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Our understanding of in situ microbial physiology is primarily based on physiological characterization of fast-growing and readily-isolatable microbes. Microbial enrichments to obtain novel isolates with slower growth rates or physiologies adapted to low nutrient environments are plagued by intrinsic biases for fastest-growing species when using standard laboratory isolation protocols. New cultivation tools to minimize these biases and enrich for less well-studied taxa are needed. In this study, we developed a high-throughput bacterial enrichment platform based on single cell encapsulation and growth within double emulsions (GrowMiDE). We showed that GrowMiDE can cultivate many different microorganisms and enrich for underrepresented taxa that are never observed in traditional batch enrichments. For example, preventing dominance of the enrichment by fast-growing microbes due to nutrient privatization within the double emulsion droplets allowed cultivation of slower-growing Negativicutes and Methanobacteria from stool samples in rich media enrichment cultures. In competition experiments between growth rate and growth yield specialist strains, GrowMiDE enrichments prevented competition for shared nutrient pools and enriched for slower-growing but more efficient strains. Finally, we demonstrated the compatibility of GrowMiDE with commercial fluorescence-activated cell sorting (FACS) to obtain isolates from GrowMiDE enrichments. Together, GrowMiDE + DE-FACS is a promising new high-throughput enrichment platform that can be easily applied to diverse microbial enrichments or screens.

 

Short tandem repeats (STRs) are enriched in eukaryoticcis-regulatory elements and alter gene expression, yet how they regulate transcription remains unknown. We found that STRs modulate transcription factor (TF)–DNA affinities and apparent on-rates by about 70-fold by directly binding TF DNA-binding domains, with energetic impacts exceeding many consensus motif mutations. STRs maximize the number of weakly preferred microstates near target sites, thereby increasing TF density, with impacts well predicted by statistical mechanics. Confirming that STRs also affect TF binding in cells, neural networks trained only on in vivo occupancies predicted effects identical to those observed in vitro. Approximately 90% of TFs preferentially bound STRs that need not resemble known motifs, providing a cis-regulatory mechanism to target TFs to genomic sites. 

Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations. 

Coral reefs, and their associated diverse ecosystems, are of enormous ecological importance. In recent years, coral health has been severely impacted by environmental stressors brought on by human activity and climate change, threatening the extinction of several major reef ecosystems. Reef damage is mediated by a process called ‘coral bleaching’ where corals, sea anemones, and other cnidarians lose their photosynthetic algal symbionts (family Symbiodiniaceae) upon stress induction, resulting in drastically decreased host energy harvest and, ultimately, coral death. The mechanism by which this critical cnidarian-algal symbiosis is lost remains poorly understood. The larvae of the sea anemone,Exaiptasia pallida(commonly referred to as ‘Aiptasia’) are an attractive model organism to study this process, but they are large (∼100 mm in length, ∼75 mm in diameter), deformable, and highly motile, complicating long-term imaging and limiting study of this critical endosymbiotic relationship in live organisms. Here, we report ‘Traptasia’, a simple microfluidic device with multiple traps designed to isolate and image individual, live larvae of Aiptasia and their algal symbionts over extended time courses. Using a trap design parameterized via fluid flow simulations and polymer bead loading tests, we trapped Aiptasia larvae containing algal symbionts and demonstrated stable imaging for >10 hours. We visualized algae within Aiptasia larvae and observed algal expulsion under an environmental stressor. To our knowledge, this device is the first to enable time-lapsed, high-throughput live imaging of cnidarian larvae and their algal symbionts and, in further implementation, could provide important insights into the cellular mechanisms of cnidarian bleaching under different environmental stressors. The ‘Traptasia’ device is simple to use, requires minimal external equipment and no specialized training to operate, and can easily be adapted using the trap optimization data presented here to study a variety of large, motile organisms.