A prominent challenge of microalgae-based biomaterial engineering is how to understand and take advantage of microalgal heterogeneity, in particular cell-to-cell variations in the content of intracellular metabolites. Unfortunately, conventional population-averaged assays such as mass spectrometry and chromatography fall short in evaluating heterogeneous populations of motile microalgae. Furthermore, although fluorescence microscopy offers molecular-specific imagery via fluorescent labeling, fluorescent dyes can cause cytotoxicity and interfere with cellular metabolism and biomaterial yield.
To tackle this important yet challenging problem, we first decided to use stimulated Raman scattering (SRS) microscopy – an optical method for chemical imaging of cells based on the vibrational signatures of their intracellular molecules. Specifically, we employed an SRS imaging system developed by Y. Ozeki in 2012 [Nature Photonics 6, 845 (2012)], which features a high frame rate of 30 frames per second per spectral point and fast spectral sweep, but it was not fast enough for our study. To further improve its speed and elucidate the microalgal heterogeneity, we formed an interdisciplinary research program (http://www.jst.go.jp/impact/serendipity/en/index.html). In this program led by K. Goda, we focused on Euglena gracilis – a microalgal species that produces industrially interesting biomaterials such as paramylon and lipids which can be used as food supplements and biofuels.
When we initially tested SRS microscopy of motile E. gracilis cells, the images were degraded by motion artifacts caused by the locomotion of the cells. Through a careful optimization of imaging parameters such as the field of view, we increased the speed of our SRS microscope and consequently achieved the world’s highest frame rate of 110 frames per second per spectral point. We also optimized the number of measured vibrational frequencies to demonstrate motion-artifact-free metabolite imaging of E. gracilis cells. Furthermore, our image analysis team adopted various digital image processing techniques for spectral decomposition and noise removal. This series of engineering efforts finally allowed us to quantify and analyze previously inaccessible cell-to-cell variations in the metabolite accumulation of a large population of motile E. gracilis cells under different culture conditions. This finding was described in our paper published in Nature Microbiology on Aug. 1st, 2016.
While our work focused on E. gracilis, our method is applicable to other microalgal species such as Botryococcus braunii and Chlamydomonas reinhardtii. We believe that our method holds great promise for understanding the enormous yet unexplored heterogeneity of microalgae, screening and characterization of microalgal mutants and highly efficient biomaterial engineering. This work exemplified the success of interdisciplinary research and teamwork that helped us tackle the important biological problem using a physical, chemical, and computational approach.