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Dynamic imaging of landmark organelles, such as nuclei, cell membrane, nuclear envelope, and lipid droplets enables image-based phenotyping of functional states of cells. Multispectral fluorescent imaging of landmark organelles requires labor-intensive labeling, limits throughput, and compromises cell health. Virtual staining of label-free images with deep neural networks is an emerging solution for this problem. Multiplexed imaging of cellular landmarks from scattered light and subsequent demultiplexing with virtual staining saves the light spectrum for imaging additional molecular reporters, photomanipulation, or other tasks. Published approaches for virtual staining of landmark organelles are fragile in the presence of nuisance variations in imaging, culture conditions, and cell types. This paper reports model training protocols for virtual staining of nuclei and membranes robust to cell types, cell states, and imaging parameters. We developed a flexible and scalable convolutional architecture, named UNeXt2, for supervised training and self-supervised pre-training. The strategies we report here enable robust virtual staining of nuclei and cell membranes in multiple cell types, including neuromasts of zebrafish, across a range of imaging conditions. We assess the models by comparing the intensity, segmentations, and application-specific measurements obtained from virtually stained and experimentally stained nuclei and membranes. The models rescue the missing label, non-uniform expression of labels, and photobleaching. We share three pre-trained models, named VSCyto3D, VSCyto2D, and VSNeuromast, as well as VisCy, a PyTorch-based pipeline for training, inference, and deployment that leverages the modern OME-Zarr format.
Vertebrate life depends on renal function to filter excess fluid and remove low-molecular-weight waste products. An essential component of the kidney filtration barrier is the slit diaphragm (SD), a specialized cell-cell junction between podocytes. Although the constituents of the SD are largely known, its molecular organization remains elusive. Here, we use super-resolution correlative light and electron microscopy to quantify a linear rate of reduction in albumin concentration across the filtration barrier under no-flow conditions. Next, we use cryo-electron tomography of vitreous lamellae from high-pressure frozen native glomeruli to analyze the molecular architecture of the SD. The resulting densities resemble a fishnet pattern. Fitting of Nephrin and Neph1, the main constituents of the SD, results in a complex interaction pattern with multiple contact sites between the molecules. Using molecular dynamics simulations, we construct a blueprint of the SD that explains its molecular architecture. Our architectural understanding of the SD reconciles previous findings and provides a mechanistic framework for the development of novel therapies to treat kidney dysfunction.
Vertebrate life depends on renal function to filter excess fluid and remove low-molecular-weight waste products. An essential component of the kidney filtration barrier is the slit diaphragm (SD), a specialized cell-cell junction between podocytes. Although the constituents of the SD are largely known, its molecular organization remains elusive. Here, we use super-resolution correlative light and electron microscopy to quantify a linear rate of reduction in albumin concentration across the filtration barrier. Next, we use cryo-electron tomography of vitreous lamellae from high-pressure frozen native glomeruli to analyze the molecular architecture of the SD. The resulting densities resemble a fishnet pattern. Fitting of Nephrin and Neph1, the main constituents of the SD, results in a complex interaction pattern with multiple contact sites between the molecules. Using molecular dynamics flexible fitting, we construct a blueprint of the SD, where we describe all interactions. Our architectural understanding of the SD reconciles previous findings and provides a mechanistic framework for the development of novel therapies to treat kidney dysfunction.
HER2 belongs to the ErbB sub-family of receptor tyrosine kinases and regulates cellular proliferation and growth. Different from other ErbB receptors, HER2 has no known ligand. Activation occurs through heterodimerization with other ErbB receptors and their cognate ligands. This suggests several possible activation paths of HER2 with ligand-specific, differential response, which so far remained unexplored. Using single-molecule tracking and the diffusion profile of HER2 as a proxy for activity, we measured the activation strength and temporal profile in live cells. We found that HER2 is strongly activated by EGFR-targeting ligands EGF and TGFα, yet with a distinguishable temporal fingerprint. The HER4-targeting ligands EREG and NRGβ1 showed weaker activation of HER2, a preference for EREG and a delayed response to NRGβ1. Our results indicate a selective ligand response of HER2 that may serve as a regulatory element. Our experimental approach is easily transferable to other membrane receptors targeted by multiple ligands.
Highlights
HER2 exhibits heterogeneous motion in the plasma membrane
The fraction of immobile HER2 correlates with phosphorylation levels
Diffusion properties serve as proxies for HER2 activation
HER2 exhibits ligand-specific activation strength and temporal profiles
HER2 belongs to the ErbB sub-family of receptor tyrosine kinases and regulates cellular proliferation and growth. Different from other ErbB receptors, HER2 has no known ligand. Activation occurs through heterodimerization with other ErbB receptors and their cognate ligands. This suggests several possible activation paths of HER2 with ligand-specific, differential response, which so far remained unexplored. Using single-molecule tracking and the diffusion profile of HER2 as a proxy for activity, we measured the activation strength and temporal profile in live cells. We found that HER2 is strongly activated by EGFR-targeting ligands EGF and TGFα, yet with a distinguishable temporal fingerprint. The HER4-targeting ligands EREG and NRGβ1 showed weaker activation of HER2, a preference for EREG, and a delayed response to NRGβ1. Our results indicate a selective ligand response of HER2 that may serve as a regulatory element. Our experimental approach is easily transferable to other membrane receptors targeted by multiple ligands.
DNA points accumulation for imaging in nanoscale topography (DNA-PAINT) is a super-resolution technique with relatively easy-to-implement multi-target imaging. However, image acquisition is slow as sufficient statistical data has to be generated from spatio-temporally isolated single emitters. Here, we trained the neural network (NN) DeepSTORM to predict fluorophore positions from high emitter density DNA-PAINT data. This achieves image acquisition in one minute. We demonstrate multi-color super-resolution imaging of structure-conserved semi-thin neuronal tissue and imaging of large samples. This improvement can be integrated into any single-molecule microscope and enables fast single-molecule super-resolution microscopy.