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Micro-Magellan: open-source, sample-adaptive, acquisition software for optical microscopy

Nature Methods volume 13, pages 807–809 (2016)Cite this article

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The past decade has seen an explosion of new techniques in optical microscopy with the potential to reveal the complex orchestra of biological processes across large scales of space and time1. Scalable usability of any new optical technique requires widespread dissemination of hardware and software for data acquisition and of software for data analysis. Commercial software and, more recently, open-source software packages have begun to meet many needs for visualization and analysis of terabyte-sized imaging volumes2,3,4. As the options for data analysis continue to expand and evolve, it becomes increasingly difficult to make ideal use of the full capabilities of widefield, confocal, and multiphoton microscopes already present in many labs because of a lack of automated and customizable acquisition software5.

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Figure 1: Overview of mMagellan's capabilities for exploring samples and running automated, sample-adaptive acquisitions.

Change history

  • 19 December 2016

    In the HTML version of this article initially published online, the online publication date is 29 September 2013; it should read 29 September 2016. This error has been corrected in this file as of 19 December 2016.

References

  1. Scherf, N. & Huisken, J. Nat. Biotechnol. 33, 815–818 (2015).

    Article  CAS  Google Scholar 

  2. Pietzsch, T., Saalfeld, S., Preibisch, S. & Tomancak, P. Nat. Methods 12, 481–483 (2015).

    Article  CAS  Google Scholar 

  3. Peng, H. et al. Nat. Commun. 5, 4342 (2014).

    Article  CAS  Google Scholar 

  4. Royer, L.A. et al. Nat. Methods 12, 480–481 (2015).

    Article  CAS  Google Scholar 

  5. Eliceiri, K.W. et al. Nat. Methods 9, 697–710 (2012).

    Article  CAS  Google Scholar 

  6. Edelstein, A.D. et al. J. Biol. Methods 1, e10 (2014).

    Article  Google Scholar 

Download references

Acknowledgements

We thank K. Thorn, M. Tsuchida, C. Weisiger, J.O. Edwards III, and M. Foxxe for helpful conversations during development; M. Headley for beta testing; E. Oswald for figure data; T. Pietzsch, C. Rueden, and G. Iannello for help with integration with BigDataViewer, FIJI, and Vaa3d/TeraFly, respectively; and C. Norberg for support. This work was supported in part by the US National Institutes of Health (grants R01EB007187 (to R.V.), R01AI52116 (to M.F.K.), and U19A1077439-06 (to M.F.K.)) and the Sandler Basic Asthma Research Center (M.F.K.).

Author information

Authors and Affiliations

  1. Department of Pathology, University of California, San Francisco, San Francisco, California, USA

    Henry Pinkard, Kaitlin Corbin & Matthew F Krummel

  2. Biological Imaging Development Center, University of California, San Francisco, San Francisco, California, USA

    Henry Pinkard, Kaitlin Corbin & Matthew F Krummel

  3. Computational Biology Graduate Group, University of California, Berkeley, Berkeley, California, USA

    Henry Pinkard

  4. Howard Hughes Medical Institute, University of California, San Francisco, San Francisco, California, USA

    Nico Stuurman & Ronald Vale

  5. Department of Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA

    Nico Stuurman & Ronald Vale

Authors
  1. Henry Pinkard
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  2. Nico Stuurman
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  3. Kaitlin Corbin
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  4. Ronald Vale
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  5. Matthew F Krummel
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Corresponding author

Correspondence to Henry Pinkard.

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Competing interests

N.S. and R.V. are cofounders of Open Imaging, the company that maintains and develops μManager.

Integrated supplementary information

Supplementary Figure 1 Delaunay triangulation of points shown in Fig. 1b

2D projection of 3D interpolation points shown in Fig. 1b. Color of points indicates Z position of the point in 3D.

Supplementary Figure 2 Automatic 3D cross correlation based drift compensation.

(a) XZ slice of popliteal lymph node explant imaging showing sample drift between start and hour 16 of imaging. (b) Maximum value of 3D cross correlation between second harmonic generation channel of two successive time points, with a 3D cubic spline fit to allow for sub-voxel resolution. (c) Applying a focus offset based on (b) compensates for sample drift. Position of lymph node cortex calculated as mean of 15 reference points. Error bars show their standard deviation. (d) Drift compensation allows stabilized imaging of lymph node over long time course

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1 and 2, and Supplementary Notes 1–9 (PDF 2674 kb)

Supplementary Software

Source code for Micro-Manager 1.4 (which contains Micro-Magellan). (ZIP 7621 kb)

Supplementary Guide

Micro-Magellan Install and User Guide (PDF 1387 kb)

Supplementary Video 1

Opening data in BigDataViewer. Narrated screencast demonstrating the acquisition of a 3D dataset in Micro-Magellan and the opening and visualization of that dataset in BigDataViewer. View in high defintion at: https://youtu.be/AOhH_0-_0wI (MOV 24120 kb)

Supplementary Video 2

Explore acquisitions. Narrated screencast demonstrating how to navigate through a sample in 3D using an explore acquisition. Explore acquisitions present a Google Maps-like interface for driving a microscope to acquire tiled 3D volumes. This demo traces the airway of a cleared lung in 3D to its branch points and sets up grids for subsequent high-resolution acquisition. View in high definition at: https://youtu.be/UuQtws9Posw (MOV 24601 kb)

Supplementary Video 3

Creating a surface. Narrated screencast demonstrating the exploration of a lymph node and the creation of a surface corresponding to its cortex. Surfaces are created by marking interpolation points on 2D slices of a 3D image. The surface is then used to run an acquisition that images only the top 120 m below the top of the lymph node. View in high definition at: https://youtu.be/wKVMWFeCF6o (MOV 58025 kb)

Supplementary Video 4

Using surfaces to image complex acquisition volumes. Narrated screencast demonstrating an example of an application of surfaces in intra-vital imaging of a tumor. Two surfaces are used in this example-one to mark the tilt of the coverslip through which the imaging takes places relative to the optical (Z) axis, and one to mark the XY outline of the imaging volume. In combination, they are used to image the volume directly below the coverslip despite it being tilted to the optical axis. Although this screencast uses intra-vital microscopy, the same principals would apply to image tissue sections or a large area of cells on a slide. View in high definition at: https://youtu.be/frW4emzcJ5I (MOV 100277 kb)

Supplementary Video 5

Using covariant pairings to increase laser excitation power with depth. Narrated screencast demonstrating how set up a gradient of excitation laser power based on the Z coordinate of the focal plane. A covariant pairing with Z position as the independent variable and laser power as the dependent variable is created. One by one, paired values are added for this covariant pairing to associate particular focus positions with different amounts of laser power. The appropriate amount of laser power for each focal position is determined empirically by repeatedly imaging the same are with different amounts of power until the desired level of signal is reached. The covariant pairing is then used in an acquisition, and laser power is calculated automatically over the full Z-stack by linearly interpolating the supplied values. View in high definition at: https://youtu.be/AWX5sUMBJiE (MOV 23102 kb)

Supplementary Video 6

Changing settings during acquisition. Narrated screencast demonstrating two examples of Micro-Magellan's ability to alter settings during an acquisition. First, it shows how a surface can be edited during acquisition in response to a sample that changes its morphology over time. Next, it shows how a new surface altogether can be created to zoom in on an area of interest during an acquisition. View in high definition at: https://youtu.be/pdOh6KFr-9U (MOV 29028 kb)

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Pinkard, H., Stuurman, N., Corbin, K. et al. Micro-Magellan: open-source, sample-adaptive, acquisition software for optical microscopy. Nat Methods 13, 807–809 (2016). https://doi.org/10.1038/nmeth.3991

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