Single-cell multi-omic integration compares and contrasts features of brain cell identity
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ABSTRACT:
PROVIDER: PRJNA523430 | ENA |
REPOSITORIES: ENA
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Project description: Not available
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Project description:To flexibly model single-cell datasets, we developed LIGER, an algorithm and software package that delineates shared and dataset-specific features of cell identity. We applied it to four diverse and challenging analyses of human and mouse brain cells, the first two of which include newly generated single-cell transcriptome datasets for mouse and human regions. First, we determined region-specific and sexually dimorphic gene expression in the mouse bed nucleus of the stria terminalis (BNST). Second, we analyzed expression in the human substantia nigra (SN), integrating cell types across donors, and relating cell types to those in the mouse (using mouse SN data from Saunders et al., 2018 (GEO: GSE116470)). The main cell types identified include neurons, astrocytes, microglia, oligodendrocytes, polydendrocytes, and endothelial cells. Third, we jointly leveraged in situ and single-cell expression data to spatially locate fine subtypes of cells present in the mouse frontal cortex. Finally, we integrated mouse cortical single-cell RNA-seq and DNA methylation profiles, revealing mechanisms of cell-type-specific gene regulation. Integrative analyses using LIGER promise to accelerate investigations of cell-type definition, gene regulation, and disease states.
2019-02-21 | GSE126836 | GEO
Project description:Uncovering the mesendoderm gene regulatory network through multi-omic data integration
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Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated singleand multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every singleomic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.
2024-01-23 | PXD037038 | Pride
Project description:This study examines the sites of HIV integration in quiescent CD4 T cells and compares them to activated T cells. The expression patterns of the sites hosting integration events were determined using microarray analysis data from quiescent and activated CD4 T cells.
2009-05-30 | GSE14596 | GEO
Project description:Background: Grape cultivars and wines are distinguishable by their color, flavor and aroma profiles. Omic analyses (transcripts, proteins and metabolites) are powerful tools for assessing biochemical differences in biological systems. Results: Berry skins of red- (Cabernet Sauvignon, Merlot, Pinot Noir) and white-skinned (Chardonnay, Semillon) wine grapes were harvested near optimum maturity from the same experimental vineyard and Ë?Brix-to-titratable acidity ratio. Identical sample aliquots were analyzed for transcripts by grapevine whole-genome oligonucleotide microarray and RNA-seq technologies, proteins by nano-liquid chromatography-mass spectroscopy, and metabolites by gas chromatography-mass spectroscopy and liquid chromatography-mass spectroscopy. Principal components analysis of each of five Omic technologies showed similar results across cultivars in all Omic datasets. Comparison of the processed data of genes mapped in RNA-seq and microarray data revealed a strong Pearson's correlation (0.80). The exclusion of probesets associated with genes with potential for cross-hybridization on the microarray improved the correlation to 0.93. The overall concordance of protein with transcript data was low with a Pearson's correlation of 0.27 and 0.24 for the RNA-seq and microarray data, respectively. Integration of metabolite with protein and transcript data produced an expected model of phenylpropanoid biosynthesis, which distinguished red from white grapes, yet provided detail of individual cultivar differences. Conclusions: The five Omic technologies were consistent in distinguishing cultivar variation. There was high concordance between transcriptomic technologies, but generally protein abundance did not correlate well with transcript abundance. The integration of multiple high-throughput Omic datasets revealed complex biochemical variation amongst five cultivars of an ancient and economically important crop species. Vitis vinifera L. cv. Cabernet Sauvignon, Chardonnay, Merlot, Pinot Noir, Semillon berries were harvested from Nevada Agricultural Experiment Station Valley Road Vineyard, Reno, NV, USA. Whole-genome microarray analysis was used to assess the transcriptomic response of berry skins at harvest, approximately 24 °Brix (2011 vintage). Vines were grown under water deficit and well-watered conditions. At least two clusters harvested from non-adjacent vines were used for each of five experimental replicates.
2015-11-12 | E-GEOD-72421 | biostudies-arrayexpress
Project description:Contemporary analyses focused on a limited number of clinical and molecular features have been unable to accurately predict clinical outcomes in pancreatic ductal adenocarcinoma (PDAC). Here we describe a novel, conceptual approach and use it to analyze clinical, computational pathology, and molecular (DNA, RNA, protein, and lipid) analyte data from 74 patients with resectable PDAC. Multiple, independent, machine learning models were developed and tested on curated single and multi-omic feature/analyte panels to determine their ability to predict clinical outcomes in patients. The multi-omic models predicted recurrence with an accuracy and positive predictive value (PPV) of 0.90, 0.91, and survival of 0.85, 0.87, respectively, outperforming every single-omic model. In predicting survival, we defined a parsimonious model with only 589 multi-omic analytes that had an accuracy and PPV of 0.85. Our approach enables discovery of parsimonious biomarker panels with similar predictive performance to that of larger and resource consuming panels and thereby has a significant potential to democratize precision cancer medicine worldwide.
2023-10-15 | MSV000093118 | MassIVE
Project description:Most existing single-cell techniques can only make one type of molecular measurements. Although computational approaches have been developed to integrate single-cell datasets, their efficacy still needs to be determined with reference to authentic single-cell multi-omic profiles. To address this challenge, we devised single-nucleus methylCytosine, Chromatin accessibility and Transcriptome sequencing (snmC2T-seq) and applied the approach to post-mortem human frontal cortex tissue. We developed a computational framework to evaluate the quality of finely defined cell types using multi-modal information and validated the efficacy of computational multi-omic integration methods. Correlation analysis in individual cells revealed gene groups showing distinct relations between methylation and expression. Integration of snmC2T-seq with other multi- and single- modal datasets enabled joint analyses of the methylome, chromatin accessibility, transcriptome, and chromatin architecture for 63 human cortical cell types. We reconstructed the regulatory lineage of these cortical cell types and found pronounced cell-type-specific enrichment of disease risks for neuropsychiatric traits, predicting causal cell types that can be targeted for treatment.
2019-12-12 | GSE140493 | GEO
Project description:An epigenetic and transcriptomic signature of immune tolerance in human monocytes by multi-omic integration
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