Homo sapiens
Ontology highlight
ABSTRACT:
PROVIDER: PRJDB10713 | ENA |
REPOSITORIES: ENA
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Project description:Cellular senescence is a tumor-suppressive program that involves chromatin reorganization and specific changes in gene expression that trigger an irreversible cell-cycle arrest. We combined quantitative mass spectrometry and ChIP deep-sequencing to identify changes in histone modification occurring during cellular senescence. ChIP-seq was carried out using H3K4me3-specific antibodies in growing, quiescent, senescent, or senescent with shRB targeting Rb, IMR90 cells. The control mock data (ChIP-seq using anti-mouse IgG antibody) is available in GEO Sample GSM497500 (Series GSE19898).
2013-01-31 | E-GEOD-43921 | biostudies-arrayexpress
Project description:We analyzed a panel of 13 cancer cell lines rendered senescent by either etoposide (7 day treatment) or alisertib (7 and 14 day treatment). We aimed to find common vulnerabilities to induce cell death, and shared features that would allow unambiguous identification of the senescent state in the context of a cancerous phenotype. The samples were treated for the given duration, followed by 24 hours without drug before RNA was extracted. The senescence phenotype was assessed by B-gal staining, growth arrest and morphology.
2021-07-18 | E-MTAB-9970 | biostudies-arrayexpress
Project description:Drug-induced cardiotoxicity and hepatotoxicity are major causes of drug attrition. To decrease late-stage drug attrition, pharmaceutical and biotechnology industries need to establish biologically relevant models that use phenotypic screening to detect drug-induced toxicity in vitro. In this study, we sought to rapidly detect patterns of cardiotoxicity using high-content image analysis with deep learning and induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs). We screened a library of 1280 bioactive compounds and identified those with potential cardiotoxic liabilities in iPSC-CMs using a single-parameter score based on deep learning. Compounds demonstrating cardiotoxicity in iPSC-CMs included DNA intercalators, ion channel blockers, epidermal growth factor receptor, cyclin-dependent kinase, and multi-kinase inhibitors. We also screened a diverse library of molecules with unknown targets and identified chemical frameworks that show cardiotoxic signal in iPSC-CMs. By using this screening approach during target discovery and lead optimization, we can de-risk early-stage drug discovery. We show that the broad applicability of combining deep learning with iPSC technology is an effective way to interrogate cellular phenotypes and identify drugs that may protect against diseased phenotypes and deleterious mutations.
2021-07-07 | GSE172181 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241833 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241835 | GEO
Project description:Cells are the singular building blocks of life, and comprehensive understanding of morphology among other properties is crucial to assessment of underlying heterogeneity. We developed Computational Sorting and Mapping of Single Cells (COSMOS), a platform based on Artificial Intelligence (AI) and microfluidics to characterize and sort single cells based on real-time deep learning interpretation of high-resolution brightfield images. Supervised deep learning models were applied to characterize and sort cell lines and dissociated primary tissue based on high-dimensional embedding vectors of morphology without need for biomarker labels and stains/dyes. We demonstrate COSMOS capabilities with multiple human cell lines and tissue samples. These early results suggest that our neural networks embedding space can capture and recapitulate deep visual characteristics and can be used to efficiently purify unlabeled viable cells with desired morphological traits. Our approach resolves a technical gap in ability to perform real-time deep learning assessment and sorting of cells based on high-resolution brightfield images.
2023-09-25 | GSE241834 | GEO
Project description:The purpose of this study was to determine whether leukemia cells can escape from drug-induced senescence; determine the phenotype of the senescent, and escaped cells, and whether senescent leukemia cells can be specifically eliminated prior to their escape. Methods: DA3/EPOR cells were treated with 200ng/ml doxorubicin for 24 hr. Cells were confirmed to enter and exit senescence based on, proliferating and SA-gal staining. mRNA transcriptional profiles of naïve proliferating, senescent, and escaped DA3/EPOR murine leukemia cells were generated by deep sequencing, in biological triplicate, using Illumina NovaSeq 6000 at a depth of 25 million pair-ended reads that were 100 bp in length. The sequence reads that passed quality filters, the first 13 nucleotides were removed from each read. Alignment to the MM10 indexed genome was done using HiSAT2. Read counts were genereated using HTSEQ, which were then used for differential expression. Differential expression was performed on Rstudio using EdgeR. qRT–PCR was performed for validation. Results: We report that DA3/EPOR leukemia cells enter and escape a doxorubicin (Dox)-induced senescence, dependent on erythropoietin (EPO). EPO is necessary for DA3/EPOR cells to survive the senescent state. Senescent DA3/EPOR cells undergo a transcriptional reprogramming, which is largely reversible upon escape. Escaped DA3/EPOR cells mirror naïve, never senescent DA3/EPOR cell, and respond similarly to Dox. Senescent DA3/EPOR cells mimic a diapause-like state, and undergo metabolic changes. Senescent DA3/EPOR cells and some drug-induced senescent human acute myeloid leukemia (AML) cells can be eliminated with chloroquine, a lysosome inhibitor. Lastly, we show with The Cancer Genome Atlas publicly available data that the senescent signature is associated with decreased survival in AML and multiple other cancers. Conclusion: Leukemia cells persist through drug treatment by entering a reversible senescent state that mimics diapause. Senescent leukemia cells depend on lysosome activity to persist, and treatment with chloroquine, a lysosome inhibitor, can eliminate senescent leukemia cells.
2023-08-09 | GSE186200 | GEO
Project description:There is a need to discover and develop non-toxic antibiotics that are effective against metabolically dormant bacteria, which underlie chronic infections and promote antibiotic resistance. Traditional antibiotic discovery has historically favored compounds effective against actively metabolizing cells, a property that is not predictive of efficacy in metabolically inactive contexts. Here, we combine a stationary-phase screening method with deep learning-powered virtual screens and toxicity filtering to discover compounds with lethality against metabolically dormant bacteria and favorable toxicity profiles. The most potent and structurally novel compound without any obvious mechanistic liability was semapimod, an anti-inflammatory drug effective against stationary-phase E. coli and A. baumannii. Integrating microbiological assays, biochemical measurements, and single-cell microscopy, we show that semapimod selectively disrupts and permeabilizes the bacterial outer membrane by binding lipopolysaccharide. This work illustrates the value of harnessing non-traditional screening methods and deep learning models to identify non-toxic antibacterial compounds that are effective in infection-relevant contexts.
2023-08-21 | PXD044230 | Pride
Project description:Senescence is a permanent cell cycle arrest that occurs in response to cellular stress. Because senescent cells promote age-related disease, there has been considerable interest in defining the proteomic alterations in senescent cells. Because senescence differs greatly depending on cell type and senescence inducer, continued progress in the characterization of senescent cells is needed. Here, we analyzed primary human mammary epithelial cells (HMECs), a model system for aging, using mass spectrometry-based proteomics. By integrating data from replicative senescence, immortalization by telomerase reactivation, and drug-induced senescence, we identified a robust proteomic signature of HMEC senescence consisting of 77 upregulated and 36 downregulated proteins. This approach identified known biomarkers, such as downregulation of the nuclear lamina protein lamin-B1 (LMNB1), and novel upregulated proteins including the β-galactoside-binding protein galectin-7 (LGALS7). Gene ontology enrichment analysis demonstrated that senescent HMECs upregulated lysosomal proteins and downregulated RNA metabolic processes. We additionally integrated our proteomic signature of senescence with transcriptomic data from senescent HMECs to demonstrate that our proteomic signature can discriminate proliferating and senescent HMECs even at the transcriptional level. Taken together, our results demonstrate the power of proteomics to identify cell type-specific signatures of senescence and advance the understanding of senescence in primary HMECs.
2021-09-28 | PXD027318 | Pride
Project description:In cell senescence, cultured cells cease proliferating and acquire aberrant gene expression patterns. MicroRNAs (miRNAs) modulate gene expression through translational repression or mRNA degradation, and have been implicated in senescence. We have used deep sequencing to carry out a comprehensive survey of miRNA expression and its involvement in cell senescence. Informatic analysis of small RNA sequence datasets from young and senescent IMR90 human fibroblasts identifies many known miRNAs, and a small number of novel miRNAs, that are regulated (either up or down) with cell senescence. Comparison with mRNA expression profiles revealed potential mRNA targets of the senescence-regulated miRNAs. The target mRNAs are enriched for genes involved in biological processes associated with cell senescence. This result greatly extends existing information on the role of miRNAs in cell senescence, and is consistent with miRNAs having a causal role in the process. Comprehensive survey of miRNA from young and senescent IMR90 fibroblasts using deep sequencing
2011-08-09 | E-GEOD-27404 | biostudies-arrayexpress