Multi-platform evaluation and optimization of single-cell RNA isoform sequencing

Webber, J. T.; Bartlett, D. A.; Al-Eryani, G.; Khorgade, A.; Shin, A.; Georgescu, C.; Yu, H.; Haas, B. J.; Popic, V.; Al'Khafaji, A. M.

doi:10.1101/2025.02.22.639662

bioRxiv · 10.1101/2025.02.22.639662

Multi-platform evaluation and optimization of single-cell RNA isoform sequencing

Webber, J. T.·Bartlett, D. A.·Al-Eryani, G.·Khorgade, A.·Shin, A.·Georgescu, C.·Yu, H.·Haas, B. J.·Popic, V.·Al'Khafaji, A. M.

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Abstract

Long-read transcriptomics enables isoform identification and quantification at single-cell resolution, but analysis is complicated by artifacts introduced during library preparation. We characterize the distinct artifact profiles of three popular single-cell platforms and demonstrate their impact on isoform identification. We introduce a new method for the identification of a wide range of artifacts in cDNA libraries and novel biochemical and bioinformatic strategies to significantly reduce their impact on downstream applications. Finally, we provide a comprehensive framework for RNA isoform sequencing analysis and interpretation as the field continues to develop.

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Webber, J. T., Bartlett, D. A., Al-Eryani, G., Khorgade, A., Shin, A., Georgescu, C., Yu, H., Haas, B. J., Popic, V., Al'Khafaji, A. M.. 2025-02-27. Multi-platform evaluation and optimization of single-cell RNA isoform sequencing. https://doi.org/10.1101/2025.02.22.639662

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