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Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms

Nature Biotechnology volume 34, pages 1267–1278 (2016)Cite this article

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Abstract

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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Figure 1: Spatiotemporally adaptive light-sheet microscopy.
Figure 2: Spatiotemporally adaptive imaging of Drosophila embryonic development.
Figure 3: Spatiotemporally adaptive imaging of zebrafish embryonic development.
Figure 4: Spatiotemporally adaptive imaging of dynamic gene expression patterns.
Figure 5: Spatiotemporally adaptive optimization of the 3D light-sheet path in vivo.
Figure 6: Spatiotemporally adaptive whole-brain functional imaging in larval zebrafish.

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Acknowledgements

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.

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Authors and Affiliations

  1. Howard Hughes Medical Institute, Janelia Research Campus, Ashburn, Virginia, USA

    Loïc A Royer, William C Lemon, Raghav K Chhetri, Yinan Wan & Philipp J Keller

  2. Max Planck Institute of Molecular Cell Biology and Genetics, Dresden, Germany

    Loïc A Royer & Eugene W Myers

  3. Coleman Technologies Incorporated, Newtown Square, Pennsylvania, USA

    Michael Coleman

Authors
  1. Loïc A Royer
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Contributions

P.J.K. and L.A.R. conceived of the research and developed the AutoPilot framework. L.A.R. designed and wrote the AutoPilot core algorithms. M.C. implemented the microscope control software, with input from P.J.K. and L.A.R. R.K.C. implemented the light-sheet microscope with digitally adjustable degrees of freedom. W.C.L. performed adaptive imaging experiments of Drosophila embryogenesis and the zebrafish larval brain. W.C.L. and Y.W. performed adaptive imaging experiments of zebrafish embryogenesis. P.J.K. supervised the project. L.A.R. and P.J.K. wrote the paper, with input from all authors.

Corresponding authors

Correspondence to Loïc A Royer or Philipp J Keller.

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Competing interests

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 information

Supplementary Text and Figures

Supplementary Methods, Supplementary Figures 1–19 and Supplementary Tables 1–11 (PDF 16230 kb)

Supplementary Software (ZIP 173448 kb)

Perturbation benchmark of spatiotemporally adaptive imaging performance (AVI 29264 kb)

Spatiotemporally adaptive imaging of Drosophila embryogenesis (AVI 51053 kb)

Recovery of high spatial resolution in Drosophila adaptive imaging (AVI 50902 kb)

Recovery of cellular resolution in deep tissue layers by adaptive imaging (AVI 4794 kb)

Spatiotemporally adaptive imaging of zebrafish embryogenesis (AVI 26395 kb)

Recovery of high spatial resolution in zebrafish adaptive imaging (AVI 7809 kb)

Quantification of resolution improvements in zebrafish adaptive imaging (AVI 7684 kb)

Spatiotemporally adaptive two-color imaging of neural development (AVI 36649 kb)

Spatiotemporally adaptive whole-brain functional imaging in larval zebrafish (AVI 17161 kb)

Example of system drift during non-adaptive long-term imaging (AVI 28293 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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