Although ensemble experiments have suggested that mitotic arrest deficient protein 2 (Mad2), a metamorphic protein, has folding intermediates, direct evidence and characterization are not available. It remains an outstanding challenge to capture the folding intermediates in real time, which is crucial to elucidate the folding mechanism, but the folding intermediates are normally unstable and only exist transiently. By combining confocal-microscopy-based and total internal reflection fluorescence (TIRF)-microscopy-based single-molecule Forster resonance energy transfer (sm-FRET) techniques, we have investigated the folding/unfolding process of Mad2 and captured its folding intermediate at the single-molecule level. This provides direct evidence for the existence of an intermediate along the folding pathway of Mad2. The folding intermediate proved to be extraordinarily stable, with an extremely long average dwell time of 2.3 s under the conditions of 3 M GdmCl at ambient temperature. The folding trajectories obtained from TIRF experiments further suggest that the intermediate is on-pathway to native Mad2.
The blood–brain barrier (BBB) is a selective barrier that controls the transport between the blood and neural tissue features and maintains brain homeostasis to protect the central nervous system (CNS). In vitro models can be useful to understand the role of the BBB in disease and assess the effects of drug delivery. Recently, we reported a 3D BBB model with perfusable microvasculature in a Transwell insert. It replicates several key features of the native BBB, as it showed size-selective permeability of different molecular weights of dextran, activity of the P-glycoprotein efflux pump, and functionality of receptor-mediated transcytosis (RMT), which is the most investigated pathway for the transportation of macromolecules through endothelial cells of the BBB. For quality control and permeability evaluation in commercial use, visualization and quantification of the 3D vascular lumen structures is absolutely crucial. Here, for the first time, we report a rapid, non-invasive optical coherence tomography (OCT)-based approach to quantify the microvessel network in the 3D in vitro BBB model. Briefly, we successfully obtained the 3D OCT images of the BBB model and further processed the images using three strategies: morphological imaging processing (MIP), random forest machine learning using the Trainable Weka Segmentation plugin (RF-TWS), and deep learning using pix2pix cGAN. The performance of these methods was evaluated by comparing their output images with manually selected ground truth images. It suggested that deep learning performed well on object identification of OCT images and its computation results of vessel counts and surface areas were close to the ground truth results. This study not only facilitates the permeability evaluation of the BBB model but also offers a rapid, non-invasive observational and quantitative approach for the increasing number of other 3D in vitro models.
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