Microalgae have been identified as a promising renewable feedstock for production of lipids for feeds and fuels. Current methods for identifying algae strains and growth conditions that support high lipid production require a variety of fluorescent chemical indicators, such as Nile Red and more recently, Bodipy. Despite notable successes using these approaches, chemical indicators exhibit several drawbacks, including non-uniform staining, low lipid specificity, cellular toxicity, and variable permeability based on cell-type, limiting their applicability for high-throughput bioprospecting. In this work, we used in vivo hyperspectral confocal fluorescence microscopy of a variety of potential microalgae production strains (Nannochloropsis sp., Dunaliella salina, Neochloris oleoabundans, and Chlamydomonas reinhardtii) to identify a label-free method for localizing lipid bodies and quantifying the lipid yield on a single-cell basis. By analyzing endogenous fluorescence from chlorophyll and resonance Raman emission from lipid-solubilized carotenoids we deconvolved pure component emission spectra and generated diffraction limited projections of the lipid bodies and chloroplast organelles, respectively. Applying this imaging method to nutrient depletion time-courses from lab-scale and outdoor cultivation systems revealed an additional autofluorescence spectral component that became more prominent over time, and varied inversely with the chlorophyll intensity, indicative of physiological compromise of the algal cell. This signal could result in false-positives for conventional measurements of lipid accumulation (via spectral overlap with Nile Red), however, the additional spectral feature was found to be useful for classification of lipid enrichment and culture crash conditions in the outdoor cultivation system. Under nutrient deprivation, increases in the lipid fraction of the cellular volume of ~. 500% were observed, as well as a correlated decrease in the chloroplast fraction of the total cellular volume. The results suggest that a membrane recycling mechanism dominates for nutrient deprivation-based lipid accumulation in the microalgae tested.
A considerable amount research is being conducted on microalgae, since microalgae are becoming a promising source of renewable energy. Most of this research is centered on lipid production in microalgae because microalgae produce triacylglycerol which is ideal for biodiesel fuels. Although we are interested in research to increase lipid production in algae, we are also interested in research to sustain healthy algal cultures in large scale biomass production farms or facilities. The early detection of fluctuations in algal health, productivity, and invasive predators must be developed to ensure that algae are an efficient and cost-effective source of biofuel. Therefore we are developing technologies to monitor the health of algae using spectroscopic measurements in the field. To do this, we have proposed to spectroscopically monitor large algal cultivations using LIDAR (Light Detection And Ranging) remote sensing technology. Before we can deploy this type of technology, we must first characterize the spectral bio-signatures that are related to algal health. Recently, we have adapted our confocal hyperspectral imaging microscope at Sandia to have two-photon excitation capabilities using a chameleon tunable laser. We are using this microscope to understand the spectroscopic signatures necessary to characterize microalgae at the cellular level prior to using these signatures to classify the health of bulk samples, with the eventual goal of using of LIDAR to monitor large scale ponds and raceways. By imaging algal cultures using a tunable laser to excite at several different wavelengths we will be able to select the optimal excitation/emission wavelengths needed to characterize algal cultures. To analyze the hyperspectral images generated from this two-photon microscope, we are using Multivariate Curve Resolution (MCR) algorithms to extract the spectral signatures and their associated relative intensities from the data. For this presentation, I will show our two-photon hyperspectral imaging results on a variety of microalgae species and show how these results can be used to characterize algal ponds and raceways.
The search is on for new renewable energy and algal-derived biofuel is a critical piece in the multi-faceted renewable energy puzzle. It has 30x more oil than any terrestrial oilseed crop, ideal composition for biodiesel, no competition with food crops, can be grown in waste water, and is cleaner than petroleum based fuels. This project discusses these three goals: (1) Conduct fundamental research into the effects that dynamic biotic and abiotic stressors have on algal growth and lipid production - Genomics/Transcriptomics, Bioanalytical spectroscopy/Chemical imaging; (2) Discover spectral signatures for algal health at the benchtop and greenhouse scale - Remote sensing, Bioanalytical spectroscopy; and (3) Develop computational model for algal growth and productivity at the raceway scale - Computational modeling.