Literature DB >> 32939729

Methodologies for Following EMT In Vivo at Single Cell Resolution.

Abdull J Massri1, Geoffrey R Schiebinger2, Alejandro Berrio1, Lingyu Wang1, Gregory A Wray1, David R McClay3.   

Abstract

An epithelial-mesenchymal transition (EMT) occurs in almost every metazoan embryo at the time mesoderm begins to differentiate. Several embryos have a long record as models for studying an EMT given that a known population of cells enters the EMT at a known time thereby enabling a detailed study of the process. Often, however, it is difficult to learn the molecular details of these model EMT systems because the transitioning cells are a minority of the population of cells in the embryo and in most cases there is an inability to isolate that population. Here we provide a method that enables an examination of genes expressed before, during, and after the EMT with a focus on just the cells that undergo the transition. Single cell RNA-seq (scRNA-seq) has advanced as a technology making it feasible to study the trajectory of gene expression specifically in the cells of interest, in vivo, and without the background noise of other cell populations. The sea urchin skeletogenic cells constitute only 5% of the total number of cells in the embryo yet with scRNA-seq it is possible to study the genes expressed by these cells without background noise. This approach, though not perfect, adds a new tool for uncovering the mechanism of EMT in this cell type.

Entities:  

Keywords:  Epithelial-mesenchymal transition; Sea urchin; Single cell RNA-sequencing; Tissue morphogenesis

Mesh:

Year:  2021        PMID: 32939729      PMCID: PMC7949293          DOI: 10.1007/978-1-0716-0779-4_23

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  48 in total

1.  Full-length RNA-seq from single cells using Smart-seq2.

Authors:  Simone Picelli; Omid R Faridani; Asa K Björklund; Gösta Winberg; Sven Sagasser; Rickard Sandberg
Journal:  Nat Protoc       Date:  2014-01-02       Impact factor: 13.491

2.  Accounting for technical noise in single-cell RNA-seq experiments.

Authors:  Philip Brennecke; Simon Anders; Jong Kyoung Kim; Aleksandra A Kołodziejczyk; Xiuwei Zhang; Valentina Proserpio; Bianka Baying; Vladimir Benes; Sarah A Teichmann; John C Marioni; Marcus G Heisler
Journal:  Nat Methods       Date:  2013-09-22       Impact factor: 28.547

3.  Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.

Authors:  Allon M Klein; Linas Mazutis; Ilke Akartuna; Naren Tallapragada; Adrian Veres; Victor Li; Leonid Peshkin; David A Weitz; Marc W Kirschner
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

4.  Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets.

Authors:  Evan Z Macosko; Anindita Basu; Rahul Satija; James Nemesh; Karthik Shekhar; Melissa Goldman; Itay Tirosh; Allison R Bialas; Nolan Kamitaki; Emily M Martersteck; John J Trombetta; David A Weitz; Joshua R Sanes; Alex K Shalek; Aviv Regev; Steven A McCarroll
Journal:  Cell       Date:  2015-05-21       Impact factor: 41.582

5.  Slide-seq: A scalable technology for measuring genome-wide expression at high spatial resolution.

Authors:  Samuel G Rodriques; Robert R Stickels; Aleksandrina Goeva; Carly A Martin; Evan Murray; Charles R Vanderburg; Joshua Welch; Linlin M Chen; Fei Chen; Evan Z Macosko
Journal:  Science       Date:  2019-03-28       Impact factor: 47.728

6.  Optimal-Transport Analysis of Single-Cell Gene Expression Identifies Developmental Trajectories in Reprogramming.

Authors:  Geoffrey Schiebinger; Jian Shu; Marcin Tabaka; Brian Cleary; Vidya Subramanian; Aryeh Solomon; Joshua Gould; Siyan Liu; Stacie Lin; Peter Berube; Lia Lee; Jenny Chen; Justin Brumbaugh; Philippe Rigollet; Konrad Hochedlinger; Rudolf Jaenisch; Aviv Regev; Eric S Lander
Journal:  Cell       Date:  2019-01-31       Impact factor: 41.582

7.  Sub-circuits of a gene regulatory network control a developmental epithelial-mesenchymal transition.

Authors:  Lindsay R Saunders; David R McClay
Journal:  Development       Date:  2014-03-05       Impact factor: 6.868

8.  Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics.

Authors:  Mireya Plass; Jordi Solana; F Alexander Wolf; Salah Ayoub; Aristotelis Misios; Petar Glažar; Benedikt Obermayer; Fabian J Theis; Christine Kocks; Nikolaus Rajewsky
Journal:  Science       Date:  2018-04-19       Impact factor: 47.728

9.  A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.

Authors:  Aaron T L Lun; Davis J McCarthy; John C Marioni
Journal:  F1000Res       Date:  2016-08-31

Review 10.  Using single-cell genomics to understand developmental processes and cell fate decisions.

Authors:  Jonathan A Griffiths; Antonio Scialdone; John C Marioni
Journal:  Mol Syst Biol       Date:  2018-04-16       Impact factor: 11.429

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  3 in total

1.  Developmental single-cell transcriptomics in the Lytechinus variegatus sea urchin embryo.

Authors:  Abdull J Massri; Laura Greenstreet; Anton Afanassiev; Alejandro Berrio; Gregory A Wray; Geoffrey Schiebinger; David R McClay
Journal:  Development       Date:  2021-09-27       Impact factor: 6.862

Review 2.  Quantifying the Epithelial-to-Mesenchymal Transition (EMT) from Bench to Bedside.

Authors:  Meredith S Brown; Kristen E Muller; Diwakar R Pattabiraman
Journal:  Cancers (Basel)       Date:  2022-02-23       Impact factor: 6.639

3.  New techniques for creating parthenogenetic larvae of the sea urchin Lytechinus pictus for gene expression studies.

Authors:  Victor D Vacquier; Amro Hamdoun
Journal:  Dev Dyn       Date:  2021-06-22       Impact factor: 3.780

  3 in total

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