title: High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy.
abstract: The development of reliable, sustainable, and economical sources of alternative fuels to petroleum is required to tackle the global energy crisis. One such alternative is microalgal biofuel, which is expected to play a key role in reducing the detrimental effects of global warming as microalgae absorb atmospheric CO2via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid amounts and fail to characterize a diverse population of microalgal cells with single-cell resolution in a non-invasive and interference-free manner. Here high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy was demonstrated. In particular, Euglena gracilis, an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement), within lipid droplets was investigated. The optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch quantitative phase microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase maps of every single cell at a high throughput of 10,000 cells/s, enabling accurate cell classification without the need for fluorescent staining. Specifically, the dataset was used to characterize heterogeneous populations of E. gracilis cells under two different culture conditions (nitrogen-sufficient and nitrogen-deficient) and achieve the cell classification with an error rate of only 2.15%. The method holds promise as an effective analytical tool for microalgae-based biofuel production. © 2017 International Society for Advancement of Cytometry.
authors.affiliation: Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
authors.forename: Baoshan
authors.initials: B
authors.lastname: Guo
authors.affiliation: Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.|Department of Electronic Engineering, Tsinghua University, Beijing, 100084, China.
authors.forename: Cheng
authors.initials: C
authors.lastname: Lei
authors.affiliation: Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
authors.forename: Hirofumi
authors.initials: H
authors.lastname: Kobayashi
authors.affiliation: Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan.
authors.forename: Takuro
authors.initials: T
authors.lastname: Ito
authors.affiliation: Laboratory for Integrated Biodevices, Quantitative Biology Center, RIKEN, Osaka, 565-0871, Japan.
authors.forename: Yaxiaer
authors.initials: Y
authors.lastname: Yalikun
authors.affiliation: Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.
authors.forename: Yiyue
authors.initials: Y
authors.lastname: Jiang
authors.affiliation: Laboratory for Integrated Biodevices, Quantitative Biology Center, RIKEN, Osaka, 565-0871, Japan.
authors.forename: Yo
authors.initials: Y
authors.lastname: Tanaka
authors.affiliation: Department of Electrical Engineering and Information Systems, University of Tokyo, Tokyo, 113-8656, Japan.
authors.forename: Yasuyuki
authors.initials: Y
authors.lastname: Ozeki
authors.affiliation: Department of Chemistry, University of Tokyo, Tokyo, 113-0033, Japan.|Japan Science and Technology Agency, Kawaguchi, 332-0012, Japan.|Department of Electrical Engineering, University of California, Los Angeles, California, 90095.
authors.forename: Keisuke
authors.initials: K
authors.lastname: Goda
mesh_terms: D002452:Cell Count; D057166:High-Throughput Screening Assays / Q000379:methods*; D000069550:Machine Learning; D058086:Microalgae / Q000166:cytology; D046210:Microfluidic Analytical Techniques / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D059010:Single-Cell Analysis / Q000379:methods*
keywords: Euglena gracilis; biofuel; global warming; high-throughput screening; machine learning; microfluidics; optofluidics; quantitative phase imaging; single-cell analysis