Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening
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Littleton_Automatic robust.pdf
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Author(s)
Wu, Chaohong
Schulte, Joost
Sepp, Katharine J.
Littleton, J. Troy
Hong, Pengyu
Date Issued
April 2010
Journal
Neuroinformatics
Publisher
Springer-Verlag
Citation
Wu, Chaohong et al. “Automatic Robust Neurite Detection and Morphological Analysis of Neuronal Cell Cultures in High-content Screening.” Neuroinformatics 8.2 (2010): 83–100.
Version
Author's final manuscript
Abstract
Cell-based high content screening (HCS) is becoming an important and increasingly favored
approach in therapeutic drug discovery and functional genomics. In HCS, changes in cellular morphology and biomarker distributions provide an information-rich profile of cellular responses to experimental treatments such as small molecules or gene knockdown probes. One obstacle that currently exists with such cell-based assays is the availability of image processing algorithms that are capable of reliably and automatically analyzing large HCS image sets. HCS images of primary neuronal cell cultures are particularly challenging to analyze due to complex cellular morphology.
Here we present a robust method for quantifying and statistically analyzing the morphology of neuronal cells in HCS images. The major advantages of our method over existing software lie in its capability to correct non-uniform illumination using the contrast-limited adaptive histogram equalization method; segment neuromeres using Gabor-wavelet texture analysis; and detect faint neurites by a novel phase-based neurite extraction algorithm that is invariant to changes in illumination and contrast and can accurately localize neurites. Our method was successfully applied to analyze a large HCS image set generated in a morphology screen for polyglutaminemediated neuronal toxicity using primary neuronal cell cultures derived from embryos of a Drosophila Huntington’s Disease (HD) model.
MIT Department
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Picower Institute for Learning and Memory
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Creative Commons Attribution-Noncommercial-Share Alike 3.0
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DOI of Published Version
https://doi.org/10.1007/s12021-010-9067-9
