Javascript must be enabled to continue!
CITE-seq Protocols v1
View through CrossRef
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 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.
Title: CITE-seq Protocols v1
Description:
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 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
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...
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 ...
Optimizing IETF multimedia signaling protocols and architectures in 3GPP networks : an evolutionary approach
Optimizing IETF multimedia signaling protocols and architectures in 3GPP networks : an evolutionary approach
Signaling in Next Generation IP-based networks heavily relies in the family of multimedia signaling protocols defined by IETF. Two of these signaling protocols are RTSP and SIP, wh...
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...
CITE-seq and Cell Hashing v1
CITE-seq and Cell Hashing v1
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 singl...
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...
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...

