Time-Lapse Imaging of Neuroblastoma Cells to Determine Cell Fate upon Gene Knockdown

<div><p>Neuroblastoma is the most common extra-cranial solid tumor of early childhood. Standard therapies are not effective in case of poor prognosis and chemotherapy resistance. To improve drug therapy, it is imperative to discover new targets that play a substantial role in tumorigenesis of neuroblastoma. The mitotic machinery is an attractive target for therapeutic interventions and inhibitors can be developed to target mitotic entry, spindle apparatus, spindle activation checkpoint, and mitotic exit. We present an elaborate analysis pipeline to determine cancer specific therapeutic targets by first performing a focused gene expression analysis to select genes followed by a gene knockdown screening assay of live cells. We interrogated gene expression studies of neuroblastoma tumors and selected 240 genes relevant for tumorigenesis and cell cycle. With these genes we performed time-lapse screening of gene knockdowns in neuroblastoma cells. We classified cellular phenotypes and used the temporal context of the perturbation effect to determine the sequence of events, particularly the mitotic entry preceding cell death. Based upon this phenotype kinetics from the gene knockdown screening, we inferred dynamic gene functions in mitosis and cell proliferation. We identified six genes (<em>DLGAP5</em>, <em>DSCC1</em>, <em>SMO</em>, <em>SNRPD1</em>, <em>SSBP1</em>, and <em>UBE2C</em>) with a vital role in mitosis and these are promising therapeutic targets for neuroblastoma. Images and movies of every time point of all screened genes are available at <a href="https://ichip.bioquant.uni-heidelberg.de">https://ichip.bioquant.uni-heidelberg.de</a>.</p> </div

Consequences of a gene knockdown on the cell cycle and cell fate.

<p>These effects can be observed (directly or indirectly) by imaging cells with silenced genes following a mitotic time-lapse screening assay. Cells may directly be affected from a loss-of-function of a gene and die (cell death), they may enter mitosis and die before completion of mitosis (cell death in mitotic arrest) or may undergo mitotic slippage followed by interphase arrest or cell death <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0050988#pone.0050988-Manchado1" target="_blank">[23]</a>.</p

Confusion matrix of the classification results after automated correction.

<p>Confusion matrix of the classification results after automated correction.</p

The workflow.

<p>(A) Neuroblastoma associated genes were selected based on gene expression profiles of neuroblastoma tumors and cell lines, (B) selected genes were subjected to image-based time-lapse siRNA knockdown screens, (C) each cell in an image was classified into one of the phenotype classes interphase, mitosis, or cell death, and (D) time series of the phenotypes were assembled into phenotype profiles to determine gene function of each gene knockdown.</p

Time series of interphase cells during five days of screening.

<p>The population shows a periodicity of ∼35 hours representing the cell cycle duration (blue bars: interphase counts (normalized by B-Score normalization) of all screened cells for each time-frame, red curve: fitting curve).</p

Determination of candidate genes that show cell death during or after mitosis.

<p>The population response to a knockdown was computed to identify the sequence of phenotype occurrence.</p

Live Cell Analysis and Mathematical Modeling Identify Determinants of Attenuation of Dengue Virus 2’-O-Methylation Mutant

<div><p>Dengue virus (DENV) is the most common mosquito-transmitted virus infecting ~390 million people worldwide. In spite of this high medical relevance, neither a vaccine nor antiviral therapy is currently available. DENV elicits a strong interferon (IFN) response in infected cells, but at the same time actively counteracts IFN production and signaling. Although the kinetics of activation of this innate antiviral defense and the timing of viral counteraction critically determine the magnitude of infection and thus disease, quantitative and kinetic analyses are lacking and it remains poorly understood how DENV spreads in IFN-competent cell systems. To dissect the dynamics of replication versus antiviral defense at the single cell level, we generated a fully viable reporter DENV and host cells with authentic reporters for IFN-stimulated antiviral genes. We find that IFN controls DENV infection in a kinetically determined manner that at the single cell level is highly heterogeneous and stochastic. Even at high-dose, IFN does not fully protect all cells in the culture and, therefore, viral spread occurs even in the face of antiviral protection of naïve cells by IFN. By contrast, a vaccine candidate DENV mutant, which lacks 2’-O-methylation of viral RNA is profoundly attenuated in IFN-competent cells. Through mathematical modeling of time-resolved data and validation experiments we show that the primary determinant for attenuation is the accelerated kinetics of IFN production. This rapid induction triggered by mutant DENV precedes establishment of IFN-resistance in infected cells, thus causing a massive reduction of virus production rate. In contrast, accelerated protection of naïve cells by paracrine IFN action has negligible impact. In conclusion, these results show that attenuation of the 2’-O-methylation DENV mutant is primarily determined by kinetics of autocrine IFN action on infected cells.</p></div

Attenuation of the 2’-O-methylation mutant depends on IFN competence of the infected cell.

<p>(<b>A</b>) Comparison of the measured time courses of susceptible, infected and protected cells (data from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005345#ppat.1005345.g006" target="_blank">Fig 6B</a> and <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005345#ppat.1005345.g008" target="_blank">Fig 8B</a> for DENV-faR and the DENV-faR E217A mutant, respectively), showing large persisting reservoirs of susceptible cells. (<b>B</b>) Release of infectious DENV from A549 cells (IFN-competent) and BHK-21 cells (IFN-incompetent). Cells were transfected with DENV RNA genomes and harvested 48 and 72 h later. Titers of infectious virus were determined by limiting-dilution assay. Arrows and numbers refer to fold mean difference of extracellular virus titers. A representative result of three independent experiments is shown. (<b>C</b>) Ratio of wildtype- to E217A mutant-infected cells with and without IFN response. A549 cells (IFN-competent) and BHK-21 cells (IFN-incompetent) were infected with DENV-faR wildtype or the DENV-faR E217A mutant at low MOI. The number of faR-positive cells was analyzed by flow cytometry at 72 h p.i. and the ratio between DENV wildtype and E217A mutant-infected cells was calculated.</p

Parameter identification and model validation.

<p>(<b>A</b>) The profile likelihoods for the model parameters show that all parameters are identified by the experimental data (CI, confidence interval). (<b>B</b>) The inferred delay for virus production (red line) matches the half-time for the rise in infectious DENV secreted by infected cells (experimental data replotted from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005345#ppat.1005345.g004" target="_blank">Fig 4C</a> on linear scale). (<b>C</b>) The predicted increase in protected cells after IFN treatment (red line with shaded 95% confidence bound) matches the experimentally observed rise in IFIT1deGFP positive cells (yellow dots). The model was simulated with a single application of 10 ng/ml IFN-λ; the corresponding data are replotted from <a href="http://www.plospathogens.org/article/info:doi/10.1371/journal.ppat.1005345#ppat.1005345.s004" target="_blank">S3B Fig</a>.</p

Characterization of stable BAC reporter cell lines.

<p>(<b>A</b>) Schematic of the BAC reporter construct. Destabilization of the Mx1deGFP or IFIT1deGFP fusion protein was achieved by C-terminal fusion with the degradation domain of mouse ornithine decarboxylase (mODC). (<b>B</b>) A549-IFIT1deGFP cells were stimulated with 10 IU/ml IFN-α for 24 h and subjected to FACS to separate cells according to expression of IFIT1deGFP. Right after sorting, GFP-positive and -negative cells were lysed and total RNA was extracted. Amounts of mRNAs specified in the bottom of the graph were quantified by RT-qPCR and normalized to GAPDH mRNA levels. Note that the IFIT1 specific RT-qPCR detected both the endogenous ISG and the reporter mRNA. Data represent the mean from two independent experiments and their respective SDs. (<b>C</b>) A549-IFIT1deGFP cells were stimulated with 100 IU/ml IFN-α (to achieve high level expression of the endogenous ISG), harvested at time points specified in the top (hours) and analyzed by Western blot to detect proteins specified in the right. A representative blot from 3 independent experiments is shown. Mock-treated cells are shown in the right lane of each panel. (<b>D</b>) Induction kinetics of IFIT1deGFP and Mx1deGFP after stimulation of A549 reporter cell lines with IFN-α. Cells were treated with 100 IU/ml of IFN-α, harvested at time points specified in the bottom of the graph and number of GFP-positive cells was determined by flow cytometry. Data are mean from 3 independent experiments and their respective SDs. (<b>E</b>) IFN-α dose response assay with A549 reporter cell lines. Cells were stimulated for 24 h with 100 IU/ml IFN-α and analyzed for mean GFP intensity (left panel) or number of GFP-expressing cells (right panel) by using flow cytometry. Data are mean from 3 independent experiments and their respective SDs. (<b>F</b>) Half-life of IFIT1deGFP (left panel) and Mx1deGFP (right panel) as determined by CHX treatment of cells after pre-stimulation with 100 IU/ml IFN-α for 15 h. Cells were harvested at time points given in the bottom of the graph and cell lysates were analyzed by Western blot using GFP-, IFIT1-, Mx1- and β-actin-specific antisera. Representative blots are shown in the bottom of each panel; quantifications from 4 independent experiments and their respective SDs are depicted in the upper graphs of each panel. In panels C and F, numbers in the left of Western blots refer to molecular weights of size standards in kiloDalton (kDa), respectively.</p