Literature DB >> 33997529

Current progress and potential opportunities to infer single-cell developmental trajectory and cell fate.

Lingfei Wang1,2,3, Qian Zhang1,2,3, Qian Qin1,2,3, Nikolaos Trasanidis1,2,4, Michael Vinyard1,2,3,5, Huidong Chen1,2,3, Luca Pinello1,2,3.   

Abstract

Rapid technological advances in transcriptomics and lineage tracing technologies provide new opportunities to understand organismal development at the single-cell level. Building on these advances, various computational methods have been proposed to infer developmental trajectories and to predict cell fate. These methods have unveiled previously uncharacterized transitional cell types and differentiation processes. Importantly, the ability to recover cell states and trajectories has been evolving hand-in-hand with new technologies and diverse experimental designs; more recent methods can capture complex trajectory topologies and infer short- and long-term cell fate dynamics. Here, we summarize and categorize the most recent and popular computational approaches for trajectory inference based on the information they leverage and describe future challenges and opportunities for the development of new methods for reconstructing differentiation trajectories and inferring cell fates.

Entities:  

Year:  2021        PMID: 33997529      PMCID: PMC8117397          DOI: 10.1016/j.coisb.2021.03.006

Source DB:  PubMed          Journal:  Curr Opin Syst Biol        ISSN: 2452-3100


  62 in total

Review 1.  Noise in gene expression: origins, consequences, and control.

Authors:  Jonathan M Raser; Erin K O'Shea
Journal:  Science       Date:  2005-09-23       Impact factor: 47.728

2.  Noise in protein expression scales with natural protein abundance.

Authors:  Arren Bar-Even; Johan Paulsson; Narendra Maheshri; Miri Carmi; Erin O'Shea; Yitzhak Pilpel; Naama Barkai
Journal:  Nat Genet       Date:  2006-05-21       Impact factor: 38.330

3.  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

4.  Quantification of mRNA translation in live cells using single-molecule imaging.

Authors:  Deepak Khuperkar; Tim A Hoek; Stijn Sonneveld; Bram M P Verhagen; Sanne Boersma; Marvin E Tanenbaum
Journal:  Nat Protoc       Date:  2020-02-19       Impact factor: 13.491

5.  Joint profiling of chromatin accessibility and gene expression in thousands of single cells.

Authors:  Junyue Cao; Darren A Cusanovich; Vijay Ramani; Delasa Aghamirzaie; Hannah A Pliner; Andrew J Hill; Riza M Daza; Jose L McFaline-Figueroa; Jonathan S Packer; Lena Christiansen; Frank J Steemers; Andrew C Adey; Cole Trapnell; Jay Shendure
Journal:  Science       Date:  2018-08-30       Impact factor: 47.728

Review 6.  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

7.  An ultra high-throughput method for single-cell joint analysis of open chromatin and transcriptome.

Authors:  Chenxu Zhu; Miao Yu; Hui Huang; Ivan Juric; Armen Abnousi; Rong Hu; Jacinta Lucero; M Margarita Behrens; Ming Hu; Bing Ren
Journal:  Nat Struct Mol Biol       Date:  2019-11-06       Impact factor: 15.369

8.  Population snapshots predict early haematopoietic and erythroid hierarchies.

Authors:  Betsabeh Khoramian Tusi; Samuel L Wolock; Caleb Weinreb; Yung Hwang; Daniel Hidalgo; Rapolas Zilionis; Ari Waisman; Jun R Huh; Allon M Klein; Merav Socolovsky
Journal:  Nature       Date:  2018-02-21       Impact factor: 49.962

Review 9.  Recording development with single cell dynamic lineage tracing.

Authors:  Aaron McKenna; James A Gagnon
Journal:  Development       Date:  2019-06-27       Impact factor: 6.868

10.  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

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  1 in total

1.  TIPD: A Probability Distribution-Based Method for Trajectory Inference from Single-Cell RNA-Seq Data.

Authors:  Jiang Xie; Yiting Yin; Jiao Wang
Journal:  Interdiscip Sci       Date:  2021-06-09       Impact factor: 2.233

  1 in total

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