Literature DB >> 32339035

Using Single-Cell and Spatial Transcriptomes to Understand Stem Cell Lineage Specification During Early Embryo Development.

Guangdun Peng1,2,3, Guizhong Cui2, Jincan Ke1, Naihe Jing1,2,3,4.   

Abstract

Embryonic development and stem cell differentiation provide a paradigm to understand the molecular regulation of coordinated cell fate determination and the architecture of tissue patterning. Emerging technologies such as single-cell RNA sequencing and spatial transcriptomics are opening new avenues to dissect cell organization, the divergence of morphological and molecular properties, and lineage allocation. Rapid advances in experimental and computational tools have enabled researchers to make many discoveries and revisit old hypotheses. In this review, we describe the use of single-cell RNA sequencing in studies of molecular trajectories and gene regulation networks for stem cell lineages, while highlighting the integratedexperimental and computational analysis of single-cell and spatial transcriptomes in the molecular annotation of tissue lineages and development during postimplantation gastrulation.

Entities:  

Keywords:  cell lineage; embryo development; single-cell genomics; spatial transcriptome; trajectory inference

Mesh:

Year:  2020        PMID: 32339035     DOI: 10.1146/annurev-genom-120219-083220

Source DB:  PubMed          Journal:  Annu Rev Genomics Hum Genet        ISSN: 1527-8204            Impact factor:   8.929


  10 in total

Review 1.  Understanding the Transcriptomic Landscape to Drive New Innovations in Musculoskeletal Regenerative Medicine.

Authors:  Stacey M Thomas; Cheryl L Ackert-Bicknell; Michael J Zuscik; Karin A Payne
Journal:  Curr Osteoporos Rep       Date:  2022-02-14       Impact factor: 5.096

Review 2.  Sample-multiplexing approaches for single-cell sequencing.

Authors:  Yulong Zhang; Siwen Xu; Zebin Wen; Jinyu Gao; Shuang Li; Sherman M Weissman; Xinghua Pan
Journal:  Cell Mol Life Sci       Date:  2022-08-05       Impact factor: 9.207

3.  Spatially and Temporally Distributed Complexity-A Refreshed Framework for the Study of GRN Evolution.

Authors:  Alessandro Minelli; Alberto Valero-Gracia
Journal:  Cells       Date:  2022-05-30       Impact factor: 7.666

Review 4.  Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.

Authors:  Minghui Wang; Won-Min Song; Chen Ming; Qian Wang; Xianxiao Zhou; Peng Xu; Azra Krek; Yonejung Yoon; Lap Ho; Miranda E Orr; Guo-Cheng Yuan; Bin Zhang
Journal:  Mol Neurodegener       Date:  2022-03-02       Impact factor: 18.879

Review 5.  Decoding and recoding plant development.

Authors:  Sarah Guiziou; Jonah C Chu; Jennifer L Nemhauser
Journal:  Plant Physiol       Date:  2021-10-05       Impact factor: 8.005

Review 6.  Computational principles and challenges in single-cell data integration.

Authors:  Ricard Argelaguet; Anna S E Cuomo; Oliver Stegle; John C Marioni
Journal:  Nat Biotechnol       Date:  2021-05-03       Impact factor: 54.908

Review 7.  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

Review 8.  Clinical and translational values of spatial transcriptomics.

Authors:  Linlin Zhang; Dongsheng Chen; Dongli Song; Xiaoxia Liu; Yanan Zhang; Xun Xu; Xiangdong Wang
Journal:  Signal Transduct Target Ther       Date:  2022-04-01

9.  Significance of single-cell and spatial transcriptomes in cell biology and toxicology.

Authors:  Duojiao Wu; Xiaozhuan Liu; Jiaqiang Zhang; Li Li; Xiangdong Wang
Journal:  Cell Biol Toxicol       Date:  2021-01-04       Impact factor: 6.691

Review 10.  Challenges and Opportunities for the Translation of Single-Cell RNA Sequencing Technologies to Dermatology.

Authors:  Alex M Ascensión; Marcos J Araúzo-Bravo; Ander Izeta
Journal:  Life (Basel)       Date:  2022-01-04
  10 in total

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