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See detailSigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies.
Ravichandran, Srikanth UL; Hartmann, Andras UL; Del Sol, Antonio

in Bioinformatics (Oxford, England) (2019)

SUMMARY: Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of ... [more ▼]

SUMMARY: Single-cell RNA-sequencing is increasingly employed to characterize disease or ageing cell subpopulation phenotypes. Despite exponential increase in data generation, systematic identification of key regulatory factors for controlling cellular phenotype to enable cell rejuvenation in disease or ageing remains a challenge. Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional networks. Targeted perturbation of these signaling hotspots can enable precise control of cell subpopulation phenotypes. SigHotSpotter correctly predicts the signaling hotspots with known experimental validations in different cellular systems. The tool is simple, user-friendly and is available as web-server or as stand-alone software. We believe SigHotSpotter will serve as a general purpose tool for the systematic prediction of signaling hotspots based on single-cell RNA-seq data, and potentiate novel cell rejuvenation strategies in the context of disease and ageing. AVAILABILITY AND IMPLEMENTATION: SigHotSpotter is at https://SigHotSpotter.lcsb.uni.lu as a web tool. Source code, example datasets and other information are available at https://gitlab.com/srikanth.ravichandran/sighotspotter. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. [less ▲]

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See detailModeling Cellular Differentiation and Reprogramming with Gene Regulatory Networks.
Hartmann, Andras UL; Ravichandran, Srikanth UL; Del Sol, Antonio

in Methods in Molecular Biology Series: Computational Stem Cell Biology (2019)

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression ... [more ▼]

Gene expression regulation is a fundamental cellular process that enables robust functioning of cells. How different genes interact among themselves to coordinate and maintain the overall gene expression profile observed in a cell is a key question in cellular biology. However, the immense complexity arising due to the scale and the nature of gene-gene interactions often hinders obtaining a global understanding of gene regulation. In this regard, network models of gene regulation based on gene-gene interactions, commonly referred to as gene regulatory networks (GRNs), serve important purpose of describing the overall interactions within a cell and provide a systematic approach to study their global behavior. In particular, in the context of cellular differentiation and reprogramming, where regulated changes in gene expression play a crucial role, precise knowledge of a cell type-specific GRN can enable control of the eventual cell fates with potential clinical applications. In this chapter, we describe our computational methodologies that we have tailor-made with purpose of applications to cell fate control. Briefly, we introduce the process of cellular differentiation and reprogramming, describe GRNs and common strategies to model them, and, finally, introduce the concept of determinants of cellular reprogramming and differentiation. In the Methods section, we elaborate on the different steps involved in the computational pipeline, including initial gene expression data processing, characterization of prior knowledge network, algorithm to remove non-cell type-specific edges, topological characterization of the inferred network, and Boolean network simulations to mimic cellular transitions. Finally, we provide a strategy to identify determinants of cellular reprogramming and differentiation based on the proposed computational methods. [less ▲]

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