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scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory Mapping

By Zihan Zheng, Xiangyu Tang, Xin Qiu, Hao Xu, Haiyang Wu, Haili Yu, Xingzhao Wen, Zhou Peng, Fa Xu, Yiwen Zhou, Qingshan Ni, Jianzhi Zhou, Liyun Zou, Gang Chen, Ying Wan

Posted 17 Apr 2020
bioRxiv DOI: 10.1101/2020.04.15.042408

The advent of single-cell RNA sequencing has provided illuminating information on complex systems. However, large numbers of genes tend to be scarcely detected in common scRNAseq approaches due to technical dropout. Although bioinformatics approaches have been developed to approximate true expression profiles, assess the dropout events on single-cell transcriptomes is still consequently challenging. In this report, we present a new plate-based method for scRNAseq that relies on Tn5 transposase to tagment cDNA following second strand synthesis. By utilizing pre-amplification tagmentation step, scSTATseq libraries are insulated against technical dropout, allowing for detailed analysis of gene-gene co-expression relationships and mapping of pathway trajectories. The entire scSTATseq library construction workflow can be completed in 7 hours, and recover transcriptome information on up to 8,000 protein-coding genes. Investigation of osteoclast differentiation using this workflow allowed us to identify novel markers of interest such as Rab15. Overall, scSTATseq is an efficient and economical method for scRNAseq that compares favorably with existing workflows. ### Competing Interest Statement The authors have declared no competing interest.

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