Single‐cell and spatial transcriptomics reveal ACKR1+ endothelial cells associated with transcytosis in Leprosy

Single‐cell and spatial transcriptomics reveal ACKR1+ endothelial cells associated with transcytosis in Leprosy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Single‐cell and spatial transcriptomics reveal ACKR1+ endothelial cells associated with transcytosis in Leprosy Heloisa Almeida Freitas, Mikael Nikson Vilela Tenório da Paz, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3829511/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Leprosy exhibits a dysregulated immune response, leading to excessive and inefficient inflammatory action. This can result in nerve damage, granuloma formation, and other clinical manifestations. Methods Single-cell RNA-sequencing (scRNA-seq) and spatial transcriptomic analyses were applied to explore the intricate cellular landscape of leprosy, focusing on endothelial cells. The study encompasses a dataset of 36,517 cells obtained from normal skin, clinical form lepromatous leprosy (LL), and reversal reaction (RR). We applied advanced techniques, including pseudotime trajectory analysis, cell–cell interaction studies, and high-dimensional weighted gene co-expression network analysis (hdWGCNA). Results The profiling of cellular composition revealed significant disparities among leprosy types, emphasizing the role of specific cell types in each condition. CellChat and hdWGCNA analysis unveiled intricate intercellular interactions in the leprosy microenvironment, with a focus on the ACKR1 gene-mediated cytokine regulation in endothelial cells. Disease-associated endothelial cells highlighted a unique gene signature associated with vesicle-related processes, suggesting their involvement in vascular alterations in leprosy. Spatial transcriptome profiling in normal skin and leprosy sections provided insights into the heterogeneity of parenchyma cells, with distinct clusters observed in lepromatous leprosy. ACKR1 exhibited high expression in regions enriched with endothelial cells only in lepromatous leprosy, indicating a localized mechanism for cytokine regulation. Conclusions The central involvement of ACKR1 + endothelial cells in transcytosis and cytokine regulation provides potential avenues for therapeutic exploration. This study underscores the importance of advanced technologies in comprehending immune microenvironments for targeted interventions in leprosy and related infectious diseases. Endothelial Heterogeneity Cytokine Signaling Disease-associated endothelial cells Granuloma Dynamics Vesicle formation Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Leprosy is a chronic infectious disease caused by the intracellular bacillus Mycobacterium leprae . Regardless of the significant reduction in prevalence after the widespread use of multidrug therapy, the incidence rates have stabilized in recent decades and leprosy remains endemic in several localized regions, such as Brazil, China, and India [ 1 ]. Importantly, the disease can cause deformities and irreversible physical disabilities, and it is still considered a public health concern. Considering its clinical aspects, leprosy stands as a compelling paradigm for elucidating the intricacies of the human immune response to intracellular bacteria, manifesting itself across a diverse clinical and immunological spectrum [ 2 , 3 ]. Central to this complex interplay is the granuloma, a chronic inflammatory structure formed through immune-mediated recruitment of white blood cells, primarily enriched with macrophages. Within the realm of infectious diseases, granulomas play a pivotal role in sequestering and degrading microbial pathogens that manage to resist the initial immune response [ 4 ]. Examining the granuloma structure across the leprosy spectrum reveals distinct patterns. Granulomas exhibit a core of mature macrophages, occasional multinucleated giant cells, and a well-organized mantle zone at the periphery containing lymphocytes, all marked by fibrosis [ 4 ]. RR lesions granulomas display intercellular edema. In contrast, LL granulomas are disorganized, prominently featuring immature lipid-laden foamy macrophages and scattered lymphocytes. A crucial aspect of the immune response in leprosy involves the role of endothelial cells, crucial in orchestrating inflammation and immune cell recruitment [ 5 ]. The gene ACKR1 (Atypical Chemokine Receptor 1) emerges as a significant player in this scenario. Expressed on both erythrocytes and endothelial cells, ACKR1 binds to inflammatory chemokines, regulating their bioavailability. Notably, endothelial ACKR1 has been implicated in vesicle formation and the induction of granuloma formation [ 6 ], critical mechanisms in the immune response to M. leprae . This dual role of ACKR1 underscores its intricate involvement in shaping the immune microenvironment during leprosy. Recent studies utilizing scRNA-seq analyses have markedly advanced our understanding of leprosy immunopathology. Investigations into human leprosy and associated cellular components are scarce, primarily due to inherent clinical, logistical, and technical challenges in collecting and profiling leprosy at the single-cell level [ 7 ]. Prior studies have predominantly relied on experimental mouse models or bulk analyses of human diseases [ 8 , 9 ]. Despite advances in understanding leprosy tissues, comprehensive insights into the intricacies of leprosy niches and the functional programs of cells in leprosy warrant further exploration. Likewise, the integration of advanced technologies like scRNA-seq holds promise in unraveling intricate cellular communications within the leprosy microenvironment, providing a foundation for enhanced strategies in surveillance, diagnosis, and treatment [ 7 , 10 ]. The progress in scRNA-seq technology has significantly propelled our understanding of the leprosy microenvironment at a single-cell resolution. Notable studies have illuminated the dynamics of stromal and immune cells contributing to a pro-leprotic and immune-suppressive microenvironment in various leprosy types, underscoring the significance of single-cell technology in deciphering leprosy complexities [ 7 , 10 ]. Despite these advances, the application of scRNA-seq in leprosy remains limited due to challenges in obtaining clinical samples. In this study, we undertake a comprehensive analysis employing scRNA-seq to investigate the intricate cellular landscape of leprosy. Our study encompasses a diverse dataset of 36,517 cells obtained from normal skin and human leprosy specimens, reversal reactions (RR) and disseminated lepromatous leprosy (LL). Furthermore, we aim to elucidate cell responses and characterize the dynamics of human leprosy, particularly focusing on endothelial cells. Through integrative analyses, including pseudotime trajectory analysis and cell-cell interaction studies, our overarching goal is to identify biomarkers, therapeutic targets, and key regulatory pathways, thereby contributing to an enhanced understanding of leprosy and improving diagnostic and treatment strategies. Methods Single cell RNA-seq Data Processed scRNA-seq data and annotation tables were downloaded from the GEO database. We performed an extensive analysis of the scRNA-seq data of normal skin (GSE130973) [ 11 ], reversal reactions (RR), a dynamic process in which some patients with disseminated lepromatous leprosy (L-lep) transition towards self-limiting tuberculoid leprosy (GSE151528) [ 5 ]. Separately, count matrices from the selected samples were introduced into R (4.3.2) and converted to a Seurat object using the Seurat package (4.4.0) [ 12 ]. Cell-Cell Interaction Analysis CellChat R package provides a comprehensive toolkit for analyzing and visualizing cell-cell communication networks in scRNA-seq data. CellChat R package provides a powerful set of tools for analyzing and visualizing cell-cell communication networks in scRNA-seq data. By following this methodology, researchers can gain new insights into the complex cellular interactions that underlie many biological processes [ 13 ]. High-dimensional WGCNA (hdWGCNA) The workflow for conducting high-dimensional Weighted Gene expression network Analysis (hdWGCNA) in R was performed with the “hdWGCNA” package. Briefly, the hdWGCNA pipeline involves the following steps: data preprocessing, gene network construction, module identification, module preservation analysis, and functional enrichment analysis. In the first step, gene expression data are preprocessed to remove noise and batch effects. In the second step, a gene co-expression network is constructed based on pairwise correlations among genes. In the third step, modules or clusters of highly correlated genes are identified, and the module eigengenes are calculated. In the fourth step, module preservation analysis is performed to assess the robustness of the identified modules. Finally, in the fifth step, functional enrichment analysis is carried out to identify the biological processes and pathways that are associated with the modules [ 14 ]. Pseudotime analysis The pseudotime dynamics of endothelial cells and astrocytes in the context of cerebral metastasis were analyzed using Monocle3, a state-of-the-art computational tool. Following the acquisition of the scRNA-seq dataset, Monocle3 was employed for trajectory inference and pseudotime estimation, bypassing preprocessing details. Leveraging the advanced algorithms within Monocle3, we elucidated the temporal progression of endothelial cells and astrocytes during cerebral metastasis. This methodology offers a comprehensive approach to uncovering the transcriptional dynamics and developmental trajectories within the microenvironment, shedding light on critical regulatory mechanisms and functional implications of these key cell types [ 15 ]. Spatial Transcriptome Normal skin and leprosy spatial transcriptome data was obtained from the GEO database (GSE167889 [ 5 ] and GSE202011 [ 16 ], respectively). Cell Ranger output was imported into SPATA2 using the import function (SPATA2::initiateSpataObject_10X). This function not only facilitated data integration but also executed baseline sample processing using the recently described pipeline. To elucidate the dynamics of spatial trajectories, we utilized the SPATA2 toolbox (v2.0.4) to manually draw trajectories simulating the tumor process, carefully selecting spots within the trajectory width [ 17 ]. To infer the spatial organization of cell types, we employed an advanced method designed to integrate spatial and single-cell data, implemented as an R package (semla, v.1.1.6, https://github.com/ludvigla/semla ) [ 18 ]. Results Profiling the cellular composition To profile the cellular composition and cell-state of human leprosy, we obtained scRNA-seq data previously published on skin biopsy specimens from five RR and LL patients, and one normal tissue sample. Following rigorous quality control and filtering procedures, a comprehensive dataset of 36,517 cells derived from 11 samples was assembled and subjected to integration using a batch effect correction algorithm (Fig. 1 A). Subsequent analysis employed a tailored computational pipeline grounded in the Seurat package, with results visualized through uniform manifold approximation and projection (UMAP). Differential expression analysis was then conducted to identify cluster markers, and the overlap of these markers with canonical signature genes for cell types facilitated the annotation of 12 primary cell types across all samples. These annotated cell types encompass T cells, B cells, plasma cells, myeloid cells, Langerhans cells, mast cells, keratinocytes, fibroblasts, smooth muscle cells, endothelial cells, eccrine gland cells, and melanocytes (Fig. 1 B). The examination revealed striking disparities in the frequency of various cell types, shedding light on the intricate cellular dynamics associated with each condition. Notably, LL exhibited a significant abundance of T cells (2,924) and keratinocytes (2,243), suggesting a prominent role for these cell types in the pathogenesis of this specific form of leprosy. Conversely, the normal tissue demonstrated a prevalence of fibroblasts (6,239) and keratinocytes (2,624), emphasizing the homeostatic nature of these cell types in unaffected tissues. The RR condition showcased a distinct cellular landscape, marked by elevated frequencies of keratinocytes (3,851) and T cells (2,434), indicative of a dynamic immunological response. Additionally, this condition revealed an increased presence of myeloid cells (1,034) and endothelial cells (1,324), underscoring the multifaceted nature of cellular interactions during RR (Fig. 1 C). CellChat and hdWCGNA Analysis Unveils Intricate Interactions in Leprosy Microenvironment Given complex intercommunications among cell components that play a critical role in human leprosy granulomas, CellChat analysis was conducted to evaluate the interactions between cells. In our comparative analysis using CellChat between leprosy samples (LL and RR) and normal tissue, a distinctive pattern emerged, particularly in the LL sample. Through CellChat, we discerned a pronounced interaction where immune cells, including leukocytes and myeloid cells, exhibited a cytokine-producing profile. Notably, these cytokines were observed to be selectively captured by the ACKR1 gene expressed in the endothelial cells of the LL leprosy sample. This interaction was notably absent in both normal tissue and the RR leprosy sample, where the ACKR1 gene was not expressed. The specific capture of immune cell-derived cytokines by endothelial cells, as indicated by the ACKR1 gene expression in the LL leprosy sample, underscores a potentially unique intercellular communication network in leprosy pathology (Fig. 2 A-B, S1 ). This finding implies a targeted modulation of cytokine signaling within the LL leprosy microenvironment, potentially contributing to the distinct immunological responses observed in leprosy lesions. The exclusive expression of the ACKR1 gene in endothelial cells suggests a localized mechanism for cytokine regulation in the LL sample, highlighting the intricate crosstalk between immune cells and endothelial cells in leprosy pathology. Further exploration of this molecular interaction may unveil novel insights into the immunopathogenesis of leprosy and provide avenues for targeted therapeutic interventions aimed at modulating this specific intercellular communication network in leprosy-affected tissues. We specifically highlight our hdWGCNA analysis for endothelial cells; we found one coexpression module significantly correlated with LL diagnosis— M2. For example, hub genes of module M2 encoded Endothelial Cell Differentiation (RPS15A, RPL30 and RPL23A, for example), consistent with its enrichment of GO terms related to protein synthesis and sorting. Notably, we found that three of the oligodendrocyte modules were significantly enriched for CDH5, FLT1, CAVIN, and PECAM1, indicating the importance of these genes in regulating gene expression in these modules. Concurrently, the RR, associated with module M3, stands out for its enrichment in genes related to the Regulation of Inflammatory Response Pathways. This finding suggests that M3 module, within the context of leprosy, may play a pivotal role in modulating neuroinflammatory responses, providing a distinctive perspective on the regulatory mechanisms implicated in the disease. The coexistence of these enriched modules, M2 and M3, underscores the intricate molecular interplay associated with leprosy, presenting potential avenues for therapeutic exploration and highlighting the need for further investigations into the functional significance of these modules in the context of disease (Fig. 2 C). Integrated trajectory analysis of disease-associated endothelial cells To further identify molecular mechanisms driving endothelial heterogeneity in leprosy, we performed pseudotime trajectory analysis using Monocle3 on the integrated snRNA-seq data in these cells. Trajectory analysis allows us to investigate the dynamics of gene expression and variability throughout a continuum of cell-state transitions. Briefly, a distinctive gene expression pattern emerged, with notable emphasis on genes associated with vesicle formation. Notably, the genes HSPG2, TXNIP, CD34, EPAS1, SPTBN1, FGD5, SPARCL1, SPARC, HLA-E, UTRN, ENG, AHNAK, VWF, A2M, FLT1, COL4A1, CD93, MYH9, ACKR1, ADGRL4, CAVIN1, MT-RNR2, NOP53, PECAM1, and RACK1 exhibited a significant upregulation in pseudotime within the context of the disease. This unique gene signature suggests their potential involvement in vesicle-related processes associated with the progression of the disease (Fig. 3 ). Furthermore, these identified genes cover diverse functions, including immune response (HLA-E), extracellular matrix organization (SPARC, COL4A1), angiogenesis (FLT1, VWF), and cell adhesion (CD34, PECAM1). The heightened expression of these genes in the pseudotime trajectory of endothelial cells implies a potential role in vesicle formation and transport, possibly contributing to the observed vascular alterations in leprosy. The identification of these vesicle-associated genes provides a focused avenue for further investigation into the molecular mechanisms underpinning vascular complications in leprosy. This refined analysis underscores the significance of vesicle-related processes in the endothelial cell response to the disease, offering insights that could inform future therapeutic strategies and diagnostic approaches. Spatial Transcriptome (ST) Profiling in normal skin and human leprosy A spatial transcriptomics (ST) analysis was conducted on normal skin tissue and leprosy sections, encompassing samples from LL and RR, as detailed in a previously published study. In order to enhance the precision of the analysis, we initially grouped similar ST spots, considering the inherent challenge posed by each spot representing multiple cells, leading to lower sequencing accuracy compared to single-cell sequencing. Recognizing the susceptibility of ST data to noise and technical artifacts, we implemented autoencoder denoising using Spata2 for improved data visualization. The identification of cell types was accomplished through specific markers. Notably, heterogeneity was observed among leprosy parenchyma cells within certain clusters, with some regions displaying a higher density of gene expression associated with the immune cells microenvironment. In the lepromatous leprosy ST sample, seven clusters were identified. Spatial trajectory screening aligned inferred gene expression changes with predefined models to identify genes exhibiting biologically relevant dynamics along a spatial trajectory. Along the trajectory, approaching the leprosy microenvironment region, the expression of the cytokine genes increased. Cell type mapping using ST data refers to a set of methods enabling the inference of cell quantities from ST expression profiles. We utilized the Semla package, leveraging scRNA-seq data to identify cell types in ST. Notably, myeloid cells were observed dispersed throughout the skin tissue region. Spatial transcriptome analysis revealed a notable absence of ACKR1 gene expression in normal tissue, further emphasizing its potential role in inflammatory processes. The identification of genes linked to vesicle formation in endothelial cells in the LL supports the hypothesis that ACKR1 and associated molecular pathways play a crucial role in the modulation of inflammatory responses. Interestingly, in RR, where the ACKR1 gene displayed low expression, it suggests a potential involvement of ACKR1 in early stages preceding the development of an extensive inflammatory infiltrate (Fig. 4 – 5 , S2 - 4 ). Discussion There are several mechanisms that adjust the immune response of the host. These include the inhibition of autophagy and cytokines, and the induction of Tregs, which are common to many intracellular bacteria [ 1 , 3 ]. Yet, there are unresolved issues, such as the specific cell subtypes that are affected by pathogens, the genes of the host that interact with these pathogens, and the communication between immune cells. The communication that occurs between immune cells and the leprosy microenvironment is another aspect that needs to be better understood. This crosstalk plays a significant role in the immune response and can influence the outcome of an infection [ 1 , 2 , 19 ]. The complex interaction between inflammatory and endothelial cells is revealed through an elaborate molecular performance, with critical genes such as ACKR1, CAVIN1, and SPARCL1 taking center stage. ACKR1 can act as a transporter for chemokines across endothelial cells, leading to apical retention of ligands and their immobilization. This transcytosis of intact chemokines supports their pro-migratory activity, contributing to an increased bioavailability of ligands for other chemokine receptors [ 6 , 20 ]. It’s important to note that while the internalization of chemokines by ACKR1 doesn’t result in their degradation, ACKR1 can still compete with conventional chemokine receptors for chemokine binding. This competition effectively reduces the availability of their ligands [ 6 , 20 , 21 ]. The presented data indicates that endothelial cells expressing ACKR1 play a role in recruiting leukocytes and other inflammatory cells through the transcytosis process. Endothelial cells request a dynamic response from leukocytes and myeloid cells, acting as essential architects in the formation of the granuloma [ 6 , 22 ]. Genes such as CAVIN1, SPARCL1 and CD34 underscore the multifaceted role of endothelial cells in this intricate interplay. The expression of SPARCL1 signals its involvement in the remodeling of local tissue, promoting a microenvironment conducive to vesicle formation [ 23 ]. Conversely, CD34, with its recognized impact on vascular permeability regulation, highlights the influence of endothelial cells in modulating interactions through the process of transcytosis [ 24 , 25 ]. At the molecular level, the interaction between the endothelial and inflammatory cells is intricately regulated by signaling pathways, with CAVIN1 emerging as a central coordinator [ 26 ]. The strong expression of CAVIN1 by endothelial cells suggests a strong activation of angiogenic pathways, influencing vascular responses and shaping the complex architecture of skin tissue [ 26 , 27 ]. The interconnected nature of these signaling pathways emphasizes the depth of molecular interactions that define the microenvironment of leprosy. Taken together, our analyses reveal the integration of single-cell and spatial transcriptomic approaches in leprosy provides a detailed understanding of the cellular dynamics, intercellular communication, and molecular mechanisms associated with leprosy immunopathology. Additionally, the identified gene signatures, spatial patterns, and immunomodulatory roles of specific genes like ACKR1 offer novel insights that can inform future therapeutic strategies, diagnostic approaches, and targeted interventions for leprosy and other intracellular bacteria-caused persistent infectious diseases. This study underscores the significance of advanced technologies in unraveling the complexities of the immune microenvironment at a spatial transcriptome and single-cell resolution. Conclusion Our extensive analysis using single-cell and spatial transcriptomics has provided valuable insights into the complex dynamics of cellular interactions, communication, and molecular processes in leprosy immunopathology. By employing advanced techniques such as scRNA-seq, CellChat analysis, hdWGCNA, and spatial transcriptome profiling, we have uncovered the composition and state of human leprosy cells, revealing diverse interactions among various cell types. Notably, ACKR1 + endothelial cells play a crucial role in the transcytosis process, modulating cytokine signaling within the leprosy microenvironment, especially in lepromatous leprosy. The identified gene signatures and spatial patterns, including the roles of critical genes like ACKR1, CAVIN1, and SPARCL1, offer insights for potential therapeutic strategies and diagnostics. This study underscores the importance of advanced technologies in understanding the complexities of the immune microenvironment at both spatial transcriptome and single-cell resolutions, contributing to a deeper understanding of leprosy and similar persistent infectious diseases caused by intracellular bacteria. Declarations Data availability statement Among input data processed in the reanalysis, two datasets were acquired from NCBI GEO (GSE130973, GSE151528, GSE167889 and GSE202011). These datasets contribute valuable information to the comprehensive evaluation and interpretation conducted in this study. The data that support the findings of this study are available from the corresponding author upon reasonable request. Funding statement This work was supported by a Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP). CACF received a (FAPESP) Postdoctoral fellowship (2022/02605-6). Conflict of interest’s disclosure The authors declare no conflict of interest. Ethics Statement Ethical review and approval were not required for the study. We reanalyzed scRNA data from other studies, strictly observing privacy policies and complying with all applicable ethical regulations. References Hambridge, Thomas, Shri Lak Nanjan Chandran, Annemieke Geluk, Paul Saunderson, and Jan Hendrik Richardus. 2021. Mycobacterium leprae transmission characteristics during the declining stages of leprosy incidence: A systematic review. PLOS Neglected Tropical Diseases 15. Public Library of Science: e0009436. https://doi.org/10.1371/JOURNAL.PNTD.0009436. van Hooij, Anouk, Susan van den Eeden, Renate Richardus, Elisa Tjon Kon Fat, Louis Wilson, Kees L.M.C. Franken, Roel Faber, et al. 2019. Application of new host biomarker profiles in quantitative point-of-care tests facilitates leprosy diagnosis in the field. EBioMedicine 47. Elsevier B.V.: 301–308. https://doi.org/10.1016/j.ebiom.2019.08.009. van Hooij, Anouk, Maria Tió-Coma, Els M. Verhard, Marufa Khatun, Khorshed Alam, Elisa Tjon Kon Fat, Danielle de Jong, et al. 2020. Household Contacts of Leprosy Patients in Endemic Areas Display a Specific Innate Immunity Profile. Frontiers in immunology 11. Front Immunol. https://doi.org/10.3389/FIMMU.2020.01811. Petersen, Helen J., and Andrew M. Smith. 2013. The Role of the Innate Immune System in Granulomatous Disorders. Frontiers in Immunology 4. Frontiers Media SA. https://doi.org/10.3389/FIMMU.2013.00120. Ma, Feiyang, Travis K. Hughes, Rosane M.B. Teles, Priscila R. Andrade, Bruno J. de Andrade Silva, Olesya Plazyo, Lam C. Tsoi, et al. 2021. The Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas. Nature immunology 22. NIH Public Access: 839. https://doi.org/10.1038/S41590-021-00956-8. Guo, Xinying, Negar Khosraviani, Sneha Raju, Joshya Singh, Nikki Zamani Farahani, Madlene Abramian, Victor J. Torres, et al. 2023. Endothelial ACKR1 is induced by neutrophil contact and down-regulated by secretion in extracellular vesicles. Frontiers in Immunology 14. Frontiers Media S.A.: 1181016. https://doi.org/10.3389/FIMMU.2023.1181016/BIBTEX. Jovic, Dragomirka, Xue Liang, Hua Zeng, Lin Lin, Fengping Xu, and Yonglun Luo. 2022. Single-cell RNA sequencing technologies and applications: A brief overview. Clinical and translational medicine 12. Clin Transl Med. https://doi.org/10.1002/CTM2.694. Ma, Chenxi, Chengzhe Yang, Ai Peng, Tianyong Sun, Xiaoli Ji, Jun Mi, Li Wei, Song Shen, and Qiang Feng. 2023. Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. Molecular Cancer 2023 22:1 22. BioMed Central: 1–23. https://doi.org/10.1186/S12943-023-01876-X. Wang, Zihao, Yaning Wang, Mengqi Chang, Yuekun Wang, Peng Liu, Jianqiang Wu, Guige Wang, et al. 2023. Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma. Neuro-oncology 25. Neuro Oncol: 1262–1274. https://doi.org/10.1093/NEUONC/NOAD017. Hao, Yuhan, Tim Stuart, Madeline H. Kowalski, Saket Choudhary, Paul Hoffman, Austin Hartman, Avi Srivastava, et al. 2023. Dictionary learning for integrative, multimodal and scalable single-cell analysis. Nature Biotechnology 2023 . Nature Publishing Group: 1–12. https://doi.org/10.1038/s41587-023-01767-y. Solé-Boldo, Llorenç, Günter Raddatz, Sabrina Schütz, Jan Philipp Mallm, Karsten Rippe, Anke S. Lonsdorf, Manuel Rodríguez-Paredes, and Frank Lyko. 2020. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. Communications biology 3. Commun Biol. https://doi.org/10.1038/S42003-020-0922-4. Hao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, et al. 2021. Integrated analysis of multimodal single-cell data. Cell 184. Elsevier B.V.: 3573-3587.e29. https://doi.org/10.1016/J.CELL.2021.04.048/ATTACHMENT/1E5EB5C1-59EE-4B2B-8BFA-14B48A54FF8F/MMC3.XLSX. Jin, Suoqin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen Hsiang Kuan, Peggy Myung, Maksim V. Plikus, and Qing Nie. 2021. Inference and analysis of cell-cell communication using CellChat. Nature Communications 2021 12:1 12. Nature Publishing Group: 1–20. https://doi.org/10.1038/s41467-021-21246-9. Morabito, Samuel, Emily Miyoshi, Neethu Michael, Saba Shahin, Alessandra Cadete Martini, Elizabeth Head, Justine Silva, Kelsey Leavy, Mari Perez-Rosendahl, and Vivek Swarup. 2021. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer’s disease. Nature Genetics 2021 53:8 53. Nature Publishing Group: 1143–1155. https://doi.org/10.1038/s41588-021-00894-z. Cao, Junyue, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, et al. 2019. The single-cell transcriptional landscape of mammalian organogenesis. Nature 2019 566:7745 566. Nature Publishing Group: 496–502. https://doi.org/10.1038/s41586-019-0969-x. Castillo, Rochelle L., Ikjot Sidhu, Igor Dolgalev, Tinyi Chu, Aleksandr Prystupa, Ipsita Subudhi, Di Yan, et al. 2023. Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems. Science immunology 8. Sci Immunol. https://doi.org/10.1126/SCIIMMUNOL.ABQ7991. Ravi, Vidhya M, Paulina Will, Jan Kueckelhaus, Na Sun, Kevin Joseph, Henrike Salié, Lea Vollmer, et al. 2022. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma Graphical abstract. Cancer Cell 40: 639-655.e13. https://doi.org/10.1016/j.ccell.2022.05.009. Larsson, Ludvig, Lovisa Franzén, Patrik L. Ståhl, and Joakim Lundeberg. 2023. Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization. Bioinformatics 39. Oxford Academic. https://doi.org/10.1093/BIOINFORMATICS/BTAD626. Ma, Feiyang, Travis K. Hughes, Rosane M.B. Teles, Priscila R. Andrade, Bruno J. de Andrade Silva, Olesya Plazyo, Lam C. Tsoi, et al. 2021. The Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas. Nature immunology 22. NIH Public Access: 839. https://doi.org/10.1038/S41590-021-00956-8. Girbl, Tamara, Tchern Lenn, Lorena Perez, Loïc Rolas, Anna Barkaway, Aude Thiriot, Carlos del Fresno, et al. 2018. Distinct Compartmentalization of the Chemokines CXCL1 and CXCL2 and the Atypical Receptor ACKR1 Determine Discrete Stages of Neutrophil Diapedesis. Immunity 49. Immunity: 1062-1076.e6. https://doi.org/10.1016/J.IMMUNI.2018.09.018. Szpakowska, Martyna, Giulia D’Uonnolo, Rafael Luís, Ana Alonso Bartolomé, Marcus Thelen, Daniel F. Legler, and Andy Chevigné. 2023. New pairings and deorphanization among the atypical chemokine receptor family — physiological and clinical relevance. Frontiers in Immunology 14. Frontiers Media S.A. https://doi.org/10.3389/FIMMU.2023.1133394/FULL. Aman, Jurjan, and Coert Margadant. 2023. Integrin-Dependent Cell–Matrix Adhesion in Endothelial Health and Disease. Circulation Research 132. Lippincott Williams & Wilkins Hagerstown, MD : 355–378. https://doi.org/10.1161/CIRCRESAHA.122.322332. Naschberger, Elisabeth, Andrea Liebl, Vera S. Schellerer, Manuela Schütz, Nathalie Britzen-Laurent, Patrick Klbel, Ute Schaal, et al. 2016. Matricellular protein SPARCL1 regulates tumor microenvironment-dependent endothelial cell heterogeneity in colorectal carcinoma. The Journal of clinical investigation 126. J Clin Invest: 4187–4204. https://doi.org/10.1172/JCI78260. Middleton, James, Stuart Neil, Jonathan Wintle, Ian Clark-Lewis, Henrietta Moore, Lam Charles, Manfred Auer, Hub Elin, and Rot Antal. 1997. Transcytosis and Surface Presentation of IL-8 by Venular Endothelial Cells. Cell 91. Cell Press: 385–395. https://doi.org/10.1016/S0092-8674(00)80422-5. Malong, Liza, Ilaria Napoli, Giulia Casal, Ian J. White, Salome Stierli, Andrew Vaughan, Anne Laure Cattin, et al. 2023. Characterization of the structure and control of the blood-nerve barrier identifies avenues for therapeutic delivery. Developmental cell 58. Dev Cell: 174-191.e8. https://doi.org/10.1016/J.DEVCEL.2023.01.002. Hansen, Carsten Gram, Elena Shvets, Gillian Howard, Kirsi Riento, and Benjamin James Nichols. 2013. Deletion of cavin genes reveals tissue-specific mechanisms for morphogenesis of endothelial caveolae. Nature communications 4. Nat Commun. https://doi.org/10.1038/NCOMMS2808. Sowa, Grzegorz. 2012. Caveolae, caveolins, cavins, and endothelial cell function: New insights. Frontiers in Physiology 2 JAN. Frontiers: 17267. https://doi.org/10.3389/FPHYS.2011.00120/BIBTEX. Additional Declarations No competing interests reported. Supplementary Files FigureS1.jpg Figure S1 - Cell–cell communications in human metastasis, related to Figure 2. A) Scatter plot comparing the outgoing and incoming interaction strength between normal skin and leprosy. B) Circle plots depicting the interaction numbers and interaction strength. C) Significant signaling pathways ranked based on differences in the overall information flow within the inferred networks between normal skin and leprosy. FigureS2.jpg Figure S2 - Spatial analysis of lepromatous leprosy. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes. FigureS3.jpg Figure S3 - Spatial analysis of reversal reaction. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes. FigureS4.jpg Figure S4 - Spatial analysis of normal skin. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3829511","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":264917762,"identity":"a6bd03a1-3226-44e7-96ef-7978fb2d41d4","order_by":0,"name":"Heloisa Almeida Freitas","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Heloisa","middleName":"Almeida","lastName":"Freitas","suffix":""},{"id":264917763,"identity":"cecc7190-c627-4a48-9bcb-af3d0d21d8c4","order_by":1,"name":"Mikael Nikson Vilela Tenório da Paz","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Mikael","middleName":"Nikson Vilela Tenório da","lastName":"Paz","suffix":""},{"id":264917764,"identity":"cbcb0353-7b4c-427a-9212-94b9926b4293","order_by":2,"name":"Gabriel Victor Lucena Silva","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Gabriel","middleName":"Victor Lucena","lastName":"Silva","suffix":""},{"id":264917765,"identity":"f7b20bf4-8af4-43fc-9d90-2af2c65bf6b0","order_by":3,"name":"Adriana Simizo","email":"","orcid":"","institution":"Universidade de São Paulo","correspondingAuthor":false,"prefix":"","firstName":"Adriana","middleName":"","lastName":"Simizo","suffix":""},{"id":264917766,"identity":"68edbdd9-30aa-461d-a893-431130c955a9","order_by":4,"name":"Jussara Almeida Oliveira Baggio","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Jussara","middleName":"Almeida Oliveira","lastName":"Baggio","suffix":""},{"id":264917767,"identity":"04840928-4fc2-45ac-918e-0d449909bddf","order_by":5,"name":"Amanda Karine Barros Ferreira Rodrigues","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Amanda","middleName":"Karine Barros Ferreira","lastName":"Rodrigues","suffix":""},{"id":264917768,"identity":"029c993f-a9ed-45a9-8e06-ce6da2b85270","order_by":6,"name":"Jammily Oliveira Vieira Moreira","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Jammily","middleName":"Oliveira Vieira","lastName":"Moreira","suffix":""},{"id":264917769,"identity":"103117d6-df6e-4275-ac6b-b1cd1036e435","order_by":7,"name":"Karol Fireman Farias","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Karol","middleName":"Fireman","lastName":"Farias","suffix":""},{"id":264917770,"identity":"d0ac80fb-4a13-4749-99df-595a6f83579e","order_by":8,"name":"Elaine Virginia Martins Souza Figueiredo","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Elaine","middleName":"Virginia Martins Souza","lastName":"Figueiredo","suffix":""},{"id":264917771,"identity":"1a4ee506-725e-4159-b4c3-06997960febc","order_by":9,"name":"Márcio Bezerra Santos","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Márcio","middleName":"Bezerra","lastName":"Santos","suffix":""},{"id":264917772,"identity":"f8afdbcd-8e37-497d-9154-68f36733c644","order_by":10,"name":"Carolinne Sales Marques","email":"","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":false,"prefix":"","firstName":"Carolinne","middleName":"Sales","lastName":"Marques","suffix":""},{"id":264917773,"identity":"2d5a4d6b-5e6d-45b2-91cd-11b9fb7e9108","order_by":11,"name":"Carlos Alberto Carvalho Fraga","email":"data:image/png;base64,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","orcid":"","institution":"Universidade Federal de Alagoas","correspondingAuthor":true,"prefix":"","firstName":"Carlos","middleName":"Alberto Carvalho","lastName":"Fraga","suffix":""}],"badges":[],"createdAt":"2024-01-02 12:44:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3829511/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3829511/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49166355,"identity":"5c8efae3-6306-4cf4-8fd2-6489c6b0aede","added_by":"auto","created_at":"2024-01-04 08:13:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1829203,"visible":true,"origin":"","legend":"\u003cp\u003eProfiling Cellular Diversity in Human normal skin and Leprosy samples through scRNAseq Analysis. A) Experimental approach. B) Visualization of 36,617 single cells. Cells are colored by cell type. C) Frequencies of single cells per sample type.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/103a652e76c7de3e96f809bd.png"},{"id":49166356,"identity":"8757a1e6-6248-4df9-9b26-ff30ef40b32a","added_by":"auto","created_at":"2024-01-04 08:13:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4144365,"visible":true,"origin":"","legend":"\u003cp\u003eCell–cell communications referenced by CellChat demonstrated notable alterations in receptors-ligands-mediated communications comparing normal and leprosy. A) Circle plots depicting the interaction numbers and interaction strength. B) Scatter plot showing the intensity of the outgoing and incoming interactions in two-dimensional manifold. The size of the circles suggests the numbers of significantly expressed receptor-ligand pathways of different cell populations. C) Schematic model representing the strongest signalings between cells. D) UMAP plot of the co-expression network. Each node represents a single gene, and edges represent co-expression links between genes and module hub genes.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/443a54f2b922f967eb92c7a1.png"},{"id":49166049,"identity":"6f046506-0309-4c3c-9a7b-ff07239c07da","added_by":"auto","created_at":"2024-01-04 08:05:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2678887,"visible":true,"origin":"","legend":"\u003cp\u003ePseudotime analysis and gene set enrichment analysis (GSEA). A) Monocle3 analysis of endothelial cells, and the cells were ordered by pseudotime. B) The heatmap for the expression patterns of the top significant genes (ranked by q value). C) Scatterplots for gene set enrichment analysis of endothelial cell DEGs.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/fe10ff7fd914391cb430fb80.png"},{"id":49166358,"identity":"282be63c-1379-421c-8c01-6cf6f14b0b40","added_by":"auto","created_at":"2024-01-04 08:13:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":7538410,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial analysis of lepromatous leprosy. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/2a86cb79f0602e72802c0e81.png"},{"id":49166047,"identity":"8c037d55-2e46-400b-8478-2d935881b8f7","added_by":"auto","created_at":"2024-01-04 08:05:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":6593081,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial analysis of reversal reaction. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/4086c7a4f6b6226d50012371.png"},{"id":51371525,"identity":"b7ab6716-51e2-4c09-b824-a5f21c66f4b4","added_by":"auto","created_at":"2024-02-20 12:19:42","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3837453,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/25727123-d0d3-42e7-9006-5631673360d3.pdf"},{"id":49166357,"identity":"00e9bede-55ad-41af-a65e-dcd006458580","added_by":"auto","created_at":"2024-01-04 08:13:11","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1554703,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S1 - Cell–cell communications in human metastasis, related to Figure 2. A) Scatter plot comparing the outgoing and incoming interaction strength between normal skin and leprosy. B) Circle plots depicting the interaction numbers and interaction strength. C) Significant signaling pathways ranked based on differences in the overall information flow within the inferred networks between normal skin and leprosy.\u003c/p\u003e","description":"","filename":"FigureS1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/cc2341af1d0cc120ee028c40.jpg"},{"id":49166053,"identity":"6a1c10e1-35ba-4bed-8da9-8eb73b88cfbf","added_by":"auto","created_at":"2024-01-04 08:05:11","extension":"jpg","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4914805,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S2 - Spatial analysis of lepromatous leprosy. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes.\u003c/p\u003e","description":"","filename":"FigureS2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/743a88ae33c1a0759318b7a3.jpg"},{"id":49166052,"identity":"81521bec-0ede-4b3f-9af2-a64ab96dd135","added_by":"auto","created_at":"2024-01-04 08:05:11","extension":"jpg","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":5106638,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S3 - Spatial analysis of reversal reaction. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes.\u003c/p\u003e","description":"","filename":"FigureS3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/3c020c0ac5173d82ad2833fd.jpg"},{"id":49166051,"identity":"087cdb91-a594-4498-adb2-135a5d08ee10","added_by":"auto","created_at":"2024-01-04 08:05:11","extension":"jpg","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":4278155,"visible":true,"origin":"","legend":"\u003cp\u003eFigure S4 - Spatial analysis of normal skin. A) Cluster’s distribution across tissue showing the trajectory drawn. B) Inferred gene expression changes along the trajectory. C) Cell type deconvolution performed by semla R package. D) Spatial dimension of the sample and colors the surface according to the expression of ACKR1, CAVIN1, CD34, SPARCL1, SPARC and PECAM1 genes.\u003c/p\u003e","description":"","filename":"FigureS4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3829511/v1/895c113f6dcf165a5edab872.jpg"}],"financialInterests":"No competing interests reported.","formattedTitle":"Single‐cell and spatial transcriptomics reveal ACKR1+ endothelial cells associated with transcytosis in Leprosy","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLeprosy is a chronic infectious disease caused by the intracellular bacillus \u003cem\u003eMycobacterium leprae\u003c/em\u003e. Regardless of the significant reduction in prevalence after the widespread use of multidrug therapy, the incidence rates have stabilized in recent decades and leprosy remains endemic in several localized regions, such as Brazil, China, and India [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Importantly, the disease can cause deformities and irreversible physical disabilities, and it is still considered a public health concern. Considering its clinical aspects, leprosy stands as a compelling paradigm for elucidating the intricacies of the human immune response to intracellular bacteria, manifesting itself across a diverse clinical and immunological spectrum [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Central to this complex interplay is the granuloma, a chronic inflammatory structure formed through immune-mediated recruitment of white blood cells, primarily enriched with macrophages. Within the realm of infectious diseases, granulomas play a pivotal role in sequestering and degrading microbial pathogens that manage to resist the initial immune response [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eExamining the granuloma structure across the leprosy spectrum reveals distinct patterns. Granulomas exhibit a core of mature macrophages, occasional multinucleated giant cells, and a well-organized mantle zone at the periphery containing lymphocytes, all marked by fibrosis [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. RR lesions granulomas display intercellular edema. In contrast, LL granulomas are disorganized, prominently featuring immature lipid-laden foamy macrophages and scattered lymphocytes. A crucial aspect of the immune response in leprosy involves the role of endothelial cells, crucial in orchestrating inflammation and immune cell recruitment [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The gene ACKR1 (Atypical Chemokine Receptor 1) emerges as a significant player in this scenario. Expressed on both erythrocytes and endothelial cells, ACKR1 binds to inflammatory chemokines, regulating their bioavailability. Notably, endothelial ACKR1 has been implicated in vesicle formation and the induction of granuloma formation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], critical mechanisms in the immune response to \u003cem\u003eM. leprae\u003c/em\u003e. This dual role of ACKR1 underscores its intricate involvement in shaping the immune microenvironment during leprosy.\u003c/p\u003e \u003cp\u003eRecent studies utilizing scRNA-seq analyses have markedly advanced our understanding of leprosy immunopathology. Investigations into human leprosy and associated cellular components are scarce, primarily due to inherent clinical, logistical, and technical challenges in collecting and profiling leprosy at the single-cell level [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Prior studies have predominantly relied on experimental mouse models or bulk analyses of human diseases [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Despite advances in understanding leprosy tissues, comprehensive insights into the intricacies of leprosy niches and the functional programs of cells in leprosy warrant further exploration. Likewise, the integration of advanced technologies like scRNA-seq holds promise in unraveling intricate cellular communications within the leprosy microenvironment, providing a foundation for enhanced strategies in surveillance, diagnosis, and treatment [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe progress in scRNA-seq technology has significantly propelled our understanding of the leprosy microenvironment at a single-cell resolution. Notable studies have illuminated the dynamics of stromal and immune cells contributing to a pro-leprotic and immune-suppressive microenvironment in various leprosy types, underscoring the significance of single-cell technology in deciphering leprosy complexities [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Despite these advances, the application of scRNA-seq in leprosy remains limited due to challenges in obtaining clinical samples. In this study, we undertake a comprehensive analysis employing scRNA-seq to investigate the intricate cellular landscape of leprosy. Our study encompasses a diverse dataset of 36,517 cells obtained from normal skin and human leprosy specimens, reversal reactions (RR) and disseminated lepromatous leprosy (LL). Furthermore, we aim to elucidate cell responses and characterize the dynamics of human leprosy, particularly focusing on endothelial cells. Through integrative analyses, including pseudotime trajectory analysis and cell-cell interaction studies, our overarching goal is to identify biomarkers, therapeutic targets, and key regulatory pathways, thereby contributing to an enhanced understanding of leprosy and improving diagnostic and treatment strategies.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSingle cell RNA-seq Data\u003c/h2\u003e \u003cp\u003eProcessed scRNA-seq data and annotation tables were downloaded from the GEO database. We performed an extensive analysis of the scRNA-seq data of normal skin (GSE130973) [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], reversal reactions (RR), a dynamic process in which some patients with disseminated lepromatous leprosy (L-lep) transition towards self-limiting tuberculoid leprosy (GSE151528) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Separately, count matrices from the selected samples were introduced into R (4.3.2) and converted to a Seurat object using the Seurat package (4.4.0) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCell-Cell Interaction Analysis\u003c/h2\u003e \u003cp\u003eCellChat R package provides a comprehensive toolkit for analyzing and visualizing cell-cell communication networks in scRNA-seq data. CellChat R package provides a powerful set of tools for analyzing and visualizing cell-cell communication networks in scRNA-seq data. By following this methodology, researchers can gain new insights into the complex cellular interactions that underlie many biological processes [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eHigh-dimensional WGCNA (hdWGCNA)\u003c/h2\u003e \u003cp\u003eThe workflow for conducting high-dimensional Weighted Gene expression network Analysis (hdWGCNA) in R was performed with the \u0026ldquo;hdWGCNA\u0026rdquo; package. Briefly, the hdWGCNA pipeline involves the following steps: data preprocessing, gene network construction, module identification, module preservation analysis, and functional enrichment analysis. In the first step, gene expression data are preprocessed to remove noise and batch effects. In the second step, a gene co-expression network is constructed based on pairwise correlations among genes. In the third step, modules or clusters of highly correlated genes are identified, and the module eigengenes are calculated. In the fourth step, module preservation analysis is performed to assess the robustness of the identified modules. Finally, in the fifth step, functional enrichment analysis is carried out to identify the biological processes and pathways that are associated with the modules [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePseudotime analysis\u003c/h2\u003e \u003cp\u003eThe pseudotime dynamics of endothelial cells and astrocytes in the context of cerebral metastasis were analyzed using Monocle3, a state-of-the-art computational tool. Following the acquisition of the scRNA-seq dataset, Monocle3 was employed for trajectory inference and pseudotime estimation, bypassing preprocessing details. Leveraging the advanced algorithms within Monocle3, we elucidated the temporal progression of endothelial cells and astrocytes during cerebral metastasis. This methodology offers a comprehensive approach to uncovering the transcriptional dynamics and developmental trajectories within the microenvironment, shedding light on critical regulatory mechanisms and functional implications of these key cell types [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Transcriptome\u003c/h2\u003e \u003cp\u003eNormal skin and leprosy spatial transcriptome data was obtained from the GEO database (GSE167889 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e] and GSE202011 [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e], respectively). Cell Ranger output was imported into SPATA2 using the import function (SPATA2::initiateSpataObject_10X). This function not only facilitated data integration but also executed baseline sample processing using the recently described pipeline. To elucidate the dynamics of spatial trajectories, we utilized the SPATA2 toolbox (v2.0.4) to manually draw trajectories simulating the tumor process, carefully selecting spots within the trajectory width [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. To infer the spatial organization of cell types, we employed an advanced method designed to integrate spatial and single-cell data, implemented as an R package (semla, v.1.1.6, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/ludvigla/semla\u003c/span\u003e\u003cspan address=\"https://github.com/ludvigla/semla\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eProfiling the cellular composition\u003c/h2\u003e \u003cp\u003eTo profile the cellular composition and cell-state of human leprosy, we obtained scRNA-seq data previously published on skin biopsy specimens from five RR and LL patients, and one normal tissue sample. Following rigorous quality control and filtering procedures, a comprehensive dataset of 36,517 cells derived from 11 samples was assembled and subjected to integration using a batch effect correction algorithm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Subsequent analysis employed a tailored computational pipeline grounded in the Seurat package, with results visualized through uniform manifold approximation and projection (UMAP). Differential expression analysis was then conducted to identify cluster markers, and the overlap of these markers with canonical signature genes for cell types facilitated the annotation of 12 primary cell types across all samples. These annotated cell types encompass T cells, B cells, plasma cells, myeloid cells, Langerhans cells, mast cells, keratinocytes, fibroblasts, smooth muscle cells, endothelial cells, eccrine gland cells, and melanocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe examination revealed striking disparities in the frequency of various cell types, shedding light on the intricate cellular dynamics associated with each condition. Notably, LL exhibited a significant abundance of T cells (2,924) and keratinocytes (2,243), suggesting a prominent role for these cell types in the pathogenesis of this specific form of leprosy. Conversely, the normal tissue demonstrated a prevalence of fibroblasts (6,239) and keratinocytes (2,624), emphasizing the homeostatic nature of these cell types in unaffected tissues. The RR condition showcased a distinct cellular landscape, marked by elevated frequencies of keratinocytes (3,851) and T cells (2,434), indicative of a dynamic immunological response. Additionally, this condition revealed an increased presence of myeloid cells (1,034) and endothelial cells (1,324), underscoring the multifaceted nature of cellular interactions during RR (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eCellChat and hdWCGNA Analysis Unveils Intricate Interactions in Leprosy Microenvironment\u003c/h2\u003e \u003cp\u003eGiven complex intercommunications among cell components that play a critical role in human leprosy granulomas, CellChat analysis was conducted to evaluate the interactions between cells. In our comparative analysis using CellChat between leprosy samples (LL and RR) and normal tissue, a distinctive pattern emerged, particularly in the LL sample. Through CellChat, we discerned a pronounced interaction where immune cells, including leukocytes and myeloid cells, exhibited a cytokine-producing profile. Notably, these cytokines were observed to be selectively captured by the ACKR1 gene expressed in the endothelial cells of the LL leprosy sample. This interaction was notably absent in both normal tissue and the RR leprosy sample, where the ACKR1 gene was not expressed. The specific capture of immune cell-derived cytokines by endothelial cells, as indicated by the ACKR1 gene expression in the LL leprosy sample, underscores a potentially unique intercellular communication network in leprosy pathology (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B, \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis finding implies a targeted modulation of cytokine signaling within the LL leprosy microenvironment, potentially contributing to the distinct immunological responses observed in leprosy lesions. The exclusive expression of the ACKR1 gene in endothelial cells suggests a localized mechanism for cytokine regulation in the LL sample, highlighting the intricate crosstalk between immune cells and endothelial cells in leprosy pathology. Further exploration of this molecular interaction may unveil novel insights into the immunopathogenesis of leprosy and provide avenues for targeted therapeutic interventions aimed at modulating this specific intercellular communication network in leprosy-affected tissues.\u003c/p\u003e \u003cp\u003eWe specifically highlight our hdWGCNA analysis for endothelial cells; we found one coexpression module significantly correlated with LL diagnosis\u0026mdash; M2. For example, hub genes of module M2 encoded Endothelial Cell Differentiation (RPS15A, RPL30 and RPL23A, for example), consistent with its enrichment of GO terms related to protein synthesis and sorting. Notably, we found that three of the oligodendrocyte modules were significantly enriched for CDH5, FLT1, CAVIN, and PECAM1, indicating the importance of these genes in regulating gene expression in these modules. Concurrently, the RR, associated with module M3, stands out for its enrichment in genes related to the Regulation of Inflammatory Response Pathways. This finding suggests that M3 module, within the context of leprosy, may play a pivotal role in modulating neuroinflammatory responses, providing a distinctive perspective on the regulatory mechanisms implicated in the disease. The coexistence of these enriched modules, M2 and M3, underscores the intricate molecular interplay associated with leprosy, presenting potential avenues for therapeutic exploration and highlighting the need for further investigations into the functional significance of these modules in the context of disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIntegrated trajectory analysis of disease-associated endothelial cells\u003c/h2\u003e \u003cp\u003eTo further identify molecular mechanisms driving endothelial heterogeneity in leprosy, we performed pseudotime trajectory analysis using Monocle3 on the integrated snRNA-seq data in these cells. Trajectory analysis allows us to investigate the dynamics of gene expression and variability throughout a continuum of cell-state transitions. Briefly, a distinctive gene expression pattern emerged, with notable emphasis on genes associated with vesicle formation. Notably, the genes HSPG2, TXNIP, CD34, EPAS1, SPTBN1, FGD5, SPARCL1, SPARC, HLA-E, UTRN, ENG, AHNAK, VWF, A2M, FLT1, COL4A1, CD93, MYH9, ACKR1, ADGRL4, CAVIN1, MT-RNR2, NOP53, PECAM1, and RACK1 exhibited a significant upregulation in pseudotime within the context of the disease. This unique gene signature suggests their potential involvement in vesicle-related processes associated with the progression of the disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, these identified genes cover diverse functions, including immune response (HLA-E), extracellular matrix organization (SPARC, COL4A1), angiogenesis (FLT1, VWF), and cell adhesion (CD34, PECAM1). The heightened expression of these genes in the pseudotime trajectory of endothelial cells implies a potential role in vesicle formation and transport, possibly contributing to the observed vascular alterations in leprosy. The identification of these vesicle-associated genes provides a focused avenue for further investigation into the molecular mechanisms underpinning vascular complications in leprosy. This refined analysis underscores the significance of vesicle-related processes in the endothelial cell response to the disease, offering insights that could inform future therapeutic strategies and diagnostic approaches.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSpatial Transcriptome (ST) Profiling in normal skin and human leprosy\u003c/h2\u003e \u003cp\u003eA spatial transcriptomics (ST) analysis was conducted on normal skin tissue and leprosy sections, encompassing samples from LL and RR, as detailed in a previously published study. In order to enhance the precision of the analysis, we initially grouped similar ST spots, considering the inherent challenge posed by each spot representing multiple cells, leading to lower sequencing accuracy compared to single-cell sequencing. Recognizing the susceptibility of ST data to noise and technical artifacts, we implemented autoencoder denoising using Spata2 for improved data visualization. The identification of cell types was accomplished through specific markers. Notably, heterogeneity was observed among leprosy parenchyma cells within certain clusters, with some regions displaying a higher density of gene expression associated with the immune cells microenvironment.\u003c/p\u003e \u003cp\u003eIn the lepromatous leprosy ST sample, seven clusters were identified. Spatial trajectory screening aligned inferred gene expression changes with predefined models to identify genes exhibiting biologically relevant dynamics along a spatial trajectory. Along the trajectory, approaching the leprosy microenvironment region, the expression of the cytokine genes increased. Cell type mapping using ST data refers to a set of methods enabling the inference of cell quantities from ST expression profiles. We utilized the Semla package, leveraging scRNA-seq data to identify cell types in ST. Notably, myeloid cells were observed dispersed throughout the skin tissue region.\u003c/p\u003e \u003cp\u003eSpatial transcriptome analysis revealed a notable absence of ACKR1 gene expression in normal tissue, further emphasizing its potential role in inflammatory processes. The identification of genes linked to vesicle formation in endothelial cells in the LL supports the hypothesis that ACKR1 and associated molecular pathways play a crucial role in the modulation of inflammatory responses. Interestingly, in RR, where the ACKR1 gene displayed low expression, it suggests a potential involvement of ACKR1 in early stages preceding the development of an extensive inflammatory infiltrate (Fig.\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"InternalRef\"\u003eS2\u003c/span\u003e-\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThere are several mechanisms that adjust the immune response of the host. These include the inhibition of autophagy and cytokines, and the induction of Tregs, which are common to many intracellular bacteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Yet, there are unresolved issues, such as the specific cell subtypes that are affected by pathogens, the genes of the host that interact with these pathogens, and the communication between immune cells. The communication that occurs between immune cells and the leprosy microenvironment is another aspect that needs to be better understood. This crosstalk plays a significant role in the immune response and can influence the outcome of an infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe complex interaction between inflammatory and endothelial cells is revealed through an elaborate molecular performance, with critical genes such as ACKR1, CAVIN1, and SPARCL1 taking center stage. ACKR1 can act as a transporter for chemokines across endothelial cells, leading to apical retention of ligands and their immobilization. This transcytosis of intact chemokines supports their pro-migratory activity, contributing to an increased bioavailability of ligands for other chemokine receptors [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. It\u0026rsquo;s important to note that while the internalization of chemokines by ACKR1 doesn\u0026rsquo;t result in their degradation, ACKR1 can still compete with conventional chemokine receptors for chemokine binding. This competition effectively reduces the availability of their ligands [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The presented data indicates that endothelial cells expressing ACKR1 play a role in recruiting leukocytes and other inflammatory cells through the transcytosis process.\u003c/p\u003e \u003cp\u003eEndothelial cells request a dynamic response from leukocytes and myeloid cells, acting as essential architects in the formation of the granuloma [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Genes such as CAVIN1, SPARCL1 and CD34 underscore the multifaceted role of endothelial cells in this intricate interplay. The expression of SPARCL1 signals its involvement in the remodeling of local tissue, promoting a microenvironment conducive to vesicle formation [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Conversely, CD34, with its recognized impact on vascular permeability regulation, highlights the influence of endothelial cells in modulating interactions through the process of transcytosis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the molecular level, the interaction between the endothelial and inflammatory cells is intricately regulated by signaling pathways, with CAVIN1 emerging as a central coordinator [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The strong expression of CAVIN1 by endothelial cells suggests a strong activation of angiogenic pathways, influencing vascular responses and shaping the complex architecture of skin tissue [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The interconnected nature of these signaling pathways emphasizes the depth of molecular interactions that define the microenvironment of leprosy.\u003c/p\u003e \u003cp\u003eTaken together, our analyses reveal the integration of single-cell and spatial transcriptomic approaches in leprosy provides a detailed understanding of the cellular dynamics, intercellular communication, and molecular mechanisms associated with leprosy immunopathology. Additionally, the identified gene signatures, spatial patterns, and immunomodulatory roles of specific genes like ACKR1 offer novel insights that can inform future therapeutic strategies, diagnostic approaches, and targeted interventions for leprosy and other intracellular bacteria-caused persistent infectious diseases. This study underscores the significance of advanced technologies in unraveling the complexities of the immune microenvironment at a spatial transcriptome and single-cell resolution.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur extensive analysis using single-cell and spatial transcriptomics has provided valuable insights into the complex dynamics of cellular interactions, communication, and molecular processes in leprosy immunopathology. By employing advanced techniques such as scRNA-seq, CellChat analysis, hdWGCNA, and spatial transcriptome profiling, we have uncovered the composition and state of human leprosy cells, revealing diverse interactions among various cell types. Notably, ACKR1 + endothelial cells play a crucial role in the transcytosis process, modulating cytokine signaling within the leprosy microenvironment, especially in lepromatous leprosy. The identified gene signatures and spatial patterns, including the roles of critical genes like ACKR1, CAVIN1, and SPARCL1, offer insights for potential therapeutic strategies and diagnostics. This study underscores the importance of advanced technologies in understanding the complexities of the immune microenvironment at both spatial transcriptome and single-cell resolutions, contributing to a deeper understanding of leprosy and similar persistent infectious diseases caused by intracellular bacteria.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData availability statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong input data processed in the reanalysis, two datasets were acquired from NCBI GEO (GSE130973, GSE151528, GSE167889 and GSE202011). These datasets contribute valuable information to the comprehensive evaluation and interpretation conducted in this study. The data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by a Funda\u0026ccedil;\u0026atilde;o de Amparo \u0026agrave; Pesquisa do Estado de S\u0026atilde;o Paulo (FAPESP).\u0026nbsp;CACF received a (FAPESP) Postdoctoral fellowship (2022/02605-6).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u0026rsquo;s disclosure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical review and approval were not required for the study. We reanalyzed scRNA data from other studies, strictly observing privacy policies and complying with all applicable ethical regulations.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHambridge, Thomas, Shri Lak Nanjan Chandran, Annemieke Geluk, Paul Saunderson, and Jan Hendrik Richardus. 2021. Mycobacterium leprae transmission characteristics during the declining stages of leprosy incidence: A systematic review. \u003cem\u003ePLOS Neglected Tropical Diseases\u003c/em\u003e 15. Public Library of Science: e0009436. https://doi.org/10.1371/JOURNAL.PNTD.0009436.\u003c/li\u003e\n\u003cli\u003evan Hooij, Anouk, Susan van den Eeden, Renate Richardus, Elisa Tjon Kon Fat, Louis Wilson, Kees L.M.C. Franken, Roel Faber, et al. 2019. Application of new host biomarker profiles in quantitative point-of-care tests facilitates leprosy diagnosis in the field. \u003cem\u003eEBioMedicine\u003c/em\u003e 47. Elsevier B.V.: 301\u0026ndash;308. https://doi.org/10.1016/j.ebiom.2019.08.009.\u003c/li\u003e\n\u003cli\u003evan Hooij, Anouk, Maria Ti\u0026oacute;-Coma, Els M. Verhard, Marufa Khatun, Khorshed Alam, Elisa Tjon Kon Fat, Danielle de Jong, et al. 2020. Household Contacts of Leprosy Patients in Endemic Areas Display a Specific Innate Immunity Profile. \u003cem\u003eFrontiers in immunology\u003c/em\u003e 11. Front Immunol. https://doi.org/10.3389/FIMMU.2020.01811.\u003c/li\u003e\n\u003cli\u003ePetersen, Helen J., and Andrew M. Smith. 2013. The Role of the Innate Immune System in Granulomatous Disorders. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e 4. Frontiers Media SA. https://doi.org/10.3389/FIMMU.2013.00120.\u003c/li\u003e\n\u003cli\u003eMa, Feiyang, Travis K. Hughes, Rosane M.B. Teles, Priscila R. Andrade, Bruno J. de Andrade Silva, Olesya Plazyo, Lam C. Tsoi, et al. 2021. The Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas. \u003cem\u003eNature immunology\u003c/em\u003e 22. NIH Public Access: 839. https://doi.org/10.1038/S41590-021-00956-8.\u003c/li\u003e\n\u003cli\u003eGuo, Xinying, Negar Khosraviani, Sneha Raju, Joshya Singh, Nikki Zamani Farahani, Madlene Abramian, Victor J. Torres, et al. 2023. Endothelial ACKR1 is induced by neutrophil contact and down-regulated by secretion in extracellular vesicles. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e 14. Frontiers Media S.A.: 1181016. https://doi.org/10.3389/FIMMU.2023.1181016/BIBTEX.\u003c/li\u003e\n\u003cli\u003eJovic, Dragomirka, Xue Liang, Hua Zeng, Lin Lin, Fengping Xu, and Yonglun Luo. 2022. Single-cell RNA sequencing technologies and applications: A brief overview. \u003cem\u003eClinical and translational medicine\u003c/em\u003e 12. Clin Transl Med. https://doi.org/10.1002/CTM2.694.\u003c/li\u003e\n\u003cli\u003eMa, Chenxi, Chengzhe Yang, Ai Peng, Tianyong Sun, Xiaoli Ji, Jun Mi, Li Wei, Song Shen, and Qiang Feng. 2023. Pan-cancer spatially resolved single-cell analysis reveals the crosstalk between cancer-associated fibroblasts and tumor microenvironment. \u003cem\u003eMolecular Cancer 2023 22:1\u003c/em\u003e 22. BioMed Central: 1\u0026ndash;23. https://doi.org/10.1186/S12943-023-01876-X.\u003c/li\u003e\n\u003cli\u003eWang, Zihao, Yaning Wang, Mengqi Chang, Yuekun Wang, Peng Liu, Jianqiang Wu, Guige Wang, et al. 2023. Single-cell transcriptomic analyses provide insights into the cellular origins and drivers of brain metastasis from lung adenocarcinoma. \u003cem\u003eNeuro-oncology\u003c/em\u003e 25. Neuro Oncol: 1262\u0026ndash;1274. https://doi.org/10.1093/NEUONC/NOAD017.\u003c/li\u003e\n\u003cli\u003eHao, Yuhan, Tim Stuart, Madeline H. Kowalski, Saket Choudhary, Paul Hoffman, Austin Hartman, Avi Srivastava, et al. 2023. Dictionary learning for integrative, multimodal and scalable single-cell analysis. \u003cem\u003eNature Biotechnology 2023\u003c/em\u003e. Nature Publishing Group: 1\u0026ndash;12. https://doi.org/10.1038/s41587-023-01767-y.\u003c/li\u003e\n\u003cli\u003eSol\u0026eacute;-Boldo, Lloren\u0026ccedil;, G\u0026uuml;nter Raddatz, Sabrina Sch\u0026uuml;tz, Jan Philipp Mallm, Karsten Rippe, Anke S. Lonsdorf, Manuel Rodr\u0026iacute;guez-Paredes, and Frank Lyko. 2020. Single-cell transcriptomes of the human skin reveal age-related loss of fibroblast priming. \u003cem\u003eCommunications biology\u003c/em\u003e 3. Commun Biol. https://doi.org/10.1038/S42003-020-0922-4.\u003c/li\u003e\n\u003cli\u003eHao, Yuhan, Stephanie Hao, Erica Andersen-Nissen, William M. Mauck, Shiwei Zheng, Andrew Butler, Maddie J. Lee, et al. 2021. Integrated analysis of multimodal single-cell data. \u003cem\u003eCell\u003c/em\u003e 184. Elsevier B.V.: 3573-3587.e29. https://doi.org/10.1016/J.CELL.2021.04.048/ATTACHMENT/1E5EB5C1-59EE-4B2B-8BFA-14B48A54FF8F/MMC3.XLSX.\u003c/li\u003e\n\u003cli\u003eJin, Suoqin, Christian F. Guerrero-Juarez, Lihua Zhang, Ivan Chang, Raul Ramos, Chen Hsiang Kuan, Peggy Myung, Maksim V. Plikus, and Qing Nie. 2021. Inference and analysis of cell-cell communication using CellChat. \u003cem\u003eNature Communications 2021 12:1\u003c/em\u003e 12. Nature Publishing Group: 1\u0026ndash;20. https://doi.org/10.1038/s41467-021-21246-9.\u003c/li\u003e\n\u003cli\u003eMorabito, Samuel, Emily Miyoshi, Neethu Michael, Saba Shahin, Alessandra Cadete Martini, Elizabeth Head, Justine Silva, Kelsey Leavy, Mari Perez-Rosendahl, and Vivek Swarup. 2021. Single-nucleus chromatin accessibility and transcriptomic characterization of Alzheimer\u0026rsquo;s disease. \u003cem\u003eNature Genetics 2021 53:8\u003c/em\u003e 53. Nature Publishing Group: 1143\u0026ndash;1155. https://doi.org/10.1038/s41588-021-00894-z.\u003c/li\u003e\n\u003cli\u003eCao, Junyue, Malte Spielmann, Xiaojie Qiu, Xingfan Huang, Daniel M. Ibrahim, Andrew J. Hill, Fan Zhang, et al. 2019. The single-cell transcriptional landscape of mammalian organogenesis. \u003cem\u003eNature 2019 566:7745\u003c/em\u003e 566. Nature Publishing Group: 496\u0026ndash;502. https://doi.org/10.1038/s41586-019-0969-x.\u003c/li\u003e\n\u003cli\u003eCastillo, Rochelle L., Ikjot Sidhu, Igor Dolgalev, Tinyi Chu, Aleksandr Prystupa, Ipsita Subudhi, Di Yan, et al. 2023. Spatial transcriptomics stratifies psoriatic disease severity by emergent cellular ecosystems. \u003cem\u003eScience immunology\u003c/em\u003e 8. Sci Immunol. https://doi.org/10.1126/SCIIMMUNOL.ABQ7991.\u003c/li\u003e\n\u003cli\u003eRavi, Vidhya M, Paulina Will, Jan Kueckelhaus, Na Sun, Kevin Joseph, Henrike Sali\u0026eacute;, Lea Vollmer, et al. 2022. Spatially resolved multi-omics deciphers bidirectional tumor-host interdependence in glioblastoma Graphical abstract. \u003cem\u003eCancer Cell\u003c/em\u003e 40: 639-655.e13. https://doi.org/10.1016/j.ccell.2022.05.009.\u003c/li\u003e\n\u003cli\u003eLarsson, Ludvig, Lovisa Franz\u0026eacute;n, Patrik L. St\u0026aring;hl, and Joakim Lundeberg. 2023. Semla: a versatile toolkit for spatially resolved transcriptomics analysis and visualization. \u003cem\u003eBioinformatics\u003c/em\u003e 39. Oxford Academic. https://doi.org/10.1093/BIOINFORMATICS/BTAD626.\u003c/li\u003e\n\u003cli\u003eMa, Feiyang, Travis K. Hughes, Rosane M.B. Teles, Priscila R. Andrade, Bruno J. de Andrade Silva, Olesya Plazyo, Lam C. Tsoi, et al. 2021. The Cellular Architecture of the Antimicrobial Response Network in Human Leprosy Granulomas. \u003cem\u003eNature immunology\u003c/em\u003e 22. NIH Public Access: 839. https://doi.org/10.1038/S41590-021-00956-8.\u003c/li\u003e\n\u003cli\u003eGirbl, Tamara, Tchern Lenn, Lorena Perez, Lo\u0026iuml;c Rolas, Anna Barkaway, Aude Thiriot, Carlos del Fresno, et al. 2018. Distinct Compartmentalization of the Chemokines CXCL1 and CXCL2 and the Atypical Receptor ACKR1 Determine Discrete Stages of Neutrophil Diapedesis. \u003cem\u003eImmunity\u003c/em\u003e 49. Immunity: 1062-1076.e6. https://doi.org/10.1016/J.IMMUNI.2018.09.018.\u003c/li\u003e\n\u003cli\u003eSzpakowska, Martyna, Giulia D\u0026rsquo;Uonnolo, Rafael Lu\u0026iacute;s, Ana Alonso Bartolom\u0026eacute;, Marcus Thelen, Daniel F. Legler, and Andy Chevign\u0026eacute;. 2023. New pairings and deorphanization among the atypical chemokine receptor family \u0026mdash; physiological and clinical relevance. \u003cem\u003eFrontiers in Immunology\u003c/em\u003e 14. Frontiers Media S.A. https://doi.org/10.3389/FIMMU.2023.1133394/FULL.\u003c/li\u003e\n\u003cli\u003eAman, Jurjan, and Coert Margadant. 2023. Integrin-Dependent Cell\u0026ndash;Matrix Adhesion in Endothelial Health and Disease. \u003cem\u003eCirculation Research\u003c/em\u003e 132. Lippincott Williams \u0026amp; Wilkins Hagerstown, MD : 355\u0026ndash;378. https://doi.org/10.1161/CIRCRESAHA.122.322332.\u003c/li\u003e\n\u003cli\u003eNaschberger, Elisabeth, Andrea Liebl, Vera S. Schellerer, Manuela Sch\u0026uuml;tz, Nathalie Britzen-Laurent, Patrick Klbel, Ute Schaal, et al. 2016. Matricellular protein SPARCL1 regulates tumor microenvironment-dependent endothelial cell heterogeneity in colorectal carcinoma. \u003cem\u003eThe Journal of clinical investigation\u003c/em\u003e 126. J Clin Invest: 4187\u0026ndash;4204. https://doi.org/10.1172/JCI78260.\u003c/li\u003e\n\u003cli\u003eMiddleton, James, Stuart Neil, Jonathan Wintle, Ian Clark-Lewis, Henrietta Moore, Lam Charles, Manfred Auer, Hub Elin, and Rot Antal. 1997. Transcytosis and Surface Presentation of IL-8 by Venular Endothelial Cells. \u003cem\u003eCell\u003c/em\u003e 91. Cell Press: 385\u0026ndash;395. https://doi.org/10.1016/S0092-8674(00)80422-5.\u003c/li\u003e\n\u003cli\u003eMalong, Liza, Ilaria Napoli, Giulia Casal, Ian J. White, Salome Stierli, Andrew Vaughan, Anne Laure Cattin, et al. 2023. Characterization of the structure and control of the blood-nerve barrier identifies avenues for therapeutic delivery. \u003cem\u003eDevelopmental cell\u003c/em\u003e 58. Dev Cell: 174-191.e8. https://doi.org/10.1016/J.DEVCEL.2023.01.002.\u003c/li\u003e\n\u003cli\u003eHansen, Carsten Gram, Elena Shvets, Gillian Howard, Kirsi Riento, and Benjamin James Nichols. 2013. Deletion of cavin genes reveals tissue-specific mechanisms for morphogenesis of endothelial caveolae. \u003cem\u003eNature communications\u003c/em\u003e 4. Nat Commun. https://doi.org/10.1038/NCOMMS2808.\u003c/li\u003e\n\u003cli\u003eSowa, Grzegorz. 2012. Caveolae, caveolins, cavins, and endothelial cell function: New insights. \u003cem\u003eFrontiers in Physiology\u003c/em\u003e 2 JAN. Frontiers: 17267. https://doi.org/10.3389/FPHYS.2011.00120/BIBTEX.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Endothelial Heterogeneity, Cytokine Signaling, Disease-associated endothelial cells, Granuloma Dynamics, Vesicle formation","lastPublishedDoi":"10.21203/rs.3.rs-3829511/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3829511/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLeprosy exhibits a dysregulated immune response, leading to excessive and inefficient inflammatory action. This can result in nerve damage, granuloma formation, and other clinical manifestations.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eSingle-cell RNA-sequencing (scRNA-seq) and spatial transcriptomic analyses were applied to explore the intricate cellular landscape of leprosy, focusing on endothelial cells. The study encompasses a dataset of 36,517 cells obtained from normal skin, clinical form lepromatous leprosy (LL), and reversal reaction (RR). We applied advanced techniques, including pseudotime trajectory analysis, cell\u0026ndash;cell interaction studies, and high-dimensional weighted gene co-expression network analysis (hdWGCNA).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe profiling of cellular composition revealed significant disparities among leprosy types, emphasizing the role of specific cell types in each condition. CellChat and hdWGCNA analysis unveiled intricate intercellular interactions in the leprosy microenvironment, with a focus on the ACKR1 gene-mediated cytokine regulation in endothelial cells. Disease-associated endothelial cells highlighted a unique gene signature associated with vesicle-related processes, suggesting their involvement in vascular alterations in leprosy. Spatial transcriptome profiling in normal skin and leprosy sections provided insights into the heterogeneity of parenchyma cells, with distinct clusters observed in lepromatous leprosy. ACKR1 exhibited high expression in regions enriched with endothelial cells only in lepromatous leprosy, indicating a localized mechanism for cytokine regulation.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe central involvement of ACKR1\u0026thinsp;+\u0026thinsp;endothelial cells in transcytosis and cytokine regulation provides potential avenues for therapeutic exploration. This study underscores the importance of advanced technologies in comprehending immune microenvironments for targeted interventions in leprosy and related infectious diseases.\u003c/p\u003e","manuscriptTitle":"Single‐cell and spatial transcriptomics reveal ACKR1+ endothelial cells associated with transcytosis in Leprosy","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-04 08:05:06","doi":"10.21203/rs.3.rs-3829511/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"91844998-8373-478a-ba9e-059c1b4b6b4e","owner":[],"postedDate":"January 4th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-20T13:51:36+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-04 08:05:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3829511","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3829511","identity":"rs-3829511","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}