École Polytechnique Fédérale de Lausanne, Laboratory of Nanoscale Biology, Lausanne, Switzerland.
Delft University of Technology, Grußmayer Lab, Department of Bionanoscience, Kavli Institute of Nanoscience, Delft, The Netherlands.
Commun Biol. 2021 May 11;4(1):550. doi: 10.1038/s42003-021-02086-1.
Localization microscopy is a super-resolution imaging technique that relies on the spatial and temporal separation of blinking fluorescent emitters. These blinking events can be individually localized with a precision significantly smaller than the classical diffraction limit. This sub-diffraction localization precision is theoretically bounded by the number of photons emitted per molecule and by the sensor noise. These parameters can be estimated from the raw images. Alternatively, the resolution can be estimated from a rendered image of the localizations. Here, we show how the rendering of localization datasets can influence the resolution estimation based on decorrelation analysis. We demonstrate that a modified histogram rendering, termed bilinear histogram, circumvents the biases introduced by Gaussian or standard histogram rendering. We propose a parameter-free processing pipeline and show that the resolution estimation becomes a function of the localization density and the localization precision, on both simulated and state-of-the-art experimental datasets.
定位显微镜是一种超分辨率成像技术,依赖于闪烁荧光发射器的空间和时间分离。这些闪烁事件可以通过单个定位来实现,其精度明显小于经典的衍射极限。这种亚衍射定位精度在理论上受到每个分子发射的光子数和传感器噪声的限制。这些参数可以从原始图像中估计。或者,可以从定位的渲染图像中估计分辨率。在这里,我们展示了定位数据集的渲染如何影响基于去相关分析的分辨率估计。我们证明了一种称为双线性直方图的修改后的直方图渲染可以避免高斯或标准直方图渲染引入的偏差。我们提出了一个无参数处理管道,并表明在模拟和最先进的实验数据集上,分辨率估计成为定位密度和定位精度的函数。