Silicon chambers for enhanced incubation and imaging of microfluidic droplets.

Droplet microfluidics has become a powerful tool in life sciences, underlying digital assays, single-cell sequencing or directed evolution, and it is making foray in physical sciences as well. Imaging and incubation of droplets are crucial, yet they are encumbered by the poor optical, thermal and mechanical properties of PDMS, a material commonly used in microfluidics labs. Here we show that Si is an ideal material for droplet chambers. Si chambers pack droplets in a crystalline and immobile monolayer, are immune to evaporation or sagging, boost the number of collected photons, and tightly control the temperature field sensed by droplets. We use the mechanical and optical benefits of Si chambers to image ≈1 million of droplets from a multiplexed digital assay - with an acquisition rate similar to the best in-line methods. Lastly, we demonstrate their applicability with a demanding assay that maps the thermal dependence of Michaelis-Menten constants with an array of ≈150 000 droplets. The design of the Si chambers is streamlined to avoid complicated fabrication and improve reproducibility, which makes Si a complementary material to PDMS in the toolbox of droplet microfluidics.

Molecular Computation for Molecular Classification.

DNA as an informational polymer has, for the past 30 years, progressively become an essential molecule to rationally build chemical reaction networks endowed with powerful signal-processing capabilities. Whether influenced by the silicon world or inspired by natural computation, molecular programming has gained attention for diagnosis applications. Of particular interest for this review, molecular classifiers have shown promising results for disease pattern recognition and sample classification. Because both input integration and computation are performed in a single tube, at the molecular level, this low-cost approach may come as a complementary tool to molecular profiling strategies, where all biomarkers are quantified independently using high-tech instrumentation. After introducing the elementary components of molecular classifiers, some of their experimental implementations are discussed either using digital Boolean logic or analog neural network architectures.

Real-Time Detection of Glyphosate by a Water-Gated Organic Field-Effect Transistor with a Microfluidic Chamber.

This paper reports the development of a real-time monitoring system utilizing the combination of a water-gated organic field-effect transistor (WG-OFET) and a microfluidic chamber for the detection of the herbicide glyphosate (GlyP). For the realization of the real-time sensing with the WG-OFET, the surface of a polymer semiconductor was utilized as a sensing unit. The aqueous solution including the target analyte, which is employed as a gate dielectric of the WG-OFET, flows into a designed microfluidic chamber on the semiconductor layer and the gate electrode. As the sensing mechanism, the WG-OFET-based sensor utilizes the competitive complexation among carboxylate-functionalized polythiophene, a copper(II) (Cu2+) ion, and GlyP. The reversible accumulation and desorption of the positively charged Cu2+ ion on the semiconductor surface induced a change in the electrical double-layer capacitance (EDLC). The optimization of the microfluidic chamber enables a uniform water flow and contributes to real-time quantitative sensing of GlyP at a micromolar level. Thus, this study would lead to practical real-time sensing in water for various fields including environmental assessment.

Morphological Manipulation of DNA Gel Microbeads with Biomolecular Stimuli.

Hydrogels are essential in many fields ranging from tissue engineering and drug delivery to food sciences or cosmetics. Hydrogels that respond to specific biomolecular stimuli such as DNA, mRNA, miRNA and small molecules are highly desirable from the perspective of medical applications, however interfacing classical hydrogels with nucleic acids is still challenging. Here were demonstrate the generation of microbeads of DNA hydrogels with droplet microfluidic, and their morphological actuation with DNA strands. Using strand displacement and the specificity of DNA base pairing, we selectively dissolved gel beads, and reversibly changed their size on-the-fly with controlled swelling and shrinking. Lastly, we performed a complex computing primitive-A Winner-Takes-All competition between two populations of gel beads. Overall, these results show that strand responsive DNA gels have tantalizing potentials to enhance and expand traditional hydrogels, in particular for applications in sequencing and drug delivery.

Accelerating the Finite-Element Method for Reaction-Diffusion Simulations on GPUs with CUDA.

DNA nanotechnology offers a fine control over biochemistry by programming chemical reactions in DNA templates. Coupled to microfluidics, it has enabled DNA-based reaction-diffusion microsystems with advanced spatio-temporal dynamics such as traveling waves. The Finite Element Method (FEM) is a standard tool to simulate the physics of such systems where boundary conditions play a crucial role. However, a fine discretization in time and space is required for complex geometries (like sharp corners) and highly nonlinear chemistry. Graphical Processing Units (GPUs) are increasingly used to speed up scientific computing, but their application to accelerate simulations of reaction-diffusion in DNA nanotechnology has been little investigated. Here we study reaction-diffusion equations (a DNA-based predator-prey system) in a tortuous geometry (a maze), which was shown experimentally to generate subtle geometric effects. We solve the partial differential equations on a GPU, demonstrating a speedup of ∼100 over the same resolution on a 20 cores CPU.

Massively parallel and multiparameter titration of biochemical assays with droplet microfluidics.

Biochemical systems in which multiple components take part in a given reaction are of increasing interest. Because the interactions between these different components are complex and difficult to predict from basic reaction kinetics, it is important to test for the effect of variations in the concentration for each reagent in a combinatorial manner. For example, in PCR, an increase in the concentration of primers initially increases template amplification, but large amounts of primers result in primer-dimer by-products that inhibit the amplification of the template. Manual titration of biochemical mixtures rapidly becomes costly and laborious, forcing scientists to settle for suboptimal concentrations. Here we present a droplet-based microfluidics platform for mapping of the concentration space of up to three reaction components followed by detection with a fluorescent readout. The concentration of each reaction component is read through its internal standard (barcode), which is fluorescent but chemically orthogonal. We describe in detail the workflow, which comprises the following: (i) production of the microfluidics chips, (ii) preparation of the biochemical mixes, (iii) their mixing and compartmentalization into water-in-oil emulsion droplets via microfluidics, (iv) incubation and imaging of the fluorescent barcode and reporter signals by fluorescence microscopy and (v) image processing and data analysis. We also provide recommendations for choosing the appropriate fluorescent markers, programming the pressure profiles and analyzing the generated data. Overall, this platform allows a researcher with a few weeks of training to acquire ∼10,000 data points (in a 1D, 2D or 3D concentration space) over the course of a day from as little as 100-1,000 µl of reaction mix.

High-throughput and long-term observation of compartmentalized biochemical oscillators.

We report the splitting of an oscillating DNA circuit into ∼700 droplets with picoliter volumes. Upon incubation at constant temperature, the droplets display sustained oscillations that can be observed for more than a day. Superimposed to the bulk behaviour, we find two intriguing new phenomena - slow desynchronization between the compartments and kinematic spatial waves - and investigate their possible origin. This approach provides a route to study the influence of small volume effects in biology, and paves the way to technological applications of compartmentalized molecular programs controlling complex dynamics.

Scaling down DNA circuits with competitive neural networks.

DNA has proved to be an exquisite substrate to compute at the molecular scale. However, nonlinear computations (such as amplification, comparison or restoration of signals) remain costly in term of strands and are prone to leak. Kim et al. showed how competition for an enzymatic resource could be exploited in hybrid DNA/enzyme circuits to compute a powerful nonlinear primitive: the winner-take-all (WTA) effect. Here, we first show theoretically how the nonlinearity of the WTA effect allows the robust and compact classification of four patterns with only 16 strands and three enzymes. We then generalize this WTA effect to DNA-only circuits and demonstrate similar classification capabilities with only 23 strands.

In vitro regulatory models for systems biology.

The reductionist approach has revolutionized biology in the past 50 years. Yet its limits are being felt as the complexity of cellular interactions is gradually revealed by high-throughput technology. In order to make sense of the deluge of "omic data", a hypothesis-driven view is needed to understand how biomolecular interactions shape cellular networks. We review recent efforts aimed at building in vitro biochemical networks that reproduce the flow of genetic regulation. We highlight how those efforts have culminated in the rational construction of biochemical oscillators and bistable memories in test tubes. We also recapitulate the lessons learned about in vivo biochemical circuits such as the importance of delays and competition, the links between topology and kinetics, as well as the intriguing resemblance between cellular reaction networks and ecosystems.

Computing with competition in biochemical networks.

Cells rely on limited resources such as enzymes or transcription factors to process signals and make decisions. However, independent cellular pathways often compete for a common molecular resource. Competition is difficult to analyze because of its nonlinear global nature, and its role remains unclear. Here we show how decision pathways such as transcription networks may exploit competition to process information. Competition for one resource leads to the recognition of convex sets of patterns, whereas competition for several resources (overlapping or cascaded regulons) allows even more general pattern recognition. Competition also generates surprising couplings, such as correlating species that share no resource but a common competitor. The mechanism we propose relies on three primitives that are ubiquitous in cells: multiinput motifs, competition for a resource, and positive feedback loops.

A microfluidic device for on-chip agarose microbead generation with ultralow reagent consumption.

Water-in-oil microdroplets offer microreactors for compartmentalized biochemical reactions with high throughput. Recently, the combination with a sol-gel switch ability, using agarose-in-oil microdroplets, has increased the range of possible applications, allowing for example the capture of amplicons in the gel phase for the preservation of monoclonality during a PCR reaction. Here, we report a new method for generating such agarose-in-oil microdroplets on a microfluidic device, with minimized inlet dead volume, on-chip cooling, and in situ monitoring of biochemical reactions within the gelified microbeads. We used a flow-focusing microchannel network and successfully generated agarose microdroplets at room temperature using the "push-pull" method. This method consists in pushing the oil continuous phase only, while suction is applied to the device outlet. The agarose phase present at the inlet is thus aspirated in the device, and segmented in microdroplets. The cooling system consists of two copper wires embedded in the microfluidic device. The transition from agarose microdroplets to microbeads provides additional stability and facilitated manipulation. We demonstrate the potential of this method by performing on-chip a temperature-triggered DNA isothermal amplification in agarose microbeads. Our device thus provides a new way to generate microbeads with high throughput and no dead volume for biochemical applications.

Reversible logic circuits made of DNA.

We report reversible logic circuits made of DNA. The circuits are based on an AND gate that is designed to be thermodynamically and kinetically reversible and to respond nonlinearly to the concentrations of its input molecules. The circuits continuously recompute their outputs, allowing them to respond to changing inputs. They are robust to imperfections in their inputs.

Remote toehold: a mechanism for flexible control of DNA hybridization kinetics.

Hybridization of DNA strands can be used to build molecular devices, and control of the kinetics of DNA hybridization is a crucial element in the design and construction of functional and autonomous devices. Toehold-mediated strand displacement has proved to be a powerful mechanism that allows programmable control of DNA hybridization. So far, attempts to control hybridization kinetics have mainly focused on the length and binding strength of toehold sequences. Here we show that insertion of a spacer between the toehold and displacement domains provides additional control: modulation of the nature and length of the spacer can be used to control strand-displacement rates over at least 3 orders of magnitude. We apply this mechanism to operate displacement reactions in potentially useful kinetic regimes: the kinetic proofreading and concentration-robust regimes.