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.
Euglena gracilis (E. gracilis) has been proposed as one of the most attractive microalgae species for biodiesel and biomass production, which exhibits a number of shapes, such as spherical, ...spindle-shaped, and elongated. Shape is an important biomarker for E. gracilis, serving as an indicator of biological clock status, photosynthetic and respiratory capacity, cell-cycle phase, and environmental condition. The ability to prepare E. gracilis of uniform shape at high purities has significant implications for various applications in biological research and industrial processes. Here, we adopt a label-free, high-throughput, and continuous technique utilizing inertial microfluidics to separate E. gracilis by a key shape parameter-cell aspect ratio (AR). The microfluidic device consists of a straight rectangular microchannel, a gradually expanding region, and five outlets with fluidic resistors, allowing for inertial focusing and ordering, enhancement of the differences in cell lateral positions, and accurate separation, respectively. By making use of the shape-activated differences in lateral inertial focusing dynamic equilibrium positions, E. gracilis with different ARs ranging from 1 to 7 are directed to different outlets.
Abstract
Sepsis is a life-threatening condition caused by the extreme release of inflammatory mediators into the blood in response to infection (e.g., bacterial infection, COVID-19), resulting in the ...dysfunction of multiple organs. Currently, there is no direct treatment for sepsis. Here we report an abiotic hydrogel nanoparticle (HNP) as a potential therapeutic agent for late-stage sepsis. The HNP captures and neutralizes all variants of histones, a major inflammatory mediator released during sepsis. The highly optimized HNP has high capacity and long-term circulation capability for the selective sequestration and neutralization of histones. Intravenous injection of the HNP protects mice against a lethal dose of histones through the inhibition of platelet aggregation and migration into the lungs. In vivo administration in murine sepsis model mice results in near complete survival. These results establish the potential for synthetic, nonbiological polymer hydrogel sequestrants as a new intervention strategy for sepsis therapy and adds to our understanding of the importance of histones to this condition.
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.
Recent advances in biophysical cytometry now make it possible to recapitulate cellular heterogeneity at the levels of throughput, precision, specificity, and sensitivity that were once inconceivable.Technological developments in state-of-the-art biophysical cytometry include single-cell mass assays, cell traction force assays, deformability and impedance cytometry, and label-free imaging cytometry.Next-generation biophysical cytometry could be more comprehensive and information-rich by exploring multimodal integration, such as simultaneous read-out of cell mass, stiffness, and morphology.How molecular signatures translate into cellular biophysical properties is not fully understood. Advanced techniques involving microfluidics, imaging, and deep learning could investigate this link.Standardizing the protocols and datasets of biophysical cytometry will be crucial to ensure wide dissemination.
The ability to rapidly assay morphological and intracellular molecular variations within large heterogeneous populations of cells is essential for understanding and exploiting cellular heterogeneity. ...Optofluidic time-stretch microscopy is a powerful method for meeting this goal, as it enables high-throughput imaging flow cytometry for large-scale single-cell analysis of various cell types ranging from human blood to algae, enabling a unique class of biological, medical, pharmaceutical, and green energy applications. Here, we describe how to perform high-throughput imaging flow cytometry by optofluidic time-stretch microscopy. Specifically, this protocol provides step-by-step instructions on how to build an optical time-stretch microscope and a cell-focusing microfluidic device for optofluidic time-stretch microscopy, use it for high-throughput single-cell image acquisition with sub-micrometer resolution at >10,000 cells per s, conduct image construction and enhancement, perform image analysis for large-scale single-cell analysis, and use computational tools such as compressive sensing and machine learning for handling the cellular 'big data'. Assuming all components are readily available, a research team of three to four members with an intermediate level of experience with optics, electronics, microfluidics, digital signal processing, and sample preparation can complete this protocol in a time frame of 1 month.
Droplet microfluidics is a powerful tool for a diverse range of biomedical and industrial applications such as single-cell biology, synthetic biology, digital PCR, biosafety monitoring, drug ...screening, and food, feed, and cosmetic industries. As an integral part of droplet microfluidics, on-chip multiplexed droplet sorting has recently gained enthusiasm, since it enables real-time sorting of single droplets containing cells with different phenotypes into multiple bins. However, conventional sorting methods are limited in throughput and scalability. Here, we present high-throughput, scalable, multiplexed droplet sorting by employing a pair of sequentially addressable dielectrophoretic arrays (SADAs) across a microchannel on a microfluidic chip. A SADA is an on-chip array of electrodes, each of which is sequentially activated and deactivated in synchronization to the position and speed of a flowing droplet of interest. The dual-SADA (dSADA) structure enables high-throughput deflection of droplets in multiple directions in a well-controlled manner. For proof-of-concept demonstration and characterization of the dSADA, we performed fluorescence-activated droplet sorting (FADS) with a 3-way dSADA at a high throughput of 2450 droplets/s. Furthermore, to show the scalability of the dSADA, we also performed FADS with a 5-way dSADA at a high throughput of 473 droplets/s.
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.
The advent of image-activated cell sorting and imaging-based cell picking has advanced our knowledge and exploitation of biological systems in the last decade. Unfortunately, they generally rely on ...fluorescent labeling for cellular phenotyping, an indirect measure of the molecular landscape in the cell, which has critical limitations. Here we demonstrate Raman image-activated cell sorting by directly probing chemically specific intracellular molecular vibrations via ultrafast multicolor stimulated Raman scattering (SRS) microscopy for cellular phenotyping. Specifically, the technology enables real-time SRS-image-based sorting of single live cells with a throughput of up to ~100 events per second without the need for fluorescent labeling. To show the broad utility of the technology, we show its applicability to diverse cell types and sizes. The technology is highly versatile and holds promise for numerous applications that are previously difficult or undesirable with fluorescence-based technologies.
In the last decade, high-content screening based on multivariate single-cell imaging has been proven effective in drug discovery to evaluate drug-induced phenotypic variations. Unfortunately, this ...method inherently requires fluorescent labeling which has several drawbacks. Here we present a label-free method for evaluating cellular drug responses only by high-throughput bright-field imaging with the aid of machine learning algorithms. Specifically, we performed high-throughput bright-field imaging of numerous drug-treated and -untreated cells (N = ~240,000) by optofluidic time-stretch microscopy with high throughput up to 10,000 cells/s and applied machine learning to the cell images to identify their morphological variations which are too subtle for human eyes to detect. Consequently, we achieved a high accuracy of 92% in distinguishing drug-treated and -untreated cells without the need for labeling. Furthermore, we also demonstrated that dose-dependent, drug-induced morphological change from different experiments can be inferred from the classification accuracy of a single classification model. Our work lays the groundwork for label-free drug screening in pharmaceutical science and industry.
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.
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