Directed evolution of custom polymerases using droplet microfluidics

Engineering TNA polymerases through iterative cycles of directed evolution

DNA polymerases are important tools for biotechnology, synthetic biology, and chemical biology as they are routinely used to amplify and edit genetic information. However, natural polymerases do not recognize artificial genetic polymers (also known as xeno-nucleic acids or XNAs) with unique sugar-phosphate backbone structures. Directed evolution offers a possible solution to this problem by facilitating the discovery of engineered versions of natural polymerases that can copy genetic information back and forth between DNA and XNA. Here we report a directed evolution strategy for discovering polymerases that can synthesize threose nucleic acid (TNA) on DNA templates. The workflow involves library generation and expression in E. coli, high-throughput microfluidics-based screening of uniform water-in-oil droplets, plasmid recovery, secondary screening, and library regeneration. This technique is sufficiently general that it could be applied to a wide range of problems involving DNA modifying enzymes.

Structure-driven protein engineering for production of valuable natural products

Proteins are the most frequently used biocatalysts, and their structures determine their functions. Modifying the functions of proteins on the basis of their structures lies at the heart of protein engineering, opening a new horizon for metabolic engineering by efficiently generating stable enzymes. Many attempts at classical metabolic engineering have focused on improving specific metabolic fluxes and producing more valuable natural products by increasing gene expression levels and enzyme concentrations. However, most naturally occurring enzymes show limitations, and such limitations have hindered practical applications. Here we review recent advances in protein engineering in synthetic biology, chemoenzymatic synthesis, and plant metabolic engineering and describe opportunities for designing and constructing novel enzymes or proteins with desirable properties to obtain more active natural products.

Programmable Droplet Microfluidics Based on Machine Learning and Acoustic Manipulation

Typical microfluidic devices are application-specific and have to be carefully designed to implement the necessary functionalities for the targeted application. Programmable microfluidic chips try to overcome this by offering reconfigurable functionalities, allowing the same chip to be used in multiple different applications. In this work, we demonstrate a programmable microfluidic chip for the two-dimensional manipulation of droplets, based on ultrasonic bulk acoustic waves and a closed-loop machine-learning-based control algorithm. The algorithm has no prior knowledge of the acoustic fields but learns to control the droplets on the fly. The manipulation is based on switching the frequency of a single ultrasonic transducer. Using this method, we demonstrate 2D transportation and merging of water droplets in oil and oil droplets in water, and we performed the chemistry that underlies the basis of a colorimetric glucose assay. We show that we can manipulate drops with volumes ranging from ∼200 pL up to ∼30 nL with our setup. We also demonstrate that our method is robust, by changing the system parameters and showing that the machine learning algorithm can still complete the manipulation tasks. In short, our method uses ultrasonics to flexibly manipulate droplets, enabling programmable droplet microfluidic devices.

A Lab-in-a-Fiber optofluidic device using droplet microfluidics and laser-induced fluorescence for virus detection

Microfluidics has emerged rapidly over the past 20 years and has been investigated for a variety of applications from life sciences to environmental monitoring. Although continuous-flow microfluidics is ubiquitous, segmented-flow or droplet microfluidics offers several attractive features. Droplets can be independently manipulated and analyzed with very high throughput. Typically, microfluidics is carried out within planar networks of microchannels, namely, microfluidic chips. We propose that fibers offer an interesting alternative format with key advantages for enhanced optical coupling. Herein, we demonstrate the generation of monodisperse droplets within a uniaxial optofluidic Lab-in-a-Fiber scheme. We combine droplet microfluidics with laser-induced fluorescence (LIF) detection achieved through the development of an optical side-coupling fiber, which we term a periscope fiber. This arrangement provides stable and compact alignment. Laser-induced fluorescence offers high sensitivity and low detection limits with a rapid response time making it an attractive detection method for in situ real-time measurements. We use the well-established fluorophore, fluorescein, to characterize the Lab-in-a-Fiber device and determine the generation of [Formula: see text] 0.9 nL droplets. We present characterization data of a range of fluorescein concentrations, establishing a limit of detection (LOD) of 10 nM fluorescein. Finally, we show that the device operates within a realistic and relevant fluorescence regime by detecting reverse-transcription loop-mediated isothermal amplification (RT-LAMP) products in the context of COVID-19 diagnostics. The device represents a step towards the development of a point-of-care droplet digital RT-LAMP platform.

Recent Advances on Sorting Methods of High-Throughput Droplet-Based Microfluidics in Enzyme Directed Evolution

Droplet-based microfluidics has been widely applied in enzyme directed evolution (DE), in either cell or cell-free system, due to its low cost and high throughput. As the isolation principles are based on the labeled or label-free characteristics in the droplets, sorting method contributes mostly to the efficiency of the whole system. Fluorescence-activated droplet sorting (FADS) is the mostly applied labeled method but faces challenges of target enzyme scope. Label-free sorting methods show potential to greatly broaden the microfluidic application range. Here, we review the developments of droplet sorting methods through a comprehensive literature survey, including labeled detections [FADS and absorbance-activated droplet sorting (AADS)] and label-free detections [electrochemical-based droplet sorting (ECDS), mass-activated droplet sorting (MADS), Raman-activated droplet sorting (RADS), and nuclear magnetic resonance-based droplet sorting (NMR-DS)]. We highlight recent cases in the last 5 years in which novel enzymes or highly efficient variants are generated by microfluidic DE. In addition, the advantages and challenges of different sorting methods are briefly discussed to provide an outlook for future applications in enzyme DE.