In Vivo Photocontrol of Microtubule Dynamics and Integrity, Migration and Mitosis, by the Potent GFP-Imaging-Compatible Photoswitchable Reagents SBTubA4P and SBTub2M.

Photoswitchable reagents are powerful tools for high-precision studies in cell biology. When these reagents are globally administered yet locally photoactivated in two-dimensional (2D) cell cultures, they can exert micron- and millisecond-scale biological control. This gives them great potential for use in biologically more relevant three-dimensional (3D) models and in vivo, particularly for studying systems with inherent spatiotemporal complexity, such as the cytoskeleton. However, due to a combination of photoswitch isomerization under typical imaging conditions, metabolic liabilities, and insufficient water solubility at effective concentrations, the in vivo potential of photoswitchable reagents addressing cytosolic protein targets remains largely unrealized. Here, we optimized the potency and solubility of metabolically stable, druglike colchicinoid microtubule inhibitors based on the styrylbenzothiazole (SBT) scaffold that are nonresponsive to typical fluorescent protein imaging wavelengths and so enable multichannel imaging studies. We applied these reagents both to 3D organoids and tissue explants and to classic model organisms (zebrafish, clawed frog) in one- and two-protein imaging experiments, in which spatiotemporally localized illuminations allowed them to photocontrol microtubule dynamics, network architecture, and microtubule-dependent processes in vivo with cellular precision and second-level resolution. These nanomolar, in vivo capable photoswitchable reagents should open up new dimensions for high-precision cytoskeleton research in cargo transport, cell motility, cell division, and development. More broadly, their design can also inspire similarly capable optical reagents for a range of cytosolic protein targets, thus bringing in vivo photopharmacology one step closer to general realization.

A MEC-2/stomatin condensate liquid-to-solid phase transition controls neuronal mechanotransduction during touch sensing.

A growing body of work suggests that the material properties of biomolecular condensates ensuing from liquid-liquid phase separation change with time. How this aging process is controlled and whether the condensates with distinct material properties can have different biological functions is currently unknown. Using Caenorhabditis elegans as a model, we show that MEC-2/stomatin undergoes a rigidity phase transition from fluid-like to solid-like condensates that facilitate transport and mechanotransduction, respectively. This switch is triggered by the interaction between the SH3 domain of UNC-89 (titin/obscurin) and MEC-2. We suggest that this rigidity phase transition has a physiological role in frequency-dependent force transmission in mechanosensitive neurons during body wall touch. Our data demonstrate a function for the liquid and solid phases of MEC-2/stomatin condensates in facilitating transport or mechanotransduction, and a previously unidentified role for titin homologues in neurons.

Automatic image annotation for fluorescent cell nuclei segmentation.

Dataset annotation is a time and labor-intensive task and an integral requirement for training and testing deep learning models. The segmentation of images in life science microscopy requires annotated image datasets for object detection tasks such as instance segmentation. Although the amount of annotated image data has been steadily reduced due to methods such as data augmentation, the process of manual or semi-automated data annotation is the most labor and cost intensive task in the process of cell nuclei segmentation with deep neural networks. In this work we propose a system to fully automate the annotation process of a custom fluorescent cell nuclei image dataset. By that we are able to reduce nuclei labelling time by up to 99.5%. The output of our system provides high quality training data for machine learning applications to identify the position of cell nuclei in microscopy images. Our experiments have shown that the automatically annotated dataset provides coequal segmentation performance compared to manual data annotation. In addition, we show that our system enables a single workflow from raw data input to desired nuclei segmentation and tracking results without relying on pre-trained models or third-party training datasets for neural networks.