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Lu T, Park S, Zhu J, et al. Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell Rep. 2021;34(1):108589doi: 10.1016/j.celrep.2020.108589.
Lu, T., Park, S., Zhu, J., Wang, Y., Zhan, X., Wang, X., Wang, L., Zhu, H., & Wang, T. (2021). Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell reports, 34(1), 108589. https://doi.org/10.1016/j.celrep.2020.108589
Lu, Tianshi, et al. "Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data." Cell reports vol. 34,1 (2021): 108589. doi: https://doi.org/10.1016/j.celrep.2020.108589
Lu T, Park S, Zhu J, Wang Y, Zhan X, Wang X, Wang L, Zhu H, Wang T. Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data. Cell Rep. 2021 Jan 05;34(1):108589. doi: 10.1016/j.celrep.2020.108589. PMID: 33406427.
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Cell Rep. 2021 Jan 05;34(1):108589. doi: 10.1016/j.celrep.2020.108589.

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

Cell reports

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

Affiliations

  1. Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  2. 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.
  3. Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  4. Department of Statistical Science, Southern Methodist University, Dallas, TX 75275, USA.
  5. Department of Mathematics and Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76019, USA.
  6. Children's Research Institute, Department of Pediatrics and Department of Internal Medicine, Center for Regenerative Science and Medicine, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA.
  7. Quantitative Biomedical Research Center, Department of Population and Data Sciences, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA; Center for the Genetics of Host Defense, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA. Electronic address: [email protected].

PMID: 33406427 DOI: 10.1016/j.celrep.2020.108589

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.

Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved.

Keywords: drop-out; genetics; lineage tracing; scRNA-seq

Conflict of interest statement

Declaration of Interests The authors declare no competing interests.

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