Measuring Gene Expression Noise in Early Drosophila Embryos: Nucleus-to-nucleus Variability

In recent years the analysis of noise in gene expression has widely attracted the attention of experimentalists and theoreticians. Experimentally, the approaches based on in vivo fluorescent reporters in single cells appear to be straightforward and effective tools for bacteria and yeast. However, t...

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Veröffentlicht in:Procedia computer science 2012, Vol.9, p.373-382
Hauptverfasser: Golyandina, Nina E., Holloway, David M., Lopes, Francisco J.P., Spirov, Alexander V., Spirova, Ekaterina N., Usevich, Konstantin D.
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container_start_page 373
container_title Procedia computer science
container_volume 9
creator Golyandina, Nina E.
Holloway, David M.
Lopes, Francisco J.P.
Spirov, Alexander V.
Spirova, Ekaterina N.
Usevich, Konstantin D.
description In recent years the analysis of noise in gene expression has widely attracted the attention of experimentalists and theoreticians. Experimentally, the approaches based on in vivo fluorescent reporters in single cells appear to be straightforward and effective tools for bacteria and yeast. However, transferring these approaches to multicellular organisms presents many methodological problems. Here we describe our approach to measure between-nucleus variability (noise) in the primary morphogenetic gradient of Bicoid (Bcd) in the precellular blastoderm stage of fruit fly (Drosophila) embryos. The approach is based on the comparison of results for fixed immunostained embryos with observations of live embryos carrying fluorescent Bcd (Bcd-GFP). We measure the noise using two-dimensional Singular Spectrum Analysis (2D SSA). We have found that the nucleus-to-nucleus noise in Bcd intensity, both for live (Bcd-GFP) and for fixed immunstained embryos, tends to be signal-independent. In addition, the character of the noise is sensitive to the nuclear masking technique used to extract quantitative intensities. Further, the method of decomposing the raw quantitative expression data into a signal (expression surface) and residual noise affects the character of the residual noise. We find that careful masking of confocal images and use of appropriate computational tools to decompose raw expression data into trend and noise makes it possible to extract and study the biological noise of gene expression.
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source Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; Access via ScienceDirect (Elsevier)
subjects 2D SSA
fluorescence imaging
gene expression noise
image segmentation
measuring gene expression
noise analysis
noise filtration
Singular Spectrum Analysis (SSA)
title Measuring Gene Expression Noise in Early Drosophila Embryos: Nucleus-to-nucleus Variability
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