Skip to main content

Thank you for visiting 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.

  • Protocol
  • Published:

A practical guide to scanning light-field microscopy with digital adaptive optics


With the development of a wide variety of animal models in recent years, there is a rapidly growing demand for long-term, high-speed intravital fluorescence imaging to observe intercellular and intracellular interactions in their native states. Scanning light-field microscopy (sLFM) with digital adaptive optics provides a compact computational solution by imaging the entire volume in a tomographic way with orders-of-magnitude improvement in spatiotemporal resolution and reduction in phototoxicity, as compared to traditional intravital microscopy. Here, we present a step-by-step protocol for both hardware and software implementation of multicolor sLFM as an add-on to a normal wide-field fluorescence microscope by using off-the-shelf lenses and devices at low cost. The procedure can be easily applied to other LFM variants, which can be advantageous in certain experimental contexts. Owing to the strong reliance of sLFM on algorithmic post-processing for high-quality data, the protocol describes various kinds of artefacts and corresponding parameters used for correcting and performance optimization. To increase the tolerance to system misalignment and differences in device fabrication, we describe a one-step calibration method for robust imaging performance up to the diffraction limit. An open-source graphical user interface is presented for hardware synchronization and real-time rendering of multiview images. The whole procedure including optical setup, software installation, system calibration and 3D reconstruction can be executed in 3–4 d with basic knowledge in optics and electronics.

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

Access options

Buy this article

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

Fig. 1: Overview of the DAOSLIMIT workflow.
Fig. 2: Schematic illustrations of DAOSLIMIT.
Fig. 3: Examples of common DAOSLIMIT artefacts.
Fig. 4: GUI software sLFdriver for data acquisition.
Fig. 5: Illustrations of the artefacts caused by NA mismatch between the objective lens and MLA.
Fig. 6: Artefacts caused by inaccurate magnification of the relay system between the MLA and the camera.
Fig. 7: The influence of inaccurate scanning amplitudes.
Fig. 8: Representative data from DAOSLIMIT experiments.

Similar content being viewed by others

Data availability

The data supporting this study are available within the article, the Supplementary Information and the primary supporting study6. All data mentioned in the protocol are available in Supplementary Software.

Code availability

The Zemax source files and all code and software used in the protocol are available in Supplementary Software. We have also uploaded the code on GitHub (, which can be updated in the future with more functions.


  1. Masedunskas, A., Porat-Shliom, N., Rechache, K., Aye, M.-P. & Weigert, R. Intravital microscopy reveals differences in the kinetics of endocytic pathways between cell cultures and live animals. Cells 1, 1121–1132 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pittet, M. J., Garris, C. S., Arlauckas, S. P. & Weissleder, R. Recording the wild lives of immune cells. Sci. Immunol. 3, eaaq0491 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  3. Winter, P. W. & Shroff, H. Faster fluorescence microscopy: advances in high speed biological imaging. Curr. Opin. Chem. Biol. 20, 46–53 (2014).

    Article  CAS  PubMed  Google Scholar 

  4. Liu, T. L. et al. Observing the cell in its native state: imaging subcellular dynamics in multicellular organisms. Science 360, eaaq1392 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  5. Icha, J., Weber, M., Waters, J. C. & Norden, C. Phototoxicity in live fluorescence microscopy, and how to avoid it. BioEssays 39, 1700003 (2017).

    Article  Google Scholar 

  6. Wu, J. et al. Iterative tomography with digital adaptive optics permits hour-long intravital observation of 3D subcellular dynamics at millisecond scale. Cell 184, 3318–3332.e17 (2021).

    Article  CAS  PubMed  Google Scholar 

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

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  Google Scholar 

  12. Yoon, Y.-G. et al. Sparse decomposition light-field microscopy for high speed imaging of neuronal activity. Optica 7, 1457–1468 (2020).

    Article  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  14. 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 

  15. Hua, X., Liu, W. & Jia, S. High-resolution Fourier light-field microscopy for volumetric multi-color live-cell imaging. Optica 8, 614–620 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  17. Truong, T. V. et al. High-contrast, synchronous volumetric imaging with selective volume illumination microscopy. Commun. Biol. 3, 1–8 (2020).

    Article  Google Scholar 

  18. Zhang, Z. et al. Imaging volumetric dynamics at high speed in mouse and zebrafish brain with confocal light field microscopy. Nat. Biotechnol. 39, 74–83 (2021).

    Article  CAS  PubMed  Google Scholar 

  19. Fu, Z. et al. Light field microscopy based on structured light illumination. Opt. Lett. 46, 3424–3427 (2021).

    Article  PubMed  Google Scholar 

  20. Zhang, Y. et al. Computational optical sectioning with an incoherent multiscale scattering model for light-field microscopy. Nat. Commun. 12, 6391 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lin, X., Wu, J. & Dai, Q. Camera array based light field microscopy. Biomed. Opt. Express 6, 3179–3189 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lu, Z. et al. Phase-space deconvolution for light field microscopy. Opt. Express 27, 18131–18145 (2019).

    Article  PubMed  Google Scholar 

  24. Zhang, Y. et al. DiLFM: an artifact-suppressed and noise-robust light-field microscopy through dictionary learning. Light Sci. Appl. 10, 152 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Wang, Z. et al. Real-time volumetric reconstruction of biological dynamics with light-field microscopy and deep learning. Nat. Methods 18, 551–556 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wagner, N. et al. Deep learning-enhanced light-field imaging with continuous validation. Nat. Methods 18, 557–563 (2021).

    Article  CAS  PubMed  Google Scholar 

  27. Alonso, M. A. Wigner functions in optics: describing beams as ray bundles and pulses as particle ensembles. Adv. Opt. Photonics 3, 272–365 (2011).

    Article  Google Scholar 

  28. Zhu, S., Lai, A., Eaton, K., Jin, P. & Gao, L. On the fundamental comparison between unfocused and focused light field cameras. Appl. Opt. 57, A1–A11 (2018).

    Article  PubMed  Google Scholar 

  29. Abrahamsson, S. et al. Fast multicolor 3D imaging using aberration-corrected multifocus microscopy. Nat. Methods 10, 60–63 (2013).

    Article  CAS  PubMed  Google Scholar 

  30. Nakano, A. Spinning-disk confocal microscopy—a cutting-edge tool for imaging of membrane traffic. Cell Struct. Funct. 27, 349–355 (2002).

    Article  PubMed  Google Scholar 

  31. Zipfel, W. R., Williams, R. M. & Webb, W. W. Nonlinear magic: multiphoton microscopy in the biosciences. Nat. Biotechnol. 21, 1369–1377 (2003).

    Article  CAS  PubMed  Google Scholar 

  32. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Fahrbach, F. O., Simon, P. & Rohrbach, A. Microscopy with self-reconstructing beams. Nat. Photonics 4, 780–785 (2010).

    Article  CAS  Google Scholar 

  35. Ji, N. Adaptive optical fluorescence microscopy. Nat. Methods 14, 374–380 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Park, J. H., Kong, L., Zhou, Y. & Cui, M. Large-field-of-view imaging by multi-pupil adaptive optics. Nat. Methods 14, 581–583 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Lin, Q. et al. Cerebellar neurodynamics predict decision timing and outcome on the single-trial level. Cell 180, 536–551 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Quicke, P. et al. Subcellular resolution three-dimensional light-field imaging with genetically encoded voltage indicators. Neurophotonics 7, 035006 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Schoonover, C. E., Ohashi, S. N., Axel, R. & Fink, A. J. P. Representational drift in primary olfactory cortex. Nature 594, 541–546 (2021).

    Article  CAS  PubMed  Google Scholar 

  40. Plaçais, P. Y. et al. Upregulated energy metabolism in the Drosophila mushroom body is the trigger for long-term memory. Nat. Commun. 8, 1–14 (2017).

    Article  CAS  Google Scholar 

  41. Jiang, D. et al. Migrasomes provide regional cues for organ morphogenesis during zebrafish gastrulation. Nat. Cell Biol. 21, 966–977 (2019).

    Article  CAS  PubMed  Google Scholar 

  42. Ma, L. et al. Discovery of the migrasome, an organelle mediating release of cytoplasmic contents during cell migration. Cell Res. 25, 24–38 (2015).

    Article  CAS  PubMed  Google Scholar 

  43. Zheng, G., Horstmeyer, R. & Yang, C. Wide-field, high-resolution Fourier ptychographic microscopy. Nat. Photonics 7, 739–745 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Humphry, M. J., Kraus, B., Hurst, A. C., Maiden, A. M. & Rodenburg, J. M. Ptychographic electron microscopy using high-angle dark-field scattering for sub-nanometre resolution imaging. Nat. Commun. 3, 1–7 (2012).

    Article  CAS  Google Scholar 

  45. Milkie, D. E., Betzig, E. & Ji, N. Pupil-segmentation-based adaptive optical microscopy with full-pupil illumination. Opt. Lett. 36, 4206–4208 (2011).

    Article  PubMed  Google Scholar 

  46. Boyd, S., Parikh, N., Chu, E., Peleato, B. & Eckstein, J. Distributed optimization and statistical learning via the alternating direction method of multipliers. Found. Trends Mach. Learn. 3, 1–122 (2010).

    Article  Google Scholar 

  47. Dabov, K., Foi, A., Katkovnik, V. & Egiazarian, K. Image denoising by sparse 3-D transform-domain collaborative filtering. IEEE Trans. Image Process. 16, 2080–2095 (2007).

    Article  PubMed  Google Scholar 

  48. Zhong, Q. et al. High-definition imaging using line-illumination modulation microscopy. Nat. Methods 18, 309–315 (2021).

    Article  CAS  PubMed  Google Scholar 

  49. Li, X. et al. Reinforcing neuron extraction and spike inference in calcium imaging using deep self-supervised denoising. Nat. Methods 18, 1395–1400 (2021).

    Article  CAS  PubMed  Google Scholar 

  50. Zhu, S., Tian, R., Antaris, A. L., Chen, X. & Dai, H. Near-infrared-II molecular dyes for cancer imaging and surgery. Adv. Mater. 31, e1900321 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Qian, Y. et al. A genetically encoded near-infrared fluorescent calcium ion indicator. Nat. Methods 16, 171–174 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Nieuwenhuizen, R. P. J. et al. Measuring image resolution in optical nanoscopy. Nat. Methods 10, 557–562 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references


We thank D. Jiang and L. Yu for their assistance in sample preparation. We thank T. Zhu and T. Yan for insightful discussions. This work was supported by the National Natural Science Foundation of China (62088102 and 62071272) and the National Key Research and Development Program of China (2020AA0105500 and 2020AAA0130000). We further acknowledge support from the Beijing Laboratory of Brain and Cognitive Intelligence, the Beijing Municipal Education Commission and the Beijing Key Laboratory of Multi-dimension & Multi-scale Computational Photography (MMCP).

Author information

Authors and Affiliations



Q.D., J.W. and Z.L. conceived the project. Z.L. and J.W. designed the whole pipeline. Z.L. and Y.C. conducted numerical simulations and biological experiments. Z.L. and Y.N. worked on data processing. J.W., Z.L. and Y.N. designed the Zemax and CAD prescriptions. Z.L. and Y.Y. prepared the acquisition and other pre-processing code for the protocol. Z.L, Y.C., J.W. and Q.D. wrote the manuscript.

Corresponding authors

Correspondence to Jiamin Wu or Qionghai Dai.

Ethics declarations

Competing interests

Q.D. and J.W. are co-founders and equity holders of Zhejiang Hehu Technology LLC, where the DAOSLIMIT technology is commercialized. Q.D., J.W. and Z.L. have patents related to the DAOSLIMIT technology (US Patent, no. 11,131,841).

Peer review

Peer review information

Nature Protocols thanks Robert Prevedel and Yicong Wu for their contribution to the peer review of this work.

Additional information

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

Related links

Key references using this protocol

Wu, J. et al. Cell 184, 3318–3332.e17 (2021):

Zhang, Y. et al. Nat. Commun. 12, 6391 (2021):

Zhang, Y. et al. Light Sci. Appl. 10, 152 (2021):

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Supplementary Methods and Supplementary Manuals 1 and 2.

Reporting Summary

Supplementary Software

The DAOSLIMIT software package contains the GUIs, related codes, reconstruction codes, Zemax files and example raw data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lu, Z., Cai, Y., Nie, Y. et al. A practical guide to scanning light-field microscopy with digital adaptive optics. Nat Protoc 17, 1953–1979 (2022).

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


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