Researchers at the New York Genome Center (NYGC) say they have developed a technique that greatly improves the ability to carry out single-cell RNA sequencing.
CITE-seq, or cellular indexing of transcriptomes and epitopes by sequencing, couples the measurement of surface protein markers on thousands of single cells with simultaneous sequencing of the messenger RNA (mRNA or transcriptomes) of those same single cells, according to the scientists..
The team’s methodology (“Simultaneous Epitope and Transcriptome Measurement in Single Cells”), published in Nature Methods, monitored 10 surface proteins, along with transcriptomes, of 8000 single cells.
“High-throughput single-cell RNA sequencing has transformed our understanding of complex cell populations, but it does not provide phenotypic information such as cell-surface protein levels,” write the investigators. “Here, we describe cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq), a method in which oligonucleotide-labeled antibodies are used to integrate cellular protein and transcriptome measurements into an efficient, single-cell readout. CITE-seq is compatible with existing single-cell sequencing approaches and scales readily with throughput increases.”
“No other method allows simultaneous measurements of transcriptomes and proteins on the same scale,” said Marlon Stoeckius, Ph.D., a research scientist in the NYGC's Technology Innovation Lab. “CITE-seq adds to already established methods for transcriptome analysis without any detrimental effects on the quality of the data generated.”
Dr. Stoeckius explained that previous approaches relied on capturing protein information of individual cells by cytometry before depositing these cells onto plates for single-cell RNA sequencing. The current methods suffer from a low throughput (the number of cells that can be analyzed) and are limited to a relatively small number of protein markers, added Dr. Stoeckius.
The protein detection component of CITE-seq is based on DNA-barcoded antibodies, which produce a sequencable readout that is captured along with the transcriptome of the cell. The integration of the protein and RNA data generated by CITE-seq required custom data analysis, which was developed in close collaboration with the lab of Rahul Satija, Ph.D., a core faculty member at the NYGC.
The team demonstrated the capabilities of CITE-seq by using the multimodal data to identify subclasses of natural killer (NK) cells that are difficult to distinguish based on transcriptomes alone.
The capacity of CITE-seq to more finely dissect cell populations has many potential applications in clinical research, continued Dr. Stoeckius.
“One possible future direction is to use CITE-seq on tumor samples to examine both individual tumor cells and the different pools of immune cells that infiltrate the tumor. This approach could be very useful in the deep characterization of tumor heterogeneity and in the development of new immunotherapeutic approaches.”