Droplet Combinations: A Scalable Microfluidic Platform for Biochemical Assays

Tailored Fluorosurfactants through Controlled/Living Radical Polymerization for Highly Stable Microfluidic Droplet Generation

Droplet-based microfluidics represents a disruptive technology in the field of chemistry and biology through the generation and manipulation of sub-microlitre droplets. To avoid droplet coalescence, fluoropolymer-based surfactants are commonly used to reduce the interfacial tension between two immiscible phases to stabilize droplet interfaces. However, the conventional preparation of fluorosurfactants involves multiple steps of conjugation reactions between fluorinated and hydrophilic segments to form multiple-block copolymers. In addition, synthesis of customized surfactants with tailored properties is challenging due to the complex synthesis process. Here, we report a highly efficient synthetic method that utilizes living radical polymerization (LRP) to produce fluorosurfactants with tailored functionalities. Compared to the commercialized surfactant, our surfactants outperform in thermal cycling for polymerase chain reaction (PCR) testing, and exhibit exceptional biocompatibility for cell and yeast culturing in a double-emulsion system. This breakthrough synthetic approach has the potential to revolutionize the field of droplet-based microfluidics by enabling the development of novel designs that generate droplets with superior stability and functionality for a wide range of applications.

Facile and scalable tubing-free sample loading for droplet microfluidics

Droplet microfluidics has in recent years found a wide range of analytical and bioanalytical applications. In droplet microfluidics, the samples that are discretized into droplets within the devices are predominantly loaded through tubings, but such tubing-based sample loading has drawbacks such as limited scalability for processing many samples, difficulty for automation, and sample wastage. While advances in autosamplers have alleviated some of these drawbacks, sample loading that can instead obviate tubings offers a potentially promising alternative but has been underexplored. To fill the gap, we introduce herein a droplet device that features a new Tubing Eliminated Sample Loading Interface (TESLI). TESLI integrates a network of programmable pneumatic microvalves that regulate vacuum and pressure sources so that successive sub-microliter samples can be directly spotted onto the open-to-atmosphere TESLI inlet, vacuumed into the device, and pressurized into nanoliter droplets within the device with minimal wastage. The same vacuum and pressure regulation also endows TESLI with cleaning and sample switching capabilities, thus enabling scalable processing of many samples in succession. Moreover, we implement a pair of TESLIs in our device to parallelize and alternate their operation as means to minimizing idle time. For demonstration, we use our device to successively process 44 samples into droplets—a number that can further scale. Our results demonstrate the feasibility of tubing-free sample loading and a promising approach for advancing droplet microfluidics.

Combinatorial nanodroplet platform for screening antibiotic combinations

The emergence and spread of multidrug resistant bacterial strains and concomitant dwindling of effective antibiotics pose worldwide healthcare challenges. To address these challenges, advanced engineering tools are developed to personalize antibiotic treatments by speeding up the diagnostics that is critical to prevent antibiotic misuse and overuse and make full use of existing antibiotics. Meanwhile, it is necessary to investigate novel antibiotic strategies. Recently, repurposing mono antibiotics into combinatorial antibiotic therapies has shown great potential for treatment of bacterial infections. However, widespread adoption of drug combinations has been hindered by the complexity of screening techniques and the cost of reagent consumptions in practice. In this study, we developed a combinatorial nanodroplet platform for automated and high-throughput screening of antibiotic combinations while consuming orders of magnitude lower reagents than the standard microtiter-based screening method. In particular, the proposed platform is capable of creating nanoliter droplets with multiple reagents in an automatic manner, tuning concentrations of each component, performing biochemical assays with high flexibility (e.g., temperature and duration), and achieving detection with high sensitivity. A biochemical assay, based on the reduction of resazurin by the metabolism of bacteria, has been characterized and employed to evaluate the combinatorial effects of the antibiotics of interest. In a pilot study, we successfully screened pairwise combinations between 4 antibiotics for a model Escherichia coli strain.

A Flexible, Microfluidic, Dispensing System for Screening Drug Combinations

It is known that in many cases a combination of drugs is more effective than single-drug treatments both for reducing toxicity and increasing efficacy. With the advent of organoid screens, personalised medicine has become possible for many diseases. Automated pipetting to well plates is the pharmaceutical industry standard for drug screening, but this is relatively expensive and slow. Here, a rotary microfluidic system is presented that can test all possible drug combinations at speed with the use of droplets. For large numbers of combinations, it is shown how the experimental scale is reduced by considering drug dilutions and machine learning. As an example, two cases are considered; the first is a three-ring and three radii configuration and the second is a four ring and forty-eight radii configuration. Between these two, all other cases are shown to be possible. The proposed commercial instrument is shown to be flexible, the user choosing which wells to fill and which driver-computational sub-routine to select. The major issues addressed here are the programming theory of the instrument and the reduction of droplets to be generated by drug dilutions and machine learning.