Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Event-driven acquisition for content-enriched microscopy

Nature Methods volume 19, pages 1262–1267 (2022)Cite this article

Subjects

Abstract

A common goal of fluorescence microscopy is to collect data on specific biological events. Yet, the event-specific content that can be collected from a sample is limited, especially for rare or stochastic processes. This is due in part to photobleaching and phototoxicity, which constrain imaging speed and duration. We developed an event-driven acquisition framework, in which neural-network-based recognition of specific biological events triggers real-time control in an instant structured illumination microscope. Our setup adapts acquisitions on-the-fly by switching between a slow imaging rate while detecting the onset of events, and a fast imaging rate during their progression. Thus, we capture mitochondrial and bacterial divisions at imaging rates that match their dynamic timescales, while extending overall imaging durations. Because event-driven acquisition allows the microscope to respond specifically to complex biological events, it acquires data enriched in relevant content.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Event-driven acquisition concept.
Fig. 2: Event recognition of mitochondrial divisions during an iSIM acquisition.
Fig. 3: EDA versus fixed-rate imaging of mitochondrial divisions.
Fig. 4: EDA versus fixed-rate imaging of bacterial divisions.

Similar content being viewed by others

Data availability

The data contained in this manuscript and the training data for the model used can be found at https://doi.org/10.5281/zenodo.5548354. Source data are provided with this paper.

Code availability

All code used in this project is available at https://github.com/LEB-EPFL/EDA. The Python plugin described can be found at https://github.com/wl-stepp/eda_plugin.

References

  1. Laissue, P. P., Alghamdi, R. A., Tomancak, P., Reynaud, E. G. & Shroff, H. Assessing phototoxicity in live fluorescence imaging. Nat. Methods 14, 657–661 (2017).

    Article  CAS  PubMed  Google Scholar 

  2. Scherf, N. & Huisken, J. The smart and gentle microscope. Nat. Biotechnol. 33, 815–818 (2015).

    Article  CAS  PubMed  Google Scholar 

  3. Grimm, J. B. & Lavis, L. D. Caveat fluorophore: an insiders’ guide to small-molecule fluorescent labels. Nat. Methods 19, 149–158 (2022).

    Article  CAS  PubMed  Google Scholar 

  4. Rodriguez, E. A. et al. The growing and glowing toolbox of fluorescent and photoactive proteins. Trends Biochem. Sci. 42, 111–129 (2017).

    Article  CAS  PubMed  Google Scholar 

  5. Weigert, M. et al. Content-aware image restoration: pushing the limits of fluorescence microscopy. Nat. Methods 15, 1090–1097 (2018).

    Article  CAS  PubMed  Google Scholar 

  6. Jin, L. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat. Commun. 11, 1934 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Ouyang, W., Aristov, A., Lelek, M., Hao, X. & Zimmer, C. Deep learning massively accelerates super-resolution localization microscopy. Nat. Biotechnol. 36, 460–468 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Chen, J. et al. Three-dimensional residual channel attention networks denoise and sharpen fluorescence microscopy image volumes. Nat. Methods 18, 678–687 (2021).

    Article  CAS  PubMed  Google Scholar 

  9. Hoebe, R. A. et al. Controlled light-exposure microscopy reduces photobleaching and phototoxicity in fluorescence live-cell imaging. Nat. Biotechnol. 25, 249–253 (2007).

    Article  CAS  PubMed  Google Scholar 

  10. Hoebe, R. A., Van der Voort, H. T. M., Stap, J., Van Noorden, C. J. F. & Manders, E. M. M. Quantitative determination of the reduction of phototoxicity and photobleaching by controlled light exposure microscopy. J. Microsc. 231, 9–20 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Heine, J. et al. Adaptive-illumination STED nanoscopy. Proc. Natl Acad. Sci. USA 114, 9797–9802 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dreier, J. et al. Smart scanning for low-illumination and fast RESOLFT nanoscopy in vivo. Nat. Commun. 10, 556 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Chu, K. K., Lim, D. & Mertz, J. Enhanced weak-signal sensitivity in two-photon microscopy by adaptive illumination. Opt. Lett. 32, 2846–2848 (2007).

    Article  PubMed  Google Scholar 

  14. Li, B., Wu, C., Wang, M., Charan, K. & Xu, C. An adaptive excitation source for high-speed multiphoton microscopy. Nat. Methods 17, 163–166 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Pinkard, H. et al. Learned adaptive multiphoton illumination microscopy for large-scale immune response imaging. Nat. Commun. 12, 1916 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Chakrova, N., Canton, A. S., Danelon, C., Stallinga, S. & Rieger, B. Adaptive illumination reduces photobleaching in structured illumination microscopy. Biomed. Opt. Express 7, 4263 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mahecic, D. et al. Mitochondrial membrane tension governs fission. Cell Rep. 35, 108947 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Lambert, A. et al. Constriction rate modulation can drive cell size control and homeostasis in C. crescentus. iScience 4, 180–189 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  19. Zhou, X. et al. Mechanical crack propagation drives millisecond daughter cell separation in Staphylococcus aureus. Science 348, 574–578 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Edelstein, A. D. et al. Advanced methods of microscope control using μManager software. J. Biol. Methods 1, e10 (2014).

    Article  PubMed  Google Scholar 

  21. Pinkard, H., Stuurman, N., Corbin, K., Vale, R. & Krummel, M. F. Micro-Magellan: open-source, sample-adaptive, acquisition software for optical microscopy. Nat. Methods 13, 807–809 (2016).

    Article  CAS  PubMed  Google Scholar 

  22. Almada, P. et al. Automating multimodal microscopy with NanoJ-Fluidics. Nat. Commun. 10, 1223 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  23. Yan, X. et al. High-content imaging-based pooled CRISPR screens in mammalian cells. J. Cell Biol. 220, e202008158 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. York, A. G. et al. Instant super-resolution imaging in live cells and embryos via analog image processing. Nat. Methods 10, 1122–1126 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Mahecic, D. et al. Homogeneous multifocal excitation for high-throughput super-resolution imaging. Nat Methods 17, 726–733 (2020).

    Article  CAS  PubMed  Google Scholar 

  26. Twig, G. et al. Fission and selective fusion govern mitochondrial segregation and elimination by autophagy. EMBO J 27, 433–446 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Sekh, A. A. et al. Physics-based machine learning for subcellular segmentation in living cells. Nat. Mach. Intell. 3, 1071–1080 (2021).

    Article  Google Scholar 

  28. Fischer, C. A. et al. MitoSegNet: easy-to-use deep learning segmentation for analyzing mitochondrial morphology. iScience 23, 101601 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Lihavainen, E., Mäkelä, J., Spelbrink, J. N. & Ribeiro, A. S. Mytoe: automatic analysis of mitochondrial dynamics. Bioinformatics 28, 1050–1051 (2012).

    Article  CAS  PubMed  Google Scholar 

  30. Peng, J.-Y. et al. Automatic morphological subtyping reveals new roles of caspases in mitochondrial dynamics. PLoS Comput. Biol. 7, 14 (2011).

    Article  Google Scholar 

  31. Valente, A. J., Maddalena, L. A., Robb, E. L., Moradi, F. & Stuart, J. A. A simple ImageJ macro tool for analyzing mitochondrial network morphology in mammalian cell culture. Acta Histochem. 119, 315–326 (2017).

    Article  CAS  PubMed  Google Scholar 

  32. Leonard, A. P. et al. Quantitative analysis of mitochondrial morphology and membrane potential in living cells using high-content imaging, machine learning, and morphological binning. Biochim. Biophys. Acta 1853, 348–360 (2015).

    Article  CAS  PubMed  Google Scholar 

  33. Lefebvre, A. E. Y. T., Ma, D., Kessenbrock, K., Lawson, D. A. & Digman, M. A. Automated segmentation and tracking of mitochondria in live-cell time-lapse images. Nat. Methods 18, 1091–1102 (2021).

    Article  CAS  PubMed  Google Scholar 

  34. Smirnova, E., Griparic, L., Shurland, D.-L. & van der Bliek, A. M. Dynamin-related protein Drp1 is required for mitochondrial division in mammalian cells. Mol. Biol. Cell 12, 2245–2256 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Kamerkar, S. C., Kraus, F., Sharpe, A. J., Pucadyil, T. J. & Ryan, M. T. Dynamin-related protein 1 has membrane constricting and severing abilities sufficient for mitochondrial and peroxisomal fission. Nat. Commun 9, 5239 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Kleele, T. et al. Distinct fission signatures predict mitochondrial degradation or biogenesis. Nature 593, 435–439 (2021).

    Article  CAS  PubMed  Google Scholar 

  37. Ugarte-Uribe, B., Müller, H.-M., Otsuki, M., Nickel, W. & García-Sáez, A. J. Dynamin-related protein 1 (Drp1) promotes structural intermediates of membrane division. J. Biol. Chem. 289, 30645–30656 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Ronneberger, O., Fischer, P. & Brox, T. in U-Net: Convolutional Networks for Biomedical Image Segmentation. in Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015 (eds. Navab, N., Hornegger, J., et al.) 234–241 (Springer International Publishing, 2015).

  39. Andersson, S. G. et al. The genome sequence of Rickettsia prowazekii and the origin of mitochondria. Nature 396, 133–140 (1998).

    Article  CAS  PubMed  Google Scholar 

  40. Kilian, N. et al. Assessing photodamage in live-cell STED microscopy. Nat. Methods 15, 755–756 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Eisenstein, M. Smart solutions for automated imaging. Nat. Methods 17, 1075–1079 (2020).

    Article  CAS  PubMed  Google Scholar 

  42. Waithe, D. et al. Object detection networks and augmented reality for cellular detection in fluorescence microscopy. J. Cell. Biol 219, e201903166 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kechkar, A., Nair, D., Heilemann, M., Choquet, D. & Sibarita, J.-B. Real-time analysis and visualization for single-molecule based super-resolution microscopy. PLoS ONE 8, e62918 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Royer, L. A. et al. Adaptive light-sheet microscopy for long-term, high-resolution imaging in living organisms. Nat. Biotechnol. 34, 1267–1278 (2016).

    Article  CAS  PubMed  Google Scholar 

  45. Štefko, M., Ottino, B., Douglass, K. M. & Manley, S. Autonomous illumination control for localization microscopy. Opt. Express 26, 30882 (2018).

    Article  PubMed  Google Scholar 

  46. Durand, A. et al. A machine learning approach for online automated optimization of super-resolution optical microscopy. Nat. Commun. 9, 5247 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Balzarotti, F. et al. Nanometer resolution imaging and tracking of fluorescent molecules with minimal photon fluxes. Science 355, 606–612 (2017).

    Article  CAS  PubMed  Google Scholar 

  48. Cohen, A. E. & Moerner, W. E. Method for trapping and manipulating nanoscale objects in solution. Appl. Phys. Lett. 86, 093109 (2005).

    Article  Google Scholar 

  49. Ely, B. Genetics of Caulobacter crescentus. Methods Enzymol. 204, 372–384 (1991).

    Article  CAS  PubMed  Google Scholar 

  50. Schrader, J. M. & Shapiro, L. Synchronization of Caulobacter crescentus for investigation of the bacterial cell cycle. J. Vis. Exp. 98, e52633 (2015).

    Google Scholar 

  51. Fiolka, R., Shao, L., Rego, E. H., Davidson, M. W. & Gustafsson, M. G. L. Time-lapse two-color 3D imaging of live cells with doubled resolution using structured illumination. Proc. Natl Acad. Sci USA 109, 5311–5315 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Czech, E., Aksoy, B. A., Aksoy, P. & Hammerbacher, J. Cytokit: a single-cell analysis toolkit for high dimensional fluorescent microscopy imaging. BMC Bioinformatics 20, 448 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank H. Perreten for technical support with cell culture and plasmid construction, and L. Casini and J. Collier (University of Lausanne) for sharing plasmids and protocols for the bacterial experiments. Imaging data used for training the neural network in this publication were produced in collaboration with the Advanced Imaging Center (AIC), a facility jointly supported by the Gordon and Betty Moore Foundation and HHMI at HHMI’s Janelia Research Campus. We thank L. Shao and T.-L. Chew at Janelia AIC for their help with SIM imaging. This work was supported by the Swiss National Science Foundation project grant (SNSF; 182429, to S.M., D.M. and W.L.S.), and National Centre for Competence in Research (NCCR Chemical Biology, to S.M. and D.M.); and the European Union’s H2020 program under the European Research Council (ERC; CoG 819823 Piko, to S.M. and C.Z.), and the Marie Skłodowska-Curie Fellowships (890169 BALTIC, to J.G.). M.W. is supported by the EPFL School of Life Sciences and a generous foundation represented by CARIGEST SA.

Author information

Author notes

  1. These authors contributed equally: Dora Mahecic and Willi L. Stepp.

Authors and Affiliations

  1. Institute of Physics, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Dora Mahecic, Willi L. Stepp, Chen Zhang, Juliette Griffié & Suliana Manley

  2. Institute of Bioengineering, Swiss Federal Institute of Technology Lausanne (EPFL), Lausanne, Switzerland

    Martin Weigert

Authors
  1. Dora Mahecic
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Willi L. Stepp
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Chen Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Juliette Griffié
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Weigert
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Suliana Manley
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

D.M., J.G. and S.M. conceived and designed the project. D.M., M.W. and S.M. supervised the project. D.M. collected the training data and performed the experiments on mitochondrial division. D.M. and M.W. implemented the neural network for event detection. W.L.S. and D.M. implemented the EDA framework and performed data analysis. W.L.S. developed the Python plugin for Micro-Manager and its documentation. W.L.S performed the experiments on C. crescentus. C.Z. cultivated the C. crescentus strains and prepared samples for imaging. W.L.S prepared the figures. D.M., W.L.S. and S.M. wrote the manuscript with contributions from all authors.

Corresponding authors

Correspondence to Dora Mahecic or Suliana Manley.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Example maximum event score regions which triggered EDA.

The event score output by the neural network can also be used to extract events of high interest from the datasets, after the acquisition is complete. Here, events that triggered EDA in different datasets are shown. The highest event score was used to define a region of interest around the event, representing a time and location of highest interest in the sample. Some regions appear twice, when the neural network event score was high enough to trigger EDA multiple times. Frames are shown in no specific order. Scale bars: 1 μm.

Extended Data Fig. 2 Bleaching behavior of a mitochondria sample during EDA imaging.

The different modes of imaging can clearly be seen in the bleaching curve represented by the signal-to-noise ratio calculated from the intensity inside the mitochondria compared to the signal outside of the mitochondria. For some parts with low frame rate, even a slight recovery of signal can be observed. (representative of n = 4 independent experiments).

Source data

Extended Data Fig. 3 EDA delivers additional frames during events of interest.

Top row: mitochondrial division as it would have been recorded with the slow fixed imaging rate without EDA. Vertical frames: additional frames captured thanks to EDA switching to the fast imaging speed showing more detail of the dynamics of the event. Both the final constriction state and the fade of the DRP1 peak can be observed with higher temporal resolution, enhancing the relevant content of the dataset. This division event can also be seen in Supplementary Video 3.0. (Scale bars: 1 μm, representative of N = 33 events in n = 4 independent experiments).

Extended Data Fig. 4 EDA imaging of synchronized bacteria populations.

C. crescentus, the strain used in this study, were synchronized via density centrifugation to obtain a population of cells that are all at the beginning of their cell cycle (G0, swarmer). This leads to a time lag before the next divisions take place. As they are synchronized, many bacteria in the sample will then divide at the same time. We used EDA to sense the onset of divisions in the sample and increase imaging speed during the divisions for high SNR and temporal resolution. We tested different times between images for fast and slow speeds, as well as different threshold event scores (gray band). a, slow: 9 min, fast 3 min. b and c, slow: 12 min, fast 2 min. (Scale bar: 1 μm, n = 4 independent synchronizations).

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1–5.

Reporting Summary

Supplementary Video 1.0

Real-time video of a 300 × 300 px FOV of mitochondria (gray) and DRP1 (red). 3:40 min recorded time, fast frame rate 3.8 fps, slow frame rate 0.2 fps.

Supplementary Video 1.1

Slow motion movie corresponding to Supplementary Video 1.0.

Supplementary Video 2.0

Real-time video of a originally 128 × 128 px FOV of mitochondria (gray) and DRP1 (red). 2:04 min recorded time, fast frame rate 3.8 fps, slow frame rate 0.2 fps.

Supplementary Video 2.1

Slow motion video corresponding to Supplementary Video 2.0.

Supplementary Video 3.0

Real-time video of a originally 128 × 128 px FOV of mitochondria (gray) and DRP1 (red). 1:12 min recorded time, fast frame rate 3.8 fps, slow frame rate 0.2 fps.

Supplementary Video 3.1

Slow motion video corresponding to Supplementary Video 3.0.

Supplementary Video 4.0

Real-time video of a 856 × 856 px FOV of C. crescentus (gray) and FtsZ (red). 3:54 h recorded time, fast frames 3 min, slow frames 9 mins.

Supplementary Video 4.1

Slow motion corresponding to Supplementary Video 4.0.

Source data

Source Data Fig. 2

Data points for EDA plot in Fig. 2c.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 2

Data points for intensity contrast plot.

Source Data Extended Data Fig. 4

Data points for EDA plots in Extended Data Fig. 4.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mahecic, D., Stepp, W.L., Zhang, C. et al. Event-driven acquisition for content-enriched microscopy. Nat Methods 19, 1262–1267 (2022). https://doi.org/10.1038/s41592-022-01589-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41592-022-01589-x

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research