Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Multiscale optical and optoacoustic imaging of amyloid-β deposits in mice

Nature Biomedical Engineering volume 6, pages 1031–1044 (2022)Cite this article

Subjects

Abstract

Deposits of amyloid-β (Aβ) in the brains of rodents can be analysed by invasive intravital microscopy on a submillimetre scale, or via whole-brain images from modalities lacking the resolution or molecular specificity to accurately characterize Aβ pathologies. Here we show that large-field multifocal illumination fluorescence microscopy and panoramic volumetric multispectral optoacoustic tomography can be combined to longitudinally assess Aβ deposits in transgenic mouse models of Alzheimer’s disease. We used fluorescent Aβ-targeted probes (the luminescent conjugated oligothiophene HS-169 and the oxazine-derivative AOI987) to transcranially detect Aβ deposits in the cortex of APP/PS1 and arcAβ mice with single-plaque resolution (8 μm) and across the whole brain (including the hippocampus and the thalamus, which are inaccessible by conventional intravital microscopy) at sub-150 μm resolutions. Two-photon microscopy, light-sheet microscopy and immunohistochemistry of brain-tissue sections confirmed the specificity and regional distributions of the deposits. High-resolution multiscale optical and optoacoustic imaging of Aβ deposits across the entire brain in rodents thus facilitates the in vivo study of Aβ accumulation by brain region and by animal age and strain.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: The multiscale optical imaging pipeline for high-precision in vivo assessment of Alzheimer’s Aβ deposits.
Fig. 2: In vivo LMI imaging of Aβ deposits in APP/PS1 and arcAβ mouse brain with targeted HS-169 fluorescent probe.
Fig. 3: Regional plaque distribution in arcAβ and APP/PS1 mice revealed by in vivo vMSOT with targeted AOI987 dye probe.
Fig. 4: Quantifying cerebral Aβ load in arcAβ mice with vMSOT at different disease stages.
Fig. 5: Comparison between in vivo LMI and ex vivo two-photon microscopy (2PM).
Fig. 6: Staining for Aβ deposition in APP/PS1 and arcAβ mouse brain tissue sections.

Data availability

The main data supporting the findings of this study are available within the paper and its Supplementary Information. Source data are provided with this paper. The raw datasets generated during the imaging studies are too large to be publicly shared, yet they are available for research purposes from the corresponding authors on reasonable request. The light-sheet microscopy dataset and GitHub code for registration and quantification with the Allen brain atlas are available at github.com/alecrimi/arcAb_mouse and figshare.com/articles/arcAB_Mouse/11473707.

Code availability

https://github.com/mesoSPIM/mesoSPIM-control (Image acquisition software for mesoSPIM light-sheet microscope); https://github.com/alecrimi/arcAb_mouse (regional quantification of amyloid plaque load for lightsheet microscope data). The custom MATLAB code used to collect and process the imaging data is available for research purposes from the corresponding author on reasonable request.

References

  1. Selkoe, D. J. & Hardy, J. The amyloid hypothesis of Alzheimer’s disease at 25 years. EMBO Mol. Med. 8, 595–608 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Clark, C. M. et al. Use of florbetapir-PET for imaging beta-amyloid pathology. JAMA 305, 275–283 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Curtis, C. et al. Phase 3 trial of flutemetamol labeled with radioactive fluorine 18 imaging and neuritic plaque density. JAMA Neurol. 72, 287–294 (2015).

    Article  PubMed  Google Scholar 

  4. Klunk, W. E. et al. Binding of the positron emission tomography tracer Pittsburgh compound-B reflects the amount of amyloid-beta in Alzheimer’s disease brain but not in transgenic mouse brain. J. Neurosci. 25, 10598–10606 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Sabri, O. et al. Florbetaben PET imaging to detect amyloid beta plaques in Alzheimer’s disease: phase 3 study. Alzheimers Dement. 11, 964–974 (2015).

    Article  PubMed  Google Scholar 

  6. Jack, C. R. Jr. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement. 14, 535–562 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Villemagne, V. L., Dore, V., Burnham, S. C., Masters, C. L. & Rowe, C. C. Imaging tau and amyloid-beta proteinopathies in Alzheimer disease and other conditions. Nat. Rev. Neurol. 14, 225–236 (2018).

    Article  CAS  PubMed  Google Scholar 

  8. Jack, C. R. Jr. et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dhenain, M. et al. Characterization of in vivo MRI detectable thalamic amyloid plaques from APP/PS1 mice. Neurobiol. Aging 30, 41–53 (2009).

    Article  CAS  PubMed  Google Scholar 

  10. Higuchi, M. et al. 19F and 1H MRI detection of amyloid beta plaques in vivo. Nat. Neurosci. 8, 527–533 (2005).

    Article  CAS  PubMed  Google Scholar 

  11. Jack, C. R. Jr. et al. In vivo magnetic resonance microimaging of individual amyloid plaques in Alzheimer’s transgenic mice. J. Neurosci. 25, 10041–10048 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Gong, N.-J., Dibb, R., Bulk, M., van der Weerd, L. & Liu, C. Imaging beta amyloid aggregation and iron accumulation in Alzheimer’s disease using quantitative susceptibility mapping MRI. NeuroImage 191, 176–185 (2019).

    Article  CAS  PubMed  Google Scholar 

  13. Dudeffant, C. et al. Contrast-enhanced MR microscopy of amyloid plaques in five mouse models of amyloidosis and in human Alzheimer’s disease brains. Sci. Rep. 7, 4955 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  14. Sehlin, D. et al. Antibody-based PET imaging of amyloid beta in mouse models of Alzheimer’s disease. Nat. Commun. 7, 10759 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Rodriguez-Vieitez, E. et al. Astrocytosis precedes amyloid plaque deposition in Alzheimer APPswe transgenic mouse brain: a correlative positron emission tomography and in vitro imaging study. Eur. J. Nucl. Med. Mol. Imaging 42, 1119–1132 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Sacher, C. et al. Longitudinal PET monitoring of amyloidosis and microglial activation in a second-generation amyloid-β mouse model. J. Nucl. Med. 60, 1787–1793 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Sacher, C. et al. Asymmetry of fibrillar plaque burden in amyloid mouse models. J. Nucl. Med. 61, 1825–1831 (2020).

    Article  CAS  PubMed  Google Scholar 

  18. Snellman, A. et al. Applicability of [11C]PIB micro-PET imaging for in vivo follow-up of anti-amyloid treatment effects in APP23 mouse model. Neurobiol. Aging 57, 84–94 (2017).

    Article  CAS  PubMed  Google Scholar 

  19. Mannheim, J. G. et al. PET/MRI hybrid systems. Semin. Nucl. Med. 48, 332–347 (2018).

    Article  PubMed  Google Scholar 

  20. Hintersteiner, M. et al. In vivo detection of amyloid-beta deposits by near-infrared imaging using an oxazine-derivative probe. Nat. Biotechnol. 23, 577–583 (2005).

    Article  CAS  PubMed  Google Scholar 

  21. Hyde, D. et al. Hybrid FMT-CT imaging of amyloid-beta plaques in a murine Alzheimer’s disease model. NeuroImage 44, 1304–1311 (2009).

    Article  PubMed  Google Scholar 

  22. Zhang, X. et al. Near-infrared fluorescence molecular imaging of amyloid beta species and monitoring therapy in animal models of Alzheimer’s disease. Proc. Natl Acad. Sci. USA 112, 9734–9739 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Whitesell, J. D. et al. Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease. J. Comp. Neurol. 527, 2122–2145 (2019).

    Article  CAS  PubMed  Google Scholar 

  24. Bacskai, B. J. et al. Four-dimensional multiphoton imaging of brain entry, amyloid binding, and clearance of an amyloid-β ligand in transgenic mice. Proc. Natl Acad. Sci. USA 100, 12462–12467 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Meyer-Luehmann, M. et al. Rapid appearance and local toxicity of amyloid-beta plaques in a mouse model of Alzheimer’s disease. Nature 451, 720–724 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Hu, S., Yan, P., Maslov, K., Lee, J.-M. & Wang, L. V. Optical-resolution photoacoustic microscopy of amyloid-beta deposits in vivo. Proc. SPIE 7564, 75643D (2010).

    Article  CAS  Google Scholar 

  27. Calvo-Rodriguez, M. et al. In vivo detection of tau fibrils and amyloid beta aggregates with luminescent conjugated oligothiophenes and multiphoton microscopy. Acta Neuropathol. Commun. 7, 171 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  28. Hefendehl, J. K. et al. Long-term in vivo imaging of beta-amyloid plaque appearance and growth in a mouse model of cerebral beta-amyloidosis. J. Neurosci. 31, 624–629 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Wilcock, D. M. et al. Intracranially administered anti-Abeta antibodies reduce beta-amyloid deposition by mechanisms both independent of and associated with microglial activation. J. Neurosci. 23, 3745–3751 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Sevigny, J. et al. The antibody aducanumab reduces Aβ plaques in Alzheimer’s disease. Nature 537, 50–56 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Chen, Z. et al. High-speed large-field multifocal illumination fluorescence microscopy. Laser Photon. Rev. 14, 1900070 (2019).

    Article  CAS  Google Scholar 

  32. Ni, R. et al. Detection of cerebral tauopathy in P301L mice using high-resolution large-field multifocal illumination fluorescence microscopy. Biomed. Opt. Express 11, 4989–5002 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Lassailly, F., Foster, K., Lopez-Onieva, L., Currie, E. & Bonnet, D. Multimodal imaging reveals structural and functional heterogeneity in different bone marrow compartments: functional implications on hematopoietic stem cells. Blood 122, 1730–1740 (2013).

    Article  CAS  PubMed  Google Scholar 

  34. Mazo, I. B. et al. Hematopoietic progenitor cell rolling in bone marrow microvessels: parallel contributions by endothelial selectins and vascular cell adhesion molecule 1. J. Exp. Med. 188, 465–474 (1998).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Deán-Ben, X. L. & Razansky, D. Adding fifth dimension to optoacoustic imaging: volumetric time-resolved spectrally enriched tomography. Light Sci. Appl. 3, e137 (2014).

    Article  CAS  Google Scholar 

  36. Deán-Ben, X. L. et al. Functional optoacoustic neuro-tomography for scalable whole-brain monitoring of calcium indicators. Light Sci. Appl. 5, e16201 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  37. Razansky, D. et al. Multispectral opto-acoustic tomography of deep-seated fluorescent proteins in vivo. Nat. Photonics 3, 412 (2009).

    Article  CAS  Google Scholar 

  38. Omar, M., Aguirre, J. & Ntziachristos, V. Optoacoustic mesoscopy for biomedicine. Nat. Biomed. Eng. 3, 354–370 (2019).

    Article  CAS  PubMed  Google Scholar 

  39. Razansky, D., Klohs, J. & Ni, R. Multi-scale optoacoustic molecular imaging of brain diseases. Eur. J. Nucl. Med. Mol. Imaging 48, 4152–4170 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Burmann, B. M. et al. Regulation of alpha-synuclein by chaperones in mammalian cells. Nature 577, 127–132 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Wang, L. V. & Hu, S. Photoacoustic tomography: in vivo imaging from organelles to organs. Science 335, 1458–1462 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Taruttis, A. & Ntziachristos, V. Advances in real-time multispectral optoacoustic imaging and its applications. Nat. Photonics 9, 219–227 (2015).

    Article  CAS  Google Scholar 

  43. Chen, Z., Dean-Ben, X. L., Gottschalk, S. & Razansky, D. Hybrid system for in vivo epifluorescence and 4D optoacoustic imaging. Opt. Lett. 42, 4577–4580 (2017).

    Article  CAS  PubMed  Google Scholar 

  44. Jährling, N. et al. Cerebral β-amyloidosis in mice investigated by ultramicroscopy. PLoS ONE 10, e0125418 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  45. Klohs, J. et al. Longitudinal assessment of amyloid pathology in transgenic ArcAβ mice using multi-parametric magnetic resonance imaging. PLoS ONE 8, e66097 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Knobloch, M., Konietzko, U., Krebs, D. C. & Nitsch, R. M. Intracellular Abeta and cognitive deficits precede beta-amyloid deposition in transgenic arcAbeta mice. Neurobiol. Aging 28, 1297–1306 (2007).

    Article  CAS  PubMed  Google Scholar 

  47. Merlini, M., Meyer, E. P., Ulmann-Schuler, A. & Nitsch, R. M. Vascular beta-amyloid and early astrocyte alterations impair cerebrovascular function and cerebral metabolism in transgenic arcAbeta mice. Acta Neuropathol. 122, 293–311 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Radde, R. et al. Abeta42-driven cerebral amyloidosis in transgenic mice reveals early and robust pathology. EMBO Rep. 7, 940–946 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Voigt, F. F. et al. The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue. Nat. Methods 16, 1105–1108 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Kayed, R. et al. Fibril specific, conformation dependent antibodies recognize a generic epitope common to amyloid fibrils and fibrillar oligomers that is absent in prefibrillar oligomers. Mol. Neurodegener. 2, 18 (2007).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  51. Shirani, H. et al. A palette of fluorescent thiophene-based ligands for the identification of protein aggregates. Chemistry 21, 15133–15137 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Tzoumas, S., Deliolanis, N., Morscher, S. & Ntziachristos, V. Unmixing molecular agents from absorbing tissue in multispectral optoacoustic tomography. IEEE Trans. Med. Imaging 33, 48–60 (2014).

    Article  PubMed  Google Scholar 

  53. Cox, B., Laufer, J. G., Arridge, S. R. & Beard, P. C. Quantitative spectroscopic photoacoustic imaging: a review. J. Biomed. Opt. 17, 061202 (2012).

    Article  PubMed  Google Scholar 

  54. Lord, A. et al. Observations in APP bitransgenic mice suggest that diffuse and compact plaques form via independent processes in Alzheimer’s disease. Am. J. Pathol. 178, 2286–2298 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Rasmussen, J. et al. Amyloid polymorphisms constitute distinct clouds of conformational variants in different etiological subtypes of Alzheimer’s disease. Proc. Natl Acad. Sci. USA 114, 13018–13023 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Ni, R. et al. Amyloid tracers binding sites in autosomal dominant and sporadic Alzheimer’s disease. Alzheimers Dement. 13, 419–430 (2017).

    Article  PubMed  Google Scholar 

  57. Ni, R., Gillberg, P. G., Bergfors, A., Marutle, A. & Nordberg, A. Amyloid tracers detect multiple binding sites in Alzheimer’s disease brain tissue. Brain 136, 2217–2227 (2013).

    Article  PubMed  Google Scholar 

  58. Herrmann, U. S. et al. Structure-based drug design identifies polythiophenes as antiprion compounds. Sci. Transl. Med. 7, 299ra123 (2015).

    Article  PubMed  CAS  Google Scholar 

  59. Schütz, A. K. et al. The amyloid-Congo red interface at atomic resolution. Angew. Chem. Int. Ed. Engl. 50, 5956–5960 (2011).

    Article  PubMed  CAS  Google Scholar 

  60. Schütz, A. K. et al. Binding of polythiophenes to amyloids: structural mapping of the pharmacophore. ACS Chem. Neurosci. 9, 475–481 (2018).

    Article  PubMed  CAS  Google Scholar 

  61. Bäck, M., Appelqvist, H., LeVine, H. 3rd & Nilsson, K. P. Anionic oligothiophenes compete for binding of X-34 but not PIB to recombinant Aβ amyloid fibrils and Alzheimer’s disease brain-derived Aβ. Chemistry 22, 18335–18338 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Aslund, A. et al. Novel pentameric thiophene derivatives for in vitro and in vivo optical imaging of a plethora of protein aggregates in cerebral amyloidoses. ACS Chem. Biol. 4, 673–684 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  63. Nyström, S. et al. Evidence for age-dependent in vivo conformational rearrangement within Aβ amyloid deposits. ACS Chem. Biol. 8, 1128–1133 (2013).

    Article  PubMed  CAS  Google Scholar 

  64. Ulrich, J. D. et al. ApoE facilitates the microglial response to amyloid plaque pathology. J. Exp. Med. 215, 1047–1058 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Liu, Y. et al. Highly specific noninvasive photoacoustic and positron emission tomography of brain plaque with functionalized croconium dye labeled by a radiotracer. Chem. Sci. 8, 2710–2716 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  66. Miao, J. et al. Cerebral microvascular amyloid beta protein deposition induces vascular degeneration and neuroinflammation in transgenic mice expressing human vasculotropic mutant amyloid beta precursor protein. Am. J. Pathol. 167, 505–515 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Fischer, V. W., Siddiqi, A. & Yusufaly, Y. Altered angioarchitecture in selected areas of brains with Alzheimer’s disease. Acta Neuropathol. 79, 672–679 (1990).

    Article  CAS  PubMed  Google Scholar 

  68. Kulic, L. et al. Early accumulation of intracellular fibrillar oligomers and late congophilic amyloid angiopathy in mice expressing the Osaka intra-Aβ APP mutation. Transl. Psychiatry 2, e183 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Kim, H. Y. et al. EPPS rescues hippocampus-dependent cognitive deficits in APP/PS1 mice by disaggregation of amyloid-β oligomers and plaques. Nat. Commun. 6, 8997 (2015).

    Article  CAS  PubMed  Google Scholar 

  70. Ni, R. et al. In vitro characterization of the regional binding distribution of amyloid PET tracer florbetaben and the glia tracers Deprenyl and PK1195 in autopsy Alzheimer’s brain tissue. J. Alzheimers Dis. 80, 1723–1737 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Kim, H. M. & Cho, B. R. Small-molecule two-photon probes for bioimaging applications. Chem. Rev. 115, 5014–5055 (2015).

    Article  CAS  PubMed  Google Scholar 

  72. Snellman, A. et al. Applicability of [(11)C]PIB micro-PET imaging for in vivo follow-up of anti-amyloid treatment effects in APP23 mouse model. Neurobiol. Aging 57, 84–94 (2017).

    Article  CAS  PubMed  Google Scholar 

  73. Zhou, Q., Chen, Z., Robin, J., Deán-Ben, X.-L. & Razansky, D. Diffuse optical localization imaging for noninvasive deep brain microangiography in the NIR-II window. Optica 8, 796–803 (2021).

    Article  Google Scholar 

  74. Ren, W. et al. Automated registration of magnetic resonance imaging and optoacoustic tomography data for experimental studies. Neurophotonics 6, 025001 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Gottschalk, S. et al. Rapid volumetric optoacoustic imaging of neural dynamics across the mouse brain. Nat. Biomed. Eng 3, 392–401 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Gottschalk, S., Fehm, T. F., Dean-Ben, X. L., Tsytsarev, V. & Razansky, D. Correlation between volumetric oxygenation responses and electrophysiology identifies deep thalamocortical activity during epileptic seizures. Neurophotonics 4, 011007 (2017).

    Article  PubMed  Google Scholar 

  77. Dean-Ben, X. L. & Razansky, D. On the link between the speckle free nature of optoacoustics and visibility of structures in limited-view tomography. Photoacoustics 4, 133–140 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Davoudi, N., Deán-Ben, X. L. & Razansky, D. Deep learning optoacoustic tomography with sparse data. Nat. Mach. Intell. 1, 453–460 (2019).

    Article  Google Scholar 

  79. Blinder, P., Shih, A. Y., Rafie, C. & Kleinfeld, D. Topological basis for the robust distribution of blood to rodent neocortex. Proc. Natl Acad. Sci. USA 107, 12670 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Laufer, J., Zhang, E., Raivich, G. & Beard, P. Three-dimensional noninvasive imaging of the vasculature in the mouse brain using a high resolution photoacoustic scanner. Appl. Opt. 48, D299–D306 (2009).

    Article  PubMed  Google Scholar 

  81. Ren, W. et al. Development of concurrent magnetic resonance imaging and volumetric optoacoustic tomography: a phantom feasibility study. J. Biophotonics 14, e202000293 (2021).

    Article  CAS  PubMed  Google Scholar 

  82. Cohen, S. I. A. et al. A molecular chaperone breaks the catalytic cycle that generates toxic Aβ oligomers. Nat. Struct. Mol. Biol. 22, 207–213 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ni, R. et al. fMRI reveals mitigation of cerebrovascular dysfunction by bradykinin receptors 1 and 2 inhibitor noscapine in a mouse model of cerebral amyloidosis. Front. Aging Neurosci. 11, 27 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  84. Klohs, J. et al. Quantitative assessment of microvasculopathy in arcAβ mice with USPIO-enhanced gradient echo MRI. J. Cereb. Blood Flow Metab. 36, 1614–1624 (2016).

    Article  CAS  PubMed  Google Scholar 

  85. Ni, R., Rudin, M. & Klohs, J. Cortical hypoperfusion and reduced cerebral metabolic rate of oxygen in the arcAbeta mouse model of Alzheimer’s disease. Photoacoustics 10, 38–47 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  86. Chen, Z., Zhou, Q., Rebling, J. & Razansky, D. Cortex-wide microcirculation mapping with ultrafast large-field multifocal illumination microscopy. J. Biophotonics 13, e202000198 (2020).

    CAS  PubMed  Google Scholar 

  87. Gregor, I. & Enderlein, J. Image scanning microscopy. Curr. Opin. Chem. Biol. 51, 74–83 (2019).

    Article  CAS  PubMed  Google Scholar 

  88. Dean-Ben, X. L., Robin, J., Ni, R. & Razansky, D. Noninvasive three-dimensional optoacoustic localization microangiography of deep tissues. Preprint at https://arxiv.org/abs/2007.00372 (2020).

  89. Ni, R. et al. In-vitro and in-vivo characterization of CRANAD-2 for multi-spectral optoacoustic tomography and fluorescence imaging of amyloid-beta deposits in Alzheimer mice. Photoacoustics 23, 100285 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  90. American National Standard for the Safe Use of Lasers (American National Standards Institute, 1993).

  91. Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

    Article  CAS  PubMed  Google Scholar 

  92. Ma, Y. et al. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience 135, 1203–1215 (2005).

    Article  CAS  PubMed  Google Scholar 

  93. Vagenknecht, P. et al. Non-invasive imaging of tau-targeted probe uptake by whole brain multi-spectral optoacoustic tomography. Eur. J. Nucl. Med. Mol. Imaging 49, 2137–2152 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  94. Chung, K. et al. Structural and molecular interrogation of intact biological systems. Nature 497, 332–337 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  95. Yang, B. et al. Single-cell phenotyping within transparent intact tissue through whole-body clearing. Cell 158, 945–958 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

D.R. acknowledges funding from the European Research Council Consolidator Grant (ERC-2015-CoG-682379), the US National Institutes of Health (UF1-NS107680) and the Swiss National Science Foundation (310030_192757). K.P.R.N. acknowledges funding from the Swedish Research Council (2016–00748). A.A. acknowledges funding from a Nomis Distinguished Scientist Award. J.K. received funding from the Swiss National Science Foundation (320030_179277), in the framework of ERA-NET NEURON (32NE30_173678/1), the Synapsis foundation and the Vontobel foundation. R.N. received funding from the Synapsis foundation career development award (2017 CDA-03), UZH Innovation (MEDEF20-021), Helmut Horten Stiftung, Vontobel Stiftung and Jubiläumsstiftung von Swiss Life. Z.C. acknowledges funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant Agreement No. 746430 – MSIOAM. We thank L. Mu (Institute of Pharmaceutical Sciences, ETH Zurich); J. Hehl, J. Kusch-Wieser and G. Bodizs (Scientific Center for Optical and Electron Microscopy (ScopeM) of ETH Zurich); L. Kulic, D. Schuppli and P. Ravikumar (Institute for Regenerative Medicine, University of Zurich) for technical support; M. Rudin (Institute for Biomedical Engineering, University of Zurich and ETH Zurich) for providing the AOI987 contrast agent; and M. Reiss and M. Rouault (Institute for Biomedical Engineering, University of Zurich and ETH Zurich) for assistance with the animal experiments.

Author information

Author notes

  1. These authors contributed equally: Ruiqing Ni, Zhenyue Chen, Xosé Luís Deán-Ben.

Authors and Affiliations

  1. Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland

    Ruiqing Ni, Zhenyue Chen, Xosé Luís Deán-Ben, Gloria Shi, Quanyu Zhou, Jan Klohs & Daniel Razansky

  2. Zurich Neuroscience Center (ZNZ), University of Zurich and ETH Zurich, Zurich, Switzerland

    Ruiqing Ni, Fabian F. Voigt, Roger M. Nitsch, Adriano Aguzzi, Fritjof Helmchen, Jan Klohs & Daniel Razansky

  3. Institute for Regenerative Medicine, University of Zurich, Zurich, Switzerland

    Ruiqing Ni & Roger M. Nitsch

  4. Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland

    Zhenyue Chen, Xosé Luís Deán-Ben, Quanyu Zhou & Daniel Razansky

  5. Brain Research Institute, University of Zurich, Zurich, Switzerland

    Fabian F. Voigt & Fritjof Helmchen

  6. Institute of Neuropathology, Universitätsspital Zurich, Zurich, Switzerland

    Daniel Kirschenbaum, Alessandro Crimi & Adriano Aguzzi

  7. Department of Chemistry and Applied Biosciences, ETH Zurich, Zurich, Switzerland

    Alessia Villois & Paolo Arosio

  8. Division of Chemistry, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden

    K. Peter R. Nilsson

Authors
  1. Ruiqing Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhenyue Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xosé Luís Deán-Ben
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Fabian F. Voigt
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Daniel Kirschenbaum
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Gloria Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Alessia Villois
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Quanyu Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Alessandro Crimi
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Paolo Arosio
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Roger M. Nitsch
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. K. Peter R. Nilsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Adriano Aguzzi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Fritjof Helmchen
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jan Klohs
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Daniel Razansky
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

R.N., X.L.D.-B., Z.C., J.K. and D.R. conceived and designed the study. R.N., X.L.D.-B., Z.C. and Q.Z. performed the LMI and vMSOT experiments. F.F.V. built the 2PM and mesoSPIM setups and performed ex vivo 2PM imaging of whole brains. D.K. performed tissue clearing and the mesoSPIM imaging experiments. G.S. performed histopathological analysis. A.V. performed the fibril-binding assay. R.N. performed MP of brain sections. K.P.R.N. synthesized the FTAAs and HS-169 probes. R.N., X.L.D.-B., Z.S., A.C. and D.K. analysed the data. R.N., X.L.D.-B., Z.C., J.K. and D.R. interpreted the results. R.N., X.L.D.-B., Z.C., J.K. and D.R. wrote the paper. D.R. supervised the project. All authors contributed to the writing and editing of the manuscript.

Corresponding authors

Correspondence to Jan Klohs or Daniel Razansky.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Biomedical Engineering thanks Liming Nie and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

Additional information

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

Extended data

Extended Data Fig. 1 Characterization of the LMI fluorescence microscope for transcranial whole-cortex imaging at single plaque resolution.

a, LMI image of fluorescent beads (1-5 μm-diameter) on a microscope slide; Repetition n = 3. b, Comparison of magnified views of wide-field (WF) and LMI images in the red boxed regions together with their signal profiles along the corresponding green lines. The Gaussian fitted full-width-at-half-maximum (FWHM) of the bead in region #1 is 18.2 μm and 8.3 μm for the WF and LMI images, respectively. The corresponding values for region #2 are 16.4 μm (WF) and 7.1 μm (LMI); For region #3: 18.9 μm (WF) and 7.4 μm (LMI); For region #4: 27.0 μm (WF) and 11.8 μm (LMI). An averaged FWHM of 20 different beads across the field-of-view (FOV) was calculated as 7.7 ± 1.4 μm (mean ± SD). c–e, The horizontal and vertical intensity profiles of one of the optical foci in the excitation illumination pattern measured with a beam profiler (SP628, Ophir Optronics). The signal intensity (represented as mean ± SD) was normalized and fitted by Gaussian function. The spot sizes along the horizontal and vertical directions were measured at 14 μm and 11 μm, respectively; Repetition n = 3. f, Curve of the spot size along the light path (z-axis). The depth-of-focus for excitation was larger than 1 mm within a spot size of 20 μm. LMI - large-field multifocal illumination. g–i, Dynamic contrast-enhanced LMI fluorescence microscopy reveals different probe dynamics in cerebral and calvarian blood vessels. To showcase the performance of the LMI fluorescence microscopy method, time-lapse imaging of the bolus passage after intravenous tail-vein injections of Cy5.5 solution was performed. (g) Cranial and intracranial vessel network shown in maximum intensity projection (MIP) over multiple image frames; (h) Analysis of perfusion dynamics (time to peak post dye injection) allows to discriminate between cerebral (fast time to peak) and calvarian (slower time to peak) vessels. As expected, the LMI approach exhibits excellent contrast and spatial resolution through the intact skull while also accurately resolving the perfusion process in the deeply embedded cerebral microvasculature. Major cerebral vessels such as anterior cerebral artery (ACA) and middle cerebral artery (MCA) were pronounced at an early stage followed by venous vessels, such as inferior cerebral vein (ICV) and superior sagittal sinus (SSS). (i) LMI image acquired at 30 min post Cy5.5 fluorescent dye injection, showing the probe retention in the suture areas. LMI - large-field multifocal illumination.

Source data

Extended Data Fig. 2 In vitro and in vivo characterization of the HS-169 probe uptake.

a, Spectrofluorometric measurements show emission peak of HS-169 in the concentration range varying from 0 to 120 µM and 200 nM Aβ42 fibril (incubation time 10 min); Data are shown as mean ± SD; Repetition n = 3. b, Fluorescence intensity of HS-169 (60 µM) and Aβ42 fibrils in the concentration range varying from 0 to 200 nM. c, Linear relation between Aβ42 fibril concentration and fluorescence intensity was observed, Data are presented as mean ± SD. Repetition n = 3. d–g, Comparison of Aβ load at 2 h and 24 h post-injection of the HS-169 probe in arcAβ mouse. (d) LMI images acquired at 2 h time point post-injection of HS-169 in a 22-month arcAβ mouse (d). LMI images acquired at 24 h post-injection of HS-169 in a 22-month arcAβ mouse with the same dose (e). Contrast-to-noise ratio (CNR) comparison between LMI and widefield (WF) fluorescence images acquired at 2 h and 24 h post-injection of HS-169 in one mouse brain (f,g). The CNR was calculated via (sig - bg)/noise; where sig and bg are the mean fluorescence intensities in ROIs as indicated in d and e; noise was calculated as the standard deviation in the ROI in a background region. Both image pattern and the CNR values of the LMI images acquired at these two time points are comparable, while there is a reduction in the CNR values in the WF image at 24 h compared to 2 h post-injection. Data is presented as mean ± SD (analysis of the 6 ROIs over one mouse brain).

Extended Data Fig. 3 Characterization of the vMSOT resolution.

a–d, Using microsphere, maximum intensity projection (MIP) along the axial direction of the vMSOT image of a 30 µm microsphere positioned in the centre of the array (a). Vertical profile of the vMSOT image along the line indicated in a (b). Fitted Gaussian curve is indicated. The spatial resolution can be estimated via the mean square difference between the width of the fitted curve and the actual microsphere diameter, resulting in a value of 113 µm. A compounded vMSOT image obtained by raster scanning the microsphere across the field of view (c). The reconstructed microsphere size for positions corresponding to various distances from the centre of the spherical array geometry, as indicated in panel c (d). e–i, Transcranial imaging of mouse brain. Maximum intensity projection (MIP) along the axial direction of the vMSOT image of a 30 µm microsphere positioned in the centre of the array’s field-of-view when an excised murine skull is placed between the microsphere and the array (e). Vertical profile of the vMSOT image along the line indicated in e (f). Fitted Gaussian curve is indicated. The mean square difference between the width of the fitted curve and the microsphere diameter corresponds to a spatial resolution of 157 µm. Ratio of amplitudes of the optoacoustic signals recorded without and with the skull as a function of the angle of each detection element with respect to the central axis of the array (g). Signals were averaged for each ring of elements. MIP of the vMSOT image of the brain of a 15-months arcAβ mouse (unmixed signal corresponding to oxygenated hemoglobin) through the intact scalp and skull (h). One-dimensional vMSOT signal profile along the white line indicated in h (i). The width of the fitted Gaussian curve is 161 µm.

Source data

Extended Data Fig. 4 Performance of vMSOT with different illumination schemes.

a, Top-view maximum intensity projection (MIP) of the volumetric vMSOT brain image recorded from a 5-months old nude mouse when the illumination was provided from a single direction through the central aperture of the array. b, The corresponding image when the brain is illuminated from four different directions using the multi-arm fiber bundle. c,d, Cross-sections of the 3D image in b for the indicated positions. The horizontal (xy) section corresponds to a depth of 3 mm from the scalp surface. The illumination wavelength was 800 nm.

Extended Data Fig. 5 In vivo Aβ imaging with vMSOT.

a, 3D rendering of vMSOT data in arcAβ mouse brain unmixed for AOI987 distribution. b, Baseline-subtracted single wavelength vMSOT image acquired at 650 nm. c, Image acquired at 600 nm excitation wavelength reveals the major cerebral vessels. d, Overlay of a and c. e,g, Overlay of a on MRI structural data. f, Time-lapse curve of the unmixed AOI987 absorbance. h, The corresponding curve for the baseline-subtracted signal at 650 nm. AOI987 was injected i.v. at 30 s. The temporal evolution of the unmixed AOI987 absorbance and baseline-subtracted signals was analysed in four different brain regions: cortex (red), superior sagittal sinus (green), hippocampus (blue), vessel (grey) on the cortical surface indicated in e,h. norm, normalized; ab, absorbance.

Extended Data Fig. 6 Assessment of nonspecific probe accumulation via blood-brain barrier leakage.

a, vMSOT image of an unspecific Cy5.5 probe distribution in a 14-month old arcAβ mouse versus non-transgenic littermate (NTL). An amount of 100 µl at a concentration of 1 mg/ml was injected intravenously. Four different time points are shown before and after Cy5.5 injection. Scale bar = 1 mm. b, Averaged Cy5.5 signals in the brains of 14-month old arcAβ mice and NTL mice at different time points post-injection (n = 3 in each group). Data are presented as mean ± SD. No significant differences were observed in probe distribution between groups.

Extended Data Fig. 7 In vivo Aβ imaging in 8, 14 and 15-months old arcAβ mice by means of vMSOT and AOI987 probe.

Regional signal analysis revealed a higher amyloid load in the cortex and cerebellum at 15 months-of-age compared to 8 months. Data is presented as mean ± SD. Two-way ANOVA followed by post-hoc Bonferroni correction for multiple comparison was used. Comparison between 8 month (n = 4) and 15 month (n = 4) arcAβ mice showed significant increase in the cortex (p = 0.012), and in the hippocampus (p = 0.0331). No different was observed between 14 (n = 4) and 15 month (n = 4).

Extended Data Fig. 8 Ex vivo whole brain imaging using vMSOT and mesoSPIM with LOCs h-FTAA and q-FTAA.

a, 3D rendering of an anatomical vMSOT image (acquired at 600 nm) overlayed with unmixed AOI987 distribution rendered by vMSOT in NTL and arcAβ mice. b, 3D rendering of mesoSPIM data of Aβ distribution (stained with LCOs h-FTAA and q-FTAA) in NTL and arcAβ mice. c, Quantification of regional fluorescence intensity in the arcAβ mouse brain. Cortex: Ctx; Hippocampus: Hip; Midbrain: MB; Striatum: Str; Thalamus: TH. d,e, Sagittal views of the brain of 24 month-old non-transgenic littermate (NTL) (d) and arcAβ mouse (e). High Aβ plaque load was observed in the cortex, hippocampus and thalamus, while fewer plaques were observed in cerebellar and other subcortical regions. Fluorescence intensity (F.I.). Scale bar = 1 mm.

Extended Data Fig. 9 Two-photon microscopy (2PM) of ex vivo brain slices from arcAβ mice after in vivo vMSOT imaging with i.v. injection of AOI987.

a,b, Lambda spectrum mapping of a representative brain slice from an 8-month old arcAβ mouse showing the peak of signal. c,e,f, Horizontal views and zoom-ins of brain slices from 8- and 15-month-old arcAβ mice. Signals were observed in the parenchyma as well as inside a cortical vessel (zoom-in in panel f). Non-specific signal was detected in the cerebellum of 8-month-old arcAβ mouse. AOI987 signals were observed in the cerebellum of 15-month-old arcAβ mice. d, Horizontal view of brain mouse slides from Allen brain atlas are shown for anatomical reference 1. Scale bar = 0.5 mm (a), 50 µm (b,f), 1 mm (c,e), Cortex: Ctx, Cerebellum: Cb.

Extended Data Fig. 10 Illustration of the differences between 2PM and LMI imaging of CAA deposits and parenchymal plaques.

a, Illustration of the high-resolution 2PM excitation of a thin CAA vascular deposit versus parenchymal plaque. b, The corresponding illustration showing a much larger fluorescence volume excited by LMI in the large densely-filled Aβ parenchymal plaque. The incident light beams and the volumes where fluorescence responses are produced for both modalities are indicated in red and green, respectively. c,d, Representative confocal microscopic images of horizontal sections from an arcAβ mouse brain. 3D rendering of vessel-wall associated CAA and parenchymal plaque indicates the major difference in their shape and overall mass, affecting the total amount of signal detected by LMI. DAPI (blue), Alexa488-6E10 (green), HS-169 (red), CAA - cerebral amyloid angiopathy; LMI - large-field multifocal illumination. Scale bar = 20 µm.

Supplementary information

Supplementary Information

Supplementary methods, tables and references.

Reporting Summary

Peer Review File

Supplementary Video 1

High-resolution Aβ imaging.

Supplementary Video 2

Non-invasive in vivo volumetric multispectral optoacoustic tomography (vMSOT) of the targeted AOI987 probe distribution in an arcAβ mouse brain at 60 min after intravenous injection.

Supplementary Video 3

MesoSPIM using luminescent conjugated oligothiophene in the whole brain of an arcAβ mouse revealed Aβ plaques.

Source data

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ni, R., Chen, Z., Deán-Ben, X.L. et al. Multiscale optical and optoacoustic imaging of amyloid-β deposits in mice. Nat. Biomed. Eng 6, 1031–1044 (2022). https://doi.org/10.1038/s41551-022-00906-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41551-022-00906-1

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing