dc.date.accessioned | 2018-03-06T16:47:32Z | |
dc.date.available | 2018-10-18T22:31:18Z | |
dc.date.created | 2017-10-11T13:19:06Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Enger, Rune . Automated gold particle quantification of immunogold labeled micrographs. Journal of Neuroscience Methods. 2017, 286, 31-37 | |
dc.identifier.uri | http://hdl.handle.net/10852/60746 | |
dc.description.abstract | Background: Immunogold cytochemistry is the method of choice for precise localization of antigens on a subcellular scale. The process of immunogold quantification in electron micrographs is laborious, especially for proteins with a dense distribution pattern.
New methods: Here I present a MATLAB based toolbox that is optimized for a typical immunogold analysis workflow. It combines automatic detection of gold particles through a multi-threshold algorithm with manual segmentation of cell membranes and regions of interests.
Results: The automated particle detection algorithm was applied to a typical immunogold dataset of neural tissue, and was able to detect particles with a high degree of precision. Without manual correction, the algorithm detected 97% of all gold particles, with merely a 0.1% false-positive rate.
Comparisons with existing method(s): To my knowledge, this is the first free and publicly available software custom made for immunogold analyses. The proposed particle detection method compares favorably to previously published algorithms.
Conclusions: The software presented here will be valuable tool for researchers in neuroscience working with immunogold cytochemistry. | en_US |
dc.language | EN | |
dc.publisher | Elsevier Science | |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 International | |
dc.rights.uri | https://creativecommons.org/licenses/by-nc-nd/4.0/ | |
dc.title | Automated gold particle quantification of immunogold labeled micrographs | en_US |
dc.type | Journal article | en_US |
dc.creator.author | Enger, Rune | |
cristin.unitcode | 185,51,12,57 | |
cristin.unitname | Gliaceller | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.cristin | 1503794 | |
dc.identifier.bibliographiccitation | info:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Journal of Neuroscience Methods&rft.volume=286&rft.spage=31&rft.date=2017 | |
dc.identifier.jtitle | Journal of Neuroscience Methods | |
dc.identifier.volume | 286 | |
dc.identifier.startpage | 31 | |
dc.identifier.endpage | 37 | |
dc.identifier.doi | http://dx.doi.org/10.1016/j.jneumeth.2017.05.018 | |
dc.identifier.urn | URN:NBN:no-63374 | |
dc.type.document | Tidsskriftartikkel | en_US |
dc.type.peerreviewed | Peer reviewed | |
dc.source.issn | 0165-0270 | |
dc.identifier.fulltext | Fulltext https://www.duo.uio.no/bitstream/handle/10852/60746/2/1-s2.0-S0165027017301450-main.pdf | |
dc.type.version | PublishedVersion | |