The cloning cycle: from amphibia to mammals and back

2001 ◽  
Vol 9 (1) ◽  
pp. 3-31 ◽  
Author(s):  
IM Lewis ◽  
MJ Munsie ◽  
AJ French ◽  
R Daniels ◽  
AO Trounson
Keyword(s):  
Developmental Biology ◽  
Nuclear Transfer ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Nuclear Material ◽  
Laboratory Animals ◽  
Cell Type ◽  
Differentiated Cells ◽  
Different Cell Types ◽  
Fertilized Egg

The process whereby a fertilized egg develops into more than 200 different cell types and forms a new individual is both intriguing and fundamental to developmental biology. The systematic pathway of cellular differentiation is generally thought to confer a restriction whereby differentiated cells lose the capacity to change into other cell types. However, the substitution of nuclear material from one cell type (egg) with that of another, in a process known as nuclear transfer or cloning, can reverse this restriction by removing epigenetic modifications to the chromatin structure. The ability to clone new individuals using this procedure was achieved some 30 years ago in amphibia in an effort to understand cellular differentiation. More recently, with the advent of improved embryo micromanipulation techniques, the technology has been applied to domestic and laboratory animals.

The Developmental Capacity of Nuclei taken from Intestinal Epithelium Cells of Feeding Tadpoles

Development ◽  
1962 ◽  
Vol 10 (4) ◽  
pp. 622-640 ◽  
Author(s):  
J. B. Gurdon
Keyword(s):  
Genetic Information ◽  
Cellular Differentiation ◽  
Cell Types ◽  
Nuclear Transplantation ◽  
Differentiated Cells ◽  
Developmental Capacity ◽  
Nuclear Changes ◽  
Different Cell Types ◽  
Epithelium Cells ◽  
Saline Medium

An important problem in embryology is whether the differentiation of cells depends upon a stable restriction of the genetic information contained in their nuclei. The technique of nuclear transplantation has shown to what extent the nuclei of differentiating cells can promote the formation of different cell types (e.g. King & Briggs, 1956; Gurdon, 1960c). Yet no experiments have so far been published on the transplantation of nuclei from fully differentiated normal cells. This is partly because it is difficult to obtain meaningful results from such experiments. The small amount of cytoplasm in differentiated cells renders their nuclei susceptible to damage through exposure to the saline medium, and this makes it difficult to assess the significance of the abnormalities resulting from their transplantation. It is, however, very desirable to know the developmental capacity of such nuclei, since any nuclear changes which are necessarily involved in cellular differentiation must have already taken place in cells of this kind.

scholarly journals SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models

2020 ◽  
Author(s):  
Yupeng Wang ◽  
Rosario B. Jaime-Lara ◽  
Abhrarup Roy ◽  
Ying Sun ◽  
Xinyue Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Dna Sequences ◽  
Cell Types ◽  
Learning Models ◽  
Cell Type ◽  
Coding Sequences ◽  
Sequence Features ◽  
Cell Type Specific ◽  
Different Cell Types

AbstractWe propose SeqEnhDL, a deep learning framework for classifying cell type-specific enhancers based on sequence features. DNA sequences of “strong enhancer” chromatin states in nine cell types from the ENCODE project were retrieved to build and test enhancer classifiers. For any DNA sequence, sequential k-mer (k=5, 7, 9 and 11) fold changes relative to randomly selected non-coding sequences were used as features for deep learning models. Three deep learning models were implemented, including multi-layer perceptron (MLP), Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN). All models in SeqEnhDL outperform state-of-the-art enhancer classifiers including gkm-SVM and DanQ, with regard to distinguishing cell type-specific enhancers from randomly selected non-coding sequences. Moreover, SeqEnhDL is able to directly discriminate enhancers from different cell types, which has not been achieved by other enhancer classifiers. Our analysis suggests that both enhancers and their tissue-specificity can be accurately identified according to their sequence features. SeqEnhDL is publicly available at https://github.com/wyp1125/SeqEnhDL.

scholarly journals Stem-cell-ubiquitous genes spatiotemporally coordinate division through regulation of stem-cell-specific gene networks

2019 ◽  
Vol 10 (1) ◽  
Author(s):  
Natalie M. Clark ◽  
Eli Buckner ◽  
Adam P. Fisher ◽  
Emily C. Nelson ◽  
Thomas T. Nguyen ◽  
...  
Keyword(s):  
Stem Cell ◽  
Gene Networks ◽  
Cell Types ◽  
Specific Gene ◽  
Cell Renewal ◽  
Cell Type ◽  
Stem Cell Maintenance ◽  
Differentiated Cells ◽  
Cell Type Specific ◽  
Stem Cell Type

AbstractStem cells are responsible for generating all of the differentiated cells, tissues, and organs in a multicellular organism and, thus, play a crucial role in cell renewal, regeneration, and organization. A number of stem cell type-specific genes have a known role in stem cell maintenance, identity, and/or division. Yet, how genes expressed across different stem cell types, referred to here as stem-cell-ubiquitous genes, contribute to stem cell regulation is less understood. Here, we find that, in the Arabidopsis root, a stem-cell-ubiquitous gene, TESMIN-LIKE CXC2 (TCX2), controls stem cell division by regulating stem cell-type specific networks. Development of a mathematical model of TCX2 expression allows us to show that TCX2 orchestrates the coordinated division of different stem cell types. Our results highlight that genes expressed across different stem cell types ensure cross-communication among cells, allowing them to divide and develop harmonically together.

scholarly journals The Emerging Role of Galectins and O-GlcNAc Homeostasis in Processes of Cellular Differentiation

Cells ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 1792 ◽  
Author(s):  
Rada Tazhitdinova ◽  
Alexander V. Timoshenko
Keyword(s):  
Cancer Biology ◽  
Cellular Differentiation ◽  
Current Knowledge ◽  
Expression Profiles ◽  
Cell Types ◽  
Innovative Strategies ◽  
Protein Levels ◽  
Differentiated Cells ◽  
Independent Functions

Galectins are a family of soluble β-galactoside-binding proteins with diverse glycan-dependent and glycan-independent functions outside and inside the cell. Human cells express twelve out of sixteen recognized mammalian galectin genes and their expression profiles are very different between cell types and tissues. In this review, we summarize the current knowledge on the changes in the expression of individual galectins at mRNA and protein levels in different types of differentiating cells and the effects of recombinant galectins on cellular differentiation. A new model of galectin regulation is proposed considering the change in O-GlcNAc homeostasis between progenitor/stem cells and mature differentiated cells. The recognition of galectins as regulatory factors controlling cell differentiation and self-renewal is essential for developmental and cancer biology to develop innovative strategies for prevention and targeted treatment of proliferative diseases, tissue regeneration, and stem-cell therapy.

scholarly journals The complement of desmosomal plaque proteins in different cell types.

1985 ◽  
Vol 101 (4) ◽  
pp. 1442-1454 ◽  
Author(s):  
P Cowin ◽  
H P Kapprell ◽  
W W Franke
Keyword(s):  
Guinea Pig ◽  
Cell Types ◽  
Cardiac Cells ◽  
Myocardial Tissue ◽  
Cell Type ◽  
Wide Range ◽  
Specific Manner ◽  
A Cell ◽  
Cell Type Specific ◽  
Different Cell Types

Desmosomal plaque proteins have been identified in immunoblotting and immunolocalization experiments on a wide range of cell types from several species, using a panel of monoclonal murine antibodies to desmoplakins I and II and a guinea pig antiserum to desmosomal band 5 protein. Specifically, we have taken advantage of the fact that certain antibodies react with both desmoplakins I and II, whereas others react only with desmoplakin I, indicating that desmoplakin I contains unique regions not present on the closely related desmoplakin II. While some of these antibodies recognize epitopes conserved between chick and man, others display a narrow species specificity. The results show that proteins whose size, charge, and biochemical behavior are very similar to those of desmoplakin I and band 5 protein of cow snout epidermis are present in all desmosomes examined. These include examples of simple and pseudostratified epithelia and myocardial tissue, in addition to those of stratified epithelia. In contrast, in immunoblotting experiments, we have detected desmoplakin II only among cells of stratified and pseudostratified epithelial tissues. This suggests that the desmosomal plaque structure varies in its complement of polypeptides in a cell-type specific manner. We conclude that the obligatory desmosomal plaque proteins, desmoplakin I and band 5 protein, are expressed in a coordinate fashion but independently from other differentiation programs of expression such as those specific for either epithelial or cardiac cells.

scholarly journals Distribution and modulation of the cellular receptor for transforming growth factor-beta.

1987 ◽  
Vol 105 (2) ◽  
pp. 965-975 ◽  
Author(s):  
L M Wakefield ◽  
D M Smith ◽  
T Masui ◽  
C C Harris ◽  
M B Sporn
Keyword(s):  
Growth Factor ◽  
Transforming Growth Factor Beta ◽  
Transforming Growth Factor ◽  
Cell Types ◽  
Tgf Beta ◽  
Cell Type ◽  
Down Regulation ◽  
Growth Factor Beta ◽  
Different Cell Types

Scatchard analyses of the binding of transforming growth factor-beta (TGF-beta) to a wide variety of different cell types in culture revealed the universal presence of high affinity (Kd = 1-60 pM) receptors for TGF-beta on every cell type assayed, indicating a wide potential target range for TGF-beta action. There was a strong (r = +0.85) inverse relationship between the receptor affinity and the number of receptors expressed per cell, such that at low TGF-beta concentrations, essentially all cells bound a similar number of TGF-beta molecules per cell. The binding of TGF-beta to various cell types was not altered by many agents that affect the cellular response to TGF-beta, suggesting that modulation of TGF-beta binding to its receptor may not be a primary control mechanism in TGF-beta action. Similarly, in vitro transformation resulted in only relatively small changes in the cellular binding of TGF-beta, and for those cell types that exhibited ligand-induced down-regulation of the receptor, down-regulation was not extensive. Thus the strong conservation of binding observed between cell types is also seen within a given cell type under a variety of conditions, and receptor expression appears to be essentially constitutive. Finally, the biologically inactive form of TGF-beta, which constitutes greater than 98% of autocrine TGF-beta secreted by all of the twelve different cell types assayed, was shown to be unable to bind to the receptor without prior activation in vitro. It is proposed that this may prevent premature interaction of autocrine ligand and receptor in the Golgi apparatus.

scholarly journals Detection of cell-type-specific risk-CpG sites in epigenome-wide association studies

2018 ◽  
Author(s):  
Xiangyu Luo ◽  
Can Yang ◽  
Yingying Wei
Keyword(s):  
High Resolution ◽  
Statistical Method ◽  
Individual Cell ◽  
Association Studies ◽  
Cell Types ◽  
Cell Type ◽  
Cpg Sites ◽  
Cpg Site ◽  
Cell Type Specific ◽  
Different Cell Types

In epigenome-wide association studies, the measured signals for each sample are a mixture of methylation profiles from different cell types. The current approaches to the association detection only claim whether a cytosine-phosphate-guanine (CpG) site is associated with the phenotype or not, but they cannot determine the cell type in which the risk-CpG site is affected by the phenotype. Here, we propose a solid statistical method, HIgh REsolution (HIRE), which not only substantially improves the power of association detection at the aggregated level as compared to the existing methods but also enables the detection of risk-CpG sites for individual cell types.

scholarly journals accuEnhancer: Accurate enhancer prediction by integration of multiple cell type data with deep learning

2020 ◽  
Author(s):  
Yi-An Tung ◽  
Wen-Tse Yang ◽  
Tsung-Ting Hsieh ◽  
Yu-Chuan Chang ◽  
June-Tai Wu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Cell Types ◽  
Regulatory Elements ◽  
Sequence Motifs ◽  
Cell Type ◽  
Enhancer Activity ◽  
Multiple Cell ◽  
Type Data ◽  
Different Cell Types ◽  
Multiple Cell Type

AbstractEnhancers are one class of the regulatory elements that have been shown to act as key components to assist promoters in modulating the gene expression in living cells. At present, the number of enhancers as well as their activities in different cell types are still largely unclear. Previous studies have shown that enhancer activities are associated with various functional data, such as histone modifications, sequence motifs, and chromatin accessibilities. In this study, we utilized DNase data to build a deep learning model for predicting the H3K27ac peaks as the active enhancers in a target cell type. We propose joint training of multiple cell types to boost the model performance in predicting the enhancer activities of an unstudied cell type. The results demonstrated that by incorporating more datasets across different cell types, the complex regulatory patterns could be captured by deep learning models and the prediction accuracy can be largely improved. The analyses conducted in this study demonstrated that the cell type-specific enhancer activity can be predicted by joint learning of multiple cell type data using only DNase data and the primitive sequences as the input features. This reveals the importance of cross-cell type learning, and the constructed model can be applied to investigate potential active enhancers of a novel cell type which does not have the H3K27ac modification data yet.AvailabilityThe accuEnhancer package can be freely accessed at: https://github.com/callsobing/accuEnhancer

scholarly journals A conserved expression signature predicts growth rate and reveals cell & lineage-specific differences

2019 ◽  
Author(s):  
Zhisheng Jiang ◽  
Serena Francesca Generoso ◽  
Marta Badia ◽  
Bernhard Payer ◽  
Lucas B. Carey
Keyword(s):  
Cell Lineage ◽  
Gene Expression Signature ◽  
Cell Types ◽  
Eukaryotic Cell ◽  
Rna Seq ◽  
Cell Type Specificity ◽  
Expression Signature ◽  
Differentiated Cells ◽  
Different Cell Types ◽  
High Degree

By performing RNA-seq on cells FACS sorted by their proliferation rate, this study identifies a gene expression signature capable of predicting proliferation rates in diverse eukaryotic cell types and species. This signature, applied to scRNAseq data from C.elegans, reveals lineage-specific differences in proliferation during development. In contrast to the universality of the proliferation signature, mitochondria and metabolism related genes show a high degree of cell-type specificity; mouse pluripotent stem cells (mESCs) and differentiated cells (fibroblasts) exhibit opposite relations between mitochondria state and proliferation. Furthermore, we identified a slow proliferating subpopulation of mESCs with higher expression of pluripotency genes. Finally, we show that fast and slow proliferating subpopulations are differentially sensitive to mitochondria inhibitory drugs in different cell types.

scholarly journals DPre: computational identification of differentiation bias and genes underlying cell type conversions

2019 ◽  
Author(s):  
Simon Steffens ◽  
Xiuling Fu ◽  
Fangfang He ◽  
Yuhao Li ◽  
Isaac A Babarinde ◽  
...  
Keyword(s):  
Ectopic Expression ◽  
Large Cell ◽  
Cell Types ◽  
Supplementary Information ◽  
Computational Techniques ◽  
Cell Type ◽  
Mixed Cell ◽  
Directional Bias ◽  
Differentiated Cells

Abstract Summary Cells are generally resistant to cell type conversions, but can be converted by the application of growth factors, chemical inhibitors and ectopic expression of genes. However, it remains difficult to accurately identify the destination cell type or differentiation bias when these techniques are used to alter cell type. Consequently, there is demand for computational techniques that can help researchers understand both the cell type and differentiation bias. While advanced tools identifying cell types exist for single cell data and the deconvolution of mixed cell populations, the problem of exploring partially differentiated cells of indeterminate transcriptional identity has not been addressed. To fill this gap, we developed driver-predictor, which relies on scoring per gene transcriptional similarity between RNA-Seq datasets to reveal directional bias of differentiation. By comparing against large cell type transcriptome libraries or a desired target expression profile, the tool enables the user to visualize both the changes in transcriptional identity as well as the genes accounting for the cell type changes. This software will be a powerful tool for researchers to explore in vitro experiments that involve cell type conversions. Availability and implementation Source code is open source under the MIT license and is freely available on https://github.com/LoaloaF/DPre. Supplementary information Supplementary data are available at Bioinformatics online.

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