Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution

ABSTRACT: Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution

PROVIDER: PRJNA986887 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
	SRR25010328_1.fastq.gz	Fastqsanger.gz
	SRR25010328_2.fastq.gz	Fastqsanger.gz
	SRR25010329_1.fastq.gz	Fastqsanger.gz
	SRR25010329_2.fastq.gz	Fastqsanger.gz
	SRR25010330_1.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 18

Similar Datasets

Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution

Project description:Mitochondrion (MT) as the energy-producing organelle participates in most metabolic activities of mammalian cells. Unidirectional mitochondrial transfer from T cells to cancer cells was recently observed “metabolically empowering” cancer cells while “depleting immune cells”, providing new insights into Tumor-T cell interaction and immune evasion. Here, we leveraged the increasingly adopted single-cell RNA-seq technology and introduced MERCI, a statistical deconvolution method for tracing and quantifying the process of mitochondrial trafficking between cancer and T cells. MERCI was benchmarked using data generated from a coculture system of MT-labeled cancer and T cells to accurately predict the recipient cells and their mitochondrial compositions. We independently demonstrated the cellular MT transfer signals and validated the MERCI performance by additionally generating a gold-standard mtscATAC-seq dataset. Application of MERCI to single cell human cancer samples identified a novel MT transfer phenotype and its signature genes involved in cytoskeleton remodeling, energy production and TNFα signaling pathways. Application of MERCI to ovarian cancer spatial transcriptomes revealed the prevalence of MT transfer in the tumor tissue of high T cell infiltration, which is elevated in the endothelium-rich regions. Finally, MT transfer is associated with high cell cycle activity and poor clinical outcome in our pan-cancer analysis through MERCI. In summary, MERCI enabled systematic investigation of a novel aspect of Tumor-T cell interaction and revealed MT-transfer related genes and pathways that may lead to new therapeutic opportunities.

2024-06-21 | GSE235675 | GEO

Systematic investigation of mitochondrial transfer between cancer cells and T cells at single-cell resolution.

Project description:Mitochondria (MT) participate in most metabolic activities of mammalian cells. A near-unidirectional mitochondrial transfer from T cells to cancer cells was recently observed to "metabolically empower" cancer cells while "depleting immune cells," providing new insights into tumor-T cell interaction and immune evasion. Here, we leverage single-cell RNA-seq technology and introduce MERCI, a statistical deconvolution method for tracing and quantifying mitochondrial trafficking between cancer and T cells. Through rigorous benchmarking and validation, MERCI accurately predicts the recipient cells and their relative mitochondrial compositions. Application of MERCI to human cancer samples identifies a reproducible MT transfer phenotype, with its signature genes involved in cytoskeleton remodeling, energy production, and TNF-α signaling pathways. Moreover, MT transfer is associated with increased cell cycle activity and poor clinical outcome across different cancer types. In summary, MERCI enables systematic investigation of an understudied aspect of tumor-T cell interactions that may lead to the development of therapeutic opportunities.

| S-EPMC10568073 | biostudies-literature

Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

Project description:Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants

| PRJNA868734 | ENA

A chemoenzymatic method for systematic investigation of the dynamics for proteins with N-terminal glycine

Project description:A systematic investigation for the dynamics of proteins with N-terminal glycine using MS-based proteomics

2023-02-07 | MSV000091216 | MassIVE

Systematic inference of super-resolution cell spatial profiles from histology images

Project description:Systematic inference of super-resolution cell spatial profiles from histology images

| PRJNA1215397 | ENA

Investigation of Thymic TCRd+ cells

Project description:Investigation of Thymic TCRd+ cells

| PRJNA914667 | ENA

Systematic investigation into the dynamic alterations in the proteome and phosphoproteome of mycobacteria following overexpression of PknG

Project description:Systematic investigation into the dynamic alterations in the proteome and phosphoproteome of mycobacteria following overexpression of PknG

2025-05-13 | PXD063872 |

Systematic investigation of cytokine signaling activity at the tissue and single-cell levels

Project description:Systematic investigation of cytokine signaling activity at the tissue and single-cell levels

| PRJNA731117 | ENA

Systematic identification of silencers in human cells

Project description:Systematic identification of silencers in human cells

| PRJNA427579 | ENA

High-throughput single-base resolution mapping of 2’-O-Methylated residues

Project description:Functional characterization of the transcriptome requires tools for the systematic investigation of RNA post-transcriptional modifications. 2’-O-Methylation of the ribose moiety is one of the most abundant post-transcriptional modifications of the RNA. We describe here a high-throughput method that enables fast and accurate mapping at single-base resolution, and quantitation, of 2’-OMe modified residues. Our approach expands the actual repertoire of methods for transcriptome-wide mapping of RNA post-transcriptional modifications.

2016-09-13 | GSE73065 | 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