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      SCANET for single-cell network-based drug repurposing candidate extraction

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      conference-abstract
        1 , , 2 , 3 , 1 , 1 , 1 , 1 , 1
      RExPO24 Conference
      REPO4EU
      RExPO24
      3-5 July 2024
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            Abstract

            Reconstructing small regulatory networks is essential for understanding cellular functions and mechanisms. Performing this task with single-cell RNA sequencing data poses a substantial computational challenge.

            In response, we present SCANet (single-cell co-expression network analysis) [1], a comprehensive tool designed for single-cell profiling. SCANet encompasses the entire workflow of differential mechanotyping, from identifying trait/cell-type-specific gene co-expression modules to predicting mechanistic drug repurposing candidates [2]. To showcase SCANet's efficacy, we examined two distinct datasets. Firstly, we uncovered monocytic drivers associated with cytokine storms in patients with acute respiratory illness, elucidating potential targets for intervention. Secondly, in obese mice, we identified 20 drugs targeting 8 potential pharmacological targets within metabolic driver mechanisms in intestinal stem cells. In another study [3], SCANet efficiently deciphers cell- and disease-specific co-expression gene modules across lesion types in patients with multiple sclerosis.

            SCANet is available as a free, open-source Python package, facilitating its integration into single-cell-based systems medicine research and mechanistic drug discovery.

            Content

            Author and article information

            Conference
            RExPO24 Conference
            REPO4EU
            2 May 2024
            Affiliations
            [1 ] Institute for Computational Systems Biology, University of Hamburg, Hamburg 22607, Germany;
            [2 ] Department of Medicine, Hamburg Center for Translational Immunology (HCTI) and Center for Biomedical AI (bAIome), University Medical Center Hamburg-Eppendorf (UKE), Hamburg 20246, Germany;
            [3 ] Center for Molecular Biology and Genetic Engineering (CBMEG), State University of Campinas (Unicamp), Campinas, SP 13083-875, Brazil;
            Author notes
            Author information
            https://orcid.org/0000-0001-5011-6683
            Article
            10.58647/REXPO.24000046.v1
            0bd9939d-eb3d-4208-8365-1efbe9f0be31

            This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

            RExPO24
            3
            Munich, Germany
            3-5 July 2024
            History
            : 2 May 2024
            Categories

            Data sharing not applicable to this article as no datasets were generated or analysed during the current study.
            Statistics,Life sciences

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