Cell Type Annotation Research Articles

A scalable sparse neural network framework for rare cell type annotation of single-cell transcriptome data

Automatic cell type annotation methods are increasingly used in single-cell RNA sequencing (scRNA-seq) analysis due to their fast and precise advantages. However, current methods often fail to account for the imbalance of scRNA-seq datasets and ignore information from smaller populations, leading to significant biological analysis errors. Here, we introduce scBalance, an integrated sparse neural network framework that incorporates adaptive weight sampling and dropout techniques for auto-annotation tasks. Using 20 scRNA-seq datasets with varying scales and degrees of imbalance, we demonstrate that scBalance outperforms current methods in both intra- and inter-dataset annotation tasks. Additionally, scBalance displays impressive scalability in identifying rare cell types in million-level datasets, as shown in the bronchoalveolar cell landscape. scBalance is also significantly faster than commonly used tools and comes in a user-friendly format, making it a superior tool for scRNA-seq analysis on the Python-based platform.

ProAct: quantifying the differential activity of biological processes in tissues, cells, and user-defined contexts.

The distinct functions and phenotypes of human tissues and cells derive from the activity of biological processes that varies in a context-dependent manner. Here, we present the Process Activity (ProAct) webserver that estimates the preferential activity of biological processes in tissues, cells, and other contexts. Users can upload a differential gene expression matrix measured across contexts or cells, or use a built-in matrix of differential gene expression in 34 human tissues. Per context, ProAct associates gene ontology (GO) biological processes with estimated preferential activity scores, which are inferred from the input matrix. ProAct visualizes these scores across processes, contexts, and process-associated genes. ProAct also offers potential cell-type annotations for cell subsets, by inferring them from the preferential activity of 2001 cell-type-specific processes. Thus, ProAct output can highlight the distinct functions of tissues and cell types in various contexts, and can enhance cell-type annotation efforts. The ProAct webserver is available at https://netbio.bgu.ac.il/ProAct/.

CIForm as a Transformer-based model for cell-type annotation of large-scale single-cell RNA-seq data.

Single-cell omics technologies have made it possible to analyze the individual cells within a biological sample, providing a more detailed understanding of biological systems. Accurately determining the cell type of each cell is a crucial goal in single-cell RNA-seq (scRNA-seq) analysis. Apart from overcoming the batch effects arising from various factors, single-cell annotation methods also face the challenge of effectively processing large-scale datasets. With the availability of an increase in the scRNA-seq datasets, integrating multiple datasets and addressing batch effects originating from diverse sources are also challenges in cell-type annotation. In this work, to overcome the challenges, we developed a supervised method called CIForm based on the Transformer for cell-type annotation of large-scale scRNA-seq data. To assess the effectiveness and robustness of CIForm, we have compared it with some leading tools on benchmark datasets. Through the systematic comparisons under various cell-type annotation scenarios, we exhibit that the effectiveness of CIForm is particularly pronounced in cell-type annotation. The source code and data are available at https://github.com/zhanglab-wbgcas/CIForm.

ScAnno: a deconvolution strategy-based automatic cell type annotation tool for single-cell RNA-sequencing data sets.

Undoubtedly, single-cell RNA sequencing (scRNA-seq) has changed the research landscape by providing insights into heterogeneous, complex and rare cell populations. Given that more such data sets will become available in the near future, their accurate assessment with compatible and robust models for cell type annotation is a prerequisite. Considering this, herein, we developed scAnno (scRNA-seq data annotation), an automated annotation tool for scRNA-seq data sets primarily based on the single-cell cluster levels, using a joint deconvolution strategy and logistic regression. We explicitly constructed a reference profile for human (30 cell types and 50 human tissues) and a reference profile for mouse (26 cell types and 50 mouse tissues) to support this novel methodology (scAnno). scAnno offers a possibility to obtain genes with high expression and specificity in a given cell type as cell type-specific genes (marker genes) by combining co-expression genes with seed genes as a core. Of importance, scAnno can accurately identify cell type-specific genes based on cell type reference expression profiles without any prior information. Particularly, in the peripheral blood mononuclear cell data set, the marker genes identified by scAnno showed cell type-specific expression, and the majority of marker genes matched exactly with those included in the CellMarker database. Besides validating the flexibility and interpretability of scAnno in identifying marker genes, we also proved its superiority in cell type annotation over other cell type annotation tools (SingleR, scPred, CHETAH and scmap-cluster) through internal validation of data sets (average annotation accuracy: 99.05%) and cross-platform data sets (average annotation accuracy: 95.56%). Taken together, we established the first novel methodology that utilizes a deconvolution strategy for automated cell typing and is capable of being a significant application in broader scRNA-seq analysis. scAnno is available at https://github.com/liuhong-jia/scAnno.

Datasets used in "Cell-type Annotation with Accurate Unseen Cell-type Identification Using Multiple References"

Datasets used in "Cell-type Annotation with Accurate Unseen Cell-type Identification Using Multiple References"

Single-cell RNA sequencing of mouse lower respiratory tract epithelial cells: A meta-analysis

The respiratory system is a vital component of our body, essential for both oxygen uptake and immune defense. Knowledge of cellular composition and function in different parts of the respiratory tract provides the basis for a better understanding of the pathological processes involved in various diseases such as chronic respiratory diseases and cancer. Single-cell RNA sequencing (scRNA-seq) is a proficient approach for the identification and transcriptional characterization of cellular phenotypes. Although the mouse is an essential tool for the study of lung development, regeneration, and disease, a scRNA-seq mouse atlas of the lung in which all epithelial cell types are included and annotated systematically is lacking. Here, we established a single-cell transcriptome landscape of the mouse lower respiratory tract by performing a meta-analysis of seven different studies in which mouse lungs and trachea were analyzed by droplet and/or plate-based scRNA-seq technologies. We provide information on the best markers for each epithelial cell type, propose surface markers for the isolation of viable cells, harmonized the annotation of cell types, and compare the mouse single-cell transcriptomes with human scRNA-seq data of the lung.

Identification of ulcerative colitis-specific immune cell signatures from public single-cell RNA-seq data.

Single-cell RNA-seq enabled microscopic studies on tissue microenvironment of many diseases. Inflammatory bowel disease, an autoimmune disease, is involved with various dysfunction of immune cells, for which single-cell RNA-seq may provide us a deeper insight into the causes and mechanism of this complex disease. In this work, we used public single-cell RNA-seq data to study tissue microenvironment around ulcerative colitis, an inflammatory bowel disease causing chronic inflammation and ulcers in large intestine. Since not all the datasets provide cell-type annotations, we first identified cell identities to select cell populations of our interest. Differentially expressed genes and gene set enrichment analysis was then performed to infer the polarization/activation state of macrophages and T cells. Cell-to-cell interaction analysis was also performed to discover distinct interactions in ulcerative colitis. Differentially expressed genes analysis of the two datasets confirmed the regulation of CTLA4, IL2RA, and CCL5 genes in the T cell subset and regulation of S100A8/A9, CLEC10A genes in macrophages. Cell-to-cell interaction analysis showed CD4+ T cells and macrophages interact actively to each other. We also identified IL-18 pathway activation in inflammatory macrophages, evidence that CD4+ T cells induce Th1 and Th2 differentiation, and also found that macrophages regulate T cell activation through different ligand-receptor pairs, viz. CD86-CTL4, LGALS9-CD47, SIRPA-CD47, and GRN-TNFRSF1B. Analysis of these immune cell subsets may suggest novel strategies for the treatment of inflammatory bowel disease.

Single-Cell and Spatial Transcriptomic Analysis of Human Skin Delineates Intercellular Communication and Pathogenic Cells

Epidermal homeostasis is governed by a balance between keratinocyte proliferation and differentiation with contributions from cell-cell interactions, but conserved or divergent mechanisms governing this equilibrium across species and how an imbalance contributes to skin disease are largely undefined. To address these questions, human skin single-cell RNA sequencing and spatial transcriptomics data were integrated and compared with mouse skin data. Human skin cell-type annotation was improved using matched spatial transcriptomics data, highlighting the importance of spatial context in cell-type identity, and spatial transcriptomics refined cellular communication inference. In cross-species analyses, we identified a human spinous keratinocyte subpopulation that exhibited proliferative capacity and a heavy metal processing signature, which was absent in mouse and may account for species differences in epidermal thickness. This human subpopulation was expanded in psoriasis and zinc-deficiency dermatitis, attesting to disease relevance and suggesting a paradigm of subpopulation dysfunction as a hallmark of the disease. To assess additional potential subpopulation drivers of skin diseases, we performed cell-of-origin enrichment analysis within genodermatoses, nominating pathogenic cell subpopulations and their communication pathways, which highlighted multiple potential therapeutic targets. This integrated dataset is encompassed in a publicly available web resource to aid mechanistic and translational studies of normal and diseased skin.

MASI enables fast model-free standardization and integration of single-cell transcriptomics data

Single-cell transcriptomics datasets from the same anatomical sites generated by different research labs are becoming increasingly common. However, fast and computationally inexpensive tools for standardization of cell-type annotation and data integration are still needed in order to increase research inclusivity. To standardize cell-type annotation and integrate single-cell transcriptomics datasets, we have built a fast model-free integration method, named MASI (Marker-Assisted Standardization and Integration). We benchmark MASI with other well-established methods and demonstrate that MASI outperforms other methods, in terms of integration, annotation, and speed. To harness knowledge from single-cell atlases, we demonstrate three case studies that cover integration across biological conditions, surveyed participants, and research groups, respectively. Finally, we show MASI can annotate approximately one million cells on a personal laptop, making large-scale single-cell data integration more accessible. We envision that MASI can serve as a cheap computational alternative for the single-cell research community.

TripletCell: a deep metric learning framework for accurate annotation of cell types at the single-cell level.

Single-cell RNA sequencing (scRNA-seq) has significantly accelerated the experimental characterization of distinct cell lineages and types in complex tissues and organisms. Cell-type annotation is of great importance in most of the scRNA-seq analysis pipelines. However, manual cell-type annotation heavily relies on the quality of scRNA-seq data and marker genes, and therefore can be laborious and time-consuming. Furthermore, the heterogeneity of scRNA-seq datasets poses another challenge for accurate cell-type annotation, such as the batch effect induced by different scRNA-seq protocols and samples. To overcome these limitations, here we propose a novel pipeline, termed TripletCell, for cross-species, cross-protocol and cross-sample cell-type annotation. We developed a cell embedding and dimension-reduction module for the feature extraction (FE) in TripletCell, namely TripletCell-FE, to leverage the deep metric learning-based algorithm for the relationships between the reference gene expression matrix and the query cells. Our experimental studies on 21 datasets (covering nine scRNA-seq protocols, two species and three tissues) demonstrate that TripletCell outperformed state-of-the-art approaches for cell-type annotation. More importantly, regardless of protocols or species, TripletCell can deliver outstanding and robust performance in annotating different types of cells. TripletCell is freely available at https://github.com/liuyan3056/TripletCell. We believe that TripletCell is a reliable computational tool for accurately annotating various cell types using scRNA-seq data and will be instrumental in assisting the generation of novel biological hypotheses in cell biology.

Cell-type classification of cancer single-cell RNA-seq data using the Subsemble ensemble-based machine learning classifier

Background The advent of single-cell RNA sequencing (scRNA-seq) has provided a high-resolution overview of the cellular heterogeneity of different tissue types. Manual cell type annotation of gene expression datasets remains a useful but time-intensive task. Ensemble machine learning methods leverage the predictive power of multiple classifiers and can be applied to classify high-dimensional gene expression data. Here, we present a novel application of the Subsemble supervised ensemble machine learning classifier used to classify novel cells with known cell type labels using gene expression data. Methods First, we tested the classification performance of different pre-processing steps used to normalize and upsample the training dataset for the Subsemble using a colorectal cancer dataset. Second, we conducted a cross-validated performance benchmark of the Subsemble classifier compared to nine other cell type classification methods across five metrics tested, using an acute myeloid leukemia dataset. Third, we conducted a comparative performance benchmark of the Subsemble classifier using a patient-based leave-one-out cross-validation scheme. Rank normalized scores were calculated for each classifier to aggregate performance across multiple metrics. Results The Subsemble classifier performed best when trained on a dataset that was log-transformed then upsampled to generate balanced class distributions. The Subsemble classifier was consistently the top-ranked classifier across five classification performance metrics compared to the nine other baseline classifiers and showed an improvement in performance as the training dataset increased. When tested using the patient-based leave-one-out cross-validation scheme, the Subsemble was the top-ranked classifier based on rank normalized scores. Conclusions Our proof-of-concept study showed that the Subsemble classifier can be used to accurately predict known cell type labels from single-cell gene expression data. The top-ranked classification performance of the Subsemble across two validation datasets, two cross-validation schemes, and five performance metrics motivates future development of accurate ensemble classifiers of scRNA-seq datasets.

Characterization of altered molecular mechanisms in Parkinson's disease through cell type-resolved multiomics analyses.

Parkinson's disease (PD) is a progressive neurodegenerative disorder. However, cell type-dependent transcriptional regulatory programs responsible for PD pathogenesis remain elusive. Here, we establish transcriptomic and epigenomic landscapes of the substantia nigra by profiling 113,207 nuclei obtained from healthy controls and patients with PD. Our multiomics data integration provides cell type annotation of 128,724 cis-regulatory elements (cREs) and uncovers cell type-specific dysregulations in cREs with a strong transcriptional influence on genes implicated in PD. The establishment of high-resolution three-dimensional chromatin contact maps identifies 656 target genes of dysregulated cREs and genetic risk loci, uncovering both potential and known PD risk genes. Notably, these candidate genes exhibit modular gene expression patterns with unique molecular signatures in distinct cell types, highlighting altered molecular mechanisms in dopaminergic neurons and glial cells including oligodendrocytes and microglia. Together, our single-cell transcriptome and epigenome reveal cell type-specific disruption in transcriptional regulations related to PD.

SigPrimedNet: A Signaling-Informed Neural Network for scRNA-seq Annotation of Known and Unknown Cell Types.

Single-cell RNA sequencing is increasing our understanding of the behavior of complex tissues or organs, by providing unprecedented details on the complex cell type landscape at the level of individual cells. Cell type definition and functional annotation are key steps to understanding the molecular processes behind the underlying cellular communication machinery. However, the exponential growth of scRNA-seq data has made the task of manually annotating cells unfeasible, due not only to an unparalleled resolution of the technology but to an ever-increasing heterogeneity of the data. Many supervised and unsupervised methods have been proposed to automatically annotate cells. Supervised approaches for cell-type annotation outperform unsupervised methods except when new (unknown) cell types are present. Here, we introduce SigPrimedNet an artificial neural network approach that leverages (i) efficient training by means of a sparsity-inducing signaling circuits-informed layer, (ii) feature representation learning through supervised training, and (iii) unknown cell-type identification by fitting an anomaly detection method on the learned representation. We show that SigPrimedNet can efficiently annotate known cell types while keeping a low false-positive rate for unseen cells across a set of publicly available datasets. In addition, the learned representation acts as a proxy for signaling circuit activity measurements, which provide useful estimations of the cell functionalities.

Spatial heterogeneity and Immune infiltration of cellular lysosomal pathways reveals a new blueprint for tumor heterogeneity in esophageal cancer.

Esophageal squamous cell carcinoma (ESCC) is a common Malignant tumor of digestive tract which have a potential association with lysosomal pathway. The purpose of this study was to explore the correlation between lysosome pathway and immune infiltration of ESCC. The cell type annotation of ESCC patients and the distribution of their gene markers were analyzed by single cell data. They were also grouped according to the expression of lysosomal pathways. Gene set variation analysis (GSVA) enriched pathway scoring, Cellchat cell communication was performed to demonstrate the tumour-associated pathway scores and interactions of different cell populations. Relevant differential genes were screened, prognostic risk markers were constructed and direct associations of lysosomal pathway-related gene risk scores with immune infiltration and tumour treatment drug sensitivity were assessed by algorithms. In cellular experiments, qPCR and flow cytometry were used to assess the role of the lysosomal pathway gene-MT1X on tumour cell development. ESCC single cell data were annotated into 7 Cluster clusters by t-sne downscaling analysis. Cellchat analysis revealed that the "MIF" cellular communication network is the main communication mode of the lysosomal pathway in ESCC cells. The lysosomal pathway genetic risk model was found to be significantly different from ESCC prognosis in both the training and validation groups. The lysosome pathway gene risk model was associated with treatment resistance in ESCC patients using oncopredict R package. The correlation between the expression of lysosomal-DEG and tumour immune infiltration and immune cell types by the MCPcounter method. Cellular assays showed that the lysosomal pathway gene MT1X was less expressed in oesophageal cancer cells than in normal oesophageal epithelial cells. Knockdown of MT1X significantly promoted the growth rate of oesophageal cancer cells. Based on the single cell sequencing technology and transcriptomic analysis, we confirmed that there is a close association between the lysosomal pathway and the immune infiltration and treatment sensitivity of ESCC, which may be a potential target for a new direction of ESCC therapy.

Adjustments to the reference dataset design improve cell type label transfer.

The transfer of cell type labels from pre-annotated (reference) to newly collected data is an important task in single-cell data analysis. As the number of publicly available annotated datasets which can be used as reference, as well as the number of computational methods for cell type label transfer are constantly growing, rationals to understand and decide which reference design and which method to use for a particular query dataset are needed. Using detailed data visualisations and interpretable statistical assessments, we benchmark a set of popular cell type annotation methods, test their performance on different cell types and study the effects of the design of reference data (e.g., cell sampling criteria, inclusion of multiple datasets in one reference, gene set selection) on the reliability of predictions. Our results highlight the need for further improvements in label transfer methods, as well as preparation of high-quality pre-annotated reference data of adequate sampling from all cell types of interest, for more reliable annotation of new datasets.

Abstract 6622: Developing spatial molecular correlates of response to immunotherapy in kidney cancer

Abstract Immunotherapy has emerged as a promising new therapy for kidney cancer with durable clinical responses in a subset of the patients. However, discovery of biomarkers that predict patient response to immunotherapy has thus far been unsuccessful. Diverse sets of biomarkers have been proposed, (e.g., PD-L1 immunohistochemistry, tumor mutation burden, gene expression signatures), but have failed to validate in clinical studies. There is an urgent need to identify predictive biomarkers for selecting kidney cancer patients most likely to respond to immunotherapy. Spatial biology technologies provide molecular data at single cell resolution along with spatial coordinates in patient’s tumor slides. We hypothesized that the presence of immune cell types and their spatial 2-D patterns in a histology image could potentially predict response to immunotherapy. For this project we are using Tissue Microarray (TMAs). We are using two cohorts of data: (Dataset-1) 70 clear cell renal cell carcinoma (CCRCC) patients treated with checkpoint inhibitors, and (Dataset-2) 180 patients, early stage papillary RCC (PRCC) cohort. Dataset-1 was assayed using CosMx (Nanostring technologies) for a RNA panel of 980 immune-related genes. Dataset-2 was assayed with PhenoCycler (Akoya Biosciences) for a 31 marker protein panel with immune-related genes. We have developed methodology and novel algorithms to process both the datasets and to identify spatial phenotypes. Our pipelines include QC, normalization, dimensionality reduction and clustering. For annotation of cell types, we used as reference single cell datasets from RCC to identify tumor-type specific cell types. These cell types were mapped back to their spatial coordinates and visualized. We verified the annotations based on visual checks of their cellular morphology and organization. Next, we identified cellular neighborhoods around each cell and identified spatial clusters. We also performed a network-based analysis to identify additional spatial patterns. We performed correlation analysis with clinical phenotypes and identified spatial clusters of immune cell subtypes located in proximity of cancer cells to be associated with response to therapy. Citation Format: Kathryn Beckermann, Scott Haake, Alex Nesta, Michael Caponegro, N R Nirmala, Anupama Reddy. Developing spatial molecular correlates of response to immunotherapy in kidney cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 6622.

Abstract 1241: A single cell landscape of malignant pancreas cancer ascites

Abstract Despite advancement in surgical and medical management of pancreatic ductal adenocarcinoma (PDAC), this tumor type is currently the 3rd leading cause of cancer-related death in the United States and expected to become the 2nd by 2030. The development of ascites in PDAC patients generally indicates a poor prognosis although the reasons for this largely remain unknown. The exact mechanism of peritoneal spread of the cancer is still largely unknown. Herein, we collected and processed sterile ascites samples of 20 PDAC samples from 19 patients and performed single-cell RNA-seq analysis. Sixteen ascites samples were obtained from abdominal paracentesis and two cases in the postmortem period. For one patient ascites was collected both pre and postmortem. All but one patient received chemotherapy prior to ascites collection. Routine clinical cytology evaluation of ascites samples from 17 patients indicated it was malignant in 11 patients and nonmalignant in six patients. All ascites samples were purified by gradient centrifuge using Ficoll-Paque Plus to remove erythrocytes and necrotic debris. DAPI negative and Calcein positive cells were selected to enrich live cells from the cell suspension by flow sorting. We adopted the 10x Genomics Chromium Single Cell Gene Expression platform for single-cell RNA-seq library preparation. Scanpy was used for computational analysis of expression data after Cell Ranger pipelines. After QC check, normalization and doublet elimination, a total of 248,263 cells were analyzed. UMAP projection accurately clustered cells from each patient. Cell type annotation identified large numbers of immune cells compared to cancer cells and mesothelial cells with the most dominant population being macrophages. A subpopulation (0.23% of all cells) representing plasmacytoid dendritic cells was also found. Cancer cells were detected in twelve samples whereas no cancer cells were detected in eight samples after exhaustive marker gene exploration, and these findings largely matched the clinical diagnoses for each sample. In the ongoing period we will compare malignant versus non-malignant ascites by differential gene expression analysis, gene set enrichment analysis and trajectory inference to determine a) the extent the immunological clusters are enriched in malignant versus benign ascites, b) the distinct phenotypes and cell states of PDAC cells in immune rich versus immune poor ascites, c) role of plasmacytoid dendritic cells or mesothelial cells contribution to formation of malignant ascites. We believe our results will shed light on the dynamics of ascites formation and in turn identify novel targets for intervention. Citation Format: Shigeaki Umeda, Elias- Ramzey Karnoub, Etay Ziv, Jean Wu, Cristian Cruz, Wungki Park, Fiyinfolu Balogun, Alejandro Jiménez- Sánchez, Ronan Chaligné, Eileen M. O'Reilly, Christine A. Iacobuzio- Donahue. A single cell landscape of malignant pancreas cancer ascites [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1241.

Single-nucleus sequencing deciphers developmental trajectories in rice pistils.

Angiosperms possess a life cycle with an alternation of sporophyte and gametophyte generations, which happens in plant organs like pistils. Rice pistils contain ovules and receive pollen for successful fertilization to produce grains. The cellular expression profile in rice pistils is largely unknown. Here, we show a cell census of rice pistils before fertilization through the use of droplet-based single-nucleus RNA sequencing. The ab initio marker identification validated by in situ hybridization assists with cell-type annotation, revealing cell heterogeneity between ovule- and carpel-originated cells. A comparison of 1N (gametophyte) and 2N (sporophyte) nuclei identifies the developmental path of germ cells in ovules with typical resetting of pluripotency before the sporophyte-gametophyte transition, while trajectory analysis of carpel-originated cells suggests previously neglected features of epidermis specification and style function. These findings gain a systems-level view of cellular differentiation and development of rice pistils before flowering and lay a foundation for understanding female reproductive development in plants.

MP60-14 A CELLULAR ATLAS OF THE NORMAL ADULT HUMAN FEMALE URETHRA

You have accessJournal of UrologyCME1 Apr 2023MP60-14 A CELLULAR ATLAS OF THE NORMAL ADULT HUMAN FEMALE URETHRA John Lafin, Alicia Malewska, Philippe Zimmern, Maude Carmel, Gary Lemack, Douglas Strand, and Ramy Goueli John LafinJohn Lafin More articles by this author , Alicia MalewskaAlicia Malewska More articles by this author , Philippe ZimmernPhilippe Zimmern More articles by this author , Maude CarmelMaude Carmel More articles by this author , Gary LemackGary Lemack More articles by this author , Douglas StrandDouglas Strand More articles by this author , and Ramy GoueliRamy Goueli More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003318.14AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Urinary voiding dysfunction is highly prevalent in aging women. A deeper understanding of the cellular composition of female urethra in the non-diseased state could provide insights into the etiology of dysfunction. Single cell RNA sequencing was performed on female urethra to generate a cellular atlas. METHODS: Urethra from female organ donors aged 18-45 (N=4) was collected fresh from surgery and enzymatically digested into a single cell suspension. Single cell RNA sequencing was performed using 10x Genomics V3 chemistry. Data were analyzed in Seurat for cell type annotation. Immunohistochemical validation of cell types was performed on FFPE sections using differentially expressed genes from clustered data. RESULTS: The female proximal urethral lumen is lined with a stratified urothelium composed of KRT5+/KRT14- basal cells with KRT13+ hillock epithelia and KRT4+ luminal epithelia. Ducts emerging from dorsal urethra (Skene's glands) contain basal and club cells similar to prostatic urethra. The urethral lumen is surrounded by two discrete layers of fibroblasts (peri-urethral and interstitial) and circumferential smooth muscle and skeletal muscle. CONCLUSIONS: The identification of 1) specialized fibroblasts surrounding the urethra, and 2) club cells in dorsal ducts could yield new insight into the regulation of infection, inflammation and obstruction, which are the putative etiologies of most urethral dysfunction. Source of Funding: DK115477, DK104310, DK129994 © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e848 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information John Lafin More articles by this author Alicia Malewska More articles by this author Philippe Zimmern More articles by this author Maude Carmel More articles by this author Gary Lemack More articles by this author Douglas Strand More articles by this author Ramy Goueli More articles by this author Expand All Advertisement PDF downloadLoading ...

Integrated Analysis of Tracheobronchial Fluid from Before and After Cardiopulmonary Bypass Reveals Activation of the Integrated Stress Response and Altered Pulmonary Microvascular Permeability.

Objective: We aim to comprehensively describe the transcriptional activity and signaling of pulmonary parenchymal and immune cells before and after cardiopulmonary bypass (CPB) by using a multi-omic approach coupled with functional cellular assays. We hypothesize that key signaling pathways from specific cells within the lung alter pulmonary endothelial cell function resulting in worsening or improving disease. Methods: We collected serial tracheobronchial lavage samples from intubated patients less than 2-years-old undergoing surgery with CPB. Samples were immediately processed for single cell RNA sequencing (10x Genomics). Cell clustering, cell-type annotation, and visualization were performed, and differentially expressed genes (DEG) between serial samples were identified. Metabolomic and proteomic analyses were performed on the supernatant using mass spectrometry and a multiplex assay (SomaScan) respectively. Functional assays were done using electric cell-substrate impedance sensing to measure resistance across human pulmonary microvascular endothelial cells (HPMECs). Results: Analysis of eight patients showed a heterogeneous mixture of pulmonary parenchymal and immune cells. Cell clustering demonstrated time-dependent changes in the transcriptomic signature indicating altered cellular phenotypes after CPB. DEG analysis was represented by genes involved in host defense, innate immunity, and the mitochondrial respiratory transport chain. Ingenuity pathway analysis showed upregulation of the integrated stress response across all cell types after CPB. Metabolomic analysis demonstrated upregulation of ascorbate and aldarate metabolism. Unbiased proteomic analysis revealed upregulation of proteins involved in cytokine and chemokine pathways. Post-CPB patient supernatant improved HMPEC barrier function, suggesting a protective cellular response to CPB. Conclusion: Children who undergo CPB for cardiac surgery have distinct cell populations, transcriptional activity, and metabolism that change over time. The response to ischemia-reperfusion injury in the lower airway of children appears to be protective, with the need to identify potential targets through future investigations.