Abstract
High-throughput automated fluorescent imaging and screening are important for studying neuronal development, functions, and pathogenesis. An automatic approach of analyzing images acquired in automated fashion, and quantifying dendritic characteristics is critical for making such screens high-throughput. However, automatic and effective algorithms and tools, especially for the images of mature mammalian neurons with complex arbors, have been lacking. Here, we present algorithms and a tool for quantifying dendritic length that is fundamental for analyzing growth of neuronal network. We employ a divide-and-conquer framework that tackles the challenges of high-throughput images of neurons and enables the integration of multiple automatic algorithms. Within this framework, we developed algorithms that adapt to local properties to detect faint branches. We also developed a path search that can preserve the curvature change to accurately measure dendritic length with arbor branches and turns. In addition, we proposed an ensemble strategy of three estimation algorithms to further improve the overall efficacy. We tested our tool on images for cultured mouse hippocampal neurons immunostained with a dendritic marker for high-throughput screen. Results demonstrate the effectiveness of our proposed method when comparing the accuracy with previous methods. The software has been implemented as an ImageJ plugin and available for use.
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Acknowledgments
We thank Dr. Hisashi Umemori, Dr. Asim Beg and Dr. Jun Zhang for their comments on the project. We thank Venkata Wunnava, Sowmya Ganugapati and Joseph Steinke who provided their help at the different stages. The project is supported by NIH R15 MH099569 (Zhou), NIH R01MH091186 (Ye), NIH R21AA021204 (Ye), and Protein Folding Disease Initiative of the University of Michigan (Ye).
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Smafield, T., Pasupuleti, V., Sharma, K. et al. Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons. Neuroinform 13, 443–458 (2015). https://doi.org/10.1007/s12021-015-9267-4
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DOI: https://doi.org/10.1007/s12021-015-9267-4