Single-cell sequencing of full-length transcripts and T-cell receptors with automated high-throughput Smart-seq3
We developed an automated high-throughput Smart-seq3 (HT Smart-seq3) workflow that integrates best practices and an optimized protocol to enhance efficiency, scalability, and method reproducibility. This workflow consistently produces high-quality data with high cell capture efficiency and gene detection sensitivity. In a rigorous comparison with the 10X platform using human primary CD4 + T-cells, HT Smart-seq3 demonstrated higher cell capture efficiency, greater gene detection sensitivity, and lower dropout rates. Additionally, when sufficiently scaled, HT Smart-seq3 achieved a comparable resolution of cellular heterogeneity to 10X. Notably, through T-cell receptor (TCR) reconstruction, HT Smart-seq3 identified a greater number of productive alpha and beta chain pairs without the need for additional primer design to amplify full-length V(D)J segments, enabling more comprehensive TCR profiling across a broader range of species. Taken together, HT Smart-seq3 overcomes key technical challenges, offering distinct advantages that position it as a promising solution for the characterization of single-cell transcriptomes and immune repertoires, particularly well-suited for low-input, low-RNA content samples.
Authors
- Subhra Chaudhuri
- Nam Bui (ORCID: https://orcid.org/0000-0001-9219-8461)
- Randy Yen
- Terence C. Wong
- Christine Wan (ORCID: https://orcid.org/0000-0001-9499-5579)
- Szu-Wen Liu
- Venina Marcela Dominical
- Silpa Suthram
- Hubert Stöppler
- Tangsheng Yi (ORCID: https://orcid.org/0000-0002-2959-4979)
- Huang Huang (ORCID: https://orcid.org/0000-0001-8202-0061)
- Li Li (ORCID: https://orcid.org/0000-0002-7720-6731)
- Ruidong Li (ORCID: https://orcid.org/0000-0002-4822-2898)
- Lili Yue (ORCID: https://orcid.org/0000-0002-6911-0449)
- Kai-Hui Sun
- Hsiu-Chun Chuang
- Olivia Ngo
Institutions
- Gilead Sciences (United States) (US)
Publication Details
- Journal
- BMC Genomics
- Published
- 2024-11-21
- DOI
- https://doi.org/10.1186/s12864-024-11036-0
- Citations
- 7
- Primary Topic
- Single-cell and spatial transcriptomics
- Type
- article
- Field-Weighted Citation Impact
- 1.64
Funders
- Gilead Sciences