Methyl halide transferase-based gas reporters for quantification of filamentous bacteria in microdroplet emulsions

The application of microfluidic techniques in experimental and environmental studies is a rapidly emerging field. Water-in-oil microdroplets can serve readily as controllable micro-vessels for studies that require spatial structure. In many applications, it is useful to monitor cell growth without breaking or disrupting the microdroplets. To this end, optical reporters based on color, fluorescence, or luminescence have been developed. However, optical reporters suffer from limitations when used in microdroplets such as inaccurate readings due to strong background interference or limited sensitivity during early growth stages. In addition, optical detection is typically not amenable to filamentous or biofilm-producing organisms that have significant nonlinear changes in opacity and light scattering during growth. To overcome such limitations, we show that volatile methyl halide gases produced by reporter cells expressing a methyl halide transferase (MHT) can serve as an alternative nonoptical detection approach suitable for microdroplets. In this study, an MHT-labeled Streptomyces venezuelae reporter strain was constructed and characterized. Protocols were established for the encapsulation and incubation of S. venezuelae in microdroplets. We observed the complete life cycle for S. venezuelae including the vegetative expansion of mycelia, mycelial fragmentation, and late-stage sporulation. Methyl bromide (MeBr) production was detected by gas chromatography-mass spectrometry (GC-MS) from S. venezuelae gas reporters incubated in either liquid suspension or microdroplets and used to quantitatively estimate bacterial density. Overall, using MeBr production as a means of quantifying bacterial growth provided a 100- to 1,000-fold increase in sensitivity over optical or fluorescence measurements of a comparable reporter strain expressing fluorescent proteins. IMPORTANCE Quantitative measurement of bacterial growth in microdroplets in situ is desirable but challenging. Current optical reporter systems suffer from limitations when applied to filamentous or biofilm-producing organisms. In this study, we demonstrate that volatile methyl halide gas production can serve as a quantitative nonoptical growth assay for filamentous bacteria encapsulated in microdroplets. We constructed an S. venezuelae gas reporter strain and observed a complete life cycle for encapsulated S. venezuelae in microdroplets, establishing microdroplets as an alternative growth environment for Streptomyces spp. that can provide spatial structure. We detected MeBr production from both liquid suspension and microdroplets with a 100- to 1,000-fold increase in signal-to-noise ratio compared to optical assays. Importantly, we could reliably detect bacteria with densities down to 106 CFU/mL. The combination of quantitative gas reporting and microdroplet systems provides a valuable approach to studying fastidious organisms that require spatial structure such as those found typically in soils.

Double Emulsion Flow Cytometry for Rapid Single Genome Detection

Established techniques in droplet microfluidics have utilized single emulsion (SE) drops to compartmentalize and analyze single cells achieving high-throughput, low input analysis. Building upon this foundation, double emulsion (DE) droplet microfluidics has emerged with distinct advantages in terms of stable compartmentalization, resistance to merging, and most importantly direct compatibility with flow cytometry. In this chapter, we describe a simple-to-fabricate, single-layer DE drop generation device that achieves spatial control over surface wetting with a plasma treatment step. This easy-to-operate device allows for the robust production of single-core DEs with excellent control over the monodispersity. We further explain the use of these DE drops for single-molecule and single-cell assays. Detailed protocols are described to perform single molecule detection using droplet digital PCR in DE drops and automated detection of DE drops on a fluorescence-activated cell sorter (FACS). Due to the wide availability of FACS instruments, DE methods can facilitate the broader adoption of drop-based screening. As the applications of FACS-compatible DE droplets are immensely varied and extend well beyond what can be explored here, this chapter should be seen as an introduction to DE microfluidics.

Ultrahigh-Throughput Screening of an Artificial Metalloenzyme using Double Emulsions

The potential for ultrahigh-throughput compartmentalization renders droplet microfluidics an attractive tool for the directed evolution of enzymes. Importantly, it ensures maintenance of the phenotype-genotype linkage, enabling reliable identification of improved mutants. Herein, we report an approach for ultrahigh-throughput screening of an artificial metalloenzyme in double emulsion droplets (DEs) using commercially available fluorescence-activated cell sorters (FACS). This protocol was validated by screening a 400 double-mutant streptavidin library for ruthenium-catalyzed deallylation of an alloc-protected aminocoumarin. The most active variants, identified by next-generation sequencing, were in good agreement with hits obtained using a 96-well plate procedure. These findings pave the way for the systematic implementation of FACS for the directed evolution of (artificial) enzymes and will significantly expand the accessibility of ultrahigh-throughput DE screening protocols.

Spatial confinement toward creating artificial living systems

Lifeforms are regulated by many physicochemical factors, and these factors could be controlled to play a role in the construction of artificial living systems. Among these factors, spatial confinement is an important one, which mediates biological behaviors at multiscale levels and participates in the biomanufacturing processes accordingly. This review describes how spatial confinement, as a fundamental biological phenomenon, provides cues for the construction of artificial living systems. Current knowledge about the role of spatial confinement in mediating individual cell behavior, collective cellular behavior, and tissue-level behavior are categorized. Endeavors on the synthesis of biomacromolecules, artificial cells, engineered tissues, and organoids in spatially confined bioreactors are then emphasized. After that, we discuss the cutting-edge applications of spatially confined artificial living systems in biomedical fields. Finally, we conclude by assessing the remaining challenges and future trends in the context of fundamental science, technical improvement, and practical applications.

Learning Strategies in Protein Directed Evolution

Synthetic biology is a fast-evolving research field that combines biology and engineering principles to develop new biological systems for medical, pharmacological, and industrial applications. Synthetic biologists use iterative "design, build, test, and learn" cycles to efficiently engineer genetic systems that are reliable, reproducible, and predictable. Protein engineering by directed evolution can benefit from such a systematic engineering approach for various reasons. Learning can be carried out before starting, throughout or after finalizing a directed evolution project. Computational tools, bioinformatics, and scanning mutagenesis methods can be excellent starting points, while molecular dynamics simulations and other strategies can guide engineering efforts. Similarly, studying protein intermediates along evolutionary pathways offers fascinating insights into the molecular mechanisms shaped by evolution. The learning step of the cycle is not only crucial for proteins or enzymes that are not suitable for high-throughput screening or selection systems, but it is also valuable for any platform that can generate a large amount of data that can be aided by machine learning algorithms. The main challenge in protein engineering is to predict the effect of a single mutation on one functional parameter-to say nothing of several mutations on multiple parameters. This is largely due to nonadditive mutational interactions, known as epistatic effects-beneficial mutations present in a genetic background may not be beneficial in another genetic background. In this work, we provide an overview of experimental and computational strategies that can guide the user to learn protein function at different stages in a directed evolution project. We also discuss how epistatic effects can influence the success of directed evolution projects. Since machine learning is gaining momentum in protein engineering and the field is becoming more interdisciplinary thanks to collaboration between mathematicians, computational scientists, engineers, molecular biologists, and chemists, we provide a general workflow that familiarizes nonexperts with the basic concepts, dataset requirements, learning approaches, model capabilities and performance metrics of this intriguing area. Finally, we also provide some practical recommendations on how machine learning can harness epistatic effects for engineering proteins in an "outside-the-box" way.

Two-Phase Biocatalysis in Microfluidic Droplets

This Perspective discusses the literature related to two-phase biocatalysis in microfluidic droplets. Enzymes used as catalysts in biocatalysis are generally less stable in organic media than in their native aqueous environments; however, chemical and pharmaceutical compounds are often insoluble in water. The use of aqueous/organic two-phase media provides a solution to this problem and has therefore become standard practice for multiple biotransformations. In batch, two-phase biocatalysis is limited by mass transport, a limitation that can be overcome with the use of microfluidic systems. Although, two-phase biocatalysis in laminar flow systems has been extensively studied, microfluidic droplets have been primarily used for enzyme screening. In this Perspective, we summarize the limited published work on two-phase biocatalysis in microfluidic droplets and discuss the limitations, challenges, and future perspectives of this technology.