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Results for “single cell RNA sequencing”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2020

CD147 (BSG) but not ACE2 expression is detectable in vascular endothelial cells within single cell RNA sequencing datasets derived from multiple tissues in healthy individuals

Coronavirus disease 2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and is associated with a wide range of systemic manifestations. Several observations support a role for vascular endothelial dysfunction in the pathogenesis including an increased incidence of thrombotic events and coagulopathy and the presence of vascular risk factors as an independent predictor of poor prognosis. It has recently been reported that endothelitis is associated with viral inclusion bodies suggesting a direct role for SARS-CoV-2 in the pathogenesis. The ACE2 receptor has been shown to mediate SARS-CoV-2 uptake and it has been proposed that CD147 (BSG) can function as an alternative cell surface receptor. To define the endothelial cell populations that are susceptible to infection with SARS-CoV-2, we investigated the expression of ACE2 as well as other genes implicated in the cellular entry of SARS-Cov-2 in the vascular endothelium through the analysis of single cell sequencing data derived from multiple human tissues (skin, liver, kidney, lung and intestine). We found that CD147 (BSG) but not ACE2 is detectable in vascular endothelial cells within single cell sequencing datasets derived from multiple tissues in healthy individuals. This implies that either ACE2 is not expressed in healthy tissue but is instead induced in response to SARS-Cov2 or that SARS-Cov2 enters endothelial cells via an alternative receptor such as CD147.

genomics↗

bioRxiv2025

Generation and Validation of a Lower Limb Muscle Single-Cell RNA Sequencing Data Set Identifies Pathogenic Endothelial Metabolism in Peripheral Arterial Disease

BackgroundPeripheral arterial disease (PAD) results from atherosclerotic occlusion(s) in leg arteries. Chronic limb-threatening ischemia (CLTI) is the most severe form of PAD. Patients with CLTI suffer from rest pain, ulcers, or gangrene. Clinical outcomes remain poor in patients with CLTI and many investigational approaches, such as promoting angiogenesis, have failed. Understanding cell-specific vs. bulk-RNA changes within muscle offers an opportunity to better understand this disease. ObjectiveTo assess cell-specific alterations in gene and metabolism pathways in endothelial and muscle cells from leg muscle between patients with PAD vs. non-PAD controls. MethodsWe analyzed publicly available bulk RNA-seq leg muscle data from non-PAD controls, patients with intermittent claudication (IC) and CLTI. An emphasis was on glycolysis and the hexosamine biosynthetic pathway (HBP). Two single-cell RNA-seq datasets were integrated, cell types identified, and metabolism pathway scores calculated across identified cell types. Validations included quantitative mRNA in flow-sorted ECs, and immunofluorescence from clinically-phenotyped human samples. ResultsBulk RNA-seq revealed downregulation of both glycolysis and HBP in CLTI vs. IC or control tissues. Single-cell analysis uncovered cell-type specific changes. In patients with CLTI, gene expression in ECs showed increased glycolysis and decreased HBP. In myonuclei gene expression of both pathways were reduced. Flow-sorted ECs confirmed higher glycolysis and reduced HBP in CLTI vs. controls. Immunofluorescence analysis of muscle revealed significantly lower HBPECs (9.5% vs. 31.3%, n=5/group, p=0.004) and increased glycolytic ECs (42.0% vs. 14.4%, n=7/group, p<0.001) in CLTI vs. control. The findings were independent of capillary density. In IC patients, supervised exercise vs. optimal medical care reduced ischemic muscle PFKFB3+ ECs (2.67% vs. 14.61%, n=11 vs. 9, p<0.001). ConclusionsWe generated and validated a single-cell RNA-seq data set that confirms maladaptive metabolic reprogramming in CLTI ECs. Strategies designed to alter metabolism need to account for differences between bulk vs. single-cell analyses.

bioinformatics↗

bioRxiv2025

scCompare: a web app for single-cell RNA sequencing dataset comparisons across multiple auto-immune diseases

MotivationSingle-cell RNA sequencing (scRNA-seq) datasets have been widely used to identify the cell types and marker genes with pivotal roles in driving pathogenesis and progression of auto-immune diseases. Comparative analysis of different cell types or diseases across multiple scRNA-seq datasets can reveal the homogeneous and heterogeneous pathogenesis, and a comprehensive web-based comparison tool that could streamline this process is not yet available. ResultsWe introduce scCompare, a web-based platform for scRNA-seq comparisons in autoimmune diseases. scCompare includes 2,125 differential gene lists from 100 scRNA-seq datasets in 22 auto-immune diseases and a query system supporting 170 standardized keywords from four attributes (disease, cell type, tissue, and treatment). scCompare also provides three modules enabling several comparative analysis and visualization options. geneQuery supports comparisons of queried genes across differential gene lists identified from multiple scRNA-seq datasets. DEGEnricher performs cell-type-specific enrichment analysis across studies based on a user-input gene list. DEGCompare allows interactive comparisons of multiple differential gene lists of many studies and performs pathway enrichment analyses. Using two case studies as examples, we demonstrated that scCompare represents a unique platform for biologists to identify, compare and validate the pathogenesis at the single-cell levels among auto-immune diseases. Availability and implementationscCompare is freely available at https://sccompare.shinyapps.io/main/. The source code is available at https://github.com/abbviegrc/scCompare.

bioinformatics↗

bioRxiv2019

scAEspy: a unifying tool based on autoencoders for the analysis of single-cell RNA sequencing data

BackgroundSingle-cell RNA sequencing (scRNA-Seq) experiments are gaining ground to study the molecular processes that drive normal development as well as the onset of different pathologies. Finding an effective and efficient low-dimensional representation of the data is one of the most important steps in the downstream analysis of scRNA-Seq data, as it could provide a better identification of known or putatively novel cell-types. Another step that still poses a challenge is the integration of different scRNA-Seq datasets. Though standard computational pipelines to gain knowledge from scRNA-Seq data exist, a further improvement could be achieved by means of machine learning approaches. ResultsAutoencoders (AEs) have been effectively used to capture the non-linearities among gene interactions of scRNA-Seq data, so that the deployment of AE-based tools might represent the way forward in this context. We introduce here scAEspy, a unifying tool that embodies: (1) four of the most advanced AEs, (2) two novel AEs that we developed on purpose, (3) different loss functions. We show that scAEspy can be coupled with various batch-effect removal tools to integrate data by different scRNA-Seq platforms, in order to better identify the cell-types. We benchmarked scAEspy against the most used batch-effect removal tools, showing that our AE-based strategies outperform the existing solutions. ConclusionsscAEspy is a user-friendly tool that enables using the most recent and promising AEs to analyse scRNA-Seq data by only setting up two user-defined parameters. Thanks to its modularity, scAEspy can be easily extended to accommodate new AEs to further improve the downstream analysis of scRNA-Seq data. Considering the relevant results we achieved, scAEspy can be considered as a starting point to build a more comprehensive toolkit designed to integrate multi single-cell omics.

bioinformatics↗

bioRxiv2020

Temporally integrated single cell RNA sequencing analysis of controlled and natural primary human DENV-1 infections

Controlled dengue human infection studies present an opportunity to address many longstanding questions in the field of flavivirus biology. However, limited data are available on how the immunological and transcriptional response elicited by an attenuated challenge virus compares to that associated with a wild-type DENV infection. To bridge this knowledge gap, we utilized scRNAseq to analyze PBMC from individuals enrolled in a DENV-1 controlled human challenge study and from individuals experiencing a natural primary DENV-1 infection. While both controlled and natural DENV infection resulted in overlapping patterns of inflammatory gene upregulation, natural DENV infection was accompanied with a more pronounced suppression in gene products associated with protein translation and mitochondrial function, principally in monocytes. This suggests that the immune response elicited by controlled and natural primary DENV infection are similar, but that natural DENV infection has a more pronounced impact on basic cellular processes to induce a multi-layered anti-viral state

immunology↗

bioRxiv2020

Single-Cell RNA Sequencing Reveals Endothelial Cell Transcriptome Heterogeneity under Homeostatic Laminar Flow

ObjectiveEndothelial cells that form the innermost layer of all vessels exhibit heterogeneous cell behaviors and responses to pro-angiogenic signals that are critical for vascular sprouting and angiogenesis. Once vessels form, remodeling and blood flow lead to endothelial cell quiescence, and homogeneity in cell behaviors and signaling responses. These changes are important for the function of mature vessels, but whether and at what level endothelial cells regulate overall expression heterogeneity during this transition is poorly understood. Here we profiled endothelial cell transcriptomic heterogeneity, and expression heterogeneity of selected proteins, under homeostatic laminar flow. Approach and ResultsSingle-cell RNA sequencing and fluorescence microscopy were used to characterize heterogeneity in RNA and protein gene expression levels of human endothelial cells under homeostatic laminar flow compared to non-flow conditions. Analysis of transcriptome variance, Gini coefficient, and coefficient of variation showed that more genes increased RNA heterogeneity under laminar flow relative to genes whose expression became more homogeneous. Analysis of a subset of genes for relative protein expression revealed that most protein profiles showed decreased heterogeneity under flow. In contrast, the magnitude of expression level changes in RNA and protein was coordinated among endothelial cells in flow vs. non-flow conditions. ConclusionsEndothelial cells exposed to homeostatic laminar flow showed increased cohort heterogeneity in RNA expression levels, while cohort expression heterogeneity of selected cognate proteins decreased under laminar flow. These findings suggest that EC homeostasis is imposed at the level of protein translation and/or stability rather than transcriptionally.

cell biology↗

bioRxiv2021

Out-of-distribution prediction with disentangled representations for single-cell RNA sequencing data

Learning robust representations can help uncover underlying biological variation in scRNA-seq data. Disentangled representation learning is one approach to obtain such informative as well as interpretable representations. Here, we learn disentangled representations of scRNA-seq data using {beta} variational autoencoder ({beta}-VAE) and apply the model for out-of-distribution (OOD) prediction. We demonstrate accurate gene expression predictions for cell-types absent from training in a perturbation and a developmental dataset. We further show that {beta}-VAE outperforms a state-of-the-art disentanglement method for scRNA-seq in OOD prediction while achieving better disentanglement performance.

bioinformatics↗

bioRxiv2021

Single cell RNA sequencing and lineage tracing confirm mesenchyme to epithelial transformation (MET) contributes to repair of the endometrium at menstruation

The human endometrium experiences repetitive cycles of tissue wounding characterised by piecemeal shedding of the surface epithelium and rapid restoration of tissue homeostasis. In this study we used a mouse model of endometrial repair and three transgenic lines of mice to investigate whether epithelial cells that become incorporated into the newly formed luminal epithelium have their origins in one or more of the mesenchymal cell types present in the stromal compartment of the endometrium. Using scRNAseq we identified a novel population of PDGFRb+ mesenchymal stromal cells that developed a unique transcriptomic signature in response to endometrial breakdown/repair. These cells expressed genes usually considered specific to epithelial cells and in silico trajectory analysis suggested they were stromal fibroblasts in transition to becoming epithelial cells. To confirm our hypothesis we used a lineage tracing strategy to compare the fate of stromal fibroblasts (PDGFRa+) and stromal perivascular cells (NG2/CSPG4+). We demonstrated that stromal fibroblasts can undergo a mesenchyme to epithelial transformation and become incorporated into the re-epithelialised luminal surface of the repaired tissue. This study is the first to discover a novel population of wound-responsive, plastic endometrial stromal fibroblasts that contribute to the rapid restoration of an intact luminal epithelium during endometrial repair. These findings form a platform for comparisons both to endometrial pathologies which involve a fibrotic response (Ashermans syndrome, endometriosis) as well as other mucosal tissues which have a variable response to wounding. HighlightsO_LISingle cell RNA sequencing identified a population of PDGFR{beta}+ mesenchymal cells with a unique transcriptomic signature that arises in response to induction of a menses-like wound C_LIO_LIThe repair-specific mesenchymal cells express genes considered specific to both mesenchymal and epithelial lineages indicative of an intermediate phenotype and a mesenchymal to epithelial transition (MET) C_LIO_LIin silico trajectory analysis revealed that repair-specific cells originate from the fibroblast cell clusters, were distinct from perivascular cells, and had a predicted trans-differentiation trajectory towards definitive epithelial cells C_LIO_LIin vivo lineage tracing in adult mice provides definitive evidence that PDGFR+ endometrial fibroblasts, and not NG2+ perivascular cells, undergo MET and can become incorporated into the luminal epithelium of the post repair tissue C_LI

physiology↗

bioRxiv2022

Neuroendocrinology of the lung revealed by single cell RNA sequencing

Pulmonary neuroendocrine cells (PNECs) are sensory epithelial cells that transmit airway status to the brain via sensory neurons and locally via CGRP and GABA. Several other neuropeptides and neurotransmitters have been detected in various species, but the number, targets, functions, and conservation of PNEC signals are largely unknown. We used scRNAseq to profile hundreds of the rare mouse and human PNECs. This revealed >40 PNEC neuropeptide and peptide hormone genes, most cells expressing unique combinations of 5-18 genes. Peptides are packaged in separate vesicles, release presumably regulated by the distinct, multimodal combinations of sensors expressed by each PNEC. Expression of cognate receptors predicts an array of local targets, and we show the new PNEC signal angiotensin directly activates innervating sensory neurons. Many signals lack lung targets so may have endocrine activity like those of PNEC- derived carcinoid tumors. PNECs are an extraordinarily rich and diverse signaling hub rivaling the enteroendocrine system.

bioinformatics↗

bioRxiv2022

Reliable and accurate gene expression quantification with subpopulation structure-aware constraints for single-cell RNA sequencing

BackgroundSingle-cell RNA sequencing (scRNA-seq) analysis analyzes the type and state of individual cells by estimating the gene expression of each cell and enables researchers to study the biological phenomena that cannot be observed in bulk RNA sequencing. MotivationHowever, the current scRNA-seq quantification tools estimate the gene expression profile of each cell independently, ignoring the fact that there are multiple cell types in the scRNA-seq data and the expression level should be highly correlated with the cell type. Since scRNA-seq suffers from a low sequencing depth, the conventional strategy leads to a high proportion of missing values in the gene expression profile, obscuring the biological characteristics of cell subpopulations and further impacting the correctness of the subsequent downstream analysis. ResultsIn this study, we proposed Quasic, a novel scRNA-seq quantification pipeline which examines the potential cell subpopulation information during quantification, and uses the information to calculate the gene expression level. Using the human peripheral blood mononuclear cells and the simulated doublet dataset, we verified that Quasic not only correctly reinforced the cell signatures, but also identified the corresponding cell subpopulations and biological pathways more accurately. In addition, we also applied Quasic to the breast cancer cell line dataset (MCF-7), and successfully identified more potentially therapeutic resistant cells of which characteristics are consistent with that from previous studies. ConclusionsThe proposed pipeline can let the gene expression profile of each cell be more consistent with the corresponding subpopulation, making the biological features unique to the subpopulation more apparent and convenient for analysis. By using Quasic, researchers can effectively extract the desired cell subpopulation information from their sampled cells, enable them to perform cell subpopulation-related studies more accurately.

bioinformatics↗

bioRxiv2023

ScRNAbox: Empowering Single-Cell RNA Sequencing on High Performance Computing Systems

MotivationSingle-cell RNA sequencing (scRNAseq) offers powerful insights, but the surge in sample sizes demands more computational power than local workstations can provide. Consequently, high-performance computing (HPC) systems have become imperative. Existing web apps designed to analyze scRNAseq data lack scalability and integration capabilities, while analysis packages demand coding expertise, hindering accessibility. ResultsIn response, we introduce scRNAbox, an innovative scRNAseq analysis pipeline meticulously crafted for HPC systems. This end-to-end solution, executed via the SLURM workload manager, efficiently processes raw data from standard and Hashtag samples. It incorporates quality control filtering, sample integration, clustering, cluster annotation tools, and facilitates cell type-specific differential gene expression analysis between two groups. ImplementationOpen-source code and comprehensive usage instructions with examples are available at https://neurobioinfo.github.io/scrnabox/site/. Supplementary InformationSupplementary data are available at Bioinformatics online.

bioinformatics↗

bioRxiv2024

Single-cell RNA sequencing of the holothurian regenerating intestine reveals the pluripotency of the coelomic epithelium

In holothurians, the regenerative process following evisceration involves the development of a "rudiment" or "anlage" at the injured end of the mesentery. This regenerating anlage plays a pivotal role in the formation of a new intestine. Despite its significance, our understanding of the molecular characteristics inherent to the constituent cells of this structure has remained limited. To address this gap, we employed state-of-the-art scRNA-seq and HCR-FISH analyses to discern the distinct cellular populations associated with the regeneration anlage. Through this approach, we successfully identified thirteen distinct cell clusters. Among these, two clusters exhibit characteristics consistent with putative mesenchymal cells, while another four show features akin to coelomocyte cell populations. The remaining seven cell clusters collectively form a large group encompassing the coelomic epithelium of the regenerating anlage and mesentery. Within this large group of clusters, we recognized previously documented cell populations such as muscle precursors, neuroepithelial cells and actively proliferating cells. Strikingly, our analysis provides data for identifying at least four other cellular populations that we define as the precursor cells of the growing anlage. Consequently, our findings strengthen the hypothesis that the coelomic epithelium of the anlage is a pluripotent tissue that gives rise to diverse cell types of the regenerating intestinal organ. Moreover, our results provide the initial view into the transcriptomic analysis of cell populations responsible for the amazing regenerative capabilities of echinoderms.

developmental biology↗

bioRxiv2024

Deciphering Metabolic Pathways and Protein-Protein Interaction Networks in Ankylosing Spondylitis through Single-Cell RNA Sequencing

Ankylosing Spondylitis (AS) is a common autoimmune disease affecting spinal joints and causing chronic pain. Understanding the roles of different cell types in AS can facilitate the development of effective treatments. In this study, we analyzed scRNA-seq data of peripheral blood mononuclear cells (PBMC) from AS patients and healthy controls collected from the literature. Using the GIMME algorithm, we created genome-scale metabolic models for each cell type to analyze reaction fluxes varying between patient and healthy conditions. Our findings revealed increased purine metabolism flux, fatty acid degradation, and glycolysis in CD14 monocytes, CD4 memory, CD4 naive, and CD8 T cells in AS patients compared to healthy individuals. Additionally, by integrating multi-omics approaches we generated cell- type-specific protein-protein interaction (PPI) networks, uncovering 63 rewired hubs across nine cell types. RPS11 emerged as the most significant hub, essential in translation and there are evidences in the literature that implicate it in AS. These results provide a detailed understanding of the metabolic and protein interaction changes in specific immune cell types in AS, highlighting RPS11 as a critical regulatory hub that could serve as a potential biomarker or therapeutic target for developing more precise and effective treatments.

systems biology↗

bioRxiv2024

Immune cell single-cell RNA sequencing analyses link an age-associated T cell subset to symptomatic benign prostatic hyperplasia

Benign prostatic hyperplasia (BPH) is among the most common age-associated diseases in men; however, the contribution of age-related changes in immune cells to BPH is not clear. The current study determined that an age-associated CD8+ T cell subset (Taa) with high Granzyme K (GZMKhi) and low Granzyme B (GZMBlow) gene expression infiltrate aged human prostates and positively correlate with International Prostate Symptom Score (IPSS). A velocity analysis indicated that CD8+ T cell differentiation is altered in large BPH prostates compared to small age-matched prostates, favoring Taa accumulation. In vitro granzyme K treatment of human BPH patient-derived large prostate fibroblasts increased secretion of pro-inflammatory senescence-associated secretory phenotype (SASP)-associated cytokines. These data suggest that granzyme K-mediated stimulation of prostate stromal fibroblast SASP cytokine and chemokine production promotes prostate immune cell recruitment and activation. Overall, these results connect symptomatic BPH with immune aging.

immunology↗

bioRxiv2025

Accurate quantification of spliced and unspliced transcripts for single-cell RNA sequencing with tidesurf

MotivationSingle-cell RNA sequencing (scRNA-seq) allows for the detailed analysis of dynamic cellular processes. In particular, this has been enabled by the estimation of RNA velocity, the derivative of gene expression, from separate count matrices for different splice states, which provides information about a cells immediate future even in snapshot data. Useful velocity estimates strongly depend on accurate counts for spliced and unspliced transcripts. Velocyto remains the standard tool for spliced and unspliced mRNA molecule quantification. However, despite considerable advances in scRNA-seq protocols, velocyto has not been updated to account for peculiarities of new protocols, such as popular approaches based on 5 chemistry. ResultsTo address this shortcoming, we present tidesurf, a command line tool for the quantification of spliced and unspliced transcript molecules from scRNA-seq libraries. Employing it on four different publicly available 10x Genomics Chromium datasets, we show the accuracy on various datasets generated with either 3 or 5 chemistry, whereas velocytos results are highly erroneous for the latter. Considering broader applicability, our results highlight tidesurf as a potential replacement for velocyto. Availability and implementationA Python implementation of tidesurf is available from PyPI and at github.com/janschleicher/tidesurf. Code for reproducing the analyses is available at github.com/janschleicher/tidesurf projects.

bioinformatics↗

bioRxiv2018

MULTI-seq: Scalable sample multiplexing for single-cell RNA sequencing using lipid-tagged indices

We describe MULTI-seq: A rapid, modular, and universal scRNA-seq sample multiplexing strategy using lipid-tagged indices. MULTI-seq reagents can barcode any cell type from any species with an accessible plasma membrane. The method is compatible with enzymatic tissue dissociation, and also preserves viability and endogenous gene expression patterns. We leverage these features to multiplex the analysis of multiple solid tissues comprising human and mouse cells isolated from patient-derived xenograft mouse models. We also utilize MULTI-seqs modular design to perform a 96-plex perturbation experiment with human mammary epithelial cells. MULTI-seq also enables robust doublet identification, which improves data quality and increases scRNA-seq cell throughput by minimizing the negative effects of Poisson loading. We anticipate that the sample throughput and reagent savings enabled by MULTI-seq will expand the purview of scRNA-seq and democratize the application of these technologies within the scientific community.

genomics↗

bioRxiv2018

Complex cell-state changes revealed by single cell RNA sequencing of 76,149 microglia throughout the mouse lifespan and in the injured brain

Microglia, the resident immune cells of the brain, rapidly change states in response to their environment, but we lack molecular and functional signatures of different microglial populations. In this study, we analyzed the RNA expression patterns of more than 76,000 individual microglia during development, old age and after brain injury. Analysis uncovered at least nine transcriptionally distinct microglial states, which expressed unique sets of genes and were localized in the brain using specific markers. The greatest microglial heterogeneity was found at young ages; however, several states - including chemokine-enriched inflammatory microglia - persisted throughout the lifespan or increased in the aged brain. Multiple reactive microglial subtypes were also found following demyelinating injury in mice, at least one of which was also found in human MS lesions. These unique microglia signatures can be used to better understand microglia function and to identify and manipulate specific subpopulations in health and disease.

neuroscience↗

bioRxiv2019

Single-Cell RNA Sequencing Reveals Regulatory Mechanism for Trophoblast Cell-Fate Divergence in Human Peri-Implantation Embryo

Multipotent trophoblasts undergo dynamic morphological movement and cellular differentiation after embryonic implantation to generate placenta. However, the mechanism controlling trophoblast development and differentiation during peri-implantation development remains elusive. In this study, we modeled human embryo peri-implantation development from blastocyst to early post-implantation stages by using an in vitro coculture system, and profiled the transcriptome of individual trophoblast cells from these embryos. We revealed the genetic networks regulating peri-implantation trophoblast development. While determining when trophoblast differentiation happens, our bioinformatic analysis identified T-box transcription factor 3 (TBX3) as a key regulator for the differentiation of cytotrophoblast into syncytiotrophoblast. The function of TBX3 in trophoblast differentiation is then validated by a loss-of-function experiment. In conclusion, our results provided a valuable resource to study the regulation of trophoblasts development and differentiation during human peri-implantation development.

cell biology↗