Ontology highlight
ABSTRACT:
PROVIDER: PRJNA813349 | ENA |
REPOSITORIES: ENA
Similar Datasets
Project description:Deep molecular phenotyping of cells at transcriptomic and proteomic levels is an essential first step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcriptome level abundances only modestly correlate with each other, complementary profiling of both levels of abundances are needed. We report a novel method to capture the cell type-specific transcriptome and proteome simultaneously from both in vitro and in vivo experimental model systems. This method leverages the ability of biotin ligase, TurboID, to biotinylate cytosolic proteins including ribosomal and RNA-binding proteins, which allows enrichment of biotinylated proteins for proteomics as well as protein-associated mRNA for transcriptomics. We validated this approach first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the ground truth, bulk proteomes and transcriptomes. We also show that the effect of a biological stimuli (e.g., neuroinflammatory activation by LPS) can be faithfully captured. We also applied this approach to obtain native-state proteomes and transcriptomes from two key brain cell types (Aldh1l1-expressing astrocytes and Camk2a-expressing neurons), thereby validating the in vivo application of this approach. We also used these data to interrogate protein mRNA concordance and discordance across two brain cell types, providing insights into shared and unique molecular processes such as cytoskeletal and mitochondrial-related functions.
2026-02-16 | PXD073954 | Pride
Project description:Deep molecular phenotyping of cells at transcriptomic and proteomic levels is an essential first step to understanding cellular contributions to development, aging, injury, and disease. Since proteome and transcriptome level abundances only modestly correlate with each other, complementary profiling of both levels of abundances are needed. We report a novel method to capture the cell type-specific transcriptome and proteome simultaneously from both in vitro and in vivo experimental model systems. This method leverages the ability of biotin ligase, TurboID, to biotinylate cytosolic proteins including ribosomal and RNA-binding proteins, which allows enrichment of biotinylated proteins for proteomics as well as protein-associated mRNA for transcriptomics. We validated this approach first using well-controlled in vitro systems to verify that the proteomes and transcriptomes obtained reflect the ground truth, bulk proteomes and transcriptomes. We also show that the effect of a biological stimuli (e.g., neuroinflammatory activation by LPS) can be faithfully captured. We also applied this approach to obtain native-state proteomes and transcriptomes from two key brain cell types (Aldh1l1-expressing astrocytes and Camk2a-expressing neurons), thereby validating the in vivo application of this approach. We also used these data to interrogate protein mRNA concordance and discordance across two brain cell types, providing insights into shared and unique molecular processes such as cytoskeletal and mitochondrial-related functions.
2026-02-16 | PXD059818 | Pride
Project description:Chloroplast genomes of subfamily Pothoideae (Araceae)
| PRJNA547619 | ENA
Project description:Few studies have investigated host-bacterial interactions at sites of infection in humans using transcriptomics and metabolomics. Haemophilus ducreyi causes cutaneous ulcers in children and the genital ulcer disease chancroid in adults. We developed a human challenge model in which healthy adult volunteers are infected with H. ducreyi on the upper arm until they develop pustules. Here, we characterized host-pathogen interactions in pustules using transcriptomics and metabolomics and examined interactions between the host transcriptome and metabolome using integrated omics. In a previous pilot study, we determined the human and H. ducreyi transcriptomes and the metabolome of pustule and wounded sites of 4 volunteers (B. Griesenauer, et al. mBio 10(3):e01193-19 https://doi.org/10.1128/mBio.01193-19). While we could form provisional transcriptional networks between the host and H. ducreyi, the study was underpowered to integrate the metabolome with the host transcriptome. To better define and integrate the transcriptomes and metabolome, we used samples from both the pilot study (n=4) and new volunteers (n=8) to identify 5,495 human differentially expressed genes (DEGs), 123 H. ducreyi DEGs, 205 differentially abundant positive ions, and 198 differentially abundant negative ions. We identified 42 positively correlated and 29 negatively correlated human-H. ducreyi transcriptome clusters. In addition, we defined human transcriptome-metabolome networks consisting of 9 total clusters, which highlighted changes in fatty acid metabolism and mitigation of oxidative damage. Taken together, the data suggest a mixed pro- and anti-inflammatory environment and rewired central metabolism in the host that provides a hostile, nutrient limited environment for H. ducreyi.
2022-11-01 | MSV000090626 | MassIVE
Project description:Few studies have investigated host-bacterial interactions at sites of infection in humans using transcriptomics and metabolomics. Haemophilus ducreyi causes cutaneous ulcers in children and the genital ulcer disease chancroid in adults. We developed a human challenge model in which healthy adult volunteers are infected with H. ducreyi on the upper arm until they develop pustules. Here, we characterized host-pathogen interactions in pustules using transcriptomics and metabolomics and examined interactions between the host transcriptome and metabolome using integrated omics. In a previous pilot study, we determined the human and H. ducreyi transcriptomes and the metabolome of pustule and wounded sites of 4 volunteers (B. Griesenauer, et al. mBio 10(3):e01193-19 https://doi.org/10.1128/mBio.01193-19). While we could form provisional transcriptional networks between the host and H. ducreyi, the study was underpowered to integrate the metabolome with the host transcriptome. To better define and integrate the transcriptomes and metabolome, we used samples from both the pilot study (n=4) and new volunteers (n=8) to identify 5,495 human differentially expressed genes (DEGs), 123 H. ducreyi DEGs, 205 differentially abundant positive ions, and 198 differentially abundant negative ions. We identified 42 positively correlated and 29 negatively correlated human-H. ducreyi transcriptome clusters. In addition, we defined human transcriptome-metabolome networks consisting of 9 total clusters, which highlighted changes in fatty acid metabolism and mitigation of oxidative damage. Taken together, the data suggest a mixed pro- and anti-inflammatory environment and rewired central metabolism in the host that provides a hostile, nutrient limited environment for H. ducreyi.
2022-11-01 | MSV000090626 | GNPS
Project description:Molecular characterization of Xanthomonas species pathogenic on Araceae
| PRJNA716613 | ENA
Project description:Endothelial cell (EC) metabolism is an emerging target for anti-angiogenic therapy in tumor and choroidal neovascularization (CNV), but little is known about individual EC metabolic transcriptomes. Here, by scRNA-sequencing 28,337 murine choroidal ECs (CECs) and sprouting CNV-ECs, we constructed a taxonomy to characterize their heterogeneity. Comparison with murine lung tumor ECs (TECs) revealed congruent marker gene expression by distinct EC phenotypes across tissues and diseases, suggesting similar angiogenic mechanisms. Trajectory inference of CNV-ECs revealed that differentiation of venous to angiogenic ECs was accompanied by metabolic transcriptome plasticity. EC phenotypes displayed metabolic transcriptome heterogeneity. Hypothesizing that conserved genes are more important, we used an integrated analysis, based on congruent transcriptome analysis, CEC-tailored genome scale metabolic modeling, and gene expression meta-analysis in multiple cross-species datasets, followed by functional validation, to identify the top-ranking metabolic targets SQLE and ALDH18A1, involved in EC proliferation and collagen production, respectively, as novel angiogenic targets. The effect of SQLE and ALDH18A1 silencing in ECs was investigated by transcriptomics and proteomics analysis.
2020-04-14 | PXD016678 | Pride