A transcriptional atlas of endothelial cell zonation along the pulmonary vascular tree

Background: The pulmonary vasculature exists as a series of branching vessels that are on gradients of size, oxygenation and pressure. Single cell transcriptomics have provided key insights into the different populations that make up the vasculature but the transcriptomic gradients remain largely undescribed. Methods: We applied a method of endothelial enrichment and deep single cell RNA sequencing to create a high resolution, transcriptomic dataset from the developing mouse lung. We developed an analytical framework to assign vessel-size scores and categorize individual endothelial cells (EC) and mural cells along a continuum of vessel sizes. We delineated a continuum of proximal arterial through distal venous EC states by uncovering transcriptional signatures associated with vessel size, spanning micro- to macrovascular zones. Our data recapitulated previously established zonally defined signaling axes, including Cxcl12 and Cxcr4 in arterioles, and identified localization of disease relevant markers such as Esr2. This vessel-size informed framework was robust across species and revealed how spatial EC heterogeneity underlies key processes in lung development and injury. Results: We generated a robust endothelial cell enriched scRNAseq dataset from the neonatal mouse lung, with deep sequencing coverage. Within this dataset, we defined transcriptomic signatures of macrovascular populations pulmonary artery (PAEC) and vein (PVEC) endothelial cells as well as for the microvascular capillary 1 (Cap1) population, incorporating canonical markers genes (e.g. Eln, Vwf, Tmem100, Scn7a). Many of these markers exhibited gradient -like expression patterns extending from arteries or veins toward Cap1, while others displayed polarized expression throughout the Cap1 cluster itself, with subsets exhibiting either artery- or vein-associated signatures. This analytical framework was successfully applied to published human lung datasets across developmental stages, demonstrating cross-species and temporal relevance. Conclusions: By linking scRNA-seq profiles with tissue context, we reconcile molecular signatures with anatomical structure of the pulmonary vasculature, enabling assignment of each individual cell to vessels with defined size. These findings provide a comprehensive transcriptional map of pulmonary endothelial cells and associated mural populations across the vascular continuum, offering valuable insights into spatial inferences and mechanistic insights within single cell RNA sequencing data sets that may help pave the way for targeted therapeutic strategies to treat pulmonary vascular diseases and expansion to tissues outside the lung. At birth, neonatal C57BL-6 (Charles River) mice were housed in room air (normoxia; N) or 80% O2 (hyperoxia; H) for 72hr before euthanasia (N=6 per exposure; 3M 3F). Followed by 10x 3' v4 scRNA-seq.背景：肺血管系统由一系列分级分支血管构成，其管径、氧合水平与压力均呈梯度分布。单细胞转录组学（single cell transcriptomics）已为解析构成血管系统的各类细胞群体提供了关键见解，但转录组梯度的相关特征在很大程度上仍未被阐明。 方法：本研究采用内皮富集联合深度单细胞RNA测序的方法，从发育中的小鼠肺组织中构建了高分辨率转录组数据集。我们开发了一套分析框架，用于分配血管大小评分，并基于血管大小的连续谱系对单个内皮细胞（endothelial cells，EC）与周细胞进行分类。通过挖掘与血管大小相关的转录特征，我们描绘了从近端动脉到远端静脉的内皮细胞状态连续谱系，覆盖了微血管到大血管区域。本研究数据重现了此前已明确的分区信号轴，包括小动脉中的Cxcl12与Cxcr4，并鉴定了Esr2等疾病相关标志物的定位。这一基于血管大小的分析框架在多个物种中均表现出稳健性，并揭示了内皮细胞空间异质性如何支撑肺发育与损伤中的关键过程。 结果：我们从新生小鼠肺组织中构建了覆盖深度充足的内皮细胞富集型单细胞RNA测序（single-cell RNA sequencing，scRNA-seq）数据集。在此数据集内，我们明确了大血管群体肺动脉内皮细胞（pulmonary artery，PAEC）、肺静脉内皮细胞（pulmonary vein，PVEC）以及微血管毛细血管1（Cap1）群体的转录特征，并纳入了Eln、Vwf、Tmem100、Scn7a等经典标记基因。其中多数标记基因呈现出从动脉或静脉向Cap1延伸的梯度样表达模式，另有部分标记基因在Cap1细胞簇内部呈现极化表达，其亚群分别表现出动脉或静脉相关的转录特征。本分析框架还成功应用于已发表的不同发育阶段的人类肺组织数据集，证实了其跨物种与时间维度的适用性。 结论：通过将单细胞RNA测序谱与组织背景相结合，本研究将分子特征与肺血管系统的解剖结构进行了关联，从而能够将单个细胞分配至对应管径的血管中。本研究成果构建了覆盖整个血管谱系的肺内皮细胞及相关周细胞群体的全面转录图谱，为单细胞RNA测序数据中的空间推断与机制解析提供了重要见解，有望为肺血管疾病的靶向治疗策略开发以及向肺外组织的研究拓展奠定基础。 实验处理：新生C57BL-6（Charles River）小鼠于出生后分别置于常氧（室温空气，N）或80%氧气（高氧，H）环境中饲养72小时，随后实施安乐死（每组n=6，雌雄各3只），并进行10x Genomics 3'端v4版单细胞RNA测序。