- Letter
- Open Access
Optical dispersions through intracellular inhomogeneities
Phys. Rev. Research 5, L022043 – Published 30 May, 2023
DOI: https://doi.org/10.1103/PhysRevResearch.5.L022043
Abstract
The transport of intensity equation (TIE) exhibits a noninterferometric correlation between the intensity and phase variations of intermediate fields (e.g., light and electrons) in biological imaging. Previous TIE formulations have generally assumed free-space propagation of monochromatic, coherent field functions crossing phase distributions along a longitudinal direction. In this study, we modify the TIE with fractal (or self-similar) organization models based on intracellular refractive index turbulence. We then implement TIE simulations over a broad range of fractal dimensions and wavelengths. Simulation results show how the intensity propagation through the spatial fluctuation of intracellular refractive index interconnects fractal dimensionality with intensity dispersion (or transmissivity) within the picometer to micrometer wavelength range. Additionally, we provide a spatial autocorrelation of phase derivatives, which allows for the direct measurement and reconstruction of intracellular fractal profiles from optical and electron microscopy imaging.
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Supplemental Material
References (38)
- M. Born and E. Wolf, Principles of Optics: Electromagnetic Theory of Propagation, Interference and Diffraction of Light, 7th ed. (Cambridge University, Cambridge, England).
- M. R. Teague, Deterministic phase retrieval: A Green's function solution, J. Opt. Soc. Am. 73, 1434 (1983).
- C. Zuo, J. Li, J. Sun, Y. Fan, J. Zhang, L. Lu, R. Zhang, B. Wang, L. Huang, and Q. Chen, Transport of intensity equation: A tutorial, Opt. Lasers Eng. 135, 106187 (2020).
- M. Mitome, Transport of intensity equation method and its applications, Microscopy 70, 69 (2021).
- S. Mahajan and T. Tang, Meeting experiments at the diffraction barrier with in silico fluorescence microscopy, ACS Photonics 9, 846 (2022).
- M. Weigert, K. Subramanian, S. T. Bundschuh, W. Myers, and M. Kreysing, Biobeam—Multiplexed wave-optical simulations of light-sheet microscopy, PLoS Comput. Biol. 14, e1006079 (2018).
- A. Girsault, T. Lukes, A. Sharipov, S. Geissbuehler, M. Leutenegger, W. Vandenberg, P. Dedecker, J. Hofkens, and T. Lasser, SOFI simulation tool: A software package for simulating and testing super-resolution optical fluctuation imaging, PLoS ONE 11, e0161602 (2016).
- M. Lindén, V. Ćurić, A. Boucharin, D. Fange, and J. Elf, Simulated single molecule microscopy with SMeagol, Bioinformatics 32, 2394 (2016); M. Lindén, V. Ćurić, E. Amselem, and J. Elf, Pointwise error estimates in localization microscopy, Nat. Commun. 8, 1 (2017).
- V. Venkataramani, F. Herrmannsdörfer, M. Heilemann, and T. Kuner, SuReSim: simulating localization microscopy experiments from ground truth models, Nat. Methods 13, 319 (2016).
- M. Watabe, S. N. V. Arjunan, W. X. Chew, K. Kaizu, and K. Takahashi, Simulation of live-cell imaging system reveals hidden uncertainties in cooperative binding measurements, Phys. Rev. E 100, 010402(R) (2019); M. Watabe, S. N. V. Arjunan, S. Fukushima, K. Iwamoto, J. Kozuka, S. Matsuoka, Y. Shindo, M. Ueda, and K. Takahashi, A computational framework for bioimaging simulation, PLoS ONE 10, e0130089 (2015).
- J. Angiolini, N. Plachta, E. Mocskos, and V. Levi, Exploring the dynamics of cell processes through simulations of fluorescence microscopy experiments, Biophys. J. 108, 2613 (2015).
- S. H. Rezatofighi, W. T. E. Pitkeathly, S. Gould, R. Hartley, K. Mele, W. E. Hughes, and J. G. Burchfield, A framework for generating realistic synthetic sequences of total internal reflection fluorescence microscopy images, in Proceedings of the IEEE 10th International Symposium on Biomedical Imaging (IEEE, New York, 2013), pp. 157–160.
- I. F. Sbalzarini, Modeling and simulation of biological systems from image data, BioEssays 35, 482 (2013).
- J. Boulanger, C. Kervrann, and P. Bouthemy, A simulation and estimation framework for intracellular dynamics and trafficking in video-microscopy and fluorescence imagery, Med. Image Anal. 13, 132 (2009).
- See Supplemental Material at http://link.aps.org/supplemental/10.1103/PhysRevResearch.5.L022043 for further details.
- J. M. Schmitt and G. Kumar, Optical scattering properties of soft tissue: a discrete particle model, Appl. Opt. 37, 2788 (1998).
- K. Kaizu, K. Nishida, Y. Sakamoto, S. Kato, T. Niina, N. Nishida, N. Aota, M. Koizumi, and K. Takahashi, E-Cell System Version 4, doi: 10.5281/zenodo.3365597 (2019).
- W. X. Chew, K. Kaizu, M. Watabe, S. V. Muniandy, K. Takahashi, and S. N. V. Arjunan, Surface reaction-diffusion kinetics on lattice at the microscopic scale, Phys. Rev. E 99, 042411 (2019); W.-x. Chew, K. Kaizu, M. Watabe, S. V. Muniandy, K. Takahashi, and S. N. V. Arjunan, Reaction-diffusion kinetics on lattice at the microscopic scale, ibid. 98, 032418 (2018); S. N. V. Arjunan and M. Tomita, A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation, Syst. Synth. Biol. 4, 35 (2010).
- A. K. Glaser, Y. Chen, and J. T. C. Liu, Fractal propagation method enables realistic optical microscopy simulations in biological tissues, Optica 3, 861 (2016).
- J. D. Rogers, A. J. Radosevich, J. Yi, and V. Backman, Modeling light scattering in tissue as continuous random media using a versatile refractive index correlation function, IEEE J. Sel. Top. Quantum Electron. 20, 1 (2013).
- A. Wax and V. Backman, Biomedical Applications of Light Scattering (McGraw–Hill, New York, 2010), p. 401; J. D. Rogers, I. R. Capoglu, and V. Backman, Nonscalar elastic light scattering from continuous media in the Born approximation: Erratum, Opt. Lett. 35, 1367 (2010).
- Electron microscopy, TEM vs SEM, Thermo Fisher Scientific, https://www.thermofisher.com/jp/ja/home/materials-science/learning-center/applications/sem-tem-difference.html, accessed: 2022-12-27.
- S. K. Rajput, O. Matoba, M. Kumar, X. Quan, Y. Awatsuji, Y. Tamada, and E. Tajahuerce, Multi-physical parameter cross-sectional imaging of quantitative phase and fluorescence by integrated multimodal microscopy, IEEE J. Sel. Top. Quantum Electron. 27, 6801809 (2021).
- N. Yudistira, M. Kavitha, T. Itabashi, A. H. Iwane, and T. Kurita, Prediction of sequential organelles localization under imbalance using a balanced deep U-net, Sci. Rep. 10, 2626 (2020).
- T. M. Ichinose and A. H. Iwane, Cytological analyses by advanced electron microscopy, in Cyanidioschyzon merolae, edited by T. Kuroiwa, S. Miyagishima, S. Matsunaga, N. Sato, H. Nozaki, K. Tanaka, and O. Misumi (Springer, Berlin, 2017), pp. 129–151.
- S. Y. Miyagishima and K. Tanaka, The unicellular red alga Cyanidioschyzon merolae—The simplest model of a photosynthetic eukaryote, Plant and Cell Physiology 62, 926 (2021).
- S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and the scikit-image contributors, scikit-image: Image processing in Python, PeerJ 2, e453 (2014).
- S. Müller, L. Schüler, A. Zech, and F. Heße, GSTools v1.3: A toolbox for geostatistical modelling in python, Geosci. Model Dev. 15, 3161 (2022).
- H. Kimura, N. Takizawa, E. Allemand, T. Hori, F. J. Iborra, N. Nozaki, M. Muraki, M. Hagiwara, A. R. Krainer, T. Fukagawa, and K. Okawa, A novel histone exchange factor, protein phosphatase , mediates the exchange and dephosphorylation of H2A-H2B, J. Cell Biol. 175, 389 (2006).
- M. Watabe, Physical constraints to phase retrieval using the transport of intensity equation in fluorescence microscopy imaging, presented at the 1st Conference for Sensing and Imaging Through Scattering and Fluctuating Field in Biology, Telecommunication and Astronomy (SI-Thru2022), April 19–22, 2022, at Pacifico Yokohama, Japan.
- S. Mazumder, Numerical Methods for Partial Differential Equations: Finite Difference and Finite Volume Methods, 1st ed. (Academic Press, San Diego, 2016).
- B. Xue and S. Zheng, Phase retrieval using the transport of intensity equation solved by the FMG-CG method, Optik 122, 2101 (2011).
- S. V. Pinhasi, R. Alimi, L. Perelmutter, and S. Eliezer, Topography retrieval using different solutions of the transport intensity equation, J. Opt. Soc. Am. A 27, 2285 (2010).
- E. Meijering, A bird's-eye view of deep learning in bioimage analysis, Comput. Struct. Biotechnol. J. 18, 2312 (2020).
- E. Moen, D. Bannon, T. Kudo, W. Graf, M. Covert, and D. Van Valen, Deep learning for cellular image analysis, Nat. Methods 16, 1233 (2019).
- M. I. Jordan and T. M. Mitchell, Machine learning: Trends, perspectives, and prospects, Science 349, 255 (2015).
- V. Marx, The big challenges of big data, Nature (London) 498, 255 (2013).
- G. Danuser, Computer vision in cell biology, Cell 147, 973 (2011).