Simultaneous epitope and transcriptome measurement in single cells
Open accessPublished version of a preprint
Abstract
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.
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Upregulation of CEBPB and CEBPD suggested a differentiation state into the granulocytic lineage.
“Upregulation of CEBPB and CEBPD ( Fig. 1e left, Supplementary Fig. 2d, Supplementary Table 3 ), implicated in emergency granulopoiesis 30 and granulopoiesis under cellular stress 31 respectively, suggested a differentiation state into the granulocytic lineage.”
From Type 1 interferon perturbates clonal competition by reshaping human blood development (2026) · cites this paper as [30]
The passages on immune-cell characterization discuss CITE-seq markers and NK-cell subsets, not CEBPB or CEBPD. Searches for those genes, emergency granulopoiesis, and granulocytic-lineage differentiation found no passage in our copy addressing the claim. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThe capture sequences do not distinguish guides and could allow simultaneous capture of other features, including antibodies and oligo-tagged markers.
“However, these capture sequences are not guide-specific, and thus, in principle, will enable multiplexed capture of additional features, such as antibodies22,23 and other oligo-tagged markers24.”
From Combinatorial single-cell CRISPR screens by direct guide RNA capture and targeted sequencing (2020) · cites this paper as [22]
Protein detection is more sensitive than mRNA detection.
“Many cells have undetectable levels of CD46 mRNA even in the absence of targeting guides, likely reflecting the high drop-out rates of scRNA-seq, and the increased sensitivity that comes with protein detection.”
From Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells (2019) · cites this paper as [8]
ECCITE-seq may detect expression phenotypes more sensitively than scRNA-seq alone.
“The low dropout of protein detection7,8 suggests that ECCITE-seq could be more sensitive in detecting expression phenotypes than scRNA-seq alone.”
From Multiplexed detection of proteins, transcriptomes, clonotypes and CRISPR perturbations in single cells (2019) · cites this paper as [8]
Using a unique reverse transcription handle and Illumina PCR priming sequence in the modified crRNA allows perturbations to be detected alongside scRNA-seq and additional modalities such as CITE-seq.
“In addition, using a unique set of reverse transcription handle and Illumina PCR priming sequence in our modified crRNA (Extended Data Fig. 2) ensures that these perturbations can be detected not only alongside scRNA-seq, but also when profiling additional molecular modalities (for example, CITE-seq12 for simultaneous transcriptome and surface protein profiling).”
From Efficient combinatorial targeting of RNA transcripts in single cells with Cas13 RNA Perturb-seq (2023) · cites this paper as [12]
Milo may also be useful for single-cell assays beyond scRNA-seq, including multi-omic assays.
“Milo may also be applicable to single-cell assays other than scRNA-seq, including multi-omic assays [35–39].”
From Differential abundance testing on single-cell data using k-nearest neighbor graphs (2022) · cites this paper as [35]
Although CyTOF can measure fewer genes than scRNA-seq, expanding the phenotyping space with oligo-labeled antibodies to detect Pro-Codes and other proteins and deconvoluting with single-cell sequencing should be feasible.
“Though the number of genes that can be analyzed by CyTOF is fewer than scRNA-seq, it should be feasible to expand the phenotyping space using oligo-labeled antibodies to detect the Pro-Codes and other proteins, and to deconvolute with single cell sequencing, as has been described (Peterson et al., 2017; Stoeckius et al., 2017).”
From Protein Barcodes Enable High-Dimensional Single-Cell CRISPR Screens (2018) · cites this paper as Stoeckius et al., 2017
CITE-seq combines highly multiplexed protein-marker detection with unbiased transcriptome profiling for thousands of single cells.
“This method combines highly multiplexed protein-marker detection with unbiased transcriptome profiling for thousands of single cells68.”
From The technological landscape and applications of single-cell multi-omics (2023) · cites this paper as [68]
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References 39 · Cited by 3,098
Research works printed but not linked: 1
- [28]van der Maaten L Accelerating t-SNE using tree-based algorithms Journal of Machine Learning Research 2014
- Authors
- Stoeckius, M., Hafemeister, C., Stephenson, W., Houck-Loomis, B., Chattopadhyay, P. K., Swerdlow, H., Satija, R., & Smibert, P.
- Affiliations
- New York Genome Center (US)
- New York University (US)
- Corresponding author
- Stoeckius, Marlon
- ORCID
- Venue
- Nature Methods
- Published
- 2017-07-31
- Volume
- 14
- Issue
- 9
- Pages
- 865-868
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC5669064
- OpenAlex
- W2739492614
- Cited by
- 3,098
- References
- 39
- Citations by year
- 2026: 376 · 2025: 386 · 2024: 433 · 2023: 448 · 2022: 449 · 2021: 428 · 2020: 297 · 2019: 179 · 2018: 80 · 2017: 20
- FWCI
- 64.24
- Type
- journal article
- Language
- en
- Open access
- green
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Nature Methods (publishedVersion)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- New York Genome Center
- Vaccine Research Center
- Deutsche Forschungsgemeinschaft
- National Institutes of Health (1dp2hg009623-01)
- National Human Genome Research Institute (DP2-HG-009623, UM1HG008901, R21HG009748)
- National Cancer Institute (P30CA016087)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Keywords
- CITE-seq, single-cell RNA sequencing, single-cell transcriptomics, cell phenotyping
- MeSH terms
- Epitopes, Sequence Analysis, RNA, Epitope Mapping, Gene Expression Profiling, Tissue Array Analysis, High-Throughput Nucleotide Sequencing, Transcriptome
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