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Event-driven acquisition for content-enriched microscopy

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.

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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.

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.

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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

Authors and Affiliations

Authors

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.

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The authors declare no competing interests.

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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.

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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

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