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Shear of a Coulombic crystal, due to repetitive collisions with its container upon shaking, simultaneously orders and melts the crystal. 

Synapses are crucial structures that mediate signal transmission between neurons in complex neural circuits and display considerable morphological and electrophysiological heterogeneity. So far we still lack a high-throughput method to profile the molecular heterogeneity among individual synapses. In the present study, we develop a droplet-based single-cell (sc) total-RNA-sequencing platform, called Multiple-Annealing-and-Tailing-based Quantitative scRNA-seq in Droplets, for transcriptome profiling of individual neurites, primarily composed of synaptosomes. In the synaptosome transcriptome, or ‘synaptome’, profiling of both mouse and human brain samples, we detect subclusters among synaptosomes that are associated with neuronal subtypes and characterize the landscape of transcript splicing that occurs within synapses. We extend synaptome profiling to synaptopathy in an Alzheimer’s disease (AD) mouse model and discover AD-associated synaptic gene expression changes that cannot be detected by single-nucleus transcriptome profiling. Overall, our results show that this platform provides a high-throughput, single-synaptosome transcriptome profiling tool that will facilitate future discoveries in neuroscience. 

Abstract Strong and tough bio‐based fibers are attractive for both fundamental research and practical applications. In this work, strong and tough hierarchical core–shell fibers with cellulose nanofibrils (CNFs) in the core and regenerated silk fibroins (RSFs) in the shell are designed and prepared, mimicking natural spider silks. CNF/RSF core–shell fibers with precisely controlled morphology are continuously wet‐spun using a co‐axial microfluidic device. Highly‐dense non‐covalent interactions are introduced between negatively‐charged CNFs in the core and positively‐charged RSFs in the shell, diminishing the core/shell interface and forming an integral hierarchical fiber. Meanwhile, shearing by microfluidic channels and post‐stretching induce a better ordering of CNFs in the core and RSFs in the shell, while ordered CNFs and RSFs are more densely packed, thus facilitating the formation of non‐covalent interactions within the fiber matrix. Therefore, CNF/RSF core–shell fibers demonstrate excellent mechanical performances; especially after post‐stretching, their tensile strength, tensile strain, Young's modulus, and toughness are up to 635 MPa, 22.4%, 24.0 GPa, and 110 MJ m⁻³, respectively. In addition, their mechanical properties are barely compromised even at −40 and 60 °C. Static load and dynamic impact tests suggest that CNF/RSF core–shell fibers are strong and tough, making them suitable for advanced structural materials. 

Abstract Elastomers generally possess low Young's modulus and high failure strain, which are widely used in soft robots and intelligent actuators. However, elastomers generally lack diverse functionalities, such as stimulated shape morphing, and a general strategy to implement these functionalities into elastomers is still challenging. Here, a microfluidic 3D droplet printing platform is developed to design composite elastomers architected with arrays of functional droplets. Functional droplets with controlled size, composition, position, and pattern are designed and implemented in the composite elastomers, imparting functional performances to the systems. The composited elastomers are sensitive to stimuli, such as solvent, temperature, and light, and are able to demonstrate multishape (bow‐ and S‐shaped), multimode (gradual and sudden), and multistep (one‐ and two‐step) deformations. Based on the unique properties of droplet‐embedded composite elastomers, a variety of stimuli‐responsive systems are developed, including designable numbers, biomimetic flowers, and soft robots, and a series of functional performances are achieved, presenting a facile platform to impart diverse functionalities into composite elastomers by microfluidic 3D droplet printing. 

The conversion and utilization of carbon dioxide (CO2) have dual significance for reducing carbon emissions and solving energy demand. Catalytic reduction of CO2 is a promising way to convert and utilize CO2. However, high-performance catalysts with excellent catalytic activity, selectivity and stability are currently lacking. High-throughput methods offer an effective way to screen high-performance CO2 reduction catalysts. Here, recent advances in high-throughput screening of electrocatalysts for CO2 reduction are reviewed. First, the mechanism of CO2 reduction reaction by electrocatalysis and potential catalyst candidates are introduced. Second, high-throughput computational methods developed to accelerate catalyst screening are presented, such as density functional theory and machine learning. Then, high-throughput experimental methods are outlined, including experimental design, high-throughput synthesis, in situ characterization and high-throughput testing. Finally, future directions of high-throughput screening of CO2 reduction electrocatalysts are outlooked. This review will be a valuable reference for future research on high-throughput screening of CO2 electrocatalysts. 

In drop-based microfluidics, an aqueous sample is partitioned into drops using individual pump sources that drive water and oil into a drop-making device. Parallelization of drop-making devices is necessary to achieve high-throughput screening of multiple experimental conditions, especially in time-sensitive studies. Here, we present the plate-interfacing parallel encapsulation (PIPE) chip, a microfluidic chip designed to generate 50 to 90 μm diameter drops of up to 96 different conditions in parallel by interfacing individual drop makers with a standard 384-well microtiter plate. The PIPE chip is used to generate two types of optically barcoded drop libraries consisting of two-color fluorescent particle combinations: a library of 24 microbead barcodes and a library of 192 quantum dot barcodes. Barcoded combinations in the drop libraries are rapidly measured within a microfluidic device using fluorescence detection and distinct barcoded populations in the fluorescence drop data are identified using DBSCAN data clustering. Signal analysis reveals that particle size defines the source of dominant noise present in the fluorescence intensity distributions of the barcoded drop populations, arising from Poisson loading for microbeads and shot noise for quantum dots. A barcoded population from a drop library is isolated using fluorescence-activated drop sorting, enabling downstream analysis of drop contents. The PIPE chip can improve multiplexed high-throughput assays by enabling simultaneous encapsulation of barcoded samples stored in a microtiter plate and reducing sample preparation time. 

This perspective highlights promising areas of application of microfluidics that have yet to be fully explored, and identifies some of the technical challenges that have impeded the widespread adoption of microfluidics. 

Single-cell RNA sequencing (scRNA-seq) is a powerful technique for describing cell states. Identifying the spatial arrangement of these states in tissues remains challenging, with the existing methods requiring niche methodologies and expertise. Here, we describe segmentation by exogenous perfusion (SEEP), a rapid and integrated method to link surface proximity and environment accessibility to transcriptional identity within three-dimensional (3D) disease models. The method utilizes the steady-state diffusion kinetics of a fluores- cent dye to establish a gradient along the radial axis of disease models. Classification of sample layers based on dye accessibility enables dissociated and sorted cells to be characterized by transcriptomic and regional identities. Using SEEP, we analyze spheroid, organoid, and in vivo tumor models of high-grade serous ovarian cancer (HGSOC). The results validate long-standing beliefs about the relationship between cell state and position while revealing new concepts regarding how spatially unique microenvironments influence the identity of individual cells within tumors. 

Despite significant advances in particle imaging technologies over the past two decades, few ad- vances have been made in particle tracking, i.e. linking individual particle positions across time series data. The state-of-the-art tracking algorithm is highly effective for systems in which the particles behave mostly independently. However, these algorithms become inaccurate when particle motion is highly correlated, such as in dense or strongly interacting systems. Accurate particle tracking is essential in the study of the physics of dense colloids, such as the study of dislocation formation, nucleation, and shear transformations. Here, we present a new method for particle tracking that incorporates information about the correlated motion of the particles. We demonstrate significant improvement over the state-of-the-art tracking algorithm in simulated data on highly correlated systems."