小鼠胎盘形成的时空转录组图谱-动脉网

A spatiotemporal transcriptomic atlas of mouse placentation

Nature 等信源发布 2024-10-22 17:28

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AbstractThe placenta, a temporary but essential organ for gestational support, undergoes intricate morphological and functional transformations throughout gestation. However, the spatiotemporal patterns of gene expression underlying placentation remain poorly understood. Utilizing Stereo-seq, we constructed a Mouse Placentation Spatiotemporal Transcriptomic Atlas (MPSTA) spanning from embryonic day (E) 7.5 to E14.5, which includes the transcriptomes of large trophoblast cells that were not captured in previous single-cell atlases.

摘要胎盘是妊娠支持的临时但必不可少的器官，在整个妊娠过程中经历了复杂的形态和功能转变。然而，胎盘基因表达的时空模式仍然知之甚少。利用Stereo-seq，我们构建了一个从胚胎期（E）7.5到E14.5的小鼠胎盘时空转录组图谱（MPSTA），其中包括以前的单细胞图谱中未捕获的大型滋养层细胞的转录组。

We defined four distinct strata of the ectoplacental cone, an early heterogeneous trophectoderm structure, and elucidated the spatial trajectory of trophoblast differentiation during early postimplantation stages before E9.5. Focusing on the labyrinth region, the interface of nutrient exchange in the mouse placenta, our spatiotemporal ligand–receptor interaction analysis unveiled pivotal modulators essential for trophoblast development and placental angiogenesis.

我们定义了外胚层锥体的四个不同层，这是一种早期的异质滋养外胚层结构，并阐明了E9.5之前植入后早期滋养层分化的空间轨迹。我们的时空配体-受体相互作用分析侧重于迷宫区域（小鼠胎盘中营养交换的界面），揭示了滋养层发育和胎盘血管生成所必需的关键调节剂。

We also found that paternally expressed genes are exclusively enriched in the placenta rather than in the decidual regions, including a cluster of genes enriched in endothelial cells that may function in placental angiogenesis. At the invasion front, we identified interface-specific transcription factor regulons, such as Atf3, Jun, Junb, Stat6, Mxd1, Maff, Fos, and Irf7, involved in gestational maintenance.

我们还发现，父系表达的基因仅在胎盘而不是蜕膜区域富集，包括一组富含内皮细胞的基因，这些基因可能在胎盘血管生成中起作用。在入侵前沿，我们确定了与妊娠维持有关的界面特异性转录因子调节子，例如Atf3，Jun，Junb，Stat6，Mxd1，Maff，Fos和Irf7。

Additionally, we revealed that maternal high-fat diet exposure preferentially affects this interface, exacerbating inflammatory responses and disrupting angiogenic homeostasis. Collectively, our findings furnish a comprehensive, spatially resolved atlas that offers valuable insights and benchmarks for future explorations into placental morphogenesis and pathology..

此外，我们发现母体高脂肪饮食暴露优先影响该界面，加剧炎症反应并破坏血管生成稳态。总的来说，我们的发现提供了一个全面的，空间分辨的图谱，为未来胎盘形态发生和病理学的探索提供了有价值的见解和基准。。

IntroductionThe placenta acts as the major organ of nutrient exchange between the mother and the embryo. It also serves as a vital fetal endocrine organ during pregnancy1. Placentation is a complex and dynamic process that is crucial for regulating communication and coadaptation between the mother and fetus2.

引言胎盘是母亲和胚胎之间营养交换的主要器官。它在怀孕期间也是胎儿重要的内分泌器官1。胎盘是一个复杂而动态的过程，对于调节母亲和胎儿之间的沟通和共适应至关重要2。

Significant placental disruptions are always associated with various pregnancy complications and adverse fetal outcomes3. However, a profound understanding of key processes related to placentation, such as trophoblast development, fetal circulation establishment, and synchronized dialog between fetal-derived cells and the maternal endometrium4, remains a challenge.

严重的胎盘破裂总是与各种妊娠并发症和不良胎儿结局有关3。然而，深入了解与胎盘相关的关键过程，如滋养层发育，胎儿循环建立以及胎儿来源细胞与母体子宫内膜之间的同步对话4，仍然是一个挑战。

The gene expression pattern of the placenta has not been systematically characterized from a spatiotemporal perspective.Previous single-cell and single-nucleus atlases have significantly enhanced our understanding of the cellular composition and cell-specific functions of the placenta, in both humans5,6,7 and rodents8,9.

胎盘的基因表达模式尚未从时空角度进行系统表征。先前的单细胞和单核地图集显着增强了我们对人类5,6,7和啮齿动物8,9中胎盘的细胞组成和细胞特异性功能的理解。

However, single-cell RNA sequencing (scRNA-seq) cannot capture multinucleated syncytiotrophoblasts (SynTs) and trophoblast giant cells (TGCs) in their mature state due to their large size9,10, nor could it provide spatial localization of diverse types of cells and gene expression patterns. Single-nucleus RNA sequencing (snRNA-seq), while capable of capturing multinucleated cells, cannot provide the complete transcriptome of the entire cell.

然而，单细胞RNA测序（scRNA-seq）不能捕获成熟状态下的多核合体滋养细胞（SynTs）和滋养层巨细胞（TGCs），因为它们的大小较大9,10，也不能提供不同类型细胞的空间定位和基因表达模式。单核RNA测序（snRNA-seq）虽然能够捕获多核细胞，但不能提供整个细胞的完整转录组。

Recently, several studies have used spatial transcriptome techniques to elucidate the locations of newly identified cell types11 and changes in gene expression in different functional regions12 of the human placenta. Nevertheless, most human placenta samples can be obtained from miscarriage samples in early pregnancy at only 6–14 gestational weeks or .

最近，一些研究使用空间转录组技术来阐明新鉴定的细胞类型11的位置以及人胎盘不同功能区域12中基因表达的变化。。

For cell type mapping analysis of trophoblasts and decidua cells, we separately downloaded scRNA-seq data from the recently published GSE156125 dataset8 and obtained data from He et al.33. After obtaining the gene expression matrix after cell segmentation, we split the spatially resolved cell bins into trophoblast and decidua groups according to their localization.

对于滋养层细胞和蜕膜细胞的细胞类型作图分析，我们分别从最近发布的GSE156125数据集8下载了scRNA-seq数据，并从He等人33获得了数据。在细胞分割后获得基因表达矩阵后，我们根据其定位将空间分辨的细胞仓分成滋养层和蜕膜组。

The trophoblast cell bins were aligned to the reference cell types of the corresponding stage in GSE156125 using the principled Bayesian model in cell2location111. Decidual cell bins were similarly mapped to the cell types from the study by He et al.33. After mapping, the trophoblast cell bins and decidua bins were then merged to obtain a complete map of placental sections with a cell type-level resolution.Calculation of the proportions of different cell types at the placenta and cluster/interfaceCombining the deconvolution results from both the trophoblast and decidua cell bins, the proportions of different cell types across the whole placenta were calculated as the number of cells of each type in a cell bin (e.g., NKp cell) divided by the total number of cells in the placenta.

使用cell2location111中的原则贝叶斯模型，将滋养层细胞仓与GSE156125中相应阶段的参考细胞类型进行比对。蜕膜细胞仓类似地映射到He等人研究的细胞类型33。作图后，将滋养层细胞仓和蜕膜仓合并，以获得具有细胞类型水平分辨率的胎盘切片的完整图谱。计算胎盘和簇/界面上不同细胞类型的比例结合滋养层和蜕膜细胞仓的解卷积结果，将整个胎盘中不同细胞类型的比例计算为细胞仓（例如NKp细胞）中每种类型的细胞数量除以胎盘中的细胞总数。

To calculate the proportion of each cell type at the interface or in a specific cluster, the bin50 cluster/interface annotation was first transferred to the cell bin according to coordinate mapping using the st.dd.set_domains function in Spateo, and then the proportions of cell types in specific clusters or at the interface were calculated as the number of cells of each type in a cell bin of the cluster/interface (e.g., biopotential progenitor in inner EPC1) divided by the total number of cells in the clusters/interfaces (e.g., inner EPC1).Spatial cell distribution analysisThe dyn.tl.neighbors functionality of Spateo was utilized to c.

为了计算界面或特定簇中每种细胞类型的比例，首先使用Spateo中的st.dd.set\u domains函数根据坐标映射将bin50簇/界面注释转移到细胞库中，然后将特定簇或界面中细胞类型的比例计算为簇/界面（例如内部EPC1中的生物势祖细胞）的细胞库中每种类型的细胞数量除以簇/界面（例如内部EPC1）中的细胞总数。空间细胞分布分析利用Spateo的dyn.tl.neighbors功能进行c。

Data availability

数据可用性

All raw data generated in this study have been deposited to CNGB Nucleotide Sequence Archive under the accession number CNP0004966 (https://db.cngb.org/search/project/CNP0004966/). Our Stereo-seq dataset can be visualized by our interactive data portal (https://db.cngb.org/stomics/mpsta/). Additional data, including raw and filtered feature-spot matrices and nucleic acid dye staining images of each section, can be accessed from https://db.cngb.org/stomics/project/STT0000055..

本研究中产生的所有原始数据均已以登录号CNP0004966保存到CNGB核苷酸序列档案库(https://db.cngb.org/search/project/CNP0004966/)。我们的Stereo-seq数据集可以通过我们的交互式数据门户可视化(https://db.cngb.org/stomics/mpsta/)。其他数据，包括原始和过滤后的特征点矩阵以及每个部分的核酸染料染色图像，可以从https://db.cngb.org/stomics/project/STT0000055..

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Download referencesAcknowledgementsWe thank Linying Wang, Binbin Jiang, Yanan Zhang, and Zhongzhen Liu for their experimental assistance. We also thank Longqi Liu, Shiping Liu, Ying Gu, Yuxiang Li, Yinqi Bai, Lei Han, Lifang Wang, Sha Liao, Ao Chen, Kailong Ma, Shuxia Cao, Xiaoming Li, Lianjie Miao, and Ruiling Zhang for their valuable scientific advice and technical support.

下载参考文献致谢我们感谢王林英，江斌斌，张亚南和刘忠贞的实验帮助。我们还要感谢刘龙琦、刘世平、顾颖、李玉祥、白银琦、雷汉、王丽芳、沙廖、陈敖、马凯龙、曹淑霞、李晓明、苗连杰和张瑞玲提供了宝贵的科学建议和技术支持。

This work was supported by the China National GeneBank (CNGB). We would also like to express our gratitude to STOmic Cloud (https://cloud.stomics.tech) for providing us with an analysis platform. This work was supported by the National Key R&D Program of China (2021YFC2700700 and 2022YFC2703500), the National Natural Science Foundation of China (82088102 and 82171686), CAMS Innovation Fund for Medical Sciences (2019-I2M-5-064), Collaborative Innovation Program of Shanghai Municipal Health Commission (2020CXJQ01), Key Discipline Construction Project (2023-2025) of Three-Year Initiative Plan for Strengthening Public Health System Construction in Shanghai (GWVI-11.1-35), Clinical research program of Shanghai Municipal Health Commission (202340222), the Natural Science Foundation of Shanghai (20ZR1463100), Clinical Research Plan of Shanghai Shenkang Hospital Development Center (SHDC2023CRD001 and SHDC2020CR1008A), Shanghai Clinical Research Center for Gynecological Diseases (22MC1940200), Shanghai Urogenital System Diseases Research Center (2022ZZ01012) and Shanghai Frontiers Science Research Center of Reproduction and Development.Author informationAuthor notesThese authors contributed equally: Yanting Wu, Kaizhen Su, Ying Zhang, Langchao Liang.Authors and AffiliationsObstetrics and Gynecology Hospital, Institute of Reproduction and Development, Fudan University, Shanghai, ChinaYanting .

这项工作得到了中国国家基因库（CNGB）的支持。我们还要感谢STOmic Cloud(https://cloud.stomics.tech)为我们提供了一个分析平台。这项工作得到了国家重点研发计划（2021YFC270700和2022YFC2703500）、国家自然科学基金（82088102和82171686）、中国医学科学院医学科学创新基金（2019-I2M-5-064）、上海市卫生委员会协同创新计划（2020CXJQ01）、加强上海市公共卫生体系建设三年计划重点学科建设项目（2023-2025）（GWVI-11.1-35）、上海市卫生委员会临床研究计划（202340222）、上海市自然科学基金（20ZR1463100）、上海申康医院临床研究发展计划的支持中心（SHDC2023CRD001和SHDC2020CR1008A），上海妇科疾病临床研究中心（22MC1940200），上海泌尿生殖系统疾病研究中心（2022ZZ01012）和上海生殖与发育前沿科学研究中心。。作者和附属机构复旦大学生殖与发育研究所妇产科医院，中国上海盐亭。

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PubMed Google ScholarContributionsH.H. conceived the idea. H.H., Y.W., Y.G., Yu Z., K.S., and L.G. designed the study. Y.W., K.S., Y.G., Yu Z., L.G., C.L., X.J., J.Z., X.X., and H.H. supervised the work. K.S., L.L., Y.S., and Q.Z. collected the samples with help from C.L. and Y.G.

PubMed谷歌学术贡献。H、构思了这个想法。H、 H.，Y.W.，Y.G.，Yu Z.，K.S。和L.G.设计了这项研究。Y、 W.，K.S.，Y.G.，Yu Z.，L.G.，C.L.，X.J.，J.Z.，X.X。和H.H.监督了这项工作。K.S.，L.L.，Y.S。和Q.Z.在C.L.和Y.G.的帮助下收集了样本。

Y.G., Ying Z., L.L., and F.W. designed the spatial transcriptome experiments. Ying Z., K.S., Y.W., L.L., C.C., Q.L., S.Z., and M.Z. performed the bioinformatics analysis, statistical analysis, and visualization of the results with help from Y.G., Yu Z., F.W. X.J., X.X., and H.H. K.S., S.C., and Y.M.

Y、 G.，Ying Z.，L.L。和F.W.设计了空间转录组实验。Ying Z.，K.S.，Y.W.，L.L.，C.C.，Q.L.，S.Z。和M.Z.在Y.G.，Yu Z.，F.W.X.J.，X.X。和H.H.K.S.，S.C。和Y.M.的帮助下进行了生物信息学分析，统计分析和结果可视化。

conducted the immunohistochemistry, qPCR and western blotting experiments. Y.W., K.S., Ying Z., and F.W. wrote the first draft of the manuscript. Yu Z., Y.G., L.G., Y.S., Q.Z., J.X., X.X., and H.H. revised and finalized the manuscript.Corresponding authorsCorrespondence to.

进行了免疫组织化学，qPCR和蛋白质印迹实验。Y、 W.，K.S.，Ying Z.和F.W.撰写了手稿的初稿。Yu Z.，Y.G.，L.G.，Y.S.，Q.Z.，J.X.，X.X。和H.H.修订并完成了手稿。通讯作者通讯。

Yanting Wu, Yu Zhang, Ya Gao or Hefeng Huang.Ethics declarations

吴艳婷、张宇、高雅雅或黄鹤峰。道德宣言

Competing interests

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Reprints and permissionsAbout this articleCite this articleWu, Y., Su, K., Zhang, Y. et al. A spatiotemporal transcriptomic atlas of mouse placentation.

转载和许可本文引用本文Wu，Y.，Su，K.，Zhang，Y。等人。小鼠胎盘的时空转录组图谱。

Cell Discov 10, 110 (2024). https://doi.org/10.1038/s41421-024-00740-6Download citationReceived: 29 January 2024Accepted: 10 September 2024Published: 22 October 2024DOI: https://doi.org/10.1038/s41421-024-00740-6Share 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.

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