Single-cell RNA-seq Datasets Research Articles

IFNɣ but not IFNα increases recognition of insulin defective ribosomal product-derived antigen to amplify islet autoimmunity.

The inflammatory milieu characteristic of insulitis affects translation fidelity and generates defective ribosomal products (DRiPs) that participate in autoimmune beta cell destruction in type 1 diabetes. Here, we studied the role of early innate cytokines (IFNα) and late immune adaptive events (IFNɣ) in insulin DRiP-derived peptide presentation to diabetogenic CD8+ T cells. Single-cell transcriptomics of human pancreatic islets was used to study the composition of the (immuno)proteasome. Specific inhibition of the immunoproteasome catalytic subunits was achieved using siRNA, and antigenic peptide presentation at the cell surface of the human beta cell line EndoC-βH1 was monitored using peptide-specific CD8 T cells. We found that IFNγ induces the expression of the PSMB10 transcript encoding the β2i catalytic subunit of the immunoproteasome in endocrine beta cells, revealing a critical role in insulin DRiP-derived peptide presentation to T cells. Moreover, we showed that PSMB10 is upregulated in a beta cell subset that is preferentially destroyed in the pancreases of individuals with type 1 diabetes. Our data highlight the role of the degradation machinery in beta cell immunogenicity and emphasise the need for evaluation of targeted immunoproteasome inhibitors to limit beta cell destruction in type 1 diabetes. The single-cell RNA-seq dataset is available from the Gene Expression Omnibus (GEO) using the accession number GSE218316 ( https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE218316 ).

Dissecting and improving gene regulatory network inference using single-cell transcriptome data.

Single-cell transcriptome data has been widely used to reconstruct gene regulatory networks (GRNs) controlling critical biological processes such as development and differentiation. Although a growing list of algorithms has been developed to infer GRNs using such data, achieving an inference accuracy consistently higher than random guessing has remained challenging. To address this, it is essential to delineate how the accuracy of regulatory inference is limited. Here, we systematically characterized factors limiting the accuracy of inferred GRNs and demonstrated that using pre-mRNA information can help improve regulatory inference compared to the typically used information (i.e., mature mRNA). Using kinetic modeling and simulated single-cell data sets, we showed that target genes' mature mRNA levels often fail to accurately report upstream regulatory activities because of gene-level and network-level factors, which can be improved by using pre-mRNA levels. We tested this finding on public single-cell RNA-seq data sets using intronic reads as proxies of pre-mRNA levels and can indeed achieve a higher inference accuracy compared to using exonic reads (corresponding to mature mRNAs). Using experimental data sets, we further validated findings from the simulated data sets and identified factors such as transcription factor activity dynamics influencing the accuracy of pre-mRNA-based inference. This work delineates the fundamental limitations of gene regulatory inference and helps improve GRN inference using single-cell RNA-seq data.

Disruption of extracellular redox balance drives persistent lung fibrosis and impairs fibrosis resolution

Lung fibrosis is a devastating outcome of various diffuse parenchymal lung diseases. Despite rigorous research efforts, the mechanisms that propagate its progressive and nonresolving nature remain enigmatic. Oxidative stress has been implicated in the pathogenesis of lung fibrosis. However, the role of extracellular redox state in disease progression and resolution remains largely unexplored. Here, we show that compartmentalized control over extracellular reactive oxygen species (ROS) by aerosolized delivery of recombinant extracellular superoxide dismutase (ECSOD) suppresses an established bleomycin-induced fibrotic process in mice. Further analysis of publicly available microarray, RNA-seq and single-cell RNAseq datasets reveals a significant decrease in ECSOD expression in fibrotic lung tissues that can be spontaneously restored during fibrosis resolution. Therefore, we investigate the effect of siRNA-mediated ECSOD depletion during the established fibrotic phase on the self-limiting nature of the bleomycin mouse model. Our results demonstrate that in vivo knockdown of ECSOD in mouse fibrotic lungs impairs fibrosis resolution. Mechanistically, we demonstrate that transforming growth factor (TGF)-β1 downregulates endogenous ECSOD expression, leading to the accumulation of extracellular superoxide via Smad-mediated signaling and the activation of additional stores of latent TGF-β1. In addition, depletion of endogenous ECSOD during the fibrotic phase in the bleomycin model induces an apoptosis-resistant phenotype in lung fibroblasts through unrestricted Akt signaling. Taken together, our data strongly support the critical role of extracellular redox state in fibrosis persistence and resolution. Based on these findings, we propose that compartment-specific control over extracellular ROS may be a potential therapeutic strategy for managing fibrotic lung disorders.

Immune cell type signature discovery and random forest classification for analysis of single cell gene expression datasets.

Robust immune cell gene expression signatures are central to the analysis of single cell studies. Nearly all known sets of immune cell signatures have been derived by making use of only single gene expression datasets. Utilizing the power of multiple integrated datasets could lead to high-quality immune cell signatures which could be used as superior inputs to machine learning-based cell type classification approaches. We established a novel workflow for the discovery of immune cell type signatures based primarily on gene-versus-gene expression similarity. It leverages multiple datasets, here seven single cell expression datasets from six different cancer types and resulted in eleven immune cell type-specific gene expression signatures. We used these to train random forest classifiers for immune cell type assignment for single-cell RNA-seq datasets. We obtained similar or better prediction results compared to commonly used methods for cell type assignment in independent benchmarking datasets. Our gene signature set yields higher prediction scores than other published immune cell type gene sets in random forest-based cell type classification. We further demonstrate how our approach helps to avoid bias in downstream statistical analyses by re-analysis of a published IFN stimulation experiment. We demonstrated the quality of our immune cell signatures and their strong performance in a random forest-based cell typing approach. We argue that classifying cells based on our comparably slim sets of genes accompanied by a random forest-based approach not only matches or outperforms widely used published approaches. It also facilitates unbiased downstream statistical analyses of differential gene expression between cell types for significantly more genes compared to previous cell classification algorithms.

A Population of Tumor-Infiltrating CD4+ T Cells Co-Expressing CD38 and CD39 Is Associated with Checkpoint Inhibitor Resistance.

We previously showed that elevated frequencies of peripheral blood CD3+CD4+CD127-GARP-CD38+CD39+ T cells were associated with checkpoint immunotherapy resistance in patients with metastatic melanoma. In the present study, we sought to further investigate this population of ectoenzyme-expressing T cells (Teee). Teee derived from the peripheral blood of patients with metastatic melanoma were evaluated by bulk RNA-sequencing (RNA-seq) and flow cytometry. The presence of Teee in the tumor microenvironment was assessed using publically available single-cell RNA-seq datasets of melanoma, lung, and bladder cancers along with multispectral immunofluorescent imaging of melanoma patient formalin-fixed, paraffin-embedded specimens. Suppressive function of Teee was determined by an in vitro autologous suppression assay. Teee had phenotypes associated with proliferation, apoptosis, exhaustion, and high expression of inhibitory molecules. Cells with a Teee gene signature were present in tumors of patients with melanoma, lung, and bladder cancers. CD4+ T cells co-expressing CD38 and CD39 in the tumor microenvironment were preferentially associated with Ki67- CD8+ T cells. Co-culture of patient Teee with autologous T cells resulted in decreased proliferation of target T cells. High baseline intratumoral frequencies of Teee were associated with checkpoint immunotherapy resistance and poor overall survival in patients with metastatic melanoma. These results demonstrate that a novel population of CD4+ T cells co-expressing CD38 and CD39 is found both in the peripheral blood and tumor of patients with melanoma and is associated with checkpoint immunotherapy resistance.

COVID-19 of differing severity: from bulk to single-cell expression data analysis

ABSTRACT Coronavirus disease 2019 (COVID-19) is raging worldwide and causes an immense disease burden. Despite this, the biomarkers and targeting drugs of COVID-19 of differing severity remain largely unknown. Based on the GSE164805 dataset, we identified modules most critical for mild COVID-19 (mCOVID-19) and severe COVID-19 (sCOVID-19) through WGCNA, respectively. We subsequently constructed a protein–protein interaction network, and detected 16 hub genes for mCOVID-19 and 10 hub genes for sCOVID-19, followed by the prediction of upstream transcription factors (TFs) and kinases. The enrichment analysis then showed downregulation of TNFA signaling via NFKB for mCOVID-19, as well as downregulation of MYC targets V1 for sCOVID-19. Infiltration degrees of many immune cells, such as macrophages, were also sharply different between mCOVID-19 and sCOVID-19 samples. Predicted protein targeting drugs with the highest scores nearly all belong to naturally derived or synthetic glucocorticoids. For the two single-cell RNA-seq datasets, we explored the expression distribution of hub genes for mCOVID-19/sCOVID-19 in each cell type. The expression levels of PRKCA, MCM5, TYMS, RBBP4, BCL6, FLOT1, KDM6B, and TLR2 were found to be cell-type-specific. Furthermore, the expression levels of 10 hub genes for mCOVID-19 were significantly upregulated in PBMCs between eight healthy controls and eight mCOVID-19 patients at our institution. Collectively, we detected critical modules, pathways, TFs, kinases, immune cells, targeting drugs, hub genes, and their expression distributions in different cell types that may involve the pathogenesis of COVID-19 of differing severity, which may propel earlier diagnosis and more effective treatment of this intractable disease in the future.

Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time.

CD4 Phenotypes Are Associated with Reduced Expansion of Tumor-Infiltrating Lymphocytes in Melanoma Patients Treated with Adoptive Cell Therapy.

Tumor-infiltrating lymphocyte (TIL) adoptive cell therapy is effective in treating malignant melanoma, but its success relies on the adequate ex vivo expansion of TIL. To assess correlates of TIL expansion, CD4+ and CD8+ TIL were analyzed by RNA sequencing (RNA-seq) and chromatin immunoprecipitation sequencing of acetylated histone 3. Patients were grouped into "TIL high" and "TIL low" based on division at the median number of TIL infused. Greater numbers of TIL infused correlated with longer overall survival, and increased frequencies of CD4+ cells infused were negatively correlated with the number of TIL infused. RNA-seq analysis of CD4+ TIL showed increases in Th2/Th17/regulatory T cell-related transcripts and pathways in the TIL-low group. Analysis of a public single-cell RNA-seq dataset validated findings that increased frequencies of CD4+ cells were negatively correlated with the number of TIL infused. TIL-low patients had significantly increased frequencies of CD4+ cells expressing ETS2 and OSM and trended toward increased expression of TNFRSF18.

A cellular and molecular spatial atlas of dystrophic muscle

Asynchronous skeletal muscle degeneration/regeneration is a hallmark feature of Duchenne muscular dystrophy (DMD); however, traditional -omics technologies that lack spatial context make it difficult to study the biological mechanisms of how asynchronous regeneration contributes to disease progression. Here, using the severely dystrophic D2-mdx mouse model, we generated a high-resolution cellular and molecular spatial atlas of dystrophic muscle by integrating spatial transcriptomics and single-cell RNAseq datasets. Unbiased clustering revealed nonuniform distribution of unique cell populations throughout D2-mdx muscle that were associated with multiple regenerative timepoints, demonstrating that this model faithfully recapitulates the asynchronous regeneration observed in human DMD muscle. By probing spatiotemporal gene expression signatures, we found that propagation of inflammatory and fibrotic signals from locally damaged areas contributes to widespread pathology and that querying expression signatures within discrete microenvironments can identify targetable pathways for DMD therapy. Overall, this spatial atlas of dystrophic muscle provides a valuable resource for studying DMD disease biology and therapeutic target discovery.

De novo detection of somatic mutations in high-throughput single-cell profiling data sets.

Characterization of somatic mutations at single-cell resolution is essential to study cancer evolution, clonal mosaicism and cell plasticity. Here, we describe SComatic, an algorithm designed for the detection of somatic mutations in single-cell transcriptomic and ATAC-seq (assay for transposase-accessible chromatin sequence) data sets directly without requiring matched bulk or single-cell DNA sequencing data. SComatic distinguishes somatic mutations from polymorphisms, RNA-editing events and artefacts using filters and statistical tests parameterized on non-neoplastic samples. Using >2.6 million single cells from 688 single-cell RNA-seq (scRNA-seq) and single-cell ATAC-seq (scATAC-seq) data sets spanning cancer and non-neoplastic samples, we show that SComatic detects mutations in single cells accurately, even in differentiated cells from polyclonal tissues that are not amenable to mutation detection using existing methods. Validated against matched genome sequencing and scRNA-seq data, SComatic achieves F1 scores between 0.6 and 0.7 across diverse data sets, in comparison to 0.2-0.4 for the second-best performing method. In summary, SComatic permits de novo mutational signature analysis, and the study of clonal heterogeneity and mutational burdens at single-cell resolution.

Comprehensive analysis of angiogenesis pattern and related immune landscape for individual treatment in osteosarcoma

Postoperative recurrence and metastasis are the main reasons for the poor prognosis of osteosarcoma (OS). Currently, an ideal predictor for not only prognosis but also drug sensitivity and immunotherapy responses in OS patients is urgently needed. Angiogenesis plays a crucial role in tumour progression, which suggests its immense potential for predicting prognosis and responses to immunotherapy for OS. Angiogenesis patterns in OS were explored in depth in this study to construct a prognostic model called ANGscore and clarify the underlying mechanism involved in the immune microenvironment. The efficacy and robustness of the model were validated in multiple datasets, including bulk RNA-seq datasets (TARGET-OS, GSE21257), a single-cell RNA-seq dataset (GSE152048) and immunotherapy-related datasets (GSE91061, GSE173839). OS patients with a high ANGscore had a worse prognosis, accompanied by the immune desert phenotype. Pseudotime and cellular communication analyses in scRNA-seq data revealed that as the ANGscore increased, the malignant degree of cells increased, and IFN-γ signalling was involved in tumour progression and regulation of the tumour immune microenvironment. Furthermore, the ANGscore was associated with immune cell infiltration and the response rate to immunotherapy. OS patients with high ANGscore might be resistant to uprosertib, and be sensitive to VE821, AZD6738 and BMS.345541. In conclusion, we established a novel ANGscore system by comprehensively analysing the expression pattern of angiogenesis genes, which can accurately differentiate the prognosis and immune characteristics of OS populations. Additionally, the ANGscore can be used for patient stratification during immunotherapy, and guide individualized treatment strategies.

Unsupervised Deep Embedded Fusion Representation of Single-Cell Transcriptomics

Cell clustering is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data, which allows us to characterize the cellular heterogeneity of transcriptional profiling at the single-cell level. Single-cell deep embedded representation models have recently become popular since they can learn feature representation and clustering simultaneously. However, the model still suffers from a variety of significant challenges, including the massive amount of data, pervasive dropout events, and complicated noise patterns in transcriptional profiling. Here, we propose a Single-Cell Deep Embedding Fusion Representation (scDEFR) model, which develop a deep embedded fusion representation to learn fused heterogeneous latent embedding that contains both the transcriptome gene-level information and the cell topology information. We first fuse them layer by layer to obtain compressed representations of intercellular relationships and transcriptome information. After that, the zero-inflated negative binomial model (ZINB)-based decoder is proposed to capture the global probabilistic structure of the data and reconstruct the final gene expression information and cell graph. Finally, by simultaneously integrating the clustering loss, crossentropy loss, ZINB loss, and the cell graph reconstruction loss, scDEFR can optimize clustering performance and learn the latent representation in fused information under a joint mutual supervised strategy. We conducted extensive and comprehensive experiments on 15 single-cell RNA-seq datasets from different sequencing platforms to demonstrate the superiority of scDEFR over a variety of state-of-the-art methods.

FERMT2 upregulation in CAFs enhances EMT of OSCC and M2 macrophage polarization.

FERMT2 upregulation was associated with malignant tumor behaviors, including epithelial-to-mesenchymal (EMT). This study aimed to characterize the expression profile of FERMT2 in oral squamous cell carcinoma (OSCC) and to explore its involvement in the tumor microenvironment sculptured by oral cancer-associated fibroblasts (OCAFs). Previous bulk-seq (TCGA-HNSC) and single-cell RNA-seq data sets were retrieved for bioinformatic analysis. Human OSCC lines SCC15 and CAL27, primary normal oral fibroblasts (NOFs), OCAFs, and THP-1 cells were used for intro studies. FERMT2 expression was significantly higher in CAFs compared with OSCC tumor cells and normal fibroblasts. Higher FERMT2 expression might independently predict unfavorable disease-specific survival (DSS) in patients with OSCC. Knockdown of FERMT2 suppressed the expression and secretion of IGFBP7, SPARC, TIMP3, COL4A1, and IGFBP4 in OCAFs. OCAFs with FERMT2 knockdown had significantly weakened capability to induce the invasion of OSCC cells and the expression of mesenchymal markers. FERMT2 knockdown impaired the inducing effect of OCAFs on the migration of M0 macrophages and the expression of M2 macrophage markers. FERMT2 could modulate the production and secretion of IGFBP7, SPARC, COL4A1, and IGFBP4 in OCAFs, thereby inducing the EMT of OSCC and M2 macrophage polarization.

Deciphering the role of NETosis-related signatures in the prognosis and immunotherapy of soft-tissue sarcoma using machine learning.

Background: Soft-tissue sarcomas (STSs) are a rare type of cancer, accounting for about 1% of all adult cancers. Treatments for STSs can be difficult to implement because of their diverse histological and molecular features, which lead to variations in tumor behavior and response to therapy. Despite the growing importance of NETosis in cancer diagnosis and treatment, researches on its role in STSs remain limited compared to other cancer types. Methods: The study thoroughly investigated NETosis-related genes (NRGs) in STSs using large cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis and Support Vector Machine Recursive Feature Elimination (SVM-RFE) were employed for screening NRGs. Utilizing single-cell RNA-seq (scRNA-seq) dataset, we elucidated the expression profiles of NRGs within distinct cellular subpopulations. Several NRGs were validated by quantitative PCR (qPCR) and our proprietary sequencing data. To ascertain the impact of NRGs on the sarcoma phenotype, we conducted a series of in vitro experimental investigations. Employing unsupervised consensus clustering analysis, we established the NETosis clusters and respective NETosis subtypes. By analyzing DEGs between NETosis clusters, an NETosis scoring system was developed. Results: By comparing the outcomes obtained from LASSO regression analysis and SVM-RFE, 17 common NRGs were identified. The expression levels of the majority of NRGs exhibited notable dissimilarities between STS and normal tissues. The correlation with immune cell infiltration were demonstrated by the network comprising 17 NRGs. Patients within various NETosis clusters and subtypes exhibited different clinical and biological features. The prognostic and immune cell infiltration predictive capabilities of the scoring system were deemed efficient. Furthermore, the scoring system demonstrated potential for predicting immunotherapy response. Conclusion: The current study presents a systematic analysis of NETosis-related gene patterns in STS. The results of our study highlight the critical role NRGs play in tumor biology and the potential for personalized therapeutic approaches through the application of the NETosis score model in STS patients.

83-OR: Defining the Transcriptome and Epitranscriptome of the ß-Cell Compensation to Insulin Resistance

Type 2 diabetes (T2D) is characterized by decreased functional β-cell mass and poor compensation to insulin resistance. Thus, understanding of the mechanism(s) responsible for the physiological adaptation of β-cells to states of high metabolic demand is desirable. We aimed to dissect the transcriptome and m6A mRNA methylation landscape in the β-cell adaptation to insulin resistance. To this end we conducted RNA-sequencing in sorted β-cells and m6A-sequencing in whole islets from three mouse models: a) a dietary model (60% high-fat diet: Control-HF), b) a genetic model (liver-specific insulin receptor KO mouse on chow: LIRKO-C), and c) a superimposed dietary and genetic model of insulin resistance (LIRKO on a high fat diet: LIRKO-HF). These data were compared to littermate controls fed a chow diet (Control-C). Control-HF, LIRKO-C and LIRKO-HF each presented an increase in insulin resistance compared to Control-C. However, LIRKO-C and LIRKO-HF presented a robust insulin secretion capacity and a graded increase in β-cell proliferation and mass compared to Control-C. First, we correlated the transcriptome with surrogates of β-cell adaptation such as insulin secretion, β-cell mass or proliferation. Next, we intersected the genes that positively correlated with β-cell compensation with single-cell RNA seq datasets performed in islets from humans with or without T2D. This bioinformatics analyses identified extracellular matrix (ECM) as the most enriched pathway in the β-cell adaptation to insulin resistance. On the other hand, m6A-sequencing of Control-C, LIRKO-C and LIRKO-HF islets and the intersection of this dataset with the differentially methylated genes in human T2D islets, revealed enrichment in pathways associated with chromatin modifications. Together, these results point to ECM pathways as being important in the transcriptomic remodeling and that m6A acts on chromatin-modifying enzymes in the β-cell compensation to mammalian insulin resistance. Disclosure D.F.De jesus: None. Z.Zhang: Research Support; SinoVac. J.Wei: None. N.K.Brown: None. J.Hu: None. S.Kahraman: Employee; Boehringer Ingelheim Pharmaceuticals Inc. E.Dirice: None. R.Kulkarni: Advisory Panel; Novo Nordisk, Inversago, Biomea Fusion, Inc., REDD Pharma, Research Support; Inversago. Funding American Diabetes Association (7-21-PDF-140 to D.F.DJ.); National Institutes of Health (R01067536)

Analysis of Notch1 signaling in mammalian sperm development

ObjectiveA mammalian Delta-Notch signaling component, Notch1, has been suggested for its expression during the normal sperm development although its conditional deletion caused no apparent abnormalities. Since we established our original transgenic mouse system that enabled labeling of past and ongoing Notch1 signaling at a cellular level, we tried to validate that observation in vivo. Our transgenic mouse system used Cre/loxP system to induce tandem dsRed expression upon Notch1 signaling.ResultsTo our surprise, we were unable to observe tandem dsRed expression in the seminiferous tubules where the sperms developed. In addition, tandem dsRed expression was lacking in the somatic cells of the next generation in our transgenic mouse system, suggesting that sperms received no Notch1 signaling during their development. To validate this result, we conducted re-analysis of four single-cell RNA-seq datasets from mouse and human testes and showed that Notch1 expression was little in the sperm cell lineage. Collectively, our results posed a question into the involvement of Notch1 in the normal sperm development although this observation may help the interpretation of the previous result that Notch1 conditional deletion caused no apparent abnormalities in murine spermatogenesis.

HGG-18. SINGLE CELL RNA-SEQ ANALYSIS OF PEDIATRIC HIGH-GRADE GLIOMA PATIENT SAMPLES IDENTIFIES COMMON GLIAL AND IMMUNE CELL TYPES THAT HELP TO EXPLAIN HETEROGENEITY AND TUMORIGENESIS

Abstract BACKGROUND Pediatric high-grade gliomas (PHGG) are aggressive, undifferentiated central nervous system (CNS) tumors. PHGG comprises subtypes that differ phenotypically, histologically and by cellular composition. Despite intensive research, overall survival rates have remained for decades around 2% for diffuse midline glioma (DMG) and 20% for non-DMG PHGG. We hypothesized that PHGG’s heterogeneity, a key difficulty in devising therapies, results from functional and lineage differences in tumor cell types. METHODS Using 19 PHGG samples from our institution’s pediatric brain tumor bank, we constructed a single-cell RNA-Seq (scRNA-Seq) dataset that included tumor and immune cells, generated predicted cell types by mapping the scRNA-Seq data onto known cell types, and performed differential gene expression and geneset enrichment analyses. We acquired bulk RNA-Seq and DNA methylation data to characterize overall tumor properties. RESULTS Samples consisted of several principal tumor cell types, including cells with astrocyte characteristics, oligodendrocyte progenitor cell (OPC) characteristics, and cells that expressed OPC markers but had slight enrichment in mesenchymal and inflammatory gene expression (OPC-like/Mes cells). We also found a potential stemlike population that expressed neural stem cell markers. The predicted astrocytes, OPCs and OPC-like/Mes cells differed in their gene expression profiles, pathway enrichment, and tendency to proliferate as assessed from expression of cell cycle genes. A combined microglia/macrophage population, strongly enriched in mesenchymal and inflammatory gene expression, may serve to induce inflammatory gene expression in tumor cells. Key conclusions from the scRNA-Seq analysis were validated using multi-channel immunofluorescence staining. CONCLUSIONS PHGG comprises a small stemlike population and varying proportions of proliferating glial lineage tumor cells. CNS-resident microglia/macrophages may trigger mesenchymal gene expression in PHGG. We will use our results to investigate the roles of PHGG’s diverse tumor cell populations in resistance to radiotherapy and to develop treatments to target individual cell types comprising PHGG.

Integrated single-cell and bulk characterization of cuproptosis key regulator PDHB and association with tumor microenvironment infiltration in clear cell renal cell carcinoma.

Renal clear cell carcinoma (ccRCC) is one of the most prevalent cancers worldwide. Accumulating evidence revealed that copper-induced cell death played a vital role in various tumors. However, the underlying mechanism of cuproptosis with molecular heterogeneity and tumor microenvironment (TME) in ccRCC remains to be elucidated. The present study aimed to discover the biological function of cuproptosis regulators with the potential to guide clinical therapy. Using Single-cell RNA-seq, bulk transcriptome and other multi-omics datasets, we identify essential cuproptosis-related hub gene PDHB for further study. The dysregulation of PDHB in ccRCC was characterized, together with survival outcomes, pathway enrichment and immune infiltration among tumor microenvironments. The functional significance and clinical association of PDHB was validated with loss of function experiments and surgical removal specimens. PDHB mRNA and protein expression level was significantly downregulated in ccRCC tissues compared with normal and paired normal tissues. Clinicopathological parameters and tissue microarray (TMA) indicated that PDHB was identified as a prognostic factor for survival outcomes among ccRCC patients. Additionally, low PDHB was negatively correlated with Treg cells, indicating an immunosuppressive microenvironment. Mechanistically, knockdown PDHB appeared to promote the RCC cells proliferation, migration, and invasion potentials. Subsequent studies showed that copper-induced cell death activation could overcome sunitinib resistance in RCC cells. This research illustrated a cuproptosis-related hub gene PDHB which could serve as a potential prognostic marker and provide therapeutic benefits for clinical treatment of ccRCC patients.

Cell-attribute aware community detection improves differential abundance testing from single-cell RNA-Seq data

Variations of cell-type proportions within tissues could be informative of biological aging and disease risk. Single-cell RNA-sequencing offers the opportunity to detect such differential abundance patterns, yet this task can be statistically challenging due to the noise in single-cell data, inter-sample variability and because such patterns are often of small effect size. Here we present a differential abundance testing paradigm called ELVAR that uses cell attribute aware clustering when inferring differentially enriched communities within the single-cell manifold. Using simulated and real single-cell and single-nucleus RNA-Seq datasets, we benchmark ELVAR against an analogous algorithm that uses Louvain for clustering, as well as local neighborhood-based methods, demonstrating that ELVAR improves the sensitivity to detect cell-type composition shifts in relation to aging, precancerous states and Covid-19 phenotypes. In effect, leveraging cell attribute information when inferring cell communities can denoise single-cell data, avoid the need for batch correction and help retrieve more robust cell states for subsequent differential abundance testing. ELVAR is available as an open-source R-package.

Split-intein Gal4 provides intersectional genetic labeling that is repressible by Gal80

The split-Gal4 system allows for intersectional genetic labeling of highly specific cell types and tissues in Drosophila. However, the existing split-Gal4 system, unlike the standard Gal4 system, cannot be repressed by Gal80, and therefore cannot be controlled temporally. This lack of temporal control precludes split-Gal4 experiments in which a genetic manipulation must be restricted to specific timepoints. Here, we describe a split-Gal4 system based on a self-excising split-intein, which drives transgene expression as strongly as the current split-Gal4 system and Gal4 reagents, yet which is repressible by Gal80. We demonstrate the potent inducibility of "split-intein Gal4" invivo using both fluorescent reporters and via reversible tumor induction in the gut. Further, we show that our split-intein Gal4 can be extended to the drug-inducible GeneSwitch system, providing an independent method for intersectional labeling with inducible control. We also show that the split-intein Gal4 system can be used to generate highly cell type-specific genetic drivers based on in silico predictions generated by single-cell RNAseq (scRNAseq) datasets, and we describe an algorithm ("Two Against Background" or TAB) to predict cluster-specific gene pairs across multiple tissue-specific scRNA datasets. We provide a plasmid toolkit to efficiently create split-intein Gal4 drivers based on either CRISPR knock-ins to target genes or using enhancer fragments. Altogether, the split-intein Gal4 system allows for the creation of highly specific intersectional genetic drivers that are inducible/repressible.