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High-Throughput Single-Cell Labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics

ABSTRACT: The importance of single-cell level data is increasingly appreciated, and significant advances in this direction have been made in recent years. Common to these technologies is the need to physically segregate individual cells into containers, such as wells or chambers of a micro-fluidics chip. High-throughput Single-Cell Labeling (Hi-SCL) in drops is a novel method that uses drop-based libraries of oligonucleotide barcodes to index individual cells in a population. The use of drops as containers, and a microfluidics platform to manipulate them en-masse, yields a highly scalable methodological framework. Once tagged, labeled molecules from different cells may be mixed without losing the cell-of-origin information. Here we demonstrate an application of the method for generating RNA-sequencing data for multiple individual cells within a population. Barcoded oligonucleotides are used to prime cDNA synthesis within drops. Barcoded cDNAs are then combined and subjected to second generation sequencing. The data are deconvoluted based on the barcodes, yielding single-cell mRNA expression data. In a proof-of-concept set of experiments we show that this method yields data comparable to other existing methods, but with unique potential for assaying very large numbers of cells.

ORGANISM(S): Mus musculus

PROVIDER: GSE62050 | GEO | 2015/06/05

SECONDARY ACCESSION(S): PRJNA263039

REPOSITORIES: GEO

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High-Throughput Single-Cell Labeling (Hi-SCL) for RNA-Seq using drop-based microfluidics

Project description:The importance of single-cell level data is increasingly appreciated, and significant advances in this direction have been made in recent years. Common to these technologies is the need to physically segregate individual cells into containers, such as wells or chambers of a micro-fluidics chip. High-throughput Single-Cell Labeling (Hi-SCL) in drops is a novel method that uses drop-based libraries of oligonucleotide barcodes to index individual cells in a population. The use of drops as containers, and a microfluidics platform to manipulate them en-masse, yields a highly scalable methodological framework. Once tagged, labeled molecules from different cells may be mixed without losing the cell-of-origin information. Here we demonstrate an application of the method for generating RNA-sequencing data for multiple individual cells within a population. Barcoded oligonucleotides are used to prime cDNA synthesis within drops. Barcoded cDNAs are then combined and subjected to second generation sequencing. The data are deconvoluted based on the barcodes, yielding single-cell mRNA expression data. In a proof-of-concept set of experiments we show that this method yields data comparable to other existing methods, but with unique potential for assaying very large numbers of cells. In this experiment we mixed 2 cell types (mES mEF) and then using single cell novel approach we could be able to find each cell (using its barcode) and assign it to mES of mEF and to produce mES and mEF aggregate bam files (converted to bed for GEO submission). 1152_RNA_RTprimers_Barcodes.txt: A list of all 1152 barcodes sequenced for Read2 fastq files.

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