Ligand-receptor Pairs Research Articles

CollectNET: a web server for integrated inference of cell-cell communication network.

Cell-cell communication (CCC) through ligand-receptor (L-R) pairs forms the cornerstone for complex functionalities in multicellular organisms. Deciphering such intercellular signaling can contribute to unraveling disease mechanisms and enable targeted therapy. Nonetheless, notable biases and inconsistencies are evident among the inferential outcomes generated by current methods for inferring CCC network. To fill this gap, we developed collectNET (http://health.tsinghua.edu.cn/collectnet) as a comprehensive web platform for analyzing CCC network, with efficient calculation, hierarchical browsing, comprehensive statistics, advanced searching, and intuitive visualization. collectNET provides a reliable online inference service with prior knowledge of three public L-R databases and systematic integration of three mainstream inference methods. Additionally, collectNET has assembled a human CCC atlas, including 126 785 significant communication pairs based on 343 023 cells. We anticipate that collectNET will benefit researchers in gaining a more holistic understanding of cell development and differentiation mechanisms. Database URL: http://health.tsinghua.edu.cn/collectnet.

Single-cell and Spatial Transcriptomic Analyses Implicate Formation of the Immunosuppressive Microenvironment during Breast Tumor Progression.

Ductal carcinoma in situ and invasive ductal carcinoma represent two stages of breast cancer progression. A multitude of studies have shown that genomic instability increases during tumor development, as manifested by higher mutation and copy number variation rates. The advent of single-cell and spatial transcriptomics has enabled the investigation of the subtle differences in cellular states during the tumor progression at single-cell level, thereby providing more nuanced understanding of the intercellular interactions within the solid tumor. However, the evolutionary trajectory of tumor cells and the establishment of the immunosuppressive microenvironment during breast cancer progression remain unclear. In this study, we performed an exploratory analysis of the single-cell sequencing dataset of 13 ductal carcinoma in situ and invasive ductal carcinoma samples. We revealed that tumor cells became more malignant and aggressive during their progression, and T cells transited to an exhausted state. The tumor cells expressed various coinhibitory ligands that interacted with the receptors of immune cells to create an immunosuppressive tumor microenvironment. Furthermore, spatial transcriptomics data confirmed the spatial colocalization of tumor and immune cells, as well as the expression of the coinhibitory ligand-receptor pairs. Our analysis provides insights into the cellular and molecular mechanism underlying the formation of the immunosuppressive landscape during two typical stages of breast cancer progression.

CellChat for systematic analysis of cell-cell communication from single-cell transcriptomics.

Recent advances in single-cell sequencing technologies offer an opportunity to explore cell-cell communication in tissues systematically and with reduced bias. A key challenge is integrating known molecular interactions and measurements into a framework to identify and analyze complex cell-cell communication networks. Previously, we developed a computational tool, named CellChat, that infers and analyzes cell-cell communication networks from single-cell transcriptomic data within an easily interpretable framework. CellChat quantifies the signaling communication probability between two cell groups using a simplified mass-action-based model, which incorporates the core interaction between ligands and receptors with multisubunit structure along with modulation by cofactors. Importantly, CellChat performs a systematic and comparative analysis of cell-cell communication using a variety of quantitative metrics and machine-learning approaches. CellChat v2 is an updated version that includes additional comparison functionalities, an expanded database of ligand-receptor pairs along with rich functional annotations, and an Interactive CellChat Explorer. Here we provide a step-by-step protocol for using CellChat v2 on single-cell transcriptomic data, including inference and analysis of cell-cell communication from one dataset and identification of altered intercellular communication, signals and cell populations from different datasets across biological conditions. The R implementation of CellChat v2 toolkit and its tutorials together with the graphic outputs are available at https://github.com/jinworks/CellChat . This protocol typically takes ~5 min depending on dataset size and requires a basic understanding of R and single-cell data analysis but no specialized bioinformatics training for its implementation.

The Interactions of T Cells with Myeloid-Derived Suppressor Cells in Peripheral Blood Stem Cell Grafts.

The interaction of myeloid-derived suppressor cells (MDSCs) with T cells within G-CSF-mobilized peripheral blood stem cell (PBSC) grafts in patients undergoing autologous or allogeneic hematopoietic stem cell transplantation remains to be elucidated. Through studying allo- and auto-PBSC grafts, we observed grafts containing large numbers of T cells and MDSCs with intergraft variability in their percentage and number. T cells from autologous grafts compared to allografts expressed relative higher percentages of inhibitory receptors PD-1, CTLA-4, TIM-3, LAG-3, TIGIT and BTLA. Autograft T cells had decreased cell proliferation and IFN-γ secretion, which supported the possible presence of T cell exhaustion. On the contrary, graft monocytic MDSCs (M-MDSCs) expressed multiple inhibitory receptor ligands, including PD-L1, CD86, Galectin-9, HVEM and CD155. The expression of inhibitory receptor ligands on M-MDSCs was correlated with their corresponding inhibitory receptors on T cells in the grafts. Isolated M-MDSCs had the ability to suppress T cell proliferation and IFN-γ secretion and/or promote Treg expansion. Blocking the PD-L1-PD-1 signaling pathway partially reversed the functions of M-MDSCs. Taken together, our data indicated that T cells and M-MDSCs in PBSC grafts express complementary inhibitory receptor-ligand pairing, which may impact the quality of immune recovery and clinical outcome post transplantation.

Single-cell RNA sequencing reveals the differentiation and regulation of endplate cells in human intervertebral disc degeneration

Low back pain (LBP) is largely attributed to intervertebral disc degeneration (IVDD), of which the endplate changes are an important component. However, the alterations in cell fate and properties within the endplates during degeneration remain unknown. Here, we firstly performed the single-cell RNA-sequencing analysis (scRNA-seq) of the cells focusing on degenerative human endplates. By unsupervised clustering of the 8,534 single-cell based on the gene expression, we identified nine distinct cell types. We employed Gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways analysis, and the single-cell regulatory network inference and clustering (SCENIC) to determine the enriched pathways and transcriptional activities across seven chondrocyte subpopulations. Furthermore, two cell fates of chondrocyte differentiation were found by trajectory analysis, one was enriched in inflammation-related genes, and the other was related to extracellular matrix (ECM). Additionally, the intercellular interactions of macrophages (MA) and chondrocytes, T cells/natural killer cells (T/NK) and chondrocytes were examined by ligand-receptor pairs analysis, showing the important regulative function of FN1 from MA and CD74 from T/NK during endplate degeneration. Overall, our findings provide novel perspectives on the endplate degeneration at the single-cell level and a whole-transcriptome size.

Immune escape between endoplasmic reticulum stress-related cancer cells and exhausted CD8+T cells leads to neoadjuvant chemotherapy resistance in ovarian cancer

Our study aims to explore the effects of neoadjuvant chemotherapy (NACT) on tumour cells and immune cells in the immune microenvironment of patients with high-grade serous ovarian cancer (HGSOC).Single-cell RNA sequencing data of paired ovarian cancer tissues were analysed before and after NACT in 11 patients with HGSOC. The effect of NACT on two major cell components of the tumour microenvironment, epithelial cells and CD8+T cells, was investigated. The mechanisms of epithelial cell evasion by NACT and immune killing were explored from the perspectives of gene expression, functional characteristics, transcriptional regulation, and cell communication. Key targets for reversing NACT resistance were identified and possible therapeutic strategies proposed.While NACT improved the de novo differentiation of anti-tumour CD8+T cells, enhancing their anti-tumour function, it increased the proportion of cancer cells with high HSP90B1 expression. Thus, the potential reasons for NACT resistance were identified as: 1) high levels of endoplasmic reticulum stress (ERS) characteristics, 2) high expression of the MDK-NCL ligand-receptor pair between them and exhausted CD8+T cells before NACT, and 3) high expression of the NECTIN2-TIGIT immune ligand-receptor pair between them and exhausted CD8+T cells after NACT.Thus, our study reveals the mechanisms underlying NACT resistance in patients with HGSOC from the perspective of the independent and interactive roles of cancer cells and CD8+T cells. We propose therapeutic strategies targeting the ERS marker HSP90B1 and the immune escape marker MDK before or during NACT, while targeting NECTIN2 blockade after NACT. This approach may offer new insights into combination treatments for patients with HGSOC displaying NACT resistance.

Dimension reduction, cell clustering, and cell–cell communication inference for single-cell transcriptomics with DcjComm

Advances in single-cell transcriptomics provide an unprecedented opportunity to explore complex biological processes. However, computational methods for analyzing single-cell transcriptomics still have room for improvement especially in dimension reduction, cell clustering, and cell–cell communication inference. Herein, we propose a versatile method, named DcjComm, for comprehensive analysis of single-cell transcriptomics. DcjComm detects functional modules to explore expression patterns and performs dimension reduction and clustering to discover cellular identities by the non-negative matrix factorization-based joint learning model. DcjComm then infers cell–cell communication by integrating ligand-receptor pairs, transcription factors, and target genes. DcjComm demonstrates superior performance compared to state-of-the-art methods.

STAN, a computational framework for inferring spatially informed transcription factor activity.

Transcription factors (TFs) drive significant cellular changes in response to environmental cues and intercellular signaling. Neighboring cells influence TF activity and, consequently, cellular fate and function. Spatial transcriptomics (ST) captures mRNA expression patterns across tissue samples, enabling characterization of the local microenvironment. However, these datasets have not been fully leveraged to systematically estimate TF activity governing cell identity. Here, we present STAN ( S patially informed T ranscription factor A ctivity N etwork), a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. We tested STAN using lymph node, breast cancer, and glioblastoma ST datasets to demonstrate its applicability by identifying TFs associated with specific cell types, spatial domains, pathological regions, and ligand‒receptor pairs. STAN augments the utility of STs to reveal the intricate interplay between TFs and spatial organization across a spectrum of cellular contexts.

A-RFP: An Adaptive Residue Flexibility Prediction Method Improving Protein-ligand Docking Based on Homologous Proteins

Background: Computational molecular docking plays an important role in determining the precise receptor-ligand conformation, which becomes a powerful tool for drug discovery. In the past 30 years, most computational docking methods have treated the receptor structure as a rigid body, although flexible docking often yields higher accuracy. The main disadvantage of flexible docking is its significantly higher computational cost. Due to the fact that different protein pocket residues exhibit different degrees of flexibility, semi-flexible docking methods, balancing rigid docking and flexible docking, have demonstrated success in predicting highly accurate conformations with a relatively low computational cost. Methods: In our study, the number of flexible pocket residues was assessed by quantitative analysis, and a novel adaptive residue flexibility prediction method, named A-RFP, was proposed to improve the docking performance. Based on the homologous information, a joint strategy is used to predict the pocket residue flexibility by combining RMSD, the distance between the residue sidechain and the ligand, and the sidechain orientation. For each receptor-ligand pair, A-RFP provides a docking conformation with the optimal affinity. Results: By analyzing the docking affinities of 3507 target-ligand pairs in 5 different values ranging from 0 to 10, we found there is a general trend that the larger number of flexible residues inevitably improves the docking results by using Autodock Vina. However, a certain number of counterexamples still exist. To validate the effectiveness of A-RFP, the experimental assessment was tested in a small-scale virtual screening on 5 proteins, which confirmed that A-RFP could enhance the docking performance. And the flexible-receptor virtual screening on a low-similarity dataset with 85 receptors validates the accuracy of residue flexibility comprehensive evaluation. Moreover, we studied three receptors with FDA-approved drugs, which further proved A-RFP can play a suitable role in ligand discovery. Conclusion: Our analysis confirms that the screening performance of the various numbers of flexible residues varies wildly across receptors. It suggests that a fine-grained docking method would offset the aforementioned deficiency. Thus, we presented A-RFP, an adaptive pocket residue flexibility prediction method based on homologous information. Without considering computational resources and time costs, A-RFP provides the optimal docking result.

Single-cell sequencing reveals glial cell involvement in development of neuropathic pain via myelin sheath lesion formation in the spinal cord

BackgroundNeuropathic pain (NP), which results from injury or lesion of the somatosensory nervous system, is intimately associated with glial cells. The roles of microglia and astrocytes in NP have been broadly described, while studies on oligodendrocytes have largely focused on axonal myelination. The mechanisms of oligodendrocytes and their interactions with other glial cells in NP development remain uncertain.MethodsTo explore the function of the interaction of the three glial cells and their interactions on myelin development in NP, we evaluated changes in NP and myelin morphology after a chronic constriction injury (CCI) model in mice, and used single-cell sequencing to reveal the subpopulations characteristics of oligodendrocytes, microglia, and astrocytes in the spinal cord tissues, as well as their relationship with myelin lesions; the proliferation and differentiation trajectories of oligodendrocyte subpopulations were also revealed using pseudotime cell trajectory and RNA velocity analysis. In addition, we identified chemokine ligand-receptor pairs between glial cells by cellular communication and verified them using immunofluorescence.ResultsOur study showed that NP peaked on day 7 after CCI in mice, a time at which myelin lesions were present in both the spinal cord and sciatic nerve. Oligodendrocytes, microglia, and astrocytes subpopulations in spinal cord tissue were heterogeneous after CCI and all were involved in suppressing the process of immune defense and myelin production. In addition, the differentiation trajectory of oligodendrocytes involved a unidirectional lattice process of OPC-1-Oligo-9, which was arrested at the Oligo-2 stage under the influence of microglia and astrocytes. And the CADM1-CADM1, NRP1-VEGFA interactions between glial cells are enhanced after CCI and they had a key role in myelin lesions and demyelination.ConclusionsOur study reveals the close relationship between the differentiation block of oligodendrocytes after CCI and their interaction with microglia and astrocytes-mediated myelin lesions and NP. CADM1/CADM1 and NRP-1/VEGFA may serve as potential therapeutic targets for use in the treatment of NP.

Single-cell transcriptome sequencing reveals altered peripheral blood immune cells in patients with severe tuberculosis

Tuberculosis is a serious global health burden, resulting in millions of deaths each year. Several circulating cell subsets in the peripheral blood are known to modulate the host immune response to Mycobacterium tuberculosis (Mtb) infection in different ways. However, the characteristics and functions of these subsets to varying stages of tuberculosis infection have not been well elucidated. Peripheral blood immune cells (PBICs) were isolated from healthy donors (HD group), individuals with mild tuberculosis (MI group), and individuals with severe tuberculosis (SE group). CD4+ naive T cells and CD8+ T cells were decreased in the SE and MI groups, while CD14+ monocytes were increased in the SE group. Further analysis revealed increased activated CD4+ T cells, transitional CD8+ T cells, memory-like NK cells, and IGHG3highTTNhighFCRL5high B cells were increased in all patients with tuberculosis (SE and MI group). In contrast, Th17 cells, cytotoxic NK cells, and cytotoxic CD4+ T cells were decreased. Moreover, the increase of CD14+CD16+ monocytes correlated with severe tuberculosis, and the GBP5highRSAD2high neutrophils were unique to patients with severe tuberculosis. Cellular communication analysis revealed that CD8+ T cells exhibited the highest incoming interaction strength in the SE group. The increased CD8+ T cell incoming interactions are associated with the MHC-I and LCK pathways, with HLA-(A-E)-CD8A, HLA-(A-E)-CD8B, and LCK-(CD8A+CD8B) being ligand–receptor pairs. Patients with tuberculosis, especially severe tuberculosis, have profound changes in peripheral blood immune cell profiles. CD8+ T cells showed the highest incoming interaction strength in patients with severe tuberculosis, with the main signals being MHC-I and LCK pathways.

Characteristics of blood–brain barrier heterogeneity between brain regions revealed by profiling vascular and perivascular cells

The blood–brain barrier (BBB) protects the brain and maintains neuronal homeostasis. BBB properties can vary between brain regions to support regional functions, yet how BBB heterogeneity occurs is poorly understood. Here, we used single-cell and spatial transcriptomics to compare the mouse median eminence, one of the circumventricular organs that has naturally leaky blood vessels, with the cortex. We identified hundreds of molecular differences in endothelial cells (ECs) and perivascular cells, including astrocytes, pericytes and fibroblasts. Using electron microscopy and an aqueous-based tissue-clearing method, we revealed distinct anatomical specializations and interaction patterns of ECs and perivascular cells in these regions. Finally, we identified candidate regionally enriched EC–perivascular cell ligand–receptor pairs. Our results indicate that both molecular specializations in ECs and unique EC–perivascular cell interactions contribute to BBB functional heterogeneity. This platform can be used to investigate BBB heterogeneity in other regions and may facilitate the development of central nervous system region-specific therapeutics.

Identification of regulatory networks and crosstalk factors in brown adipose tissue and liver of a cold-exposed cardiometabolic mouse model

BackgroundActivation of brown adipose tissue (BAT) has gained attention due to its ability to dissipate energy and counteract cardiometabolic diseases (CMDs).MethodsThis study investigated the consequences of cold exposure on the BAT and liver proteomes of an established CMD mouse model based on LDL receptor-deficient (LdlrKO) mice fed a high-fat, high-sucrose, high-cholesterol diet for 16 weeks. We analyzed energy metabolism in vivo and performed untargeted proteomics on BAT and liver of LdlrKO mice maintained at 22 °C or 5 °C for 7 days.ResultsWe identified several dysregulated pathways, miRNAs, and transcription factors in BAT and liver of cold-exposed Ldlrko mice that have not been previously described in this context. Networks of regulatory interactions based on shared downstream targets and analysis of ligand-receptor pairs identified fibrinogen alpha chain (FGA) and fibronectin 1 (FN1) as potential crosstalk factors between BAT and liver in response to cold exposure. Importantly, genetic variations in the genes encoding FGA and FN1 have been associated with cardiometabolic-related phenotypes and traits in humans.DiscussionThis study describes the key factors, pathways, and regulatory networks involved in the crosstalk between BAT and the liver in a cold-exposed CMD mouse model. These findings may provide a basis for future studies aimed at testing whether molecular mediators, as well as regulatory and signaling mechanisms involved in tissue adaption upon cold exposure, could represent a target in cardiometabolic disorders.Graphical abstract

Integrative analysis of single-Cell RNA sequencing and experimental validation in the study of abdominal aortic aneurysm progression

BackgroundAbdominal aortic aneurysm (AAA) is a complex vascular disorder characterized by the progressive dilation of the abdominal aorta, with a high risk of rupture and mortality.Understanding the cellular interactions and molecular mechanisms underlying AAA development is critical for identifying potential therapeutic targets. MethodsThis study utilized datasets GSE197748, GSE164678 and GSE183464 from the GEO database, encompassing bulk and single-cell RNA sequencing data from AAA and control samples. We performed principal component analysis, differential expression analysis, and functional enrichment analysis to identify key pathways involved in AAA. Cell-cell interactions were investigated using CellPhoneDB, focusing on fibroblasts, vascular smooth muscle cells (VSMCs), and macrophages. We further validated our findings using a mouse model of AAA induced by porcine pancreatic enzyme infusion, followed by gene expression analysis and co-immunoprecipitation experiments. ResultsOur analysis revealed significant alterations in gene expression profiles between AAA and control samples, with a pronounced immune response and cell adhesion pathways being implicated. Single-cell RNA sequencing data highlighted an increased proportion of pro-inflammatory macrophages, along with changes in the composition of fibroblasts and VSMCs in AAA. CellPhoneDB analysis identified critical ligand-receptor interactions, notably collagen type I alpha 1 chain (COL1A1)/COL1A2-CD18 and thrombospondin 1 (THBS1)-CD3, suggesting complex communication networks between fibroblasts and VSMCs. In vivo experiments confirmed the upregulation of these genes in AAA mice and demonstrated the functional interaction between COL1A1/COL1A2 and CD18. ConclusionThe interaction between fibroblasts and VSMCs, mediated by specific ligand-receptor pairs such as COL1A1/COL1A2-CD18 and THBS1-CD3, plays a pivotal role in AAA pathogenesis.

Abstract We024: Spatial Transcriptomics Identifies a Regeneration-Permissive Microenvironment in the Neonatal Mammalian Heart

Ischemic heart disease is the leading cause of death globally, largely driven by inability of the adult heart to restore tissue structure and function after injury. However, the neonatal mouse heart is able to regenerate up to one week after birth, circumventing scarring and functional impairment. Neonatal heart regeneration is a spatially and temporally regulated process, involving coordinated communication of different cardiac cell types. Our previous work identified a regenerative cardiomyocyte population (CM4) that proliferates after myocardial infarction (MI), but the intercellular communication that induces their regenerative response is not fully understood. To study the cellular environment of CM4 cells during neonatal heart regeneration, we profiled spatial transcriptomes of regenerative P1 hearts at 3 days and 7 days after MI and compared them to nonregenerative P8 hearts. We identified unique spatial regions with distinct gene expression signatures, that we refer to as “molecular niches”, in the ischemic and border zones (IZ, BZ) of the P1 heart post-MI compared to P8 hearts. Cell type deconvolution of molecular niches showed a distinct temporal change of fibroblast and macrophage abundance in the IZ of P1 hearts compared to P8 hearts and also showed the localization of CM4 cells to the BZ of P1 hearts at 7 days post-MI. To identify intercellular communication that contributes to the differential injury response in P1 and P8 hearts, we analyzed ligand-receptor pairs that were expressed at higher levels in the IZ and BZ of P1 compared to P8 hearts. Among the nearly 50 signaling pairs identified, OSM and SLIT signaling have been previously implicated in heart regeneration. Notably, many of these signaling factors, including cytokines, chemokines and growth factors, have receptors expressed in multiple cardiac cell types. Thus, ongoing studies are systematically distilling the effect of each secreted factor on individual cell types in the heart using a cardiac cell co-culture and multiplexed single-cell RNA sequencing approach. In summary, our study provides insight into the spatial organization and tissue microenvironment of neonatal heart regeneration and identifies a number of candidate signaling pathways that potentially mediate critical intercellular communication during neonatal heart regeneration.

Single-cell RNA sequencing and spatial transcriptome reveal potential molecular mechanisms of lung cancer brain metastasis

BackgroundLung cancer is a highly aggressive and prevalent disease worldwide. By the time it is first diagnosed, distant metastases have usually already occurred. Among them, the prognosis of patients with brain metastasis from lung cancer is very poor. Therefore, it is particularly important to identify the evolutionary status of tumor cells during lung cancer brain metastases and discover the underlying mechanisms of lung cancer brain metastases. MethodsIn this study, we analysed three types of data: single-cell RNA sequencing, bulk RNA sequencing, and spatial transcriptome. Firstly, we identified early metastatic epithelial cell clusters (EMEC) using CNV and trajectory analysis in scRNA-seq data. Secondly, we integrated scRNA-seq and spatial transcriptome data with the help of MIA (Multimodal intersection analysis) to explore the biological characteristics of EMEC. Finally, we used bulk RNA-seq data to validate the molecular characteristics of EMEC. ResultA total of 55,763 single cells were obtained and divided into 9 cell types. In brain metastasis, we found a significantly higher proportion of epithelial cells. In addition, we identified a specific subpopulation of epithelial cells, which was named as “early metastatic epithelial cell clusters (EMEC)”. It is enriched in oxidative phosphorylation, coagulation, complement. Moreover, we also found that EMEC underwent cellular communication with other immune cells through ligand-receptor pairs such as MIF-(CD74 + CXCR4) and MIF-(CD74 + CD44). Next, we validated that EMEC were associated with poor clinical prognosis using three independent external datasets. Finally, spatial transcriptome analysis revealed specificity in the spatial distribution of EMEC, which shifted from the peripheral regions to the central regions of the tumour as the depth of tumor invasion progressed. ConclusionThis study reveals the potential molecular mechanisms of lung cancer brain metastasis from both single-cell and spatial transcriptomic perspectives, providing biological insights and clinical reference value for detecting patients suffering from lung cancer brain metastasis.

Function of unconventional T cells in oral lichen planus revealed by single-cell RNA sequencing.

We intended to map the single-cell profile of OLP, explore the molecular characteristics of unconventional T cells in OLP tissues. Buccal mucosa samples from OLP patients and healthy individuals were used to prepare single-cell suspension. Single-cell RNA sequencing was used to analyze the proportion of all the cells, and the molecular characteristics of unconventional T cells. Immunohistochemical staining was used to detect the expression of unconventional T cells marker genes. The cell clusters from buccal mucosa were categorized into immune cells, fibroblasts, endothelial cells, and epithelial cells. Unconventional T cells with phenotype of CD247+TRDC+NCAM1+ were identified. Immunohistochemical staining revealed higher expression of unconventional T cell marker genes in OLP tissue, predominantly in the lamina propria. In OLP, unconventional T cells are in a unique stress response state, exhibited enhanced NF-κB signaling and apoptosis inhibition, enhanced heat shock protein genes expression, weakened cytotoxic function. A large number of ligand-receptor pairs were found between unconventional T cells and other cells, particularly with fibroblasts and endothelial cells. This study mapped the single-cell profile of OLP, delineated the molecular characteristics of unconventional T cells in OLP, and uncovered that these unconventional T cells are in a stress response state.

ScHyper: reconstructing cell-cell communication through hypergraph neural networks.

Cell-cell communications is crucial for the regulation of cellular life and the establishment of cellular relationships. Most approaches of inferring intercellular communications from single-cell RNA sequencing (scRNA-seq) data lack a comprehensive global network view of multilayered communications. In this context, we propose scHyper, a new method that can infer intercellular communications from a global network perspective and identify the potential impact of all cells, ligand, and receptor expression on the communication score. scHyper designed a new way to represent tripartite relationships, by extracting a heterogeneous hypergraph that includes the source (ligand expression), the target (receptor expression), and the relevant ligand-receptor (L-R) pairs. scHyper is based on hypergraph representation learning, which measures the degree of match between the intrinsic attributes (static embeddings) of nodes and their observed behaviors (dynamic embeddings) in the context (hyperedges), quantifies the probability of forming hyperedges, and thus reconstructs the cell-cell communication score. Additionally, to effectively mine the key mechanisms of signal transmission, we collect a rich dataset of multisubunit complex L-R pairs and propose a nonparametric test to determine significant intercellular communications. Comparing with other tools indicates that scHyper exhibits superior performance and functionality. Experimental results on the human tumor microenvironment and immune cells demonstrate that scHyper offers reliable and unique capabilities for analyzing intercellular communication networks. Therefore, we introduced an effective strategy that can build high-order interaction patterns, surpassing the limitations of most methods that can only handle low-order interactions, thus more accurately interpreting the complexity of intercellular communications.

A Single Trophoblast Layer Acts as the Gatekeeper at the Endothelial-Hematopoietic Crossroad in the Placenta.

During embryonic development the placental vasculature acts as a major hematopoietic niche, where endothelial to hematopoietic transition ensures emergence of hematopoietic stem cells (HSCs). However, the molecular mechanisms that regulate the placental hematoendothelial niche are poorly understood. Using a parietal trophoblast giant cell (TGC)-specific knockout mouse model and single-cell RNA-sequencing, we show that the paracrine factors secreted by the TGCs are critical in the development of this niche. Disruptions in the TGC-specific paracrine signaling leads to the loss of HSC population and the concomitant expansion of a KDR+/DLL4+/PROM1+ hematoendothelial cell-population in the placenta. Combining single-cell transcriptomics and receptor-ligand pair analyses, we also define the parietal TGC-dependent paracrine signaling network and identify Integrin signaling as a fundamental regulator of this process. Our study elucidates novel mechanisms by which non-autonomous signaling from the primary parietal TGCs maintain the delicate placental hematopoietic-angiogenic balance and ensures embryonic and extraembryonic development.

Single-cell transcriptomics analysis of bullous pemphigoid unveils immune-stromal crosstalk in type 2 inflammatory disease

Bullous pemphigoid (BP) is a type 2 inflammation- and immunity-driven skin disease, yet a comprehensive understanding of the immune landscape, particularly immune-stromal crosstalk in BP, remains elusive. Herein, using single-cell RNA sequencing (scRNA-seq) and in vitro functional analyzes, we pinpoint Th2 cells, dendritic cells (DCs), and fibroblasts as crucial cell populations. The IL13-IL13RA1 ligand–receptor pair is identified as the most significant mediator of immune-stromal crosstalk in BP. Notably, fibroblasts and DCs expressing IL13RA1 respond to IL13-secreting Th2 cells, thereby amplifying Th2 cell-mediated cascade responses, which occurs through the specific upregulation of PLA2G2A in fibroblasts and CCL17 in myeloid cells, creating a positive feedback loop integral to immune-stromal crosstalk. Furthermore, PLA2G2A and CCL17 contribute to an increased titer of pathogenic anti-BP180-NC16A autoantibodies in BP patients. Our work provides a comprehensive insight into BP pathogenesis and shows a mechanism governing immune-stromal interactions, providing potential avenues for future therapeutic research.