scholarly journals Single-cell transcriptomes of the aging human skin reveal loss of fibroblast priming

2019 ◽  
Author(s):  
Llorenç Solé-Boldo ◽  
Günter Raddatz ◽  
Sabrina Schütz ◽  
Jan-Philipp Mallm ◽  
Karsten Rippe ◽  
...  
Keyword(s):  
Single Cell ◽  
Human Skin ◽  
Expression Patterns ◽  
Cell Types ◽  
Skin Aging ◽  
Dermal Fibroblasts ◽  
Skin Cell ◽  
Age Related ◽  
Dermal Cell ◽  
And Function

SummaryFibroblasts are the main dermal cell type and are essential for the architecture and function of human skin. Important differences have been described between fibroblasts localized in distinct dermal layers, and these cells are also known to perform varied functions. However, this phenomenon has not been analyzed comprehensively yet. Here we have used single-cell RNA sequencing to analyze >15,000 cells from a sun-protected area in young and old donors. Our results define four main fibroblast subpopulations that can be spatially localized and functionally distinguished. Importantly, intrinsic aging reduces this fibroblast ‘priming’, generates distinct expression patterns of skin aging-associated genes, and substantially reduces the interactions of dermal fibroblasts with other skin cell types. Our work thus provides comprehensive evidence for a functional specialization of human dermal fibroblasts and suggests that the age-related loss of fibroblast priming contributes to human skin aging.

scholarly journals High Glucose Causes Distinct Expression Patterns of Primary Human Skin Cells by RNA Sequencing

2021 ◽  
Vol 12 ◽  
Author(s):  
Shan Zhang ◽  
Zunxiang Ke ◽  
Chao Yang ◽  
Peng Zhou ◽  
Huanzong Jiang ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Human Skin ◽  
High Glucose ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Skin Cells ◽  
Skin Cell ◽  
Glucose Treatment

Diabetes-related skin problems represent the most common long-term complications in diabetes mellitus patients. These complications, which include diabetic dermopathy, diabetic blisters, necrobiosis lipoidica diabeticorum, and eruptive xanthomatosis, may dramatically impair patients’ quality of life and cause long-lasting disability. However, the cellular and molecular mechanisms linking diabetes-related hyperglycemia and skin complications are still incompletely understood. To assess the role of the various skin-cell types in hyperglycemia-induced skin disorders, we performed RNA sequencing-based transcriptome analysis, measuring gene expression patterns in biological replicates in normal- and high glucose-stimulated skin cells. Three primary human skin-cell types were examined, i.e., epidermal keratinocytes, dermal fibroblasts, and dermal microvascular endothelial cells. For each separate cell type, we identified gene expression. Comparing gene abundances and expression levels revealed that transcription profiles exhibit distinct patterns in the three skin-cell types exposed to normal (i.e., physiological) glucose treatment and high (i.e., supraphysiological) glucose treatment. The obtained data indicate that high glucose induced differential gene expression and distinct activity patterns in signaling pathways in each skin-cell type. We are adding these data to the public database in the hope that they will facilitate future studies to develop novel targeted interventions for diabetic skin complications.

scholarly journals Single-Cell RNA Profiling of Human Skin Reveals Age-Related Loss of Dermal Sheath Cells and Their Contribution to a Juvenile Phenotype

2022 ◽  
Vol 12 ◽  
Author(s):  
Juliane M. D. Ahlers ◽  
Cassandra Falckenhayn ◽  
Nicholas Holzscheck ◽  
Llorenç Solé-Boldo ◽  
Sabrina Schütz ◽  
...  
Keyword(s):  
Stem Cells ◽  
Stem Cell ◽  
Single Cell ◽  
Human Skin ◽  
Dermal Papilla ◽  
Dermal Fibroblasts ◽  
Self Renewal ◽  
Paracrine Effects ◽  
Rna Profiling ◽  
Age Related

The dermal sheath (DS) is a population of mesenchyme-derived skin cells with emerging importance for skin homeostasis. The DS includes hair follicle dermal stem cells, which exhibit self-renewal and serve as bipotent progenitors of dermal papilla (DP) cells and DS cells. Upon aging, stem cells exhibit deficiencies in self-renewal and their number is reduced. While the DS of mice has been examined in considerable detail, our knowledge of the human DS, the pathways contributing to its self-renewal and differentiation capacity and potential paracrine effects important for tissue regeneration and aging is very limited. Using single-cell RNA sequencing of human skin biopsies from donors of different ages we have now analyzed the transcriptome of 72,048 cells, including 50,149 fibroblasts. Our results show that DS cells that exhibit stem cell characteristics were lost upon aging. We further show that HES1, COL11A1, MYL4 and CTNNB1 regulate DS stem cell characteristics. Finally, the DS secreted protein Activin A showed paracrine effects on keratinocytes and dermal fibroblasts, promoting proliferation, epidermal thickness and pro-collagen production. Our work provides a detailed description of human DS identity on the single-cell level, its loss upon aging, its stem cell characteristics and its contribution to a juvenile skin phenotype.

scholarly journals Human skin aging is associated with increased expression of the histone variant H2A.J in the epidermis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Claudia E. Rübe ◽  
Caroline Bäumert ◽  
Nadine Schuler ◽  
Anna Isermann ◽  
Zoé Schmal ◽  
...  
Keyword(s):  
Dna Damage ◽  
Human Skin ◽  
Cellular Senescence ◽  
Skin Aging ◽  
Histone Variant ◽  
Epidermal Keratinocytes ◽  
Basal Cells ◽  
Telomere Attrition ◽  
Age Related ◽  
And Function

AbstractCellular senescence is an irreversible growth arrest that occurs as a result of damaging stimuli, including DNA damage and/or telomere shortening. Here, we investigate histone variant H2A.J as a new biomarker to detect senescent cells during human skin aging. Skin biopsies from healthy volunteers of different ages (18–90 years) were analyzed for H2A.J expression and other parameters involved in triggering and/or maintaining cellular senescence. In the epidermis, the proportions of H2A.J-expressing keratinocytes increased from ≈20% in young to ≈60% in aged skin. Inverse correlations between Ki67- and H2A.J staining in germinative layers may reflect that H2A.J-expressing cells having lost their capacity to divide. As cellular senescence is triggered by DNA-damage signals, persistent 53BP1-foci, telomere lengths, and telomere-associated damage foci were analyzed in epidermal keratinocytes. Only slight age-related telomere attrition and few persistent nuclear 53BP1-foci, occasionally colocalizing with telomeres, suggest that unprotected telomeres are not a significant cause of senescence during skin aging. Quantification of integrin-α6+ basal cells suggests that the number and function of stem/progenitor cells decreased during aging and their altered proliferation capacities resulted in diminished tissue renewal with epidermal thinning. Collectively, our findings suggest that H2A.J is a sensitive marker of epidermal aging in human skin.

scholarly journals Identifying cell types from single-cell data based on similarities and dissimilarities between cells

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Yuanyuan Li ◽  
Ping Luo ◽  
Yi Lu ◽  
Fang-Xiang Wu
Keyword(s):  
Gene Expression ◽  
Single Cell ◽  
Spectral Clustering ◽  
Incidence Matrix ◽  
Expression Patterns ◽  
Cell Types ◽  
Clustering Method ◽  
Different Types ◽  
Cell Data ◽  
Spectral Clustering Method

Abstract Background With the development of the technology of single-cell sequence, revealing homogeneity and heterogeneity between cells has become a new area of computational systems biology research. However, the clustering of cell types becomes more complex with the mutual penetration between different types of cells and the instability of gene expression. One way of overcoming this problem is to group similar, related single cells together by the means of various clustering analysis methods. Although some methods such as spectral clustering can do well in the identification of cell types, they only consider the similarities between cells and ignore the influence of dissimilarities on clustering results. This methodology may limit the performance of most of the conventional clustering algorithms for the identification of clusters, it needs to develop special methods for high-dimensional sparse categorical data. Results Inspired by the phenomenon that same type cells have similar gene expression patterns, but different types of cells evoke dissimilar gene expression patterns, we improve the existing spectral clustering method for clustering single-cell data that is based on both similarities and dissimilarities between cells. The method first measures the similarity/dissimilarity among cells, then constructs the incidence matrix by fusing similarity matrix with dissimilarity matrix, and, finally, uses the eigenvalues of the incidence matrix to perform dimensionality reduction and employs the K-means algorithm in the low dimensional space to achieve clustering. The proposed improved spectral clustering method is compared with the conventional spectral clustering method in recognizing cell types on several real single-cell RNA-seq datasets. Conclusions In summary, we show that adding intercellular dissimilarity can effectively improve accuracy and achieve robustness and that improved spectral clustering method outperforms the traditional spectral clustering method in grouping cells.

scholarly journals Exploring Human Skin Aging at the Single-Cell Level

2021 ◽  
Vol 56 (3) ◽  
pp. 253-254
Author(s):  
Mary Mohrin ◽  
Heinrich Jasper
Keyword(s):  
Single Cell ◽  
Human Skin ◽  
Skin Aging ◽  
Single Cell Level ◽  
Cell Level

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

PLoS ONE ◽  
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.

scholarly journals Infinity Flow: High-throughput single-cell quantification of 100s of proteins using conventional flow cytometry and machine learning

2020 ◽  
Author(s):  
Etienne Becht ◽  
Daniel Tolstrup ◽  
Charles-Antoine Dutertre ◽  
Florent Ginhoux ◽  
Evan W. Newell ◽  
...  
Keyword(s):  
Machine Learning ◽  
Flow Cytometry ◽  
Single Cell ◽  
Low Cost ◽  
Expression Patterns ◽  
Cell Types ◽  
Cellular Heterogeneity ◽  
Supervised Machine Learning ◽  
Melanoma Metastasis ◽  
Immunologic Research

AbstractModern immunologic research increasingly requires high-dimensional analyses in order to understand the complex milieu of cell-types that comprise the tissue microenvironments of disease. To achieve this, we developed Infinity Flow combining hundreds of overlapping flow cytometry panels using machine learning to enable the simultaneous analysis of the co-expression patterns of 100s of surface-expressed proteins across millions of individual cells. In this study, we demonstrate that this approach allows the comprehensive analysis of the cellular constituency of the steady-state murine lung and to identify novel cellular heterogeneity in the lungs of melanoma metastasis bearing mice. We show that by using supervised machine learning, Infinity Flow enhances the accuracy and depth of clustering or dimensionality reduction algorithms. Infinity Flow is a highly scalable, low-cost and accessible solution to single cell proteomics in complex tissues.

scholarly journals Spatial and single-cell transcriptional landscape of human cerebellar development

2020 ◽  
Author(s):  
Kimberly A. Aldinger ◽  
Zach Thomson ◽  
Parthiv Haldipur ◽  
Mei Deng ◽  
Andrew E. Timms ◽  
...  
Keyword(s):  
Single Cell ◽  
Emotional Regulation ◽  
Cell Types ◽  
Molecular Organization ◽  
Cerebellar Development ◽  
Disease Genes ◽  
Spatially Resolved ◽  
Human Cerebellum ◽  
Spatial Composition ◽  
And Function

ABSTRACTCerebellar development and function require precise regulation of molecular and cellular programs to coordinate motor functions and integrate network signals required for cognition and emotional regulation. However, molecular understanding of human cerebellar development is limited. Here, we combined spatially resolved and single-cell transcriptomics to systematically map the molecular, cellular, and spatial composition of early and mid-gestational human cerebellum. This enabled us to transcriptionally profile major cell types and examine the dynamics of gene expression within cell types and lineages across development. The resulting ‘Developmental Cell Atlas of the Human Cerebellum’ demonstrates that the molecular organization of the cerebellar anlage reflects cytoarchitecturally distinct regions and developmentally transient cell types that are insufficiently captured in bulk transcriptional profiles. By mapping disease genes onto cell types, we implicate the dysregulation of specific cerebellar cell types, especially Purkinje cells, in pediatric and adult neurological disorders. These data provide a critical resource for understanding human cerebellar development with implications for the cellular basis of cerebellar diseases.

scholarly journals Single cell analysis reveals immune cell–adipocyte crosstalk regulating the transcription of thermogenic adipocytes

eLife ◽  
2019 ◽  
Vol 8 ◽  
Author(s):  
Prashant Rajbhandari ◽  
Douglas Arneson ◽  
Sydney K Hart ◽  
In Sook Ahn ◽  
Graciel Diamante ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Single Cell Analysis ◽  
Metabolic Response ◽  
Cell Types ◽  
Specific Cell ◽  
Beneficial Effects ◽  
Thermogenic Adipocytes ◽  
And Function

Immune cells are vital constituents of the adipose microenvironment that influence both local and systemic lipid metabolism. Mice lacking IL10 have enhanced thermogenesis, but the roles of specific cell types in the metabolic response to IL10 remain to be defined. We demonstrate here that selective loss of IL10 receptor α in adipocytes recapitulates the beneficial effects of global IL10 deletion, and that local crosstalk between IL10-producing immune cells and adipocytes is a determinant of thermogenesis and systemic energy balance. Single Nuclei Adipocyte RNA-sequencing (SNAP-seq) of subcutaneous adipose tissue defined a metabolically-active mature adipocyte subtype characterized by robust expression of genes involved in thermogenesis whose transcriptome was selectively responsive to IL10Rα deletion. Furthermore, single-cell transcriptomic analysis of adipose stromal populations identified lymphocytes as a key source of IL10 production in response to thermogenic stimuli. These findings implicate adaptive immune cell-adipocyte communication in the maintenance of adipose subtype identity and function.

scholarly journals Cell Atlas technologies and insights into tissue architecture

2020 ◽  
Vol 477 (8) ◽  
pp. 1427-1442 ◽  
Author(s):  
Anna Wilbrey-Clark ◽  
Kenny Roberts ◽  
Sarah A. Teichmann
Keyword(s):  
Single Cell ◽  
Molecular Level ◽  
Cell Types ◽  
Tissue Architecture ◽  
Robert Hooke ◽  
Spatially Resolved ◽  
Gene And Protein Expression ◽  
Basic Biology ◽  
And Function ◽  
Fundamental Units

Since Robert Hooke first described the existence of ‘cells’ in 1665, scientists have sought to identify and further characterise these fundamental units of life. While our understanding of cell location, morphology and function has expanded greatly; our understanding of cell types and states at the molecular level, and how these function within tissue architecture, is still limited. A greater understanding of our cells could revolutionise basic biology and medicine. Atlasing initiatives like the Human Cell Atlas aim to identify all cell types at the molecular level, including their physical locations, and to make this reference data openly available to the scientific community. This is made possible by a recent technology revolution: both in single-cell molecular profiling, particularly single-cell RNA sequencing, and in spatially resolved methods for assessing gene and protein expression. Here, we review available and upcoming atlasing technologies, the biological insights gained to date and the promise of this field for the future.

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