Photoactivated Selective Release of Droplets from Microwell Arrays

SCO-pH: Microfluidic dynamic phenotyping platform for high-throughput screening of single cell acidification

Studies on the dynamics of single cell phenotyping have been hampered by the lack of quantitative high-throughput metabolism assays. Extracellular acidification, a prominent phenotype, yields significant insights into cellular metabolism, including tumorigenicity. Here, we develop a versatile microfluidic system for single cell optical pH analysis (SCO-pH), which compartmentalizes single cells in 140-pL droplets and immobilizes approximately 40,000 droplets in a two-dimensional array for temporal extracellular pH analysis. SCO-pH distinguishes cells undergoing hyperglycolysis induced by oligomycin A from untreated cells by monitoring their extracellular acidification. To facilitate pH sensing in each droplet, we encapsulate a cell-impermeable pH probe whose fluorescence intensities are quantified. Using this approach, we can differentiate hyperglycolytic cells and concurrently observe single cell heterogeneity in extracellular acidification dynamics. This high-throughput system will be useful in applications that require dynamic phenotyping of single cells with significant heterogeneity.

Learning Consistency and Specificity of Cells From Single-Cell Multi-Omic Data

Advancements in single-cell technologies concomitantly develop the epigenomic and transcriptomic profiles at the cell levels, providing opportunities to explore the potential biological mechanisms. Even though significant efforts have been dedicated to them, it remains challenging for the integration analysis of multi-omic data of single-cell because of the heterogeneity, complicated coupling and interpretability of data. To handle these issues, we propose a novel self-representation Learning-based Multi-omics data Integrative Clustering algorithm (sLMIC) for the integration of single-cell epigenomic profiles (DNA methylation or scATAC-seq) and transcriptomic (scRNA-seq), which the consistent and specific features of cells are explicitly extracted facilitating the cell clustering. Specifically, sLMIC constructs a graph for each type of single-cell data, thereby transforming omics data into multi-layer networks, which effectively removes heterogeneity of omic data. Then, sLMIC employs the low-rank and exclusivity constraints to separate the self-representation of cells into two parts, i.e., the shared and specific features, which explicitly characterize the consistency and diversity of omic data, providing an effective strategy to model the structure of cell types. Feature extraction and cell clustering are jointly formulated as an overall objective function, where latent features of data are obtained under the guidance of cell clustering. The extensive experimental results on 13 multi-omics datasets of single-cell from diverse organisms and tissues indicate that sLMIC observably exceeds the advanced algorithms regarding various measurements.

Development and future of droplet microfluidics

Over the past two decades, advances in droplet-based microfluidics have facilitated new approaches to process and analyze samples with unprecedented levels of precision and throughput. A wide variety of applications has been inspired across multiple disciplines ranging from materials science to biology. Understanding the dynamics of droplets enables optimization of microfluidic operations and design of new techniques tailored to emerging demands. In this review, we discuss the underlying physics behind high-throughput generation and manipulation of droplets. We also summarize the applications in droplet-derived materials and droplet-based lab-on-a-chip biotechnology. In addition, we offer perspectives on future directions to realize wider use of droplet microfluidics in industrial production and biomedical analyses.

Light-Responsive Materials in Droplet Manipulation for Biochemical Applications

Miniaturized droplets, characterized by well-controlled microenvironments and capability for parallel processing, have significantly advanced the studies on enzymatic evolution, molecular diagnostics, and single-cell analysis. However, manipulation of small-sized droplets, including moving, merging, and trapping of the targeted droplets for complex biochemical assays and subsequent analysis, is not trivial and remains technically demanding. Among various techniques, light-driven methods stand out as a promising candidate for droplet manipulation in a facile and flexible manner, given the features of contactless interaction, high spatiotemporal resolution, and biocompatibility. This review therefore compiles an in-depth discussion of the governing mechanisms underpinning light-driven droplet manipulation. Besides, light-responsive materials, representing the core of light-matter interaction and the key character converting light into different forms of energy, are particularly assessed in this review. Recent advancements in light-responsive materials and the most notable applications are comprehensively archived and evaluated. Continuous innovations and rational engineering of light-responsive materials are expected to propel the development of light-driven droplet manipulation, equip droplets with enhanced functionality, and broaden the applications of droplets for biochemical studies and routine biochemical investigations.

Optical-based microbubble for on-demand droplet release from static droplet array (SDA) for dispensing one droplet into one tube

Static droplet array (SDA) is a pivotal tool for high-capacity screening assays, yet extraction and collection the target droplets that contain unique analytes or cells from the SDA remains one major technical bottleneck that limits its broader application. Here we present an optical-based on-demand droplet release (OODR) system by incorporating a 1064 nm laser-responsive indium tin oxide (ITO) layer into a chamber array-based droplet microfluidic chip. By focusing the 1064 nm laser onto the ITO layer, microbubbles can be created via local heating to selectively push-out the droplets from the chamber. Then the released droplet is readily exported in a one-droplet-one-tube (ODOT) manner by the inherent capillary force into pipette tip. Releasing of the droplets containing fluorescein sodium demonstrated ∼100% successful rate (9 out of 6400 droplets were successfully released) and low residual (only ∼5% of the droplet volume remains in the chamber). White or fluorescence image-based releasing of single-cell-droplets directly after cell loading or multi-cells-droplets derived from on-chip single-cell cultivation for both E. coli and yeast cells further demonstrated the wide applicability of OODR. The present system is user-friendly and has the potential to be applied in various high-throughput screening assays, including single molecule/cell analysis, drug screening, and phenotype-based cell sorting.

Polymer-Grafted, Gold Nanoparticle-Based Nano-Capsules as Reversible Colorimetric Tensile Strain Sensors

Colloidal colorimetric microsensors enable the in-situ detection of mechanical strains within materials. Enhancing the sensitivity of these sensors to small scale deformation while enabling reversibility of the sensing capability would expand their utility in applications including biosensing and chemical sensing. In this study, we introduce the synthesis of colloidal colorimetric nano-sensors using a simple and readily scalable fabrication method. Colloidal nano sensors are prepared by emulsion-templated assembly of polymer-grafted gold nanoparticles (AuNP). To direct the adsorption of AuNP to the oil-water interface of emulsion droplets, AuNP (≈11nm) are functionalized with thiol-terminated polystyrene (PS, Mn  = 11k). These PS-grafted gold nanoparticles are suspended in toluene and subsequently emulsified to form droplets with a diameter of ≈30µm. By evaporating the solvent of the oil-inwater emulsion, we form nanocapsules (AuNC) (diameter < 1µm) decorated by PS-grafted AuNP. To test mechanical sensing, the AuNC are embedded in an elastomer matrix. The addition of a plasticizer reduces the glass transition temperature of the PS brushes, and in turn imparts reversible deformability to the AuNC. The plasmonic peak of the AuNC shifts towards lower wavelengths upon application of uniaxial tensile tension, indicating increased inter-nanoparticle distance, and reverts back as the tension is released.

On-demand light-driven release of droplets stabilized via a photoresponsive fluorosurfactant

Water-in-oil droplets have emerged as promising microreactors for high-throughput biochemical analysis due to their features of reduced sample consumption and automated operation. For a typical screening application, droplets are often trapped for continuous monitoring of the reaction over an extended period, followed by the selective retrieval of targeted droplets based on the after-effect of biochemical reactions. While techniques for droplet trapping are well developed, retrieval of targeted droplets mainly demands complicated device fabrication or sophisticated control. Herein, facile and rapid selective droplet release is achieved by utilizing a new class of photoresponsive fluorosurfactant based on plasmonic nanoparticles. The intense photothermal response provided by this novel photoresponsive fluorosurfactant is capable of vaporizing the fluorocarbon oil at the droplet interface under laser illumination, resulting in a bubble releasing a trapped droplet on demand. A fully automated fluorescence-activated droplet release platform has also been developed to demonstrate its potential for droplet-based large-scale screening applications.

Multi-omics integration in the age of million single-cell data

An explosion in single-cell technologies has revealed a previously underappreciated heterogeneity of cell types and novel cell-state associations with sex, disease, development and other processes. Starting with transcriptome analyses, single-cell techniques have extended to multi-omics approaches and now enable the simultaneous measurement of data modalities and spatial cellular context. Data are now available for millions of cells, for whole-genome measurements and for multiple modalities. Although analyses of such multimodal datasets have the potential to provide new insights into biological processes that cannot be inferred with a single mode of assay, the integration of very large, complex, multimodal data into biological models and mechanisms represents a considerable challenge. An understanding of the principles of data integration and visualization methods is required to determine what methods are best applied to a particular single-cell dataset. Each class of method has advantages and pitfalls in terms of its ability to achieve various biological goals, including cell-type classification, regulatory network modelling and biological process inference. In choosing a data integration strategy, consideration must be given to whether the multi-omics data are matched (that is, measured on the same cell) or unmatched (that is, measured on different cells) and, more importantly, the overall modelling and visualization goals of the integrated analysis.

Method for Passive Droplet Sorting after Photo-Tagging

We present a method to photo-tag individual microfluidic droplets for latter selection by passive sorting. The use of a specific surfactant leads to the interfacial tension to be very sensitive to droplet pH. The photoexcitation of droplets containing a photoacid, pyranine, leads to a decrease in droplet pH. The concurrent increase in droplet interfacial tension enables the passive selection of irradiated droplets. The technique is used to select individual droplets within a droplet array as illuminated droplets remain in the wells while other droplets are eluted by the flow of the external oil. This method was used to select droplets in an array containing cells at a specific stage of apoptosis. The technique is also adaptable to continuous-flow sorting. By passing confined droplets over a microfabricated trench positioned diagonally in relation to the direction of flow, photo-tagged droplets were directed toward a different chip exit based on their lateral movement. The technique can be performed on a conventional fluorescence microscope and uncouples the observation and selection of droplets, thus enabling the selection on a large variety of signals, or based on qualitative user-defined features.

Static array of droplets and on-demand recovery for biological assays

Microfluidics has revolutionized several research areas by providing compact yet powerful microanalytical devices that in many cases outperform conventional systems. Among different microfluidics technologies, droplet microfluidics has emerged as a powerful platform to enable analyses of biological samples and phenomena because of its simplicity and versatility. Droplet microfluidics enables high-throughput encapsulation, manipulation, and analysis of single cells while drastically reducing the cost and time required by conventional technologies. For many of these microanalysis systems, manipulation of individual droplets is extremely important as it enables multiplexed high dimensional phenotyping of the targets, going beyond surface phenotyping. One of the key manipulation steps that needs to be implemented with high precision is enabling long-term observation of droplets and recovery of a subset of these droplets for further analysis. This Perspective highlights the recent advances and provides an outlook on future developments that will enable highly complex analyses of biological samples.