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Identifying new mechanisms that underlie the complex process of metastasis is vital to combat this fatal step in prostate cancer (PCa) progression. Small non-coding RNAs are emerging as important regulators of tumor cell biology. Here we take an integrative approach to elucidate the contribution of microRNAs to metastatic progression, combining transcriptomic analysis with functional screens for migration and morphology. We developed high-content microscopy, high-throughput functional screens for migration and morphology in PCa cells using a microRNA library. RNA-Seq analysis of paired epithelial and mesenchymal PCa cells identified differential expression of 200 microRNAs. Data integration identified two microRNAs that inhibited migration, induced an epithelial-like morphology and were increased in epithelial PCa cells. An overrepresentation of the AAGUGC seed sequence was detected in all three datasets. Analysis of published datasets of patients with PCa identified microRNAs of clinical relevance. The integration of high-throughput functional and expression analyses identifies microRNAs with clinical significance that modulate metastatic behavior in PCa.
Pubmed ID: 32231998
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Software repository for R packages related to analysis and comprehension of high throughput genomic data. Uses separate set of commands for installation of packages. Software project based on R programming language that provides tools for analysis and comprehension of high throughput genomic data.
View all literature mentionsSoftware tool to enable biologists without training in computer vision or programming to quantitatively measure phenotypes from thousands of images automatically. It counts cells and also measures the size, shape, intensity and texture of every cell (and every labeled subcellular compartment) in every image. It was designed for high throughput screening but can perform automated image analysis for images from time-lapse movies and low-throughput experiments. CellProfiler has an increasing number of algorithms to identify and measure properties of neuronal cell types.
View all literature mentionsSoftware package for differential gene expression analysis based on the negative binomial distribution. Used for analyzing RNA-seq data for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates.
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