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Updated: Jan 7, 2026

An Ultrahigh-throughput Microfluidic Platform for Single-cell Genome Sequencing
Published on: May 23, 2018
A protocol for high-quality single-cell RNA sequencing with cell surface protein quantification
Sichong Han1,2, Siqi Liu1,2, Changya Chen1,2
1State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin 300020, China.
Cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) offers a standardized method for single-cell RNA sequencing and protein analysis. This approach enhances cell type classification and integrates multimodal data for biological research.
Area of Science:
- Single-cell multi-omics analysis
- Molecular biology techniques
- Biotechnology applications
Background:
- Single-cell analysis reveals cellular heterogeneity.
- Simultaneous transcriptomic and proteomic data provide deeper biological insights.
- Existing methods may lack comprehensive cell surface protein information.
Purpose of the Study:
- To present a standardized protocol for CITE-seq.
- To enable high-quality single-cell RNA sequencing coupled with cell surface protein quantification.
- To facilitate integration of multimodal single-cell data.
Main Methods:
- Detailed protocol for sample preparation and antibody-oligo conjugation.
- Gel bead-in-emulsion (GEM) generation and cDNA amplification.
- Library construction for high-throughput sequencing and protein detection.
Main Results:
- Demonstrated compatibility with existing scRNA-seq workflows.
- Achieved cost-efficient, high-throughput protein detection.
- Enhanced resolution in cell type classification and identification of rare cell subsets.
Conclusions:
- The standardized CITE-seq protocol ensures reproducibility and robust data quality.
- This workflow advances insights in immunology, oncology, and developmental biology.
- The protocol is flexible, scalable, and applicable across diverse research settings.
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