β淀粉样蛋白斑块的高分辨全脑三维定量研究
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摘要
中枢神经系统中β淀粉样蛋白斑块是阿尔兹海默症的主要病理特征之一,其负荷和数目的变化是病程发展的重要标志.已有研究主要是对局部脑组织进行二维切片成像,尚缺少在全脑三维空间对斑块进行高分辨率定量分析的研究方法.本文建立了适用于哺乳动物三维完整脑内β淀粉样蛋白斑块定量分析策略,包括全脑斑块快速荧光染色方法、基于荧光显微光学切片断层成像技术的高分辨全脑数据获取,以及斑块自动定位、统计数目等.与免疫组化染色比较,证明本方法对直径大于10μm的斑块检出率为97.71%±0.18%.并以0.32μm×0.32μm×2μm的成像分辨率,获取了5XFAD转基因小鼠全脑Aβ斑块分布数据集,首次以脑区/核团的三维轮廓划分出立体区域,定量统计了90个亚区内Aβ斑块的数量及分布密度.本文建立的快速、精准、价廉的方法将有助于全面高效地研究阿尔兹海默症致病机理和药效评估.
Amyloidosis of the central nervous system is one of the main pathological hallmarks of Alzheimer's disease(AD). Changes in the number and the load of amyloid plaque(Aβ) deposition are important indicators of the progression of AD. However, the quantitative analysis of amyloidosis at the subcellular level in the whole brain remains a substantial challenge. In this study, an automatic analysis method was established for assessing amyloid plaques in the whole brain, including convenient whole brain labeling, imaging with a fluorescence micro-optical sectioning tomography system(fMOST) and an automated three-dimensional(3 D) analysis method.Validation using immunostaining showed that the detection rate of amyloid plaques larger than 10 μm was 97.71%±0.18%. We achieved a 3 D dataset of amyloid plaques in the whole brain of a 5 XFAD transgenic mouse at a resolution of 0.32 μm×0.32 μm×2 μm and quantitatively analyzed the 3 D distribution of amyloid plaques in different brain regions and nuclei through automated registration, segmentation and counting. The results indicated that the combination of the fMOST system with a whole brain staining method enables the acquisition and quantitative analysis of a brain-wide dataset of amyloid plaques, which may contribute to elucidating the underlying pathogenesis of AD and therapeutic drug discovery.
Amyloidosis of the central nervous system is one of the main pathological hallmarks of Alzheimer's disease(AD). Changes in the number and the load of amyloid plaque(Aβ) deposition are important indicators of the progression of AD. However, the quantitative analysis of amyloidosis at the subcellular level in the whole brain remains a substantial challenge. In this study, an automatic analysis method was established for assessing amyloid plaques in the whole brain, including convenient whole brain labeling, imaging with a fluorescence micro-optical sectioning tomography system(fMOST) and an automated three-dimensional(3 D) analysis method.Validation using immunostaining showed that the detection rate of amyloid plaques larger than 10 μm was 97.71%±0.18%. We achieved a 3 D dataset of amyloid plaques in the whole brain of a 5 XFAD transgenic mouse at a resolution of 0.32 μm×0.32 μm×2 μm and quantitatively analyzed the 3 D distribution of amyloid plaques in different brain regions and nuclei through automated registration, segmentation and counting. The results indicated that the combination of the fMOST system with a whole brain staining method enables the acquisition and quantitative analysis of a brain-wide dataset of amyloid plaques, which may contribute to elucidating the underlying pathogenesis of AD and therapeutic drug discovery.
引文
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27 He Y,Meng Y,Gong H,et al.An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.PLoS ONE,2014,9:e104437
28 Li Y,Gong H,Yang X,et al.TDat:an efficient platform for processing petabyte-scale whole-brain volumetric images.Front Neural Circuits,2017,11:51
29 Kuan L,Li Y,Lau C,et al.Neuroinformatics of the Allen mouse brain connectivity atlas.Methods,2015,73:4-17
30 Dong H.The Allen reference atlas:a digital color brain atlas of the C57BL/6J male mouse.New Jersey:John Wiley&Sons,2008
31 Peng J,Long B,Yuan J,et al.A quantitative analysis of the distribution of CRH Neurons in whole mouse brain.Front Neuroanat,2017,11:63
32 Zhang C,Yan C,Ren M,et al.A platform for stereological quantitative analysis of the brain-wide distribution of type-specific neurons.Sci Rep,2017,7:14334
33 Serrano-Pozo A,Mielke M L,Muzitansky A,et al.Stable size distribution of amyloid plaques over the course of Alzheimer disease.JNeuropathol Exp Neurol,2012,71:694-701
34 Bittner T,Burgold S,Dorostkar M M,et al.Amyloid plaque formation precedes dendritic spine loss.Acta Neuropathol,2012,124:797-807
35 Hefendehl J K,Wegenast-Braun B M,Liebig C,et al.Long-term in vivo imaging ofβ-amyloid plaque appearance and growth in a mouse model of cerebralβ-amyloidosis.J Neurosci,2011,31:624-629
36 Li X,Yu B,Sun Q,et al.Generation of a whole-brain atlas for the cholinergic system and mesoscopic projectome analysis of basal forebrain cholinergic neurons.Proc Natl Acad Sci USA,2018,115:415-420
37 Luo Q M.Brainsmatics-bridging the brain science and brain-inspired artificial intelligence(in Chinese).Sci Sin-Vitae,2017,47:1015-1024[骆清铭.脑空间信息学-连接脑科学与类脑人工智能的桥梁.中国科学:生命科学,2017,47:1015-1024]
2 Goedert M,Spillantini M G.A century of Alzheimer’s disease.Science,2006,314:777-781
3 Barage S H,Sonawane K D.Amyloid cascade hypothesis:pathogenesis and therapeutic strategies in Alzheimer’s disease.Neuropeptides,2015,52:1-18
4 Musiek E S,Holtzman D M.Three dimensions of the amyloid hypothesis:time,space and“wingmen”.Nat Neurosci,2015,18:800-806
5 Duyckaerts C,Delatour B,Potier M C.Classification and basic pathology of Alzheimer disease.Acta Neuropathol,2009,118:5-36
6 LesnéS E,Sherman M A,Grant M,et al.Brain amyloid-βoligomers in ageing and Alzheimer’s disease.Brain,2013,136:1383-1398
7 J?hrling N,Becker K,Wegenast-Braun B M,et al.Cerebralβ-amyloidosis in mice investigated by ultramicroscopy.PLoS ONE,2015,10:e0125418
8 Amato S P,Pan F,Schwartz J,et al.Whole brain imaging with serial two-photon tomography.Front Neuroanat,2016,10:31
9 Liebmann T,Renier N,Bettayeb K,et al.Three-dimensional study of Alzheimer’s disease hallmarks using the iDISCO clearing method.Cell Rep,2016,16:1138-1152
10 Whitesell J D,Buckley A R,Knox J E,et al.Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease.bioRxiv,2018,doi:10.1101/395236
11 Osten P,Margrie T W.Mapping brain circuitry with a light microscope.Nat Methods,2013,10:515-523
12 Yuan J,Gong H,Li A,et al.Visible rodent brain-wide networks at single-neuron resolution.Front Neuroanat,2015,9:70
13 Richardson D S,Lichtman J W.Clarifying tissue clearing.Cell,2015,162:246-257
14 Kim S Y,Chung K,Deisseroth K.Light microscopy mapping of connections in the intact brain.Trends Cogn Sci,2013,17:596-599
15 Ando K,Laborde Q,Lazar A,et al.Inside Alzheimer brain with CLARITY:senile plaques,neurofibrillary tangles and axons in 3-D.Acta Neuropathol,2014,128:457-459
16 Hama H,Hioki H,Namiki K,et al.ScaleS:an optical clearing palette for biological imaging.Nat Neurosci,2015,18:1518-1529
17 Canter R G,Choi H,Wang J,et al.3D Mapping reveals network-specific amyloid progression and subcortical susceptibility.bioRxiv,2017,doi:10.1101/116244
18 Gong H,Zeng S,Yan C,et al.Continuously tracing brain-wide long-distance axonal projections in mice at a one-micron voxel resolution.NeuroImage,2013,74:87-98
19 Gong H,Xu D,Yuan J,et al.High-throughput dual-colour precision imaging for brain-wide connectome with cytoarchitectonic landmarks at the cellular level.Nat Commun,2016,7:12142
20 Jiang T,Long B,Gong H,et al.A platform for efficient identification of molecular phenotypes of brain-wide neural circuits.Sci Rep,2017,7:13891
21 Oakley H,Cole S L,Logan S,et al.Intraneuronal beta-amyloid aggregates,neurodegeneration,and neuron loss in transgenic mice with five familial Alzheimer’s disease mutations:potential factors in amyloid plaque formation.J Neurosci,2006,26:10129-10140
22 Quan T,Zheng T,Yang Z,et al.NeuroGPS:automated localization of neurons for brain circuits using L1 minimization model.Sci Rep,2013,3:1414
23 Ni H,Tan C,Feng Z,et al.A robust image registration interface for large volume brain atlas.bioRxiv,2018,doi:10.1101/377044
24 Fu H,Tu P,Zhao L,et al.Amyloid-βdeposits target efficient near-infrared fluorescent probes:synthesis,in vitro evaluation,and in vivo imaging.Anal Chem,2016,88:1944-1950
25 Seiriki K,Kasai A,Hashimoto T,et al.High-speed and scalable whole-brain imaging in rodents and primates.Neuron,2017,94:1085-1100.e6
26 Youmans K L,Tai L M,Kanekiyo T,et al.Intraneuronal Aβdetection in 5xFAD mice by a new Aβ-specific antibody.Mol Neurodegenerat,2012,7:8
27 He Y,Meng Y,Gong H,et al.An automated three-dimensional detection and segmentation method for touching cells by integrating concave points clustering and random walker algorithm.PLoS ONE,2014,9:e104437
28 Li Y,Gong H,Yang X,et al.TDat:an efficient platform for processing petabyte-scale whole-brain volumetric images.Front Neural Circuits,2017,11:51
29 Kuan L,Li Y,Lau C,et al.Neuroinformatics of the Allen mouse brain connectivity atlas.Methods,2015,73:4-17
30 Dong H.The Allen reference atlas:a digital color brain atlas of the C57BL/6J male mouse.New Jersey:John Wiley&Sons,2008
31 Peng J,Long B,Yuan J,et al.A quantitative analysis of the distribution of CRH Neurons in whole mouse brain.Front Neuroanat,2017,11:63
32 Zhang C,Yan C,Ren M,et al.A platform for stereological quantitative analysis of the brain-wide distribution of type-specific neurons.Sci Rep,2017,7:14334
33 Serrano-Pozo A,Mielke M L,Muzitansky A,et al.Stable size distribution of amyloid plaques over the course of Alzheimer disease.JNeuropathol Exp Neurol,2012,71:694-701
34 Bittner T,Burgold S,Dorostkar M M,et al.Amyloid plaque formation precedes dendritic spine loss.Acta Neuropathol,2012,124:797-807
35 Hefendehl J K,Wegenast-Braun B M,Liebig C,et al.Long-term in vivo imaging ofβ-amyloid plaque appearance and growth in a mouse model of cerebralβ-amyloidosis.J Neurosci,2011,31:624-629
36 Li X,Yu B,Sun Q,et al.Generation of a whole-brain atlas for the cholinergic system and mesoscopic projectome analysis of basal forebrain cholinergic neurons.Proc Natl Acad Sci USA,2018,115:415-420
37 Luo Q M.Brainsmatics-bridging the brain science and brain-inspired artificial intelligence(in Chinese).Sci Sin-Vitae,2017,47:1015-1024[骆清铭.脑空间信息学-连接脑科学与类脑人工智能的桥梁.中国科学:生命科学,2017,47:1015-1024]