Controlled Oil/Water Partitioning of Hydrophobic Substrates Extending the Bioanalytical Applications of Droplet-Based Microfluidics

Functional characterization of luciferase in a brittle star indicates parallel evolution influenced by genomic availability of haloalkane dehalogenase

Determining why convergent traits use distinct versus shared genetic components is crucial for understanding how evolutionary processes generate and sustain biodiversity. However, the factors dictating the genetic underpinnings of convergent traits remain incompletely understood. Here, we use heterologous protein expression, biochemical assays, and phylogenetic analyses to confirm the origin of a luciferase gene from haloalkane dehalogenases in the brittle star Amphiura filiformis . Through database searches and gene tree analyses, we also show a complex pattern of presence and absence of haloalkane dehalogenases across organismal genomes. These results first confirm parallel evolution across a vast phylogenetic distance, because octocorals like Renilla also use luciferase derived from haloalkane dehalogenases. This parallel evolution is surprising, even though previously hypothesized, because many organisms that also use coelenterazine as the bioluminescence substrate evolved completely distinct luciferases. The inability to detect haloalkane dehalogenases in the genomes of several bioluminescent groups suggests that the distribution of this gene family influences its recruitment as a luciferase. Together, our findings highlight how biochemical function and genomic availability help determine whether distinct or shared genetic components are used during the convergent evolution of traits like bioluminescence.

Superimposed Electric Field Enhanced Electrospray for High-Throughput and Consistent Cell Encapsulation

Cell encapsulation technology, crucial for advanced biomedical applications, faces challenges in existing microfluidic and electrospray methods. Microfluidic techniques, while precise, can damage vulnerable cells, and conventional electrospray methods often encounter instability and capsule breakage during high-throughput encapsulation. Inspired by the transformation of the working state from unstable dripping to stable jetting triggered by local electric potential, this study introduces a superimposed electric field (SEF)-enhanced electrospray method for cell encapsulation, with improved stability and biocompatibility. Utilizing stiffness theory, the stability of the electrospray, whose stiffness is five times stronger under conical confinement, is quantitatively analyzed. The SEF technique enables rapid, continuous production of ≈300 core-shell capsules per second in an aqueous environment, significantly improving cell encapsulation efficiency. This method demonstrates remarkable potential as exemplified in two key applications: (1) a 92-fold increase in human-derived induced pluripotent stem cells (iPSCs) expansion over 10 d, outperforming traditional 2D cultures in both growth rate and pluripotency maintenance, and (2) the development of liver capsules for steatosis modeling, exhibiting normal function and biomimetic lipid accumulation. The SEF-enhanced electrospray method presents a significant advancement in cell encapsulation technology. It offers a more efficient, stable, and biocompatible approach for clinical transplantation, drug screening, and cell therapy.

Advancing Enzyme's Stability and Catalytic Efficiency through Synergy of Force-Field Calculations, Evolutionary Analysis, and Machine Learning

Thermostability is an essential requirement for the use of enzymes in the bioindustry. Here, we compare different protein stabilization strategies using a challenging target, a stable haloalkane dehalogenase DhaA115. We observe better performance of automated stabilization platforms FireProt and PROSS in designing multiple-point mutations over the introduction of disulfide bonds and strengthening the intra- and the inter-domain contacts by in silico saturation mutagenesis. We reveal that the performance of automated stabilization platforms was still compromised due to the introduction of some destabilizing mutations. Notably, we show that their prediction accuracy can be improved by applying manual curation or machine learning for the removal of potentially destabilizing mutations, yielding highly stable haloalkane dehalogenases with enhanced catalytic properties. A comparison of crystallographic structures revealed that current stabilization rounds were not accompanied by large backbone re-arrangements previously observed during the engineering stability of DhaA115. Stabilization was achieved by improving local contacts including protein-water interactions. Our study provides guidance for further improvement of automated structure-based computational tools for protein stabilization.

Ultrahigh-Throughput Enzyme Engineering and Discovery in In Vitro Compartments

Novel and improved biocatalysts are increasingly sourced from libraries via experimental screening. The success of such campaigns is crucially dependent on the number of candidates tested. Water-in-oil emulsion droplets can replace the classical test tube, to provide in vitro compartments as an alternative screening format, containing genotype and phenotype and enabling a readout of function. The scale-down to micrometer droplet diameters and picoliter volumes brings about a >107-fold volume reduction compared to 96-well-plate screening. Droplets made in automated microfluidic devices can be integrated into modular workflows to set up multistep screening protocols involving various detection modes to sort >107 variants a day with kHz frequencies. The repertoire of assays available for droplet screening covers all seven enzyme commission (EC) number classes, setting the stage for widespread use of droplet microfluidics in everyday biochemical experiments. We review the practicalities of adapting droplet screening for enzyme discovery and for detailed kinetic characterization. These new ways of working will not just accelerate discovery experiments currently limited by screening capacity but profoundly change the paradigms we can probe. By interfacing the results of ultrahigh-throughput droplet screening with next-generation sequencing and deep learning, strategies for directed evolution can be implemented, examined, and evaluated.

In-depth analysis of biocatalysts by microfluidics: An emerging source of data for machine learning

Nowadays, the vastly increasing demand for novel biotechnological products is supported by the continuous development of biocatalytic applications which provide sustainable green alternatives to chemical processes. The success of a biocatalytic application is critically dependent on how quickly we can identify and characterize enzyme variants fitting the conditions of industrial processes. While miniaturization and parallelization have dramatically increased the throughput of next-generation sequencing systems, the subsequent characterization of the obtained candidates is still a limiting process in identifying the desired biocatalysts. Only a few commercial microfluidic systems for enzyme analysis are currently available, and the transformation of numerous published prototypes into commercial platforms is still to be streamlined. This review presents the state-of-the-art, recent trends, and perspectives in applying microfluidic tools in the functional and structural analysis of biocatalysts. We discuss the advantages and disadvantages of available technologies, their reproducibility and robustness, and readiness for routine laboratory use. We also highlight the unexplored potential of microfluidics to leverage the power of machine learning for biocatalyst development.

Tools for computational design and high-throughput screening of therapeutic enzymes

Therapeutic enzymes are valuable biopharmaceuticals in various biomedical applications. They have been successfully applied for fibrinolysis, cancer treatment, enzyme replacement therapies, and the treatment of rare diseases. Still, there is a permanent demand to find new or better therapeutic enzymes, which would be sufficiently soluble, stable, and active to meet specific medical needs. Here, we highlight the benefits of coupling computational approaches with high-throughput experimental technologies, which significantly accelerate the identification and engineering of catalytic therapeutic agents. New enzymes can be identified in genomic and metagenomic databases, which grow thanks to next-generation sequencing technologies exponentially. Computational design and machine learning methods are being developed to improve catalytically potent enzymes and predict their properties to guide the selection of target enzymes. High-throughput experimental pipelines, increasingly relying on microfluidics, ensure functional screening and biochemical characterization of target enzymes to reach efficient therapeutic enzymes.

Miniaturized single-cell technologies for monoclonal antibody discovery

Antibodies (Abs) are among the most important class of biologicals, showcasing a high therapeutic and diagnostic value. In the global therapeutic Ab market, fully-human monoclonal Abs (FH-mAbs) are flourishing thanks to their low immunogenicity and high specificity. The rapidly emerging field of single-cell technologies has paved the way to efficiently discover mAbs by facilitating a fast screening of the antigen (Ag)-specificity and functionality of Abs expressed by B cells. This review summarizes the principles and challenges of the four key concepts to discover mAbs using these technologies, being confinement of single cells using either droplet microfluidics or microstructure arrays, identification of the cells of interest, retrieval of those cells and single-cell sequence determination required for mAb production. This review reveals the enormous potential for mix-and-matching of the above-mentioned strategies, which is illustrated by the plethora of established, highly integrated devices. Lastly, an outlook is given on the many opportunities and challenges that still lie ahead to fully exploit miniaturized single-cell technologies for mAb discovery.

Recent trends in biocatalysis

Biocatalysis has undergone revolutionary progress in the past century. Benefited by the integration of multidisciplinary technologies, natural enzymatic reactions are constantly being explored. Protein engineering gives birth to robust biocatalysts that are widely used in industrial production. These research achievements have gradually constructed a network containing natural enzymatic synthesis pathways and artificially designed enzymatic cascades. Nowadays, the development of artificial intelligence, automation, and ultra-high-throughput technology provides infinite possibilities for the discovery of novel enzymes, enzymatic mechanisms and enzymatic cascades, and gradually complements the lack of remaining key steps in the pathway design of enzymatic total synthesis. Therefore, the research of biocatalysis is gradually moving towards the era of novel technology integration, intelligent manufacturing and enzymatic total synthesis.

Engineering the protein dynamics of an ancestral luciferase

Protein dynamics are often invoked in explanations of enzyme catalysis, but their design has proven elusive. Here we track the role of dynamics in evolution, starting from the evolvable and thermostable ancestral protein AncHLD-RLuc which catalyses both dehalogenase and luciferase reactions. Insertion-deletion (InDel) backbone mutagenesis of AncHLD-RLuc challenged the scaffold dynamics. Screening for both activities reveals InDel mutations localized in three distinct regions that lead to altered protein dynamics (based on crystallographic B-factors, hydrogen exchange, and molecular dynamics simulations). An anisotropic network model highlights the importance of the conformational flexibility of a loop-helix fragment of Renilla luciferases for ligand binding. Transplantation of this dynamic fragment leads to lower product inhibition and highly stable glow-type bioluminescence. The success of our approach suggests that a strategy comprising (i) constructing a stable and evolvable template, (ii) mapping functional regions by backbone mutagenesis, and (iii) transplantation of dynamic features, can lead to functionally innovative proteins.

Ultrahigh throughput screening for enzyme function in droplets

Water-in-oil droplets, made and handled in microfluidic devices, provide a new experimental format, in which ultrahigh throughput experiments can be conducted faster and with minimal reagent consumption. An increasing number of studies have emerged that applied this approach to directed evolution and metagenomic screening of enzyme catalysts. Here, we review the considerations necessary to implement robust workflows, based on choices of device design, detection modes, emulsion formulations and substrates, and scope out which enzyme classes have become amenable to droplet screening.

Exploration of enzyme diversity: High-throughput techniques for protein production and microscale biochemical characterization

Enzymes are being increasingly utilized for acceleration of industrially and pharmaceutically critical chemical reactions. The strong demand for finding robust and efficient biocatalysts for these applications can be satisfied via the exploration of enzyme diversity. The first strategy is to mine the natural diversity, represented by millions of sequences available in the public genomic databases, by using computational approaches. Alternatively, metagenomic libraries can be targeted experimentally or computationally to explore the natural diversity of a specific environment. The second strategy, known as directed evolution, is to generate man-made diversity in the laboratory using gene mutagenesis and screen the constructed library of mutants. The selected hits must be experimentally characterized in both strategies, which currently represent the rate-limiting step in the process of diversity exploration. The traditional techniques used for biochemical characterization are time-demanding, cost, and sample volume ineffective, and low-throughput. Therefore, the development and implementation of high-throughput experimental methods are essential for discovering novel enzymes. This chapter describes the experimental protocols employing the combination of robust production and high-throughput microscale biochemical characterization of enzyme variants. We validated its applicability against the model enzyme family of haloalkane dehalogenases. These protocols can be adapted to other enzyme families, paving the way towards the functional characterization and quick identification of novel biocatalysts.