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.

Imaging volumetric dynamics at high speed in mouse and zebrafish brain with confocal light field microscopy

Abstract

A detailed understanding of the function of neural networks and how they are supported by a dynamic vascular system requires fast three-dimensional imaging in thick tissues. Here we present confocal light field microscopy, a method that enables fast volumetric imaging in the brain at depths of hundreds of micrometers. It uses a generalized confocal detection scheme that selectively collects fluorescent signals from the in-focus volume and provides optical sectioning capability to improve imaging resolution and sensitivity in thick tissues. We demonstrate recording of whole-brain calcium transients in freely swimming zebrafish larvae and observe behaviorally correlated activities in single neurons during prey capture. Furthermore, in the mouse brain, we detect neural activities at depths of up to 370 μm and track blood cells at 70 Hz over a volume of diameter 800 μm × thickness 150 μm and depth of up to 600 μm.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: Design and characterization of confocal LFM.
Fig. 2: Whole-brain functional imaging of neural activity in movement-restrained larval zebrafish.
Fig. 3: Tracking and imaging whole-brain neural activity during larval zebrafish’s prey capture behavior.
Fig. 4: Volumetric functional imaging of neural activity in awake mouse brain.
Fig. 5: Imaging and tracking of circulating blood cells in awake mouse brain.

Data availability

We have no restriction on data availability. Data can be requested from the corresponding author.

Code availability

Custom LabVIEW codes for system control and data acquisition, MATLAB codes for volume reconstruction, system aberration correction, registration of imaging volumes of freely moving larval zebrafish and tracking circulating blood cells can be found in Supplementary Software.

References

  1. 1.

    Denk, W., Strickler, J. H. & Webb, W. W. Two-photon laser scanning fluorescence microscopy. Science 248, 73–76 (1990).

    CAS  PubMed  Article  Google Scholar 

  2. 2.

    Katona, G. et al. Fast two-photon in vivo imaging with three-dimensional random-access scanning in large tissue volumes. Nat. Methods 9, 201–208 (2012).

    CAS  PubMed  Article  Google Scholar 

  3. 3.

    Kong, L. et al. Continuous volumetric imaging via an optical phase-locked ultrasound lens. Nat. Methods 12, 759–762 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  4. 4.

    Nadella, K. M. N. S. et al. Random-access scanning microscopy for 3D imaging in awake behaving animals. Nat. Methods 13, 1001–1004 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  5. 5.

    Amir, W. et al. Simultaneous imaging of multiple focal planes using a two-photon scanning microscope. Opt. Lett. 32, 1731–1733 (2007).

    CAS  PubMed  Article  Google Scholar 

  6. 6.

    Cheng, A., Gonçalves, J. T., Golshani, P., Arisaka, K. & Portera-Cailliau, C. Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing. Nat. Methods 8, 139–142 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  7. 7.

    Prevedel, R. et al. Fast volumetric calcium imaging across multiple cortical layers using sculpted light. Nat. Methods 13, 1021–1028 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Yang, W. et al. Simultaneous multi-plane imaging of neural circuits. Neuron 89, 269–284 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Zhang, T. et al. Kilohertz two-photon brain imaging in awake mice. Nat. Methods 16, 1119–1122 (2019).

    CAS  PubMed  Article  Google Scholar 

  10. 10.

    Lu, R. et al. Video-rate volumetric functional imaging of the brain at synaptic resolution. Nat. Neurosci. 20, 620–628 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  11. 11.

    Song, A. et al. Volumetric two-photon imaging of neurons using stereoscopy (vTwINS). Nat. Methods 14, 420–426 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Kazemipour, A. et al. Kilohertz frame-rate two-photon tomography. Nat. Methods 16, 778–786 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  13. 13.

    Kerr, J. N. D. & Denk, W. Imaging in vivo: watching the brain in action. Nat. Rev. Neurosci. 9, 195–205 (2008).

    CAS  PubMed  Article  Google Scholar 

  14. 14.

    Ji, N., Freeman, J. & Smith, S. L. Technologies for imaging neural activity in large volumes. Nat. Neurosci. 19, 1154–1164 (2016).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  15. 15.

    Yang, W. & Yuste, R. In vivo imaging of neural activity. Nat. Methods 14, 349–359 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Weisenburger, S. & Vaziri, A. A guide to emerging technologies for large-scale and whole-brain optical imaging of neuronal activity. Ann. Rev. Neurosci. 41, 431–452 (2018).

    CAS  PubMed  Article  Google Scholar 

  17. 17.

    Bouchard, M. B. et al. Swept confocally-aligned planar excitation (SCAPE) microscopy for high-speed volumetric imaging of behaving organisms. Nat. Photonics 9, 113–119 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. 18.

    Voleti, V. et al. Real-time volumetric microscopy of in vivo dynamics and large-scale samples with SCAPE 2.0. Nat. Methods 16, 1054–1062 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  19. 19.

    Schrödel, T., Prevedel, R., Aumayr, K., Zimmer, M. & Vaziri, A. Brain-wide 3D imaging of neuronal activity in Caenorhabditis elegans with sculpted light. Nat. Methods 10, 1013–1020 (2013).

    PubMed  Article  CAS  Google Scholar 

  20. 20.

    Ahrens, M. B., Orger, M. B., Robson, D. N., Li, J. M. & Keller, P. J. Whole-brain functional imaging at cellular resolution using light-sheet microscopy. Nat. Methods 10, 413–420 (2013).

    CAS  PubMed  Article  Google Scholar 

  21. 21.

    Tomer, R. et al. SPED light sheet microscopy: fast mapping of biological system structure and function. Cell. 163, 1796–1806 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Holekamp, T. F., Turaga, D. & Holy, T. E. Fast three-dimensional fluorescence imaging of activity in neural populations by objective-coupled planar illumination microscopy. Neuron 57, 661–672 (2008).

    CAS  PubMed  Article  Google Scholar 

  23. 23.

    Levoy, M., Ng, R., Adams, A., Footer, M. & Horowitz, M. Light field microscopy. ACM Trans. Graph. 25, 924–934 (2006).

    Article  Google Scholar 

  24. 24.

    Broxton, M. et al. Wave optics theory and 3-D deconvolution for the light field microscope. Opt. Express 21, 25418–25439 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Prevedel, R. et al. Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy. Nat. Methods 11, 727–730 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. 26.

    Cong, L. et al. Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (Danio rerio). eLife. 6, e28158 (2017).

    PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Wagner, N. et al. Instantaneous isotropic volumetric imaging of fast biological processes. Nat. Methods 16, 497–500 (2019).

    CAS  PubMed  Article  Google Scholar 

  28. 28.

    Pégard, N. C. et al. Compressive light-field microscopy for 3D neural activity recording. Optica. 3, 517–524 (2016).

    Article  Google Scholar 

  29. 29.

    Nöbauer, T. et al. Video rate volumetric Ca2+ imaging across cortex using seeded iterative demixing (SID) microscopy. Nat. Methods 14, 811–818 (2017).

    PubMed  Article  CAS  Google Scholar 

  30. 30.

    Madrid-Wolff, J., Castro, D., Arbeláez, P. & Forero-Shelton, M. Light-sheet enhanced resolution of light field microscopy for rapid imaging of large volumes. In Proc. SPIE Vol. 10499 (eds Brown, T. G. et al.) (SPIE, 2018).

  31. 31.

    Wang, D. et al. Hybrid light-sheet and light-field microscope for high resolution and large volume neuroimaging. Biomed. Opt. Express 10, 6595–6610 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Truong, T. V. et al. High-contrast, synchronous volumetric imaging with selective volume illumination microscopy. Commun Biol 3, 74–74 (2020).

    PubMed  PubMed Central  Article  Google Scholar 

  33. 33.

    Yang, S. J. et al. Extended field-of-view and increased-signal 3D holographic illumination with time-division multiplexing. Opt. Express 23, 32573–32581 (2015).

    PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Hsu, F.-C., Da Sie, Y., Lai, F.-J. & Chen, S.-J. Volumetric bioimaging based on light field microscopy with temporal focusing illumination. In Proc. SPIE Vol. 10499 (eds Brown, T. G. et al.) (SPIE, 2018).

  35. 35.

    Taylor, M. A., Nöbauer, T., Pernia-Andrade, A., Schlumm, F. & Vaziri, A. Brain-wide 3D light-field imaging of neuronal activity with speckle-enhanced resolution. Optica 5, 345–353 (2018).

    Article  Google Scholar 

  36. 36.

    Hardy, J. W. Adaptive Optics for Astronomical Telescopes (Oxford Univ. Press, 1998).

  37. 37.

    Llavador, A., Sola-Pikabea, J., Saavedra, G., Javidi, B. & Martínez-Corral, M. Resolution improvements in integral microscopy with Fourier plane recording. Opt. Express 24, 20792–20798 (2016).

    CAS  PubMed  Article  Google Scholar 

  38. 38.

    Guo, C., Liu, W., Hua, X., Li, H. & Jia, S. Fourier light-field microscopy. Opt. Express 27, 25573–25594 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Mertz, J. Optical sectioning microscopy with planar or structured illumination. Nat. Methods 8, 811–819 (2011).

    CAS  PubMed  Article  Google Scholar 

  40. 40.

    Chen, T.-W. et al. Ultrasensitive fluorescent proteins for imaging neuronal activity. Nature 499, 295–300 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  41. 41.

    Nguyen, J. P. et al. Whole-brain calcium imaging with cellular resolution in freely behaving Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 113, E1074–E1081 (2016).

    CAS  PubMed  Article  Google Scholar 

  42. 42.

    Venkatachalam, V. et al. Pan-neuronal imaging in roaming Caenorhabditis elegans. Proc. Natl Acad. Sci. USA 113, E1082–E1088 (2016).

    CAS  PubMed  Article  Google Scholar 

  43. 43.

    Kim, D. H. et al. Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish. Nat. Methods 14, 1107–1114 (2017).

    CAS  PubMed  Article  Google Scholar 

  44. 44.

    Thouvenin, O. & Wyart, C. Tracking microscopy enables whole-brain imaging in freely moving zebrafish. Nat. Methods 14, 1041–1082 (2017).

    CAS  PubMed  Article  Google Scholar 

  45. 45.

    Semmelhack, J. L. et al. A dedicated visual pathway for prey detection in larval zebrafish. eLife 3, e04878 (2014).

    PubMed Central  Article  PubMed  Google Scholar 

  46. 46.

    Henriques, P. M., Rahman, N., Jackson, S. E. & Bianco, I. H. Nucleus isthmi is required to sustain target pursuit during visually guided prey-catching. Current Biology 29, 1771–1786 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  47. 47.

    Bianco, I. H. & Engert, F. Visuomotor transformations underlying hunting behavior in zebrafish. Curr. Biol. 25, 831–846 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  48. 48.

    Muto, A. et al. Activation of the hypothalamic feeding centre upon visual prey detection. Nat. Commun. 8, 15029 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Muto, A., Ohkura, M., Abe, G., Nakai, J. & Kawakami, K. Real-time visualization of neuronal activity during perception. Current Biology 23, 307–311 (2013).

    CAS  PubMed  Article  Google Scholar 

  50. 50.

    Zhou, P. et al. Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data. eLife 7, e28728 (2018).

    PubMed  PubMed Central  Article  Google Scholar 

  51. 51.

    Pnevmatikakis, EftychiosA. et al. Simultaneous denoising, deconvolution, and demixing of calcium imaging data. Neuron 89, 285–299 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  52. 52.

    Mickoleit, M. et al. High-resolution reconstruction of the beating zebrafish heart. Nat. Methods 11, 919–922 (2014).

    CAS  PubMed  Article  Google Scholar 

  53. 53.

    Grutzendler, J. & Nedergaard, M. Cellular Control of brain capillary blood flow: in vivo imaging Veritas. Trends Neurosci. 42, 528–536 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  54. 54.

    Kornfield, T. E. & Newman, E. A. Measurement of retinal blood flow using fluorescently labeled red blood cells. eNeuro 2, https://doi.org/10.1523/ENEURO.0005-15.2015 (2015).

  55. 55.

    Dana, H. et al. High-performance calcium sensors for imaging activity in neuronal populations and microcompartments. Nat. Methods 16, 649–657 (2019).

    CAS  PubMed  Article  Google Scholar 

  56. 56.

    Iadecola, C. The neurovascular unit coming of age: a journey through neurovascular coupling in health and disease. Neuron 96, 17–42 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  57. 57.

    Zhang, B., Yao, Y., Zhang, H., Kawakami, K. & Du, J. Left Habenula mediates light-preference behavior in Zebrafish via an asymmetrical visual pathway. Neuron 93, 914–928 (2017).

    CAS  PubMed  Article  Google Scholar 

  58. 58.

    Filosa, A., Barker, A. J., Dal Maschio, M. & Baier, H. Feeding state modulates behavioral choice and processing of prey stimuli in the Zebrafish tectum. Neuron 90, 596–608 (2016).

    CAS  PubMed  Article  Google Scholar 

  59. 59.

    Khoobehi, B., Peyman, G. A., Carnahan, L. G. & Hayes, R. L. A novel approach for freeze-frame video determination of volumetric blood flow in the rat retina. Ophthal. Surgery Lasers Imag. 34, 505–514 (2003).

    Google Scholar 

  60. 60.

    Smith, S. L. & Häusser, M. Parallel processing of visual space by neighboring neurons in mouse visual cortex. Nat. Neurosci. 13, 1144–1149 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

Download references

Acknowledgements

K.W. acknowledges support from National Key R&D Program of China (grant no. 2017YFA0700500), ‘Strategic Priority Research Program’ of the Chinese Academy of Sciences (grant no. XDB32030200), NSFC (grant no. 31871086), International Partnership Program of Chinese Academy of Sciences (grant no. 153D31KYSB20170059), Shanghai Municipal Science and Technology Major Project (grant nos. 2018SHZDZX05 and 18JC1410100). J.D. acknowledges support from Shanghai Municipal Science and Technology Major Project (grant no. 18JC1410100). We thank J. He at the Institute of Neuroscience for his support on zebrafish handling and helpful discussions. We thank the reviewers for their constructive comments and suggestions.

Author information

Affiliations

Authors

Contributions

Z.Z. and L.C. built confocal LFM. Z.Z. wrote the code for image reconstruction. Z.Z. and T.Z. performed zebrafish-related experiments and data analysis. L.B. and P.Y. performed the mouse experiments. L.B. performed data analysis on the mouse calcium imaging. L.B. and W.S. performed data analysis on blood flow in the mouse brain. Z.Z., F.L. and J.D. designed zebrafish behavioral experiments. K.W. conceived and led the project. Z.Z., L.C., L.B. and K.W. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Kai Wang.

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.

Supplementary information

Supplementary Information

Supplementary Figs. 1–24, Note 1 and Tables 1–3.

Reporting Summary

Supplementary Video 1

Animated schematics of confocal LFM with fast axial scanning. The excitation laser path and fluorescence imaging light path are in blue and green, respectively. The rotating wheel carrying masks and glass plates is rotated at constant speed. Trigger signals are generated by a trigger laser passing through trigger slits on masks (Supplementary Figs. 1 and 2) to synchronize scanning galvo, laser and camera. Every time a trigger slit on a mask is rotated to the focal spot of the trigger laser, the trigger detector detects a rising edge of laser power, which serves as the trigger signal. Rotation of galvo mirror and rotating wheel are synchronized to ensure the excitation laser sheet always goes through a thin slit in the center of mask. During the scan, fluorescence signals collected by microlenses are spatially filtered by apertures on the same mask before they are imaged on the camera. More detailed descriptions of the system can be found in Fig. 1 and Supplementary Note 1.

Supplementary Video 2

Comparison of confocal LFM and nonconfocal LFM when imaging spontaneous neural activities in restrained larval zebrafish brain. Spontaneous neural activities on a single plane in larval zebrafish brain (Tg(HuC:GCaMP6s), 6 dpf) imaged by confocal LFM with (left) and without (right) confocal mask. Sampling rates were 1 Hz in both conditions.

Supplementary Video 3

3D volumetric imaging of larval zebrafish brain. MIP in time of the calcium responses on flash light stimuli in a larval zebrafish brain (Tg(HuC:GCaMP6s), 5 dpf). The larval zebrafish is movement-restrained. Five imaging volumes obtained sequentially from five different mask–glass plate combinations were stitched together to cover an entire imaging depth of ~200 μm. The same data are shown in Fig. 2b–d and Supplementary Video 4.

Supplementary Video 4

Whole-brain functional imaging in larval zebrafish under light stimulation. Larval zebrafish (Tg(HuC:GCaMP6s), 5 dpf) with pan-neuronal labeling of cytosol-localized GCamp6s was imaged by confocal LFM at a 6-Hz volume rate. A flash of light was applied at time window of 0–4 s. The same data are shown in Fig. 2b–d and Supplementary Video 3.

Supplementary Video 5

Whole-brain functional imaging of a freely swimming larval zebrafish during prey capture behavior. Confocal LFM imaging of a freely swimming larval zebrafish expressing cytosol-localized, pan-neuronal GCaMP6s (Tg(HuC:GCaMP6s), 8 dpf). Left, behavioral recording of the zebrafish and paramecia. Right, MIPs of reconstructed volumes obtained in five different sets of masks and glass plates focusing at different depths. The raw reconstructions and registration of them in top views are displayed in the top and middle rows, respectively. Shown in the bottom row are the MIPs in the y–z view of registered results. At 15.03 s, the zebrafish larva initiated hunting, identified by the converging eyes onto the paramecium marked in blue and a J-turn to align its heading direction. It successfully ate the paramecium indicated in orange at 15.72 s. Video plays four times slower than real time. Scale bar, 100 µm. The same data are shown in Fig. 3.

Supplementary Video 6

Unbiased volumetric reconstruction of active neural structures in an awake mouse brain imaged by confocal LFM. Volumetric rendering of spontaneously active neural structures imaged by confocal LFM in an awake mouse brain. Neurons in mouse brain were densely labeled with cytosol-localized GCamp6. The displayed images are standard deviations of unbiased volumetric reconstruction time series with 1,600 time points. Detailed description of data processing can be found in the Methods. Three volumes were captured at the indicated different depths in different sessions. The same data are shown in Fig. 4a–e.

Supplementary Video 7

Active neural structures extracted by CNMF-E on each z plane independently in an imaging volume captured by confocal LFM. Volumetric rendering of time series of neural activities on their corresponding spatial footprints extracted by CNMF-E. The CNMF-E was separately applied on each z plane within the reconstructed volume (Methods). The imaging volume was from 100 to 200 μm below the cortical surface. The spatial footprints of the same neuron at different z planes showed highly correlated temporal traces, which validated the CNMF-E extraction results. The same data are shown in Fig. 4a–e.

Supplementary Video 8

Imaging circulating blood cells in an awake mouse brain by confocal LFM. Imaging circulating blood cells in awake mouse brain in six volumes starting from the cortex surface to 650 μm below at 70 Hz volume rate. Circulating cells labeled with red fluorescent dye are in red. Presumed blood vessels, as found by averaging over time images of labeled blood cells, are in green. Blood cells circulating as deep as 600 μm below the cortex surface can be imaged by confocal LFM.

Supplementary Video 9

Imaging circulating blood cells in an awake mouse brain over extended period of time. Volumetric imaging of circulating blood cells 100–250 μm below the mouse cortex surface over entire imaging session (1,260 frames in 18 s). These data are the same as in the second volume in Supplementary Video 8 but with more time points.

Supplementary Video 10

Analysis of instantaneous speeds of circulating cells in blood vessels. By tracking single cells in blood vessels, their speeds can be inferred. The skeletons of blood vessels are colored based on the average speeds of circulating blood cells in them over the entire imaging session (1,260 frames in 18 s). The change in brightness of each blood vessel skeleton indicates the change of instantaneous speeds of cells in it. This analysis was carried on an imaging volume covering ~100–250 μm below mouse cortex surface (same as in Supplementary Video 9). The skeletons of blood vessels in the volume were projected in the z direction for display.

Supplementary Software 1

MATLAB and LabVIEW source codes for system control, confocal LFM 3D reconstruction, system aberration calibration, 3D registration of zebrafish imaging data, analysis of circulating blood cells and example data for doing 3D reconstruction.

Supplementary Data 1

Design files for several key elements in confocal LFM.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Z., Bai, L., Cong, L. et al. Imaging volumetric dynamics at high speed in mouse and zebrafish brain with confocal light field microscopy. Nat Biotechnol 39, 74–83 (2021). https://doi.org/10.1038/s41587-020-0628-7

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing