Specific and dynamic lignification at the cell-type level controls plant physiology and adaptability

Lignins, abundant phenolic cell wall polymers that accumulate in vascular tissue, were essential for plant terrestrialization as they enable sap conduction and mechanical support. Although lignification is currently understood as a random process, different cell types accumulate lignins with different compositions. The biological significance of these cellular differences is however still unknown. We performed single cell analyses to decipher the specific roles of different lignins and their residues on sap conduction and mechanical strengthening in plant xylem, using inducible pluripotent cell cultures and genetically modified whole plants. We show that specific lignins dynamically accumulate in each cell type and their morphotypes using distinct genetic programs, and that different lignin residues have non-redundant roles on plant biomechanical and hydraulic properties. Lignin is therefore a dynamic polymer changing composition to tailor the load bearing and sap conduction properties of each cells, in order for plants to adapt to developmental and environmental constraints.

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

10.1101/2020.05.13.093997 ◽

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.

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The complement of desmosomal plaque proteins in different cell types.

The Journal of Cell Biology ◽

10.1083/jcb.101.4.1442 ◽

1985 ◽

Vol 101 (4) ◽

pp. 1442-1454 ◽

Cited By ~ 122

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.

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

The Journal of Cell Biology ◽

10.1083/jcb.105.2.965 ◽

1987 ◽

Vol 105 (2) ◽

pp. 965-975 ◽

Cited By ~ 374

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.

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

10.1101/415109 ◽

2018 ◽

Cited By ~ 1

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.

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

10.1101/2020.11.10.375717 ◽

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

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

Biochemical Journal ◽

10.1042/bj2320859 ◽

1985 ◽

Vol 232 (3) ◽

pp. 859-862 ◽

Cited By ~ 14

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.

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The ultrastructure of the dimorphic infection cells of Haptoglossa heteromorpha illustrates the developmental plasticity of infection apparatus structures in a nematode parasite

Canadian Journal of Botany ◽

10.1139/b00-074 ◽

2000 ◽

Vol 78 (8) ◽

pp. 1095-1107 ◽

Cited By ~ 2

Author(s):

Sally L Glockling ◽

Gordon W Beakes

Keyword(s):

Developmental Plasticity ◽

Biological Significance ◽

Cell Types ◽

Ventral Side ◽

Nematophagous Fungus ◽

Fibrillar Material ◽

Host Parasite Interactions ◽

Host Parasite ◽

Different Cell Types ◽

Direct Confirmation

The nematophagous fungus Haptoglossa heteromorpha Glockling & Beakes is unusual in this genus, as it consistently produces two morphologically distinct infection cells that develop respectively from the large and small aplanospores. The large infection cells are typical "gun cells" and are uninucleate with over half the cytoplasmic volume occupied by the basal vacuole. However, the small infection cells are considerably modified in their structure. They have an elongate tapered morphology with a bilobed base. Small infection cells are binucleate and contain both apical and basal vacuolar regions. The inverted injection tube is highly modified and originates at a raised padlike region on the mid ventral side of the cell. This protruding pad is associated with a diffuse covering of fibrillar material. The bore region extends deep into the cell and the highly compressed missile chamber is located beneath the hindmost nucleus, near the base of the cell. Finally, there is a more typical tube tail segment, which winds between the nuclei and eventually terminates beneath the anterior vacuole. The differences with gun cells of previously described Haptoglossa species are documented. The functional and biological significance of these two different infection cells is discussed. Only the large gun cells were observed to discharge to form sporidia. It seems unlikely that the highly modified smaller cells can infect in this way. It may be that the two different cell types have evolved to infect different hosts, but no direct confirmation of this could be obtained.Key words: Haptoglossa, host-parasite interactions, infection (gun) cell, nematophagous fungi, ultrastructure.

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

International Journal of Molecular Sciences ◽

10.3390/ijms21103525 ◽

2020 ◽

Vol 21 (10) ◽

pp. 3525 ◽

Cited By ~ 4

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.

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Transport properties of the lens

AJP Cell Physiology ◽

10.1152/ajpcell.1985.249.3.c181 ◽

1985 ◽

Vol 249 (3) ◽

pp. C181-C190 ◽

Cited By ~ 55

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.

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Identification of cell states using super-enhancer RNA

BMC Genomics ◽

10.1186/s12864-021-08092-1 ◽

2021 ◽

Vol 22 (S3) ◽

Author(s):

Yueh-Hua Tu ◽

Hsueh-Fen Juan ◽

Hsuan-Cheng Huang

Keyword(s):

Messenger Rna ◽

Time Course ◽

Developmental Process ◽

Cell Types ◽

Regulatory Elements ◽

Cell Type ◽

Sequencing Data ◽

Enhancer Rna ◽

Super Enhancer ◽

Different Cell Types

Abstract Background A new class of regulatory elements called super-enhancers, comprised of multiple neighboring enhancers, have recently been reported to be the key transcriptional drivers of cellular, developmental, and disease states. Results Here, we defined super-enhancer RNAs as highly expressed enhancer RNAs that are transcribed from a cluster of localized genomic regions. Using the cap analysis of gene expression sequencing data from FANTOM5, we systematically explored the enhancer and messenger RNA landscapes in hundreds of different cell types in response to various environments. Applying non-negative matrix factorization (NMF) to super-enhancer RNA profiles, we found that different cell types were well classified. In addition, through the NMF of individual time-course profiles from a single cell-type, super-enhancer RNAs were clustered into several states with progressive patterns. We further investigated the enriched biological functions of the proximal genes involved in each pattern, and found that they were associated with the corresponding developmental process. Conclusions The proposed super-enhancer RNAs can act as a good alternative, without the complicated measurement of histone modifications, for identifying important regulatory elements of cell type specification and identifying dynamic cell states.

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