Cosmos bipinnatus
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA384085 | ENA |
REPOSITORIES: ENA
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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:Lung cancer is the leading cause of cancer death worldwide. Low-dose computed tomography screening (LDCT) was recently shown to anticipate the time of diagnosis, thus reducing lung cancer mortality. We identifed a serum microRNA signature (the miR-Test) that could identify the optimal target population for LDCT screening. Here, we performed a large-scale validation study of the miR-Test in high-risk individuals enrolled in the Continuous Observation of Smoking Subjects (COSMOS) lung cancer screening program.
2016-02-29 | GSE76462 | 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.
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Project description:The integration of multi-omic data sets can provide unique information about molecular processes in a cell. Despite the development of many tools to extract information from such data sets, there are limited strategies to systematically extract mechanistic hypotheses from them. We here present COSMOS (Causal Oriented Search of Multi-Omic Space), a method that integrates cell signaling pathways, transcriptional, and metabolics data sets. COSMOS leverages extensive prior knowledge of interactions between biomolecules with computational methods to estimate activities of transcription factors and kinases as well as network-level causal reasoning. COSMOS can provide mechanistic explanations for experimental observations across multiple omic data sets. We applied COSMOS to a dataset comprising transcriptomic, phosphoproteomic, and metabolomic data from nine renal cell carcinoma patients comparing healthy non affected kidney tissue and kidney cancer. We used COSMOS to generate novel hypotheses such as the impact of CDK7 on nucleoside metabolism and its influence on citrulline production, that we validated experimentally. We expect that our freely available method will be broadly useful to extract mechanistic insights from multi-omic studies.
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