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从单分子定位中提取纳米级膜形态。

Extracting nanoscale membrane morphology from single-molecule localizations.

机构信息

Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand; Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut; Department of Biomedical Engineering, Yale University, New Haven, Connecticut.

Department of Cell Biology, Yale University School of Medicine, New Haven, Connecticut.

出版信息

Biophys J. 2023 Aug 8;122(15):3022-3030. doi: 10.1016/j.bpj.2023.06.010. Epub 2023 Jun 23.

Abstract

Membrane surface reconstruction at the nanometer scale is required for understanding mechanisms of subcellular shape change. This historically has been the domain of electron microscopy, but extraction of surfaces from specific labels is a difficult task in this imaging modality. Existing methods for extracting surfaces from fluorescence microscopy have poor resolution or require high-quality super-resolution data that are manually cleaned and curated. Here, we present NanoWrap, a new method for extracting surfaces from generalized single-molecule localization microscopy data. This makes it possible to study the shape of specifically labeled membranous structures inside cells. We validate NanoWrap using simulations and demonstrate its reconstruction capabilities on single-molecule localization microscopy data of the endoplasmic reticulum and mitochondria. NanoWrap is implemented in the open-source Python Microscopy Environment.

摘要

为了理解亚细胞形状变化的机制,需要在纳米尺度上重建膜表面。这在历史上一直是电子显微镜的领域,但在这种成像模式中,从特定标记物中提取表面是一项困难的任务。从荧光显微镜中提取表面的现有方法分辨率较差,或者需要高质量的超分辨率数据,这些数据需要手动清理和整理。在这里,我们提出了 NanoWrap,这是一种从广义单分子定位显微镜数据中提取表面的新方法。这使得研究细胞内特定标记的膜状结构的形状成为可能。我们使用模拟验证了 NanoWrap,并展示了它在内质网和线粒体单分子定位显微镜数据上的重建能力。NanoWrap 是在开源 Python 显微镜环境中实现的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8548/10432223/bf6d38b296c3/gr1.jpg

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