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Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Neuroinformatics Springer Journals

Automatic Dendritic Length Quantification for High Throughput Screening of Mature Neurons

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Publisher
Springer Journals
Copyright
Copyright © 2015 by Springer Science+Business Media New York
Subject
Biomedicine; Neurosciences; Bioinformatics; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Neurology
ISSN
1539-2791
eISSN
1559-0089
DOI
10.1007/s12021-015-9267-4
pmid
25854493
Publisher site
See Article on Publisher Site

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.

Journal

Neuroinformatics – Springer Journals

Published: Apr 9, 2015

References

  • Rapid automated three-dimensional tracing of neurons from confocal image stacks
    Al-Kofahi, KA; Lasek, S; Szarowski, DH; Pace, CJ; Nagy, G; Turner, JN
  • Dendritic atrophy in children with Down’s syndrome
    Becker, L; Armstrong, D; Chan, F
  • Survival and growth of hippocampal neurons in defined medium at low density: advantages of a sandwich culture technique or low oxygen
    Brewer, GJ; Cotman, CW
  • Rapid automated tracing and feature extraction from retinal fundus images
    Can, A; Shen, H; Turner, JN; Tanenbaum, HL; Roysam, B
  • A note on two problems in connexion with graphs
    Dijkstra, EW
  • Role of image informatics in accelerating drug discovery and development
    Dunkle, R
  • A Golgi-Cox morphological analysis of neuronal changes induced by environmental enrichment
    Faherty, CJ; Kerley, D; Smeyne, RJ
  • Accommodating Cells in HTS
    Fox, S
  • Fibonacci heaps and their uses in improved network optimization algorithms
    Fredman, ML; Tarjan, RE
  • Dendritic spine pathology in schizophrenia
    Glausier, J; Lewis, D
  • Laboratory automation: total and subtotal
    Hawker, CD
  • Neuron tracing in perspective
    Meijering, E
  • Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images
    Meijering, E; Jacob, M; Sarria, J; Steiner, P; Hirling, H; Unser, M
  • Neuromantic - from semi-manual to semi-automatic reconstruction of neuron morphology
    Myatt, DR; Hadlington, T; Ascoli, GA; Nasuto, SJ
  • High content image analysis identifies novel regulators of synaptogenesis in a high-throughput RNAi screen of primary neurons
    Nieland, TJ; Logan, DJ; Saulnier, J; Lam, D; Johnson, C; Root, DE
  • A threshold selection method from gray-level histograms
    Otsu, N
  • Pathological changes in dendrites of substantia nigra neurons in Parkinson’s disease: a Golgi study
    Patt, S; Gertz, H; Gerhard, L; Cervós-Navarro, J
  • V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets
    Peng, H; Ruan, Z; Long, F; Simpson, JH; Myers, EW
  • Stress, depression, and neuroplasticity: a convergence
    Pittenger, C; Duman, RS
  • NeuriteTracer: a novel imagej plugin for automated
    Pool, M; Thiemann, J; Bar-Or, A; Fournier, AE
  • Rayburst sampling, an algorithm for automated three-dimensional shape analysis from laser scanning microscopy images
    Rodriguez, A; Ehlenberger, D; Hof, P; Wearne, S
  • A simple procedure for quantification of neurite outgrowth based on stereological principles
    Rønn, LC; Ralets, I; Hartz, BP; Bech, M; Berezin, A; Berezin, V
  • High-throughput genetic screen for synaptogenic factors: identification of LRP6 as critical for excitatory synapse development
    Sharma, K; Choi, S-Y; Zhang, Y; Nieland, TJ; Long, S; Li, M
  • Automated analysis of neurite branching in cultured cortical neurons using HCA-Vision
    Vallotton, P; Lagerstrom, R; Sun, C; Buckley, M; Wang, D; Silva, MD
  • Digital image analysis of microbes: Imaging, morphometry, fluorometry and motility techniques and applications
    Wilkinson, MH
  • Automatic robust neurite detection and morphological analysis of neuronal cell cultures in high-content screening
    Wu, C; Schulte, J; Sepp, KJ; Littleton, JT; Hong, P
  • Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays
    Zhang, Y; Zhou, X; Alexei, D; Marta, L; Adjeroh, D; Yuan, J