Neurons are specialized cells of the Central Nervous System whose function is intricately related to the neuritic network they develop to transmit information. Morphological evaluation of this network and other neuronal structures is required to establish relationships between neuronal morphology and function, and may allow monitoring physiological and pathophysiologic alterations. Fluorescence-based microphotographs are the most widely used in cellular bioimaging, but phase contrast (PhC) microphotographs are easier to obtain, more affordable, and do not require invasive, complicated and disruptive techniques. Despite the various freeware tools available for fluorescence-based images analysis, few exist that can tackle the more elusive and harder-to-analyze PhC images. To surpass this, an interactive semi-automated image processing workflow was developed to easily extract relevant information (e.g. total neuritic length, average cell body area) from both PhC and fluorescence neuronal images. This workflow, named ‘NeuronRead’, was developed in the form of an ImageJ macro. Its robustness and adaptability were tested and validated on rat cortical primary neurons under control and differentiation inhibitory conditions. Validation included a comparison to manual determinations and to a golden standard freeware tool for fluorescence image analysis. NeuronRead was subsequently applied to PhC images of neurons at distinct differentiation days and exposed or not to DAPT, a pharmacological inhibitor of the γ-secretase enzyme, which cleaves the well-known Alzheimer's amyloid precursor protein (APP) and the Notch receptor. Data obtained confirms a neuritogenic regulatory role for γ-secretase products and validates NeuronRead as a time- and cost-effective useful monitoring tool.

神经元是中枢神经系统的专用细胞，其功能与它们发展来传递信息的神经网络有着密切的关系。需要对该网络和其他神经元结构进行形态学评估，以建立神经元形态与功能之间的关系，并且可能允许监视生理和病理生理变化。基于荧光的显微照片在细胞生物成像中使用最广泛，但是相衬（PhC）显微照片更容易获得，更实惠，并且不需要侵入性，复杂和破坏性技术。尽管有许多免费的工具可用于基于荧光的图像分析，但很少有工具可以处理更难以捉摸和难以分析的PhC图像。要超越这一点，开发了交互式半自动图像处理工作流程，以轻松地从PhC和荧光神经元图像中提取相关信息（例如，总神经长，平均细胞体面积）。这个名为“ NeuronRead”的工作流程是以ImageJ宏的形式开发的。在控制和分化抑制条件下，在大鼠皮质原代神经元上测试并验证了其鲁棒性和适应性。验证包括与手动确定的比较以及与用于荧光图像分析的黄金标准免费软件的比较。随后将NeuronRead应用于神经元在不同分化天的PhC图像，并暴露于或不暴露于DAPT（一种γ-分泌酶的药理抑制剂）中，该酶裂解了著名的阿尔茨海默氏症淀粉样蛋白（APP）和Notch受体。