Single-cell RNA Sequencing Datasets Research Articles

OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing

Single-cell sequencing is frequently affected by “omission” due to limitations in sequencing throughput, yet bulk RNA-seq may contain these ostensibly “omitted” cells. Here, we introduce the single cell trajectory blending from Bulk RNA-seq (BulkTrajBlend) algorithm, a component of the OmicVerse suite that leverages a Beta-Variational AutoEncoder for data deconvolution and graph neural networks for the discovery of overlapping communities. This approach effectively interpolates and restores the continuity of “omitted” cells within single-cell RNA sequencing datasets. Furthermore, OmicVerse provides an extensive toolkit for both bulk and single cell RNA-seq analysis, offering seamless access to diverse methodologies, streamlining computational processes, fostering exquisite data visualization, and facilitating the extraction of significant biological insights to advance scientific research.

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.

T-distributed Stochastic Neighbor Network for unsupervised representation learning

Unsupervised representation learning (URL) is still lack of a reasonable operator (e.g. convolution kernel) for exploring meaningful structural information from generic data including vector, image and tabular data. In this paper, we propose a simple end-to-end T-distributed Stochastic Neighbor Network (TsNet) for URL with clustering downstream task. Concretely, our TsNet model has three major components: (1) an adaptive connectivity distribution learning module is presented to construct a pairwise graph for preserving the local structure of generic data; (2) a T-distributed stochastic neighbor embedding based loss function is designed to learn a transformation between embeddings and original data, which improves the discrimination of representations; (3) a nonlinear parametric mapping is learned via our TsNet on an unsupervised generalized manner, which can address the “out-of-sample” issue. By combining these components, our method is able to considerably outperform previous related unsupervised learning approaches on visualization and clustering of generic data. A simple deep neural network equipped on our model respectively achieves 74.90%, 76.56% ACC and NMI, which is 8% relative improvement over previous state-of-the-art on real single-cell RNA-sequencing (scRNA-seq) datasets clustering.

Single-Cell Analysis Identifies Distinct Populations of Cytotoxic CD4+ T Cells Linked to the Therapeutic Efficacy of Immune Checkpoint Inhibitors in Metastatic Renal Cell Carcinoma.

The involvement of cytotoxic CD4+ T cells (CD4+ CTLs) and their potential role in dictating the response to immune checkpoint inhibitors (ICIs) in patients with metastatic renal cell carcinoma (mRCC) remains an unexplored area of research. Utilizing single-cell RNA sequencing, we analyzed the immunophenotype and expression patterns of CD4+ T lymphocyte subtypes in mRCC patients, followed by preliminary validation via multi-immunofluorescent staining. In addition, we obtained a comprehensive immunotherapy dataset encompassing single-cell RNA sequencing datasets and bulk RNA-seq cohorts from the European Genome-Phenome Archive and ArrayExpress database. Utilizing the CIBERSORTx deconvolution algorithms, we derived a signature score for CD4+ CTLs from the bulk-RNA-seq datasets of the CheckMate 009/025 clinical trials. Single-cell analysis of CD4+ T lymphocytes in mRCC reveals several cancer-specific states, including diverse phenotypes of regulatory T cells. Remarkably, we observe that CD4+ CTLs cells constitute a substantial proportion of all CD4+ T lymphocyte sub-clusters in mRCC patients, highlighting their potential significance in the disease. Furthermore, within mRCC patients, we identify two distinct cytotoxic states of CD4+ T cells: CD4+GZMK+ T cells, which exhibit a weaker cytotoxic potential, and CD4+GZMB+ T cells, which demonstrate robust cytotoxic activity. Both regulatory T cells and CD4+ CTLs originate from proliferating CD4+ T cells within mRCC tissues. Intriguingly, our trajectory analysis indicates that the weakly cytotoxic CD4+GZMK+ T cells differentiate from their more cytotoxic CD4+GZMB+ counterparts. In comparing patients with lower CD4+ CTLs levels to those with higher CD4+ CTLs abundance in the CheckMate 009 and 25 immunotherapy cohorts, the latter group exhibited significantly improved OS and PFS probability. Our study underscores the pivotal role that intratumoral CD4+ CTLs may play in bolstering anti-tumor immunity, suggesting their potential as a promising biomarker for predicting response to ICIs in patients with mRCC.

Disease-specific transcriptional programs govern airway goblet cell metaplasia

Hypersecretion of airway mucus caused by goblet cell metaplasia is a characteristic of chronic pulmonary inflammatory diseases including asthma, cystic fibrosis (CF), and chronic obstructive pulmonary disease (COPD). Goblet cells originate from airway progenitor club cells. However, the molecular mechanisms and features of goblet cell metaplasia in lung disease are poorly understood. Herein, public single-cell RNA sequencing datasets of human lungs were reanalyzed to explore the transitional phase as club cells differentiate into goblet cells in asthma, CF, and COPD. We found that changes in club and goblet cells during pathogenesis and cellular transition were associated with signalling pathways related to immune response, oxidative stress, and apoptosis. Moreover, other key drivers of goblet cell specification appeared to be pathologically specific, with interleukin (IL)-13 and hypoxia inducible factor 1 (HIF-1)-induced genetic changes in asthma, cystic fibrosis transmembrane conductance regulator (CFTR) mutation being present in CF, and interactions with CD8+ T cells, mitophagy, and mitochondria-induced apoptosis in COPD. In conclusion, this study revealed the similarities and differences in goblet cell metaplasia in asthma, CF, and COPD at the transcriptome level, thereby providing insights into possible novel therapeutic approaches for these diseases.

Integrated Single-Cell Analysis Reveals Spatially and Temporally Dynamic Heterogeneity in Fibroblast States during Wound Healing

Wound healing is a dynamic process over temporal and spatial scales. Key to repair outcomes are fibroblasts, yet how they modulate healing across time and in different wound regions remains incompletely understood. By integrating single-cell RNA-sequencing datasets of mouse skin and wounds, we infer that fibroblasts are the most transcriptionally dynamic skin-resident cells, evolving during postnatal skin maturation, and rapidly after injury towards distinct late scar states. We show that transcriptional dynamics in fibroblasts are largely driven by genes encoding extracellular matrix and signaling factors. Lineage trajectory inference and spatial gene mapping reveal that Prg4-expressing fibroblasts transiently emerge along early wound edges. Within days, they become replaced by long-lasting and likely non-interconverting fibroblast populations, including Col25a1-expressing and Pamr1-expressing fibroblasts that occupy subepidermal and deep scar regions, respectively, where they engage in reciprocal signaling with immune cells. Signaling inference shows that fibroblast-immune crosstalk repeatedly uses some signaling pathways across wound healing time, while use of other signaling pathways is time- and space-limited. Collectively, we uncovered high transcriptional plasticity by wound fibroblasts, with early states transiently forming distinct micro-niches along wound edges and in the fascia, followed by stable states, that stratify scar tissue into molecularly dissimilar upper and lower layers.

Role of TAP1 in the identification of immune-hot tumor microenvironment and its prognostic significance for immunotherapeutic efficacy in gastric carcinoma.

Gastric cancer (GC), a multifaceted gastrointestinal malignancy, is the fourth most prevalent contributor to cancer-related fatalities globally. As a member of the ATP-binding cassette (ABC) family, transporter associated with antigen processing 1 (TAP1) is crucial for conveying antigen peptides from the cytoplasm to the lumen of the endoplasmic reticulum and subsequently loading them onto the major histocompatibility complex (MHC) class I molecules. Recent studies have established the biological significance of TAP1 in upholding tumor survival and facilitating immune evasion by remodeling the tumor microenvironment (TME) and orchestrating immune infiltration. The study was conducted to elucidate the association of TAP1 expression with immunological characteristics, and sought to exploit the value of TAP1 as a biomarker reflecting the inflamed TME and immunotherapeutic response. RNA-sequencing profiles and clinical annotations were obtained from The Cancer Genome Atlas-stomach adenocarcinoma (TCGA-STAD) cohort and Gene Expression Omnibus (GEO) portal. Preprocessing was conducting using the limma package. Weighted gene co-expression network analysis (WGCNA) was used to identify gene modules and TAP1 co-expressed genes (CEGs) based on correlation patterns. Consensus clustering and silhouette analysis determined the optimal number of TAP1-related groups. Gene expression profiles were integrated and classified using the pamr package. The Estimation of STromal and Immune cells in MAlignant Tumors using Expression data (ESTIMATE) algorithm and single-sample gene set enrichment analysis (ssGSEA) were used to evaluate immunological characteristics. Differential expression analysis was conducted using the limma package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed. Single-cell RNA sequencing (scRNA-seq) datasets were analyzed using the Seurat toolkit to characterize cell types. Within this investigation, no significant differences in TAP1 expression were observed among patients exhibiting various clinicopathological features, indicating that TAP1 expression was not specific to molecular subtypes. Subsequent analysis revealed a positive correlation between TAP1 and diverse immunological traits, encompassing immunomodulators, tumor-infiltrating immune cells, as well as immune checkpoints across multiple datasets. Besides, within a GC immunotherapy cohort, individuals displaying high TAP1 expression demonstrated an increased likelihood of achieving complete remission (CR) post-treatment, suggesting heightened sensitivity to immunotherapy. In the clinical cohort, TAP1 overexpression in GC patients was positively correlated with CD8. TAP1 appears linked to an inflamed TME and serves as a prospective biomarker for discerning immunological attributes and gauging immunotherapeutic responses in GC, particularly in identifying immune-reactive tumors.

Optimal sequencing budget allocation for trajectory reconstruction of single cells.

Charting cellular trajectories over gene expression is key to understanding dynamic cellular processes and their underlying mechanisms. While advances in single-cell RNA-sequencing technologies and computational methods have pushed forward the recovery of such trajectories, trajectory inference remains a challenge due to the noisy, sparse, and high-dimensional nature of single-cell data. This challenge can be alleviated by increasing either the number of cells sampled along the trajectory (breadth) or the sequencing depth, i.e. the number of reads captured per cell (depth). Generally, these two factors are coupled due to an inherent breadth-depth tradeoff that arises when the sequencing budget is constrained due to financial or technical limitations. Here we study the optimal allocation of a fixed sequencing budget to optimize the recovery of trajectory attributes. Empirical results reveal that reconstruction accuracy of internal cell structure in expression space scales with the logarithm of either the breadth or depth of sequencing. We additionally observe a power law relationship between the optimal number of sampled cells and the corresponding sequencing budget. For linear trajectories, non-monotonicity in trajectory reconstruction across the breadth-depth tradeoff can impact downstream inference, such as expression pattern analysis along the trajectory. We demonstrate these results for five single-cell RNA-sequencing datasets encompassing differentiation of embryonic stem cells, pancreatic beta cells, hepatoblast and multipotent hematopoietic cells, as well as induced reprogramming of embryonic fibroblasts into neurons. By addressing the challenges of single-cell data, our study offers insights into maximizing the efficiency of cellular trajectory analysis through strategic allocation of sequencing resources.

Synthetic DNA barcodes identify singlets in scRNA-seq datasets and evaluate doublet algorithms

Single-cell RNA sequencing (scRNA-seq) datasets contain true single cells, or singlets, in addition to cells that coalesce during the protocol, or doublets. Identifying singlets with high fidelity in scRNA-seq is necessary to avoid false negative and false positive discoveries. Although several methodologies have been proposed, they are typically tested on highly heterogeneous datasets and lack a priori knowledge of true singlets. Here, we leveraged datasets with synthetically introduced DNA barcodes for a hitherto unexplored application: to extract ground-truth singlets. We demonstrated the feasibility of our framework, "singletCode," to evaluate existing doublet detection methods across a range of contexts. We also leveraged our ground-truth singlets to train a proof-of-concept machine learning classifier, which outperformed other doublet detection algorithms. Our integrative framework can identify ground-truth singlets and enable robust doublet detection in non-barcoded datasets.

COL10A1 expression distinguishes a subset of cancer-associated fibroblasts present in the stroma of high-risk basal cell carcinoma.

Basal cell carcinoma (BCC) is the most frequently diagnosed skin cancer and the most common malignancy in humans. Different morphological subtypes of BCC are associated with a low or high risk of recurrence and aggressiveness, but the underlying biology of how the individual subtypes arise remains largely unknown. As the majority of BCCs appear to arise from mutations in the same pathway, we hypothesized that BCC development, growth and invasive potential is also influenced by the tumour microenvironment and, in particular, by cancer-associated fibroblasts (CAFs) and the factors they secrete. To characterize the stroma of the different BCC subtypes with a focus on CAF populations. To investigate the stromal features of the different BCC subtypes, we used laser capture microdissection (LCM) followed by RNA sequencing (RNA-Seq). Fifteen BCC samples from five different 'pure' subtypes (i.e. superficial, nodular, micronodular, sclerosing and basosquamous; n = 3 each) were selected and included in the analysis. Healthy skin was used as a control (n = 6). The results were confirmed by immunohistochemistry (IHC). We validated our findings in two independent public single-cell RNA-Seq (scRNA-Seq) datasets and by RNAscope. The stroma of the different BCC subtypes were found to have distinct gene expression signatures. Nodular and micronodular appeared to have the most similar signatures, while superficial and sclerosing the most different. By comparing low- and high-risk BCC subtypes, we found that COL10A1 is overexpressed in the stroma of sclerosing/infiltrative and basosquamous but not in micronodular high-risk subtypes. Those findings were confirmed by IHC in 93 different BCC and 13 healthy skin samples. Moreover, scRNA-Seq analysis of BCCs from two independent datasets found that the COL10A1-expressing population of cells is associated with the stroma adjacent to infiltrative BCC and shows extracellular matrix remodelling features. We identified COL10A1 as a marker of high-risk BCC, in particular of the sclerosing/infiltrative and basosquamous subtypes. We demonstrated at the single-cell level that COL10A1 is expressed by a specific CAF population associated with the stroma of infiltrative BCC. This opens up new, tailored treatment options, and suggests COL10A1 as a new prognostic biomarker for BCC progression.

Deregulated immune cell recruitment orchestrated by c-MET impairs pulmonary inflammation and fibrosis

BackgroundPulmonary fibrosis (PF) represents the pathologic end stage of several interstitial lung diseases (ILDs) associated with high morbidity and mortality rates. However, current treatments can only delay disease progression rather than provide a cure. The role of inflammation in PF progression is well-established, but new insights into immune regulation are fundamental for developing more efficient therapies. c-MET signaling has been implicated in the migratory capacity and effector functions of immune cells. Nevertheless, the role of this signaling pathway in the context of PF-associated lung diseases remains unexplored.MethodsTo determine the influence of c-MET in immune cells in the progression of pulmonary fibrosis, we used a conditional deletion of c-Met in immune cells. To induce pulmonary fibrosis mice were administered with bleomycin (BLM) intratracheally. Over the course of 21 days, mice were assessed for weight change, and after euthanasia at different timepoints, bronchoalveolar lavage fluid cells and lung tissue were assessed for inflammation and fibrosis. Furthermore, c-MET expression was assessed in cryobiopsy sections, bronchoalveolar lavage fluid cells samples and single cell RNA-sequencing dataset from human patients with distinct interstitial lung diseases.Resultsc-MET expression was induced in lung immune cells, specifically in T cells, interstitial macrophages, and neutrophils, during the inflammatory phase of BLM-induced PF mouse model. Deletion of c-Met in immune cells correlated with earlier weight recovery and improved survival of BLM-treated mice. Moreover, the deletion of c-Met in immune cells was associated with early recruitment of the immune cell populations, normally found to express c-MET, leading to a subsequent attenuation of the cytotoxic and proinflammatory environment. Consequently, the less extensive inflammatory response, possibly coupled with tissue repair, culminated in less exacerbated fibrotic lesions. Furthermore, c-MET expression was up-regulated in lung T cells from patients with fibrosing ILD, suggesting a potential involvement of c-MET in the development of fibrosing disease.ConclusionsThese results highlight the critical contribution of c-MET signaling in immune cells to their enhanced uncontrolled recruitment and activation toward a proinflammatory and profibrotic phenotype, leading to the exacerbation of lung injury and consequent development of fibrosis.

Cell type-specific expression of angiotensin receptors in the human lung with implications for health, aging, and chronic disease.

The renin-angiotensin system is a highly characterized integrative pathway in mammalian homeostasis whose clinical spectrum has been expanded to lung disorders such as chronic obstructive pulmonary disease (COPD)-emphysema, idiopathic pulmonary fibrosis (IPF), and COVID pathogenesis. Despite this widespread interest, specific localization of this receptor family in the mammalian lung is limited, partially due to the imprecision of available antibody reagents. In this study, we establish the expression pattern of the two predominant angiotensin receptors in the human lung, AGTR1 and AGTR2, using complementary and comprehensive bulk and single-cell RNA-sequence datasets that are publicly available. We show these two receptors have distinct localization patterns and developmental trajectories in the human lung, pericytes for AGTR1 and a subtype of alveolar epithelial type 2 cells for AGTR2. In the context of disease, we further pinpoint AGTR2 localization to the COPD-associated subpopulation of alveolar epithelial type 2 (AT2B) and AGTR1 localization to fibroblasts, where their expression is upregulated in individuals with COPD, but not in individuals with IPF. Finally, we examine the genetic variation of the angiotensin receptors, finding AGTR2 associated with lung phenotype (i.e., cystic fibrosis) via rs1403543. Together, our findings provide a critical foundation for delineating this pathway's role in lung homeostasis and constructing rational approaches for targeting specific lung disorders.

DNA damage response-related signatures characterize the immune landscape and predict the prognosis of HCC via integrating single-cell and bulk RNA-sequencing

BackgroundThe occurrence and progression of hepatocellular carcinoma (HCC) are significantly affected by DNA damage response (DDR). Exploring DDR-related biomarkers can help predict the prognosis and immune characteristics of HCC. MethodsFirst, the single-cell RNA sequencing (scRNA-seq) dataset GSE242889 was processed and performed manual annotation. Then we found the marker genes of DDR-active subgroups based on “AUCell” algorithm. The “Limma” R package was used to identify differentially expressed genes (DEGs) between tumor and normal samples of HCC. The risk prognostic model was constructed by filtering genes using univariate Cox and LASSO regression analyses. Finally, the signatures were analyzed for immune infiltration, gene mutation, and drug sensitivity. Last but not least, KPNA2, which had the largest coefficient in our model was validated by experiments including western blot, MTT, colony formation and γ-H2AX assays. ResultsWe constructed a prognostic model based on 5 DDR marker genes including KIF2C, CDC20, KPNA2, UBE2S and ADH1B for HCC. We also proved that the model had an excellent performance in both training and validation cohorts. Patients in the high-risk group had a poorer prognosis, different immune features, gene mutation frequency, immunotherapy response and drug sensitivity compared with the low-risk group. Besides, our experimental results proved that KPNA2 was up-regulated in liver cancer cells than in hepatocytes. More importantly, the knockdown of KPNA2 significantly inhibited cell variability, proliferation and promoted DNA damage. ConclusionsWe innovatively integrated scRNA-seq and bulk RNA sequencing to construct the DDR-related prognostic model. Our model could effectively predict the prognosis, immune landscape and therapy response of HCC.

Integrated analysis of tertiary lymphoid structures and immune infiltration in ccRCC microenvironment revealed their clinical significances: a multicenter cohort study

BackgroundTertiary lymphoid structures (TLSs) serve as organized lymphoid aggregates that influence immune responses within the tumor microenvironment. This study aims to investigate the characteristics and clinical significance of TLSs and...

Recessive variants in MYO1C as a potential novel cause of proteinuric kidney disease.

Steroid-resistant nephrotic syndrome is the second leading cause of chronic kidney disease among patients < 25years of age. Through exome sequencing, identification of > 65 monogenic causes has revealed insights into disease mechanisms of nephrotic syndrome (NS). To elucidate novel monogenic causes of NS, we combined homozygosity mapping with exome sequencing in a worldwide cohort of 1649 pediatric patients with NS. We identified homozygous missense variants in MYO1C in two unrelated children with NS (c.292C > T, p.R98W; c.2273 A > T, p.K758M). We evaluated publicly available kidney single-cell RNA sequencing datasets and found MYO1C to be predominantly expressed in podocytes. We then performed structural modeling for the identified variants in PyMol using aligned shared regions from two available partial structures of MYO1C (4byf and 4r8g). In both structures, calmodulin, a common regulator of myosin activity, is shown to bind to the IQ motif. At both residue sites (K758; R98), there are ion-ion interactions stabilizing intradomain and ligand interactions: R98 binds to nearby D220 within the myosin motor domain and K758 binds to E14 on a calmodulin molecule. Variants of these charged residues to non-charged amino acids could ablate these ionic interactions, weakening protein structure and function establishing the impact of these variants. We here identified recessive variants in MYO1C as a potential novel cause of NS in children.

Noninvasive assessment of the lung inflammation-fibrosis axis by targeted imaging of CMKLR1.

Precision management of fibrotic lung diseases is challenging due to their diverse clinical trajectories and lack of reliable biomarkers for risk stratification and therapeutic monitoring. Here, we validated the accuracy of CMKLR1 as an imaging biomarker of the lung inflammation-fibrosis axis. By analyzing single-cell RNA sequencing datasets, we demonstrated CMKLR1 expression as a transient signature of monocyte-derived macrophages (MDMφ) enriched in patients with idiopathic pulmonary fibrosis (IPF). Consistently, we identified MDMφ as the major driver of the uptake of CMKLR1-targeting peptides in a murine model of bleomycin-induced lung fibrosis. Furthermore, CMKLR1-targeted positron emission tomography in the murine model enabled quantification and spatial mapping of inflamed lung regions infiltrated by CMKLR1-expressing macrophages and emerged as a robust predictor of subsequent lung fibrosis. Last, high CMKLR1 expression by bronchoalveolar lavage cells identified an inflammatory endotype of IPF with poor survival. Our investigation supports the potential of CMKLR1 as an imaging biomarker for endotyping and risk stratification of fibrotic lung diseases.

Integrated analysis of single-cell and bulk RNA-sequencing identifies a signature based on drug response genes to predict prognosis and therapeutic response in ovarian cancer

Ovarian cancer represents a severe gynecological malignancy with a dire prognosis, underscoring the imperative need for dependable biomarkers that can accurately predict drug response and guide therapeutic choices. In this study, we harnessed online single-cell RNA sequencing (scRNAseq) and bulk RNA sequencing (RNAseq) datasets, applying the Scissor algorithm to identify cells responsive to paclitaxel. From these cells, we derived a gene signature, subsequently used to construct a prognostic model that demonstrated high sensitivity and specificity in predicting patient outcomes. Moreover, we conducted pathway and functional enrichment analyses to uncover potential molecular mechanisms driving the prognostic gene signature. This study illustrates the critical role of scRNAseq and bulk RNAseq in developing precise prognostic models for ovarian cancer, potentially transforming clinical decision-making.

The transcriptomic and spatial organization of telencephalic GABAergic neuronal types.

The telencephalon of the mammalian brain comprises multiple regions and circuit pathways that play adaptive and integrative roles in a variety of brain functions. There is a wide array of GABAergic neurons in the telencephalon; they play a multitude of circuit functions, and dysfunction of these neurons has been implicated in diverse brain disorders. In this study, we conducted a systematic and in-depth analysis of the transcriptomic and spatial organization of GABAergic neuronal types in all regions of the mouse telencephalon and their developmental origins. This was accomplished by utilizing 611,423 single-cell transcriptomes from the comprehensive and high-resolution transcriptomic and spatial cell type atlas for the adult whole mouse brain we have generated, supplemented with an additional single-cell RNA-sequencing dataset containing 99,438 high-quality single-cell transcriptomes collected from the pre- and postnatal developing mouse brain. We present a hierarchically organized adult telencephalic GABAergic neuronal cell type taxonomy of 7 classes, 52 subclasses, 284 supertypes, and 1,051 clusters, as well as a corresponding developmental taxonomy of 450 clusters across different ages. Detailed charting efforts reveal extraordinary complexity where relationships among cell types reflect both spatial locations and developmental origins. Transcriptomically and developmentally related cell types can often be found in distant and diverse brain regions indicating that long-distance migration and dispersion is a common characteristic of nearly all classes of telencephalic GABAergic neurons. Additionally, we find various spatial dimensions of both discrete and continuous variations among related cell types that are correlated with gene expression gradients. Lastly, we find that cortical, striatal and some pallidal GABAergic neurons undergo extensive postnatal diversification, whereas septal and most pallidal GABAergic neuronal types emerge simultaneously during the embryonic stage with limited postnatal diversification. Overall, the telencephalic GABAergic cell type taxonomy can serve as a foundational reference for molecular, structural and functional studies of cell types and circuits by the entire community.

DRA involvement in linaclotide-stimulated bicarbonate secretion during loss of CFTR function.

Duodenal bicarbonate secretion is critical to epithelial protection, as well as nutrient digestion and absorption, and is impaired in cystic fibrosis (CF). We examined if linaclotide, typically used to treat constipation, may also stimulate duodenal bicarbonate secretion. Bicarbonate secretion was measured in vivo and in vitro using mouse and human duodenum (biopsies and enteroids). Ion transporter localization was identified with confocal microscopy, and de novo analysis of human duodenal single-cell RNA sequencing (scRNA-Seq) data sets was performed. Linaclotide increased bicarbonate secretion in mouse and human duodenum in the absence of cystic fibrosis transmembrane conductance regulator (CFTR) expression (Cftr-knockout mice) or function (CFTRinh-172). Na+/H+ exchanger 3 inhibition contributed to a portion of this response. Linaclotide-stimulated bicarbonate secretion was eliminated by down-regulated in adenoma (DRA, SLC26A3) inhibition during loss of CFTR activity. ScRNA-Seq identified that 70% of villus cells expressed SLC26A3, but not CFTR, mRNA. Loss of CFTR activity and linaclotide increased apical brush border expression of DRA in non-CF and CF differentiated enteroids. These data provide further insights into the action of linaclotide and how DRA may compensate for loss of CFTR in regulating luminal pH. Linaclotide may be a useful therapy for CF individuals with impaired bicarbonate secretion.

Disease activity drives divergent epigenetic and transcriptomic reprogramming of monocyte subpopulations in systemic lupus erythematosus

ObjectivesSystemic lupus erythematosus (SLE) is characterised by systemic inflammation involving various immune cell types. Monocytes, pivotal in promoting and regulating inflammation in SLE, differentiate from classic monocytes into intermediate and...