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Fully-automated image processing software to analyze calcium traces in populations of single cells

Fully-automated image processing software to analyze calcium traces in populations of single cells

Wong, Loo Chin, Lu, Bo, Tan, Kia Wee and Fivaz, Marc ORCID: 0000-0003-1003-7934 (2010) Fully-automated image processing software to analyze calcium traces in populations of single cells. Cell calcium, 48 (5). pp. 270-4. ISSN 1532-1991 (doi:https://doi.org/10.1016/j.ceca.2010.09.008)

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Abstract

Advances in fluorescence live cell imaging over the last decade have revolutionized cell biology by providing access to single-cell information in space and time. One current limitation of live-cell imaging is the lack of automated procedures to analyze single-cell data in large cell populations. Most commercially available image processing softwares do not have built-in image segmentation tools that can automatically and accurately extract single-cell data in a time series. Consequently, individual cells are usually identified manually, a process which is time consuming and inherently low-throughput. We have developed a MATLAB-based image segmentation algorithm that reliably detects individual cells in dense populations and measures their fluorescence intensity over time. To demonstrate the value of this algorithm, we measured store-operated calcium entry (SOCE) in hundreds of individual cells. Rapid access to single-cell calcium signals in large populations allowed us to precisely determine the relationship between SOCE activity and STIM1 levels, a key component of SOCE. Our image processing tool can in principle be applied to a wide range of live-cell imaging modalities and cell-based drug screening platforms.

Item Type: Article
Uncontrolled Keywords: Imaging, STIM, SOCE, Segmentation Software, Image analysis
Faculty / Department / Research Group: Faculty of Engineering & Science
Faculty of Engineering & Science > Department of Life & Sports Sciences
Last Modified: 11 Jan 2018 17:10
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/18509

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