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BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples

Nature Methods volume 16, pages 870–874 (2019)Cite this article

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Abstract

Light-sheet imaging of cleared and expanded samples creates terabyte-sized datasets that consist of many unaligned three-dimensional image tiles, which must be reconstructed before analysis. We developed the BigStitcher software to address this challenge. BigStitcher enables interactive visualization, fast and precise alignment, spatially resolved quality estimation, real-time fusion and deconvolution of dual-illumination, multitile, multiview datasets. The software also compensates for optical effects, thereby improving accuracy and enabling subsequent biological analysis.

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Fig. 1: BigStitcher principles.
Fig. 2: Optical aberrations, light simulations and alignment quantification.
Fig. 3: Reconstructed samples.

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Data availability

Small example datasets are available for download from the Open Science Foundation at https://osf.io/bufza/. Larger datasets are available on request. Additional datasets uploaded at a later stage will be linked from the documentation page which can be found at https://imagej.net/BigStitcher#Example_Datasets. Example datasets are explained in detail in Supplementary Note 18.

Code availability

All source code used in this publication (BigStitcher, phase correlation simulation and benchmarks, and the simulation of light propagation in tissue using ray tracing) is open-source and published under the GNU General Public License version 2. The latest stable releases used in this publication are provided as Supplementary Software; current versions that include bugfixes and updates can be downloaded from GitHub (at https://github.com/PreibischLab/BigStitcher; https://github.com/PreibischLab/multiview-reconstruction; and https://github.com/PreibischLab/multiview-simulation; see Supplementary Notes 19 and 20 for further explanations). Details on how to use the software are described in Supplementary Note 21.

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Acknowledgements

We thank T. Pietzsch and S. Saalfeld for insightful discussions and BigDataViewer and ImgLib2 support; N. Vladimirov for very helpful microscopy discussions; C. Rueden for Fiji support and maintenance; N. Gompel for early-stage project discussions; and the Caenorhabditis Genetics Center at the University of Minnesota for providing C. elegans strains. S.P., F.P. and M.T. were funded by MDC Berlin; S.P. was supported by HFSP grant RGP0021/2018-102; F.P. was funded by a PhD fellowship from Studienstiftung des deutschen Volkes; F.R.R. and M.T. were funded by the Helmholtz Alliances ICEMED and AMPro; D.H., H.H. and H.L. were funded by the Deutsche Forschungsgemeinschaft (DFG, Nanosystems Initiative Munich), the NHGRI/NIH Center for Photogenomics (grant RM1 HG007743) and LMU Munich; and P.T., N.R., R.K.C., A.C. and P.J.K. were funded by HHMI Janelia.

Author information

Author notes

  1. These authors contributed equally: David Hörl, Fabio Rojas Rusak.

Authors and Affiliations

  1. Department of Biology II, Ludwig-Maximilians-Universität München, Munich, Germany

    David Hörl, Hartmann Harz & Heinrich Leonhardt

  2. Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    David Hörl, Friedrich Preusser & Stephan Preibisch

  3. Cardiovascular and Metabolic Sciences, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany

    Fabio Rojas Rusak & Mathias Treier

  4. Janelia Research Campus, Howard Hughes Medical Institute, Ashburn, VA, USA

    Paul Tillberg, Nadine Randel, Raghav K. Chhetri, Albert Cardona & Philipp J. Keller

  5. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Albert Cardona

  6. Charité-Universitätsmedizin Berlin, Berlin, Germany

    Mathias Treier

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  1. David Hörl
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Contributions

S.P. conceived the idea in discussions with H.H., H.L. and M.T.; D.H. and S.P. developed the algorithms and implemented the software; F.R.R. performed all clearing experiments, reconstructions and benchmarks; F.P. imaged and reconstructed C. elegans; P.T. and N.R. performed ExM sample preparation; R.K.C. and P.J.K. developed the ExM-optimized IsoView microscope and imaged the sample; S.P. reconstructed the ExM sample; S.P., M.T., H.L., H.H., P.J.K. and A.C. supported and supervised the project; and S.P., D.H. and F.R.R. wrote the manuscript with input from the co-authors.

Corresponding author

Correspondence to Stephan Preibisch.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information: Rita Strack was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Supplementary information

Supplementary Information

Supplementary Figs. 1–23, Supplementary Tables 1 and 2 and Supplementary Notes 1–21.

Reporting Summary

Supplementary Video 1

Interactive link verification.

Supplementary Video 2

Simulations of light propagation in tissue.

Supplementary Video 3

Interactive walk through a cleared sample.

Supplementary Video 4

Quality of multiview registration on the expanded sample.

Supplementary Video 5

3D maximum-intensity projection of the expanded sample.

Supplementary Video 6

Low-resolution overview of reconstructed mouse brain.

Supplementary Video 7

3D maximum-intensity projection of the reconstructed C. elegans dauer.

Supplementary Video 8

Quality measurement by rFRC (single image tile).

Supplementary Video 9

Quality measurement by rFRC (large sample).

Supplementary Software

Source code for BigStitcher, phase correlation simulation and benchmark, and the simulation of light propagation in tissue using ray tracing, all licensed under the GNU General Public License version 2.

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Hörl, D., Rojas Rusak, F., Preusser, F. et al. BigStitcher: reconstructing high-resolution image datasets of cleared and expanded samples. Nat Methods 16, 870–874 (2019). https://doi.org/10.1038/s41592-019-0501-0

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