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TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history
Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071466/ https://www.ncbi.nlm.nih.gov/pubmed/35412632 http://dx.doi.org/10.1093/nar/gkac235 |
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author | Pan, Xinhai Li, Hechen Zhang, Xiuwei |
author_facet | Pan, Xinhai Li, Hechen Zhang, Xiuwei |
author_sort | Pan, Xinhai |
collection | PubMed |
description | Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed. |
format | Online Article Text |
id | pubmed-9071466 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-90714662022-05-06 TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history Pan, Xinhai Li, Hechen Zhang, Xiuwei Nucleic Acids Res Computational Biology Recently, lineage tracing technology using CRISPR/Cas9 genome editing has enabled simultaneous readouts of gene expressions and lineage barcodes, which allows for the reconstruction of the cell division tree and makes it possible to reconstruct ancestral cell types and trace the origin of each cell type. Meanwhile, trajectory inference methods are widely used to infer cell trajectories and pseudotime in a dynamic process using gene expression data of present-day cells. Here, we present TedSim (single-cell temporal dynamics simulator), which simulates the cell division events from the root cell to present-day cells, simultaneously generating two data modalities for each single cell: the lineage barcode and gene expression data. TedSim is a framework that connects the two problems: lineage tracing and trajectory inference. Using TedSim, we conducted analysis to show that (i) TedSim generates realistic gene expression and barcode data, as well as realistic relationships between these two data modalities; (ii) trajectory inference methods can recover the underlying cell state transition mechanism with balanced cell type compositions; and (iii) integrating gene expression and barcode data can provide more insights into the temporal dynamics in cell differentiation compared to using only one type of data, but better integration methods need to be developed. Oxford University Press 2022-04-12 /pmc/articles/PMC9071466/ /pubmed/35412632 http://dx.doi.org/10.1093/nar/gkac235 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com |
spellingShingle | Computational Biology Pan, Xinhai Li, Hechen Zhang, Xiuwei TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title | TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title_full | TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title_fullStr | TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title_full_unstemmed | TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title_short | TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history |
title_sort | tedsim: temporal dynamics simulation of single-cell rna sequencing data and cell division history |
topic | Computational Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9071466/ https://www.ncbi.nlm.nih.gov/pubmed/35412632 http://dx.doi.org/10.1093/nar/gkac235 |
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