Research Article

Combinatorial labeling of single cells for gene expression cytometry

See allHide authors and affiliations

Science  06 Feb 2015:
Vol. 347, Issue 6222, 1258367
DOI: 10.1126/science.1258367

You are currently viewing the abstract.

View Full Text

Log in to view the full text

Log in through your institution

Log in through your institution

Single-cell expression analysis on a large scale

To understand why cells differ from each other, we need to understand which genes are transcribed at a single-cell level. Several methods measure messenger RNA (mRNA) expression in single cells, but most are limited to relatively low numbers of cells or genes. Fan et al. labeled each mRNA molecule in a cell with both a cellular barcode and a molecular barcode. Further analysis did not then require single-cell technologies. Instead, the labeled mRNA from all cells was pooled, amplified, and sequenced, and the gene expression profile of individual cells was reconstructed based on the barcodes. The technique successfully revealed heterogeneity across several thousand blood cells.

Science, this issue 10.1126/science.1258367

Structured Abstract


The measurement of specific proteins and transcripts in individual cells is critical for understanding the role of cellular diversity in development, health, and disease. Flow cytometry has become a standard technology for high-throughput detection of protein markers on single cells and has been widely adopted in basic research and clinical diagnostics. In contrast, nucleic acid measurements such as mRNA expression are typically conducted on bulk samples, obscuring the contributions from individual cells. Ideally, in order to characterize the complexity of cellular systems, it is desirable to have an affordable approach to examine the expression of a large number of genes across many thousands of cells.


Here, we have developed a scalable approach that enables routine, digital gene expression profiling of thousands of single cells across an arbitrary number of genes, without using robotics or automation. The approach, termed “CytoSeq,#x201D; employs a recursive Poisson strategy. First, single cells are randomly deposited into an array of picoliter wells. A combinatorial library of beads bearing cell- and transcript-barcoding capture probes is then added so that each cell is partitioned alongside a bead. The bead library has a diversity of ~106 so that each cell is paired with a unique cell barcode, whereas the transcript barcode diversity is ~105 so that each mRNA molecule within a cell becomes specifically labeled. After cell lysis, mRNAs hybridize to beads, which are pooled for reverse transcription, amplification, and sequencing. Because cDNAs from all polyadenylated transcripts of each cell are covalently archived on the bead surface, any selection of genes can be analyzed. The digital gene expression profile for each cell is reconstructed when barcoded transcripts are assigned to the cell of origin and counted.


We applied CytoSeq to characterize complex heterogeneous samples in the human hematopoietic system by examining thousands of cells per experiment. In addition to surface proteins that are traditional cell type markers, we examined genes coding for cytokines, transcription factors, and intracellular proteins of various cellular functions that may not be readily accessible by flow cytometry. We demonstrated the ability to identify major subsets within human peripheral blood mononuclear cells (PBMCs). We compared cellular heterogeneity in resting CD3+ T cells versus those stimulated with antibodies to CD3 and CD28, as well as resting CD8+ T cells versus those stimulated with CMV peptides, and identified the rare cells that were specific to the antigen. Highlighting the specificity of large-scale single-cell analysis compared with bulk sample measurements, we found that the up-regulation of a number of genes in the stimulated samples originated from only a few cells (<0.1% of the population).


The routine availability of gene expression cytometry will help transform our understanding of cellular diversity in complex biological systems and drive novel research and clinical applications. The massively parallel single-cell barcoding strategy described here may be applied to assay other biological molecules, including other RNAs, genomic DNA, and the genome and the transcriptome together.

Gene expression cytometry (CytoSeq).

Massively parallel, stochastic barcoding of RNA content from single cells in a microwell bead array enables digital gene expression profiling of thousands of single cells simultaneously. Shown here is the principal component analysis for human PBMCs. Each point represents a single cell. Cells with correlated expression profiles are coded with similar colors.


We present a technically simple approach for gene expression cytometry combining next-generation sequencing with stochastic barcoding of single cells. A combinatorial library of beads bearing cell- and molecular-barcoding capture probes is used to uniquely label transcripts and reconstruct the digital gene expression profile of thousands of individual cells in a single experiment without the need for robotics or automation. We applied the technology to dissect the human hematopoietic system and to characterize heterogeneous response to in vitro stimulation. High sensitivity is demonstrated by detection of low-abundance transcripts and rare cells. Under current implementation, the technique can analyze a few thousand cells simultaneously and can readily scale to 10,000s or 100,000s of cells.

View Full Text