High-Throughput Screening of Chlorella Vulgaris Growth Kinetics inside a Droplet-Based Microfluidic Device under Irradiance and Nitrate Stress Conditions

A high throughput array microhabitat platform reveals how light and nitrogen colimit the growth of algal cells

A mechanistic understanding of algal growth is essential for maintaining a sustainable environment in an era of climate change and population expansion. It is known that algal growth is tightly controlled by complex interactive physical and chemical conditions. Many mathematical models have been proposed to describe the relation of algal growth and environmental parameters, but experimental verification has been difficult due to the lack of tools to measure cell growth under precise physical and chemical conditions. As such, current models depend on the specific testing systems, and the fitted growth kinetic constants vary widely for the same organisms in the existing literature. Here, we present a microfluidic platform where both light intensity and nutrient gradients can be well controlled for algal cell growth studies. In particular, light shading is avoided, a common problem in macroscale assays. Our results revealed that light and nitrogen colimit the growth of algal cells, with each contributing a Monod growth kinetic term in a multiplicative model. We argue that the microfluidic platform can lead towards a general culture system independent algal growth model with systematic screening of many environmental parameters. Our work advances technology for algal cell growth studies and provides essential information for future bioreactor designs and ecological predictions.

Colimitation of light and nitrogen on algal growth revealed by an array microhabitat platform

Microalgae are key players in the global carbon cycle and emerging producers of biofuels. Algal growth is critically regulated by its complex microenvironment, including nitrogen and phosphorous levels, light intensity, and temperature. Mechanistic understanding of algal growth is important for maintaining a balanced ecosystem at a time of climate change and population expansion, as well as providing essential formulations for optimizing biofuel production. Current mathematical models for algal growth in complex environmental conditions are still in their infancy, due in part to the lack of experimental tools necessary to generate data amenable to theoretical modeling. Here, we present a high throughput microfluidic platform that allows for algal growth with precise control over light intensity and nutrient gradients, while also performing real-time microscopic imaging. We propose a general mathematical model that describes algal growth under multiple physical and chemical environments, which we have validated experimentally. We showed that light and nitrogen colimited the growth of the model alga Chlamydomonas reinhardtii following a multiplicative Monod kinetic model. The microfluidic platform presented here can be easily adapted to studies of other photosynthetic micro-organisms, and the algal growth model will be essential for future bioreactor designs and ecological predictions.

Microalgae growth in ultra-thin steady-state continuous photobioreactors: assessing self-shading effects

To disclose the net effect of light on microalgal growth in photobioreactors, self-shading and mixing-induced light–dark cycles must be minimized and discerned from the transient phenomena of acclimation. In this work, we performed experiments of continuous microalgal cultivation in small-scale photobioreactors with different thicknesses (from 2 to 35 mm): working at a steady state allowed us to describe the effect of light after acclimation, while the geometry of the reactor was adjusted to find the threshold light path that can discriminate different phenomena. Experiments showed an increased inhibition under smaller culture light paths, suggesting a strong shading effect at thicknesses higher than 8 mm where mixing-induced light–dark cycles may occur. A Haldane-like model was applied and kinetic parameters retrieved, showing possible issues in the scalability of experimental results at different light paths if mixing-induced light–dark cycles are not considered. To further highlight the influence of mixing cycles, we proposed an analogy between small-scale operations with continuous light and PBR operations with pulsed light, with the computation of characteristic parameters from pulsed-light microalgae growth mathematical modeling.

Strain Development in Microalgal Biotechnology-Random Mutagenesis Techniques

Microalgal biomass and metabolites can be used as a renewable source of nutrition, pharmaceuticals and energy to maintain or improve the quality of human life. Microalgae’s high volumetric productivity and low impact on the environment make them a promising raw material in terms of both ecology and economics. To optimize biotechnological processes with microalgae, improving the productivity and robustness of the cell factories is a major step towards economically viable bioprocesses. This review provides an overview of random mutagenesis techniques that are applied to microalgal cell factories, with a particular focus on physical and chemical mutagens, mutagenesis conditions and mutant characteristics.

Droplet-based microfluidic screening and sorting of microalgal populations for strain engineering applications

The application of microfluidic technologies to microalgal research is particularly appealing since these approaches allow the precise control of the extracellular environment and offer a high-throughput approach to studying dynamic cellular processes. To expand the portfolio of applications, here we present a droplet-based microfluidic method for analysis and screening of Phaeodactylum tricornutum and Nannochloropsis gaditana, which can be integrated into a genetic transformation workflow. Following encapsulation of single cells in picolitre-sized droplets, fluorescence signals arising from each cell can be used to assess its phenotypic state. In this work, the chlorophyll fluorescence intensity of each cell was quantified and used to identify populations of P. tricornutum cells grown in different light conditions. Further, individual P. tricornutum or N. gaditana cells engineered to express green fluorescent protein were distinguished and sorted from wild-type cells. This has been exploited as a rapid screen for transformed cells within a population, bypassing a major bottleneck in algal transformation workflows and offering an alternative strategy for the identification of genetically modified strains.

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Droplet-based microfluidic systems are promising tools for algal single cell analysis.

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Improved intracellular fluorescence detection allows effective sorting of algae cells.

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The physiological status of single encapsulated algae cells can be determined.

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Sorting in microdroplets enables faster identification of transformed cells.

Robotics for enzyme technology: innovations and technological perspectives

The use of robotics in the life science sector has created a considerable and significant impact on a wide range of research areas, including enzyme technology due to their immense applications in enzyme and microbial engineering as an indispensable tool in high-throughput screening applications. Scientists are experiencing the advanced applications of various biological robots (nanobots), fabricated based on bottom-up or top-down approaches for making nanotechnology scaffolds. Nanobots and enzyme-powered nanomotors are particularly attractive because they are self-propelled vehicles, which consume biocompatible fuels. These smart nanostructures are widely used as drug delivery systems for the efficient treatment of various diseases. This review gives insights into the escalating necessity of robotics and nanobots and their ever-widening applications in enzyme technology, including biofuel production and biomedical applications. It also offers brief insights into high-throughput robotic platforms that are currently being used in enzyme screening applications for monitoring and control of microbial growth conditions. KEY POINTS: • Robotics and their applications in biotechnology are highlighted. • Robotics for high-throughput enzyme screening and microbial engineering are described. • Nanobots and enzyme-powered nanomotors as controllable drug delivery systems are reviewed.

Using microalgae for remediation of crude petroleum oil-water emulsions

Crude petroleum oil spills are among the most important organic contaminations. While the separated oils accumulated on the surface water are relatively easily removed, the emulsified portions are more difficult to remove and pose significant threats to the environment. Bioremediation using bacteria has proven to be an effective method, but the biomass produced in this case does not have any significant remunerative value. In this work, microalgae were proposed to combine emulsified oil remediation process with the potential of microalgae as a biofuel feedstock, thus enhancing the economic and environmental benefits of the process. A freshwater strain of Chlorella vulgaris was grown in water containing different concentrations of emulsified crude oil at different temperatures. The specific growth rate (μ_max ) of the microalgae for each initial oil concentration was determined and was found to increase with the increase in initial oil concentration. For example, at 30°C, the specific growth rate, μ increased from 0.477 to 0.784 per day as the oil concentration increased from 57 to 222 mg/L. At 30°C, the effect of substrate concentration agreed with that of the microalgae growth, whereas at 40°C, the drop in oil concentration decreased with the increase in concentration. The results were fitted to a modified Monod kinetics model that used specific interfacial area as the influential substrate instead of the actual concentration. The results of this study clearly show the potential of using microalgae for emulsified oil remediation at relatively high concentrations.

Impacts of Endocrine Disruptor di-n-Butyl Phthalate Ester on Microalga Chlorella vulgaris Verified by Approaches of Proteomics and Gene Ontology

Di-n-butyl phthalate (DBP) is an extensively used plasticizer. Most investigations on DBP have been concentrated on its environmental distribution and toxicity to humans. However, information on the effects of plasticizers on algal species is scarce. This study verified the impacts of endocrine disruptor di-n-butyl phthalate ester on microalga Chlorella vulgaris by approaches of proteomics and gene ontology. The algal acute biotoxicity results showed that the 24h-EC50 of DBP for C. vulgaris was 4.95 mg L^-1, which caused a decrease in the chlorophyll a content and an increase in the DBP concentration of C. vulgaris. Proteomic analysis led to the identification of 1257 C. vulgaris proteins. Sixty-one more proteins showed increased expression, compared to proteins with decreased expression. This result illustrates that exposure to DBP generally enhances protein expression in C. vulgaris. GO annotation showed that both acetolactate synthase (ALS) and GDP-L-fucose synthase 2 (GER2) decreased more than 1.5-fold after exposure to DBP. These effects could inhibit both the valine biosynthetic process and the nucleotide-sugar metabolic process in C. vulgaris. The results of this study demonstrate that DBP could inhibit growth and cause significant changes to the biosynthesis-relevant proteins in C. vulgaris.