A fast fluorescence imaging flow cytometer for phytoplankton analysis

Optofluidic imaging meets deep learning: from merging to emerging

Propelled by the striking advances in optical microscopy and deep learning (DL), the role of imaging in lab-on-a-chip has dramatically been transformed from a silo inspection tool to a quantitative "smart" engine. A suite of advanced optical microscopes now enables imaging over a range of spatial scales (from molecules to organisms) and temporal window (from microseconds to hours). On the other hand, the staggering diversity of DL algorithms has revolutionized image processing and analysis at the scale and complexity that were once inconceivable. Recognizing these exciting but overwhelming developments, we provide a timely review of their latest trends in the context of lab-on-a-chip imaging, or coined optofluidic imaging. More importantly, here we discuss the strengths and caveats of how to adopt, reinvent, and integrate these imaging techniques and DL algorithms in order to tailor different lab-on-a-chip applications. In particular, we highlight three areas where the latest advances in lab-on-a-chip imaging and DL can form unique synergisms: image formation, image analytics and intelligent image-guided autonomous lab-on-a-chip. Despite the on-going challenges, we anticipate that they will represent the next frontiers in lab-on-a-chip imaging that will spearhead new capabilities in advancing analytical chemistry research, accelerating biological discovery, and empowering new intelligent clinical applications.

Online Monitoring of the Concentrations of Amorphous and Crystalline Mesoscopic Species Present in Solution

Despite the growing evidence for the existence of amorphous mesoscopic species in a solution and their crucial roles in crystallization, there has been the lack of a suitable method to measure the time-resolved concentrations of amorphous and crystalline mesospecies in a lab-scale stirred reactor. This has limited experimental investigations to understand the kinetics of amorphous and crystalline mesospecies formation in stirred solutions and made it challenging to measure the crystal nucleation rate directly. Here, we used depolarized light sheet microscopy to achieve time-resolved measurements of amorphous and crystalline mesospecies concentrations in solutions at varying temperatures. After demonstrating that the concentration measurement method is reasonably accurate, precise, and sensitive, we utilized this method to examine mesospecies formation both in a mixture of two miscible liquids and in an undersaturated solution of dl-valine, thus revealing the importance of a temperature change in the formation of metastable and amorphous mesospecies as well as the reproducibility of the measurements. Moreover, we used the presented method to monitor both mesospecies formation and crystal nucleation in dl-valine solutions at four different levels of supersaturation, while achieving the direct measurement of the crystal nucleation rates in stirred solutions. Our results show that, as expected, the inherent variability in nucleation originating from its stochastic nature reduces with increasing supersaturation, and the dependence of the measured nucleation rate on supersaturation is in reasonable agreement with that predicted by the classical nucleation theory.

Deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM) for easy isotropic volumetric imaging of large biological specimens

Isotropic 3D histological imaging of large biological specimens is highly desired but remains highly challenging to current fluorescence microscopy technique. Here we present a new method, termed deep-learning super-resolution light-sheet add-on microscopy (Deep-SLAM), to enable fast, isotropic light-sheet fluorescence imaging on a conventional wide-field microscope. After integrating a minimized add-on device that transforms an inverted microscope into a 3D light-sheet microscope, we further integrate a deep neural network (DNN) procedure to quickly restore the ambiguous z-reconstructed planes that suffer from still insufficient axial resolution of light-sheet illumination, thereby achieving isotropic 3D imaging of thick biological specimens at single-cell resolution. We apply this easy and cost-effective Deep-SLAM approach to the anatomical imaging of single neurons in a meso-scale mouse brain, demonstrating its potential for readily converting commonly-used commercialized 2D microscopes to high-throughput 3D imaging, which is previously exclusive for high-end microscopy implementations.

Automatic Detection of Freshwater Phytoplankton Specimens in Conventional Microscopy Images

Water safety and quality can be compromised by the proliferation of toxin-producing phytoplankton species, requiring continuous monitoring of water sources. This analysis involves the identification and counting of these species which requires broad experience and knowledge. The automatization of these tasks is highly desirable as it would release the experts from tedious work, eliminate subjective factors, and improve repeatability. Thus, in this preliminary work, we propose to advance towards an automatic methodology for phytoplankton analysis in digital images of water samples acquired using regular microscopes. In particular, we propose a novel and fully automatic method to detect and segment the existent phytoplankton specimens in these images using classical computer vision algorithms. The proposed method is able to correctly detect sparse colonies as single phytoplankton candidates, thanks to a novel fusion algorithm, and is able to differentiate phytoplankton specimens from other image objects in the microscope samples (like minerals, bubbles or detritus) using a machine learning based approach that exploits texture and colour features. Our preliminary experiments demonstrate that the proposed method provides satisfactory and accurate results.

AI on a chip

Artificial intelligence (AI) has dramatically changed the landscape of science, industry, defence, and medicine in the last several years. Supported by considerably enhanced computational power and cloud storage, the field of AI has shifted from mostly theoretical studies in the discipline of computer science to diverse real-life applications such as drug design, material discovery, speech recognition, self-driving cars, advertising, finance, medical imaging, and astronomical observation, where AI-produced outcomes have been proven to be comparable or even superior to the performance of human experts. In these applications, what is essentially important for the development of AI is the data needed for machine learning. Despite its prominent importance, the very first process of the AI development, namely data collection and data preparation, is typically the most laborious task and is often a limiting factor of constructing functional AI algorithms. Lab-on-a-chip technology, in particular microfluidics, is a powerful platform for both the construction and implementation of AI in a large-scale, cost-effective, high-throughput, automated, and multiplexed manner, thereby overcoming the above bottleneck. On this platform, high-throughput imaging is a critical tool as it can generate high-content information (e.g., size, shape, structure, composition, interaction) of objects on a large scale. High-throughput imaging can also be paired with sorting and DNA/RNA sequencing to conduct a massive survey of phenotype-genotype relations whose data is too complex to analyze with traditional computational tools, but is analyzable with the power of AI. In addition to its function as a data provider, lab-on-a-chip technology can also be employed to implement the developed AI for accurate identification, characterization, classification, and prediction of objects in mixed, heterogeneous, or unknown samples. In this review article, motivated by the excellent synergy between AI and lab-on-a-chip technology, we outline fundamental elements, recent advances, future challenges, and emerging opportunities of AI with lab-on-a-chip technology or "AI on a chip" for short.

Efficient and cost-effective 3D cellular imaging by sub-voxel-resolving light-sheet add-on microscopy

Light-sheet fluorescence microscopy (LSFM) allows volumetric live imaging at high-speed and with low photo-toxicity. Various LSFM modalities are commercially available, but their size and cost limit their access by the research community. A new method, termed sub-voxel-resolving (SVR) light-sheet add-on microscopy (SLAM), is presented to enable fast, resolution-enhanced light-sheet fluorescence imaging from a conventional wide-field microscope. This method contains two components: a miniature add-on device to regular wide-field microscopes, which contains a horizontal laser light-sheet illumination path to confine fluorophore excitation at the vicinity of the focal plane for optical sectioning; an off-axis scanning strategy and a SVR algorithm that utilizes sub-voxel spatial shifts to reconstruct the image volume that results in a twofold increase in resolution. SLAM method has been applied to observe the muscle activity change of crawling C. elegans, the heartbeat of developing zebrafish embryo, and the neural anatomy of cleared mouse brains, at high spatiotemporal resolution. It provides an efficient and cost-effective solution to convert the vast number of in-service microscopes for fast 3D live imaging with voxel-super-resolved capability.

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, various microorganisms and parasites in the blood during acute blood infections. All of these rare diagnostic objects can be detected and identified very rapidly to save a patient's life. This review outlines the main techniques of visualization of rare objects in the blood flow, methods for extraction of such objects from the blood flow for further investigations and new approaches to identify the objects automatically with the modern deep learning methods.

Scattering of an Airy light sheet by a chiral sphere

Scattering of a 1D Airy beam light sheet by a chiral sphere is numerically studied within Mie theory and the plane-wave spectrum method. To testify to the validity of our code and method, the results of scattering intensity of a chiral sphere by an Airy beam light sheet reducing to a homogeneous isotropic sphere are compared with those in existing literature, which shows that these results are in good agreement. Influences of different parameters on differential scattering cross sections in the far field are investigated in detail, including the chiral parameters, sphere radius, and beam position. It is found in the scattering intensity of an Airy beam by a chiral sphere that the chiral sphere, which is compared with a homogeneous isotropic sphere, can decrease the scattering intensity in a region, and the scattering intensity distribution is sensitive to the x₀ position of the Airy beam.

On-chip light-sheet fluorescence imaging flow cytometry at a high flow speed of 1 m/s

We present on-chip fluorescence imaging flow cytometry by light-sheet excitation on a mirror-embedded microfluidic chip. The method allows us to obtain microscopy-grade fluorescence images of cells flowing at a high speed of 1 m/s, which is comparable to the flow speed of conventional non-imaging flow cytometers. To implement the light-sheet excitation of flowing cells in a microchannel, we designed and fabricated a mirror-embedded PDMS-based microfluidic chip. To show its broad utility, we used the method to classify large populations of microalgal cells (Euglena gracilis) and human cancer cells (human adenocarcinoma cells). Our method holds promise for large-scale single-cell analysis.

Droplet-based light-sheet fluorescence microscopy for high-throughput sample preparation, 3-D imaging and quantitative analysis on a chip

We report a novel fusion of droplet microfluidics and light-sheet microscopy, to achieve high-throughput sample compartmentalization, manipulation and three-dimensional imaging on a chip. This optofluidic device characterized by orthogonal plane illumination and rapid liquid handling is compact and cost-effective, and capable of preparing sample droplets with tunable size, frequency and ingredient. Each droplet flowing through the device's imaging region is self-scanned by a laser-sheet, three-dimensionally reconstructed and quantitatively analysed. This simple-and-robust platform combines fast 3-D imaging with efficient sample preparation and eliminates the need of a complicated mechanical scan at the same time. Achieving 500 measurements per second and screening over 30 samples per minute, it shows great potential for various lab-on-a-chip biological studies, such as embryo sorting and cell growth assays.

Simultaneous dual-color light sheet fluorescence imaging flow cytometry for high-throughput marine phytoplankton analysis

This paper reports the development of a dual-color light sheet fluorescence imaging flow cytometer exclusively designed for rapid phytoplankton analysis. By simultaneously exciting chlorophyll and phycoerythrin fluorescence, the system is enabled to discriminate phycoerythrin-containing and phycoerythrin-lacking phytoplankton groups through simultaneous two-channel spectral imaging-in-flow. It is demonstrated the system has good sensitivity and resolution to detect picophytoplankton down to the size of ~1μm, high throughput of 1.3 × 10⁵cells/s and 5 × 10³cells/s at 100μL/min and 3mL/min volume flow rates for cultured picophytoplankton and nanophytoplankton detection, respectively, and a broad imaging range from ~1μm up to 300μm covering most marine phytoplankton cell sizes with just one 40 × objective. The simultaneous realization of high resolution, high sensitivity and high throughput with spectral resolving power of the system is expected to promote the technology towards more practical applications that demand automated phytoplankton analysis.

Automated quantitative cytological analysis using portable microfluidic microscopy

In this article, a portable microfluidic microscopy based approach for automated cytological investigations is presented. Inexpensive optical and electronic components have been used to construct a simple microfluidic microscopy system. In contrast to the conventional slide-based methods, the presented method employs microfluidics to enable automated sample handling and image acquisition. The approach involves the use of simple in-suspension staining and automated image acquisition to enable quantitative cytological analysis of samples. The applicability of the presented approach to research in cellular biology is shown by performing an automated cell viability assessment on a given population of yeast cells. Further, the relevance of the presented approach to clinical diagnosis and prognosis has been demonstrated by performing detection and differential assessment of malaria infection in a given sample.

Optofluidic time-stretch imaging - an emerging tool for high-throughput imaging flow cytometry

Optical imaging is arguably the most effective tool to visualize living cells with high spatiotemporal resolution and in a nearly noninvasive manner. Driven by this capability, state-of-the-art cellular assay techniques have increasingly been adopting optical imaging for classifying different cell types/stages, and thus dissecting the respective cellular functions. However, it is still a daunting task to image and characterize cell-to-cell variability within an enormous and heterogeneous population - an unmet need in single-cell analysis, which is now widely advocated in modern biology and clinical diagnostics. The challenge stems from the fact that current optical imaging technologies still lack the practical speed and sensitivity for measuring thousands to millions of cells down to the single-cell precision. Adopting the wisdom in high-speed fiber-optics communication, optical time-stretch imaging has emerged as a completely new optical imaging concept which is now proven for ultrahigh-throughput optofluidic single-cell imaging, at least 1-2 orders-of-magnitude higher (up to ∼100 000 cells per second) compared to the existing imaging flow cytometers. It also uniquely enables quantification of intrinsic biophysical markers of individual cells - a largely unexploited class of single-cell signatures that is known to be correlated with the overwhelmingly investigated biochemical markers. With the aim of reaching a wider spectrum of experts specializing in cellular assay developments and applications, this paper highlights the essential basics of optical time-stretch imaging, followed by reviewing the recent developments and applications of optofluidic time-stretch imaging. We will also discuss the current challenges of this technology, in terms of providing new insights in basic biology and enriching the clinical diagnostic toolsets.

High throughput imaging cytometer with acoustic focussing

We demonstrate an imaging flow cytometer that uses acoustic levitation to assemble cells and other particles into a sheet structure. This technique enables a high resolution, low noise CMOS camera to capture images of thousands of cells with each frame. While ultrasonic focussing has previously been demonstrated for 1D cytometry systems, extending the technology to a planar, much higher throughput format and integrating imaging is non-trivial, and represents a significant jump forward in capability, leading to diagnostic possibilities not achievable with current systems. A galvo mirror is used to track the images of the moving cells permitting exposure times of 10 ms at frame rates of 50 fps with motion blur of only a few pixels. At 80 fps, we demonstrate a throughput of 208 000 beads per second. We investigate the factors affecting motion blur and throughput, and demonstrate the system with fluorescent beads, leukaemia cells and a chondrocyte cell line. Cells require more time to reach the acoustic focus than beads, resulting in lower throughputs; however a longer device would remove this constraint.

Non-linear optical flow cytometry using a scanned, Bessel beam light-sheet

Modern flow cytometry instruments have become vital tools for high-throughput analysis of single cells. However, as issues with the cellular labeling techniques often used in flow cytometry have become more of a concern, the development of label-free modalities for cellular analysis is increasingly desired. Non-linear optical phenomena (NLO) are of growing interest for label-free analysis because of the ability to measure the intrinsic optical response of biomolecules found in cells. We demonstrate that a light-sheet consisting of a scanned Bessel beam is an optimal excitation geometry for efficiently generating NLO signals in a microfluidic environment. The balance of photon density and cross-sectional area provided by the light-sheet allowed significantly larger two-photon fluorescence intensities to be measured in a model polystyrene microparticle system compared to measurements made using other excitation focal geometries, including a relaxed Gaussian excitation beam often used in conventional flow cytometers.

Automated cell viability assessment using a microfluidics based portable imaging flow analyzer

In this work, we report a system-level integration of portable microscopy and microfluidics for the realization of optofluidic imaging flow analyzer with a throughput of 450 cells/s. With the use of a cellphone augmented with off-the-shelf optical components and custom designed microfluidics, we demonstrate a portable optofluidic imaging flow analyzer. A multiple microfluidic channel geometry was employed to demonstrate the enhancement of throughput in the context of low frame-rate imaging systems. Using the cell-phone based digital imaging flow analyzer, we have imaged yeast cells present in a suspension. By digitally processing the recorded videos of the flow stream on the cellphone, we demonstrated an automated cell viability assessment of the yeast cell population. In addition, we also demonstrate the suitability of the system for blood cell counting.