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A strategy for identifying fluorescence intensity profiles of single rod-shaped cells

The extraction of fluorescence intensity profiles of single cells from image data is a common challenge in cell biology. The manual segmentation of cells, the extraction of cell orientation and finally the extraction of intensity profiles are time-consuming tasks. This article proposes a routine for...

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Main Authors: Herzog, Alexandra (Author) , Voss, Björn Magnus (Author) , Keilberg, Daniela (Author) , Hot, Edina (Author) , Søgaard-Andersen, Lotte (Author) , Garbe, Christoph S. (Author) , Kostina, Ekaterina (Author)
Format: Article (Journal)
Language:English
Published: 2013
In: Journal of bioinformatics and computational biology
Year: 2012, Volume: 11, Issue: 2
ISSN:1757-6334
DOI:10.1142/S0219720012500242
Online Access:Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1142/S0219720012500242
Verlag, lizenzpflichtig, Volltext: https://www.worldscientific.com/doi/abs/10.1142/S0219720012500242
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Author Notes:Alexandra Herzog, Björn Voss, Daniela Keilberg, Edina Hot, Lotte Søgaard-Andersen, Christoph Garbe, and Ekaterina Kostina

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520 |a The extraction of fluorescence intensity profiles of single cells from image data is a common challenge in cell biology. The manual segmentation of cells, the extraction of cell orientation and finally the extraction of intensity profiles are time-consuming tasks. This article proposes a routine for the segmentation of single rod-shaped cells (i.e. without neighboring cells in a distance of the cell length) from image data combined with an extraction of intensity distributions along the longitudinal cell axis under the aggravated conditions of (i) a low spatial resolution and (ii) lacking information on the imaging system i.e. the point spread function and signal-to-noise ratio. The algorithm named cipsa transfers a new approach from particle streak velocimetry to cell classification interpreting the rod-shaped as streak-like structures. An automatic reduction of systematic errors such as photobleaching and defocusing is included to guarantee robustness of the proposed approach under the described conditions and to the convenience of end-users unfamiliar with image processing. Performance of the algorithm has been tested on image sequences with high noise level produced by an overlay of different error sources. The developed algorithm provides a user-friendly, stand-alone procedure. 
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