LCCC 1108 (UNCseqTM)
Ontology highlight
ABSTRACT: The primary objective of this specimen correlative study was two-fold: to provide a mechanism for the association of known molecular alterations with clinical outcomes, and to provide rapid genetic profiling of alterations with known clinical utility using tumor and germline specimens to support treatment decisions.
REPOSITORIES: dbGaP
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| GapExchange_phs001713.v1.p1.xml | Xml | |||
| dbGaPEx2.1.5.xsd | Other | |||
| Study_Report.phs001713.LCCC_1108.v1.p1.MULTI.pdf | ||||
| manifest_phs001713.LCCC_1108.v1.p1.c2.GRU.pdf | ||||
| datadict_v2.xsl | Other |
Items per page: 5 1 - 5 of 15 |
Similar Datasets
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236518 | GEO
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236517 | GEO
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236508 | GEO
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236512 | GEO
Project description:Developmental cell fate decisions are dynamic processes driven by the complex behaviour of gene regulatory networks. A challenge in studying these processes using single-cell genomics is that the data provides only a static snapshot with no detail of dynamics. Metabolic labelling and splicing can provide time-resolved information, but current methods have limitations. Here, we present experimental and computational methods that overcome these limitations to allow dynamical modelling of gene expression from single-cell data. We developed sci-FATE2, an optimised metabolic labelling method that substantially increases data quality, and profiled approximately 45,000 embryonic stem cells differentiating into multiple neural tube identities. To recover dynamics, we developed velvet, a deep learning framework that extends beyond instantaneous velocity estimation by modelling gene expression dynamics through a neural stochastic differential equation system within a variational autoencoder. Velvet outperforms current velocity tools across quantitative benchmarks, and predicts trajectory distributions that accurately recapitulate underlying dataset distributions while conserving known biology. Velvet trajectory distributions capture dynamical aspects such as decision boundaries between alternative fates and correlative gene regulatory structure. Using velvet to provide a dynamical description of in vitro neural patterning, we highlight a process of sequential decision making and fate-specific patterns of developmental signalling. Together, these experimental and computational methods recast single-cell analyses from descriptions of observed data distributions to models of the dynamics that generated them, providing a new framework for investigating developmental gene regulation and cell fate decisions.
2023-07-06 | GSE236378 | GEO
Project description:ALFAOMEGA-RETRÒ will be exploited to retrospectively collect clinical and imaging data and archival samples to be used for validation and correlative studies on markers discovered by cutting-edge translational projects within the AIRC5x1000 program "Insights into the evolving heterogeneity of colorectal cancer (CRC): from mechanism to therapies" (an ongoing multi-institutional research program).
| 2394032 | ecrin-mdr-crc
Project description:The purpose of this study is to evaluate the utility of the QLQ-GINET21 in making clinical and therapeutic decisions.
| 2221368 | ecrin-mdr-crc
Project description:We profiled 798 miRNAs using NanoString technology in plasma, urine, and liquid-based cytology (LBC) from 24 women with and without high-grade cervical lesions (CIN2/3). LBC showed the highest detection efficiency and consistency, with the lowest proportion of low-count miRNAs and the highest expression levels, supporting its utility as the most effective specimen type for miRNA-based screening.
2025-07-08 | GSE302097 | GEO
Project description:NSCLC remains to be the leading cause of cancer incidence and mortality in the Philippines. Early diagnosis, better understanding on the mechanisms of drug resistance, and proper treatment monitoring is necessary to lower the incidence and mortality rate of NSCLC. This study was able to provide initial insights on the proteomic profile of Filipino NSCLC by quantitative proteomic analysis and identification of aberrant expressions in the tumor tissue relative to the adjacent normal tissue specimen. A total of 4518 proteins were identified and from this, 1855 proteins were found to be significantly upregulated and downregulated. Functional and pathway analysis was done to the upregulated and downregulated proteins with at least 4-fold expression change. Pathway analysis revealed the possible activation of the upregulated protein, MUC1, which is a known oncogenic driver involved in the stimulation of pathways that promote angiogenesis in NSCLC tumor, and prevention of apoptosis. STAT1 and STAT3, also found upregulated in the tumor specimen, were known to have interactions with MUC1 which results to expression of proteins for cell proliferation. The analysis also suggests the stabilization of HIF which allows cell growth in the hypoxic tumor environment, and the activation of EIF signaling correlated with low survival rate of NSCLC patients. Our data demonstrate the rich proteomic information that can be obtained from comprehensive profiling of the NSCLC proteome and applications for better understanding of NSCLC tumorigenicity.
2022-02-10 | PXD027710 | Pride
Project description:This serum proteomic profile provides rigorous evidence that enables understanding of the heterogeneity of IPF and protein alterations that could help in its diagnosis and treatment decisions.
2024-05-16 | PXD052329 |