Cheng Lei

Optical time-stretch imaging: Principles and applications

Breathtaking innovations in optical imaging have opened new exciting avenues for science, industry, and medicine over the last few decades. One of such innovations is optical time-stretch imaging—an emerging method for ultrafast optical imaging that builds on temporally stretching broadband pulses by using dispersive properties of light in both spatial and temporal domains. It achieves continuous image acquisition at an ultrahigh frame rate of 10–1000 million frames per second by overcoming technical and fundamental limitations that exist in traditional imaging methods. By virtue of its inherent affinity with optical signal processing, optical time-stretch imaging can be combined with various optical techniques such as amplification, nonlinear processing, compressive sensing, and pattern correlation to realize unique capabilities that are not possible with the traditional imaging methods. Applications enabled by such capabilities are versatile and include surface inspection, surface vibrometry, particle analysis, and cell screening. In this paper, we review the principles and limitations of conventional optical imaging, the principles and applications of optical time-stretch imaging, and discuss our future perspective.

High-throughput optofluidic particle profiling with morphological and chemical specificity

We present a method for high-throughput optofluidic particle analysis that provides both the morphological and chemical profiles of individual particles in a large heterogeneous population. This method is based on an integration of a time-stretch optical microscope with a submicrometer spatial resolution of 780 nm and a three-color fluorescence analyzer on top of an inertial-focusing microfluidic device. The integrated system can perform image- and fluorescence-based screening of particles with a high throughput of 10,000 particles/s, exceeding previously demonstrated imaging particle analyzers in terms of specificity without sacrificing throughput.

High-throughput label-free image cytometry and image-based classification of live Euglena gracilis.

We demonstrate high-throughput label-free single-cell image cytometry and image-based classification of Euglena gracilis (a microalgal species) under different culture conditions. We perform it with our high-throughput optofluidic image cytometer composed of a time-stretch microscope with 780-nm resolution and 75-Hz line rate, and an inertial-focusing microfluidic device. By analyzing a large number of single-cell images from the image cytometer, we identify differences in morphological and intracellular phenotypes between E. gracilis cell groups and statistically classify them under various culture conditions including nitrogen deficiency for lipid induction. Our method holds promise for real-time evaluation of culture techniques for E. gracilis and possibly other microalgae in a non-invasive manner.

High-Throughput Accurate Single-Cell Screening of Euglena gracilis with Fluorescence-Assisted Optofluidic Time-Stretch Microscopy

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 high-throughput, high-accuracy, single-cell screening of E. gracilis with fluorescence-assisted optofluidic time-stretch microscopy–a method that combines the strengths of microfluidic cell focusing, optical time-stretch microscopy, and fluorescence detection used in conventional flow cytometry. Specifically, our fluorescence-assisted optofluidic time-stretch microscope consists of an optical time-stretch microscope and a fluorescence analyzer on top of a hydrodynamically focusing microfluidic device and can detect fluorescence from every E. gracilis cell in a population and simultaneously obtain its image with 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 holds promise for evaluating cultivation techniques and selective breeding for microalgae-based biofuel production.

Inertial focusing of ellipsoidal Euglena gracilis cells in a stepped microchannel

Euglena gracilis (E. gracilis) has recently been attracting attention as a potential renewable source for the production of biofuels, livestock feed, cosmetics, and dietary supplements. Research has focused on strain isolation, productivity improvement, nutrient and resource allocation, and co-product production, key steps that ultimately determine the economic viability and compatibility of the biomass produced. To achieve these characteristics, approaches to select E. gracilis mutants with desirable properties, such as high wax ester content, high growth rate, and high environmental tolerance for biodiesel and biomass production, are needed. Flow-based analysis and sorting can be rapid and highly automated but calls for techniques that can precisely control the position of E. gracilis with varying sizes and shapes in a tightly focused stream in a high-throughput manner. In this work, we use a stepped microchannel consisting of a low-aspect-ratio straight channel and a series of expansion regions along the channel height. We study horizontal and vertical focusing, orientation, rotational, and translational behaviors of E. gracilis as a function of aspect ratio (AR) and channel Reynolds number (Re). By making use of inertial focusing and local secondary flows, E. gracilis with diverse shapes are directed to a single equilibrium position in a single focal stream. As an application of on-chip flow cytometry, we integrate a focusing microchip with a custom laser-two-focus (L2F) optical system and demonstrate the detection of chlorophyll autofluorescence as well as the measurement of the velocity of E. gracilis cells flowing through the microchannel.

High-throughput, label-free, single-cell, microalgal lipid screening by machine-learning-equipped optofluidic time-stretch quantitative phase microscopy

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 CO2 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 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.

Label-free detection of cellular drug responses by high-throughput bright-field imaging and machine learning

In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (Nu2009=u2009~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000u2009cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.

GHz Optical Time-Stretch Microscopy by Compressive Sensing

Optical time-stretch microscopy has recently attracted intensive attention for its capability of acquiring images at an ultrahigh frame rate. Unfortunately, its achievable frame rate is limited by the requirement of having no overlap between consecutive frames, which leads to a tradeoff between the frame rate (pulse repetition rate) and the amount of the temporal dispersion used for optical image serialization. In this paper, we demonstrate compressive sensing on the platform of optical time-stretch microscopy to overcome the tradeoff between frame rate and temporal dispersion (time stretch) and achieve 50 times higher frame rate than conventional optical time-stretch microscopy. Specifically, we computationally perform compressed optical time-stretch microscopy with an experimental dataset acquired by conventional optical time-stretch microscopy and demonstrate its effects in terms of spatial resolution and cell classification accuracy. Our results indicate that the spatial resolution and cell classification accuracy reach 780xa0nm and 95% at a line scan rate of 675xa0MHz and 6.75xa0GHz, respectively, which correspond to five times and 50 times higher frame rates than what conventional optical time-stretch microscopy can achieve with the same dispersion amount and digitizer sampling rate.

Optofluidic time-stretch quantitative phase microscopy

Innovations in optical microscopy have opened new windows onto scientific research, industrial quality control, and medical practice over the last few decades. One of such innovations is optofluidic time-stretch quantitative phase microscopy - an emerging method for high-throughput quantitative phase imaging that builds on the interference between temporally stretched signal and reference pulses by using dispersive properties of light in both spatial and temporal domains in an interferometric configuration on a microfluidic platform. It achieves the continuous acquisition of both intensity and phase images with a high throughput of more than 10,000 particles or cells per second by overcoming speed limitations that exist in conventional quantitative phase imaging methods. Applications enabled by such capabilities are versatile and include characterization of cancer cells and microalgal cultures. In this paper, we review the principles and applications of optofluidic time-stretch quantitative phase microscopy and discuss its future perspective.

High-throughput time-stretch microscopy with morphological and chemical specificity

Particle analysis is an effective method in analytical chemistry for sizing and counting microparticles such as emulsions, colloids, and biological cells. However, conventional methods for particle analysis, which fall into two extreme categories, have severe limitations. Sieving and Coulter counting are capable of analyzing particles with high throughput, but due to their lack of detailed information such as morphological and chemical characteristics, they can only provide statistical results with low specificity. On the other hand, CCD or CMOS image sensors can be used to analyze individual microparticles with high content, but due to their slow charge download, the frame rate (hence, the throughput) is significantly limited. Here by integrating a time-stretch optical microscope with a three-color fluorescent analyzer on top of an inertial-focusing microfluidic device, we demonstrate an optofluidic particle analyzer with a sub-micrometer spatial resolution down to 780 nm and a high throughput of 10,000 particles/s. In addition to its morphological specificity, the particle analyzer provides chemical specificity to identify chemical expressions of particles via fluorescence detection. Our results indicate that we can identify different species of microparticles with high specificity without sacrificing throughput. Our method holds promise for high-precision statistical particle analysis in chemical industry and pharmaceutics.