scholarly journals Structural differences between plasma-membrane 5′-nucleotidase in different cell types as evidenced by antibodies

1985 ◽  
Vol 232 (3) ◽  
pp. 859-862 ◽  
Author(s):  
J Harb ◽  
K Meflah ◽  
S Bernard
Keyword(s):  
Cell Types ◽  
Antigenic Determinants ◽  
Nucleotidase Activity ◽  
Cell Type ◽  
Caudate Nuclei ◽  
The Third ◽  
Common Structure ◽  
Structural Differences ◽  
Structural Nature ◽  
Different Cell Types

Antibodies raised against bovine 5′-nucleotidase inhibit this enzyme as well as 5′-nucleotidase from other bovine tissues, showing common structure(s) between these proteins. However, an IgG fraction directed against the glucidic moiety of the liver enzyme did not cross-react with the enzyme from lymphocyte or caudate nuclei, a clear indication that within the same species the 5′-nucleotidase differs from one cell type to another. In addition, immunoblots after electrophoresis show that the previous antibodies recognize 5′-nucleotidase from human, mouse or chicken origin. However, only human 5′-nucleotidase activity can be inhibited by the antibodies. Thus at least three groups of antigenic determinants must exist on the 5′-nucleotidase: one related to the glucidic moiety of the glycoprotein whose binding inhibits the enzyme activity, another related to the catalytic site, as its binding also led to enzyme inhibition, and a last one of structural nature. It seems that the third group of determinant is common to many species, whereas the second one is more restricted.

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 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.

Anatomy and ultrastructure of the endophytic system of Pilostyles thurberi (Rafflesiaceae)

1985 ◽  
Vol 63 (7) ◽  
pp. 1231-1240 ◽  
Author(s):  
Job Kuijt ◽  
D. Bray ◽  
A. R. Olson
Keyword(s):  
Cell Types ◽  
Sieve Element ◽  
Cell Type ◽  
Discontinuous System ◽  
Sieve Elements ◽  
The Third ◽  
Sieve Plates ◽  
Host Parasite ◽  
Anatomy And Ultrastructure ◽  
Sieve Pores

The endophytic system of Pilostyles thurberi Gray consists of initially uniseriate filaments which develop into an anastomosing complex of larger cortical strands and radial sinkers. In the cortical strands three cell types are recognized, two of which differ largely in the density of the cytoplasm, the shape of the nucleus, and the degree to which the cytoplasm becomes plasmolyzed during fixation. The nuclei of both cell types contain two nucleoli which are physically connected by a nucleolar bridge. The third cell type demonstrates sieve plates, including a calloselike substance in the sieve pores and is consequently considered to be a sieve element. The sieve elements appear to form a discontinuous system and are regarded as a vestigial cell type. Plasmodesmal connections across the host–parasite interface have not been observed.

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 A Rare Case of Third Ventricular Glioblastoma

2021 ◽  
Author(s):  
Asmira Gacic ◽  
Hakija Beculic ◽  
Rasim Skomorac ◽  
Alma Efendic
Keyword(s):  
Spinal Cord ◽  
Glioblastoma Multiforme ◽  
Blood Vessels ◽  
Rare Case ◽  
Third Ventricle ◽  
Cell Types ◽  
The Third ◽  
Different Cell Types ◽  
Brain And Spinal Cord ◽  
The Brain

Glioblastoma, also known as glioblastoma multiforme, is an aggressive type of cancer that is made up of abnormal astrocytic cells, but also contain a mixture of different cell types (including blood vessels) and areas of necrosis. It is often seen in the brain and spinal cord, but glioblastomas are rarely found in the third ventricle. In this case, it was diagnosed in a 22-year-old male patient and we intended to draw

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 CARs: Beyond T Cells and T Cell-Derived Signaling Domains

2020 ◽  
Vol 21 (10) ◽  
pp. 3525 ◽  
Author(s):  
Nico M. Sievers ◽  
Jan Dörrie ◽  
Niels Schaft
Keyword(s):  
Cell Types ◽  
Antigen Binding ◽  
Cell Type ◽  
Transfected Cells ◽  
Effector Functions ◽  
Binding Domains ◽  
Specific Effector ◽  
Different Cell Types ◽  
Intracellular Domains ◽  
Signaling Domains

When optimizing chimeric antigen receptor (CAR) therapy in terms of efficacy, safety, and broadening its application to new malignancies, there are two main clusters of topics to be addressed: the CAR design and the choice of transfected cells. The former focuses on the CAR construct itself. The utilized transmembrane and intracellular domains determine the signaling pathways induced by antigen binding and thereby the cell-specific effector functions triggered. The main part of this review summarizes our understanding of common signaling domains employed in CARs, their interactions among another, and their effects on different cell types. It will, moreover, highlight several less common extracellular and intracellular domains that might permit unique new opportunities. Different antibody-based extracellular antigen-binding domains have been pursued and optimized to strike a balance between specificity, affinity, and toxicity, but these have been reviewed elsewhere. The second cluster of topics is about the cellular vessels expressing the CAR. It is essential to understand the specific attributes of each cell type influencing anti-tumor efficacy, persistence, and safety, and how CAR cells crosstalk with each other and bystander cells. The first part of this review focuses on the progress achieved in adopting different leukocytes for CAR therapy.

Transport properties of the lens

1985 ◽  
Vol 249 (3) ◽  
pp. C181-C190 ◽  
Author(s):  
R. T. Mathias ◽  
J. L. Rae
Keyword(s):  
Transport Properties ◽  
Membrane Transport ◽  
Cell Membranes ◽  
Spatial Localization ◽  
Cell Types ◽  
Physical Structure ◽  
Present Knowledge ◽  
Cell Type ◽  
Electrophysiological Properties ◽  
Different Cell Types

Many studies have shown that the lens is a multicellular syncytial tissue whose electrophysiological properties are the integrated result of membrane transport, low-resistance gap junctions interconnecting the cells, and the restricted extracellular space between cells. There are at least three structurally distinct populations of cells within the lens, and the membrane transport properties of each cell type appear to differ. Indeed, there may be subcellular specialization of membrane transport properties in the surface epithelial cells. We review the physical structure of the lens, its electrical structure, and our present knowledge of the membrane transport properties of the different cell types. Our recent work has focused on radially circulating fluxes generated by the spatial localization of membrane transport in surface cell membranes versus inner fiber cell membranes. We review this work and present some simplified models of the results with some discussion of physiological implications.

Selective staining of secretory granules of adrenal medullary cells by silver methenamine: A light and electron microscopic study

1968 ◽  
Vol 46 (5) ◽  
pp. 745-747 ◽  
Author(s):  
William W. L. Chang ◽  
Sergio A. Bencosme
Keyword(s):  
Microscopic Study ◽  
Adrenal Medulla ◽  
Electron Microscopic Study ◽  
Secretory Granules ◽  
Cell Types ◽  
Cell Type ◽  
Glutaraldehyde Fixation ◽  
Electron Microscopic ◽  
Selective Staining ◽  
The Third

A reevaluation of the silver methenamine reaction as an electron stain has ensued from recent use of glutaraldehyde fixation alone. By this technique, three cell types of rat adrenal medulla were found: (i) the norepinephrine-containing cells showed selectively stained, irregular, black granules; (ii) the epinephrine-containing cells showed round, light-grey granules; and (iii) the third cell type showed round granules like those of epinephrine-containing cells, but black in color, similar to those of the norepinephrine-containing cells.

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