High-throughput optimisation of light-driven microalgae biotechnologies

Abstract

Microalgae biotechnologies are rapidly developing into new commercial settings. Several high value products already exist on the market, and systems development is focused on cost reduction to open up future economic opportunities for food, fuel and freshwater production. Light is a key environmental driver for photosynthesis and optimising light capture is therefore critical for low cost, high efficiency systems. Here a novel high-throughput screen that simulates fluctuating light regimes in mass cultures is presented. The data was used to model photosynthetic efficiency (PE_µ, mol photon^-1 m²) and chlorophyll fluorescence of two green algae, Chlamydomonas reinhardtii and Chlorella sp. Response surface methodology defined the effect of three key variables: density factor (D_f, 'culture density'), cycle time (t_c, 'mixing rate'), and maximum incident irradiance (I_max). Both species exhibited a large rise in PE_µ with decreasing I_max and a minimal effect of t_c (between 3-20 s). However, the optimal D_f of 0.4 for Chlamydomonas and 0.8 for Chlorella suggested strong preferences for dilute and dense cultures respectively. Chlorella had a two-fold higher optimised PE_µ than Chlamydomonas, despite its higher light sensitivity. These results demonstrate species-specific light preferences within the green algae clade. Our high-throughput screen enables rapid strain selection and process optimisation.

Figure 1

Experimental design for high-throughput light simulations of cells cycling in outdoor microalgae mass cultures. (A) Depicts the 3 factors that affect the light regime experienced by cells cycling in mass cultures: I_max, D_f and t_c, and the levels used for the full factorial experimental design which are based on ‘typical’ outdoor conditions. (B) Each combination of light factors was programmed by changing the light intensity of the LEDs over the cycle time, assuming cell cycling occurs in a sinusoidal trajectory. Here, I_max, is the amplitude of the sine, simulating the maximum irradiance that a cell would receive when at the ‘surface’ of a mass culture, D_f, is the proportion of time that PAR is below 5 µmol m⁻² s⁻¹ in one period; this simulates the fraction of time that a cell spends in the dark, depending on the culture density, and t_c is the period of one sine wave, that simulates the time required for a cell to cycle through the reactor. I_avg is the integration of light received, simulating the average irradiance or light dose received the by cell. Here t_light and t_dark are the time cells receive PAR (>5 µmol m⁻² s⁻¹) and no PAR (<5 µmol m⁻² s⁻¹) respectively. (C) The programmed LEDs form part of an 18-plate microwell robotic system. Chlamydomonas and Chlorella were incubated in 96-well plates placed on LED arrays with one LED per microwell and one unique light regime per plate. All light regimes occurred over a photoperiod of 16 h day⁻¹ and a dark period of 8 h day⁻¹.

Figure 2

Trends in photosynthetic efficiency (PE_µ) under different light regimes of Chlamydomonas (grey bars) and Chlorella (blue bars). (A and B) individual PE_µ data of the 27 light treatments for Chlamydomonas and Chlorella, respectively (n = 3), (C) the overall trends in averaged PE_µ values over all conditions of D_f, I_max and t_c tested (n = 27), (D) the averaged PE_µ values of D_f and t_c combined to show effect of I_max (n = 9), (E) the averaged PE_µ values of I_max and t_c combined to show effect of D_f (n = 9) and (F) the averaged PE_µ values of D_f and I_max combined to show effect of t_c (n = 9). Error bars represent the standard deviation (SD) of individual treatments within biological triplicates (A,B) and between different treatments (C–F).

Figure 3

Response surface (3D) and contour (2D) plots of two-way interactions of factors affecting the PE_µ (mol photon⁻¹ m²) of Chlamydomonas (A,C,E) and Chlorella (B,D,F). The colour bar depicts high PE_µ values in red and lower PE_µ values in blue.

Figure 4

Trends in underlying photosynthetic mechanisms. Plots depict averaged effects of I_max, D_f and t_c on ΦPSII (A,B and C) (n = 2); F_v/F_m (D,E and F) and OD₆₈₀/OD₇₅₀ (G,H and I) respectively for Chlamydomonas (grey bars) and Chlorella (blue bars) (n = 3, Error bars represent standard deviation).