Single-cell Transcriptomic Data Research Articles

Type I Interferonopathy among Non-Elderly Female Patients with Post-Acute Sequelae of COVID-19.

The pathophysiological mechanisms of the post-acute sequelae of COVID-19 (PASC) remain unclear. Sex differences not only exist in the disease severity of acute SARS-CoV-2 infection but also in the risk of suffering from PASC. Women have a higher risk of suffering from PASC and a longer time to resolution than men. To explore the possible immune mechanisms of PASC among non-elderly females, we mined single-cell transcriptome data from peripheral blood samples of non-elderly female patients with PASC and acute SARS-CoV-2 infection, together with age- and gender-matched non-PASC and healthy controls available from the Gene Expression Omnibus database. By comparing the differences, we found that a CD14+ monocyte subset characterized by higher expression of signal transducers and activators of transcription 2 (STAT2) (CD14+STAT2high) was notably increased in the PASC patients compared with the non-PASC individuals. The transcriptional factor (TF) activity analysis revealed that STAT2 and IRF9 were the key TFs determining the function of CD14+STAT2high monocytes. STAT2 and IRF9 are TFs exclusively involving type I and III interferon (IFN) signaling pathways, resulting in uncontrolled IFN-I signaling activation and type I interferonopathy. Furthermore, increased expression of common interferon-stimulated genes (ISGs) has also been identified in most monocyte subsets among the non-elderly female PASC patients, including IFI6, IFITM3, IFI44L, IFI44, EPSTI1, ISG15, and MX1. This study reveals a featured CD14+STAT2high monocyte associated with uncontrolled IFN-I signaling activation, which is indicative of a possible type I interferonopathy in the non-elderly female patients with PASC.

CNTools: A computational toolbox for cellular neighborhood analysis from multiplexed images.

Recent studies show that cellular neighborhoods play an important role in evolving biological events such as cancer and diabetes. Therefore, it is critical to accurately and efficiently identify cellular neighborhoods from spatially-resolved single-cell transcriptomic data or single-cell resolution tissue imaging data. In this work, we develop CNTools, a computational toolbox for end-to-end cellular neighborhood analysis on annotated cell images, comprising both the identification and analysis steps. It includes state-of-the-art cellular neighborhood identification methods and post-identification smoothing techniques, with our newly proposed Cellular Neighbor Embedding (CNE) method and Naive Smoothing technique, as well as several established downstream analysis approaches. We applied CNTools on three real-world CODEX datasets and evaluated identification methods with smoothing techniques quantitatively and qualitatively. It shows that CNE with Naive Smoothing overall outperformed other methods and revealed more convincing biological insights. We also provided suggestions on how to choose proper identification methods and smoothing techniques according to input data.

Prognostic and immunological implications of heterogeneous cell death patterns in prostate cancer

BackgroundProstate cancer is one of the most common cancers in men with a significant proportion of patients developing biochemical recurrence (BCR) after treatment. Programmed cell death (PCD) mechanisms are known to play critical roles in tumor progression and can potentially serve as prognostic and therapeutic biomarkers in PCa. This study aimed to develop a prognostic signature for BCR in PCa using PCD-related genes.Materials and methodsWe conducted an analysis of 19 different modes of PCD to develop a comprehensive model. Bulk transcriptomic, single-cell transcriptomic, genomic, and clinical data were collected from multiple cohorts, including TCGA-PRAD, GSE58812, METABRIC, GSE21653, and GSE193337. We analyzed the expression and mutations of the 19 PCD modes and constructed, evaluated, and validated the model.ResultsTen PCD modes were found to be associated with BCR in PCa, with specific PCD patterns exhibited by various cell components within the tumor microenvironment. Through Lasso Cox regression analysis, we established a Programmed Cell Death Index (PCDI) utilizing an 11-gene signature. High PCDI values were validated in five independent datasets and were found to be associated with an increased risk of BCR in PCa patients. Notably, older age and advanced T and N staging were associated with higher PCDI values. By combining PCDI with T staging, we constructed a nomogram with enhanced predictive performance. Additionally, high PCDI values were significantly correlated with decreased drug sensitivity, including drugs such as Docetaxel and Methotrexate. Patients with lower PCDI values demonstrated higher immunophenoscores (IPS), suggesting a potentially higher response rate to immune therapy. Furthermore, PCDI was associated with immune checkpoint genes and key components of the tumor microenvironment, including macrophages, T cells, and NK cells. Finally, clinical specimens validated the differential expression of PCDI-related PCDRGs at both the gene and protein levels.ConclusionIn conclusion, we developed a novel PCD-based prognostic feature that successfully predicted BCR in PCa patients and provided insights into drug sensitivity and potential response to immune therapy. These findings have significant clinical implications for the treatment of PCa.

In vitro differentiation of mouse pluripotent stem cells into corticosteroid-producing adrenocortical cells

Directed differentiation of pluripotent stem cells into specialized cell types represents an invaluable tool for a wide range of applications. Here, we have exploited single-cell transcriptomic data to develop a stepwise invitro differentiation system from mouse embryonic stem cells into adrenocortical cells. We show that during development, the adrenal primordium is embedded in an extracellular matrix containing tenascin and fibronectin. Culturing cells on fibronectin during differentiation increased the expression of the steroidogenic marker NR5A1. Furthermore, 3D cultures in the presence of protein kinase A (PKA)-pathway activators led to the formation of aggregates composed of different cell types expressing adrenal progenitor or steroidogenic markers, including the adrenocortical-specific enzyme CYP21A1. Importantly, in-vitro-differentiated cells responded to adrenocorticotropic hormone (ACTH) and angiotensin II with the production of glucocorticoids and mineralocorticoids, respectively, thus confirming the specificity of differentiation toward the adrenal lineage.

Comprehensive analysis of consensus molecular subtypes for ovarian cancer from bulk to single-cell perspectives

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1,987 ovarian cancer patients spanning 26 independent GEO cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust 10-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

In utero human intestine contains maternally derived bacterial metabolites.

Understanding when host-microbiome interactions are first established is crucial for comprehending normal development and identifying disease prevention strategies. Furthermore, bacterially derived metabolites play critical roles in shaping the intestinal immune system. Recent studies have demonstrated that memory T cells infiltrate human intestinal tissue early in the second trimester, suggesting that intestinal immune education begins in utero. Our previous study reported a unique fetal intestinal metabolomic profile with an abundance of several bacterially derived metabolites and aryl hydrocarbon receptor (AHR) ligands implicated in mucosal immune regulation. To follow up on this work, in the current study, we demonstrate that a number of microbial byproducts present in fetal intestines in utero are maternally derived and vertically transmitted to the fetus. Notably, these bacterially derived metabolites, particularly short chain fatty acids and secondary bile acids, are likely biologically active and functional in regulating the fetal immune system and preparing the gastrointestinal tract for postnatal microbial encounters, as the transcripts for their various receptors and carrier proteins are present in second trimester intestinal tissue through single-cell transcriptomic data.

Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics.

Single-cell transcriptomics has broadened our understanding of cellular diversity and gene expression dynamics in healthy and diseased tissues. Recently, spatial transcriptomics has emerged as a tool to contextualize single cells in multicellular neighbourhoods and to identify spatially recurrent phenotypes, or ecotypes. These technologies have generated vast datasets with targeted-transcriptome and whole-transcriptome profiles of hundreds to millions of cells. Such data have provided new insights into developmental hierarchies, cellular plasticity and diverse tissue microenvironments, and spurred a burst of innovation in computational methods for single-cell analysis. In this Review, we discuss recent advancements, ongoing challenges and prospects in identifying and characterizing cell states and multicellular neighbourhoods. We discuss recent progress in sample processing, data integration, identification of subtle cell states, trajectory modelling, deconvolution and spatial analysis. Furthermore, we discuss the increasing application of deep learning, including foundation models, in analysing single-cell and spatial transcriptomics data. Finally, we discuss recent applications of these tools in the fields of stem cell biology, immunology, and tumour biology, and the future of single-cell and spatial transcriptomics in biological research and its translation to the clinic.

Prognostic and immune infiltration implications of SIGLEC9 in SKCM

The occurrence and progression of skin cutaneous melanoma (SKCM) is strongly associated with immune cells infiltrating the tumor microenvironment (TME). This study examined the expression, prognosis, and immune relevance of SIGLEC9 in SKCM using multiple online databases. Analysis of the GEPIA2 and Ualcan databases revealed that SIGLEC9 is highly expressed in SKCM, and patients with high SIGLEC9 expression had improved overall survival (OS). Furthermore, the mutation rate of SIGLEC9 in SKCM patients was found to be 5.41%, the highest observed. The expression of SIGLEC9 was positively correlated with macrophages, neutrophils and B cells, CD8 + T cells, CD4 + T cells, and dendritic cells, according to TIMER. Based on TCGA-SKCM data, we verified that high SIGLEC9 expression is closely associated with a good prognosis for SKCM patients, including overall survival, progression-free interval, and disease-specific survival. This positive prognosis could be due to the infiltration of immune cells into the TME. Additionally, our analysis of single-cell transcriptome data revealed that SIGLEC9 not only played a role in the normal skin immune microenvironment, but is also highly expressed in immune cell subpopulations of SKCM patients, regulating the immune response to tumors. Our findings suggest that the close association between SIGLEC9 and SKCM prognosis is primarily mediated by its effect on the tumor immune microenvironment.

Integrative single-cell and bulk transcriptome analyses identify a distinct pro-tumor macrophage signature that has a major prognostic impact on glioblastomas

Glioblastoma (GBM) is a highly heterogeneous disease with poor clinical outcomes. To comprehensively dissect the molecular landscape of GBM and heterogeneous macrophage clusters in the progression of GBM, this study integrates single-cell and bulk transcriptome data to recognize a distinct pro-tumor macrophage cluster significantly associated with the prognosis of GBM and develop a GBM prognostic signature to facilitate prior subtypes. Leveraging glioma single-cell sequencing data, we identified a novel pro-tumor macrophage subgroup, marked by S100A9, which might interact with endothelial cells to facilitate tumor progression via angiogenesis. To further benefit clinical application, a prognostic signature was established with the genes associated with pro-tumor macrophages. Patients classified within the high-risk group characterized with enrichment in functions related to tumor progression, including epithelial-mesenchymal transition and hypoxia, displays elevated mutations in the TERT promoter region, reduced methylation in the MGMT promoter region, poorer prognoses, and diminished responses to temozolomide therapy, thus effectively discriminating between the prognostic outcomes of GBM patients. Our research sheds light on the intricate microenvironment of gliomas and identifies potential molecular targets for the development of novel therapeutic approaches.

Building a learnable universal coordinate system for single-cell atlas with a joint-VAE model

A universal coordinate system that can ensemble the huge number of cells and capture their heterogeneities is of vital importance for constructing large-scale cell atlases as references for molecular and cellular studies. Studies have shown that cells exhibit multifaceted heterogeneities in their transcriptomic features at multiple resolutions. This nature of complexity makes it hard to design a fixed coordinate system through a combination of known features. It is desirable to build a learnable universal coordinate model that can capture major heterogeneities and serve as a controlled generative model for data augmentation. We developed UniCoord, a specially-tuned joint-VAE model to represent single-cell transcriptomic data in a lower-dimensional latent space with high interpretability. Each latent dimension can represent either discrete or continuous feature, and either supervised by prior knowledge or unsupervised. The latent dimensions can be easily reconfigured to generate pseudo transcriptomic profiles with desired properties. UniCoord can also be used as a pre-trained model to analyze new data with unseen cell types and thus can serve as a feasible framework for cell annotation and comparison. UniCoord provides a prototype for a learnable universal coordinate framework to enable better analysis and generation of cells with highly orchestrated functions and heterogeneities.

Multi-omics analyses reveal aberrant differentiation trajectory with WNT1 loss-of-function in type XV osteogenesis imperfecta.

Osteogenesis imperfecta (OI) is a group of severe genetic bone disorders characterized by congenital low bone mass, deformity, and frequent fractures. Type XV OI is a moderate to severe form of skeletal dysplasia caused by WNT1 variants. In this cohort study from southern China, we summarized the clinical phenotypes of patients with WNT1 variants and found that the proportion of type XV patients was around 10.3% (25 out of 243) with a diverse spectrum of phenotypes. Functional assays indicated that variants of WNT1 significantly impaired its secretion and effective activity, leading to moderate to severe clinical manifestations, porous bone structure, and enhanced osteoclastic activities. Analysis of proteomic data from human skeleton indicated that the expression of SOST (sclerostin) was dramatically reduced in type XV patients compared to patients with COL1A1 quantitative variants. Single-cell transcriptome data generated from human tibia samples of patients diagnosed with type XV OI and leg-length discrepancy, respectively, revealed aberrant differentiation trajectories of skeletal progenitors and impaired maturation of osteocytes with loss of WNT1, resulting in excessive CXCL12+ progenitors, fewer mature osteocytes, and the existence of abnormal cell populations with adipogenic characteristics. The integration of multi-omics data from human skeleton delineates how WNT1 regulates the differentiation and maturation of skeletal progenitors, which will provide a new direction for the treatment strategy of type XV OI and relative low bone mass diseases such as early onset osteoporosis.

Genetically and Functionally Distinct Immunoglobulin Heavy Chain Locus Duplication in Bats.

The genetic locus encoding immunoglobulin heavy chains (IgH) is critical for vertebrate humoral immune responses and diverse antibody repertoires. Immunoglobulin and T cell receptor loci of most bat species have not been annotated, despite the recurrent role of bats as viral reservoirs and sources of zoonotic pathogens. We investigated the genetic structure and function of IgH loci across the largest bat family, Vespertilionidae, focusing on big brown bats (Eptesicus fuscus ). We discovered that E. fuscus and ten other species within Vespertilionidae have two complete, functional, and distinct immunoglobulin heavy chain loci on separate chromosomes. This locus organization is previously unknown in mammals, but is reminiscent of more limited duplicated loci in teleost fish. Single cell transcriptomic data validate functional rearrangement and expression of immunoglobulin heavy chains of both loci in the expressed repertoire of Eptesicus fuscus , with maintenance of allelic exclusion, bias of usage toward the smaller and more compact IgH locus, and evidence of differential selection of antigen-experienced B cells and plasma cells varying by IgH locus use. This represents a unique mechanism for mammalian humoral immunity and may contribute to bat resistance to viral pathogenesis.

Disrupting YAP1-mediated glutamine metabolism induces synthetic lethality alongside ODC1 inhibition in osteosarcoma.

Osteosarcoma, a highly malignant primary bone tumor primarily affecting adolescents, frequently develops resistance to initial chemotherapy, leading to metastasis and limited treatment options. Our study aims to uncover novel therapeutic targets for metastatic and recurrent osteosarcoma. In this study, we proved the potential of modulating the YAP1-regulated glutamine metabolic pathway to augment the response of OS to DFMO. We initially employed single-cell transcriptomic data to gauge the activation level of polyamine metabolism in MTAP-deleted OS patients. This was further substantiated by transcriptome sequencing data from recurrent and non-recurrent patient tissues, confirming the activation of polyamine metabolism in progressive OS. Through high-throughput drug screening, we pinpointed CIL56, a YAP1 inhibitor, as a promising candidate for a combined therapeutic strategy with DFMO. In vivo, we utilized PDX and CDX models to validate the therapeutic efficacy of this drug combination. In vitro, we conducted western blot analysis, qPCR analysis, immunofluorescence staining, and PuMA experiments to monitor alterations in molecular expression, distribution, and tumor metastasis capability. We employed CCK-8 and colony formation assays to assess the proliferative capacity of cells in the experimental group. We used flow cytometry and reactive oxygen probes to observe changes in ROS and glutamine metabolism within the cells. Finally, we applied RNA-seq in tandem with metabolomics to identify metabolic alterations in OS cells treated with a DFMO and CIL56 combination. This enabled us to intervene and validate the role of the YAP1-mediated glutamine metabolic pathway in DFMO resistance. Through single-cell RNA-seq data analysis, we pinpointed a subset of late-stage OS cells with significantly upregulated polyamine metabolism. This upregulation was further substantiated by transcriptomic profiling of recurrent and non-recurrent OS tissues. High-throughput drug screening revealed a promising combination strategy involving DFMO and CIL56. DFMO treatment curbs the phosphorylation of YAP1 protein in OS cells, promoting nuclear entry and initiating the YAP1-mediated glutamine metabolic pathway. This reduces intracellular ROS levels, countering DFMO's anticancer effect. The therapeutic efficacy of DFMO can be amplified both in vivo and in vitro by combining it with the YAP1 inhibitor CIL56 or the glutaminase inhibitor CB-839. This underscores the significant potential of targeting the YAP1-mediated glutamine metabolic pathway to enhance efficacy of DFMO. Our findings elucidate YAP1-mediated glutamine metabolism as a crucial bypass mechanism against DFMO, following the inhibition of polyamine metabolism. Our study provides valuable insights into the potential role of DFMO in an "One-two Punch" therapy of metastatic and recurrent osteosarcoma.

Robust analysis of allele-specific copy number alterations from scRNA-seq data with XClone

Somatic copy number alterations (CNAs) are major mutations that contribute to the development and progression of various cancers. Despite a few computational methods proposed to detect CNAs from single-cell transcriptomic data, the technical sparsity of such data makes it challenging to identify allele-specific CNAs, particularly in complex clonal structures. In this study, we present a statistical method, XClone, that strengthens the signals of read depth and allelic imbalance by effective smoothing on cell neighborhood and gene coordinate graphs to detect haplotype-aware CNAs from scRNA-seq data. By applying XClone to multiple datasets with challenging compositions, we demonstrated its ability to robustly detect different types of allele-specific CNAs and potentially indicate whole genome duplication, therefore enabling the discovery of corresponding subclones and the dissection of their phenotypic impacts.

14 Natural killer cells have impaired cytotoxicity in advanced renal cell carcinoma

Abstract Background Natural killer (NK) cells are key mediators of anti-tumor activity in multiple cancers, including clear cell renal cell carcinoma (ccRCC). However, the phenotype and function of NK cells across different disease stages of ccRCC is incompletely characterized. Methods Single-cell RNA (scRNA)-sequencing (10x Genomics) data from tumor and adjacent normal kidney from ccRCC was analyzed at multiple clinical stages (localized – stage I/II/III; and advanced – stage IV). Graph-based clustering and lineage markers were used to identify distinct NK cell populations. Differential gene expression analysis was performed to further investigate NK cell phenotype and to derive a gene expression signature (GES). Gene signatures from NK cell subclusters of interest were used to interrogate bulk transcriptomic datasets and expression with clinical outcomes. Tumor-infiltrating NK cell function (cytokine production and cytotoxicity) was assessed by isolation of live NK cells from ccRCC tissue, co-culture with K562 target cells, and measurement of cytokine (IFNgamma) and cytotoxicity (CD107a) markers by flow cytometry. Results Single-cell transcriptomic data were analyzed from 13 patients with ccRCC (tumor and normal kidney), resulting in 21,139 high-quality NK cells. Patients with advanced/metastatic RCC had a lower proportion of NK cells versus total immune cells in the tumor microenvironment compared to normal kidney (p=0.036) and localized ccRCC (p=0.088). Clustering analysis revealed 6 distinct NK cell subsets. The C0.Bright-like NK cell cluster was significantly enriched in advanced ccRCC compared to localized ccRCC (p=0.048) and normal kidney (p=0.0059), expressed markers of tissue residency (ZNF683, ITGA1, CD9, ITGAE), and had decreased expression of cytotoxicity genes (GZMB, PRF1). A GES signature composed of genes upregulated in the dysfunctional C0.Bright-like NK cell cluster was used to interrogate bulk RNA-sequencing data from The Cancer Genome Atlas clear cell cohort, providing validation in a large, independent cohort, of an increase in this dysfunctional NK subset in advanced ccRCC compared to localized ccRCC (p=0.00039) and normal tissue (p=5.3e-05). To functionally confirm the decreased cytotoxicity of this dysfunctional NK population, CD45+CD56+CD3- NK cells were isolated from advanced ccRCC, and co-cultured with K562 target cells. Consistent with the transcriptomic phenotype, tumor-resident CD49a+CD9+ NK cells had cytokine production (IFNgamma) but decreased markers of cytotoxicity (CD107a) compared to CD49a-CD9- NK cells. By contrast, CD49a+CD9+ NK cells isolated from peripheral blood or normal kidney tissue maintained cytotoxic activity. Conclusions Among patients with ccRCC, a single-cell transcriptomic analysis revealed heterogeneous NK cell populations. A dysfunctional tumor resident NK cell phenotype was enriched among patients with advanced disease in ccRCC, and functionally confirmed to have diminished cytotoxicity. This study therefore provides insight into a new axis of immune dysfunction – decreased NK-mediated cytotoxicity – in advanced ccRCC. An improved understanding of NK cell dysfunction within ccRCC may provide a foundation for therapeutic restoration of NK-mediated anti-tumor activity in ccRCC. DOD CDMRP Funding: yes

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and drug response. We utilized 13 PCD pathways, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis to develop predictive models based on 73 machine learning combinations from 10 algorithms. Bulk RNA-sequencing, single-cell RNA-sequencing transcriptomic data, and matched clinicopathological information were obtained from the TCGA-AML, Tyner, and GSE37642-GPL96 cohorts. These datasets were leveraged to construct and validate the models. Additionally, in vitro experiments were conducted to substantiate the bioinformatics findings. The machine learning approach established a 6-gene pan-programmed cell death-related genes index (PPCDI) signature. Validation in two external cohorts showed high PPCDI associated with worse prognosis in AML patients. Incorporating PPCDI with clinical variables, we constructed several robust prognostic nomograms that accurately predicted prognosis of AML patients. Multi-omics analysis integrating bulk and single-cell transcriptomics revealed correlations between PPCDI and immunological features, delineating the immune microenvironment landscape in AML. Patients with high PPCDI exhibited resistance to conventional chemotherapy like doxorubicin but retained sensitivity to dasatinib and methotrexate (FDA-approved drugs for other leukemias), suggesting the potential of PPCDI to guide personalized therapy selection in AML. In summary, we developed a novel PPCDI model through comprehensive analysis of diverse programmed cell death pathways. This PPCDI signature demonstrates great potential in predicting clinical prognosis and drug sensitivity phenotypes in AML patients.

Intramyocardial Sprouting Tip Cells Specify Coronary Arterialization.

The elaborate patterning of coronary arteries critically supports the high metabolic activity of the beating heart. How coronary endothelial cells coordinate hierarchical vascular remodeling and achieve arteriovenous specification remains largely unknown. Understanding the molecular and cellular cues that pattern coronary arteries is crucial to develop innovative therapeutic strategies that restore functional perfusion within the ischemic heart. Single-cell transcriptomics and histological validation were used to delineate heterogeneous transcriptional states of the developing and mature coronary endothelium with a focus on sprouting endothelium and arterial cell specification. Genetic lineage tracing and high-resolution 3-dimensional imaging were used to characterize the origin and mechanisms of coronary angiogenic sprouting, as well as to fate-map selective endothelial lineages. Integration of single-cell transcriptomic data from ischemic adult mouse hearts and human embryonic data served to assess the conservation of transcriptional states across development, disease, and species. We discover that coronary arteries originate from cells that have previously transitioned through a specific tip cell phenotype. We identify nonoverlapping intramyocardial and subepicardial tip cell populations with differential gene expression profiles and regulatory pathways. Esm1-lineage tracing confirmed that intramyocardial tip cells selectively contribute to coronary arteries and endocardial tunnels, but not veins. Notably, prearterial cells are detected from development stages to adulthood, increasingly in response to ischemic injury, and in human embryos, suggesting that tip cell-to-artery specification is a conserved mechanism. A tip cell-to-artery specification mechanism drives arterialization of the intramyocardial plexus and endocardial tunnels throughout life and is reactivated upon ischemic injury. Differential sprouting programs govern the formation and specification of the venous and arterial coronary plexus.

Multiomics Analysis of Disulfidptosis Patterns and Integrated Machine Learning to Predict Immunotherapy Response in Lung Adenocarcinoma.

Recent studies have unveiled disulfidptosis as a phenomenon intimately associated with cellular damage, heralding new avenues for exploring tumor cell dynamics. We aimed to explore the impact of disulfide cell death on the tumor immune microenvironment and immunotherapy in lung adenocarcinoma (LUAD). We initially utilized pan-cancer transcriptomics to explore the expression, prognosis, and mutation status of genes related to disulfidptosis. Using the LUAD multi- -omics cohorts in the TCGA database, we explore the molecular characteristics of subtypes related to disulfidptosis. Employing various machine learning algorithms, we construct a robust prognostic model to predict immune therapy responses and explore the model's impact on the tumor microenvironment through single-cell transcriptome data. Finally, the biological functions of genes related to the prognostic model are verified through laboratory experiments. Genes related to disulfidptosis exhibit high expression and significant prognostic value in various cancers, including LUAD. Two disulfidptosis subtypes with distinct prognoses and molecular characteristics have been identified, leading to the development of a robust DSRS prognostic model, where a lower risk score correlates with a higher response rate to immunotherapy and a better patient prognosis. NAPSA, a critical gene in the risk model, was found to inhibit the proliferation and migration of LUAD cells. Our research introduces an innovative prognostic risk model predicated upon disulfidptosis genes for patients afflicted with Lung Adenocarcinoma (LUAD). This model proficiently forecasts the survival rates and therapeutic outcomes for LUAD patients, thereby delineating the high-risk population with distinctive immune cell infiltration and a state of immunosuppression. Furthermore, NAPSA can inhibit the proliferation and invasion capabilities of LUAD cells, thereby identifying new molecules for clinical targeted therapy.

Exploring the Common Pathogenic Mechanisms of Psoriasis and Atopic Dermatitis: The Interaction between SGK1 and TIGIT Signaling Pathways.

This study aims to explore the common pathogenic mechanisms of psoriasis and atopic dermatitis, two T-cell-mediated autoimmune diseases. Utilizing single-cell transcriptomic sequencing data, we revealed that Treg cells primarily express TIGIT in both psoriasis and atopic dermatitis, and identified a subset of macrophages that highly express SGK1. These cells can interact with T cells via the NECTIN2-TIGIT signaling pathway, inhibiting the differentiation of T cells into a pro-inflammatory phenotype, thereby uncovering a common immunoregulatory mechanism in both diseases. Furthermore, we discovered that inhibition of SGK1 exacerbates the inflammatory response in disease models of both conditions. These findings not only provide a new perspective for a common therapeutic strategy for psoriasis and atopic dermatitis but also highlight the importance of considering these molecular interactions in future treatments. Validation of these observations through further qPCR, immunofluorescence, and animal studies has identified potential new targets for the treatment of psoriasis and atopic dermatitis.

Integrating single-cell transcriptomics and machine learning to predict breast cancer prognosis: A study based on natural killer cell-related genes.

Breast cancer (BC) is the most commonly diagnosed cancer in women globally. Natural killer (NK) cells play a vital role in tumour immunosurveillance. This study aimed to establish a prognostic model using NK cell-related genes (NKRGs) by integrating single-cell transcriptomic data with machine learning. We identified 44 significantly expressed NKRGs involved in cytokine and T cell-related functions. Using 101 machine learning algorithms, the Lasso + RSF model showed the highest predictive accuracy with nine key NKRGs. We explored cell-to-cell communication using CellChat, assessed immune-related pathways and tumour microenvironment with gene set variation analysis and ssGSEA, and observed immune components by HE staining. Additionally, drug activity predictions identified potential therapies, and gene expression validation through immunohistochemistry and RNA-seq confirmed the clinical applicability of NKRGs. The nomogram showed high concordance between predicted and actual survival, linking higher tumour purity and risk scores to a reduced immune score. This NKRG-based model offers a novel approach for risk assessment and personalized treatment in BC, enhancing the potential of precision medicine.