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.
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We thank all members of the Keller laboratory for extensive testing of the AutoPilot framework; Tzumin Lee (Janelia Research Campus) for deadpanEE–Gal4 Drosophila stocks; Misha Ahrens for Tg(elavl3:GCaMP6f) zebrafish stocks; Nicola Maghelli for help with illustrations; Martin Weigert for 3D rendering software; Vanessa de Oliveira Carlos for D. rerio graphics; and Misha Ahrens, Chen Wang, Katie McDole, Kaspar Podgorski, Na Ji, Eric Betzig and Iain Patten for helpful discussions and thoughtful comments on the manuscript. This work was supported by the Howard Hughes Medical Institute, the Max Planck Institute for Cell Biology and Genetics and the German Federal Ministry of Research and Education (BMBF) under the funding code 031A099.
P.J.K., R.K.C. and L.A.R. filed a provisional US patent application for adaptive light-sheet microscopy on 24 June 2016 (application number 62,354,384).
Supplementary Methods, Supplementary Figures 1–19 and Supplementary Tables 1–11 (PDF 16230 kb)
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Royer, L., Lemon, W., Chhetri, R. et al. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat Biotechnol 34, 1267–1278 (2016). https://doi.org/10.1038/nbt.3708
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