The molecular organization of differentially curved caveolae indicates bendable structural units at the plasma membrane

Caveolae are small coated plasma membrane invaginations with diverse functions. Caveolae undergo curvature changes. Yet, it is unclear which proteins regulate this process. To address this gap, we develop a correlative stimulated emission depletion (STED) fluorescence and platinum replica electron microscopy imaging (CLEM) method to image proteins at single caveolae. Caveolins and cavins are found at all caveolae, independent of curvature. EHD2 is detected at both low and highly curved caveolae. Pacsin2 associates with low curved caveolae and EHBP1 with mostly highly curved caveolae. Dynamin is absent from caveolae. Cells lacking dynamin show no substantial changes to caveolae, suggesting that dynamin is not directly involved in caveolae curvature. We propose a model where caveolins, cavins, and EHD2 assemble as a cohesive structural unit regulated by intermittent associations with pacsin2 and EHBP1. These coats can flatten and curve to enable lipid traffic, signaling, and changes to the surface area of the cell

Genome-edited human stem cells expressing fluorescently labeled endocytic markers allow quantitative analysis of clathrin-mediated endocytosis during differentiation.

We developed a general approach for investigation of how cellular processes become adapted for specific cell types during differentiation. Previous studies reported substantial differences in the morphology and dynamics of clathrin-mediated endocytosis (CME) sites. However, associating specific CME properties with distinct differentiated cell types and determining how these properties are developmentally specified during differentiation have been elusive. Using genome-edited human embryonic stem cells, and isogenic fibroblasts and neuronal progenitor cells derived from them, we established by live-cell imaging and platinum replica transmission electron microscopy that CME site dynamics and ultrastructure on the plasma membrane are precisely reprogrammed during differentiation. Expression levels for the endocytic adaptor protein AP2μ2 were found to underlie dramatic changes in CME dynamics and structure. Additionally, CME dependency on actin assembly and phosphoinositide-3 kinase activity are distinct for each cell type. Collectively, our results demonstrate that key CME properties are reprogrammed during differentiation at least in part through AP2μ2 expression regulation

Cytoplasmic Protein Mobility in Osmotically Stressed Escherichia coli▿ †

Facile diffusion of globular proteins within a cytoplasm that is dense with biopolymers is essential to normal cellular biochemical activity and growth. Remarkably, Escherichia coli grows in minimal medium over a wide range of external osmolalities (0.03 to 1.8 osmol). The mean cytoplasmic biopolymer volume fraction (〈φ〉) for such adapted cells ranges from 0.16 at 0.10 osmol to 0.36 at 1.45 osmol. For cells grown at 0.28 osmol, a similar 〈φ〉 range is obtained by plasmolysis (sudden osmotic upshift) using NaCl or sucrose as the external osmolyte, after which the only available cellular response is passive loss of cytoplasmic water. Here we measure the effective axial diffusion coefficient of green fluorescent protein (DGFP) in the cytoplasm of E. coli cells as a function of 〈φ〉 for both plasmolyzed and adapted cells. For plasmolyzed cells, the median DGFP (\documentclass[10pt]{article} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \begin{equation*}D_{GFP}^{m}\end{equation*}\end{document}) decreases by a factor of 70 as 〈φ〉 increases from 0.16 to 0.33. In sharp contrast, for adapted cells, \documentclass[10pt]{article} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{pmc} \usepackage[Euler]{upgreek} \pagestyle{empty} \oddsidemargin -1.0in \begin{document} \begin{equation*}D_{GFP}^{m}\end{equation*}\end{document} decreases only by a factor of 2.1 as 〈φ〉 increases from 0.16 to 0.36. Clearly, GFP diffusion is not determined by 〈φ〉 alone. By comparison with quantitative models, we show that the data cannot be explained by crowding theory. We suggest possible underlying causes of this surprising effect and further experiments that will help choose among competing hypotheses. Recovery of the ability of proteins to diffuse in the cytoplasm after plasmolysis may well be a key determinant of the time scale of the recovery of growth