Enhanced CRISPR/Cas12a-based quantitative detection of nucleic acids using double emulsion droplets.

Pairing droplet microfluidics and CRISPR/Cas12a techniques creates a powerful solution for the detection and quantification of nucleic acids at the single-molecule level, due to its specificity, sensitivity, and simplicity. However, traditional water-in-oil (W/O) single emulsion (SE) droplets often present stability issues, affecting the accuracy and reproducibility of assay results. As an alternative, water-in-oil-in-water (W/O/W) double emulsion (DE) droplets offer superior stability and uniformity for droplet digital assays. Moreover, unlike SE droplets, DE droplets are compatible with commercially available flow cytometry instruments for high-throughput analysis. Despite these advantages, no study has demonstrated the use of DE droplets for CRISPR-based nucleic acid detection. In our study, we conducted a comparative analysis to assess the performance of SE and DE droplets in quantitative detection of human papillomavirus type 18 (HPV18) DNA based on CRISPR/Cas12a. We evaluated the stability of SEs and DEs by examining size variation, merging extent, and content interaction before and after incubation at different temperatures and time points. By integrating DE droplets with flow cytometry, we achieved high-throughput and high-accuracy CRISPR/Cas12a-based quantification of target HPV18 DNA. The DE platform, when paired with CRISPR/Cas12a and flow cytometry techniques, emerges as a reliable tool for absolute quantification of nucleic acid biomarkers.

Enhancing the sensitivity and stability of electrochemical aptamer-based sensors by AuNPs@MXene nanocomposite for continuous monitoring of biomarkers.

Electrochemical aptamer-based (E-AB) sensors offer exciting potential for real-time tracking of various biomarkers, such as proteins and small molecules, due to their exceptional selectivity and adaptability. However, most E-AB sensors rely on planar gold structures, which inherently limit their sensitivity and operational stability for continuous monitoring of biomarkers. Although gold nanostructures have recently enhanced E-AB sensor performance, no studies have explored the combination of gold nanostructure with other types of nanomaterials for continuous molecular monitoring. To fill this gap, we employed gold nanoparticles and MXene Ti3C2 (AuNPs@MXene), a versatile nanocomposite, in designing an E-AB sensor targeted at vascular endothelial growth factor (VEGF), a crucial human signaling protein. Remarkably, the AuNPs@MXene nanocomposite achieved over thirty-fold and half-fold increases in active surface area compared to bare and AuNPs-modified gold electrodes, respectively, significantly elevating the analytical capabilities of E-AB sensors during continuous operation. After a systematic optimization and characterization process, the newly developed E-AB sensor, powered by AuNPs@MXene nanocomposite, demonstrated both enhanced stability and heightened sensitivity. Overall, our findings open new avenues for the incorporation of nanocomposites in E-AB sensor design, enabling the creation of more sensitive and durable real-time monitoring systems.

Color-scalable flow cytometry with Raman tags.

Flow cytometry is an indispensable tool in biology and medicine for counting and analyzing cells in large heterogeneous populations. It identifies multiple characteristics of every single cell, typically via fluorescent probes that specifically bind to target molecules on the cell surface or within the cell. However, flow cytometry has a critical limitation: the color barrier. The number of chemical traits that can be simultaneously resolved is typically limited to several due to the spectral overlap between fluorescence signals from different fluorescent probes. Here, we present color-scalable flow cytometry based on coherent Raman flow cytometry with Raman tags to break the color barrier. This is made possible by combining a broadband Fourier-transform coherent anti-Stokes Raman scattering (FT-CARS) flow cytometer, resonance-enhanced cyanine-based Raman tags, and Raman-active dots (Rdots). Specifically, we synthesized 20 cyanine-based Raman tags whose Raman spectra are linearly independent in the fingerprint region (400 to 1,600 cm-1). For highly sensitive detection, we produced Rdots composed of 12 different Raman tags in polymer nanoparticles whose detection limit was as low as 12 nM for a short FT-CARS signal integration time of 420 µs. We performed multiplex flow cytometry of MCF-7 breast cancer cells stained by 12 different Rdots with a high classification accuracy of 98%. Moreover, we demonstrated a large-scale time-course analysis of endocytosis via the multiplex Raman flow cytometer. Our method can theoretically achieve flow cytometry of live cells with >140 colors based on a single excitation laser and a single detector without increasing instrument size, cost, or complexity.

Typing of acute leukemia by intelligent optical time-stretch imaging flow cytometry on a chip.

Acute leukemia (AL) is one of the top life-threatening diseases. Accurate typing of AL can significantly improve its prognosis. However, conventional methods for AL typing often require cell staining, which is time-consuming and labor-intensive. Furthermore, their performance is highly limited by the specificity and availability of fluorescent labels, which can hardly meet the requirements of AL typing in clinical settings. Here, we demonstrate AL typing by intelligent optical time-stretch (OTS) imaging flow cytometry on a microfluidic chip. Specifically, we employ OTS microscopy to capture the images of cells in clinical bone marrow samples with a spatial resolution of 780 nm at a high flowing speed of 1 m s-1 in a label-free manner. Then, to show the clinical utility of our method for which the features of clinical samples are diverse, we design and construct a deep convolutional neural network (CNN) to analyze the cellular images and determine the AL type of each sample. We measure 30 clinical samples composed of 7 acute lymphoblastic leukemia (ALL) samples, 17 acute myelogenous leukemia (AML) samples, and 6 samples from healthy donors, resulting in a total of 227 620 images acquired. Results show that our method can distinguish ALL and AML with an accuracy of 95.03%, which, to the best of our knowledge, is a record in label-free AL typing. In addition to AL typing, we believe that the high throughput, high accuracy, and label-free operation of our method make it a potential solution for cell analysis in scientific research and clinical settings.

Virtual-freezing fluorescence imaging flow cytometry with 5-aminolevulinic acid stimulation and antibody labeling for detecting all forms of circulating tumor cells.

Circulating tumor cells (CTCs) are precursors to cancer metastasis. In blood circulation, they take various forms such as single CTCs, CTC clusters, and CTC-leukocyte clusters, all of which have unique characteristics in terms of physiological function and have been a subject of extensive research in the last several years. Unfortunately, conventional methods are limited in accurately analysing the highly heterogeneous nature of CTCs. Here we present an effective strategy for simultaneously analysing all forms of CTCs in blood by virtual-freezing fluorescence imaging (VIFFI) flow cytometry with 5-aminolevulinic acid (5-ALA) stimulation and antibody labeling. VIFFI is an optomechanical imaging method that virtually freezes the motion of fast-flowing cells on an image sensor to enable high-throughput yet sensitive imaging of every single event. 5-ALA stimulates cancer cells to induce the accumulation of protoporphyrin (PpIX), a red fluorescent substance, making it possible to detect all cancer cells even if they show no expression of the epithelial cell adhesion molecule, a typical CTC biomarker. Although PpIX signals are generally weak, VIFFI flow cytometry can detect them by virtue of its high sensitivity. As a proof-of-principle demonstration of the strategy, we applied cancer cells spiked in blood to the strategy to demonstrate image-based detection and accurate classification of single cancer cells, clusters of cancer cells, and clusters of a cancer cell(s) and a leukocyte(s). To show the clinical utility of our method, we used it to evaluate blood samples of four breast cancer patients and four healthy donors and identified EpCAM-positive PpIX-positive cells in one of the patient samples. Our work paves the way toward the determination of cancer prognosis, the guidance and monitoring of treatment, and the design of antitumor strategies for cancer patients.

Studying the efficacy of antiplatelet drugs on atherosclerosis by optofluidic imaging on a chip.

Vascular stenosis caused by atherosclerosis instigates activation and aggregation of platelets, eventually resulting in thrombus formation. Although antiplatelet drugs are commonly used to inhibit platelet activation and aggregation, they unfortunately cannot prevent recurrent thrombotic events in patients with atherosclerosis. This is partially due to the limited understanding of the efficacy of antiplatelet drugs in the complex hemodynamic environment of vascular stenosis. Conventional methods for evaluating the efficacy of antiplatelet drugs under stenosis either fail to simulate the hemodynamic environment of vascular stenosis characterized by high shear stress and recirculatory flow or lack spatial resolution in their analytical techniques to statistically identify and characterize platelet aggregates. Here we propose and experimentally demonstrate a method comprising an in vitro 3D stenosis microfluidic chip and an optical time-stretch quantitative phase imaging system for studying the efficacy of antiplatelet drugs under stenosis. Our method simulates the atherogenic flow environment of vascular stenosis while enabling high-resolution and statistical analysis of platelet aggregates. Using our method, we distinguished the efficacy of three antiplatelet drugs, acetylsalicylic acid (ASA), cangrelor, and eptifibatide, for inhibiting platelet aggregation induced by stenosis. Specifically, ASA failed to inhibit stenosis-induced platelet aggregation, while eptifibatide and cangrelor showed high and moderate efficacy, respectively. Furthermore, we demonstrated that the drugs tested also differed in their efficacy for inhibiting platelet aggregation synergistically induced by stenosis and agonists (e.g., adenosine diphosphate, and collagen). Taken together, our method is an effective tool for investigating the efficacy of antiplatelet drugs under vascular stenosis, which could assist the development of optimal pharmacologic strategies for patients with atherosclerosis.

High-Throughput Raman Flow Cytometry and Beyond.

Flow cytometry is a powerful tool with applications in diverse fields such as microbiology, immunology, virology, cancer biology, stem cell biology, and metabolic engineering. It rapidly counts and characterizes large heterogeneous populations of cells in suspension (e.g., blood cells, stem cells, cancer cells, and microorganisms) and dissociated solid tissues (e.g., lymph nodes, spleen, and solid tumors) with typical throughputs of 1,000-100,000 events per second (eps). By measuring cell size, cell granularity, and the expression of cell surface and intracellular molecules, it provides systematic insights into biological processes. Flow cytometers may also include cell sorting capabilities to enable subsequent additional analysis of the sorted sample (e.g., electron microscopy and DNA/RNA sequencing), cloning, and directed evolution. Unfortunately, traditional flow cytometry has several critical limitations as it mainly relies on fluorescent labeling for cellular phenotyping, which is an indirect measure of intracellular molecules and surface antigens. Furthermore, it often requires time-consuming preparation protocols and is incompatible with cell therapy. To overcome these difficulties, a different type of flow cytometry based on direct measurements of intracellular molecules by Raman spectroscopy, or "Raman flow cytometry" for short, has emerged. Raman flow cytometry obtains a chemical fingerprint of the cell in a nondestructive manner, allowing for single-cell metabolic phenotyping. However, its slow signal acquisition due to the weak light-molecule interaction of spontaneous Raman scattering prevents the throughput necessary to interrogate large cell populations in reasonable time frames, resulting in throughputs of about 1 eps. The remedy to this throughput limit lies in coherent Raman scattering methods such as stimulated Raman scattering (SRS) and coherent anti-Stokes Raman scattering (CARS), which offer a significantly enhanced light-sample interaction and hence enable high-throughput Raman flow cytometry, Raman imaging flow cytometry, and even Raman image-activated cell sorting (RIACS). In this Account, we outline recent advances, technical challenges, and emerging opportunities of coherent Raman flow cytometry. First, we review the principles of various types of SRS and CARS and introduce several techniques of coherent Raman flow cytometry such as CARS, multiplex CARS, Fourier-transform CARS, SRS, SRS imaging flow cytometry, and RIACS. Next, we discuss a unique set of applications enabled by coherent Raman flow cytometry, from microbiology and lipid biology to cancer detection and cell therapy. Finally, we describe future opportunities and challenges of coherent Raman flow cytometry including increasing sensitivity and throughput, integration with droplet microfluidics, utilizing machine learning techniques, or achieving in vivo flow cytometry. This Account summarizes the growing field of high-throughput Raman flow cytometry and the bright future it can bring.

Direct control of store-operated calcium channels by ultrafast laser.

Ca2+ channels are essential to cell birth, life, and death. They can be externally activated by optogenetic tools, but this requires robust introduction of exogenous optogenetic genes for expression of photosensitive proteins in biological systems. Here we present femtoSOC, a method for direct control of Ca2+ channels solely by ultrafast laser without the need for optogenetic tools or any other exogenous reagents. Specifically, by focusing and scanning wavelength-tuned low-power femtosecond laser pulses on the plasma membrane for multiphoton excitation, we directly induced Ca2+ influx in cultured cells. Mechanistic study reveals that photoexcited flavins covalently bind cysteine residues in Orai1 via thioether bonds, which facilitates Orai1 polymerization to form store-operated calcium channels (SOCs) independently of STIM1, a protein generally participating in SOC formation, enabling all-optical activation of Ca2+ influx and downstream signaling pathways. Moreover, we used femtoSOC to demonstrate direct neural activation both in brain slices in vitro and in intact brains of living mice in vivo in a spatiotemporal-specific manner, indicating potential utility of femtoSOC.

GEM-IL: A highly responsive fluorescent lactate indicator.

Lactate metabolism has been shown to have increasingly important implications in cellular functions as well as in the development and pathophysiology of disease. The various roles as a signaling molecule and metabolite have led to interest in establishing a new method to detect lactate changes in live cells. Here we report our development of a genetically encoded metabolic indicator specifically for probing lactate (GEM-IL) based on superfolder fluorescent proteins and mutagenesis. With improvements in its design, specificity, and sensitivity, GEM-IL allows new applications compared with the previous lactate indicators, Laconic and Green Lindoblum. We demonstrate the functionality of GEM-IL to detect differences in lactate changes in human oncogenic neural progenitor cells and mouse primary ventricular myocytes. The development and application of GEM-IL show promise for enhancing our understanding of lactate dynamics and roles.

Intelligent classification of platelet aggregates by agonist type.

Platelets are anucleate cells in blood whose principal function is to stop bleeding by forming aggregates for hemostatic reactions. In addition to their participation in physiological hemostasis, platelet aggregates are also involved in pathological thrombosis and play an important role in inflammation, atherosclerosis, and cancer metastasis. The aggregation of platelets is elicited by various agonists, but these platelet aggregates have long been considered indistinguishable and impossible to classify. Here we present an intelligent method for classifying them by agonist type. It is based on a convolutional neural network trained by high-throughput imaging flow cytometry of blood cells to identify and differentiate subtle yet appreciable morphological features of platelet aggregates activated by different types of agonists. The method is a powerful tool for studying the underlying mechanism of platelet aggregation and is expected to open a window on an entirely new class of clinical diagnostics, pharmacometrics, and therapeutics.

Platelets are small cells in the blood that primarily help stop bleeding after an injury by sticking together with other blood cells to form a clot that seals the broken blood vessel. Blood clots, however, can sometimes cause harm. For example, if a clot blocks the blood flow to the heart or the brain, it can result in a heart attack or stroke, respectively. Blood clots have also been linked to harmful inflammation and the spread of cancer, and there are now preliminary reports of remarkably high rates of clotting in COVID-19 patients in intensive care units. A variety of chemicals can cause platelets to stick together. It has long been assumed that it would be impossible to tell apart the clots formed by different chemicals (which are also known as agonists). This is largely because these aggregates all look very similar under a microscope, making it incredibly time consuming for someone to look at enough microscopy images to reliably identify the subtle differences between them. However, finding a way to distinguish the different types of platelet aggregates could lead to better ways to diagnose or treat blood vessel-clogging diseases. To make this possible, Zhou, Yasumoto et al. have developed a method called the "intelligent platelet aggregate classifier" or iPAC for short. First, numerous clot-causing chemicals were added to separate samples of platelets taken from healthy human blood. The method then involved using high-throughput techniques to take thousands of images of these samples. Then, a sophisticated computer algorithm called a deep learning model analyzed the resulting image dataset and "learned" to distinguish the chemical causes of the platelet aggregates based on subtle differences in their shapes. Finally, Zhou, Yasumoto et al. verified iPAC method's accuracy using a new set of human platelet samples. The iPAC method may help scientists studying the steps that lead to clot formation. It may also help clinicians distinguish which clot-causing chemical led to a patient's heart attack or stroke. This could help them choose whether aspirin or another anti-platelet drug would be the best treatment. But first more studies are needed to confirm whether this method is a useful tool for drug selection or diagnosis.

Intelligent image-activated cell sorting 2.0.

The advent of intelligent image-activated cell sorting (iIACS) has enabled high-throughput intelligent image-based sorting of single live cells from heterogeneous populations. iIACS is an on-chip microfluidic technology that builds on a seamless integration of a high-throughput fluorescence microscope, cell focuser, cell sorter, and deep neural network on a hybrid software-hardware data management architecture, thereby providing the combined merits of optical microscopy, fluorescence-activated cell sorting (FACS), and deep learning. Here we report an iIACS machine that far surpasses the state-of-the-art iIACS machine in system performance in order to expand the range of applications and discoveries enabled by the technology. Specifically, it provides a high throughput of ∼2000 events per second and a high sensitivity of ∼50 molecules of equivalent soluble fluorophores (MESFs), both of which are 20 times superior to those achieved in previous reports. This is made possible by employing (i) an image-sensor-based optomechanical flow imaging method known as virtual-freezing fluorescence imaging and (ii) a real-time intelligent image processor on an 8-PC server equipped with 8 multi-core CPUs and GPUs for intelligent decision-making, in order to significantly boost the imaging performance and computational power of the iIACS machine. We characterize the iIACS machine with fluorescent particles and various cell types and show that the performance of the iIACS machine is close to its achievable design specification. Equipped with the improved capabilities, this new generation of the iIACS technology holds promise for diverse applications in immunology, microbiology, stem cell biology, cancer biology, pathology, and synthetic biology.

Large-scale label-free single-cell analysis of paramylon in Euglena gracilis by high-throughput broadband Raman flow cytometry.

Microalga-based biomaterial production has attracted attention as a new source of drugs, foods, and biofuels. For enhancing the production efficiency, it is essential to understand its differences between heterogeneous microalgal subpopulations. However, existing techniques are not adequate to address the need due to the lack of single-cell resolution or the inability to perform large-scale analysis and detect small molecules. Here we demonstrated large-scale single-cell analysis of Euglena gracilis (a unicellular microalgal species that produces paramylon as a potential drug for HIV and colon cancer) with our recently developed high-throughput broadband Raman flow cytometer at a throughput of >1,000 cells/s. Specifically, we characterized the intracellular content of paramylon from single-cell Raman spectra of 10,000 E. gracilis cells cultured under five different conditions and found that paramylon contents in E. gracilis cells cultured in an identical condition is given by a log-normal distribution, which is a good model for describing the number of chemicals in a reaction network. The capability of characterizing distribution functions in a label-free manner is an important basis for isolating specific cell populations for synthetic biology via directed evolution based on the intracellular content of metabolites.

High-speed single-pixel imaging by frequency-time-division multiplexing.

We propose and experimentally demonstrate high-speed single-pixel imaging by integrating frequency-division multiplexing and time-division multiplexing (techniques used widely in telecommunications) and applying the combined technique, namely, frequency-time-division multiplexing (FTDM), to optical imaging. Specifically, FTDM single-pixel imaging uses an array of broadband, spatially distributed, dual-frequency combs (i.e., spatial dual combs) for multidimensional illumination and detects an image-encoded time-domain signal with a single-pixel photodetector in a FTDM manner. As a proof-of-principle demonstration, we use the method to show ultrafast two-color (bright-field and fluorescence) single-pixel microscopy of breast cancer cells at a high frame rate of 32,000 fps and ultrafast image velocimetry of fluorescent particles flowing at a high speed of ${ \gt }{2}\;{\rm m/s}$>2m/s.

Intelligent frequency-shifted optofluidic time-stretch quantitative phase imaging.

Optofluidic time-stretch quantitative phase imaging (OTS-QPI) is a powerful tool as it enables high-throughput (>10,000 cell/s) QPI of single live cells. OTS-QPI is based on decoding temporally stretched spectral interferograms that carry the spatial profiles of cells flowing on a microfluidic chip. However, the utility of OTS-QPI is troubled by difficulties in phase retrieval from the high-frequency region of the temporal interferograms, such as phase-unwrapping errors, high instrumentation cost, and large data volume. To overcome these difficulties, we propose and experimentally demonstrate frequency-shifted OTS-QPI by bringing the phase information to the baseband region. Furthermore, to show its boosted utility, we use it to demonstrate image-based classification of leukemia cells with high accuracy over 96% and evaluation of drug-treated leukemia cells via deep learning.

Virtual-freezing fluorescence imaging flow cytometry.

By virtue of the combined merits of flow cytometry and fluorescence microscopy, imaging flow cytometry (IFC) has become an established tool for cell analysis in diverse biomedical fields such as cancer biology, microbiology, immunology, hematology, and stem cell biology. However, the performance and utility of IFC are severely limited by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present an optomechanical imaging method that overcomes the trade-off by virtually freezing the motion of flowing cells on the image sensor to effectively achieve 1000 times longer exposure time for microscopy-grade fluorescence image acquisition. Consequently, it enables high-throughput IFC of single cells at >10,000 cells s-1 without sacrificing sensitivity and spatial resolution. The availability of numerous information-rich fluorescence cell images allows high-dimensional statistical analysis and accurate classification with deep learning, as evidenced by our demonstration of unique applications in hematology and microbiology.

Intelligent whole-blood imaging flow cytometry for simple, rapid, and cost-effective drug-susceptibility testing of leukemia.

Drug susceptibility (also called chemosensitivity) is an important criterion for developing a therapeutic strategy for various cancer types such as breast cancer and leukemia. Recently, functional assays such as high-content screening together with genomic analysis have been shown to be effective for predicting drug susceptibility, but their clinical applicability is poor since they are time-consuming (several days long), labor-intensive, and costly. Here we present a highly simple, rapid, and cost-effective liquid biopsy for ex vivo drug-susceptibility testing of leukemia. The method is based on an extreme-throughput (>1 million cells per second), label-free, whole-blood imaging flow cytometer with a deep convolutional autoencoder, enabling image-based identification of the drug susceptibility of every single white blood cell in whole blood within 24 hours by simply flowing a drug-treated whole blood sample as little as 500 µL into the imaging flow cytometer without labeling. Our results show that the method accurately evaluates the drug susceptibility of white blood cells from untreated patients with acute lymphoblastic leukemia. Our method holds promise for affordable precision medicine.

Label-free chemical imaging flow cytometry by high-speed multicolor stimulated Raman scattering.

Combining the strength of flow cytometry with fluorescence imaging and digital image analysis, imaging flow cytometry is a powerful tool in diverse fields including cancer biology, immunology, drug discovery, microbiology, and metabolic engineering. It enables measurements and statistical analyses of chemical, structural, and morphological phenotypes of numerous living cells to provide systematic insights into biological processes. However, its utility is constrained by its requirement of fluorescent labeling for phenotyping. Here we present label-free chemical imaging flow cytometry to overcome the issue. It builds on a pulse pair-resolved wavelength-switchable Stokes laser for the fastest-to-date multicolor stimulated Raman scattering (SRS) microscopy of fast-flowing cells on a 3D acoustic focusing microfluidic chip, enabling an unprecedented throughput of up to ∼140 cells/s. To show its broad utility, we use the SRS imaging flow cytometry with the aid of deep learning to study the metabolic heterogeneity of microalgal cells and perform marker-free cancer detection in blood.

Selenium and Glutathione-Depleted Rats as a Sensitive Animal Model to Predict Drug-Induced Liver Injury in Humans.

Drug-induced liver injury (DILI) is one of the most serious and frequent drug-related adverse events in humans. Selenium (Se) and glutathione (GSH) have a crucial role for the hepatoprotective effect against reactive metabolites or oxidative damage leading to DILI. The hepatoprotective capacity related to Se and GSH in rodents is considered to be superior compared to the capacity in humans. Therefore, we hypothesize that Se/GSH-depleted rats could be a sensitive animal model to predict DILI in humans. In this study, Se-deficiency is induced by feeding a Se-deficient diet and GSH-deficiency is induced by l-buthionine-S,R-sulfoxinine treatment via drinking water. The usefulness of this animal model is validated using flutamide, which is known to cause DILI in humans but not in intact rats. In the Se/GSH-depleted rats from the present study, decreases in glutathione peroxidase-1 protein expression and GSH levels and an increase in malondialdehyde levels in the liver are observed without any increase in plasma liver function parameters. Five-day repeated dosing of flutamide at 150 mg/kg causes hepatotoxicity in the Se/GSH-depleted rats but not in normal rats. In conclusion, Se/GSH-depleted rats are the most sensitive for detecting flutamide-induced hepatotoxicity in all the reported animal models.

Simple, stable, compact implementation of frequency-division-multiplexed microscopy by inline interferometry.

Frequency-division-multiplexed (FDM) imaging is a powerful method for high-speed imaging that surpasses the speed limit of conventional imaging constrained by the frame rate of image sensors. However, its complexity, instability, and bulkiness deriving from the implementation with a Mach-Zehnder interferometer hamper its practical applications. Here we demonstrate a simple, stable, and compact implementation of FDM imaging by inline interferometry that makes the method readily available to practical situations. As a proof-of-concept demonstration, we demonstrate 2D bright-field and fluorescence image acquisition of fluorescent beads, microalgal cells, and breast cancer cells within 65.5 µs, corresponding to 15,300 frames per second.

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.