Open AccessLINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis
Author(s) -
Lin Liu,
Yufeng Zhang,
Weizhou Qian,
Yao Liu,
Yingkun Zhang,
Feng Lin,
Cenxi Liu,
Guangxing Lu,
Di Sun,
Xiaoxu Guo,
Yanling Song,
Jia Song,
Chaoyong Yang,
Jin Li
Publication year - 2022
Publication title -
proceedings of the national academy of sciences of the united states of america
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.011
H-Index - 771
eISSN - 1091-6490
pISSN - 0027-8424
DOI - 10.1073/pnas.2119767119
Subject(s) - lineage (genetic) , computational biology , biology , lineage markers , genetics , chromatin , gene , phenotype
Significance Lineage analysis is an important assay for developmental biology, cancer biology, etc. Traditional tools in this field are time consuming, technically challenging, and in demand of preexisting knowledge. By integrating exogenous barcodes into cells, single-cell RNA-sequencing (scRNA-seq) can be used to conduct such tasks, but these assays required significant expertise in both wet- and dry-laboratory experiments. We developed a user-friendly algorithm to conduct cell-lineage inference solely based on endogenous markers of label-free scRNA-seq. This algorithm is able to identify lineage-informative mutations from a bunch of interfering mitochondrial RNA variants with high accuracy and efficiency. With this algorithm, we removed most of the technical hurdles of lineage analysis on scRNA-seq and will dramatically accelerate its application in biological research.