Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data

Tianshi Lu, Seongoh Park, James Zhu, Yunguan Wang, Xiaowei Zhan, Xinlei Wang, Li Wang, Hao Zhu, Tao Wang

Research output: Contribution to journalArticlepeer-review

Abstract

Single-cell lineage tracing provides crucial insights into the fates of individual cells. Single-cell RNA sequencing (scRNA-seq) is commonly applied in modern biomedical research, but genetics-based lineage tracing for scRNA-seq data is still unexplored. Variant calling from scRNA-seq data uniquely suffers from “expressional drop-outs,” including low expression and allelic bias in gene expression, which presents significant obstacles for lineage reconstruction. We introduce SClineager, which infers accurate evolutionary lineages from scRNA-seq data by borrowing information from related cells to overcome expressional drop-outs. We systematically validate SClineager and show that genetics-based lineage tracing is applicable for single-cell-sequencing studies of both tumor and non-tumor tissues using SClineager. Overall, our work provides a powerful tool that can be applied to scRNA-seq data to decipher the lineage histories of cells and that could address a missing opportunity to reveal valuable information from the large amounts of existing scRNA-seq data.

Original languageEnglish (US)
Article number108589
JournalCell Reports
Volume34
Issue number1
DOIs
StatePublished - Jan 5 2021

Keywords

  • drop-out
  • genetics
  • lineage tracing
  • scRNA-seq

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)

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