Split Luciferase Molecular Tension Sensors for Bioluminescent Readout of Mechanical Forces in Biological Systems.

The ability of proteins to sense and transmit mechanical forces underlies many biological processes, but characterizing these forces in biological systems remains a challenge. Existing genetically encoded force sensors typically rely on fluorescence or bioluminescence resonance energy transfer (FRET or BRET) to visualize tension. However, these force sensing modules are relatively large, and interpreting measurements requires specialized image analysis and careful control experiments. Here, we report a compact molecular tension sensor that generates a bioluminescent signal in response to tension. This sensor (termed PILATeS) makes use of the split NanoLuc luciferase and consists of the H. sapiens titin I10 domain with the insertion of a 10-15 amino acid tag derived from the C-terminal ß-strand of NanoLuc. Mechanical load across PILATeS mediates exposure of this tag to recruit the complementary split NanoLuc fragment, resulting in force-dependent bioluminescence. We demonstrate the ability of PILATeS to report biologically meaningful forces by visualizing forces at the interface between integrins and extracellular matrix substrates. We further use PILATeS as a genetically encoded sensor of tension experienced by the mechanosensing protein vinculin. We anticipate that PILATeS will provide an accessible means of visualizing molecular-scale forces in biological systems.

Lumen expansion is initially driven by apical actin polymerization followed by osmotic pressure in a human epiblast model.

Post-implantation, the pluripotent epiblast in a human embryo forms a central lumen, paving the way for gastrulation. Osmotic pressure gradients are considered the drivers of lumen expansion across development, but their role in human epiblasts is unknown. Here, we study lumenogenesis in a pluripotent-stem-cell-based epiblast model using engineered hydrogels. We find that leaky junctions prevent osmotic pressure gradients in early epiblasts and, instead, forces from apical actin polymerization drive lumen expansion. Once the lumen reaches a radius of ∼12 µm, tight junctions mature, and osmotic pressure gradients develop to drive further growth. Computational modeling indicates that apical actin polymerization into a stiff network mediates initial lumen expansion and predicts a transition to pressure-driven growth in larger epiblasts to avoid buckling. Human epiblasts show transcriptional signatures consistent with these mechanisms. Thus, actin polymerization drives lumen expansion in the human epiblast and may serve as a general mechanism of early lumenogenesis.

Content-aware frame interpolation (CAFI): deep learning-based temporal super-resolution for fast bioimaging.

The development of high-resolution microscopes has made it possible to investigate cellular processes in 3D and over time. However, observing fast cellular dynamics remains challenging because of photobleaching and phototoxicity. Here we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo and Depth-Aware Video Frame Interpolation, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series post-acquisition. We show that CAFI is capable of understanding the motion context of biological structures and can perform better than standard interpolation methods. We benchmark CAFI's performance on 12 different datasets, obtained from four different microscopy modalities, and demonstrate its capabilities for single-particle tracking and nuclear segmentation. CAFI potentially allows for reduced light exposure and phototoxicity on the sample for improved long-term live-cell imaging. The models and the training and testing data are available via the ZeroCostDL4Mic platform.