Engineered tissues can be used to understand fundamental features of biology, develop organotypic in vitro model systems, and as engineered tissue constructs for replacing damaged tissue in vivo. However, a key limitation is an inability to test the wide range of parameters that might impact the engineered tissue in a high-throughput manner and in an environment that mimics the three-dimensional (3D) native architecture. We developed a microfabricated platform to generate arrays of microtissues embedded within 3D micropatterned matrices. Microcantilevers simultaneously constrain microtissue formation and report forces generated by the microtissues in real time, opening the possibility to use high-throughput, low-volume screening for studies on engineered tissues. Thanks to the micrometer scale of the microtissues, this platform is also suitable for high-throughput monitoring of drug-induced effect on architecture and contractility in engineered tissues. Moreover, independent variations of the mechanical stiffness of the cantilevers and collagen matrix allow the measurement and manipulation of the mechanics of the microtissues. Thus, our approach will likely provide valuable opportunities to elucidate how biomechanical, electrical, biochemical, and genetic/epigenetic cues modulate the formation and maturation of 3D engineered tissues. In this chapter, we describe the microfabrication, preparation, and experimental use of such microfabricated tissue gauges.

MicroRNAs (miRNAs) are small, 18-22 nt long, noncoding RNAs that act as potent negative gene regulators in a variety of physiological and pathological processes. To repress gene expression, miRNAs are packaged into RNA-induced silencing complexes (RISCs) that target mRNAs for degradation and/or translational repression in a sequence-specific manner. Recently, miRNAs have been shown to also interact with proteins outside RISCs, impacting cellular processes through mechanisms not involving gene silencing. Here, we define a previously unappreciated activity of miRNAs in inhibiting RNA-protein interactions that in the context of HIV-1 biology blocks HIV virus budding and reduces virus infectivity. This occurs by miRNA binding to the nucleocapsid domain of the Gag protein, the main structural component of HIV-1 virions. The resulting miRNA-Gag complexes interfere with viral-RNA-mediated Gag assembly and viral budding at the plasma membrane, with imperfectly assembled Gag complexes endocytosed and delivered to lysosomes. The blockade of virus production by miRNA is reversed by adding the miRNA's target mRNA and stimulated by depleting Argonaute-2, suggesting that when miRNAs are not mediating gene silencing, they can block HIV-1 production through disruption of Gag assembly on membranes. Overall, our findings have significant implications for understanding how cells modulate HIV-1 infection by miRNA expression and raise the possibility that miRNAs can function to disrupt RNA-mediated protein assembly processes in other cellular contexts.

Nine human neurodegenerative diseases are due to expansion of a CAG repeat- encoding glutamine within the open reading frame of the respective genes. Polyglutamine (polyQ) expansion confers dominant toxicity, resulting in neuronal degeneration. MicroRNAs (miRNAs) have been shown to modulate programmed cell death during development. To address whether miRNA pathways play a role in neurodegeneration, we tested whether genes critical for miRNA processing modulated toxicity induced by the spinocerebellar ataxia type 3 (SCA3) protein. These studies revealed a striking enhancement of polyQ toxicity upon reduction of miRNA processing in Drosophila and human cells. In parallel genetic screens, we identified the miRNA bantam (ban) as a potent modulator of both polyQ and tau toxicity in flies. Our studies suggest that ban functions downstream of toxicity of the SCA3 protein, to prevent degeneration. These findings indicate that miRNA pathways dramatically modulate polyQ- and tau-induced neurodegeneration, providing the foundation for new insight into therapeutics.

Fluorescence microscopy, a key driver for progress in the life sciences, faces limitations due to the microscope’s optics, fluorophore chemistry, and photon exposure limits, necessitating trade-offs in imaging speed, resolution, and depth. Here, we introduce MicroSplit, a computational multiplexing technique based on deep learning that allows multiple cellular structures to be imaged in a single fluorescent channel and then unmixed computationally, allowing faster imaging and reduced photon exposure. We show that MicroSplit efficiently separates up to four superimposed noisy structures into distinct denoised fluorescent image channels. Furthermore, using Variational Splitting Encoder-Decoder (VSE) networks, our approach can sample diverse predictions from a trained posterior of solutions. The diversity of these samples scales with the uncertainty in a given input, allowing us to estimate the true prediction errors by computing the variability between posterior samples. We demonstrate the robustness of MicroSplit across various datasets and noise levels and show its utility to image more, image faster, and improve downstream analysis. We provide MicroSplit along with all associated training and evaluation datasets as open resources, enabling life scientists to benefit from the potential of computational multiplexing and accelerate the pace of scientific discovery.

We present a method for microtubule tracking in electron microscopy volumes. Our method first identifies a sparse set of voxels that likely belong to microtubules. Similar to prior work, we then enumerate potential edges between these voxels, which we represent in a candidate graph. Tracks of microtubules are found by selecting nodes and edges in the candidate graph by solving a constrained optimization problem incorporating biological priors on microtubule structure. For this, we present a novel integer linear programming formulation, which results in speed-ups of three orders of magnitude and an increase of 53% in accuracy compared to prior art (evaluated on three 1 . 2 × 4 × 4µm volumes of Drosophila neural tissue). We also propose a scheme to solve the optimization problem in a block-wise fashion, which allows distributed tracking and is necessary to process very large electron microscopy volumes. Finally, we release a benchmark dataset for microtubule tracking, here used for training, testing and validation, consisting of eight 30 x 1000 x 1000 voxel blocks (1 . 2 × 4 × 4µm) of densely annotated microtubules in the CREMI data set (https://github.com/nilsec/micron).

We studied two aspects of vimentin intermediate filament dynamics-transport of filaments and subunit exchange. We observed transport of long filaments in the periphery of cells using live-cell structured illumination microscopy. We studied filament transport elsewhere in cells using a photoconvertible-vimentin probe and total internal reflection microscopy. We found that filaments were rapidly transported along linear tracks in both anterograde and retrograde directions. Filament transport was microtubule dependent but independent of microtubule polymerization and/or an interaction with the plus end-binding protein APC. We also studied subunit exchange in filaments by long-term imaging after photoconversion. We found that converted vimentin remained in small clusters along the length of filaments rather than redistributing uniformly throughout the network, even in cells that divided after photoconversion. These data show that vimentin filaments do not depolymerize into individual subunits; they recompose by severing and reannealing. Together these results show that vimentin filaments are very dynamic and that their transport is required for network maintenance.

Using imidazole as promotion agent, primary, secondary and phenolic alcohol compounds were esterified with aliphatic and aromatic carboxylic acid anhydrides. Heating a ternary mixture of alcohol, anhydride and imidazole in an unmodified microwave oven produced esters in low to high yields, depending on the steric bulk of the alcohol.

Tethered midbody remnants dancing across apical microvilli, encountering the centrosome, and beckoning forth a cilium-who would have guessed this is how polarized epithelial cells coordinate the end of mitosis and the beginning of ciliogenesis? New evidence from Bernabé-Rubio et al. (2016. J. Cell Biol http://dx.doi.org/10.1083/jcb.201601020) supports this emerging model.

Although nervous system sexual dimorphisms are known in many species, relatively little is understood about the molecular mechanisms generating these dimorphisms. Recent findings in Drosophila provide the tools for dissecting how neurogenesis and neuronal differentiation are modulated by the Drosophila sex-determination regulatory genes to produce nervous system sexual dimorphisms. Here we report studies aimed at illuminating the basis of the sexual dimorphic axonal projection patterns of foreleg gustatory receptor neurons (GRNs): only in males do GRN axons project across the midline of the ventral nerve cord. We show that the sex determination genes fruitless (fru) and doublesex (dsx) both contribute to establishing this sexual dimorphism. Male-specific Fru (Fru(M)) acts in foreleg GRNs to promote midline crossing by their axons, whereas midline crossing is repressed in females by female-specific Dsx (Dsx(F)). In addition, midline crossing by these neurons might be promoted in males by male-specific Dsx (Dsx(M)). Finally, we (1) demonstrate that the roundabout (robo) paralogs also regulate midline crossing by these neurons, and (2) provide evidence that Fru(M) exerts its effect on midline crossing by directly or indirectly regulating Robo signaling.

Since the demonstration that the sequence of a protein encodes its structure, the prediction of structure from sequence remains an outstanding problem that impacts numerous scientific disciplines, including many genome projects. By iteratively fixing secondary structure assignments of residues during Monte Carlo simulations of folding, our coarse-grained model without information concerning homology or explicit side chains can outperform current homology-based secondary structure prediction methods for many proteins. The computationally rapid algorithm using only single (phi,psi) dihedral angle moves also generates tertiary structures of accuracy comparable with existing all-atom methods for many small proteins, particularly those with low homology. Hence, given appropriate search strategies and scoring functions, reduced representations can be used for accurately predicting secondary structure and providing 3D structures, thereby increasing the size of proteins approachable by homology-free methods and the accuracy of template methods that depend on a high-quality input secondary structure.