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 CO 2 via 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 labelfree single-cell screening of lipid-producing microalgal cells with optofluidic timestretch 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. V C 2017 International Society for Advancement of Cytometry

A high‐throughput all‐optical laser‐scanning imaging flow cytometer with biomolecular specificity and subcellular resolution

“… Not only can this integration accelerate image processing of numerous cell images, but also allows deep data analytics based on the aforementioned image‐derived phenotypes or deep machine‐learning‐based classification in real‐time. All in all, these advancements in FACED imaging flow cytometry could be a potent approach for large‐scale and deep characterization of single cells and their heterogeneity with the unprecedented statistical power—an ability to become increasingly critical in single‐cell analysis adopted in cancer research , stem cell research , drug discovery , neuroscience to name a few.…”

New on the block: The workshop reports

“…His strong expertise in technology development and instrumentation is due to his physics background in the Laser Interferometer Gravitational‐Wave Observatory (LIGO) group that received the 2017 Nobel Prize in physics. Over the last decade, he has made significant contributions to the development of diverse high‐speed optical imaging methods and their applications in cytometry . For example, he and his colleagues developed a new type of ultrafast imaging based on a radically different image acquisition principle in 2009 and combined it with microfluidics to demonstrate high‐throughput imaging flow cytometry in 2012 .…”

Multi‐ATOM: Ultrahigh‐throughput single‐cell quantitative phase imaging with subcellular resolution

“…The major barrier is the exceedingly high group velocity dispersion (GVD) required for achieving diffraction‐limited imaging resolution that inevitably comes at the cost of severe optical loss and thus image signal‐to‐noise ratio (SNR) . Consequently, the degraded SNR lowers the yield of phase‐image retrieval and hinders subcellular resolution in ultra‐large‐population single‐cell time‐stretch QPI (practically >10 5 cells), which has yet been reported so far .…”