Background

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.

New method

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.

Results

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.

Comparison with existing methods

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.

Conclusions

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.

中文翻译：

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一种使用 3D 重建共聚焦 z 堆栈对细胞内囊泡隔室进行量化的方法：比较 ImageJ 和 Imaris 以计算基底前脑胆碱能神经元内的早期内体

背景

水泡室的表型变化是许多外周和中枢疾病的早期病理标志。例如，准确评估早期内体病理学对于唐氏综合征 (DS) 和阿尔茨海默病 (AD) 以及其他内体-溶酶体病理学的神经系统疾病的研究至关重要。

新方法

我们描述了一种使用 Imaris 软件使用 3 维 (3D) 重建的共聚焦z堆栈来量化发射器识别的基底前脑胆碱能神经元 (BFCNs) 内免疫标记的早期内体的方法。

结果

使用两种不同的图像分析程序：ImageJ 和 Imaris 对 3D 重建 z 堆栈进行量化。我们发现 ImageJ 始终高估了单个 BFCN 中存在的早期内体的数量。与 Imaris 相比，在 ImageJ 中观察到在定义的 BFCNs 中难以分离密集的早期内体。

与现有方法的比较

以前量化内体-溶酶体病理学的方法依赖于在单个焦点平面上拍摄的共聚焦显微镜图像。由于早期内体分布在 BFCNs 的整个体细胞和神经元过程中，因此仅分析单个核周图像可能会丢失对异常早期内体表型的关键洞察力。该协议不依赖于具有代表性的采样，而是能够对定义的感兴趣细胞内的所有免疫标记囊泡进行精确、直接的量化。

结论

Imaris 是在双标签共聚焦显微镜下准确计数点状囊泡的理想程序。Imaris 提供的卓越图像分辨率和详细算法使得在 3D 空间中分散在 BFCN 中的单个早期内体的精确和严格量化很容易实现。