Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration

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Abstract

Whole-body regeneration of planarians is a natural wonder but how it occurs remains elusive. It requires coordinated responses from each cell in the remaining tissue with spatial awareness to regenerate new cells and missing body parts. While previous studies identified new genes essential to regeneration, a more efficient screening approach that can identify regeneration-associated genes in the spatial context is needed. Here, we present a comprehensive three-dimensional spatiotemporal transcriptomic landscape of planarian regeneration. We describe a pluripotent neoblast subtype, and show that depletion of its marker gene makes planarians more susceptible to sub-lethal radiation. Furthermore, we identified spatial gene expression modules essential for tissue development. Functional analysis of hub genes in spatial modules, such as plk1, shows their important roles in regeneration. Our three-dimensional transcriptomic atlas provides a powerful tool for deciphering regeneration and identifying homeostasis-related genes, and provides a publicly available online spatiotemporal analysis resource for planarian regeneration research.

Abstract

Cui et al. present a comprehensive three-dimensional spatiotemporal transcriptome landscape of planarian regeneration. They identified a novel pluripotent neoblast subtype and new spatially specific genes essential to tissue regeneration.

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Integrated analysis of multimodal single-cell data

Yuhan Hao, Stephanie Hao, Erica Andersen-Nissen … (2021)

Summary The simultaneous measurement of multiple modalities represents an exciting frontier for single-cell genomics and necessitates computational methods that can define cellular states based on multimodal data. Here, we introduce “weighted-nearest neighbor” analysis, an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities. We apply our procedure to a CITE-seq dataset of 211,000 human peripheral blood mononuclear cells (PBMCs) with panels extending to 228 antibodies to construct a multimodal reference atlas of the circulating immune system. Multimodal analysis substantially improves our ability to resolve cell states, allowing us to identify and validate previously unreported lymphoid subpopulations. Moreover, we demonstrate how to leverage this reference to rapidly map new datasets and to interpret immune responses to vaccination and coronavirus disease 2019 (COVID-19). Our approach represents a broadly applicable strategy to analyze single-cell multimodal datasets and to look beyond the transcriptome toward a unified and multimodal definition of cellular identity.

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SCANPY : large-scale single-cell gene expression data analysis

F. Alexander Wolf, Philipp Angerer, Fabian Theis (2018)

Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).

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Massively parallel digital transcriptional profiling of single cells

Grace Zheng, Jessica M. Terry, Phillip Belgrader … (2017)

Characterizing the transcriptome of individual cells is fundamental to understanding complex biological systems. We describe a droplet-based system that enables 3′ mRNA counting of tens of thousands of single cells per sample. Cell encapsulation, of up to 8 samples at a time, takes place in ∼6 min, with ∼50% cell capture efficiency. To demonstrate the system's technical performance, we collected transcriptome data from ∼250k single cells across 29 samples. We validated the sensitivity of the system and its ability to detect rare populations using cell lines and synthetic RNAs. We profiled 68k peripheral blood mononuclear cells to demonstrate the system's ability to characterize large immune populations. Finally, we used sequence variation in the transcriptome data to determine host and donor chimerism at single-cell resolution from bone marrow mononuclear cells isolated from transplant patients.

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Author and article information

Contributors

Jia-Yi Zhou:

ORCID: http://orcid.org/0000-0002-3274-1802

zhoujiayi2018sd@big.ac.cn

Jun Cai:

ORCID: http://orcid.org/0000-0003-2733-9373

juncai@big.ac.cn

Shihua Zhang:

ORCID: http://orcid.org/0000-0003-0192-7118

zsh@amss.ac.cn

Yun-Gui Yang:

ORCID: http://orcid.org/0000-0002-2821-8541

ygyang@big.ac.cn

Journal

Journal ID (nlm-ta): Nat Commun

Journal ID (iso-abbrev): Nat Commun

Title: Nature Communications

Publisher: Nature Publishing Group UK (London )

ISSN (Electronic): 2041-1723

Publication date (Electronic): 2 June 2023

Publication date PMC-release: 2 June 2023

Publication date Collection: 2023

Volume: 14

Electronic Location Identifier: 3205

Affiliations

[1 ]GRID grid.9227.e, ISNI 0000000119573309, CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, , Chinese Academy of Sciences, ; Beijing, 100101 China

[2 ]GRID grid.464209.d, ISNI 0000 0004 0644 6935, China National Center for Bioinformation, ; Beijing, 100101 China

[3 ]GRID grid.9227.e, ISNI 0000000119573309, NCMIS, CEMS, RCSDS, Academy of Mathematics and Systems Science, , Chinese Academy of Sciences, ; Beijing, 100190 China

[4 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, School of Mathematical Sciences, , University of Chinese Academy of Sciences, ; Beijing, 100049 China

[5 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, University of Chinese Academy of Sciences, ; Beijing, China

[6 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Sino-Danish College, , University of Chinese Academy of Sciences, ; Beijing, 101408 China

[7 ]GRID grid.9227.e, ISNI 0000000119573309, Institute of Stem Cell and Regeneration, , Chinese Academy of Sciences, ; Beijing, 100101 China

[8 ]GRID grid.9227.e, ISNI 0000000119573309, Center for Excellence in Animal Evolution and Genetics, , Chinese Academy of Sciences, ; Kunming, 650223 China

[9 ]GRID grid.410726.6, ISNI 0000 0004 1797 8419, Key Laboratory of Systems Health Science of Zhejiang Province, School of Life Science, Hangzhou Institute for Advanced Study, , University of Chinese Academy of Sciences, ; Hangzhou, 310024 China

Author information

Guanshen Cui http://orcid.org/0000-0003-1352-7664

Jia-Yi Zhou http://orcid.org/0000-0002-3274-1802

Ying Wu http://orcid.org/0000-0002-6751-9953

Yong-Liang Zhao http://orcid.org/0000-0003-0121-1312

Ying Yang http://orcid.org/0000-0002-8104-5985

Jun Cai http://orcid.org/0000-0003-2733-9373

Shihua Zhang http://orcid.org/0000-0003-0192-7118

Yun-Gui Yang http://orcid.org/0000-0002-2821-8541

Article

Publisher ID: 39016

DOI: 10.1038/s41467-023-39016-0

PMC ID: 10238425

PubMed ID: 37268637

SO-VID: 1b350f56-b41d-40c9-b303-f869f81cdba7

License:

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

History

Date received : 28 September 2022

Date accepted : 25 May 2023

Funding

Funded by: the Innovation Grant of National Natural Science Foundation of China (32121001); the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDA16010500); CAS Key Research Projects of the Frontier Science (QYZDY-SSW-SMC027); Shanghai Municipal Science and Technology Major Project (2017SHZDZX01)

Funded by: CAS Project for Young Scientists in Basic Research (YSBR-073)

Funded by: the National Key Research and Development Program of China (2021YFF1200904); the National Natural Science Foundation of China (32070795)

Funded by: the Strategic Priority Research Program of the Chinese Academy of Sciences (CAS) (XDA16021400); CAS Project for Young Scientists in Basic Research (YSBR-034); the National Key Research and Development Program of China (2019YFA0709501), the National Natural Science Foundation of China (12126605, 61621003)

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ScienceOpen disciplines: Uncategorized

Keywords: regeneration,differentiation,stem-cell differentiation

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ScienceOpen disciplines: Uncategorized

Keywords: regeneration, differentiation, stem-cell differentiation

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Spatiotemporal transcriptomic atlas reveals the dynamic characteristics and key regulators of planarian regeneration

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Most cited references 51

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Cited by 6

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