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Cell Calcium. 2010 Nov;48(5):270-4. doi: 10.1016/j.ceca.2010.09.008. Epub 2010 Oct 16.

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

Author information

1
DUKE-NUS Graduate Medical School, Program in Neuroscience and Neurobehavioral Disorders, 8 College Road, Singapore 169857, Singapore.

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.

PMID:
20952058
DOI:
10.1016/j.ceca.2010.09.008
[Indexed for MEDLINE]

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