A spatiotemporal atlas of cerebrovascular development in zebrafish

Domain: neuroscience
Published: 2026-01-31
Editorial outcome: accept

Abstract
The vertebrate brain requires precisely patterned vasculature, yet the molecular and cellular dynamics of cerebrovascular development remain incompletely understood. While single-cell studies have profiled brain vasculature, integrating these data across molecular, cellular, and architectural scales remains challenging. We combine in situ sequencing, single-cell transcriptomics, and 3D vascular reconstruction to map zebrafish brain endothelial development (3-11 dpf). Quantitative 3D analysis reveals a developmental shift from lateral vascularization to intraparenchymal angiogenesis, coinciding with blood-brain barrier (BBB) maturation. We identify 6 endothelial subtypes with conserved mammalian signatures, showing capillary endothelial cells (CapECs), the predominant intracranial subtype exhibiting stage-specific enrichment of transporter and tight junction modules. Spatial transcriptomics show progressive CapEC enrichment in mesencephalic/metencephalic regions versus arterial EC accumulation in prosencephalic vessels. Functional assays confirm BBB competency by 11 dpf, while genetic perturbation of three uncharacterized CapEC-enriched genes (slc16a1a, zgc:158423, cldc1) disrupts vascular patterning and BBB integrity. This multidimensional atlas provides an evolutionary perspective on cerebrovascular development, bridges molecular profiles with vascular architecture, and offers insights into developmental dynamics across scales.

Introduction
The maintenance of cerebrovascular structural and functional integrity constitutes a fundamental pillar of cerebral homeostasis1. Dysregulation of cerebrovascular development contributes to the pathogenesis of diverse neurological and cerebrovascular pathologies, such as Alzheimer’s disease, vascular dementia, capillary telangiectasias, arteriovenous malformations, and cerebral cavernous malformations2. Consequently, a comprehensive understanding of cerebrovascular architecture, cellular dynamics, and molecular properties is essential for deciphering the pathophysiology of neurovascular diseases.

For the past decades, diverse methodologies have emerged for drawing the brain vascular graph, ranging from vascular corrosion casting and dye filling to transgenic vessel labelling and, more recently, lectin labelling combined with tissue clearing3–6. Through immunolabelling, advanced tissue clearing, and optimized vasculature segmentation, the adult mouse cerebrovascular network has been comprehensively reconstructed, enabling systematic analysis of whole-brain vascular properties7. Beyond structure visualization, deep learning-based vessel segmentation has been employed to distinguish and quantify brain angio-architecture, facilitating detailed analysis of vascular alterations in neurovascular diseases8–10. Despite these advances, the structural characteristics of the developing cerebrovasculature during the early developmental stage remain largely unexplored.

Recent advances in single-cell sequencing have enabled unprecedented resolution in decoding the cellular and molecular mechanisms governing brain vasculature11–15. Utilizing single-nucleus RNA-seq (snRNA-seq) on ex vivo and post-mortem human brain tissue, characteristics and new cell-type-specific markers of endothelial, mural, and perivascular cells were uncovered16. Single-cell RNA-seq (scRNA-seq) analysis of endothelial and mural cells from the prenatal human brain has elucidated the transcriptomic and functional diversity of vascular cells during the second trimester17. Furthermore, comparative scRNA-seq studies across human fetal and adult stages, as well as various vascular-related diseases, have mapped the molecular heterogeneity characterizing brain vascular cells across life stages and pathological contexts2. In addition, scRNA-seq revealed region-specific functional heterogeneity of the blood-brain barrier (BBB) between the mouse cortex and median eminence, with distinct molecular signatures underpinning this spatial divergence18. However, despite these significant strides, the relationship between the inherent molecular signatures of cerebrovascular cells and their precise anatomical positioning within the brain remains poorly characterized.

Within the brain vasculature, capillaries serve as the most critical component, as they constitute the BBB, which regulates the exchange of compounds between the brain and the blood while safeguarding the brain against harmful substances in the bloodstream19. Several key endothelial-enriched genes are highly expressed in capillaries, including Glut1, Cldn5, and Mfsd2a, being functionally linked to BBB integrity20. Notwithstanding progress in comprehending the molecular mechanisms regulating vascular development, the cellular and molecular attributes of capillary formation and maturation remain enigmatic. Hence, there is considerable interest in unraveling the molecular signatures of capillary development, which could facilitate the identification of novel drug delivery targets to enhance therapeutic efficacy.

The zebrafish model offers an exceptional avenue for uncovering brain vascular development mechanisms due to its remarkable evolutionary conservation with mammals in cerebrovascular structure, function, and molecular regulation, as well as its optically transparent embryos for high-resolution cerebrovascular imaging21,22. Crucially, the brain endothelium serves as the anatomical foundation of cerebrovascular, playing a pivotal role in maintaining neurovascular equilibrium and governing cerebral blood flow. To elucidate the vascular, cellular, and molecular characteristics of brain vasculature during the early developmental stage, we leveraged an endothelial-specific transgenic zebrafish line to reconstruct three-dimensional (3D) cerebrovascular architectures across 3 days post-fertilization (dpf), 6 dpf, and 11 dpf. Complementing this structural analysis, we performed scRNA-seq of brain vascular endothelial cells (ECs) at 3, 6, and 11 dpf, systematically investigating gene expression profiles across endothelial subtypes. Finally, integrating these datasets, we established a spatiotemporally resolved transcriptome atlas aligned with 3D vascular anatomy, revealing stage-specific molecular signatures governing cerebrovascular maturation. Notably, leveraging our comprehensive dataset, we identified the critical roles of capillary-specific genes zgc:158423, slc16a1a, and cldc1 for BBB integrity. Our dataset furnishes a valuable resource for elucidating the integrated vascular, cellular, and molecular networks underlying cerebrovascular development.

Results
3D cerebrovascular network of zebrafish from embryo to larva
To systematically investigate the spatiotemporal characteristics of zebrafish vasculature in the developing brain, we first generated the 3D cerebrovascular atlas spanning embryonic to larval stages (3, 6, and 11 dpf22,23, Fig. 1A). We performed confocal imaging with the EC-specific transgenic zebrafish line of Tg(kdrl:eGFP)s843 and then reconstructed the cerebrovascular network using Imaris. Similar to mammals, the zebrafish brain is perfused via the circle of Willis (COW), which is supplied by the caudal division of the internal carotid artery (CaDI). Blood then distributes to the parenchyma of the prosencephalon, the mesencephalon, and metencephalon. For further quantitative analysis, the cerebrovascular architecture was subdivided into lateral, dorsal, ventral, and intraparenchymal subnetworks (Fig. 1A), with vascular anatomical nomenclature primarily following previous convention24 (Table S1).

3D reconstruction of the developmental zebrafish cerebrovasculature uncovered region-specific dynamic changes in vascular architecture. Quantitative analysis demonstrated significant increases in both vascular length (Fig. 1B) and segment number (Fig. 1C), with particularly pronounced angiogenesis observed in five vessels: the prosencephalic communicating vessel (PrCm), middle mesencephalic central artery (MMCtA), medulla oblongata central artery (MoCtA), basilar artery (BA) and nasal vein (NV) (Fig. 1D, E). These findings indicate robust intraparenchymal vascular expansion during this developmental period. Consistent with previous reports that early metencephalic vascularization originates from the sprouting of the primordial hindbrain channel (PHBC)22, both the number of PHBC and MoCtA segments increased significantly from 3 to 6 dpf. However, beyond this stage, the number of MoCtA segments continued to rise (69.40±4.925 vs. 113.0±10.28, mean±SEM), whereas PHBC counts remained unchanged (36.80±1.158 vs. 32.80±1.463, mean±SEM). These spatiotemporal transitions suggest a developmental pattern in the zebrafish cerebrovasculature, progressing from lateral to intraparenchymal vascularization.

Characteristics of cerebrovascular EC subtypes
During early development, the cerebrovascular network undergoes regionally vascular enrichment. ECs, serving as both structural units and functional hubs of vasculature, are closely associated with this process. However, the molecular signatures of ECs and their spatial organization within the vascular network have not yet been systematically integrated. To address this gap, we performed scRNA-seq of fluorescence-activated cell sorting (FACS)-purified cerebrovascular ECs at these 3, 6, and 11 dpf (Fig. 1F), alongside whole-brain spatial transcriptomic profiling using improved in situ sequencing (IISS)25,26 and vascular mapping (Fig. 1G).

Single-cell transcriptomic profiling has profoundly advanced our characterization of distinct EC types27. Firstly, we integrated EC populations from 3, 6, and 11 dpf (Fig. 2A), which were filtered using four canonical EC markers (cdh5, kdrl, pecam1, and fli1a) (Fig. S1). Following integration, the cell populations were unbiasedly clustered and visualized using Uniform Manifold Approximation and Projection (UMAP). Through cross-referencing differentially expressed genes in each cluster with previously reported marker genes28,29, we identified six distinct clusters of ECs, including angiogenic ECs (AngECs), mitotic ECs (MECs), lymphatic ECs (LymECs), venous ECs (VECs), arterial ECs (AECs), and capillary ECs (CapECs) (Fig. 2B, C).

EC subtypes exhibit functional heterogeneity mediated by subtype-specific gene expression profiles. We characterized the top 25 highly expressed genes (HEGs) in six EC subtypes, providing a set of candidate markers for these cell populations (Fig. S2A and Supplementary Data 1). We then profiled the temporal dynamics of these marker genes across 3, 6, and 11 dpf, uncovering diverse developmental patterns of distinct EC subtypes (Fig. S2B and Supplementary Data 2). Notably, AngECs, AECs, and CapECs displayed progressive upregulation of specific gene modules. In contrast, VECs reached peak module expression at 6 dpf, while MECs exhibited reduced expression by 11 dpf. LymECs maintained consistent expression across all three developmental stages (Fig. S2B). These findings highlight subtype-specific functional transitions, suggesting dynamic and coordinated regulation of endothelial specialization during cerebrovascular development.

Dynamic expression module of EC subtypes during development
Following the identification of ECs subtype-specific genes, we conducted functional enrichment analysis and categorized significantly enriched pathways according to their biological functions (Fig. 2D). Pathways involving signal transduction and signaling interactions were broadly enriched across most EC subtypes. In contrast, CapECs uniquely exhibited significant enrichment in substance transport, indicating their specialized role in BBB formation and maintenance. Subsequent validation confirmed marked significant upregulation of key functional modules in CapECs, including ATP-binding cassette (ABC) transporters, solute carrier (SLC) transporters, and tight junction components, compared to other EC subtypes (Fig. 2E-G).

Within these modules, we analyzed the developmental profiles at 3, 6, and 11 dpf. The expression of transporter-related modules significantly increases from 3 dpf to 6 dpf and remains elevated thereafter, indicating that BBB transport functionality is still immature at the molecular level at 3 dpf (Fig. 2H, I), aligning with the high BBB leakage at this developmental stage30. In comparison, tight junction-associated modules exhibited a distinct developmental trajectory marked by a sharp expression increase at 6 dpf, persisting through 11 dpf, indicating functional establishment of tight junctions around 6 dpf (Fig. 2J), consistent with the previous conclusion31. These results indicate that the coordinated activity of these modules regulates the stepwise maturation of the BBB. We further performed permeability assays to assess BBB functionality at 3, 6, and 11 dpf by injecting Evans Blue (961 Da) into the embryonic common cardinal vein. Quantitative analysis revealed significant tracer extravasation at 3 and 6 dpf, with peak leakage observed at 3 dpf. Since Evans Blue could bind to serum albumin, forming a protein complex with a molecular size around 70 kDa32,33, this indicates an immature BBB function of 3 to 6 dpf zebrafish. By 11 dpf, Evans Blue was robustly confined to the cerebral vasculature (Fig. S3A, B), demonstrating functional maturation of the BBB.

Conservation of cerebrovascular EC subtypes between mammals and zebrafish
Characterizing EC features during development enables an in-depth investigation of the conservation of cerebrovascular development between mammals and zebrafish. To systematically compare the developmental processes between species, we integrated published scRNA-seq data of human brain ECs (from GW14 to GW18, GW for gestational week)2 and mouse brain ECs (from E12.5 to E17.5, E for embryonic day)34, which correspond to the zebrafish developmental window from 3 to 6 dpf. The HEGs of zebrafish EC populations exhibited conserved expression profiles in mammalian (human/mouse) EC subtypes (Fig. 3A), with cross-species correlation analysis confirming robust transcriptional conservation across EC subtypes (Fig. S4A, B). Furthermore, we present the specific functions associated with gene modules of CapECs during developmental progression in human and mouse (Fig. 3B-D). Interestingly, the expression trends of both the transporter module and tight junction protein module in mammals are consistent with those in zebrafish during development (Fig. 2H-J). The conservation of ECs, particularly CapECs, during zebrafish development demonstrates the species’ strong potential as an alternative model for mammalian vascular research.

Spatiotemporal transcriptome atlas of cerebrovascular EC subtypes
To delineate the spatial characteristics of ECs during vascular development, we integrated scRNA-seq data with IISS information mapped onto a whole-brain vascular network. In this experimental design, a workflow combining 3D imaging in vivo of Tg(kdrl:eGFP)s843 zebrafish with subsequent serial sectioning and IISS was used for spatially mapping the ECs (Fig. 1G, “Methods”). Only the zebrafish with entire brain sections and strong eGFP expression were processed for IISS. During IISS, the 488nm channel was exclusively allocated for the vascular eGFP fluorescence acquisition. Vascular matching was achieved by adjusting slice views in 3D reconstructions, enabling the location of eGFP+ regions of interest (ROI) in IISS within the whole-brain network (Fig. S5).

With this design, we systematically profiled the expression of 36 genes specifically associated with CapECs, AECs, VECs, and AngECs in 3, 6, and 11 dpf zebrafish (Figs. 4A and S6-7). The analysis yielded robust data: at 3 dpf, 28 slices comprising 8,769 cells and 1,970 vascular regions, with 45,993 transcript counts; at 6 dpf, 25 slices with 6,873 cells, 1,258 regions, and 30,919 counts; and at 11 dpf, 25 slices with 5425 cells, 1,924 regions, and 33,118 counts. These data demonstrate the high efficiency and depth of our spatial transcriptomic profiling. To validate our spatiotemporal transcriptomic data, we selected slc2a1a as a representative gene. Comparative analysis revealed a strong concordance between spatial transcriptomics-derived slc2a1a expression patterns and slc2a1a:eGFP fluorescence distributions in transgenic zebrafish (Fig. S8A), confirming the reliability of our approach (Fig. S8B, C).

Through comprehensive classification, we established a spatiotemporal transcriptomic atlas of cerebrovascular EC types (Figs. 4B and S6). The procedure involved three key steps: firstly, after capturing the IISS data from these serial sections, we used the spatial distribution of the vasculature from the eGFP channel to align the 2D section with its corresponding, pre-acquired in vivo 3D cerebrovasculature map from the same animal (Fig. 4B, left); subsequently, this 2D-3D registration allowed us to transfer the vessel type annotations from the 3D atlas onto the 2D section (Fig. 4B, middle); finally, based on this precise vascular annotation, we performed statistical analyses of the cellular and gene information from the IISS data for each vessel type (Fig. 4B, right). This atlas revealed the distribution of four EC subtypes, with CapECs consistently predominating in the vast majority of intracranial vasculature (>50%, Fig. 4C). Unlike the majority pattern, the lateral vessels (NV and PMBC) showed progressive CapEC reduction coupled with VEC accumulation during development.

To elucidate the subtype-specific developmental changes in vascular patterning, we mapped the density distributions of individual major EC subtypes (CapEC, AEC, VEC, and AngEC) within our 3D vascular atlas (Fig. 4D). All subtypes exhibited preferential localization in lateral vasculature (PMBC and PHBC), with progressive developmental enrichment into intraparenchymal vessels of mesencephalon and metencephalon, including the posterior mesencephalic central artery (PMCtA) and medulla oblongata central artery (MoCtA). PrCm displayed an increasing AEC enrichment during 6 and 11 dpf, indicating gradual arterial specialization. In contrast, the cerebellar central artery (CCtA) and metencephalic artery (MtA) maintained weak EC specification throughout all stages, suggesting delayed vascular angiogenesis that may parallel its neurodevelopment (Fig. 4D, E). Intriguingly, our spatiotemporal transcriptomic atlas exhibited highly concordant distribution patterns among these EC subpopulations. To validate these findings, we implemented an amplification-based single-molecules fluorescence in situ hybridization (asmFISH) assay, which further verified the co-localization of distinct EC subtypes within the same vascular segments (Fig. S10A, B). Furthermore, we also detected that the cells expressed markers of EC subtypes (Fig. S10C-H). These findings demonstrate progressive spatiotemporal specialization of EC properties in the developing zebrafish cerebrovasculature from embryonic to larval stages.

Functional evaluation of genes enriched in CapECs
During cerebrovascular development, the BBB emerges as a highly specialized functional interface. Here, we focused on CapECs as they constitute the pivotal cellular component responsible for BBB functionality. Leveraging our spatiotemporal transcriptomic dataset, we found that several genes enriched in CapECs relative to other EC subtypes upon isolation (cldn5a, lef1, and fabp11a, et al.) have been investigated to be associated with BBB35,36. Fortunately, we also identified three uncharacterized zebrafish capillary highly expressed genes, including zgc:158423, slc16a1a, and si:ch73-86n18.1 (also known as cldc1) in our scRNA-seq (Fig. S11A). Through in situ hybridization in zebrafish embryos, we verified their specific expression within the cerebrovasculature (Fig. S11B).

Both slc16a1a (orthologous to mouse Slc16a1 and human SLC16A1) and zgc:158423 (conserved with mouse Slc3a2 and human SLC3A2) have been reported as transporters located at the BBB37,38, whereas their functions in BBB development remain undefined. We analyzed the density distribution of slc16a1a+ ECs and zgc:158423+ ECs using our spatiotemporal transcriptome atlas (Fig. 5A, B). At 3 dpf, both EC populations were primarily distributed in the lateral PHBC and PMBC, followed by the AMCtA, MMCtA, PMCtA in mesencephalon, and the MoCtA in metencephalon. During subsequent development, these ECs gradually enriched in the major mesencephalon and metencephalon vessels, exhibiting spatial distribution characteristics similar to those of CapECs in spatial transcriptomics.

To investigate the functions of slc16a1a, zgc:158423, and cldc1, we generated corresponding crispants in zebrafish using CRISPR/Cas9 technology (Figs. 5C and S12A). At 3 dpf, both slc16a1a and zgc:158423 crispants exhibited comparable vascular defects, including significant reductions in total vessel length and segment number, predominantly in the mesencephalic region (Fig. 5C-G). By 6 dpf, only a subset of vessels in slc16a1a crispants displayed impairments in length or segment number (Fig. S12A-E). In contrast, cldc1 crispants showed no obvious vascular abnormalities at 3 dpf; however, a significant decrease in vascular length and segment number was detected by 6 dpf (Figs. 5C-G and S12A-E). We also calculated vascular straightness and found no substantial difference between the crispants and the controls (Fig. S12F-I). Notably, cldc1 crispants also showed a high hemorrhage rate in the cerebral vasculature at 3 dpf, indicating its essential role in cerebrovascular stability (Fig. S11C). We next assessed BBB permeability in the crispants. Evans Blue leakage assays revealed significantly increased extravasation in both slc16a1a and zgc:158423 crispants at 3 dpf, which persisted through 6 dpf (Fig. 5H-K). In contrast, cldc1 crispants exhibited no leakage at either time point, confirming intact BBB integrity.

Moreover, we performed morpholino oligonucleotide (MO)-mediated knockdown of zgc:158423 and slc16a1a. The morphants recapitulated the vascular defects observed in crispants. At 3 dpf, both morphants showed significant reductions in vessel length in the PrCm, AMCtA, CCtA, and MoCtA regions (Fig. S13A-E). By 6 dpf, these vascular defects were attenuated, consistent with the phenotype seen in crispants (Fig. S13F-J). BBB assessment in morphants also revealed permeability defects consistent with those in crispants (Fig. S14). Collectively, the vascular abnormalities and increased permeability observed in both slc16a1a and zgc:158423 crispants and morphants underscore the critical roles of these genes in zebrafish cerebrovascular development and BBB integrity.

In summary, through integrating single-cell transcriptomics, spatial transcriptomics, and 3D vascular reconstruction, we delineate the molecular characteristics and spatial distribution patterns of EC subtypes during the early development of zebrafish cerebrovasculature. This work establishes both a comprehensive molecular atlas of cerebrovascular ECs and a multidimensional analytical framework for investigating cerebrovascular development and related pathologies.

Methods
Fish lines and maintenance
Zebrafish (Danio rerio) were raised and maintained following the standard procedure in ZFIN in accordance with the Guangdong Provincial Regulations on the Administration of Laboratory Animals, under rigorous oversight by the animal ethics committee of Guangdong Medical University. Zebrafish at 3, 6, and 11 dpf from the Tg(kdrl:eGFP)s84353, Tg(kdrl:ras-Cherry)s91654, and Tg(slc2a1a:eGFP) (a gift from Dr. Dong Liu’s laboratory, Nantong University, China) zebrafish line were used in this study, in which sex is not yet determined at these developmental stages.

3D imaging and vascular reconstruction
3D imaging of zebrafish embryos was performed using the confocal laser scanning microscope Nikon AX (Nikon, Japan) with a 10× objective (zoom 1.6-2.0). To facilitate 3D reconstruction, optical sections were acquired at 1 μm z-intervals with the pinhole adjusted to 0.4 Airy units. Zebrafish were treated with 1-phenyl 2-thiourea (PTU, 0.03 g/L) from 10 h post-fertilization to prevent pigmentation. Before imaging, the transgenic zebrafish were anesthetized with 0.03% tricaine (Sigma-Aldrich) and mounted in 1% low-melting point agarose (LMA, Sigma-Aldrich) in a confocal dish with ventral side up. After imaging, the surface and filaments modules of Imaris 10.2.0 (Oxford Instruments, UK) were used for vascular reconstruction. The segment length and number of connected vasculatures were quantified. The vessel nomenclature follows established literature24, with modified abbreviations for clearer brain region representation (Table S1).

scRNA-seq of zebrafish brain ECs
At 3, 6, and 11 dpf, zebrafish were anesthetized in the E3 medium, and brain tissues were isolated within 30 min. 1500–2000 brain tissues were isolated from the above zebrafish at the corresponding time point and incubated at 37 °C with tissue digestion solution containing 2 mg/L of papain (9001-73-4, Sigma-Aldrich) for about 25 min. Then the tissues were incubated with Dulbecco’s Modified Eagle Medium (DMEM) medium containing 5% fetal bovine serum (FBS) to terminate the digestion. Cell suspensions were centrifuged at 500 g for 30 min at 4 °C and resuspended in DMEM medium containing 5% FBS. Then cell suspensions were filtered through the 70 μm and 40 μm cell strainers to prepare single-cell suspensions. All filtration procedures were performed on ice to preserve cell viability. eGFP-positive cells were sorted by FACSAria II and collected with DMEM medium containing 5% FBS. After centrifugation of sorted cells, the cells were resuspended in DMEM medium with 5% FBS to obtain the appropriate cell density. Samples with more than 80% viable cells were labeled with barcodes. Then, sequencing libraries were constructed using Chromium Next GEM Single Cell 3’ Reagent Kits v3.1 and sequenced using the Illumina NovaSeq 6000.

Processing of scRNA-seq data
Raw sequencing reads of the samples were generated by the Cell Ranger (v.7.1.0) workflow. Quality control, removing doublets, empty droplets, and cells with higher mitochondrial containment, were performed with the Seurat (v.4.3.0) package in R (4.3.0). To exclude the contamination of non-ECs, four canonical makers (cdh5, kdrl, pecam1, and fli1a) were used to identify the EC clusters. ECs filtered from three data sets were then integrated by the Integrated Data function in Seurat55. Then the data were subjected to unsupervised clustering and visualized with UMAP. The differentially expressed genes were identified by the FindAllMarkers and FindMarkers function provided by the Seurat package56. Functional pathway enrichment analysis on the gene sets was performed by clusterProfiler57.

Cell conservation analysis
The differentially expressed genes among CapECs, AECs, and VECs were identified by the Find Markers function. Correlation analysis between mammals and zebrafish was performed by the MetaNeighbor package. Mouse and human genes were transferred to their homologous genes of zebrafish by the biomaRt package58,59. scRNA-seq of mouse and human cerebrovascular was obtained from previous literature2,34. The correspondence of cerebrovascular developmental stages is as follows: zebrafish (3 dpf to 6 dpf), mouse (E14.5 to E17.5), and human (GW14 to GW18).

Sample preparation for IISS
The live imaging of the 3D brain vascular was conducted as described above. The imaged zebrafish were recovered in the egg water for 2 h, then were fixed at room temperature in the 4% (w/v) paraformaldehyde (PFA) fixed solution for 0.5 h to preserve the vascular eGFP signal. After washing with 1× PBS three times, the head of fixed zebrafish was isolated with Venus scissors. Then the tissue was transferred into the mold, and the excess PBS was removed. OCT was used to embed the zebrafish brain in a supine position, and immediately frozen with dry ice powder. The samples were preserved at −80 °C before frozen section. Zebrafish brain tissues were cryosectioned at 10 μm using a freezing microtome (Dakewe, CT520), with collection beginning from the dorsal first tissue slice until the gills appeared. The sections were collected in a 1.5 × 1.5 cm2 region on one adhesion microscope slide (CITOTEST, China). To check whether the vascular signal (eGFP) is still retained, these sections were imaged by Mica microscopy (Leica, Germany). To verify whether the blood vessels in the slices were intact, the vasculatures were carefully compared artificially with the 3D vascular network. Only the zebrafish with entire brain sections (without loss, damage, or folding) and strong eGFP expression were processed for IISS.

IISS assay procedure
Before IISS, ubc and dapb were used as the positive and negative controls, respectively, to detect whether the sections could be used.
IISS was performed by Suzhou Dynamic Biosystem Co., Ltd. In detail, sections were first fixed with 4% PFA for 5 min, followed by washing twice with 1× DEPC-PBS and dehydrated with a gradient of ethanol. After being rinsed three times with washing buffer (SEERNA Bioscience, Xiamen, China), permeabilization of the sections was conducted with 0.1 M HCl at 37 °C for 5 min. Then the sections were incubated in order with 1) hybridization mix solution containing target probes at 37 °C for 4 h; 2) ligation mix at 37 °C for 30 min; 3) splicing primers in circulation mix at 37 °C for 30 min; 4) rolled circular amplification mix at 30 °C overnight. After completing each of these steps, the sections were washed three times by using the washing buffer at 30 °C. Then, sections were incubated with ligation buffer containing anchored primers, labeled probes, and ligases at 30 °C following the mounting of sections by utilizing SlowFade Gold Antifade Mountant (Thermo Fisher Scientific, Shanghai, China) that contains 0.5 μg/mL DAPI (Sigma, Shanghai, China). Leica DM6B microscope equipped with DFC9000GT camera was utilized to image with a 20x objective. Following imaging, sequencing primers were eluted with stripping buffer (SEERNA Bioscience, Xiamen, China), and then the sections were washed three times with washing buffer for 5 min. These tissue sections would be reused in the next rounds until the completion of the whole experimental procedure.

Process of IISS data
Registration of multi-round imaging data. Due to the multiple rounds of imaging in the IISS experiment, images between different rounds would produce displacement, and image registration is necessary. Scale-invariant feature transform algorithm was used to extract the matching feature points between the common reference image and the image to be registered. Then, the change matrix between the two images was calculated according to the relative position relationship between each set of matching feature points. At last, the image was corrected by an affine transformation to achieve image registration.

Cell segmentation. To assign gene signals to cells for downstream analysis, the delineation of cellular spatial boundaries relative to mRNA transcripts is required. First, DAPI images were used to detect nuclei using a deep learning algorithm, cellpose, for cellular segmentation. Then each nucleus was expanded outwards until either 20 pixels max distance was reached or the boundary of another cell was reached. Finally, we have obtained the cell outline coordinates for further analysis.

Vascular localization of cells. The fluorescent signal (eGFP) of blood vessels was also captured in our tissue sections to provide vascular positional information for cells. To reduce misalignment between multi-channel images, the registration process between DAPI images and eGFP images was performed as follows: using the tissue boundary in DAPI-stained images as a spatial reference, a feature-based rigid registration algorithm was applied to achieve the initial alignment of cross-modal images, ensuring the consistency of tissue structures across different channels. Based on the coarse registration, non-rigid optimization is performed by leveraging the distribution features of eGFP-labeled blood vessels. The optical flow method is used to establish a local deformation field, and sub-pixel-level registration is achieved through mutual information maximization or feature matching optimization, ensuring that the spatial correspondence of blood vessels meets the requirements of subsequent quantitative analysis.

Then, the cerebrovascular segmentation was performed. Morphological transformations are employed to eliminate non-uniform background interference in fluorescence imaging, and high-frequency vascular features are extracted through structural element matching, effectively enhancing the signal-to-noise ratio in low-contrast regions. Initial segmentation is achieved based on a local adaptive threshold algorithm, with the threshold dynamically calculated according to the local regional features of the image. By dynamically computing the statistical characteristics of the pixel neighborhood, a vascular binary mask robust to illumination variations is generated. Optimize the segmentation results based on the distribution of marker labels, remove pseudo-vascular noise, and output a binary image of the vascular network with complete structure.

For spatial matching of cerebrovascular segments, the longer eGFP+ segments served as landmarks to identify the slice in the 3D vascular network (reconstructed from prior 3D imaging of the same fish), with relative positioning guiding vascular annotation in other eGFP+ ROIs. We then calculated the absolute positions of vascular regions and the centroid coordinates of cells to identify intravascular cells and annotate their vascular localization.

Cells whose centroids fell within the vascular regions were classified as vascular cells. These cells were subsequently assigned through the correlation of their transcriptional profiles with scRNA-seq data.
Gene expression of IISS was evaluated at the cellular level. The density distributions of vascular ECs were displayed on the reconstructed 3D cerebrovascular network.

Further analysis of IISS data. To gain the signal position in the images, the watershed algorithm based on local extremum was used to identify and locate the signals in the images. By combining the signal coordinates, cell segment outline, and blood vessel segment information, gene signals were assigned to the cells, and the cells were then assigned to the vascular region. Finally, we obtained the information that included the coordinates of gene signal and cell centroids, the outline of cells and blood vessels, and the matrices of signal-cell and cell-blood vessel.

Amplification-based single-molecule in situ hybridization assays. The RNA amplification-based single-molecule in situ hybridization (asmFISH) assays were performed using the SEERNA® FISH RNA Fluorescence in situ Hybridization Kit according to the manufacturer’s standard protocol60. Two sets of probes were used: the stabl, cldn5a, and heyl probes were conjugated with Cy3, Cy3.5, and Cy7 respectively, and the dab2, slc2ala, and hey2 probes were correspondingly labeled with Cy3, Cy3.5, and Cy7. All probes were synthesized by Thermo Fisher Scientific.

Whole-mount RNA in situ hybridization. The whole-mount in situ hybridization was performed as previously described61. For zgc:158423, slc16a1a, and cldc1 probes synthesis, total RNA was extracted from 3 dpf zebrafish and reverse-transcribed into cDNA using Hifair® Advance Fast 1st Strand cDNA Synthesis Kit (11150ES10, Yeasen, China). Then, target gene sequences were amplified from the aforementioned cDNA by 2× Hieff® PCR Master Mix (With Dye, 10102ES08, Yeasen, China) with the primers listed in Table S2. The PCR products were cloned into the pGM-T vector using the pGM-T Ligation Kit (VT202-01, TIANGEN, China). Then plasmid DNA was isolated from positive clones, and the correct insert sequence was confirmed by Sanger sequencing. The verified plasmids were linearized by digestion with NdeI and antisense RNA probes were synthesized by in vitro transcription using the T7 RNA polymerase with the linearized plasmids as templates. Images were captured by Leica stereomicroscope (M205).

Generation of crispants by CRISPR/Cas9. The gRNA target sites were designed using CRISPOR, and those with high specificity score and predicted efficiency were selected for synthesized by Tsingke (Table S3). For microinjection, the ribonucleoprotein (RNP) complex was prepared by mixing 600 ng of Cas9 protein (M0646T, NEB) with 800 ng of each sgRNA in a 2 μL reaction system (consisting of 0.2 μL Cas9 mRNA [3000 ng/μL stock], 0.8 μL sgRNA1 [1000 ng/μL stock], 0.8 μL sgRNA2 [1000 ng/μL stock] and 0.2 μL 10× buffer).
Subsequently, 2 nL of the RNP mixture was injected into one-cell stage Tg(kdrl:eGFP)s843 zebrafish embryos. Control embryos were injected with an equivalent amount of Cas9 protein. The mutagenesis efficiency was evaluated at 1 dpf using the T7 Endonuclease I (T7E1), followed by sequencing. The primers for mutagenesis efficiency detection were listed in Table S2.

Morpholino (MO) injection. Translation blocking MOs were designed and synthesized by Gene Tools, LLC. The MO sequences used were zgc:158423 (5’-CAGTTCTATGTCCTTCATTTCTCCT-3’) and slc16a1a (5’-CATGGCTCCAGGTGTTTACAAACCA-3’). 0.5 ng of zgc:158423 and 2.5 ng of slc16a1a MO were injected into the Tg(kdrl:eGFP)s843 zebrafish yolk at one cell stage, respectively.

Imaging and analysis of cerebrovascular permeability. Evans Blue (961 Da) was injected into the common cardinal vein of zebrafish at 3 and 6 dpf. After 20 minutes of circulation, the brains of anesthetized larvae were imaged using Nikon AX confocal microscopy with dual-channel laser excitation at 488 nm and 561 nm. For leakage quantification, maximum intensity projections of 99 μm thickness were generated by Image J. Extravascular tracer puncta within the brain parenchyma were counted and normalized to the control group.

Gene-disease association analysis. The GWAS summaries were obtained from the Neale lab website (https://www.nealelab.is/uk-biobank). The association was defined as the distances from significant risk SNPs to the transcriptional start site (TSS) of genes. The significance threshold for risk-associated SNPs was set at p < 5 × 10-8. Disease selection was based on the ICD-10 classification system.

Statistical analysis. Statistical tests were conducted in the R statistical computing environment (R Core Team, 2023). Unpaired two-tailed Student’s t-test was used to compare the difference between the two experimental groups. The Wilcoxon test was used to assess the difference between two groups with more than 30 samples, such as marker genes identified by FindMarkers and FindAllMarkers. Kruskal-Wallis test with Dunn’s multiple comparisons was used to compare the differences among 3, 6, and 11 dpf. p value < 0.05 was considered statistically significant, *p < 0.05, **p < 0.01, ***p < 0.001, ****p < 0.0001.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript presents a well-constructed spatiotemporal atlas of cerebrovascular development in zebrafish that will\ngreatly address the gap in this field. The 3D vascular construction really impressed me, as it plays a pivotal role in\nconstructing the spatiotemporal map of vascular development. Also, the spatiotemporal atlas reveals the CapEC enrichment\nalternation and dynamic expression module of transporter and tight junction pathway. Finally, three CapEC-enriched genes\nwere recruited to validate their contribution to the establishment of the blood brain barrier. The resource is thoughtfully\ndesigned and of long-term significance to the field, qualifing the publications for Nature Communications. Yet it remains\nsome revise to improve clarity and interpretability. Also, I have some concerns about the methods. All the comments are\nlisted below.\nMajor comments\n1. The spatiotemporal atlas was constructed within three larval stages, which are 3, 6, and 11 dpf, respectively. I am\nwondering why the authors selected the three stages, are these stages special developmental stages to the zebrafish? Is\nthere any solid and reliable reference mentioning these three stages? Please cite the reference.\n2. The authors devoted considerable effort to constructing the 3D vascular atlas. They measured vascular length and\nsegments during three stages. Are these quantitative measurements necessary? Do they help in constructing the\nsubsequent spatiotemporal atlas? Are there other possible quantitative measurements, I don’t know, such as vessel\nthickness? If it is feasible.\n3. Single-cell transcriptomic analysis identified six types of ECs. The marker gene information of these ECs should be\nincluded in the supplementary tables, such as pseudobulk and fold change. In addition, based on the expression levels of\nthe marker genes, angiogenic ECs appear to be similar to the other ECs, which may suggest that this cluster of ECs are\nprogenitor cells of other ECs. The authors should add more analysis to discuss this cluster, as they may be related to\nvasculogenic events.\n4. These HEGs in Figure 3A should be analyzed for their correlations across different species to verify their conservation.\nThe results should be presented in the manuscript.\n5. The spatial transcriptome profiles expression of 36 genes during different periods. What criteria were used to select these\ngenes? The changes of these genes across different stages should be present and discussed.\n6. This is very appealing, as the authors have generated a whole-brain transcriptome of endothelial cell-specific genes\nbased on the 3D vascular network. However, for the vascular localization in series zebrafish sections, more detailed\nexplanations are needed. For example, the source of the vascular annotation image in Figure 4B. The Results section\nshould also provide a clearer description of the relevant process and key steps.\n7. In figure 4C, the cell proportion of AngECs in different vessels is low when compared to other ECs, which is inconsistent\nwith the cell proportions from single cell analysis. Please explain this discrepancy.\n8. The efficiency of in-situ sequencing should be presented in the manuscript and discussing the specific advantages of\nperforming this method for cerebral vasculature.\n9. Cell denstiy in different regions of cerebral vasculature is dynamic throughout three stages, which is interesting to me.\nWhether the alternation of cell density has association with any biological functions should be explained in detail. Also,\ndetailed information of cell density to each vessel should be presented in the supplemental table.\n10. Why do not present the cell density of angEC? Is it due to the consideration of figure arrangement or because the results\nare negative?\n11. The authors validated the reliability of expression patterns in spatiotemporal transcriptomic data with a transgenic\nfluorescence zebrafish. The expression modules presented in the supplemental figure 6B-C should be plotted in a\nconsistent manner. And a correlation or similarity test should also be performed.\n12. In the end, the authors identified three uncharacterized zebrafish capillary-specific genes (zgc:158423, slc16a1a, and\nsi:ch73-86n18.1) from scRNA-seq analysis. But there are only two genes demonstrated in figure 5 and supplemental figure\n9. The information of all three genes should be demonstrated even if the results are negative. Also, it lacks information of 11\ndpf, which confused me.\nMinor comments\n1. For figure 2D, I recommend counting the number of cell clusters enriched in each pathway, as this would make the\npresentation clearer and more compelling.\n2. The number of significance analyses in figure 3B should remain consistent with those in figure 3C and 3D.\n3. What does AUROC mean in the supplemental figure 4? Please explain.\n4. Why is the vascular classification in the transcriptome less than that in the developing 3D vasculature? Please confirm.\n5. The 3 dpf control zebrafish in Figure 5C missed the pro vasculatures (in yellow color).\n6. Statistical analysis of methods included 4 significant thresholds, which is too much. The authors should follow the\nstandards provided by the journal.", "reviewer_2": "The manuscript by Li et al is a resource paper which utilizes single-cell transcriptomics, 3D vascular reconstruction and in\nsitu sequencing to describe zebrafish brain vascular development. The authors have identified 6 endothelial subtypes,\nwhich show a conserved transcriptional signature with mammalian cells. They also have performed some functional assays,\nwhich preliminarily implicate 3 new genes in vascular patterning and blod-brain barrier (BBB) integrity.\nOverall, this study provides new and detailed information on brain endothelial cell transcriptomics using state-of-the-art\napproaches, and potentially could be a very useful resource for the scientific community. However, the biggest limitation is\nthat the spatial transcriptomic approach using improved in situ sequencing (IISS) approach is not very convincing, and the\nresults do not convincingly demonstrate distinct spatial localization of different endothelial cell subtypes. This approach or\nanalysis may need an improvement for it to be a useful resource. Specific comments:\n1. As noted above, IISS approach aims to detect spatial localization of different marker genes that can be assigned to\ndistince brain endothelial cell populations. However, the results in Fig. 4 show largely overlapping expression of many cell\nmarkers. For example, CapEC, AEC and VECs show very similar enrichment patterns (Fig. 4D and E), all of them enriched\nin central arteries, including MoCtA. The approach fails to detect distinct expression patterns, which might be expected for\narterial and venous cells, for example. It seems that this could be an artifact of the methods used rather than the true\nexpression pattern. To verify this, the authors could perform hybridization chain reaction (HCR) using top marker genes for\neach cell population and verify if these expression patterns match their IISS results.\n2. While the authors have performed very detailed description of zebrafish brain vascular anatomy at 3-11 dpf, this\ndescription is not entirely new and builds upon several previous studies (perhaps less detailed), such as Isogai et al, Dev\nBiol 2001, and others. When describing 3D cerebrovascular atlas, the authors need to acknowledge clearly what has been\ndone in the previous studies, and how their approach goes beyond what has been previously described.\n3. Please note how many endothelial cells were identified at each time point in scRNA-seq analysis. From the Fig. 2A it\nappears that there were many more cells identified at 3 dpf stage compared to 6 and 11 dpf; it is unclear why. There may be\nvery few cells (only 10-15) identified in some cell populations at 6 and 11 dpf (such as CapECs), which may limit the\ntranscriptional profile analysis; this needs to be acknowledged.\n4. Please move the legend in Fig. 2C (the inset box which shows percent expressed and average expression) outside of the\ndata panel. It currently obstructs the view of dot plot expression for MEC, VEC and AngEC populations.\n5. It is difficult to read the gene list in Fig. S2. Either the figure resolution can be improved, or the list can be presented in a\nseparate supplementary table.\n6. Please clarify how the proportion of endothelial cells in different vessels was determined in Fig. 4C. scRNA-seq analysis\nidentified AngECs as the most abundant cell type, and CapEC make a relatively small proportion. However, Fig. 4C shows\nCapECs as the most abundant cell type, and there is only a small proportion of AngECs. Why is there such a discrepancy\nbetween the two approaches?\n7. Morpholino knockdown experiments (Fig. 5H-I and S9F-H) currently have no specificity controls and show data that are\nvery preliminary. Ideal verification would be to generate genetic mutants. At the very least, the authors should show that F0\nCRISPR / Cas9 knockout of the same genes produces similar phenotypes to the morpholino knockdown.\n8. The authors state that F0 knockdown of si:ch73-86n18.1 produced erythrocyte extravasation, arguing that this\ndemonstrates role in BBB development (Fig. S8C). The results here show a hemorrhage, likely due to vessel rupture, which\nis different from BBB formation. In defective BBB vessels may still be intact and may not necessarily show hemorrhages.\nPlease include “n”, how many embryos were analyzed and how many showed hemorrhages. Also, please include in the\nmethods the sequence of gRNA (or gRNAs) used for targeting, how was gRNA made (in vitro transcription or synthetic) and\nspecify what was the mutagenesis success rate based on DNA sequencing, restriction analysis, or another appropriate\nmethod.\n9. It is very difficult to see any color in Figure S7, orange, green, purple or blue.", "reviewer_3": "The authors created a detailed 3D and molecular atlas of how blood vessels develop in the zebrafish brain from 3 to 11 days\nafter fertilization. By combining state of the art techniques in imaging, single-cell RNA sequencing, and spatial\ntranscriptomics, they identified different endothelial cell subtypes, tracked how the blood–brain barrier matures, and\ndiscovered several previously unknown genes essential for proper vascular development and barrier integrity.\nThe authors deserve much credit for the excellent representation of the complicated data sets in their figures regarding the\nthe vascular anatomy and their Spatiotemporal analysis.\nIn general the study is well worth publishing, however especially regarding the functional analysis of the novel BBB genes, I\na more detailed and better controlled analysis is needed, as the functional data is a major part elevating the paper from a\ndescriptional study.\nMajor comments\nThe Authors identified novel CapEc enriched gene expression and in addition to expression analysis performed morpholino\nmediated knockdown.\nImportantly: the authors show No data of injected control Morpholinos!! which of course have to be performed as a control\n(uninjected is by no means sufficient)\nThe effects of knockdown, knockout on the developing vasculatur should be analysed at earlier stages, and especially the\nday3 analysis of vascular patterning should be shown in more detail.\nAlternative validation of the MO knockdown effects:\nWhile it might be outside of the scope of the manuscript to generate stable genetic knockout lines there have been many\nreports showing a specifically negative effect of morpholinos in the brain (usually resulting in the use of less than 1ng Mo\nand additional combination with p53 Mo). I would suggest at least a comparison with a Crispr/Cas9 transient approach to\nbypass toxicity of the Morphant approach. this would have the benefit, that the effects should not become attenuated due to\ndiminishing MO concentration\nSupplementary Figure S8\nVascular permeability shown by \"leakage\" of erythrocytes into the brain:\nthe shown phenotype is very unusual even for cerebrovascular hemorrhages, as large portions of the brain are filled with\nerythrocytes. Here the authors should perform time lapse analysis to show the influx of erythrocytes into the brain... the\nimage does not represent BBB leakage as much as a maybe never established vasculature?\nNormally if vessel maturation is perturbed, hemorrhages occur at sites of higher blood pressure, however the shown image\nis not enough to support the authors conclusions.\nSupplementary Figure S9 the 3 day images for leakage should be shown (and not only quantified), especially as showing\nthe vascular phenotype at day3 is alsoimportant\nwhat is happening to the AEC?\nthe auhtors state: Notably, CapECs, MECs, and LymECs displayed progressive downregulation of specific gene modules,\nwhereas VECs exhibited sustained transcriptional upregulation. These findings highlight subtype-specific functional\ntransitions, suggesting dynamic and coordinated regulation of endothelial specialization during cerebrovascular\ndevelopment.. what is happening to the AEC population is there no change? in any case please state...\ndisease SNP association\nIt is difficult to address the validity of the disease association using the public GWAS dataset. how high are the disease\nassociation scores? how does that compare to other known brain EC associated gene defects? is this really a meaningful\nresult? how different are the scores from non-associated genes?\nfrom my point of view, this could at max be a carefully phrased mention in the discussion but in its current form should not be\nconsidered a result.\nin your expression dataset: how high is the contamination with endocardial ECs? could you identify a small endocardial Ec\npopulation or will these ECs be \"mixed in\" into your aretrial and venous cell populations?\nSupplementary Figure S8\nVascular permeability shown by \"leakage\" of erythrocytes into the brain:\nthe shown phenotype is very unusual even for cerebrovascular hemorrhages, as large portions of the brain are filled with\nerythrocytes. Here the authors should perform time lapse analysis to show the influx of erythrocytes into the brain... the\nimage does not represent BBB leakage as much as a maybe never established vasculature?\nNormally if vessel maturation is perturbed, hemorrhages occur at sites of higher blood pressure, however the shown image\nis not enough to support the authors conclusions.\nminor comments:\nthere has been a detailed publication of the vascular anatomy of the zebrafish. It is vital to keep the existing denomiations for\nthe vessels: for example\ncerebellar central artery is abbreviated CCtA, not CeCtA\nPosterior mesencephalic central artery is abbreviated PMCtA, not PMsCtA\nplease check ALL used abbreviations against table 1 in Isogai in Isogai et al, Developmental Biology 230, 278–301 (2001)\nUsing the abbreviation Pro, Mes and Met for the brain regions does not improve readbility of the manuscript. as there are so\nmany vessel abbreviation for the reader, it would make it easier to keep the full length name.\nthe Methods are missing important information:\nThe sequences of the guide RNAs for Crispr/Cas9 mediated mosaic knockouts (Crispants) have to be given, as well as the\ninjection amounts of guide as well as Cas9 RNA.\nThe primers for the in situ hybridisation probes include no T7, T3 or Sp6 sequence... which primers were finally used, which\nenzymes, and which kit?, did you amplify from cDNA (which stage embryos or was it cDNA from FACS sorted EC´s?\nWhat concentration of EVANS blue solution was used, approximately how much was injected? (please note, that Evans\nblue binds to Albumin or similar proteins in the blood, therefore the size of the leaking product is approximaltely around\n70kDA)\nIn line 246/247: several CapEC-specific genes (cldn5a, lef1, and fabp11a, et.al) is misleading as these are not capillary\nspecific genes, but only when ECs are isolated are more highly expressed in the subpopulation\nGrammatical editing of the manuscript is necessary :\nfor example (but not limited to)\nin line 39/40: \"Dysregulation of cerebrovascular contributes to the..\"is lacking a noun after cerebrovascular.\nin line 144 \"we integrated EC population from 3, 6, and 11 dpf\" should be \"EC populations\"\nrephrase:\nin line 180 \"suggesting that tight junction is already established by 3 dpf (Figure 2J), consistent with the previous\nconclusion\" ... tight junctions are established?.\nline 257: \"At 3 dpf, both ECs were primarily distributed in the lateral PHBC and PMBC, followed by...\" should refer to both\nEC populations\nline 256: as F stands for \"Filial\" meaning offspring: there can never be a F0 generation. please elaborate in the text, what\nmethod you refer to as \"Instantaneous knockout\" (morphants or crispants?).\nThe title of figure 4 \"Mapping of different EC types in cerebrovascular network based on\nISS during zebrafish development.\" should be IISS"}, "author_response": "Point-by-point response\n(NCOMMS-25-57684)\nReviewer #1\nThis manuscript presents a well-constructed spatiotemporal atlas of cerebrovascular\ndevelopment in zebrafish that will greatly address the gap in this field. The 3D vascular\nconstruction really impressed me, as it plays a pivotal role in constructing the\nspatiotemporal map of vascular development. Also, the spatiotemporal atlas reveals the\nCapEC enrichment alternation and dynamic expression module of transporter and tight\njunction pathway. Finally, three CapEC-enriched genes were recruited to validate their\ncontribution to the establishment of the blood brain barrier. The resource is thoughtfully\ndesigned and of long-term significance to the field, qualifying the publications for\nNature Communications. Yet it remains some revise to improve clarity and\ninterpretability. Also, I have some concerns about the methods. All the comments are\nlisted below.\nResponse:\nWe are grateful to the reviewer for his/her encouraging and constructive assessment,\nparticularly their recognition of the value of our 3D vascular reconstructions and\nspatiotemporal atlas, which mirrors our own goals for this work. In response to the\nfeedback, we have thoroughly revised the manuscript to enhance clarity and have\nprovided detailed explanations and additional information to address the\nmethodological points raised. Please find our specific responses to each comment below.\nMajor comments\n1. The spatiotemporal atlas was constructed within three larval stages, which are 3, 6,\nand 11 dpf, respectively. I am wondering why the authors selected the three stages, are\nthese stages special developmental stages to the zebrafish? Is there any solid and\nreliable reference mentioning these three stages? Please cite the reference.\nResponse:\nWe thank the reviewer for this comment. The time points of 3-, 6-, and 11-days post-\nfertilization (dpf) were selected as they correspond to key stages in the development of\nthe zebrafish blood-brain barrier (BBB). As established in previous literature [1-5], the\ninitiation of BBB development begins at approximately 3 dpf and reaches maturity\naround 11 dpf. We have now included the key references in the revised manuscript\n(Line 107) to provide the necessary context and justification for our experimental\ndesign.\n2. The authors devoted considerable effort to constructing the 3D vascular atlas. They\nmeasured vascular length and segments during three stages. Are these quantitative\nmeasurements necessary? Do they help in constructing the subsequent spatiotemporal\natlas? Are there other possible quantitative measurements, I don’t know, such as vessel\nthickness? If it is feasible.\nResponse:\nWe thank the reviewer for this insightful question regarding the necessity and scope of\nour quantitative measurements. The measurements of vascular length and segment\nnumber are indeed fundamental, as they provide direct, quantitative evidence of the\nmorphological differences in cerebral vessels across stages and locations (Response\nfigures 1/figure 1B-E). These parameters served as critical benchmarks to validate the\naccuracy of our subsequent spatiotemporal atlas.\nRegarding other possible measurements, we agree that vascular thickness is a relevant\nmetric. However, as the reviewer might also consider, it is highly susceptible to\nvariations in imaging resolution and reconstruction thresholds within a dataset like ours.\nWe therefore did not deem it a reliable parameter for consistent cross-stage comparison.\nImportantly, our quantitative analysis extended beyond length and segments. As shown\nin Response figure 2, we also quantified vessel straightness from 3 to 11 dpf, providing\nan additional layer of morphological insight. We believe this suite of measurements\ncollectively offers a robust foundation for our atlas.\nResponse figure 1. A, B Quantification of the total length (A) and segment number (B) of\ncerebrovasculature in zebrafish at 3, 6, and 11 dpf. C, D Quantification of the length (C) and\nsegment number (D) of cerebrovascular subnetworks in zebrafish at 3, 6, and 11 dpf. Data were\npresented as mean ± SEM and n = 5 at each stage. The Kruskal-Wallis test revealed an overall effect\nof development stage (shown in the top, ns: no-significant, *p < 0.05, **p < 0.01, ***p < 0.001).\nSubsequent Dunn’s test was applied for post hoc multiple comparisons (showed follow the columns,\n*p < 0.05, **p < 0.01). Red asterisks indicate the vascular subnetworks exhibiting significant\ndifferences in both vessel length and segment number. Red asterisks indicate the vascular\nsubnetworks exhibiting significant differences in both vessel length and segment number.\nResponse figure 2.A Quantification of the total straightness of cerebrovasculature in zebrafish at\n3, 6, and 11 dpf. B Quantification of the straightness of cerebrovascular subnetworks in zebrafish at\n3, 6, and 11 dpf. Data were presented as mean ± SEM and n = 5 at each stage.\n3. Single-cell transcriptomic analysis identified six types of ECs. The marker gene\ninformation of these ECs should be included in the supplementary tables, such as\npseudobulk and fold change. In addition, based on the expression levels of the marker\ngenes, angiogenic ECs appear to be similar to the other ECs, which may suggest that\nthis cluster of ECs are progenitor cells of other ECs. The authors should add more\nanalysis to discuss this cluster, as they may be related to vasculogenic events.\nResponse:\nWe thank the reviewer for this insightful comment. In response, we have taken\nadditional steps to evaluate the stability and reliability of our findings. The requested\nmarker gene information, including pseudobulk expression and fold change, has now\nbeen included in the revised Supplementary Table 3.\nWe agree with the reviewer’s astute observation that angiogenic ECs (AngECs) share\ngene expression features with other subtypes, suggesting a potential progenitor role. To\ninvestigate this hypothesis directly, we performed a pseudotime trajectory analysis. The\nresults (Response figure 3A) indicate that AngECs are present throughout the entire\npseudotime trajectory, while other subtypes like capillary ECs (CapECs) emerge\npredominantly at the terminal stage (Response figure 3B). This pattern strongly\nsupports the notion that AngECs may serve as progenitor cells.\nTo further characterize the terminal cell fate, we performed an enrichment analysis on\ngenes highly expressed at the end of the trajectory (Response figure 3C). These genes\nwere significantly enriched in pathways related to substance transport, a hallmark\nfunction of mature CapECs. Additionally, pathways governing vessel morphogenesis\nand cell proliferation were also enriched, indicating ongoing angiogenic processes even\nat later stages. These new analyses collectively reinforce the progenitor-like nature of\nthe AngEC population.\nResponse figure 3. Pseudotime analysis of zebrafish brain endothelial cells. A. Single-cell\npseudotime trajectory of endothelial cells, colored by inferred pseudotime values. B. Pseudotime\ntrajectories of AEC, AngEC, CapEC, MEC, VEC. C. Functional enrichment analysis of pseudotime\nspecific expression genes.\n4. These HEGs in Figure 3A should be analyzed for their correlations across different\nspecies to verify their conservation. The results should be presented in the manuscript.\nResponse:\nIn direct response to the reviewer’s valuable suggestion, we performed a cross-species\ncorrelation analysis to assess the conservation of the HEGs identified in Figure 3A. The\nresults, now presented in Response figure 4/Figure 3A (with correlation coefficients\nto zebrafish indicated numerically), reveal significant positive correlations across\nspecies. This important finding has been incorporated into the revised Figure 3A and\nits legend, further strengthening our manuscript by underscoring the evolutionary\nconservation of these key genes.\nResponse figure 4. Comparative analysis of endothelial cell subtype-specific gene expression\npatterns between zebrafish and mammals. Correlation coefficients, quantifying the degree of\nconservation for each species pair, are listed beneath the species names.\n5. The spatial transcriptome profiles expression of 36 genes during different periods.\nWhat criteria were used to select these genes? The changes of these genes across\ndifferent stages should be present and discussed.\nResponse:\nWe thank the reviewer for raising this important point regarding gene selection. The 36\ngenes profiled were specifically chosen based on their high and specific expression\nwithin distinct endothelial cell (EC) subtypes, as identified in our single-cell RNA-seq\ndata. This criterion ensured that the spatial transcriptome analysis focused on robust,\nsubtype-specific markers to accurately characterize the spatiotemporal dynamics of the\ncerebrovasculature. The dynamic expression changes of these key genes across\ndevelopmental stages are now detailed in Response figures 5-8, and we have expanded\nthe discussion of these patterns in the revised manuscript (Lines 365-368).\nResponse figure 5. Spatial transcriptomic analysis of 36-gene expression in Arterial EC across three\ndevelopmental stages.\nResponse figure 6. Spatial transcriptomic analysis of 36-gene expression in Venous EC across three\ndevelopmental stages.\nResponse figure 7. Spatial transcriptomic analysis of 36-gene expression in Capillary EC across\nthree developmental stages.\nResponse figure 8. Spatial transcriptomic analysis of 36-gene expression in Angiogenic EC across\nthree developmental stages.\n6. This is very appealing, as the authors have generated a whole-brain transcriptome\nof endothelial cell-specific genes based on the 3D vascular network. However, for the\nvascular localization in series zebrafish sections, more detailed\nexplanations are needed. For example, the source of the vascular annotation image in Figure 4B. The\nResults section should also provide a clearer description of the relevant process and\nkey steps.\nResponse:\nWe thank the reviewer for this comment. Figure 4B aims to illustrate our pipeline for\nlocalizing genes and cells within the annotated vascular network. The specific slice\nshown was selected from serial sections of an 11 dpf zebrafish. Our methodological\nworkflow was as follows: firstly, after capturing the IISS data from these serial sections,\nwe used the spatial distribution of the vasculature from the eGFP channel to align the\n2D section with its corresponding, pre-acquired in vivo 3D cerebrovasculature map\nfrom the same animal (Figure 4B, left); subsequently, this registration allowed us to\ntransfer the vessel type annotations from the 3D atlas onto the 2D section (Figure 4B,\nmiddle); finally, based on this precise vascular annotation, we performed statistical\nanalyses of the cellular and gene information from the IISS data for each vessel type\n(Figure 4B, right). In the figure, colored curves represent blood vessels, polygons\nrepresent cells, and dots represent genes. A clearer description of this process and its\nkey steps have been revised in the Materials and Methods section on (Lines 226-233).\n7. In figure 4C, the cell proportion of AngECs in different vessels is low when compared\nto other ECs, which is inconsistent with the cell proportions from single cell analysis.\nPlease explain this discrepancy.\nResponse:\nWe appreciate the reviewer’s insightful comment, which allows us to highlight a key\nbiological insight. The apparent discrepancy in AngEC proportions between the single-\ncell and IISS datasets actually underscores the high transcriptional similarity between\nAngECs and CapECs. Our single-cell data initially revealed shared marker genes\nbetween these subtypes. The IISS technology, with its more focused gene panel, lacks\nthe resolution to perfectly distinguish these highly similar populations, leading to some\nAngECs being annotated as CapECs. This observation is consistent with recent\nliterature [6] that describes hybrid populations like “angiogenic capillary ECs”,\nsupporting the concept of a developmental continuum. This observation clarifies that\nthe differences in proportions among these endothelial subtypes should be interpreted\nas evidence of their close relationship, rather than as a contradiction.\n8. The efficiency of in-situ sequencing should be presented in the manuscript and\ndiscussing the specific advantages of performing this method for cerebral vasculature.\nResponse:\nWe thank the reviewer for this suggestion. We have now included the sequencing\nefficiency metrics for our IISS analysis in the revised manuscript (Lines 216-220, 355-\n364).\nBriefly, we performed IISS on serial sections of zebrafish embryos at 3, 6, and 11 dpf,\ntargeting a panel of 36 candidate genes. The analysis yielded robust data: at 3 dpf, 28\nslices comprising 8,769 cells and 1,970 vascular regions, with 45,993 transcript counts;\nat 6 dpf, 25 slices with 6,873 cells, 1,258 regions, and 30,919 counts; and at 11 dpf, 25\nslices with 5,425 cells, 1,924 regions, and 33,118 counts. These data demonstrate the\nhigh efficiency and depth of our spatial transcriptomic profiling.\nRegarding its specific advantages for cerebral vasculature research, IISS is uniquely\npowerful. First, its high sensitivity and specificity, achieved via rolling circle\namplification (RCA), are crucial for detecting lowly expressed regulatory genes within\nthe sparse endothelial cell population. Second, its customizable gene panel allowed us\nto focus precisely on endothelial-specific and developmentally relevant targets. Most\nimportantly, IISS provides single-cell resolution while perfectly preserving tissue\nmorphology. This is indispensable for unambiguously assigning transcriptional\nidentities to specific vessel types (e.g., arteries, capillaries, veins) within the complex,\nintricate architecture of the developing brain, which is simply not possible with bulk or\ndissociated single-cell methods.\n9. Cell denstiy in different regions of cerebral vasculature is dynamic throughout three\nstages, which is interesting to me. Whether the alternation of cell density has\nassociation with any biological functions should be explained in detail. Also,\ndetailed information of cell density to each vessel should be presented in the supplemental table.\nResponse:\nWe thank the reviewer for this insightful comment. Our analysis revealed that all\nendothelial cell (EC) subtypes initially exhibited preferential localization in the lateral\nvasculature (PMBC and PHBC). As development proceeded, there was a progressive\nspatiotemporal enrichment into intraparenchymal vessels of the mesencephalon and\nmetencephalon, including the posterior mesencephalic central artery (PMCtA) and the\nmedulla oblongata central artery (MoCtA). Concurrently, the primordial cerebral artery\n(PrCm) showed increasing arterial EC (AEC) enrichment from 6 to 11 dpf, indicating\na process of gradual arterial specialization. In contrast, the cerebellar central artery\n(CCtA) and metencephalic artery (MtA) maintained weak EC specification across all\nstages, suggesting a delayed vascular maturation that may parallel the region’s specific\nneurodevelopmental timeline. These dynamic changes in cell density reflect an\norchestrated process of spatiotemporal specialization, likely supporting the evolving\nmetabolic and functional demands of the developing brain. As requested, the detailed\ncell density data for each vessel are now provided in the revised Supplementary Table\n1.\n10. Why do not present the cell density of angEC? Is it due to the consideration of figure\narrangement or because the results are negative?\nResponse:\nWe thank the reviewer for this question. The cell density data for angiogenic ECs\n(AngECs) was not included in the main figure primarily to maintain a clear and focused\nnarrative on the major, spatially-resolved endothelial subtypes that constitute the\nmature vascular network. As our study aimed to chart the established cerebrovascular\narchitecture, the AngEC population, which we hypothesize to be a transient progenitor\nstate, was not the central focus of our spatial density analysis. Importantly, this was not\ndue to negative results; the cell density data for AngECs is available in Response figure\n9, and we confirm that their distribution is consistent with a sparse, progenitor-like\npopulation.\nResponse figure 9.A. Spatiotemporal density gradients for AngECs. Colors encode the cell density\nnormalized to the population size of the corresponding EC type. Scale bar: 100 μm. B. Heatmaps\ndisplay the density of AngECs in different blood vessels across 3, 6, and 11 dpf.\n11. The authors validated the reliability of expression patterns in spatiotemporal\ntranscriptomic data with a transgenic fluorescence zebrafish. The expression modules\npresented in the supplemental figure 6B-C should be plotted in a consistent manner.\nAnd a correlation or similarity test should also be performed.\nResponse:\nWe thank the reviewer for these constructive suggestions. We have now plotted the\nexpression modules in Supplementary Figure 6B-C in a consistent manner for direct\nvisual comparison. Furthermore, in direct response to the reviewer's recommendation,\nwe performed a correlation analysis between the two expression modules for the\nprosencephalon, mesencephalon, and metencephalon. The results show strong positive\ncorrelations, with coefficients of 0.609, 0.870, and 0.871, respectively. These new data\nare presented in Response figure 10.\nResponse figure 10. Statistics of relative expression of slc2a1a in Tg(kdrl:mCherry;slc2a1a:eGFP)\n(A) and IISS results (B) at 3, 6, and 11 dpf.\n12. In the end, the authors identified three uncharacterized zebrafish capillary-specific\ngenes (zgc:158423, slc16a1a, and si:ch73-86n18.1) from scRNA-seq analysis. But there\nare only two genes demonstrated in figure 5 and supplemental figure 9. The information\nof all three genes should be demonstrated even if the results are negative. Also, it lacks\ninformation of 11 dpf, which confused me.\nResponse:\nWe thank the reviewer for this important comment, which has helped us improve the\ncompleteness of our data presentation.\n- Characterization of all three genes: In response, we have conducted additional\nCRISPR/Cas9 experiments to ensure a consistent analysis for all three scRNA-seq-\nidentified genes (zgc:158423, slc16a1a, and cldc1). The full dataset, including BBB\nintegrity and vascular structure for all targets, is now presented in the revised Figure\n5H-K and Response figure 11.\n- Clarification on 11 dpf: We agree that the absence of the 11 dpf data required\nexplanation. We found that the BBB assay was not reliable at this late stage due to\ndecreased morpholino efficacy and the extreme technical difficulty of intravascular dye\ninjection, which could introduce artifacts. Therefore, we limited our conclusive analysis\nto the earlier time points where the assay is robust.\nResponse figure 11. A. Representative images of zebrafish cerebrovascular permeability in 3 dpf\ncontrol, slc16a1a, zgc:158423, and cldc1 crispants.Evans Blue leakage is indicated by white arrows.\nScale bar: 100 μm. N (Control) = 11, n (slc16a1a) = 14, n (zgc:158423) = 16 and n (cldc1) = 13. B.\nQuantification of whole-brain BBB permeability (leakage points) for the groups shown in (A) at 3\ndpf. C. Representative images of zebrafish cerebrovascular permeability in 6 dpf control, slc16a1a,\nzgc:158423, and cldc1 crispants. Evans Blue leakage is indicated by white arrows. Scale bar: 100\nμm. N (Control) = 13, n (slc16a1a) = 9, n (zgc:158423) = 10 and n (cldc1) = 12. D. Quantification\nof whole-brain BBB permeability (leakage points) for the groups shown in (C) at 6 dpf. Statistical\nsignificance is represented by ns: no-significant, ***p < 0.001.\nMinor comments\n1. For figure 2D, I recommend counting the number of cell clusters enriched in each\npathway, as this would make the presentation clearer and more compelling.\nResponse:\nWe have added the counting number of cell clusters enriched in each pathway as bar\nplot in the right portion of Figure 2D, as in Response figure 12.\nResponse figure 12. Dot plot shows differential transcription profile of six EC types by gathering\nimportant signaling pathways with significant enrichments.\n2. The number of significance analyses in figure 3B should remain consistent with those\nin figure 3C and 3D.\nResponse:\nWe have revised Figure 3, as in Response figure 13.\nResponse figure 13. Expression evaluation of gene modules including ABC transporters (A), SLC\ntransporters (B), and tight junction proteins (C) during cerebrovascular development between\nmouse and human. Statistical significance was assessed using Student’s t-test. Statistical\nsignificance is represented by ns: no-significant, *p < 0.05, **p < 0.01, ***p < 0.001.\n3. What does AUROC mean in the supplemental figure 4? Please explain.\nResponse:\nThanks for this comment. In the present study, MetaNeighbor, a R package, was\nemployed to evaluate the cross-species cell conservation. MetaNeighbor outputs a\nperformance score for each gene set and task. This score is the mean Area Under the\nReceiver Operator characteristic Curve (AUROC) across all folds of cross-dataset\nvalidation, and it can be interpreted as the probability that we will rank a cell of the type\nwe aim to characterize (a “positive”) higher than a cell from the out-group (a\n“negative”). Details were described by Crow M et.al. [7].\n4. Why is the vascular classification in the transcriptome less than that in the\ndeveloping 3D vasculature?\nResponse:\nWe appreciate the reviewer’s question. The difference is due to sampling limitations\nduring tissue sectioning. The dorsal vessels (D-Pr/D-MM) were partially lost due to the\nregion's raised morphology, and the CaDI could not be fully captured due to its deep\nlocation. Consequently, their transcriptomic data were excluded to maintain analytical\nrigor.\n5. The 3 dpf control zebrafish in Figure 5C missed the pro vasculatures (in yellow color).\nResponse:\nWe appreciate the reviewer’s thorough evaluation. We have carefully reviewed and\nrevised Figure 5C, ensuring that the previously missing information has been properly\nadded.\n6. Statistical analysis of methods included 4 significant thresholds, which is too much.\nThe authors should follow the standards provided by the journal.\nResponse:\nWe have uniformly applied 3 significant thresholds according to standards of the journal.\nReference\n1. Jeong, J. Y., Kwon, H. B., Ahn, J. C., Kang, D., Kwon, S. H., Park, J. A., & Kim, K.\nW. (2008). Functional and developmental analysis of the blood–brain barrier in\nzebrafish. Brain research bulletin, 75(5), 619-628.\n2. Quiñonez-Silvero, C., Hübner, K., & Herzog, W. (2020). Development of the brain\nvasculature and the blood-brain barrier in zebrafish. Developmental biology, 457(2),\n181-190.\n3. Fleming, A., Diekmann, H., & Goldsmith, P. (2013). Functional characterisation of\nthe maturation of the blood-brain barrier in larval zebrafish. PloS one, 8(10), e77548.\n4. Li, Y., Chen, T., Miao, X., Yi, X., Wang, X., Zhao, H., ... & Zheng, Y. (2017).\nZebrafish: A promising in vivo model for assessing the delivery of natural products,\nfluorescence dyes and drugs across the blood-brain barrier. Pharmacological\nresearch, 125, 246-257.\n5. Jackson, S., Meeks, C., Vezina, A., Robey, R. W., Tanner, K., & Gottesman, M. M.\n(2019). Model systems for studying the blood-brain barrier: Applications and\nchallenges. Biomaterials, 214, 119217.\n6. Wälchli, T., Ghobrial, M., Schwab, M., Takada, S., Zhong, H., Suntharalingham,\nS., ... & Radovanovic, I. (2024). Single-cell atlas of the human brain vasculature\n vasculature\nacross development, adulthood and disease. Nature, 632(8025), 603-613.\n7. Crow, M., Paul, A., Ballouz, S., Huang, Z. J., & Gillis, J. (2018). Characterizing the\nreplicability of cell types defined by single cell RNA-sequencing data using\nMetaNeighbor. Nature communications, 9(1), 884.\nReviewer #2\nThe manuscript by Li et al is a resource paper which utilizes single-cell transcriptomics,\n3D vascular reconstruction and in situ sequencing to describe zebrafish brain vascular\ndevelopment. The authors have identified 6 endothelial subtypes, which show a\nconserved transcriptional signature with mammalian cells. They also have performed\nsome functional assays, which preliminarily implicate 3 new genes in vascular\npatterning and blood-brain barrier (BBB) integrity.\nOverall, this study provides new and detailed information on brain endothelial cell\ntranscriptomics using state-of-the-art approaches, and potentially could be a very\nuseful resource for the scientific community. However, the biggest limitation is that the\nspatial transcriptomic approach using improved in situ sequencing (IISS) approach is\nnot very convincing, and the results do not convincingly demonstrate distinct spatial\nlocalization of different endothelial cell subtypes. This approach or analysis may need\nan improvement for it to be a useful resource.\nResponse:\nWe thank the reviewer for the supportive and constructive comments. In direct response\nto the main limitation noted, we have revised the manuscript to strengthen the validation\nand presentation of our spatial transcriptomic (IISS) data, providing a more convincing\nanalysis of endothelial cell subtype localization. Our point-by-point responses below\ndetail the specific changes made to address this and all other points.\nSpecific comments:\n1. As noted above, IISS approach aims to detect spatial localization of different marker\ngenes that can be assigned to distince brain endothelial cell populations. However, the\nresults in Fig. 4 show largely overlapping expression of many cell markers. For\nexample, CapEC, AEC and VECs show very similar enrichment patterns (Fig. 4D and\nE), all of them enriched in central arteries, including MoCtA. The approach fails to\ndetect distinct expression patterns, which might be expected for arterial and venous\ncells, for example. It seems that this could be an artifact of the methods used rather\nthan the true expression pattern. To verify this, the authors could perform hybridization\nchain reaction (HCR) using top marker genes for each cell population and verify if\nthese expression patterns match their IISS results.\nResponse:\nWe thank the reviewer for this insightful comment. It is noteworthy that amplification-\nbased single molecule in situ hybridization (asmFISH) provides superior spatial\nresolution (10-50 nm) through its spatially encoded probes and compatibility with\nsuper-resolution microscopy techniques. The high-resolution capability enables precise\nmapping of densely transcribed genomic loci, overcoming the inherent limitations of\nHCR, which is constrained by the optical diffraction limit (~200 nm). Therefore, we\nselected asmFISH to validate and reproduce the spatial expression patterns initially\nidentified by IISS (Figure S8). This approach ensures higher-resolution and rigorous\nverification of the original findings.\nThe results show that hybridization signals of different endothelial cell subtype markers\n(CapEC: cldn5a, VEC: stab1 and AEC: hey1) co-localize within the same vascular\nstructures, with co-expression in some regions (Response figure 14A), suggesting that\nmultiple endothelial cell subtypes may coexist within the same vascular regions.\nQuantitative analysis of inter-signal distances revealed that pairwise marker signals\nwere predominantly localized within < 10 μm, which is a spatial scale comparable to\nthe diameter of zebrafish cerebral vasculature (Response figure 14C, E). These\nfindings were corroborated using an alternative marker panel (CapEC: slc2a1a, VEC:\ndab2 and AEC: hey2), which yielded consistent co-localization and spatial distribution\npatterns (Response figure 14B, D, F). Consequently, we conclude that: (1) the IISS\ndata robustly captures the spatial organization of endothelial cells, and (2) the observed\ndistribution similarities among endothelial subtypes reflect the biological\ncharacteristics rather than methodological artifacts. We include the asmFISH analysis\nin the revised manuscript (Lines 248-252).\nResponse figure 14. A-B Representative images showing co-expression of cerebrovascular\nendothelial cell (EC) subtype-specific markers within the same vascular segment. (A) Markers:\ncldn5a (CapEC), stab2 (VEC), hey1 (AEC). (B) Markers: slc2a1a (CapEC), dab2 (VEC), hey2\n(AEC). Scale bars: 50 µm; high magnification, 10µm. C-F Quantification of the proportion of EC\nsubtypes localized within a 10 μm distance across four serial sections of the zebrafish brain.\n2. While the authors have performed very detailed description of zebrafish brain\nvascular anatomy at 3-11 dpf, this description is not entirely new and builds upon\nseveral previous studies (perhaps less detailed), such as Isogai et al, Dev Biol 2001,\nand others. When describing 3D cerebrovascular atlas, the authors need to\nacknowledge clearly what has been done in the previous studies, and how their\napproach goes beyond what has been previously described.\nResponse:\nWe thank the reviewer for this important comment. We fully agree that foundational\nstudies, such as the seminal work by Isogai et al. [1], established a critical 2D\nanatomical framework. Our study builds upon this by introducing a quantitative, 3D\nperspective. Whereas prior work primarily provided descriptive 2D atlases, we have\ngenerated a complete 3D reconstruction of the intracranial vasculature, enabling for the\nfirst time the precise quantification of dynamic changes in vascular length, segment\nnumber, and network topology. This technical advancement, powered by modern\nimaging and multidimensional processing, allows us to move beyond descriptive\nanatomy to a quantitative analysis that can be directly correlated with underlying\nmolecular events. We have revised the Discussion to explicitly acknowledge these\nfoundational studies and to clarify the specific advances our 3D atlas provides (Lines\n324-326).\n3. Please note how many endothelial cells were identified at each time point in scRNA-\nseq analysis. From the Fig. 2A it appears that there were many more cells identified at\n3 dpf stage compared to 6 and 11 dpf; it is unclear why. There may be very few cells\n(only 10-15) identified in some cell populations at 6 and 11 dpf (such as CapECs), which may limit the\ntranscriptional profile analysis; this needs to be acknowledged.\nResponse:\nWe thank the reviewer for raising this critical point regarding cell numbers. To ensure\nthe robustness of our analysis, we have performed additional single-cell RNA-seq\nexperiments for the 6 and 11 dpf stages. This has improved the cell count distribution\nacross time points and subtypes, enhancing the reliability of our transcriptional profiles.\nThe updated cell proportions are presented in Response figure 15.\nResponse figure 15. The proportion of each cell type at 3, 6, and 11 dpf.\n4. Please move the legend in Fig. 2C (the inset box which shows percent expressed and\naverage expression) outside of the data panel. It currently obstructs the view of dot plot\nexpression for MEC, VEC and AngEC populations.\nResponse:\nWe thank the reviewer for this suggestion. We have moved the legend outside the main\npanel in revised Figure 2C to provide an unobstructed view of the dot plot data, as\nshown in the updated version presented in Response figure 16/Figure 2C.\nResponse figure 16. Dot plot depicting the expression patterns of canonical markers and cell type-\nspecific genes across different EC types.\n5. It is difficult to read the gene list in Fig. S2. Either the figure resolution can be\nimproved, or the list can be presented in a separate supplementary table.\nResponse:\nWe thank the reviewer for this suggestion. To improve readability, we have moved the\ngene list from Figure S2 into a new Supplementary Table 2, which now provides the\ncorresponding gene expression levels in a clear tabular format.\n6. Please clarify how the proportion of endothelial cells in different vessels was\ndetermined in Fig. 4C. scRNA-seq analysis identified AngECs as the most abundant\ncell type, and CapEC make a relatively small proportion. However, Fig. 4C shows\nCapECs as the most abundant cell type, and there is only a small proportion of AngECs.\nWhy is there such a discrepancy between the two approaches?\nResponse:\nWe thank the reviewer for this insightful observation regarding the discrepancy in\nendothelial cell (EC) proportions. We agree that this is an important point to clarify.\nThe difference likely stems from a fundamental technical distinction between the two\nmethods. Single-cell RNA-seq provides a comprehensive transcriptomic profile,\nallowing for high-resolution clustering. In contrast, the improved in situ sequencing\n(IISS) method profiles a limited, targeted panel of genes. Our single-cell data indeed\nrevealed that AngECs and CapECs share a subset of marker genes. Consequently, in the\nIISS data where this finer transcriptional resolution is lost, a significant portion of\nAngECs is likely incorrected annotated as CapECs, leading to the observed\nunderestimation of AngECs and overestimation of CapECs in Fig. 4C. This very\nchallenge, however, reinforces their high transcriptional similarity. Supporting this\ninterpretation, a recent study [2] also describes “angiogenic capillary ECs”,\nblurring the clear distinction between these subtypes and suggesting a close developmental\nrelationship.\n7. Morpholino knockdown experiments (Fig. 5H-I and S9F-H) currently have no\nspecificity controls and show data that are very preliminary. Ideal verification would\nbe to generate genetic mutants. At the very least, the authors should show that F0\nCRISPR/Cas9 knockout of the same genes produces similar phenotypes to the\nmorpholino knockdown.\nResponse:\nWe thank the reviewer for the constructive suggestion regarding the need for genetic\nvalidation. In direct response, we have performed F0 CRISPR/Cas9 knockout\nexperiments for zgc:158423 and slc16a1a. Importantly, the crispants for both genes\nrecapitulated the pronounced blood-brain barrier (BBB) disruption phenotype that we\ninitially observed with morpholino knockdown, as demonstrated at both 3 and 6 dpf in\nResponse figure 17. This independent genetic validation strongly supports the specific\nand essential role of these genes in BBB integrity. Consequently, we have incorporated\nthese conclusive crispants data into the revised main Figure 5H-K to strengthen our\ncentral findings, while the supporting morpholino data have also been moved to the\nrevised Supplementary figure 10 for completeness.\nResponse figure 17. A. Representative images of zebrafish cerebrovascular permeability in 3 dpf\ncontrol, slc16a1a, and zgc:158423 crispants. Evans Blue leakage is indicated by white arrows. Scale\nbar: 100 μm. N (Control) = 11, n (slc16a1a) = 14, and n (zgc:158423) = 16. B. Quantification of\nwhole-brain BBB permeability (leakage points) for the groups shown in (A) at 3 dpf. C.\nRepresentative images of zebrafish cerebrovascular permeability in 6 dpf control, slc16a1a, and\nzgc:158423 crispants. Evans Blue leakage is indicated by white arrows. Scale bar: 100 μm. N\n(Control) = 13, n (slc16a1a) = 9, and n (zgc:158423) = 10. D. Quantification of whole-brain BBB\npermeability (leakage points) for the groups shown in (C) at 6 dpf. Statistical significance is\nrepresented by ns: no-significant, ***p < 0.001.\n8. The authors state that F0 knockdown of si:ch73-86n18.1 produced erythrocyte\nextravasation, arguing that this demonstrates role in BBB development (Fig. S8C). The\nresults here show a hemorrhage, likely due to vessel rupture, which is different from\nBBB formation. In defective BBB vessels may still be intact and may not necessarily\nshow hemorrhages. Please include “n”, how many embryos were analyzed and how\nmany showed hemorrhages. Also, please include in the methods the sequence of gRNA\n(or gRNAs) used for targeting, how was gRNA made (in vitro transcription or synthetic)\nand specify what was the mutagenesis success rate based on DNA sequencing,\nrestriction analysis, or another appropriate method.\nResponse:\nWe thank the reviewer for this critical insight. We agree that the observed erythrocyte\nextravasation is more consistent with vessel rupture and hemorrhage than with a\nselective impairment of the BBB. Following this important distinction, we have re-\nevaluated the phenotype and revised our interpretation throughout the manuscript.\n(1) Phenotype quantification: We provide the requested sample sizes (“n”) for the\nhemorrhage analysis. In cldc1 (formerly si:ch73-86n18.1) crispants, a subset of fish\n(8/95) exhibited significant cerebral hemorrhage, which was rare in controls (1/130)\n(Response figure 18/Supplementary figure 9C).\n(2) BBB integrity assessment: To specifically assess BBB function, we performed\ndedicated BBB integrity assays. We found no significant difference between cldc1\ncrispants and controls at 3 or 6 dpf (Figure 5H-J), confirming that Cldc1 is not essential\nfor BBB integrity.\n(3) Vascular morphology analysis: We further analyzed the vasculature and found that\ncldc1 loss led to reduced vascular straightness at 3 dpf and a significant decrease in\nvascular segments at 6 dpf (Supplementary figure 9). This suggests its role lies in\nvascular patterning and structural integrity, not in the BBB per se.\n(4) Experimental methods: As requested, the gRNA sequences are now listed in\nSupplementary Table 4. The gRNAs were synthesized via in vitro transcription by\nTsingke Biotechnology. Mutagenesis efficiency was confirmed by T7E1 assay and\nSanger sequencing, with detailed protocols provided in the Methods section (Lines 582-\n590).\nResponse figure 18.A. Brain hemorrhage induced by CRISPR/Cas9-mediated cldc1 instantaneous\nknock out at 3 dpf. Scale bars: 100 μm B. Statistical analysis of the brain hemorrhage ratio (Fisher's\nexact test). **p < 0.01.\n9. It is very difficult to see any color in Figure S7, orange, green, purple or blue.\nResponse:\nWe are grateful to the reviewer for highlighting the difficulty in distinguishing colors\nin the original Figure S7. To rectify this, we have optimized the figure for clarity by:\n(1) adopting a high-contrast color palette (e.g., red, green, blue, cyan) and enlarging the\ndata points for better visibility; and (2) generating a new high-resolution version of the\nfigure for the manuscript. For the reviewer’s convenience, the original high-resolution\nimages have also been compiled in Response figure 19 (Figure S7).\nResponse figure 19. Distribution of cerebrovascular AEC, AngEC, CapEC, VEC in serial sections\nof zebrafish at 3, 6, and 11 dpf. Scale bars: 100 µm.\nReference\n1. Isogai, S., Horiguchi, M., & Weinstein, B. M. (2001). The vascular anatomy of the\ndeveloping zebrafish: an atlas of embryonic and early larval development.\nDevelopmental biology, 230(2), 278-301.\n2. Wälchli, T., Ghobrial, M., Schwab, M., Takada, S., Zhong, H., Suntharalingham,\nS., ... & Radovanovic, I. (2024). Single-cell atlas of the human brain vasculature\nacross development, adulthood and disease. Nature, 632(8025), 603-613.\nReviewer #3\nThe authors created a detailed 3D and molecular atlas of how blood vessels develop in\nthe zebrafish brain from 3 to 11 days after fertilization. By combining state of the art\ntechniques in imaging, single-cell RNA sequencing, and spatial transcriptomics, they\nidentified different endothelial cell subtypes, tracked how the blood–brain barrier\nmatures, and discovered several previously unknown genes essential for proper\nvascular development and barrier integrity.\nThe authors deserve much credit for the excellent representation of the complicated\ndata sets in their figures regarding the vascular anatomy and their Spatiotemporal\nanalysis.\nIn general, the study is well worth publishing, however especially regarding the\nfunctional analysis of the novel BBB genes, a more detailed and better controlled\nanalysis is needed, as the functional data is a major part elevating the paper from a\ndescriptional study.\nResponse:\nWe sincerely appreciate the reviewer’s generous evaluation of our manuscript and their\nconstructive guidance. We agree that rigorous functional validation is crucial to elevate\nthe study beyond description. Accordingly, we have performed a more detailed and\ncontrolled analysis of the novel blood-brain barrier (BBB) genes, as recommended.\nThese revisions, detailed in our Point-by-point responses, have significantly\nstrengthened this key aspect of our work. We are confident that the manuscript is now\nsubstantially improved and fully addresses the reviewer’s insightful comments. \nMajor comments\n1. The Authors identified novel CapEC enriched gene expression and in addition to\nexpression analysis performed morpholino mediated knockdown.\nImportantly: the authors show No data of injected control Morpholinos!! which of\ncourse have to be performed as a control (uninjected is by no means sufficient)\nAlternative validation of the MO knockdown effects:\nWhile it might be outside of the scope of the manuscript to generate stable genetic\nknockout lines there have been many reports showing a specifically negative effect of\nmorpholinos in the brain (usually resulting in the use of less than 1ng Mo and additional\ncombination with p53 Mo). I would suggest at least a comparison with a Crispr/Cas9\ntransient approach to bypass toxicity of the Morphant approach. This would have the\nbenefit, that the effects should not become attenuated due to diminishing MO\nconcentration.\nResponse:\nWe thank the reviewer for raising these critical points regarding morpholino controls\nand the need for genetic validation. We note that a similar concern was also highlighted\nby Reviewer 2 (please see our response to Question 7 above). In response to both\nreviewers, we fully acknowledge the limitation of using ddH O as the sole injection\n2\ncontrol in our initial morpholino experiments. To directly address this issue and\nunequivocally validate our functional findings, we have performed F0 CRISPR/Cas9\nknockout experiments for zgc:158423, slc16a1a and cldc1. The crispants recapitulated\nthe pronounced BBB disruption phenotype observed in our morpholino studies at both\n3 and 6 dpf (Response figure 20). This independent genetic validation effectively\naddresses potential concerns regarding morpholino specificity and toxicity.\nConsequently, we have elevated these robust crispants data to the revised main Figure\n5H-K, while the original morpholino data have been repositioned to Supplemental\nfigure 11 for referenceand transparency.\nResponse figure 20. A. Representative images of zebrafish cerebrovascular permeability in 3 dpf\ncontrol, slc16a1a, and zgc:158423 crispants. Evans Blue leakage is indicated by white arrows. Scale\nbar: 100 μm. N (Control) = 11, n (slc16a1a) = 14, and n (zgc:158423) = 16. B. Quantification of\nwhole-brain BBB permeability (leakage points) for the groups shown in (A) at 3 dpf. C.\nRepresentative images of zebrafish cerebrovascular permeability in 6 dpf control, slc16a1a, and\nzgc:158423 crispants. Evans Blue leakage is indicated by white arrows. Scale bar: 100 μm. N\n(Control) = 13, n (slc16a1a) = 9, and n (zgc:158423) = 10. D. Quantification of whole-brain BBB\npermeability (leakage points) for the groups shown in (C) at 6 dpf. Statistical significance is\nrepresented by ns: no-significant, ***p < 0.001.\n3. The effects of knockdown, knockout on the developing vasculatur should be\nanalyzed at earlier stages, and especially the day 3 analysis of vascular patterning\nshould be shown in more detail.\nResponse:\nWe thank the reviewer for this suggestion to deepen our analysis of vascular patterning\nat earlier stages. In direct response, we have performed a more detailed quantitative\nassessment of the developing vasculature at 3 dpf. In addition to vascular length and\nsegment number, we introduced an analysis of vascular straightness as a key\nmorphological parameter. This new metric reveals subtle but significant alterations in\nvascular patterning in the knockdown/knockout models at this critical early stage. The\ncomplete dataset from 3 to 6 dpf, including this new analysis, is presented in Response\nfigure 21 and has been integrated into the revised Figure S10F–I.\nResponse figure 21. A–B. Statistics on the straightness of the whole cerebrovasculature (A) and\ndifferent types of vasculatures (B) in 3-dpf zebrafish among control, slc16a1a, zgc:158423, and\ncldc1 crispants. Data were presented as mean ± SEM, n (control) = 6, n (slc16a1a) = 6, n\n(zgc:158423) = 7, n (cldc1) = 6. C–D. Statistics on the straightness of the whole cerebrovasculature\n(C) and different types of vasculatures (D) in 6 dpf zebrafish among control, slc16a1a, zgc:158423,\nand cldc1 crispants. Data were presented as mean ± SEM, n (control) = 7, n (slc16a1a) = 5, n\n(zgc:158423) = 6, n (cldc1) = 7. *p < 0.05.\n4. Supplementary Figure S8: Vascular permeability shown by \"leakage\" of\nerythrocytes into the brain: the shown phenotype is very unusual even for\ncerebrovascular hemorrhages, as large portions of the brain are filled with erythrocytes.\nHere the authors should perform time lapse analysis to show the influx of erythrocytes\ninto the brain... the image does not represent BBB leakage as much as a maybe never\nestablished vasculature?\nNormally if vessel maturation is perturbed, hemorrhages occur at sites of higher blood pressure, however the shown image\nis not enough to support the authors conclusions.\nResponse:\nWe thank the reviewer for this critical insight. We agree that the extensive erythrocyte\ninfiltration shown in Supplementary Figure S9 is more indicative of a profound\nstructural defect in the vasculature than of selective BBB leakage.\nIn response, we conducted a systematic analysis of cldc1 crispants. We first quantified\nthe phenotype and found that a subset (8/95) indeed exhibited severe cerebral\nhemorrhage, which was rare in controls (1/130) (Response figure 22/Figure S9C).\nThis supports the reviewer's interpretation that the underlying issue may be one of failed\nvascular development or integrity.\nTo test this hypothesis directly, we performed two lines of investigation:\n(1) BBB Integrity: we confirmed that cldc1 loss does not affect selective BBB function,\nas no significant integrity defect was detected at 3 or 6 dpf.\n(2) Vascular Morphogenesis: we analyzed the vasculature itself and found clear\npatterning defects: a reduction in vascular straightness at 3 dpf, followed by a\nsignificant loss of vascular segments by 6 dpf (Response figures 21, 23, 24/Figure\n5C–G).\nCollectively, these data have led us to revise our conclusion: cldc1 is essential for proper\nvascular morphogenesis and structural integrity, rather than for the maintenance of the\nBBB.\nResponse figure 22. A. Brain hemorrhage induced byCRISPR/Cas9-mediated cldc1 instantaneous\nknock outat 3 dpf. B. statistical analysis of the brain hemorrhage ratio (Fisher’s exact test). **p <\n0.01.\nResponse figure 23. A. Represent images of 3-dpf zebrafish brain vasculature and their\nreconstruction for Control, slc16a1a, zgc:158423 and cldc1 crispants. Scale bars: 100 μm. B–C.\nStatistics on the length of the whole cerebrovasculature (B) and different types of vasculatures (C)\nin 3-dpf zebrafish. Data were presented as mean ± SEM, n (control) = 6, n (slc16a1a) = 6, n\n(zgc:158423) = 7, n (cldc1) = 6. D–E. Statistics on the total number of vascular segments (D) and\nthe number of various vascular segments (E) in 3-dpf zebrafish. Data were presented as mean ±\nSEM, n (control) = 6, n (slc16a1a) = 6, n (zgc:158423) = 7, n (cldc1) = 6. Statistical significance is\nrepresented by *p < 0.05, **p < 0.01, ***p < 0.001.\nResponse figure 24. A. Represent images of 6-dpf zebrafish brain vasculature and their\nreconstruction for Control, slc16a1a, zgc:158423 and cldc1 crispants. Scale bars: 100 μm. B–C.\nStatistics on the length of the whole cerebrovasculature (B) and different types of vasculatures (C)\nin 6-dpf zebrafish. Data were presented as mean ± SEM, n (control) = 7, n (slc16a1a) = 5, n\n(zgc:158423) = 6, n (cldc1) = 7. D–E. Statistics on the total number of vascular segments (D) and\nthe number of various vascular segments (E) in 6-dpf zebrafish. Data were presented as mean ±\nSEM, n (control) = 7, n (slc16a1a) = 5, n (zgc:158423) = 6, n (cldc1) = 7. Statistical significance is\nrepresented by *p < 0.05, **p < 0.01.\n4. Supplementary Figure S9 the 3 day images for leakage should be shown (and not\nonly quantified), especially as showing the vascular phenotype at day 3 is also\nimportant.\nResponse:\nWe agree with the reviewer that visualizing the early phenotype at 3 dpf is crucial. In\nresponse, we have now included representative images showcasing the leakage\nphenotype at this stage directly in the main text (revised Figure 5H–I) to provide\nimmediate visual context for the quantitative data. These images are also available in\nResponse figures 21 and 23/Figure 5H–I.\n5. What is happening to the AEC? The authors state: notably, CapECs, MECs, and\nLymECs displayed progressive downregulation of specific gene modules, whereas\nVECs exhibited sustained transcriptional upregulation. These findings highlight\nsubtype-specific functional transitions, suggesting dynamic and coordinated regulation\nof endothelial specialization during cerebrovascular development. What is happening\nto the AEC population is there no change? in any case please state...\nResponse:\nWe thank the reviewer for prompting us to clarify the dynamics of the AEC population.\nOur initial analysis did not highlight AECs as their transcriptional changes were less\npronounced. However, our expanded single-cell RNA-seq dataset (repeated for 6/11\ndpf) now reveals a more nuanced picture of coordinated specialization. We find that\nAECs, AngECs, and CapECs all show progressive transcriptional upregulation, while\nVECs and MECs follow distinct temporal patterns (Response figure 25/Figure S2).\nThis complete analysis, now included in the manuscript, underscores that all EC\nsubtypes, including AECs, undergo stage-specific functional transitions (Lines 155-\n158).\nResponse figure 25. Heatmap of top 25 HEGs of six EC types during the cerebrovascular\ndevelopment.\n6. Disease SNP association: it is difficult to address the validity of the disease\nassociation using the public GWAS dataset. How high are the disease association\nscores? How does that compare to other known brain EC associated gene defects? Is\nthis really a meaningful result? How different are the scores from non-associated genes?\nFrom my point of view, this could at max be a carefully phrased mention in the\ndiscussion but in its current form should not be considered a result.\nResponse:\nWe thank the reviewer for raising important questions regarding the validity and\ninterpretation of the disease SNP association analysis.\nTo assess potential links between our candidate genes and human disorders, we\nanalyzed public GWAS datasets for cerebrovascular and neurodegenerative diseases\n(Response figure 26A). In this visualization, the dot size represents the physical\ndistance from a gene's transcription start site (TSS) to a significant disease-associated\nSNP. A larger dot indicates a closer distance, and we considered genes within 10 kb of\na significant SNP as potentially implicated through linkage disequilibrium.\nFor context, we compared our candidates to known EC-related genes, finding that most\nof the latter were also associated with one or two such diseases. Furthermore, a control\nset of EC-unrelated genes tended to be located significantly farther from the disease-\nassociated SNPs (Response figure 26B), suggesting our observation is non-random.\nWe agree with the reviewer that while this in silico analysis is statistically suggestive,\nit lacks rigorous clinical validation and does not constitute a definitive result. In line\nwith the reviewer’s suggestion, we have therefore carefully rephrased this part, moving\nit from the Results to the Discussion and presenting it as a potential implication for\nfuture research rather than a firm conclusion (Lines 393-400).\nResponse figure 26. A. Association between EC-associated genes and two categories of diseases\nby measuring the distance of significant risk SNPs to the transcriptional start site. B. Distances from\nthe TSS to the significant SNPs between EC-associated and non-associated genes.\n7. In your expression dataset: how high is the contamination with endocardial ECs?\nCould you identify a small endocardial EC population or will these ECs be \"mixed in\"\ninto your arterial and venous cell populations?\nResponse:\nWe thank the reviewer for this insightful question. We acknowledge that our initial\ndataset included a population of endocardial ECs. To address this, we rigorously re-\nclustered our data using a panel of known lineage-specific markers. This process\nenabled us to clearly distinguish and subsequently re-annotate the endocardial cells,\nwhich were then excluded from the final analysis of the intracranial cerebrovascular\nsubtypes presented in the manuscript. This ensures that our reported populations are\nspecific to the brain vasculature.\nMinor comments\n1. There has been a detailed publication of the vascular anatomy of the zebrafish. It is\nvital to keep the existing denomiations for the vessels: for example cerebellar central\nartery is abbreviated CCtA, not CeCtA. Posterior mesencephalic central artery is\nabbreviated PMCtA, not PMsCtA. Please check ALL used abbreviations against table\n1 in Isogai et al, Developmental Biology 230, 278–301 (2001).\nResponse:\nWe are grateful to the reviewer for pointing out the discrepancies in our vascular\nnomenclature. We have systematically revised all vessel abbreviations throughout the\ntext and figures to conform precisely to the canonical reference (Isogai et al., 2001). A\ncomplete list of the approved abbreviations is provided in Supplementary Table 1 for\nreference.\n2. Using the abbreviation Pro, Mes and Met for the brain regions does not improve\nreadbility of the manuscript. As there are so many vessel abbreviation for the reader, it\nwould make it easier to keep the full length name.\nResponse:\nWe agree with the reviewer that minimizing abbreviations can significantly enhance\nreadability, especially given the numerous vessel acronyms already present. We have\ntherefore adopted the full terms, prosencephalon, mesencephalon, and metencephalon,\nacross the entire manuscript to ensure clarity for the reader.\n3. The Methods are missing important information: The sequences of the guide RNAs\nfor Crispr/Cas9 mediated mosaic knockouts (Crispants) have to be given, as well as the\ninjection amounts of guide as well as Cas9 RNA.\nResponse:\nWe thank the reviewer for this comment. We have now provided all requested\nmethodological details for the CRISPR/Cas9 experiments as follows:\n(1) The sequences of all guide RNAs (gRNAs) used are listed in Supplementary Table\n4.\n(2) For microinjection, a ribonucleoprotein (RNP) complex was prepared by mixing\n600 pg of Cas9 protein with 300 pg of each sgRNA.\n(3) A volume of 2 nL of the RNP mixture was injected into the one-cell stage of\nkdrl:eGFP zebrafish embryos.\n(4) Control embryos were injected with an equivalent amount of Cas9 protein alone.\nThese details have been incorporated into the Materials and Methods section to ensure\nfull reproducibility (Lines 582-590).\n4. The primers for the in situ hybridization probes include no T7, T3 or Sp6 sequence...\nwhich primers were finally used, which enzymes, and which kit?, did you amplify from\ncDNA (which stage embryos or was it cDNA from FACS sorted EC’s?\nResponse:\nWe thank the reviewer for the opportunity to clarify the probe synthesis methodology.\nWe apologize for the lack of detail in the original manuscript and have now provided a\ncomprehensive description in the Methods section (Lines 569-580). The complete\nprocedure is summarized as follows:\n1. cDNA Source: cDNA was synthesized from total RNA extracted from whole 3 dpf\nzebrafish embryos.\n2. Cloning and Verification:\n- Target gene sequences were amplified from the aforementioned cDNA using gene-\nspecific primers (sequences provided in the original response).\n- The PCR products were cloned into the pGM-T vector using the pGM-T Ligation\nKit (TIANGEN).\n- Plasmid DNA was isolated from positive clones, and the correct insert sequence\nwas confirmed by Sanger sequencing.\n3. Probe Synthesis:\n- The verified plasmids were linearized by digestion with NdeI.\n- Antisense RNA probes were synthesized by in vitro transcription using the T7 RNA\npolymerase system, with the linearized plasmids as templates.\n4. Hybridization: Whole-mount in situ hybridization was then performed following a\nstandard established protocol, and images were acquired using a Leica\nstereomicroscope (M205).\n5. What concentration of EVANS blue solution was used, approximately how much was\ninjected? (please note, that Evans blue binds to Albumin or similar proteins in the blood,\ntherefore the size of the leaking product is approximately around 70kDa).\nResponse:\nWe thank the reviewer for this comment. In our experiments, we injected 2 nL of a 0.5%\n(w/v) Evans Blue solution into the common cardinal vein (CCV) of zebrafish embryos.\nWe acknowledge the reviewer’s point that Evans Blue binds to serum albumin, forming\na complex with a molecular weight of approximately 70 kDa. This property is precisely\nwhy it serves as a well-established and validated tracer for assessing blood-brain barrier\nintegrity, as it effectively reports on the leakage of serum proteins [1, 2].\n6. In line 246/247: several CapEC-specific genes (cldn5a, lef1, and fabp11a, et.al) is\nmisleading as these are not capillary specific genes, but only when ECs are isolated are\nmore highly expressed in the subpopulation.\nResponse:\nWe thank the reviewer for this precise correction. We agree that the original phrasing\n“CapEC-specific genes” was inaccurate, as cldn5a, lef1, and fabp11a are not\nexclusively expressed in capillary ECs but are significantly enriched in this subtype\nrelative to other arterial and venous ECs upon isolation. We have revised the text in\nLines 255 and 259 to accurately describe them as “genes enriched in CapECs” to reflect\ntheir comparative expression profile correctly.\n7. Grammatical editing of the manuscript is necessary: for example (but not limited to)\nin line 39/40: “Dysregulation of cerebrovascular contributes to th.” is lacking a noun\nafter cerebrovascular.\nResponse:\nWe thank the reviewer for catching this grammatical error. We have corrected the\nsentence as suggested, and it now reads: “Dysregulation of cerebrovascular\ndevelopment contributes to the pathogenesis of diverse neurological and\ncerebrovascular pathologies...” (Lines 39-42). The manuscript has undergone thorough\ngrammatical editing to address this and similar issues throughout the text.\n8. In line 144 “we integrated EC population from 3, 6, and 11 dpf” should be“EC\npopulations”.\nResponse:\nWe thank the reviewer for this correction. The term “population” has been revised to\n“populations”, as suggested (Line 142).\n9. Rephrase: in line 180 \"suggesting that tight junction is already established by 3 dpf\n(Figure 2J), consistent with the previous conclusion\" ... tight junctions are established?\nResponse:\nWe thank the reviewer for this correction. We have revised “tight junction” to the plural\nform “tight junctions” in the revised manuscript, as suggested (Lines 176-179).\n10. Line 257: “At 3 dpf, both ECs were primarily distributed in the lateral PHBC and\nPMBC, followed by...” should refer to both EC populations.\nResponse:\nWe appreciate the reviewer’s precise feedback on the terminology. The text has been\ncorrected from “ECs” to “EC populations” accordingly (Line 270).\n11. Line 256: as F stands for “Filial” meaning offspring: there can never be a F0\ngeneration. Please elaborate in the text, what method you refer to as “Instantaneous\nknockout” (morphants or crispants?)\nResponse:\nWe thank the reviewer for correcting the inaccurate use of “F0 generation”. This term\nhas been removed from the manuscript. As the reviewer rightly points out,\n“Instantaneous knockout” refers to the CRISPR/Cas9-mediated method, and we have\nreplaced it with the precise term “crispant” throughout the revised text to avoid any\nambiguity.\n12. The title of figure 4 “mapping of different EC types in cerebrovascular network\nbased on ISS during zebrafish development.” should be IISS.\nResponse:\nWe thank the reviewer for this precise correction. The title of Figure 4 has been updated\nto “Mapping of different EC types in cerebrovascular network based on IISS during\nzebrafish development” (Line 801).\nReference\n1. Shabanzadeh, A. P., Ringuette, D., Syonov, M., Wu, Q., Tassew, N. G., Mun, E. K., ... &\nMonnier, P. P. (2025). Inhibition of proprotein convertase SKI-1 prevents blood vessel\nalteration after stroke. Nature Cardiovascular Research, 1-20.\n2. Yoon, S. H., Kim, C. Y., Lee, E., Lee, C., Lee, K. S., Lee, J., ... & Yu, J. W. (2025).\nMicroglial NLRP3-gasdermin D activation impairs blood-brain barrier integrity through\ninterleukin-1β-independent neutrophil chemotaxis upon peripheral inflammation in mice.\nNature communications, 16(1), 699.\nPoint-by-point response\n(NCOMMS-25-57684A)\nReviewer #1\nThanks for the author’s careful revision of the manuscript. I appreciate the efforts\nmade to address the first-round comments. The manuscript has improved substantially\nin clarity and scientific rigor. A few minor comments listed below still needs to be\naddressed.\nResponse\nWe are grateful for the reviewer’s encouraging comments regarding the improvements\nmade in clarity and scientific rigor following the first-round revision and appreciate\nthe reviewer’s additional minor suggestions. A point-by-point response to all\ncomments is provided below, and corresponding changes have been incorporated into\nthe revised manuscript.\n1. The spatial transcriptome analysis reveals the dynamic expression of 36 genes\nacross three stages. The findings are highly informative and of compelling\ninterpretative value. Therefore, Response figure 5-8 should be included either in the\nmain text or as supplementary information.\nResponse\nWe sincerely appreciate the reviewer’s thoughtful comment. We agree that the spatial\ntranscriptomic dynamics of the 36 genes across the three stages offer important\ninterpretative value. Following the reviewer’s suggestion, we have now included these\nfigures in the revised supplementary information (Figures S6 and S7) to improve the\ncompleteness and transparency of the presented results. We believe that this addition\nwill facilitate a more comprehensive understanding of spatial gene expression\npatterns.\n2. The cell density of angEC (response figure 9) should be included in figure 4, since\nthe distribution is consistent with a sparse, progenitor-like population.\nResponse\nWe sincerely appreciate the reviewer’s thoughtful comment. We have incorporated the\nAngEC cell density data into revised Figure 4.\n3. Line 179, “... indicating functional establishment of tight junctions by 6 dpf” is\nnot accurate, the stage of establishment could also be 5 dpf or else. I suggest revised\nit to “… indicating functional establishment of tight junctions around 6 dpf”.\nResponse\nWe appreciate the reviewer’s careful reading and agree that stating a precise day could\nbe misleading. The text has been corrected from “by 6 dpf” to “around 6 dpf” in the\nrevised manuscript (Line 182).\n4. Line 230, the reference of “this registration” is insufficiently clear. It is\nrecommended to revise it to “this 2D-3D registration.”\nResponse\nWe thank the reviewer for this correction. We have revised “this registration” to the\nplural form “this 2D-3D registration” in the revised manuscript, as suggested (Line\n237).\n5. Line 359, “allowed us” should be “allows us”, maintain consistency with the\nprevious sections.\nResponse\nWe thank the reviewer for this correction. The term “allowed us” has been revised to\n“allows us”, as suggested (Line 372).\nReviewer #2\nThe manuscript by Li et al is a resource paper which utilizes single-cell\ntranscriptomics, 3D vascular reconstruction and in situ sequencing to describe\nzebrafish brain vascular development. The authors have identified 6 endothelial\nsubtypes, which show a conserved transcriptional signature with mammalian cells.\nThey also have performed some functional assays, which preliminarily implicate 3\nnew genes in vascular patterning and blood-brain barrier (BBB) integrity. Overall,\nthis study provides new and detailed information on brain endothelial cell\ntranscriptomics using state-of-the-art approaches, and potentially could be a very\nuseful resource for the scientific community.\nThe revised manuscript has been greatly improved and the authors have\nsatisfactorily addressed most of my previous concerns. I only have two minor\ncomments, which relate to the newly obtained data.\nResponse: We sincerely thank the reviewer for the positive and encouraging\nassessment of our revised manuscript. It is pleased that the reviewer finds the study to\nbe a valuable resource for the community and appreciates the improvements made\nduring revision. Below, we provide detailed responses to the two remaining minor\ncomments related to the newly acquired data.\n1. In Fig. S8. The authors show co-expression of different EC markers. The figure title\nstates that different EC subtypes are co-located in the same vessels. Can the authors\nclarify if the staining shown in the small panels a,b,c,… corresponds to multiple\ndistinct vascular endothelial cells, or if the fluorescent RNA molecules are localized in\nthe same endothelial cell. Because this is single molecule FISH, and each subpanel\nappears to show a cros-section through a single vessel, it is likely that many of these\n“dot”like signals are single RNA molecules present in the same endothelial cells. In\nwhich case, the interpretation would be that multiple markers are co-expressed in the\nsame cells, and not that different EC subtypes are located in the same vessel.\nResponse:\nThank you for raising this point. Based on careful re-examination of the DAPI signals,\nwe have updated the figure to include longitudinally sectioned vessels (Figure\nS10/Response figure 1). The revised data clearly shows two phenotypes: (1) cells\nexpressing markers of slc2a1a, dab2, and hey2 (Response figure 1A) or cldn5a,\nstab1 and hey1 (Response figure 1B), respectively, are all located within the same\nvessel; and (2) multiple markers are indeed co-expressed within the same cells\n(indicated by the orange arrow in Response figure 1A-i and Response figure 1C-D).\nIn the light of these observations, we have revised the figure legend title to “Different\nEC cells expressing specific markers are located in the same vessels”.\nResponse figure 1. Different EC cells expressing specific markers are located in the same\nvessels\nA-B Representative images showing markers of cerebrovascular endothelial cell (EC)\nsubtype-specific expressed in distinct cells within the same blood vessel. Markers in A: slc2a1a\n(CapEC, red), dab2 (VEC, yellow), hey2 (AEC, magenta). Markers in B: cldn5a (CapEC, red),\nstab2 (VEC, yellow), hey1 (AEC, magenta). The views within the red dashed rectangle have been\nenlarged, with the vascular segments filled in cyan. The green dashed circle indicates the markers\nexpressed in the distinct cells within the same vessels. Scale bars: 100 µm; high magnification,\n25\nµm. Further magnified view in A-i was indicated by red arrow. Scale bar: 5 μm. C-D\nRepresentative images showing co-expression of cerebrovascular EC subtype-specific markers\nwithin the same vascular segment. Markers in C: cldn5a (CapEC, red), stab2 (VEC, yellow),\nhey1 (AEC, magenta). Markers in D: slc2a1a (CapEC, red), dab2 (VEC, yellow), hey2 (AEC,\nmagenta). Scale bars: 100 µm; high magnification, 10 µm. E-H Quantification of the proportion of EC\nsubtypes localized within a 10 μm distance across four serial sections of the zebrafish brain.\n2. In Fig. S8A,B it would be helpful to choose a different color instead of yellow to\nshow expression of stab1 or dab2. Because red and green overlap results in a yellow\nsignal, it is not possible to tell if yellow cells have overlap of red and green, or are\npositive for the third marker expression.\nResponse\nWe agree that using yellow to indicate expression could lead to ambiguity due to\noverlap of red and green signals. In the Response figure 1C-D (Figure S10C-D), we\nhave chosen a different color scheme to clearly separate stab1 or dab2 expression\nfrom the other markers, ensuring unambiguous visualization of co-expression\npatterns.\n#Reviewer 3\n1. In Line 291 the authors claim: \"Notably, cldc1 crispants also showed a high\nhemorrhage rate in the cerebral vasculature at 3 dpf, indica;ng its essential role in\ncerebrovascular stability (Figure S9C)\". However, figure S9C does not indicate a\nhemorrhage rate, it only shows one image and leaves out any numbers (i.e. of\nembryos analyzed, and of embryos with hemorrhage) and the reviewers response\nletter shows a quantification of roughly 8% . (cldc1 crispants: the cldc1 gene should\nbe in italics)\nResponse\nWe thank the reviewer for this important comment. This figure only presented a\nrepresentative image and did not include quantitative information regarding the\nhemorrhage rate, which could lead to ambiguity. To address this concern, we have\nnow added a quantification bar plot of hemorrhage rate in the revised Figure S11C,\nincluding both the exact embryo numbers analyzed and the percentage of embryos\nexhibiting cerebral hemorrhage. Additionally, we have corrected gene cldc1 in italics\nin the revised manuscript (Line 301).\n2. Line 293: We next assessed BBB permeability in the crispants. Evans Blue leakage\nassays revealed significantly increased extravasation in both slc16a1a and\nzgc:158423 crispants at 3 dpf, which persisted through 6 dpf. In contrast, cldc1\ncrispants exhibited no leakage at either time point, confirming intact BBB integrity. A\nreference to the respective figure is missing (figure 5 H-K?)\nResponse\nWe thank the reviewer for pointing this out. We apologize for the missing of the figure\ncitation in the original manuscript. We have now added the appropriate figure\nreference of Figure 5H-K in the revised manuscript (Line 300).\n3. As mentioned in my initial comments: \"please note, that Evans blue binds to\nAlbumin or similar proteins in the blood, therefore the size of the leaking product is\napproximately around 70 kDa\". However; the manuscript still indicates in line 184 ff,\nthat Evans Blue would represent a 961 Da permeability assay. It does so also in the\nmethod section in line 611.\nResponse\nWe sincerely appreciate the reviewer for pointing this out. We have added one\nsentence describe this phenomenon as “Since Evans Blue could bind to serum albumin,\nforming protein complex with a molecular size around 70 kDa, this indicates an immature\nBBB function of 3 to 6 dpf zebrafish.” (Lines 187-189).\n4. Figure S9: \"Green arrows highlight the expression signals in blood vessels\". the\ngreen arrows indicate some but definitely not all blood vessels, so this description is\nrather meaningless or confusing. If the authors wanted to point out specific\nexpression in a subset of blood vessels (like capillary ECs, of which caplillaries?)\nthese should be mentioned in detail. The in situ images could be larger.\nResponse\nWe agree with the reviewer’s comment. In the in situ hybridization experiment, we\nobserved expression signals of the target genes in zebrafish cerebral blood vessels and\ninitially indicated them with green arrows. To avoid potential misinterpretation, we\nhave now removed these arrows in the revised manuscript. The in situ images have\nbeen shown in enlarged version (updated Figure S11B).\n5. Methods: The used zebrafish transgenic lines are still lacking the allele numbers.\nPlease correct\nResponse\nThank you for this comment. We have added the allele numbers [Tg(kdrl:eGFP)s843,\nTg(kdrl:ras-Cherry)s916] and the origin references of these zebrafish transgenic lines\nin Methods section (Line 431).\n6. The method section (line 598) now states: \"For microinjection, the\nribonucleoprotein (RNP) complex was prepared by mixing 600 pg of Cas9 protein\nwith 800 pg of each sgRNA. 2 nL of the RNP mixture was injected into the one-cell\nstage of Tg(kdrl:eGFP) zebrafish embryos.\" As there is no mentioning in which\nvolume 600 pg of Cas9 and 800 pg sgRNA were contained, the 2 nl can not be\ncalculated into an amount injected per embryo (which would be meaningful).\nResponse\nThank you for the comments. We apologize for the ambiguity in the original\nmethodological description. To address the lack of clarity, we have revised the\nmethodological description. For microinjection, the ribonucleoprotein (RNP) complex\nwas prepared by mixing 600 ng of Cas9 protein (M0646T, NEB) with 800 ng of each\nsgRNA in a 2 μL reaction system (consisting of 0.2 μL Cas9 mRNA [3000 ng/μL\nstock], 0.8 μL sgRNA1 [1000 ng/μL stock], 0.8 μL sgRNA2 [1000 ng/μL stock] and\n0.2 μL 10×buffer). Subsequently, 2 nL of the RNP mixture was injected into one-cell\nstage Tg(kdrl:eGFP)s843 zebrafish embryos (Lines 604-609).\n7. The quantification of tracer leakage in reference 31 has been done completely\ndifferent than in the present paper, please describe correctly what was quantified how.\nThe images of the supplemental figure showing the leakage analysis are much too\nsmall.\nResponse\nWe apologize for this mistake. We have removed this citation of reference 31 and\nelaborated on the quantification for leakage in the Methods section (Lines 623-625).\nAdditionally, the images of the supplemental figure showing the leakage are now\ndisplayed in an enlarged format as Figure S14."}]

Decision letter