The world is faced with environmental problems and the energy crisis due to the combustion and depletion of fossil
fuels. The development of reliable, sustainable, and economical sources of alternative fuels is an important, but
challenging goal for the world. As an alternative to liquid fossil fuels, algal biofuel is expected to play a key role in
alleviating global warming since algae absorb atmospheric CO2 via photosynthesis. Among various algae for fuel
production, Euglena gracilis is an attractive microalgal species as it is known to produce wax ester (good for biodiesel
and aviation fuel) within lipid droplets. To date, while there exist many techniques for inducing microalgal cells to
produce and accumulate lipid with high efficiency, few analytical methods are available for characterizing a population
of such lipid-accumulated microalgae including E. gracilis with high throughout, high accuracy, and single-cell
resolution simultaneously. Here we demonstrate a high-throughput optofluidic Euglena gracilis profiler which consists
of an optical time-stretch microscope and a fluorescence analyzer on top of an inertial-focusing microfluidic device that
can detect fluorescence from lipid droplets in their cell body and provide images of E. gracilis cells simultaneously at a
high throughput of 10,000 cells/s. With the multi-dimensional information acquired by the system, we classify nitrogen-sufficient
(ordinary) and nitrogen-deficient (lipid-accumulated) E. gracilis cells with a low false positive rate of 1.0%.
This method provides a promise for evaluating the efficiency of lipid-inducing techniques for biofuel production, which
is also applicable for identifying biomedical samples such as blood cells and cancer cells.