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神经科学中的高内涵分析。

High-content analysis in neuroscience.

作者信息

Dragunow Mike

机构信息

Department of Pharmacology and National Research Centre for Growth and Development, Faculty of Medical and Health Sciences, The University of Auckland, Auckland 1142, New Zealand.

出版信息

Nat Rev Neurosci. 2008 Oct;9(10):779-88. doi: 10.1038/nrn2492. Epub 2008 Sep 11.

Abstract

High-content analysis (HCA) combines automated microscopy and automated image analysis to quantify complex cellular anatomy and biochemistry objectively, accurately and quickly. High-content assays that are applicable to neuroscience include those that can quantify various aspects of dendritic trees, protein aggregation, transcription factor translocation, neurotransmitter receptor internalization, neuron and synapse number, cell migration, proliferation and apoptosis. The data that are generated by HCA are rich and multiplexed. HCA thus provides a powerful high-throughput tool for neuroscientists.

摘要

高内涵分析(HCA)结合了自动化显微镜和自动化图像分析技术,能够客观、准确且快速地对复杂的细胞解剖结构和生物化学进行量化。适用于神经科学的高内涵分析方法包括那些能够量化树突状树突的各个方面、蛋白质聚集、转录因子易位、神经递质受体内化、神经元和突触数量、细胞迁移、增殖和凋亡的方法。高内涵分析产生的数据丰富且具有多重性。因此,高内涵分析为神经科学家提供了一个强大的高通量工具。

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