Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Homo sapiens

PROVIDER: GSE150084 | GEO | 2020/05/08

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Similar Datasets

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

Project description:We subject multiple versions and single cell-derived sublines of an archetypal non-small cell lung cancer cell line to experimental scrutiny at the genomic, transcriptomic, and cell population levels. We find evidence of genetic variability among cell line versions and one subline. Additional sublines show no genetic variation but epigenetic differences are detected at the transcriptomic level. Stochastic simulations of subline growth dynamics confirm that one subline likely contains at least two genetic states while all others are isogenic. Overall, our results substantiate a view of intratumoral heterogeneity in which different genetic states give rise to multiple epigenetic “basins of attraction,” across which cells can transition driven by stochastic factors such as gene expression noise and asymmetric cell division. Deconvolving intratumoral heterogeneity into genetic, epigenetic, and stochastic components provides a lens through which to view tumor drug response and acquired treatment resistance that may lead to novel therapeutic strategies in the future.

2020-05-08 | GSE150083 | GEO

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

2020-05-08 | GSE150025 | GEO

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities

| PRJNA631050 | ENA

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [scRNA-seq]

| PRJNA631051 | ENA

Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

Project description:Multi-omics data and stochastic simulations support a cancer heterogeneity framework unifying genetic, epigenetic, and stochastic variabilities [Bulk-RNA-seq]

| PRJNA630953 | ENA

Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas.

Project description: Not available

| S-EPMC4783472 | biostudies-literature

Multi-Omics Factor Analysis-a framework for unsupervised integration of multi-omics data sets.

Project description: Not available

| S-EPMC6010767 | biostudies-literature

Single Cell triple omics sequencing reveals genetic and epigenetic heterogeneity in hepatocellular carcinoma

Project description:Single cell genome, DNA methylome, and transcriptome sequencing has been achieved separately. However, to analyze the regulation of RNA expression by genetic and epigenetic factors within an individual cell, it is necessary to analyze these omics simultaneously from the same single cell. Here we developed a single cell triple omics sequencing technique- scTrio-seq, to analyze the genome, DNA methylome, and transcriptome concurrently of a mammalian cell. 6 single human HepG2 cell line cells were sequenced using the newly developed scTrio-seq, other 2 HepG2 cells were sequenced using scRNA-seq and other 2 HepG2 cells were sequenced using scRRBS as technique control. 6 single mouse embryonic stem cells (mESCs) were sequenced using the newly developted scTrio-seq. Meanwhile, two scRNA-seq and two scRRBS were also completed using two mESCs separately. 26 single cells from hepatocellular carcinoma were sequenced using scTrio-seq to analyze the regulation relations between three omics of cancer cells.

2016-03-01 | E-GEOD-65364 | biostudies-arrayexpress

Omics Pipe: a community-based framework for reproducible multi-omics data analysis.

Project description: Not available

| S-EPMC4443682 | biostudies-literature

An in vitro model of tumor heterogeneity resolves genetic, epigenetic, and stochastic sources of cell state variability.

Project description: Not available

| S-EPMC8195356 | biostudies-literature