Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA; Program of Pathobiology and Translational Medicine, Vilcek Institute of Graduate Biomedical Sciences, NYU Grossman School of Medicine, New York, NY, USA; NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.
Center for Dementia Research, Nathan Kline Institute, Orangeburg, NY, USA; Department of Psychiatry, NYU Grossman School of Medicine, New York, NY, USA; Department of Neuroscience & Physiology, NYU Grossman School of Medicine, New York, NY, USA; NYU Neuroscience Institute, NYU Grossman School of Medicine, New York, NY, USA.
J Neurosci Methods. 2021 Feb 15;350:109038. doi: 10.1016/j.jneumeth.2020.109038. Epub 2020 Dec 15.
Phenotypic changes in vesicular compartments are an early pathological hallmark of many peripheral and central diseases. For example, accurate assessment of early endosome pathology is crucial to the study of Down syndrome (DS) and Alzheimer's disease (AD), as well as other neurological disorders with endosomal-lysosomal pathology.
We describe a method for quantification of immunolabeled early endosomes within transmitter-identified basal forebrain cholinergic neurons (BFCNs) using 3-dimensional (3D) reconstructed confocal z-stacks employing Imaris software.
Quantification of 3D reconstructed z-stacks was performed using two different image analysis programs: ImageJ and Imaris. We found ImageJ consistently overcounted the number of early endosomes present within individual BFCNs. Difficulty separating densely packed early endosomes within defined BFCNs was observed in ImageJ compared to Imaris.
Previous methods quantifying endosomal-lysosomal pathology relied on confocal microscopy images taken in a single plane of focus. Since early endosomes are distributed throughout the soma and neuronal processes of BFCNs, critical insight into the abnormal early endosome phenotype may be lost as a result of analyzing only a single image of the perikaryon. Rather than relying on a representative sampling, this protocol enables precise, direct quantification of all immunolabeled vesicles within a defined cell of interest.
Imaris is an ideal program for accurately counting punctate vesicles in the context of dual label confocal microscopy. Superior image resolution and detailed algorithms offered by Imaris make precise and rigorous quantification of individual early endosomes dispersed throughout a BFCN in 3D space readily achievable.
囊泡隔间的表型变化是许多周围和中枢疾病的早期病理标志。例如,早期内体病理学的准确评估对于唐氏综合征(DS)和阿尔茨海默病(AD)的研究以及其他具有内体溶酶体病理学的神经退行性疾病至关重要。
我们描述了一种使用 3 维(3D)重建共聚焦 z 堆栈,通过 Imaris 软件对递质鉴定的基底前脑胆碱能神经元(BFCN)中的免疫标记早期内体进行定量的方法。
使用两种不同的图像分析程序(ImageJ 和 Imaris)对 3D 重建 z 堆栈进行了量化。我们发现 ImageJ 始终高估了单个 BFCN 中存在的早期内体数量。与 Imaris 相比,在 ImageJ 中观察到在定义的 BFCN 中难以分离密集堆积的早期内体。
以前量化内体溶酶体病理学的方法依赖于在单个焦平面上拍摄的共聚焦显微镜图像。由于早期内体分布在 BFCN 的体和神经元突起中,因此仅分析一个细胞体的图像可能会丢失对内体异常表型的关键认识。该方案不依赖于代表性采样,而是能够精确,直接地定量在感兴趣的定义细胞内的所有免疫标记的囊泡。
Imaris 是在双标记共聚焦显微镜背景下准确计数点状囊泡的理想程序。Imaris 提供的优越的图像分辨率和详细算法使在 3D 空间中精确和严格地量化分散在 BFCN 中的单个早期内体变得容易实现。