Automated Analysis of NF-κB Nuclear Translocation Kinetics in High-Throughput Screening

<div><p>Nuclear entry and exit of the NF-κB family of dimeric transcription factors plays an essential role in regulating cellular responses to inflammatory stress. The dynamics of this nuclear translocation can vary significantly within a cell population and may dramatically change e.g. upon drug exposure. Furthermore, there is significant heterogeneity in individual cell response upon stress signaling. In order to systematically determine factors that define NF-κB translocation dynamics, high-throughput screens that enable the analysis of dynamic NF-κB responses in individual cells in real time are essential. Thus far, only NF-κB downstream signaling responses of whole cell populations at the transcriptional level are in high-throughput mode. In this study, we developed a fully automated image analysis method to determine the time-course of NF-κB translocation in individual cells, suitable for high-throughput screenings in the context of compound screening and functional genomics. Two novel segmentation methods were used for defining the individual nuclear and cytoplasmic regions: watershed masked clustering (WMC) and best-fit ellipse of Voronoi cell (BEVC). The dynamic NFκB oscillatory response at the single cell and population level was coupled to automated extraction of 26 analogue translocation parameters including number of peaks, time to reach each peak, and amplitude of each peak. Our automated image analysis method was validated through a series of statistical tests demonstrating computational efficient and accurate NF-κB translocation dynamics quantification of our algorithm. Both pharmacological inhibition of NF-κB and short interfering RNAs targeting the inhibitor of NFκB, IκBα, demonstrated the ability of our method to identify compounds and genetic players that interfere with the nuclear transition of NF-κB.</p> </div

Application of the individual cell NF-κB nuclear translocation analysis in siRNA screening assays.

<p>(A) The average nuclear translocation response graphs for negative controls siCASP8, siCntrl#1, transfection reagent without siRNA (mock), and positive control siNFKBIA. Inset: representative images of mock and siNFKBIA treated GFP-p65 cells, at 0 and 30 minutes after TNFα stimulation (B) Table showing the univariate Z'-factors of all 32 individual parameters. The definitions of the 26 analogue parameters are given in <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0052337#pone.0052337.s008" target="_blank">Table S2</a></b>. Absolute Curve Difference: the absolute point-by-point difference between control and treatment averages. (C) Multivariate Z'-factor calculation based on top-scoring univariate Z'-factors. Both the conventional as well as the robust multivariate Z'-factors exceed the confidence threshold of 0.5 by combining ≥5 top-scoring univariate Z'-factors by linear projection.</p

Image-based NF-κB nuclear translocation analysis.

<p>Time series images of GFP-p65 expressing HepG2 cells stimulated with 10 ng/mL TNFα. (A') Nuclear channel. (A'') GFP-p65 channel. Examples of multiple nuclear translocations at 30, 150 and 270 minutes (white arrow) and at 30, 120, 210 and 330 minutes (yellow arrow). Example of only one, long, nuclear translocation event (red arrow). (B) Flowchart of the individual cell NF-κB nuclear translocation analysis. 1. Splitting of the two-channel image time series of the NF-κB response 2. Nuclear image preprocessing and segmentation. 3. Tracking of nuclear mask throughout the time series. 4. Segmentation of cell locations. 5. Definition of the best ellipse fitting within a Voronoi cell (BEVC) as the cytoplasmic mask. 6. Quantification of the ratio of the nuclear and cytoplasmic GFP intensity per time-point, per cell. 7. Analysis of the nuclear translocation profile of individual cells. 8. Categorization of responses to perform population analyses.</p

NF-κB oscillation is regulated by an auto-regulatory negative feedback loop.

<p>Simplified schematic overview of the TNFα-induced canonical NF-κB response. TNFα binding to the TNF receptor (TNFR) activates the inhibitor of kappa-B kinase (IKK) complex, leading to phosphorylation of the inhibitor of NF-κB, IκB, upon which NF-κB is free to enter the nucleus to activate transcription of its target genes. One of the primary NF-κB target genes is IκB, which may retrieve NF-κB from the nucleus to maintain inactive IκB::NF-κB complex in the cytoplasm. Ongoing TNFR signaling can re-initiate the induction-inhibition cycle.</p

Classification of human breast cancer cell lines.

<p>(a) According to the cross-validation result, the smallest error rate was achieved when 8 features were selected. (b) A 3 dimensional PCA plot was generated based on these 8 selected features. Percentages of data variation preserved in each principle component were shown with each axis. Different categories of breast cancer cells are colored differently to show the separation between the various human breast cancer cell classes.</p

Stepwise demonstration of the image analysis method. Scale bar represents 100 micrometer.

<p>(a) Image stack obtained from the Hoechst stained nuclei channel. (b) Image stack obtained from the Rhodamine stained F-actin channel. (c) In-focus 2D image projected from the stacks of Hoechst stained nuclei channel. (d) In-focus 2D image projected from the stacks of Rhodamine stained F-actin channel. (e) Binary nuclear mask after segmentation by Watershed Masked Clustering. (f) Binary cellular mask after segmentation. The subpopulation classification result is also shown here. The green contour represents branched and interconnected complex networks. The red contour represents spherical colonies. (g) Quantitative parameters measured for each well of the 384-well plates. See <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109688#s2" target="_blank">Methods</a> for further description.</p

Characterization of cellular phenotype by clustering and classification.

<p>(a) Hierarchical clustering result using an average matrix as distance matrix. The scale of dendrogram is the natural logarithm of . (b) Five defined classes of test compounds and corresponding compounds and number of data points. (c) Classification result using multiple classification methods. Feature selection with search algorithm “forward” and criterion “Mahalanobis distance” was applied to detect optimal number of features. For each classification method and each number of selected features, 10 fold cross-validation was repeated 10 times, resulting in 10 error rates. The average error rates are shown in the chart with standard deviation as error bar. SVC means support vector machine classification.</p

2D PCA plot of phenotype profiles for various active compounds and their concentration dependent phenotypic trajectories.

<p>(a) 2D PCA plot of phenotype profiles for negative control (DMSO) and 12 active compounds at different concentrations. Percentages of data variation preserved in each principle component are shown with each axis. Compounds with the same biological target are colored identically. Red: BCR-ABL target inhibitor; Yellow: VEFGR inhibitor; Green: EGFR inhibitor; Purple: HDAC inhibitor; Blue: c-MET inhibitor. Concentration is represented by the size of data points. The trend lines were added for each effective compound using 2nd polynomial regression models. (b) Comparison of microscope images of four example compounds with two DMSO control images. Each compound has a different biological target. 2D projected images from the Rhoadamine stained F-actin channel are shown here. Scale bar represents 500 micrometer</p

An overview of the project workflow.

<p>The details are explained in the Methods and Results of the manuscript and more information on the individual data analysis steps can be found in the <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0109688#pone.0109688.s014" target="_blank">File S1</a>.</p