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Multiplex imaging of amyloid-β plaques dynamics in living brains with quinoline-malononitrile-based probes

Nature Biomedical Engineering (2025)Cite this article

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Abstract

The dynamic behaviour of amyloid-β (Aβ) plaques in Alzheimer’s disease remains poorly understood, and accumulation and distribution of Aβ plaques must be inferred from in vitro pathological changes in brain tissue. In situ detection of Aβ plaques in live imaging is challenging because of the lack of adequate probes. Here we report the design of unimolecular quinoline-malononitrile-based Aβ probes, termed QMFluor integrative framework, that binds in vivo to Aβ plaques, making them detectable via near-infrared fluorescence imaging, magnetic resonance imaging, positron emission tomography and computed tomography. QMFluor probes are permeable to the blood–brain barrier, and, upon systematic injection, enable real-time magnetic resonance imaging and positron emission tomography–computed tomography imaging of the Aβ biodistribution in the hippocampus and cerebral cortex, and accurately differentiate the brains of living Alzheimer’s disease mouse models from wild-type controls. We further demonstrate the ability of QMFluor probes to reach the brain after intravenous injection in a large animal model. This strategy expands the toolbox of probes for in vivo visualization of amyloids in Alzheimer’s disease pathological analysis, drug screening and clinical applications.

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Fig. 1: Unimolecular design for spatio-temporally quantifying Aβ plaques in the brain.
Fig. 2: Activatable fluorescence/MRI/PET properties of QMFluor-based probes.
Fig. 3: Lighting-up Aβ with NIR fluorescence in brain slices and living mice.
Fig. 4: Dynamic fluorescence and two-photon imaging of the cerebral cortex in living mice.
Fig. 5: 3D light-sheet fluorescence imaging of AD mouse brain after intravenous injection.
Fig. 6: MRI and PET–CT imaging in living mice.
Fig. 7: PET–CT and PET–MRI imaging in a beagle dog.

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Data availability

The main data supporting the results in this study are available within the paper and its Supplementary Information. All data generated and analysed during the study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

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Acknowledgements

This work was supported by the National Key Research and Development Program (2021YFA0910000 to W.-H.Z.), NSFC/China (22225805, 32394001, 32121005, T2488302, 92356301, 22338006 and 22378122 to Z.G., Z.G., Z.G., W.-H.Z., W.-H.Z., W.-H.Z. and C.Y., respectively), Shanghai Frontier Science Research Base of Optogenetic Techniques for Cell Metabolism (Shanghai Municipal Education Commission, grant 2021 Sci & Tech 03-28 to Z.G.), Science and Technology Commission of Shanghai Municipality (24DX1400200 to Z.G.), Shanghai Science and Technology Committee (23J21901600 and 23ZR1416600 to Z.G. and C.Y., respectively). We thank H.C. (Shanghai Institute of Materia Medica) and Z.A. (Shanghai Institute of Materia Medica) for allowing us to use the Photometrics Prime 95B sCMOS camera in dynamic in vivo fluorescence imaging.

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Author notes

  1. These authors contributed equally: Jianfeng Dai, Weijun Wei.

Authors and Affiliations

  1. Key Laboratory for Advanced Materials and Joint International Research Laboratory of Precision Chemistry and Molecular Engineering, Feringa Nobel Prize Scientist Joint Research Center, Institute of Fine Chemicals, Frontiers Science Center for Materiobiology and Dynamic Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai, China

    Jianfeng Dai, Chenxu Yan, Caiqi Liu, Jialiang Huang, Zhiqian Guo & Wei-Hong Zhu

  2. State Key Laboratory of Bioreactor Engineering, East China University of Science and Technology, Shanghai, China

    Jianfeng Dai & Zhiqian Guo

  3. Department of Nuclear Medicine, Institute of Clinical Nuclear Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China

    Weijun Wei, Ding-Kun Ji, Chenyi Liang & Jianjun Liu

  4. Center of Photosensitive Chemicals Engineering, East China University of Science and Technology, Shanghai, China

    Wei-Hong Zhu

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  1. Jianfeng Dai
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Contributions

All the experiments were conducted by J.D., W.W., D.-K.J., C. Liu, J.H., C. Liang and J.L. with the supervision of C.Y., Z.G. and W.-H.Z. All the authors analysed the data and contributed to the paper writing.

Corresponding authors

Correspondence to Chenxu Yan, Zhiqian Guo or Wei-Hong Zhu.

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Nature Biomedical Engineering thanks Jorge R. Barrio, Hak Soo Choi and Makoto Higuchi for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 In vivo two-photon fluorescence images.

In vivo two-photon fluorescence images of 12-month-old and 6-month-old APP/PS1 mice after being administered with Gd-QM-FN (2.35 mg kg−1) & FITC (50 mg kg−1) via tail vein at 2 min (a) and 10 min (b) post-injection. Channel 1: λex = 900 nm, λem = 660–750 nm. Channel 2: λex = 900 nm, λem = 575–645 nm. Note: white single-arrows refer to Aβ plaques, white double-arrows refer to cerebral amyloid angiopathy. The experiments in panels a and b were performed with 3 biological replicates and single measurement per mouse.

Extended Data Fig. 2 Maximum intensity projection (MIP) PET images.

MIP PET images of APP/PS1 and WT mice at 2, 5, and 10 min after intravenous injection of 68Ga-QM-FN (10 MBq). Note: MIP is a type of three-dimensional imaging technique used to visualize high-intensity areas within a PET scan (Supplementary Video 5). The experiments were performed with 3 biological replicates and single measurement per mouse.

Extended Data Fig. 3 Radioactivity uptake in the hippocampus, cerebral cortex, and midbrain.

Quantification analysis of radioactivity uptake in the hippocampus, cerebral cortex, and midbrain at 2, 5, and 10 min after intravenous injection of 68Ga-QM-FN (10 MBq) in APP/PS1 (a) and WT (b) mice. Radioactivity uptake ratios of hippocampus-to-midbrain (H/M, c) and cerebral-cortex-to-midbrain (CC/M, d) at 2, 5, and 10 min after intravenous injection of 68Ga-QM-FN (10 MBq) in APP/PS1 and WT mice. Data are expressed as the mean ± SD of three independent mice (n = 3).

Source data

Extended Data Fig. 4 Dynamic PET imaging within 1 h after bolus injection.

a, PET imaging of a beagle dog at different time points after intravenous injection of 68Ga-QM-FN (67 MBq). b, Quantitative analysis of the dynamic 68Ga-QM-FN distribution, accumulation, and clearance in terms of SUVmean in major organs and tissues within 1 h. The experiments in panel a were performed with single dog and 3 technical replicates.

Source data

Supplementary information

Supplementary Information

Supplementary Methods, Figs. 1–70, Tables 1 and 2, and references.

Reporting Summary

Supplementary Video 1

Dynamic mapping of Aβ plaques.

Supplementary Video 2

Dynamic two-photon microscopy imaging by using fluorescein isothiocyanate (FITC).

Supplementary Video 3

Dynamic two-photon microscopy imaging by using FITC and Gd-QM-FN.

Supplementary Video 4

Light-sheet fluorescence imaging of the brain tissue.

Supplementary Video 5

Maximum intensity projection (MIP) PET images of APP/PS1 and WT mice.

Supplementary Video 6

PET–CT imaging of a beagle dog.

Supplementary Video 7

PET imaging of a beagle dog.

Source data

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Dai, J., Wei, W., Yan, C. et al. Multiplex imaging of amyloid-β plaques dynamics in living brains with quinoline-malononitrile-based probes. Nat. Biomed. Eng (2025). https://doi.org/10.1038/s41551-025-01392-x

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