Single-cell RNA-Aequencing Reveals Novel Myofibroblasts with Epithelial Cell-Like Features in the Mammary Gland of Dairy Cattle

Abstract Background: Cow’s milk is a highly-nutritious dairy product that is widely consumed worldwide. It is secreted by the developed mammary gland (MG) of dairy cattle. However, a comprehensive understanding of cell-type diversity and cell function within bovine MG is lacking. In the current study, we used single-cell RNA sequencing to investigate the transcriptome of 24,472 high-quality MG cells isolated from newborn and adult cows. Results: Unbiased clustering analysis revealed the existence of 24 cell types, which could be divided into four categories: 9 immune, 3 epithelial, 9 fibroblast, and 3 endothelial cell types. Other cell subtypes were further identified based on re-clustering and pseudotemporal reconstruction of epithelial cells that included 3 mature luminal epithelial, 1 intermediate, and 2 progenitor cell subtypes. The individual top marker genes of these 3 mature luminal epithelial cell subtypes (L0, L1, and L5) were APOA1, STC2, and PTX3, which were further validated using immunofluorescence. Based on functional analysis, the L0, L1, and L5 cell subtypes were all involved in the upregulation of lipid metabolism, protein and hormone metabolism, and the immune response, respectively. Furthermore, we discovered a novel myofibroblast that expresses COL1A1 and CSN3, has visible epithelial-like characteristics, and shows the potential to differentiate into luminal epithelial cells, especially immune-sensing luminal cells (L5). Conclusions: We constructed the first single-cell atlas of the dairy cow MG, and our new findings of epithelial-like myofibroblast cells and their differentiation trajectories into luminal cells may provide novel insights into the development and lactogenesis in dairy cattle MGs.

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Aging-associated alterations in the mammary gland revealed by single-cell RNA sequencing

10.1101/773408 ◽

2019 ◽

Cited By ~ 2

Author(s):

Carman Man-Chung Li ◽

Hana Shapiro ◽

Christina Tsiobikas ◽

Laura Selfors ◽

Huidong Chen ◽

...

Keyword(s):

Mammary Gland ◽

Single Cell ◽

Rna Sequencing ◽

Expression Patterns ◽

Cell Types ◽

Single Cell Rna Sequencing ◽

Numerous Cell ◽

Luminal Cells ◽

Increased Susceptibility ◽

Mammary Tissues

AbstractAging of the mammary gland is closely associated with increased susceptibility to diseases such as cancer, but there have been limited systematic studies of aging-induced alterations within this organ. We performed high-throughput single-cell RNA-sequencing (scRNA-seq) profiling of mammary tissues from young and old nulliparous mice, including both epithelial and stromal cell types. Our analysis identified altered proportions and distinct gene expression patterns in numerous cell populations as a consequence of the aging process, independent of parity and lactation. In addition, we detected a subset of luminal cells that express both hormone-sensing and alveolar markers and decrease in relative abundance with age. These data provide a high-resolution landscape of aging mammary tissues, with potential implications for normal tissue functions and cancer predisposition.

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Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states

Nature Communications ◽

10.1038/s41467-021-27322-4 ◽

2022 ◽

Vol 13 (1) ◽

Author(s):

Hanbing Song ◽

Hannah N. W. Weinstein ◽

Paul Allegakoen ◽

Marc H. Wadsworth ◽

Jamie Xie ◽

...

Keyword(s):

Prostate Cancer ◽

Tumor Microenvironment ◽

Epithelial Cell ◽

Epithelial Cells ◽

Single Cell ◽

Single Cell Analysis ◽

Cell Types ◽

Prostate Biopsies ◽

Prostate Carcinogenesis ◽

Cancer Tumor

AbstractProstate cancer is the second most common malignancy in men worldwide and consists of a mixture of tumor and non-tumor cell types. To characterize the prostate cancer tumor microenvironment, we perform single-cell RNA-sequencing on prostate biopsies, prostatectomy specimens, and patient-derived organoids from localized prostate cancer patients. We uncover heterogeneous cellular states in prostate epithelial cells marked by high androgen signaling states that are enriched in prostate cancer and identify a population of tumor-associated club cells that may be associated with prostate carcinogenesis. ERG-negative tumor cells, compared to ERG-positive cells, demonstrate shared heterogeneity with surrounding luminal epithelial cells and appear to give rise to common tumor microenvironment responses. Finally, we show that prostate epithelial organoids harbor tumor-associated epithelial cell states and are enriched with distinct cell types and states from their parent tissues. Our results provide diagnostically relevant insights and advance our understanding of the cellular states associated with prostate carcinogenesis.

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Single-cell RNA-seq analysis reveals lung epithelial cell-specific contributions of Tet1 to allergic inflammation

10.1101/2021.12.22.473869 ◽

2021 ◽

Author(s):

Tao Zhu ◽

Anthony P Brown ◽

Lucy Cai ◽

Gerald Quon ◽

Hong Ji

Keyword(s):

Epithelial Cell ◽

Epithelial Cells ◽

Single Cell ◽

Lung Inflammation ◽

Trajectory Analysis ◽

Cell Types ◽

Lung Epithelial Cells ◽

Lung Epithelial Cell ◽

Alveolar Type ◽

Lung Epithelial

Background: Tet1 protects against house dust mite (HDM)-induced lung inflammation in mice and alters the lung methylome and transcriptome. We explored the role of Tet1 in individual lung epithelial cell types in HDM-induced inflammation. Methods: A model of HDM-induced lung inflammation was established in Tet1 knockout and littermate wildtype mice. EpCAM+ lung epithelial cells were isolated. Libraries were generated using the 10X Chromium workflow and sequenced. ScRNA-seq analysis was performed using Cell Ranger, scAlign, and Seurat. Cell types were labeled using known markers. Enriched pathways were identified using Ingenuity Pathway Analysis. Transcription factor (TF) activity was analyzed by DoRothEA. Single-cell trajectory analysis was performed with Monocle to explore Alveolar type 2 (AT2) cell differentiation. Results: AT2 cells were the most abundant among the eight EpCAM+ lung epithelial cell types. HDM challenge increased the percentage of alveolar progenitor cells (AP), broncho alveolar stem cells (BAS), and goblet cells, and decreased the percentage of AT2 and ciliated cells. Bulk and cell-type-specific analysis identified genes subject to Tet1 regulation and linked to augmented lung inflammation, including alarms, detoxification enzymes and oxidative stress response genes, and gene in tissue repair. The transcriptomic regulation was accompanied by alterations in TF activities. Trajectory analysis supports that HDM may enhance the differentiation of AP and BAS cells into AT2 cells, independent of Tet1. Conclusions: Collectively, lung epithelial cells had common and unique transcriptomic signatures of allergic lung inflammation. Tet1 deletion altered transcriptomic networks in various lung epithelial cells, with an overall effect of promoting allergen-induced lung inflammation.

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Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states

10.1101/2020.11.06.359802 ◽

2020 ◽

Author(s):

Hanbing Song ◽

Hannah N.W. Weinstein ◽

Paul Allegakoen ◽

Marc H. Wadsworth ◽

Jamie Xie ◽

...

Keyword(s):

Prostate Cancer ◽

Tumor Microenvironment ◽

Epithelial Cell ◽

Epithelial Cells ◽

Single Cell ◽

Single Cell Analysis ◽

Cell Types ◽

Prostate Biopsies ◽

Prostate Epithelial Cells ◽

Cancer Tumor

AbstractProstate cancer is the second most common malignancy in men worldwide and consists of a mixture of tumor and non-tumor cell types. To characterize the prostate cancer tumor microenvironment, we performed single-cell RNA-sequencing on prostate biopsies, prostatectomy specimens, and patient-derived organoids from localized prostate cancer patients. We identify a population of tumor-associated club cells that may act as progenitor cells and uncover heterogeneous cellular states in prostate epithelial cells marked by high androgen signaling states that are enriched in prostate cancer. ERG- tumor cells, compared to ERG+ cells, demonstrate shared heterogeneity with surrounding luminal epithelial cells and appear to give rise to common tumor microenvironment responses. Finally, we show that prostate epithelial organoids recapitulate tumor-associated epithelial cell states and are enriched with distinct cell types and states from their parent tissues. Our results provide diagnostically relevant insights and advance our understanding of the cellular states associated with prostate carcinogenesis.

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Molecular characteristics and spatial distribution of adult human corneal cell subtypes

Scientific Reports ◽

10.1038/s41598-021-94933-8 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Ann J. Ligocki ◽

Wen Fury ◽

Christian Gutierrez ◽

Christina Adler ◽

Tao Yang ◽

...

Keyword(s):

Single Cell ◽

Rna Sequencing ◽

Cross Sections ◽

Cell Types ◽

Marker Genes ◽

Molecular Characteristics ◽

Transcriptional Level ◽

Human Cornea ◽

Adult Human ◽

And Migration

AbstractBulk RNA sequencing of a tissue captures the gene expression profile from all cell types combined. Single-cell RNA sequencing identifies discrete cell-signatures based on transcriptomic identities. Six adult human corneas were processed for single-cell RNAseq and 16 cell clusters were bioinformatically identified. Based on their transcriptomic signatures and RNAscope results using representative cluster marker genes on human cornea cross-sections, these clusters were confirmed to be stromal keratocytes, endothelium, several subtypes of corneal epithelium, conjunctival epithelium, and supportive cells in the limbal stem cell niche. The complexity of the epithelial cell layer was captured by eight distinct corneal clusters and three conjunctival clusters. These were further characterized by enriched biological pathways and molecular characteristics which revealed novel groupings related to development, function, and location within the epithelial layer. Moreover, epithelial subtypes were found to reflect their initial generation in the limbal region, differentiation, and migration through to mature epithelial cells. The single-cell map of the human cornea deepens the knowledge of the cellular subsets of the cornea on a whole genome transcriptional level. This information can be applied to better understand normal corneal biology, serve as a reference to understand corneal disease pathology, and provide potential insights into therapeutic approaches.

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Chemerin Regulates Epithelial Barrier Function of Mammary Glands in Dairy Cows

Animals ◽

10.3390/ani11113194 ◽

2021 ◽

Vol 11 (11) ◽

pp. 3194

Author(s):

Yutaka Suzuki ◽

Sachi Chiba ◽

Koki Nishihara ◽

Keiichi Nakajima ◽

Akihiko Hagino ◽

...

Keyword(s):

Mammary Gland ◽

Epithelial Cell ◽

Epithelial Cells ◽

Dairy Cows ◽

Barrier Function ◽

Mammary Epithelial Cells ◽

Mammary Epithelial ◽

Epithelial Barrier ◽

Epithelial Tissue ◽

Epithelial Barrier Function

Epithelial barrier function in the mammary gland acts as a forefront of the defense mechanism against mastitis, which is widespread and a major disorder in dairy production. Chemerin is a chemoattractant protein with potent antimicrobial ability, but its role in the mammary gland remains unelucidated. The aim of this study was to determine the function of chemerin in mammary epithelial tissue of dairy cows in lactation or dry-off periods. Mammary epithelial cells produced chemerin protein, and secreted chemerin was detected in milk samples. Chemerin treatment promoted the proliferation of cultured bovine mammary epithelial cells and protected the integrity of the epithelial cell layer from hydrogen peroxide (H2O2)-induced damage. Meanwhile, chemerin levels were higher in mammary tissue with mastitis. Tumor necrosis factor alpha (TNF-α) strongly upregulated the expression of the chemerin-coding gene (RARRES2) in mammary epithelial cells. Therefore, chemerin was suggested to support mammary epithelial cell growth and epithelial barrier function and to be regulated by inflammatory stimuli. Our results may indicate chemerin as a novel therapeutic target for diseases in the bovine mammary gland.

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JIND: Joint Integration and Discrimination for Automated Single-Cell Annotation

10.1101/2020.10.06.327601 ◽

2020 ◽

Author(s):

Mohit Goyal ◽

Guillermo Serrano ◽

Ilan Shomorony ◽

Mikel Hernaez ◽

Idoia Ochoa

Keyword(s):

Single Cell ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Rna Seq ◽

Batch Effects ◽

Cell Type ◽

Latent Space ◽

Cell Type Specific ◽

Low Dimensional

AbstractSingle-cell RNA-seq is a powerful tool in the study of the cellular composition of different tissues and organisms. A key step in the analysis pipeline is the annotation of cell-types based on the expression of specific marker genes. Since manual annotation is labor-intensive and does not scale to large datasets, several methods for automated cell-type annotation have been proposed based on supervised learning. However, these methods generally require feature extraction and batch alignment prior to classification, and their performance may become unreliable in the presence of cell-types with very similar transcriptomic profiles, such as differentiating cells. We propose JIND, a framework for automated cell-type identification based on neural networks that directly learns a low-dimensional representation (latent code) in which cell-types can be reliably determined. To account for batch effects, JIND performs a novel asymmetric alignment in which the transcriptomic profile of unseen cells is mapped onto the previously learned latent space, hence avoiding the need of retraining the model whenever a new dataset becomes available. JIND also learns cell-type-specific confidence thresholds to identify and reject cells that cannot be reliably classified. We show on datasets with and without batch effects that JIND classifies cells more accurately than previously proposed methods while rejecting only a small proportion of cells. Moreover, JIND batch alignment is parallelizable, being more than five or six times faster than Seurat integration. Availability: https://github.com/mohit1997/JIND.

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Revealing immune responses in the Mycobacterium avium subsp. paratuberculosis-infected THP-1 cells using single cell RNA-sequencing

PLoS ONE ◽

10.1371/journal.pone.0254194 ◽

2021 ◽

Vol 16 (7) ◽

pp. e0254194

Author(s):

Hong-Tae Park ◽

Woo Bin Park ◽

Suji Kim ◽

Jong-Sung Lim ◽

Gyoungju Nah ◽

...

Keyword(s):

Crohn’S Disease ◽

Crohn's Disease ◽

Single Cell ◽

Mycobacterium Avium ◽

Expression Patterns ◽

Cell Types ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Cytokines And Chemokines

Mycobacterium avium subsp. paratuberculosis (MAP) is a causative agent of Johne’s disease, which is a chronic and debilitating disease in ruminants. MAP is also considered to be a possible cause of Crohn’s disease in humans. However, few studies have focused on the interactions between MAP and human macrophages to elucidate the pathogenesis of Crohn’s disease. We sought to determine the initial responses of human THP-1 cells against MAP infection using single-cell RNA-seq analysis. Clustering analysis showed that THP-1 cells were divided into seven different clusters in response to phorbol-12-myristate-13-acetate (PMA) treatment. The characteristics of each cluster were investigated by identifying cluster-specific marker genes. From the results, we found that classically differentiated cells express CD14, CD36, and TLR2, and that this cell type showed the most active responses against MAP infection. The responses included the expression of proinflammatory cytokines and chemokines such as CCL4, CCL3, IL1B, IL8, and CCL20. In addition, the Mreg cell type, a novel cell type differentiated from THP-1 cells, was discovered. Thus, it is suggested that different cell types arise even when the same cell line is treated under the same conditions. Overall, analyzing gene expression patterns via scRNA-seq classification allows a more detailed observation of the response to infection by each cell type.

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A computational method to aid the design and analysis of single cell RNA-seq experiments for cell type identification

10.1101/247114 ◽

2018 ◽

Cited By ~ 1

Author(s):

Douglas Abrams ◽

Parveen Kumar ◽

R. Krishna Murthy Karuturi ◽

Joshy George

Keyword(s):

Experimental Design ◽

Single Cell ◽

Single Cells ◽

Cell Types ◽

Cell Number ◽

Fold Change ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Estimate Sample Size

AbstractBackgroundThe advent of single cell RNA sequencing (scRNA-seq) enabled researchers to study transcriptomic activity within individual cells and identify inherent cell types in the sample. Although numerous computational tools have been developed to analyze single cell transcriptomes, there are no published studies and analytical packages available to guide experimental design and to devise suitable analysis procedure for cell type identification.ResultsWe have developed an empirical methodology to address this important gap in single cell experimental design and analysis into an easy-to-use tool called SCEED (Single Cell Empirical Experimental Design and analysis). With SCEED, user can choose a variety of combinations of tools for analysis, conduct performance analysis of analytical procedures and choose the best procedure, and estimate sample size (number of cells to be profiled) required for a given analytical procedure at varying levels of cell type rarity and other experimental parameters. Using SCEED, we examined 3 single cell algorithms using 48 simulated single cell datasets that were generated for varying number of cell types and their proportions, number of genes expressed per cell, number of marker genes and their fold change, and number of single cells successfully profiled in the experiment.ConclusionsBased on our study, we found that when marker genes are expressed at fold change of 4 or more than the rest of the genes, either Seurat or Simlr algorithm can be used to analyze single cell dataset for any number of single cells isolated (minimum 1000 single cells were tested). However, when marker genes are expected to be only up to fC 2 upregulated, choice of the single cell algorithm is dependent on the number of single cells isolated and proportion of rare cell type to be identified. In conclusion, our work allows the assessment of various single cell methods and also aids in examining the single cell experimental design.

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ICTD: A semi-supervised cell type identification and deconvolution method for multi-omics data

10.1101/426593 ◽

2018 ◽

Cited By ~ 2

Author(s):

Wennan Chang ◽

Changlin Wan ◽

Xiaoyu Lu ◽

Szu-wei Tu ◽

Yifan Sun ◽

...

Keyword(s):

Single Cell ◽

Cell Types ◽

Training Data ◽

Marker Genes ◽

Cell Detection ◽

Omics Data ◽

Deconvolution Method ◽

Cell Type ◽

Data Set ◽

Cell Type Specific

AbstractWe developed a novel deconvolution method, namely Inference of Cell Types and Deconvolution (ICTD) that addresses the fundamental issue of identifiability and robustness in current tissue data deconvolution problem. ICTD provides substantially new capabilities for omics data based characterization of a tissue microenvironment, including (1) maximizing the resolution in identifying resident cell and sub types that truly exists in a tissue, (2) identifying the most reliable marker genes for each cell type, which are tissue and data set specific, (3) handling the stability problem with co-linear cell types, (4) co-deconvoluting with available matched multi-omics data, and (5) inferring functional variations specific to one or several cell types. ICTD is empowered by (i) rigorously derived mathematical conditions of identifiable cell type and cell type specific functions in tissue transcriptomics data and (ii) a semi supervised approach to maximize the knowledge transfer of cell type and functional marker genes identified in single cell or bulk cell data in the analysis of tissue data, and (iii) a novel unsupervised approach to minimize the bias brought by training data. Application of ICTD on real and single cell simulated tissue data validated that the method has consistently good performance for tissue data coming from different species, tissue microenvironments, and experimental platforms. Other than the new capabilities, ICTD outperformed other state-of-the-art devolution methods on prediction accuracy, the resolution of identifiable cell, detection of unknown sub cell types, and assessment of cell type specific functions. The premise of ICTD also lies in characterizing cell-cell interactions and discovering cell types and prognostic markers that are predictive of clinical outcomes.

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