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Label-free measurement of algal triacylglyceride production using fluorescence hyperspectral imaging

Algal Research

Ricken, Bryce; Collins, Aaron M.; Sinclair, Michael B.; Timlin, Jerilyn A.; Singh, Seema S.

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.

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Preprocessing strategies to improve MCR analyses of hyperspectral images

Chemometrics and Intelligent Laboratory Systems

Jones, Howland D.T.; Sinclair, Michael B.; Melgaard, David K.; Collins, Aaron M.; Timlin, Jerilyn A.

Multivariate curve resolution (MCR) is a useful and important analysis tool for extracting quantitative information from hyperspectral image data. However, in the case of hyperspectral fluorescence microscope images acquired with CCD-type technologies, cosmic spikes and the presence of detector artifacts in the spectral data can make the extraction of the pure-component spectra and their relative concentrations challenging when applying MCR to the images. In this paper, we present new generalized and automated approaches for preprocessing spectral image data to improve the robustness of the MCR analysis of spectral images. These novel preprocessing steps remove cosmic spikes, correct for the presence of detector offsets and structured noise as well as select spectral and spatial regions to reduce the detrimental effects of detector noise. These preprocessing and MCR analysis techniques incorporate the use of an optical filter to prevent light from impinging on a small number of spectral pixels in the CCD detector. This dark spectral region can be incorporated into any spectral imaging system to enhance modeling of detector offset and structured noise components as well as the automated selection of spatial regions to restrict the analysis to only those regions containing viable spectral information. The success of these automated preprocessing methods combined with new MCR modeling approaches are demonstrated with realistically simulated data derived from spectral images of macrophage cells with green fluorescence protein (GFP). Further, we demonstrate using spectral images from the green alga, Chlorella, approaches for the analyses when fluorescent species with widely different relative spectral intensities are present in the image. We believe that the preprocessing and MCR approaches introduced in this paper can be generalized to several other hyperspectral image technologies and can improve the success of automated MCR analyses with little or no a priori information required about the spectral components present in the samples. © 2012 Elsevier B.V.

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Results 1–50 of 53
Results 1–50 of 53