Baoshan Guo

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 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 (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/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.

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 label-free screening of euglena gracilis with optofluidic time-stretch quantitative phase 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, microalgal biofuel is expected to play a key role in reducing the detrimental effects of global warming since microalgae absorb atmospheric CO2 via photosynthesis. Unfortunately, conventional analytical methods only provide population-averaged lipid contents and fail to characterize a diverse population of microalgal cells with single-cell resolution in a noninvasive and interference-free manner. Here we demonstrate high-throughput label-free single-cell screening of lipid-producing microalgal cells with optofluidic time-stretch quantitative phase microscopy. In particular, we use Euglena gracilis – an attractive microalgal species that produces wax esters (suitable for biodiesel and aviation fuel after refinement) within lipid droplets. Our optofluidic time-stretch quantitative phase microscope is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch phase-contrast microscope, and a digital image processor equipped with machine learning. As a result, it provides both the opacity and phase contents of every single cell at a high throughput of 10,000 cells/s. We characterize heterogeneous populations of E. gracilis cells under two different culture conditions to evaluate their lipid production efficiency. Our method holds promise as an effective analytical tool for microalgaebased biofuel production.

Optofluidic time-stretch quantitative phase microscopy for high-throughput label-free single-cell analysis (Conference Presentation)

The ability to sift through a large heterogeneous population of cells is of paramount importance in a diverse range of biomedical and green applications. Furthermore, the capability of identifying various features of cells in a label-free manner is useful for high-throughput screening. Here we present optofluidic time-stretch quantitative phase microscopy for high-throughput label-free single-cell screening. This method is based on an integration of a hydrodynamic-focusing microfluidic chip, an optical time-stretch microscope for high-speed imaging with a spatial resolution of ~800 nm at a frame rate of ~10 million frames per second, and a digital image processor for image-based characterization, classification, and statistical analysis of biological cells such as blood cells and microalgae. It provides both the opacity (amplitude) and thickness (phase) content of every cell at a high throughput of ~10,000 cells per second. This method is expected to be effective for a diverse range of applications such as cancer detection and biofuel production.

Label-free image-based detection of drug resistance with optofluidic time-stretch microscopy (Conference Presentation)

Acquired drug resistance is a fundamental predicament in cancer therapy. Early detection of drug-resistant cancer cells during or after treatment is expected to benefit patients from unnecessary drug administration and thus play a significant role in the development of a therapeutic strategy. However, the development of an effective method of detecting drug-resistant cancer cells is still in its infancy due to their complex mechanism in drug resistance. To address this problem, we propose and experimentally demonstrate label-free image-based drug resistance detection with optofluidic time-stretch microscopy using leukemia cells (K562 and K562/ADM). By adding adriamycin (ADM) to both K562 and K562/ADM (ADM-resistant K562 cells) cells, both types of cells express unique morphological changes, which are subsequently captured by an optofluidic time-stretch microscope. These unique morphological changes are extracted as image features and are subjected to supervised machine learning for cell classification. We hereby have successfully differentiated K562 and K562/ADM solely with label-free images, which suggests that our technique is capable of detecting drug-resistant cancer cells. Our optofluidic time-stretch microscope consists of a time-stretch microscope with a high spatial resolution of 780 nm at a 1D frame rate of 75 MHz and a microfluidic device that focuses and orders cells. We compare various machine learning algorithms as well as various concentrations of ADM for cell classification. Owing to its unprecedented versatility of using label-free image and its independency from specific molecules, our technique holds great promise for detecting drug resistance of cancer cells for which its underlying mechanism is still unknown or chemical probes are still unavailable.

High-throughput label-free detection of aggregate platelets with optofluidic time-stretch microscopy (Conference Presentation)

According to WHO, approximately 10 million new cases of thrombotic disorders are diagnosed worldwide every year. In the U.S. and Europe, their related diseases kill more people than those from AIDS, prostate cancer, breast cancer and motor vehicle accidents combined. Although thrombotic disorders, especially arterial ones, mainly result from enhanced platelet aggregability in the vascular system, visual detection of platelet aggregates in vivo is not employed in clinical settings. Here we present a high-throughput label-free platelet aggregate detection method, aiming at the diagnosis and monitoring of thrombotic disorders in clinical settings. With optofluidic time-stretch microscopy with a spatial resolution of 780 nm and an ultrahigh linear scanning rate of 75 MHz, it is capable of detecting aggregated platelets in lysed blood which flows through a hydrodynamic-focusing microfluidic device at a high throughput of 10,000 particles/s. With digital image processing and statistical analysis, we are able to distinguish them from single platelets and other blood cells via morphological features. The detection results are compared with results of fluorescence-based detection (which is slow and inaccurate, but established). Our results indicate that the method holds promise for real-time, low-cost, label-free, and minimally invasive detection of platelet aggregates, which is potentially applicable to detection of platelet aggregates in vivo and to the diagnosis and monitoring of thrombotic disorders in clinical settings. This technique, if introduced clinically, may provide important clinical information in addition to that obtained by conventional techniques for thrombotic disorder diagnosis, including ex vivo platelet aggregation tests.