was observed. Our results provide the potential exploitation of microalgal swimming signatures as possible indicators of the cultivation conditions and the timing of microalgal harvest to maximize the lipid yield for biofuel production. The findings can also be implemented to explore the production of food and antibiotics from other microalgal metabolites with low energy costs.

Active reconfiguration of cytoplasmic lipid droplets governs migration of nutrient-limited phytoplankton

“…The steady volume of energy-rich LDs—despite the nutrient-poor settings during stationary stage (fig. S7)—may equip cells to secure biophysical advantages ( 50 , 51 , 55 , 56 ) while reserving essential molecules for the maintenance (or modification) of behavioral and physiological traits over longer time scales.…”

“…LDs are energetically expensive; however, phytoplankton swimming under nutrient limitation could offset competitive advantage from larger storage capacity (45,46) because of enhanced molecular encounters and expansion of potential nutrient pools (40). While nutrient limitation has been associated with initiation of phytoplankton migration during blooms (47)(48)(49), contrasting reports suggest suppression of motility under depleted settings (50,51). Confounding environmental factors such as local pH (35) can additionally regulate phytoplankton swimming, alongside biomolecular parameters like variations in the cellular ATP (52), which has been reported to shift energy allocations to favor cellular motility.…”

Aerosol generation from different wind instruments

“…The recorded holograms are then numerically reconstructed using different diffraction kernels to obtain in-focus objects within the sample volume. DIH has been broadly used as a high-fidelity tool for in situ measurements of particle concentration, size, and shape over a broad range of applications ( Beals et al, 2015 ; ( Graham & Nimmo Smith, 2010 ); Katz & Sheng, 2010 ; Kumar et al, 2019 ; Li, Miller, Wang, Koley, & Katz, 2017 ; Shao, Li, & Hong, 2019 ; You et al, 2020 ). Particularly, DIH has been recently used for in situ measurements of aerosol generation from human exhalation, capable of capturing the presence of droplets and crystalline particles in the respiratory gas flow as well as their concentration and size distribution ( Shao et al, 2020 ).…”

Aerosol Generation from Different Wind Instruments

“…The recorded holograms are then numerically reconstructed using different diffraction kernels to obtain in-focus objects within the sample volume. DIH has been broadly used as a high-fidelity tool for in situ measurements of particle concentration, size, and shape over a broad range of applications (Beals et al, 2015; Graham & Nimmo Smith., 2010; Katz & Sheng, 2010; Kumar et al, 2019; Li, Miller, Wang, Koley, & Katz, 2017; Shao, Li, & Hong, 2019; You et al, 2020). Particularly, DIH has been recently used for in situ measurements of aerosol generation from human exhalation, capable of capturing the presence of droplets and crystalline particles in the respiratory gas flow as well as their concentration and size distribution (Shao et al, 2020).…”