Myofibroblasts reduce angiogenesis and vasculogenesis in a vascularized microphysiological model of lung fibrosis.

Lung fibrosis, characterized by chronic and progressive scarring, has no cure. Hallmarks are the accumulation of myofibroblasts and extracellular matrix, as well as vascular remodeling. The crosstalk between myofibroblasts and vasculature is poorly understood, with conflicting reports on whether angiogenesis and vessel density are increased or decreased in lung fibrosis.

SARS-CoV-2 infection of human pluripotent stem cell-derived vascular cells reveals smooth muscle cells as key mediators of vascular pathology during infection.

Although respiratory symptoms are the most prevalent disease manifestation of infection by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), nearly 20% of hospitalized patients are at risk for thromboembolic events. This prothrombotic state is considered a key factor in the increased risk of stroke, which is observed clinically during both acute infection and long after symptoms clear. Here, we develop a model of SARS-CoV-2 infection using human-induced pluripotent stem cell-derived endothelial cells (ECs), pericytes (PCs), and smooth muscle cells (SMCs) to recapitulate the vascular pathology associated with SARS-CoV-2 exposure.

Deep and dynamic metabolic and structural imaging in living tissues.

Label-free imaging through two-photon autofluorescence of NAD(P)H allows for nondestructive, high-resolution visualization of cellular activities in living systems. However, its application to thick tissues has been restricted by its limited penetration depth within 300 μm, largely due to light scattering. Here, we demonstrate that the imaging depth for NAD(P)H can be extended to more than 700 μm in living engineered human multicellular microtissues by adopting multimode fiber-based, low repetition rate, high peak power, three-photon excitation of NAD(P)H at 1100 nm.

VONet: A deep learning network for 3D reconstruction of organoid structures with a minimal number of confocal images.

Organoids and 3D imaging techniques are crucial for studying human tissue structure and function, but traditional 3D reconstruction methods are expensive and time consuming, relying on complete z stack confocal microscopy data. This paper introduces VONet, a deep learning-based system for 3D organoid rendering that uses a fully convolutional neural network to reconstruct entire 3D structures from a minimal number of z stack images. VONet was trained on a library of over 39,000 virtual organoids (VOs) with diverse structural features and achieved an average intersection over union of 0.

miR126-mediated impaired vascular integrity in Rett syndrome.

Rett syndrome (RTT) is a neurodevelopmental disorder that is caused by mutations in melty-CpG binding protein 2 (MeCP2). MeCP2 is a non-cell type-specific DNA binding protein, and its mutation influences not only neural cells but also non-neural cells in the brain, including vasculature associated with endothelial cells. Vascular integrity is crucial for maintaining brain homeostasis, and its alteration may be linked to the pathology of neurodegenerative disease, but a non-neurogenic effect, especially the relationship between vascular alternation and Rett syndrome pathogenesis, has not been shown.