Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Department of Statistics, Sungshin Women's University, Seoul 02844, Republic of Korea.
Cell Rep. 2021 Jan 5;34(1):108589. doi: 10.1016/j.celrep.2020.108589.
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.
单细胞谱系追踪为了解单个细胞的命运提供了关键的见解。单细胞 RNA 测序 (scRNA-seq) 在现代生物医学研究中得到了广泛应用，但基于遗传学的 scRNA-seq 数据的谱系追踪仍未得到探索。从 scRNA-seq 数据中进行变体调用独特地受到“表达性缺失”的影响，包括基因表达中的低表达和等位基因偏倚，这给谱系重建带来了重大障碍。我们引入了 SClineager，它通过从相关细胞中借用信息来克服表达性缺失，从而从 scRNA-seq 数据中推断出准确的进化谱系。我们系统地验证了 SClineager，并表明基于遗传学的谱系追踪可用于使用 SClineager 对肿瘤和非肿瘤组织的单细胞测序研究。总的来说，我们的工作提供了一种强大的工具，可以应用于 scRNA-seq 数据，以破译细胞的谱系历史，并解决从大量现有 scRNA-seq 数据中揭示有价值信息的缺失机会。
