基于小分子筛选的嗅上皮类器官培养体系的建立

嗅上皮接收和传导气味信号是嗅觉系统的重要组成部分。嗅上皮的损伤在通常情况下可自发恢复,但特定疾病或衰老造成的嗅上皮损伤会引起嗅觉功能减退和嗅觉障碍。嗅上皮主要由基底细胞、支持细胞以及嗅感觉神经元组成。为了在体外建立包含多种细胞类型的嗅上皮类器官,本研究采用3D细胞培养技术,通过筛选小分子药物,构建了包含多种细胞类型的嗅上皮类器官模型,包含水平基底样细胞、球形基底样细胞、支持样细胞和嗅感觉神经元样细胞多种细胞类型。类器官培养体系中多种生长因子和小分子化合物在细胞增殖速度、细胞组成以及不同细胞类型标志基因的表达水平等方面对类器官产生影响。Wnt信号通路激活剂CHIR-99021能够提高嗅上皮类器官的成克隆率和增殖速度且有利于提高嗅上皮类器官中嗅感觉神经元样细胞标志基因的表达水平;培养体系的任一因子均能提高类器官中cKit阳性的球形基底样细胞克隆比例;表皮生长因子(epidermal growth factor,EGF)和维生素C均有利于类器官中水平基底样细胞标志基因的表达。本研究建立的嗅上皮类器官系统模拟了嗅上皮干细胞分化产生多种嗅上皮细胞类型的过程,为研究嗅上皮组织损伤再生、嗅觉障碍病理...     

Topological and organizational properties of the products of house-keeping and tissue-specific genes in protein-protein interaction networks

Background  

Human cells of various tissue types differ greatly in morphology despite having the same set of genetic information. Some genes are expressed in all cell types to perform house-keeping functions, while some are selectively expressed to perform tissue-specific functions. In this study, we wished to elucidate how proteins encoded by human house-keeping genes and tissue-specific genes are organized in human protein-protein interaction networks. We constructed protein-protein interaction networks for different tissue types using two gene expression datasets and one protein-protein interaction database. We then calculated three network indices of topological importance, the degree, closeness, and betweenness centralities, to measure the network position of proteins encoded by house-keeping and tissue-specific genes, and quantified their local connectivity structure.     

Evolutionary insights into primate skeletal gene regulation using a comparative cell culture model

The evolution of complex skeletal traits in primates was likely influenced by both genetic and environmental factors. Because skeletal tissues are notoriously challenging to study using functional genomic approaches, they remain poorly characterized even in humans, let alone across multiple species. The challenges involved in obtaining functional genomic data from the skeleton, combined with the difficulty of obtaining such tissues from nonhuman apes, motivated us to consider an alternative in vitro system with which to comparatively study gene regulation in skeletal cell types. Specifically, we differentiated six human (Homo sapiens) and six chimpanzee (Pan troglodytes) induced pluripotent stem cell lines (iPSCs) into mesenchymal stem cells (MSCs) and subsequently into osteogenic cells (bone cells). We validated differentiation using standard methods and collected single-cell RNA sequencing data from over 100,000 cells across multiple samples and replicates at each stage of differentiation. While most genes that we examined display conserved patterns of expression across species, hundreds of genes are differentially expressed (DE) between humans and chimpanzees within and across stages of osteogenic differentiation. Some of these interspecific DE genes show functional enrichments relevant in skeletal tissue trait development. Moreover, topic modeling indicates that interspecific gene programs become more pronounced as cells mature. Overall, we propose that this in vitro model can be used to identify interspecific regulatory differences that may have contributed to skeletal trait differences between species.     

MarcoPolo: a method to discover differentially expressed genes in single-cell RNA-seq data without depending on prior clustering

The standard analysis pipeline for single-cell RNA-seq data consists of sequential steps initiated by clustering the cells. An innate limitation of this pipeline is that an imperfect clustering result can irreversibly affect the succeeding steps. For example, there can be cell types not well distinguished by clustering because they largely share the global structure, such as the anterior primitive streak and mid primitive streak cells. If one searches differentially expressed genes (DEGs) solely based on clustering, marker genes for distinguishing these types will be missed. Moreover, clustering depends on many parameters and can often be subjective to manual decisions. To overcome these limitations, we propose MarcoPolo, a method that identifies informative DEGs independently of prior clustering. MarcoPolo sorts out genes by evaluating if the distributions are bimodal, if similar expression patterns are observed in other genes, and if the expressing cells are proximal in a low-dimensional space. Using real datasets with FACS-purified cell labels, we demonstrate that MarcoPolo recovers marker genes better than competing methods. Notably, MarcoPolo finds key genes that can distinguish cell types that are not distinguishable by the standard clustering. MarcoPolo is built in a convenient software package that provides analysis results in an HTML file.     

Single-cell transcriptomics reveals cell type diversity of human prostate

Extensive studies have been performed to describe the phenotypic changes occurring during malignant transformation of the prostate. However, the cell types and associated changes that contribute to the development of prostate diseases and cancer remain elusive, largely due to the heterogeneous composition of prostatic tissues. Here, we conduct a comprehensive evaluation of four human prostate tissues by single-cell RNA sequencing (scRNA-seq) to analyze their cellular compositions. We identify 18 clusters of cell types, each with distinct gene expression profiles and unique features; of these, one cluster of epithelial cells (Ep) is found to be associated with immune function. In addition, we characterize a special cluster of fibroblasts and aberrant signaling changes associated with prostate cancer (PCa). Moreover, we provide insights into the epithelial changes that occur during the cellular senescence and aging. These results expand our understanding of the unique functional associations between the diverse prostatic cell types and the contributions of specific cell clusters to the malignant transformation of prostate tissues and PCa development.     

Single-cell RNA sequencing analysis to characterize cells and gene expression landscapes in atrial septal defect

This study aimed to characterize the cells and gene expression landscape in atrial septal defect (ASD). We performed single-cell RNA sequencing of cells derived from cardiac tissue of an ASD patient. Unsupervised clustering analysis was performed to identify different cell populations, followed by the investigation of the cellular crosstalk by analysing ligand-receptor interactions across cell types. Finally, differences between ASD and normal samples for all cell types were further investigated. An expression matrix of 18,411 genes in 6487 cells was obtained and used in this analysis. Five cell types, including cardiomyocytes, endothelial cells, smooth muscle cells, fibroblasts and macrophages were identified. ASD showed a decreased proportion of cardiomyocytes and an increased proportion of fibroblasts. There was more cellular crosstalk among cardiomyocytes, fibroblasts and macrophages, especially between fibroblast and macrophage. For all cell types, the majority of the DEGs were downregulated in ASD samples. For cardiomyocytes, there were 199 DEGs (42 upregulated and 157 downregulated) between ASD and normal samples. PPI analysis showed that cardiomyocyte marker gene FABP4 interacted with FOS, while FOS showed interaction with NPPA. Cell trajectory analysis showed that FABP4, FOS, and NPPA showed different expression changes along the pseudotime trajectory. Our results showed that single-cell RNA sequencing provides a powerful tool to study DEG profiles in the cell subpopulations of interest at the single-cell level. These findings enhance the understanding of the underlying mechanisms of ASD at both the cellular and molecular level and highlight potential targets for the treatment of ASD.     

Geometry of the Gene Expression Space of Individual Cells

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.     

Discriminative feature of cells characterizes cell populations of interest by a small subset of genes

Organisms are composed of various cell types with specific states. To obtain a comprehensive understanding of the functions of organs and tissues, cell types have been classified and defined by identifying specific marker genes. Statistical tests are critical for identifying marker genes, which often involve evaluating differences in the mean expression levels of genes. Differentially expressed gene (DEG)-based analysis has been the most frequently used method of this kind. However, in association with increases in sample size such as in single-cell analysis, DEG-based analysis has faced difficulties associated with the inflation of P-values. Here, we propose the concept of discriminative feature of cells (DFC), an alternative to using DEG-based approaches. We implemented DFC using logistic regression with an adaptive LASSO penalty to perform binary classification for discriminating a population of interest and variable selection to obtain a small subset of defining genes. We demonstrated that DFC prioritized gene pairs with non-independent expression using artificial data and that DFC enabled characterization of the muscle satellite/progenitor cell population. The results revealed that DFC well captured cell-type-specific markers, specific gene expression patterns, and subcategories of this cell population. DFC may complement DEG-based methods for interpreting large data sets. DEG-based analysis uses lists of genes with differences in expression between groups, while DFC, which can be termed a discriminative approach, has potential applications in the task of cell characterization. Upon recent advances in the high-throughput analysis of single cells, methods of cell characterization such as scRNA-seq can be effectively subjected to the discriminative methods.     

Cancer attractors: A systems view of tumors from a gene network dynamics and developmental perspective

Cell lineage commitment and differentiation are governed by a complex gene regulatory network. Disruption of these processes by inappropriate regulatory signals and by mutational rewiring of the network can lead to tumorigenesis. Cancer cells often exhibit immature or embryonic traits and dysregulated developmental genes can act as oncogenes. However, the prevailing paradigm of somatic evolution and multi-step tumorigenesis, while useful in many instances, offers no logically coherent reason for why oncogenesis recapitulates ontogenesis. The formal concept of “cancer attractors”, derived from an integrative, complex systems approach to gene regulatory network may provide a natural explanation. Here we present the theory of attractors in gene network dynamics and review the concept of cell types as attractors. We argue that cancer cells are trapped in abnormal attractors and discuss this concept in the light of recent ideas in cancer biology, including cancer genomics and cancer stem cells, as well as the implications for differentiation therapy.     

Cells differentiated from mouse embryonic stem cells via embryoid bodies express renal marker molecules

Differentiation of mouse embryonic stem (ES) cells via embryoid bodies (EB) is established as a suitable model to study cellular processes of development in vitro. ES cells are known to be pluripotent because of their capability to differentiate into cell types of all three germ layers including germ cells. Here, we show that ES cells differentiate into renal cell types in vitro. We found that genes were expressed during EB cultivation, which have been previously described to be involved in renal development. Marker molecules characteristic for terminally differentiated renal cell types were found to be expressed predominantly during late stages of EB cultivation, while marker molecules involved in the initiation of nephrogenesis were already expressed during early steps of EB development. On the cellular level--using immunostaining--we detected cells expressing podocin, nephrin and wt-1, characteristic for differentiated podocytes and other cells, which expressed Tamm-Horsfall protein, a marker for distal tubule epithelial cells of kidney tissue. Furthermore, the proximal tubule marker molecules renal-specific oxido reductase, kidney androgen-related protein and 25-hydroxyvitamin D3alpha-hydroxylase were found to be expressed in EBs. In particular, we could demonstrate that cells expressing podocyte marker molecules assemble to distinct ring-like structures within the EBs. Because the differentiation efficiency into these cell types is still relatively low, application of fibroblast growth factor (FGF)-2 in combination with leukaemia inhibitory factor was tested for induction, but did not enhance ES cell-derived renal differentiation in vitro.     

Comparative analysis of gene expression profiles in ruminal tissue from Holstein dairy cows fed high or low concentrate diets

Cattle are often fed high concentrate (HC) diets to increase productivity, although HC diets cause changes in ruminal environment such as pH reduction. Despite those well-documented changes in cattle fed HC diets, there is currently a paucity of data describing the molecular events regulating the ruminal environment. Our objective was to gain an understanding of which genes are differentially expressed in ruminal tissue from Holstein cows fed a HC comparing to low concentrate (LC) diet using microarray analysis using a bovine 24 k microarray. A total of 5,200 differentially expressed genes (DEG) were detected for cows fed HC relative to LC. The DEG were firstly annotated with gene ontology (GO) and Kyoto encyclopedia of Genes and Genomes (KEGG), indicating that the DEG were associated with catalytic activity and MAPK pathway, respectively. Further characterization using GeneCodis identified patterns of interrelated annotations for the DEG to elucidate the relationships among annotation groups revealed that a cAMP-dependent protein kinase A catalytic subunit beta (PRKACB), may be associated with ruminal tissue maintenance. The results contributed to understanding of the regulatory mechanisms at the mRNA level for Holstein cows fed at different concentrate ratio diets.