Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.ceca.2010.09.008
Title: Fully-automated image processing software to analyze calcium traces in populations of single cells
Authors: Wong, L.C. 
Lu, B.
Tan, K.W. 
Fivaz, M. 
Keywords: Calcium
Imaging
Segmentation
SOCE
Software
STIM
Issue Date: Nov-2010
Citation: Wong, L.C., Lu, B., Tan, K.W., Fivaz, M. (2010-11). Fully-automated image processing software to analyze calcium traces in populations of single cells. Cell Calcium 48 (5) : 270-274. ScholarBank@NUS Repository. https://doi.org/10.1016/j.ceca.2010.09.008
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. © 2010 Elsevier Ltd.
Source Title: Cell Calcium
URI: http://scholarbank.nus.edu.sg/handle/10635/124742
ISSN: 01434160
DOI: 10.1016/j.ceca.2010.09.008
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