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Research ArticleMethods/New Tools, Novel Tools and Methods

Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning

J. A. Fantuzzo, V. R. Mirabella, A. H. Hamod, R. P. Hart, J. D. Zahn and Z. P. Pang
eNeuro 29 November 2017, 4 (6) ENEURO.0219-17.2017; DOI: https://doi.org/10.1523/ENEURO.0219-17.2017
J. A. Fantuzzo
1Child Health Institute of New Jersey, New Brunswick, NJ 08901
5Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854
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V. R. Mirabella
1Child Health Institute of New Jersey, New Brunswick, NJ 08901
3Department of Neuroscience and Cell Biology, Rutgers University, Piscataway, NJ 08854
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A. H. Hamod
1Child Health Institute of New Jersey, New Brunswick, NJ 08901
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  • ORCID record for A. H. Hamod
R. P. Hart
2Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ 08854
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J. D. Zahn
5Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854
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Z. P. Pang
1Child Health Institute of New Jersey, New Brunswick, NJ 08901
3Department of Neuroscience and Cell Biology, Rutgers University, Piscataway, NJ 08854
4Department of Pediatrics, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08903
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Article Information

DOI 
https://doi.org/10.1523/ENEURO.0219-17.2017
PubMed 
29218324
Published By 
Society for Neuroscience
History 
  • Received June 23, 2017
  • Revision received October 18, 2017
  • Accepted November 6, 2017
  • Published online November 29, 2017.
Copyright & Usage 
Copyright © 2017 Fantuzzo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.

Author Information

  1. J. A. Fantuzzo1,5,*,
  2. V. R. Mirabella1,3,*,
  3. A. H. Hamod1,
  4. R. P. Hart2,
  5. J. D. Zahn5 and
  6. Z. P. Pang1,3,4
  1. 1Child Health Institute of New Jersey, New Brunswick, NJ 08901
  2. 2Department of Cell Biology and Neuroscience, Rutgers University, Piscataway, NJ 08854
  3. 3Department of Neuroscience and Cell Biology, Rutgers University, Piscataway, NJ 08854
  4. 4Department of Pediatrics, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ 08903
  5. 5Department of Biomedical Engineering, Rutgers University, Piscataway, NJ 08854
  1. Correspondence should be addressed to Zhiping P. Pang, Child Health Institute of New Jersey, Rutgers University, 89 French Street, Room 3277, New Brunswick, NJ 08901. E-mail: pangzh{at}rwjms.rutgers.edu.
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Author contributions

  1. Author contributions: J.A.F. and V.R.M. designed research with guidance from Z.P.P., J.D.Z., and R.P.H. J.A.F. performed all software coding. J.A.F. and V.R.M. performed research. J.A.F., and V.R.M. analyzed the data. A.H.H. performed staining and imaging of synaptogenesis time course data. J.A.F. and V.R.M. wrote the paper with guidance from Z.P.P, J.D.Z. and R.P.H. All authors read and approved the final version of the manuscript.

  2. ↵* J.A.F. and V.R.M. contributed equally to this work.

View Abstract

Disclosures

  • The authors declare no competing financial interests.

  • This work was supported by the National Institutes of Health (NIH) Grant R21 DA039686, the Robert Wood Johnson Foundation, the NIH-National Institute on Alcohol Abuse and Alcoholism Grant R01 AA023797, and a Ruth L. Kirschstein Institutional National Research Service Award to V.R.M. from NIMH (Grant F30 MH108321). This work was also supported by New Jersey Governors Council on Autism Research and Treatment. Z.P.P. is supported by U19MH104172, part of the National Cooperative Reprogrammed Cell Research Groups (NCRCRG) to Study Mental Illness.

Funding

  • HHS | NIH | National Institute of Mental Health (NIMH)

    U19MH104172; F30MH108321
  • National Institutes of Alcohol Abuse and Alcoholism

    NIAAA 023797
  • New Jersey Governors Council on Autism Research and Treatment

  • HHS | NIH | National Institute on Drug Abuse (NIDA)

    DA039686

Other Version

  • previous version (November 29, 2017).
  • You are viewing the most recent version of this article.

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Mar 2018407014
Apr 20186911756
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Jul 202063822
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Total 202115530
Total181019691249
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Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning
J. A. Fantuzzo, V. R. Mirabella, A. H. Hamod, R. P. Hart, J. D. Zahn, Z. P. Pang
eNeuro 29 November 2017, 4 (6) ENEURO.0219-17.2017; DOI: 10.1523/ENEURO.0219-17.2017

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Intellicount: High-Throughput Quantification of Fluorescent Synaptic Protein Puncta by Machine Learning
J. A. Fantuzzo, V. R. Mirabella, A. H. Hamod, R. P. Hart, J. D. Zahn, Z. P. Pang
eNeuro 29 November 2017, 4 (6) ENEURO.0219-17.2017; DOI: 10.1523/ENEURO.0219-17.2017
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Keywords

  • Automated Image Analysis
  • high-throughput
  • Immunofluorescence
  • machine learning
  • synapse formation
  • Synapse Quantification

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