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Isotropic super-resolution light-sheet microscopy of dynamic intracellular structures at subsecond timescales

Nature Methods volume 19, pages 359–369 (2022)Cite this article

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Abstract

Long-term visualization of the dynamic interactions between intracellular structures throughout the three-dimensional space of whole live cells is essential to better understand their functions, but this task remains challenging due to the limitations of existing three-dimensional fluorescence microscopy techniques, such as an insufficient axial resolution, low volumetric imaging rate and photobleaching. Here, we present the combination of a progressive deep-learning super-resolution strategy with a double-ring-modulated selective plane illumination microscopy design capable of visualizing the dynamics of intracellular structures in live cells for hours at an isotropic spatial resolution of roughly 100 nm in three dimensions at speeds up to roughly 17 Hz. Using this approach, we reveal the complex spatial relationships and interactions between endoplasmic reticulum (ER) and mitochondria throughout live cells, providing new insights into ER-mediated mitochondrial division. We also examined the motion of Drp1 oligomers involved in mitochondrial fission and revealed the dynamic interactions between Drp1 and mitochondria in three dimensions.

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Fig. 1: Principle and performance of IDDR–SPIM.
Fig. 2: 4D SR imaging revealing the morphological changes and locomotion of mitochondria or ER in live cells.
Fig. 3: 5D SR imaging revealing the complex spatial relationships and the dynamic interactions between ER (red or magenta) and mitochondria (green) in live cells.
Fig. 4: 5D SR imaging revealing the dynamic interactions between the Drp1 oligomers (green) and mitochondria (red).
Fig. 5: Forward and backward cross-mode SR applications of mitochondria structures using ID network.

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Data availability

The data that support the findings of this study are publicly accessible at Zenodo: https://doi.org/10.5281/zenodo.5720599. The example model checkpoints and data are packaged with the code. Source data for Figs. 1–5 are provided with this paper.

Code availability

Codes for deep-learning model, codes for data analysis and synthetic data generation used in current study are available at https://github.com/feilab-hust/ID-Net.

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Acknowledgements

We thank Z. Yu for his help with image processing. We thank the Optical Bioimaging Core Facility of WNLO-HUST (Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology) and the Analytical and Testing Centre of HUST for spectral measurements and data acquisition. We also thank the State Key Laboratory of Agricultural Microbiology Core Facility, Huazhong Agricultural University for assistance in SIM, and are grateful to Q. Zong and Z. Hu for their support of data acquisition. This work was supported by the following grants: National Natural Science Foundation of China (grant nos. 92054110, 61827825, 21874052, 21927802 and 31770924), National Key R&D program of China (grant no. 2017YFA0700501), Science Fund for Creative Research Group of China (grant no. 61721092), Innovation Fund of WNLO, the Fundamental Research Funds for the Central Universities (grant no. 2018JYCXJJ021), the Academic Frontier Youth Team Project to X. Wang from HUST and Director Fund of WNLO.

Author information

Author notes

  1. These authors contributed equally: Yuxuan Zhao, Meng Zhang, Wenting Zhang, Yao Zhou.

Authors and Affiliations

  1. School of Optical and Electronic Information-Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China

    Yuxuan Zhao, Yao Zhou, Longbiao Chen, Qing Liu, Peng Wang, Rong Chen & Peng Fei

  2. Britton Chance Center and MoE Key Laboratory for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, Wuhan, China

    Meng Zhang, Wenting Zhang, Xinxin Duan, Feifan Chen, Huan Deng, Yunfei Wei & Yu-Hui Zhang

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Contributions

P.F. and Y.-H.Z. conceived and oversaw the project. P.F. initiated the designs for computational imaging and Y.-H.Z. initiated the designs for biological experiments. Y.Zhao, Y.Zhou, Q.L. and R.C. developed the microscope. Y.Zhao, L.C. and Q.L. developed the programs. M.Z., W.Z. and F.C. synthesized the fluorescent probes and prepared the experimental samples. Y.Zhao, Y.Zhou, L.C., M.Z., W.Z., Q.L., P.W., H.D., X.D. and Y.W. processed the images and analyzed the data. Y.Zhao, M.Z., P.F. and Y.-H.Z. wrote the manuscript with discussion and improvements from all authors.

Corresponding authors

Correspondence to Peng Fei or Yu-Hui Zhang.

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Nature Methods thanks Brian Glancy, Jonathan Ventura, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. 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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Extended data

Extended Data Fig. 1 Double-ring selective plane illumination microscopy (DR–SPIM) setup.

a, Schematic illustrating the optical path of DR-SPIM. b, The 3D solidworks model of DR-SPIM. c, A photograph of the optical setup of DR-SPIM, showing the global view of the microscope. d, e show some key parts of the microscope. d, Double-ring plane illumination/detection assembly. e, Double-ring optical mask.

Extended Data Fig. 2 Endoplasmic reticulum (ER, tagged with EGFP-Sec61β) in a live U2OS cell imaged via single-ring Bessel light sheet and DR-SPIM.

a, X-y plane max intensity projection (MIP) of ER by 500-nm single-ring Bessel sheet21 with 70% side lobes (left) and 500-nm DR-SPIM with 20% side lobes (right). The magnified views (blue boxed regions) further compare ER signals from the same region of interest (white boxed region) by single-ring Bessel sheet (i) and DR-SPIM (ii), respectively. The laser power at the back focal plane was 1.2 mw and 0.5 mw for single-ring Bessel sheet and DR-SPIM, respectively. The exposure time was 10 ms for both setups. b, X-z slices of ER by single-ring Bessel sheet (top) and DR-SPIM (bottom). The white arrows indicate the artifacts induced by relatively strong side lobes in single-ring Bessel sheet. All the experiments were repeated five times independently with similar results. Scale bar, 10 μm and 1μm (inserts).

Extended Data Fig. 3 Comparison of spatial resolution and fidelity between iso-DSP 2.0 (ID) restoration and other state-of-the-art 3D SR networks on expanded microtubules (4× expansion microscopy36).

a, Comparative x-y and x-z planes of simulated diffraction limited DR-SPIM (LR), 3D RCAN (RCAN), 1st-generation DSP (DSP 1.0), 2nd-generation DSP (DSP 2.0), iso-DSP 2.0 (ID), and iso-enhanced expansion microscopy results (ISO-GT). Experiments were repeated five times independently with similar results. Scale bar, 5 μm. b, Magnified views of the boxed regions in a for each method. Scale bar, 1 μm. c, Decorrelation analysis quantifying the lateral and axial resolution of LR, RCAN, DSP 1.0, DSP 2.0, ID, and ISO-GT. d-e, 3D SSIM (left) and 3D PSNR (right) metrics of LR, RCAN, DSP 1.0, DSP 2.0 and ID reconstruction results. The SSIM and PSNR metrices for different reconstructions are all calculated with using raw expansion microscopy data as ground truth reference, except for ID metrices, which are calculated by comparing ID reconstruction to iso-enhanced expansion microscopy data (ISO-GT). n = 10 volumes for decorrelation analysis, 3D SSIM and 3D PSNR metrics, respectively. All boxplots are drawn from the 25th to 75th percentile with the horizontal bar at the median and the whiskers extending to the minima and maxima.

Source data

Extended Data Fig. 4 3D SR visualization of 9 diverse intracellular structures imaged via IDDR-SPIM.

Scale bar, 5 μm (whole cell) and 1 μm (insert box).

Extended Data Fig. 5 Comparative photobleaching and phototoxicity rates of N-SIM and IDDR-SPIM.

Time-lapse images of ER in live U2OS cells by 2D N-SIM and IDDR-SPIM are shown in a and b, respectively, indicating notably lower photobleaching and phototoxicity by IDDR-SPIM. 9 raw frames were acquired to reconstruct a single N-SIM image. To yield similar SNR in the reconstructed images at the initial time point, the illumination intensities for 2D N-SIM and IDDR-SPIM were 50 W/cm2 and 1 W/cm2, respectively.

Extended Data Fig. 6 Reliable interpretation of late endosomes’ locomotion requires 3D SR imaging.

a, A color-coded MIP of the late endosomes (tagged with EGFP-Rab7). The left part shows the SR result reconstructed via IDDR-SPIM, and the right part shows the diffraction-limited DR-SPIM result. Experiments were repeated five times independently with similar results. Scale bar, 10 μm. b and c, Magnified views of late endosomes in x-y and x-z planes. Scale bar, 1 μm (left), 0.5μm (right). d, Time-lapse images show the dynamics of a pair of late endosomes in x-y and y-z planes. Merely with 2D SR images, a false “kiss-and-run” process would be observed. Experiments were repeated five times independently with similar results. Scale bar, 1 μm. e, Decorrelation analysis quantifying the spatial resolution of late endosomes imaged by raw DR-SPIM (LR) and IDDR-SPIM (SR). f, SQUIRREL analysis quantifying the SR accuracy (left: RSP; right: RSE) of late endosomes reconstructed by IDDR-SPIM. n = 10 volumes for decorrelation analysis and SQUIRREL analysis, respectively. All boxplots are drawn from the 25th to 75th percentile with the horizontal bar at the median and the whiskers extending to the minima and maxima.

Source data

Extended Data Fig. 7 3D Trajectories of 28 Drp1 oligomers not located on the mitochondria.

The different colors of the trajectories indicate the diverse average speed of oligomers locomotion. The numbers show the total length and tracking time for each trajectory. Scale bar, 2 μm.

Extended Data Fig. 8 Cross-mode SR applications based on established IDDR-SPIM.

An ID model trained on DR-SPIM database was successfully applied to the SR restoration of 3D images of 8 diverse intracellular structures acquired via a spinning disk confocal microscope (SDCM). Scale bar, 5 μm.

Extended Data Fig. 9 Extending the imaging depth through the integration of axial scanning with DR-SPIM.

a, An entire expanded nuclear of U2OS cell (~4×) imaged via axial scanning-enabled DR-SPIM. Scale bar: 10 μm. Combined with the axial scanning scheme, the imaging depth of DR-SPIM could be increased from ~10 μm to ~50 μm while keeping the same high axial resolution. b, X-y, x-z, and y-z plane MIPs of the boxed region, showing the fine structures of nuclear pores. Scale bar: 1 μm.

Supplementary information

Supplementary Information

Supplementary Figs. 1–20, Table 1 and Notes 1 and 2.

Reporting Summary

Supplementary Data

Statistical source data for supplementary figures.

Supplementary Video 1

The time-lapse 3D SR imaging of microtubules (labeled with Tubulin-Atto 488) in a live U2OS cell via IDDR–SPIM at a volumetric speed of 0.8 s per cell.

Supplementary Video 2

Comparison of phototoxicity of ER (tagged with EGFP-Sec61β) in live U2OS cells between 2D N-SIM and IDDR–SPIM at speeds of 1.1 frames per s, 1 volume (51 frames per volume) per s.

Supplementary Video 3

The time-lapse imaging of the ER (tagged with EGFP-Sec61β) in live U2OS cells via 2D N-SIM, SCDM and DR–SPIM at speeds of 0.9 s per frame, 0.1 s per frame and 1 s per volume, respectively.

Supplementary Video 4

The time-lapse 3D SR imaging of mitochondrial matrix (tagged with Cox4-mKate2) throughout a whole live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 3 Hz.

Supplementary Video 5

The time-lapse 3D SR imaging of mitochondrial matrix (tagged with Cox4-mKate2) throughout a whole live U2OS cell via IDDR–SPIM at a high volumetric imaging rate of 10 Hz.

Supplementary Video 6

The video-rate time-lapse 3D SR imaging of the ER (tagged with EGFP-Sec61β) in a live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 17 Hz.

Supplementary Video 7

The time-lapse 3D SR imaging of mito OM (tagged with Tomm20-EGFP) throughout a whole live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz.

Supplementary Video 8

The time-lapse simultaneous dual-color 3D SR imaging of the ER (tagged with EGFP-Sec61β, red) and mitochondrial matrix (tagged with Cox4-mKate2, green) throughout a whole live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz (188 planes per cell) at 300 time points.

Supplementary Video 9

The time-lapse simultaneous dual-color 3D SR imaging of the ER (tagged with EGFP-Sec61β, red) and mitochondrial matrix (tagged with Cox4-mKate2, green) in a live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz reveals the dynamic process of ER-mediated mitochondrial division.

Supplementary Video 10

The magnified view of 5D visualization revealing the dynamic process of ER-mediated mitochondrial division.

Supplementary Video 11

The time-lapse simultaneous dual-color 3D SR imaging of the ER (tagged with EGFP-Sec61β, red) and mitochondrial matrix (tagged with Cox4-mKate2, green) in a live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz reveals the dynamic process involving the cooperation of the ER and one mitochondrion in the division of another mitochondrion.

Supplementary Video 12

The time-lapse simultaneous dual-color 3D SR imaging of mito OM (tagged with Tomm20-EGFP, red) and the Drp1 oligomers (tagged with mCherry-Drp1, green) in a live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz reveals the dynamic process of Drp1-mediated mitochondrial division.

Supplementary Video 13

The time-lapse simultaneous dual-color 3D SR imaging of mito OM (tagged with Tomm20-EGFP, red) and the Drp1 oligomers (tagged with mCherry-Drp1, green) in a live U2OS cell via IDDR–SPIM at a volumetric imaging rate of 1 Hz reveals the dynamic process of Drp1-mediated mitochondrial branching.

Supplementary Video 14

Another example reveals the dynamic process of Drp1-mediated mitochondrial branching.

Source data

Source Data Fig. 1

Statistical Source Data of Fig. 1d,g.

Source Data Fig. 2

Statistical Source Data of Fig. 2f.

Source Data Fig. 3

Statistical Source Data of Fig. 3d,j.

Source Data Fig. 4

Statistical Source Data of Fig. 4b.

Source Data Fig. 5

Statistical Source Data of Fig. 5d,g.

Source Data Extended Data Fig. 3

Statistical Source Data of Extended Data Fig. 3.

Source Data Extended Data Fig. 6

Statistical Source Data of Extended Data Fig. 6e,f.

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Zhao, Y., Zhang, M., Zhang, W. et al. Isotropic super-resolution light-sheet microscopy of dynamic intracellular structures at subsecond timescales. Nat Methods 19, 359–369 (2022). https://doi.org/10.1038/s41592-022-01395-5

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