Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Label-free single-cell RNA Multiplexing leveraging genetic variability

ABSTRACT: Label-free single-cell RNA Multiplexing leveraging genetic variability

PROVIDER: PRJNA1040187 | ENA |

REPOSITORIES: ENA

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other
	SRR26826519_1.fastq.gz	Fastqsanger.gz
	SRR26826519_2.fastq.gz	Fastqsanger.gz
	SRR26823420_1.fastq.gz	Fastqsanger.gz
	SRR26823420_2.fastq.gz	Fastqsanger.gz

Items per page:

1 - 5 of 57

Similar Datasets

Label-free single-cell RNA Multiplexing leveraging genetic variability [scRNA-seq]

Project description:Label-free single-cell RNA Multiplexing leveraging genetic variability [scRNA-seq]

| PRJNA1040191 | ENA

Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

Project description:Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

| PRJNA1040192 | ENA

Label-free single-cell RNA Multiplexing leveraging genetic variability [scRNA-seq]

Project description:Single cell RNA sequencing has enabled unprecedented insights into the molecular cues and cellular heterogeneity underlying human disease. However, the high costs and complexity of single cell methods remain a major obstacle for generating large scale human cohorts. Here we compare current state-of-the-art single cell multiplexing technologies, and provide a new widely applicable demultiplexing method, SNP-Fishing, that enables simple, robust high-throughput multiplexing leveraging genetic variability of patients.

2024-09-06 | GSE247707 | GEO

Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-seq]

2024-09-06 | GSE247706 | GEO

Label-free single-cell RNA Multiplexing leveraging genetic variability

Project description:This SuperSeries is composed of the SubSeries listed below.

2024-09-06 | GSE247708 | GEO

null

Project description:Label-free single-cell RNA Multiplexing leveraging genetic variability

| PRJEB68214 | ENA

Label-free single-cell RNA multiplexing leveraging genetic variability.

Project description:Single cell RNA sequencing has provided unprecedented insights into the molecular cues and cellular heterogeneity underlying human disease. However, the high costs and complexity of single cell methods remain a major obstacle for generating large-scale human cohorts. Here, we compare current state-of-the-art single cell multiplexing technologies, and provide a widely applicable demultiplexing method, SoupLadle, that enables simple, yet robust high-throughput multiplexing leveraging genetic variability of patients.

| S-EPMC11621319 | biostudies-literature

Label-free melanoma phenotype classification using AI-based morphological profiling

Project description:Label-free melanoma phenotype classification using AI-based morphological profiling

| PRJNA1140860 | ENA

Multiplexing droplet-based single cell RNA-sequencing using genetic barcodes

Project description:Here, we introduce an in-silico algorithm demuxlet that harnesses naturally occurring genetic variation in a pool of cells from unrelated individuals to discover the sample identity of each cell and identify droplets containing cells from two different individuals (doublets). These two capabilities enable a simple multiplexing design that increases single cell library construction throughput by experimental design where cells from genetically diverse samples are multiplexed and captured at 2-10x over standard workflows. We further demonstrate the utility of sample multiplexing by characterizing the interindividual variability in cell type-specific responses of ~15k PBMCs to interferon-beta, a potent cytokine. Our computational tool enables sample multiplexing of droplet-based single cell RNA-seq for large-scale studies of population variation and could be extended to other single cell datasets that incorporate natural or synthetic DNA barcodes.

2017-06-13 | GSE96583 | GEO

Immune profiling and label-free detection of 2D MXenes at the single-cell and tissue level

Project description:There is a critical unmet need to image 2D materials within single cells and tissues. Here we propose a versatile label-free single-cell detection strategy based on mass cytometry. We use single-cell mass cytometry by time-of-flight (CyTOF), imaging mass cytometry, and multiplexed ion beam imaging by time-of-flight (MIBI-TOF) with three representative MXenes. Nb4C3, Mo2Ti2C3, and Ta4C3 were selected to ensure mass-detection within the range and to avoid overlap with currently available tags. We demonstrated their biocompatibility, detection, and quantification in 16 primary human immune cell subpopulations also showing the impact of MXene lateral size on the detection signal and cell binding. We report the biological and immune functional compatibility of MXenes. In vivo biodistribution experiments in mice using a mixture of MXenes revealed their presence in liver, blood, spleen, and lungs. Lastly, we applied MIBI-TOF to capture the presence of MXenes in different organs. The label-free detection of 2D materials by mass cytometry at the single cell level, on multiple subpopulations and in multiple organs in vivo opens exciting new opportunities in biomedicine.

2025-04-01 | GSE200036 | 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