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PSF decomposition of nanoscopy images via Bayesian analysis unravels distinct molecular organization of the cell membrane

机译:通过贝叶斯分析的纳米显微镜图像的PSF分解揭示了细胞膜的独特分子结构

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摘要

The spatial organization of membrane receptors at the nanoscale has major implications in cellular function and signaling. The advent of super-resolution techniques has greatly contributed to our understanding of the cellular membrane. Yet, despite the increased resolution, unbiased quantification of highly dense features, such as molecular aggregates, remains challenging. Here we describe an algorithm based on Bayesian inference of the marker intensity distribution that improves the determination of molecular positions inside dense nanometer-scale molecular aggregates. We tested the performance of the method on synthetic images representing a broad range of experimental conditions, demonstrating its wide applicability. We further applied this approach to STED images of GPI-anchored and model transmembrane proteins expressed in mammalian cells. The analysis revealed subtle differences in the organization of these receptors, emphasizing the role of cortical actin in the compartmentalization of the cell membrane.
机译:纳米级的膜受体的空间组织对细胞功能和信号传导具有重要意义。超分辨率技术的出现极大地促进了我们对细胞膜的理解。然而,尽管分辨率提高了,但对高密度特征(例如分子聚集体)的无偏量化仍然具有挑战性。在这里,我们描述了一种基于标记强度分布的贝叶斯推断的算法,该算法改进了在密集的纳米级分子聚集体内分子位置的确定。我们在代表广泛实验条件的合成图像上测试了该方法的性能,证明了其广泛的适用性。我们进一步将该方法应用于在哺乳动物细胞中表达的GPI锚定和模型化跨膜蛋白的STED图像。分析显示这些受体的组织之间存在细微的差异,强调了皮质肌动蛋白在细胞膜分隔中的作用。

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