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spatial transcriptomics database (stomicsdb)  (Spatial Transcriptomics Inc)
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Spatial Transcriptomics Inc spatial transcriptomics database (stomicsdb)
Proposed bioinformatics pipeline with experimental validation for translational neuroscience applications. (A) The high dimensional omics analysis includes (i) the overlap between <t>transcriptomics</t> and proteomics/metabolomics/lipidomics datasets which identifies the differentially expressed genes (DEGs) that are altered across all omics layers; (ii) the overlap with single-cell/single-nucleus RNA-sequencing (sc/snRNA-seq) indicates the identification of DEGs in different cell types or cell subpopulations; (iii) the overlap with spatial analysis enables the understanding of the location of the DEGs in the respective brain regions; and (iv) the overlap with data with time element will provide further information on how the DEGs and the corresponding phenotypes could change over time. (B) Key cluster of DEGs can be further analyzed by pathway analysis, gene-enrichment analysis, network analysis and/or protein-protein interaction analysis, among many other analyses that can be performed. (C) The key DEGs should be validated by cell experiments or animal studies with strategies such as gene knockout or knockin, gene silencing or overexpression, or treatments with native ligands or protein inhibitors/activators. (D) Changes in gene/protein levels or alteration in protein activities associated with certain disease phenotypes may provide insights to disease mechanisms, as well as the identification of potential biomarkers or advancement of potential therapeutic developments. The figure was created with BioRender.com .
Spatial Transcriptomics Database (Stomicsdb), supplied by Spatial Transcriptomics Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Proposed bioinformatics pipeline with experimental validation for translational neuroscience applications. (A) The high dimensional omics analysis includes (i) the overlap between transcriptomics and proteomics/metabolomics/lipidomics datasets which identifies the differentially expressed genes (DEGs) that are altered across all omics layers; (ii) the overlap with single-cell/single-nucleus RNA-sequencing (sc/snRNA-seq) indicates the identification of DEGs in different cell types or cell subpopulations; (iii) the overlap with spatial analysis enables the understanding of the location of the DEGs in the respective brain regions; and (iv) the overlap with data with time element will provide further information on how the DEGs and the corresponding phenotypes could change over time. (B) Key cluster of DEGs can be further analyzed by pathway analysis, gene-enrichment analysis, network analysis and/or protein-protein interaction analysis, among many other analyses that can be performed. (C) The key DEGs should be validated by cell experiments or animal studies with strategies such as gene knockout or knockin, gene silencing or overexpression, or treatments with native ligands or protein inhibitors/activators. (D) Changes in gene/protein levels or alteration in protein activities associated with certain disease phenotypes may provide insights to disease mechanisms, as well as the identification of potential biomarkers or advancement of potential therapeutic developments. The figure was created with BioRender.com .

Journal: Journal of Pharmaceutical Analysis

Article Title: Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

doi: 10.1016/j.jpha.2023.06.011

Figure Lengend Snippet: Proposed bioinformatics pipeline with experimental validation for translational neuroscience applications. (A) The high dimensional omics analysis includes (i) the overlap between transcriptomics and proteomics/metabolomics/lipidomics datasets which identifies the differentially expressed genes (DEGs) that are altered across all omics layers; (ii) the overlap with single-cell/single-nucleus RNA-sequencing (sc/snRNA-seq) indicates the identification of DEGs in different cell types or cell subpopulations; (iii) the overlap with spatial analysis enables the understanding of the location of the DEGs in the respective brain regions; and (iv) the overlap with data with time element will provide further information on how the DEGs and the corresponding phenotypes could change over time. (B) Key cluster of DEGs can be further analyzed by pathway analysis, gene-enrichment analysis, network analysis and/or protein-protein interaction analysis, among many other analyses that can be performed. (C) The key DEGs should be validated by cell experiments or animal studies with strategies such as gene knockout or knockin, gene silencing or overexpression, or treatments with native ligands or protein inhibitors/activators. (D) Changes in gene/protein levels or alteration in protein activities associated with certain disease phenotypes may provide insights to disease mechanisms, as well as the identification of potential biomarkers or advancement of potential therapeutic developments. The figure was created with BioRender.com .

Article Snippet: Spatial TranscriptOmics DataBase (STOmicsDB) , Spatial Omics , [ ] .

Techniques: Biomarker Discovery, RNA Sequencing, Gene Knockout, Knock-In, Over Expression