Role of synovial fibroblast subsets across synovial pathotypes in rheumatoid arthritis: a deconvolution analysis

ObjectivesTo integrate published single-cell RNA sequencing (scRNA-seq) data and assess the contribution of synovial fibroblast (SF) subsets to synovial pathotypes and respective clinical characteristics in treatment-naïve early arthritis.MethodsIn this in silico study, we integrated scRNA-seq data from published studies with additional unpublished in-house data. Standard Seurat, Harmony and Liger workflow was performed for integration and differential gene expression analysis. We estimated single cell type proportions in bulk RNA-seq data (deconvolution) from synovial tissue from 87 treatment-naïve early arthritis patients in the Pathobiology of Early Arthritis Cohort using MuSiC. SF proportions across synovial pathotypes (fibroid, lymphoid and myeloid) and relationship of disease activity measurements across different synovial pathotypes were assessed.ResultsWe identified four SF clusters with respective marker genes: PRG4+ SF (CD55, MMP3, PRG4, THY1neg); CXCL12+ SF (CXCL12, CCL2, ADAMTS1, THY1low); POSTN+ SF (POSTN, collagen genes, THY1); CXCL14+ SF (CXCL14, C3, CD34, ASPN, THY1) that correspond to lining (PRG4+ SF) and sublining (CXCL12+ SF, POSTN+ + and CXCL14+ SF) SF subsets. CXCL12+ SF and POSTN+ + were most prominent in the fibroid while PRG4+ SF appeared highest in the myeloid pathotype. Corresponding, lining assessed by histology (assessed by Krenn-Score) was thicker in the myeloid, but also in the lymphoid pathotype + the fibroid pathotype. PRG4+ SF correlated positively with disease severity parameters in the fibroid, POSTN+ SF in the lymphoid pathotype whereas CXCL14+ SF showed negative association with disease severity in all pathotypes.ConclusionThis study shows a so far unexplored association between distinct synovial pathologies and SF subtypes defined by scRNA-seq. The knowledge of the diverse interplay of SF with immune cells will advance opportunities for tailored targeted treatments.

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Gene expression markers of Tumor Infiltrating Leukocytes

10.1101/068940 ◽

2016 ◽

Cited By ~ 5

Author(s):

Patrick Danaher ◽

Sarah Warren ◽

Lucas Dennis ◽

Leonard D’Amico ◽

Andrew White ◽

...

Keyword(s):

Gene Expression ◽

Flow Cytometry ◽

Single Cell ◽

Immune Cells ◽

Immune Cell ◽

Expression Patterns ◽

Cell Populations ◽

Marker Genes ◽

Cell Type ◽

Single Cell Type

AbstractBackgroundAssays of the abundance of immune cell populations in the tumor microenvironment promise to inform immune oncology research and the choice of immunotherapy for individual patients. We propose to measure the intratumoral abundance of various immune cells populations with gene expression. In contrast to IHC and flow cytometry, gene expression assays yield high information content from a clinically practical workflow. Previous studies of gene expression in purified immune cells have reported hundreds of genes showing enrichment in a single cell type, but the utility of these genes in tumor samples is unknown. We describe a novel statistical method for using co-expression patterns in large tumor gene expression datasets to validate previously reported candidate cell type marker genes, and we use this method to winnow previously published gene lists down to a subset of high confidence marker genes.MethodsWe use co-expression patterns in 9986 samples from The Cancer Genome Atlas (TCGA) to validate previously reported cell type marker genes. We compare immune cell scores derived from these genes to measurements from flow cytometry and immunohistochemistry. We characterize the reproducibility of our cell scores in replicate runs of RNA extracted from FFPE tumor tissue.ResultsWe identify a list of 60 marker genes whose expression levels quantify 14 immune cell populations. Cell type scores calculated from these genes are concordant with flow cytometry and IHC readings, show high reproducibility in replicate RNA samples from FFPE tissue, and reveal an intricate picture of the immune infiltrate in TCGA. Most genes previously reported to be enriched in a single cell type have co-expression patterns inconsistent with cell type specificity.ConclusionsDue to their concise gene set, computational simplicity and utility in tumor samples, these cell type gene signatures may be useful in future discovery research and clinical trials to understand how tumors and therapeutic intervention shape the immune response.

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Fully-automated cell-type identification with specific markers extracted from single-cell transcriptomic data

10.1101/812131 ◽

2019 ◽

Author(s):

Aleksandr Ianevski ◽

Anil K Giri ◽

Tero Aittokallio

Keyword(s):

Single Cell ◽

Cell Types ◽

Computational Method ◽

Marker Genes ◽

Cell Type ◽

Cell Clusters ◽

Mouse Tissues ◽

Single Cell Type ◽

The Individual ◽

Selection Of

AbstractSingle-cell transcriptomics enables systematic charting of cellular composition of complex tissues. Identification of cell populations often relies on unsupervised clustering of cells based on the similarity of the scRNA-seq profiles, followed by manual annotation of cell clusters using established marker genes. However, manual selection of marker genes for cell-type annotation is a laborious and error-prone task since the selected markers must be specific both to the individual cell clusters and various cell types. Here, we developed a computational method, termed ScType, which enables data-driven selection of marker genes based solely on given scRNA-seq data. Using a compendium of 7 scRNA-seq datasets from various human and mouse tissues, we demonstrate how ScType enables unbiased, accurate and fully-automated single-cell type annotation by guaranteeing the specificity of marker genes both across cell clusters and cell types. The widely-applicable method is implemented as an interactive web-tool (https://sctype.fimm.fi), connected with comprehensive database of specific markers.

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A Treatment-Naïve Cellular Atlas of Pediatric Crohn’s Disease Predicts Disease Severity and Therapeutic Response

10.1101/2021.09.17.21263540 ◽

2021 ◽

Author(s):

Hengqi Betty Zheng ◽

Benjamin A. Doran ◽

Kyle Kimler ◽

Alison Yu ◽

Victor Tkachev ◽

...

Keyword(s):

Crohn’S Disease ◽

High Resolution ◽

Crohn's Disease ◽

Single Cell ◽

Disease Severity ◽

Inflammatory Disease ◽

Terminal Ileum ◽

Severe Disease ◽

Cell Types ◽

Treatment Naïve

AbstractCrohn’s disease is an inflammatory bowel disease (IBD) which most often presents with patchy lesions in the terminal ileum and colon and requires complex clinical care. Recent advances in the targeting of cytokines and leukocyte migration have greatly advanced treatment options, but most patients still relapse and inevitably progress. Although single-cell approaches are transforming our ability to understand the barrier tissue biology of inflammatory disease, comprehensive single-cell RNA-sequencing (scRNA-seq) atlases of IBD to date have largely sampled pre-treated patients with established disease. This has limited our understanding of which cell types, subsets, and states at diagnosis are predictive of disease severity and response to treatment. Here, through a combined clinical, flow cytometric, and scRNA-seq study, we profile diagnostic human biopsies from the terminal ileum of treatment-naïve pediatric patients with Crohn’s disease (pediCD; n=14) and from non-inflamed pediatric controls with functional gastrointestinal disorders (FGID; n=13). To fully resolve and annotate epithelial, stromal, and immune cell states among the 201,883 single-cell transcriptomes, we develop and deploy a principled and unbiased tiered clustering approach, ARBOL, yielding 138 FGID and 305 pediCD end cell clusters. Notably, through both flow cytometry and scRNA-seq, we observe that at the level of broad cell types, treatment-naïve pediCD is not readily distinguishable from FGID in cellular composition. However, by integrating high-resolution scRNA-seq analysis, we identify significant differences in cell states that arise during pediCD relative to FGID. Furthermore, by closely linking our scRNA-seq analysis with clinical meta-data, we resolve a vector of lymphoid, myeloid, and epithelial cell states in treatment-naïve samples which can distinguish patients with less severe disease (those not on anti-TNF therapies (NOA)), from those with more severe disease at presentation who require anti-TNF therapies. Moreover, this vector was also able to distinguish those patients that achieve a full response (FR) to anti-TNF blockade from those more treatment-resistant patients who only achieve a partial response (PR). Our study jointly leverages a treatment-naïve cohort, high-resolution principled scRNA-seq data analysis, and clinical outcomes to understand which baseline cell states may predict inflammatory disease trajectory.

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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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Author Correction: iLoF: An intelligent Lab on Fiber Approach for Human Cancer Single-Cell Type Identification

Scientific Reports ◽

10.1038/s41598-020-67916-4 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Joana S. Paiva ◽

Pedro A. S. Jorge ◽

Rita S. R. Ribeiro ◽

Meritxell Balmaña ◽

Diana Campos ◽

...

Keyword(s):

Single Cell ◽

Human Cancer ◽

Cell Type ◽

Single Cell Type

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Differential calcium signalling by m2 and m3 muscarinic acetylcholine receptors in a single cell type

Naunyn-Schmiedeberg s Archives of Pharmacology ◽

10.1007/bf00169379 ◽

1995 ◽

Vol 352 (5) ◽

pp. 469-476 ◽

Cited By ~ 22

Author(s):

Martina Schmidt ◽

Christine Bienek ◽

Chris J. van Koppen ◽

Martin C. Michel ◽

Karl H. Jakobs

Keyword(s):

Single Cell ◽

Acetylcholine Receptors ◽

Calcium Signalling ◽

Muscarinic Acetylcholine Receptors ◽

Cell Type ◽

Muscarinic Acetylcholine ◽

Single Cell Type

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Divergent functions of endotrophin on different cell populations in adipose tissue

AJP Endocrinology and Metabolism ◽

10.1152/ajpendo.00314.2016 ◽

2016 ◽

Vol 311 (6) ◽

pp. E952-E963 ◽

Cited By ~ 20

Author(s):

Yueshui Zhao ◽

Xue Gu ◽

Ningyan Zhang ◽

Mikhail G. Kolonin ◽

Zhiqiang An ◽

...

Keyword(s):

Adipose Tissue ◽

Lipid Accumulation ◽

Lysyl Oxidase ◽

Stromal Vascular Fraction ◽

Cell Types ◽

Marker Genes ◽

New Perspective ◽

Collagen Genes ◽

Different Cell Types

Endotrophin is a cleavage product of collagen 6 (Col6) in adipose tissue (AT). Previously, we demonstrated that endotrophin serves as a costimulator to trigger fibrosis and inflammation within the unhealthy AT milieu. However, how endotrophin affects lipid storage and breakdown in AT and how different cell types in AT respond to endotrophin stimulation remain unknown. In the current study, by using a doxycycline-inducible mouse model, we observed significant upregulation of adipogenic genes in the white AT (WAT) of endotrophin transgenic mice. We further showed that the mice exhibited inhibited lipolysis and accelerated hypertrophy and hyperplasia in WAT. To investigate the effects of endotrophin in vitro, we incubated different cell types from AT with conditioned medium from endotrophin-overexpressing 293T cells. We found that endotrophin activated multiple pathological pathways in different cell types. Particularly in 3T3-L1 adipocytes, endotrophin triggered a fibrotic program by upregulating collagen genes and promoted abnormal lipid accumulation by downregulating hormone-sensitive lipolysis gene and decreasing HSL phosphorylation levels. In macrophages isolated from WAT, endotrophin stimulated higher expression of the collagen-linking enzyme lysyl oxidase and M1 proinflammatory marker genes. In the stromal vascular fraction isolated from WAT, endotrophin induced upregulation of both profibrotic and proinflammatory genes. In conclusion, our study provides a new perspective on the effect of endotrophin in abnormal lipid accumulation and a mechanistic insight into the roles played by adipocytes and a variety of other cell types in AT in shaping the unhealthy microenvironment upon endotrophin treatment.

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Identification of cell-type-specific marker genes from co-expression patterns in tissue samples

Bioinformatics ◽

10.1093/bioinformatics/btab257 ◽

2021 ◽

Author(s):

Yixuan Qiu ◽

Jiebiao Wang ◽

Jing Lei ◽

Kathryn Roeder

Keyword(s):

Single Cell ◽

Expression Patterns ◽

R Package ◽

Supplementary Information ◽

Marker Genes ◽

Specific Marker ◽

Cell Type ◽

Correlation Pattern ◽

Tissue Samples ◽

Bulk Data

Abstract Motivation Marker genes, defined as genes that are expressed primarily in a single cell type, can be identified from the single cell transcriptome; however, such data are not always available for the many uses of marker genes, such as deconvolution of bulk tissue. Marker genes for a cell type, however, are highly correlated in bulk data, because their expression levels depend primarily on the proportion of that cell type in the samples. Therefore, when many tissue samples are analyzed, it is possible to identify these marker genes from the correlation pattern. Results To capitalize on this pattern, we develop a new algorithm to detect marker genes by combining published information about likely marker genes with bulk transcriptome data in the form of a semi-supervised algorithm. The algorithm then exploits the correlation structure of the bulk data to refine the published marker genes by adding or removing genes from the list. Availability and implementation We implement this method as an R package markerpen, hosted on CRAN (https://CRAN.R-project.org/package=markerpen). Supplementary information Supplementary data are available at Bioinformatics online.

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There are no A1 and A2 astrocytes in brain

10.21203/rs.3.rs-737027/v1 ◽

2021 ◽

Author(s):

Shuang-qi Gao

Keyword(s):

Endothelial Cells ◽

Single Cell ◽

Rna Sequencing ◽

Cell Sorting ◽

Reactive Astrocytes ◽

Marker Genes ◽

Set Up ◽

The Brain

Abstract Objectives The subsets of astrocytes in the brain have not been fully elucidated. Using bulk RNA sequencing, reactive astrocytes were divided into A1 versus A2. However, using single-cell RNAseq (ScRNAseq), astrocytes were divided into over two subsets. Our aim was to set up the correspondence between the fluorescent-activated cell sorting (FACS)-bulk RNAseq and ScRNAseq data. Results We found that most of reactive astrocytes (RAs) marker genes were expressed in endothelial cells but not in astrocytes, suggesting those marker genes are not suitable for astrocytic activation. The absence of A1 and A2 astrocytes in the brain.

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A proximal-to-distal survey of healthy adult human small intestine and colon epithelium by single-cell transcriptomics

10.1101/2021.10.05.460818 ◽

2021 ◽

Author(s):

Joseph Burclaff ◽

R. Jarrett Bliton ◽

Keith A Breau ◽

Meryem T Ok ◽

Ismael Gomez-Martinez ◽

...

Keyword(s):

Small Intestine ◽

Single Cell ◽

Drug Targets ◽

Healthy Adult ◽

Cellular Level ◽

Cell Junction ◽

Marker Genes ◽

Human Small Intestine ◽

Adult Human ◽

Human Intestinal Epithelium

Background and Aims: Single-cell transcriptomics offer unprecedented resolution of tissue function at the cellular level, yet studies in healthy adult human small intestine and colon are sparse. Here, we present single-cell transcriptomics from 3 humans covering the duodenum, jejunum, ileum, and ascending, transverse, and descending colon. Methods: 12,590 single epithelial cells from three independently processed organ donors were evaluated for organ-specific lineage biomarkers, differentially regulated genes, receptors, and drug targets. Analyses focused on intrinsic cell properties and capacity for response to extrinsic signals along the gut axis across different humans. Results: Cells were assigned to 25 epithelial lineage clusters. Human intestinal stem cells (ISCs) are not specifically marked by many murine ISC markers. Lysozyme expression is not unique to Paneth cells (PCs), and PCs lack expression of expected niche-factors. BEST4 cells express NPY and show functional and maturational differences between SI and colon. Tuft cells possess a broad ability to interact with the innate and adaptive immune systems through previously unreported receptors. Some classes of mucins, hormones, cell-junction, and nutrient absorption genes show unappreciated regional expression differences across lineages. Differential expression of receptors and drug targets across lineages reveals biological variation and potential for variegated responses. Conclusions: Our study identifies novel lineage marker genes; covers regional differences; shows important differences between mouse and human gut epithelium; and reveals insight into how the epithelium responds to the environment and drugs. This comprehensive cell atlas of the healthy adult human intestinal epithelium resolves data gaps in anatomical regions along the gastrointestinal tract and advances our understanding of human intestinal physiology.

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