Overcoming Expressional Drop-outs in Lineage Reconstruction from Single-Cell RNA-Sequencing Data
Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved..
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.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2021 |
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| Erschienen: |
2021 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:34 |
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| Enthalten in: |
Cell reports - 34(2021), 1 vom: 05. Jan., Seite 108589 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Lu, Tianshi [VerfasserIn] |
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| Links: |
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| Themen: |
Drop-out |
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| Anmerkungen: |
Date Completed 06.12.2021 Date Revised 15.03.2023 published: Print Citation Status MEDLINE |
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| doi: |
10.1016/j.celrep.2020.108589 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2020 The Author(s). Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a 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 | ||
| 650 | 4 | |a Journal Article | |
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| 650 | 4 | |a drop-out | |
| 650 | 4 | |a genetics | |
| 650 | 4 | |a lineage tracing | |
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| 700 | 1 | |a Park, Seongoh |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, James |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Yunguan |e verfasserin |4 aut | |
| 700 | 1 | |a Zhan, Xiaowei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Xinlei |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Li |e verfasserin |4 aut | |
| 700 | 1 | |a Zhu, Hao |e verfasserin |4 aut | |
| 700 | 1 | |a Wang, Tao |e verfasserin |4 aut | |
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