New genetic tools for central and peripheral vascular endothelia  

New genetic tools for central and peripheral vascular endothelia | 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 Research Article New genetic tools for central and peripheral vascular endothelia Shangzhou Xia, Tenghuan Ge, Ruocen Song, Qinghai Liu, Xiao He, and 6 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7421061/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The divergence between the central and peripheral vascular system, particularly the emergence of the blood-brain barrier (BBB), is central to the brain’s homeostasis and functions. However, the molecular and genetic constituents that separating the BBB vascular cells from the rest remain elusive. Using single cell transcriptomics, we identified new cerebrovascular markers, e.g. zinc finger protein Zic3 is explicitly found in adult brain endothelial cells and the Atp13a5 ATPase is only expressed in brain pericytes. Using new genetic models, we further confirmed the specificity of Zic3 in cerebrovasculature. Additionally, we developed a mouse model based on Plvap , and confirmed it is specific for endothelial cells of the peripheral tissue and circumventricular organs in brain. In-depth transcriptomics analysis between Zic3 + and Plvap + endothelial cells revealed that genetic programs associated with lipid metabolism, transporter systems and tight junction signaling are critical drivers behind the separation of central and peripheral endothelia. These new murine genetic tools will further aid our understanding of vascular heterogeneity and BBB specialization. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Main The blood-brain barrier (BBB), blood-spinal cord barrier and blood-retinal barrier provide the physical barriers limiting the entrance of circulating pathogens and toxins, immune cells and body’s metabolic waste products into the central nervous system (CNS), and supplies critical energy metabolites such as glucose and lactate, essential amino acids, fatty acids, vitamins and growth hormones via selective transporter systems 1,2 . The BBB also helps clear brain’s own metabolic wastes including excess of neurotransmitters and proteinaceous molecules such as Alzheimer’s amyloid-β species, providing neurons with a tightly controlled microenvironment 3 . On the other hand, the vasculatures in the peripheral organs are more permeable, particularly in organs such as liver and kidney 4 . This functional heterogeneity also suggests that tissue microenvironment may provide important guidance cues for the specialization of their own vasculatures 5 . For example, Wnt signaling regulates both neural and BBB development in the vertebrate brain 6 , while hepatocyte growth factor (HGF) stimulates liver angiogenesis 7 . Endothelial cells are the bedrocks of the vasculature. Anatomically, CNS endothelia are similar to the ones in the peripheral organs, connected by intercellular junctions and embedded in basement membrane. Yet, they are in close interactions with perivascular pericytes and astrocytic endfeet 8 , resulting in more tightly sealed tight junctions and minimal level of transcytosis 9 . At the molecular level, brain endothelial cells carry high levels of tight junctional proteins such as claudins and occludins 10 , and are enriched with special transporters such as glucose transporter 1 (GLUT1, encoded by SLC2A1 ) 10 , and major facilitator superfamily domain containing protein MFSD2A, a sodium-dependent lysophosphatidylcholine (LPC) transporter 11 . The peripheral endothelia on the other hand are often fenestrated 12 , providing non-selective transcellular transport through pinocytosis. The plasmalemma vesicle-associated protein (PLVAP), functioning as the diaphragm of these fenestrae, is in general absent in the adult brain 13 . However, the intrinsic programs underlying the central and peripheral divergence and the heterogeneity of vascular system remain underexplored. In addition, existing genetic tools (e.g., reporters and Cre drivers) are inadequate to differentiate or specifically manipulate central or peripheral vasculature endothelia, posing a significant research barrier. To address these gaps, we analyzed single-cell transcriptomic datasets from 11 mouse tissues to identify endothelial markers with organ-specific expression patterns and revealed that Zic Family Member 3 ( Zic3 ) is explicitly expressed in brain endothelial cells. We generated a Zic3-T2A-tdTomato-IRES-CreERT2 knock-in model, with both fluorescent tdTomato reporter and CreERT2 recombinase expressed from the endogenous alleles, and confirmed both tdTomato reporter expression profiles and CreERT2 recombinase activity are specific to the endothelial cells of the central nervous system, including brain, spinal cord and retina. Furthermore, we generated a Plvap-T2A-EGFP-IRES-CreERT2 knock-in model, and found that it reliably targets the peripheral endothelial cells in heart, lung, liver and kidney, as well as the endothelial cells in the choroid plexus and meningeal vessels in the brain. Based on these two markers, we further analyzed the endothelial transcriptomics at single-cell level, which revealed that transporter system and tight junction signaling are the major divergent points between the central and peripheral vasculatures. In sum, these new Zic3 and Plvap genetic models represent unique tools towards a better understanding of the molecular and genetic underpinning of the complexity of our vascular system in development, aging and diseases. Results Differential vascular permeabilities between CNS and peripheral organs The divergence between the central and peripheral vascularsystems in terms of permeabilities has been known for more than a century 14 , since the early observations based on dye injections in embryos 15 . Using HRP as a traditional tracer administered in the circulation 16 , we compared the vascular permeabilities between the central and peripheral organs ( Extended Data Fig. 1a ). Briefly, mice received a single dose of 0.5 mg/g of body weight of HRP solution in saline, and tissues were harvested 2 h later after perfusion. With fluorescent Tyramide signal amplification (TSA) 17 , we can amplify the HRP tracer and compare the vascular permeabilities between different organs ( Fig. 1a and Extended Data Fig. 1b ). Among peripheral organs, kidney exhibited the highest vascular permeability, followed by liver, intestine and lung, while heart and spleen showed relatively low levels of HPR molecule accumulation. On the other hand, the brain and spinal cord had very minimal HRP signals, particularly in the cortex, or the grey matter of spinal cord ( Fig. 1a ). Zic3 is a new marker for brain endothelial cells To explore the molecular basis of vascular heterogeneity, we integrated and performed secondary analysis on three single cell RNA sequencing (scRNA-seq) datasets covering both mouse brain and peripheral vasculatures 18–20 . 48,526 single-cell transcriptomes were collected for the secondary analysis, as we recently reported 21 . In Uniform Manifold Approximation and Projection (UMAP), four major vascular cell types were separated into 20 clusters across different tissues including endothelial cells, mural cells, Oligodendrocytes, and fibroblasts ( Fig. 1b ), based on their specific genetic markers. These include but are not limited to Cldn5 , Podxl and Kdr for endothelial cells, Pdgfrb , Vtn and Cspg4 for mural cells. Interestingly, we found that Zic3 is unique to the brain endothelium ( Fig. 1c ), and confirmed that Atp13a5 is a brain mural cell marker 21 ( Fig. 1d ). Zic3 encodes a zinc finger protein and transcription factor, essential for mammalian embryonic cardiovascular and neural development 22 . It is highly specific to the brain based on the Tabula Muris dataset 23 ( Extended Data Fig. 1c-d ). To further confirm its specificity, we analyzed the brain vascular scRNA-seq dataset 18 and found that Zic3 expression is exceedingly precise to brain endothelial cells of capillaries, arterioles and veins, but not expressed in any other vasculature cells including VSMCs, oligodendrocytes, fibroblasts, microglia, or astrocytes ( Extended Data Fig. 1e ). In addition, analysis of a large dataset of 1,093,785 cells combining adult mouse cortical and hippocampal areas 24 confirmed that Zic3 expression is exclusively in endothelial cells, but not in any neuronal and glial cells ( Extended Data Fig. 1f ). Besides Zic3 , we examined transporters that are currently known to be enriched in brain endothelial cells. For example, lysolipid transporter Mfsd2a and glucose transporter Slc2a1 are well-known for their roles in cerebrovasculature 25,26 , but their expressions are evident in testicular endothelial cells based on the integrated transcriptomics ( Extended Data Fig. 1g ). In addition, although organic anion transporter Slco1c1 is specific to brain endothelial cells in the integration dataset, it is highly expressed in astrocytes as well, based on datasets including the Tabula Muris 23 ( Extended Data Fig. 1h ). Fluorescent in situ hybridization (FISH) with RNAscope probes validated that Zic3 transcripts are expressed throughout the brain regions including cortex, but not observed in peripheral organs such as liver or heart ( Fig. 1e-f ), and Zic3 mRNA transcripts were colocalized exclusively within the lectin-positive brain endothelium ( Extended Data Fig. 2a ). In addition, immunohistochemistry analysis with antibodies that react with murine ZIC3 protein confirmed its presence in vasculature across brain regions ( Fig. 1g ), including cortex, hippocampus, cerebellum, hypothalamus, striatum, medulla, midbrain, thalamus and pons ( Extended Data Fig. 2b ). This indicates that Zic3 is potentially a new specific marker for CNS endothelial cells. Zic3-T2A-tdTomato-IRES-CreERT2 knock-in model for CNS endothelial cells Next, we generated a new transgenic model targeting the Zic3 locus. This Zic3-T2A-tdTomato-IRES-CreERT2 knock-in model carries expressions of both tdTomato reporter and CreERT2 recombinase through the endogenous Zic3 locus ( Fig. 2a , also see Methods). One F0 founder was selected based on germline transmission and genome sequencing, and the F1 generation was further tested with southern blot analysis and validated by PCR ( Extended Data Fig. 3a-c ). As Zic3 is located on X-chromosome, the model is maintained as hemizygous ( Zic3 tdT/Y for male, and Zic3 tdT/+ for female). Homozygous female Zic3 tdT/tdT mice can be obtained from breeding of hemizygous, and appear normal. Importantly, tdTomato is reliably expressed in adult hemizygotes and homozygotes ( Fig. 2b ). The Zic3- tdTomato profiles overlay well with Lectin-labeled endothelium throughout the brain, including cortex, hippocampus and thalamus ( Fig. 2c ), but not in peripheral tissues such as liver, heart or kidney ( Fig. 2d ). To verify the expression of Zic3 marker in the CNS, we also examined the spinal cord and retina in our transgenic model. We found robust Zic3- tdTomato signals in both white and gray matter of the spinal cord ( Fig. 2e ), with a little higher coverage in gray matter. In the retina, Zic3- tdTomato expressing cells were found in most of the vessels ( Fig. 2f and Extended Data Fig. 3d ). Additional immunostainings further confirmed that Zic3 -tdTomato expressing cells are indeed brain endothelial cells, as they are CD31-positive ( Fig. 2g and Extended Data Fig. 3e ), but not other cell types in the brain such as CD13-positive pericytes ( Fig. 2h and Extended Data Fig. 3f-g ), NeuN-positive neurons, GFAP-positive astrocytes, or IBA1-positive microglia ( Extended Data Fig. 3h-j ). Zic3 -tdTomato reporter is found in over 90% of capillary endothelial cells, as well as in ~40% of endothelial cells on VCAM1-positive veins and venules or SMA-positive arteries and arterioles ( Fig. 2i-k ). It is estimated to cover at least 80% of the cerebrovasculature ( Extended Data Fig. 3k ). Characterization of the Zic3 -CreER recombinase activity To test the CreER activity in our Zic3-T2A-tdTomato-IRES-CreERT2 model, we crossed it with the Ai3-EYFP floxed reporter line 27 , and induced the CreER activity with tamoxifen administration ( Fig. 3a , also see Methods). With 7 injections of Tamoxifen, more than 90% of Zic3 -tdTomato + endothelial cells in the brain, retina and spinal cord expressed robust EYFP signals ( Fig. 3b-d ). Yet, no recombination or EYFP reporter signal was observed in peripheral tissues such as heart, kidney or liver ( Fig. 3c-d ). Hence, our data demonstrated that Zic3 marker is unique for the CNS endothelium, and Zic3-T2A-tdTomato-IRES-CreERT2 model may help us decode the molecular and genetic underpinning of its specification . Plvap-T2A-EGFP-IRES-CreERT2 model for peripheral and non-BBB endothelial cells MECA-32 (an antibody against PLVAP glycoprotein) is widely used for staining of endothelial cells in embryos and most adult mouse peripheral tissues 28 , and it also label the vasculature in some of the circumventricular organs, such as the choroid plexus. This is highly consistent with single cell transcriptomic ( Fig. 1c ). Therefore, we also generated the Plvap-T2A-EGFP-IRES-CreERT2 knock-in mouse model, carrying the EGFP reporter and CreERT2 recombinase ( Fig. 4a ). One F0 founder was selected based on germline transmission and genome sequencing, and the F1 generation was further tested with southern blot analysis and PCR for the integrity of the knock-in allele ( Extended Data Fig. 4a-c ). Plvap-T2A-EGFP-IRES-CreERT2 hemizygous mice are healthy, and we observed robust Plvap- EGFP signal in the lung and heart vasculature ( Fig. 4b-c ), which is consistent with the transcriptomic profiles in the Tabula Muris database 23 ( Extended Data Fig. 4d ). To access the CreER activity of Plvap-T2A-EGFP-IRES-CreERT2 model, we crossed it with the Ai14-tdTomato floxed reporter mice 27 ( Fig. 4d ). Following Tamoxifen injections, we analyzed Plvap-CreERT2 driven Ai14-tdTomato reporter ( Plvap- Cre::Ai14-tdT), and found very robust labeling of peripheral vasculatures including kidney and liver ( Fig. 4e ). Interestingly, we also observed Plvap- EGFP signals in the choroid plexus within the brain ventricles, but not in the cortex or hippocampus ( Fig. 4f ). This is further confirmed with the Plvap- Cre::Ai14-tdT model, which showed robust labeling of choroid plexus vasculature following Tamoxifen injections ( Fig. 4g-h ). In addition, the Plvap model also reliably targets the meningeal vessels. As meningeal vasculature is known to be vulnerable in head injuries 29 , we conducted mild traumatic brain injury (mTBI) on this model using a controlled cortical impact system 30 ( Fig. 4i ). Even though the impact did not result in direct damage to the skull ( Fig. 4j ) 30 , the meningeal vessel were severely damaged in the ipsilateral side after the injury, as shown by the dramatic loss of Plvap- Cre::Ai14-tdT reporter under the impact site( Fig. 4k ). Interestingly, the Plvap -EGFP reporter showed an increased pattern 3 days after mTBI, indicating robust angiogenesis and vascular remodeling occurred shortly after the injury( Fig. 4k ). This demonstrated the new Plvap knock-in model is suitable for studying peripheral endothelial cells, and the built-in reporter and CreERT2 recombinase can be used synchronously for effective lineage tracing in vivo . Transcriptomic heterogeneity between CNS and peripheral endothelial cells To further explore the differences between Zic3 -positive and Plvap -positive endothelial cells at single cell transcriptomic level, we analyzed 37,083 single endothelial cells from various organs, including the brain, intestine, colon, heart, kidney, liver, lung, muscle, spleen, and testis ( Fig. 5a-c ). Specifically, we compared 1,903 Zic3 -positive ECs with 20,517 Plvap -positive ECs. These two populations exhibit distinct transcriptomic profiles, and we identified 341 genes with significant differential expression: 277 genes are significantly enriched in Zic3 -positive ECs, while 64 genes show higher expression in Plvap -positive ECs. For instance, transporter genes such as Slc2a1, Slco1a4, Slco1c1, Slc22a8, Slc7a5, Slc38a3 and Mfsd2a are more abundantly expressed in Zic3 -positive ECs, whereas lipid metabolism associated genes such as Phospholipid Phosphatases Plpp1 and Plpp3 , fatty acid binding protein Fabp4 , glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1 ( Gpihbp1 ) are commonly expressed in Plvap -positive ECs ( Fig. 5d-e ). In addition, a few genes showed organ specificities in the peripheral, e.g. surfactant protein C (Sftpc) is only found in lung ECs, C-Type Lectin domain protein Clec4g is mainly expressed in liver ECs, Insulin-like growth factor binding protein Igfbp5 is exclusive to kidney ECs, Stabilin 2 ( Stab2 ) is highly enriched in spleen ECs ( Fig. 5d ). With these differentially expressed genes (DEGs), we conducted pathway analysis based on Gene Ontology enrichment method. Based on biological processes, these DEGs are related to transporter systems and regulate brain development ( Fig. 5f ). Their cellular functions are connected to membrane functions and intercellular junctions ( Fig. 5g ), and their molecular functions are associated with lipid binding and protein transporting ( Fig. 5h ). This was further confirmed with Ingenuity canonical pathway analysis, which showed that transporter system and tight junction signaling are the top pathways differentiating the Zic3 -positive ECs from Plvap -positive ECs ( Extended Data Fig. 5e ). Taken together, these data further confirmed Zic3 and Plvap as unique markers for the CNS and peripheral endothelial cell, respectively. Discussion The mammalian BBB is central to the CNS health and functions. It is an evolutionary milestone for overall brain fitness, yet in same time became a major research challenge, as our understanding of how the highly specialized BBB structure is established during development and how different brain vascular cell types orchestrate together to support brain functions remain limited 10 . This obstacle can be partly attributed to the lack of specific markers and genetic tools that can separate the cerebrovascular cells from the peripheral ones. For example, current murine genetic tools of vascular cells are often based on the well-known endothelial cell markers such as podocalyxin ( Podxl ), claudin 5 ( Cldn5 ), tyrosine kinase ( Tek/Tie2 ) and tyrosine kinase receptor ( Kdr ), which all tend to express throughout the body. Therefore, transgenic mouse models based on these alleles, including Tek-Cre and Tek-Cre/ERT2, Cdh5-Cre and Cdh5-Cre/ERT2, Kdr-Cre , all exhibit limitations and constraints when applied to brain vasculature 31 . Recently, we used single cell transcriptomics and identified a brain pericyte specific marker — Atp13a5 , and successfully generated a knock-in mouse model for the brain pericyte 21 . With similar approaches, we further identified Zic3 as a brain endothelial specific marker from single cell transcriptomics, and confirmed its expression with both in situ hybridization and antibody staining. The finding of Zic3 markers is very meaningful, as it guided us to generate a new transgenic model — Zic3-T2A-tdTomato-IRES-CreERT2 . Based on the Zic3- tdTomato expression and the corresponding CreER recombinase activity after tamoxifen induction, we further confirmed that the Zic3-T2A-tdTomato-IRES-CreERT2 model is specific to the endothelial cells in the CNS, and not for peripheral cells. For peripheral endothelial cells, we also generated a new Plvap-T2A-EGFP-IRES-CreERT2 knock-in model, and the characterization of Plvap -EGFP reporter and corresponding CreER recombinase activity showed it targets the endothelial cells of the peripheral organs such as lung, kidney, liver, etc., as well as the circumventricular organs in the brain which are known to be outside of the BBB. Therefore, the Zic3 and Plvap genetic tools are sufficient to separate and selectively target the CNS and peripheral endothelial cells, respectively. Interestingly, the presence of Plvap in choroid plexus and meningeal vessels also indicates that the heterogeneity of cerebrovascular endothelial cells, at least between the BBB and non-BBB areas, which could be potentially probed with the genetic tools we have generated. There is a list of receptors and transporters known to be highly enriched at the BBB endothelium, including lysolipid transporter Mfsd2a , glucose transporter Slc2a1 and organic anion transporter Slco1c1. Their candidacy for generating genetic models for brain or BBB endothelial cells were proposed, but none of them are specific enough, as shown by single cell transcriptomics (e.g. Extended Data Fig. 1 ). Based on our data from RNAscope, immunostaining and reporter models, Zic3 is indeed a CNS endothelial specific genetic marker. Zic3 encodes a highly conserved C2H2 zinc finger domain protein and a member of the GLI superfamily of transcription factors 32 . Its mutations are found in patients with X-linked heterotaxy, and its deficiency in mice disrupts the left–right asymmetry of cardiovascular development and results in neural tube deficits 33 . While ZIC3 plays an important role in early development, it is still surprising to see its expression remained in the BBB endothelial cells even in the adult brain. In human endothelial cells derived from pluripotent stem cells, ZIC3 can act downstream of Wnt signaling and promote the endothelial barrier functions 34 . Ectopic expression of Zic3 in human umbilical vein endothelial cells could also induce BBB marker gene expression 35 . Therefore, it is important to determine if Zic3 is a master regulator of an intrinsic program that maintains the BBB functions in future studies. Interestingly, Zic3 is also located on X-chromosome, and may contribute to the sex-difference in the cerebrovascular functions associated with aging and Alzheimer’s disease 36 . The cerebrovascular development is guided by some of the classic growth factor pathways, such as VEGF, Wnt and Notch 8 . Take Wnt signaling as an example, Wnt7a/7b and Norrin 37 , Frizzled receptor 38 , co-receptors low-density lipoprotein receptor-related protein (LRP) 5 and 6 39 and co-activator orphan G-protein coupled receptor Gpr124 40 are all indispensable the proper development of the BBB. To understand the vascular heterogeneity, we analyzed current single cell transcriptomics gathered from different organs. Besides Zic3 , Plvap and Atp13a5 , we identified a list of genetic markers that can potentially be utilized to develop next-generation tools. For example, the transcriptomic comparison between CNS Zic3 + endothelial cells with Plvap + peripheral ones pointed to hundreds of DEGs that changed preferentially in BBB endothelial cells (Fig. 5 ). These genes are linked to interesting pathways associated with the establishment of the blood-brain barrier, brain development, lipid metabolism, transport systems and tight junction signaling. While Zic3 has a preference for CNS endothelial cells, Clec4g, Igfbp5 and Stab2 may potentially be utilized to target endothelial cells of the liver, kidney and spleen, respectively, and worth further investigation. Nevertheless, this endothelial heterogeneity is proof that ECs are heavily influenced by the local environment of each individual organs. Last but not the least, BBB dysfunctions with endothelial activation is commonly found in a spectrum of CNS disorders, including AD and other dementia 10 . Therefore, the identification of Zic3 as a BBB endothelial cells marker will likely advance our studies towards a better understanding of BBB vascular cell changes in aging and CNS diseases, including vascular contributions to AD and dementia. Beyond the application of these transgenic models, these specific markers can be used to develop other tools, such as BBB cell-type specific antibodies and new viral vector designs to target BBB endothelial cells more specifically with artificial promoters based on tissue and cell-type specific enhancers 41 . Declarations Acknowledgement We thank Dr. Fan Gao for his valuable bioinformatics support and insightful discussions that greatly contributed to the completion of this study. We thank the support National Institutes of Health (NIH) grant nos. R01AG061288, RF1NS122060, RF1NS135617, and R21AG085559, BrightFocus Foundation grant no. A2019218S, and Alzheimer’s Association grant no. AA-ADP 23-1051406. References Daneman R, Prat A (2015) The Blood–Brain Barrier. Cold Spring Harb Perspect Biol 7:a020412 Zlokovic BV (2008) The blood-brain barrier in health and chronic neurodegenerative disorders. 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Nat Neurosci 22:1089–1098 < Methods KEY RESOURCES TABLE REAGENT or RESOURCE SOURCE IDENTIFIER Genomic data sets Mouse brain vasculature Gene Expression Omnibus GSE98816 GSE99058 Atlas of Murine Endothelial Cells Array Express E-MTAB-8077 Fibroblast and mural cell in muscular organs Gene Expression Omnibus GSE150294 Tabula Muris Gene Expression Omnibus GSE109774 Single cell RNA-sequencing data processing Seurat Package (v.3.1.5) R v.3.6.2 FindVariableFeatures function R v.3.6.2 FindAllMarkers function R v.3.6.2 FindMarkers function R v.3.6.2 Principal component analysis (PCA) R v.3.6.2 Uniform Manifold Approximation and Projection (UMAP) R v.3.6.2 Shared Nearest Neighbor (SNN) clustering R v.3.6.2 Experimental Models: Organisms/Strains Zic3-tdTomato-CreERT2 This study Plvap-tdTomato-CreERT2 This study DNA isolation and genotyping GoTaq Green Master Mix Promega M7122 Zic3-tdTomato- CreERT2 forward primer AATGGCTCTCCTCAAGCGTATTC Zic3-tdTomato- CreERT2 reverse primer GTTATTCAACTTGCACCATGCCG Plvap-EGFP- CreERT2 forward primer GCTGTGTAGCAGAGACAAACCTTA Plvap-EGFP-CreERT2 reverse primer GGTGGTGCAGATGAACTTCAGG Antibodies Goat polyclonal anti-CD13 R&D systems AF2335 Rat monoclonal anti- CD31 BD Pharmingen 550274 Rabbit polyclonal anti-ZIC3 Thermo Fisher Scientific PA5-29073 Rat monoclonal anti-VCAM1 Millipore Sigma CBL1300 Mouse monoclonal anti-SMA Millipore Sigma A5228 Rabbit polyclonal anti-Iba-1 Wako 019-19741 Rabbit polyclonal anti-GFAP Dako z0334 Rabbit polyclonal anti-NeuN Millipore Sigma ABN78 Rabbit anti-mouse Olig2 Millipore Sigma AB9610 Dylight 488-conjugated L. esculentum lectin Thermo Fisher Scientific L32470 Dylight 649-conjugated L. esculentum lectin Thermo Fisher Scientific L32472 Immunohistochemistry Vibratome Leica VT1200 Cryostat Leica CM3050 S Normal Donkey Serum Jackson ImmunoResearch AB_2337258 VECTASTAIN ABC universal kit Vector laboratories PK6200 ImmPRESS Universal Polymer kit Vector laboratories MP-7500 in situ hybridization SuperFrost Plus Micro Slide VWR 48311-703 Zic3 RNAscope probe Advanced Cell Diagnostics Cat. #480351 Positive control RNAscope probe Advanced Cell Diagnostics Cat. #320881 Negative control RNAscope probe Advanced Cell Diagnostics Cat. #320871 HybEZ Oven Advanced Cell Diagnostics RNAscope® 2.5 HD Reagent Kit-RED Advanced Cell Diagnostics 322350 RNAscope® Multiplex Fluorescent V2 Assay Advanced Cell Diagnostics 323100 Nikon A1R MP+ confocal/ multiphoton microscope Nikon Nikon A1R Revolve 4 brightfield and fluorescence microscope Echo RVL-100-G Software GraphPad Prism GraphPad Software GraphPad Prism 8 Metascape http://metascape.org ClueGO 2.5.4 Cytoscape 3.8.1 Image J NIH CONTACT FOR REAGENT AND RESOURCE SHARING Further information and request for resources and reagents should be directed to and will be fulfilled by Lead Contact Zhen Zhao ( [email protected] ). EXPERIMENTAL MODEL AND SUBJECT DETAILS Animals Mice were housed in plastic cages on a 12 h light/dark cycle with access to water ad libitum and a standard laboratory diet. All procedures were approved by the Institutional Animal Care and Use Committee at the University of Southern California and followed National Institutes of Health guidelines. All animals were included in the study. Male and female animals of 2–3 months of age were used in the experiments. All animals were randomized for their genotype information. All experiments were carried out blind: the operators responsible for the experimental procedures and data analysis were blinded and unaware of group allocation throughout the experiments. For all experiments, at least three independent mice were analyzed, which included both sexes and no apparent sex difference were observed. Generation of the Zic3-tdTomato-CreERT2 knock-in model To generate Zic3-T2A-tdTomato-IRES-CreERT2 knock-in mouse , donor DNA templates encoding self-cleaving T2A peptide, tdTomato, internal ribosome entry site and CreERT2 were synthesized. These sequences were flanked by 375bp sequences and 4606bp sequences homologous to the third exon and 3’ UTR region of Zic3 gene. Next, these donor vector containing the T2A-tdTomato-IRES-CreERT2 cassette, and gRNA (TTTAACGAATGGTACGTCTGAGG) were co-injected into fertilized eggs to generate targeted conditional knock-in offspring. The F0 founder animals were genotyped by PCR and sequence analysis, and four F1 mice were generated and further confirmed with southern blotting for both 5’ arm and 3’ arm insertion sequences. Generation of the Plvap-EGFP-CreERT2 knock-in model We generated the Plvap-T2A-EGFP-IRES-CreERT2 knock-in mouse (Fig. 4b), with a donor DNA template encoding self-cleaving T2A peptide, EGFP, internal ribosome entry site, CreERT2. These sequences were flanked by 405bp sequences and 3834bp sequences homologous to the 6 th exon and 3’ UTR region of Plvap gene. These donor vector containing the T2A-EGFP-IRES-CreERT2 cassette, and gRNA (GCAGCTGGGTCCTCAACCGCTGG) were co-injected into fertilized eggs to generate targeted conditional knock-in offspring. The F0 founder animals were genotyped by PCR and sequence analysis, and three F1 mice were generated and further confirmed with southern blotting for both 5’ arm and 3’ arm insertion sequences. Mild Traumatic Brain Injury model To induce mild traumatic brain injury (mTBI) in mice, we followed a previously described protocol 42 . Briefly, we used the KOPF stereotaxic system to position the mouse’s head under the impactor at a specific angle, targeting a point 2 mm posterior and 2.5 mm lateral to Bregma. A 4 mm flat plastic tip (RWD Life Science) was used to deliver a controlled impact using a brain injury device (RWD #68099). Mice were anesthetized with ketamine and xylazine (90 mg/kg and 9 mg/kg, i.p.). After exposing the skull, we delivered an impact at a velocity of 3 m/s, a depth of 1 mm, and a duration of 180 milliseconds. Mice were then placed in warmed cages to recover. METHOD DETAILS Bioinformatics ScRNA-seq data for mouse brain vasculature and multiple organs For scRNA-seq integration dataset, we obtained the cell count matrix from Gene Expression Omnibus (GEO) with the series record GSE98816, GSE150294 and Array express E-MTAB-8077, and did secondary analysis after integration. Then we performed secondary analysis of 3 single cell sequencing (scRNA-seq) datasets on brain and peripheral vascular cells, 48,526 single-cell transcriptomes were collected using R Seurat Package. For scRNA-seq dataset for mouse brain vasculature, we obtained the cell count matrix from GEO with the series record GSE98816 and GSE99058 18 . The data represent the expression levels of 18435 genes in 3186 cells. The mouse brain tissue was harvested for Smart-seq2 and sequencing was performed on a HiSeq2500 at the National Genomics Infrastructure (NGI), Science for Life Laboratory, Sweden, with single 50-bp reads (dual indexing reads). For scRNA-seq dataset for multiple organs, we obtained the cell count matrix from GEO with the series record GSE109774 23 . The data represent the expression levels of 23433 genes in 53760 cells. All organs were single-cell-sorted into plated using flurescence-activated cell sorting. Libraries were sequenced on the NovaSeq 6000 Sequencing System (Illumina) using 2 x 100-bp paired-end reads. ScRNA-seq data preprocessing The data processing of the scRNA-seq data were performed with the Seurat Package (v.3.1.5) in R (v.3.6.2). The basic scRNA-seq analysis was run using the pipeline provided by Seurat Tutorial ( https://satijalab.org/seurat/v3.0/immune_alignment.html ) as of June 24, 2019. In general, we set up the Seurat objects from different groups in experiments for normalizing the count data present in the assay. This achieves log-normalization of all datasets with a size factor of 10,000 transcript per cell. For different Seurat objects, FindVariableFeatures() function was used to identify outlier genes on a ‘mean variability plot’ for each object. The nFeatures parameter is 2000 as the default for the selection method called ‘vst’. These resulted genes serve to illustrate priority for further analysis. Data processing The dataset on all cells were used to scale and center the genes. First of all, principal component analysis (PCA) was used for linear dimensionality reduction with default computes the top 30 principal components. By applying the JackStraw() function, JackStrawPlot() function and ElbowPlot() function, we identified the principal components for further analysis. Then, PCA results were used as the input for the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction. We identified clusters of cells by a shared nearest neighbor (SNN) modularity optimization-based clustering algorithm. The algorithm first calculated k-nearest neighbors and computed the k-NN graph, and then optimizes the modularity function to determine clusters. Determination of cell-type identity To determine the cell type, we used FindAllMarkers() function with parameters min.pct and thresh.use set to 0.25 to find markers in each cluster and known marker genes that have been previously reported could be used to determine cell-type identity. These include, but are not limited to Snap25 for Neuron, Cldn10 for Astrocyte, Mbp for Oligodendrocyte, Cldn5 for EC, Kcnj8 for PC, Acta2 for VSMC, Ctss for microglial, Col1a1 for Fibroblast-like cell. Pathway analysis and visualization by Metascape, ClueGO and Cytoscape Using the Metascape online tool (http://metascape.org), we performed functional enrichment analysis of ZIC3-positive enriched genes. Enrichment of pathways from KEGG, GO Biological Process and GO Molecular Function was analyzed by Metascape. The terms with P-value 1.5 would be considered. The ClueGO Cytoscape pluin 2.5.4 and Cytoscape version 3.8.1 will be used for secondary KEGG pathway analysis and network visualization. Cellular Biology Related Procedures HPR injection and Lectin injection and mapping The HRP solution was prepared by dissolving 125 mg (0.125 g) of HRP Type II (Sigma, P8250) in 2.5 ml of PBS, yielding a concentration of 0.5 mg/10 µl. Each animal was injected with a single dose of 0.5 mg/g of body weight and harvested 2 hours later. The mice were then sacrificed and perfused with PBS and PFA at 5 min after lectin (ThermoFisher Scientific, #L32470) injection. To visualize HRP in the injected samples under light microscopy, the samples were washed with PBS and incubated in Tris buffer containing 0.1% tyramide reagent and 0.0015% H₂O₂ for 10 minutes at room temperature, in the dark 43 . All sections were then scanned using the Li-Cor Odyssey Dlx at a resolution of 21 µm, or on a NikonTi2 confocal microscope. The ImageJ plugin ‘Neuro J’ length analysis tool was used to measure the length of lectin-positive or HRP-positive endothelial capillary profiles. The capillary length was quantified and expressed as mm of lectin+ endothelial capillary profiles per mm2 of brain tissue. The HRP-occupied vascular area ratio was calculated by measuring the HRP-positive signal within lectin-positive regions. Fluorescence in situ hybridization Fluorescence in situ hybridization was performed using the RNAscope technology (Advanced Cell Diagnostics, Hayward, CA). Tissue sample preparation and pretreatment were performed on fixed brains cut into 15 µm sections mounted onto SuperFrost Plus glass slides following the manufacturer’s protocol (ACD documents 323100). After dehydration and pretreatment, slides were subjected to RNAscope Multiplex Fluorescent Assay (ACD documents 323100). RNAscope probes for mouse Zic3, positive control and negative control were hybridized for 2h at 40ºC in the HybEZ Oven and the remainder of the assay protocol was implements. Subsequently, the slides were subjected to immunohistochemistry. The fluorescent signal emanating from RNA probes and antibodies was visualized and captured using a Nikon AIR MP+ confocal/ multiphoton microscope (Nikon). All FISH images presented are projection of 10-image stacks (0.5 µm intervals) obtained from cerebral cortex, and a smoothing algorithm was applied during image post-processing (Nikon NIS-Elements Software). Immunohistochemistry Animals were anesthetized, perfused and brains were removed and postfixed as we described previously 44 . Brain, spinal cord, kidney, liver, and heart tissue were also collected, postfixed and cut at 35 µm thickness using a vibratome (Leica). After that, sections were blocked with 5% normal donkey serum (Vector Laboratories) and 0.1% Triton-X in 0.01M PBS and incubated with primary antibodies diluted in blocking solution overnight at 4ºC. The primary antibody information is as following: Goat anti-mouse aminopeptidase N/ANPEP (CD13; R&D systems; AF2335; 1:100), ZIC3 polyclonal antibody (Invitrogen; PA5-29073; 1:100), Rat anti-mouse vascular adhesion molecule (VCAM1; MilliporeSigma; CBL1300; 1:200), Mouse anti-α-smooth muscle actin (SMA, MilliporeSigma; A5228, 1:200), Rabbit anti-mouse ionized calcium binding adaptor molecule 1 (Iba-1; Wako, 019-19741; 1:200), Rabbit anti-Glial Fibrillary Acidic Protein (GFAP; Dako, z0334; 1:500), Rabbit anti-mouse NeuN (Millipore, ABN78, 1:500). To visualize brain microvessels, sections were incubated with Dylight 488 or 647-conjugated L. esculentum lectin as we have described previously 44 . After incubation with primary antibodies, sections were washed with PBS for three times and incubated with fluorophore-conjugated secondary antibodies. Sections were imaged with a Nikon AIR MP+ confocal/ multiphoton microscope (Nikon). Z-stack projections and pseudo-coloring were performed using Nikon NIS-Elements Software. Image post analysis was performed using ImageJ software. Molecular Biology Related Procedures DNA isolation and genotyping Mouse genomic DNA was isolated from tail biopsies (2 - 5 mm) and following overnight digestion at 56 into 100 μL of tail digestion buffer containing 10 mM Tris-HCl (pH 9.0), 50 mM KCl, 0.1% Triton X-100 and 0.4 mg/mL Proteinase K. Next, the tail will be incubated at 98 for 13 minutes to denature the Proteinase K. After centrifugation at 12000 rpm for 15 min, the supernatants were collected for PCR. The primers details are listed in the KEY RESOURCES TABLE. The PCR conditions were as follows: 1) 94 °C for 3 min; 2) 35 cycles at 94 °C for 30 sec, 60 °C for 30 sec, and 72 °C for 35 sec; 3) 72 °C for 5 min. PCR products were separated on 2% agarose gel. Quantification and statistical analysis Sample sizes were calculated using nQUERY, assuming a two-side alpha-level of 0.05, 80% power and homogeneous variances for the 2 samples to be compared, with the means and SEM for different parameters predicted from pilot study. All the data are presented as mean ± SEM as indicated in the figure legends and were analyzed by GraphPad Prism 8. For multiple comparisons, Bartlett’s test for equal variances was used to determine the variances between the multiple groups and one-way analysis of variance (ANOVA) followed by Tukey test was used to test statistical significance, using GraphPad Prism 8 software. A P value of less than 0.05 was considered statistically significant. Additional Declarations No competing interests reported. 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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-7421061","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506029845,"identity":"93098d63-a4af-4edd-9090-9d05808c98bf","order_by":0,"name":"Shangzhou Xia","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Shangzhou","middleName":"","lastName":"Xia","suffix":""},{"id":506029846,"identity":"65626cf1-7190-492c-81b2-571df241d2fa","order_by":1,"name":"Tenghuan Ge","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Tenghuan","middleName":"","lastName":"Ge","suffix":""},{"id":506029847,"identity":"27b91981-2a40-48e3-85e4-d843b6602a18","order_by":2,"name":"Ruocen Song","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Ruocen","middleName":"","lastName":"Song","suffix":""},{"id":506029848,"identity":"294c9f23-39bb-422b-9f8e-f278f93ad9e8","order_by":3,"name":"Qinghai Liu","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Qinghai","middleName":"","lastName":"Liu","suffix":""},{"id":506029849,"identity":"78f5b128-74d7-42d8-90e3-fefe91547a31","order_by":4,"name":"Xiao He","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Xiao","middleName":"","lastName":"He","suffix":""},{"id":506029850,"identity":"a234a10e-6d98-4482-825d-16bafc014f88","order_by":5,"name":"Yvonne Shen","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Yvonne","middleName":"","lastName":"Shen","suffix":""},{"id":506029851,"identity":"e97f82cf-b716-4844-a1a9-27fa204d1b1b","order_by":6,"name":"Haowen Qiao","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Haowen","middleName":"","lastName":"Qiao","suffix":""},{"id":506029852,"identity":"fdb1ae14-50fa-4af7-96d8-12f34e90cfd8","order_by":7,"name":"Yafei Qu","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Yafei","middleName":"","lastName":"Qu","suffix":""},{"id":506029853,"identity":"168b669b-8061-40b7-a4d4-e7dd8fe313a4","order_by":8,"name":"Jian-Fu Chen","email":"","orcid":"","institution":"University of Southern California","correspondingAuthor":false,"prefix":"","firstName":"Jian-Fu","middleName":"","lastName":"Chen","suffix":""},{"id":506029854,"identity":"7d9dd19a-1780-4898-80bb-fbf84f35ade0","order_by":9,"name":"Zhen Zhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAApElEQVRIiWNgGAWjYFACHiCugDAlSNByhmQtjG2kaJHvP3vww8d5h+35G5gP3uYhRgtjw7lkyZnbDifOOMCWbE2UFmbGHgNp3m2HEwwYeMykidLCxsxj/Jt3zmF7Awb+b8Rp4WEDGs7bcJhxA5BJnBYJHh4zyxnH0hNnHGYztpxDjBb5/jPGNz7UWNvztzc/vPGGGC0IwEya8lEwCkbBKBgF+AAAAA8oHK/KrzMAAAAASUVORK5CYII=","orcid":"","institution":"University of Southern California","correspondingAuthor":true,"prefix":"","firstName":"Zhen","middleName":"","lastName":"Zhao","suffix":""},{"id":506029855,"identity":"a22bb2ee-0a1f-4130-b6f9-94d73ff0ed91","order_by":10,"name":"Xinying Guo","email":"","orcid":"","institution":"Guangzhou Women and Children’s Medical Center, Guangzhou Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinying","middleName":"","lastName":"Guo","suffix":""}],"badges":[],"createdAt":"2025-08-21 00:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7421061/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7421061/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89974857,"identity":"7bb71523-b527-4611-aa00-ba1689317b3e","added_by":"auto","created_at":"2025-08-27 05:55:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1592293,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNew cerebrovascular marker based on single cell transcriptomics.\u003c/strong\u003e \u003cstrong\u003e(a) \u003c/strong\u003eRepresentative images of mouse kidney, liver, intestine, heart, brain, spinal cord following the intravenous injection of HRP. Scale bar: 50 µm. \u003cstrong\u003e(b) \u003c/strong\u003eUMAP of vascular cells transcriptomics from 11 tissues transcriptomes. Each dot was color-coded and annotated by both organs and cell types. EC: endothelial cell; mural: mural cell; FB: fibroblast; Oligo: oligodendrocytes. \u003cstrong\u003e(c) \u003c/strong\u003eViolin plots showing the distribution of expression level of the endothelial markers across 20 cell types. \u003cstrong\u003e(d) \u003c/strong\u003eViolin plots showing the distribution of expression level of the pericyte markers across 20 cell types. \u003cstrong\u003e(e)\u003c/strong\u003e Representative images showing RNAscope FISH for \u003cem\u003eZic3\u003c/em\u003e transcripts (Green), and immunostaining for Lectin\u003csup\u003e+ \u003c/sup\u003eendothelial\u003csup\u003e \u003c/sup\u003ecells (Magenta) and Dapi for nuclei (Blue) in the cortex of brain, the ventricular wall of heart and liver. Scale bar: 50 µm. \u003cstrong\u003e(f) \u003c/strong\u003ePercentage of \u003cem\u003eZic3\u003c/em\u003e mRNA-expressing cells in different mouse tissues. n = 3 mice. Data are presented as mean ± SEM. n = 3 mice. Data are presented in mean ± SEM. \u003cstrong\u003e(g)\u003c/strong\u003e Immunohistochemistry staining of paraffin-embedded mouse cortical sections using rabbit polyclonal anti-ZIC3 antibody (PA5-29073).\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/dfbebc823bc7bc8ac3554589.png"},{"id":89974860,"identity":"135347fc-3ed4-4c80-8de6-532ea6a3cb20","added_by":"auto","created_at":"2025-08-27 05:55:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1853867,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eZic3\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e model for blood-brain barrier specific endothelial cells. (a)\u003c/strong\u003e Schematic diagram showing the strategy for generating the \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e knock-in mice. See Methods for more details. \u003cstrong\u003e(b)\u003c/strong\u003e A representative tiled image of brain section from a homozygous \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e knock-in mouse. Scale bar: 500 µm. \u003cstrong\u003e(c)\u003c/strong\u003e Representative confocal images of \u003cem\u003eZic3\u003c/em\u003e‑tdTomato (red), endothelial lectin (green), and DAPI (blue) in multiple brain regions from a homozygous \u003cem\u003eZic3‑T2A‑tdTomato‑IRES‑CreERT2\u003c/em\u003e mouse. Scale bar, 100 µm. \u003cstrong\u003e(d)\u003c/strong\u003e Representative confocal images of \u003cem\u003eZic3\u003c/em\u003e-tdTomato, endothelial marker Lectin (green) and Dapi (blue) in different tissues from a homozygous \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2 \u003c/em\u003emouse, including liver, heart (ventricular wall) and kidney. Scale bar: 100 µm.\u003cstrong\u003e (e-f) \u003c/strong\u003eRepresentative confocal images showing \u003cem\u003eZic3\u003c/em\u003e-tdTomato expression, lectin (green) and Dapi (blue) in spinal cord and retina from homozygous \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2 \u003c/em\u003emouse. Scale bar: 100 µm. \u003cstrong\u003e(g-h)\u003c/strong\u003e High magnification confocal images showing \u003cem\u003eZic3\u003c/em\u003e-tdTomato expression colocalized with CD31+ endothelial cell, but not CD13+ pericytes. Scale bar: 25 µm. \u003cstrong\u003e(i) \u003c/strong\u003e\u003cem\u003eZic3\u003c/em\u003e-tdTomato expression on brain capillary, and VCAM1\u003csup\u003e+\u003c/sup\u003e vein/venules. Scale bar: 50 µm. \u003cstrong\u003e(j) \u003c/strong\u003e\u003cem\u003eZic3\u003c/em\u003e-tdTomato expression on brain SMA\u003csup\u003e+\u003c/sup\u003e artery/arterioles. Scale bar: 50 µm. \u003cstrong\u003e(k) \u003c/strong\u003eThe percentage of\u003cstrong\u003e \u003c/strong\u003eZic3-tdTomato\u003csup\u003e+\u003c/sup\u003e cells distributed among artery/arterioles, capillaries and vein/venules in the cortex. Arteries and arterioles are identified by vessel diameter in combination with the presence of SMA. Veins and venules are identified by vessel diameter in combination with the presence of VCAM1 and the absence of SMA. Lectin\u003csup\u003e+\u003c/sup\u003e vessels with diameters smaller than 6 µm are considered as capillaries. n = 3 mice.\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/280ef2d5bae895de740d39c0.png"},{"id":89974863,"identity":"4f87f013-82f9-4f0f-aa06-3390a63bd1e1","added_by":"auto","created_at":"2025-08-27 05:55:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1976599,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional characterization of Zic3-T2A-tdTomato-IRES-CreERT2 knock-in model. (a)\u003c/strong\u003e Schematic diagram showing the breeding strategy for generating\u003cem\u003e Zic3-T2A-tdTomato-IRES-CreERT2; Ai3\u003c/em\u003e mice. \u003cstrong\u003e(b)\u003c/strong\u003e Representative confocal images of \u003cem\u003eZic3-\u003c/em\u003edriven tdTomato (red), tamoxifen-induced EYFP (green), Dapi (blue) and lectin-labelled endothelial profiles (gray) in different tissues from a \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2; Ai3\u003c/em\u003e mice, including cortex, hippocampus and retina. Scale bar: 100 µm. \u003cstrong\u003e(c)\u003c/strong\u003e Representative confocal images of heart, kidney and liver sections from \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2; Ai3\u003c/em\u003e mice, showing no tdTomato or EYFP expression. Lectin (gray): endothelial profiles. Scale bar: 100 µm. \u003cstrong\u003e(d\u003c/strong\u003e) Quantification of the percentages of tdTomato\u003csup\u003e+\u003c/sup\u003e and EYFP\u003csup\u003e+\u003c/sup\u003e double positive cells in endothelia cells in different tissues as indicated. SC: spinal cord. n = 3 mice. Data are presented in mean ± SEM.\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/28ce3435cb28718f90875baf.png"},{"id":89976490,"identity":"9a8372df-b4dd-4d8f-9045-ed02ef54c9be","added_by":"auto","created_at":"2025-08-27 06:03:25","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2216613,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003ePlvap\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e model for peripheral and non-BBB endothelial cells. (a)\u003c/strong\u003e Schematic diagram showing the strategy for generating the \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in mice. See Methods for more details. \u003cstrong\u003e(b)\u003c/strong\u003e Representative image of fixed frozen lung section from a hemizygous \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in mouse. Scale bar: 50 µm. \u003cstrong\u003e(c) \u003c/strong\u003eRepresentative image of heart section from a hemizygous \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in mouse. Scale bar: 50 µm. \u003cstrong\u003e(d) \u003c/strong\u003eSchematic diagram showing the breeding strategy for generating\u003cem\u003e Plvap-T2A-EGFP-IRES-CreERT2; Ai14 \u003c/em\u003emice. \u003cstrong\u003e(e)\u003c/strong\u003e Representative confocal images of kidney and liver sections from \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2; Ai14 \u003c/em\u003emice, showing Ai14-tdTomato expression. Lectin (gray): endothelial profiles. Dapi (blue): nuclear staining. Scale bar: 100 µm.\u0026nbsp; \u003cstrong\u003e(f)\u003c/strong\u003e A representative image of brain section from a hemizygous \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in mouse. Scale bar: 500 µm. \u003cstrong\u003e(g)\u003c/strong\u003e Representative confocal image showing a whole mount of choroid plexus from a hemizygous \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e; Ai14-tdTomato mice, 2 weeks after tamoxifen induction. \u003cem\u003ePlvap-\u003c/em\u003eCreERT2 driven tdTomato\u003cem\u003e (Plvap-\u003c/em\u003eCre::Ai14-tdT) are shown in red. Scale bar: 100 µm. \u003cstrong\u003e(h)\u003c/strong\u003e Representative confocal images showing \u003cem\u003ePlvap-\u003c/em\u003eCre::Ai14-tdT and \u003cem\u003ePlvap-\u003c/em\u003eEGFP expression in the choroid plexus of 3\u003csup\u003erd\u003c/sup\u003e ventricle from a brain section. Dapi (blue), nuclei staining. Scale bar: 50 µm. \u003cstrong\u003e(i)\u003c/strong\u003e Schematic diagram showing the mild traumatic brain injury (mTBI) model with a controlled cortical impact system. \u003cstrong\u003e(j)\u003c/strong\u003e A whole mount of skull preparation from a mouse 3 days after mTBI injury. Red circle indicates the impacted region. \u003cstrong\u003e(k)\u003c/strong\u003e Representative confocal images showing \u003cem\u003ePlvap-\u003c/em\u003eCre::Ai14-tdT, \u003cem\u003ePlvap-\u003c/em\u003eEGFP and lectin angiogram (Magenta) in the boxed region of the contralateral side (green box) and ipsilateral side (brown box).\u0026nbsp; Scale bar: 100 µm.\u003cbr\u003e\n\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/46e5b49f1e03c4524d0ba5cd.png"},{"id":89974874,"identity":"a70c22b4-2a21-43b4-bc07-a2463ca9ddd9","added_by":"auto","created_at":"2025-08-27 05:55:26","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":378803,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTranscriptomic differences between CNS and peripheral endothelial cells. (a)\u003c/strong\u003e UMAP of single endothelial cells from mouse. Each dot was color-coded and annotated by different organs. \u003cstrong\u003e(b) \u003c/strong\u003eViolin plot showing the distribution of expression level of \u003cem\u003eZic3\u003c/em\u003e in mouse endothelial transcriptomics. \u003cstrong\u003e(c) \u003c/strong\u003eViolin plots showing the distribution of expression level of \u003cem\u003ePlvap\u003c/em\u003e in mouse endothelial transcriptomics. \u003cstrong\u003e(d)\u003c/strong\u003e Dot plots showing the expression of differentially expressed genes across all organs.\u003cstrong\u003e (e) \u003c/strong\u003eDot plots showing the expression level of the differentially expressed genes in \u003cem\u003eZic3\u003c/em\u003e+ and \u003cem\u003ePlvap\u003c/em\u003e+ cells, respectively. \u003cstrong\u003e(f-h) \u003c/strong\u003eGO Pathway analysis\u003cstrong\u003e \u003c/strong\u003eon 341 BBB endothelial cells enriched genes. f, Cell proliferation: Positive regulation of cell proliferation; Cell migration: Positive regulation of cell migration; Phosphorylation: Positive regulation of peptidyl−tyrosine phosphorylation; NG of angiogenesis: Negative regulation of angiogenesis; BBB establishment: Establishment of blood-brain barrier. g, Integral membrane: Integral component of membrane. h, Transmembrane activity: Transmembrane transporter activity; Xenobiotic activity: Xenobiotic transporter activity; Amino acid transporter activity: Amino acid transmembrane transporter activity; Insulin like GF factor I binding: Insulin like growth factor I binding; ; Insulin like GF factor II binding: Insulin like growth factor II binding.\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/7e6a20a4d202792adac8b6f5.png"},{"id":100359892,"identity":"c5caed6a-20f9-47b7-b584-058264951bf3","added_by":"auto","created_at":"2026-01-16 07:27:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9162101,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/6d34b5c7-6f92-4a34-9210-1117ae04e84a.pdf"},{"id":89974858,"identity":"e44b4d18-8898-4ecf-92b7-ca22c3dd0cfe","added_by":"auto","created_at":"2025-08-27 05:55:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4317654,"visible":true,"origin":"","legend":"","description":"","filename":"ExtendedFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7421061/v1/95a2161d9beb67a628c14074.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"New genetic tools for central and peripheral vascular endothelia ","fulltext":[{"header":"Main","content":"\u003cp\u003eThe blood-brain barrier (BBB), blood-spinal cord barrier and blood-retinal barrier\u0026nbsp;provide the physical barriers limiting the entrance of circulating pathogens and toxins, immune cells and body’s metabolic waste products into the central nervous system (CNS), and supplies critical energy metabolites such as glucose and lactate, essential amino acids, fatty acids, vitamins and growth hormones via selective transporter systems\u003csup\u003e1,2\u003c/sup\u003e. The BBB also helps clear brain’s own metabolic wastes including excess of neurotransmitters and proteinaceous molecules such as Alzheimer’s amyloid-β species, providing neurons with a tightly controlled microenvironment\u003csup\u003e3\u003c/sup\u003e. On the other hand, the vasculatures in the peripheral organs are more permeable, particularly in organs such as liver and kidney\u003csup\u003e4\u003c/sup\u003e. This functional heterogeneity also suggests that tissue microenvironment may provide important guidance cues for the specialization of their own vasculatures\u003csup\u003e5\u003c/sup\u003e. For example, Wnt signaling regulates both neural and BBB development in the vertebrate brain\u003csup\u003e6\u003c/sup\u003e, while hepatocyte growth factor (HGF) stimulates liver angiogenesis\u003csup\u003e7\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEndothelial cells are the bedrocks of the vasculature. Anatomically, CNS endothelia are similar to the ones in the peripheral organs, connected by intercellular junctions and embedded in basement membrane. Yet, they are in close interactions with perivascular pericytes and astrocytic endfeet\u003csup\u003e8\u003c/sup\u003e, resulting in more tightly sealed tight junctions and minimal level of transcytosis\u003csup\u003e9\u003c/sup\u003e. At the molecular level, brain endothelial cells carry high levels of tight junctional proteins such as claudins and occludins\u003csup\u003e10\u003c/sup\u003e, and are enriched with special transporters such as glucose transporter 1 (GLUT1, encoded by \u003cem\u003eSLC2A1\u003c/em\u003e)\u003csup\u003e10\u003c/sup\u003e, and major facilitator superfamily domain containing protein MFSD2A, a sodium-dependent lysophosphatidylcholine (LPC) transporter\u003csup\u003e11\u003c/sup\u003e. The peripheral endothelia on the other hand are often fenestrated\u003csup\u003e12\u003c/sup\u003e, providing non-selective transcellular transport through pinocytosis. The plasmalemma vesicle-associated protein (PLVAP), functioning as the diaphragm of these fenestrae, is in general absent in the adult brain\u003csup\u003e13\u003c/sup\u003e.\u0026nbsp;However, the intrinsic programs underlying the central and peripheral divergence and the heterogeneity of vascular system remain underexplored. In addition,\u0026nbsp;existing genetic tools (e.g., reporters and Cre drivers) are inadequate to differentiate or specifically manipulate central or peripheral vasculature endothelia, posing a significant research barrier.\u003c/p\u003e\n\u003cp\u003eTo address these gaps, we analyzed single-cell transcriptomic datasets from 11 mouse tissues to identify endothelial markers with organ-specific expression patterns and revealed that Zic Family Member 3 (\u003cem\u003eZic3\u003c/em\u003e) is explicitly expressed in brain endothelial cells. We generated a \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e knock-in model, with both fluorescent tdTomato reporter and CreERT2 recombinase expressed from the endogenous alleles, and confirmed both \u003cem\u003etdTomato\u0026nbsp;\u003c/em\u003ereporter expression profiles and CreERT2 recombinase activity are specific to the endothelial cells of the central nervous system, including brain, spinal cord and retina. Furthermore, we generated a \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in model, and found that it reliably targets the peripheral endothelial cells in heart, lung, liver and kidney, as well as the endothelial cells in the choroid plexus and meningeal vessels in the brain. Based on these two markers, we further analyzed the endothelial transcriptomics at single-cell level, which revealed that transporter system and tight junction signaling are the major divergent points between the central and peripheral vasculatures. In sum, these new \u003cem\u003eZic3\u003c/em\u003e and \u003cem\u003ePlvap\u0026nbsp;\u003c/em\u003egenetic models represent unique tools towards a better understanding of the molecular and genetic underpinning of the complexity of our vascular system in development, aging and diseases.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eDifferential vascular permeabilities between CNS and peripheral organs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe divergence between the central and peripheral vascularsystems in terms of permeabilities has been known for more than a century\u003csup\u003e14\u003c/sup\u003e, since the early observations based on dye injections in embryos\u003csup\u003e15\u003c/sup\u003e. Using HRP as a traditional tracer administered in the circulation\u003csup\u003e16\u003c/sup\u003e, we compared the vascular permeabilities between the central and peripheral organs (\u003cstrong\u003eExtended Data Fig. 1a\u003c/strong\u003e).\u0026nbsp;\u0026nbsp;Briefly, mice received a single dose of 0.5 mg/g of body weight of HRP solution in saline, and tissues were harvested 2 h later after perfusion. With fluorescent Tyramide signal amplification (TSA)\u003csup\u003e17\u003c/sup\u003e, we can amplify the HRP tracer and compare the vascular permeabilities between different organs\u0026nbsp;(\u003cstrong\u003eFig. 1a\u0026nbsp;\u003c/strong\u003eand \u003cstrong\u003eExtended Data Fig. 1b\u003c/strong\u003e). Among peripheral organs, kidney exhibited the highest vascular permeability, followed by liver, intestine and lung, while heart and spleen showed relatively low levels of HPR molecule accumulation. On the other hand, the brain and spinal cord had very minimal HRP signals, particularly in the cortex, or the grey matter of spinal cord\u0026nbsp;(\u003cstrong\u003eFig. 1a\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eZic3\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;is a new marker for brain endothelial cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the molecular basis of vascular heterogeneity, we integrated and performed secondary analysis on three single cell RNA sequencing (scRNA-seq) datasets covering both mouse brain and peripheral vasculatures\u003csup\u003e18–20\u003c/sup\u003e. 48,526 single-cell transcriptomes were collected for the secondary analysis, as we recently reported\u003csup\u003e21\u003c/sup\u003e. In Uniform Manifold Approximation and Projection (UMAP), four major vascular cell types were separated into 20 clusters across different tissues including endothelial cells, mural cells, Oligodendrocytes, and fibroblasts (\u003cstrong\u003eFig. 1b\u003c/strong\u003e), based on their specific genetic markers. These include but are not limited to \u003cem\u003eCldn5\u003c/em\u003e, \u003cem\u003ePodxl\u003c/em\u003e and \u003cem\u003eKdr\u003c/em\u003e for endothelial cells, \u003cem\u003ePdgfrb\u003c/em\u003e, \u003cem\u003eVtn\u003c/em\u003e and \u003cem\u003eCspg4\u003c/em\u003e for mural cells. Interestingly, we found that \u003cem\u003eZic3\u003c/em\u003e is unique to the brain endothelium (\u003cstrong\u003eFig. 1c\u003c/strong\u003e), and confirmed that \u003cem\u003eAtp13a5\u003c/em\u003e is a brain mural cell marker\u003csup\u003e21\u003c/sup\u003e (\u003cstrong\u003eFig. 1d\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eZic3\u003c/em\u003e encodes a zinc finger protein and transcription factor, essential for mammalian embryonic cardiovascular and neural \u0026nbsp;development\u003csup\u003e22\u003c/sup\u003e. It is highly specific to the brain based on the Tabula Muris dataset\u003csup\u003e23\u003c/sup\u003e (\u003cstrong\u003eExtended Data Fig. 1c-d\u003c/strong\u003e). To further confirm its specificity, we analyzed the brain vascular scRNA-seq dataset\u003csup\u003e18\u003c/sup\u003e and found that \u003cem\u003eZic3\u003c/em\u003e expression is exceedingly precise to brain endothelial cells of capillaries, arterioles and veins, but not expressed in any other vasculature cells including VSMCs, oligodendrocytes, fibroblasts, microglia, or astrocytes (\u003cstrong\u003eExtended Data Fig. 1e\u003c/strong\u003e). In addition, analysis of a large dataset of 1,093,785 cells combining adult mouse cortical and hippocampal areas\u003csup\u003e24\u003c/sup\u003e confirmed that \u003cem\u003eZic3\u003c/em\u003e expression is exclusively in endothelial cells, but not in any neuronal and glial cells (\u003cstrong\u003eExtended Data Fig. 1f\u003c/strong\u003e). Besides \u003cem\u003eZic3\u003c/em\u003e, we examined transporters that are currently known to be enriched in brain endothelial cells. For example, lysolipid transporter \u003cem\u003eMfsd2a\u003c/em\u003e and glucose transporter \u003cem\u003eSlc2a1\u003c/em\u003e are well-known for their roles in cerebrovasculature\u003csup\u003e25,26\u003c/sup\u003e, but their expressions are evident in testicular endothelial cells based on the integrated transcriptomics (\u003cstrong\u003eExtended Data Fig. 1g\u003c/strong\u003e). In addition, although organic anion transporter \u003cem\u003eSlco1c1\u003c/em\u003e is specific to brain endothelial cells in the integration dataset, it is highly expressed in astrocytes as well, based on datasets including the Tabula Muris\u003csup\u003e23\u003c/sup\u003e (\u003cstrong\u003eExtended Data Fig. 1h\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFluorescent \u003cem\u003ein situ\u003c/em\u003e hybridization (FISH) with RNAscope probes validated that \u003cem\u003eZic3\u003c/em\u003e transcripts are expressed throughout the brain regions including cortex, but not observed in peripheral organs such as liver or heart (\u003cstrong\u003eFig. 1e-f\u0026nbsp;\u003c/strong\u003e), and \u003cem\u003eZic3\u003c/em\u003e mRNA transcripts were colocalized exclusively within the lectin-positive brain endothelium (\u003cstrong\u003eExtended Data Fig. 2a\u003c/strong\u003e). In addition, immunohistochemistry analysis with antibodies that react with murine ZIC3 protein confirmed its presence in vasculature across brain regions (\u003cstrong\u003eFig. 1g\u003c/strong\u003e), including cortex, hippocampus, cerebellum, hypothalamus, striatum, medulla, midbrain, thalamus and pons (\u003cstrong\u003eExtended Data Fig. 2b\u003c/strong\u003e). This indicates that \u003cem\u003eZic3\u003c/em\u003e is potentially a new specific marker for CNS endothelial cells.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;knock-in model for CNS endothelial cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNext, we generated a new transgenic model targeting the \u003cem\u003eZic3\u003c/em\u003e locus. This \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e knock-in model carries expressions of both tdTomato reporter and CreERT2 recombinase through the endogenous \u003cem\u003eZic3\u003c/em\u003e locus (\u003cstrong\u003eFig. 2a\u003c/strong\u003e, also see Methods). One F0 founder was selected based on germline transmission and genome sequencing, and the F1 generation was further tested with southern blot analysis and validated by PCR (\u003cstrong\u003eExtended Data Fig. 3a-c\u003c/strong\u003e). As \u003cem\u003eZic3\u003c/em\u003e is located on X-chromosome, the model is maintained as hemizygous (\u003cem\u003eZic3\u003csup\u003etdT/Y\u003c/sup\u003e\u003c/em\u003e for male, and \u003cem\u003eZic3\u003csup\u003etdT/+\u003c/sup\u003e\u003c/em\u003e for female). Homozygous female \u003cem\u003eZic3\u003csup\u003etdT/tdT\u003c/sup\u003e\u003c/em\u003e mice can be obtained from breeding of hemizygous, and appear normal. Importantly, tdTomato is reliably expressed in adult hemizygotes and homozygotes (\u003cstrong\u003eFig. 2b\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe \u003cem\u003eZic3-\u003c/em\u003etdTomato profiles overlay well with Lectin-labeled endothelium throughout the brain, including cortex, hippocampus and thalamus (\u003cstrong\u003eFig. 2c\u003c/strong\u003e), but not in peripheral tissues such as liver, heart or kidney (\u003cstrong\u003eFig. 2d\u003c/strong\u003e). To verify the expression of \u003cem\u003eZic3\u003c/em\u003e marker in the CNS, we also examined the spinal cord and retina in our transgenic model. We found robust \u003cem\u003eZic3-\u003c/em\u003etdTomato signals in both white and gray matter of the spinal cord (\u003cstrong\u003eFig. 2e\u003c/strong\u003e), with a little higher coverage in gray matter. In the retina, \u003cem\u003eZic3-\u003c/em\u003etdTomato expressing cells were found in most of the vessels (\u003cstrong\u003eFig. 2f\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Extended Data Fig. 3d\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003eAdditional immunostainings further confirmed that \u003cem\u003eZic3\u003c/em\u003e-tdTomato expressing cells are indeed brain endothelial cells, as they are CD31-positive (\u003cstrong\u003eFig. 2g\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Extended Data Fig. 3e\u003c/strong\u003e), but not other cell types in the brain such as CD13-positive pericytes (\u003cstrong\u003eFig. 2h\u0026nbsp;\u003c/strong\u003eand\u003cstrong\u003e\u0026nbsp;Extended Data Fig. 3f-g\u003c/strong\u003e), NeuN-positive neurons, GFAP-positive astrocytes, or IBA1-positive microglia (\u003cstrong\u003eExtended Data Fig. 3h-j\u003c/strong\u003e). \u003cem\u003eZic3\u003c/em\u003e-tdTomato reporter is found in over 90% of capillary endothelial cells, as well as in ~40% of endothelial cells on VCAM1-positive veins and venules or SMA-positive arteries and arterioles (\u003cstrong\u003eFig. 2i-k\u003c/strong\u003e). It is estimated to cover at least 80% of the cerebrovasculature (\u003cstrong\u003eExtended Data Fig. 3k\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of the \u003cem\u003eZic3\u003c/em\u003e-CreER recombinase activity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test the CreER activity in our \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e model, we crossed it with the Ai3-EYFP floxed reporter line\u003csup\u003e27\u003c/sup\u003e, and induced the CreER activity with tamoxifen administration (\u003cstrong\u003eFig. 3a\u003c/strong\u003e, also see Methods). With 7 injections of Tamoxifen, more than 90% of \u003cem\u003eZic3\u003c/em\u003e-tdTomato\u003csup\u003e+\u003c/sup\u003e endothelial cells in the brain, retina and spinal cord expressed robust EYFP signals (\u003cstrong\u003eFig. 3b-d\u003c/strong\u003e). Yet, no recombination or EYFP reporter signal was observed in peripheral tissues such as heart, kidney or liver (\u003cstrong\u003eFig. 3c-d\u003c/strong\u003e). Hence, our data demonstrated that \u003cem\u003eZic3\u003c/em\u003e marker is unique for the CNS endothelium, and \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e model may help us decode the molecular and genetic underpinning of its specification\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;model for peripheral and non-BBB endothelial cells\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMECA-32 (an antibody against PLVAP glycoprotein) is widely used for staining of endothelial cells in embryos and most adult mouse peripheral tissues\u003csup\u003e28\u003c/sup\u003e, and it also label the vasculature in some of the circumventricular organs, such as the choroid plexus. This is highly consistent with single cell transcriptomic (\u003cstrong\u003eFig. 1c\u003c/strong\u003e). Therefore, we also generated the \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in mouse model, carrying the EGFP reporter and CreERT2 recombinase (\u003cstrong\u003eFig. 4a\u003c/strong\u003e). One F0 founder was selected based on germline transmission and genome sequencing, and the F1 generation was further tested with southern blot analysis and PCR for the integrity of the knock-in allele (\u003cstrong\u003eExtended Data Fig. 4a-c\u003c/strong\u003e). \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e hemizygous mice are healthy, and we observed robust \u003cem\u003ePlvap-\u003c/em\u003eEGFP signal in the lung and heart vasculature (\u003cstrong\u003eFig. 4b-c\u003c/strong\u003e), which is consistent with the transcriptomic profiles in the Tabula Muris database\u003csup\u003e23\u003c/sup\u003e (\u003cstrong\u003eExtended Data Fig. 4d\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo access the CreER activity of \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e model, we crossed it with the Ai14-tdTomato floxed reporter mice\u003csup\u003e27\u003c/sup\u003e (\u003cstrong\u003eFig. 4d\u003c/strong\u003e). Following Tamoxifen injections, we analyzed \u003cem\u003ePlvap-CreERT2\u0026nbsp;\u003c/em\u003edriven Ai14-tdTomato reporter (\u003cem\u003ePlvap-\u003c/em\u003eCre::Ai14-tdT), and found very robust labeling of peripheral vasculatures including kidney and liver (\u003cstrong\u003eFig. 4e\u003c/strong\u003e). Interestingly, we also observed \u003cem\u003ePlvap-\u003c/em\u003eEGFP signals in the choroid plexus within the brain ventricles, but not in the cortex or hippocampus (\u003cstrong\u003eFig. 4f\u003c/strong\u003e). This is further confirmed with the\u0026nbsp;\u003cem\u003ePlvap-\u003c/em\u003eCre::Ai14-tdT model, which showed robust labeling of choroid plexus vasculature\u0026nbsp;following\u0026nbsp;Tamoxifen injections (\u003cstrong\u003eFig. 4g-h\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn addition, the\u003cem\u003ePlvap\u0026nbsp;\u003c/em\u003emodel also reliably targets the\u0026nbsp;meningeal vessels. As meningeal vasculature is known to be vulnerable in head injuries\u003csup\u003e29\u003c/sup\u003e, we conducted mild traumatic brain injury (mTBI) on this model using a controlled cortical impact system\u003csup\u003e30\u003c/sup\u003e (\u003cstrong\u003eFig. 4i\u003c/strong\u003e). Even though the impact did not result in direct damage to the skull (\u003cstrong\u003eFig. 4j\u003c/strong\u003e)\u003csup\u003e30\u003c/sup\u003e, the meningeal vessel were severely damaged in the ipsilateral side after the injury, as shown by the dramatic loss of \u003cem\u003ePlvap-\u003c/em\u003eCre::Ai14-tdT reporter under the impact site(\u003cstrong\u003eFig. 4k\u003c/strong\u003e). Interestingly, the \u003cem\u003ePlvap\u003c/em\u003e-EGFP reporter showed an increased pattern 3 days after mTBI, indicating robust angiogenesis and vascular remodeling occurred shortly after the injury(\u003cstrong\u003eFig. 4k\u003c/strong\u003e). This demonstrated the new \u003cem\u003ePlvap\u0026nbsp;\u003c/em\u003eknock-in model is suitable for studying peripheral endothelial cells, and the built-in reporter and CreERT2 recombinase can be used synchronously for effective lineage tracing \u003cem\u003ein vivo\u003c/em\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTranscriptomic heterogeneity between CNS and peripheral endothelial cells\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further explore the differences between \u003cem\u003eZic3\u003c/em\u003e-positive and \u003cem\u003ePlvap\u003c/em\u003e-positive endothelial cells at single cell transcriptomic level, we analyzed 37,083 single endothelial cells from various organs, including the brain, intestine, colon, heart, kidney, liver, lung, muscle, spleen, and testis (\u003cstrong\u003eFig. 5a-c\u003c/strong\u003e). Specifically, we compared 1,903 \u003cem\u003eZic3\u003c/em\u003e-positive ECs with 20,517 \u003cem\u003ePlvap\u003c/em\u003e-positive ECs. These two populations exhibit distinct transcriptomic profiles, and we identified 341 genes with significant differential expression: 277 genes are significantly enriched in\u003cem\u003e\u0026nbsp;Zic3\u003c/em\u003e-positive ECs, while 64 genes show higher expression in \u003cem\u003ePlvap\u003c/em\u003e-positive ECs. For instance, transporter genes such as \u003cem\u003eSlc2a1, Slco1a4, Slco1c1,\u003c/em\u003e \u003cem\u003eSlc22a8, Slc7a5, Slc38a3 and Mfsd2a\u003c/em\u003e are more abundantly expressed in \u003cem\u003eZic3\u003c/em\u003e-positive ECs, whereas lipid metabolism associated genes such as Phospholipid Phosphatases \u003cem\u003ePlpp1\u003c/em\u003e and \u003cem\u003ePlpp3\u003c/em\u003e, fatty acid binding protein \u003cem\u003eFabp4\u003c/em\u003e, \u0026nbsp; glycosylphosphatidylinositol anchored high density lipoprotein binding protein 1 (\u003cem\u003eGpihbp1\u003c/em\u003e) are commonly expressed in \u003cem\u003ePlvap\u003c/em\u003e-positive ECs (\u003cstrong\u003eFig. 5d-e\u003c/strong\u003e). In addition, a few genes showed organ specificities in the peripheral, e.g. surfactant protein C\u003cem\u003e\u0026nbsp;(Sftpc)\u0026nbsp;\u003c/em\u003eis only found in lung ECs, C-Type Lectin domain protein \u003cem\u003eClec4g\u0026nbsp;\u003c/em\u003eis mainly expressed in liver ECs, Insulin-like growth factor binding protein \u003cem\u003eIgfbp5\u003c/em\u003e is exclusive to kidney ECs, Stabilin 2 (\u003cem\u003eStab2\u003c/em\u003e) is highly enriched in spleen ECs (\u003cstrong\u003eFig. 5d\u003c/strong\u003e). With these differentially expressed genes (DEGs), we conducted pathway analysis based on Gene Ontology enrichment method. Based on biological processes, these DEGs are related to transporter systems and regulate brain development (\u003cstrong\u003eFig. 5f\u003c/strong\u003e). \u0026nbsp;Their cellular functions are connected to membrane functions and intercellular junctions (\u003cstrong\u003eFig. 5g\u003c/strong\u003e), and their molecular functions are associated with lipid binding and protein transporting (\u003cstrong\u003eFig. 5h\u003c/strong\u003e). This was further confirmed with Ingenuity canonical pathway analysis, which showed that transporter system and tight junction signaling are the top pathways differentiating the \u003cem\u003eZic3\u003c/em\u003e-positive ECs from \u003cem\u003ePlvap\u003c/em\u003e-positive ECs (\u003cstrong\u003eExtended Data Fig. 5e\u003c/strong\u003e). Taken together, these data further confirmed \u003cem\u003eZic3\u0026nbsp;\u003c/em\u003eand \u003cem\u003ePlvap\u003c/em\u003e as unique markers for the CNS and peripheral endothelial cell, respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe mammalian BBB is central to the CNS health and functions. It is an evolutionary milestone for overall brain fitness, yet in same time became a major research challenge, as our understanding of how the highly specialized BBB structure is established during development and how different brain vascular cell types orchestrate together to support brain functions remain limited\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. This obstacle can be partly attributed to the lack of specific markers and genetic tools that can separate the cerebrovascular cells from the peripheral ones. For example, current murine genetic tools of vascular cells are often based on the well-known endothelial cell markers such as podocalyxin (\u003cem\u003ePodxl\u003c/em\u003e), claudin 5 (\u003cem\u003eCldn5\u003c/em\u003e), tyrosine kinase (\u003cem\u003eTek/Tie2\u003c/em\u003e) and tyrosine kinase receptor (\u003cem\u003eKdr\u003c/em\u003e), which all tend to express throughout the body. Therefore, transgenic mouse models based on these alleles, including \u003cem\u003eTek-Cre\u003c/em\u003e and \u003cem\u003eTek-Cre/ERT2, Cdh5-Cre and Cdh5-Cre/ERT2, Kdr-Cre\u003c/em\u003e, all exhibit limitations and constraints when applied to brain vasculature\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Recently, we used single cell transcriptomics and identified a brain pericyte specific marker \u0026mdash; \u003cem\u003eAtp13a5\u003c/em\u003e, and successfully generated a knock-in mouse model for the brain pericyte\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. With similar approaches, we further identified \u003cem\u003eZic3\u003c/em\u003e as a brain endothelial specific marker from single cell transcriptomics, and confirmed its expression with both in situ hybridization and antibody staining.\u003c/p\u003e\u003cp\u003eThe finding of \u003cem\u003eZic3\u003c/em\u003e markers is very meaningful, as it guided us to generate a new transgenic model \u0026mdash; \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e. Based on the \u003cem\u003eZic3-\u003c/em\u003etdTomato expression and the corresponding CreER recombinase activity after tamoxifen induction, we further confirmed that the \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2\u003c/em\u003e model is specific to the endothelial cells in the CNS, and not for peripheral cells. For peripheral endothelial cells, we also generated a new \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u003c/em\u003e knock-in model, and the characterization of \u003cem\u003ePlvap\u003c/em\u003e-EGFP reporter and corresponding CreER recombinase activity showed it targets the endothelial cells of the peripheral organs such as lung, kidney, liver, etc., as well as the circumventricular organs in the brain which are known to be outside of the BBB. Therefore, the \u003cem\u003eZic3\u003c/em\u003e and \u003cem\u003ePlvap\u003c/em\u003e genetic tools are sufficient to separate and selectively target the CNS and peripheral endothelial cells, respectively. Interestingly, the presence of \u003cem\u003ePlvap\u003c/em\u003e in choroid plexus and meningeal vessels also indicates that the heterogeneity of cerebrovascular endothelial cells, at least between the BBB and non-BBB areas, which could be potentially probed with the genetic tools we have generated.\u003c/p\u003e\u003cp\u003eThere is a list of receptors and transporters known to be highly enriched at the BBB endothelium, including lysolipid transporter \u003cem\u003eMfsd2a\u003c/em\u003e, glucose transporter \u003cem\u003eSlc2a1\u003c/em\u003e and organic anion transporter \u003cem\u003eSlco1c1.\u003c/em\u003e Their candidacy for generating genetic models for brain or BBB endothelial cells were proposed, but none of them are specific enough, as shown by single cell transcriptomics (e.g. Extended Data Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Based on our data from RNAscope, immunostaining and reporter models, \u003cem\u003eZic3\u003c/em\u003e is indeed a CNS endothelial specific genetic marker. \u003cem\u003eZic3\u003c/em\u003e encodes a highly conserved C2H2 zinc finger domain protein and a member of the GLI superfamily of transcription factors\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Its mutations are found in patients with X-linked heterotaxy, and its deficiency in mice disrupts the left\u0026ndash;right asymmetry of cardiovascular development and results in neural tube deficits\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. While ZIC3 plays an important role in early development, it is still surprising to see its expression remained in the BBB endothelial cells even in the adult brain. In human endothelial cells derived from pluripotent stem cells, ZIC3 can act downstream of Wnt signaling and promote the endothelial barrier functions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Ectopic expression of Zic3 in human umbilical vein endothelial cells could also induce BBB marker gene expression\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. Therefore, it is important to determine if Zic3 is a master regulator of an intrinsic program that maintains the BBB functions in future studies. Interestingly, \u003cem\u003eZic3\u003c/em\u003e is also located on X-chromosome, and may contribute to the sex-difference in the cerebrovascular functions associated with aging and Alzheimer\u0026rsquo;s disease\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe cerebrovascular development is guided by some of the classic growth factor pathways, such as VEGF, Wnt and Notch\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Take Wnt signaling as an example, Wnt7a/7b and Norrin\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, Frizzled receptor\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, co-receptors low-density lipoprotein receptor-related protein (LRP) 5 and 6\u003csup\u003e39\u003c/sup\u003e and co-activator orphan G-protein coupled receptor Gpr124\u003csup\u003e40\u003c/sup\u003e are all indispensable the proper development of the BBB. To understand the vascular heterogeneity, we analyzed current single cell transcriptomics gathered from different organs. Besides \u003cem\u003eZic3\u003c/em\u003e, \u003cem\u003ePlvap\u003c/em\u003e and \u003cem\u003eAtp13a5\u003c/em\u003e, we identified a list of genetic markers that can potentially be utilized to develop next-generation tools. For example, the transcriptomic comparison between CNS \u003cem\u003eZic3\u003c/em\u003e\u0026thinsp;+\u0026thinsp;endothelial cells with \u003cem\u003ePlvap\u003c/em\u003e\u0026thinsp;+\u0026thinsp;peripheral ones pointed to hundreds of DEGs that changed preferentially in BBB endothelial cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). These genes are linked to interesting pathways associated with the establishment of the blood-brain barrier, brain development, lipid metabolism, transport systems and tight junction signaling. While \u003cem\u003eZic3\u003c/em\u003e has a preference for CNS endothelial cells, \u003cem\u003eClec4g, Igfbp5\u003c/em\u003e and \u003cem\u003eStab2\u003c/em\u003e may potentially be utilized to target endothelial cells of the liver, kidney and spleen, respectively, and worth further investigation. Nevertheless, this endothelial heterogeneity is proof that ECs are heavily influenced by the local environment of each individual organs.\u003c/p\u003e\u003cp\u003eLast but not the least, BBB dysfunctions with endothelial activation is commonly found in a spectrum of CNS disorders, including AD and other dementia\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Therefore, the identification of \u003cem\u003eZic3\u003c/em\u003e as a BBB endothelial cells marker will likely advance our studies towards a better understanding of BBB vascular cell changes in aging and CNS diseases, including vascular contributions to AD and dementia. Beyond the application of these transgenic models, these specific markers can be used to develop other tools, such as BBB cell-type specific antibodies and new viral vector designs to target BBB endothelial cells more specifically with artificial promoters based on tissue and cell-type specific enhancers\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Dr. Fan Gao for his valuable bioinformatics support and insightful discussions that greatly contributed to the completion of this study. We thank the support National Institutes of Health (NIH) grant nos. R01AG061288, RF1NS122060, RF1NS135617, and R21AG085559, BrightFocus Foundation grant no. A2019218S, and Alzheimer’s Association grant no. AA-ADP 23-1051406.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eDaneman R, Prat A (2015) The Blood\u0026ndash;Brain Barrier. Cold Spring Harb Perspect Biol 7:a020412\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZlokovic BV (2008) The blood-brain barrier in health and chronic neurodegenerative disorders. Neuron 57:178\u0026ndash;201\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZlokovic BV (2011) Neurovascular pathways to neurodegeneration in Alzheimer\u0026rsquo;s disease and other disorders. Nat Rev Neurosci 12:723\u0026ndash;738\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eF\u0026eacute;l\u0026eacute;tou M (2011) The Endothelium: Part 1: Multiple Functions of the Endothelial Cells\u0026mdash;Focus on Endothelium-Derived Vasoactive Mediators. 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Dev Cell 58:174\u0026ndash;191e8\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNikolakopoulou AM et al (2019) Pericyte loss leads to circulatory failure and pleiotrophin depletion causing neuron loss. Nat Neurosci 22:1089\u0026ndash;1098\u003c/span\u003e\u003c/li\u003e\u003c\u003c/ol\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eKEY RESOURCES TABLE\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eREAGENT or RESOURCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSOURCE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eIDENTIFIER\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenomic data sets\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMouse brain vasculature\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE98816\u003c/p\u003e\n \u003cp\u003eGSE99058\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAtlas of Murine Endothelial Cells\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eArray Express\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eE-MTAB-8077\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFibroblast and mural cell in muscular organs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE150294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eTabula Muris\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGene Expression Omnibus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGSE109774\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSingle cell RNA-sequencing data processing\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSeurat Package (v.3.1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFindVariableFeatures function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFindAllMarkers function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eFindMarkers function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePrincipal component analysis (PCA)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eUniform Manifold Approximation and Projection (UMAP)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eShared Nearest Neighbor (SNN) clustering\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ev.3.6.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eExperimental Models: Organisms/Strains\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZic3-tdTomato-CreERT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePlvap-tdTomato-CreERT2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThis study\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eDNA isolation and genotyping\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGoTaq Green Master Mix\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePromega\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eM7122\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZic3-tdTomato-\u003c/em\u003eCreERT2 forward primer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAATGGCTCTCCTCAAGCGTATTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZic3-tdTomato-\u003c/em\u003eCreERT2 reverse primer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGTTATTCAACTTGCACCATGCCG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePlvap-EGFP-\u003c/em\u003eCreERT2 forward primer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGCTGTGTAGCAGAGACAAACCTTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003ePlvap-EGFP-CreERT2\u003c/em\u003e reverse primer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGGTGGTGCAGATGAACTTCAGG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntibodies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGoat polyclonal anti-CD13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eR\u0026amp;D systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAF2335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRat monoclonal anti- CD31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eBD Pharmingen\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e550274\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRabbit polyclonal anti-ZIC3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePA5-29073\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRat monoclonal anti-VCAM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMillipore Sigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCBL1300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMouse monoclonal anti-SMA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMillipore Sigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eA5228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRabbit polyclonal anti-Iba-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eWako\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e019-19741\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRabbit polyclonal anti-GFAP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDako\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ez0334\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRabbit polyclonal anti-NeuN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMillipore Sigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eABN78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRabbit anti-mouse Olig2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMillipore Sigma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB9610\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDylight 488-conjugated\u003cem\u003e\u0026nbsp;L. esculentum\u003c/em\u003e lectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eL32470\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eDylight 649-conjugated\u003cem\u003e\u0026nbsp;L. esculentum\u003c/em\u003e lectin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eThermo Fisher Scientific\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eL32472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVibratome\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeica\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVT1200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCryostat\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eLeica\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCM3050 S\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNormal Donkey Serum\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eJackson ImmunoResearch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAB_2337258\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVECTASTAIN ABC universal kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVector laboratories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePK6200\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImmPRESS Universal Polymer kit\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVector laboratories\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMP-7500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003ein situ\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;hybridization\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eSuperFrost Plus Micro Slide\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eVWR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e48311-703\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eZic3\u0026nbsp;\u003c/em\u003eRNAscope probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCat. #480351\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ePositive control RNAscope probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCat. #320881\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNegative control RNAscope probe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCat. #320871\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eHybEZ Oven\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNAscope® 2.5 HD Reagent Kit-RED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e322350\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRNAscope® Multiplex Fluorescent V2 Assay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eAdvanced Cell Diagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e323100\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNikon A1R MP+ confocal/ multiphoton microscope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNikon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNikon A1R\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRevolve 4 brightfield and fluorescence microscope\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eEcho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eRVL-100-G\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoftware\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraphPad Prism\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraphPad Software\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eGraphPad Prism 8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eMetascape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003ehttp://metascape.org\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eClueGO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e2.5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eCytoscape\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003e3.8.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eImage J\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003cp\u003eNIH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\"\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eCONTACT FOR REAGENT AND RESOURCE SHARING\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther information and request for resources and reagents should be directed to and will be fulfilled by Lead Contact Zhen Zhao ([email protected]).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEXPERIMENTAL MODEL AND SUBJECT DETAILS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMice were housed in plastic cages on a 12 h light/dark cycle with access to water ad libitum and a standard laboratory diet. All procedures were approved by the Institutional Animal Care and Use Committee at the University of Southern California and followed National Institutes of Health guidelines. All animals were included in the study. Male and female animals of 2–3 months of age were used in the experiments. All animals were randomized for their genotype information. All experiments were carried out blind: the operators responsible for the experimental procedures and data analysis were blinded and unaware of group allocation throughout the experiments. For all experiments, at least three independent mice were analyzed, which included both sexes and no apparent sex difference were observed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGeneration of the Zic3-tdTomato-CreERT2 knock-in model\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo generate \u003cem\u003eZic3-T2A-tdTomato-IRES-CreERT2 knock-in mouse\u003c/em\u003e, donor DNA templates encoding self-cleaving T2A peptide, tdTomato, internal ribosome entry site and CreERT2 were synthesized. These sequences were flanked by 375bp sequences and 4606bp sequences homologous to the third exon and 3’ UTR region of \u003cem\u003eZic3\u0026nbsp;\u003c/em\u003egene. Next, these donor vector containing the \u003cem\u003eT2A-tdTomato-IRES-CreERT2\u003c/em\u003e cassette, and gRNA (TTTAACGAATGGTACGTCTGAGG) were co-injected into fertilized eggs to generate targeted conditional knock-in offspring. The F0 founder animals were genotyped by PCR and sequence analysis, and four F1 mice were generated and further confirmed with southern blotting for both 5’ arm and 3’ arm insertion sequences.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eGeneration of the Plvap-EGFP-CreERT2 knock-in model\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe generated the \u003cem\u003ePlvap-T2A-EGFP-IRES-CreERT2\u0026nbsp;\u003c/em\u003eknock-in mouse (Fig. 4b), with a donor DNA template encoding self-cleaving T2A peptide, EGFP, internal ribosome entry site, CreERT2. These sequences were flanked by 405bp sequences and 3834bp sequences homologous to the 6\u003csup\u003eth\u0026nbsp;\u003c/sup\u003eexon and 3’ UTR region of \u003cem\u003ePlvap\u0026nbsp;\u003c/em\u003egene. These donor vector containing the \u003cem\u003eT2A-EGFP-IRES-CreERT2\u0026nbsp;\u003c/em\u003ecassette, and gRNA (GCAGCTGGGTCCTCAACCGCTGG) were co-injected into fertilized eggs to generate targeted conditional knock-in offspring. The F0 founder animals were genotyped by PCR and sequence analysis, and three F1 mice were generated and further confirmed with southern blotting for both 5’ arm and 3’ arm insertion sequences.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMild Traumatic Brain Injury model\u0026nbsp;\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo induce mild traumatic brain injury (mTBI) in mice, we followed a previously described protocol\u003csup\u003e42\u003c/sup\u003e. Briefly, we used the KOPF stereotaxic system to position the mouse’s head under the impactor at a specific angle, targeting a point 2 mm posterior and 2.5 mm lateral to Bregma. A 4 mm flat plastic tip (RWD Life Science) was used to deliver a controlled impact using a brain injury device (RWD #68099). Mice were anesthetized with ketamine and xylazine (90 mg/kg and 9 mg/kg, i.p.). After exposing the skull, we delivered an impact at a velocity of 3 m/s, a depth of 1 mm, and a duration of 180 milliseconds. Mice were then placed in warmed cages to recover.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHOD DETAILS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eBioinformatics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScRNA-seq data for mouse brain vasculature and multiple organs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor scRNA-seq integration dataset, we obtained the cell count matrix from Gene Expression Omnibus (GEO) with the series record GSE98816, GSE150294 and Array express E-MTAB-8077, and did secondary analysis after integration. Then we performed secondary analysis of 3 single cell sequencing (scRNA-seq) datasets on brain and peripheral vascular cells, 48,526 single-cell transcriptomes were collected using R Seurat Package.\u003c/p\u003e\n\u003cp\u003eFor scRNA-seq dataset for mouse brain vasculature, we obtained the cell count matrix from GEO with the series record GSE98816 and GSE99058\u003csup\u003e18\u003c/sup\u003e. The data represent the expression levels of 18435 genes in 3186 cells. The mouse brain tissue was harvested for Smart-seq2 and sequencing was performed on a HiSeq2500 at the National Genomics Infrastructure (NGI), Science for Life Laboratory, Sweden, with single 50-bp reads (dual indexing reads).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor scRNA-seq dataset for multiple organs, we obtained the cell count matrix from GEO with the series record GSE109774\u003csup\u003e23\u003c/sup\u003e. The data represent the expression levels of 23433 genes in 53760 cells. All organs were single-cell-sorted into plated using flurescence-activated cell sorting. Libraries were sequenced on the NovaSeq 6000 Sequencing System (Illumina) using 2 x 100-bp paired-end reads.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScRNA-seq data preprocessing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data processing of the scRNA-seq data were performed with the Seurat Package (v.3.1.5) in R (v.3.6.2). The basic scRNA-seq analysis was run using the pipeline provided by Seurat Tutorial (\u003ca href=\"https://satijalab.org/seurat/v3.0/immune_alignment.html\"\u003ehttps://satijalab.org/seurat/v3.0/immune_alignment.html\u003c/a\u003e) as of June 24, 2019. In general, we set up the Seurat objects from different groups in experiments for normalizing the count data present in the assay. This achieves log-normalization of all datasets with a size factor of 10,000 transcript per cell. For different Seurat objects, FindVariableFeatures() function\u0026nbsp;was used to identify outlier genes on a ‘mean variability plot’ for each object. The nFeatures parameter is 2000 as the default for the selection method called ‘vst’. These resulted genes serve to illustrate priority for further analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe dataset on all cells were used to scale and center the genes. First of all, principal component analysis (PCA) was used for linear dimensionality reduction with default computes the top 30 principal components. By applying the JackStraw() function, JackStrawPlot() function and ElbowPlot() function, we identified the principal components for further analysis. Then, PCA results were used as the input for the Uniform Manifold Approximation and Projection (UMAP) dimensional reduction.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe identified clusters of cells by a shared nearest neighbor (SNN) modularity optimization-based clustering algorithm. The algorithm first calculated k-nearest neighbors and computed the k-NN graph, and then optimizes the modularity function to determine clusters. \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDetermination of cell-type identity\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo determine the cell type, we used FindAllMarkers() function with parameters min.pct and thresh.use set to 0.25 to find markers in each cluster and known marker genes that have been previously reported could be used to determine cell-type identity. These include, but are not limited to \u003cem\u003eSnap25\u003c/em\u003e for Neuron, \u003cem\u003eCldn10\u003c/em\u003e for Astrocyte, \u003cem\u003eMbp\u003c/em\u003e for Oligodendrocyte, \u003cem\u003eCldn5\u003c/em\u003e for EC, \u003cem\u003eKcnj8\u003c/em\u003e for PC, \u003cem\u003eActa2\u003c/em\u003e for VSMC, \u003cem\u003eCtss\u003c/em\u003e for microglial, \u003cem\u003eCol1a1\u003c/em\u003e for Fibroblast-like cell.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePathway analysis and visualization by Metascape, ClueGO and Cytoscape\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUsing the Metascape online tool (http://metascape.org), we performed functional enrichment analysis of ZIC3-positive enriched genes. Enrichment of pathways from KEGG, GO Biological Process and GO Molecular Function was analyzed by Metascape. The terms with P-value \u0026lt; 0.01, minimum counts of 3, and enrichment factors of \u0026gt; 1.5 would be considered. The ClueGO Cytoscape pluin 2.5.4 and Cytoscape version 3.8.1 will be used for secondary KEGG pathway analysis and network visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCellular Biology Related Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eHPR injection and Lectin injection and mapping\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe HRP solution was prepared by dissolving 125 mg (0.125 g) of HRP Type II (Sigma, P8250) in 2.5 ml of PBS, yielding a concentration of 0.5 mg/10 µl. Each animal was injected with a single dose of 0.5 mg/g of body weight and harvested 2 hours later. The mice were then sacrificed and perfused with PBS and PFA at 5 min after lectin (ThermoFisher Scientific, #L32470) injection. To visualize HRP in the injected samples under light microscopy, the samples were washed with PBS and incubated in Tris buffer containing 0.1% tyramide reagent and 0.0015% H₂O₂\u0026nbsp;for 10 minutes at room temperature, in the dark\u003csup\u003e43\u003c/sup\u003e. All sections were then scanned using the Li-Cor Odyssey Dlx at a resolution of 21 µm, or on a NikonTi2 confocal microscope.\u0026nbsp;The ImageJ plugin ‘Neuro J’ length analysis tool was used to measure the length of lectin-positive or HRP-positive endothelial capillary profiles. The capillary length was quantified and expressed as mm of lectin+ endothelial capillary profiles per mm2 of brain tissue. The HRP-occupied vascular area ratio was calculated by measuring the HRP-positive signal within lectin-positive regions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFluorescence \u003cem\u003ein situ\u003c/em\u003e hybridization\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFluorescence \u003cem\u003ein situ\u0026nbsp;\u003c/em\u003ehybridization was performed using the RNAscope technology (Advanced Cell Diagnostics, Hayward, CA). Tissue sample preparation and pretreatment were performed on fixed brains cut into 15 µm sections mounted onto SuperFrost Plus glass slides following the manufacturer’s protocol (ACD documents 323100). After dehydration and pretreatment, slides were subjected to RNAscope Multiplex Fluorescent Assay (ACD documents 323100). RNAscope probes for mouse \u003cem\u003eZic3,\u0026nbsp;\u003c/em\u003epositive control and negative control were hybridized for 2h at 40ºC in the HybEZ Oven and the remainder of the assay protocol was implements. Subsequently, the slides were subjected to immunohistochemistry. The fluorescent signal emanating from RNA probes and antibodies was visualized and captured using a Nikon AIR MP+ confocal/ multiphoton microscope (Nikon). All FISH images presented are projection of 10-image stacks (0.5 µm intervals) obtained from cerebral cortex, and a smoothing algorithm was applied during image post-processing (Nikon NIS-Elements Software).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAnimals were anesthetized, perfused and brains were removed and postfixed as we described previously\u003csup\u003e44\u003c/sup\u003e. Brain, spinal cord, kidney, liver, and heart tissue were also collected, postfixed and cut at 35 µm thickness using a vibratome (Leica). After that, sections were blocked with 5% normal donkey serum (Vector Laboratories) and 0.1% Triton-X in 0.01M PBS and incubated with primary antibodies diluted in blocking solution overnight at 4ºC. The primary antibody information is as following: Goat anti-mouse aminopeptidase N/ANPEP (CD13; R\u0026amp;D systems; AF2335; 1:100), ZIC3 polyclonal antibody (Invitrogen; PA5-29073; 1:100), Rat anti-mouse vascular adhesion molecule (VCAM1; MilliporeSigma; CBL1300; 1:200), Mouse anti-α-smooth muscle actin (SMA, MilliporeSigma; A5228, 1:200), Rabbit anti-mouse ionized calcium binding adaptor molecule 1 (Iba-1; Wako, 019-19741; 1:200), Rabbit anti-Glial Fibrillary Acidic Protein (GFAP; Dako, z0334; 1:500), Rabbit anti-mouse NeuN (Millipore, ABN78, 1:500). To visualize brain microvessels, sections were incubated with Dylight 488 or 647-conjugated \u003cem\u003eL. esculentum\u003c/em\u003e lectin as we have described previously\u003csup\u003e44\u003c/sup\u003e. After incubation with primary antibodies, sections were washed with PBS for three times and incubated with fluorophore-conjugated secondary antibodies. Sections were imaged with a Nikon AIR MP+ confocal/ multiphoton microscope (Nikon). Z-stack projections and pseudo-coloring were performed using Nikon NIS-Elements Software. Image post analysis was performed using ImageJ software.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMolecular Biology Related Procedures\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA isolation and genotyping\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMouse genomic DNA was isolated from tail biopsies (2 - 5 mm) and following overnight digestion at 56 \u0026nbsp;\u0026nbsp;into 100 μL of tail digestion buffer containing 10 mM Tris-HCl (pH 9.0), 50 mM KCl, 0.1% Triton X-100 and 0.4 mg/mL Proteinase K. Next, the tail will be incubated at 98\u0026nbsp;\u0026nbsp;\u0026nbsp;for 13 minutes to denature the Proteinase K. After centrifugation at 12000 rpm for 15 min, the supernatants were collected for PCR. The primers details are listed in the KEY RESOURCES TABLE. The PCR conditions were as follows: 1) 94 °C for 3 min; 2) 35 cycles at 94 °C for 30 sec, 60 °C for 30 sec, and 72 °C for 35 sec; 3) 72 °C for 5 min. PCR products were separated on 2% agarose gel.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSample sizes were calculated using nQUERY, assuming a two-side alpha-level of 0.05, 80% power and homogeneous variances for the 2 samples to be compared, with the means and SEM for different parameters predicted from pilot study. All the data are presented as mean ± SEM as indicated in the figure legends and were analyzed by GraphPad Prism 8. For multiple comparisons, Bartlett’s test for equal variances was used to determine the variances between the multiple groups and one-way analysis of variance (ANOVA) followed by Tukey test was used to test statistical significance, using GraphPad Prism 8 software. A P value of less than 0.05 was considered statistically significant.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":true,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7421061/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7421061/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe divergence between the central and peripheral vascular system, particularly the emergence of the blood-brain barrier (BBB), is central to the brain’s homeostasis and functions. However, the molecular and genetic constituents that separating the BBB vascular cells from the rest remain elusive. Using single cell transcriptomics, we identified new cerebrovascular markers, e.g. zinc finger protein \u003cem\u003eZic3\u003c/em\u003e is explicitly found in adult brain endothelial cells and the \u003cem\u003eAtp13a5\u003c/em\u003e ATPase is only expressed in brain pericytes. Using new genetic models, we further confirmed the specificity of \u003cem\u003eZic3\u003c/em\u003e in cerebrovasculature. Additionally, we developed a mouse model based on \u003cem\u003ePlvap\u003c/em\u003e, and confirmed it is specific for endothelial cells of the peripheral tissue and circumventricular organs in brain. In-depth transcriptomics analysis between \u003cem\u003eZic3\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e and \u003cem\u003ePlvap\u003c/em\u003e\u003csup\u003e\u003cem\u003e+\u003c/em\u003e\u003c/sup\u003e endothelial cells revealed that genetic programs associated with lipid metabolism, transporter systems and tight junction signaling are critical drivers behind the separation of central and peripheral endothelia. These new murine genetic tools will further aid our understanding of vascular heterogeneity and BBB specialization.\u003c/p\u003e","manuscriptTitle":"New genetic tools for central and peripheral vascular endothelia ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-27 05:55:20","doi":"10.21203/rs.3.rs-7421061/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"6e3d9b30-8a85-466b-93dc-8558e44f0994","owner":[],"postedDate":"August 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-10T16:38:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-27 05:55:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7421061","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7421061","identity":"rs-7421061","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}