LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.
Li Lin, Yufeng Zhang, Weizhou Qian, Yao Liu, Yingkun Zhang, Fanghe Lin, Cenxi Liu, Guangxing Lu, Di Sun, Xiaoxu Guo, YanLing Song, Jia Song, Chaoyong Yang, Jin Li
Author Information
Li Lin: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China. ORCID
Yufeng Zhang: State Key Laboratory of Genetic Engineering and School of Life Sciences, Fudan University, Shanghai 200433, China.
Weizhou Qian: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China. ORCID
Yao Liu: Department of Endocrinology and Metabolism, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai 200072, China.
Yingkun Zhang: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China. ORCID
Fanghe Lin: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China.
Cenxi Liu: State Key Laboratory of Genetic Engineering and School of Life Sciences, Fudan University, Shanghai 200433, China.
Guangxing Lu: State Key Laboratory of Genetic Engineering and School of Life Sciences, Fudan University, Shanghai 200433, China.
Di Sun: Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China.
Xiaoxu Guo: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China.
YanLing Song: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China. ORCID
Jia Song: Institute of Molecular Medicine, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai 200127, China; li_jin_lifescience@fudan.edu.cn songjiajia2010@shsmu.edu.cn cyyang@xmu.edu.cn.
Chaoyong Yang: State Key Laboratory for Physical Chemistry of Solid Surfaces, Key Laboratory for Chemical Biology of Fujian Province, Key Laboratory of Analytical Chemistry, and Department of Chemical Biology, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, People's Republic of China; li_jin_lifescience@fudan.edu.cn songjiajia2010@shsmu.edu.cn cyyang@xmu.edu.cn. ORCID
Jin Li: State Key Laboratory of Genetic Engineering and School of Life Sciences, Fudan University, Shanghai 200433, China; li_jin_lifescience@fudan.edu.cn songjiajia2010@shsmu.edu.cn cyyang@xmu.edu.cn. ORCID
Single-cell RNA-sequencing (scRNA-seq) has become a powerful tool for biomedical research by providing a variety of valuable information with the advancement of computational tools. Lineage analysis based on scRNA-seq provides key insights into the fate of individual cells in various systems. However, such analysis is limited by several technical challenges. On top of the considerable computational expertise and resources, these analyses also require specific types of matching data such as exogenous barcode information or bulk assay for transposase-accessible chromatin with high throughput sequencing (ATAC-seq) data. To overcome these technical challenges, we developed a user-friendly computational algorithm called "LINEAGE" (label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis). Aiming to screen out endogenous markers of lineage located on mitochondrial reads from label-free scRNA-seq data to conduct lineage inference, LINEAGE integrates a marker selection strategy by feature subspace separation and de novo "low cross-entropy subspaces" identification. In this process, the mutation type and subspace-subspace "cross-entropy" of features were both taken into consideration. LINEAGE outperformed three other methods, which were designed for similar tasks as testified with two standard datasets in terms of biological accuracy and computational efficiency. Applied on a label-free scRNA-seq dataset of BRAF-mutated cancer cells, LINEAGE also revealed genes that contribute to BRAF inhibitor resistance. LINEAGE removes most of the technical hurdles of lineage analysis, which will remarkably accelerate the discovery of the important genes or cell-lineage clusters from scRNA-seq data.
