Single-cell RNA-seq Data Research Articles

Aggressive high-grade NF2 mutant meningiomas downregulate oncogenic YAP signaling via the upregulation of VGLL4 and FAT3/4.

Meningiomas are the most common primary central nervous system tumors in adults. Although generally benign, a subset is of higher grade and ultimately fatal. Around half of all meningiomas harbor inactivating mutations in NF2, leading to deregulation of oncogenic YAP1 activity. While benign NF2 mutant meningiomas exhibit few genetic events in addition to NF2 inactivation, aggressive high-grade NF2 mutant meningiomas frequently harbor a highly aberrant genome. It is unclear if NF2 mutant meningiomas of different grades are equally reliant on YAP activity. We analyzed bulk and single-cell RNA-Seq data from a large cohort of human meningiomas for the expression of YAP1 target genes and Hippo effectors as well as in vitro cell line experiments. Aggressive NF2 mutant meningiomas harbor decreased expression levels of YAP1 target genes and increased expression levels of the YAP1 antagonist VGLL4 and the upstream regulators FAT3/4 compared to their benign counterparts. Decreased expression of YAP1 target genes as well as high expression of VGLL4 and FAT3/4 is significantly associated with an increased risk of recurrence. In vitro, overexpression of VGLL4 resulted in the downregulation of YAP activity in benign NF2 mutant meningioma cells, confirming the direct link between VGLL4 expression and decreased levels of YAP activity observed in aggressive NF2 mutant meningiomas. Our results shed new insight into the biology of benign and aggressive NF2 mutant meningiomas and may have important implications for the efficacy of therapies targeting oncogenic YAP1 activity in NF2 mutant meningiomas.

Lamc1 promotes osteogenic differentiation and inhibits adipogenic differentiation of bone marrow-derived mesenchymal stem cells

Bone marrow-derived mesenchymal stem cells (BMSCs) exhibit multi-lineage differentiation potential and robust proliferative capacity. The late stage of differentiation signifies the functional maturation and characterization of specific cell lineages, which is crucial for studying lineage-specific differentiation mechanisms. However, the molecular processes governing late-stage BMSC differentiation remain poorly understood. This study aimed to elucidate the key biological processes involved in late-stage BMSC differentiation. Publicly available transcriptomic data from human BMSCs were analyzed after approximately 14 days of osteogenic, adipogenic, and chondrogenic differentiation. Thirty-one differentially expressed genes (DEGs) associated with differentiation were identified. Pathway enrichment analysis indicated that the DEGs were involved in extracellular matrix (ECM)-receptor interactions, focal adhesion, and glycolipid biosynthesis, a ganglion series process. Subsequently, the target genes were validated using publicly available single-cell RNA-seq data from mouse BMSCs. Lamc1 exhibited predominant distribution in adipocytes and osteoblasts, primarily during the G2/M phase. Tln2 and Hexb were expressed in chondroblasts, osteoblasts, and adipocytes, while St3gal5 was abundantly distributed in stem cells. Cell communication analysis identified two receptors that interact with LAMCI. q-PCR results confirmed the upregulation of Lamc1, Tln2, Hexb, and St3gal5 during osteogenic differentiation and their downregulation during adipogenic differentiation. Knockdown of Lamc1 inhibited adipogenic and osteogenic differentiation. In conclusion, this study identified four genes, Lamc1, Tln2, Hexb, and St3gal5, that may play important roles in the late-stage differentiation of BMSCs. It elucidated their interactions and the pathways they influence, providing a foundation for further research on BMSC differentiation.

Comprehensive characterization of cytokines in patients under extracorporeal membrane oxygenation: Evidence from integrated bulk and single-cell RNA sequencing data using multiple machine learning approaches.

ECMO (extracorporeal membrane oxygenation) is an effective technique for providing short-term mechanical support to the heart, lungs, or both. During ECMO treatment, the inflammatory response, particularly involving cytokines, plays a crucial role in pathophysiology. However, the potential effects of cytokines on patients receiving ECMO are not comprehensively understood. We acquired three ECMO support datasets, namely two bulk and one single-cell RNA sequencing (RNA-seq), from the GEO (Gene Expression Omnibus) combined with hospital cohorts to investigate the expression pattern and potential biological processes of cytokine-related genes (CRGs) in patients under ECMO. Subsequently, machine learning approaches, including support vector machine (SVM), random forest (RF), modified Lasso penalized regression, extreme gradient boosting (XGBoost), and artificial neural network (ANN), were applied to identify hub CRGs, thus developing a prediction model called CRG classifier. The predictive and prognostic performance of the model was comprehensively evaluated in GEO and hospital cohorts. Finally, we mechanistically analyzed the relationship between hub cytokines, immune cells, and pivotal molecular pathways. Analyzing bulk and single-cell RNA-seq data revealed that most CRGs were significantly differentially expressed, the enrichment scores of cytokine and cytokine cytokine receptor (CCR) interaction were significantly higher during ECMO. Based on multiple machine learning algorithms, nine key CRGs (CCL2, CCL4, IFNG, IL1R2, IL20RB, IL31RA, IL4, IL7, and IL7R) were used to develop the CRG classifier. The CRG classifier exhibited excellent prognostic values (AUC > 0.85), serving as an independent risk factor. It performed better in predicting mortality and yielded a larger net benefit than other clinical features in GEO and hospital cohorts. Additionally, IL1R2, CCL4, and IL7R were predominantly expressed in monocytes, NK cells, and T cells, respectively. Their expression was significantly positively correlated with the relative abundance of corresponding immune cells. Gene set variation analysis (GSVA) revealed that parainflammation, complement and coagulation cascades, and IL6/JAK/STAT3 signaling were significantly enriched in the subgroup that died after receiving ECMO. Spearman correlation analyses and Mantel tests revealed that the expression of hub cytokines (IL1R2, CCL4, and IL7R) and pivotal molecular pathways scores (complement and coagulation cascades, IL6/JAK/STAT3 signaling, and parainflammation) were closely related. A predictive model (CRG classifier) comprising nine CRGs based on multiple machine learning algorithms was constructed, potentially assisting clinicians in guiding individualized ECMO treatment. Additionally, elucidating the underlying mechanistic pathways of cytokines during ECMO will provide new insights into its treatment.

Constructing a prognostic model for colon cancer patients on basis of coagulation genes enriched in cancer-associated fibroblasts to guide personalized immunotherapy

Abstract Objectives Colon cancer is a global health challenge. This research is designed to build a prognostic model that can personalize the guidance of immunotherapy among colon cancer patients. Methods Coagulation-associated prognostic genes which were subsequently integrated into a Least Absolute Shrinkage and Selection Operator algorithm for constructing a prognostic model were identified with the univariate Cox analyses. The potential of coagulation-related risk score (CRRS) in prognosis and immunotherapy outcomes was rigorously assessed. Finally, the cellular origin of genes in the CRRS model was explored with single-cell RNA-seq data, and the biological functions of core genes were further confirmed by cell function experiments. Results Our findings showed the CRRS model usefully classified patients into high-risk and low-risk groups. High-risk patients exhibited worse total survival. A nomogram was subsequently devised, enabling quantitative survival prediction by incorporating CRRS, age, sex, and TNM stage. Moreover, the CRRS model predicted the extent of cancer-associated fibroblasts (CAFs) infiltration. The analysis further indicated diminished immune responsiveness in high-risk patients, and single-cell data analysis pinpointed TIMP1+ CAF as a potential contributor to cancer progression. Conclusions The CRRS model can be adopted as a prognostic device for colon cancer patients and low-risk patients are more suitable for treatment with immune checkpoint inhibitors. TIMP1 secreted by CAF can promote the malignant progression of colon cancer.

Denoiseit: denoising gene expression data using rank based isolation trees

BackgroundSelecting informative genes or eliminating uninformative ones before any downstream gene expression analysis is a standard task with great impact on the results. A carefully curated gene set significantly enhances the likelihood of identifying meaningful biomarkers.MethodIn contrast to the conventional forward gene search methods that focus on selecting highly informative genes, we propose a backward search method, DenoiseIt, that aims to remove potential outlier genes yielding a robust gene set with reduced noise. The gene set constructed by DenoiseIt is expected to capture biologically significant genes while pruning irrelevant ones to the greatest extent possible. Therefore, it also enhances the quality of downstream comparative gene expression analysis. DenoiseIt utilizes non-negative matrix factorization in conjunction with isolation forests to identify outlier rank features and remove their associated genes.ResultsDenoiseIt was applied to both bulk and single-cell RNA-seq data collected from TCGA and a COVID-19 cohort to show that it proficiently identified and removed genes exhibiting expression anomalies confined to specific samples rather than a known group. DenoiseIt also showed to reduce the level of technical noise while preserving a higher proportion of biologically relevant genes compared to existing methods. The DenoiseIt Software is publicly available on GitHub at https://github.com/cobi-git/DenoiseIt

CytoKernel: Robust kernel embeddings for assessing differential expression of single cell data.

High-throughput sequencing of single-cell data can be used to rigorously evlauate cell specification and enable intricate variations between groups or conditions. Many popular existing methods for differential expression target differences in aggregate measurements (mean, median, sum) and limit their approaches to detect only global differential changes. We present a robust method for differential expression of single-cell data using a kernel-based score test, cytoKernel. cytoKernel is specifically designed to assess the differential expression of single cell RNA sequencing and high-dimensional flow or mass cytometry data using the full probability distribution pattern. cytoKernel is based on kernel embeddings which employs the probability distributions of the single cell data, by calculating the pairwise divergence/distance between distributions of subjects. It can detect both patterns involving aggregate changes, as well as more elusive variations that are often overlooked due to the multimodal characteristics of single cell data. We performed extensive benchmarks across both simulated and real data sets from mass cytometry data and single-cell RNA sequencing. The cytoKernel procedure effectively controls the False Discovery Rate (FDR) and shows favourable performance compared to existing methods. The method is able to identify more differential patterns than existing approaches. We apply cytoKernel to assess gene expression and protein marker expression differences from cell subpopulations in various publicly available single-cell RNAseq and mass cytometry data sets. The methods described in this paper are implemented in the open-source R package cytoKernel, which is freely available from Bioconductor at http://bioconductor.org/packages/cytoKernel.

Pediatric glioma immune profiling identifies TIM3 as a therapeutic target in BRAF fusion pilocytic astrocytoma.

Despite being the leading cause of cancer-related childhood mortality, pediatric gliomas have been relatively understudied, and the repurposing of immunotherapies has not been successful. Whole-transcriptome sequencing, single-cell sequencing, and sequential multiplex immunofluorescence were used to identify an immunotherapeutic strategy that could be applied to multiple preclinical glioma models. MAPK-driven pediatric gliomas have a higher IFN signature relative to other molecular subgroups. Single-cell sequencing identified an activated and cytotoxic microglia (MG) population designated MG-Act in BRAF-fused, MAPK-activated pilocytic astrocytoma (PA), but not in high-grade gliomas or normal brain. T cell immunoglobulin and mucin domain 3 (TIM3) was expressed on MG-Act and on the myeloid cells lining the tumor vasculature but not normal brain vasculature. TIM3 expression became upregulated on immune cells in the PA microenvironment, and anti-TIM3 reprogrammed ex vivo immune cells from human PAs to a proinflammatory cytotoxic phenotype. In a genetically engineered murine model of MAPK-driven, low-grade gliomas, anti-TIM3 treatment increased median survival over IgG- and anti-PD-1-treated mice. Single-cell RNA-Seq data during the therapeutic window of anti-TIM3 revealed enrichment of the MG-Act population. The therapeutic activity of anti-TIM3 was abrogated in mice on the CX3CR1 MG-KO background. These data support the use of anti-TIM3 in clinical trials of pediatric low-grade, MAPK-driven gliomas.

Detecting Rhythmic Gene Expression in Single Cell Transcriptomics.

An autonomous, environmentally-synchronizable circadian rhythm is a ubiquitous feature of life on Earth. In multicellular organisms, this rhythm is generated by a transcription-translation feedback loop present in nearly every cell that drives daily expression of thousands of genes in a tissue-dependent manner. Identifying the genes that are under circadian control can elucidate the mechanisms by which physiological processes are coordinated in multicellular organisms. Today, transcriptomic profiling at the single-cell level provides an unprecedented opportunity to understand the function of cell-level clocks. However, while many cycling detection algorithms have been developed to identify genes under circadian control in bulk transcriptomic data, it is not known how best to adapt these algorithms to single-cell RNAseq data. Here, we benchmark commonly used circadian detection methods on their reliability and efficiency when applied to single cell RNAseq data. Our results provide guidance on adapting existing cycling detection methods to the single-cell domain, and elucidate opportunities for more robust and efficient rhythm detection in single-cell data. We also propose a subsampling procedure combined with harmonic regression as an efficient strategy to detect circadian genes in the single-cell setting.

Occlusion enhanced pan-cancer classification via deep learning

Quantitative measurement of RNA expression levels through RNA-Seq is an ideal replacement for conventional cancer diagnosis via microscope examination. Currently, cancer-related RNA-Seq studies focus on two aspects: classifying the status and tissue of origin of a sample and discovering marker genes. Existing studies typically identify marker genes by statistically comparing healthy and cancer samples. However, this approach overlooks marker genes with low expression level differences and may be influenced by experimental results. This paper introduces “GENESO,” a novel framework for pan-cancer classification and marker gene discovery using the occlusion method in conjunction with deep learning. we first trained a baseline deep LSTM neural network capable of distinguishing the origins and statuses of samples utilizing RNA-Seq data. Then, we propose a novel marker gene discovery method called “Symmetrical Occlusion (SO)”. It collaborates with the baseline LSTM network, mimicking the “gain of function” and “loss of function” of genes to evaluate their importance in pan-cancer classification quantitatively. By identifying the genes of utmost importance, we then isolate them to train new neural networks, resulting in higher-performance LSTM models that utilize only a reduced set of highly relevant genes. The baseline neural network achieves an impressive validation accuracy of 96.59% in pan-cancer classification. With the help of SO, the accuracy of the second network reaches 98.30%, while using 67% fewer genes. Notably, our method excels in identifying marker genes that are not differentially expressed. Moreover, we assessed the feasibility of our method using single-cell RNA-Seq data, employing known marker genes as a validation test.

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

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

ScRNMF: An imputation method for single-cell RNA-seq data by robust and non-negative matrix factorization.

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

Establishment of a chemokine-based prognostic model and identification of CXCL10+ M1 macrophages as predictors of neoadjuvant therapy efficacy in colorectal cancer.

Although neoadjuvant therapy has brought numerous benefits to patients, not all patients can benefit from it. Chemokines play a crucial role in the tumor microenvironment and are closely associated with the prognosis and treatment of colorectal cancer. Therefore, constructing a prognostic model based on chemokines will help risk stratification and providing a reference for the personalized treatment. Employing LASSO-Cox predictive modeling, a chemokine-based prognostic model was formulated, harnessing the data from TCGA and GEO databases. Then, our exploration focused on the correlation between the chemokine signature and elements such as the immune landscape, somatic mutations, copy number variations, and drug sensitivity. CXCL10+M1 macrophages identified via scRNA-seq. Monocle2 showed cell pseudotime trajectories, CellChat characterized intercellular communication. CytoTRACE analyzed neoadjuvant therapy stemness, SCENIC detected cell type-specific regulation. Lastly, validation was performed through multiplex immunofluorescence experiments. A model based on 15 chemokines was constructed and validated. High-risk scores correlated with poorer prognosis and advanced TNM and clinical stages. Individuals presenting elevated risk scores demonstrated an increased propensity towards the development of chemotherapy resistance. Subsequent scRNA-seq data analysis indicated that patients with higher presence of CXCL10+ M1 macrophages in tumor tissues are more likely to benefit from neoadjuvant therapy. We developed a chemokine-based prognostic model by integrating both single-cell and bulk RNA-seq data. Furthermore, we revealed epithelial cell heterogeneity in neoadjuvant outcomes and identified CXCL10+ M1 macrophages as potential therapy response predictors. These findings could significantly contribute to risk stratification and serve as a key guide for the advancement of personalized therapeutic approaches.

COMSE: analysis of single-cell RNA-seq data using community detection-based feature selection

BackgroundSingle-cell RNA sequencing enables studying cells individually, yet high gene dimensions and low cell numbers challenge analysis. And only a subset of the genes detected are involved in the biological processes underlying cell-type specific functions.ResultIn this study, we present COMSE, an unsupervised feature selection framework using community detection to capture informative genes from scRNA-seq data. COMSE identified homogenous cell substates with high resolution, as demonstrated by distinguishing different cell cycle stages. Evaluations based on real and simulated scRNA-seq datasets showed COMSE outperformed methods even with high dropout rates in cell clustering assignment. We also demonstrate that by identifying communities of genes associated with batch effects, COMSE parses signals reflecting biological difference from noise arising due to differences in sequencing protocols, thereby enabling integrated analysis of scRNA-seq datasets of different sources.ConclusionsCOMSE provides an efficient unsupervised framework that selects highly informative genes in scRNA-seq data improving cell sub-states identification and cell clustering. It identifies gene subsets that reveal biological and technical heterogeneity, supporting applications like batch effect correction and pathway analysis. It also provides robust results for bulk RNA-seq data analysis.

A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.

Probabilistic topic modelling has become essential in many types of single-cell data analysis. Based on probabilistic topic assignments in each cell, we identify the latent representation of cellular states. A dictionary matrix, consisting of topic-specific gene frequency vectors, provides interpretable bases to be compared with known cell type-specific marker genes and other pathway annotations. However, fitting a topic model on a large number of cells would require heavy computational resources-specialized computing units, computing time and memory. Here, we present a scalable approximation method customized for single-cell RNA-seq data analysis, termed ASAP, short for Annotating a Single-cell data matrix by Approximate Pseudobulk estimation. Our approach is more accurate than existing methods but requires orders of magnitude less computing time, leaving much lower memory consumption. We also show that our approach is widely applicable for atlas-scale data analysis; our method seamlessly integrates single-cell and bulk data in joint analysis, not requiring additional preprocessing or feature selection steps.

Integration of single-cell datasets depicts profiles of macrophages and fibro/adipogenic progenitors in dystrophic muscle

Single-cell technologies have recently expanded the possibilities for researchers to gain, at an unprecedented resolution level, knowledge about tissue composition, cell complexity, and heterogeneity. Moreover, the integration of data coming from different technologies and sources also offers, for the first time, the possibility to draw a holistic portrait of how cells behave to sustain tissue physiology during the human lifespan and disease.Here, we interrogated and integrated publicly available single-cell RNAseq data to advance the understanding of how macrophages, fibro/adipogenic progenitors, and other cell types establish gene regulatory networks and communicate with each other in the muscle tissue. We identified altered gene signatures and signaling pathways associated with the dystrophic condition, including an enhanced Spp1-Cd44 signaling in dystrophic macrophages. We shed light on the differences among dystrophic muscle aging, considering wild type, mdx, and more severe conditions as in the case of the mdx-2d model. Contextually, we provided details on existing communication relations between muscle niche cell populations, highlighting increased interactions and distinct signaling events that these cells stablish in the dystrophic microenvironment.We believe our findings can help scientists to formulate and test new hypotheses by moving towards a more complete understanding of muscle regeneration and immune system biology.

Identification of RNA-binding protein RBMS3 as a potential biomarker for immunotherapy in bladder cancer.

RNA-binding protein (RBP) plays pivotal roles in the malignant progression of cancer by regulating gene expression. In this paper, we aimed to develop RBP-based prognostic signature and identify critical hub RBPs in bladder cancer (BLCA). Firstly, a risk model based on differentially expressed RBP gens (DERBPs) between normal and tumor tissues was successfully established, which can predict the tumor stromal score and drug sensitivity. Then two another RBP risk models based on miRNA-correlated RBPs or lncRNA-correlated RBPs were also established, and RBMS3 was identified as the overlapping gene in the three models. Data from multiple bioinformatics databases revealed that RBMS3 was an independent prognostic factor for overall survival (OS), and was associated with an immunosuppressive tumor microenvironment (TME) in BLCA. Further, Single-cell RNA-Seq (scRNA-Seq) data and the human protein altas (HPA) database showed that RBMS3 expression (both mRNA and protein) were up-regulated in BLCA tumor and tumor stromal cells. Finally, RBMS3 was shown to be associated with worse response to BLCA immunotherapy. Overall, RBMS3 is a key prognostic RBP with TME remodeling function and may serve as a target for BLCA immunotherapy.

ScTab: Scaling cross-tissue single-cell annotation models

Identifying cellular identities is a key use case in single-cell transcriptomics. While machine learning has been leveraged to automate cell annotation predictions for some time, there has been little progress in scaling neural networks to large data sets and in constructing models that generalize well across diverse tissues. Here, we propose scTab, an automated cell type prediction model specific to tabular data, and train it using a novel data augmentation scheme across a large corpus of single-cell RNA-seq observations (22.2 million cells). In this context, we show that cross-tissue annotation requires nonlinear models and that the performance of scTab scales both in terms of training dataset size and model size. Additionally, we show that the proposed data augmentation schema improves model generalization. In summary, we introduce a de novo cell type prediction model for single-cell RNA-seq data that can be trained across a large-scale collection of curated datasets and demonstrate the benefits of using deep learning methods in this paradigm.

Combining Bulk and Single Cell RNA-Sequencing Data to Identify Hub Genes of Fibroblasts in Dilated Cardiomyopathy.

Dilated cardiomyopathy (DCM) is the second leading cause of heart failure, with intricate pathophysiological underpinnings. In order to shed fresh light on the mechanistic research of DCM, we combined bulk RNA-seq and single-cell RNA-seq (scRNA-seq) data to examine significant cells and genes implicated in the disease. This analysis employed publicly accessible bulk RNA-seq and scRNA-seq DCM datasets. The scRNA-seq data underwent normalization, principal component, and t-distribution stochastic neighbor embedding analysis. Cell-to-cell communication networks and activity analysis were conducted using CellChat. Utilizing enrichment analysis, the marker genes' role in the active cells was evaluated. After screening by limma software and weighted gene co-expression network analysis, the differentially expressed genes (DEGs) served as hub genes. Furthermore, these hub genes were subjected to immunological studies, transcription factor expression, and gene set enrichment. Lastly, the expression of the four hub genes and their connection to DCM were verified using the rat models. Fibroblasts and monocytes were chosen as hub cells from among the eight identified cell clusters; their marker genes intersected with DEGs to yield six hub genes. In addition, the six hub genes and the essential module genes intersected to yield four essential genes (ASPN,SFRP4,LUM, andFRZB) that were connected to the Wnt signaling pathway and highly expressed in fibroblast. The four hub DEGs had an expression pattern in the DCM rat model experiment results that was in line with the findings of the bioinformatics study. Additionally, there was a strong correlation between decreased cardiac function and the up-regulation of ASPN,SFRP4,LUM, and FRZB. Ultimately, bulk RNA-seq and scRNA-seq data identified fibroblasts and monocytes as the main cell types implicated in DCM. The highly expressed genes ASPN,FRZB,LUM,and SFRP4 in fibroblasts may aid in the mechanistic investigation of DCM.

Chromosome-contiguous genome for the Haecon-5 strain of Haemonchus contortus reveals marked genetic variability and enables the discovery of essential gene candidates

Millions of livestock animals worldwide are infected with the haematophagous barber’s pole worm, Haemonchus contortus, the aetiological agent of haemonchosis. Despite the major significance of this parasite worldwide and its widespread resistance to current treatments, the lack of a high-quality genome for the well-defined strain of this parasite from Australia, called Haecon-5, has constrained research in a number of areas including host-parasite interactions, drug discovery and population genetics. To enable research in these areas, we report here a chromosome-contiguous genome (∼280 Mb) for Haecon-5 with high-quality models for 19,234 protein-coding genes. Comparative genomic analyses show significant genomic similarity (synteny) with a UK strain of H. contortus, called MHco3(ISE).N1 (abbreviated as “ISE”), but we also discover marked differences in genomic structure/gene arrangements, distribution of nucleotide variability (single nucleotide polymorphisms (SNPs) and indels) and orthology between Haecon-5 and ISE. We used the genome and extensive transcriptomic resources for Haecon-5 to predict a subset of essential single-copy genes employing a “cross-species” machine learning (ML) approach using a range of features from nucleotide/protein sequences, protein orthology, subcellular localisation, single-cell RNA-seq and/or histone methylation data available for the model organisms Caenorhabditis elegans and Drosophila melanogaster. From a set of 1,464 conserved single copy genes, transcribed in key life-cycle stages of H. contortus, we identified 232 genes whose homologs have critical functions in C. elegans and/or D. melanogaster, and prioritised 10 of them for further characterisation; nine of the 10 genes likely play roles in neurophysiological processes, germline, hypodermis and/or respiration, and one is an unknown (orphan) gene for which no detailed functional information exists. Future studies of these genes/gene products are warranted to elucidate their roles in parasite biology, host-parasite interplay and/or disease. Clearly, the present Haecon-5 reference genome and associated resources now underpin a broad range of fundamental investigations of H. contortus and could assist in accelerating the discovery of novel intervention targets and drug candidates to combat haemonchosis.

Skin microdialysis detects distinct immunologic patterns in chronic inflammatory skin diseases

BackgroundInsight into the pathophysiology of inflammatory skin diseases, especially at the proteomic level, is severely hampered by the lack of adequate in situ data. ObjectiveCharacterize lesional and nonlesional skin of inflammatory skin diseases using skin microdialysis. MethodsSkin microdialysis samples from patients with atopic dermatitis (AD, n=6), psoriasis vulgaris (PSO, n=7) or prurigo nodularis (PN, n=6), as well as healthy controls (n=7) were subjected to proteomics and multiplex cytokine analysis. Single-cell RNA sequencing of skin biopsy specimens was used to identify the cellular origin of cytokines. ResultsAmong the top 20 enriched GO annotations, NAD metabolic process, regulation of secretion by cell, and pyruvate metabolic process were elevated in microdialysates from lesional AD skin compared with both nonlesional skin and controls. The top 20 enriched KEGG pathways in these three groups overlapped almost completely. In contrast, nonlesional skin from patients with PSO or PN and control skin showed no overlap with lesional skin in this KEGG pathway analysis. Lesional skin from patients with PSO, but not AD or PN, showed significantly elevated protein levels of MCP-1 compared to nonlesional skin. IL-8 was elevated in lesional vs nonlesional AD and PSO skin, whereas IL-12p40 and IL-22 were higher only in lesional PSO skin. Integrated single-cell RNA-seq data revealed identical cellular sources of these cytokines in AD, PSO and PN. ConclusionBased on microdialysate, proteomic data of lesional PSO and PN skin, but not lesional AD skin, differed significantly from those of nonlesional skin. IL-8, IL-22, MCP-1 and IL-12p40 might be suitable markers for minimally invasive molecular profiling.