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Single-cell RNA sequencing (scRNA-seq) has contributed to understanding cellular heterogeneity and immune profiling in cancer. The aim of the study was to investigate gene expression and immune profiling in colorectal cancer (CRC) using scRNA-seq. We analyzed single-cell gene expression and T cell receptor (TCR) sequences in 30 pairs of CRC and matched normal tissue. Intratumoral lymphocytes were measured with digital image analysis. CRC had more T cells, epithelial cells, and myeloid cells than normal colorectal tissue. CRCs with microsatellite instability had more abundant T cells than those without microsatellite instability. Immune cell compositions of CRC and normal colorectal tissue were inversely correlated. CD4 + or CD8 + proliferating T cells, CD4 + effector memory T cells, CD8 + naïve T cells, and regulatory T cells of CRC showed higher TCR clonal expansion. Tumor epithelial cells interacted with immune cells more strongly than normal. T cells, myeloid cells, and fibroblasts from CRCs of expanded T cell clonotypes showed increased expression of genes related to TNF and NFKB signaling and T cell activation. CRCs of expanded T cell clonotypes also showed stronger cellular interactions among immune cells, fibroblasts, and endothelial cells. Pro-inflammatory CXCL and TNF signaling were activated in CRCs of expanded T cell clonotype. In conclusion, scRNA-seq analysis revealed different immune cell compositions, differential gene expression, and diverse TCR clonotype dynamics in CRC. TCR clonality expansion is associated with immune activation through T cell signaling and chemokine signaling. Patients with CRCs of expanded clonotype can be promising candidates for immunotherapy.

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PURPOSE: By utilizing both protein and mRNA expression patterns, we can identify more detailed and diverse immune cells, providing insights into understanding the complex immune landscape in cancer ecosystems. MATERIALS AND METHODS: This study was performed by obtaining publicly available Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq) data of peripheral blood mononuclear cells (PBMCs) from the Gene Expression Omnibus database. A total of 94674 total cells were analyzed, of which 32412 were T cells. There were 228 protein features and 16262 mRNA features in the data. The Seurat package was used for quality control and preprocessing, principal component analysis was performed, and Uniform Manifold Approximation and Projection was used to visualize the clusters. Protein and mRNA levels in the CITE-seq were analyzed. RESULTS: We observed that a subset of T cells in the clusters generated at the protein level divided better. By identifying mRNA markers that were highly correlated with the CD4 and CD8 proteins and cross-validating CD26 and CD99 markers using flow cytometry, we found that CD4+ and CD8+ T cells were better discriminated in PBMCs. Weighted Nearest Neighbor clustering results identified a previously unobserved T cell subset. CONCLUSION: In this study, we used CITE-seq data to confirm that protein expression patterns could be used to identify cells more precisely. These findings will improve our understanding of the heterogeneity of immune cells in the future and provide valuable insights into the complexity of the immune response in health and disease.

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BACKGROUND: Cerebral vascular malformations (CCMs) are primarily found within the brain, where they result in increased risk for stroke, seizures, and focal neurological deficits. The unique feature of the brain vasculature is the blood-brain barrier formed by the brain neurovascular unit. Recent studies suggest that loss of CCM genes causes disruptions of blood-brain barrier integrity as the inciting events for CCM development. CCM lesions are proposed to be initially derived from a single clonal expansion of a subset of angiogenic venous capillary endothelial cells (ECs) and respective resident endothelial progenitor cells (EPCs). However, the critical signaling events in the subclass of brain ECs/EPCs for CCM lesion initiation and progression are unclear. METHODS: Brain EC-specific CCM3-deficient (Pdcd10BECKO) mice were generated by crossing Pdcd10fl/fl mice with Mfsd2a-CreERT2 mice. Single-cell RNA-sequencing analyses were performed by the chromium single-cell platform (10× genomics). Cell clusters were annotated into EC subtypes based on visual inspection and GO analyses. Cerebral vessels were visualized by 2-photon in vivo imaging and tissue immunofluorescence analyses. Regulation of mTOR (mechanistic target of rapamycin) signaling by CCM3 and Cav1 (caveolin-1) was performed by cell biology and biochemical approaches. RESULTS: Single-cell RNA-sequencing analyses from P10 Pdcd10BECKO mice harboring visible CCM lesions identified upregulated CCM lesion signature and mitotic EC clusters but decreased blood-brain barrier-associated EC clusters. However, a unique EPC cluster with high expression levels of stem cell markers enriched with mTOR signaling was identified from early stages of the P6 Pdcd10BECKO brain. Indeed, mTOR signaling was upregulated in both mouse and human CCM lesions. Genetic deficiency of Raptor (regulatory-associated protein of mTOR), but not of Rictor (rapamycin-insensitive companion of mTOR), prevented CCM lesion formation in the Pdcd10BECKO model. Importantly, the mTORC1 (mTOR complex 1) pharmacological inhibitor rapamycin suppressed EPC proliferation and ameliorated CCM pathogenesis in Pdcd10BECKO mice. Mechanistic studies suggested that Cav1/caveolae increased in CCM3-depleted EPC-mediated intracellular trafficking and complex formation of the mTORC1 signaling proteins. CONCLUSIONS: CCM3 is critical for maintaining blood-brain barrier integrity and CCM3 loss-induced mTORC1 signaling in brain EPCs initiates and facilitates CCM pathogenesis.

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Dermatofibrosarcoma protuberans (DFSP) is a rare cutaneous sarcoma characterized by the COL1A1-PDGFB fusion gene. This study utilized single-cell RNA sequencing to dissect the cellular and molecular landscape of primary DFSP. Distinct DFSP cell clusters, exhibiting fibroblast-like traits, revealed variations in pathways associated with proliferation, inflammation and metabolism. Differential gene expression analysis during the differentiation from tumour stem cells to DFSP cells unveiled SMOC2, DCN and TGFBR3 as potential regulators of tumour invasion and immune infiltration through VEGF/TGF-ß signalling modulation. Cellular communication analysis highlighted interactions within DFSP cell clusters and with endothelial cells, implicating molecules such as NAMPT, ANGPT2 and PTN in pathogenesis and treatment resistance. These findings offer insights into DFSP intratumour heterogeneity, elucidate molecular mechanisms underlying tumour behaviour, and suggest potential therapeutic targets.

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Background: Metabolic dysregulation is a hallmark of type 2 diabetes mellitus (T2DM), in which the abnormalities in brown adipose tissue (BAT) play important roles. However, the cellular composition and function of BAT as well as its pathological significance in diabetes remain incompletely understood. Our objective is to delineate the single-cell landscape of BAT-derived stromal vascular fraction (SVF) and their characteristic alterations in T2DM rats. Methods: T2DM was induced in rats by intraperitoneal injection of low-dose streptozotocin and high-fat diet feeding. Single-cell mRNA sequencing was then performed on BAT samples and compared to normal rats to characterize changes in T2DM rats. Subsequently, the importance of key cell subsets in T2DM was elucidated using various functional studies. Results: Almost all cell types in the BAT-derived SVF of T2DM rats exhibited enhanced inflammatory responses, increased angiogenesis, and disordered glucose and lipid metabolism. The multidirectional differentiation potential of adipose tissue-derived stem cells was also reduced. Moreover, macrophages played a pivotal role in intercellular crosstalk of BAT-derived SVF. A novel Rarres2+macrophage subset promoted the differentiation and metabolic function of brown adipocytes via adipose-immune crosstalk. Conclusion: BAT SVF exhibited strong heterogeneity in cellular composition and function and contributed to T2DM as a significant inflammation source, in which a novel macrophage subset was identified that can promote brown adipocyte function.

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BACKGROUND: The lack of reliable biomarkers in response to anti-TNFα biologicals hinders personalized therapy for Crohn's disease (CD) patients. The motivation behind our study is to shift the paradigm of anti-TNFα biomarker discovery toward specific immune cell sub-populations using single-cell RNA sequencing and an innovative approach designed to uncover PBMCs gene expression signals, which may be masked due to the treatment or ongoing inflammation; Methods: The single-cell RNA sequencing was performed on PBMC samples from CD patients either naïve to biological therapy, in remission while on adalimumab, or while on ustekinumab but previously non-responsive to adalimumab. Sieves for stringent downstream gene selection consisted of gene ontology and independent cohort genomic profiling. Replication and meta-analyses were performed using publicly available raw RNA sequencing files of sorted immune cells and an association analysis summary. Machine learning, Mendelian randomization, and oligogenic risk score methods were deployed to validate DEGs highly relevant to anti-TNFα therapy response; Results: This study found PLCB1 in CD4+ T cells and CRTAM in double-negative T cells, which met the stringent statistical thresholds throughout the analyses. An additional assessment proved causal inference of both genes in response to anti-TNFα therapy; Conclusions: This study, jointly with an innovative design, uncovered novel candidate genes in the anti-TNFα response landscape of CD, potentially obscured by therapy or inflammation.

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BACKGROUND: The immune system's role in ST-segment-elevated myocardial infarction (STEMI) remains poorly characterized but is an important driver of recurrent cardiovascular events. While anti-inflammatory drugs show promise in reducing recurrence risk, their broad immune system impairment may induce severe side effects. To overcome these challenges, a nuanced understanding of the immune response to STEMI is needed. METHODS: For this, we compared peripheral blood mononuclear single-cell RNA-sequencing (scRNA-seq) and plasma protein expression over time (hospital admission, 24 hours, and 6-8 weeks post-STEMI) in 38 patients and 38 controls (95 995 diseased and 33 878 control peripheral blood mononuclear cells). RESULTS: Compared with controls, classical monocytes were increased and CD56dim natural killer cells were decreased in patients with STEMI at admission and persisted until 24 hours post-STEMI. The largest gene expression changes were observed in monocytes, associating with changes in toll-like receptor, interferon, and interleukin signaling activity. Finally, a targeted cardiovascular biomarker panel revealed expression changes in 33/92 plasma proteins post-STEMI. Interestingly, interleukin-6R, MMP9 (matrix metalloproteinase-9), and LDLR (low-density lipoprotein receptor) were affected by coronary artery disease-associated genetic risk variation, disease status, and time post-STEMI, indicating the importance of considering these aspects when defining potential future therapies. CONCLUSIONS: Our analyses revealed the immunologic pathways disturbed by STEMI, specifying affected cell types and disease stages. Additionally, we provide insights into patients expected to benefit most from anti-inflammatory treatments by identifying the genetic variants and disease stage at which these variants affect the outcome of these (drug-targeted) pathways. These findings advance our knowledge of the immune response post-STEMI and provide guidance for future therapeutic studies.

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Regulation of gene expression hinges on the interplay between enhancers and promoters, traditionally explored through pairwise analyses. Recent advancements in mapping genome folding, like GAM, SPRITE, and multi-contact Hi-C, have uncovered multi-way interactions among super-enhancers (SEs), spanning megabases, yet have not measured their frequency in single cells or the relationship between clustering and transcription. To close this gap, here we used multiplexed imaging to map the 3D positions of 376 SEs across thousands of mammalian nuclei. Notably, our single-cell images reveal that while SE-SE contacts are rare, SEs often form looser associations we termed "communities". These communities, averaging 4-5 SEs, assemble cooperatively under the combined effects of genomic tethers, Pol2 clustering, and nuclear compartmentalization. Larger communities are associated with more frequent and larger transcriptional bursts. Our work provides insights about the SE interactome in single cells that challenge existing hypotheses on SE clustering in the context of transcriptional regulation.

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BACKGROUND: In recent years, gene clustering analysis has become a widely used tool for studying gene functions, efficiently categorizing genes with similar expression patterns to aid in identifying gene functions. Caenorhabditis elegans is commonly used in embryonic research due to its consistent cell lineage from fertilized egg to adulthood. Biologists use 4D confocal imaging to observe gene expression dynamics at the single-cell level. However, on one hand, the observed tree-shaped time-series datasets have characteristics such as non-pairwise data points between different individuals. On the other hand, the influence of cell type heterogeneity should also be considered during clustering, aiming to obtain more biologically significant clustering results. RESULTS: A biclustering model is proposed for tree-shaped single-cell gene expression data of Caenorhabditis elegans. Detailedly, a tree-shaped piecewise polynomial function is first employed to fit non-pairwise gene expression time series data. Then, four factors are considered in the objective function, including Pearson correlation coefficients capturing gene correlations, p-values from the Kolmogorov-Smirnov test measuring the similarity between cells, as well as gene expression size and bicluster overlapping size. After that, Genetic Algorithm is utilized to optimize the function. CONCLUSION: The results on the small-scale dataset analysis validate the feasibility and effectiveness of our model and are superior to existing classical biclustering models. Besides, gene enrichment analysis is employed to assess the results on the complete real dataset analysis, confirming that the discovered biclustering results hold significant biological relevance.

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BACKGROUND: Oral submucous fibrosis (OSF) is a precancerous lesion characterized by fibrous tissue deposition, the incidence of which correlates positively with the frequency of betel nut chewing. Prolonged betel nut chewing can damage the integrity of the oral mucosal epithelium, leading to chronic inflammation and local immunological derangement. However, currently, the underlying cellular events driving fibrogenesis and dysfunction are incompletely understood, such that OSF has few treatment options with limited therapeutic effectiveness. Dental pulp stem cells (DPSCs) have been recognized for their anti-inflammatory and anti-fibrosis capabilities, making them promising candidates to treat a range of immune, inflammatory, and fibrotic diseases. However, the application of DPSCs in OSF is inconclusive. Therefore, this study aimed to explore the pathogenic mechanism of OSF and, based on this, to explore new treatment options. METHODS: A human cell atlas of oral mucosal tissues was compiled using single-cell RNA sequencing to delve into the underlying mechanisms. Epithelial cells were reclustered to observe the heterogeneity of OSF epithelial cells and their communication with immune cells. The results were validated in vitro, in clinicopathological sections, and in animal models. In vivo, the therapeutic effect and mechanism of DPSCs were characterized by histological staining, immunohistochemical staining, scanning electron microscopy, and atomic force microscopy. RESULTS: A unique epithelial cell population, Epi1.2, with proinflammatory and profibrotic functions, was predominantly found in OSF. Epi1.2 cells also induced the fibrotic process in fibroblasts by interacting with T cells through receptor-ligand crosstalk between macrophage migration inhibitory factor (MIF)-CD74 and C-X-C motif chemokine receptor 4 (CXCR4). Furthermore, we developed OSF animal models and simulated the clinical local injection process in the rat buccal mucosa using DPSCs to assess their therapeutic impact and mechanism. In the OSF rat model, DPSCs demonstrated superior therapeutic effects compared with the positive control (glucocorticoids), including reducing collagen deposition and promoting blood vessel regeneration. DPSCs mediated immune homeostasis primarily by regulating the numbers of KRT19 + MIF + epithelial cells and via epithelial-stromal crosstalk. CONCLUSIONS: Given the current ambiguity surrounding the cause of OSF and the limited treatment options available, our study reveals that epithelial cells and their crosstalk with T cells play an important role in the mechanism of OSF and suggests the therapeutic promise of DPSCs.

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Stem cells and the cells they produce are unique because they vary from one cell to another. Traditional methods of studying cells often overlook these differences. However, the development of new technologies for studying individual cells has greatly changed biological research in recent years. Among these innovations, single-cell RNA sequencing (scRNA-seq) stands out. This technique allows scientists to examine the activity of genes in each cell, across thousands or even millions of cells. This makes it possible to understand the diversity of cells, identify new types of cells, and see how cells differ across different tissues, individuals, species, times, and conditions. This paper discusses the importance of scRNA-seq and the computational tools and software that are essential for analyzing the vast amounts of data generated by scRNA-seq studies. Our goal is to provide practical advice for bioinformaticians and biologists who are using scRNA-seq to study stem cells. We offer an overview of the scRNA-seq field, including the tools available, how they can be used, and how to present the results of these studies effectively. Our findings include a detailed overview and classification of tools used in scRNA-seq analysis, based on a review of 2,733 scientific publications. This review is complemented by information from the scRNA-tools database, which lists over 1,400 tools for analyzing scRNA-seq data. This database is an invaluable resource for researchers, offering a wide range of options for analyzing their scRNA-seq data.

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Background: Renal ischemia-reperfusion injury (RIRI) is an inevitable complication in the process of kidney transplantation and lacks specific therapy. The study aims to determine the underlying mechanisms of RIRI to uncover a promising target for efficient renoprotection. Method: Four bulk RNA-seq datasets including 495 renal samples of pre- and post-reperfusion were collected from the GEO database. The machine learning algorithms were utilized to ascertain pivotal endoplasmic reticulum stress genes. Then, we incorporated correlation analysis and determined the interaction pathways of these key genes. Considering the heterogeneous nature of bulk-RNA analysis, the single-cell RNA-seq analysis was performed to investigate the mechanisms of key genes at the single-cell level. Besides, 4-PBA was applied to inhibit endoplasmic reticulum stress and hence validate the pathological role of these key genes in RIRI. Finally, three clinical datasets with transcriptomic profiles were used to assess the prognostic role of these key genes in renal allograft outcomes after RIRI. Results: In the bulk-RNA analysis, endoplasmic reticulum stress was identified as the top enriched pathway and three endoplasmic reticulum stress-related genes (PPP1R15A, JUN, and ATF3) were ranked as top performers in both LASSO and Boruta analyses. The three genes were found to significantly interact with kidney injury-related pathways, including apoptosis, inflammatory response, oxidative stress, and pyroptosis. For oxidative stress, these genes were more strongly related to oxidative markers compared with antioxidant markers. In single-cell transcriptome, the three genes were primarily upregulated in endothelium, distal convoluted tubule cells, and collecting duct principal cells among 12 cell types of renal tissues in RIRI. Furthermore, distal convoluted tubule cells and collecting duct principal cells exhibited pro-inflammatory status and the highest pyroptosis levels, suggesting their potential as main effectors of three key genes for mediating RIRI-associated injuries. Importantly, inhibition of these key genes using 4-phenyl butyric acid alleviated functional and histological damage in a mouse RIRI model. Finally, the three genes demonstrated highly prognostic value in predicting graft survival outcomes. Conclusion: The study identified three key endoplasmic reticulum stress-related genes and demonstrated their prognostic value for graft survival, providing references for individualized clinical prevention and treatment of postoperative complications after renal transplantation.

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BACKGROUND: Mature endothelial cells (ECs) are heterogeneous, with subtypes defined by tissue origin and position within the vascular bed (ie, artery, capillary, vein, and lymphatic). How this heterogeneity is established during the development of the vascular system, especially arteriovenous specification of ECs, remains incompletely characterized. METHODS: We used droplet-based single-cell RNA sequencing and multiplexed error-robust fluorescence in situ hybridization to define EC and EC progenitor subtypes from E9.5, E12.5, and E15.5 mouse embryos. We used trajectory inference to analyze the specification of arterial ECs (aECs) and venous ECs (vECs) from EC progenitors. Network analysis identified candidate transcriptional regulators of arteriovenous differentiation, which we tested by CRISPR (clustered regularly interspaced short palindromic repeats) loss of function in human-induced pluripotent stem cells undergoing directed differentiation to aECs or vECs (human-induced pluripotent stem cell-aECs or human-induced pluripotent stem cell-vECs). RESULTS: From the single-cell transcriptomes of 7682 E9.5 to E15.5 ECs, we identified 19 EC subtypes, including Etv2+Bnip3+ EC progenitors. Spatial transcriptomic analysis of 15 448 ECs provided orthogonal validation of these EC subtypes and established their spatial distribution. Most embryonic ECs were grouped by their vascular-bed types, while ECs from the brain, heart, liver, and lung were grouped by their tissue origins. Arterial (Eln, Dkk2, Vegfc, and Egfl8), venous (Fam174b and Clec14a), and capillary (Kcne3) marker genes were identified. Compared with aECs, embryonic vECs and capillary ECs shared fewer markers than their adult counterparts. Early capillary ECs with venous characteristics functioned as a branch point for differentiation of aEC and vEC lineages. CONCLUSIONS: Our results provide a spatiotemporal map of embryonic EC heterogeneity at single-cell resolution and demonstrate that the diversity of ECs in the embryo arises from both tissue origin and vascular-bed position. Developing aECs and vECs share common venous-featured capillary precursors and are regulated by distinct transcriptional regulatory networks.

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BACKGROUND: Coronary artery lesions (CALs) are the most common and major complication of Kawasaki disease (KD) in developed countries. However, the underlying immunologic mechanisms of CAL development in KD remain unclear. METHODS: Here, we conducted single-cell transcriptome analyses of 212 210 peripheral blood mononuclear cells collected from a cross-sectional cohort of 16 children, including 4 patients with KD with CALs, 5 patients with KD without CALs, 4 healthy controls, and 3 febrile controls. RESULTS: KD altered the proportion of peripheral blood mononuclear cells, including an increasing trend in inflammatory cells (megakaryocytes and monocytes) and a decreasing trend in lymphocytes (eg, CD4+ T, CD8+ T, mucosal-associated invariant T, natural killer, and γδ T cells), highlighting the potential presence of lymphopenia phenomenon in KD. Our data indicated the presence of inflammatory cytokine storm in patients with KD with CALs, caused by systemic upregulation of TNFSF13B (tumor necrosis factor superfamily member 13b), CXCL16 (C-X-C motif chemokine ligand 16), TNFSF10 (tumor necrosis factor superfamily member 10), and IL1RN (interleukin 1 receptor antagonist), mainly produced by monocytes (especially for the Mono_CD14-CD16 cluster) and megakaryocytes. We also found that myeloid cells of patients with KD, particularly in those with CALs, might play a role in vascular injury (eg, increased MMP [matrix metalloproteinase] 9, MMP17, and MMP25) and immune cell recruitment. The immune landscape of patients with KD with CALs was featured by lower exhaustion levels in natural killer cells, a high cytotoxic state in the CD8_Pro cluster, and activation of the complement system in monocytes. Additionally, the activation of B cells was more pronounced in the early stage of KD. CONCLUSIONS: Collectively, this study provides a comprehensive understanding of the roles of various immune cells and inflammatory cytokine storms in the development of CALs in KD and offers a valuable resource for identifying novel therapeutic targets for patients with KD with CALs.

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BACKGROUND: A main obstacle in current valvular heart disease research is the lack of high-quality homogeneous functional heart valve cells. Human induced pluripotent stem cells (hiPSCs)-derived heart valve cells may help with this dilemma. However, there are no well-established protocols to induce hiPSCs to differentiate into functional heart valve cells, and the networks that mediate the differentiation have not been fully elucidated. METHODS: To generate heart valve cells from hiPSCs, we sequentially activated the Wnt, BMP4, VEGF (vascular endothelial growth factor), and NFATc1 signaling pathways using CHIR-99021, BMP4, VEGF-165, and forskolin, respectively. The transcriptional and functional similarity of hiPSC-derived heart valve cells compared with primary heart valve cells were characterized. Longitudinal single-cell RNA sequencing was used to uncover the trajectory, switch genes, pathways, and transcription factors of the differentiation. RESULTS: An efficient protocol was developed to induce hiPSCs to differentiate into functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells. After 6-day differentiation and CD144 magnetic bead sorting, ≈70% CD144+ cells and 30% CD144- cells were obtained. On the basis of single-cell RNA sequencing data, the CD144+ cells and CD144- cells were found to be highly similar to primary heart valve endothelial cells and primary heart valve interstitial cells in gene expression profile. Furthermore, CD144+ cells had the typical function of primary heart valve endothelial cells, including tube formation, uptake of low-density lipoprotein, generation of endothelial nitric oxide synthase, and response to shear stress. Meanwhile, CD144- cells could secret collagen and matrix metalloproteinases, and differentiate into osteogenic or adipogenic lineages like primary heart valve interstitial cells. Therefore, we identified CD144+ cells and CD144- cells as hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells, respectively. Using single-cell RNA sequencing analysis, we demonstrated that the trajectory of heart valve cell differentiation was consistent with embryonic valve development. We identified the main switch genes (NOTCH1, HEY1, and MEF2C), signaling pathways (TGF-ß, Wnt, and NOTCH), and transcription factors (MSX1, SP5, and MECOM) that mediated the differentiation. Finally, we found that hiPSC-derived valve interstitial-like cells might derive from hiPSC-derived valve endothelial-like cells undergoing endocardial-mesenchymal transition. CONCLUSIONS: In summary, this is the first study to report an efficient strategy to generate functional hiPSC-derived valve endothelial-like cells and hiPSC-derived valve interstitial-like cells from hiPSCs, as well as to elucidate the differentiation trajectory and transcriptional dynamics of hiPSCs differentiated into heart valve cells.

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PURPOSE: Eosinophilic asthma (EA) and non-asthmatic eosinophilic bronchitis (EB) share similar eosinophilic airway inflammation. Unlike EA, EB did not present airway hyperresponsiveness or airflow obstruction. We aimed to compare the mechanism underlying the different manifestations between EA and EB via sputum transcriptomics analysis. METHODS: Induced-sputum cells from newly physician-diagnosed EA, EB patients, and healthy controls (HCs) were collected for RNA sequencing. RESULTS: Bulk RNA sequencing was performed using sputum cells from patients with EA (n = 18), EB (n = 15) and HCs (n = 28). Principal component analysis revealed similar gene expression patterns in EA and EB. The most differentially expressed genes in EB compared with HC were also shared by EA, including IL4, IL5 IL13, CLC, CPA3, and DNASE1L3. However, gene set enrichment analysis showed that the signatures regulating macrophage activation were enriched in EA compared to EB. Sputum cells were profiled using single-cell RNA sequencing. FABP4+ macrophages, SPP1+ macrophages, FCN1+ macrophages, dendritic cells, T cells, B cells, mast cells, and epithelial cells were identified based on gene expression profiling. Analysis of cell-cell communication revealed that interactions between FCN1+ macrophages and other cells were higher in EA than in EB. A wealth of transforming growth factor beta (TGF-ß) and vascular endothelial growth factor (VEGF) interactions between FCN1+ macrophages and other cells have been shown in EA. The gene expression levels of EREG, TGFBI, and VEGFA in FCN1+ macrophages of EA were significantly higher than those of EB. Furthermore, signatures associated with the response to TGF-ß, cellular response to VEGF stimulus and developmental cell growth were enriched in FCN1+ macrophages of EA compared to those of EB. CONCLUSIONS: FCN1+ macrophage activation associated with airway remodeling processes was upregulated in EA compared to that in EB, which may contribute to airway hyperresponsiveness and airflow obstruction.

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The choice of developing thymocytes to become CD8+ cytotoxic or CD4+ helper T cells has been intensely studied, but many of the underlying mechanisms remain to be elucidated. Recent multiomics approaches have provided much higher resolution analysis of gene expression in developing thymocytes than was previously achievable, thereby offering a fresh perspective on this question. Focusing on our recent studies using CITE-seq (cellular indexing of transcriptomes and epitopes) analyses of mouse thymocytes, we present a detailed timeline of RNA and protein expression changes during CD8 versus CD4 T cell differentiation. We also revisit our current understanding of the links between T cell receptor signaling and expression of the lineage-defining transcription factors ThPOK and RUNX3. Finally, we propose a sequential selection model to explain the tight linkage between MHC-I versus MHC-II recognition and T cell lineage choice. This model incorporates key aspects of previously proposed kinetic signaling, instructive, and stochastic/selection models.

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Adult muscle stem cells (MuSCs) are known to replicate upon activation before differentiating and fusing to regenerate myofibers. It is unclear whether MuSC differentiation is intrinsically linked to cell division, which has implications for stem cell population maintenance. We use single-cell RNA-sequencing to identify transcriptionally diverse subpopulations of MuSCs after 5 days of a growth stimulus in adult muscle. Trajectory inference in combination with a novel mouse model for tracking MuSC-derived myonuclei and in vivo labeling of DNA replication revealed an MuSC population that exhibited division-independent differentiation and fusion. These findings demonstrate that in response to a growth stimulus in the presence of intact myofibers, MuSC division is not obligatory.

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BACKGROUND: Although single-cell RNA-sequencing is commonly applied to dissect the heterogeneity in human tissues, it involves the preparation of single-cell suspensions via cell dissociation, causing loss of spatial information. In this study, we employed high-resolution single-cell transcriptome imaging to reveal rare smooth muscle cell (SMC) types in human thoracic aortic aneurysm (TAA) tissue samples. METHODS: Single-molecule spatial distribution of transcripts from 140 genes was analyzed in fresh-frozen human TAA samples with region and sex-matched controls. In vitro studies and tissue staining were performed to examine human CART prepropeptide (CARTPT) regulation and function. RESULTS: We captured thousands of cells per sample including a spatially distinct CARTPT-expressing SMC subtype enriched in male TAA samples. Immunoassays confirmed human CART (cocaine- and amphetamine-regulated transcript) protein enrichment in male TAA tissue and truncated CARTPT secretion into cell culture medium. Oxidized low-density lipoprotein, a cardiovascular risk factor, induced CARTPT expression, whereas CARTPT overexpression in human aortic SMCs increased the expression of key osteochondrogenic transcription factors and reduced contractile gene expression. Recombinant human CART treatment of human SMCs further confirmed this phenotype. Alizarin red staining revealed calcium deposition in male TAA samples showing similar localization with human CART staining. CONCLUSIONS: Here, we demonstrate the feasibility of single-molecule imaging in uncovering rare SMC subtypes in the diseased human aorta, a difficult tissue to dissociate. We identified a spatially distinct CARTPT-expressing SMC subtype enriched in male human TAA samples. Our functional studies suggest that human CART promotes osteochondrogenic switch of aortic SMCs, potentially leading to medial calcification of the thoracic aorta.

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