Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA.
Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany.
Nat Biotechnol. 2016 Dec;34(12):1267-1278. doi: 10.1038/nbt.3708. Epub 2016 Oct 31.
Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.
在光片显微镜中获得最佳的图像质量需要照明光片与检测物镜的焦平面之间实现完美的重叠。然而，由于活标本的时空变化的光学特性，光片和检测平面之间的不匹配是很常见的。在这里，我们提出了 AutoPilot 框架，这是一种用于时空自适应成像的自动化方法，它集成了（i）一个多视角光片显微镜，能够在三维空间中数字平移和旋转光片和检测平面，以及（ii）一种计算方法，能够实时跨整个标本体积连续优化空间分辨率。我们展示了整个发育中的斑马鱼（Danio rerio）和黑腹果蝇（Drosophila melanogaster）胚胎的长期自适应成像，并在斑马鱼幼虫中进行了自适应全脑功能成像。我们的方法将空间分辨率和信号强度提高了两到五倍，恢复了许多非自适应成像无法分辨的区域的细胞和亚细胞结构，适应了遗传编码荧光标记物的时空动态，并在生物体的大规模形态发生变化期间稳健地优化了成像性能。
