Large-scale simultaneous measurement of epitopes and transcriptomes in single cells

By Marlon Stoeckius, Christoph Hafemeister, William Stephenson, B. Houck-Loomis, Pratip K. Chattopadhyay, H. Swerdlow, Rahul Satija, Peter Smibert. 2017

2,234 citations recorded by the source corpus.

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. 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.

Subfields: Immunology, Molecular Biology

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Citations in the corpus

Automated optimized parameters for T-distributed stochastic neighbor embedding improve visualization and analysis of large datasets (2019, methodology): “DNA-barcoded antibodies have been recently utilized to allow simultaneous protein-epitope and transcriptome measurements in single cells 34 thus expanding the repertoire of traditional cytometry methods that could employ t-SNE as a staple method of data visualization and presentation.”
Machine Learning for Integrating Data in Biology and Medicine: Principles, Practice, and Opportunities (2018, methodology): “For example, studies [178, 179] adopt the method that first clusters cells based on each omic and then perform extensive comparisons between clusters using statistical associ-”
Integrated analysis of multimodal single-cell data (2020, positive): “We first introduce and demonstrate our solution on our previously generated CITE-seq dataset of 8,617 cord blood mononuclear cells, with a panel of 10 immunophenotypic markers (Stoeckius et al., 2017).”
Comparison and evaluation of statistical error models for scRNA-seq (2021, neutral): “, CITE-seq [56]), chromatin accessibility profiles (i.”
Orchestrating single-cell analysis with Bioconductor (2019, background): “In addition, recent advances in technology and protocols allow the simultaneous measurement of genetic, epigenetic, and transcriptomic information from the same cells [46–52].”
Best practices for single-cell analysis across modalities (2023, background): “The integration of data in such set-ups is known as ‘mosaic integration’, for which tools recently started to emerge (see the figure, part c ).”
Comprehensive integration of single-cell data (2018, positive): “Cells were resuspended in CITE-seq [Stoeckius et al., 2017] staining buffer (2%BSA/0.01%Tween in PBS) and incubated with FcX blocking reagent for 10 min (BioLegend, cat #: 422302) to block nonspeciﬁc antibody binding.”
Integrative single-cell analysis (2019, methodology): “Nature reviews | Genetics R e v i e w s barcodes, rather than fluorescent moieties, to label antibodies 20,21 .”