A novel tool developed by scientists at the New York Genome Center (NYGC) represents an important step forward for single-cell RNA sequencing- an advancing field of genomics that provides detailed insights into individual cells. The sequencing of single cells also holds the key to be able to distinguish between different cell types and study disease mechanisms at the level of individual cells.
The study appearing in Nature Methods demonstrates how, the technique, 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
“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.”
Approaches previously employed capturing protein information of individual cells by cytometry before depositing these cells onto plates for single-cell RNA sequencing and the ones used in the present suffer from a low throughput and are limited to a relatively small number of protein markers.
The protein detection component of CITE-seq is based on DNA-barcoded antibodies, which produce a readout that can be easily sequenced which is further captured along with the transcriptome of the cell. The integration of the protein and RNA data generated by CITE-seq in the last step requires custom data analysis.
And as an example of the power of CITE-seq, the investigators used 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.
Going forward, the team plans to continue to use the method to better profile single cells, primarily from blood. “There are some cell types that are easily distinguishable by protein markers but not so easy to distinguish using transcriptomics,” he said. “But, with multi-modal data, we can look at both.”
