Search engine for discovering works of Art, research articles, and books related to Art and Culture
ShareThis
Javascript must be enabled to continue!

CITE-seq and Cell Hashing v1

View through CrossRef
This protocol is for performing CITE-seq and Cell Hashing in parallel. CITE-seq: Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a multimodal single cell phenotyping method developed in theTechnology Innovation labat the New York Genome Center in collaboration with the Satija lab. CITE-seq uses DNA-barcoded antibodies to convert detection of proteins into a quantitative, sequenceable readout. Antibody-bound oligos act as synthetic transcripts that are captured during most large-scale oligodT-based scRNA-seq library preparation protocols (e.g. 10x Genomics, Drop-seq,ddSeq). This allows for immunophenotyping of cells with a potentially limitless number of markers and unbiased transcriptome analysis using existing single-cell sequencing approaches. Cell Hashing: Sample multiplexing and super-loading onsingle cellRNA-sequencing platforms. Cell Hashinguses a series of oligo-tagged antibodies against ubiquitously expressed surface proteins with different barcodes to uniquely label cells from distinct samples, which can be subsequently pooled in onescRNA-seq run. By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples.
Springer Science and Business Media LLC
Title: CITE-seq and Cell Hashing v1
Description:
This protocol is for performing CITE-seq and Cell Hashing in parallel.
CITE-seq: Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) is a multimodal single cell phenotyping method developed in theTechnology Innovation labat the New York Genome Center in collaboration with the Satija lab.
CITE-seq uses DNA-barcoded antibodies to convert detection of proteins into a quantitative, sequenceable readout.
Antibody-bound oligos act as synthetic transcripts that are captured during most large-scale oligodT-based scRNA-seq library preparation protocols (e.
g.
10x Genomics, Drop-seq,ddSeq).
This allows for immunophenotyping of cells with a potentially limitless number of markers and unbiased transcriptome analysis using existing single-cell sequencing approaches.
Cell Hashing: Sample multiplexing and super-loading onsingle cellRNA-sequencing platforms.
Cell Hashinguses a series of oligo-tagged antibodies against ubiquitously expressed surface proteins with different barcodes to uniquely label cells from distinct samples, which can be subsequently pooled in onescRNA-seq run.
By sequencing these tags alongside the cellular transcriptome, we can assign each cell to its sample of origin, and robustly identify doublets originating from multiple samples.

Related Results

MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing v1
MARS-seq2.0: an experimental and analytical pipeline for indexed sorting combined with single-cell RNA sequencing v1
Human tissues comprise trillions of cells that populate a complex space of molecular phenotypes and functions and that vary in abundance by 4–9 orders of magnitude. Relying solely ...
Artificial-Cell-Type Aware Cell Type Classification in CITE-seq
Artificial-Cell-Type Aware Cell Type Classification in CITE-seq
AbstractCellular Indexing of Transcriptomes and Epitopes by sequencing (CITE-seq), couples the measurement of surface marker proteins with simultaneous sequencing of mRNA at single...
CITE-seq Protocols v1
CITE-seq Protocols v1
This collection contains our main protocols for performing CITE-seq and Cell Hashing, specifically on Drop-seq or10x Genomics single cell 3′ v2 chemistry. CITE-seq: Cellular In...
Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data
Benchmarking Algorithms for Gene Set Scoring of Single-cell ATAC-seq Data
AbstractGene set scoring (GSS) has been routinely conducted for gene expression analysis of bulk or single-cell RNA-seq data, which helps to decipher single-cell heterogeneity and ...
Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational Autoencoder
Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational Autoencoder
AbstractSingle cell RNA sequencing (scRNA-seq) is a powerful approach which generates genome-wide gene expression profiles at single cell resolution. Among its many applications, i...
Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells
Global Prediction of Chromatin Accessibility Using RNA-seq from Small Number of Cells
ABSTRACT Conventional high-throughput technologies for mapping regulatory element activities such as ChIP-seq, DNase-seq and FAIRE-seq cannot analyze samples with s...
MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data
MuSiC2: cell type deconvolution for multi-condition bulk RNA-seq data
ABSTRACTCell type composition of intact bulk tissues can vary across samples. Deciphering cell type composition and its changes during disease progression is an important step towa...
Abstract P1-05-23: Utilities and challenges of RNA-Seq based expression and variant calling in a clinical setting
Abstract P1-05-23: Utilities and challenges of RNA-Seq based expression and variant calling in a clinical setting
Abstract Introduction Variant calling based on DNA samples has been the gold standard of clinical testing since the advent of Sanger sequencing. The u...

Back to Top