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Toti-N-Seq breakthrough enables universal and scalable single-cell profiling

Your Health 247 by Your Health 247
July 22, 2025
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Toti-N-Seq breakthrough enables universal and scalable single-cell profiling
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Background

The rapid advancement of single-cell and single-nucleus RNA sequencing (sc/snRNA-seq) has opened unprecedented windows into cellular diversity, yet existing methods for multiplexing samples struggle with scalability and accuracy. Traditional techniques relying on antibodies or lipid-based barcodes often fail to uniformly label cells across different types or species, particularly in complex clinical samples. These limitations—cell-type bias, cross-contamination risks, and loss of rare cell populations—hinder large-scale studies and clinical translation. To overcome these challenges, a team led by Professor Yiwei Li at Huazhong University of Science and Technology (HUST) has pioneered Toti-N-Seq, a groundbreaking technology that harnesses the universal presence of N-glycans on cell and nuclear surfaces. Published as a cover story in Research (2025, DOI: 10.34133/research.0678), this innovation redefines how researchers approach high-throughput cellular profiling.

Research progress

At the heart of Toti-N-Seq lies an engineered protein, Stv-Fg, derived from modifying the natural glycan-binding protein Fbs1. This fusion protein binds non-selectively to all N-glycan types, enabling universal tagging of cells and nuclei. By attaching DNA barcodes to Stv-Fg, the team achieved precise sample multiplexing without cell-type or species restrictions. Experimental validations underscored its robustness: flow cytometry revealed labeling efficiencies as low as 37.5 pM for cell membranes and 75.0 pM for nuclei, with cross-contamination below 2% even after prolonged sample mixing.

In practical applications, Toti-N-Seq demonstrated exceptional accuracy. When applied to single-nucleus sequencing, it achieved an overall classification accuracy (OCA) of 0.987, outperforming conventional antibody- or lipid-based methods. Notably, the technology preserved rare cell populations, such as the 0.5% plasmacytoid dendritic cells (pDCs) in human peripheral blood samples, while reducing doublet rates to 0.04% for single cells and 0.02% for nuclei. These capabilities were further validated in 12-plex experiments, where sample ratio deviations remained under 4%, proving its reliability for large-scale studies.

Future prospects

Looking ahead, the Toti-N-Seq platform is set to transform both basic and applied research. The team plans to expand its multiplexing capacity to 24-plex or higher, facilitating ambitious projects like cross-organ cell atlases and high-throughput drug screening. Integration with epigenetic and proteomic tools will enable multi-dimensional single-cell analyses, shedding light on complex regulatory networks.

Clinically, Toti-N-Seq’s ability to retain rare cell subsets positions it as a powerful tool for dissecting tumor microenvironments and predicting immunotherapy responses. Upcoming multi-center studies will explore its diagnostic potential in cancer patient cohorts. Beyond academia, the technology’s compatibility with platforms like MobiNova microfluidics promises to streamline industrial workflows, accelerating drug development and toxicity testing through standardized, reproducible protocols.

Conclusion

Toti-N-Seq represents a leap forward in single-cell genomics, addressing long-standing bottlenecks in multiplexing accuracy and scalability. By leveraging the ubiquity of N-glycans, Professor Li’s team has created a versatile tool that bridges species and cell types while preserving biological nuance. As the technology moves toward clinical and industrial adoption, it holds the potential to democratize high-resolution cellular profiling, empowering discoveries from developmental biology to personalized medicine.

Source:

Journal reference:

Li, Y., et al. (2025). Toti-N-glycan recognition enables universal multiplexed single nucleus RNA sequencing. Research. doi.org/10.34133/research.0678.



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Tags: BreakthroughenablesprofilingscalableSinglecellTotiNSequniversal
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