Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

ABSTRACT: Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

PROVIDER: PRJNA622559 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth

Project description:CD4+ T-cells represent a heterogeneous collection of specialised sub-types and are a key cell type in the pathogenesis of many diseases due to their role in the adaptive immune system. By investigating CD4+ T-cells at the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific cell states driving disease or treatment response. However, the impact of sequencing depth and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell population such as CD4+ T-cells. We therefore generated a high depth, high cell number dataset to determine the effect of reduced sequencing depth and cell number on the ability to accurately identify CD4+ T-cell subtypes. Furthermore, we investigated T-cell signatures under resting and stimulated conditions to assess cluster specific effects of stimulation. We found that firstly, cell number has a much more profound effect than sequencing depth on the ability to classify cells; secondly, this effect is greater when cells are unstimulated and finally, resting and stimulated samples can be combined to leverage additional power whilst still allowing differences between samples to be observed. These results will inform future scRNA-seq studies to ensure the most efficient experimental design.

2020-11-13 | GSE147928 | GEO

Characterisation of CD4+ T-cell subtypes using single cell RNA sequencing and the impact of cell number and sequencing depth.

Project description:CD4+ T-cells represent a heterogeneous collection of specialised sub-types and are a key cell type in the pathogenesis of many diseases due to their role in the adaptive immune system. By investigating CD4+ T-cells at the single cell level, using RNA sequencing (scRNA-seq), there is the potential to identify specific cell states driving disease or treatment response. However, the impact of sequencing depth and cell numbers, two important factors in scRNA-seq, has not been determined for a complex cell population such as CD4+ T-cells. We therefore generated a high depth, high cell number dataset to determine the effect of reduced sequencing depth and cell number on the ability to accurately identify CD4+ T-cell subtypes. Furthermore, we investigated T-cell signatures under resting and stimulated conditions to assess cluster specific effects of stimulation. We found that firstly, cell number has a much more profound effect than sequencing depth on the ability to classify cells; secondly, this effect is greater when cells are unstimulated and finally, resting and stimulated samples can be combined to leverage additional power whilst still allowing differences between samples to be observed. While based on one individual, these results could inform future scRNA-seq studies to ensure the most efficient experimental design.

| S-EPMC7666207 | biostudies-literature

Genomic copy number number characterisation of glioblastoma (GBM) cell lines.

Project description:Genomic copy number number characterisation of glioblastoma (GBM) cell lines.

| PRJNA489666 | ENA

Human T cell characterisation

Project description:Human T cell characterisation

| PRJNA910133 | ENA

Human T cell characterisation

Project description:Characterisation of human CD4+ cells to gain more insights into regulatory processes.

2023-03-30 | GSE220530 | GEO

Characterisation of pancreatic ductal adenocarcinoma subtypes by global phosphotyrosine profiling

Project description:Here we use high-resolution mass spectrometry (MS) to characterise the magnitude and complexity of global tyrosine phosphorylation PDAC. We profiled the endogenous tyrosine phosphorylation-based signaling in 36 cell lines from two cell line populations, quantifying around 2,000 tyrosine phosphorylation sites. This approach facilitated consistent segregation of PDAC cell lines into three subtypes with distinct signalling profiles, using both their global tyrosine phosphorylation patterns and a Random Forest derived 8 phosphorylation site signature. We extracted segregation-driving phosphosites, and subsequent pathway and network analysis to characterise the identified subtypes.

2016-06-09 | PXD003198 | Pride

Epigenetic subtypes of neuroblastoma [cell lines]

Project description:Epigenetic subtypes of neuroblastoma [cell lines]

| PRJNA527794 | ENA

Characterisation of the functional and transcriptomic effects of pro-inflammatory cytokines on human EndoC-βH5 beta cells

Project description:EndoC-βH5 is a newly established human beta-cell model which may be superior to previous models of native human beta cells. Exposure of beta cells to proinflammatory cytokines is a widely used in vitro model of immune-mediated beta-cell failure in type 1 diabetes and we therefore performed an in-depth characterisation of the effects of cytokines on EndoC-βH5 cells. Global gene expression was characterised by stranded RNA sequencing.

2023-04-01 | GSE218735 | GEO

mouse dendritic cell subtypes proteomics

Project description:dendritic cells = improtant APC -different subtypes characterized by surface markers by FACS -combination of FACS and LC-MS powerful tool, especially for low cell numbers -direct sorting of cells into SP3-compatible lysis buffer -new protocol without washing steps after sort and before protein digestion

2024-07-25 | PXD031960 | JPOST Repository

Molecular Subtypes in Head and Neck Cancer [copy number]

Project description:Head and neck squamous cell carcinoma (HNSCC) is a heterogeneous disease whose underlying etiology has not been explained by traditional prognostic factors such as tumor site, stage, or histology. Although previous studies have shown that molecular subtypes of HNSCC exist, the benefit of such a classification scheme has not been fully realized. We show that molecular subtypes of HNSCC exist; that these subtypes have distinct patterns of chromosomal gain and loss, some of which affect canonical oncogenes and tumor suppressors; and that the subtypes are biologically and clinically relevant. These subtypes provide new insight into HNSCC etiology, as well as a valuable method for classifying HNSCC tumors.

2013-03-01 | GSE39367 | 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