The Cellular Origin of the Pulmonary Pericyte

The Cellular Origin of the Pulmonary Pericyte | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article The Cellular Origin of the Pulmonary Pericyte Robbert Rottier, Isabel Sreeram, Ruben Boers, Joachim Boers, Beatrice Tan, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4349859/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Emerging evidence suggests pericytes play a significant role in various lung diseases. However, characterizing pericytes remains challenging, impeding our understanding of their role in lung development and disease. Using single-cell RNA sequencing and DCM-time machine technology, we tracked the development of mouse pulmonary pericytes. Our study revealed the differentiation of perivascular progenitors into pericytes and vascular smooth muscle cells. Temporal analysis uncovered dynamic gene expression profiles during pericyte differentiation, highlighting pathways crucial for pulmonary vascular development. Further analysis showed intricate signaling interactions between pericyte progenitors and mature pericytes, and we validated Mcam as a bona fide pulmonary pericyte marker. These findings challenge conventional views on pericyte origin and underscore the importance of accurate pericyte identification in developmental and disease contexts. Overall, this study enhances our understanding of pulmonary pericyte ontogeny and differentiation, offering insights into their potential as therapeutic targets in pericyte-associated lung diseases. Biological sciences/Developmental biology/Morphogenesis/Cell lineage Biological sciences/Molecular biology/Transcriptomics Biological sciences/Biotechnology/Functional genomics/Gene expression profiling Health sciences/Molecular medicine pericytes lung development single cell analysis lineage tracing mouse Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 INTRODUCTION Pericytes, a perivascular subset of mesenchymal cells, play a pivotal role in orchestrating lung development and maintaining vascular homeostasis ( 1 , 2 ). Their multifaceted contributions span angiogenesis, branching morphogenesis, and regulation of the pulmonary vascular tone ( 3 , 4 ). Pericytes reside within the abluminal surface of the microvasculature, where they interact directly with endothelial cells (ECs) to modulate angiogenic cues and provide structural support, thus facilitating the expansion of the pulmonary vasculature ( 3 ). In the lung, this is defined by distal angiogenesis, a process during which new vessels sprout from preexisting ones ( 5 ). Pericytes initiate angiogenesis by detaching from the basement membrane and are later recruited by ECs to structurally support the newly formed vessels ( 2 , 3 ). This pericyte-EC interplay is central to the intricate orchestration of vascular development and function within the lung ( 3 , 6 , 7 ). The expansion of the pulmonary vasculature is essential to three-dimensional airway branching ( 1 , 8 , 9 ). Moreover, pericytes have been found to directly regulate postnatal lung morphogenesis and are thus indispensable for proper lung development ( 4 ). A growing body of evidence points to the involvement of pericytes in the onset and progression of numerous vascular and nonvascular lung diseases, including pulmonary hypertension, asthma, and chronic obstructive pulmonary disease (COPD) ( 10 – 14 ). Altered pericyte behavior is particularly evident in both congenital and acquired types of pulmonary hypertension. This is characterized by extensive vascular remodeling, including increased pericyte coverage and aberrant expression of ACTA2 , which in some diseases is already present early during gestation ( 12 , 13 , 15 ). Consequently, pericytes are a subject of intense research. Given their capacity to differentiate into a wide range of mesenchymal subtypes in vitro and following transplantation in vivo , pericytes were long regarded as mesenchymal stem cells ( 16 , 17 ). However, lineage tracing studies have challenged this assumption, suggesting that pericytes represent a more differentiated cell type ( 18 , 19 ). The notion that they share functional and molecular characteristics with vascular smooth muscle cells (VSMCs), including the expression of the smooth muscle cell marker ACTA2 , has led to them being generally considered to be VSMC progenitors, although discriminative markers have not yet been found ( 1 , 2 , 20 , 21 ). One of the prominent challenges in pericyte research is the absence of definitive molecular markers that exclusively identify this cell population, especially at the early stages of development ( 3 , 11 , 22 ). Conventionally used markers, such as CSPG4 and PDGFRB, have fallen short in providing distinct and consistent pericyte identification. As such, the origin of the pulmonary pericytes has remained enigmatic ( 23 ). Previously, we showed that perivascular cells are prime candidates to underlie the vascular abnormalities found in congenital diaphragmatic hernia (CDH) associated pulmonary hypertension ( 13 ). In CDH, perivascular cells were found to differentiate prematurely towards a more contractile phenotype and to aberrantly cover the pulmonary vessels, which was found to be the result of inhibited retinoic acid (RA) signaling ( 13 , 24 ). To increase our understanding of pericytes during lung development, we combined single-cell RNA sequencing (scRNA-seq) and DCM-time machine (DCM-TM) technology ( 25 ) to uncover the developmental origin of the mouse pulmonary pericyte. This DCM-TM technology utilizes an inducible bacterial DNA cytosine methyltransferase (DCM) fused to RNA polymerase II, to DCM label active genes and enhancers. This bacterial DNA methylation mark, which is absent in eukaryotes, is propagated during cell division, ensuring its presence in all descendants. By applying DCM-TM in combination with scRNA-seq, we identified a pericyte progenitor that gives rise to both pericytes and VSMCs. Furthermore, we explore the expression of novel pericyte-specific markers throughout lung development. RESULTS scRNA-seq identifies three molecularly distinct lung perivascular populations Pericytes, a mesenchymal cell type closely linked to vascular smooth muscle cells (VSMCs) ( 2 ), remain poorly characterized. We investigated the ontogeny of lung pericytes using whole-lung scRNA-seq at multiple stages of mouse lung development, ranging from E10.5 to E18.5. Through unbiased clustering and cell type annotation, we identified endothelial ( Pecam1 +), epithelial ( Epcam + ) , mesothelial ( Wt1+ ), immune ( Ptprc +), and mesenchymal ( Pecam1 -, Epcam-, Ptprc -) cell compartments (Fig. S1 A-B). Mesenchymal cells were subsequently extracted and analyzed separately (Fig S1 C). This revealed numerous transitional cells as well as more mature cell types (Fig. S1 D). To characterize pericyte-specific developmental changes, we performed RNA velocity analysis of the mesenchymal populations (Fig. S1 E). Based on these predictions, we isolated and reclustered closely related smooth muscle and non-smooth muscle cell types. This revealed eight transcriptionally distinct populations, including pericytes/perivascular type III cells ( Cspg4 +, Pdgfrb +), airway smooth muscle ( Foxf1 +, Acta2 +, Myh11 +), myofibroblasts ( Tgfbi +, Pdgfra +), lipofibroblasts ( Tcf21 +, Gyg +), and chondrocytes ( Sox9 +, Col2a1 +) (Fig. 1 A-C). The fraction of cells and mean expression of signature genes for each population were visualized (Fig. 1 D). We identified an Ebf1 + population as early as E10.5 (Fig. 1 B-C). At E12.5, two molecularly distinct Ebf1 + populations emerged, one of which we identified as true pericytes/type III perivascular cells (perivascular cell III) due to the expression of general pericyte markers Pdgfrb , Cspg4 , and Mcam (Fig. 1 D). The second Ebf1 + cluster, which we defined as type II perivascular cells (perivascular cell II), was found to be molecularly distinct from pericytes, although it did co-express many pericyte markers, albeit to a lower extent. Both Ebf1 + populations expressed Notch3 , a key regulator of VSMC maturation and vessel stabilization ( 11 , 26 ). The top differentially expressed markers of the perivascular II cells included Cxcl12 and Sparc , both of which are part of the pericyte secretome and are involved in pulmonary artery muscularization and extracellular matrix (ECM) formation and angiogenesis, respectively (Fig. 1 C) ( 27 , 28 ). Recently, Ebf1 positive fibroblasts were identified as a novel pulmonary mesenchymal subpopulation in the embryonic mouse lung from gestational age 14.5 (E14.5) onwards ( 29 ). Although their transcriptomic profile closely resembled that of pericytes, suggesting that they may share a common lineage, a distinct pericyte population could not be identified. Ebf1 has also been shown to be pericyte specific and to contribute to pericyte and VSMC cell commitment. By contrast, Foxf1, an airway smooth muscle cell (ASMC)–specific transcription factor, has been found to drive airway smooth muscle cell (ASMC) development ( 30 , 31 ). Here, we identified two different smooth muscle cell lineages, one of which was identified as the VSMC compartment due to the expression of Ebf1 and Heyl (perivascular cell II and III ) , the other which was determined to represent the ASMC compartment due to the expression of Foxf1 (airway smooth muscle and myofibroblast, Fig. 1 D). Both perivascular type II and III cells were found to specifically express Heyl , another transcription factor tied to VSMC cell fate determination ( 31 ). Interestingly, only the perivascular II cells expressed smooth muscle cell (SMC) markers Acta2 and Tagln , which implies that they might be pulmonary VSMC progenitors. We uncovered a third distinct mesenchymal population which was only detected between E10.5 and E14.5 These cells, which have previously been described as adventitial fibroblasts ( 32 ), were termed perivascular type I cells (perivascular cell I), were defined by the expression of Col1a1 , which was recently shown to regulate pericyte proliferation, migration, and differentiation, and Dlk1 , a more recently discovered pericyte marker (Fig. 1 C) ( 27 , 33 ). Based on these findings, we hypothesized that these perivascular type I cells might give rise to type II and III perivascular cells. To define the temporal trajectories of the perivascular clusters, we performed RNA velocity analysis on our scRNA-seq data. Both the dynamical and stochastic models implied a developmental relationship between the three perivascular cell types, although a single progenitor could not be clearly established (Fig. 1 E, S1F). DCM-time machine tracks pericyte cell state changes throughout lung development To determine the development of pulmonary perivascular cells, we applied DCM-time machine (DCM-TM) technology ( 25 ). This recently developed method offers superior flexibility as it is not constrained by temporal resolution or gene detection limits, which strongly contrasts with RNA velocity. Consequently, it allows for comprehensive whole-transcriptome lineage tracing of target cell populations. To explore these trajectories, we conducted four distinct pulse-chase experiments, each involving a 48-hour regimen of doxycycline administration to induce the DCM tagging system, followed by the isolation of Cspg4 +/ Pdgfrb + perivascular cells (perivascular cells I and II) and Pecam1 + endothelial cells (ECs) at E18.5 using FACS (Fig. 2 ). Sequencing of methylated DNA (MeD-seq) was performed on genomic DNA isolated from these cells, followed by the identification of methylation specific DNA regions (see genome browser plots in supplementary Fig. 2) and normalization for DCM induction efficiency (Fig. S3 A, table S2 ) ( 34 ). The time points, ranging from E8.5 to E16.5, were explicitly chosen to reflect the early and late embryonic and pseudoglandular stages of lung development, during which most of the pulmonary vascular growth takes place. DCM methylation levels for each time point were analyzed to determine to what extent perivascular cell development could be traced retrospectively. Gene meta-analysis indicated that DCM methylation levels of gene bodies in both perivascular cells and ECs were found to be similar to those observed in controls at E8.5-10.5, indicating that the methylation of active genes at his time point was insufficiently maintained after 8 days (Fig. S3 B). Consequently, we set the cutoff to E10.5-12.5. By leveraging the DCM labeling of active genes across the early (E10.5-12.5), middle (E12.5-14.5), and late (E14.5-16.5) time intervals, we meticulously tracked changes in gene expression within pericyte precursors over time. Notably, the DCM labeling of Col1a1 and Dlk1 confirmed their enrichment at the early and middle time points, respectively (Fig. 3 A). Furthermore, we observed that genes specific to pericytes attained their peak expression levels at the late time point, with Mcam , Notch3 , and Ebf1 demonstrating higher expression levels in comparison to Cpsg4 and Pdgfrb (Fig. 3 A). Additionally, examination of smooth muscle cell markers revealed an increasing expression of VSMC-specific genes in the perivascular cells, including Heyl , Egfl6 , and Mef2c , thus providing further confirmation that pericytes follow the developmental trajectory of VSMCs (Fig. 3 B). To map temporal gene activity throughout pericyte differentiation, we clustered genes with a DCM signal significantly higher than background levels based on their peak day. We visualized the average expression of these gene clusters on the uniform manifold approximation and projection (UMAP) of our mesenchymal scRNA-seq data, which shows enrichment at specific clusters (Fig. 3 C). This analysis revealed that genes with a temporal DCM methylation profile peaking at the early time point were enriched in perivascular type I cells, while genes peaking at the late time point were enriched in perivascular type III cells (Fig. 3 C). Genes with maximal temporal signals at the middle time point were broadly expressed across all three perivascular clusters, indicating that this peak pattern mostly represents ubiquitously expressed genes. Gene ontology (GO) analysis of the DCM labeled genes highlighted gene set enrichment for stem cell pluripotency at the early time point and for VSMC contraction at the late time point (Fig. 3 D-F). In summary, we conclude that perivascular type I cells serve as pericyte and VSMC progenitors, giving rise to both perivascular type II cells, termed intermediate pericytes, and pericytes (Fig. 3 G). Pathways determining pericyte differentiation and pulmonary vascular development To gain deeper insights into cell state changes during pericyte differentiation, we conducted a KEGG pathway analysis on DCM-labeled genes. Mesenchymal Wnt, Shh, and Vegf signaling pathways play crucial roles in the development of various mesenchymal lineages, including airway and vascular smooth muscle cells and pericytes, and the establishment and expansion of the pulmonary vascular plexus ( 35 – 38 ). Consistent with this, we observed the early enrichment of these signaling pathways during lung development (Fig. 4 A-B). At the middle time point, we observed enrichment of the Notch signaling pathway, aligning with prior reports highlighting its significance in distal angiogenesis, arterial specification, and vessel maturation ( 39 ). By contrast, the late time point revealed enrichment of Tgfb and Hippo signaling pathways, both of which are well-known regulators of mesenchymal ECM production, a process that directly follows blood vessel formation ( 40 ). To unravel the cell-cell signaling mechanisms driving pericyte differentiation, we examined the expression of ligands and receptors by performing Cellphone DB analyses on our scRNA-seq data ( 41 ). These analyses highlighted the pericyte progenitors and intermediate as sources of ligands that not only signal to each other but also communicate with mature pericytes. This finding implies a regulatory role for these pericyte progenitors on mature pericytes (Fig. 4 C). The number of predicted ligand-receptor interactions also revealed that mature pericytes exhibit a lower number of interactions compared to their progenitors, showing that progenitor cells can process more signaling cues due to their ability to differentiate into different cell types. Among the ligands expressed by the pericyte progenitors were Wnt2, Wnt5b, while intermediate pericytes emerged as the primary source of Tgfb signaling effectors, including Tgfb2 and Tgfb3 (Fig. 4 D). Interestingly, retinoic acid signaling was highly enriched between the three perivascular populations, with pericyte progenitors emerging as the main source of retinoic acid (Fig. 4 E). We further aimed to elucidate the interplay between ECs and pericyte progenitors in the context of pulmonary vascular development through receptor-ligand analysis of our whole-lung scRNA-seq data. The interaction between ECs and pericytes is essential for the development and expansion of the pulmonary vasculature ( 6 ). In line with this, our finding indicated consistent interactions between ECs and both intermediate pericytes and mature pericytes, primarily involving well-established angiogenic pathways including Pdgfb, Angiopoetin, and Notch (Fig. 4 F) ( 6 ). Notably, we observed that Vegf signaling, a key regulator of early vasculogenesis, mostly occurred between ECs and intermediate pericytes, corroborating the notion that intermediate pericytes represent an early and less differentiated type of pericyte (Fig. 4 G). Temporal expression of known and novel pulmonary pericyte markers While Cspg4 and Pdgfrb are commonly used molecular markers for pericytes, they have generally failed to serve as unique markers ( 2 , 11 , 42 , 43 ). The absence of a single, definitive marker hinders our understanding of pulmonary pericyte ontogeny and differentiation capacity. Based on the differentially expressed genes found in our scRNA-seq and DCM-TM experiments, we evaluated the specificity of novel potential pulmonary pericyte-specific markers using immunocytochemistry. Notch3, Ebf1, and Mcam were analyzed for their temporal and spatial expression in Cspg4/Pdgfrb double positive cells within mouse lungs at E12.5, E15.5, and E18.5. Consistent with prior research by our group, Cspg4 maintained perivascular expression throughout lung development, while Pdgfrb expression became increasingly restricted to the vasculature by E18.5 ( 13 ) (Fig. 5 A, S4A, S5). Notably, very few Cspg4/Pdgfrb double positive cells were detected. At all time points, we found Notch3 expression in all Cspg4 positive cells, with the highest expression occurring in Csp4/Pdgfrb double positive cells (Fig. 5 B, S4A). A similar expression pattern was observed for Ebf1 (Fig. S5 ). This observation confirmed that Ebf1 and Notch3 serve as more selective markers for VSMCs and pericytes in the lung when compared to Cspg4 and Pdgfrb. To determine whether pericytes and perivascular type II cells could be distinguished from one another, we co-stained lungs for Notch3, Ebf1, and Mcam. Mcam was identified as highly specific for lung perivascular cells at all time points (Fig. 5 B, S4B). Furthermore, not all Ebf1 + cells co-expressed Mcam, demonstrating that Mcam can effectively differentiate between intermediate and mature pericytes in the lung. DISCUSSION In this study, we investigated pericyte differentiation throughout critical stages of lung development. We uncovered a Col1a1 + pericyte progenitor and identified an intermediate pericyte expressing Cxcl12 . Moreover, we established that pulmonary pericytes follow the developmental trajectory of VSMCs, affirming a shared lineage between pericytes and VSMCs. Additionally, we validated Mcam as a bona fide marker for pulmonary pericytes. There are currently only few publications that have studied lung pericyte origin in vivo . Peng et al. ( 44 ) used lineage tracing coupled with clonal analysis starting at E8.5 and identified a multipotent Wnt2 + progenitor that could generate the pulmonary smooth muscle compartment as well as pericytes in the proximal part of the murine lung. To date, these findings have only been corroborated by Zepp et al. ( 45 ), who integrated scRNA-seq and genomic lineage tracing of the murine distal lung, ranging from E12.5 to postnatal day 42, to study the developmental origin of various mesenchymal cell types. Although they did not define a specific pericyte population, Pgdfrb + cells were found two develop into either VSMCs or a cell type enriched for Hey l and Ebf1 , which is in line with our findings. Although widely used to predict cell trajectories, RNA velocity modeling is subject to many limitations ( 46 ). Projections can be distorted due to the simultaneous occurrence of multiple dynamic processes, including cell cycle and differentiation, making it particularly difficult to apply to developmental data, as illustrated by our RNA velocity analyses, which predicted backwards differentiation from later to earlier perivascular populations ( 47 , 48 ). By combining scRNA-seq with DCM-TM, we were able to bypass these challenges by retrospectively tracking genome wide gene transcription activity. This allowed us to identify and characterize pericyte progenitors in the developing lung for the first time. Unfortunately, the temporal window of our analysis was limited due to low methylation levels at the earliest time point (E8.5-10.5), which was subsequently excluded from further analysis. This might have been the result of the number of cell divisions, which have been shown to affect the DCM propagation rate ( 25 ). Moreover, the DCM methylation profile peaking at the middle time point did not show enrichment of one specific perivascular population. The consensus that pericytes are precursors to VSMCs is based on the observation that pericytes increasingly express smooth muscle cell markers in pathological settings such as pulmonary hypertension ( 21 , 49 ). Here, we demonstrate that pericytes increasingly express Heyl and Ebf1 , both transcription factors that determine VSMC cell fate ( 30 , 31 ), thus confirming that they originate from the same progenitor. In pericytes, Hey l acts as a downstream target of Notch3 , which plays an important role during in assuring pericyte coverage ( 50 ). Ebf1 plays an important role in pericyte function and cell fate commitment, as illustrated by the downregulation of Pdgfrb , Cspg4 , Mcam , Angpt1 , and Vegf in Ebf1 -silenced human brain pericytes ( 30 ). Interestingly, we found that intermediate pericytes express smooth muscle cell markers to a higher extent than mature pericytes, which contests the consensus that pericytes are VSMC progenitors. The absence of a single pericyte marker has complicated research into the ontogeny and differentiation of pericytes ( 3 , 42 ). Although Cspg4 and Pdgfrb are the most used markers for identifying pericytes in both mouse and human lungs, our data show that not all mature pericytes express both markers and that they are insufficient in distinguishing between intermediate and mature pericytes. Based on our experiments, we conclude that Mcam specifically marks mature pericytes in the developing lung. Mcam has been shown to function as a coreceptor of Pdgfrb in human brain pericytes, with specific deletion of Mcam leading to defects in pericyte coverage and blood-brain barrier integrity ( 51 ). Although little is known about its function in lung pericytes, our findings suggest a similar role for Mcam in the mouse lung. The role of retinoic acid (RA) as a key regulator of lung organogenesis and its involvement in both fetal and chronic respiratory disorders has been well established ( 52 ). Our current findings that RA signaling is highly enriched in all perivascular cell types supports our previous work, in which we demonstrated that interference with RA signaling prior to lung development leads to aberrant pericytes and subsequently to the abnormal lung development also observed in CDH ( 13 ). Interestingly, Ebf1 , which we found to be differentially expressed by intermediate and mature pericytes, has also been linked to RA signaling through its activation of the RA transcriptional coactivators CREB-binding protein and p300, implicating this transcription factor as a potential therapeutic target in pericyte-associated lung disease ( 53 , 54 ). This is further highlighted by its role in regulating the expression of Cxcl12 ( 55 ), a chemokine that has recently been found to drive pericyte accumulation in allergic asthma ( 14 ), and was found to be expressed solely by intermediate pericytes. In conclusion, by combining scRNA-seq with DCM-TM technology, we identified a pulmonary pericyte progenitor characterized by the expression of Col1a1 + that gives rise to pericytes through an intermediate Ebf1 + pericyte. Our findings pave the way for the understanding of pericyte ontogeny and differentiation in vivo and may contribute to new therapeutic strategies for diseases associated with pericyte dysfunction. METHODS Mice All mouse experimental protocols adhered to ethical guidelines and were approved by the Dutch Central Committee on the Ethics of Animal Experiments (AVD10100202216366). We conducted these experiments following national guidelines. The DCM–Polr2b:m2rtTA mouse line contains the m2rtTA trans-activator inserted in the Rosa locus and the DCM-Polr2b in the Col1a1 locus. To generate embryos heterozygous for DCM–Polr2b:m2rtTA , Wild-Type (WT) C57BL/6J females were crossed with homozygous DCM–Polr2b:m2rtTA transgenic males. The fusion transgene was induced by administering 2.0 mg/ml doxycycline in the drinking water for two days at four different gestational ages: embryonic day (E) 8.5–10.5, E10.5-12.5, E12.5-14.5, and E14.5-16.5 (Fig. 2 ). Sex determination of embryos was performed by PCR of the the sex-determining region of chromosome Y (SRY) and thyroid stimulating hormone β (TSH; control) previously published primer sequences ( 56 ). Fluorescence-activated cell sorting (FACS) Embryos were harvested at E18.5 and their lungs were isolated. For each induction, three independent cell preparations were processed. Three to four fetal lungs of male embryos were pooled in 750 µl of DMEM (Lonza) and mechanically dissociated them using dissection scissors. Following the addition of collagenase I, II, and IV (Sigma-Aldrich) at a final concentration of 5 µg/ml, the samples were incubated at 37°C in an Eppendorf shaker at 1000 rpm for 10 minutes. Subsequent mechanical dissociation involved vigorous resuspension, after which the cell suspensions were filtered through a 40-µm cell strainer. The filters were rinsed with 750 µl of DMEM with 10% FCS, and the resulting cell suspensions were centrifuged at 4°C for 10 minutes at 1000 rpm. After removing the supernatant, the cells were resuspended in 300 µl of 1x red blood cell lysis buffer at 4°C for 1 minute, diluted them in 900 µl of PBS with 10% FCS, and centrifuged them (10 minutes at 4°C, 1000 rpm). The cells were then resuspended in 300 µl of PBS with 5% FCS. Cells were counted and incubated at 4°C in the dark for 40 minutes with the following antibodies: CD31-PE-Cy7 (1:100, ThermoFisher), CD45-PE-Texas Red (1:100, ThermoFisher), CSPG4 -Alexa-488 (1:100, Merck), and PDGFR-β-APC (1:100, ThermoFisher). Prior to sorting, the cells were concentrated and resuspended in 800 µl of PBS with 5% FCS. DAPI (1:10000, BD Biosciences). CD45 − cells were sorted for CD31 and for CSPG4/PDGFRB using a BD FACS Aria III and BD FACSDiva software version 8.0.1. DNA was isolated from the sorted cells using QIAamp DNA micro kit according to the manufacturer’s protocol, and RNA was isolated by Trizol extraction. At the E8.5-10.10 induction time point, cells were sorted using an unconjugated CSPG4 antibody (1:100, Millipore) for 40 minutes, and subsequently incubated with goat anti-rabbit Alexa 488 secondary antibody (1:200, Jackson) for 30 minutes MeD-seq sample preparations and data analysis MeD-seq analyses were performed as previously described, with all experimental time points conducted in triplicate ( 25 , 34 ). Data processing was carried out using custom scripts in Python and MATLAB. Raw FASTQ files were subjected to Illumina adaptor trimming, and reads were filtered based on LpnPI restriction site occurrence between 13 bp and 17 bp from either 5′ or 3′ end of the read. DCM methylation data (CCWGG sites) and CpG methylation data (CCG, CGG and GCGC sites) were separated during filtering and mapped separately to mm10 using bowtie2. Genome-wide individual DCM site scores were used to generate read count scores for all annotated genes from UCSC (GRCm38.p2). BAM files were generated using SAMtools version 0.1.19 for visualization in IGV51,52. Because DCM and CpG methylation can be detected separately using MeD-seq, DCM enrichment was determined by either data normalization using CpG read coverage (for absolute DCM enrichment) or DCM read coverage (for relative DCM enrichment) between samples. For both situations, normalization is done using reads per million (RPM), where absolute DCM levels indicate the level of DCM–Rpol2b induction, and relative DCM levels are used to correct for differences in DCM–Rpol2b induction between mice and/or time points. Single cell RNA-sequencing (scRNA-seq) WT male fetal mouse lungs were isolated at E10.5, E12.5, E14.5, E16.5, and E18.5 and processed for single cell as described (N = 1). For samples isolated at E10.5 and E12.5, duplicates were obtained, one containing only one lung and the other containing three lungs. Samples isolated at later time points consisted of one single lung. The resulting cell suspensions were processed by the Erasmus Center for Biomics and/or Department for Hematology. Single-cell libraries were prepared using the Chromium Single Cell 3’ Reagent Kit v3 (10x Genomics). Next-generation sequencing (28-8-0-91 cycles) was conducted on an Illumina NovaSeq6000 platform (Illumina). Raw data were processed into FASTQ files, and sequences were quality-checked using FastQC (version v0.11.5). Pre-processing of scRNA-seq data Reads from each batch were processed separately using the 10x Genomics Cell Ranger v6.6.0 pipeline ( 57 )with the mm10 2020-A reference dataset. Cellranger count was run to map the reads to mm10 and generate count matrices based on Ensembl v98 annotation. The counts were further preprocessed using R v4.0.5 and Seurat v4.3.0 ( 58 ). SoupX v1.5.2 ( 59 ) was used to remove ambient background RNAs from the datasets. We removed doublets using doubletfinder v2.0.3 ( 60 ) with the settings pN = 0.25, pK = 0.09, PCs = 1:10, nExp = number of expected doublets based on each library size. Cells with less 1,000 or more than 9,000 detected genes, more than 70,000 counts or more than 20 percent mitochondrial reads were removed. Furthermore, blood cells were filtered out by excluding cells with over 25% of all reads mapped to hemoglobin genes. Genes expressed in less than 3 cells, Malat1 and Gm42418 were removed for further analysis. Integration of scRNA-seq datasets The datasets were normalized using SCTransform v2 ( 61 , 62 ) with the glmGamPoi method ( 63 ) and percentages of ribosomal, percentage of mitochondrial genes and difference in cell cycle as vars.to.regress. Integration of the datasets was prepared using the SelectIntegrationFeatures (nfeatures = 3000), PrepSCTIntegration and RunPCA functions. Integration anchors were found using FindIntegrationAnchors with SCT as normalization.method, 30 dimensions, rpca reduction, and 20 anchors. Finally, the cells were integrated using IntegrateData with SCT as normalization.method and 30 dimensions. An Uniform Manifold Approximation and Projection (UMAP) ( 64 ) was created using RunUMAP based on the first 30 principal components from RunPCA. Last, FindNeighbors and FindClusters with a resolution of 0.5 were used to identify clusters. We subsetted the mesenchymal cell clusters and reanalyzed these cells separately as described above to focus further on the dynamics within the mesenchyme. Cluster annotation The clusters were annotated based on the expression of known markers and differentially expressed genes between clusters. Differential expression analysis was performed using scanpy v1.9.1 ( 65 ) rank_genes_groups with the Wilcoxon method. The expression of the 5 most differential expressed genes per cluster were visualized using rank_genes_groups_heatmap. Furthermore, the expression of known marker genes was plotted using the scanpy dotplot function. RNA velocity analysis Velocyto v0.17.17 ( 66 ) run10x was run on each dataset seperately to create loom files with spliced and unspliced counts. The loom files were analyzed using scvelo v0.3.0 ( 67 ). The counts were pre-processed using scvelo filter_and_normalize with 20 min_shared_counts and 2000 n_top_genes, scanpy v1.9.1 pca and scanpy neighbors with 30 n_pcs and 30 n_neighbors. We ran scvelo moments and recover_dynamics to compute moments for velocity estimation and recover the full splicing kinetics, respectively. Finally, scvelo velocity and velocity_graph was run with mode=“dynamical” to obtain the velocity estimates. The velocity of the cells was plotted on the mesenchyme UMAP using scvelo velocity_embedding_stream with smooth = 0.8 and min_mass = 2. The velocity was also estimated using the “stochastic” mode of scvelo with the same settings. Based on the velocity predictions, we isolated and reclustered closely related smooth muscle and non-smooth muscle cell types (cluster 6, 7, 8, 10, 11, 13) as described above. Moreover, the RNA velocity analysis was rerun on the perivascular clusters seperately. CellphoneDB Cell-cell interactions between the different clusters were identified using CellphoneDB v2.1.7 ( 41 ) using the statistical analysis method. The number of interactions between each cluster with a P-value below 0.05 was visualized in a heatmap, which was plotted using cellphonedb heatmap_plot. Immunofluorescence (IF) on paraffin sections IF stainings 5 µm thick paraffin sections of either isolated lungs or whole embryos were performed following established protocols ( 68 ). Primary and secondary antibodies used for IF are detailed in Table 1 . To facilitate antigen retrieval, antigen retrieval was performed using Tris-EDTA buffer with a pH of 9.0, followed by blocking using either a 3% BSA fraction V (Roche) or 5% ELK (Campina) in PBS-Tween 20 0.05%. For double stainings with both CSPG4 and PDGFRb, a tyramide signal amplification kit (ThermoFisher) was used. For triple stainings including CSPG4 and PDGFRb, two tyramide signal amplification kits were used. Autofluorescence of erythrocytes was quenched using Vector TrueVIEW autofluorescence quenching kit (Vector Laboratories, SP8400). Table 1 Primary and secondary antibodies used for IF. IF = immunofluorescence. Notch3 = notch receptor 3. Ebf1 = early B-cell factor 1. Mcam = melanoma cell adhesion molecule. Cspg4 = chondroitin sulfate proteoglycan 4. Pdgfrb = platelet-derived growth factor receptor beta. Antibody Source Dilution Notch3 R&D systems, AF1308 1:200 in 5% ELK/PBS-T Ebf1 Merck, AB10523 1:1000 in 5% ELK/PBS-T Mcam BioLegend, 134712 1:25 in 5% ELK/PBS-T Cspg4 Merck, AB5320 1:250 in 5% ELK/PBS-T Pdgfrb Cell signaling Technology, 3169 1:100 in 5% ELK/PBS-T Donkey anti-rabbit HRP Jackson ImmunoResearch, 711-035-152 1:500 in 3% BSA/PBS-T Donkey anti-rabbit Alexa 488 Jackson ImmunoResearch, 711-545-152 1:500 in 3% BSA/PBS-T Donkey anti-rabbit Alexa 647 Jackson ImmunoResearch 711-605-152 1:500 in 3% BSA/PBS- Donkey anti-goat Alexa 594 Jackson ImmunoResearch, 705-585-147 1:500 in 3% BSA/PBS-T Donkey anti-goat Alexa 647 Jackson ImmunoResearch, 705-605-147 1:500 in 3% BSA/PBS-T Donkey anti-rat Alexa 647 Jackson ImmunoResearch, 712-605-153 1:500 in 3% BSA/PBS-T Declarations ACKNOWLEDGEMENTS We thank Gabriëla Edel for her help with the experimental setup, Tsung Wai Kan and dr. Alex Maas for performing the FACS sorts, and dr. Eric Bindels for RNA-sequencing the first samples. This work was supported by grants from the Sophia Foundation for Medical Research (grant number S18-19), and ZonMw (114025011; RJR). DATA AVAILABILITY All raw and processed high-throughput sequencing data (MeD-seq, scRNA-seq) generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO) under accession number GSE######. References Whitsett JA, Kalin TV, Xu Y, Kalinichenko VV (2019) Building and Regenerating the Lung Cell by Cell. Physiol Rev 99(1):513–554 Barron L, Gharib SA, Duffield JS (2016) Lung Pericytes and Resident Fibroblasts: Busy Multitaskers. Am J Pathol 186(10):2519–2531 van Splunder H, Villacampa P, Martínez-Romero A, Graupera M (2023) Pericytes in the disease spotlight. Trends Cell Biol Kato K, Diéguez-Hurtado R, Park DY, Hong SP, Kato-Azuma S, Adams S et al (2018) Pulmonary pericytes regulate lung morphogenesis. Nat Commun 9(1):2448 Parera MC, van Dooren M, van Kempen M, de Krijger R, Grosveld F, Tibboel D et al (2005) Distal angiogenesis: a new concept for lung vascular morphogenesis. Am J Physiol Lung Cell Mol Physiol 288(1):L141–L149 Sweeney M, Foldes G (2018) It Takes Two: Endothelial-Perivascular Cell Cross-Talk in Vascular Development and Disease. Front Cardiovasc Med 5:154 Yuan K, Agarwal S, Chakraborty A, Condon DF, Patel H, Zhang S et al (2021) Lung Pericytes in Pulmonary Vascular Physiology and Pathophysiology. Compr Physiol 11(3):2227–2247 Lazarus A, Del-Moral PM, Ilovich O, Mishani E, Warburton D, Keshet E (2011) A perfusion-independent role of blood vessels in determining branching stereotypy of lung airways. Development 138(11):2359–2368 van Tuyl M, Liu J, Wang J, Kuliszewski M, Tibboel D, Post M (2005) Role of oxygen and vascular development in epithelial branching morphogenesis of the developing mouse lung. Am J Physiol Lung Cell Mol Physiol 288(1):L167–L178 Holm A, Heumann T, Augustin HG (2018) Microvascular Mural Cell Organotypic Heterogeneity and Functional Plasticity. Trends Cell Biol 28(4):302–316 Garrison AT, Bignold RE, Wu X, Johnson JR (2023) Pericytes: The lung-forgotten cell type. Front Physiol 14:1150028 Bordenave J, Tu L, Berrebeh N, Thuillet R, Cumont A, Le Vely B et al (2020) Lineage Tracing Reveals the Dynamic Contribution of Pericytes to the Blood Vessel Remodeling in Pulmonary Hypertension. Arterioscler Thromb Vasc Biol 40(3):766–782 Kool HM, Bürgisser PE, Edel GG, de Kleer I, Boerema-de Munck A, de Laat I et al (2019) Inhibition of retinoic acid signaling induces aberrant pericyte coverage and differentiation resulting in vascular defects in congenital diaphragmatic hernia. Am J Physiol Lung Cell Mol Physiol 317(3):L317–L31 Bignold R, Shammout B, Rowley JE, Repici M, Simms J, Johnson JR (2022) Chemokine CXCL12 drives pericyte accumulation and airway remodeling in allergic airway disease. Respir Res 23(1):183 Sluiter I, Reiss I, Kraemer U, Krijger R, Tibboel D, Rottier RJ (2011) Vascular abnormalities in human newborns with pulmonary hypertension. Expert Rev Respir Med 5(2):245–256 Mirko C, Chien-Wen C, Mihaela C, Lorenza L, Bruno P, Arteriosclerosis (2010) Thromb Vascular Biology 30(6):1104–1109 Crisan M, Yap S, Casteilla L, Chen CW, Corselli M, Park TS et al (2008) A perivascular origin for mesenchymal stem cells in multiple human organs. Cell Stem Cell 3(3):301–313 de Souza LE, Malta TM, Kashima Haddad S, Covas DT (2016) Mesenchymal Stem Cells and Pericytes: To What Extent Are They Related? Stem Cells Dev 25(24):1843–1852 Guimarães-Camboa N, Cattaneo P, Sun Y, Moore-Morris T, Gu Y, Dalton ND et al (2017) Pericytes of Multiple Organs Do Not Behave as Mesenchymal Stem Cells In Vivo. Cell Stem Cell 20(3):345–359 e5 Volz KS, Jacobs AH, Chen HI, Poduri A, McKay AS, Riordan DP et al (2015) Pericytes are progenitors for coronary artery smooth muscle. Elife. ;4 Sheikh AQ, Misra A, Rosas IO, Adams RH, Greif DM (2015) Smooth muscle cell progenitors are primed to muscularize in pulmonary hypertension. Sci Transl Med 7(308):308ra159 Ronkainen J, Aro P, Storskrubb T, Johansson SE, Lind T, Bolling-Sternevald E et al (2005) Prevalence of Barrett's esophagus in the general population: an endoscopic study. Gastroenterology 129(6):1825–1831 Wu Y, Fu J, Huang Y, Duan R, Zhang W, Wang C et al (2023) Biology and function of pericytes in the vascular microcirculation. Anim Models Experimental Med 6(4):337–345 Sluiter I, van der Horst I, van der Voorn P, Boerema-de Munck A, Buscop-van Kempen M, de Krijger R et al (2013) Premature differentiation of vascular smooth muscle cells in human congenital diaphragmatic hernia. Exp Mol Pathol 94(1):195–202 Boers R, Boers J, Tan B, van Leeuwen ME, Wassenaar E, Sanchez EG et al (2023) Retrospective analysis of enhancer activity and transcriptome history. Nat Biotechnol Tefft JB, Bays JL, Lammers A, Kim S, Eyckmans J, Chen CS (2022) Notch1 and Notch3 coordinate for pericyte-induced stabilization of vasculature. Am J Physiol Cell Physiol 322(2):C185–C96 Su H, Cantrell AC, Zeng H, Zhu SH, Chen JX (2021) Emerging Role of Pericytes and Their Secretome in the Heart. Cells. ;10(3) Yuan K, Liu Y, Zhang Y, Nathan A, Tian W, Yu J et al (2020) Mural Cell SDF1 Signaling Is Associated with the Pathogenesis of Pulmonary Arterial Hypertension. Am J Respir Cell Mol Biol 62(6):747–759 Liu X, Rowan SC, Liang J, Yao C, Huang G, Deng N et al (2021) Categorization of lung mesenchymal cells in development and fibrosis. iScience 24(6):102551 Pagani F, Tratta E, Dell'Era P, Cominelli M, Poliani PL (2021) EBF1 is expressed in pericytes and contributes to pericyte cell commitment. Histochem Cell Biol 156(4):333–347 Cao S, Feng H, Yi H, Pan M, Lin L, Zhang YS et al (2023) Single-cell RNA sequencing reveals the developmental program underlying proximal-distal patterning of the human lung at the embryonic stage. Cell Res 33(6):421–433 Negretti NM, Plosa EJ, Benjamin JT, Schuler BA, Habermann AC, Jetter CS et al (2021) A single-cell atlas of mouse lung development. Development. ;148(24) Xia M, Jiao L, Wang XH, Tong M, Yao MD, Li XM et al (2023) Single-cell RNA sequencing reveals a unique pericyte type associated with capillary dysfunction. Theranostics 13(8):2515–2530 Boers R, Boers J, de Hoon B, Kockx C, Ozgur Z, Molijn A et al (2018) Genome-wide DNA methylation profiling using the methylation-dependent restriction enzyme LpnPI. Genome Res 28(1):88–99 De Langhe SP, Reynolds SD (2008) Wnt signaling in lung organogenesis. Organogenesis 4(2):100–108 Aros CJ, Pantoja CJ, Gomperts BN (2021) Wnt signaling in lung development, regeneration, and disease progression. Commun Biol 4(1):601 White AC, Lavine KJ, Ornitz DM (2007) FGF9 and SHH regulate mesenchymal Vegfa expression and development of the pulmonary capillary network. Development 134(20):3743–3752 Goodwin K, Jaslove JM, Tao H, Zhu M, Hopyan S, Nelson CM (2022) Patterning the embryonic pulmonary mesenchyme. iScience 25(3):103838 Kiyokawa H, Morimoto M (2020) Notch signaling in the mammalian respiratory system, specifically the trachea and lungs, in development, homeostasis, regeneration, and disease. Dev Growth Differ 62(1):67–79 Akira S, Takahide N (2015) Hippo and TGF-β interplay in the lung field. Am J Physiology-Lung Cell Mol Physiol 309(8):L756–L67 Efremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R (2020) CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc 15(4):1484–1506 Yamazaki T, Mukouyama YS (2018) Tissue Specific Origin, Development, and Pathological Perspectives of Pericytes. Front Cardiovasc Med 5:78 Baek SH, Maiorino E, Kim H, Glass K, Raby BA, Yuan K (2022) Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities. Front Cardiovasc Med 9:876591 Peng T, Tian Y, Boogerd CJ, Lu MM, Kadzik RS, Stewart KM et al (2013) Coordination of heart and lung co-development by a multipotent cardiopulmonary progenitor. Nature 500(7464):589–592 Zepp JA, Morley MP, Loebel C, Kremp MM, Chaudhry FN, Basil MC et al (2021) Genomic, epigenomic, and biophysical cues controlling the emergence of the lung alveolus. Science. ;371(6534) Zheng SC, Stein-O’Brien G, Boukas L, Goff LA, Hansen KD (2023) Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates. Genome Biol 24(1):246 Bergen V, Soldatov RA, Kharchenko PV, Theis FJ (2021) RNA velocity—current challenges and future perspectives. Mol Syst Biol 17(8):e10282 Barile M, Imaz-Rosshandler I, Inzani I, Ghazanfar S, Nichols J, Marioni JC et al (2021) Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. Genome Biol 22(1):197 Gaskill CF, Carrier EJ, Kropski JA, Bloodworth NC, Menon S, Foronjy RF et al (2017) Disruption of lineage specification in adult pulmonary mesenchymal progenitor cells promotes microvascular dysfunction. J Clin Invest 127(6):2262–2276 Kofler NM, Cuervo H, Uh MK, Murtomäki A, Kitajewski J (2015) Combined deficiency of Notch1 and Notch3 causes pericyte dysfunction, models CADASIL and results in arteriovenous malformations. Sci Rep 5(1):16449 Chen J, Luo Y, Hui H, Cai T, Huang H, Yang F et al (2017) CD146 coordinates brain endothelial cell-pericyte communication for blood-brain barrier development. Proc Natl Acad Sci U S A 114(36):E7622–E31 Fernandes-Silva H, Araújo-Silva H, Correia-Pinto J, Moura RS (2020) Retinoic Acid: A Key Regulator of Lung Development. Biomolecules. ;10(1) Zhao F, McCarrick-Walmsley R, Akerblad P, Sigvardsson M, Kadesch T (2003) Inhibition of p300/CBP by early B-cell factor. Mol Cell Biol 23(11):3837–3846 Chen W, Jia W, Wang K, Si X, Zhu S, Duan T et al (2013) Distinct roles for CBP and p300 on the RA-mediated expression of the meiosis commitment gene Stra8 in mouse embryonic stem cells. PLoS ONE 8(6):e66076 Lagergren A, Månsson R, Zetterblad J, Smith E, Basta B, Bryder D et al (2007) The Cxcl12, periostin, and Ccl9 genes are direct targets for early B-cell factor in OP-9 stroma cells. J Biol Chem 282(19):14454–14462 Lavrovsky Y, Song CS, Chatterjee B, Roy AK (1998) A rapid and reliable PCR-based assay for gene transmission and sex determination in newborn transgenic mice. Transgenic Res 7(4):319–320 Zheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049 Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495–502 Young MD, Behjati S (2020) SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. ;9(12) McGinnis CS, Murrow LM, Gartner ZJ, DoubletFinder (2019) Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst 8(4):329–337 e4 Choudhary S, Satija R (2022) Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol 23(1):27 Hafemeister C, Satija R (2019) Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20(1):296 Ahlmann-Eltze C, Huber W (2020) glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data. Bioinformatics 36(24):5701–5702 McInnes LH, Melville J (2018) J.;. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction Wolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15 La Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V et al (2018) RNA velocity of single cells. Nature 560(7719):494–498 Bergen V, Lange M, Peidli S, Wolf FA, Theis FJ (2020) Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol 38(12):1408–1414 Eenjes E, Benthem F, Boerema-de Munck A, Buscop-van Kempen M, Tibboel D, Rottier RJ (2023) Distinct roles for SOX2 and SOX21 in differentiation, distribution and maturation of pulmonary neuroendocrine cells. Cell Mol Life Sci 80(3):79 Additional Declarations There is NO Competing Interest. Supplementary Files DCMpericyteSupplementarymaterialsfinalversion.docx SreerametalSupplementaryfigure1.pdf SreerametalSupplementaryfigure2.pdf SreerametalSupplementaryfigure3.pdf SreerametalSupplementaryfigure4.pdf SreerametalSupplementaryfigure5.pdf Rob.pdf Reporting Summary Cite Share Download PDF Status: Under Review Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4349859","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":331271148,"identity":"cdff5d45-8cfd-4147-8fc2-b4fa4d6225e6","order_by":0,"name":"Robbert Rottier","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3PoQ6CQBzH8WP/DcrZzwH6CrgLGHwYbmwkApGkJJrVwQi8gsnMxmZy8wGuwKgGnYVA8GgEBaLhvvG3ffa/Q0gm+98IQlo0HIIJUDiC4GI4WZOkP+TMJHa0qJtXu13ZaXN9tt2F5aeygmCEGIVGLfEwanDPTY8xZ2fuWZCMEIJUlQjCEt2naBEJovsI8AxySJb3t9J1nOXZTOIQggGwylnEpwioYN08skmwR8GMOe3/Uo4SLVaqcLdfE61slEfHzTxz6waHvwmCb2MxAmQymUw2ow8RtEdc0ZbFOgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-9291-4971","institution":"Erasmus Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Robbert","middleName":"","lastName":"Rottier","suffix":""},{"id":331271149,"identity":"461d4599-0cca-49b3-8c28-a44b971a80dc","order_by":1,"name":"Isabel Sreeram","email":"","orcid":"","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Isabel","middleName":"","lastName":"Sreeram","suffix":""},{"id":331271150,"identity":"0eb4e1f6-0b5a-442f-922c-b76c19a73526","order_by":2,"name":"Ruben Boers","email":"","orcid":"https://orcid.org/0000-0002-3377-2897","institution":"Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Ruben","middleName":"","lastName":"Boers","suffix":""},{"id":331271151,"identity":"b072a2bd-108a-4902-82e6-5a54925af7c9","order_by":3,"name":"Joachim Boers","email":"","orcid":"","institution":"Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Joachim","middleName":"","lastName":"Boers","suffix":""},{"id":331271152,"identity":"c83d8fd2-2f72-4d49-ae93-08bf757446a4","order_by":4,"name":"Beatrice Tan","email":"","orcid":"https://orcid.org/0000-0001-6362-7474","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Beatrice","middleName":"","lastName":"Tan","suffix":""},{"id":331271153,"identity":"c6c6b8c3-d98b-4161-a8b4-9e869da781fb","order_by":5,"name":"Anne Boerema-de Munck","email":"","orcid":"","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Anne","middleName":"Boerema-de","lastName":"Munck","suffix":""},{"id":331271154,"identity":"a5a5257c-ff1d-42ee-9cff-993d50704df5","order_by":6,"name":"Marjon Buscop-van Kempen","email":"","orcid":"","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Marjon","middleName":"Buscop-van","lastName":"Kempen","suffix":""},{"id":331271155,"identity":"cc59a6ba-8c07-4ccd-81b8-854560db7078","order_by":7,"name":"Wilfred van IJcken","email":"","orcid":"https://orcid.org/0000-0002-0421-8301","institution":"Erasmus University Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Wilfred","middleName":"van","lastName":"IJcken","suffix":""},{"id":331271156,"identity":"4461e20b-5f8f-4627-8a1a-3ebe5f59f678","order_by":8,"name":"J. Marco Schnater","email":"","orcid":"https://orcid.org/0000-0003-4910-8293","institution":"Erasmus Medical Center","correspondingAuthor":false,"prefix":"","firstName":"J.","middleName":"Marco","lastName":"Schnater","suffix":""},{"id":331271157,"identity":"9d376392-bc4e-4cf9-93aa-5df3c9fdb979","order_by":9,"name":"René Wijnen","email":"","orcid":"","institution":"Department of Pediatric Surgery, Erasmus University Medical Centre Rotterdam","correspondingAuthor":false,"prefix":"","firstName":"René","middleName":"","lastName":"Wijnen","suffix":""},{"id":331271158,"identity":"f2b0c0e0-ab2c-4bc9-bedb-5a107f93aac6","order_by":10,"name":"Joost Gribnau","email":"","orcid":"https://orcid.org/0000-0001-5645-4691","institution":"Erasmus MC","correspondingAuthor":false,"prefix":"","firstName":"Joost","middleName":"","lastName":"Gribnau","suffix":""}],"badges":[],"createdAt":"2024-04-30 14:47:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4349859/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4349859/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61060433,"identity":"29938fa2-aa55-44f9-a2c8-7812331625a8","added_by":"auto","created_at":"2024-07-25 06:34:14","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":260505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLongitudinal single-cell transcriptomics of mouse pulmonary mesenchymal cells at the pseudoglandular and canalicular stages of lung development.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. UMAP of a mesenchymal subset of mouse embryonic lung scRNA-seq data ranging from E 10.5 to E18.5 showing clusters annotated as specific cell types. (N=2 for E10.5 and E12.5, N=1 for E14.5, E16.5, and E18.5) B. UMAP visualization of sample integration of E10.5, E12.5, E14.5, E16.5, and E18.5 mouse pulmonary mesenchymal cell data. C. UMAP layout mapping the distribution of single cell profiles of each time point denoted as the embryonic day. D. Heatmap showing the top 5 differentially expressed genes for each mesenchymal subtype. E. Dot plot showing the expression of the most important cell type markers. F. Stochastic model of the RNA velocity analysis of mouse pulmonary mesenchymal subset.\u003c/p\u003e","description":"","filename":"SreerametalFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/2511c89b0a2e5c88a7c65d57.png"},{"id":61062874,"identity":"c6039243-b48c-40d1-9c1c-13e8b4004b83","added_by":"auto","created_at":"2024-07-25 06:58:14","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73597,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSchematic overview of the experimental setup and DCM-TM and MeD-seq pipelines.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SreerametalFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/0746c3e050edfc7edaf4e9e0.png"},{"id":61060435,"identity":"8c3beb72-5738-4f91-abca-23995d93089f","added_by":"auto","created_at":"2024-07-25 06:34:14","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":188159,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDCM labeling tracks pericyte differentiation back in time.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, B. DCM labeling (fold change in DCM reads relative and to total and normalized to controls) of pericyte (A) and VSMC-specific (B) genes (N=3). C. DCM labeling of all significantly labeled genes (control samples compared to all time points) clustered according to the maximum DCM signal. For each cluster, the capture by scRNA-seq is shown as the percentage of cells with signal (blue to red) and the average expression of clustered genes is plotted in the UMAP shown in figure 1A. Here, only the 3 perivascular cell types are visualized. D. KEGG pathway enrichment analysis of DCM labeled genes in pericytes at the three induction time points. E, F. DCM labeling (fold change in DCM reads relative and to total and normalized to controls) of genes associated with stem cell pluripotency (E) and VSMC contraction (F). G. Model of pulmonary pericyte differentiation derived from the combination of scRNA-seq and DCM-TM shown in C.\u003c/p\u003e","description":"","filename":"SreerametalFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/b1700302aa3523f32a8932d0.png"},{"id":61060432,"identity":"b1d92fb6-b45e-4ab1-af0b-01a160f3b993","added_by":"auto","created_at":"2024-07-25 06:34:14","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74620,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSignaling pathways in pericyte differentiation and vascular development.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, B. KEGG pathway enrichment analysis of DCM labeled genes of signaling pathways in pericytes (A) and endothelial cells (B) at the three induction time points. C. Heatmap showing the number of significant interactions between the three perivascular cell types based on the scRNA-seq analysis of the mesenchymal subset. D. Diagram of the most significant receptor-ligand interactions between the perivascular cell types. E. Diagram of retinoic acid signaling between the pericyte progenitor and intermediate pericyte. F. Heatmap showing the number of significant interactions between the perivascular cell types and endothelial cells based on the scRNA-seq analysis of the whole lung. G. Diagrams of the most significant receptor-ligand interactions between the perivascular cell types and endothelial cells.\u003c/p\u003e","description":"","filename":"SreerametalFigure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/df030d57406c07e12731ae54.png"},{"id":61061278,"identity":"0b50205d-7394-48f6-ba77-dbd61358f342","added_by":"auto","created_at":"2024-07-25 06:42:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4414958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTemporal expression of known and novel pulmonary pericyte markers.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA, B. Immunofluorescence staining with antibodies directed against (A), Cspg4 (green), Pdgfrb (red), and (B) Notch3 (gray) and Mcam (green) Notch3 (red) and Ebf1 (gray) at E12.5, E15.5, and E18.5 (500x500 selection of pulmonary blood vessels. DNA is DAPI stained. Representative image shown from n = 3. All images taken at 40x magnification).\u003c/p\u003e","description":"","filename":"SreerametalFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/fd64aa9d0b0af944ed3545a4.png"},{"id":61063235,"identity":"690496e5-88ff-4920-8c6d-e8326be67729","added_by":"auto","created_at":"2024-07-25 07:06:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5455339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/b0098b6d-d572-4795-87be-b2a130748875.pdf"},{"id":61060440,"identity":"a8da2f01-60fe-4eee-865a-a80084f99911","added_by":"auto","created_at":"2024-07-25 06:34:14","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":21666,"visible":true,"origin":"","legend":"","description":"","filename":"DCMpericyteSupplementarymaterialsfinalversion.docx","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/8a45690746907275c3b913cc.docx"},{"id":61061279,"identity":"2a24bf40-c07a-4ce3-a8af-2911bdbc1feb","added_by":"auto","created_at":"2024-07-25 06:42:14","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":17652964,"visible":true,"origin":"","legend":"","description":"","filename":"SreerametalSupplementaryfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/03287133aa9e4e24f43bba1f.pdf"},{"id":61062173,"identity":"29c2718a-7e6d-43b9-9663-7fad2ee87d7b","added_by":"auto","created_at":"2024-07-25 06:50:14","extension":"pdf","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":618033,"visible":true,"origin":"","legend":"","description":"","filename":"SreerametalSupplementaryfigure2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/498a65e67d0660c8f5511735.pdf"},{"id":61061280,"identity":"3699aca7-9b12-42ba-ae05-d8054c08c1d7","added_by":"auto","created_at":"2024-07-25 06:42:14","extension":"pdf","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":3071305,"visible":true,"origin":"","legend":"","description":"","filename":"SreerametalSupplementaryfigure3.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/3a70dec7572f093793018638.pdf"},{"id":61061285,"identity":"f105905f-ae45-4f60-ab82-193da7ec01bc","added_by":"auto","created_at":"2024-07-25 06:42:14","extension":"pdf","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":27645274,"visible":true,"origin":"","legend":"","description":"","filename":"SreerametalSupplementaryfigure4.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/823462595737a67ff1c75e12.pdf"},{"id":61061284,"identity":"d05a3807-363e-408a-96f4-7c2824991b76","added_by":"auto","created_at":"2024-07-25 06:42:14","extension":"pdf","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":10155334,"visible":true,"origin":"","legend":"","description":"","filename":"SreerametalSupplementaryfigure5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/40ed98f22bdc614fe5c87c4c.pdf"},{"id":61060441,"identity":"9e7d8c44-3796-44b9-b512-1eacf0a16fcb","added_by":"auto","created_at":"2024-07-25 06:34:14","extension":"pdf","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":4112211,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"Rob.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4349859/v1/66e050517f5aa4ebe7c64ab0.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"\u003cp\u003eThe Cellular Origin of the Pulmonary Pericyte\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003ePericytes, a perivascular subset of mesenchymal cells, play a pivotal role in orchestrating lung development and maintaining vascular homeostasis (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). Their multifaceted contributions span angiogenesis, branching morphogenesis, and regulation of the pulmonary vascular tone (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Pericytes reside within the abluminal surface of the microvasculature, where they interact directly with endothelial cells (ECs) to modulate angiogenic cues and provide structural support, thus facilitating the expansion of the pulmonary vasculature (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). In the lung, this is defined by distal angiogenesis, a process during which new vessels sprout from preexisting ones (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Pericytes initiate angiogenesis by detaching from the basement membrane and are later recruited by ECs to structurally support the newly formed vessels (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). This pericyte-EC interplay is central to the intricate orchestration of vascular development and function within the lung (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). The expansion of the pulmonary vasculature is essential to three-dimensional airway branching (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, pericytes have been found to directly regulate postnatal lung morphogenesis and are thus indispensable for proper lung development (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA growing body of evidence points to the involvement of pericytes in the onset and progression of numerous vascular and nonvascular lung diseases, including pulmonary hypertension, asthma, and chronic obstructive pulmonary disease (COPD) (\u003cspan additionalcitationids=\"CR11 CR12 CR13\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Altered pericyte behavior is particularly evident in both congenital and acquired types of pulmonary hypertension. This is characterized by extensive vascular remodeling, including increased pericyte coverage and aberrant expression of \u003cem\u003eACTA2\u003c/em\u003e, which in some diseases is already present early during gestation (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Consequently, pericytes are a subject of intense research.\u003c/p\u003e \u003cp\u003eGiven their capacity to differentiate into a wide range of mesenchymal subtypes \u003cem\u003ein vitro\u003c/em\u003e and following transplantation \u003cem\u003ein vivo\u003c/em\u003e, pericytes were long regarded as mesenchymal stem cells (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, lineage tracing studies have challenged this assumption, suggesting that pericytes represent a more differentiated cell type (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The notion that they share functional and molecular characteristics with vascular smooth muscle cells (VSMCs), including the expression of the smooth muscle cell marker \u003cem\u003eACTA2\u003c/em\u003e, has led to them being generally considered to be VSMC progenitors, although discriminative markers have not yet been found (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne of the prominent challenges in pericyte research is the absence of definitive molecular markers that exclusively identify this cell population, especially at the early stages of development (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Conventionally used markers, such as CSPG4 and PDGFRB, have fallen short in providing distinct and consistent pericyte identification. As such, the origin of the pulmonary pericytes has remained enigmatic (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePreviously, we showed that perivascular cells are prime candidates to underlie the vascular abnormalities found in congenital diaphragmatic hernia (CDH) associated pulmonary hypertension (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). In CDH, perivascular cells were found to differentiate prematurely towards a more contractile phenotype and to aberrantly cover the pulmonary vessels, which was found to be the result of inhibited retinoic acid (RA) signaling (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). To increase our understanding of pericytes during lung development, we combined single-cell RNA sequencing (scRNA-seq) and DCM-time machine (DCM-TM) technology (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) to uncover the developmental origin of the mouse pulmonary pericyte. This DCM-TM technology utilizes an inducible bacterial DNA cytosine methyltransferase (DCM) fused to RNA polymerase II, to DCM label active genes and enhancers. This bacterial DNA methylation mark, which is absent in eukaryotes, is propagated during cell division, ensuring its presence in all descendants. By applying DCM-TM in combination with scRNA-seq, we identified a pericyte progenitor that gives rise to both pericytes and VSMCs. Furthermore, we explore the expression of novel pericyte-specific markers throughout lung development.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003escRNA-seq identifies three molecularly distinct lung perivascular populations\u003c/h2\u003e \u003cp\u003ePericytes, a mesenchymal cell type closely linked to vascular smooth muscle cells (VSMCs) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), remain poorly characterized. We investigated the ontogeny of lung pericytes using whole-lung scRNA-seq at multiple stages of mouse lung development, ranging from E10.5 to E18.5. Through unbiased clustering and cell type annotation, we identified endothelial (\u003cem\u003ePecam1\u003c/em\u003e+), epithelial (\u003cem\u003eEpcam\u003c/em\u003e+\u003cem\u003e)\u003c/em\u003e, mesothelial (\u003cem\u003eWt1+\u003c/em\u003e), immune (\u003cem\u003ePtprc\u003c/em\u003e+), and mesenchymal (\u003cem\u003ePecam1\u003c/em\u003e-, \u003cem\u003eEpcam-, Ptprc\u003c/em\u003e-) cell compartments (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA-B). Mesenchymal cells were subsequently extracted and analyzed separately (Fig \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC). This revealed numerous transitional cells as well as more mature cell types (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eD). To characterize pericyte-specific developmental changes, we performed RNA velocity analysis of the mesenchymal populations (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eE). Based on these predictions, we isolated and reclustered closely related smooth muscle and non-smooth muscle cell types. This revealed eight transcriptionally distinct populations, including pericytes/perivascular type III cells (\u003cem\u003eCspg4\u003c/em\u003e+, \u003cem\u003ePdgfrb\u003c/em\u003e+), airway smooth muscle (\u003cem\u003eFoxf1\u003c/em\u003e+, \u003cem\u003eActa2\u003c/em\u003e+, \u003cem\u003eMyh11\u003c/em\u003e+), myofibroblasts (\u003cem\u003eTgfbi\u003c/em\u003e+, \u003cem\u003ePdgfra\u003c/em\u003e+), lipofibroblasts (\u003cem\u003eTcf21\u003c/em\u003e+, \u003cem\u003eGyg\u003c/em\u003e+), and chondrocytes (\u003cem\u003eSox9\u003c/em\u003e+, \u003cem\u003eCol2a1\u003c/em\u003e+) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-C). The fraction of cells and mean expression of signature genes for each population were visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe identified an \u003cem\u003eEbf1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;population as early as E10.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB-C). At E12.5, two molecularly distinct \u003cem\u003eEbf1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;populations emerged, one of which we identified as true pericytes/type III perivascular cells (perivascular cell III) due to the expression of general pericyte markers \u003cem\u003ePdgfrb\u003c/em\u003e, \u003cem\u003eCspg4\u003c/em\u003e, and \u003cem\u003eMcam\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). The second Ebf1\u0026thinsp;+\u0026thinsp;cluster, which we defined as type II perivascular cells (perivascular cell II), was found to be molecularly distinct from pericytes, although it did co-express many pericyte markers, albeit to a lower extent. Both \u003cem\u003eEbf1\u0026thinsp;+\u003c/em\u003e\u0026thinsp;populations expressed \u003cem\u003eNotch3\u003c/em\u003e, a key regulator of VSMC maturation and vessel stabilization (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The top differentially expressed markers of the perivascular II cells included \u003cem\u003eCxcl12\u003c/em\u003e and \u003cem\u003eSparc\u003c/em\u003e, both of which are part of the pericyte secretome and are involved in pulmonary artery muscularization and extracellular matrix (ECM) formation and angiogenesis, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecently, Ebf1 positive fibroblasts were identified as a novel pulmonary mesenchymal subpopulation in the embryonic mouse lung from gestational age 14.5 (E14.5) onwards (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Although their transcriptomic profile closely resembled that of pericytes, suggesting that they may share a common lineage, a distinct pericyte population could not be identified. Ebf1 has also been shown to be pericyte specific and to contribute to pericyte and VSMC cell commitment. By contrast, Foxf1, an airway smooth muscle cell (ASMC)\u0026ndash;specific transcription factor, has been found to drive airway smooth muscle cell (ASMC) development (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Here, we identified two different smooth muscle cell lineages, one of which was identified as the VSMC compartment due to the expression of \u003cem\u003eEbf1\u003c/em\u003e and \u003cem\u003eHeyl\u003c/em\u003e (perivascular cell II and III\u003cem\u003e)\u003c/em\u003e, the other which was determined to represent the ASMC compartment due to the expression of \u003cem\u003eFoxf1\u003c/em\u003e (airway smooth muscle and myofibroblast, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003eBoth perivascular type II and III cells were found to specifically express \u003cem\u003eHeyl\u003c/em\u003e, another transcription factor tied to VSMC cell fate determination (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). Interestingly, only the perivascular II cells expressed smooth muscle cell (SMC) markers \u003cem\u003eActa2\u003c/em\u003e and \u003cem\u003eTagln\u003c/em\u003e, which implies that they might be pulmonary VSMC progenitors.\u003c/p\u003e \u003cp\u003eWe uncovered a third distinct mesenchymal population which was only detected between E10.5 and E14.5 These cells, which have previously been described as adventitial fibroblasts (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e), were termed perivascular type I cells (perivascular cell I), were defined by the expression of \u003cem\u003eCol1a1\u003c/em\u003e, which was recently shown to regulate pericyte proliferation, migration, and differentiation, and \u003cem\u003eDlk1\u003c/em\u003e, a more recently discovered pericyte marker (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC) (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Based on these findings, we hypothesized that these perivascular type I cells might give rise to type II and III perivascular cells. To define the temporal trajectories of the perivascular clusters, we performed RNA velocity analysis on our scRNA-seq data. Both the dynamical and stochastic models implied a developmental relationship between the three perivascular cell types, although a single progenitor could not be clearly established (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE, S1F).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDCM-time machine tracks pericyte cell state changes throughout lung development\u003c/h2\u003e \u003cp\u003eTo determine the development of pulmonary perivascular cells, we applied DCM-time machine (DCM-TM) technology (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). This recently developed method offers superior flexibility as it is not constrained by temporal resolution or gene detection limits, which strongly contrasts with RNA velocity. Consequently, it allows for comprehensive whole-transcriptome lineage tracing of target cell populations.\u003c/p\u003e \u003cp\u003eTo explore these trajectories, we conducted four distinct pulse-chase experiments, each involving a 48-hour regimen of doxycycline administration to induce the DCM tagging system, followed by the isolation of \u003cem\u003eCspg4\u003c/em\u003e+/\u003cem\u003ePdgfrb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;perivascular cells (perivascular cells I and II) and \u003cem\u003ePecam1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;endothelial cells (ECs) at E18.5 using FACS (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sequencing of methylated DNA (MeD-seq) was performed on genomic DNA isolated from these cells, followed by the identification of methylation specific DNA regions (see genome browser plots in supplementary Fig.\u0026nbsp;2) and normalization for DCM induction efficiency (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eA, table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). The time points, ranging from E8.5 to E16.5, were explicitly chosen to reflect the early and late embryonic and pseudoglandular stages of lung development, during which most of the pulmonary vascular growth takes place.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDCM methylation levels for each time point were analyzed to determine to what extent perivascular cell development could be traced retrospectively. Gene meta-analysis indicated that DCM methylation levels of gene bodies in both perivascular cells and ECs were found to be similar to those observed in controls at E8.5-10.5, indicating that the methylation of active genes at his time point was insufficiently maintained after 8 days (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eB). Consequently, we set the cutoff to E10.5-12.5.\u003c/p\u003e \u003cp\u003eBy leveraging the DCM labeling of active genes across the early (E10.5-12.5), middle (E12.5-14.5), and late (E14.5-16.5) time intervals, we meticulously tracked changes in gene expression within pericyte precursors over time. Notably, the DCM labeling of \u003cem\u003eCol1a1\u003c/em\u003e and \u003cem\u003eDlk1\u003c/em\u003e confirmed their enrichment at the early and middle time points, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Furthermore, we observed that genes specific to pericytes attained their peak expression levels at the late time point, with \u003cem\u003eMcam\u003c/em\u003e, \u003cem\u003eNotch3\u003c/em\u003e, and \u003cem\u003eEbf1\u003c/em\u003e demonstrating higher expression levels in comparison to \u003cem\u003eCpsg4\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Additionally, examination of smooth muscle cell markers revealed an increasing expression of VSMC-specific genes in the perivascular cells, including \u003cem\u003eHeyl\u003c/em\u003e, \u003cem\u003eEgfl6\u003c/em\u003e, and \u003cem\u003eMef2c\u003c/em\u003e, thus providing further confirmation that pericytes follow the developmental trajectory of VSMCs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo map temporal gene activity throughout pericyte differentiation, we clustered genes with a DCM signal significantly higher than background levels based on their peak day. We visualized the average expression of these gene clusters on the uniform manifold approximation and projection (UMAP) of our mesenchymal scRNA-seq data, which shows enrichment at specific clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eThis analysis revealed that genes with a temporal DCM methylation profile peaking at the early time point were enriched in perivascular type I cells, while genes peaking at the late time point were enriched in perivascular type III cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Genes with maximal temporal signals at the middle time point were broadly expressed across all three perivascular clusters, indicating that this peak pattern mostly represents ubiquitously expressed genes. Gene ontology (GO) analysis of the DCM labeled genes highlighted gene set enrichment for stem cell pluripotency at the early time point and for VSMC contraction at the late time point (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD-F).\u003c/p\u003e \u003cp\u003eIn summary, we conclude that perivascular type I cells serve as pericyte and VSMC progenitors, giving rise to both perivascular type II cells, termed intermediate pericytes, and pericytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePathways determining pericyte differentiation and pulmonary vascular development\u003c/h2\u003e \u003cp\u003eTo gain deeper insights into cell state changes during pericyte differentiation, we conducted a KEGG pathway analysis on DCM-labeled genes. Mesenchymal Wnt, Shh, and Vegf signaling pathways play crucial roles in the development of various mesenchymal lineages, including airway and vascular smooth muscle cells and pericytes, and the establishment and expansion of the pulmonary vascular plexus (\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Consistent with this, we observed the early enrichment of these signaling pathways during lung development (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-B).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the middle time point, we observed enrichment of the Notch signaling pathway, aligning with prior reports highlighting its significance in distal angiogenesis, arterial specification, and vessel maturation (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). By contrast, the late time point revealed enrichment of Tgfb and Hippo signaling pathways, both of which are well-known regulators of mesenchymal ECM production, a process that directly follows blood vessel formation (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTo unravel the cell-cell signaling mechanisms driving pericyte differentiation, we examined the expression of ligands and receptors by performing Cellphone DB analyses on our scRNA-seq data (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). These analyses highlighted the pericyte progenitors and intermediate as sources of ligands that not only signal to each other but also communicate with mature pericytes. This finding implies a regulatory role for these pericyte progenitors on mature pericytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC). The number of predicted ligand-receptor interactions also revealed that mature pericytes exhibit a lower number of interactions compared to their progenitors, showing that progenitor cells can process more signaling cues due to their ability to differentiate into different cell types. Among the ligands expressed by the pericyte progenitors were Wnt2, Wnt5b, while intermediate pericytes emerged as the primary source of Tgfb signaling effectors, including Tgfb2 and Tgfb3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD). Interestingly, retinoic acid signaling was highly enriched between the three perivascular populations, with pericyte progenitors emerging as the main source of retinoic acid (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003eWe further aimed to elucidate the interplay between ECs and pericyte progenitors in the context of pulmonary vascular development through receptor-ligand analysis of our whole-lung scRNA-seq data. The interaction between ECs and pericytes is essential for the development and expansion of the pulmonary vasculature (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In line with this, our finding indicated consistent interactions between ECs and both intermediate pericytes and mature pericytes, primarily involving well-established angiogenic pathways including Pdgfb, Angiopoetin, and Notch (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF) (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Notably, we observed that Vegf signaling, a key regulator of early vasculogenesis, mostly occurred between ECs and intermediate pericytes, corroborating the notion that intermediate pericytes represent an early and less differentiated type of pericyte (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eTemporal expression of known and novel pulmonary pericyte markers\u003c/h2\u003e \u003cp\u003eWhile Cspg4 and Pdgfrb are commonly used molecular markers for pericytes, they have generally failed to serve as unique markers (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e). The absence of a single, definitive marker hinders our understanding of pulmonary pericyte ontogeny and differentiation capacity. Based on the differentially expressed genes found in our scRNA-seq and DCM-TM experiments, we evaluated the specificity of novel potential pulmonary pericyte-specific markers using immunocytochemistry. Notch3, Ebf1, and Mcam were analyzed for their temporal and spatial expression in Cspg4/Pdgfrb double positive cells within mouse lungs at E12.5, E15.5, and E18.5.\u003c/p\u003e \u003cp\u003eConsistent with prior research by our group, Cspg4 maintained perivascular expression throughout lung development, while Pdgfrb expression became increasingly restricted to the vasculature by E18.5 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, S4A, S5). Notably, very few Cspg4/Pdgfrb double positive cells were detected.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt all time points, we found Notch3 expression in all Cspg4 positive cells, with the highest expression occurring in Csp4/Pdgfrb double positive cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, S4A). A similar expression pattern was observed for Ebf1 (Fig. \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e). This observation confirmed that Ebf1 and Notch3 serve as more selective markers for VSMCs and pericytes in the lung when compared to Cspg4 and Pdgfrb.\u003c/p\u003e \u003cp\u003eTo determine whether pericytes and perivascular type II cells could be distinguished from one another, we co-stained lungs for Notch3, Ebf1, and Mcam. Mcam was identified as highly specific for lung perivascular cells at all time points (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB, S4B). Furthermore, not all Ebf1\u0026thinsp;+\u0026thinsp;cells co-expressed Mcam, demonstrating that Mcam can effectively differentiate between intermediate and mature pericytes in the lung.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we investigated pericyte differentiation throughout critical stages of lung development. We uncovered a \u003cem\u003eCol1a1\u003c/em\u003e\u0026thinsp;+\u0026thinsp;pericyte progenitor and identified an intermediate pericyte expressing \u003cem\u003eCxcl12\u003c/em\u003e. Moreover, we established that pulmonary pericytes follow the developmental trajectory of VSMCs, affirming a shared lineage between pericytes and VSMCs. Additionally, we validated Mcam as a \u003cem\u003ebona fide\u003c/em\u003e marker for pulmonary pericytes.\u003c/p\u003e \u003cp\u003eThere are currently only few publications that have studied lung pericyte origin \u003cem\u003ein vivo\u003c/em\u003e. Peng et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) used lineage tracing coupled with clonal analysis starting at E8.5 and identified a multipotent \u003cem\u003eWnt2\u003c/em\u003e\u0026thinsp;+\u0026thinsp;progenitor that could generate the pulmonary smooth muscle compartment as well as pericytes in the proximal part of the murine lung. To date, these findings have only been corroborated by Zepp et al. (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), who integrated scRNA-seq and genomic lineage tracing of the murine distal lung, ranging from E12.5 to postnatal day 42, to study the developmental origin of various mesenchymal cell types. Although they did not define a specific pericyte population, \u003cem\u003ePgdfrb\u003c/em\u003e\u0026thinsp;+\u0026thinsp;cells were found two develop into either VSMCs or a cell type enriched for \u003cem\u003eHey\u003c/em\u003el and \u003cem\u003eEbf1\u003c/em\u003e, which is in line with our findings.\u003c/p\u003e \u003cp\u003eAlthough widely used to predict cell trajectories, RNA velocity modeling is subject to many limitations (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Projections can be distorted due to the simultaneous occurrence of multiple dynamic processes, including cell cycle and differentiation, making it particularly difficult to apply to developmental data, as illustrated by our RNA velocity analyses, which predicted backwards differentiation from later to earlier perivascular populations (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). By combining scRNA-seq with DCM-TM, we were able to bypass these challenges by retrospectively tracking genome wide gene transcription activity. This allowed us to identify and characterize pericyte progenitors in the developing lung for the first time. Unfortunately, the temporal window of our analysis was limited due to low methylation levels at the earliest time point (E8.5-10.5), which was subsequently excluded from further analysis. This might have been the result of the number of cell divisions, which have been shown to affect the DCM propagation rate (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Moreover, the DCM methylation profile peaking at the middle time point did not show enrichment of one specific perivascular population.\u003c/p\u003e \u003cp\u003eThe consensus that pericytes are precursors to VSMCs is based on the observation that pericytes increasingly express smooth muscle cell markers in pathological settings such as pulmonary hypertension (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). Here, we demonstrate that pericytes increasingly express \u003cem\u003eHeyl\u003c/em\u003e and \u003cem\u003eEbf1\u003c/em\u003e, both transcription factors that determine VSMC cell fate (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e), thus confirming that they originate from the same progenitor. In pericytes, \u003cem\u003eHey\u003c/em\u003el acts as a downstream target of \u003cem\u003eNotch3\u003c/em\u003e, which plays an important role during in assuring pericyte coverage (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e). \u003cem\u003eEbf1\u003c/em\u003e plays an important role in pericyte function and cell fate commitment, as illustrated by the downregulation of \u003cem\u003ePdgfrb\u003c/em\u003e, \u003cem\u003eCspg4\u003c/em\u003e, \u003cem\u003eMcam\u003c/em\u003e, \u003cem\u003eAngpt1\u003c/em\u003e, and \u003cem\u003eVegf\u003c/em\u003e in \u003cem\u003eEbf1\u003c/em\u003e-silenced human brain pericytes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Interestingly, we found that intermediate pericytes express smooth muscle cell markers to a higher extent than mature pericytes, which contests the consensus that pericytes are VSMC progenitors.\u003c/p\u003e \u003cp\u003eThe absence of a single pericyte marker has complicated research into the ontogeny and differentiation of pericytes (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). Although \u003cem\u003eCspg4\u003c/em\u003e and \u003cem\u003ePdgfrb\u003c/em\u003e are the most used markers for identifying pericytes in both mouse and human lungs, our data show that not all mature pericytes express both markers and that they are insufficient in distinguishing between intermediate and mature pericytes. Based on our experiments, we conclude that \u003cem\u003eMcam\u003c/em\u003e specifically marks mature pericytes in the developing lung. \u003cem\u003eMcam\u003c/em\u003e has been shown to function as a coreceptor of Pdgfrb in human brain pericytes, with specific deletion of Mcam leading to defects in pericyte coverage and blood-brain barrier integrity (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e). Although little is known about its function in lung pericytes, our findings suggest a similar role for Mcam in the mouse lung.\u003c/p\u003e \u003cp\u003eThe role of retinoic acid (RA) as a key regulator of lung organogenesis and its involvement in both fetal and chronic respiratory disorders has been well established (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e). Our current findings that RA signaling is highly enriched in all perivascular cell types supports our previous work, in which we demonstrated that interference with RA signaling prior to lung development leads to aberrant pericytes and subsequently to the abnormal lung development also observed in CDH (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Interestingly, \u003cem\u003eEbf1\u003c/em\u003e, which we found to be differentially expressed by intermediate and mature pericytes, has also been linked to RA signaling through its activation of the RA transcriptional coactivators CREB-binding protein and p300, implicating this transcription factor as a potential therapeutic target in pericyte-associated lung disease (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). This is further highlighted by its role in regulating the expression of \u003cem\u003eCxcl12\u003c/em\u003e (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e), a chemokine that has recently been found to drive pericyte accumulation in allergic asthma (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e), and was found to be expressed solely by intermediate pericytes.\u003c/p\u003e \u003cp\u003eIn conclusion, by combining scRNA-seq with DCM-TM technology, we identified a pulmonary pericyte progenitor characterized by the expression of Col1a1\u0026thinsp;+\u0026thinsp;that gives rise to pericytes through an intermediate Ebf1\u0026thinsp;+\u0026thinsp;pericyte. Our findings pave the way for the understanding of pericyte ontogeny and differentiation \u003cem\u003ein vivo\u003c/em\u003e and may contribute to new therapeutic strategies for diseases associated with pericyte dysfunction.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eMice\u003c/h2\u003e \u003cp\u003e All mouse experimental protocols adhered to ethical guidelines and were approved by the Dutch Central Committee on the Ethics of Animal Experiments (AVD10100202216366). We conducted these experiments following national guidelines.\u003c/p\u003e \u003cp\u003eThe \u003cem\u003eDCM\u0026ndash;Polr2b:m2rtTA\u003c/em\u003e mouse line contains the m2rtTA trans-activator inserted in the Rosa locus and the \u003cem\u003eDCM-Polr2b\u003c/em\u003e in the \u003cem\u003eCol1a1\u003c/em\u003e locus. To generate embryos heterozygous for \u003cem\u003eDCM\u0026ndash;Polr2b:m2rtTA\u003c/em\u003e, Wild-Type (WT) C57BL/6J females were crossed with homozygous \u003cem\u003eDCM\u0026ndash;Polr2b:m2rtTA\u003c/em\u003e transgenic males. The fusion transgene was induced by administering 2.0 mg/ml doxycycline in the drinking water for two days at four different gestational ages: embryonic day (E) 8.5\u0026ndash;10.5, E10.5-12.5, E12.5-14.5, and E14.5-16.5 (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSex determination of embryos was performed by PCR of the the sex-determining region of chromosome Y (SRY) and thyroid stimulating hormone β (TSH; control) previously published primer sequences (\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eFluorescence-activated cell sorting (FACS)\u003c/h2\u003e \u003cp\u003eEmbryos were harvested at E18.5 and their lungs were isolated. For each induction, three independent cell preparations were processed. Three to four fetal lungs of male embryos were pooled in 750 \u0026micro;l of DMEM (Lonza) and mechanically dissociated them using dissection scissors. Following the addition of collagenase I, II, and IV (Sigma-Aldrich) at a final concentration of 5 \u0026micro;g/ml, the samples were incubated at 37\u0026deg;C in an Eppendorf shaker at 1000 rpm for 10 minutes.\u003c/p\u003e \u003cp\u003eSubsequent mechanical dissociation involved vigorous resuspension, after which the cell suspensions were filtered through a 40-\u0026micro;m cell strainer. The filters were rinsed with 750 \u0026micro;l of DMEM with 10% FCS, and the resulting cell suspensions were centrifuged at 4\u0026deg;C for 10 minutes at 1000 rpm. After removing the supernatant, the cells were resuspended in 300 \u0026micro;l of 1x red blood cell lysis buffer at 4\u0026deg;C for 1 minute, diluted them in 900 \u0026micro;l of PBS with 10% FCS, and centrifuged them (10 minutes at 4\u0026deg;C, 1000 rpm). The cells were then resuspended in 300 \u0026micro;l of PBS with 5% FCS.\u003c/p\u003e \u003cp\u003eCells were counted and incubated at 4\u0026deg;C in the dark for 40 minutes with the following antibodies: CD31-PE-Cy7 (1:100, ThermoFisher), CD45-PE-Texas Red (1:100, ThermoFisher), \u003cem\u003eCSPG4\u003c/em\u003e-Alexa-488 (1:100, Merck), and PDGFR-β-APC (1:100, ThermoFisher).\u003c/p\u003e \u003cp\u003ePrior to sorting, the cells were concentrated and resuspended in 800 \u0026micro;l of PBS with 5% FCS. DAPI (1:10000, BD Biosciences). CD45\u003csup\u003e\u0026minus;\u003c/sup\u003e cells were sorted for CD31 and for CSPG4/PDGFRB using a BD FACS Aria III and BD FACSDiva software version 8.0.1. DNA was isolated from the sorted cells using QIAamp DNA micro kit according to the manufacturer\u0026rsquo;s protocol, and RNA was isolated by Trizol extraction.\u003c/p\u003e \u003cp\u003eAt the E8.5-10.10 induction time point, cells were sorted using an unconjugated CSPG4 antibody (1:100, Millipore) for 40 minutes, and subsequently incubated with goat anti-rabbit Alexa 488 secondary antibody (1:200, Jackson) for 30 minutes\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eMeD-seq sample preparations and data analysis\u003c/h2\u003e \u003cp\u003eMeD-seq analyses were performed as previously described, with all experimental time points conducted in triplicate (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eData processing was carried out using custom scripts in Python and MATLAB. Raw FASTQ files were subjected to Illumina adaptor trimming, and reads were filtered based on LpnPI restriction site occurrence between 13 bp and 17 bp from either 5\u0026prime; or 3\u0026prime; end of the read. DCM methylation data (CCWGG sites) and CpG methylation data (CCG, CGG and GCGC sites) were separated during filtering and mapped separately to mm10 using bowtie2. Genome-wide individual DCM site scores were used to generate read count scores for all annotated genes from UCSC (GRCm38.p2). BAM files were generated using SAMtools version 0.1.19 for visualization in IGV51,52. Because DCM and CpG methylation can be detected separately using MeD-seq, DCM enrichment was determined by either data normalization using CpG read coverage (for absolute DCM enrichment) or DCM read coverage (for relative DCM enrichment) between samples. For both situations, normalization is done using reads per million (RPM), where absolute DCM levels indicate the level of DCM\u0026ndash;Rpol2b induction, and relative DCM levels are used to correct for differences in DCM\u0026ndash;Rpol2b induction between mice and/or time points.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell RNA-sequencing (scRNA-seq)\u003c/h2\u003e \u003cp\u003eWT male fetal mouse lungs were isolated at E10.5, E12.5, E14.5, E16.5, and E18.5 and processed for single cell as described (N\u0026thinsp;=\u0026thinsp;1). For samples isolated at E10.5 and E12.5, duplicates were obtained, one containing only one lung and the other containing three lungs. Samples isolated at later time points consisted of one single lung.\u003c/p\u003e \u003cp\u003eThe resulting cell suspensions were processed by the Erasmus Center for Biomics and/or Department for Hematology. Single-cell libraries were prepared using the Chromium Single Cell 3\u0026rsquo; Reagent Kit v3 (10x Genomics). Next-generation sequencing (28-8-0-91 cycles) was conducted on an Illumina NovaSeq6000 platform (Illumina). Raw data were processed into FASTQ files, and sequences were quality-checked using FastQC (version v0.11.5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePre-processing of scRNA-seq data\u003c/h2\u003e \u003cp\u003eReads from each batch were processed separately using the 10x Genomics Cell Ranger v6.6.0 pipeline (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e)with the mm10 2020-A reference dataset. Cellranger count was run to map the reads to mm10 and generate count matrices based on Ensembl v98 annotation. The counts were further preprocessed using R v4.0.5 and Seurat v4.3.0 (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e). SoupX v1.5.2 (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) was used to remove ambient background RNAs from the datasets. We removed doublets using doubletfinder v2.0.3 (\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e) with the settings pN\u0026thinsp;=\u0026thinsp;0.25, pK\u0026thinsp;=\u0026thinsp;0.09, PCs\u0026thinsp;=\u0026thinsp;1:10, nExp\u0026thinsp;=\u0026thinsp;number of expected doublets based on each library size. Cells with less 1,000 or more than 9,000 detected genes, more than 70,000 counts or more than 20 percent mitochondrial reads were removed. Furthermore, blood cells were filtered out by excluding cells with over 25% of all reads mapped to hemoglobin genes. Genes expressed in less than 3 cells, Malat1 and Gm42418 were removed for further analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eIntegration of scRNA-seq datasets\u003c/h2\u003e \u003cp\u003eThe datasets were normalized using SCTransform v2 (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e) with the glmGamPoi method (\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) and percentages of ribosomal, percentage of mitochondrial genes and difference in cell cycle as vars.to.regress. Integration of the datasets was prepared using the SelectIntegrationFeatures (nfeatures\u0026thinsp;=\u0026thinsp;3000), PrepSCTIntegration and RunPCA functions. Integration anchors were found using FindIntegrationAnchors with SCT as normalization.method, 30 dimensions, rpca reduction, and 20 anchors. Finally, the cells were integrated using IntegrateData with SCT as normalization.method and 30 dimensions. An Uniform Manifold Approximation and Projection (UMAP) (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e) was created using RunUMAP based on the first 30 principal components from RunPCA. Last, FindNeighbors and FindClusters with a resolution of 0.5 were used to identify clusters. We subsetted the mesenchymal cell clusters and reanalyzed these cells separately as described above to focus further on the dynamics within the mesenchyme.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCluster annotation\u003c/h2\u003e \u003cp\u003eThe clusters were annotated based on the expression of known markers and differentially expressed genes between clusters. Differential expression analysis was performed using scanpy v1.9.1 (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e) rank_genes_groups with the Wilcoxon method. The expression of the 5 most differential expressed genes per cluster were visualized using rank_genes_groups_heatmap. Furthermore, the expression of known marker genes was plotted using the scanpy dotplot function.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eRNA velocity analysis\u003c/h2\u003e \u003cp\u003eVelocyto v0.17.17 (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) run10x was run on each dataset seperately to create loom files with spliced and unspliced counts. The loom files were analyzed using scvelo v0.3.0 (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e). The counts were pre-processed using scvelo filter_and_normalize with 20 min_shared_counts and 2000 n_top_genes, scanpy v1.9.1 pca and scanpy neighbors with 30 n_pcs and 30 n_neighbors. We ran scvelo moments and recover_dynamics to compute moments for velocity estimation and recover the full splicing kinetics, respectively. Finally, scvelo velocity and velocity_graph was run with mode=\u0026ldquo;dynamical\u0026rdquo; to obtain the velocity estimates. The velocity of the cells was plotted on the mesenchyme UMAP using scvelo velocity_embedding_stream with smooth\u0026thinsp;=\u0026thinsp;0.8 and min_mass\u0026thinsp;=\u0026thinsp;2. The velocity was also estimated using the \u0026ldquo;stochastic\u0026rdquo; mode of scvelo with the same settings. Based on the velocity predictions, we isolated and reclustered closely related smooth muscle and non-smooth muscle cell types (cluster 6, 7, 8, 10, 11, 13) as described above. Moreover, the RNA velocity analysis was rerun on the perivascular clusters seperately.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCellphoneDB\u003c/h2\u003e \u003cp\u003eCell-cell interactions between the different clusters were identified using CellphoneDB v2.1.7 (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) using the statistical analysis method. The number of interactions between each cluster with a P-value below 0.05 was visualized in a heatmap, which was plotted using cellphonedb heatmap_plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence (IF) on paraffin sections\u003c/h2\u003e \u003cp\u003eIF stainings 5 \u0026micro;m thick paraffin sections of either isolated lungs or whole embryos were performed following established protocols (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e). Primary and secondary antibodies used for IF are detailed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. To facilitate antigen retrieval, antigen retrieval was performed using Tris-EDTA buffer with a pH of 9.0, followed by blocking using either a 3% BSA fraction V (Roche) or 5% ELK (Campina) in PBS-Tween 20 0.05%. For double stainings with both CSPG4 and PDGFRb, a tyramide signal amplification kit (ThermoFisher) was used. For triple stainings including CSPG4 and PDGFRb, two tyramide signal amplification kits were used. Autofluorescence of erythrocytes was quenched using Vector TrueVIEW autofluorescence quenching kit (Vector Laboratories, SP8400).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimary and secondary antibodies used for IF. IF\u0026thinsp;=\u0026thinsp;immunofluorescence. Notch3\u0026thinsp;=\u0026thinsp;notch receptor 3. Ebf1\u0026thinsp;=\u0026thinsp;early B-cell factor 1. Mcam\u0026thinsp;=\u0026thinsp;melanoma cell adhesion molecule. Cspg4\u0026thinsp;=\u0026thinsp;chondroitin sulfate proteoglycan 4. Pdgfrb\u0026thinsp;=\u0026thinsp;platelet-derived growth factor receptor beta.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAntibody\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDilution\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNotch3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u0026amp;D systems, AF1308\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:200 in 5% ELK/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEbf1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerck, AB10523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:1000 in 5% ELK/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMcam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBioLegend, 134712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:25 in 5% ELK/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCspg4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMerck, AB5320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:250 in 5% ELK/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePdgfrb\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCell signaling Technology, 3169\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:100 in 5% ELK/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-rabbit HRP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch, 711-035-152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-rabbit Alexa 488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch, 711-545-152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-rabbit Alexa 647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch 711-605-152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-goat Alexa 594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch, 705-585-147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-goat Alexa 647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch, 705-605-147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDonkey anti-rat Alexa 647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJackson ImmunoResearch, 712-605-153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1:500 in 3% BSA/PBS-T\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eACKNOWLEDGEMENTS\u003c/h2\u003e \u003cp\u003eWe thank Gabri\u0026euml;la Edel for her help with the experimental setup, Tsung Wai Kan and dr. Alex Maas for performing the FACS sorts, and dr. Eric Bindels for RNA-sequencing the first samples. This work was supported by grants from the Sophia Foundation for Medical Research (grant number S18-19), and ZonMw (114025011; RJR).\u003c/p\u003e\u003ch2\u003eDATA AVAILABILITY\u003c/h2\u003e \u003cp\u003eAll raw and processed high-throughput sequencing data (MeD-seq, scRNA-seq) generated in this study have been submitted to the NCBI Gene Expression Omnibus (GEO) under accession number GSE######.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWhitsett JA, Kalin TV, Xu Y, Kalinichenko VV (2019) Building and Regenerating the Lung Cell by Cell. Physiol Rev 99(1):513\u0026ndash;554\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarron L, Gharib SA, Duffield JS (2016) Lung Pericytes and Resident Fibroblasts: Busy Multitaskers. Am J Pathol 186(10):2519\u0026ndash;2531\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Splunder H, Villacampa P, Mart\u0026iacute;nez-Romero A, Graupera M (2023) Pericytes in the disease spotlight. Trends Cell Biol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKato K, Di\u0026eacute;guez-Hurtado R, Park DY, Hong SP, Kato-Azuma S, Adams S et al (2018) Pulmonary pericytes regulate lung morphogenesis. Nat Commun 9(1):2448\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParera MC, van Dooren M, van Kempen M, de Krijger R, Grosveld F, Tibboel D et al (2005) Distal angiogenesis: a new concept for lung vascular morphogenesis. Am J Physiol Lung Cell Mol Physiol 288(1):L141\u0026ndash;L149\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSweeney M, Foldes G (2018) It Takes Two: Endothelial-Perivascular Cell Cross-Talk in Vascular Development and Disease. Front Cardiovasc Med 5:154\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan K, Agarwal S, Chakraborty A, Condon DF, Patel H, Zhang S et al (2021) Lung Pericytes in Pulmonary Vascular Physiology and Pathophysiology. Compr Physiol 11(3):2227\u0026ndash;2247\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLazarus A, Del-Moral PM, Ilovich O, Mishani E, Warburton D, Keshet E (2011) A perfusion-independent role of blood vessels in determining branching stereotypy of lung airways. Development 138(11):2359\u0026ndash;2368\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan Tuyl M, Liu J, Wang J, Kuliszewski M, Tibboel D, Post M (2005) Role of oxygen and vascular development in epithelial branching morphogenesis of the developing mouse lung. Am J Physiol Lung Cell Mol Physiol 288(1):L167\u0026ndash;L178\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHolm A, Heumann T, Augustin HG (2018) Microvascular Mural Cell Organotypic Heterogeneity and Functional Plasticity. Trends Cell Biol 28(4):302\u0026ndash;316\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarrison AT, Bignold RE, Wu X, Johnson JR (2023) Pericytes: The lung-forgotten cell type. Front Physiol 14:1150028\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordenave J, Tu L, Berrebeh N, Thuillet R, Cumont A, Le Vely B et al (2020) Lineage Tracing Reveals the Dynamic Contribution of Pericytes to the Blood Vessel Remodeling in Pulmonary Hypertension. Arterioscler Thromb Vasc Biol 40(3):766\u0026ndash;782\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKool HM, B\u0026uuml;rgisser PE, Edel GG, de Kleer I, Boerema-de Munck A, de Laat I et al (2019) Inhibition of retinoic acid signaling induces aberrant pericyte coverage and differentiation resulting in vascular defects in congenital diaphragmatic hernia. Am J Physiol Lung Cell Mol Physiol 317(3):L317\u0026ndash;L31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBignold R, Shammout B, Rowley JE, Repici M, Simms J, Johnson JR (2022) Chemokine CXCL12 drives pericyte accumulation and airway remodeling in allergic airway disease. Respir Res 23(1):183\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSluiter I, Reiss I, Kraemer U, Krijger R, Tibboel D, Rottier RJ (2011) Vascular abnormalities in human newborns with pulmonary hypertension. Expert Rev Respir Med 5(2):245\u0026ndash;256\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMirko C, Chien-Wen C, Mihaela C, Lorenza L, Bruno P, Arteriosclerosis (2010) Thromb Vascular Biology 30(6):1104\u0026ndash;1109\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrisan M, Yap S, Casteilla L, Chen CW, Corselli M, Park TS et al (2008) A perivascular origin for mesenchymal stem cells in multiple human organs. Cell Stem Cell 3(3):301\u0026ndash;313\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede Souza LE, Malta TM, Kashima Haddad S, Covas DT (2016) Mesenchymal Stem Cells and Pericytes: To What Extent Are They Related? Stem Cells Dev 25(24):1843\u0026ndash;1852\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuimar\u0026atilde;es-Camboa N, Cattaneo P, Sun Y, Moore-Morris T, Gu Y, Dalton ND et al (2017) Pericytes of Multiple Organs Do Not Behave as Mesenchymal Stem Cells In Vivo. Cell Stem Cell 20(3):345\u0026ndash;359 e5\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVolz KS, Jacobs AH, Chen HI, Poduri A, McKay AS, Riordan DP et al (2015) Pericytes are progenitors for coronary artery smooth muscle. Elife. ;4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheikh AQ, Misra A, Rosas IO, Adams RH, Greif DM (2015) Smooth muscle cell progenitors are primed to muscularize in pulmonary hypertension. Sci Transl Med 7(308):308ra159\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRonkainen J, Aro P, Storskrubb T, Johansson SE, Lind T, Bolling-Sternevald E et al (2005) Prevalence of Barrett's esophagus in the general population: an endoscopic study. Gastroenterology 129(6):1825\u0026ndash;1831\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu Y, Fu J, Huang Y, Duan R, Zhang W, Wang C et al (2023) Biology and function of pericytes in the vascular microcirculation. Anim Models Experimental Med 6(4):337\u0026ndash;345\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSluiter I, van der Horst I, van der Voorn P, Boerema-de Munck A, Buscop-van Kempen M, de Krijger R et al (2013) Premature differentiation of vascular smooth muscle cells in human congenital diaphragmatic hernia. Exp Mol Pathol 94(1):195\u0026ndash;202\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers R, Boers J, Tan B, van Leeuwen ME, Wassenaar E, Sanchez EG et al (2023) Retrospective analysis of enhancer activity and transcriptome history. Nat Biotechnol\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTefft JB, Bays JL, Lammers A, Kim S, Eyckmans J, Chen CS (2022) Notch1 and Notch3 coordinate for pericyte-induced stabilization of vasculature. Am J Physiol Cell Physiol 322(2):C185\u0026ndash;C96\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu H, Cantrell AC, Zeng H, Zhu SH, Chen JX (2021) Emerging Role of Pericytes and Their Secretome in the Heart. Cells. ;10(3)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan K, Liu Y, Zhang Y, Nathan A, Tian W, Yu J et al (2020) Mural Cell SDF1 Signaling Is Associated with the Pathogenesis of Pulmonary Arterial Hypertension. Am J Respir Cell Mol Biol 62(6):747\u0026ndash;759\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu X, Rowan SC, Liang J, Yao C, Huang G, Deng N et al (2021) Categorization of lung mesenchymal cells in development and fibrosis. iScience 24(6):102551\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePagani F, Tratta E, Dell'Era P, Cominelli M, Poliani PL (2021) EBF1 is expressed in pericytes and contributes to pericyte cell commitment. Histochem Cell Biol 156(4):333\u0026ndash;347\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao S, Feng H, Yi H, Pan M, Lin L, Zhang YS et al (2023) Single-cell RNA sequencing reveals the developmental program underlying proximal-distal patterning of the human lung at the embryonic stage. Cell Res 33(6):421\u0026ndash;433\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNegretti NM, Plosa EJ, Benjamin JT, Schuler BA, Habermann AC, Jetter CS et al (2021) A single-cell atlas of mouse lung development. Development. ;148(24)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXia M, Jiao L, Wang XH, Tong M, Yao MD, Li XM et al (2023) Single-cell RNA sequencing reveals a unique pericyte type associated with capillary dysfunction. Theranostics 13(8):2515\u0026ndash;2530\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoers R, Boers J, de Hoon B, Kockx C, Ozgur Z, Molijn A et al (2018) Genome-wide DNA methylation profiling using the methylation-dependent restriction enzyme LpnPI. Genome Res 28(1):88\u0026ndash;99\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Langhe SP, Reynolds SD (2008) Wnt signaling in lung organogenesis. Organogenesis 4(2):100\u0026ndash;108\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAros CJ, Pantoja CJ, Gomperts BN (2021) Wnt signaling in lung development, regeneration, and disease progression. Commun Biol 4(1):601\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhite AC, Lavine KJ, Ornitz DM (2007) FGF9 and SHH regulate mesenchymal Vegfa expression and development of the pulmonary capillary network. Development 134(20):3743\u0026ndash;3752\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGoodwin K, Jaslove JM, Tao H, Zhu M, Hopyan S, Nelson CM (2022) Patterning the embryonic pulmonary mesenchyme. iScience 25(3):103838\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiyokawa H, Morimoto M (2020) Notch signaling in the mammalian respiratory system, specifically the trachea and lungs, in development, homeostasis, regeneration, and disease. Dev Growth Differ 62(1):67\u0026ndash;79\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkira S, Takahide N (2015) Hippo and TGF-β interplay in the lung field. Am J Physiology-Lung Cell Mol Physiol 309(8):L756\u0026ndash;L67\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfremova M, Vento-Tormo M, Teichmann SA, Vento-Tormo R (2020) CellPhoneDB: inferring cell-cell communication from combined expression of multi-subunit ligand-receptor complexes. Nat Protoc 15(4):1484\u0026ndash;1506\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYamazaki T, Mukouyama YS (2018) Tissue Specific Origin, Development, and Pathological Perspectives of Pericytes. Front Cardiovasc Med 5:78\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaek SH, Maiorino E, Kim H, Glass K, Raby BA, Yuan K (2022) Single Cell Transcriptomic Analysis Reveals Organ Specific Pericyte Markers and Identities. Front Cardiovasc Med 9:876591\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng T, Tian Y, Boogerd CJ, Lu MM, Kadzik RS, Stewart KM et al (2013) Coordination of heart and lung co-development by a multipotent cardiopulmonary progenitor. Nature 500(7464):589\u0026ndash;592\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZepp JA, Morley MP, Loebel C, Kremp MM, Chaudhry FN, Basil MC et al (2021) Genomic, epigenomic, and biophysical cues controlling the emergence of the lung alveolus. Science. ;371(6534)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng SC, Stein-O\u0026rsquo;Brien G, Boukas L, Goff LA, Hansen KD (2023) Pumping the brakes on RNA velocity by understanding and interpreting RNA velocity estimates. Genome Biol 24(1):246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergen V, Soldatov RA, Kharchenko PV, Theis FJ (2021) RNA velocity\u0026mdash;current challenges and future perspectives. Mol Syst Biol 17(8):e10282\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarile M, Imaz-Rosshandler I, Inzani I, Ghazanfar S, Nichols J, Marioni JC et al (2021) Coordinated changes in gene expression kinetics underlie both mouse and human erythroid maturation. Genome Biol 22(1):197\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaskill CF, Carrier EJ, Kropski JA, Bloodworth NC, Menon S, Foronjy RF et al (2017) Disruption of lineage specification in adult pulmonary mesenchymal progenitor cells promotes microvascular dysfunction. J Clin Invest 127(6):2262\u0026ndash;2276\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKofler NM, Cuervo H, Uh MK, Murtom\u0026auml;ki A, Kitajewski J (2015) Combined deficiency of Notch1 and Notch3 causes pericyte dysfunction, models CADASIL and results in arteriovenous malformations. Sci Rep 5(1):16449\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen J, Luo Y, Hui H, Cai T, Huang H, Yang F et al (2017) CD146 coordinates brain endothelial cell-pericyte communication for blood-brain barrier development. Proc Natl Acad Sci U S A 114(36):E7622\u0026ndash;E31\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes-Silva H, Ara\u0026uacute;jo-Silva H, Correia-Pinto J, Moura RS (2020) Retinoic Acid: A Key Regulator of Lung Development. Biomolecules. ;10(1)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao F, McCarrick-Walmsley R, Akerblad P, Sigvardsson M, Kadesch T (2003) Inhibition of p300/CBP by early B-cell factor. Mol Cell Biol 23(11):3837\u0026ndash;3846\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen W, Jia W, Wang K, Si X, Zhu S, Duan T et al (2013) Distinct roles for CBP and p300 on the RA-mediated expression of the meiosis commitment gene Stra8 in mouse embryonic stem cells. PLoS ONE 8(6):e66076\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLagergren A, M\u0026aring;nsson R, Zetterblad J, Smith E, Basta B, Bryder D et al (2007) The Cxcl12, periostin, and Ccl9 genes are direct targets for early B-cell factor in OP-9 stroma cells. J Biol Chem 282(19):14454\u0026ndash;14462\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLavrovsky Y, Song CS, Chatterjee B, Roy AK (1998) A rapid and reliable PCR-based assay for gene transmission and sex determination in newborn transgenic mice. Transgenic Res 7(4):319\u0026ndash;320\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng GX, Terry JM, Belgrader P, Ryvkin P, Bent ZW, Wilson R et al (2017) Massively parallel digital transcriptional profiling of single cells. Nat Commun 8:14049\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatija R, Farrell JA, Gennert D, Schier AF, Regev A (2015) Spatial reconstruction of single-cell gene expression data. Nat Biotechnol 33(5):495\u0026ndash;502\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoung MD, Behjati S (2020) SoupX removes ambient RNA contamination from droplet-based single-cell RNA sequencing data. Gigascience. ;9(12)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcGinnis CS, Murrow LM, Gartner ZJ, DoubletFinder (2019) Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors. Cell Syst 8(4):329\u0026ndash;337 e4\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoudhary S, Satija R (2022) Comparison and evaluation of statistical error models for scRNA-seq. Genome Biol 23(1):27\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHafemeister C, Satija R (2019) Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression. Genome Biol 20(1):296\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhlmann-Eltze C, Huber W (2020) glmGamPoi: fitting Gamma-Poisson generalized linear models on single cell count data. Bioinformatics 36(24):5701\u0026ndash;5702\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcInnes LH, Melville J (2018) J.;. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWolf FA, Angerer P, Theis FJ (2018) SCANPY: large-scale single-cell gene expression data analysis. Genome Biol 19(1):15\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLa Manno G, Soldatov R, Zeisel A, Braun E, Hochgerner H, Petukhov V et al (2018) RNA velocity of single cells. Nature 560(7719):494\u0026ndash;498\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBergen V, Lange M, Peidli S, Wolf FA, Theis FJ (2020) Generalizing RNA velocity to transient cell states through dynamical modeling. Nat Biotechnol 38(12):1408\u0026ndash;1414\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEenjes E, Benthem F, Boerema-de Munck A, Buscop-van Kempen M, Tibboel D, Rottier RJ (2023) Distinct roles for SOX2 and SOX21 in differentiation, distribution and maturation of pulmonary neuroendocrine cells. Cell Mol Life Sci 80(3):79\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"pericytes, lung development, single cell analysis, lineage tracing, mouse","lastPublishedDoi":"10.21203/rs.3.rs-4349859/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4349859/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmerging evidence suggests pericytes play a significant role in various lung diseases. However, characterizing pericytes remains challenging, impeding our understanding of their role in lung development and disease. Using single-cell RNA sequencing and DCM-time machine technology, we tracked the development of mouse pulmonary pericytes. Our study revealed the differentiation of perivascular progenitors into pericytes and vascular smooth muscle cells. Temporal analysis uncovered dynamic gene expression profiles during pericyte differentiation, highlighting pathways crucial for pulmonary vascular development. Further analysis showed intricate signaling interactions between pericyte progenitors and mature pericytes, and we validated Mcam as a bona fide pulmonary pericyte marker. These findings challenge conventional views on pericyte origin and underscore the importance of accurate pericyte identification in developmental and disease contexts. Overall, this study enhances our understanding of pulmonary pericyte ontogeny and differentiation, offering insights into their potential as therapeutic targets in pericyte-associated lung diseases.\u003c/p\u003e","manuscriptTitle":"The Cellular Origin of the Pulmonary Pericyte","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-25 06:34:09","doi":"10.21203/rs.3.rs-4349859/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-communications","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"NCOMMS","sideBox":"Learn more about [Nature Communications](http://www.nature.com/ncomms/)","snPcode":"","submissionUrl":"https://mts-ncomms.nature.com/","title":"Nature Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Communications","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8eaebb40-df94-4fac-a73d-c0a92565df68","owner":[],"postedDate":"July 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":35101146,"name":"Biological sciences/Developmental biology/Morphogenesis/Cell lineage"},{"id":35101147,"name":"Biological sciences/Molecular biology/Transcriptomics"},{"id":35101148,"name":"Biological sciences/Biotechnology/Functional genomics/Gene expression profiling"},{"id":35101149,"name":"Health sciences/Molecular medicine"}],"tags":[],"updatedAt":"2024-10-09T13:21:37+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-25 06:34:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4349859","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4349859","identity":"rs-4349859","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}