Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.

The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell d...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Mike J Downey, Danuta M Jeziorska, Sascha Ott, T Katherine Tamai, Georgy Koentges, Keith W Vance, Till Bretschneider
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/986f69cff5644414add7b98f87a58b61
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:986f69cff5644414add7b98f87a58b61
record_format	dspace
spelling	oai:doaj.org-article:986f69cff5644414add7b98f87a58b612021-11-18T07:32:03ZExtracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.1932-620310.1371/journal.pone.0027886https://doaj.org/article/986f69cff5644414add7b98f87a58b612011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22194797/?tool=EBIhttps://doaj.org/toc/1932-6203The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.Mike J DowneyDanuta M JeziorskaSascha OttT Katherine TamaiGeorgy KoentgesKeith W VanceTill BretschneiderPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 12, p e27886 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Mike J Downey Danuta M Jeziorska Sascha Ott T Katherine Tamai Georgy Koentges Keith W Vance Till Bretschneider Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
description	The extraction of fluorescence time course data is a major bottleneck in high-throughput live-cell microscopy. Here we present an extendible framework based on the open-source image analysis software ImageJ, which aims in particular at analyzing the expression of fluorescent reporters through cell divisions. The ability to track individual cell lineages is essential for the analysis of gene regulatory factors involved in the control of cell fate and identity decisions. In our approach, cell nuclei are identified using Hoechst, and a characteristic drop in Hoechst fluorescence helps to detect dividing cells. We first compare the efficiency and accuracy of different segmentation methods and then present a statistical scoring algorithm for cell tracking, which draws on the combination of various features, such as nuclear intensity, area or shape, and importantly, dynamic changes thereof. Principal component analysis is used to determine the most significant features, and a global parameter search is performed to determine the weighting of individual features. Our algorithm has been optimized to cope with large cell movements, and we were able to semi-automatically extract cell trajectories across three cell generations. Based on the MTrackJ plugin for ImageJ, we have developed tools to efficiently validate tracks and manually correct them by connecting broken trajectories and reassigning falsely connected cell positions. A gold standard consisting of two time-series with 15,000 validated positions will be released as a valuable resource for benchmarking. We demonstrate how our method can be applied to analyze fluorescence distributions generated from mouse stem cells transfected with reporter constructs containing transcriptional control elements of the Msx1 gene, a regulator of pluripotency, in mother and daughter cells. Furthermore, we show by tracking zebrafish PAC2 cells expressing FUCCI cell cycle markers, our framework can be easily adapted to different cell types and fluorescent markers.
format	article
author	Mike J Downey Danuta M Jeziorska Sascha Ott T Katherine Tamai Georgy Koentges Keith W Vance Till Bretschneider
author_facet	Mike J Downey Danuta M Jeziorska Sascha Ott T Katherine Tamai Georgy Koentges Keith W Vance Till Bretschneider
author_sort	Mike J Downey
title	Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
title_short	Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
title_full	Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
title_fullStr	Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
title_full_unstemmed	Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
title_sort	extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/986f69cff5644414add7b98f87a58b61
work_keys_str_mv	AT mikejdowney extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT danutamjeziorska extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT saschaott extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT tkatherinetamai extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT georgykoentges extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT keithwvance extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution AT tillbretschneider extractingfluorescentreportertimecoursesofcelllineagesfromhighthroughputmicroscopyatlowtemporalresolution
_version_	1718423302823739392

Extracting fluorescent reporter time courses of cell lineages from high-throughput microscopy at low temporal resolution.

Ejemplares similares