早期人胎肺图谱揭示上皮细胞可塑性的时间动力学-动脉网

Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity

Nature 等信源发布 2024-07-13 01:13

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AbstractStudying human fetal lungs can inform how developmental defects and disease states alter the function of the lungs. Here, we sequenced >150,000 single cells from 19 healthy human pseudoglandular fetal lung tissues ranging between gestational weeks 10–19. We capture dynamic developmental trajectories from progenitor cells that express abundant levels of the cystic fibrosis conductance transmembrane regulator (CFTR).

摘要研究人类胎儿肺可以了解发育缺陷和疾病状态如何改变肺的功能。在这里，我们对妊娠10-19周之间的19个健康人假腺胎儿肺组织中的>150000个单细胞进行了测序。。

These cells give rise to multiple specialized epithelial cell types. Combined with spatial transcriptomics, we show temporal regulation of key signalling pathways that may drive the temporal and spatial emergence of specialized epithelial cells including ciliated and pulmonary neuroendocrine cells. Finally, we show that human pluripotent stem cell-derived fetal lung models contain CFTR-expressing progenitor cells that capture similar lineage developmental trajectories as identified in the native tissue.

这些细胞产生多种特殊的上皮细胞类型。结合空间转录组学，我们显示了关键信号通路的时间调节，这些信号通路可能会驱动特化上皮细胞（包括纤毛和肺神经内分泌细胞）的时空出现。最后，我们显示人类多能干细胞衍生的胎儿肺模型包含表达CFTR的祖细胞，这些祖细胞捕获了与天然组织中鉴定出的相似的谱系发育轨迹。

Overall, this study provides a comprehensive single-cell atlas of the developing human lung, outlining the temporal and spatial complexities of cell lineage development and benchmarks fetal lung cultures from human pluripotent stem cell differentiations to similar developmental window..

总体而言，这项研究提供了发育中的人类肺的全面单细胞图谱，概述了细胞谱系发育的时空复杂性，并将胎儿肺培养物从人类多能干细胞分化到类似的发育窗口进行了基准测试。。

IntroductionSingle-cell technologies are redefining the molecular signatures of canonical cell types and identifying new cell types and cell states in organs and tissues1,2. Specifically, single-cell RNA sequencing (scRNA-seq) has revealed cellular transcriptomic heterogeneities and has enriched our understanding of how these cells contribute to development and disease.

引言单细胞技术正在重新定义典型细胞类型的分子特征，并在器官和组织中鉴定新的细胞类型和细胞状态1,2。具体而言，单细胞RNA测序（scRNA-seq）揭示了细胞转录组的异质性，并丰富了我们对这些细胞如何促进发育和疾病的理解。

Single-cell atlases of the adult human pulmonary system have illuminated the roles of key cell types in homeostasis and some diseases3,4,5,6,7. On the contrary, our understanding of the cells that form the developing fetal lung has largely been extrapolated from mouse studies8,9. Recently, there have been studies leveraging scRNA-seq to characterize the developing human fetal lung tissue10,11,12,13,14.

成人肺系统的单细胞图谱阐明了关键细胞类型在体内平衡和一些疾病中的作用3,4,5,6,7。相反，我们对形成发育中的胎儿肺的细胞的理解在很大程度上是从小鼠研究中推断出来的8,9。最近，有研究利用scRNA-seq来表征发育中的人类胎儿肺组织10,11,12,13,14。

Through single-cell studies, species-specific differences in lung cell types and cellular distributions have been identified, which can impact function and disease pathogenesis. Understanding the molecular and cellular networks that drive the formation of the respiratory system during fetal development may provide important insight into the regenerative processes during repair and potentially identify the developmental origins of postnatal lung disease and disorders.Recent sequencing datasets from a small collection of normal human fetal lungs obtained from early to mid-gestation fetuses have highlighted some novel cell types and cell lineage trajectories10,12 that are unique to human fetal development.

通过单细胞研究，已经确定了肺细胞类型和细胞分布的物种特异性差异，这可能会影响功能和疾病发病机理。了解在胎儿发育过程中驱动呼吸系统形成的分子和细胞网络可能会为修复过程中的再生过程提供重要见解，并可能确定出生后肺部疾病和疾病的发育起源。最近从妊娠早期至中期胎儿获得的一小部分正常人胎儿肺的测序数据集突出了一些人类胎儿发育特有的新型细胞类型和细胞谱系轨迹10,12。

Unfortunately, following up on these observations and their potential impact on post-natal lung function is hindered by a lack of relevant experimentally tractable models.Human pluripotent stem cells (hPSCs) are becoming a resourceful tool to generate organoids or in vitro tissue mimetics to study fundament.

不幸的是，由于缺乏相关的实验易处理模型，这些观察结果及其对产后肺功能的潜在影响的后续行动受到阻碍。人类多能干细胞（hPSCs）正在成为产生类器官或体外组织模拟物以研究基础的资源丰富的工具。

Data availability

数据可用性

The single-cell RNA sequencing and spatial sequencing data generated in this study have been deposited in the NCBI GEO database under accession codes GSE264398, GSE264425, GSE264407, and GSE266789. The raw sequencing files can be accessed on the NCBI website through the accession codes and all processed sequencing data can be obtained at Synapse (https://www.synapse.org/#!Synapse:syn53437291/files/).

本研究中产生的单细胞RNA测序和空间测序数据已保存在NCBI GEO数据库中，登录号为GSE264398，GSE264425，GSE264407和GSE266789。原始测序文件可以通过登录码在NCBI网站上访问，所有处理过的测序数据都可以在Synapse获得(https://www.synapse.org/#哦！突触：syn53437291/files/）。

The source data generated in this study are available at https://www.synapse.org/#!Synapse:syn59808546..

这项研究产生的源数据可在https://www.synapse.org/#哦！突触：syn59808546。。

The published human fetal lung datasets were obtained from He et al.10 (Array Express; E-MTAB-11278), Sountoulidis et al.11 (GEO; GSE215898) and Cao et al.12 (OMIX; OMIX003147). The mouse embryonic dataset was obtained from Negretti et al.9 (GEO; GSE165063). The adult lung single-cell RNA sequencing dataset was obtained from the Human Cell Atlas7 portal (https://datasets.cellxgene.cziscience.com/2aa90e63-9a6d-444d-8343-8fc2a9921797.rds). Source data are provided with this paper..

已发表的人类胎儿肺数据集来自He等人10（Array Express；E-MTAB-11278），Sountoulidis等人11（GEO；GSE215898）和Cao等人12（OMIX；OMIX003147）。小鼠胚胎数据集获自Negretti等人9（GEO；GSE165063）。成人肺单细胞RNA测序数据集是从人类细胞图谱7门户获得的(https://datasets.cellxgene.cziscience.com/2aa90e63-9a6d-444d-8343-8fc2a9921797.rds)。本文提供了源数据。。

Code availability

代码可用性

Code for the computational analyses is available at https://github.com/spencerfar/fetal_lung_analysis (https://doi.org/10.5281/zenodo.11231149) and https://github.com/The-HQQ/Human_fetal_lung_atlas/ (https://doi.org/10.5281/zenodo.11238128) respectively.

计算分析代码可在https://github.com/spencerfar/fetal_lung_analysis（笑声）(https://doi.org/10.5281/zenodo.11231149)和https://github.com/The-HQQ/Human_fetal_lung_atlas/（笑声）(https://doi.org/10.5281/zenodo.11238128)分别。

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Polański, K. et al. BBKNN: fast batch alignment of single cell transcriptomes. Bioinformatics 36, 964–965 (2020).Article

。生物信息学36964-965（2020）。文章

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Download referencesAcknowledgementsWe would like to thank Zoe (Shuk-Yee) Ngan for her help in optimizing the single-cell library prep procedures. We would also like to thank Dien Nguyen for her help in data analysis. We would also like to thank Mr. Max Nilt and Ms. Dragica Curovic from the Research Center for Women’s and Infants’ Health (RCWIH) BioBank for their help with the fetal lung tissue collection.

下载参考文献致谢我们要感谢Zoe（Shuk Yee）Ngan在优化单细胞文库制备程序方面的帮助。我们还要感谢Dien Nguyen在数据分析方面的帮助。我们还要感谢妇女和婴儿健康研究中心（RCWIH）生物库的Max Nilt先生和Dragica Curovic女士对胎儿肺组织收集的帮助。

We would also like to thank Mr. Alper Celik from the Center of Computation Medicine (CCM) for his guidance in the bioinformatics analyses. Computational power was enabled by support provided by Compute Canada (www.computecanada.ca) and the internal SickKids High Power Computing (HPC) platform for processing our scRNA-seq datasets.

我们还要感谢计算医学中心（CCM）的Alper Celik先生在生物信息学分析方面的指导。计算能力是由Compute Canada（www.computecanada.ca）和内部SickKids高性能计算（HPC）平台提供的支持实现的，用于处理我们的scRNA-seq数据集。

Funding support for this work was provided by the SickKids Foundation & CIHR-IHDCYH grant (NI20-1070), and the Stem Cell Network Early Career Investigator - Innovation Award (FY21/ECI23) to A.P.W. H.Q. received the University of Toronto, Data Science Institute Student Fellowship award (2022-2025) and the Ontario Graduate Studentship award (2020 and 2022).

这项工作的资金支持由SickKids基金会和CIHR-IHDCYH赠款（NI20-1070）提供，A.P.W.H.Q.的干细胞网络早期职业研究者创新奖（FY21/ECI23）获得了多伦多大学数据科学研究所学生奖学金（2022-2025）和安大略省研究生奖学金（2020和2022）。

S.F. received a University of Toronto Data Sciences Institute Postdoctoral Fellowship in 2023. K.K. received the 2022 Canada Graduate Student Masters Award. Schematic illustrations for Figs. 1A, 8A, 10 and Supplementary Fig. 1A were created using BioRender.com.Author informationAuthor notesThese authors contributed equally: Henry Quach, Spencer Farrell.Authors and AffiliationsProgram in Developmental and Stem Cell Biology, Hospital for Sick Children, Toronto, Ontario, CanadaHenry Quach, Ming Jia Michael Wu, Kayshani Kanagarajah & Amy P.

S、 F.于2023年获得多伦多大学数据科学研究所博士后奖学金。K、 K.获得2022年加拿大研究生硕士奖。图1A，8A，10和补充图1A的示意图是使用BioRender.com创建的。作者信息作者注意到这些作者做出了同样的贡献：Henry Quach，Spencer Farrell。作者和附属机构安大略省多伦多市病童医院发育与干细胞生物学项目，CanadaHenry Quach，Ming Jia Michael Wu，Kayshani Kanagarajah＆Amy P。

WongDepartment of Laboratory Medicine & Pathobiology, University of Toronto, Toronto, Ontario, CanadaHenry Quach, Kayshani Kanagarajah & Amy .

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PubMed Google ScholarContributionsH.Q. and A.P.W. designed the study. H.Q. and K.K. processed the tissues. H.Q. performed the scRNA-seq experiments. H.Q. and J.L. performed the Visium and Xenium experiments while H.Q. and M.W. performed the analyses. K.K. performed the immunohistochemical staining and analyses.

PubMed谷歌学术贡献。Q、 A.P.W.设计了这项研究。H、 Q.和K.K.处理了组织。H、 Q.进行了scRNA-seq实验。H、 Q.和J.L.进行了Visium和Xenium实验，而H.Q.和M.W.进行了分析。K、 K.进行免疫组织化学染色和分析。

H.Q. and A.P.W. selected the gene panel for Xenium analysis. H.Q., S.F., M.W., X.X., P.K., A.T., and B.K. performed the computational analyses. C.E.B., F.R., S.G., and T.J.M. contributed to the conceptual design of the study and reviewed the data. H.Q., S.F., and A.P.W. wrote the manuscript. All authors read the manuscript and suggested improvements to its contents and forms.

H、 Q.和A.P.W.选择了用于Xenium分析的基因组。H、 Q.，S.F.，M.W.，X.X.，P.K.，A.T。和B.K.进行了计算分析。C、 E.B.，F.R.，S.G。和T.J.M.为研究的概念设计做出了贡献，并审查了数据。H、 Q.，S.F。和A.P.W.撰写了手稿。所有作者都阅读了手稿，并建议改进其内容和形式。

All funding support for this work was awarded to A.P.W.Corresponding authorCorrespondence to.

这项工作的所有资金支持都授予了A.P.W.通讯作者通讯社。

Amy P. Wong.Ethics declarations

艾米·P·王。道德宣言

Competing interests

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All authors declare no competing interests.

所有作者都声明没有利益冲突。

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Nature Communications thanks Christos Samakovlis and the other anonymous, reviewers for their contribution to the peer review of this work. A peer review file is available.

《自然通讯》感谢克里斯托斯·萨马科利斯（ChristosSamakovlis）和其他匿名审稿人对这项工作的同行评审所做的贡献。同行评审文件可用。

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary informationSupplementary InformationPeer review fileDescription of additional supplementary filesSupplementary Data 1Supplementary Data 2Reporting SummarySource dataSource DataRights and permissions.

Additional informationPublisher的注释Springer Nature在已发布的地图和机构隶属关系中的管辖权主张方面保持中立。补充信息补充信息同行评审文件其他补充文件的描述补充数据1补充数据2报告摘要源数据源数据权限。

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 licence, and indicate if changes were made.

开放获取本文是根据知识共享署名4.0国际许可证授权的，该许可证允许以任何媒体或格式使用，共享，改编，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出是否进行了更改。

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Reprints and permissionsAbout this articleCite this articleQuach, H., Farrell, S., Wu, M.J.M. et al. Early human fetal lung atlas reveals the temporal dynamics of epithelial cell plasticity.

转载和许可本文引用本文Quach，H.，Farrell，S.，Wu，M.J.M.等人。早期人类胎儿肺图谱揭示了上皮细胞可塑性的时间动态。

Nat Commun 15, 5898 (2024). https://doi.org/10.1038/s41467-024-50281-5Download citationReceived: 16 November 2023Accepted: 05 July 2024Published: 13 July 2024DOI: https://doi.org/10.1038/s41467-024-50281-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

《国家公社》155898（2024）。https://doi.org/10.1038/s41467-024-50281-5Download引文接收日期：2023年11月16日接收日期：2024年7月5日发布日期：2024年7月13日OI：https://doi.org/10.1038/s41467-024-50281-5Share。复制到剪贴板。

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