Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Single-cell Multi-omics Sequencing and Analyses of Human Colorectal Cancer

ABSTRACT: Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single-cell resolution. Using optimized single-cell multi-omics sequencing together with multi-regional sampling of the primary tumor, lymphatic and distant metastases, we provide insights beyond intratumoral heterogeneity. Genome-wide DNA methylation levels were relatively consistent within a single genetic sub-lineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 sequenced patients. Our work demonstrates the feasibility of reconstructing genetic lineages, and tracing their epigenomic and transcriptomic dynamics with single-cell multi-omics sequencing.

ORGANISM(S): Homo sapiens

PROVIDER: GSE97693 | GEO | 2018/11/29

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Genomic DNA of tumor tissues, adjacent normal tissues, and peripheral blood were extracted using QIAamp DNA mini Kit (QIAGEN, cat. #51306)

Project description:Although genomic instability, epigenetic abnormality, and gene expression dysregulation are hallmarks of colorectal cancer, these features have not been simultaneously analyzed at single cell resolution. Using optimized single cell multiomics sequencing together with multiregional sampling of the primary tumor, lymphatic and distant metastases, we provide insights beyond intratumoral heterogeneity. Genome wide DNA methylation levels were relatively consistent within a single genetic sublineage. The genome-wide DNA demethylation patterns of cancer cells were consistent in all 10 sequenced patients. The cancer cells’ DNA demethylation degrees clearly correlated with the densities of the heterochromatin-associated histone modification H3K9me3 of normal tissue, and those of repetitive element Long Interspersed Nuclear Element 1. Our work demonstrates the feasibility of reconstructing genetic lineages, and tracing their epigenomic and transcriptomic dynamics with single cell multiomics sequencing.

| EGAS00001003242 | EGA

Single Cell Multi-Omics Profiling Using ISSAAC-seq

Project description:Joint profiling of chromatin accessibility and gene expression from the same single cell provides critical information about cell types in a tissue and cell states during a dynamic process. These emerging multi-omics techniques help the investigation of cell-type resolved gene regulatory mechanisms. Here, we developed in situ SHERRY after ATAC-seq (ISSAAC-seq), a highly sensitive and flexible single cell multi-omics method to interrogate chromatin accessibility and gene expression from the same single cell. We demonstrated that ISSAAC-seq is sensitive and provides high quality data with orders of magnitude more features than existing methods. Using the joint profiles from thousands of nuclei from the mouse cerebral cortex, we uncovered major and rare cell types together with their cell-type specific regulatory elements and expression profiles. Finally, we revealed distinct dynamics and relationships of transcription and chromatin accessibility during an oligodendrocyte maturation trajectory.

2022-06-15 | E-MTAB-11264 | biostudies-arrayexpress

Single-cell co-profiling of gene expression and chromatin accessibility in the mice striatum with varied genetic backgrounds

Project description:To investigate the influence of genetic background and sex on addiction, we conducted a study on the striatum of mice, a crucial brain structure linked to various substance abuse disorders, at the single-cell level. We utilized single-cell multi-omics technology to analyze the gene expression and chromatin accessibility of individual cells in the mice striatum.

2023-05-11 | GSE228530 | GEO

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for Osteoporosis Risk [methylome]

Project description:In this study, we analyzed transcriptome gene expression microarray, epigenomic miRNA microarray and methylome sequencing data simultaneously in PBMs from 5 high hip BMD subjects and 5 low hip BMD subjects. Through integrating the transcriptomic and epigenomic data, firstly we identified BMD-related genetic factors, including 9 protein coding genes and 2 miRNAs, of which 3 genes (FAM50A, ZNF473 and TMEM55B) and one miRNA (hsa-mir-4291) showed the consistent association evidence from both gene expression and methylation data, and 3 genes (TMEM55B, RNF40 and ALDOA) were confirmed in the meta-analysis of 7 GWAS samples and GEnetic Factors for OSteoporosis consortium (GEFOS-2) GWAS results. Secondly in network analysis we identified an interaction network module with 12 genes and 11 miRNAs including AKT1, STAT3, STAT5A, FLT3, hsa-mir-141 and hsa-mir-34a which have been associated with BMD in previous studies. This module revealed the crosstalk among miRNAs, mRNAs and DNA methylation and showed four potential regulatory patterns of gene expression to influence the BMD status, including regulation by gene methylation, by miRNA and its methylation, by transcription factors and co-regulation by miRNA and gene methylation. In conclusion, the integration of multiple layers of omics can yield more in-depth results than analysis of individual omics data respectively. Integrative analysis from transcriptomics and epigenomic data improves our ability to identify causal genetic factors, and more importantly uncover functional regulation pattern of multi-omics for osteoporosis etiology. 5 high hip BMD subjects and 5 low hip BMD subjects

2014-10-22 | E-GEOD-62588 | biostudies-arrayexpress

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for Osteoporosis Risk [miRNA]

2014-11-19 | E-GEOD-63446 | biostudies-arrayexpress

Integrative Analysis of Transcriptomic and Epigenomic Data to Reveal Regulation Patterns for Osteoporosis Risk [Transcriptome]

2014-10-17 | E-GEOD-62402 | biostudies-arrayexpress

Simultaneous deep transcriptome and proteome profiling in single mouse oocytes

Project description:Deep proteome data in the single-cell multi-omics analysis of single mouse oocytes.

2023-07-20 | PXD043935 |

Neurulation of the cynomolgus monkey embryo achieved from 3D blastocyst culture [Bisulfite-Seq]

Project description:To further reveal the major cell types of developing pIVC embryos and underlying epigenetic dynamics, the optimized single-cell based multi-omics sequencing method scChaRM-seq was performed (Yan et al., 2021b). 1,862 single cells Bisulfite-seq datasets were further analyzed. We then performed multi-omics profiling analysis using data obtained from9 pIVC embryos at 8 sequential developmental stages.

2023-03-24 | GSE218524 | GEO

Neurulation of the cynomolgus monkey embryo achieved from 3D blastocyst culture [RNA-Seq]

Project description:To further reveal the major cell types of developing pIVC embryos and underlying epigenetic dynamics, the optimized single-cell based multi-omics sequencing method scChaRM-seq was performed (Yan et al., 2021b). After stringent filtration, 3,682 single cells RNA-seq datasets were further analyzed We then performed multi-omics profiling analysis using data obtained from9 pIVC embryos at 8 sequential developmental stages.

2023-03-24 | GSE218523 | GEO

Bisulfite-free epigenomics and genomics of single cells analyzed by methylation sensitive restriction (epi-gSCAR)

Project description:Single-cell multi-omics are powerful means to study cell-to-cell heterogeneity. Here, we present a single-tube, bisulfite-free method for the simultaneous, genome-wide analysis of DNA methylation and genetic variants in single cells: epigenomics and genomics of single cells analyzed by restriction (epi-gSCAR). By applying this method, we obtained DNA methylation measurements of up to 506,063 CpGs and up to 1,244,188 single-nucleotide variants from single leukemia-derived cells. We demonstrate that epi-gSCAR generates accurate and reproducible measurements of DNA methylation and allows to differentiate between two cell lines based on the DNA methylation and genetic profiles.

2020-10-28 | GSE131723 | GEO

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data