意大利佛罗伦萨大学L. Silvestri等研究人员开发出用于小鼠全脑定量体积显微镜的通用自动对焦系统。相关论文于2021年7月26日在线发表在《自然—方法学》杂志上。

研究人员报道了RAPID（rapid autofocusing via pupil-split image phase detection），一种适用于每一个基于宽视场显微镜的实时自动对焦方法。支持RAPID的光片显微镜以亚细胞分辨率可靠地重建了完整的、已透明化的小鼠大脑，并使研究人员能够描述整个脑内生长抑素阳性神经元的三维（3D）空间集群的特征，包括密集标记的区域。

此外，它还能对整个大脑中的小胶质细胞进行三维形态学分析。除了光片显微镜，研究人员还证明了RAPID在各种环境下都能保持较高的图像质量，从体内荧光成像到快速移动的生物体的三维跟踪。因此，RAPID提供了一个灵活的自动对焦解决方案，适用于传统的自动显微镜任务，以及大型生物标本的定量分析。

据了解，对宏观生物样本进行无偏见的定量分析需要快速的成像系统，能够在大体积中保持高分辨率。

附：英文原文

Title: Universal autofocus for quantitative volumetric microscopy of whole mouse brains

Author: L. Silvestri, M. C. Mllenbroich, I. Costantini, A. P. Di Giovanna, G. Mazzamuto, A. Franceschini, D. Kutra, A. Kreshuk, C. Checcucci, L. O. Toresano, P. Frasconi, L. Sacconi, F. S. Pavone

Issue&Volume: 2021-07-26

Abstract: Unbiased quantitative analysis of macroscopic biological samples demands fast imaging systems capable of maintaining high resolution across large volumes. Here we introduce RAPID (rapid autofocusing via pupil-split image phase detection), a real-time autofocus method applicable in every widefield-based microscope. RAPID-enabled light-sheet microscopy reliably reconstructs intact, cleared mouse brains with subcellular resolution, and allowed us to characterize the three-dimensional (3D) spatial clustering of somatostatin-positive neurons in the whole encephalon, including densely labeled areas. Furthermore, it enabled 3D morphological analysis of microglia across the entire brain. Beyond light-sheet microscopy, we demonstrate that RAPID maintains high image quality in various settings, from in vivo fluorescence imaging to 3D tracking of fast-moving organisms. RAPID thus provides a flexible autofocus solution that is suitable for traditional automated microscopy tasks as well as for quantitative analysis of large biological specimens. RAPID (rapid autofocusing via pupil-split image phase detection) is a sample-agnostic real-time autofocus method for widefield microscopy. RAPID removes most image degradation in large, cleared samples for enhanced quantitative analyses.

DOI: 10.1038/s41592-021-01208-1

Source: https://www.nature.com/articles/s41592-021-01208-1