Literature DB >> 34146472

Benchmarked approaches for reconstruction of in vitro cell lineages and in silico models of C. elegans and M. musculus developmental trees.

Wuming Gong1, Alejandro A Granados2, Jingyuan Hu3, Matthew G Jones4, Ofir Raz5, Irepan Salvador-Martínez6, Hanrui Zhang7, Ke-Huan K Chow2, Il-Youp Kwak8, Renata Retkute9, Alidivinas Prusokas10, Augustinas Prusokas11, Alex Khodaverdian12, Richard Zhang12, Suhas Rao12, Robert Wang12, Phil Rennert13, Vangala G Saipradeep14, Naveen Sivadasan14, Aditya Rao14, Thomas Joseph14, Rajgopal Srinivasan14, Jiajie Peng15, Lu Han15, Xuequn Shang15, Daniel J Garry1, Thomas Yu16, Verena Chung16, Michael Mason16, Zhandong Liu3, Yuanfang Guan7, Nir Yosef12, Jay Shendure17, Maximilian J Telford6, Ehud Shapiro5, Michael B Elowitz2, Pablo Meyer18.   

Abstract

The recent advent of CRISPR and other molecular tools enabled the reconstruction of cell lineages based on induced DNA mutations and promises to solve the ones of more complex organisms. To date, no lineage reconstruction algorithms have been rigorously examined for their performance and robustness across dataset types and number of cells. To benchmark such methods, we decided to organize a DREAM challenge using in vitro experimental intMEMOIR recordings and in silico data for a C. elegans lineage tree of about 1,000 cells and a Mus musculus tree of 10,000 cells. Some of the 22 approaches submitted had excellent performance, but structural features of the trees prevented optimal reconstructions. Using smaller sub-trees as training sets proved to be a good approach for tuning algorithms to reconstruct larger trees. The simulation and reconstruction methods here generated delineate a potential way forward for solving larger cell lineage trees such as in mouse.
Copyright © 2021 The Authors. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  C. elegans; CRISPR; M. musculus; benchmarking; cell lineage tracing; crowdsourcing; intmemoir; lineage reconstruction; machine learning; simulation

Mesh:

Year:  2021        PMID: 34146472     DOI: 10.1016/j.cels.2021.05.008

Source DB:  PubMed          Journal:  Cell Syst        ISSN: 2405-4712            Impact factor:   10.304


  5 in total

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

Authors:  Xinhai Pan; Hechen Li; Xiuwei Zhang
Journal:  Nucleic Acids Res       Date:  2022-05-06       Impact factor: 16.971

2.  Simultaneous brain cell type and lineage determined by scRNA-seq reveals stereotyped cortical development.

Authors:  Donovan J Anderson; Florian M Pauler; Aaron McKenna; Jay Shendure; Simon Hippenmeyer; Marshall S Horwitz
Journal:  Cell Syst       Date:  2022-04-21       Impact factor: 11.091

Review 3.  Connecting past and present: single-cell lineage tracing.

Authors:  Cheng Chen; Yuanxin Liao; Guangdun Peng
Journal:  Protein Cell       Date:  2022-04-19       Impact factor: 15.328

4.  LINEAGE: Label-free identification of endogenous informative single-cell mitochondrial RNA mutation for lineage analysis.

Authors:  Li Lin; Yufeng Zhang; Weizhou Qian; Yao Liu; Yingkun Zhang; Fanghe Lin; Cenxi Liu; Guangxing Lu; Di Sun; Xiaoxu Guo; YanLing Song; Jia Song; Chaoyong Yang; Jin Li
Journal:  Proc Natl Acad Sci U S A       Date:  2022-02-01       Impact factor: 12.779

5.  Single cell lineage reconstruction using distance-based algorithms and the R package, DCLEAR.

Authors:  Wuming Gong; Hyunwoo J Kim; Daniel J Garry; Il-Youp Kwak
Journal:  BMC Bioinformatics       Date:  2022-03-24       Impact factor: 3.169

  5 in total

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