Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data.
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.
['Lu|Tianshi|T|', 'Park|Seongoh|S|', 'Zhu|James|J|', 'Wang|Yunguan|Y|', 'Zhan|Xiaowei|X|', 'Wang|Xinlei|X|', 'Wang|Li|L|', 'Zhu|Hao|H|', 'Wang|Tao|T|']
drop-out lineage tracing scRNA-seq genetics