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dc.contributor.authorFerguson, Sophie
dc.contributor.authorSteyer, Anna M.
dc.contributor.authorMayhew, Terry M.
dc.contributor.authorSchwab, Yannick
dc.contributor.authorLucocq, John Milton
dc.date.accessioned2017-05-08T09:30:13Z
dc.date.available2017-05-08T09:30:13Z
dc.date.issued2017-06
dc.identifier.citationFerguson , S , Steyer , A M , Mayhew , T M , Schwab , Y & Lucocq , J M 2017 , ' Quantifying Golgi structure using EM : combining volume-SEM and stereology for higher throughput ' , Histochemistry and Cell Biology , vol. 147 , no. 6 , pp. 653-669 . https://doi.org/10.1007/s00418-017-1564-6en
dc.identifier.issn0948-6143
dc.identifier.otherPURE: 249465430
dc.identifier.otherPURE UUID: 9c54225d-bda4-472c-bbda-92bd304202b2
dc.identifier.otherScopus: 85018493203
dc.identifier.otherWOS: 000401334200002
dc.identifier.urihttp://hdl.handle.net/10023/10719
dc.descriptionJohn Lucocq was supported by a Programme grant from the Wellcome Trust (Number 045404). Sophie Ferguson was a recipient of a 600th anniversary studentship from the University of St Andrews.en
dc.description.abstractInvestigating organelles such as the Golgi complex depends increasingly on high-throughput quantitative morphological analyses from multiple experimental or genetic conditions. Light microscopy (LM) has been an effective tool for screening but fails to reveal fine details of Golgi structures such as vesicles, tubules and cisternae. Electron microscopy (EM) has sufficient resolution but traditional transmission EM (TEM) methods are slow and inefficient. Newer volume scanning EM (volume-SEM) methods now have the potential to speed up 3D analysis by automated sectioning and imaging. However, they produce large arrays of sections and/or images, which require labour-intensive 3D reconstruction for quantitation on limited cell numbers. Here, we show that the information storage, digital waste and workload involved in using volume-SEM can be reduced substantially using sampling-based stereology. Using the Golgi as an example, we describe how Golgi populations can be sensed quantitatively using single random slices and how accurate quantitative structural data on Golgi organelles of individual cells can be obtained using only 5–10 sections/images taken from a volume-SEM series (thereby sensing population parameters and cell–cell variability). The approach will be useful in techniques such as correlative LM and EM (CLEM) where small samples of cells are treated and where there may be variable responses. For Golgi study, we outline a series of stereological estimators that are suited to these analyses and suggest workflows, which have the potential to enhance the speed and relevance of data acquisition in volume-SEM.
dc.format.extent17
dc.language.isoeng
dc.relation.ispartofHistochemistry and Cell Biologyen
dc.rights© The Author(s) 2017. Open Access. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.en
dc.subjectGolgien
dc.subjectStereologyen
dc.subjectVolume-SEMen
dc.subjectFIBSEMen
dc.subjectSBF-SEMen
dc.subjectSamplingen
dc.subjectQuantificationen
dc.subjectQH301 Biologyen
dc.subjectQD Chemistryen
dc.subject.lccQH301en
dc.subject.lccQDen
dc.titleQuantifying Golgi structure using EM : combining volume-SEM and stereology for higher throughputen
dc.typeJournal itemen
dc.contributor.sponsorThe Wellcome Trusten
dc.description.versionPublisher PDFen
dc.contributor.institutionUniversity of St Andrews. School of Medicineen
dc.contributor.institutionUniversity of St Andrews. Cellular Medicine Divisionen
dc.contributor.institutionUniversity of St Andrews. Biomedical Sciences Research Complexen
dc.identifier.doihttps://doi.org/10.1007/s00418-017-1564-6
dc.description.statusPeer revieweden
dc.identifier.urlhttp://europepmc.org/articles/PMC5429891en
dc.identifier.grantnumber089803/B/09/Zen


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