Identification of novel mammalian growth regulatory factors by genome-scale quantitative image analysis

Harada, Josephine N.; Bower, Kristen E.; Orth, Anthony P.; Callaway, Scott; Nelson, Christine; Laris, Casey; Hogenesch, John B.; Vogt, Peter K.; Chanda, Sumit K.

doi:10.1101/gr.3889305

Cited by 46 publications

(40 citation statements)

References 52 publications

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“…In a previously published genome-scale quantitative image analysis, overexpression of a cDNA that we now recognize to be the Plg-R KT cDNA, resulted in dramatic increases in cell proliferation whereas knockdown of the corresponding mRNA resulted in apoptosis (Harada et al, 2005). Consistent with an anti-apoptotic role of Plg-R KT , we have shown that cell-bound plasminogen inhibits TNF -induced apoptosis (Mitchell et al, 2006).…”

Section: Tissue and Cellular Distribution And Regulation Of The Plg-rsupporting

confidence: 53%

Identification of the Novel Plasminogen Receptor, Plg-RKT

Miles¹,

Andronicos²,

Chen³

et al. 2012

Proteomics - Human Diseases and Protein Functions

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Section: Tissue and Cellular Distribution And Regulation Of The Plg-rsupporting

confidence: 53%

Identification of the Novel Plasminogen Receptor, Plg-RKT

Miles¹,

Andronicos²,

Chen³

et al. 2012

Proteomics - Human Diseases and Protein Functions

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“…We are currently testing whether other monoclonal antibodies specific for discrete linear epitopes of intracellular targets identified by proteomic studies or profiling technologies (reviewed in ref. 47 and references therein) could be used for similar purposes.…”

Section: Discussionmentioning

confidence: 99%

Suppression of cervical carcinoma cell growth by intracytoplasmic codelivery of anti-oncoprotein E6 antibody and small interfering RNA

Courtête¹,

Sibler²,

Zeder‐Lutz³

et al. 2007

Molecular Cancer Therapeutics

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Cervical cancer is caused by high-risk types of human papillomaviruses (HPV) that encode the E6 and E7 oncogenes. Silencing of E6 gene expression in HPVpositive cell lines by transfection of small interfering RNA (siRNA) with cationic lipids restores the dormant p53 tumor suppressor pathway. Because cationic lipids can also be used for intracytoplasmic delivery of proteins, we tested whether the delivery of monoclonal antibodies that bind to HPV16 E6 and neutralize its biological activity in vitro could restore p53 function in tumor cells. Here, we show that the 4C6 antibody is efficiently delivered into the cell cytoplasm using a lipidic reagent used for siRNA transfection. The delivery of 4C6 resulted in the nuclear accumulation of p53 protein in CaSki and SiHa cells but not in HeLa cells. Furthermore, the antibody-mediated p53 response was dramatically increased when a peptide corresponding to the 4C6 epitope and bearing a COOHterminal cysteine residue was added to the transduction mixture. We found that a fraction of the added peptides were dimers that allowed the formation of antibody polymers adsorbed onto the lipidic matrix. With this system, the proliferation of CaSki and SiHa cells was strongly diminished, but no apoptosis was detectable. Remarkably, cell growth was almost totally suppressed by the addition of E6-specific siRNA to the transduction complex. The results indicate that the activity of E6 oncoprotein can be down-regulated in vivo by lipidmediated antibody delivery and that antibodies and siRNA act synergistically when codelivered. This novel targeting strategy is simple to implement and may find therapeutic applications.

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“…Quantitative studies have been performed based on populations of cells to analyze high-throughput RNAi (Wheeler et al 2004;Neumann et al 2006;Goshima et al 2007), protein overexpression (Harada et al 2005), or drug profiling screens (Perlman et al 2004;Loo et al 2007). Such studies require methods for segmentation and feature extraction, and classification if different object classes are considered.…”

mentioning

confidence: 99%

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

Harder¹,

Mora-Bermúdez²,

Godinez

et al. 2009

Genome Res.

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Live-cell imaging allows detailed dynamic cellular phenotyping for cell biology and, in combination with small molecule or drug libraries, for high-content screening. Fully automated analysis of live cell movies has been hampered by the lack of computational approaches that allow tracking and recognition of individual cell fates over time in a precise manner. Here, we present a fully automated approach to analyze time-lapse movies of dividing cells. Our method dynamically categorizes cells into seven phases of the cell cycle and five aberrant morphological phenotypes over time. It reliably tracks cells and their progeny and can thus measure the length of mitotic phases and detect cause and effect if mitosis goes awry. We applied our computational scheme to annotate mitotic phenotypes induced by RNAi gene knockdown of CKAP5 (also known as ch-TOG) or by treatment with the drug nocodazole. Our approach can be readily applied to comparable assays aiming at uncovering the dynamic cause of cell division phenotypes.[Supplemental material is available online at http://www.genome.org.]High-content image-based screening is a powerful technology for gene function studies or drug profiling. This technology is characterized by the combination of automated microscopy to rapidly acquire high-content images of treated cells and sophisticated computational methods to extract quantitative information in an automatic and unbiased way.Quantitative studies have been performed based on populations of cells to analyze high-throughput RNAi (Wheeler et al. 2004;Neumann et al. 2006;Goshima et al. 2007), protein overexpression (Harada et al. 2005), or drug profiling screens (Perlman et al. 2004;Loo et al. 2007). Such studies require methods for segmentation and feature extraction, and classification if different object classes are considered. Publicly available software platforms like CellProfiler (Carpenter et al. 2006) can be applied. For population-based studies, however, results are often limited to general features of entire cell populations at certain time points.By contrast, following single cells over time allows studying the inherent dynamics of cellular and molecular processes more accurately and is consequently widely used in state-of-the-art cell biology. To make time-lapse imaging of single cells applicable for high content screening, additional methods for tracking of cells throughout image sequences and recognition of their phenotypic changes are required. Tracking approaches have been used, e.g., to quantify the level of fluorescently tagged proteins over time (e.g., Sigal et al. 2006; or to quantify cell-cell interactions and cell migration dynamics (e.g., Chen et al. 2009).Automated classification methods have also been used on static images to distinguish different phenotype classes, providing information on the structure and location of subcellular phenotypes at a single cell level (e.g., Conrad et al. 2004;Huang and Murphy 2004;Chen et al. 2007;Hamilton et al. 2007).Combining classification and tracking methods to stu...

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Identification of novel mammalian growth regulatory factors by genome-scale quantitative image analysis

Cited by 46 publications

References 52 publications

Identification of the Novel Plasminogen Receptor, Plg-RKT

Identification of the Novel Plasminogen Receptor, Plg-RKT

Suppression of cervical carcinoma cell growth by intracytoplasmic codelivery of anti-oncoprotein E6 antibody and small interfering RNA

Automatic analysis of dividing cells in live cell movies to detect mitotic delays and correlate phenotypes in time

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