Literature DB >> 33690599

Transcriptional bursts explain autosomal random monoallelic expression and affect allelic imbalance.

Anton J M Larsson1, Christoph Ziegenhain1, Michael Hagemann-Jensen1, Björn Reinius2, Tina Jacob3, Tim Dalessandri1, Gert-Jan Hendriks1, Maria Kasper1, Rickard Sandberg1.   

Abstract

Transcriptional bursts render substantial biological noise in cellular transcriptomes. Here, we investigated the theoretical extent of allelic expression resulting from transcriptional bursting and how it compared to the amount biallelic, monoallelic and allele-biased expression observed in single-cell RNA-sequencing (scRNA-seq) data. We found that transcriptional bursting can explain the allelic expression patterns observed in single cells, including the frequent observations of autosomal monoallelic gene expression. Importantly, we identified that the burst frequency largely determined the fraction of cells with monoallelic expression, whereas the burst size had little effect on monoallelic observations. The high consistency between the bursting model predictions and scRNA-seq observations made it possible to assess the heterogeneity of a group of cells as their deviation in allelic observations from the expected. Finally, both burst frequency and size contributed to allelic imbalance observations and reinforced that studies of allelic imbalance can be confounded from the inherent noise in transcriptional bursting. Altogether, we demonstrate that allele-level transcriptional bursting renders widespread, although predictable, amounts of monoallelic and biallelic expression in single cells and cell populations.

Entities:  

Year:  2021        PMID: 33690599      PMCID: PMC7978379          DOI: 10.1371/journal.pcbi.1008772

Source DB:  PubMed          Journal:  PLoS Comput Biol        ISSN: 1553-734X            Impact factor:   4.475


  27 in total

Review 1.  Random monoallelic expression of autosomal genes: stochastic transcription and allele-level regulation.

Authors:  Björn Reinius; Rickard Sandberg
Journal:  Nat Rev Genet       Date:  2015-10-07       Impact factor: 53.242

2.  Smart-seq2 for sensitive full-length transcriptome profiling in single cells.

Authors:  Simone Picelli; Åsa K Björklund; Omid R Faridani; Sven Sagasser; Gösta Winberg; Rickard Sandberg
Journal:  Nat Methods       Date:  2013-09-22       Impact factor: 28.547

3.  Comparative Analysis of Single-Cell RNA Sequencing Methods.

Authors:  Christoph Ziegenhain; Beate Vieth; Swati Parekh; Björn Reinius; Amy Guillaumet-Adkins; Martha Smets; Heinrich Leonhardt; Holger Heyn; Ines Hellmann; Wolfgang Enard
Journal:  Mol Cell       Date:  2017-02-16       Impact factor: 17.970

Review 4.  What shapes eukaryotic transcriptional bursting?

Authors:  Damien Nicolas; Nick E Phillips; Felix Naef
Journal:  Mol Biosyst       Date:  2017-06-27

5.  Benchmarking single-cell RNA-sequencing protocols for cell atlas projects.

Authors:  Elisabetta Mereu; Atefeh Lafzi; Catia Moutinho; Christoph Ziegenhain; Davis J McCarthy; Adrián Álvarez-Varela; Eduard Batlle; Dominic Grün; Julia K Lau; Stéphane C Boutet; Chad Sanada; Aik Ooi; Robert C Jones; Kelly Kaihara; Chris Brampton; Yasha Talaga; Yohei Sasagawa; Kaori Tanaka; Tetsutaro Hayashi; Caroline Braeuning; Cornelius Fischer; Sascha Sauer; Timo Trefzer; Christian Conrad; Xian Adiconis; Lan T Nguyen; Aviv Regev; Joshua Z Levin; Swati Parekh; Aleksandar Janjic; Lucas E Wange; Johannes W Bagnoli; Wolfgang Enard; Marta Gut; Rickard Sandberg; Itoshi Nikaido; Ivo Gut; Oliver Stegle; Holger Heyn
Journal:  Nat Biotechnol       Date:  2020-04-06       Impact factor: 54.908

6.  A stochastic epigenetic switch controls the dynamics of T-cell lineage commitment.

Authors:  Kenneth Kh Ng; Mary A Yui; Arnav Mehta; Sharmayne Siu; Blythe Irwin; Shirley Pease; Satoshi Hirose; Michael B Elowitz; Ellen V Rothenberg; Hao Yuan Kueh
Journal:  Elife       Date:  2018-11-20       Impact factor: 8.140

7.  Transcriptional pulsing of a developmental gene.

Authors:  Jonathan R Chubb; Tatjana Trcek; Shailesh M Shenoy; Robert H Singer
Journal:  Curr Biol       Date:  2006-05-23       Impact factor: 10.834

8.  Single-Cell Transcriptomics Reveals that Differentiation and Spatial Signatures Shape Epidermal and Hair Follicle Heterogeneity.

Authors:  Simon Joost; Amit Zeisel; Tina Jacob; Xiaoyan Sun; Gioele La Manno; Peter Lönnerberg; Sten Linnarsson; Maria Kasper
Journal:  Cell Syst       Date:  2016-09-15       Impact factor: 10.304

9.  Stochastic models of gene transcription with upstream drives: exact solution and sample path characterization.

Authors:  Justine Dattani; Mauricio Barahona
Journal:  J R Soc Interface       Date:  2017-01       Impact factor: 4.118

10.  Genomic encoding of transcriptional burst kinetics.

Authors:  Michael Hagemann-Jensen; Leonard Hartmanis; Anton J M Larsson; Per Johnsson; Omid R Faridani; Björn Reinius; Åsa Segerstolpe; Chloe M Rivera; Bing Ren; Rickard Sandberg
Journal:  Nature       Date:  2019-01-02       Impact factor: 49.962

View more
  3 in total

1.  Haplotype-aware analysis of somatic copy number variations from single-cell transcriptomes.

Authors:  Teng Gao; Ruslan Soldatov; Hirak Sarkar; Adam Kurkiewicz; Evan Biederstedt; Po-Ru Loh; Peter V Kharchenko
Journal:  Nat Biotechnol       Date:  2022-09-26       Impact factor: 68.164

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

3.  Transcriptional kinetics and molecular functions of long noncoding RNAs.

Authors:  Per Johnsson; Christoph Ziegenhain; Leonard Hartmanis; Gert-Jan Hendriks; Michael Hagemann-Jensen; Björn Reinius; Rickard Sandberg
Journal:  Nat Genet       Date:  2022-03-03       Impact factor: 41.307

  3 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.