Single cell spatial biology over developmental time can decipher pediatric brain pathologies.

Pediatric low grade brain tumors and neurodevelopmental disorders share proteins, signaling pathways, and networks. They also share germline mutations and an impaired prenatal differentiation origin. They may differ in the timing of the events and proliferation. We suggest that their pivotal distinct, albeit partially overlapping, outcomes relate to the cell states, which depend on their spatial location, and timing of gene expression during brain development. These attributes are crucial as the brain develops sequentially, and single-cell spatial organization influences cell state, thus function. Our underlying premise is that the root cause in neurodevelopmental disorders and pediatric tumors is impaired prenatal differentiation. Data related to pediatric brain tumors, neurodevelopmental disorders, brain cell (sub)types, locations, and timing of expression in the developing brain are scant. However, emerging single cell technologies, including transcriptomic, spatial biology, spatial high-resolution imaging performed over the brain developmental time, could be transformational in deciphering brain pathologies thereby pharmacology.

Single-Cell RNA Sequencing Reveals the Spatial Heterogeneity and Functional Alteration of Endothelial Cells in Chronic Hepatitis B Infection.

Chronic Hepatitis B virus (CHB) infection is a global health challenge, causing damage ranging from hepatitis to cirrhosis and hepatocellular carcinoma. In our study, single-cell RNA sequencing (scRNA-seq) analysis was performed in livers from mice models with chronic inflammation induced by CHB infection and we found that endothelial cells (ECs) exhibited the largest number of differentially expressed genes (DEGs) among all ten cell types. NF-κB signaling was activated in ECs to induce cell dysfunction and subsequent hepatic inflammation, which might be mediated by the interaction of macrophage-derived and cholangiocyte-derived VISFATIN/Nampt signaling. Moreover, we divided ECs into three subclusters, including periportal ECs (EC_Z1), midzonal ECs (EC_Z2), and pericentral ECs (EC_Z3) according to hepatic zonation. Functional analysis suggested that pericentral ECs and midzonal ECs, instead of periportal ECs, were more vulnerable to HBV infection, as the VISFATIN/Nampt- NF-κB axis was mainly altered in these two subpopulations. Interestingly, pericentral ECs showed increasing communication with macrophages and cholangiocytes via the Nampt-Insr and Nampt-Itga5/Itgb1 axis upon CHB infection, which contribute to angiogenesis and vascular capillarization. Additionally, ECs, especially pericentral ECs, showed a close connection with nature killer (NK) cells and T cells via the Cxcl6-Cxcr6 axis, which is involved in shaping the microenvironment in CHB mice livers. Thus, our study described the heterogeneity and functional alterations of three subclusters in ECs. We revealed the potential role of VISFATIN/Nampt signaling in modulating ECs characteristics and related hepatic inflammation, and EC-derived chemokine Cxcl16 in shaping NK and T cell recruitment, providing key insights into the multifunctionality of ECs in CHB-associated pathologies.

Opportunities and Challenges in Advancing Plant Research with Single-cell Omics.

Plants possess diverse cell types and intricate regulatory mechanisms to adapt to the ever-changing environment of nature. Various strategies have been employed to study cell types and their developmental progressions, including single-cell sequencing methods which provide high-dimensional catalogs to address biological concerns. In recent years, single-cell sequencing technologies in transcriptomics, epigenomics, proteomics, metabolomics, and spatial transcriptomics have been increasingly used in plant science to reveal intricate biological relationships at the single-cell level. However, the application of single-cell technologies to plants is more limited due to the challenges posed by cell structure. This review outlines the advancements in single-cell omics technologies, their implications in plant systems, future research applications, and the challenges of single-cell omics in plant systems.

Neoadjuvant PARPi or chemotherapy in ovarian cancer informs targeting effector Treg cells for homologous-recombination-deficient tumors.

Homologous recombination deficiency (HRD) is prevalent in cancer, sensitizing tumor cells to poly (ADP-ribose) polymerase (PARP) inhibition. However, the impact of HRD and related therapies on the tumor microenvironment (TME) remains elusive. Our study generates single-cell gene expression and T cell receptor profiles, along with validatory multimodal datasets from >100 high-grade serous ovarian cancer (HGSOC) samples, primarily from a phase II clinical trial (NCT04507841). Neoadjuvant monotherapy with the PARP inhibitor (PARPi) niraparib achieves impressive 62.5% and 73.6% response rates per RECIST v.1.1 and GCIG CA125, respectively. We identify effector regulatory T cells (eTregs) as key responders to HRD and neoadjuvant therapies, co-occurring with other tumor-reactive T cells, particularly terminally exhausted CD8+ T cells (Tex). TME-wide interferon signaling correlates with cancer cells upregulating MHC class II and co-inhibitory ligands, potentially driving Treg and Tex fates. Depleting eTregs in HRD mouse models, with or without PARP inhibition, significantly suppresses tumor growth without observable toxicities, underscoring the potential of eTreg-focused therapeutics for HGSOC and other HRD-related tumors.

scDrug+: predicting drug-responses using single-cell transcriptomics and molecular structure.

Predicting drug responses based on individual transcriptomic profiles holds promise for refining prognosis and advancing precision medicine. Although many studies have endeavored to predict the responses of known drugs to novel transcriptomic profiles, research into predicting responses for newly discovered drugs remains sparse. In this study, we introduce scDrug+, a comprehensive pipeline that seamlessly integrates single-cell analysis with drug-response prediction. Importantly, scDrug+ is equipped to predict the response of new drugs by analyzing their molecular structures. The open-source tool is available as a Docker container, ensuring ease of deployment and reproducibility. It can be accessed at https://github.com/ailabstw/scDrugplus.

A single-cell transcriptomic study of heterogeneity in human embryonic tanycytes.

Disruptions in energy homeostasis can lead to diseases like obesity and diabetes, affecting millions of people each year. Tanycytes, the adult stem cells in the hypothalamus, play crucial roles in assisting hypothalamic neurons in maintaining energy balance. Although tanycytes have been extensively studied in rodents, our understanding of human tanycytes remains limited. In this study, we utilized single-cell transcriptomics data to explore the heterogeneity of human embryonic tanycytes, investigate their gene regulatory networks, analyze their intercellular communication, and examine their developmental trajectory. Our analysis revealed the presence of two clusters of ß tanycytes and three clusters of α tanycytes in our dataset. Surprisingly, human embryonic tanycytes displayed significant similarities to mouse tanycytes in terms of marker gene expression and transcription factor activities. Trajectory analysis indicated that α tanycytes were the first to be generated, giving rise to ß tanycytes in a dorsal-ventral direction along the third ventricle. Furthermore, our CellChat analyses demonstrated that tanycytes generated earlier along the developmental lineages exhibited increased intercellular communication compared to those generated later. In summary, we have thoroughly characterized the heterogeneity of human embryonic tanycytes from various angles. We are confident that our findings will serve as a foundation for future research on human tanycytes.

Spatial heterogeneity and functional zonation of living tissues and organs in situ.

In most organs, resources such as nutrients, oxygen, and physiologically active substances are unevenly supplied within the tissue spaces. Consequently, different tissue functions are exhibited in each space. This spatial heterogeneity of tissue environments arises depending on the spatial arrangement of nutrient vessels and functional vessels, leading to continuous changes in the metabolic states and functions of various cell types from regions proximal to these vessels to distant regions. This phenomenon is referred to as "zonation". Traditional analytical methods have made it difficult to investigate this zonation in detail. However, recent advancements in intravital imaging, spatial transcriptomics, and single-cell transcriptomics technologies have facilitated the discovery of "zones" in various organs and elucidated their physiological roles. Here, we outline the spatial differences in the immune system within each zone of organs. This information provides a deeper understanding of organs' immune systems.

Single Cell Transcriptome Analysis During Development in Dictyostelium.

Dictyostelium represents a stripped-down model for understanding how cells make decisions during development. The complete life cycle takes around a day and the fully differentiated structure is composed of only two major cell types. With this apparent reduction in "complexity," single cell transcriptomics has proven to be a valuable tool in defining the features of developmental transitions and cell fate separation events, even providing causal information on how mechanisms of gene expression can feed into cell decision-making. These scientific outputs have been strongly facilitated by the ease of non-disruptive single cell isolation-allowing access to more physiological measures of transcript levels. In addition, the limited number of cell states during development allows the use of more straightforward analysis tools for handling the ensuing large datasets, which provides enhanced confidence in inferences made from the data. In this chapter, we will outline the approaches we have used for handling Dictyostelium single cell transcriptomic data, illustrating how these approaches have contributed to our understanding of cell decision-making during development.

An integrated approach identifies the molecular underpinnings of murine anterior visceral endoderm migration.

The anterior visceral endoderm (AVE) differs from the surrounding visceral endoderm (VE) in its migratory behavior and ability to restrict primitive streak formation to the opposite side of the mouse embryo. To characterize the molecular bases for the unique properties of the AVE, we combined single-cell RNA sequencing of the VE prior to and during AVE migration with phosphoproteomics, high-resolution live-imaging, and short-term lineage labeling and intervention. This identified the transient nature of the AVE with attenuation of "anteriorizing" gene expression as cells migrate and the emergence of heterogeneities in transcriptional states relative to the AVE's position. Using cell communication analysis, we identified the requirement of semaphorin signaling for normal AVE migration. Lattice light-sheet microscopy showed that Sema6D mutants have abnormalities in basal projections and migration speed. These findings point to a tight coupling between transcriptional state and position of the AVE and identify molecular controllers of AVE migration.

Single-cell and spatial transcriptomics reveal a high glycolysis B cell and tumor-associated macrophages cluster correlated with poor prognosis and exhausted immune microenvironment in diffuse large B-cell lymphoma.

BACKGROUND: Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous malignancy characterized by varied responses to treatment and prognoses. Understanding the metabolic characteristics driving DLBCL progression is crucial for developing personalized therapies. METHODS: This study utilized multiple omics technologies including single-cell transcriptomics (n = 5), bulk transcriptomics (n = 966), spatial transcriptomics (n = 10), immunohistochemistry (n = 34), multiple immunofluorescence (n = 20) and to elucidate the metabolic features of highly malignant DLBCL cells and tumor-associated macrophages (TAMs), along with their associated tumor microenvironment. Metabolic pathway analysis facilitated by scMetabolism, and integrated analysis via hdWGCNA, identified glycolysis genes correlating with malignancy, and the prognostic value of glycolysis genes (STMN1, ENO1, PKM, and CDK1) and TAMs were verified. RESULTS: High-glycolysis malignant DLBCL tissues exhibited an immunosuppressive microenvironment characterized by abundant IFN_TAMs (CD68+CXCL10+PD-L1+) and diminished CD8+ T cell infiltration. Glycolysis genes were positively correlated with malignancy degree. IFN_TAMs exhibited high glycolysis activity and closely communicating with high-malignancy DLBCL cells identified within datasets. The glycolysis score, evaluated by seven genes, emerged as an independent prognostic factor (HR = 1.796, 95% CI: 1.077-2.995, p = 0.025 and HR = 2.631, 95% CI: 1.207-5.735, p = 0.015) along with IFN_TAMs were positively correlated with poor survival (p < 0.05) in DLBCL. Immunohistochemical validation of glycolysis markers (STMN1, ENO1, PKM, and CDK1) and multiple immunofluorescence validation of IFN_TAMs underscored their prognostic value (p < 0.05) in DLBCL. CONCLUSIONS: This study underscores the significance of glycolysis in tumor progression and modulation of the immune microenvironment. The identified glycolysis genes and IFN_TAMs represent potential prognostic markers and therapeutic targets in DLBCL.

Machine learning and related approaches in transcriptomics.

Data acquisition for transcriptomic studies used to be the bottleneck in the transcriptomic analytical pipeline. However, recent developments in transcriptome profiling technologies have increased researchers' ability to obtain data, resulting in a shift in focus to data analysis. Incorporating machine learning to traditional analytical methods allows the possibility of handling larger volumes of complex data more efficiently. Many bioinformaticians, especially those unfamiliar with ML in the study of human transcriptomics and complex biological systems, face a significant barrier stemming from their limited awareness of the current landscape of ML utilisation in this field. To address this gap, this review endeavours to introduce those individuals to the general types of ML, followed by a comprehensive range of more specific techniques, demonstrated through examples of their incorporation into analytical pipelines for human transcriptome investigations. Important computational aspects such as data pre-processing, task formulation, results (performance of ML models), and validation methods are encompassed. In hope of better practical relevance, there is a strong focus on studies published within the last five years, almost exclusively examining human transcriptomes, with outcomes compared with standard non-ML tools.

Determining cellular lineage directed networks in hematopoiesis using single-cell transcriptomic data and volatility-constrained correlation.

Single-cell transcriptome sequencing (scRNA-seq) has revolutionized our understanding of cellular processes by enabling the analysis of expression profiles at an individual cell level. This technology has shown promise in uncovering new cell types, gene functions, cell differentiation, and trajectory inference through the study of various biological processes, such as hematopoiesis. Recent scRNA-seq analysis of mouse bone marrow cells has provided a network model of hematopoietic lineage. However, all data analyses have predicted undirected network maps for the associated cell trajectories. Moreover, the debate regarding the origin of basophil cells still persists. In this work, we apply the Volatility Constrained (VC) correlation method to predict not only the network structure but also the causality or directionality between the cell types present in the hematopoietic process. Our findings suggest a dual origin of basophils, from both granulocyte/macrophage and erythrocyte progenitors, the latter being a trajectory less explored in previous research. The proposed approach and predictions may assist in developing a complete hematopoietic process map, impacting our understanding of hematopoiesis and providing a robust directional network framework for further biomedical research.

Systematic evaluation with practical guidelines for single-cell and spatially resolved transcriptomics data simulation under multiple scenarios.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) and spatially resolved transcriptomics (SRT) have led to groundbreaking advancements in life sciences. To develop bioinformatics tools for scRNA-seq and SRT data and perform unbiased benchmarks, data simulation has been widely adopted by providing explicit ground truth and generating customized datasets. However, the performance of simulation methods under multiple scenarios has not been comprehensively assessed, making it challenging to choose suitable methods without practical guidelines. RESULTS: We systematically evaluated 49 simulation methods developed for scRNA-seq and/or SRT data in terms of accuracy, functionality, scalability, and usability using 152 reference datasets derived from 24 platforms. SRTsim, scDesign3, ZINB-WaVE, and scDesign2 have the best accuracy performance across various platforms. Unexpectedly, some methods tailored to scRNA-seq data have potential compatibility for simulating SRT data. Lun, SPARSim, and scDesign3-tree outperform other methods under corresponding simulation scenarios. Phenopath, Lun, Simple, and MFA yield high scalability scores but they cannot generate realistic simulated data. Users should consider the trade-offs between method accuracy and scalability (or functionality) when making decisions. Additionally, execution errors are mainly caused by failed parameter estimations and appearance of missing or infinite values in calculations. We provide practical guidelines for method selection, a standard pipeline Simpipe ( https://github.com/duohongrui/simpipe ; https://doi.org/10.5281/zenodo.11178409 ), and an online tool Simsite ( https://www.ciblab.net/software/simshiny/ ) for data simulation. CONCLUSIONS: No method performs best on all criteria, thus a good-yet-not-the-best method is recommended if it solves problems effectively and reasonably. Our comprehensive work provides crucial insights for developers on modeling gene expression data and fosters the simulation process for users.

A deep learning method for classification of HNSCC and HPV patients using single-cell transcriptomics.

Background: Head and Neck Squamous Cell Carcinoma (HNSCC) is the seventh most highly prevalent cancer type worldwide. Early detection of HNSCC is one of the important challenges in managing the treatment of the cancer patients. Existing techniques for detecting HNSCC are costly, expensive, and invasive in nature. Methods: In this study, we aimed to address this issue by developing classification models using machine learning and deep learning techniques, focusing on single-cell transcriptomics to distinguish between HNSCC and normal samples. Furthermore, we built models to classify HNSCC samples into HPV-positive (HPV+) and HPV-negative (HPV-) categories. In this study, we have used GSE181919 dataset, we have extracted 20 primary cancer (HNSCC) samples, and 9 normal tissues samples. The primary cancer samples contained 13 HPV- and 7 HPV+ samples. The models developed in this study have been trained on 80% of the dataset and validated on the remaining 20%. To develop an efficient model, we performed feature selection using mRMR method to shortlist a small number of genes from a plethora of genes. We also performed Gene Ontology (GO) enrichment analysis on the 100 shortlisted genes. Results: Artificial Neural Network based model trained on 100 genes outperformed the other classifiers with an AUROC of 0.91 for HNSCC classification for the validation set. The same algorithm achieved an AUROC of 0.83 for the classification of HPV+ and HPV- patients on the validation set. In GO enrichment analysis, it was found that most genes were involved in binding and catalytic activities. Conclusion: A software package has been developed in Python which allows users to identify HNSCC in patients along with their HPV status. It is available at https://webs.iiitd.edu.in/raghava/hnscpred/.

Ly6Chi Monocytes Are Metabolically Reprogrammed in the Blood during Inflammatory Stimulation and Require Intact OxPhos for Chemotaxis and Monocyte to Macrophage Differentiation.

Acute inflammation is a rapid and dynamic process involving the recruitment and activation of multiple cell types in a coordinated and precise manner. Here, we investigate the origin and transcriptional reprogramming of monocytes using a model of acute inflammation, zymosan-induced peritonitis. Monocyte trafficking and adoptive transfer experiments confirmed that monocytes undergo rapid phenotypic change as they exit the blood and give rise to monocyte-derived macrophages that persist during the resolution of inflammation. Single-cell transcriptomics revealed significant heterogeneity within the surface marker-defined CD11b+Ly6G-Ly6Chi monocyte populations within the blood and at the site of inflammation. We show that two major transcriptional reprogramming events occur during the initial six hours of Ly6Chi monocyte mobilisation, one in the blood priming monocytes for migration and a second at the site of inflammation. Pathway analysis revealed an important role for oxidative phosphorylation (OxPhos) during both these reprogramming events. Experimentally, we demonstrate that OxPhos via the intact mitochondrial electron transport chain is essential for murine and human monocyte chemotaxis. Moreover, OxPhos is needed for monocyte-to-macrophage differentiation and macrophage M(IL-4) polarisation. These new findings from transcriptional profiling open up the possibility that shifting monocyte metabolic capacity towards OxPhos could facilitate enhanced macrophage M2-like polarisation to aid inflammation resolution and tissue repair.

Single-Nucleus RNA-Seq Reveals Spermatogonial Stem Cell Developmental Pattern in Shaziling Pigs.

Normal testicular development ensures the process of spermatogenesis, which is a complex biological process. The sustained high productivity of spermatogenesis throughout life is predominantly attributable to the constant proliferation and differentiation of spermatogonial stem cells (SSCs). The self-renewal and differentiation processes of SSCs are strictly regulated by the SSC niche. Therefore, understanding the developmental pattern of SSCs is crucial for spermatogenesis. The Shaziling pig is a medium-sized indigenous pig breed originating from central China. It is renowned for its superior meat quality and early male sexual maturity. The spermatogenic ability of the boars is of great economic importance to the pig industry. To investigate testicular development, particularly the pattern of SSC development in Shaziling pigs, we used single-cell transcriptomics to identify gene expression patterns in 82,027 individual cells from nine Shaziling pig testes at three key postnatal developmental stages. We generated an unbiased cell developmental atlas of Shaziling pig testicular tissues. We elucidated the complex processes involved in the development of SSCs within their niche in the Shaziling pig. Specifically, we identified potential marker genes and cellular signaling pathways that regulate SSC self-renewal and maintenance. Additionally, we proposed potential novel marker genes for SSCs that could be used for SSC isolation and sorting in Shaziling pigs. Furthermore, by immunofluorescence staining of testicular tissues of different developmental ages using marker proteins (UCHL1 and KIT), the developmental pattern of the spermatogonia of Shaziling pigs was intensively studied. Our research enhances the comprehension of the development of SSCs and provides a valuable reference for breeding Shaziling pigs.

Single-cell transcriptomic profiling reveals decreased ER protein Reticulon3 drives the progression of renal fibrosis.

Chronic kidney disease (CKD) poses a significant global health dilemma, emerging from complex causes. Although our prior research has indicated that a deficiency in Reticulon-3 (RTN3) accelerates renal disease progression, a thorough examination of RTN3 on kidney function and pathology remains underexplored. To address this critical need, we generated Rtn3-null mice to study the consequences of RTN3 protein deficiency on CKD. Single-cell transcriptomic analyses were performed on 47,885 cells from the renal cortex of both healthy and Rtn3-null mice, enabling us to compare spatial architectures and expression profiles across 14 distinct cell types. Our analysis revealed that RTN3 deficiency leads to significant alterations in the spatial organization and gene expression profiles of renal cells, reflecting CKD pathology. Specifically, RTN3 deficiency was associated with Lars2 overexpression, which in turn caused mitochondrial dysfunction and increased reactive oxygen species levels. This shift induced a transition in renal epithelial cells from a functional state to a fibrogenic state, thus promoting renal fibrosis. Additionally, RTN3 deficiency was found to drive the endothelial-to-mesenchymal transition process and disrupt cell-cell communication, further exacerbating renal fibrosis. Immunohistochemistry and Western-Blot techniques were used to validate these observations, reinforcing the critical role of RTN3 in CKD pathogenesis. The deficiency of RTN3 protein in CKD leads to profound changes in cellular architecture and molecular profiles. Our work seeks to elevate the understanding of RTN3's role in CKD's narrative and position it as a promising therapeutic contender.

Novel cell states arise in embryonic cells devoid of key reprogramming factors.

The capacity for embryonic cells to differentiate relies on a large-scale reprogramming of the oocyte and sperm nucleus into a transient totipotent state. In zebrafish, this reprogramming step is achieved by the pioneer factors Nanog, Pou5f3, and Sox19b (NPS). Yet, it remains unclear whether cells lacking this reprogramming step are directed towards wild type states or towards novel developmental canals in the Waddington landscape of embryonic development. Here we investigate the developmental fate of embryonic cells mutant for NPS by analyzing their single-cell gene expression profiles. We find that cells lacking the first developmental reprogramming steps can acquire distinct cell states. These states are manifested by gene expression modules that result from a failure of nuclear reprogramming, the persistence of the maternal program, and the activation of somatic compensatory programs. As a result, most mutant cells follow new developmental canals and acquire new mixed cell states in development. In contrast, a group of mutant cells acquire primordial germ cell-like states, suggesting that NPS-dependent reprogramming is dispensable for these cell states. Together, these results demonstrate that developmental reprogramming after fertilization is required to differentiate most canonical developmental programs, and loss of the transient totipotent state canalizes embryonic cells into new developmental states in vivo.

Single-Cell Transcriptomic Profiling Unveils Dynamic Immune Cell Responses during Haemonchus contortus Infection.

BACKGROUND: Haemonchus contortus is a parasite widely distributed in tropical, subtropical, and warm temperate regions, causing significant economic losses in the livestock industry worldwide. However, little is known about the genetics of H. contortus resistance in livestock. In this study, we monitor the dynamic immune cell responses in diverse peripheral blood mononuclear cells (PBMCs) during H. contortus infection in goats through single-cell RNA sequencing (scRNA-Seq) analysis. METHODS AND RESULTS: A total of four Boer goats, two goats with oral infection with the L3 larvae of H. contortus and two healthy goats as controls, were used in the animal test. The infection model in goats was established and validated by the fecal egg count (FEC) test and qPCR analysis of the gene expression of IL-5 and IL-6. Using scRNA-Seq, we identified seven cell types, including T cells, monocytes, natural killer cells, B cells, and dendritic cells with distinct gene expression signatures. After identifying cell subpopulations of differentially expressed genes (DEGs) in the case and control groups, we observed the upregulation of multiple inflammation-associated genes, including NFKBIA and NFKBID. Kyoto Encyclopedia of the Genome (KEGG) enrichment analysis revealed significant enrichment of NOD-like receptor pathways and Th1/Th2 cell differentiation signaling pathways in CD4 T cells DEGs. Furthermore, the analysis of ligand-receptor interaction networks showed a more active state of cellular communication in the PBMCs from the case group, and the inflammatory response associated MIF-(CD74 + CXCR4) ligand receptor complex was significantly more activated in the case group, suggesting a potential inflammatory response. CONCLUSIONS: Our study preliminarily revealed transcriptomic profiling characterizing the cell type specific mechanisms in host PBMCs at the single-cell level during H. contortus infection.

Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids.

The human blood-brain barrier (hBBB) is a highly specialized structure that regulates passage across blood and central nervous system (CNS) compartments. Despite its critical physiological role, there are no reliable in vitro models that can mimic hBBB development and function. Here, we constructed hBBB assembloids from brain and blood vessel organoids derived from human pluripotent stem cells. We validated the acquisition of blood-brain barrier (BBB)-specific molecular, cellular, transcriptomic, and functional characteristics and uncovered an extensive neuro-vascular crosstalk with a spatial pattern within hBBB assembloids. When we used patient-derived hBBB assembloids to model cerebral cavernous malformations (CCMs), we found that these assembloids recapitulated the cavernoma anatomy and BBB breakdown observed in patients. Upon comparison of phenotypes and transcriptome between patient-derived hBBB assembloids and primary human cavernoma tissues, we uncovered CCM-related molecular and cellular alterations. Taken together, we report hBBB assembloids that mimic the core properties of the hBBB and identify a potentially underlying cause of CCMs.