scRNA-seq Datasets Research Articles

Construction of mitochondrial quality regulation genes-related prognostic model based on bulk-RNA-seq analysis in multiple myeloma.

Mitochondrial quality regulation plays an important role in affecting the treatment sensitivity of multiple myeloma (MM). We aimed to develop a mitochondrial quality regulation genes (MQRGs)-related prognostic model for MM patients. The Genomic Data Commons-MM of bulk RNA-seq, mutation, and single-cell RNA-seq (scRNA-seq) dataset were downloaded, and the MQRGs gene set was collected previous study. "maftools" and CIBERSORT were used for mutation and immune-infiltration analysis. Subsequently, the "ConsensusClusterPlus" was used to perform the unsupervised clustering analysis, "survminer" and "ssGSEA" R package was used for the Kaplan-Meier survival and enrichment analysis, "limma" R, univariate and Least Absolute Shrinkage and Selection Operator Cox were used for RiskScore model. The "timeROC" R package was used for Receiver Operating Characteristic Curve analysis. Finally, the "Seurat" R package was used for scRNA-seq analysis. These MQRGs are mainly located on chromosome-1,2,3,7, and 22 and had significant expression differences among age, gender, and stage groups, in which PPARGC1A and PPARG are the high mutation genes. Most MQRGs expression are closely associated with the plasma cells infiltration and can divide the patients into 2 different prognostic clusters (C1, C2). Then, 8 risk models were screened from 60 DEGs for RiskScore, which is an independent prognostic factor and effectively divided the patients into high and low risk groups with significant difference of immune checkpoint expression. Nomogram containing RiskScore can accurately predict patient prognosis, and a series of specific transcription factor PRDM1 and IRF1 were identified. We described the based molecular features and developed a high effective MQRGs-related prognostic model in MM.

Batch effects correction in scRNA-seq based on biological-noise decoupling autoencoder and central-cross loss

Technical or biologically irrelevant differences caused by different experiments, times, or sequencing platforms can generate batch effects that mask the true biological information. Therefore, batch effects are typically removed when analyzing single-cell RNA sequencing (scRNA-seq) datasets for downstream tasks. Existing batch correction methods usually mitigate batch effects by reducing the data from different batches to a lower dimensional space before clustering, potentially leading to the loss of rare cell types. To address this problem, we introduce a novel single-cell data batch effect correction model using Biological-noise Decoupling Autoencoder (BDA) and Central-cross Loss termed BDACL. The model initially reconstructs raw data using an auto-encoder and conducts preliminary clustering. We then construct a similarity matrix and a hierarchical clustering tree to delineate relationships within and between different batches. Finally, we introduce a Central-cross Loss (CL). This loss leverages cross-entropy loss to prompt the model to better distinguish between different cluster labels. Additionally, it employs the Central Loss to encourage samples to form more compact clusters in the embedding space, thereby enhancing the consistency and interpretability of clustering results to mitigate differences between different batches. The primary innovation of this model lies in reconstructing data with an auto-encoder and gradually merging smaller clusters into larger ones using a hierarchical clustering tree. By using reallocated cluster labels as training labels and employing the Central-cross Loss, the model effectively eliminates batch effects in an unsupervised manner. Compared to current methods, BDACL can mitigate batch effects without losing rare cell types.

Multi-scalar data integration decoding risk genes for chronic kidney disease

BackgroundChronic Kidney Disease (CKD) impacts over 10% of the global population, and recent advancements in high-throughput analytical technologies are uncovering the complex physiology underlying this condition. By integrating Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq/RNA array), and single-cell RNA sequencing (scRNA-seq) data, our study aimed to explore the genes and cell types relevant to CKD traits.MethodsGWAS summary data for end-stage renal failure (ESRD) and decreased eGFR (CKD) with or without diabetes and (micro)proteinuria were obtained from the GWAS Catalog and the UK Biobank (UKB) database. Two gene Expression Omnibus (GEO) transcriptome datasets were used to establish glomerular and tubular gene expression differences between CKD patients and healthy individuals. Two scRNA-seq datasets were utilized to obtain the expression of key genes at the single-cell level. The expression profile, differentially expressed genes (DEGs), gene-gene interaction, and pathway enrichment were analysed for these CKD risk genes.ResultsA total of 779 distinct SNPs were identified from GWAS across different CKD traits, involving 681 genes. While many of these risk genes are specific to the CKD traits of renal failure, decreased eGFR, and (micro)proteinuria, they share common pathways, including extracellular matrix (ECM). ECM modeling was enriched in upregulated glomerular and tubular DEGs from CKD kidneys compared to healthy controls, with the expression of relevant collagen genes, such as COL1A2, prevalent in fibroblasts/myofibroblasts. Additionally, immune responses, including T cell differentiation, were dysregulated in CKD kidneys. The late podocyte signature gene THSD7A was enriched in podocytes but downregulated in CKD. We also highlighted that the regulated risk genes of CKD are mainly expressed in tubular cells and immune cells in the kidney.ConclusionsOur integrated analysis highlight the genes, pathways, and relevant cell types associational with the pathogenesis of kidney traits, as a basis for further mechanistic studies to understand the pathogenesis of CKD.

Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data.

Aortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD. Four AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein-protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model. A total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice. By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.

Single-cell spatiotemporal analysis reveals alveolar dendritic cell–T cell immunity hubs defending against pulmonary infection

How immune cells are spatiotemporally coordinated in the lung to effectively monitor, respond to, and resolve infection and inflammation in primed form needs to be fully illustrated. Here we apply immunocartography, a high-resolution technique that integrates spatial and single-cell RNA sequencing (scRNA-seq) through deconvolution and co-localization analyses, to the SARS-CoV-2-infected Syrian hamster model. We generate a comprehensive transcriptome map of the whole process of pulmonary infection from physiological condition, infection initiation, severe pneumonia to natural recovery at organ scale and single-cell resolution, with 142,965 cells and 45 lung lobes from 25 hamsters at 5 time points. Integrative analysis identifies that alveolar dendritic cell–T cell immunity hubs, where Ccr7+Ido1+ dendritic cells, Cd160+Cd8+ T cells, and Tnfrsf4+Cd4+ T cells physiologically co-localize, rapidly expand during SARS-CoV-2 infection, eliminate SARS-CoV-2 with the aid of Slamf9+ macrophages, and then restore to physiological levels after viral clearance. We verify the presence of these cell subpopulations in the immunity hubs in normal and SARS-CoV-2-infected hACE2 mouse models, as well as in publicly available human scRNA-seq datasets, demonstrating the potential broad relevance of our findings in lung immunity.

SCAPE-APA: a package for estimating alternative polyadenylation events from scRNA-seq data

SCAPE was a package that was previously developed to estimate alternative polyadenylation events from single-cell RNA-seq (scRNA-seq) data. It was composed of ad-hoc python scripts and has speed issues when handling large scRNA-seq data. To suit the needs of analyzing large scRNA-seq datasets, we present SCAPE-APA, which is a reimplementation of SCAPE with substantial changes. We made the following updates to the package: (1) we binned similar reads together to accelerate the estimation; (2) we re-derived the mixture model to tailor it for binned reads; (3) we implemented the inference algorithm using Taichi language for acceleration; (4) we re-implemented the untranslated region (UTR) annotation extraction script using the professional package gffutils for better maintenance; (5) we wrote a script to detect spurious alternative polyadenylation sites generated due to junction reads, and (6) we created a formal Python package and uploaded it to the Python Package Index website (Pypi).

Theoretical framework for the difference of two negative binomial distributions and its application in comparative analysis of sequencing data.

High-throughput sequencing (HTS) technologies have been instrumental in investigating biological questions at the bulk and single-cell levels. Comparative analysis of two HTS data sets often relies on testing the statistical significance for the difference of two negative binomial distributions (DOTNB). Although negative binomial distributions are well studied, the theoretical results for DOTNB remain largely unexplored. Here, we derive basic analytical results for DOTNB and examine its asymptotic properties. As a state-of-the-art application of DOTNB, we introduce DEGage, a computational method for detecting differentially expressed genes (DEGs) in scRNA-seq data. DEGage calculates the mean of the sample-wise differences of gene expression levels as the test statistic and determines significant differential expression by computing the P-value with DOTNB. Extensive validation using simulated and real scRNA-seq data sets demonstrates that DEGage outperforms five popular DEG analysis tools: DEGseq2, DEsingle, edgeR, Monocle3, and scDD. DEGage is robust against high dropout levels and exhibits superior sensitivity when applied to balanced and imbalanced data sets, even with small sample sizes. We utilize DEGage to analyze prostate cancer scRNA-seq data sets and identify marker genes for 17 cell types. Furthermore, we apply DEGage to scRNA-seq data sets of mouse neurons with and without fear memory and reveal eight potential memory-related genes overlooked in previous analyses. The theoretical results and supporting software for DOTNB can be widely applied to comparative analyses of dispersed count data in HTS and broad research questions.

Accurate identification of locally aneuploid cells by incorporating cytogenetic information in single cell data analysis

Single-cell RNA sequencing is a powerful tool to investigate the cellular makeup of tumor samples. However, due to the sparse data and the complex tumor microenvironment, it can be challenging to identify neoplastic cells that play important roles in tumor growth and disease progression. This is especially relevant for blood cancers, where neoplastic cells may be highly similar to normal cells. To address this challenge, we have developed partCNV and partCNVH, two methods for rapid and accurate detection of aneuploid cells with local copy number deletion or amplification. PartCNV uses an expectation-maximization (EM) algorithm with mixtures of Poisson distributions and incorporates cytogenetic information to guide the classification. PartCNVH further improves partCNV by integrating a hidden Markov model for feature selection. We have thoroughly evaluated the performance of partCNV and partCNVH through simulation studies and real data analysis using three scRNA-seq datasets from blood cancer patients. Our results show that partCNV and partCNVH have favorable accuracy and provide more interpretable results compared to existing methods. In the real data analysis, we have identified multiple biological processes involved in the oncogenesis of myelodysplastic syndromes and acute myeloid leukemia.

Computational approach for decoding malaria drug targets from single-cell transcriptomics and finding potential drug molecule

Malaria is a deadly disease caused by Plasmodium parasites. While potent drugs are available in the market for malaria treatment, over the years, Plasmodium parasites have successfully developed resistance against many, if not all, front-line drugs. This poses a serious threat to global malaria eradication efforts, and the continued discovery of new drugs is necessary to tackle this debilitating disease. With recent unprecedented progress in machine learning techniques, single-cell transcriptomic in Plasmodium offers a powerful tool for identifying crucial proteins as a drug target and subsequent computational prediction of potential drugs. In this study, We have implemented a mutual-information-based feature reduction algorithm with a classification algorithm to select important proteins from transcriptomic datasets (sexual and asexual stages) for Plasmodium falciparum and then constructed the protein-protein interaction (PPI) networks of the proteins. The analysis of this PPI network revealed key proteins vital for the survival of Plasmodium falciparum. Based on the function and identification of a few strong binding sites on a couple of these key proteins, we computationally predicted a set of potential drug molecules using a deep learning-based technique. Lead drug molecules that satisfy ADMET and drug-likeliness properties are finally reported out of the generated drugs. The study offers a general computational pipeline to identify crucial proteins using scRNA-seq data sets and further development of potential new drugs.

TET2 promotes tumor antigen presentation and T cell IFN-γ which is enhanced by vitamin C.

Immune evasion by tumors is promoted by low T cell infiltration, ineffective T cell activity directed against the tumor and reduced tumor antigen presentation. The TET2 DNA dioxygenase gene is frequently mutated in hematopoietic malignancies and loss of TET enzymatic activity is found in a variety of solid tumors. We showed previously that vitamin C (VC), a co-factor of TET2, enhances tumor-associated T cell recruitment and checkpoint inhibitor therapy responses in a TET2-dependent manner. Using single-cell RNA sequencing (scRNA-seq) analysis performed on B16-OVA melanoma tumors, we have shown here that an additional function for TET2 in tumors is to promote expression of certain antigen presentation machinery genes, which is potently enhanced by VC. Consistently, VC promoted antigen presentation in cell-based and tumor assays in a TET2-dependent manner. Quantifying intercellular signaling from the scRNA-seq dataset showed that T cell-derived IFNγ-induced signaling within the tumor and tumor microenvironment requires tumor-associated TET2 expression which is enhanced by VC treatment. Analysis of patient tumor samples indicated that TET activity directly correlates with antigen-presentation gene expression and with patient outcomes. Our results demonstrate the importance of tumor-associated TET2 activity as a critical mediator of tumor immunity which is augmented by high-dose VC therapy.

Visualizing scRNA-Seq data at population scale with GloScope

Increasingly, scRNA-Seq studies explore cell populations across different samples and the effect of sample heterogeneity on organism’s phenotype. However, relatively few bioinformatic methods have been developed which adequately address the variation between samples for such population-level analyses. We propose a framework for representing the entire single-cell profile of a sample, which we call a GloScope representation. We implement GloScope on scRNA-Seq datasets from study designs ranging from 12 to over 300 samples and demonstrate how GloScope allows researchers to perform essential bioinformatic tasks at the sample-level, in particular visualization and quality control assessment.

RUNX2 as a novel biomarker for early identification of patients progressing to advanced-stage mycosis fungoides.

The majority of patients with mycosis fungoides (MF) have an indolent disease course, but a substantial fraction (20-30%) of patients progress to advanced stages - usually with a grave prognosis. Early differentiation between indolent and aggressive types of MF is important for the choice of treatment regimen and monitoring of the individual patient. Good biomarkers are therefore desired. Here, we used spatial transcriptomics on skin samples at time-of-diagnosis to enable prediction of patients who later progressed to advanced stages of MF. Formalin-fixed, paraffin-embedded skin biopsies at time of diagnosis from six patients with MF who progressed to advanced stages of disease within 4 months to 12 years after diagnosis, and nine patients who remained in early-stage disease over 9 to 27 years were analyzed using the GeoMx Digital Spatial Profiler to capture spatially resolved high-plex RNA gene expression data. Five different regions of interest (the epidermis, the basal layer of epidermis, CD4+ T-cells and neighboring cells, and Pautrier's microabscesses) were profiled for further assessment. Interestingly, RUNX2, SHMT2, and MCM7 were upregulated in the enriched population of malignant T-cells in Pautrier's microabscesses in patients who later developed advanced stages of disease. Expression of RUNX2, SHMT2 and MCM7 in malignant T-cells was confirmed in a subset of patients in MF skin using scRNA-seq datasets across multiple studies and correlating with stage of disease. Taken together, we provide first evidence that RUNX2 has potential as a biomarker to identify MF patients progressing to advanced stage disease. As RUNX2 has not previously been linked to MF, our data also shows the analytical strength of combining spatial transcriptomics with scRNA-seq analysis.

Imputing spatial transcriptomics through gene network constructed from protein language model.

Image-based spatial transcriptomic sequencing technologies have enabled the measurement of gene expression at single-cell resolution, but with a limited number of genes. Current computational approaches attempt to overcome these limitations by imputing missing genes, but face challenges regarding prediction accuracy and identification of cell populations due to the neglect of gene-gene relationships. In this context, we present stImpute, a method to impute spatial transcriptomics according to reference scRNA-seq data based on the gene network constructed from the protein language model ESM-2. Specifically, stImpute employs an autoencoder to create gene expression embeddings for both spatial transcriptomics and scRNA-seq data, which are used to identify the nearest neighboring cells between scRNA-seq and spatial transcriptomics datasets. According to the neighbored cells, the gene expressions of spatial transcriptomics cells are imputed through a graph neural network, where nodes are genes, and edges are based on cosine similarity between the ESM-2 embeddings of the gene-encoding proteins. The gene prediction uncertainty is further measured through a deep learning model. stImpute was shown to consistently outperform state-of-the-art methods across multiple datasets concerning imputation and clustering. stImpute also demonstrates robustness in producing consistent results that are insensitive to model parameters.

C-ziptf: stable tensor factorization for zero-inflated multi-dimensional genomics data.

In the past two decades, genomics has advanced significantly, with single-cell RNA-sequencing (scRNA-seq) marking a pivotal milestone. ScRNA-seq provides unparalleled insights into cellular diversity and has spurred diverse studies across multiple conditions and samples, resulting in an influx of complex multidimensional genomics data. This highlights the need for robust methodologies capable of handling the complexity and multidimensionality of such genomics data. Furthermore, single-cell data grapples with sparsity due to issues like low capture efficiency and dropout effects. Tensor factorizations (TF) have emerged as powerful tools to unravel the complex patterns from multi-dimensional genomics data. Classic TF methods, based on maximum likelihood estimation, struggle with zero-inflated count data, while the inherent stochasticity in TFs further complicates result interpretation and reproducibility. Our paper introduces Zero Inflated Poisson Tensor Factorization (ZIPTF), a novel method for high-dimensional zero-inflated count data factorization. We also present Consensus-ZIPTF (C-ZIPTF), merging ZIPTF with a consensus-based approach to address stochasticity. We evaluate our proposed methods on synthetic zero-inflated count data, simulated scRNA-seq data, and real multi-sample multi-condition scRNA-seq datasets. ZIPTF consistently outperforms baseline matrix and tensor factorization methods, displaying enhanced reconstruction accuracy for zero-inflated data. When dealing with high probabilities of excess zeros, ZIPTF achieves up to better accuracy. Moreover, C-ZIPTF notably enhances the factorization's consistency. When tested on synthetic and real scRNA-seq data, ZIPTF and C-ZIPTF consistently uncover known and biologically meaningful gene expression programs. Access our data and code at: https://github.com/klarman-cell-observatory/scBTF and https://github.com/klarman-cell-observatory/scbtf_experiments .

Single-cell RNA Sequencing Reveals the Diversity of the Immunological Landscape Response to Genital Herpes

Genital herpes (GH) is a common sexually transmitted disease, which is primarily caused by herpes simplex virus type 2 (HSV-2), and continues to be a global health concern. Although our understanding of the alterations in immune cell populations and immunomodulation in GH patients is still limited, it is evident that systemic intrinsic immunity, innate immunity, and adaptive immunity play crucial roles during HSV-2 infection and GH reactivation. To investigate the mechanisms underlying HSV-2 infection and recurrence, single-cell RNA sequencing (scRNA-seq) was performed on immune cells isolated from the peripheral blood of both healthy individuals and patients with recurrent GH. Furthermore, the systemic immune response in patients with recurrent GH showed activation of classical monocytes, CD4+ T cells, natural killer cells (NK cells), and plasmacytoid dendritic cells (pDCs), especially of genes associated with the Toll-like receptor signaling pathway and T cell activation. Circulating immune cells in GH patients show higher expression of genes associated with inflammation and antiviral responses both in the scRNA-Seq data set and in independent quantitative real-time polymerase chain reaction (qRT-PCR) analysis and ELISA experiments. This study demonstrated that localized genital herpes, resulting from HSV reactivation, may influence the functionality of circulating immune cells, suggesting a potential avenue for future research into the role of systemic immunity during HSV infection and recurrence.

Fam3a-mediated prohormone convertase switch in α-cells regulates pancreatic GLP-1 production in an Nr4a2-Foxa2-dependent manner

BackgroundFam3a has been demonstrated to regulate pancreatic β-cell function and glucose homeostasis. However, the role and mechanism of Fam3a in regulating α-cell function remain unexplored. MethodsGlucagon and glucagon-like peptide-1 (GLP-1) levels in pancreas and plasma were measured in global Fam3a knockout (Fam3a−/−) mice. Human islet single-cell RNA sequencing (scRNA-seq) datasets were utilized to analyze gene expression correlations between FAM3A and PCSK1 (encoding PC1/3, which processes proglucagon into GLP-1). Mouse pancreatic α-cell line αTC1.9 cells were transfected with Fam3a siRNA or plasmid for Fam3a knockdown or overexpression to explore the effects of Fam3a on PC1/3 expression and GLP-1 production. The downstream mediator (including Nr4a2) was identified by transcriptomic analysis, and its role was confirmed by Fam3a knockdown or overexpression in αTC1.9 cells. Based on the interacted protein of Nr4a2 and the direct binding to Pcsk1 promoter, the transcription factor Foxa2 was selected for further verification. Nuclear translocation assay and dual-luciferase reporter assay were used to clarify the involvement of Fam3a-Nr4a2-Foxa2 pathway in PC1/3 expression and GLP-1 production. Moreover, α-cell-specific Fam3a knockout (Fam3aα−/−) mice were constructed to evaluate the metabolic variables and hormone levels under normoglycemic, high-fat diet (HFD)-fed and streptozotocin (STZ)-induced diabetic conditions. Exendin 9–39 (Ex9), a GLP-1 receptor antagonist, was used to investigate GLP-1 paracrine effects in Fam3aα−/− mice and in their primary islets. ResultsCompared with wild-type mice, pancreatic and plasma active GLP-1 levels were increased in Fam3a−/− mice. Analysis of human islet scRNA-seq datasets showed a significant negative correction between FAM3A and PCSK1 in α-cells. Fam3a knockdown upregulated PC1/3 expression and GLP-1 production in αTC1.9 cells, while Fam3a overexpression displayed inverse effects. Transcriptomic analysis identified Nr4a2 as a key downstream mediator of Fam3a, and Nr4a2 expression in αTC1.9 cells was downregulated and upregulated by Fam3a knockdown and overexpression, respectively. Nr4a2 silencing increased PC1/3 expression, albeit Nr4a2 did not directly bind to Pcsk1 promoter. Instead, Nr4a2 formed a complex with Foxa2 to facilitate Fam3a-mediated Foxa2 nuclear translocation. Foxa2 negatively regulated PC1/3 expression and GLP-1 production. Besides, Foxa2 inhibited the transcriptional activity of Pcsk1 promoter at specific binding sites 10 and 6, and this inhibition was intensified by Nr4a2 in αTC1.9 cells. Compared with Flox/cre littermates, improved glucose tolerance, increased active GLP-1 level in pancreas and plasma, upregulated plasma insulin level in response to glucose, and decreased plasma glucagon level were observed in Fam3aα−/− mice. Primary islets isolated from Fam3aα−/− mice also showed an increase in active GLP-1 and insulin release. In addition, the insulinotropic effect of intra-islet GLP-1 was blocked by Ex9 in Fam3aα−/− mice and in their primary islets. Similarly, HFD-fed Fam3aα−/− mice also exhibited an improved glucose tolerance. Both HFD-fed and STZ-induced diabetic Fam3aα−/− mice showed an increased pancreatic active GLP-1 level, an elevated plasma insulin level and a reduced plasma glucagon level. ConclusionsFam3a deficiency in α-cells enhances pancreatic GLP-1 production to improve β-cell function via paracrine signaling in an Nr4a2-Foxa2-PC1/3-dependent manner. Our study unveils a novel strategy for reprogramming α-cell proglucagon processing output from glucagon to GLP-1 and deepen the understanding of crosstalk between α-cells and β-cells.

Cluster of differentiation-44 as a novel biomarker of lupus nephritis and its role in kidney inflammation and fibrosis.

CD44 is a transmembrane glycoprotein implicated in tissue inflammation and fibrosis. We investigated its role in kidney inflammation and fibrosis in a murine model of lupus nephritis (LN), and the clinico-pathological association of serum CD44 level in patients with biopsy-proven Class III/IV ± V LN. NZB/W F1 mice were treated with control IgG or anti-CD44 monoclonal antibody for 4 weeks and disease parameters assessed. Serum CD44 level in LN patients was determined by ELISA. Control groups included healthy subjects and patients with non-renal SLE or non-lupus renal disease. CD44 expression was absent in the normal kidney, but it was expressed in proximal and distal tubular epithelial cells and infiltrating cells in renal biopsies from patients with active proliferative LN. ScRNA-Seq datasets confirmed that CD44 was predominantly expressed in tubular cells and all immune cells identified in LN patients including tissue resident, inflammatory and phagocytic macrophages, Treg cells, effector and central memory CD4+ T cells, resident memory CD8+ T cells and naïve and activated B cells. Treatment of NZB/W F1 mice with anti-CD44 antibody preserved kidney histology and reduced proteinuria, tubulo-interstitial infiltration of CD3+, CD4+ and CD19+ immune cells, and mediators of kidney fibrosis compared to Control mice. Longitudinal studies showed that serum CD44 level increased prior to clinical renal flare by 4.5 months and the level decreased after treatment. ROC curve analysis showed that CD44 level distinguished patients with active LN from healthy subjects and patients with quiescent LN, active non-renal lupus, and non-lupus CKD (ROC AUC of 0.99, 0.96, 0.99 and 0.99 respectively). CD44 level correlated with leukocyte infiltration and interstitial inflammation scores in active LN kidney biopsies. Our findings suggest that CD44 plays a pathogenic role in renal parenchymal inflammation and fibrosis in active LN and monitoring CD44 may facilitate early diagnosis of flare.

SAE-Impute: imputation for single-cell data via subspace regression and auto-encoders

BackgroundSingle-cell RNA sequencing (scRNA-seq) technology has emerged as a crucial tool for studying cellular heterogeneity. However, dropouts are inherent to the sequencing process, known as dropout events, posing challenges in downstream analysis and interpretation. Imputing dropout data becomes a critical concern in scRNA-seq data analysis. Present imputation methods predominantly rely on statistical or machine learning approaches, often overlooking inter-sample correlations.ResultsTo address this limitation, We introduced SAE-Impute, a new computational method for imputing single-cell data by combining subspace regression and auto-encoders for enhancing the accuracy and reliability of the imputation process. Specifically, SAE-Impute assesses sample correlations via subspace regression, predicts potential dropout values, and then leverages these predictions within an autoencoder framework for interpolation. To validate the performance of SAE-Impute, we systematically conducted experiments on both simulated and real scRNA-seq datasets. These results highlight that SAE-Impute effectively reduces false negative signals in single-cell data and enhances the retrieval of dropout values, gene-gene and cell-cell correlations. Finally, We also conducted several downstream analyses on the imputed single-cell RNA sequencing (scRNA-seq) data, including the identification of differential gene expression, cell clustering and visualization, and cell trajectory construction.ConclusionsThese results once again demonstrate that SAE-Impute is able to effectively reduce the droupouts in single-cell dataset, thereby improving the functional interpretability of the data.

Patient-based multilevel transcriptome exploration highlights relevant chemokines and chemokine receptor axes in glioblastoma

Chemokines and their receptors form a complex interaction network, crucial for precise leukocyte positioning and trafficking. In cancer, they promote malignant cell proliferation and survival but are also critical for immune cell infiltration in the tumor microenvironment. Glioblastoma (GBM) is the most common and lethal brain tumor, characterized by an immunosuppressive TME, with restricted immune cell infiltration. A better understanding of chemokine-receptor interactions is therefore essential for improving tumor immunogenicity. In this study, we assessed the expression of all human chemokines in adult-type diffuse gliomas, with particular focus on GBM, based on patient-derived samples. Publicly available bulk RNA sequencing datasets allowed us to identify the chemokines most abundantly expressed in GBM, with regard to disease severity and across different tumor subregions. To gain insight into the chemokines–receptor network at the single cell resolution, we explored GBmap, a curated resource integrating multiple scRNAseq datasets from different published studies. Our study constitutes the first patient–based handbook highlighting the relevant chemokine–receptor crosstalks, which are of significant interest in the perspective of a therapeutic modulation of the TME in GBM.

BootCellNet, a resampling-based procedure, promotes unsupervised identification of cell populations via robust inference of gene regulatory networks.

Recent advances in measurement technologies, particularly single-cell RNA sequencing (scRNA-seq), have revolutionized our ability to acquire large amounts of omics-level data on cellular states. As measurement techniques evolve, there has been an increasing need for data analysis methodologies, especially those focused on cell-type identification and inference of gene regulatory networks (GRNs). We have developed a new method named BootCellNet, which employs smoothing and resampling to infer GRNs. Using the inferred GRNs, BootCellNet further infers the minimum dominating set (MDS), a set of genes that determines the dynamics of the entire network. We have demonstrated that BootCellNet robustly infers GRNs and their MDSs from scRNA-seq data and facilitates unsupervised identification of cell clusters using scRNA-seq datasets of peripheral blood mononuclear cells and hematopoiesis. It has also identified COVID-19 patient-specific cells and their potential regulatory transcription factors. BootCellNet not only identifies cell types in an unsupervised and explainable way but also provides insights into the characteristics of identified cell types through the inference of GRNs and MDS.