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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2019

scds: Computational Annotation of Doublets in Single Cell RNA Sequencing Data

MotivationSingle cell RNA sequencing (scRNA-seq) technologies enable the study of transcriptional heterogeneity at the resolution of individual cells and have an increasing impact on biomedical research. Specifically, high-throughput approaches that employ micro-fluidics in combination with unique molecular identifiers (UMIs) are capable of assaying many thousands of cells per experiment and are rapidly becoming commonplace. However, it is known that these methods sometimes wrongly consider two or more cells as single cells, and that a number of so-called doublets is present in the output of such experiments. Treating doublets as single cells in downstream analyses can severely bias a studys conclusions, and therefore computational strategies for the identification of doublets are needed. Here we present single cell doublet scoring (scds), a software tool for the in silico identification of doublets in scRNA-seq data. ResultsWith scds, we propose two new and complementary approaches for doublet identification: Co-expression based doublet scoring (cxds) and binary classification based doublet scoring (bcds). The co-expression based approach, cxds, utilizes binarized (absence/presence) gene expression data and employs a binomial model for the co-expression of pairs of genes and yields interpretable doublet annotations. bcds, on the other hand, uses a binary classification approach to discriminate artificial doublets from the original data. We apply our methods and existing doublet identification approaches to four data sets with experimental doublet annotations and find that our methods perform at least as well as the state of the art, but at comparably little computational cost. We also find appreciable differences between methods and across data sets, that no approach dominates all others, and we believe there is room for improvement in computational doublet identification as more data with experimental annotations becomes available. In the meanwhile, scds presents a scalable, competitive approach that allows for doublet annotations in thousands of cells in a matter of seconds. Availability and Implementationscds is implemented as an R package and freely available at https://github.com/kostkalab/scds. Contactkostka@pitt.edu

bioinformatics↗

bioRxiv2025

Rethinking the Estrogen Receptor Beta Dominance Hypothesis in Endometriosis: Insights from Single Cell RNA Sequencing Meta-analysis

Structured AbstractO_ST_ABSBackgroundC_ST_ABSO_LIEndometriosis is a chronic, estrogen-dependent disease characterized by the presence of endometrial-like tissue growing outside the uterus. The molecular and clinical heterogeneity of endometriosis complicate diagnostic and treatment options -- diagnostic delays of seven to ten years are common and therapies often lack long-term efficacy. Estrogen signaling and estrogen receptor beta (ER{beta}) expression is thought to be increased in endometriosis, contributing to increased cell proliferation in lesions. The "ER{beta} dominance hypothesis" is a prevailing hypothesis in the field, setting ER{beta} as a high-priority therapeutic target. If effectively modulated, ER{beta} could be the first therapy to directly target lesion biology, rather than only managing symptoms. C_LI Objective(s)O_LIWe aimed to characterize ER{beta}s expression in endometriosis by cell type and evaluate its therapeutic relevance, primarily assessing the validity of the ER{beta} dominance hypothesis. C_LI Study DesignO_LIWe reanalyzed scRNAseq data from eight previously published studies. Our final filtered dataset included 557,061 cells, the largest endometriosis single cell atlas ever constructed. We quantified gene expression levels of ESR1 and ESR2, which encode ER[a] and ER{beta} respectively, across each tissue and cell type, to identify cell-type specific drivers of ESR2/ER{beta} expression across diseased and healthy tissues. To characterize the differences between cells that uniquely express ESR1 versus those that uniquely express ESR2, we performed differential gene expression and pathway enrichment analyses. C_LI ResultsO_LICount and distribution analyses revealed no significant ESR2/ER{beta} dominance in any cell or tissue type by Fishers Exact Tests and Wilcoxon Rank Sum Tests. Differential gene expression and pathway enrichment analyses suggest distinct roles of each estrogen receptor isoform. C_LI Conclusion(s)O_LIOverall, our results argue against a simplified model of ER{beta} dominance and instead propose a dual-isoform and cell and tissue-specific framework for understanding estrogen receptor signaling in endometriosis. These findings hold important implications for future therapeutic strategies. Specifically, treatments that target ER{beta} alone may fail to account for the functional role and relative abundance of ER. In the future, therapeutic approaches that consider isoform-specific, tissue-specific, and cell-specific expression patterns may prove most effective in reducing recurrence and improving outcomes for patients. C_LI Condensation pageO_ST_ABSTweetable statementC_ST_ABSSingle cell RNA Sequencing meta-analysis shows estrogen receptor beta is not dominantly expressed in most endometriosis tissues. Estrogen receptor alpha to estrogen receptor beta ratios vary by cell type and tissue type. Each isoform directs cell-type specific behavior in endometriosis and disease-free tissues. AJOG at a GlanceO_LIWhy was this study conducted? O_LIWe wanted to characterize estrogen receptor betas expression in endometriosis and evaluate its therapeutic relevance. C_LI C_LIO_LIWhat are the key findings? O_LIEstrogen receptor beta is not dominantly expressed in any tissue. Estrogen receptor alpha and estrogen receptor beta have disease- and cell-type specific behaviors. C_LI C_LIO_LIWhat does this study add to what is already known? O_LIIt characterizes estrogen receptor isoform expression and signaling by cell type. It also challenges the current estrogen receptor beta dominance hypothesis, meaning estrogen receptor beta may not be a key driver of endometriosis. C_LI C_LI

systems biology↗

bioRxiv2025

High-throughput RNA and Single-cell RNA Sequencing to Identify Sepsis associated Molecular Mechanisms and the Blood Immune Cell Landscape

Sepsis, a leading cause of death in intensive care units (ICUs), is a complex systemic inflammatory response to infection with high morbidity and mortality. Its pathogenesis involves dysregulated inflammation, immune dysfunction, and metabolic alterations, particularly in lactate metabolism. This study employed bioinformatics analyses to explore sepsis mechanisms and identify potential therapeutic targets. We analyzed two GEO datasets and found the lactate metabolism pathway significantly enriched in sepsis patients. Seventeen key genes were identified and used to classify sepsis into two subtypes via WGCNA and consensus clustering. These subtypes exhibited distinct clinical and immune profiles. Seven hub genes (BPI, HGF, HP, LCN2, LTF, MMP8, RETN) showed differential expression between subtypes and may serve as diagnostic biomarkers. MMP8 was identified as a critical regulator in lactate metabolism, with associated miRNAs and transcription factors predicted. Single-cell analysis revealed altered immune cell compositions and interactions in sepsis patients. Our findings offer novel insights into sepsis pathogenesis and potential therapeutic strategies targeting lactate metabolism and immune regulation. Keywords: sepsis; lactate metabolism; bioinformatics; immune microenvironment

bioinformatics↗

bioRxiv2023

Single-cell RNA sequencing unveils the hidden powers of zebrafish kidney for generating both hematopoiesis and adaptive antiviral immunity

The vertebrate kidneys play two evolutionary conserved roles in waste excretion and osmoregulation. Besides, the kidney of fish is considered as a functional ortholog of mammalian bone marrow that serves as a hematopoietic hub for generating blood cell lineages and immunological responses. However, knowledge about the properties of kidney hematopoietic cells, and the functionality of kidney in fish immune systems remain to be elucidated. To this end, our present study generated a comprehensive atlas with 59 hematopoietic stem/progenitor cell (HSPC) and immune-cell types from zebrafish kidney via single-cell transcriptome profiling analysis. These populations included almost all known cells associated with innate and adaptive immunity, and displayed differential responses to viral infection, indicating their diverse functional roles in antiviral immunity. Remarkably, HSPCs were found to have extensive reactivities to viral infection, and the trained immunity can be effectively induced in certain HSPCs. In addition, the antigen-stimulated adaptive immunity can be fully generated in kidney, suggesting kidney acting as a secondary lymphoid organ. These results indicated that fish kidney is a dual-functional entity with functionalities of both primary and secondary lymphoid organs. Our findings illustrated the unique features of fish immune system, and highlighted the multifaced biology of kidney in ancient vertebrates.

immunology↗

bioRxiv2020

A monotonicity-based gene clustering algorithm for enhancing clarity in single-cell RNA sequencing data

MotivationSingle-cell RNA sequencing (scRNA-seq) technologies and analysis tools have allowed researchers to achieve remarkably detailed understandings of the roles and relationships between cells and genes. However, conventional distance metrics, such as Euclidean, Pearson, and Spearman distances, fail to simultaneously take into account the high dimensionality, monotonicity, and magnitude of gene expression data. To address several shortcomings in these commonly used metrics, we present a magnitude-contingent monotonic correlation metric called Polaratio which is designed to enhance the quality of scRNA-seq data analysis. ResultsWe integrate three interpretable clustering algorithms - Single-Cell Consensus Clustering (SC3), Hierarchical Clustering (HC), and K-Medoids (KM) - through a consensus cell clustering procedure, which we evaluate on various biological datasets to benchmark Polaratio against several well-known metrics. Our results demonstrate Polaratios ability to improve the accuracy of cell clustering on 5 out of 7 publicly available datasets. Availabilityhttps://github.com/dubai03nsr/Polaratio Contactpcicalese@uh.edu

bioinformatics↗

bioRxiv2024

Optimizing single-cell RNA sequencing methods for human colon biopsies: droplet-based vs. picowell-based platforms

Background & AimsSingle-cell RNA sequencing (scRNA) has empowered many insights into gastrointestinal microenvironments. However, profiling human biopsies using droplet-based scRNA (D-scRNA) is challenging since it requires immediate processing to minimize epithelial cell damage. In contrast, picowell-based (P-scRNA) platforms permit short-term frozen storage before sequencing. We compared P- and D-scRNA platforms on cells derived from human colon biopsies. MethodsEndoscopic rectosigmoid mucosal biopsies were obtained from two adults and conducted D-scRNA (10X Chromium) and P-scRNA (Honeycomb HIVE) in parallel using an individuals pool of single cells (> 10,000 cells/participant). Three experiments were performed to evaluate 1) P-scRNA with cells under specific storage conditions (immediately processed [fresh], vs. frozen at -20C vs. -80C [2 weeks]); 2) fresh P-scRNA versus fresh D-scRNA; and 3) P-scRNA stored at -80C with fresh D-scRNA. ResultsSignificant recovery of loaded cells was achieved for fresh (80.9%) and -80C (48.5%) P-scRNA and D-scRNA (76.6%), but not -20C P-scRNA (3.7%). However, D-scRNA captures more typeable cells among recovered cells (71.5% vs. 15.8% Fresh and 18.4% -80C P-scRNA), and these cells exhibit higher gene coverage at the expense of higher mitochondrial read fractions across most cell types. Cells profiled using D-scRNA demonstrated more consistent gene expression profiles among the same cell type than those profiled using P-scRNA. Significant intra-cell-type differences were observed in profiled gene classes across platforms. ConclusionsOur results highlight non-overlapping advantages of P-scRNA and D-scRNA and underscore the need for innovation to enable high-fidelity capture of colonic epithelial cells. The platform-specific variation highlights the challenges of maintaining rigor and reproducibility across studies that use different platforms.

genomics↗

bioRxiv2023

Mechanistic modeling with a variational autoencoder for multimodal single-cell RNA sequencing data

We motivate and present biVI, which combines the variational autoencoder framework of scVI with biophysically motivated, bivariate models for nascent and mature RNA distributions. While previous approaches to integrate bimodal data via the variational autoencoder framework ignore the causal relationship between measurements, biVI models the biophysical processes that give rise to observations. We demonstrate through simulated benchmarking that biVI captures cell type structure in a low-dimensional space and accurately recapitulates parameter values and copy number distributions. On biological data, biVI provides a scalable route for identifying the biophysical mechanisms underlying gene expression. This analytical approach outlines a generalizable strateg for treating multimodal datasets generated by high-throughput, single-cell genomic assays.

biophysics↗

bioRxiv2022

RevGel-seq: instrument-free single-cell RNA sequencing using a reversible hydrogel for cell-specific barcoding

Progress in sample preparation for scRNA-seq is reported based on RevGel-seq, a reversible-hydrogel technology optimized for samples of fresh cells. Complexes of one cell paired with one barcoded bead are stabilized by a chemical linker and dispersed in a hydrogel in the liquid state. Upon gelation on ice the complexes are immobilized and physically separated without requiring nanowells or droplets. Cell lysis is triggered by detergent diffusion, and RNA molecules are captured on the adjacent barcoded beads for further processing with reverse transcription and preparation for cDNA sequencing. As a proof of concept, analysis of PBMC using RevGel-seq achieves results similar to microfluidic-based technologies when using the same original sample and the same data analysis software. In addition, a clinically relevant application of RevGel-seq is presented for pancreatic islet cells. Furthermore, characterizations carried out on cardiomyocytes demonstrate that the hydrogel technology readily accommodates very large cells. Standard analyses are in the 10,000-input cell range with the current gelation device, in order to satisfy common requirements for single-cell research. A convenient stopping point after two hours has been established by freezing at the cell lysis step, with full preservation of gene expression profiles. Overall, our results show that RevGel-seq represents an accessible and efficient instrument-free alternative, enabling flexibility in terms of experimental design and timing of sample processing, while providing broad coverage of cell types.

cell biology↗

bioRxiv2017

Defining the Transcriptional Landscape during Cytomegalovirus Latency with Single-Cell RNA Sequencing

Primary infection with human cytomegalovirus (HCMV) results in a lifelong infection due to its ability to establish latent infection, one characterized viral reservoir being hematopoietic cells. Although reactivation from latency causes serious disease in immunocompromised individuals, our molecular understanding of latency is limited. Here, we delineate viral gene expression during natural HCMV persistent infection by analyzing the massive RNA-seq atlas generated by the Genotype-Tissue Expression (GTEx) project. This systematic analysis reveals that HCMV persistence in-vivo is prevalent in diverse tissues. Unexpectedly, we find only viral transcripts that resemble gene expression during stages of lytic infection with no evidence of any highly restricted latency-associated viral gene expression program. To further define the transcriptional landscape during HCMV latent infection, we also used single cell RNA-seq and a tractable experimental latency model. In contrast to current views on latency, we also find no evidence for a specific restricted latency-associated viral gene expression program. Instead, we reveal that latency-associated gene expression largely mirrors a late lytic viral program albeit at much lower levels of expression. Overall, our work has the potential to revolutionize our understanding of HCMV persistence and suggests that latency is governed mainly by quantitative changes, with a limited number of qualitative changes, in viral gene expression.

microbiology↗

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↗