Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Dissection of cMyc+ GC-B cells at single-cell level

ABSTRACT: Dissection of cMyc+ GC-B cells at single-cell level

PROVIDER: PRJNA533063 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR8906280_1.fastq.gz	Fastqsanger.gz
	SRR8906280_2.fastq.gz	Fastqsanger.gz
	SRR8906290_1.fastq.gz	Fastqsanger.gz
	SRR8906290_2.fastq.gz	Fastqsanger.gz
	SRR8906300_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 226

Similar Datasets

Dissection of cMyc+ GC-B cells

Project description:Dissection of cMyc+ GC-B cells

| PRJNA612687 | ENA

Dissection of cMyc+ GC-B cells at single-cell level

Project description:Transcriptomic profiling and unsupervised clustering of cMyc+ GC-B cells identify the four subpopulations representing positive-selection stages.

2021-01-12 | GSE129857 | GEO

Dissection of cMyc+ GC-B cells [WT]

Project description:Dissection of cMyc+ GC-B cells [WT]

| PRJNA613033 | ENA

Dissection of cMyc+ GC-B cells

Project description:Transcriptomic profiling of the cMyc+ LZ-GC-B cell subpopulations delineated by flow-cytometry

2021-01-12 | GSE146987 | GEO

Dissection of anterior mesendoderm segregation at single cell level in zebrafish

Project description:Dissection of anterior mesendoderm segregation at single cell level in zebrafish

| PRJNA1048453 | ENA

Dissection of cMyc+ GC-B cells [WT]

Project description:Transcriptomic profiling of plasma cells derived from wild type mice

2021-01-12 | GSE147098 | GEO

Gene expression analysis of cMyc-deficient CD8 T cells retrovirally overexpressing cMyc or AP4

Project description:To determine functional overlap between cMyc and AP4 in CD8+ T cell priming, we retrovirally expressed cMyc or AP4 in cMyc-deficient CD8+ T cells and examined gene expression after activation.

2014-07-13 | GSE58079 | GEO

Single-molecule RNA-FISH analysis reveals stochasticity in reactivation of latent HIV-1 regulated by Nuclear Orphan Receptors NR4A and cMYC

Project description:HIV-1 eradication strategies require reactivation of all latent cells. Current methods lack effectiveness due to incomplete knowledge of proviral reactivation. We employed a single-molecule RNA-FISH (smRNA-FISH) analysis and found that proviral reactivation is highly variable, stochastic, and occurs in bursts and waves, with different kinetics in response to diverse LRAs. Approximately 1-5% of latent cells displayed stochastic reactivation even without stimulation. Through single-cell RNA-seq analysis, we identified NR4A3 and cMYC as extrinsic factors selectively associated with HIV-1 reactivation. Concomitant with HIV-1 reactivation cMYC was downregulated and NR4A3 was upregulated in both latent cell lines and primary CD4+ T-cells from aviremic patients. By inhibiting cMYC using SN-38, an active metabolite of irinotecan, we induced NR4A3 and HIV-1 expression. Our results suggest that inherent stochasticity in proviral reactivation contributes to cell-to-cell variability, which could potentially be modulated by drugs targeting cMYC and NR4A3.

2024-05-24 | GSE241207 | GEO

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (tubulointerstitium)

Project description:To identify genes with cell-lineage-specific expression not accessible by experimental micro-dissection, we developed a genome-scale iterative method, in-silico nano-dissection, which leverages high-throughput functional-genomics data from tissue homogenates using a machine-learning framework. This study applied nano-dissection to chronic kidney disease and identified transcripts specific to podocytes, key cells in the glomerular filter responsible for hereditary proteinuric syndromes and acquired CKD. In-silico prediction accuracy exceeded predictions derived from fluorescence-tagged-murine podocytes, identified genes recently implicated in hereditary glomerular disease and predicted genes significantly correlated with kidney function. The nano-dissection method is broadly applicable to define lineage specificity in many functional and disease contexts. We applied a machine-learning framework on high-throughput gene expression data from human kidney biopsy tissue homogenates and predict novel podocyte-specific genes. The prediction was validated by Human Protein Atlas at protein level. Prediction accuracy was compared with predictions derived from experimental approach using fluorescence-tagged-murine podocytes.

2013-08-06 | E-GEOD-47184 | biostudies-arrayexpress

In silico nano-dissection: defining cell type specificity at transcriptional level in human disease (glomeruli)

2013-08-06 | E-GEOD-47183 | biostudies-arrayexpress