scholarly journals Hyperbolic geometry of gene expression

2020 ◽  
Author(s):  
Yuansheng Zhou ◽  
Tatyana Sharpee
Keyword(s):  
Gene Expression ◽  
Hyperbolic Geometry ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Cell Types ◽  
Hyperbolic Metric ◽  
High Dimensional ◽  
Multiple Sources ◽  
Number Of Genes ◽  
The Hyperbolic Metric

AbstractUnderstanding the patterns of gene expression is key to elucidating the differences between cell types and across disease conditions. The overwhelmingly large number of genes involved generally makes this problem intractable. Yet, we find that gene expression patterns in five different data datasets can all be described using a small number of variables. These variables describe differences between cells according to a hyperbolic metric. We reach this conclusion by developing methods that, starting with an initial assumption of a Euclidean geometry, can detect the presence of other geometries in the data. The Euclidean metric is used in most of current studies of gene expression, primarily because it is difficult to use other non-linear metrics in high dimensional spaces. The hyperbolic metric is much more suitable for describing data produced by a hierarchically organized network, which is relevant for many biological processes. We find that the hyperbolic effects, but not the space dimensionality, increase with the number of genes that are taken into account. The hyperbolic curvature was the smallest for mouse embryonic stem cells, stronger for mouse kidney, lung and brain cells, and reached the largest value in a set of human cells integrated from multiple sources. We show that taking into account hyperbolic geometry strongly improves the visualization of gene expression data compared to leading visualization methods. These results demonstrate the advantages of knowing the underlying geometry when analyzing high-dimensional data.

scholarly journals Impact of AHR Ligand TCDD on Human Embryonic Stem Cells and Early Differentiation

2020 ◽  
Vol 21 (23) ◽  
pp. 9052
Author(s):  
Indrek Teino ◽  
Antti Matvere ◽  
Martin Pook ◽  
Inge Varik ◽  
Laura Pajusaar ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Human Embryonic Stem Cells ◽  
Target Genes ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Stem Cell Differentiation ◽  
Cell Types ◽  
Early Differentiation

Aryl hydrocarbon receptor (AHR) is a ligand-activated transcription factor, which mediates the effects of a variety of environmental stimuli in multiple tissues. Recent advances in AHR biology have underlined its importance in cells with high developmental potency, including pluripotent stem cells. Nonetheless, there is little data on AHR expression and its role during the initial stages of stem cell differentiation. The purpose of this study was to investigate the temporal pattern of AHR expression during directed differentiation of human embryonic stem cells (hESC) into neural progenitor, early mesoderm and definitive endoderm cells. Additionally, we investigated the effect of the AHR agonist 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) on the gene expression profile in hESCs and differentiated cells by RNA-seq, accompanied by identification of AHR binding sites by ChIP-seq and epigenetic landscape analysis by ATAC-seq. We showed that AHR is differentially regulated in distinct lineages. We provided evidence that TCDD alters gene expression patterns in hESCs and during early differentiation. Additionally, we identified novel potential AHR target genes, which expand our understanding on the role of this protein in different cell types.

scholarly journals Changes in the Expression of Mitochondrial Morphology-Related Genes during the Differentiation of Murine Embryonic Stem Cells

2020 ◽  
Vol 2020 ◽  
pp. 1-12 ◽  
Author(s):  
Jeong Eon Lee ◽  
Bong Jong Seo ◽  
Min Ji Han ◽  
Yean Ju Hong ◽  
Kwonho Hong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Mitochondrial Fission ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Cell Types ◽  
Mitochondrial Fusion ◽  
Mitochondrial Morphology ◽  
Stems Cells

During embryonic development, cells undergo changes in gene expression, signaling pathway activation/inactivation, metabolism, and intracellular organelle structures, which are mediated by mitochondria. Mitochondria continuously switch their morphology between elongated tubular and fragmented globular via mitochondrial fusion and fission. Mitochondrial fusion is mediated by proteins encoded by Mfn1, Mfn2, and Opa1, whereas mitochondrial fission is mediated by proteins encoded by Fis1 and Dnm1L. Here, we investigated the expression patterns of mitochondria-related genes during the differentiation of mouse embryonic stem cells (ESCs). Pluripotent ESCs maintain stemness in the presence of leukemia inhibitory factor (LIF) via the JAK-STAT3 pathway but lose pluripotency and differentiate in response to the withdrawal of LIF. We analyzed the expression levels of mitochondrial fusion- and fission-related genes during the differentiation of ESCs. We hypothesized that mitochondrial fusion genes would be overexpressed while the fission genes would be downregulated during the differentiation of ESCs. Though the mitochondria exhibited an elongated morphology in ESCs differentiating in response to LIF withdrawal, only the expression of Mfn2 was increased and that of Dnm1L was decreased as expected, the other exceptions being Mfn1, Opa1, and Fis1. Next, by comparing gene expression and mitochondrial morphology, we proposed an index that could precisely represent mitochondrial changes during the differentiation of pluripotent stem cells by analyzing the expression ratios of three fusion- and two fission-related genes. Surprisingly, increased Mfn2/Dnm1L ratio was correlated with elongation of mitochondria during the differentiation of ESCs. Moreover, application of this index to other specialized cell types revealed that neural stems cells (NSCs) and mouse embryonic fibroblasts (MEFs) showed increased Mfn2/Dnm1L ratio compared to ESCs. Thus, we suggest that the Mfn2/Dnm1L ratio could reflect changes in mitochondrial morphology according to the extent of differentiation.

scholarly journals Changes in the expression of mitochondrial morphology-related genes during the differentiation of murine embryonic stem cells

2019 ◽  
Author(s):  
Jeong Eon Lee ◽  
Bong Jong Seo ◽  
Min Ji Han ◽  
Yean Ju Hong ◽  
Kwonho Hong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Mitochondrial Fission ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Cell Types ◽  
Mitochondrial Fusion ◽  
Mitochondrial Morphology ◽  
Stems Cells

AbstractDuring embryonic development, cells undergo changes in gene expression, signaling pathway activation/inactivation, metabolism, and intracellular organelle structures, which are mediated by mitochondria. Mitochondria continuously switch their morphology between elongated tubular and fragmented globular via mitochondrial fusion and fission. Mitochondrial fusion is mediated by proteins encoded by Mfn1, Mfn2, and Opa1, whereas mitochondrial fission is mediated by proteins encoded by Fis1 and Dmn1L. Here, we investigated the expression patterns of mitochondria-related genes during the differentiation of mouse embryonic stem cells (ESCs) in response to leukemia inhibitory factor (LIF) withdrawal. The expression of Mfn2 and Dnm1L was, as expected, increased and decreased, respectively. By comparing gene expression and mitochondrial morphology, we proposed an index that could precisely represent mitochondrial changes during the differentiation of pluripotent stem cells by analyzing the expression ratios of three fusion- and two fission-related genes. Surprisingly, increased Mfn2/Dnm1L ratio was correlated with elongation of mitochondria during the differentiation of ESCs. Moreover, application of this index to other specialized cell types revealed that neural stems cells (NSCs) and mouse embryonic fibroblasts (MEFs) showed increased Mfn2/Dnm1L ratio compared to ESCs. Thus, we suggest that the Mfn2/Dnm1L ratio could reflect changes in mitochondrial morphology according to the extent of differentiation.

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 Histone modifications form a cell-type-specific chromosomal bar code that persists through the cell cycle

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
John A. Halsall ◽  
Simon Andrews ◽  
Felix Krueger ◽  
Charlotte E. Rutledge ◽  
Gabriella Ficz ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Cycle ◽  
Histone Modifications ◽  
Expression Patterns ◽  
Cell Types ◽  
Cell Type ◽  
Bar Code ◽  
Genes Encoding ◽  
Cell Type Specific ◽  
Rolling Windows

AbstractChromatin configuration influences gene expression in eukaryotes at multiple levels, from individual nucleosomes to chromatin domains several Mb long. Post-translational modifications (PTM) of core histones seem to be involved in chromatin structural transitions, but how remains unclear. To explore this, we used ChIP-seq and two cell types, HeLa and lymphoblastoid (LCL), to define how changes in chromatin packaging through the cell cycle influence the distributions of three transcription-associated histone modifications, H3K9ac, H3K4me3 and H3K27me3. We show that chromosome regions (bands) of 10–50 Mb, detectable by immunofluorescence microscopy of metaphase (M) chromosomes, are also present in G1 and G2. They comprise 1–5 Mb sub-bands that differ between HeLa and LCL but remain consistent through the cell cycle. The same sub-bands are defined by H3K9ac and H3K4me3, while H3K27me3 spreads more widely. We found little change between cell cycle phases, whether compared by 5 Kb rolling windows or when analysis was restricted to functional elements such as transcription start sites and topologically associating domains. Only a small number of genes showed cell-cycle related changes: at genes encoding proteins involved in mitosis, H3K9 became highly acetylated in G2M, possibly because of ongoing transcription. In conclusion, modified histone isoforms H3K9ac, H3K4me3 and H3K27me3 exhibit a characteristic genomic distribution at resolutions of 1 Mb and below that differs between HeLa and lymphoblastoid cells but remains remarkably consistent through the cell cycle. We suggest that this cell-type-specific chromosomal bar-code is part of a homeostatic mechanism by which cells retain their characteristic gene expression patterns, and hence their identity, through multiple mitoses.

Specificity of gene expression in adipocytes

1985 ◽  
Vol 5 (2) ◽  
pp. 419-421
Author(s):  
K M Zezulak ◽  
H Green
Keyword(s):  
Gene Expression ◽  
Cell Types ◽  
Cell Type ◽  
3T3 Cells ◽  
Cell Type Specificity ◽  
Number Of Genes ◽  
Enhanced Expression ◽  
Adipose Cells ◽  
Distinctive Phenotype

During the differentiation of preadipose 3T3 cells into adipose cells, the mRNAs for three proteins increase strikingly in abundance. To determine the degree of cell-type specificity in the expression of these mRNAs, we estimated their abundances in several nonadipose tissues of the mouse. None of these mRNAs was strictly confined to adipocytes, but the ensemble of three mRNAs was rather specific to adipocytes. Insofar as is revealed by these three markers, the distinctive phenotype of adipocytes is the result of the enhanced expression of a number of genes, none of which is completely silent in all other cell types.

scholarly journals Cell-type Specific Expression Quantitative Trait Loci Associated with Alzheimer Disease in Blood and Brain Tissue

2020 ◽  
Author(s):  
Devanshi Patel ◽  
Xiaoling Zhang ◽  
John J. Farrell ◽  
Jaeyoon Chung ◽  
Thor D. Stein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Quantitative Trait ◽  
Expression Patterns ◽  
Regulation Of Gene Expression ◽  
Cell Types ◽  
Eqtl Analysis ◽  
Cell Type ◽  
Specific Expression ◽  
Cell Type Specific Expression ◽  
Cell Type Specific

ABSTRACTBecause regulation of gene expression is heritable and context-dependent, we investigated AD-related gene expression patterns in cell-types in blood and brain. Cis-expression quantitative trait locus (eQTL) mapping was performed genome-wide in blood from 5,257 Framingham Heart Study (FHS) participants and in brain donated by 475 Religious Orders Study/Memory & Aging Project (ROSMAP) participants. The association of gene expression with genotypes for all cis SNPs within 1Mb of genes was evaluated using linear regression models for unrelated subjects and linear mixed models for related subjects. Cell type-specific eQTL (ct-eQTL) models included an interaction term for expression of “proxy” genes that discriminate particular cell type. Ct-eQTL analysis identified 11,649 and 2,533 additional significant gene-SNP eQTL pairs in brain and blood, respectively, that were not detected in generic eQTL analysis. Of note, 386 unique target eGenes of significant eQTLs shared between blood and brain were enriched in apoptosis and Wnt signaling pathways. Five of these shared genes are established AD loci. The potential importance and relevance to AD of significant results in myeloid cell-types is supported by the observation that a large portion of GWS ct-eQTLs map within 1Mb of established AD loci and 58% (23/40) of the most significant eGenes in these eQTLs have previously been implicated in AD. This study identified cell-type specific expression patterns for established and potentially novel AD genes, found additional evidence for the role of myeloid cells in AD risk, and discovered potential novel blood and brain AD biomarkers that highlight the importance of cell-type specific analysis.

scholarly journals Projecting genetic associations through gene expression patterns highlights disease etiology and drug mechanisms

2021 ◽  
Author(s):  
Milton Pividori ◽  
Sumei Lu ◽  
Binglan Li ◽  
Chun Su ◽  
Matthew E. Johnson ◽  
...  
Keyword(s):  
Gene Expression ◽  
Association Studies ◽  
Expression Patterns ◽  
Cell Types ◽  
Joint Analysis ◽  
Genetic Associations ◽  
Disease Etiology ◽  
Mediating Role ◽  
Gene Modules ◽  
Trait Associations

Understanding how dysregulated transcriptional processes result in tissue-specific pathology requires a mechanistic interpretation of expression regulation across different cell types. It has been shown that this insight is key for the development of new therapies. These mechanisms can be identified with transcriptome-wide association studies (TWAS), which have represented an important step forward to test the mediating role of gene expression in GWAS associations. However, due to pervasive eQTL sharing across tissues, TWAS has not been successful in identifying causal tissues, and other methods generally do not take advantage of the large amounts of RNA-seq data publicly available. Here we introduce a polygenic approach that leverages gene modules (genes with similar co-expression patterns) to project both gene-trait associations and pharmacological perturbation data into a common latent representation for a joint analysis. We observed that diseases were significantly associated with gene modules expressed in relevant cell types, such as hypothyroidism with T cells and thyroid, hypertension and lipids with adipose tissue, and coronary artery disease with cardiomyocytes. Our approach was more accurate in predicting known drug-disease pairs and revealed stable trait clusters, including a complex branch involving lipids with cardiovascular, autoimmune, and neuropsychiatric disorders. Furthermore, using a CRISPR-screen, we show that genes involved in lipid regulation exhibit more consistent trait associations through gene modules than individual genes. Our results suggest that a gene module perspective can contextualize genetic associations and prioritize alternative treatment targets when GWAS hits are not druggable.

scholarly journals Identification of Split-GAL4 Drivers and Enhancers That Allow Regional Cell Type Manipulations of the Drosophila melanogaster Intestine

Genetics ◽  
2020 ◽  
Vol 216 (4) ◽  
pp. 891-903
Author(s):  
Ishara S. Ariyapala ◽  
Jessica M. Holsopple ◽  
Ellen M. Popodi ◽  
Dalton G. Hartwick ◽  
Lily Kahsai ◽  
...  
Keyword(s):  
Gene Expression ◽  
Progenitor Cells ◽  
Expression Patterns ◽  
Cell Types ◽  
Enteroendocrine Cells ◽  
Epithelial Tissue ◽  
Cell Type ◽  
Cell Functions ◽  
Distinct Subset ◽  
Targeted Gene Expression

The Drosophila adult midgut is a model epithelial tissue composed of a few major cell types with distinct regional identities. One of the limitations to its analysis is the lack of tools to manipulate gene expression based on these regional identities. To overcome this obstacle, we applied the intersectional split-GAL4 system to the adult midgut and report 653 driver combinations that label cells by region and cell type. We first identified 424 split-GAL4 drivers with midgut expression from ∼7300 drivers screened, and then evaluated the expression patterns of each of these 424 when paired with three reference drivers that report activity specifically in progenitor cells, enteroendocrine cells, or enterocytes. We also evaluated a subset of the drivers expressed in progenitor cells for expression in enteroblasts using another reference driver. We show that driver combinations can define novel cell populations by identifying a driver that marks a distinct subset of enteroendocrine cells expressing genes usually associated with progenitor cells. The regional cell type patterns associated with the entire set of driver combinations are documented in a freely available website, providing information for the design of thousands of additional driver combinations to experimentally manipulate small subsets of intestinal cells. In addition, we show that intestinal enhancers identified with the split-GAL4 system can confer equivalent expression patterns on other transgenic reporters. Altogether, the resource reported here will enable more precisely targeted gene expression for studying intestinal processes, epithelial cell functions, and diseases affecting self-renewing tissues.

scholarly journals Characterization of Gene Expression Patterns among Artificially Developed Cancer Stem Cells Using Spherical Self-Organizing Map

2016 ◽  
Vol 15 ◽  
pp. CIN.S39839 ◽  
Author(s):  
Akimasa Seno ◽  
Tomonari Kasai ◽  
Masashi Ikeda ◽  
Arun Vaidyanath ◽  
Junko Masuda ◽  
...  
Keyword(s):  
Gene Expression ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Human Cancer ◽  
Expression Patterns ◽  
Embryonic Stem ◽  
Culture Conditions ◽  
Self Organizing Map ◽  
Cancer Tissues ◽  
Self Organizing

We performed gene expression microarray analysis coupled with spherical self-organizing map (sSOM) for artificially developed cancer stem cells (CSCs). The CSCs were developed from human induced pluripotent stem cells (hiPSCs) with the conditioned media of cancer cell lines, whereas the CSCs were induced from primary cell culture of human cancer tissues with defined factors ( OCT3/4, SOX2, and KLF4). These cells commonly expressed human embryonic stem cell (hESC)/hiPSC-specific genes ( POU5F1, SOX2, NANOG, LIN28, and SALL4) at a level equivalent to those of control hiPSC 201B7. The sSOM with unsupervised method demonstrated that the CSCs could be divided into three groups based on their culture conditions and original cancer tissues. Furthermore, with supervised method, sSOM nominated TMED9, RNASE1, NGFR, ST3GAL1, TNS4, BTG2, SLC16A3, CD177, CES1, GDF15, STMN2, FAM20A, NPPB, CD99, MYL7, PRSS23, AHNAK, and LOC152573 genes commonly upregulating among the CSCs compared to hiPSC, suggesting the gene signature of the CSCs.

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