scholarly journals Snapshot: clustering and visualizing epigenetic history during cell differentiation

2018 ◽  
Author(s):  
Guanjue Xiang ◽  
Belinda Giardine ◽  
Lin An ◽  
Chen Sun ◽  
Cheryl A. Keller ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Epigenetic Modification ◽  
Cell Lineage ◽  
Cell Types ◽  
New Method ◽  
Pivotal Role ◽  
International Project ◽  
Hierarchical Visualization ◽  
Using Data

AbstractEpigenetic modification of chromatin plays a pivotal role in regulating gene expression during cell differentiation. The scale and complexity of epigenetic data pose significant challenges for biologists to identify the regulatory events controlling cell differentiation. Here, we present a new method, called Snapshot, that uses epigenetic data to generate a hierarchical visualization for DNA regions with epigenetic features segregating along any given cell differentiation hierarchy of interest. Different hierarchies of cell types may be used to highlight the epigenetic history specific to any particular cell lineage. We demonstrate the utility of Snapshot using data from the VISION project, an international project for ValIdated Systematic IntegratiON of epigenomic data in mouse and human hematopoiesis.Availability and implementation: https://github.com/guanjue/snapshot

scholarly journals JAZF1-SUZ12 dysregulates PRC2 function and gene expression during cell differentiation

2021 ◽  
Author(s):  
Manuel Tavares ◽  
Garima Khandelwal ◽  
Joanne Mutter ◽  
Keijo Viiri ◽  
Manuel Beltran ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Target Genes ◽  
Histone H3 ◽  
Endometrial Stromal Sarcoma ◽  
Cell Types ◽  
Polycomb Repressive Complex 2 ◽  
Stromal Sarcoma ◽  
N Terminus ◽  
Undifferentiated Cells

Polycomb repressive complex 2 (PRC2) methylates histone H3 lysine 27 (H3K27me3) to maintain repression of genes specific for other cell types and is essential for cell differentiation. In endometrial stromal sarcoma, the PRC2 subunit SUZ12 is often fused with the NuA4/TIP60 subunit JAZF1. Here, we show that JAZF1-SUZ12 dysregulates PRC2 composition, recruitment, histone modification, gene expression and cell differentiation. The loss of the SUZ12 N-terminus in the fusion protein disrupted interaction with the PRC2 accessory factors JARID2, EPOP and PALI1 and prevented recruitment of PRC2 from RNA to chromatin. In undifferentiated cells, JAZF1-SUZ12 occupied PRC2 target genes but gained a JAZF1-like binding profile during cell differentiation. JAZF1-SUZ12 reduced H3K27me3 and increased H4Kac at PRC2 target genes, and this was associated with disruption in gene expression and cell differentiation programs. These results reveal the defects in chromatin regulation caused by JAZF1-SUZ12, which may underlie its role in oncogenesis.

scholarly journals Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

2020 ◽  
Author(s):  
Nan Papili Gao ◽  
Olivier Gandrillon ◽  
András Páldi ◽  
Ulysse Herbach ◽  
Rudiyanto Gunawan
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Single Cell ◽  
Burst Size ◽  
Mechanistic Model ◽  
Cell Lineage ◽  
Transcriptional Analysis ◽  
Cell Gene Expression ◽  
Branching Point ◽  
Cell Gene

ABSTRACTWe employed our previously-described single-cell gene expression analysis CALISTA (Clustering And Lineage Inference in Single-Cell Transcriptional Analysis) to evaluate transcriptional uncertainty at the single-cell level using a stochastic mechanistic model of gene expression. We reconstructed a transcriptional uncertainty landscape during cell differentiation by visualizing single-cell transcriptional uncertainty surface over a two dimensional representation of the single-cell gene expression data. The reconstruction of transcriptional uncertainty landscapes for ten publicly available single-cell gene expression datasets from cell differentiation processes with linear, single or multi-branching cell lineage, reveals universal features in the cell differentiation trajectory that include: (i) a peak in single-cell uncertainty during transition states, and in systems with bifurcating differentiation trajectories, each branching point represents a state of high transcriptional uncertainty; (ii) a positive correlation of transcriptional uncertainty with transcriptional burst size and frequency; (iii) an increase in RNA velocity preceeding the increase in the cell transcriptional uncertainty. Finally, we provided biological interpretations of the universal rise-then-fall profile of the transcriptional uncertainty landscape, including a link with the Waddington’s epigenetic landscape, that is generalizable to every cell differentiation system.

scholarly journals Mapping the evolving landscape of super-enhancers during cell differentiation

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yan Kai ◽  
Bin E. Li ◽  
Ming Zhu ◽  
Grace Y. Li ◽  
Fei Chen ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Differentiation ◽  
Target Gene ◽  
Target Genes ◽  
De Novo ◽  
Cell Types ◽  
Functional Enrichment ◽  
Target Gene Expression ◽  
Differentiated Cells ◽  
Super Enhancer

Abstract Background Super-enhancers are clusters of enhancer elements that play critical roles in the maintenance of cell identity. Current investigations on super-enhancers are centered on the established ones in static cell types. How super-enhancers are established during cell differentiation remains obscure. Results Here, by developing an unbiased approach to systematically analyze the evolving landscape of super-enhancers during cell differentiation in multiple lineages, we discover a general trend where super-enhancers emerge through three distinct temporal patterns: conserved, temporally hierarchical, and de novo. The three types of super-enhancers differ further in association patterns in target gene expression, functional enrichment, and 3D chromatin organization, suggesting they may represent distinct structural and functional subtypes. Furthermore, we dissect the enhancer repertoire within temporally hierarchical super-enhancers, and find enhancers that emerge at early and late stages are enriched with distinct transcription factors, suggesting that the temporal order of establishment of elements within super-enhancers may be directed by underlying DNA sequence. CRISPR-mediated deletion of individual enhancers in differentiated cells shows that both the early- and late-emerged enhancers are indispensable for target gene expression, while in undifferentiated cells early enhancers are involved in the regulation of target genes. Conclusions In summary, our analysis highlights the heterogeneity of the super-enhancer population and provides new insights to enhancer functions within super-enhancers.

scholarly journals Cell Type Assignments for Spatial Transcriptomics Data

2021 ◽  
Author(s):  
Haotian Teng ◽  
Ye Yuan ◽  
Ziv Bar-Joseph
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
New Method ◽  
Inhibitory Neurons ◽  
Cell Type ◽  
Inference Algorithms ◽  
Probabilistic Function ◽  
Transcriptomics Data

ABSTRACTMotivationRecent advancements in fluorescence in situ hybridization (FISH) techniques enable them to concurrently obtain information on the location and gene expression of single cells. A key question in the initial analysis of such spatial transcriptomics data is the assignment of cell types. To date, most studies used methods that only rely on the expression levels of the genes in each cell for such assignments. To fully utilize the data and to improve the ability to identify novel sub-types we developed a new method, FICT, which combines both expression and neighborhood information when assigning cell types.ResultsFICT optimizes a probabilistic function that we formalize and for which we provide learning and inference algorithms. We used FICT to analyze both simulated and several real spatial transcriptomics data. As we show, FICT can accurately identify cell types and sub-types improving on expression only methods and other methods proposed for clustering spatial transcriptomics data. Some of the spatial sub-types identified by FICT provide novel hypotheses about the new functions for excitatory and inhibitory neurons.AvailabilityFICT is available at: https://github.com/haotianteng/[email protected]

scholarly journals Bcl11b, a novel GATA3-interacting protein, suppresses Th1 while limiting Th2 cell differentiation

2018 ◽  
Vol 215 (5) ◽  
pp. 1449-1462 ◽  
Author(s):  
Difeng Fang ◽  
Kairong Cui ◽  
Gangqing Hu ◽  
Rama Krishna Gurram ◽  
Chao Zhong ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Cell Differentiation ◽  
Epigenetic Modification ◽  
B Cell Lymphoma ◽  
Regulatory Elements ◽  
Th2 Cells ◽  
Th2 Cell

GATA-binding protein 3 (GATA3) acts as the master transcription factor for type 2 T helper (Th2) cell differentiation and function. However, it is still elusive how GATA3 function is precisely regulated in Th2 cells. Here, we show that the transcription factor B cell lymphoma 11b (Bcl11b), a previously unknown component of GATA3 transcriptional complex, is involved in GATA3-mediated gene regulation. Bcl11b binds to GATA3 through protein–protein interaction, and they colocalize at many important cis-regulatory elements in Th2 cells. The expression of type 2 cytokines, including IL-4, IL-5, and IL-13, is up-regulated in Bcl11b-deficient Th2 cells both in vitro and in vivo; such up-regulation is completely GATA3 dependent. Genome-wide analyses of Bcl11b- and GATA3-regulated genes (from RNA sequencing), cobinding patterns (from chromatin immunoprecipitation sequencing), and Bcl11b-modulated epigenetic modification and gene accessibility suggest that GATA3/Bcl11b complex is involved in limiting Th2 gene expression, as well as in inhibiting non-Th2 gene expression. Thus, Bcl11b controls both GATA3-mediated gene activation and repression in Th2 cells.

scholarly journals Histone methylations in the developing central nervous system and in neural tube defects

Neuroforum ◽  
2018 ◽  
Vol 24 (2) ◽  
pp. A85-A94
Author(s):  
Alejandro Villarreal ◽  
Henriette Franz ◽  
Tanja Vogel
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Nervous System ◽  
Cell Differentiation ◽  
Neural Tube ◽  
Neural Tube Defects ◽  
System Development ◽  
Epigenetic Modification ◽  
Nervous System Development ◽  
Central Nervous System Development

Abstract Understanding central nervous system genesis is of crucial relevance to decode different human diseases such as microcephaly or neural tube defects, which arise from incorrect developmental processes. Epigenetic mechanisms regulate gene expression in a spatio-temporal manner and are implicated in diverse cellular actions one of which is cell differentiation. Therefore, the study of these mechanisms is of great relevance in the context of development and disease. In this article, we will review histone methylations as epigenetic modification and how they impact on gene expression and cell differentiation in central nervous system development and neural differentiation. Further, we will discuss an emerging link between histone methylation in the etiology of neural tube defects. We will specifically highlight the role of the disruptor of telomeric silencing like 1 (DOT1L) and histone H3 lysine 79 methylation (H3K79me), which is an unusual histone modification with implication for proper central nervous system development.

scholarly journals Interleukin (IL)-4 inhibits IL-10 to promote IL-12 production by dendritic cells

2005 ◽  
Vol 201 (12) ◽  
pp. 1899-1903 ◽  
Author(s):  
Yongxue Yao ◽  
Wei Li ◽  
Mark H. Kaplan ◽  
Cheong-Hee Chang
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Dendritic Cells ◽  
Cell Differentiation ◽  
B Cells ◽  
Histone Acetylation ◽  
Cell Lineage ◽  
Down Regulation ◽  
Th1 Cell ◽  
Th2 Cell

Interleukin (IL)-4 is known to be the most potent cytokine that can initiate Th2 cell differentiation. Paradoxically, IL-4 instructs dendritic cells (DCs) to promote Th1 cell differentiation. We investigated the mechanisms by which IL-4 directs CD4 T cells toward the Th1 cell lineage. Our study demonstrates that the IL-4–mediated induction of Th1 cell differentiation requires IL-10 production by DCs. IL-4 treatment of DCs in the presence of lipopolysaccharide or CpG resulted in decreased production of IL-10, which was accompanied by enhanced IL-12 production. In IL-10–deficient DCs, the level of IL-12 was greatly elevated and, more importantly, the ability of IL-4 to up-regulate IL-12 was abrogated. Interestingly, IL-4 inhibited IL-10 production by DCs but not by B cells. The down-regulation of IL-10 gene expression by IL-4 depended on Stat6 and was at least partly caused by decreased histone acetylation of the IL-10 promoter. These data indicate that IL-4 plays a key role in inducing Th1 cell differentiation by instructing DCs to produce less IL-10.

scholarly journals Expression of BMPRIA on human thymic NK cell precursors: role of BMP signaling in intrathymic NK cell development

Blood ◽  
2012 ◽  
Vol 119 (8) ◽  
pp. 1861-1871 ◽  
Author(s):  
Laura Hidalgo ◽  
Víctor G. Martínez ◽  
Jaris Valencia ◽  
Carmen Hernández-López ◽  
Miriam N. Vázquez ◽  
...  
Keyword(s):  
Transcription Factors ◽  
Cell Differentiation ◽  
Nk Cells ◽  
Nk Cell ◽  
Cell Lineage ◽  
Cell Types ◽  
Cell Development ◽  
Bmp Signaling ◽  
Type Ia ◽  
Nk Cell Differentiation

Abstract The bone morphogenetic protein (BMP) signaling pathway regulates survival, proliferation, and differentiation of several cell types in multiple tissues, including the thymus. Previous reports have shown that BMP signaling negatively regulates T-cell development. Here, we study the subpopulation of early human intrathymic progenitors expressing the type IA BMP receptor (BMPRIA) and provide evidence that CD34+CD1a−BMPRIA+ precursor cells mostly express surface cell markers and transcription factors typically associated with NK cell lineage. These CD34+ cells mostly differentiate into functional CD56+ natural killer (NK) cells when they are cocultured with thymic stromal cells in chimeric human-mouse fetal thymic organ cultures and also in the presence of SCF and IL-15. Moreover, autocrine BMP signaling can promote the differentiation of thymic NK cells by regulating the expression of key transcription factors required for NK cell lineage (eg, Id3 and Nfil3) as well as one of the components of IL-15 receptor, CD122. Subsequently, the resulting population of IL-15–responsive NK cell precursors can be expanded by IL-15, whose action is mediated by BMP signaling during the last steps of thymic NK cell differentiation. Our results strongly suggest that BMPRIA expression identifies human thymic NK cell precursors and that BMP signaling is relevant for NK cell differentiation in the human thymus.

scholarly journals Transcriptomic correlates of electrophysiological and morphological diversity within and across neuron types

2019 ◽  
Author(s):  
Claire Bomkamp ◽  
Shreejoy Tripathy ◽  
Carolina Bengtsson Gonzales ◽  
Jens Hjerling Leffler ◽  
Ann Marie Craig ◽  
...  
Keyword(s):  
Gene Expression ◽  
Morphological Diversity ◽  
Cell Types ◽  
Morphological Properties ◽  
Cell Type ◽  
Phenotypic Differences ◽  
Inhibitory Cell ◽  
Cell Class ◽  
Using Data

In order to further our understanding of how gene expression contributes to key functional properties of neurons, we combined publicly accessible gene expression, electrophysiology, and morphology measurements to identify cross-cell type correlations between these data modalities. Building on our previous work using a similar approach, we distinguished between correlations which were "class-driven," meaning those that could be explained by differences between excitatory and inhibitory cell classes, and those that reflected graded phenotypic differences within classes. Taking cell class identity into account increased the degree to which our results replicated in an independent dataset as well as their correspondence with known modes of ion channel function based on the literature. We also found a smaller set of genes whose relationships to electrophysiological or morphological properties appear to be specific to either excitatory or inhibitory cell types. Next, using data from Patch-seq experiments, allowing simultaneous single-cell characterization of gene expression and electrophysiology, we found that some of the gene-property correlations observed across cell types were further predictive of within-cell type heterogeneity. In summary, we have identified a number of relationships between gene expression, electrophysiology, and morphology that provide testable hypotheses for future studies.

scholarly journals Connection up- and down-regulation expression analysis of microarrays (CU-DREAM): a physiogenomic discovery tool

2011 ◽  
Vol 5 (2) ◽  
pp. 257-262 ◽  
Author(s):  
Chatchawit Aporntewan ◽  
Apiwat Mutirangura
Keyword(s):  
Gene Expression ◽  
Gene Regulation ◽  
Statistical Significance ◽  
Cell Types ◽  
Gene Expression Omnibus ◽  
Hek293 Cells ◽  
Genetic Changes ◽  
Microarray Experiments ◽  
Lung Cancers ◽  
Using Data

Abstract Background: Many microarray experiments have been conducted during recent years, and scores of gene expression data have been archived in public databases. The use of data from multiple experiments can provide valuable information. However, there is a lack of convenient tools to compare datasets in this manner. Objective: Implement software, called CU-DREAM, to compare the datasets of two microarray experiments. CUDREAM is easy to use and compatible with Gene Expression Omnibus (GEO). Subjects and methods: Five experiments were used to demonstrate the functionality of CU-DREAM. These are GSE6791, GSE7803, GSE5816, GSE4246, and GSE13638 for studies of cancers and RNA interference. Results: All six showcases demonstrated the validity of the CU-DREAM approach. One showcase could confirm the regulation of genes identified in two independent experiments on cervical cancer. The statistical significance was lower compared with cervical and lung cancers. In addition, CU-DREAM could identify isoform changes in lung cancer. The last showcase demonstrated that Dicer- and Ago2-depleted cells or Dicer-depleted HeLa and HEK293 cells shared the same gene regulation pathways. CU-DREAM had seven main functions: 1) to identify genes that are up- and down-regulated in an experiment, 2) to validate significantly regulated genes using data from another experiment, 3) to determine if two different diseases have a similar effect on gene regulation, 4) to identify isoform-changed genes, 5) to determine if cells share gene regulation mechanisms, 6) to identify common gene regulation pathways even when comparing two different cell types, and 7) to identify down-stream genes that are regulated by the conditions of the analyzed experiments. Conclusion: CU-DREAM is an effective tool for the pre-screening of drugs, substances or environmental insults or the identification of the genetic changes that are associated with pathological conditions (CU-DREAM can be downloaded from: http://pioneer.netserv.chula.ac.th/~achatcha/cu-dream).

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