Browse
Submit Data
Databases
API
Help

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Progressively overloaded single-cell RNA-seq with cell hashing

ABSTRACT: Barcode-based multiplexing methods can be used to increase throughput and reduce batch effects in large single-cell genomics studies. To evaluate methods for demultiplexing barcode-multiplexed data, we generated a dataset by labeling samples separately with barcode-tagged antibodies, mixing those samples, and progressively overloading a droplet-based scRNA-seq system.

ORGANISM(S): Homo sapiens

PROVIDER: GSE181862 | GEO | 2021/12/01

REPOSITORIES: GEO

ACCESS DATA

Json Xml

Dataset's files

Source:

			Action	DRS
		Other

Items per page:

1 - 1 of 1

Similar Datasets

Comparison of different hashing methods on human PBMCs and primary mice tissues

Project description:the aim 1 was to evaluate hashing (sample multiplexing) accuracy of TotalSeq-B antibodies and CellPlex (a multiplexing solution from 10x Genomics) on 2 pools of PBMCs from 3 different donors. Thus the genetic difference between donors was used as a ground truth for sample demultiplexing based on antibody or lipid hashing hashing. the aim 2 was to evaluate TotalSeq antibody and lipid hashing on different mice primary tissues using different hashing protocols.

2022-02-12 | E-MTAB-11401 | biostudies-arrayexpress

Progressively overloaded single-cell RNA-seq with cell hashing

Project description:Progressively overloaded single-cell RNA-seq with cell hashing

| PRJNA753770 | ENA

High throughput error correction using dual nucleotide dimer blocks allows direct single-cell nanopore transcriptome sequencing

Project description:Droplet-based single-cell sequencing techniques have provided unprecedented insight into cellular heterogeneities within tissues. However, these approaches only allow for the measurement of the distal parts of a transcript following short-read sequencing. Therefore, splicing and sequence diversity information is lost for the majority of the transcript. The application of long-read Nanopore sequencing to droplet-based methods is challenging because of the low base-calling accuracy currently associated with Nanopore sequencing. Although several approaches that use additional short-read sequencing to error-correct the barcode and UMI sequences have been developed, these techniques are limited by the requirement to sequence a library using both short- and long-read sequencing. Here we introduce a novel approach termed single-cell Barcode UMI Correction sequencing (scBUC-seq) to efficiently error-correct barcode and UMI oligonucleotide sequences synthesized by using blocks of dimeric nucleotides. The method can be applied to correct both short-read and long-read sequencing, thereby allowing users to recover more reads per cell that permits direct single-cell Nanopore sequencing for the first time. We illustrate our method by using species-mixing experiments to evaluate barcode assignment accuracy and multiple myeloma cell lines to evaluate differential isoform usage and Ewing’s sarcoma cells to demonstrate Ig fusion transcript analysis.

2021-01-31 | GSE162053 | GEO

Label-free single-cell RNA Multiplexing leveraging genetic variability [bulk RNA-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 | GSE247706 | GEO

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

2024-09-06 | GSE247707 | GEO

Cell hashing enable sample multiplexing, multiplet identification and super-loading on droplet-based single cell RNA-sequencing platforms

Project description:We reasoned that by using a distinct set of oligo-tagged antibodies against ubiquitously expressed proteins, we could uniquely label multiple populations of cells, multiplex them together, and use the barcoded antibody signal as a fingerprint. We refer to this approach as cellular "hashing", as our set of oligos defines a "look up table" to assign each multiplexed cell to its original sample. We demonstrate application of the technique to combine eight samples and run them simultaneously in a single droplet based scRNA-seq run. We show that cell hashtags allow sample multiplexing, confident multiplet identification and super-loading in the context of a commonly used droplet-based scRNA-seq method to drive down the per-cell cost of large-scale scRNA-seq experiments

2018-01-02 | GSE108313 | GEO

Single Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting (SNACS): A tool for demultiplexing single-cell DNA sequencing data

Project description:Motivation Recently, single-cell DNA sequencing (scDNA-seq) and multi-modal profiling with the addition of cell-surface antibodies (scDAb-seq) have provided key insights into cancer heterogeneity. Scaling these technologies across large patient cohorts, however, is frequently cost and time prohibitive. Multiplexing, in which cells from unique patients and pooled into a single experiment, offers a possible solution. While multiplexing methods are available for scRNAseq, accurate demultiplexing in scDNAseq remains an unmet need. Results Here, we introduce SNACS: Single-Nucleotide Polymorphism (SNP) and Antibody-based Cell Sorting. SNACS relies on a combination of patient-level cell-surface identifiers and natural variation in genetic polymorphisms to demultiplex scDNAseq data. We demonstrated the performance of SNACS on a dataset consisting of multi-sample experiments from patients with leukemia where we knew truth from single-sample experiments from the same patients. Relative to demultiplexing methods derived from the scRNAseq literature, SNACS offered superior accuracy. Availability Implementation SNACS is available at https://github.com/olshena/SNACS.

2024-02-09 | GSE255224 | GEO

Extracellular matrix remodelling is associated with muscle force increase in overloaded mouse plantaris muscle.

Project description:Aims: Transforming growth factor-β (TGF-β) signalling is thought to contribute to the remodelling of extracellular matrix (ECM) of skeletal muscle and to functional decline in patients with muscular dystrophies. We wanted to determine the role of TGF-β-induced ECM remodelling in dystrophic muscle. Methods: We experimentally induced the pathological hallmarks of severe muscular dystrophy by mechanically overloading the plantaris muscle in mice. Furthermore, we determined the role of TGF-β signalling on dystrophic tissue modulation and on muscle function by (i) overloading myostatin knockout (Mstn-/- ) mice and (ii) by additional pharmacological TGF-β inhibition via halofuginone. Results: Transcriptome analysis of overloaded muscles revealed upregulation predominantly of genes associated with ECM, inflammation and metalloproteinase activity. Histology revealed in wild-type mice signs of severe muscular dystrophy including myofibres with large variation in size and internalized myonuclei, as well as increased ECM deposition. At the same time, muscle weight had increased by 208% and muscle force by 234%. Myostatin deficiency blunted the effect of overload on muscle mass (59% increase) and force (76% increase), while having no effect on ECM deposition. Concomitant treatment with halofuginone blunted overload-induced muscle hypertrophy and muscle force increase, while reducing ECM deposition and increasing myofibre size. Conclusions: ECM remodelling is associated with an increase in muscle mass and force in overload-modelled dystrophic muscle. Lack of myostatin is not advantageous and inhibition of ECM deposition by halofuginone is disadvantageous for muscle plasticity in response to stimuli that induce dystrophic muscle.

2020-08-01 | GSE127255 | GEO

Hashing (sample multiplexing) of MCF7, PC3, DU145 and MDA-MB-231 cells and nuclei using different hashing methods

Project description:Hashing (sample multiplexing) of MCF7, PC3, DU145 and MDA-MB-231 cells and nuclei using different hashing methods

| PRJEB42512 | ENA

Probability of stealth multiplets in sample-multiplexing for droplet-based single-cell analysis

Project description:These datasets are test datasets of sample-multiplexed scRNA-seq, consisting of cDNA (transcriptome) and sample barcode read files: Three-sample multiplexing experiment (JS009) is a MULTI-seq dataset containing mesenchyme embryonic hind limb bud cells, embryonic stem (ES) cells, and NIH3T3 cells. Each cell sample was labelled with a distinct MULTI-seq barcode. The barcode sequences were, CATAGAGC, TCCTCGAA, and GTGTACCT for the limb bud mesenchyme cells, the ES cells, and the NIH3T3 cells, respectively. Two-sample multiplexing experiment (JS010) is a CellPlex detaset, containing ES cells and NIH3T3 cells. Each cell sample was labelled with the 3'CellPlex Kit (10X Genomics). NIH3T3 cells and ES cells were labelled with CMO301 and CMO302, respectively.

2024-01-09 | E-MTAB-13181 | biostudies-arrayexpress

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