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High-speed laser-scanning biological microscopy using FACED

Nature Protocols volume 16, pages 4227–4264 (2021)Cite this article

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Abstract

Laser scanning is used in advanced biological microscopy to deliver superior imaging contrast, resolution and sensitivity. However, it is challenging to scale up the scanning speed required for interrogating a large and heterogeneous population of biological specimens or capturing highly dynamic biological processes at high spatiotemporal resolution. Bypassing the speed limitation of traditional mechanical methods, free-space angular-chirp-enhanced delay (FACED) is an all-optical, passive and reconfigurable laser-scanning approach that has been successfully applied in different microscopy modalities at an ultrafast line-scan rate of 1–80 MHz. Optimal FACED imaging performance requires optimized experimental design and implementation to enable specific high-speed applications. In this protocol, we aim to disseminate information allowing FACED to be applied to a broader range of imaging modalities. We provide (i) a comprehensive guide and design specifications for the FACED hardware; (ii) step-by-step optical implementations of the FACED module including the key custom components; and (iii) the overall image acquisition and reconstruction pipeline. We illustrate two practical imaging configurations: multimodal FACED imaging flow cytometry (bright-field, fluorescence and second-harmonic generation) and kHz 2D two-photon fluorescence microscopy. Users with basic experience in optical microscope operation and software engineering should be able to complete the setup of the FACED imaging hardware and software in ~2–3 months.

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Fig. 1: Overall schematic of a FACED microscope.
Fig. 2: Ray-tracing diagram of a FACED device based on a conjugate-mirror model.
Fig. 3: Design considerations for FACED imaging.
Fig. 4: Overview of FACED image acquisition flow diagram.
Fig. 5: General workflow of the protocol.
Fig. 6: Schematics of the FACED imaging system of three different implementations (I) (II) and (III).
Fig. 7: Mounting of the FACED mirror pair and vertical imaging part.
Fig. 8: Data acquisition and image processing.
Fig. 9: Characterization of basic FACED imaging performance.
Fig. 10: Applications of FACED imaging.

Data availability

The custom codes and data used in this protocol are available in the Supplementary Software and upon request, respectively.

References

  1. Pawley, J. Handbook of Biological Confocal Microscopy Vol. 236 (Springer Science & Business Media, 2006).

  2. Villette, V. et al. Ultrafast two-photon imaging of a high-gain voltage indicator in awake behaving mice. Cell 179, 1590–1608 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Gong, Y. et al. High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor. Science 350, 1361–1366 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Regev, A. et al. Science forum: the human cell atlas. eLife 6, e27041 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  5. Usaj, M. M. et al. High-content screening for quantitative cell biology. Trends Cell Biol 26, 598–611 (2016).

    Article  CAS  Google Scholar 

  6. Pegoraro, G. & Misteli, T. High-throughput imaging for the discovery of cellular mechanisms of disease. Trends Genet 33, 604–615 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Wu, J.-L. et al. Ultrafast laser-scanning time-stretch imaging at visible wavelengths. Light Sci. Appl. 6, e16196–e16196 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Yan, W., Wu, J., Wong, K. K. Y. & Tsia, K. K. A high‐throughput all‐optical laser‐scanning imaging flow cytometer with biomolecular specificity and subcellular resolution. J. Biophotonics 11, e201700178 (2018).

    Article  Google Scholar 

  9. Wu, J. et al. Multi-MHz laser-scanning single-cell fluorescence microscopy by spatiotemporally encoded virtual source array. Biomed. Opt. Express 8, 4160–4171 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Ren, Y.-X. et al. Parallelized volumetric fluorescence microscopy with a reconfigurable coded incoherent light-sheet array. Light Sci. Appl. 9, 8 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Wu, J. et al. Kilohertz two-photon fluorescence microscopy imaging of neural activity in vivo. Nat. Methods 17, 287–290 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Barteneva, N. S. & Vorobjev, I. A. Imaging Flow Cytometry (Springer, 2016).

  13. Tang, A. H. L. et al. Time-stretch microscopy on a DVD for high-throughput imaging cell-based assay. Biomed. Opt. Express 8, 640–652 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Li, Y. et al. Deep cytometry: deep learning with real-time inference in cell sorting and flow cytometry. Sci. Rep. 9, 11088 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lau, A. K. et al. Interferometric time-stretch microscopy for ultrafast quantitative cellular and tissue imaging at 1 μm. J. Biomed. Opt. 19, 76001 (2014).

    Article  PubMed  Google Scholar 

  16. Guo, B. et al. Optofluidic time-stretch quantitative phase microscopy. Methods 136, 116–125 (2018).

    Article  CAS  PubMed  Google Scholar 

  17. Jin, D. et al. Large population cell characterization using quantitative phase cytometer. Cytom. A 91, 450–459 (2017).

    Article  CAS  Google Scholar 

  18. Guo, B. et al. High‐throughput, label‐free, single‐cell, microalgal lipid screening by machine‐learning‐equipped optofluidic time‐stretch quantitative phase microscopy. Cytom. A 91, 494–502 (2017).

    Article  CAS  Google Scholar 

  19. Merola, F. et al. Tomographic flow cytometry by digital holography. Light Sci. Appl. 6, e16241–e16241 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Bianco, V. et al. Endowing a plain fluidic chip with micro-optics: a holographic microscope slide. Light Sci. Appl. 6, e17055–e17055 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Mandracchia, B. et al. Holographic microscope slide in a spatio-temporal imaging modality for reliable 3D cell counting. Lab Chip 17, 2831–2838 (2017).

    Article  CAS  PubMed  Google Scholar 

  22. Huang, D. et al. High-speed live-cell interferometry: a new method for quantifying tumor drug resistance and heterogeneity. Anal. Chem. 90, 3299–3306 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Lee, K. C. M. et al. Multi‐ATOM: ultrahigh‐throughput single‐cell quantitative phase imaging with subcellular resolution. J. Biophotonics 12, e201800479 (2019).

    PubMed  PubMed Central  Google Scholar 

  24. Lee, K. C. M. et al. Quantitative phase imaging flow cytometry for ultra‐large‐scale single‐cell biophysical phenotyping. Cytom. A 95, 510–520 (2019).

    Article  Google Scholar 

  25. Ugele, M. et al. Label‐free high‐throughput leukemia detection by holographic microscopy. Adv. Sci. 5, 1800761 (2018).

    Article  Google Scholar 

  26. Mugnano, M. et al. Label-free optical marker for red-blood-cell phenotyping of inherited anemias. Anal. Chem. 90, 7495–7501 (2018).

    Article  CAS  PubMed  Google Scholar 

  27. Karandikar, S. H. et al. Reagent-free and rapid assessment of T cell activation state using diffraction phase microscopy and deep learning. Anal. Chem. 91, 3405–3411 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Wang, D. & Bodovitz, S. Single cell analysis: the new frontier in ‘omics’. Trends Biotechnol 28, 281–290 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Doan, M. et al. Diagnostic potential of imaging flow cytometry. Trends Biotechnol 36, 649–652 (2018).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Marvin, J. S. et al. An optimized fluorescent probe for visualizing glutamate neurotransmission. Nat. Methods 10, 162–170 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Huang, C. et al. All-optical volumetric physiology for connectomics in dense neuronal structures. iScience 22, 133–146 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Marshall, G. F. & Stutz, G. E. Handbook of Optical and Laser Scanning (CRC Press, 2011).

  35. Choi, S. et al. Development of a high speed laser scanning confocal microscope with an acquisition rate up to 200 frames per second. Opt. Express 21, 23611–23618 (2013).

    Article  CAS  PubMed  Google Scholar 

  36. Römera, G. & Bechtoldb, P. Electro-optic and acousto-optic laser beam scanners—invited paper. Phys. Procedia 56, 29–39 (2014).

    Article  Google Scholar 

  37. Schlachter, S. C. et al. Spectrally encoded confocal microscopy of esophageal tissues at 100 kHz line rate. Biomed. Opt. Express 4, 1636–1645 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Goda, K., Tsia, K. K. & Jalali, B. Serial time-encoded amplified imaging for real-time observation of fast dynamic phenomena. Nature 458, 1145–1149 (2009).

    Article  CAS  PubMed  Google Scholar 

  39. Lau, A. K. S., Shum, H. C., Wong, K. K. Y. & Tsia, K. K. Optofluidic time-stretch imaging—an emerging tool for high-throughput imaging flow cytometry. Lab Chip 16, 1743–1756 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Karpf, S. et al. Spectro-temporal encoded multiphoton microscopy and fluorescence lifetime imaging at kilohertz frame-rates. Nat. Commun. 11, 2062 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Kubitscheck, U. (ed.). Fluorescence Microscopy: From Principles to Biological Applications (Wiley-Blackwell, 2013).

  42. Mazumdar, A. Principles and techniques of Schlieren imaging systems (Columbia University Computer Science Technical Reports, CUCS-016-13, 2013).

  43. Wong, T. T. W. et al. Asymmetric-detection time-stretch optical microscopy (ATOM) for ultrafast high-contrast cellular imaging in flow. Sci. Rep. 4, 3656 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  44. Chen, X., Nadiarynkh, O., Plotnikov, S. & Campagnola, P. J. Second harmonic generation microscopy for quantitative analysis of collagen fibrillar structure. Nat. Protoc. 7, 654–669 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sun, W., Tan, Z., Mensh, B. D. & Ji, N. Thalamus provides layer 4 of primary visual cortex with orientation-and direction-tuned inputs. Nat. Neurosci. 19, 308 (2016).

    Article  CAS  PubMed  Google Scholar 

  46. Siu, D. M. D. et al. Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity. Lab Chip (2020).

  47. Cox, G. C., Moreno, N. & Feijo, J. Second-harmonic imaging of plant polysaccharides. J. Biomed. Opt. 10, 24013 (2005).

    Article  CAS  Google Scholar 

  48. Singh, J. & Gu, S. Commercialization potential of microalgae for biofuels production. Renew. Sustain. Energy Rev. 14, 2596–2610 (2010).

    Article  CAS  Google Scholar 

  49. Dragone, G., Fernandes, B. D., Abreu, A. P., Vicente, A. A. & Teixeira, J. A. Nutrient limitation as a strategy for increasing starch accumulation in microalgae. Appl. Energy 88, 3331–3335 (2011).

    Article  CAS  Google Scholar 

  50. Nakagawa, S. & Cuthill, I. C. Effect size, confidence interval and statistical significance: a practical guide for biologists. Biol. Rev. 82, 591–605 (2007).

    Article  PubMed  Google Scholar 

  51. Koyande, A. K. et al. Microalgae: a potential alternative to health supplementation for humans. Food Sci. Hum. Wellness 8, 16–24 (2019).

    Article  Google Scholar 

  52. Blasi, T. et al. Label-free cell cycle analysis for high-throughput imaging flow cytometry. Nat. Commun. 7, 10256 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The work is supported by the Research Grants Council of the Hong Kong Special Administrative Region of China (grant nos. 17209017, 17259316, 17207715, C7047-16G and RFS2021-7S06), Innovation and Technology Support Programme (ITS/204/18), NIH BRAIN Initiative grants 1UF1NS107696.

Author information

Author notes

  1. Jianglai Wu

    Present address: Chinese Institute for Brain Research, Beijing, China

  2. These authors contributed equally: Queenie T. K. Lai, Gwinky G. K. Yip, Jianglai Wu.

Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China

    Queenie T. K. Lai, Gwinky G. K. Yip, Justin S. J. Wong, Michelle C. K. Lo, Kelvin C. M. Lee, Tony T. H. D. Le, Hayden K. H. So & Kevin K. Tsia

  2. Department of Physics, University of California, Berkeley, Berkeley, CA, USA

    Jianglai Wu & Na Ji

  3. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

    Na Ji

  4. Helen Wills Neuroscience Institute, University of California, Berkeley, Berkeley, CA, USA

    Na Ji

  5. Molecular Biophysics and Integrated Bioimaging Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Na Ji

  6. Advanced Biomedical Instrumentation Centre, Hong Kong Science Park, Shatin New Town, Hong Kong

    Kevin K. Tsia

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Contributions

All authors contributed to the development of the protocol and the writing of the manuscript.

Corresponding authors

Correspondence to Na Ji or Kevin K. Tsia.

Ethics declarations

Competing interests

K.K.T. and the University of Hong Kong have filed a US patent application (14/733,454) that relates to the all-optical laser-scanning imaging methods.

Additional information

Peer review information Nature Protocols thanks Pietro Ferraro and the other, anonymous reviewer(s) for their contribution to the peer review of this work.

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

Related links

Key references associated with this protocol

Wu, J. L. et al. Light Sci. Appl. 6, e16196 (2017): https://doi.org/10.1038/lsa.2016.196

Wu, J. et al. Nat. Methods 17, 287–290 (2020): https://doi.org/10.1038/s41592-020-0762-7

Ren, Y. X. et al. Light Sci. Appl. 9, 8 (2020): https://doi.org/10.1038/s41377-020-0245-8

Supplementary information

Supplementary Information

Supplememtary Manuals 1–3, Supplementary Note 1 and Supplementary Tables 1 and 2.

Reporting Summary

Supplementary Software

The data acquisition and visualizing program for FPGA system and oscilloscope.

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Lai, Q.T.K., Yip, G.G.K., Wu, J. et al. High-speed laser-scanning biological microscopy using FACED. Nat Protoc 16, 4227–4264 (2021). https://doi.org/10.1038/s41596-021-00576-4

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