Fluorescence-Encoded Time-Domain Coherent Raman Spectroscopy in the Visible Range

Fluorescence-encoded vibrational spectroscopy has attracted increasing attention by virtue of its high sensitivity and high chemical specificity. We recently demonstrated fluorescence-encoded time-domain coherent Raman spectroscopy (FLETCHERS), which enables low-frequency vibrational spectroscopy of low-concentration fluorophores using near-infrared (800-900 nm) light excitation. However, the feasibility of this study was constrained by the scarcity of excitable molecules in the near-infrared range. Consequently, the broader applicability of FLETCHERS has not been investigated. Here we extend the capabilities of FLETCHERS into the visible range by employing a noncollinear optical parametric amplifier as a light source, significantly enhancing its versatility. Specifically, we use the method, which we refer to as visible FLETCHERS (vFLETCHERS), to individually acquire Raman spectra from five visible fluorophores that have absorption peaks in the 600-700 nm region. These results not only confirm the versatility of vFLETCHERS for a wide range of molecules but also allude to its widespread applicability in biological research through highly sensitive supermultiplexed imaging.

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.

Passive microfluidic devices for cell separation

The separation of specific cell populations is instrumental in gaining insights into cellular processes, elucidating disease mechanisms, and advancing applications in tissue engineering, regenerative medicine, diagnostics, and cell therapies. Microfluidic methods for cell separation have propelled the field forward, benefitting from miniaturization, advanced fabrication technologies, a profound understanding of fluid dynamics governing particle separation mechanisms, and a surge in interdisciplinary investigations focused on diverse applications. Cell separation methodologies can be categorized according to their underlying separation mechanisms. Passive microfluidic separation systems rely on channel structures and fluidic rheology, obviating the necessity for external force fields to facilitate label-free cell separation. These passive approaches offer a compelling combination of cost-effectiveness and scalability when compared to active methods that depend on external fields to manipulate cells. This review delves into the extensive utilization of passive microfluidic techniques for cell separation, encompassing various strategies such as filtration, sedimentation, adhesion-based techniques, pinched flow fractionation (PFF), deterministic lateral displacement (DLD), inertial microfluidics, hydrophoresis, viscoelastic microfluidics, and hybrid microfluidics. Besides, the review provides an in-depth discussion concerning cell types, separation markers, and the commercialization of these technologies. Subsequently, it outlines the current challenges faced in the field and presents a forward-looking perspective on potential future developments. This work hopes to aid in facilitating the dissemination of knowledge in cell separation, guiding future research, and informing practical applications across diverse scientific disciplines.

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.

Neuronal sensorimotor integration guiding salt concentration navigation in Caenorhabditis elegans

Animals navigate their environment by manipulating their movements and adjusting their trajectory which requires a sophisticated integration of sensory data with their current motor status. Here, we utilize the nematode Caenorhabditis elegans to explore the neural mechanisms of processing the sensory and motor information for navigation. We developed a microfluidic device which allows animals to freely move their heads while receiving temporal NaCl stimuli. We found that C. elegans regulates neck bending direction in response to temporal NaCl concentration changes in a way which is consistent with a C. elegans' navigational strategy which regulates traveling direction toward preferred NaCl concentrations. Our analysis also revealed that the activity of a neck motor neuron is significantly correlated with neck bending and activated by the decrease in NaCl concentration in a phase-dependent manner. By combining the analysis of behavioral and neural response to NaCl stimuli and optogenetic perturbation experiments, we revealed that NaCl decrease during ventral bending activates the neck motor neuron which counteracts ipsilateral bending. Simulations further suggest that this phase-dependent response of neck motor neurons can facilitate curving toward preferred salt concentrations.

Manipulation of photosynthetic energy transfer by vibrational strong coupling

Uncovering the mystery of efficient and directional energy transfer in photosynthetic organisms remains a critical challenge in quantum biology. Recent experimental evidence and quantum theory developments indicate the significance of quantum features of molecular vibrations in assisting photosynthetic energy transfer, which provides the possibility of manipulating the process by controlling molecular vibrations. Here, we propose and theoretically demonstrate efficient manipulation of photosynthetic energy transfer by using vibrational strong coupling between the vibrational state of a Fenna-Matthews-Olson (FMO) complex and the vacuum state of an optical cavity. Specifically, based on a full-quantum analytical model to describe the strong coupling effect between the optical cavity and molecular vibration, we realize efficient manipulation of energy transfer efficiency (from 58% to 92%) and energy transfer time (from 20 to 500 ps) in one branch of FMO complex by actively controlling the coupling strength and the quality factor of the optical cavity under both near-resonant and off-resonant conditions, respectively. Our work provides a practical scenario to manipulate photosynthetic energy transfer by externally interfering molecular vibrations via an optical cavity and a comprehensible conceptual framework for researching other similar systems.

Deciphering the Potential of Multidimensional Carbon Materials for Surface-Enhanced Raman Spectroscopy through Density Functional Theory

Surface-enhanced Raman spectroscopy (SERS) is a potent analytical tool, particularly for molecular identification and structural analysis. Conventional metallic SERS substrates, however, suffer from low reproducibility and compatibility with biological molecules. Recently, metal-free SERS substrates based on chemical enhancement have emerged as a promising alternative with carbon-based materials offering excellent reproducibility and compatibility. Nevertheless, our understanding of carbon materials in SERS remains limited, which hinders their rational design. Here we systematically explore multidimensional carbon materials, including zero-dimensional fullerenes (C60), one-dimensional carbon nanotubes, two-dimensional graphene, and their B-, N-, and O-doped derivatives, for SERS applications. Using density functional theory, we elucidate the nonresonant polarizability-enhanced and resonant charge-transfer-based chemical enhancement mechanisms of these materials by evaluating their static/dynamic polarizability and electron excitation properties. This work provides a critical reference for the future design of carbon-based SERS substrates, opening a new avenue in this field.

Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae

Artificial intelligence (AI) has become a focal point across a multitude of societal sectors, with science not being an exception. Particularly in the life sciences, imaging flow cytometry has increasingly integrated AI for automated management and categorization of extensive cell image data. However, the necessity of AI over traditional classification methods when extending imaging flow cytometry to include cell sorting remains uncertain, primarily due to the time constraints between image acquisition and sorting actuation. AI-enabled image-activated cell sorting (IACS) methods remain substantially limited, even as recent advancements in IACS have found success while largely relying on traditional feature gating strategies. Here we assess the necessity of AI for image classification in IACS by contrasting the performance of feature gating, classical machine learning (ML), and deep learning (DL) with convolutional neural networks (CNNs) in the differentiation of Saccharomyces cerevisiae mutant images. We show that classical ML could only yield a 2.8-fold enhancement in target enrichment capability, albeit at the cost of a 13.7-fold increase in processing time. Conversely, a CNN could offer an 11.0-fold improvement in enrichment capability at an 11.5-fold increase in processing time. We further executed IACS on mixed mutant populations and quantified target strain enrichment via downstream DNA sequencing to substantiate the applicability of DL for the proposed study. Our findings validate the feasibility and value of employing DL in IACS for morphology-based genetic screening of S. cerevisiae, encouraging its incorporation in future advancements of similar technologies.

Time-dependent cell-state selection identifies transiently expressed genes regulating ILC2 activation

The decision of whether cells are activated or not is controlled through dynamic intracellular molecular networks. However, the low population of cells during the transition state of activation renders the analysis of the transcriptome of this state technically challenging. To address this issue, we have developed the Time-Dependent Cell-State Selection (TDCSS) technique, which employs live-cell imaging of secretion activity to detect an index of the transition state, followed by the simultaneous recovery of indexed cells for subsequent transcriptome analysis. In this study, we used the TDCSS technique to investigate the transition state of group 2 innate lymphoid cells (ILC2s) activation, which is indexed by the onset of interleukin (IL)-13 secretion. The TDCSS approach allowed us to identify time-dependent genes, including transiently induced genes (TIGs). Our findings of IL4 and MIR155HG as TIGs have shown a regulatory function in ILC2s activation.

Boosting the Brightness of Raman Tags Using Cyanostar Macrocycles

Raman probes have received growing attention for their potential use in super-multiplex biological imaging and flow cytometry applications that cannot be achieved using fluorescent probes. However, obtaining strong Raman scattering signals from small Raman probes has posed a challenge that holds back their practical implementation. Here, we present new types of Raman-active nanoparticles (Rdots) that incorporate ionophore macrocycles, known as cyanostars, to act as ion-driven and structure-directing spacers to address this problem. These macrocycle-enhanced Rdots (MERdots) exhibit sharper and higher electronic absorption peaks than Rdots. When combined with resonant broadband time-domain Raman spectroscopy, these MERdots show a ∼3-fold increase in Raman intensity compared to conventional Rdots under the same particle concentration. Additionally, the detection limit on the concentration of MERdots is improved by a factor of 2.5 compared to that of Rdots and a factor of 430 compared to that of Raman dye molecules in solution. The compact size of MERdots (26 nm in diameter) and their increased Raman signal intensity, along with the broadband capabilities of time-domain resonant Raman spectroscopy, make them promising candidates for a wide range of biological applications.

Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies

Keisuke Goda, Hang Lu, Peng Fei, and Jochen Guck introduce the AI in Microfluidics themed collection, on revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies.

Unlocking the secrets of the invisible world: incredible deep optical imaging through in-silico clearing

In-silico clearing enables deep optical imaging of biological samples by correcting image blur caused by scattering and aberration. This breakthrough method offers researchers unprecedented insights into three-dimensional biological systems, with enormous potential for advancing biology and medicine to better understand living organisms and human health.

The marriage of coherent Raman scattering imaging and advanced computational tools

Coherent Raman scattering microscopy can provide high-contrast tissue and single-cell images based on the inherent molecular vibrations of the sample. However, conventional techniques face a three-way trade-off between Raman spectral bandwidth, imaging speed, and image fidelity. Although currently challenging to address via optical design, this trade-off can be overcome via emerging computational tools such as compressive sensing and machine learning.

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.

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.

Place & Play SERS: sample collection and preparation-free surface-enhanced Raman spectroscopy

The ability to perform sensitive, real-time, in situ, multiplex chemical analysis is indispensable for diverse applications such as human health monitoring, food safety testing, forensic analysis, environmental sensing, and homeland security. Surface-enhanced Raman spectroscopy (SERS) is an effective tool to offer the ability by virtue of its high sensitivity and rapid label-free signal detection as well as the availability of portable Raman spectrometers. Unfortunately, the practical utility of SERS is limited because it generally requires sample collection and preparation, namely, collecting a sample from an object of interest and placing the sample on top of a SERS substrate to perform a SERS measurement. In fact, not all analytes can satisfy this requirement because the sample collection and preparation process may be undesirable, laborious, difficult, dangerous, costly, or time-consuming. Here we introduce "Place & Play SERS" based on an ultrathin, flexible, stretchable, adhesive, biointegratable gold-deposited polyvinyl alcohol (PVA) nanomesh substrate that enables placing the substrate on top of an object of interest and performing a SERS measurement of the object by epi-excitation without the need for touching, destroying, and sampling it. Specifically, we characterized the sensitivity of the gold/PVA nanomesh substrate in the Place & Play SERS measurement scheme and then used the scheme to conduct SERS measurements of both wet and dry objects under nearly real-world conditions. To show the practical utility of Place & Play SERS, we demonstrated two examples of its application: food safety testing and forensic analysis. Our results firmly verified the new measurement scheme of SERS and are expected to extend the potential of SERS by opening up untapped applications of sensitive, real-time, in situ multiplex chemical analysis.

Label-free multiphoton imaging flow cytometry

Label-free imaging flow cytometry is a powerful tool for biological and medical research as it overcomes technical challenges in conventional fluorescence-based imaging flow cytometry that predominantly relies on fluorescent labeling. To date, two distinct types of label-free imaging flow cytometry have been developed, namely optofluidic time-stretch quantitative phase imaging flow cytometry and stimulated Raman scattering (SRS) imaging flow cytometry. Unfortunately, these two methods are incapable of probing some important molecules such as starch and collagen. Here, we present another type of label-free imaging flow cytometry, namely multiphoton imaging flow cytometry, for visualizing starch and collagen in live cells with high throughput. Our multiphoton imaging flow cytometer is based on nonlinear optical imaging whose image contrast is provided by two optical nonlinear effects: four-wave mixing (FWM) and second-harmonic generation (SHG). It is composed of a microfluidic chip with an acoustic focuser, a lab-made laser scanning SHG-FWM microscope, and a high-speed image acquisition circuit to simultaneously acquire FWM and SHG images of flowing cells. As a result, it acquires FWM and SHG images (100 × 100 pixels) with a spatial resolution of 500 nm and a field of view of 50 μm × 50 μm at a high event rate of four to five events per second, corresponding to a high throughput of 560-700 kb/s, where the event is defined by the passage of a cell or a cell-like particle. To show the utility of our multiphoton imaging flow cytometer, we used it to characterize Chromochloris zofingiensis (NIES-2175), a unicellular green alga that has recently attracted attention from the industrial sector for its ability to efficiently produce valuable materials for bioplastics, food, and biofuel. Our statistical image analysis found that starch was distributed at the center of the cells at the early cell cycle stage and became delocalized at the later stage. Multiphoton imaging flow cytometry is expected to be an effective tool for statistical high-content studies of biological functions and optimizing the evolution of highly productive cell strains.

Real-time intelligent classification of COVID-19 and thrombosis via massive image-based analysis of platelet aggregates

Microvascular thrombosis is a typical symptom of COVID-19 and shows similarities to thrombosis. Using a microfluidic imaging flow cytometer, we measured the blood of 181 COVID-19 samples and 101 non-COVID-19 thrombosis samples, resulting in a total of 6.3 million bright-field images. We trained a convolutional neural network to distinguish single platelets, platelet aggregates, and white blood cells and performed classical image analysis for each subpopulation individually. Based on derived single-cell features for each population, we trained machine learning models for classification between COVID-19 and non-COVID-19 thrombosis, resulting in a patient testing accuracy of 75%. This result indicates that platelet formation differs between COVID-19 and non-COVID-19 thrombosis. All analysis steps were optimized for efficiency and implemented in an easy-to-use plugin for the image viewer napari, allowing the entire analysis to be performed within seconds on mid-range computers, which could be used for real-time diagnosis.

Long-term effects of Pfizer-BioNTech COVID-19 vaccinations on platelets

There is a global concern about the safety of COVID-19 vaccines associated with platelet function. However, their long-term effects on overall platelet activity remain poorly understood. Here we address this problem by image-based single-cell profiling and temporal monitoring of circulating platelet aggregates in the blood of healthy human subjects, before and after they received multiple Pfizer-BioNTech (BNT162b2) vaccine doses over a time span of nearly 1 year. Results show no significant or persisting platelet aggregation trends following the vaccine doses, indicating that any effects of vaccinations on platelet turnover, platelet activation, platelet aggregation, and platelet-leukocyte interaction was insignificant.

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.

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.

Intelligent sort-timing prediction for image-activated cell sorting

Intelligent image-activated cell sorting (iIACS) has enabled high-throughput image-based sorting of single cells with artificial intelligence (AI) algorithms. This AI-on-a-chip technology combines fluorescence microscopy, AI-based image processing, sort-timing prediction, and cell sorting. Sort-timing prediction is particularly essential due to the latency on the order of milliseconds between image acquisition and sort actuation, during which image processing is performed. The long latency amplifies the effects of the fluctuations in the flow speed of cells, leading to fluctuation and uncertainty in the arrival time of cells at the sort point on the microfluidic chip. To compensate for this fluctuation, iIACS measures the flow speed of each cell upstream, predicts the arrival timing of the cell at the sort point, and activates the actuation of the cell sorter appropriately. Here, we propose and demonstrate a machine learning technique to increase the accuracy of the sort-timing prediction that would allow for the improvement of sort event rate, yield, and purity. Specifically, we trained an algorithm to predict the sort timing for morphologically heterogeneous budding yeast cells. The algorithm we developed used cell morphology, position, and flow speed as inputs for prediction and achieved 41.5% lower prediction error compared to the previously employed method based solely on flow speed. As a result, our technique would allow for an increase in the sort event rate of iIACS by a factor of ~2.

Indirect impact of the COVID-19 pandemic on the incidence of non-COVID-19 infectious diseases: a region-wide, patient-based database study in Japan

OBJECTIVES

The COVID-19 pandemic has forced people to change many behaviours, including physical distancing, hygiene measures and lifestyles. This study aimed to evaluate the indirect impact of the COVID-19 pandemic on the incidence of non-COVID-19 infections and medical care costs/visits using health insurance claims.

STUDY DESIGN

This was an observational study using patient-based administrative claims covering approximately 800,000 insured persons and their dependents in the Mie Prefecture in Japan.

METHODS

This study identified non-COVID-19 infectious disease incidences, number of outpatient visits and healthcare costs between 2017 and 2021. Each year was divided into quarters. The adjusted incidence rate ratios (IRRs) during the pandemic (January 2020 to September 2021) and during the prepandemic period (January 2017 to December 2019) were determined using Poisson regression.

RESULTS

The adjusted influenza IRRs from April 2020 were close to zero. The incidence of upper respiratory tract infections and bacterial pneumonia was significantly reduced (IRRs range: 0.39-0.73 and 0.43-0.84, respectively). Gastrointestinal and urinary tract infection incidences decreased by approximately 30% and 10%, respectively. In contrast, sexually transmitted infections (STIs), including syphilis, gonococcal infection and Chlamydia trachomatis infection, did not decrease during the pandemic but increased significantly between April and June 2021 (adjusted IRR, 1.37; 95% confidence interval, 1.18-1.60). The adjusted IRRs for outpatient visits and healthcare costs were 0.86-0.93 and 0.91-0.97, respectively.

CONCLUSIONS

In contrast to other infections, STIs did not decrease during the COVID-19 pandemic. The IRR of STIs during the pandemic period is an area of public health concern. Appropriate screening and medical consultations are strongly recommended.

Intelligent image-activated sorting of Chlamydomonas reinhardtii by mitochondrial localization

Organelle positioning in cells is associated with various metabolic functions and signaling in unicellular organisms. Specifically, the microalga Chlamydomonas reinhardtii repositions its mitochondria, depending on the levels of inorganic carbon. Mitochondria are typically randomly distributed in the Chlamydomonas cytoplasm, but relocate toward the cell periphery at low inorganic carbon levels. This mitochondrial relocation is linked with the carbon-concentrating mechanism, but its significance is not yet thoroughly understood. A genotypic understanding of this relocation would require a high-throughput method to isolate rare mutant cells not exhibiting this relocation. However, this task is technically challenging due to the complex intracellular morphological difference between mutant and wild-type cells, rendering conventional non-image-based high-event-rate methods unsuitable. Here, we report our demonstration of intelligent image-activated cell sorting by mitochondrial localization. Specifically, we applied an intelligent image-activated cell sorting system to sort for C. reinhardtii cells displaying no mitochondrial relocation. We trained a convolutional neural network (CNN) to distinguish the cell types based on the complex morphology of their mitochondria. The CNN was employed to perform image-activated sorting for the mutant cell type at 180 events per second, which is 1-2 orders of magnitude faster than automated microscopy with robotic pipetting, resulting in an enhancement of the concentration from 5% to 56.5% corresponding to an enrichment factor of 11.3. These results show the potential of image-activated cell sorting for connecting genotype-phenotype relations for rare-cell populations, which require a high throughput and could lead to a better understanding of metabolic functions in cells.

Graphene oxide bulk material reinforced by heterophase platelets with multiscale interface crosslinking

Graphene oxide (GO) and reduced GO possess robust mechanical, electrical and chemical properties. Their nanocomposites have been extensively explored for applications in diverse fields. However, due to the high flexibility and weak interlayer interactions of GO nanosheets, the flexural mechanical properties of GO-based composites, especially in bulk materials, are largely constrained, which hinders their performance in practical applications. Here, inspired by the amorphous/crystalline feature of the heterophase within nacreous platelets, we present a centimetre-sized, GO-based bulk material consisting of building blocks of GO and amorphous/crystalline leaf-like MnO₂ hexagon nanosheets adhered together with polymer-based crosslinkers. These building blocks are stacked and hot-pressed with further crosslinking between the layers to form a GO/MnO₂-based layered (GML) bulk material. The resultant GML bulk material exhibits a flexural strength of 231.2 MPa. Moreover, the material exhibits sufficient fracture toughness and strong impact resistance while being light in weight. Experimental and numerical analyses indicate that the ordered heterophase structure and synergetic crosslinking interactions across multiscale interfaces lead to the superior mechanical properties of the material. These results are expected to provide insights into the design of structural materials and potential applications of high-performance GO-based bulk materials in aerospace, biomedicine and electronics.

Deep imaging flow cytometry

Imaging flow cytometry (IFC) has become a powerful tool for diverse biomedical applications by virtue of its ability to image single cells in a high-throughput manner. However, there remains a challenge posed by the fundamental trade-off between throughput, sensitivity, and spatial resolution. Here we present deep-learning-enhanced imaging flow cytometry (dIFC) that circumvents this trade-off by implementing an image restoration algorithm on a virtual-freezing fluorescence imaging (VIFFI) flow cytometry platform, enabling higher throughput without sacrificing sensitivity and spatial resolution. A key component of dIFC is a high-resolution (HR) image generator that synthesizes "virtual" HR images from the corresponding low-resolution (LR) images acquired with a low-magnification lens (10×/0.4-NA). For IFC, a low-magnification lens is favorable because of reduced image blur of cells flowing at a higher speed, which allows higher throughput. We trained and developed the HR image generator with an architecture containing two generative adversarial networks (GANs). Furthermore, we developed dIFC as a method by combining the trained generator and IFC. We characterized dIFC using Chlamydomonas reinhardtii cell images, fluorescence in situ hybridization (FISH) images of Jurkat cells, and Saccharomyces cerevisiae (budding yeast) cell images, showing high similarities of dIFC images to images obtained with a high-magnification lens (40×/0.95-NA), at a high flow speed of 2 m s^-1. We lastly employed dIFC to show enhancements in the accuracy of FISH-spot counting and neck-width measurement of budding yeast cells. These results pave the way for statistical analysis of cells with high-dimensional spatial information.

Massive image-based single-cell profiling reveals high levels of circulating platelet aggregates in patients with COVID-19

A characteristic clinical feature of COVID-19 is the frequent incidence of microvascular thrombosis. In fact, COVID-19 autopsy reports have shown widespread thrombotic microangiopathy characterized by extensive diffuse microthrombi within peripheral capillaries and arterioles in lungs, hearts, and other organs, resulting in multiorgan failure. However, the underlying process of COVID-19-associated microvascular thrombosis remains elusive due to the lack of tools to statistically examine platelet aggregation (i.e., the initiation of microthrombus formation) in detail. Here we report the landscape of circulating platelet aggregates in COVID-19 obtained by massive single-cell image-based profiling and temporal monitoring of the blood of COVID-19 patients (n = 110). Surprisingly, our analysis of the big image data shows the anomalous presence of excessive platelet aggregates in nearly 90% of all COVID-19 patients. Furthermore, results indicate strong links between the concentration of platelet aggregates and the severity, mortality, respiratory condition, and vascular endothelial dysfunction level of COVID-19 patients.

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.

High-throughput sorting of nanoliter droplets enabled by a sequentially addressable dielectrophoretic array

Droplet microfluidics has emerged as a powerful tool for a diverse range of biomedical and industrial applications such as single-cell analysis, directed evolution, and metabolic engineering. In these applications, droplet sorting has been effective for isolating small droplets encapsulating molecules, cells, or crystals of interest. Recently, there is an increased interest in extending the applicability of droplet sorting to larger droplets to utilize their size advantage. However, sorting throughputs of large droplets have been limited, hampering their wide adoption. Here, we report our demonstration of high-throughput fluorescence-activated droplet sorting of 1 nL droplets using an upgraded version of the sequentially addressable dielectrophoretic array (SADA), which we reported previously. The SADA is an array of electrodes that are individually and sequentially activated/deactivated according to the speed and position of a droplet passing nearby the array. We upgraded the SADA by increasing the number of driving electrodes constituting the SADA and incorporating a slanted microchannel. By using a ten-electrode SADA with the slanted microchannel, we achieved fluorescence-activated droplet sorting of 1 nL droplets at a record high throughput of 1752 droplets/s, twice as high as the previously reported maximum sorting throughput of 1 nL droplets.

Are droplets really suitable for single-cell analysis? A case study on yeast in droplets

Single-cell analysis has become one of the main cornerstones of biotechnology, inspiring the advent of various microfluidic compartments for cell cultivation such as microwells, microtrappers, microcapillaries, and droplets. A fundamental assumption for using such microfluidic compartments is that unintended stress or harm to cells derived from the microenvironments is insignificant, which is a crucial condition for carrying out unbiased single-cell studies. Despite the significance of this assumption, simple viability or growth tests have overwhelmingly been the assay of choice for evaluating culture conditions while empirical studies on the sub-lethal effect on cellular functions have been insufficient in many cases. In this work, we assessed the effect of culturing cells in droplets on the cellular function using yeast morphology as an indicator. Quantitative morphological analysis using CalMorph, an image-analysis program, demonstrated that cells cultured in flasks, large droplets, and small droplets significantly differed morphologically. From these differences, we identified that the cell cycle was delayed in droplets during the G1 phase and during the process of bud growth likely due to the checkpoint mechanism and impaired mitochondrial function, respectively. Furthermore, comparing small and large droplets, cells cultured in large droplets were morphologically more similar to those cultured in a flask, highlighting the advantage of increasing the droplet size. These results highlight a potential source of bias in cell analysis using droplets and reinforce the significance of assessing culture conditions of microfluidic cultivation methods for specific study cases.

Highly sensitive Fourier-transform coherent anti-Stokes Raman scattering spectroscopy via genetic algorithm pulse shaping

We report highly sensitive Fourier-transform coherent anti-Stokes Raman scattering spectroscopy enabled by genetic algorithm (GA) pulse shaping for adaptive dispersion compensation. We show that the non-resonant four-wave mixing signal from water can be used as a fitness indicator for successful GA training. This method allows GA adaptation to sample measurement conditions and offers significantly improved performance compared to training using second-harmonic generation from a nonlinear crystal in place of the sample. Results include a 3× improvement to peak signal-to-noise ratio for 2-propanol measurement, as well as a 10× improvement to peak intensities from the high-throughput measurement of polystyrene microbeads under flow.

Highly Sensitive Low-Frequency Time-Domain Raman Spectroscopy via Fluorescence Encoding

Fluorescence-encoded vibrational spectroscopy has become increasingly more popular by virtue of its high chemical specificity and sensitivity. However, current fluorescence-encoded vibrational spectroscopy methods lack sensitivity in the low-frequency region, which if addressed could further enhance their capabilities. Here, we present a method for highly sensitive low-frequency fluorescence-encoded vibrational spectroscopy, termed fluorescence-encoded time-domain coherent Raman spectroscopy (FLETCHERS). By first exciting molecules into vibrationally excited states and then promoting the vibrating molecules to electronic states at varying times, the molecular vibrations can be encoded onto the emitted time-domain fluorescence intensity. We demonstrate the sensitive low-frequency detection capability of FLETCHERS by measuring vibrational spectra in the lower fingerprint region of rhodamine 800 solutions as dilute as 250 nM, which is ∼1000 times more sensitive than conventional vibrational spectroscopy. These results, along with further improvement of the method, open up the prospect of performing single-molecule vibrational spectroscopy in the low-frequency region.

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.

Morphological Indicator for Directed Evolution of Euglena gracilis with a High Heavy Metal Removal Efficiency

In the past few decades, microalgae-based bioremediation methods for treating heavy metal (HM)-polluted wastewater have attracted much attention by virtue of their environment friendliness, cost efficiency, and sustainability. However, their HM removal efficiency is far from practical use. Directed evolution is expected to be effective for developing microalgae with a much higher HM removal efficiency, but there is no non-invasive or label-free indicator to identify them. Here, we present an intelligent cellular morphological indicator for identifying the HM removal efficiency of Euglena gracilis in a non-invasive and label-free manner. Specifically, we show a strong monotonic correlation (Spearman's ρ = -0.82, P = 2.1 × 10^-5) between a morphological meta-feature recognized via our machine learning algorithms and the Cu²⁺ removal efficiency of 19 E. gracilis clones. Our findings firmly suggest that the morphology of E. gracilis cells can serve as an effective HM removal efficiency indicator and hence have great potential, when combined with a high-throughput image-activated cell sorter, for directed-evolution-based development of E. gracilis with an extremely high HM removal efficiency for practical wastewater treatment worldwide.

Droplet flow cytometry for single-cell analysis

The interrogation of single cells has revolutionised biology and medicine by providing crucial unparalleled insights into cell-to-cell heterogeneity. Flow cytometry (including fluorescence-activated cell sorting) is one of the most versatile and high-throughput approaches for single-cell analysis by detecting multiple fluorescence parameters of individual cells in aqueous suspension as they flow past through a focus of excitation lasers. However, this approach relies on the expression of cell surface and intracellular biomarkers, which inevitably lacks spatial and temporal phenotypes and activities of cells, such as secreted proteins, extracellular metabolite production, and proliferation. Droplet microfluidics has recently emerged as a powerful tool for the encapsulation and manipulation of thousands to millions of individual cells within pico-litre microdroplets. Integrating flow cytometry with microdroplet architectures surrounded by aqueous solutions (e.g., water-in-oil-in-water (W/O/W) double emulsion and hydrogel droplets) opens avenues for new cellular assays linking cell phenotypes to genotypes at the single-cell level. In this review, we discuss the capabilities and applications of droplet flow cytometry (DFC). This unique technique uses standard commercially available flow cytometry instruments to characterise or select individual microdroplets containing single cells of interest. We explore current challenges associated with DFC and present our visions for future development.

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.

Toward deep biophysical cytometry: prospects and challenges

The biophysical properties of cells reflect their identities, underpin their homeostatic state in health, and define the pathogenesis of disease. Recent leapfrogging advances in biophysical cytometry now give access to this information, which is obscured in molecular assays, with a discriminative power that was once inconceivable. However, biophysical cytometry should go 'deeper' in terms of exploiting the information-rich cellular biophysical content, generating a molecular knowledge base of cellular biophysical properties, and standardizing the protocols for wider dissemination. Overcoming these barriers, which requires concurrent innovations in microfluidics, optical imaging, and computer vision, could unleash the enormous potential of biophysical cytometry not only for gaining a new mechanistic understanding of biological systems but also for identifying new cost-effective biomarkers of disease.

Intelligent Platelet Morphometry

Technological advances in image-based platelet analysis or platelet morphometry are critical for a better understanding of the structure and function of platelets in biological research as well as for the development of better clinical strategies in medical practice. Recently, the advent of high-throughput optical imaging and deep learning has boosted platelet morphometry to the next level by providing a new set of capabilities beyond what is achievable with traditional platelet morphometry, shedding light on the unexplored domain of platelet analysis. This Opinion article introduces emerging opportunities in 'intelligent' platelet morphometry, which are expected to pave the way for a new class of diagnostics, pharmacometrics, and therapeutics.

Sequentially timed all-optical mapping photography boosted by a branched 4f system with a slicing mirror

We present sequentially timed all-optical mapping photography (STAMP) with a slicing mirror in a branched 4f system for an increased number of frames without sacrificing pixel resolution. The branched 4f system spectrally separates the laser light path into multiple paths by the slicing mirror placed in the Fourier plane. Fabricated by an ultra-precision end milling process, the slicing mirror has 18 mirror facets of differing mirror angles. We used the boosted STAMP to observe dynamics of laser ablation with two image sensors which captured 18 subsequent frames at a frame rate of 126 billion frames per second, demonstrating this technique's potential for imaging unexplored ultrafast non-repetitive phenomena.

Analysis of signal detection configurations in optical time-stretch imaging

Optical time-stretch (OTS) imaging is effective for observing ultra-fast dynamic events in real time by virtue of its capability of acquiring images with high spatial resolution at high speed. In different implementations of OTS imaging, different configurations of its signal detection, i.e. fiber-coupled and free-space detection schemes, are employed. In this research, we quantitatively analyze and compare the two detection configurations of OTS imaging in terms of sensitivity and image quality with the USAF-1951 resolution chart and diamond films, respectively, providing a valuable guidance for the system design of OTS imaging in diverse fields.

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.

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.

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.

Sequentially addressable dielectrophoretic array for high-throughput sorting of large-volume biological compartments

Droplet microfluidics has become a powerful tool in precision medicine, green biotechnology, and cell therapy for single-cell analysis and selection by virtue of its ability to effectively confine cells. However, there remains a fundamental trade-off between droplet volume and sorting throughput, limiting the advantages of droplet microfluidics to small droplets (<10 pl) that are incompatible with long-term maintenance and growth of most cells. We present a sequentially addressable dielectrophoretic array (SADA) sorter to overcome this problem. The SADA sorter uses an on-chip array of electrodes activated and deactivated in a sequence synchronized to the speed and position of a passing target droplet to deliver an accumulated dielectrophoretic force and gently pull it in the direction of sorting in a high-speed flow. We use it to demonstrate large-droplet sorting with ~20-fold higher throughputs than conventional techniques and apply it to long-term single-cell analysis of Saccharomyces cerevisiae based on their growth rate.

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.