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@article{794068,
author = {Matula, Petr and Kumar, Anil and Wörz, Ilka and Erfle, Holger and Bartenschlager, Ralf and Eils, Roland and Rohr, Karl},
article_location = {UNITED STATES},
article_number = {4},
keywords = {image analysis; cell nucleus segmentation; quantification of viral infection; siRNA screening; cell-based arrays; immunofluorescence microscopy; image quality control},
language = {eng},
issn = {1552-4922},
journal = {Cytometry Part A},
title = {Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection},
url = {http://www3.interscience.wiley.com/journal/121511933/abstract},
volume = {75A},
year = {2009}
}
TY - JOUR
ID - 794068
AU - Matula, Petr - Kumar, Anil - Wörz, Ilka - Erfle, Holger - Bartenschlager, Ralf - Eils, Roland - Rohr, Karl
PY - 2009
TI - Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection
JF - Cytometry Part A
VL - 75A
IS - 4
SP - 309-318
EP - 309-318
PB - John Wiley & Sons, Inc.
SN - 15524922
KW - image analysis
KW - cell nucleus segmentation
KW - quantification of viral infection
KW - siRNA screening
KW - cell-based arrays
KW - immunofluorescence microscopy
KW - image quality control
UR - http://www3.interscience.wiley.com/journal/121511933/abstract
N2 - The identification of eukaryotic genes involved in virus entry and replication is important for understanding viral infection. Our goal is to develop a siRNA-based screening system using cell arrays and high-throughput (HT) fluorescence microscopy. A central issue is efficient, robust, and automated single-cell-based analysis of massive image datasets. We have developed an image analysis approach that comprises (i) a novel, gradient-based thresholding scheme for cell nuclei segmentation which does not require subsequent postprocessing steps for separation of clustered nuclei, (ii) quantification of the virus signal in the neighborhood of cell nuclei, (iii) localization of regions with transfected cells by combining model-based circle fitting and grid fitting, (iv) cell classification as infected or noninfected, and (v) image quality control (e.g., identification of out-of-focus images). We compared the results of our nucleus segmentation approach with a previously developed scheme of adaptive thresholding with subsequent separation of nuclear clusters. Our approach, which does not require a postprocessing step for the separation of nuclear clusters, correctly segmented 97.1% of the nuclei, whereas the previous scheme achieved 95.8%. Using our algorithm for the detection of out-of-focus images, we obtained a high discrimination power of 99.4%. Our overall approach has been applied to more than 55,000 images of cells infected by either hepatitis C or dengue virus. Reduced infection rates were correctly detected in positive siRNA controls, as well as for siRNAs targeting, for example, cellular genes involved in viral infection. Our image analysis approach allows for the automatic and accurate determination of changes in viral infection based on high-throughput single-cell-based siRNA cell array imaging experiments.
ER -
MATULA, Petr, Anil KUMAR, Ilka WÖRZ, Holger ERFLE, Ralf BARTENSCHLAGER, Roland EILS and Karl ROHR. Single-cell-based image analysis of high-throughput cell array screens for quantification of viral infection. \textit{Cytometry Part A}. UNITED STATES: John Wiley \&{} Sons, Inc., 75A, No~4, p.~309-318. ISSN~1552-4922. 2009.
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