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Updated: Jan 12, 2026

Multiplexed Single Cell mRNA Sequencing Analysis of Mouse Embryonic Cells
Published on: January 7, 2020
A universal and cost-efficient sample labeling approach for multiplexed single-cell RNA-seq based on recombinant
Quanyong Zhang1, Maorong Li1, Luemou Shen1
1State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, Yunnan 650500, China; Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, Guangdong 511458, China.
Researchers developed HEATag, a cost-effective cell membrane labeling method for single-cell transcriptomics. This technique enhances library preparation efficiency and reduces batch effects in high-throughput studies.
Area of Science:
- Single-cell biology
- Molecular biology
- Genomics
Background:
- Single-cell transcriptomics offers deep insights into cellular diversity but is limited by high costs.
- Existing multiplexing strategies for single-cell studies are often complex or expensive.
Purpose of the Study:
- To develop a universal, cost-effective, and scalable cell membrane labeling method for single-cell transcriptomics.
- To address the limitations of current multiplexing techniques in single-cell research.
Main Methods:
- Developed HEATag (HUH-endonuclease-agglutinin tagging), combining Duck circovirus HUH endonuclease (DCV) and wheat germ agglutinin (WGA).
- Utilized DCV for sequence-specific ssDNA conjugation and WGA for robust cell labeling.
- Demonstrated compatibility with commercial and custom single-cell omics platforms.
Main Results:
- HEATag efficiently tags cell membranes with indexed single-stranded DNA (ssDNA).
- The method is robust for both fresh and fixed cells across species.
- Achieved enhanced library preparation efficiency and minimized batch effects.
Conclusions:
- HEATag offers a universal, cost-effective solution for high-throughput single-cell studies.
- This approach facilitates broader application of single-cell transcriptomics.
- Improves scalability and efficiency in single-cell omics workflows.
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