Ligand–Receptor Analysis of Brain Cell Type Marker Data Reveals Intricate Endothelial Interaction

Abstract

Background

Brain endothelial interaction with neurons, astrocytes, oligodendrocytes and microglial cells is critical for brain physiology; it is still far from being mapped. Understanding of the endothelial communication with other brain cell type could unravel novel insight into neurovascular homeostasis.

Purpose

This study aims to construct neurovascular interaction network, focusing on brain endothelial cell interactome using brain cell marker gene dataset and ligand–receptor (LR) pair.

Methods

We curated brain marker gene list from McKenzie et al.’s brain cell type top 1000 marker list of endothelial, microglia, astrocyte, neuron, oligodendrocyte and oligodendrocyte progenitor cell (OPC) and extracted LR interaction between them. Subsequently, using Cytoscape, endothelial cell interaction map was constructed and top interaction and hub gene were derived. Moreover, we performed Kyoto encyclopedia of genes and genomes (KEGG) pathways enrichment (p value < .1) to infer biological information hidden.

Results

Neurovascular LR interaction showed endothelial cells as the top network having 25.34% of total interaction with 176 outgoing and 171 incoming interactions. A considerable portion of signalling (11%) is involved in autocrine signalling functionally related to vascular tone, angiogenesis and others. Paracrine signalling between endothelial cells with microglia, astrocytes, neurons and OPC constituted 13.5%, 8.9%, 5.8% and 4.9% of total interactions, respectively. Functional enrichment of LR interaction in endothelial–microglia, endothelial–astrocyte and endothelial–neuron networks constitutes 49, 45 and 36 significant KEGG pathways (p value < .1) respectively. These pathways include extracellular matrix (ECM) receptor, axon guidance, chemokine, nuclear factor kappa B (NF-kB) and signalling pathways, among others. Hub gene analysis showed ITGB1 in endothelial cells, ITGA4 in microglia, NOTCH2 in astrocytes and LAMC2 in neurons having maximum interaction in the endothelial network.

Conclusion

This study recapitulated not only previously known gene interactions using a markers gene list but also identified novel interactions between endothelial and other brain cell types. In conclusion, this analysis underscores the critical role of endothelial cell interactions in brain physiology.

Keywords

Function genetics neurology physiology

Introduction

Homeostasis in the brain depends on coordinated and harmonious interactions between different cell types, such as neurons, vasculature, glial cells (microglia, astrocyte) and others.^{1, 2} Notably, the brain consists of an extensive endothelial cell network that not only extends structural support but also engages in communication with other cell types of the brain.^{3, 4} Endothelial interaction in the neurovascular unit (NVU) is important for many things, including axon guidance, neurogenesis, synaptogenesis blood–brain barrier (BBB) integrity and keeping the connection between brain activity and blood flow.^{3, 5} Neurovascular interactions are critical for neurovascular coupling (NVC) between neural activity and cerebral blood flow. Moreover, it is also associated with the formation and maintenance of the BBB.^{6, 7} Despite endothelial signalling being regarded as the central signalling axis, the molecular basis of NVU function is complex, involving multiple autocrine and paracrine endothelial signalling between multiple NVU cells, still systemic understanding of novel molecular mediators of endothelial interaction remains unknown, and has recently drowned interest in the wider scientific community.^{1, 8} Recent single-cell studies have provided insights into the interactions of brain endothelial cells in both health and disease; Walchli et al. characterised different endothelial cell clusters across developmental stages, adulthood and neuropathological conditions such as glioblastoma, meningioma and arteriovenous malformations, highlighting endothelial interactions associated with angiogenic and immune-related pathways.^{9, 10} Changes in endothelial gene expression and interactions are consistently observed across various models of BBB disruption in neurological disorders, highlighting their involvement in disease processes.¹¹ Notably, multiple studies suggest that dysfunctional endothelial cells perturb the intercellular interaction in NVU and result in various neuropathologies, including AD, VaD (Alzheimer’s disease and vascular dementia) and impaired endothelial cells play a role in the development of oedema inflammation limits migration and affects barrier function.^11–13 Thus, identifying and quantifying the intercellular signalling of endothelial cells and other cell types of NVU are pertinent to understanding the molecular basis of NVU homeostasis and its underlying pathology.¹⁴ Further, there is an incomplete understanding of feed-forward and feedback mechanisms in endothelial function and a lack of understanding of how neurovascular dysfunction causes neurodegenerative disease.¹⁵ Although several vasculature brain single-cell sequencing studies have been carried out over the past year, research on the brain vasculature at the single-cell level remains limited due to the molecular heterogeneity of endothelial cells and technical challenges, such as the low abundance of vascular cells.^16–18 Therefore, multiple new approaches and independent brain endothelial datasets analysis are necessary to better understand their function. In this context, identifying central regulatory hub genes associated with endothelial interactions with other brain cell types is also crucial to unravel mechanistic insight of brain endothelial cells. To fill this knowledge gap, it is imperative to identify cell-specific ligand–receptor (LR) interactions for the potential novel molecular players within NVU. A system-wide approach is necessary to unravel complex interactions among each cell’s role within NVU to decipher how endothelial cell-specific communication orchestrates brain homeostasis and disease.

Remarkably, the system biology approach to drawing LR networks has been instrumental in unravelling new organ functions in various studies related to the brain, heart, kidney, liver, lungs, placenta, retina and visual cortex.^19–23 Furthermore, this research has revealed novel concepts in a wide range of disease-related conditions, including aging, infections and injuries. Likely, cell–cell communication mediated by LR complex is also critical to coordinating the diverse biological processes of NVU. However, the use of this approach with brain cell type marker data has not received much attention until recently. Interestingly, cell-type marker genes are specific genes whose expression is characteristic of a particular cell type and these genes often reflect the functionality and identity of that cell type.²⁴

The emergence of RNA-seq technology has enabled the generation of brain cell-type transcriptomic data in humans and mice. This has aided in defining marker genes (genes expressed in a specific subset of cells) for each cell type in the brain.^{25, 26} Though the identification of cell type markers in the mammalian brain is dependent on the sensibility and specificity of the technology used to classify cells, despite methodological and technological limitations, there are significant opportunities to understand intercellular interactions using these data. The resource of brain cell-specific expression is an opportunity to comprehend the novel molecular characteristics of endothelial cell types.^{27, 28}

We hypothesised that enriching cell-type-specific top markers genes with the curated LR list could unravel autocrine and paracrine signalling between major cell types of NVU.²⁹ Here, we aim to utilise brain cell-type marker resources and the recently updated list of LR pairs, to enhance our understanding of the endothelial communication within the NVU and its neurophysiological significance.

Methods

Gene Expression Data Utilised for Study

We used the top 1000 genes extracted from the brain for each of the six cell types, including endothelial, astrocytes, microglia, neurons, oligodendrocytes and oligodendrocyte progenitor cell (OPC) from McKenzie et al.’s Brain Cell Type Specific Gene Expression and Co-expression Network Architectures data that provides marker gene expression data (Supplementary Table S1). This marker data set by McKenzie et al was generated by combining human gene expression datasets (GEO: GSE67835,³⁰ GSE73721)³¹ and mouse brain cell gene expression datasets (GEO: GSE52564,³² GSE60361,³³ GSE71585)³⁴ to create a list of brain cell type markers. We next sought to validate top-cell cell interacting genes from each cell type. We employed the neuroexpresso web interface, which also catalogued marker gene expression using microarray and RNA-seq databases.²⁷

Ligand–receptor Pairs Database

We used the NATMI database of LRs, which is a highly curated and filtered database,³⁵ and from Cellphone DB v2.0, PPI pairs, STRINGDB (https://stringdb.org/cgi/download.pl?sessionId=oCkT8UeKh8rN&species_text=Homo+sapiens, RNA-magnet, Single CellSignalR and ICELLNET.

Functional Annotation and Data Visualisation

LR interaction data from endothelial cells and other cell types was subjected to DAVID Bioinformatics Resources³⁶ to extract statistically significant (p value < .1) biological annotation (e.g., Gene Ontology Terms and KEGG Pathways) to infer biological information hidden. Cytoscape, a visualisation tool, was also utilised to make a LR interaction map exhibited in our dataset.³⁷ Nodes represent LRs and are coloured differently for each cell type. These entities are connected through edges representing LR interaction, and the edge length is directly proportional to the value of the LR interaction. The weight of each interaction was calculated by multiplying the expression of the LR and accordingly edge thickness was chosen. Networks of each cell type are represented as degree-sorted networks and maximum degree was also noted. A circular plot was used to illustrate intracellular interaction circularly, depicting the number of LR interactions between each brain cell type and the percentage of total LRs in each cell type.³⁸

Result

Assuming the cell markers are upregulated LRs may indicate possible intracellular interaction among endothelial cells, microglial cell, neuronal cell, astrocytes cell, oligodendroglial cell and OPCs. We aimed to utilise brain cell type marker resources and the recently updated curated set of LR pairings from connectome DB2020 to develop intercellular communication associated with the NVU.

We utilised the curated brain cell type data from ref.,²⁹ which encompasses five cell type-specific RNA expressions identified in five RNA-seq studies including human and animal brains. This dataset has six distinct cell types: astrocytic, endothelial cells, microglial cell, neuronal cell, oligodendroglial cell and OPCs. The average fold change across datasets was utilised to organise genes based on cell type enrichment and specificity. The top 1000 marker genes for each of the six cell types, derived from aggregated signatures of humans and mice, were utilised for the investigation of NVU LR interactions (Figure 1). Our analysis process revealed 348 ligands, 388 receptors and 1,101 LR interactions across the six distinct brain cell types (Figure 1A). We developed a cell–cell communication network for each brain cell type based on edge counts; each edge represents the number of LR pairs interaction. The cell–cell communication network is depicted as a heatmap, network graph and circos plot in (Figure 1B, C and D) respectively. Supplementary Table S2 includes all the LR interaction in endothelial–endothelial, endothelial–microglia, endothelial–astrocyte, endothelial–neuron, endothelial–oligodendrocyte and endothelial–OPC.

Figure 1.

The Total Number of Ligands, Receptors and Ligand–receptor Pairings Identified in the Top 1000 Dataset. Cells Expressing the Ligands Are Represented in Rows, and Cells Expressing the Receptors Are Represented in Columns. The Network-graph View Indicates the Number of Ligand–receptor Pairs Between and Within Each Cell Type. The Colour of the Edge Is Coded According to the Number of Interactions, from Green at the Lowest to Red at the Highest. The Arrows at the End of the Edge Show the Ligand–receptor direction. Circos View for the Same Network in a Circular Fashion. The Percentage of Each Cell Type’s Interaction Is Derived from the Total Ligand–receptor Interaction.

The analysis of interactions among cell types indicated that the number of outgoing interactions was predominantly influenced by endothelial cells, neurons, astrocytes, microglia, oligodendrocyte precursor cells and oligodendrocytes. The total outgoing interactions shown in parenthesis for each cell type are as follows: endothelial cell (174), microglia (148), neuron (110), astrocyte (93), oligodendrocyte precursor cell (87) and oligodendrocyte (76). The overall number of incoming interactions for each cell type is as follows: endothelial cell (171), microglia (132), astrocyte (127), neuron (103), oligodendrocyte precursor cell (63) and oligodendrocyte (60). Endothelial cells exhibit the highest level of contact with microglia (29), followed by astrocytes (26). In addition to this, neurons exhibit maximum outgoing interactions with astrocytes (25) and incoming interactions with endothelial cells (14) and oligodendrocyte precursor cells (14). Oligodendrocyte cells engage in outgoing interactions with astrocytes (19) and incoming interactions with OPCs (15).

In addition to paracrine signalling, autocrine signalling is highly substantial and robust. In autocrine signalling, microglia have the highest number of interactions (70), followed by endothelial cells (66), neurons (45) and astrocytes (26).

Notably, the total interaction abundance of endothelial cells (25.34) is much higher than that of other cells within the NVU (Figure 1E).

Supplementary Table 1 shows lists of LR pairs present in top, 1000 brain cell type marker gene list.

This investigation indicated that each cell type expresses several LRs, with endothelial cells exhibiting the highest interaction, followed by microglia, astrocytes, neurons, oligodendrocytes and OPC. It also suggested a significant potential for autocrine signalling, among brain cell types including endothelial cell. Our findings indicate that all endothelial cell types possess a substantial capacity for cell–cell communication, with other brain cell types of NVU cells. These findings need further exploration of potentially novel and under-recognised endothelial cell interaction with different cell types to elucidate a new mechanism of the NVU functional.

Table 1.

Top Ligand–receptor Endothelial Interaction with Their Function.

S.No	Inter	Gene	COM. SCORE	Function	Ref
1	EN- EN	MMRN2(EN)-CD93(EN)	11.4	This axis regulates angiogenesis process	64
		CD34(EN)-SELE(EN)	11.3	SELE role in circulating leukocytes with vascular endothelium.CD34 role in vasculogenesis, angiogenesis	33,34,35
		ANXA2(EN)-ROBO4(EN)	11.0	Maintains vascular integrity	36
		MMRN2(EN)-CLEC14A(EN)	10.4	A role in inhibiting sprouting angiogenesis	65
		COL4A1(EN)-ITGA1(EN)	10.4	Extra-cellular matrix	66,67
2	EN-MIC	ICAM2(EN)-ITGAM(MIC)	18.2	ICAM-2 is involved in lymphocyte extravasation and ITGAM regulates the release of key inflammatory cytokines	40,41
		ICAM2(EN)-ITGB2(MIC)	15.8	Energy metabolism, angiogenesis and cytoskeleton regulation.	42
		IL1A(MIC)-IL1R1(EN)	15.2	Neuroinflammation.	43
		C1QA(MIC)-CD93(EN)	15.0	For myelination in central nervous system induction of myelination	68
		IL1B(MIC)-IL1R1(EN)	9.3	Increased permeability, inflammation	69
3	EN-AST	TNC(AST)-PTPRB(EN)	8.7	TNC is biomarker of cerebrovascular and neurodegenerative diseases. PTPRB is implicated in regulating vascular permeability during inflammatory response	46
		BGN(EN)-FGFR3(AST)	7.2	BGN is involved in neuroprotection and FGFR3 influences neurogenesis.	47,70
		ANXA1(EN)-EGFR(AST)	6.4	Maintenance of the BBB integrity	71
		FN1(EN)-SDC2(AST)	5.7	SDC2 induces maturation of neuronal dendritic spine	72
		EDN3(EN)-EDNRB(AST)	5.6	Maintains vascular homeostasis	73
4	EN-NEU	RELN(NEU)-LRP8(EN)	6.7	Through LRP8, Reelin signalling modulates synaptic plasticity as well as learning and memory. RELN also plays a role in controlling dendritic spine	50,51
		IGF1(NEU)-IGF1R(EN)	4.9	IGF1 with IGF1R involved in neurogenesis, myelination and neuronal survival	74
		EFNA1(EN)-EPHA6(NEU)	3.7	Regulation of neuronal and spine morphology	52
		RELN(NEU)-ITGB1(EN)	3.4	Embryonic neuronal migration to spine formation in the adult brain	50
		EFNA1(EN)-EPHA7(NEU)	3.3	Neuronal migration, axon guidance and synaptic formation	53
5	EN-OL	FN1(EN)-MAG(OL)	24.6	BBB maintaining, and synaptic integrity	56
		TF(OL)-TFRC(EN)	5.3	Iron homeostasis, myelination and neurotransmission	54
		ECM1(EN)-ITGB4(OL)	5.1	This axis is important for proliferation of OPC	75
		MATN1(OL)-ITGA1(EN)	4.5	Matn1 inhibits angiogenesis	76
		ANGPT4(OL)-TEK(EN)	1.9	ANGPT4 is a regulator of endothelial barrier integrity, and it might exert a protective effect during ischemic stroke	77
6	EN-OP	FN1(EN)-ITGB8(OP)	4.1	FN1 plays important role in neurite outgrowth	60
		COL4A1(EN)-ITGB8(OP)	2.3	COL4A1 mutants lead to haemorrhagic stroke	78
		DLL3(OP)-NOTCH4(EN)	2.3	Neurogenesis and maintenance of neural stem cell	61
		SEMA3D(OP)-NRP1(EN)	1.8	Sema3d for vascular development. NRP1for vascular permeability	62,79
		FN1(EN)-ITGA9(OP)	1.8	Cell adhesion molecule/BBB maintenance	80

Note: NO: Number; INTER: Interaction; COM.SCORE: Communication score; Ref: References; EN: Endothelial cell; MIC: Microglia; AST: Astrocyte; NEU: Neuron; OL: Oligodendrocyte; OP: Oligodendrocyte progenitor cell.

As shown that endothelial cells possess the highest transmission capability among brain cell types and to better understand potential communication between endothelial cells within the NVU, we constructed a cell–cell communication network featuring LR pairs in source and target brain cell type along with weighted edges representing communication score (Figure 2). We filtered LR interactions to develop a network having a communication score exceeding 0.5 only, evaluated by the product of the log fold changes of LR pairs across all cell types.

Figure 2.

The Putative Paracrine and Autocrine Ligand–receptor Signalling Networks of Endothelial Cells with Microglia, Astrocytes, Neurons, Oligodendrocytes and OPC. Network Nodes Indicate Ligand or Receptor Pairs, Differentially Coloured for Each Cell Type. Arrows Show Signals from Ligands to Receptors. Only Ligand–receptor Interactions Equal to or Greater Than 0.5 Weight Are Used for Interaction Analysis.

We also conducted KEGG pathway enrichment analysis with the DAVID bioinformatics tool on LR pairs identified between endothelial cells and various cell types to elucidate the functionality of each cell–cell communication. Moreover, we analysed network topological properties like the degree to find the top interacting pathway and hub gene in each interaction. Supplementary Table S3 includes a list of enriched significant KEGG pathways in each interaction depicted in Figure 3. The top interacting LR of endothelial cells with other cell types based on communication score is tabulated in Table 1, and the following section includes details of endothelial autocrine and paracrine interactions.

Figure 3.

The Bar Graph Shows the KEGG Pathways That Were Significantly Enriched After Multiple Testing Adjustments (p < .1) in the Identified Ligand–receptor Pair of (A) Endothelial–endothelial and (B) endothelial–microglia. (C) Endothelial–astrocytes, (D) neuronal endothelial–endothelial oligo, (E) endothelial OPC, (F) endothelial OPC, (F) endothelial OPC and (F) endothelial. The Y-axis Indicates the Total Number of Ligands or Receptors Modulated in Each Pathway in Each Group. For Pathway Analysis, Only Ligand–receptor Interactions Equal or Greater Than 0.5 Weight Are Used.

Endothelial–endothelial Interaction Network and Functional Annotation

The endothelial cell–cell communication network demonstrated significant autocrine signalling between endothelial LRs, with a total of 66 distinct connections. The pathways markedly enriched in LR pairings include PI3K–Akt signalling, focal adhesion, Rap1 signalling and ECM interaction pathways (Figure 3A). The top interaction based on the LR communication score in endothelial cells suggests pathways and interactions potentially implicated in angiogenesis and homeostasis. The most potent (indicated by communication score) LR interaction includes ligand multimerin 2 (MMRN2), a pan-endothelial extracellular matrix protein and its receptor cluster of differentiation 93 (CD93), which is functionally associated with endothelial cell adhesion, migration and in vitro angiogenesis.³⁹ The endothelial cells also exhibit elevated expression of ligands encoding a cluster of differentiation 34 (CD34) a transmembrane glycoprotein, and hallmark of hematopoietic progenitor cells, as well as ANXA2, which maintain vascular integrity bind with the receptors prominently expressed in endothelial cells, such as SELE which plays role in circulating leukocytes and ROBO4 for maintain vascular integrity.^40–43 Among the endothelium receptors, ITGB1 exhibits the important hub having multiple endothelial ligands like fibronectin 1 (FN1), COL4A1 involved in inflammation.⁴⁴ Furthermore, ITGA4 is a hub gene for many ligand on endothelial cell like SPON2, CXCL12 and VCAM-1 recruiting endothelial progenitor cells from the bone marrow to nascent blood arteries, and it facilitates their adhesion to VCAM-1 in endothelial as well as mural cells within these vessels.^{45, 46}

Endothelial–microglia Interaction Network and Functional Annotation

The paracrine signalling between endothelial cells and microglia demonstrates 72 LR interaction enriched in the cytokinin–cytokinin receptor interaction and chemokine signalling pathway. Additional enhanced pathways encompass different inflammation-related pathways, including nuclear factor kappa B (NF-kB), toll-like receptor (TLR) signalling, complement and coagulation cascades and hematopoietic cell lineage (Figure 3B). Interactions with higher communication scores include intercellular adhesion molecule 2 (ICAM2) an endothelial ligand and bind with integrin alpha M and beta 2 (ITGAM), (ITGB2) microglial receptor, known for angiogenesis, and cytoskeleton regulation, and also interleukin-1 (IL-1), microglial ligand and interleukin 1 receptor type 1(IL-1R1) endothelial receptor involved in neuroinflammation.^47–50 Other interaction of cytokinin receptors includes several endothelium ligands, including chemokine ligands 21 and 19 (CCL21, CCL19) c-x-c motif chemokine 12,2 and 3 (CXCL12, CXCL2, CXCL3), as well as microglial receptors like as c-c chemokine receptor type 7(CCR7) c-x-c chemokine receptor type 3 and 2 (CXCR3, CXCR2,) x-c chemokine receptor 1(XCR1). Moreover, microglial transforming growth factor beta 1(TGFB1) ligand and its endothelium receptors transforming growth factor beta receptor 3 (TGFBR3) endoglin (ENG) and activin A receptor like type 1(ACVRL1) associated with cell proliferation, angiogenesis are also present.^{51, 52} In this paracrine signaling network, ITGA4 serves as a central hub among endothelial receptors, binding to various microglial ligands such as SPP1, ADAM28, and CD14. Moreover, ITGB3 acts as a key hub gene for microglial receptors, interacting with endothelial ligands including FN1, NID1, and TGM2. While the significance of inflammation in endothelial–microglial interactions is well recognised, the role of these LR interactions in brain homeostasis and NVC remains inadequately understood.

Endothelial–astrocyte Interaction Network and Functional Annotation

Signals from endothelial cells to astrocytes included 52 LR which are enriched for proteins associated with structural stabilisation, and many pathways and related enriched in LR interaction include ECM receptor interaction, focal adhesion signalling, BBB maintenance and are related to CBF regulation (Figure 3C). The interaction with the highest communication score involves biglycan (BGN), an endothelial ligand that binds to the fibroblast growth factor receptor 3 (FGFR3) on astrocytes, a pairing known for its neuroprotective effects. Additionally, the astrocytic ligand tenascin C (TNC) interacts with the endothelial receptor protein tyrosine phosphatase receptor type B (PTPRB). Notably, TNC serves as a biomarker for neurodegenerative diseases, while PTPRB plays a key role in regulating vascular permeability.^{53, 54} Astrocytes are also a essential for the maintaining cerebral blood flow, as demonstrated by several key molecular interactions. These include the binding of endothelin 1 and 3 to endothelin receptor type B (EDN1,3–EDNRB), and interactions related to the blood-brain barrier (BBB) such as platelet-derived growth factor D with its receptor PDGFRβ (PDGFD–PDGFRβ), and norrin with frizzled class receptor 4 (NDP–FZD4). Our analysis is also on the involvement of ligand vascular endothelial growth factor A(VEGFA) on astrocytes and its interaction with the endothelial receptors neuropilin 1 (NRP1) involved in neurodevelopment, fms-related tyrosine kinase 1(FLT1) and kinase insert domain receptor (KDR), known to modulate BBB permeability and encourage the structural rearrangement of endothelial cell.^{55, 56} Furthermore, the LR interaction identified NOTCH2 is a central hub for an astrocytic receptor bind with various endothelial ligands jagged-1 (JAG1), jagged-2 (JAG2) and delta-4 (DLL4).

This signalling axis also suggests a potential role for endothelial cells in promoting astrocyte development.

Endothelial–Neuronal Interaction Network and Functional Annotation

The molecular interaction of the neuronal and vascular LR network indicates 28 enrichment of axon guidance-related gene sets. In addition to axon guidance, other pathways include Rap1 signalling, the Hippo signalling pathway and long-term depression, among others (Figure 3D). The topmost interaction based on communication score involves neuronal ligand reelin (RELN), an extracellular matrix glycoprotein that regulates neuronal migration, along with its endothelial receptor (ITGB1) and the low-density lipoprotein receptor-related protein 8 (LRP8) receptor indicates involvement in synaptic plasticity.^{57, 58} The recognition of endothelial–neuronal connection offers a novel insight into the vascular regulation of axon guidance signals through SEMA4A, NRP1 and EPHA2. Furthermore, the finding of endothelial ligand ephrin A1 (EFNA1) emerged as a central hub among outgoing endothelial ligands, binding to various receptors such as neuronal eph receptor A6, 7, 5 and 3 (EPHA6, EPHA7, EPHA5 and EPHA3) involved in neuronal migration, synaptic formation and axon guidence.^{59, 60} These receptors constitute the largest subfamily of receptor protein–tyrosine kinases and are associated with axon guidance.

Endothelial–Oligodendrocyte Interaction Network and Functional Annotation

The 27 connections between oligodendrocytes with endothelial cells are primarily facilitated by the RAP1 signalling pathway, MAPK signalling, focal adhesion, ECM and RAS signalling pathway among other (Figure 3E). The topmost and strongest interaction of endothelial–oligodendrocyte involves ligand FN1 on endothelial cell and receptor myelin-associated glycoprotein (MAG) on oligodendrocyte and the transferrin (TF) ligand in oligodendrocytes and the transferrin receptor 1 (TFRC) in endothelial cells. Notably, FN1 serves as an attachment surface for immune cells and plays a crucial role in cell adhesion and migration. Additionally, it facilitates the secretion of transferrin (TF), a key regulator of iron homeostasis, which is essential for myelin formation in oligodendrocytes.^61–63 Notably, other interaction involved engagement of ligand matrilin 1 (MATN1), angiopoietin 4 (ANGPT4) on oligodendrocyte which interacts with receptor (ITGA1) andTEK receptor tyrosin kinase (TEK) on endothelial cell. ANGPT4 protects blood vessel integrity and cerebrovascular protection in various disease model.⁶⁴ Moreover, gene LR like MATN1 an outgoing oligodendrocyte ligand, neurogenic locus notch homolog protein 3 (NOTCH3) an incoming endothelial receptor, is a central hub for oligodendrocyte and endothelial interaction. (MATN1) is an adaptor protein of cartilage extracellular matrix and NOTCH3 maintains blood vessel integrity.^{65, 66} The complex interaction between oligodendrocytes and endothelial cells underscores the importance of vascular support in maintaining brain homeostasis and function.

Endothelial–Oligodendrocyte Progenitor Cell Interaction Network and Functional Annotation

The interaction between OPC and endothelial cells involved 35 LR interactions that encompass various classes of vascular remodelling pathways, including ECM interactions and focal adhesion (Figure 3F). Among these, the highest communication score interactions involve endothelial ligands, including collagen type 4-alpha1 (COL4A1) and fibronectin 1 (FN1). These ligands interacts with integrin 8 (ITGB8) receptors on oligodendrocyte precursor cells (OPCs) and are involved in neurite outgrowth, respectively.⁶⁷ Furthermore, we also noted some interaction includes semaphorin 3D (SEMA3AD) on OPC with NRP1 on endothelial cell, which is involved in vascular development and DLL3 an OPC ligand, bind with NOTCH4, an endothelial receptor involves in neurogenesis.^{68, 69} This relationship reinforces the concepts of the oligovascular niche, wherein endothelial cells signal to OPCs, promoting their proliferation, while OPCs also emit signals that may aid in vascular remodelling. In the interaction of endothelial cell to OPC network analysis also shows ITGB1 is central hub for incoming endothelial receptor bind with OPC’s ligand metalloproteinase 9 and 12 (ADAM9, ADAM12), versican (VCAN) having role in neural development, synaptic plasticity and neurodegeneration.⁷⁰

We further validated the cell-specific markers for each cell type utilising Neuroexpresso, a database containing brain cell type-specific gene expression profiles derived from publicly available microarray and RNA sequencing data. This database independently validates the marker genes used in our analysis, demonstrating robust interactions among ICAM2 (endothelial), ITGAM (microglia), IL1A (microglia), IL1R1 (endothelial), BGN (endothelial), FGFR3 (astrocyte), TNC (astrocyte), PTPRB (endothelial), EFNA1 (endothelial), EPHA6 (neuronal), RELN (neuronal), and LRP8 (endothelial). These genes are enriched in the same cell types as those identified in the brain cell-specific marker gene list reported by B.O. Mancarci et al. (Figure 4A–L).

Figure 4.

NeuroExpresso Confirms Marker Gene Expression Patterns of Ligand–receptor Obtained from Top Weighted Ligand–receptor Interaction in (A) Endothelial Ligand–microglia Receptor, (B) Microglia Ligand–endothelial Receptor, (C) Endothelial Ligand–astrocytes Receptor, (D) Astrocytes Ligand–endothelial Receptor, (E) Endothelial Ligand–neuron receptor, (F) Neuron Ligand–endothelial Receptor from the Cortex of Each Points Indicates Expression in the Microarray or RNA seq Dataset Curated in This Software.

Consequently, these LR interaction networks elucidate the extent, orientation and intricacy of connections among neurovascular cells in both homeostasis and injury. Analysing communications between endothelial networks with microglia, astrocytes, neurons and oligodendrocytes with endothelial cells revealed varied biological processes pertinent to their functions. These data indicate that numerous multicellular mechanisms are in operation. Figure 5 summarises the endothelial network along with top interaction between other brain cell types.

Figure 5.

Endothelial Autocrine and Paracrine Ligand–receptor Interactions Within the Neurovascular Unit. Top Ligand–receptors Interaction Having High Communication Scores (2.3–24.2) Is Shown in Each Interaction. E. CELL: Endothelial Cell, OPC: Oligodendrocyte Progenitor Cell.

Discussion

This study highlighted the role of endothelial interaction with other key brain cells, which are essential for comprehending neurovascular physiology. Our findings indicate that the majority of interactions were previously unrecognised in NVU physiology while also reiterating established relationships and neurobiological functional categories crucial for homeostasis.

A considerable percentage of this endothelial interaction is involved in autocrine signalling, as receptors and ligands are expressed in endothelial cell. Significant endothelial autocrine signalling encompassed HIF-1 signalling, which is crucial for angiogenesis, vascular tone regulation, adhesion control and leukocyte recruitment to the vessel wall, particularly through its effects on endothelial cells and autocrine signalling.^71–73

Significant autocrine interactions of endothelial cells include VEGF-FLT1, ANGPT2-TEK and EphA2-EFNA1, having a role in angiogenesis and BBB maintenance. Another autocrine axis identified in our study involves MMRN2-CD93 and ANXA2-ROBO4. These interactions play crucial roles in key endothelial functions, including promoting cell survival during hypoxia, supporting angiogenesis, maintaining cerebrovascular integrity, and preserving hematopoietic stem cells.^74–76

Identifying the interaction of autocrine signalling loops is essential for grasping NVU physiology and advantageous for maintaining brain homeostasis. Moreover, the bulk of ligands synthesised by cells are also recognised to interact with receptors present on adjacent cells, serving as paracrine signals.

Our analysis indicates that a significant fraction of endothelial paracrine intracellular connections involves the extracellular matrix and focal adhesion. Matrix receptors have two major functions: Initially, they regulate signalling pathways, enabling cells to adapt to alterations in the microenvironment. Second, they serve as a physical connection between the extracellular matrix and the cytoskeleton, anchoring cells and regulating their movement. The pervasive occurrence of ECM LR interactions inside the NVU facilitates essential cellular adhesion, linking the extracellular matrix to the cytoskeleton, influencing cell morphology and polarity and transmitting extracellular signals to the cytoplasm.⁷⁷

Furthermore, diverse extracellular matrix components in the proximity to the cells constituting the BBB within the brain microvasculature serve as an optimal mechanism for modulating the dynamic alterations in BBB activities.⁷⁸ This is evidenced in multiple stroke models by reduced ECM LRs, including laminin, FN and collagen IV which correlate with heightened BBB permeability. The connections between cells and the matrix of the BBB are facilitated by two principal receptors/adhesion proteins: dystroglycan and integrins. Integrins are receptors for the extracellular matrix that are crucial for brain development and function. They play significant roles in axon guidance, synaptogenesis and synaptic plasticity.^{78, 79} Dystroglycan comprises a highly glycosylated extracellular alpha component and a transmembrane beta-subunit having a role in dendritic morphology and BBB.

In addition, the presence of focal adhesion in all the paracrine and autocrine signalling in the network also shows how vascular wall constituents detect external biochemical and biophysical signals like shear stress, angiogenic signals and proinflammatory stimuli. Focal adhesion involved in bidirectional signalling detects any change in circulation and transmits to alter the extracellular matrix and adjacent cells and tissue.

Our data indicate that endothelial–microglial LR interactions occur in a significant fraction of NVU intercellular signalling, corroborating recent research indicating microglia regulate brain microvasculature and cerebral blood flow and facilitate the repair of the damaged BBB. The functional role of purinergic receptor P2RY12 on microglia, found in our investigation, is thoroughly described in a recent publication. This study demonstrated that microglia rapidly migrate to the site of capillary injury to repair the compromised BBB, dependent on P2RY12 signalling.⁸⁰ In P2RY12-/- mice, there was a reduction in capillary-associated microglia density, along with increased baseline cerebral blood flow and a diminished response to carbon dioxide inhalation challenges. These findings indicate a hitherto unrecognised role of microglia in the control of capillary vascular tone.⁸¹ Furthermore, recent studies highlight the significance of endothelial–microglial interactions, with inflammation-mediated cross-talk between microglia and endothelial cells implicated in various pathological conditions.⁸² Many cerebrovascular disorders (stroke, AD, multiple sclerosis, VaD, transient ischaemic attack) involve perturbation of microglial-specific pathways like PI3k-AKT signalling, IL-17 signalling pathways, TLR signalling pathway, NF-kβ pathway and chemokine signalling pathways, caused by inflammation. Another paracrine signalling assessing the functional significance of the diversity within neural and vascular LR networks, together with their associated downstream signalling cascades, is crucial for comprehending neurovascular interactions in the brain. Moreover, endothelial cell–neuron interactions are now recognised in neurogenesis, and neuroprotection regulates the BBB.^{83, 84} EPHA2/ephrin and semaphoring–plexin signalling are two examples of pathways associated with neuron and vascular interaction associated with synaptic plasticity; these factors also regulate vascular integrity at the BBB.^{60, 85} Our analysis found several LR interactions between the endothelium and neurons related to axon guidance, RAP1 signalling and the RAS signalling pathway.

Our analysis has also curated multiple astrocyte endothelial interactions. Astrocytes maintain close association with both endothelium and neurons. Astrocytes predominantly envelop vascular cells and maintain close interaction with neurons. This structural characteristic allows astrocytes to facilitate bi-directional communication between neurons and blood vessels.^{86, 87}

Given that astrocytes develop simultaneously with brain vasculature and envelop capillaries throughout the brain, regulation of astrocyte function by endothelial cells is both physically and physiologically feasible having roles in BBB maintenance.^{87, 88}

Recent evidence implies that cerebral endothelial cell and oligodendrocyte lineage cells interact to maintain white matter homeostasis.⁸⁹

Our analysis further identified a significant role for the interactions between oligodendrocytes and endothelial cells, as well as between OPCs and endothelial cells. This relationship reinforces the concepts of the oligovascular niche, wherein endothelial cells signal to OPCs, perhaps promoting their proliferation, while OPCs also emit signals that may aid in vascular remodelling. Endothelial proteins stimulate OPC migration and proliferation in vitro.

NVC dysregulation occurs in multiple cerebrovascular diseases such as AD, stroke, VaD and multiple sclerosis but remains unidentified. Our analysis indicates several endothelial cell interactions that can be explored in the disease context. Moreover, investigating endothelial-specific intracellular LR interaction is significant for formulating novel treatment strategies for cerebrovascular disease. Our study has certain limitations, like the determination of cell-type enrichment robustness is complex, given the brain’s intricate neuronal systems that may be compromised during separation. Moreover, endothelial cell are also heterogenous and there is the absence of pericyte, other endothelial subtype in the marker list; thus, this study cannot be regarded as the complete spectrum of signalling interaction in endothelial cell. Consequently, it is imperative to experimentally investigate these interactions through cell type-specific loss or gain of function studies, imaging and special transcriptomics.

Conclusion

Our findings offer a comprehensive map of autocrine, paracrine and intercellular signalling of endothelial cells. Analysis also indicates that the majority of interactions were previously unrecognised in NVU physiology, while also reiterating established relationships and neurobiological functional categories crucial for homeostasis. Investigating these endothelial-specific intracellular LR interaction is significant for formulating novel treatment strategies for cerebrovascular disease. To gain further insight, endothelial-specific interactions are required to be validated through functional studies, explored in a disease context using animal models, organoid or three-dimensional (3D) cell cultures. Despite methodological and technological limitations, current analysis could serve as a significant resource for investigating the complexity of brain endothelial cell types, its function and relevance in several neurological disorders.

Footnotes

Acknowledgement

The authors would like to thank School of Life Science and Biotechnology, CSJMU, for their technical supporting.

Authors’ Contribution

GK conceived the work.

GK and AM helped in data curation and methodology and writing—original draft of the manuscript.

Statement of Ethics

Ethical permission was not required for this article, as it is a research study that analyses data from previous articles rather than directly involving patients.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The author thankfully acknowledges funding support from CSJM University CV Raman grant (CSJMU/CV/12/2022).

ICMJE Statement

This article complies with the ICMJE guidelines.

Patient Consent

As this research article analyses data exclusively from previous studies and does not involve direct patient interaction, consent was not applicable.

Supplemental Material

ORCID iD

Gaurav Kumar

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