Single-cell Transcriptomic Datasets Research Articles

An integrated single-cell reference atlas of the human endometrium.

The complex and dynamic cellular composition of the human endometrium remains poorly understood. Previous endometrial single-cell atlases profiled few donors and lacked consensus in defining cell types. We introduce the Human Endometrial Cell Atlas (HECA), a high-resolution single-cell reference atlas (313,527 cells) combining published and new endometrial single-cell transcriptomics datasets of 63 women with and without endometriosis. HECA assigns consensus and identifies previously unreported cell types, mapped in situ using spatial transcriptomics and validated using a new independent single-nuclei dataset (312,246 nuclei, 63 donors). In the functionalis, we identify intricate stromal-epithelial cell coordination via transforming growth factor beta (TGFβ) signaling. In the basalis, we define signaling between fibroblasts and an epithelial population expressing progenitor markers. Integration of HECA with large-scale endometriosis genome-wide association study data pinpoints decidualized stromal cells and macrophages as most likely dysregulated in endometriosis. The HECA is a valuable resource for studying endometrial physiology and disorders, and for guiding microphysiological in vitro systems development.

AKNNO: single-cell and spatial transcriptomics clustering with an optimized adaptive k-nearest neighbor graph

Typical clustering methods for single-cell and spatial transcriptomics struggle to identify rare cell types, while approaches tailored to detect rare cell types gain this ability at the cost of poorer performance for grouping abundant ones. Here, we develop aKNNO to simultaneously identify abundant and rare cell types based on an adaptive k-nearest neighbor graph with optimization. Benchmarking on 38 simulated and 20 single-cell and spatial transcriptomics datasets demonstrates that aKNNO identifies both abundant and rare cell types more accurately than general and specialized methods. Using only gene expression aKNNO maps abundant and rare cells more precisely compared to integrative approaches.

Muscle cell-derived Ccl8 is a negative regulator of skeletal muscle regeneration.

Skeletal muscles undergo robust regeneration upon injury, and infiltrating immune cells play a major role in not only clearing damaged tissues but also regulating the myogenic process through secreted cytokines. Chemokine C-C motif ligand 8 (Ccl8), along with Ccl2 and Ccl7, has been reported to mediate inflammatory responses to suppress muscle regeneration. Ccl8 is also expressed by muscle cells, but a role of the muscle cell-derived Ccl8 in myogenesis has not been reported. In this study, we found that knockdown of Ccl8, but not Ccl2 or Ccl7, led to increased differentiation of C2C12 myoblasts. Analysis of existing single-cell transcriptomic datasets revealed that both immune cells and muscle stem cells (MuSCs) in regenerating muscles express Ccl8, with the expression by MuSCs at a much lower level, and that the temporal patterns of Ccl8 expression were different in MuSCs and macrophages. To probe a function of muscle cell-derived Ccl8 invivo, we utilized a mouse system in which Cas9 was expressed in Pax7+ myogenic progenitor cells (MPCs) and Ccl8 gene editing was induced by AAV9-delivered sgRNA. Depletion of Ccl8 in Pax7+ MPCs resulted in accelerated muscle regeneration after barium chloride-induced injury in both young and middle-aged mice, and intramuscular administration of a recombinant Ccl8 reversed the phenotype. Accelerated regeneration was also observed when Ccl8 was depleted in Myf5+ or MyoD+ MPCs by similar approaches. Our results suggest that muscle cell-derived Ccl8 plays a unique role in regulating the initiation of myogenic differentiation during injury-induced muscle regeneration.

Identification of tumor-specific T cell signature predicting cancer immunotherapy response in bladder cancer by multi-omics analysis and experimental verification

BackgroundNumerous gene signatures predicting the prognosis of bladder cancer have been identified. However, a tumor-specific T cell signature related to immunotherapy response in bladder cancer remains under investigation.MethodsSingle-cell RNA and TCR sequencing from the Gene expression omnibus (GEO) database were used to identify tumor-specific T cell-related genes in bladder cancer. Subsequently, we constructed a tumor-specific T cell signature (TstcSig) and validated its clinical relevance for predicting immunotherapy response in multiple immunotherapy cohorts. Further analyses explored the immune characteristics of TstcSig in bladder cancer patients from other cohorts in the TCGA and GEO databases. Western blot (WB), multicolor immunofluorescence (MIF), qRT-PCR and flow cytometry assays were performed to validate the results of bioinformatics analysis.ResultsThe established TstcSig, based on five tumor-specific T cell-related genes, could predict outcomes in a bladder cancer immunotherapy cohort. This was verified using two additional immunotherapy cohorts and showed better predictive performance compared to 109 published T cell signatures. TstcSig was strongly correlated with immune characteristics such as immune checkpoint gene expression, tumor mutation burden, and T cell infiltration, as validated by single-cell and spatial transcriptomics datasets. Notably, the positive correlation between TstcSig and T cell infiltration was confirmed in the TCGA cohort. Furthermore, pan-cancer analysis demonstrated the heterogeneity of the prognostic value of TstcSig. Tumor-specific T cells highly expressed CD27, IFNG, GZMB and CXCL13 and secreted more effector cytokines for tumor cell killing, as validated experimentally.ConclusionWe developed a five-gene signature (including VAMP5, TIGIT, LCK, CD27 and CACYBP) based on tumor-specific T cell-related genes to predict the immunotherapy response in bladder cancer patients.

Cathepsin D is essential for the degradomic shift of macrophages required to resolve liver fibrosis

Background and objectivesFibrosis contributes to 45% of deaths in industrialized nations and is characterized by an abnormal accumulation of extracellular matrix (ECM). There are no specific anti-fibrotic treatments for liver fibrosis, and previous unsuccessful attempts at drug development have focused on preventing ECM deposition. Because liver fibrosis is largely acknowledged to be reversible, regulating fibrosis resolution could offer novel therapeutical options. However, little is known about the mechanisms controlling ECM remodeling during resolution. Changes in proteolytic activity are essential for ECM homeostasis and macrophages are an important source of proteases. Herein, in this study we evaluate the role of macrophage-derived cathepsin D (CtsD) during liver fibrosis. MethodsCtsD expression and associated pathways were characterized in single-cell RNA sequencing and transcriptomic datasets in human cirrhosis. Liver fibrosis progression, reversion and functional characterization were assessed in novel myeloid-CtsD and hepatocyte-CtsD knock-out mice. ResultsAnalysis of single-cell RNA sequencing datasets demonstrated CtsD was expressed in macrophages and hepatocytes in human cirrhosis. Liver fibrosis progression, reversion and functional characterization were assessed in novel myeloid-CtsD (CtsDΔMyel) and hepatocyte-CtsD knock-out mice. CtsD deletion in macrophages, but not in hepatocytes, resulted in enhanced liver fibrosis. Both inflammatory and matrisome proteomic signatures were enriched in fibrotic CtsDΔMyel livers. Besides, CtsDΔMyel liver macrophages displayed functional, phenotypical and secretomic changes, which resulted in a degradomic phenotypical shift, responsible for the defective proteolytic processing of collagen I in vitro and impaired collagen remodeling during fibrosis resolution in vivo. Finally, CtsD-expressing mononuclear phagocytes of cirrhotic human livers were enriched in lysosomal and ECM degradative signaling pathways. ConclusionsOur work describes for the first-time CtsD-driven lysosomal activity as a central hub for restorative macrophage function during fibrosis resolution and opens new avenues to explore their degradome landscape to inform drug development.

Fatecode enables cell fate regulator prediction using classification-supervised autoencoder perturbation

Cell reprogramming, which guides the conversion between cell states, is a promising technology for tissue repair and regeneration, with the ultimate goal of accelerating recovery from diseases or injuries. To accomplish this, regulators must be identified and manipulated to control cell fate. We propose Fatecode, a computational method that predicts cell fate regulators based only on single-cell RNA sequencing (scRNA-seq) data. Fatecode learns a latent representation of the scRNA-seq data using a deep learning-based classification-supervised autoencoder and then performs in silico perturbation experiments on the latent representation to predict genes that, when perturbed, would alter the original cell type distribution to increase or decrease the population size of a cell type of interest. We assessed Fatecode's performance using simulations from a mechanistic gene-regulatory network model and scRNA-seq data mapping blood and brain development of different organisms. Our results suggest that Fatecode can detect known cell fate regulators from single-cell transcriptomics datasets.

Loss of NLRP6 increases the severity of kidney fibrosis.

While NLRP3 contributes to kidney fibrosis, the function of most NOD-like receptors (NLRs) in chronic kidney disease (CKD) remains unexplored. To identify further NLR members involved in the pathogenesis of CKD, we searched for NLR genes expressed by normal kidneys and differentially expressed in human CKD transcriptomics databases. For NLRP6, lower kidney expression correlated with decreasing glomerular filtration rate. The role and molecular mechanisms of Nlrp6 in kidney fibrosis were explored in wild-type and Nlrp6-deficient mice and cell cultures. Data mining of single-cell transcriptomics databases identified proximal tubular cells as the main site of Nlrp6 expression in normal human kidneys and tubular cell Nlrp6 was lost in CKD. We confirmed kidney Nlrp6 downregulation following murine unilateral ureteral obstruction. Nlrp6-deficient mice had higher kidney p38 MAPK activation and more severe kidney inflammation and fibrosis. Similar results were obtained in adenine-induced kidney fibrosis. Mechanistically, profibrotic cytokines transforming growth factor beta 1 (TGF-β1) and TWEAK decreased Nlrp6 expression in cultured tubular cells, and Nlrp6 downregulation resulted in increased TGF-β1 and CTGF expression through p38 MAPK activation, as well as in downregulation of the antifibrotic factor Klotho, suggesting that loss of Nlrp6 promotes maladaptive tubular cell responses. The pattern of gene expression following Nlrp6 targeting in cultured proximal tubular cells was consistent with maladaptive transitions for proximal tubular cells described in single-cell transcriptomics datasets. In conclusion, endogenous constitutive Nlrp6 dampens sterile kidney inflammation and fibrosis. Loss of Nlrp6 expression by tubular cells may contribute to CKD progression.

A broken network of susceptibility genes in the monocytes of Crohn's disease patients.

Genome-wide association studies have identified over 200 genetic loci associated with inflammatory bowel disease; however, the mechanism of such a large amount of susceptibility genes remains uncertain. In this study, we integrated bioinformatics analysis and two independent single-cell transcriptome datasets to investigate the expression network of 232 susceptibility genes in Crohn's disease (CD) patients and healthy controls. The study revealed that most of the susceptibility genes are specifically and strictly expressed in the monocytes of the human intestinal tract. The susceptibility genes established a network within the monocytes of health control. The robustness of a gene network may prevent disease onset that is influenced by the genetic and environmental alteration in the expression of susceptibility genes. In contrast, we showed a sparse network in pediatric/adult CD patients, suggesting the broken network contributed to the CD etiology. The network status of susceptibility genes at the single-cell level of monocytes provided novel insight into the etiology.

Cross-species transcriptomics identifies obesity associated genes between human and mouse studies

BackgroundFundamentally defined by an imbalance in energy consumption and energy expenditure, obesity is a significant risk factor of several musculoskeletal conditions including osteoarthritis (OA). High-fat diets and sedentary lifestyle leads to increased adiposity resulting in systemic inflammation due to the endocrine properties of adipose tissue producing inflammatory cytokines and adipokines. We previously showed serum levels of specific adipokines are associated with biomarkers of bone remodelling and cartilage volume loss in knee OA patients. Whilst more recently we find the metabolic consequence of obesity drives the enrichment of pro-inflammatory fibroblast subsets within joint synovial tissues in obese individuals compared to those of BMI defined ‘health weight’. As such this present study identifies obesity-associated genes in OA joint tissues which are conserved across species and conditions.MethodsThe study utilised 6 publicly available bulk and single-cell transcriptomic datasets from human and mice studies downloaded from Gene Expression Omnibus (GEO). Machine learning models were employed to model and statistically test datasets for conserved gene expression profiles. Identified genes were validated in OA tissues from obese and healthy weight individuals using quantitative PCR method (N = 38). Obese and healthy-weight patients were categorised by BMI > 30 and BMI between 18 and 24.9 respectively. Informed consent was obtained from all study participants who were scheduled to undergo elective arthroplasty.ResultsPrincipal component analysis (PCA) was used to investigate the variations between classes of mouse and human data which confirmed variation between obese and healthy populations. Differential gene expression analysis filtered on adjusted p-values of p < 0.05, identified differentially expressed genes (DEGs) in mouse and human datasets. DEGs were analysed further using area under curve (AUC) which identified 12 genes. Pathway enrichment analysis suggests these genes were involved in the biosynthesis and elongation of fatty acids and the transport, oxidation, and catabolic processing of lipids. qPCR validation found the majority of genes showed a tendency to be upregulated in joint tissues from obese participants. Three validated genes, IGFBP2 (p = 0.0363), DOK6 (0.0451) and CASP1 (0.0412) were found to be significantly different in obese joint tissues compared to lean-weight joint tissues.ConclusionsThe present study has employed machine learning models across several published obesity datasets to identify obesity-associated genes which are validated in joint tissues from OA. These results suggest obesity-associated genes are conserved across conditions and may be fundamental in accelerating disease in obese individuals. Whilst further validations and additional conditions remain to be tested in this model, identifying obesity-associated genes in this way may serve as a global aid for patient stratification giving rise to the potential of targeted therapeutic interventions in such patient subpopulations.

Macrophage and fibroblast trajectory inference and crosstalk analysis during myocardial infarction using integrated single-cell transcriptomic datasets

BackgroundCardiac fibrosis after myocardial infarction (MI) has been considered an important part of cardiac pathological remodeling. Immune cells, especially macrophages, are thought to be involved in the process of fibrosis and constitute a niche with fibroblasts to promote fibrosis. However, the diversity and variability of fibroblasts and macrophages make it difficult to accurately depict interconnections.MethodsWe collected and reanalyzed scRNA-seq and snRNA-seq datasets from 12 different studies. Differentiation trajectories of these subpopulations after MI injury were analyzed by using scVelo, PAGA and Slingshot. We used CellphoneDB and NicheNet to infer fibroblast-macrophage interactions. Tissue immunofluorescence staining and in vitro experiments were used to validate our findings.ResultsWe discovered two subsets of ECM-producing fibroblasts, reparative cardiac fibroblasts (RCFs) and matrifibrocytes, which appeared at different times after MI and exhibited different transcriptional profiles. We also observed that CTHRC1+ fibroblasts represent an activated fibroblast in chronic disease states. We identified a macrophage subset expressing the genes signature of SAMs conserved in both human and mouse hearts. Meanwhile, the SPP1hi macrophages were predominantly found in the early stages after MI, and cell communication analysis indicated that SPP1hi macrophage-RCFs interactions are mainly involved in collagen deposition and scar formation.ConclusionsOverall, this study comprehensively analyzed the dynamics of fibroblast and macrophage subsets after MI and identified specific subsets of fibroblasts and macrophages involved in scar formation and collagen deposition.

Scanorama: integrating large and diverse single-cell transcriptomic datasets.

Merging diverse single-cell RNA sequencing (scRNA-seq) data from numerous experiments, laboratories and technologies can uncover important biological insights. Nonetheless, integrating scRNA-seq data encounters special challenges when the datasets are composed of diverse cell type compositions. Scanorama offers a robust solution for improving the quality and interpretation of heterogeneous scRNA-seq data by effectively merging information from diverse sources. Scanorama is designed to address the technical variation introduced by differences in sample preparation, sequencing depth and experimental batches that can confound the analysis of multiple scRNA-seq datasets. Here we provide a detailed protocol for using Scanorama within a Scanpy-based single-cell analysis workflow coupled with Google Colaboratory, a cloud-based free Jupyter notebook environment service. The protocol involves Scanorama integration, a process that typically spans 0.5-3 h. Scanorama integration requires a basic understanding of cellular biology, transcriptomic technologies and bioinformatics. Our protocol and new Scanorama-Colaboratory resource should make scRNA-seq integration more widely accessible to researchers.

Specialized Tfh cell subsets driving type-1 and type-2 humoral responses in lymphoid tissue

Effective antibody responses are essential to generate protective humoral immunity. Different inflammatory signals polarize T cells towards appropriate effector phenotypes during an infection or immunization. Th1 and Th2 cells have been associated with the polarization of humoral responses. However, T follicular helper cells (Tfh) have a unique ability to access the B cell follicle and support the germinal center (GC) responses by providing B cell help. We investigated the specialization of Tfh cells induced under type-1 and type-2 conditions. We first studied homogenous Tfh cell populations generated by adoptively transferred TCR-transgenic T cells in mice immunized with type-1 and type-2 adjuvants. Using a machine learning approach, we established a gene expression signature that discriminates Tfh cells polarized towards type-1 and type-2 response, defined as Tfh1 and Tfh2 cells. The distinct signatures of Tfh1 and Tfh2 cells were validated against datasets of Tfh cells induced following lymphocytic choriomeningitis virus (LCMV) or helminth infection. We generated single-cell and spatial transcriptomics datasets to dissect the heterogeneity of Tfh cells and their localization under the two immunizing conditions. Besides a distinct specialization of GC Tfh cells under the two immunizations and in different regions of the lymph nodes, we found a population of Gzmk+ Tfh cells specific for type-1 conditions. In human individuals, we could equally identify CMV-specific Tfh cells that expressed Gzmk. Our results show that Tfh cells acquire a specialized function under distinct types of immune responses and with particular properties within the B cell follicle and the GC.

Transcriptomic and genomic characteristics of intrahepatic metastases of primary liver cancer

BackgroundPatients with primary multifocal hepatocellular carcinoma (HCC) have a poor prognosis and often experience a high rate of treatment failure. Multifocal HCC is mainly caused by intrahepatic metastasis (IM), and though portal vein tumor thrombosis (PVTT) is considered a hallmark of IM, the molecular mechanism by which primary HCC cells invade the portal veins remains unclear. Therefore, it is necessary to recognize the early signs of metastasis of HCC to arrange better treatment for patients.ResultsTo determine the differential molecular features between primary HCC with and without phenotype of metastasis, we used the CIBERSORTx software to deconvolute cell types from bulk RNA-Seq based on a single-cell transcriptomic dataset. According to the relative abundance of tumorigenic and metastatic hepatoma cells, VEGFA+ macrophages, effector memory T cells, and natural killer cells, HCC samples were divided into five groups: Pro-T, Mix, Pro-Meta, NKC, and MemT, and the transcriptomic and genomic features of the first three groups were analyzed. We found that the Pro-T group appeared to retain native hepatic metabolic activity, whereas the Pro-Meta group underwent dedifferentiation. Genes highly expressed in the group Pro-Meta often signify a worse outcome.ConclusionsThe HCC cohort can be well-typed and prognosis predicted according to tumor microenvironment components. Primary hepatocellular carcinoma may have obtained corresponding molecular features before metastasis occurred.

Deep Batch Integration and Denoise of Single-Cell RNA-Seq Data.

Numerous single-cell transcriptomic datasets from identical tissues or cell lines are generated from different laboratories or single-cell RNA sequencing (scRNA-seq) protocols. The denoising of these datasets to eliminate batch effects is crucial for data integration, ensuring accurate interpretation and comprehensive analysis of biological questions. Although many scRNA-seq data integration methods exist, most are inefficient and/or not conducive to downstream analysis. Here, DeepBID, a novel deep learning-based method for batch effect correction, non-linear dimensionality reduction, embedding, and cell clustering concurrently, is introduced. DeepBID utilizes a negative binomial-based autoencoder with dual Kullback-Leibler divergence loss functions, aligning cell points from different batches within a consistent low-dimensional latent space and progressively mitigating batch effects through iterative clustering. Extensive validation on multiple-batch scRNA-seq datasets demonstrates that DeepBID surpasses existing tools in removing batch effects and achieving superior clustering accuracy. When integrating multiple scRNA-seq datasets from patients with Alzheimer's disease, DeepBID significantly improves cell clustering, effectively annotating unidentified cells, and detecting cell-specific differentially expressed genes.

Single-cell RNA-seqreveals novel interaction between muscle satellite cells and fibro-adipogenic progenitors mediated with FGF7 signalling.

Muscle satellite cells (MuSCs) exert essential roles in skeletal muscle adaptation to growth, injury and ageing, and their functions are extensively modulated by microenvironmental factors. However, the current knowledge about the interaction of MuSCs with niche cells is quite limited. A 10× single-cell RNA sequencing (scRNA-seq) was performed on porcine longissimus dorsi and soleus (SOL) muscles to generate a single-cell transcriptomic dataset of myogenic cells and other cell types. Sophisticated bioinformatic analyses, including unsupervised clustering analysis, marker gene, gene set variation analysis (GSVA), AUCell, pseudotime analysis and RNA velocity analysis, were performed to explore the heterogeneity of myogenic cells. CellChat analysis was used to demonstrate cell-cell communications across myogenic cell subpopulations and niche cells, especially fibro-adipogenic progenitors (FAPs). Integrated analysis with human and mice datasets was performed to verify the expression of FGF7 across diverse species. The role of FGF7 on MuSC proliferation was evaluated through administering recombinant FGF7 to porcine MuSCs, C2C12, cardiotoxin (CTX)-injured muscle and d-galactose (d-gal)-induced ageing model. ScRNA-seq totally figured out five cell types including myo-lineage cells and FAPs, and myo-lineage cells were further classified into six subpopulations, termed as RCN3+, S100A4+, ID3+, cycling (MKI67+), MYF6+ and MYMK+ satellite cells, respectively. There was a higher proportion of cycling and MYF6+ cells in the SOL population. CellChat analysis uncovered a particular impact of FAPs on myogenic cells mediated by FGF7, which was relatively highly expressed in SOL samples. Administration of FGF7 (10ng/mL) significantly increased the proportion of EdU+ porcine MuSCs and C2C12 by 4.03±0.81% (P<0.01) and 6.87±2.17% (P<0.05), respectively, and knockdown of FGFR2 dramatically abolished the pro-proliferating effects (P<0.05). In CTX-injured muscle, FGF7 significantly increased the ratio of EdU+/Pax7+ cells by 15.68±5.45% (P<0.05) and elevated the number of eMyHC+ regenerating myofibres by 19.7±4.25% (P<0.01). Under d-gal stimuli, FGF7 significantly reduced γH2AX+ cells by 17.19±3.05% (P<0.01) in porcine MuSCs, induced EdU+ cells by 4.34±1.54% (P<0.05) in C2C12, and restored myofibre size loss and running exhaustion in vivo (all P<0.05). Our scRNA-seq reveals a novel interaction between muscle FAPs and satellite cells mediated by FGF7-FGFR2. Exogenous FGF7 augments the proliferation of satellite cells and thus benefits muscle regeneration and counteracts age-related myopathy.

Beaconet: A Reference-Free Method for Integrating Multiple Batches of Single-Cell Transcriptomic Data in Original Molecular Space.

Integrating multiple single-cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single-cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low-dimensional feature space. To overcome these limitations, a reference-free method, Beaconet, for integrating multiple single-cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state-of-the-art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene-expression features. Additionally, when applying to large-scale atlas data integration, Beaconet shows notable advantages in both time- and space-efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single-cell datasets in a reference-free and molecular feature-preserved mode.

HepScope: CNN-based single-cell discrimination of malignant hepatocytes

Background: Hepatocellular carcinoma (HCC) poses a significant global health challenge. This study aims to identify robust single-cell biomarkers specifically designed to distinguish between malignant and non-malignant hepatocytes, thereby advancing precision medicine strategies for HCC. Methods: At the single-cell level, we investigated the reliability of established marker genes for malignant hepatocytes in distinguishing them from their non-malignant counterparts. Leveraging one of the largest integrated single-cell transcriptomics (scRNA-seq) datasets of healthy and diseased liver tissues, alongside independent scRNA-seq, spatial transcriptomics (stRNA-seq), bulk RNA-seq, and bulk proteome datasets, we defined a signature gene set, denoted as HepScope. Five other publicly available gene sets related to HCC were used for comparison with HepScope. The development of machine learning (ML) and 1-dimensional convolutional neural network (1D-CNN) models involved utilizing normalized expression of signature genes from scRNA-seq datasets. The HepScope-1D-CNN model was developed to assess differentiation performance by considering the expression values of each signature gene across different tissues and within the same tissue. Finally, we calculated a risk score based on the signature genes to evaluate its prognostic capability. Result: We demonstrated the lack of specificity and consistency among current HCC marker genes in distinguishing malignant hepatocytes from non-malignant hepatocytes. The differential expression (DEG) analysis identified 113 genes upregulated in malignant hepatocytes, forming the HepScope. In contrast to HepScope, the expression values of the five other publicly available gene sets did not exhibit the ability to discriminate between malignant and non-malignant hepatocytes. Utilizing the genes from HepScope, the Gaussian Naive Bayes ML model proficiently distinguished between malignant and non-malignant hepatocytes. The HepScope-CNN model showed the highest performance and effciency compared to other 1D-CNN models trained with different gene sets. Conclusion: In conclusion, our study reveals a novel gene set designed to precisely identify malignant hepatocytes at the single-cell level. This research holds promise for advancing precision medicine in HCC, providing valuable insights into the development of targeted therapies and personalized treatment strategies. We acknowledge support provided by the National Research Foundation (NRF) of Korea (2020R1A6A1A03043539, 2020M3A9D8037604, and 2022R1C1C1004756) and the Korea Health Technology R&amp;D Project of the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health &amp; Welfare, Republic of Korea (HR22C1734). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Senescence of human pancreatic beta cells enhances functional maturation through chromatin reorganization and promotes interferon responsiveness.

Senescent cells can influence the function of tissues in which they reside, and their propensity for disease. A portion of adult human pancreatic beta cells express the senescence marker p16, yet it is unclear whether they are in a senescent state, and how this affects insulin secretion. We analyzed single-cell transcriptome datasets of adult human beta cells, and found that p16-positive cells express senescence gene signatures, as well as elevated levels of beta-cell maturation genes, consistent with enhanced functionality. Senescent human beta-like cells in culture undergo chromatin reorganization that leads to activation of enhancers regulating functional maturation genes and acquisition of glucose-stimulated insulin secretion capacity. Strikingly, Interferon-stimulated genes are elevated in senescent human beta cells, but genes encoding senescence-associated secretory phenotype (SASP) cytokines are not. Senescent beta cells in culture and in human tissue show elevated levels of cytoplasmic DNA, contributing to their increased interferon responsiveness. Human beta-cell senescence thus involves chromatin-driven upregulation of a functional-maturation program, and increased responsiveness of interferon-stimulated genes, changes that could increase both insulin secretion and immune reactivity.

Axon guidance cue SEMA3A promotes the aggressive phenotype of basal-like PDAC

ObjectiveThe dysregulation of the axon guidance pathway is common in pancreatic ductal adenocarcinoma (PDAC), yet our understanding of its biological relevance is limited. Here, we investigated the functional role of...

Bulk and single-cell transcriptome datasets of the mouse fetal and adult rete ovarii and surrounding tissues

The rete ovarii (RO) is an epithelial structure that arises during development in close proximity to the ovary and persists throughout adulthood. However, the functional significance of the RO remains elusive, and it is absent from recent discussions of female reproductive anatomy. The RO comprises three regions: the intraovarian rete within the ovary, the extraovarian rete in the periovarian tissue, and the connecting rete linking the two. We hypothesize that the RO plays a pivotal role in ovarian homeostasis and responses to physiological changes. To begin to uncover the nature and function of RO cells, we conducted transcriptomic profiling of the RO. This study presents three datasets, and reports our analysis and quality control approaches for bulk, single-cell, and nucleus-level transcriptomics of the fetal and adult RO tissues using the Pax8-rtTA; Tre-H2B-GFP mouse line, where all RO regions express nuclear GFP. The integration and rigorous validation of these datasets will advance our understanding of the RO’s roles in ovarian development, female maturation, and adult female fertility.