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  • Primer
  • Published:

Holotomography

Abstract

Holotomography (HT) represents a 3D, label-free optical imaging methodology that leverages refractive index as an inherent quantitative contrast for imaging. This technique has recently seen notable advancements, creating novel opportunities for the comprehensive visualization and analysis of living cells and their subcellular organelles. It has manifested wide-ranging applications spanning cell biology, biophysics, microbiology and biotechnology, substantiating its vast potential. In this Primer, we elucidate the foundational physical principles underpinning HT, detailing its experimental implementations and providing case studies of representative research employing this methodology. We also venture into interdisciplinary territories, exploring how HT harmonizes with emergent technologies, such as regenerative medicine, 3D biology and organoid-based drug discovery and screening. Looking ahead, we engage in a prospective analysis of potential future trajectories for HT, discussing innovation-focused initiatives that may further elevate this field. We also propose possible future applications of HT, identifying opportunities for its integration into diverse realms of scientific research and technological development.

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Fig. 1: An overview of holotomography and diverse implementations.
Fig. 2: General landscape of contemporary holotomography applications.
Fig. 3: Optical measurement and numerical reconstruction in holotomography.
Fig. 4: Label-free 3D imaging of diverse live cells.
Fig. 5: Applications of holotomography in microbiology.
Fig. 6: Applications of holotomography in the study of organoids.
Fig. 7: Typical imaging artefacts in holotomography.
Fig. 8: Artificial-intelligence-assisted holotomography for investigating the organization of a 3D tissue.

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References

  1. Robertson, L. A. van Leeuwenhoek microscopes — where are they now? FEMS Microbiol. Lett. 362, fnv056 (2015).

    Article  Google Scholar 

  2. Wiedenmann, J., Oswald, F. & Nienhaus, G. U. Fluorescent proteins for live cell imaging: opportunities, limitations, and challenges. IUBMB Life 61, 1029–1042 (2009).

    Article  Google Scholar 

  3. Ghosh, B. & Agarwal, K. Viewing life without labels under optical microscopes. Commun. Biol. 6, 559 (2023).

    Article  Google Scholar 

  4. Shaked, N. T., Boppart, S. A., Wang, L. V. & Popp, J. Label-free biomedical optical imaging. Nat. Photon. 17, 1031–1041 (2023).

    Article  ADS  Google Scholar 

  5. Park, Y., Depeursinge, C. & Popescu, G. Quantitative phase imaging in biomedicine. Nat. Photon. 12, 578–589 (2018).

    Article  ADS  Google Scholar 

  6. 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 

  7. Lee, M. J. et al. Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids using low-coherence holotomography. Preprint at bioRxiv https://doi.org/10.1101/2023.09.16.558039 (2023).

  8. 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 

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

    Article  Google Scholar 

  10. Sung, Y. et al. Three-dimensional holographic refractive-index measurement of continuously flowing cells in a microfluidic channel. Phys. Rev. Appl. 1, 014002 (2014).

    Article  ADS  Google Scholar 

  11. Pirone, D. et al. Stain-free identification of cell nuclei using tomographic phase microscopy in flow cytometry. Nat. Photon. 16, 851–859 (2022).

    Article  ADS  Google Scholar 

  12. Lee, C. et al. Label-free three-dimensional observations and quantitative characterisation of on-chip vasculogenesis using optical diffraction tomography. Lab Chip 21, 494–501 (2021).

    Article  Google Scholar 

  13. Tebon, P. J. et al. Drug screening at single-organoid resolution via bioprinting and interferometry. Nat. Commun. 14, 3168 (2023).

    Article  ADS  Google Scholar 

  14. Jo, Y. et al. Label-free multiplexed microtomography of endogenous subcellular dynamics using generalizable deep learning. Nat. Cell Biol. 23, 1329–1337 (2021).

    Article  Google Scholar 

  15. Yasuhiko, O. & Takeuchi, K. In-silico clearing approach for deep refractive index tomography by partial reconstruction and wave-backpropagation. Light Sci. Appl. 12, 101 (2023).

    Article  ADS  Google Scholar 

  16. Park, J. et al. Quantification of structural heterogeneity in H&E stained clear cell renal cell carcinoma using refractive index tomography. Biomed. Opt. Express 14, 1071–1081 (2023).

    Article  Google Scholar 

  17. Lee, A. J. et al. Label-free monitoring of 3D cortical neuronal growth in vitro using optical diffraction tomography. Biomed. Opt. Express 12, 6928–6939 (2021).

    Article  Google Scholar 

  18. Wang, Z. et al. Spatial light interference tomography (SLIT). Opt. Express 19, 19907–19918 (2011).

    Article  ADS  Google Scholar 

  19. Oh, J. et al. Three-dimensional label-free observation of individual bacteria upon antibiotic treatment using optical diffraction tomography. Biomed. Opt. Express 11, 1257–1267 (2020).

    Article  Google Scholar 

  20. Kim, G. et al. Rapid species identification of pathogenic bacteria from a minute quantity exploiting three-dimensional quantitative phase imaging and artificial neural network. Light Sci. Appl. 11, 190 (2022).

    Article  ADS  Google Scholar 

  21. Wolf, E. Three-dimensional structure determination of semi-transparent objects from holographic data. Opt. Commun. 1, 153–156 (1969).

    Article  ADS  Google Scholar 

  22. Kak, A. C. & Slaney, M. Principles of Computerized Tomographic Imaging (SIAM, 2001).

  23. Takeda, M., Ina, H. & Kobayashi, S. Fourier-transform method of fringe-pattern analysis for computer-based topography and interferometry. J. Opt. Soc. Am. 72, 156–160 (1982).

    Article  ADS  Google Scholar 

  24. Yamaguchi, I. & Zhang, T. Phase-shifting digital holography. Opt. Lett. 22, 1268–1270 (1997).

    Article  ADS  Google Scholar 

  25. Kim, K. et al. Diffraction optical tomography using a quantitative phase imaging unit. Opt. Lett. 39, 6935–6938 (2014).

    Article  ADS  Google Scholar 

  26. Zdańkowski, P. et al. Common-path intrinsically achromatic optical diffraction tomography. Biomed. Opt. Express 12, 4219–4234 (2021).

    Article  Google Scholar 

  27. Kim, Y. et al. Common-path diffraction optical tomography for investigation of three-dimensional structures and dynamics of biological cells. Opt. Express 22, 10398–10407 (2014).

    Article  ADS  Google Scholar 

  28. Bon, P., Maucort, G., Wattellier, B. & Monneret, S. Quadriwave lateral shearing interferometry for quantitative phase microscopy of living cells. Opt. Express 17, 13080–13094 (2009).

    Article  ADS  Google Scholar 

  29. Baek, Y. & Park, Y. Intensity-based holographic imaging via space-domain Kramers–Kronig relations. Nat. Photon. 15, 354–360 (2021).

    Article  ADS  Google Scholar 

  30. Ling, R., Tahir, W., Lin, H.-Y., Lee, H. & Tian, L. High-throughput intensity diffraction tomography with a computational microscope. Biomed. Opt. Express 9, 2130–2141 (2018).

    Article  Google Scholar 

  31. Gbur, G. & Wolf, E. Hybrid diffraction tomography without phase information. J. Opt. Soc. Am. A 19, 2194–2202 (2002).

    Article  ADS  Google Scholar 

  32. Lee, J. et al. High-precision and low-noise dielectric tensor tomography using a micro-electromechanical system mirror. Opt. Express 32, 23171–23179 (2024).

    Article  Google Scholar 

  33. Choi, W. et al. Tomographic phase microscopy. Nat. Methods 4, 717–719 (2007).

    Article  Google Scholar 

  34. Lauer, V. New approach to optical diffraction tomography yielding a vector equation of diffraction tomography and a novel tomographic microscope. J. Microsc. 205, 165–176 (2002).

    Article  MathSciNet  Google Scholar 

  35. Kim, K. et al. High-resolution three-dimensional imaging of red blood cells parasitized by Plasmodium falciparum and in situ hemozoin crystals using optical diffraction tomography. J. Biomed. Opt. 19, 011005 (2014).

    ADS  Google Scholar 

  36. Park, C., Lee, K., Baek, Y. & Park, Y. Low-coherence optical diffraction tomography using a ferroelectric liquid crystal spatial light modulator. Opt. Express 28, 39649–39659 (2020).

    Article  ADS  Google Scholar 

  37. Shin, S., Kim, K., Yoon, J. & Park, Y. Active illumination using a digital micromirror device for quantitative phase imaging. Opt. Lett. 40, 5407–5410 (2015).

    Article  ADS  Google Scholar 

  38. Charrière, F. et al. Cell refractive index tomography by digital holographic microscopy. Opt. Lett. 31, 178–180 (2006).

    Article  ADS  Google Scholar 

  39. Habaza, M., Gilboa, B., Roichman, Y. & Shaked, N. T. Tomographic phase microscopy with 180 rotation of live cells in suspension by holographic optical tweezers. Opt. Lett. 40, 1881–1884 (2015).

    Article  ADS  Google Scholar 

  40. Lee, K., Shin, S., Yaqoob, Z., So, P. T. C. & Park, Y. Low-coherent optical diffraction tomography by angle-scanning illumination. J. Biophotonics 12, e201800289 (2019).

    Article  Google Scholar 

  41. Streibl, N. Three-dimensional imaging by a microscope. J. Opt. Soc. Am. A 2, 121–127 (1985).

    Article  ADS  Google Scholar 

  42. Chen, M., Tian, L. & Waller, L. 3D differential phase contrast microscopy. Biomed. Opt. Express 7, 3940–3950 (2016).

    Article  Google Scholar 

  43. Hugonnet, H., Lee, M. & Park, Y. Optimizing illumination in three-dimensional deconvolution microscopy for accurate refractive index tomography. Opt. Express 29, 6293–6301 (2021).

    Article  ADS  Google Scholar 

  44. Soto, J. M., Rodrigo, J. A. & Alieva, T. Label-free quantitative 3D tomographic imaging for partially coherent light microscopy. Opt. Express 25, 15699–15712 (2017).

    Article  ADS  Google Scholar 

  45. Kim, T. et al. White-light diffraction tomography of unlabelled live cells. Nat. Photon. 8, 256–263 (2014).

    Article  ADS  Google Scholar 

  46. 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 

  47. Li, J. et al. Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy. Light Sci. Appl. 11, 154 (2022).

    Article  ADS  Google Scholar 

  48. Phillips, Z. F., Chen, M. & Waller, L. Single-shot quantitative phase microscopy with color-multiplexed differential phase contrast (cDPC). PLoS ONE 12, e0171228 (2017).

    Article  Google Scholar 

  49. Lee, C. et al. Single-shot refractive index slice imaging using spectrally multiplexed optical transfer function reshaping. Opt. Express 31, 13806–13816 (2023).

    Article  ADS  Google Scholar 

  50. Lee, M., Hugonnet, H. & Park, Y. Inverse problem solver for multiple light scattering using modified Born series. Optica 9, 177–182 (2022).

    Article  ADS  Google Scholar 

  51. Yasuhiko, O., Takeuchi, K., Yamada, H. & Ueda, Y. Multiple-scattering suppressive refractive index tomography for the label-free quantitative assessment of multicellular spheroids. Biomed. Opt. Express 13, 962–979 (2022).

    Article  Google Scholar 

  52. Bruning, J. H. et al. Digital wavefront measuring interferometer for testing optical surfaces and lenses. Appl. Opt. 13, 2693–2703 (1974).

    Article  ADS  Google Scholar 

  53. Born, M. & Wolf, E. Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light 7th edn (Cambridge Univ. Press, 1999).

  54. Sung, Y. et al. Optical diffraction tomography for high resolution live cell imaging. Opt. Express 17, 266–277 (2009).

    Article  ADS  Google Scholar 

  55. Rienzi, L., Vajta, G. & Ubaldi, F. Predictive value of oocyte morphology in human IVF: a systematic review of the literature. Hum. Reprod. Update 17, 34–45 (2010).

    Article  Google Scholar 

  56. Novotny, L. & Hecht, B. Principles of Nano-Optics (Cambridge Univ. Press, 2012).

  57. Shin, S. et al. Tomographic measurement of dielectric tensors at optical frequency. Nat. Mater. 21, 317–324 (2022).

    Article  ADS  Google Scholar 

  58. Saba, A., Lim, J., Ayoub, A. B., Antoine, E. E. & Psaltis, D. Polarization-sensitive optical diffraction tomography. Optica 8, 402–408 (2021).

    Article  ADS  Google Scholar 

  59. Yeh, L.-H. et al. uPTI: uniaxial permittivity tensor imaging of intrinsic density and anisotropy. in Biophotonic Congress 2021 NM3C.4 (Optica Publishing Group, 2021).

  60. Jenkins, M. H. & Gaylord, T. K. Three-dimensional quantitative phase imaging via tomographic deconvolution phase microscopy. Appl. Opt. 54, 9213–9227 (2015).

    Article  ADS  Google Scholar 

  61. Hugonnet, H., Lee, M. J. & Park, Y. K. Quantitative phase and refractive index imaging of 3D objects via optical transfer function reshaping. Opt. Express 30, 13802–13809 (2022).

    Article  ADS  Google Scholar 

  62. Bon, P., Aknoun, S., Monneret, S. & Wattellier, B. Enhanced 3D spatial resolution in quantitative phase microscopy using spatially incoherent illumination. Opt. Express 22, 8654–8671 (2014).

    Article  ADS  Google Scholar 

  63. Lim, J., Ayoub, A. B., Antoine, E. E. & Psaltis, D. High-fidelity optical diffraction tomography of multiple scattering samples. Light Sci. Appl. 8, 82 (2019).

    Article  ADS  Google Scholar 

  64. Kamilov, U. S. et al. Learning approach to optical tomography. Optica 2, 517–522 (2015).

    Article  ADS  Google Scholar 

  65. Pham, T.-A. et al. Versatile reconstruction framework for diffraction tomography with intensity measurements and multiple scattering. Opt. Express 26, 2749–2763 (2018).

    Article  ADS  Google Scholar 

  66. 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 

  67. Soubies, E., Pham, T.-A. & Unser, M. Efficient inversion of multiple-scattering model for optical diffraction tomography. Opt. Express 25, 21786–21800 (2017).

    Article  ADS  Google Scholar 

  68. Hugonnet, H., Lee, M., Shin, S. & Park, Y. Vectorial inverse scattering for dielectric tensor tomography: overcoming challenges of reconstruction of highly scattering birefringent samples. Opt. Express 31, 29654–29663 (2023).

    Article  ADS  Google Scholar 

  69. Lim, J. et al. Comparative study of iterative reconstruction algorithms for missing cone problems in optical diffraction tomography. Opt. Express 23, 16933–16948 (2015).

    Article  ADS  Google Scholar 

  70. Gerchberg, R. Super-resolution through error energy reduction. Opt. Acta Int. J. Opt. 21, 709–720 (1974).

    Article  ADS  Google Scholar 

  71. LaRoque, S. J., Sidky, E. Y. & Pan, X. Accurate image reconstruction from few-view and limited-angle data in diffraction tomography. J. Opt. Soc. Am. A 25, 1772–1782 (2008).

    Article  ADS  Google Scholar 

  72. Lustig, M., Donoho, D. & Pauly, J. M. Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007).

    Article  Google Scholar 

  73. Rudin, L. I., Osher, S. & Fatemi, E. Nonlinear total variation based noise removal algorithms. Phys. D Nonli. Phenom. 60, 259–268 (1992).

    Article  ADS  MathSciNet  Google Scholar 

  74. Lefkimmiatis, S., Ward, J. P. & Unser, M. Hessian Schatten-norm regularization for linear inverse problems. IEEE Trans. Image Process. 22, 1873–1888 (2013).

    Article  ADS  MathSciNet  Google Scholar 

  75. Pham, T.-A et al. Three-dimensional optical diffraction tomography with Lippmann–Schwinger model. IEEE Trans. Comput. Imaging 6, 727–738 (2020).

    Article  MathSciNet  Google Scholar 

  76. Delaney, A. H. & Bresler, Y. Globally convergent edge-preserving regularized reconstruction: an application to limited-angle tomography. IEEE Trans. Image Process. 7, 204–221 (1998).

    Article  ADS  Google Scholar 

  77. Sung, Y., Choi, W., Lue, N., Dasari, R. R. & Yaqoob, Z. Stain-free quantification of chromosomes in live cells using regularized tomographic phase microscopy. PLoS ONE 7, e49502 (2012).

    Article  ADS  Google Scholar 

  78. Charbonnier, P., Blanc-Féraud, L., Aubert, G. & Barlaud, M. Deterministic edge-preserving regularization in computed imaging. IEEE Trans. Image Process. 6, 298–311 (1997).

    Article  ADS  Google Scholar 

  79. Krauze, W. Optical diffraction tomography with finite object support for the minimization of missing cone artifacts. Biomed. Opt. Express 11, 1919–1926 (2020).

    Article  Google Scholar 

  80. Hugonnet, H., Shin, S. & Park, Y. Regularization of dielectric tensor tomography. Opt. Express 31, 3774–3783 (2023).

    Article  ADS  Google Scholar 

  81. Chung, H., Huh, J., Kim, G., Park, Y. K. & Ye, J. C. Missing cone artifact removal in ODT using unsupervised deep learning in the projection domain. IEEE Trans. Comput. Imaging 7, 747–758 (2021).

    Article  Google Scholar 

  82. Zhou, K. C. & Horstmeyer, R. Diffraction tomography with a deep image prior. Opt. Express 28, 12872–12896 (2020).

    Article  ADS  Google Scholar 

  83. Ryu, D. et al. DeepRegularizer: rapid resolution enhancement of tomographic imaging using deep learning. IEEE Trans. Med. Imaging 40, 1508–1518 (2021).

    Article  Google Scholar 

  84. Yang, F., Pham, T.-A, Gupta, H., Unser, M. & Ma, J. Deep-learning projector for optical diffraction tomography. Opt. Express 28, 3905–3921 (2020).

    Article  ADS  Google Scholar 

  85. Tam, K. C. & Perez-Mendez, V. Tomographical imaging with limited-angle input. J. Opt. Soc. Am. 71, 582–592 (1981).

    Article  ADS  MathSciNet  Google Scholar 

  86. Medoff, B. P., Brody, W. R., Nassi, M. & Macovski, A. Iterative convolution backprojection algorithms for image reconstruction from limited data. J. Opt. Soc. Am. 73, 1493–1500 (1983).

    Article  ADS  Google Scholar 

  87. McNally, J. G., Preza, C., Conchello, J.-A. & Thomas, L. J. Artifacts in computational optical-sectioning microscopy. J. Opt. Soc. Am. A 11, 1056–1067 (1994).

    Article  ADS  Google Scholar 

  88. Andersen, A. H. & Kak, A. C. Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm. Ultrason. Imaging 6, 81–94 (1984).

    Article  Google Scholar 

  89. Midgley, P. A. & Weyland, M. 3D electron microscopy in the physical sciences: the development of Z-contrast and EFTEM tomography. Ultramicroscopy 96, 413–431 (2003).

    Article  Google Scholar 

  90. Moser, S., Jesacher, A. & Ritsch-Marte, M. Efficient and accurate intensity diffraction tomography of multiple-scattering samples. Opt. Express 31, 18274–18289 (2023).

    Article  ADS  Google Scholar 

  91. Xu, J., Zhao, Y., Li, H. & Zhang, P. An image reconstruction model regularized by edge-preserving diffusion and smoothing for limited-angle computed tomography. Inverse Probl. 35, 085004 (2019).

    Article  ADS  MathSciNet  Google Scholar 

  92. Chen, M., Mi, D., He, P., Deng, L. & Wei, B. A CT reconstruction algorithm based on L 1/2 regularization. Comput. Math. Methods Med. 2014, 862910 (2014).

    Article  MathSciNet  Google Scholar 

  93. Yu, W. & Zeng, L. ℓ0 gradient minimization based image reconstruction for limited-angle computed tomography. PLoS ONE 10, e0130793 (2015).

    Article  Google Scholar 

  94. Deng, X., Liu, X., & Li, H. Limited-angle CT Reconstruction with ℓp Regularization. in Proceedings of the Third International Symposium on Image Computing and Digital Medicine 182–186 (ACM, 2019).

  95. Zuo, C., Sun, J., Li, J., Asundi, A. & Chen, Q. Wide-field high-resolution 3D microscopy with Fourier ptychographic diffraction tomography. Opt. Lasers Eng. 128, 106003 (2020).

    Article  Google Scholar 

  96. Horstmeyer, R., Chung, J., Ou, X., Zheng, G. & Yang, C. Diffraction tomography with Fourier ptychography. Optica 3, 827–835 (2016).

    Article  ADS  Google Scholar 

  97. Syga, Ł., Spakman, D., Punter, C. M. & Poolman, B. Method for immobilization of living and synthetic cells for high-resolution imaging and single-particle tracking. Sci. Rep. 8, 1–12 (2018).

    Article  Google Scholar 

  98. Lee, P. J., Helman, N. C., Lim, W. A. & Hung, P. J. A microfluidic system for dynamic yeast cell imaging. Biotechniques 44, 91–95 (2008).

    Article  Google Scholar 

  99. Lee, S. S., Vizcarra, I. A., Huberts, D. H., Lee, L. P. & Heinemann, M. Whole lifespan microscopic observation of budding yeast aging through a microfluidic dissection platform. Proc. Natl Acad. Sci. USA 109, 4916–4920 (2012).

    Article  ADS  Google Scholar 

  100. Shin, J., Kim, G., Park, J., Lee, M. & Park, Y. Long-term label-free assessments of individual bacteria using three-dimensional quantitative phase imaging and hydrogel-based immobilization. Sci. Rep. 13, 46 (2023).

    Article  ADS  Google Scholar 

  101. Peddie, C. J. et al. Volume electron microscopy. Nat. Rev. Methods Primers 2, 51 (2022).

    Article  Google Scholar 

  102. Li, D. et al. Extended-resolution structured illumination imaging of endocytic and cytoskeletal dynamics. Science 349, aab3500 (2015).

    Article  Google Scholar 

  103. Seifert, T. S. et al. Longitudinal and transverse electron paramagnetic resonance in a scanning tunneling microscope. Sci. Adv. 6, eabc5511 (2020).

    Article  ADS  Google Scholar 

  104. Jensen, E. C. Use of fluorescent probes: their effect on cell biology and limitations. Anat. Rec. 295, 2031–2036 (2012).

    Article  Google Scholar 

  105. Kim, K. et al. Optical diffraction tomography techniques for the study of cell pathophysiology. J. Biomed. Photon. Eng. 2, 020201 (2016).

    Google Scholar 

  106. Elmer-Dixon, M. M. & Bowler, B. E. Rapid quantification of vesicle concentration for DOPG/DOPC and cardiolipin/DOPC mixed lipid systems of variable composition. Anal. Biochem. 553, 12–14 (2018).

    Article  Google Scholar 

  107. Tasic, A. Z., Djordjevic, B. D., Grozdanic, D. K. & Radojkovic, N. Use of mixing rules in predicting refractive indexes and specific refractivities for some binary liquid mixtures. J. Chem. Eng. Data 37, 310–313 (1992).

    Article  Google Scholar 

  108. Kim, K. et al. Three-dimensional label-free imaging and quantification of lipid droplets in live hepatocytes. Sci. Rep. 6, 36815 (2016).

    Article  ADS  Google Scholar 

  109. Rodrigo, J. A., Soto, J. M., & Alieva, T. Fast label-free optical diffraction tomography compatible with conventional wide-field microscopes. Optical Methods for Inspection, Characterization, and Imaging of Biomaterials IV 11060, 139–148 (2019).

    Google Scholar 

  110. Lee, S. et al. Refractive index tomograms and dynamic membrane fluctuations of red blood cells from patients with diabetes mellitus. Sci. Rep. 7, 1039 (2017).

    Article  ADS  Google Scholar 

  111. Pachitariu, M. & Stringer, C. Cellpose 2.0: how to train your own model. Nat. Methods 19, 1634–1641 (2022).

    Article  Google Scholar 

  112. Ma, J. et al. Segment anything in medical images. Nat. Commun. 15, 654 (2024).

    Article  ADS  Google Scholar 

  113. Berg, S. et al. ilastik: interactive machine learning for (bio) image analysis. Nat. Methods 16, 1226–1232 (2019).

    Article  Google Scholar 

  114. Barer, R. & Tkaczyk, S. Refractive index of concentrated protein solutions. Nature 173, 821–822 (1954).

    Article  ADS  Google Scholar 

  115. Barer, R. Refractometry and interferometry of living cells. J. Opt. Soc. Am. 47, 545–556 (1957).

    Article  ADS  Google Scholar 

  116. Lee, S. Y., Park, H. J., Best-Popescu, C., Jang, S. & Park, Y. K. The effects of ethanol on the morphological and biochemical properties of individual human red blood cells. PLoS ONE 10, e0145327 (2015).

    Article  Google Scholar 

  117. Hur, J., Kim, K., Lee, S., Park, H. & Park, Y. Melittin-induced alterations in morphology and deformability of human red blood cells using quantitative phase imaging techniques. Sci. Rep. 7, 9306 (2017).

    Article  ADS  Google Scholar 

  118. Lee, H. J., Lee, S., Park, H., Park, Y. & Shin, J. Three-dimensional shapes and cell deformability of rat red blood cells during and after asphyxial cardiac arrest. Emerg. Med. Int. 2019, 6027236 (2019).

    Article  Google Scholar 

  119. Choi, S. Y., Oh, J., Jung, J., Park, Y. & Lee, S. Y. Three-dimensional label-free visualization and quantification of polyhydroxyalkanoates in individual bacterial cell in its native state. Proc. Natl Acad. Sci. USA 118, e2103956118 (2021).

    Article  Google Scholar 

  120. Park, C., Shin, S. & Park, Y. Generalized quantification of three-dimensional resolution in optical diffraction tomography using the projection of maximal spatial bandwidths. J. Opt. Soc. Am. A 35, 1891–1898 (2018).

    Article  ADS  Google Scholar 

  121. Schürmann, M. et al. Three‐dimensional correlative single‐cell imaging utilizing fluorescence and refractive index tomography. J. Biophotonics 11, e201700145 (2018).

    Article  Google Scholar 

  122. Kim, K. & Guck, J. The relative densities of cytoplasm and nuclear compartments are robust against strong perturbation. Biophys. J. 119, 1946–1957 (2020).

    Article  ADS  Google Scholar 

  123. Bakhshandeh, S. et al. Optical quantification of intracellular mass density and cell mechanics in 3D mechanical confinement. Soft Matter 17, 853–862 (2021).

    Article  ADS  Google Scholar 

  124. Roffay, C. et al. Passive coupling of membrane tension and cell volume during active response of cells to osmosis. Proc. Natl Acad. Sci. USA 118, e2103228118 (2021).

    Article  Google Scholar 

  125. Yoon, J. et al. Label-free characterization of white blood cells by measuring 3D refractive index maps. Biomed. Opt. Express 6, 3865–3875 (2015).

    Article  Google Scholar 

  126. Banani, S. F., Lee, H. O., Hyman, A. A. & Rosen, M. K. Biomolecular condensates: organizers of cellular biochemistry. Nat. Rev. Mol. Cell Biol. 18, 285–298 (2017).

    Article  Google Scholar 

  127. Shin, Y. & Brangwynne, C. P. Liquid phase condensation in cell physiology and disease. Science 357, eaaf4382 (2017).

    Article  Google Scholar 

  128. Kim, T.-K., Lee, B.-W., Fujii, F., Kim, J. K. & Pack, C.-G. Physicochemical properties of nucleoli in live cells analyzed by label-free optical diffraction tomography. Cells 8, 699 (2019).

    Article  Google Scholar 

  129. Kim, Y. et al. Characterizing organelles in live stem cells using label-free optical diffraction tomography. Mol. Cell 44, 851 (2021).

    Article  Google Scholar 

  130. Kim, T.-K. et al. Mitotic chromosomes in live cells characterized using high-speed and label-free optical diffraction tomography. Cells 8, 1368 (2019).

    Article  Google Scholar 

  131. Biswas, A., Kim, K., Cojoc, G., Guck, J. & Reber, S. The Xenopus spindle is as dense as the surrounding cytoplasm. Dev. Cell 56, 967–975.e5 (2021).

    Article  Google Scholar 

  132. Sandoz, P. A., Tremblay, C., van der Goot, F. G. & Frechin, M. Image-based analysis of living mammalian cells using label-free 3D refractive index maps reveals new organelle dynamics and dry mass flux. PLoS Biol. 17, e3000553 (2019).

    Article  Google Scholar 

  133. Dong, D. et al. Super-resolution fluorescence-assisted diffraction computational tomography reveals the three-dimensional landscape of the cellular organelle interactome. Light Sci. Appl. 9, 11 (2020).

    Article  ADS  Google Scholar 

  134. Kim, K. et al. Correlative three-dimensional fluorescence and refractive index tomography: bridging the gap between molecular specificity and quantitative bioimaging. Biomed. Opt. Express 8, 5688–5697 (2017).

    Article  Google Scholar 

  135. Simon, B., Debailleul, M., Beghin, A., Tourneur, Y. & Haeberlé, O. High‐resolution tomographic diffractive microscopy of biological samples. J. Biophoton. 3, 462–467 (2010).

    Article  Google Scholar 

  136. Chowdhury, S., Eldridge, W. J., Wax, A. & Izatt, J. A. Structured illumination microscopy for dual-modality 3D sub-diffraction resolution fluorescence and refractive-index reconstruction. Biomed. Opt. Express 8, 5776–5793 (2017).

    Article  Google Scholar 

  137. Shin, S., Kim, D., Kim, K. & Park, Y. Super-resolution three-dimensional fluorescence and optical diffraction tomography of live cells using structured illumination generated by a digital micromirror device. Sci. Rep. 8, 9183 (2018).

    Article  ADS  Google Scholar 

  138. Guo, R., Barnea, I. & Shaked, N. T. Limited-angle tomographic phase microscopy utilizing confocal scanning fluorescence microscopy. Biomed. Opt. Express 12, 1869–1881 (2021).

    Article  Google Scholar 

  139. Paidi, S. K. et al. Coarse Raman and optical diffraction tomographic imaging enable label-free phenotyping of isogenic breast cancer cells of varying metastatic potential. Biosens. Bioelectron. 175, 112863 (2021).

    Article  Google Scholar 

  140. Hsieh, C.-M. et al. Regulation of lipid droplets in live preadipocytes using optical diffraction tomography and Raman spectroscopy. Opt. Express 27, 22994–23008 (2019).

    Article  ADS  Google Scholar 

  141. Oh, S. et al. Protein and lipid mass concentration measurement in tissues by stimulated Raman scattering microscopy. Proc. Natl Acad. Sci. USA 119, e2117938119 (2022).

    Article  Google Scholar 

  142. Bailey, M. et al. Predicting the refractive index of tissue models using light scattering spectroscopy. Appl. Spectrosc. 75, 574–580 (2021).

    Article  ADS  Google Scholar 

  143. Scarcelli, G. et al. Noncontact three-dimensional mapping of intracellular hydromechanical properties by Brillouin microscopy. Nat. Methods 12, 1132–1134 (2015).

    Article  Google Scholar 

  144. Schlüßler, R. et al. Mechanical mapping of spinal cord growth and repair in living zebrafish larvae by Brillouin imaging. Biophys. J. 115, 911–923 (2018).

    Article  ADS  Google Scholar 

  145. Hauck, N. et al. PNIPAAm microgels with defined network architecture as temperature sensors in optical stretchers. Mater. Adv. 3, 6179–6190 (2022).

    Article  Google Scholar 

  146. Schlüßler, R. et al. Correlative all-optical quantification of mass density and mechanics of subcellular compartments with fluorescence specificity. eLife 11, e68490 (2022).

    Google Scholar 

  147. Abuhattum, S. et al. Adipose cells and tissues soften with lipid accumulation while in diabetes adipose tissue stiffens. Sci. Rep. 12, 1–17 (2022).

    Article  Google Scholar 

  148. Kolb, J. et al. Small leucine-rich proteoglycans inhibit CNS regeneration by modifying the structural and mechanical properties of the lesion environment. Nat. Commun. 14, 6814 (2023).

    Article  ADS  Google Scholar 

  149. Youle, R. J. & Van Der Bliek, A. M. Mitochondrial fission, fusion, and stress. Science 337, 1062–1065 (2012).

    Article  ADS  Google Scholar 

  150. Meyer, P. & Dworkin, J. Applications of fluorescence microscopy to single bacterial cells. Res. Microbiol. 158, 187–194 (2007).

    Article  Google Scholar 

  151. Costerton, J. The role of electron microscopy in the elucidation of bacterial structure and function. Annu. Rev. Microbiol. 33, 459–479 (1979).

    Article  Google Scholar 

  152. Joyner, R. P. et al. A glucose-starvation response regulates the diffusion of macromolecules. eLife 5, e09376 (2016).

    Article  Google Scholar 

  153. Rappaz, B. et al. Noninvasive characterization of the fission yeast cell cycle by monitoring dry mass with digital holographic microscopy. J. Biomed. Opt. 14, 034049 (2009).

    Article  ADS  Google Scholar 

  154. Odermatt, P. D. et al. Variations of intracellular density during the cell cycle arise from tip-growth regulation in fission yeast. eLife 10, e64901 (2021).

    Article  Google Scholar 

  155. Randazzo, A. et al. Optimal turnaround time for direct identification of microorganisms by mass spectrometry in blood culture. J. Microbiol. Methods 130, 1–5 (2016).

    Article  Google Scholar 

  156. Mukherjee, A. & Koller, M. Polyhydroxyalkanoate (PHA) biopolyesters — emerging and major products of industrial biotechnology. EuroBiotech. J. 6, 49–60 (2022).

    Article  Google Scholar 

  157. Peters, V. & Rehm, B. H. In vivo monitoring of PHA granule formation using GFP-labeled PHA synthases. FEMS Microbiol. Lett. 248, 93–100 (2005).

    Article  Google Scholar 

  158. Tian, J., Sinskey, A. J. & Stubbe, J. Kinetic studies of polyhydroxybutyrate granule formation in Wautersia eutropha H16 by transmission electron microscopy. J. Bacteriol. 187, 3814–3824 (2005).

    Article  Google Scholar 

  159. Park, H. et al. Measuring cell surface area and deformability of individual human red blood cells over blood storage using quantitative phase imaging. Sci. Rep. 6, 34257 (2016).

    Article  ADS  Google Scholar 

  160. Kim, G., Jo, Y., Cho, H., Min, H.-S. & Park, Y. Learning-based screening of hematologic disorders using quantitative phase imaging of individual red blood cells. Biosens. Bioelectron. 123, 69–76 (2019).

    Article  Google Scholar 

  161. Yoon, J. et al. Identification of non-activated lymphocytes using three-dimensional refractive index tomography and machine learning. Sci. Rep. 7, 6654 (2017).

    Article  ADS  Google Scholar 

  162. Lee, S., Jang, S. & Park, Y. Measuring three-dimensional dynamics of platelet activation using 3-D quantitative phase imaging. Preprint at bioRxiv https://doi.org/10.1101/827436 (2019).

  163. Stanly, T. A. et al. Quantitative optical diffraction tomography imaging of mouse platelets. Front. Physiol. 11, 568087 (2020).

    Article  Google Scholar 

  164. Mathiowetz, A. J. & Olzmann, J. A. Lipid droplets and cellular lipid flux. Nat. Cell Biol. https://doi.org/10.1038/s41556-024-01364-4 (2024).

  165. Fam, T. K., Klymchenko, A. S. & Collot, M. Recent advances in fluorescent probes for lipid droplets. Materials 11, 1768 (2018).

    Article  ADS  Google Scholar 

  166. Kähärä, I. et al. Phototoxicity of BODIPY in long-term imaging can be reduced by intramolecular motion. Photochem. Photobiol. Sci. 21, 1677–1687 (2022).

    Article  Google Scholar 

  167. Bumpus, T. W. & Baskin, J. M. Clickable substrate mimics enable imaging of phospholipase D activity. ACS Cent. Sci. 3, 1070–1077 (2017).

    Article  Google Scholar 

  168. Sandoz, P. A. et al. Label free 3D analysis of organelles in living cells by refractive index shows pre-mitotic organelle spinning in mammalian stem cells. Preprint at bioRxiv https://doi.org/10.1101/407239 (2018).

  169. Silva, L. M. et al. Besnoitia besnoiti infection alters both endogenous cholesterol de novo synthesis and exogenous LDL uptake in host endothelial cells. Sci. Rep. 9, 6650 (2019).

    Article  ADS  Google Scholar 

  170. Park, S. et al. Label-free tomographic imaging of lipid droplets in foam cells for machine-learning-assisted therapeutic evaluation of targeted nanodrugs. ACS Nano 14, 1856–1865 (2020).

    Article  Google Scholar 

  171. Nuiyen, A. et al. Lack of Nck1 protein and Nck–CD3 interaction caused the increment of lipid content in Jurkat T cells. BMC Mol. Cell Biol. 23, 1–14 (2022).

    Article  Google Scholar 

  172. Brangwynne, C. P. et al. Germline P granules are liquid droplets that localize by controlled dissolution/condensation. Science 324, 1729–1732 (2009).

    Article  ADS  Google Scholar 

  173. Li, P. et al. Phase transitions in the assembly of multivalent signalling proteins. Nature 483, 336–340 (2012).

    Article  ADS  Google Scholar 

  174. Lyon, A. S., Peeples, W. B. & Rosen, M. K. A framework for understanding the functions of biomolecular condensates across scales. Nat. Rev. Mol. Cell Biol. 22, 215–235 (2021).

    Article  Google Scholar 

  175. Boija, A., Klein, I. A. & Young, R. A. Biomolecular condensates and cancer. Cancer Cell 39, 174–192 (2021).

    Article  Google Scholar 

  176. Mathieu, C., Pappu, R. V. & Taylor, J. P. Beyond aggregation: pathological phase transitions in neurodegenerative disease. Science 370, 56–60 (2020).

    Article  ADS  Google Scholar 

  177. Kim, T. et al. RNA-mediated demixing transition of low-density condensates. Nat. Commun. 14, 2425 (2023).

    Article  ADS  Google Scholar 

  178. Posey, A. E., Holehouse, A. S. & Pappu, R. V. Methods in Enzymology Vol. 611, 1–30 (Elsevier, 2018).

  179. Hong, Y. et al. Label‐free quantitative analysis of coacervates via 3D phase imaging. Adv. Opt. Mater. 9, 2100697 (2021).

    Article  Google Scholar 

  180. McCall, P. et al. Label-free composition determination for biomolecular condensates with an arbitrarily large number of components. Preprint at bioRxiv https://doi.org/10.1101/2020.10.25.352823 (2023).

  181. Guillén-Boixet, J. et al. RNA-induced conformational switching and clustering of G3BP drive stress granule assembly by condensation. Cell 181, 346–361.e17 (2020).

    Article  Google Scholar 

  182. Qian, X. et al. Generation of human brain region-specific organoids using a miniaturized spinning bioreactor. Nat. Protoc. 13, 565–580 (2018).

    Article  Google Scholar 

  183. Yoon, K.-J. et al. Zika-virus-encoded NS2A disrupts mammalian cortical neurogenesis by degrading adherens junction proteins. Cell Stem Cell 21, 349–358.e6 (2017).

    Article  Google Scholar 

  184. Nishimura, K. et al. Live-cell imaging of subcellular structures for quantitative evaluation of pluripotent stem cells. Sci. Rep. 9, 1777 (2019).

    Article  ADS  Google Scholar 

  185. Orozco-Fuentes, S. et al. Quantification of the morphological characteristics of hESC colonies. Sci. Rep. 9, 17569 (2019).

    Article  ADS  Google Scholar 

  186. Wakui, T. et al. Method for evaluation of human induced pluripotent stem cell quality using image analysis based on the biological morphology of cells. J. Med. Imaging 4, 044003 (2017).

    Article  Google Scholar 

  187. Jiang, H. et al. Reconstruction of bovine spermatozoa substances distribution and morphological differences between Holstein and Korean native cattle using three-dimensional refractive index tomography. Sci. Rep. 9, 8774 (2019).

    Article  ADS  Google Scholar 

  188. Dardikman-Yoffe, G., Mirsky, S. K., Barnea, I. & Shaked, N. T. High-resolution 4-D acquisition of freely swimming human sperm cells without staining. Sci. Adv. 6, eaay7619 (2020).

    Article  ADS  Google Scholar 

  189. Chowdhury, S. et al. High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images. Optica 6, 1211–1219 (2019).

    Article  ADS  Google Scholar 

  190. Kim, J., Koo, B.-K. & Knoblich, J. A. Human organoids: model systems for human biology and medicine. Nat. Rev. Mol. Cell Biol. 21, 571–584 (2020).

    Article  Google Scholar 

  191. Stępień, P. et al. Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging. Biomed. Opt. Express 13, 5709–5720 (2022).

    Article  Google Scholar 

  192. Yang, F. et al. Robust phase unwrapping via deep image prior for quantitative phase imaging. IEEE Trans. Image Process. 30, 7025–7037 (2021).

    Article  ADS  Google Scholar 

  193. Park, D. et al. Cryobiopsy: a breakthrough strategy for clinical utilization of lung cancer organoids. Cells 12, 1854 (2023).

    Article  Google Scholar 

  194. Artegiani, B. et al. Fast and efficient generation of knock-in human organoids using homology-independent CRISPR–Cas9 precision genome editing. Nat. Cell Biol. 22, 321–331 (2020).

    Article  Google Scholar 

  195. Roshanzadeh, A. et al. Surface charge-dependent cytotoxicity of plastic nanoparticles in alveolar cells under cyclic stretches. Nano Lett. 20, 7168–7176 (2020).

    Article  ADS  Google Scholar 

  196. Larrazabal, C., Hermosilla, C., Taubert, A. & Conejeros, I. 3D holotomographic monitoring of Ca++ dynamics during ionophore-induced Neospora caninum tachyzoite egress from primary bovine host endothelial cells. Parasitol. Res. 121, 1169–1177 (2021).

    Article  Google Scholar 

  197. Balk, M. et al. Cellular SPION uptake and toxicity in various head and neck cancer cell lines. Nanomaterials 11, 726 (2021).

    Article  Google Scholar 

  198. Kim, E. H. et al. Self-luminescent photodynamic therapy using breast cancer targeted proteins. Sci. Adv. 6, eaba3009 (2020).

    Article  ADS  Google Scholar 

  199. Park, S. et al. Detection of intracellular monosodium urate crystals in gout synovial fluid using optical diffraction tomography. Sci. Rep. 11, 10019 (2021).

    Article  ADS  Google Scholar 

  200. Sohn, M., Lee, J. E., Ahn, M., Park, Y. & Lim, S. Correlation of dynamic membrane fluctuations in red blood cells with diabetes mellitus and cardiovascular risks. Sci. Rep. 11, 1–10 (2021).

    ADS  Google Scholar 

  201. Ziemczonok, M., Kuś, A., Wasylczyk, P. & Kujawińska, M. 3D-printed biological cell phantom for testing 3D quantitative phase imaging systems. Sci. Rep. 9, 18872 (2019).

    Article  ADS  Google Scholar 

  202. Park, Y. et al. Refractive index maps and membrane dynamics of human red blood cells parasitized by Plasmodium falciparum. Proc. Natl Acad. Sci. USA 105, 13730–13735 (2008).

    Article  ADS  Google Scholar 

  203. Kim, G. et al. Measurements of three-dimensional refractive index tomography and membrane deformability of live erythrocytes from Pelophylax nigromaculatus. Sci. Rep. 8, 9192 (2018).

    Article  ADS  Google Scholar 

  204. Choi, I., Lee, K. & Park, Y. Compensation of aberration in quantitative phase imaging using lateral shifting and spiral phase integration. Opt. Express 25, 30771–30779 (2017).

    Article  ADS  Google Scholar 

  205. Ryu, D. et al. Deep learning-based optical field screening for robust optical diffraction tomography. Sci. Rep. 9, 15239 (2019).

    Article  ADS  Google Scholar 

  206. Choi, G. et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt. Express 27, 4927–4943 (2019).

    Article  ADS  Google Scholar 

  207. Wang, Z. et al. Spatial light interference microscopy (SLIM). Opt. Express 19, 1016–1026 (2011).

    Article  ADS  Google Scholar 

  208. Charrière, F. et al. Shot-noise influence on the reconstructed phase image signal-to-noise ratio in digital holographic microscopy. Appl. Opt. 45, 7667–7673 (2006).

    Article  ADS  Google Scholar 

  209. Ghiglia, D. C. & Romero, L. A. Minimum Lp-norm two-dimensional phase unwrapping. J. Opt. Soc. Am. A 13, 1999–2013 (1996).

    Article  ADS  Google Scholar 

  210. Lee, D. et al. High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching. Biomed. Opt. Express 13, 6404–6415 (2022).

    Article  Google Scholar 

  211. Kostencka, J., Kozacki, T., Kuś, A., Kemper, B. & Kujawińska, M. Holographic tomography with scanning of illumination: space-domain reconstruction for spatially invariant accuracy. Biomed. Opt. Express 7, 4086–4101 (2016).

    Article  Google Scholar 

  212. Chung, Y. et al. Label‐free histological analysis of retrieved thrombi in acute ischemic stroke using optical diffraction tomography and deep learning. J. Biophotonics 16, e202300067 (2023).

    Article  Google Scholar 

  213. Zheng, Y. et al. A graph-transformer for whole slide image classification. IEEE Trans. Med. Imaging 41, 3003–3015 (2022).

    Article  ADS  Google Scholar 

  214. Lee, Y. et al. Derivation of prognostic contextual histopathological features from whole-slide images of tumours via graph deep learning. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-022-00923-0 (2022).

  215. Schelkens, P. et al. Compression strategies for digital holograms in biomedical and multimedia applications. Light Adv. Manuf. 3, 601–621 (2022).

    Google Scholar 

  216. Lim, J., Ayoub, A. B. & Psaltis, D. Three-dimensional tomography of red blood cells using deep learning. Adv. Photon. 2, 026001 (2020).

    Article  ADS  Google Scholar 

  217. Ryu, D. et al. Label-free white blood cell classification using refractive index tomography and deep learning. BME Front. 2021, 9893804 (2021).

    Article  Google Scholar 

  218. Hassaan, M. et al. Breast cancer diagnosis using spatial light interference microscopy. J. Biomed. Opt. 20, 111210 (2015).

    Article  Google Scholar 

  219. Tu, H. et al. Stain-free histopathology by programmable supercontinuum pulses. Nat. Photon. 10, 534–540 (2016).

    Article  ADS  Google Scholar 

  220. Lee, M. et al. Label-free optical quantification of structural alterations in Alzheimer’s disease. Sci. Rep. 6, 31034 (2016).

    Article  ADS  Google Scholar 

  221. Zhuo, W., Gabriel, P., Krishnarao, V. T. & Andre, B. Tissue refractive index as marker of disease. J. Biomed. Opt. 16, 116017 (2011).

    Google Scholar 

  222. Rivenson, Y. et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl. 8, 23 (2019).

    Article  ADS  Google Scholar 

  223. Costantini, I., Cicchi, R., Silvestri, L., Vanzi, F. & Pavone, F. S. In-vivo and ex-vivo optical clearing methods for biological tissues. Biomed. Opt. Express 10, 5251–5267 (2019).

    Article  Google Scholar 

  224. Kandel, M. E. et al. Reproductive outcomes predicted by phase imaging with computational specificity of spermatozoon ultrastructure. Proc. Natl Acad. Sci. USA 117, 18302–18309 (2020).

    Article  ADS  Google Scholar 

  225. Herrero, J. & Meseguer, M. Selection of high potential embryos using time-lapse imaging: the era of morphokinetics. Fertil. Steril. 99, 1030–1034 (2013).

    Article  Google Scholar 

  226. Dimitriadis, I., Zaninovic, N., Badiola, A. C. & Bormann, C. L. Artificial intelligence in the embryology laboratory: a review. Reprod. BioMedicine Online 44, 435–448 (2022).

    Article  Google Scholar 

  227. Barnes, J. et al. A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study. Lancet Digital Health 5, e28–e40 (2023).

    Article  Google Scholar 

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Acknowledgements

This work was supported by the National Research Foundation of Korea (2015R1A3A2066550, 2022M3H4A1A02074314), Institute for Information and Communications Technology Planning and Evaluation (IITP; 2021-0-00745) grant funded by the Ministry of Science and ICT, Republic of Korea, the Korea Health Technology R&D Project through the Korea Health Industry Development Institute, funded by the Ministry of Health and Welfare, Republic of Korea (HI21C0977, HR22C1605), the Korean Fund for Regenerative Medicine (KFRM) grant funded by the Korea government (the Ministry of Science and ICT and the Ministry of Health & Welfare) (21A0101L0), Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education, Republic of Korea (RS-2023-00241278).

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Authors and Affiliations

Authors

Contributions

Introduction (Y.K.P. and G.K.); Experimentation (Y.K.P. and H.H.); Results (Y.K.P., G.K., H.H., K.K., J.-H.L., S.S.L., J.H., C.L., H.P., K.-J.Y., Y.S., G.C., I.H., L.M., J.H.K., T.H.H., S.L., P.O., B.-K.K. and J.G.); Applications (Y.K.P., G.K., K.K., J.-H.L., S.S.L., J.H., C.L., H.P., K.-J.Y., Y.S., G.C., I.H., L.M., J.H.K., T.H.H., S.L., P.O., B.-K.K. and J.G.); Reproducibility and data deposition (Y.K.P. and G.K.); Limitations and optimizations (Y.K.P. and H.H.); Outlook (Y.K.P. and G.K.); overview of the Primer (Y.K.P.).

Corresponding author

Correspondence to YongKeun Park.

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Competing interests

H.H., K.K. and Y.K.P. have financial interests in Tomocube Inc., a company that commercializes holotomography instruments. The other authors declare no competing interests.

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Nature Reviews Methods Primers thanks Kevin Tsia, Thomas Zangle and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Glossary

Biomolecular condensates

Membrane-less assemblies of proteins and nucleic acids, characterized by inhomogeneous, non-stoichiometric molecular arrangements, encompassing structures such as nucleoli, nuclear speckles, heterochromatin, cytoplasmic stress granules, germ granules and others.

Coherent HT

A holotomography (HT) technique that uses a coherent light source such as a laser for angle-scanned illumination.

Coherent light illumination

Light with well-defined propagation angle that can be described by a plane wave.

Fourier diffraction theorem

A theorem that states that the Fourier transform of the aperture function of an object is proportional to the far-field diffraction pattern, linking the spatial domain with the frequency domain and enabling the reconstruction of 3D refractive index tomogram via the analysis inverse wave scattering and propagation.

Kramers–Kronig relations

A fundamental principle in physics that describes the relationship between the real and imaginary parts of a complex function, used in holotomography to retrieve phase information from intensity measurements.

Lipid droplets

Subcellular organelles primarily involved in the storage and regulation of lipids, serving as energy reservoirs and playing a role in cellular lipid metabolism and signalling pathways within cells.

Mach–Zehnder interferometric techniques

Optical techniques that split a laser beam into two distinct paths, which are then recombined to form interference patterns, providing an optical field with detailed amplitude and phase information.

Missing cone problem

The issue in tomographic imaging in which certain angles cannot be sampled owing to geometric or physical constraints, resulting in a loss of information and potentially leading to inaccuracies in the reconstructed images.

Non-negativity

In regularization algorithms addressing the missing cone problem, the non-negativity is a constraint applied during reconstruction that ensures all predicted values for the missing data are greater than or equal to zero.

Refractive index

A dimensionless number given by the ratio of speed of light in a medium to that in vacuum.

Spatially low-coherence HT

A holotomography (HT) technique that exploits spatially low-coherent light source for illumination and axially scans the sample.

Temporally low-coherence HT

A holotomography (HT) technique that exploits temporally low-coherent light sources such as light-emitting diode for angle-scanned illumination.

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Kim, G., Hugonnet, H., Kim, K. et al. Holotomography. Nat Rev Methods Primers 4, 51 (2024). https://doi.org/10.1038/s43586-024-00327-1

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