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Artificial confocal microscopy for deep label-free imaging

Nature Photonics volume 17, pages 250–258 (2023)Cite this article

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Abstract

Wide-field microscopy of optically thick specimens typically features reduced contrast due to spatial cross-talk, in which the signal at each point in the field of view is the result of a superposition from neighbouring points that are simultaneously illuminated. In 1955, Marvin Minsky proposed confocal microscopy as a solution to this problem. Today, laser scanning confocal fluorescence microscopy is broadly used due to its high depth resolution and sensitivity, but comes at the price of photobleaching, chemical and phototoxicity. Here we present artificial confocal microscopy (ACM) to achieve confocal-level depth sectioning, sensitivity and chemical specificity non-destructively on unlabelled specimens. We equipped a commercial laser scanning confocal instrument with a quantitative phase imaging module, which provides optical path-length maps of the specimen in the same field of view as the fluorescence channel. Using pairs of phase and fluorescence images, we trained a convolution neural network to translate the former into the latter. The training to infer a new tag is very practical as the input and ground truth data are intrinsically registered and the data acquisition is automated. The ACM images present much stronger depth sectioning than the input (phase) images, enabling us to recover confocal-like tomographic volumes of microspheres, hippocampal neurons in culture, and three-dimensional liver cancer spheroids. By training on nucleus-specific tags, ACM allows for segmenting individual nuclei within dense spheroids for both cell counting and volume measurements. In summary, ACM can provide quantitative, dynamic data, non-destructively from thick samples while chemical specificity is recovered computationally.

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Fig. 1: ACM optical path and image processing.
Fig. 2: ACM network architecture and inference.
Fig. 3: ACM estimates volume and dry mass from inferred fluorescence signals.
Fig. 4: Label-free intracellular segmentation in turbid spheroids.
Fig. 5: Automated segmentation of cells inside spheroids.

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Data availability

Due to size considerations, the data that support the findings of this study are available from the corresponding author on reasonable request.

Code availability

The code that supports the findings of this study are available from the corresponding author on reasonable request.

References

  1. Freedman, B. S. et al. Modelling kidney disease with CRISPR-mutant kidney organoids derived from human pluripotent epiblast spheroids. Nat. Commun. 6, 1–13 (2015).

    Article  Google Scholar 

  2. Langhans, S. A. Three-dimensional in vitro cell culture models in drug discovery and drug repositioning. Front. Pharmacol. 9, 6 (2018).

    Article  Google Scholar 

  3. Bissell, M. J., Hall, H. G. & Parry, G. How does the extracellular matrix direct gene expression? J. Theor. Biol. 99, 31–68 (1982).

    Article  ADS  Google Scholar 

  4. Laschke, M. W. & Menger, M. D. Life is 3D: boosting spheroid function for tissue engineering. Trends Biotechnol. 35, 133–144 (2017).

    Article  Google Scholar 

  5. Bell, C. C. et al. Comparison of hepatic 2D sandwich cultures and 3D spheroids for long-term toxicity applications: a multicenter study. Toxicol. Sci. 162, 655–666 (2018).

    Article  Google Scholar 

  6. Fong, E. L. S., Toh, T. B., Yu, H. & Chow, E. K.-H. 3D culture as a clinically relevant model for personalized medicine. SLAS Tecnol. 22, 245–253 (2017).

    Article  Google Scholar 

  7. Kamm, R. D. et al. Perspective: the promise of multi-cellular engineered living systems. APL Bioeng. 2, 040901 (2018).

    Article  Google Scholar 

  8. Cvetkovic, C. et al. Three-dimensionally printed biological machines powered by skeletal muscle. Proc. Natl Acad. Sci. USA 111, 10125–10130 (2014).

    Article  ADS  Google Scholar 

  9. Williams, B. J., Anand, S. V., Rajagopalan, J. & Saif, M. T. A. A self-propelled biohybrid swimmer at low Reynolds number. Nat. Commun. 5, 1–8 (2014).

    Article  Google Scholar 

  10. Chen, X. & Korotkova, O. Optical beam propagation in soft anisotropic biological tissues. Osa Continuum 1, 1055–1067 (2018).

    Article  Google Scholar 

  11. Tuchin, V. V. & Society of Photo-optical Instrumentation Engineers. Tissue Optics: Light Scattering Methods and Instruments for Medical Diagnosis 2nd edn (SPIE/International Society for Optical Engineering, 2007).

  12. Chen, W. et al. High-throughput image analysis of tumor spheroids: a user-friendly software application to measure the size of spheroids automatically and accurately. J Vis Exp. https://doi.org/10.3791/51639 (2014).

  13. Minsky, M. S. Memoir on inventing the confocal. Scanning Microsc. 10, 128–138 (1988).

    Google Scholar 

  14. Wilson, T. & Sheppard, C. Theory and Practice of Scanning Optical Microscopy (Academic, 1984).

  15. Diaspro, A. Optical Fluorescence Microscopy (Springer, 2011).

  16. Lippincott-Schwartz, J., Altan-Bonnet, N. & Patterson, G. H. Photobleaching and photoactivation: following protein dynamics in living cells. Nat. Cell Biol. S7–S14 (2003).

  17. Hoebe, R. A. et al. Controlled light-exposure microscopy reduces photobleaching and phototoxicity in fluorescence live-cell imaging. Nat. Biotechnol. 25, 249–253 (2007).

    Article  Google Scholar 

  18. Crivat, G. & Taraska, J. W. Imaging proteins inside cells with fluorescent tags. Trends Biotechnol. 30, 8–16 (2012).

    Article  Google Scholar 

  19. Graf, B. W. & Boppart, S. A. in Live Cell Imaging: Methods and Protocols 211–227 (Springer, 2010).

  20. North, A. J. Seeing is believing? A beginners’ guide to practical pitfalls in image acquisition. J. Cell Biol. 172, 9–18 (2006).

    Article  Google Scholar 

  21. Hoover, E. E. & Squier, J. A. Advances in multiphoton microscopy technology. Nat. Photon. 7, 93–101 (2013).

    Article  ADS  Google Scholar 

  22. Stelzer, E. H. et al. Light sheet fluorescence microscopy. Nat. Rev. Methods Primers 1, 1–25 (2021).

    Article  Google Scholar 

  23. Choi, W. J., Pepple, K. L. & Wang, R. K. Automated three‐dimensional cell counting method for grading uveitis of rodent eye in vivo with optical coherence tomography. J. Biophoton. 11, e201800140 (2018).

    Article  Google Scholar 

  24. Huang, Y. et al. Optical coherence tomography detects necrotic regions and volumetrically quantifies multicellular tumor spheroids. Cancer Res. 77, 6011–6020 (2017).

    Article  Google Scholar 

  25. Schnell, M. et al. High-resolution label-free imaging of tissue morphology with confocal phase microscopy. Optica 7, 1173–1180 (2020).

    Article  ADS  Google Scholar 

  26. Hase, E. et al. Scan-less confocal phase imaging based on dual-comb microscopy. Optica 5, 634–643 (2018).

    Article  ADS  Google Scholar 

  27. Singh, V. R. et al. Studying nucleic envelope and plasma membrane mechanics of eukaryotic cells using confocal reflectance interferometric microscopy. Nat. Commun. 10, 1–8 (2019).

    Article  ADS  Google Scholar 

  28. Liu, C. et al. High-speed line-field confocal holographic microscope for quantitative phase imaging. Opt. Express 24, 9251–9265 (2016).

    Article  ADS  Google Scholar 

  29. Popescu, G. Quantitative Phase Imaging of Cells and Tissues (McGraw-Hill, 2011).

  30. Park, Y., Depeursinge, C. & Popescu, G. Quantitative phase imaging in biomedicine. Nat. Photon. 12, 578 (2018).

    Article  ADS  Google Scholar 

  31. Chen, X., Kandel, M. E. & Popescu, G. Spatial light interference microscopy: principle and applications to biomedicine. Adv. Opt. Photon. 13, 353–425 (2021).

    Article  Google Scholar 

  32. Chen, X., Kandel, M. E., Hu, C., Lee, Y. J. & Popescu, G. Wolf phase tomography (WPT) of transparent structures using partially coherent illumination. Light Sci. Appl. 9, 1–9 (2020).

    Article  Google Scholar 

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

    Article  Google Scholar 

  34. Chen, M., Ren, D., Liu, H.-Y., Chowdhury, S. & Waller, L. Multi-layer Born multiple-scattering model for 3D phase microscopy. Optica 7, 394–403 (2020).

    Article  ADS  Google Scholar 

  35. Hugonnet, H. et al. Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution. Adv. Photon. 3, 026004 (2021).

    Article  ADS  Google Scholar 

  36. Ledwig, P. & Robles, F. E. Epi-mode tomographic quantitative phase imaging in thick scattering samples. Biomed. Opt. Express 10, 3605–3621 (2019).

    Article  Google Scholar 

  37. Kandel, M. E. et al. Epi-illumination gradient light interference microscopy for imaging opaque structures. Nat. Commun. 10, 4691 (2019).

    Article  ADS  Google Scholar 

  38. Nguyen, T. H., Kandel, M. E., Rubessa, M., Wheeler, M. B. & Popescu, G. Gradient light interference microscopy for 3D imaging of unlabeled specimens. Nat. Commun. 8, 210 (2017).

    Article  ADS  Google Scholar 

  39. Wang, H. et al. Deep learning enables cross-modality super-resolution in fluorescence microscopy. Nat. Methods 16, 103–110 (2019).

    Article  Google Scholar 

  40. Wu, Y. et al. Three-dimensional virtual refocusing of fluorescence microscopy images using deep learning. Nat. Methods 16, 1323–1331 (2019).

    Article  Google Scholar 

  41. Williams, B. M. et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia 63, 419–430 (2020).

    Article  Google Scholar 

  42. Cheng, S., Li, H., Luo, Y., Zheng, Y. & Lai, P. Artificial intelligence-assisted light control and computational imaging through scattering media. J. Innov. Opt. Health Sci. 12, 1930006 (2019).

    Article  Google Scholar 

  43. Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F. & Johnson, G. R. Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy. Nat. Methods 15, 917–920 (2018).

    Article  Google Scholar 

  44. Lee, M. et al. Deep-learning-based three-dimensional label-free tracking and analysis of immunological synapses of CAR-T cells. eLife 9, e49023 (2020).

    Article  Google Scholar 

  45. Tan, M. & Le, Q. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. In Proc. 36th International Conference on Machine Learning 6105–6114 (PMLR, 2019).

  46. Chen, X. & Korotkova, O. Probability density functions of instantaneous Stokes parameters on weak scattering. Opt. Commun. 400, 1–8 (2017).

    Article  ADS  Google Scholar 

  47. Kanai, Y. & Hirokawa, N. Sorting mechanisms of Tau and MAP2 in neurons: suppressed axonal transit of MAP2 and locally regulated microtubule binding. Neuron 14, 421–432 (1995).

    Article  Google Scholar 

  48. Rein, A. Retroviral RNA packaging: a review. Arch. Viol. Suppl. 9, 513–522 (1994).

  49. Kingma, D.P. & Ba, J. Adam: a method for stochastic optimization. CoRR, abs/1412.6980 (2014).

  50. Deng, J. et al. ImageNet: A large-scale hierarchical image database. In 2009 IEEE Conference on Computer Vision and Pattern Recognition 248–255 (IEEE, 2009).

  51. Kandel, M. E. et al. Multiscale assay of unlabeled neurite dynamics using phase imaging with computational specificity. ACS Sens. 6, 1864–1874 (2021).

    Article  Google Scholar 

  52. Barer, R. Determination of dry mass, thickness, solid and water concentration in living cells. Nature 172, 1097–1098 (1953).

    Article  ADS  Google Scholar 

  53. Gonzalez, R. C. & Woods, R. E. Digital Image Processing (Prentice-Hall, 2002).

  54. Stalling, D., Westerhoff M., & Hege H.-C. Amira: A highly interactive system for visual data analysis. Visualization Handb. 38, 749–767 (2005).

Download references

Acknowledgements

This work is supported by the National Science Foundation (grant nos. CBET0939511 STC, NRT-UtB 1735252, CBET-1932192), the National Institute of General Medical Sciences (grant no. GM129709), the National Insititute of Neurological Disorders and Stroke (grant nos. NS097610 and NS100019) and the National Cancer Institute (grant no. CA238191).

Author information

Author notes

  1. Xi Chen

    Present address: School of Applied and Engineering Physics, Cornell University, Ithaca, USA

  2. Mikhail E. Kandel

    Present address: Groq, Mountain View, CA, USA

Authors and Affiliations

  1. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Xi Chen, Mikhail E. Kandel, Chenfei Hu, Young Jae Lee, Hee Jung Chung, Hyun Joon Kong, Mark Anastasio & Gabriel Popescu

  2. Department of Computer Science and Engineering, Washington University in St Louis, St Louis, Missouri, USA

    Shenghua He

  3. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Chenfei Hu & Gabriel Popescu

  4. Neuroscience Program, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Young Jae Lee & Hee Jung Chung

  5. Department of Bioengineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Kathryn Sullivan, Hyun Joon Kong, Mark Anastasio & Gabriel Popescu

  6. Department of Molecular and Integrative Physiology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Gregory Tracy & Hee Jung Chung

  7. Carl Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Hee Jung Chung, Hyun Joon Kong & Gabriel Popescu

  8. Chemical and Biomolecular Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    Hyun Joon Kong

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Contributions

X.C., M.E.K., and G.P. conceived the project. X.C. and M.E.K. designed the experiments. X.C. and M.E.K. built the system. X.C. performed imaging. S.H. trained the machine learning network. X.C. and M.E.K. analysed the data. G.T & H.J.C. provided neurons. Y.J.L. cultured neurons and performed immunocytochemistry. K.M.S. & H.K. provided spheroids. X.C., C.H. and G.P. derived the theoretical model. X.C., M.E.K., S.H., C.H. and G.P. wrote the manuscript. M.A. supervised the AI work. G.P. supervised the project.

Corresponding author

Correspondence to Xi Chen.

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Competing interests

G.P. had a financial interest in Phi Optics, a company developing QPI technology for materials and life science applications. The remaining authors declare no competing interests.

Peer review

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Nature Photonics thanks Adam Wax and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Comparison of ground truth to ACM power spectra from Fig. 3a–l.

Contours circumscribing theoretical resolution limits of confocal fluorescence system (ground truth) are shown in as red dotted circles. The theoretical lateral resolution of the system is 0.22 μm (NA = 1.3, 1 Airy Unit (AU), excitation wavelength at 561 nm), corresponding to a maximum lateral frequency of 14.3 rad/μm. The theoretical axial resolution of the system is about 0.50 μm, corresponding to a maximum axial frequency of 6.3 rad/μm. The 3D frequency coverage of the ground truth and ACM spectra agree, and both reach the theoretical resolution limits.

Supplementary information

Supplementary Information

Supplementary Figs. 1–15 and Notes 1–5.

Reporting Summary

Supplementary Video 1

ACM-predicted 3D tomography of unlabelled live neurons.

Supplementary Video 2

ACM-predicted timelapse of unlabelled live neurons.

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Chen, X., Kandel, M.E., He, S. et al. Artificial confocal microscopy for deep label-free imaging. Nat. Photon. 17, 250–258 (2023). https://doi.org/10.1038/s41566-022-01140-6

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