A deep learning approach to identifying immunogold particles in electron microscopy images

Abstract Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious...

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Autores principales:	Diego Jerez, Eleanor Stuart, Kylie Schmitt, Debbie Guerrero-Given, Jason M. Christie, Naomi Kamasawa, Michael S. Smirnov
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/5a0c7fe657b7450eb58edf43c22c8435
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spelling	oai:doaj.org-article:5a0c7fe657b7450eb58edf43c22c84352021-12-02T18:15:33ZA deep learning approach to identifying immunogold particles in electron microscopy images10.1038/s41598-021-87015-22045-2322https://doaj.org/article/5a0c7fe657b7450eb58edf43c22c84352021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87015-2https://doaj.org/toc/2045-2322Abstract Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.Diego JerezEleanor StuartKylie SchmittDebbie Guerrero-GivenJason M. ChristieNaomi KamasawaMichael S. SmirnovNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diego Jerez Eleanor Stuart Kylie Schmitt Debbie Guerrero-Given Jason M. Christie Naomi Kamasawa Michael S. Smirnov A deep learning approach to identifying immunogold particles in electron microscopy images
description	Abstract Electron microscopy (EM) enables high-resolution visualization of protein distributions in biological tissues. For detection, gold nanoparticles are typically used as an electron-dense marker for immunohistochemically labeled proteins. Manual annotation of gold particle labels is laborious and time consuming, as gold particle counts can exceed 100,000 across hundreds of image segments to obtain conclusive data sets. To automate this process, we developed Gold Digger, a software tool that uses a modified pix2pix deep learning network capable of detecting and annotating colloidal gold particles in biological EM images obtained from both freeze-fracture replicas and plastic sections prepared with the post-embedding method. Gold Digger performs at near-human-level accuracy, can handle large images, and includes a user-friendly tool with a graphical interface for proof reading outputs by users. Manual error correction also helps for continued re-training of the network to improve annotation accuracy over time. Gold Digger thus enables rapid high-throughput analysis of immunogold-labeled EM data and is freely available to the research community.
format	article
author	Diego Jerez Eleanor Stuart Kylie Schmitt Debbie Guerrero-Given Jason M. Christie Naomi Kamasawa Michael S. Smirnov
author_facet	Diego Jerez Eleanor Stuart Kylie Schmitt Debbie Guerrero-Given Jason M. Christie Naomi Kamasawa Michael S. Smirnov
author_sort	Diego Jerez
title	A deep learning approach to identifying immunogold particles in electron microscopy images
title_short	A deep learning approach to identifying immunogold particles in electron microscopy images
title_full	A deep learning approach to identifying immunogold particles in electron microscopy images
title_fullStr	A deep learning approach to identifying immunogold particles in electron microscopy images
title_full_unstemmed	A deep learning approach to identifying immunogold particles in electron microscopy images
title_sort	deep learning approach to identifying immunogold particles in electron microscopy images
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/5a0c7fe657b7450eb58edf43c22c8435
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A deep learning approach to identifying immunogold particles in electron microscopy images

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