Recent advances in next-generation sequencing technologies have made it possible to investigate cellular systems at the level of individual cells. Single-cell RNA sequencing (scRNA-seq) is now widely employed to study cellular heterogeneity and dynamics. It has helped uncover rare cell populations and regulatory relationships between genes and track the trajectories of distinct cell lineages during development. scRNA-seq comes with its own advantages and disadvantages. The islands in the sc-RNA sequencing method are identified by visualizing overlapping transcription profiles of a few distinguishable marker genes which makes it possible to quantify the RNA in a large number of cells in a short time. The main disadvantage is that this technique does not provide any phenotypic information.
The story of an individual cell extends to much more than its RNA. Proteins form the actual workforce of the cell and are involved in a myriad of cellular processes. Messenger RNA (mRNA) transcripts are often considered mimics of proteins, though we know the relative abundance of proteins and mRNA is not one-to-one. Over the past decade, researchers have used flow cytometry to detect protein markers expressed on single cells and classify the cell islands based on the tagged protein expression patterns. The limitation of flow cytometry is that it uses fluorescently-labeled antibodies that can only bind to and assess the levels of cell surface proteins, thus not providing any information on intracellular proteins.
Principles of CITE-Seq
Cellular Indexing of Transcriptomes and Epitopes (CITE-seq)is a new technique devised by scientists at the NY Genome Centre and the Satija lab, that captures a snapshot of both the transcriptome and the cell surface proteins of single cells. It combines highly multiplexed antibody-based detection of protein markers with unbiased transcriptome profiling, for thousands of single cells in parallel.
CITE-seq utilizes antibodies conjugated to oligonucleotides with DNA barcodes. This:
The identity and abundance of proteins are reflected by the number of reads corresponding to each conjugated DNA barcode. Downstream bioinformatics analysis then provides multimodal information on the state of the cells. Some tools available for CITE-seq single-cell analysis are the CITE-seq Python package and Cellranger. Seurat has also made available a multimodal data analysis pipeline for this application.
Advantages of CITE-seq
CITE-seq, thus, adds a new dimension to scRNA-seq by combining transcriptomics with immunophenotyping.
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