Optofluidic Time-stretch Microscopy Research Articles

Fast intelligent cell phenotyping for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm.

.Significance: The use of optofluidic time-stretch flow cytometry enables extreme-throughput cell imaging but suffers from the difficulties of capturing and processing a large amount of data. As significant amounts of continuous image data are generated, the images require identification with high speed.Aim: We present an intelligent cell phenotyping framework for high-throughput optofluidic time-stretch microscopy based on the XGBoost algorithm, which is able to classify obtained cell images rapidly and accurately. The applied image recognition consists of density-based spatial clustering of applications with noise outlier detection, histograms of oriented gradients combining gray histogram fused feature, and XGBoost classification.Approach: We tested the ability of this framework against other previously proposed or commonly used algorithms to phenotype two groups of cell images. We quantified their performances with measures of classification ability and computational complexity based on AUC and test runtime. The tested cell image datasets were acquired from high-throughput imaging of over 20,000 drug-treated and untreated cells with an optofluidic time-stretch microscope.Results: The framework we built beats other methods with an accuracy of over 97% and a classification frequency of . In addition, we determined the optimal structure of training sets according to model performances under different training set components.Conclusions: The proposed XGBoost-based framework acts as a promising solution to processing large flow image data. This work provides a foundation for future cell sorting and clinical practice of high-throughput imaging cytometers.

Effects of Flow-Induced Microfluidic Chip Wall Deformation on Imaging Flow Cytometry.

Imaging flow cytometry is a powerful tool by virtue of its capability for high-throughput cell analysis. The advent of high-speed optical imaging methods on a microfluidic platform has significantly improved cell throughput and brought many degrees of freedom to instrumentation and applications over the last decade, but it also poses a predicament on microfluidic chips. Specifically, as the throughput increases, the flow speed also increases (currently reaching 10 m/s): consequently, the increased hydrodynamic pressure on the microfluidic chip deforms the wall of the microchannel and produces detrimental effects lead to defocused and blur image. Here, we present a comprehensive study of the effects of flow-induced microfluidic chip wall deformation on imaging flow cytometry. We fabricated three types of microfluidic chips with the same geometry and different degrees of stiffness made of polydimethylsiloxane (PDMS) and glass to investigate material influence on image quality. First, we found the maximum deformation of a PDMS microchannel was >60 μm at a pressure of 0.6 MPa, while no appreciable deformation was identified in a glass microchannel at the same pressure. Second, we found the deviation of lag time that indicating velocity difference of migrating microbeads due to the deformation of the microchannel was 29.3 ms in a PDMS microchannel and 14.9 ms in a glass microchannel. Third, the glass microchannel focused cells into a slightly narrower stream in the X-Y plane and a significantly narrower stream in the Z-axis direction (focusing percentages were increased 30%, 32%, and 5.7% in the glass channel at flow velocities of 0.5, 1.5, and 3 m/s, respectively), and the glass microchannel showed stabler equilibrium positions of focused cells regardless of flow velocity. Finally, we achieved the world's fastest imaging flow cytometry by combining a glass microfluidic device with an optofluidic time-stretch microscopy imaging technique at a flow velocity of 25 m/s. © 2019 International Society for Advancement of Cytometry.

High-throughput microfluidic particle velocimetry using optical time-stretch microscopy

We report the velocity measurement of microscopic particles flowing at ultrahigh speed with optofluidic time-stretch microscopy at high throughput. This is a study of using optical time-stretch microcopy as a tool for particle velocimetry, where we developed a custom algorithm to process the images acquired from the optofluidic platform for the velocity calculation of individual particles. We experimentally determined the actual flow velocities for polystyrene microspheres with different sizes and traveling through the microchannel at a throughput of ∼10 000 particles/s. We also examined microfluidic channels with different aspect ratios (depth-to-width) for particle velocimetry. The result indicates a measurable flow velocity up to 2.51 m/s. Our method provides a promising tool for label-free and high-throughput particle velocimetry at high velocity magnitudes.

High-throughput imaging flow cytometry by optofluidic time-stretch microscopy.

The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular 'big data'. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.

Optofluidic time-stretch microscopy: recent advances

Flow cytometry is an indispensable method for valuable applications in numerous fields such as immunology, pathology, pharmacology, molecular biology, and marine biology. Optofluidic time-stretch microscopy is superior to conventional flow cytometry methods for its capability to acquire high-quality images of single cells at a high-throughput exceeding 10,000 cells per second. This makes it possible to extract copious information from cellular images for accurate cell detection and analysis with the assistance of machine learning. Optofluidic time-stretch microscopy has proven its effectivity in various applications, including microalga-based biofuel production, evaluation of thrombotic disorders, as well as drug screening and discovery. In this review, we discuss the principles and recent advances of optofluidic time-stretch microscopy.

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.

Label-free detection of aggregated platelets in blood by machine-learning-aided optofluidic time-stretch microscopy.

According to WHO, about 10 million new cases of thrombotic disorders are diagnosed worldwide every year. Thrombotic disorders, including atherothrombosis (the leading cause of death in the US and Europe), are induced by occlusion of blood vessels, due to the formation of blood clots in which aggregated platelets play an important role. The presence of aggregated platelets in blood may be related to atherothrombosis (especially acute myocardial infarction) and is, hence, useful as a potential biomarker for the disease. However, conventional high-throughput blood analysers fail to accurately identify aggregated platelets in blood. Here we present an in vitro on-chip assay for label-free, single-cell image-based detection of aggregated platelets in human blood. This assay builds on a combination of optofluidic time-stretch microscopy on a microfluidic chip operating at a high throughput of 10 000 blood cells per second with machine learning, enabling morphology-based identification and enumeration of aggregated platelets in a short period of time. By performing cell classification with machine learning, we differentiate aggregated platelets from single platelets and white blood cells with a high specificity and sensitivity of 96.6% for both. Our results indicate that the assay is potentially promising as predictive diagnosis and therapeutic monitoring of thrombotic disorders in clinical settings.

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.