Literature DB >> 35412632

TedSim: temporal dynamics simulation of single-cell RNA sequencing data and cell division history.

Xinhai Pan1, Hechen Li1, Xiuwei Zhang1.   

Abstract

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.
© The Author(s) 2022. Published by Oxford University Press on behalf of Nucleic Acids Research.

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Mesh:

Year:  2022        PMID: 35412632      PMCID: PMC9071466          DOI: 10.1093/nar/gkac235

Source DB:  PubMed          Journal:  Nucleic Acids Res        ISSN: 0305-1048            Impact factor:   16.971


  35 in total

1.  A comparison of single-cell trajectory inference methods.

Authors:  Wouter Saelens; Robrecht Cannoodt; Helena Todorov; Yvan Saeys
Journal:  Nat Biotechnol       Date:  2019-04-01       Impact factor: 54.908

Review 2.  Single-Cell Transcriptomics Meets Lineage Tracing.

Authors:  Lennart Kester; Alexander van Oudenaarden
Journal:  Cell Stem Cell       Date:  2018-05-10       Impact factor: 24.633

3.  SERGIO: A Single-Cell Expression Simulator Guided by Gene Regulatory Networks.

Authors:  Payam Dibaeinia; Saurabh Sinha
Journal:  Cell Syst       Date:  2020-08-31       Impact factor: 10.304

Review 4.  Lineage tracing meets single-cell omics: opportunities and challenges.

Authors:  Daniel E Wagner; Allon M Klein
Journal:  Nat Rev Genet       Date:  2020-03-31       Impact factor: 53.242

5.  ETE 3: Reconstruction, Analysis, and Visualization of Phylogenomic Data.

Authors:  Jaime Huerta-Cepas; François Serra; Peer Bork
Journal:  Mol Biol Evol       Date:  2016-02-26       Impact factor: 16.240

6.  Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics.

Authors:  Kelly Street; Davide Risso; Russell B Fletcher; Diya Das; John Ngai; Nir Yosef; Elizabeth Purdom; Sandrine Dudoit
Journal:  BMC Genomics       Date:  2018-06-19       Impact factor: 3.969

7.  Reversed graph embedding resolves complex single-cell trajectories.

Authors:  Xiaojie Qiu; Qi Mao; Ying Tang; Li Wang; Raghav Chawla; Hannah A Pliner; Cole Trapnell
Journal:  Nat Methods       Date:  2017-08-21       Impact factor: 47.990

8.  Splatter: simulation of single-cell RNA sequencing data.

Authors:  Luke Zappia; Belinda Phipson; Alicia Oshlack
Journal:  Genome Biol       Date:  2017-09-12       Impact factor: 13.583

9.  Simultaneous single-cell profiling of lineages and cell types in the vertebrate brain.

Authors:  Bushra Raj; Daniel E Wagner; Aaron McKenna; Shristi Pandey; Allon M Klein; Jay Shendure; James A Gagnon; Alexander F Schier
Journal:  Nat Biotechnol       Date:  2018-03-28       Impact factor: 54.908

10.  Is it possible to reconstruct an accurate cell lineage using CRISPR recorders?

Authors:  Irepan Salvador-Martínez; Marco Grillo; Michalis Averof; Maximilian J Telford
Journal:  Elife       Date:  2019-01-28       Impact factor: 8.140

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