Actin, alpha-actinin, and tropomyosin interaction in the structural organization of actin filaments in nonmuscle cells

During the spreading of a population of rat embryo cells, approximately 40% of the cells develop a strikingly regular network which precedes the formation of the straight actin filament bundles seen in the fully spread out cells. Immunofluorescence studies with antibodies specific for the skeletal muscle structural proteins actin, alpha-actinin, and tropomyosin indicate that this network is composed of foci containing actin and alpha-actinin, connected by tropomyosin-associated actin filaments. Actin filaments, having both tropomyosin and alpha-actinin associated with them, are also seen to extend from the vertices of this network to the edges of the cell. These results demonstrate a specific interaction of alpha-actinin and tropomyosin with actin filaments during the assembly and organization of the actin filament bundles of tissue culture cells. The three-dimensional network they form may be regarded as the structural precursor and the vertices of this network as the organization centers of the ultimately formed actin filament bundles of the fully spread out cells.     

Geometry of the Gene Expression Space of Individual Cells

There is a revolution in the ability to analyze gene expression of single cells in a tissue. To understand this data we must comprehend how cells are distributed in a high-dimensional gene expression space. One open question is whether cell types form discrete clusters or whether gene expression forms a continuum of states. If such a continuum exists, what is its geometry? Recent theory on evolutionary trade-offs suggests that cells that need to perform multiple tasks are arranged in a polygon or polyhedron (line, triangle, tetrahedron and so on, generally called polytopes) in gene expression space, whose vertices are the expression profiles optimal for each task. Here, we analyze single-cell data from human and mouse tissues profiled using a variety of single-cell technologies. We fit the data to shapes with different numbers of vertices, compute their statistical significance, and infer their tasks. We find cases in which single cells fill out a continuum of expression states within a polyhedron. This occurs in intestinal progenitor cells, which fill out a tetrahedron in gene expression space. The four vertices of this tetrahedron are each enriched with genes for a specific task related to stemness and early differentiation. A polyhedral continuum of states is also found in spleen dendritic cells, known to perform multiple immune tasks: cells fill out a tetrahedron whose vertices correspond to key tasks related to maturation, pathogen sensing and communication with lymphocytes. A mixture of continuum-like distributions and discrete clusters is found in other cell types, including bone marrow and differentiated intestinal crypt cells. This approach can be used to understand the geometry and biological tasks of a wide range of single-cell datasets. The present results suggest that the concept of cell type may be expanded. In addition to discreet clusters in gene-expression space, we suggest a new possibility: a continuum of states within a polyhedron, in which the vertices represent specialists at key tasks.     

Evidence for initiation of microtubules in discrete regions of the cell cortex in Azolla root-tip cells,and an hypothesis on the development of cortical arrays of microtubules

Complexes of microtubules, vesicles, and (to varying degrees) dense matrix material around the microtubules were seen along the edges of cells in root apices of Azolla pinnata R.Br. (viewing the cells as polyhedra with faces, vertices and edges). They are best developed after cytokinesis has been completed, when the daughter cells are reinstating their interphase arrays of microtubules. They are not confined to edges made by the junction of new cell plates with parental walls, but occur also along older edges. Similar matrices and vesicles are seen amongst phragmoplast microtubules and where pre-prophase bands intersect the edges of cells. It is suggested that the complexes participate in the development of cortical arrays of microtubules. The observations are combined with others, made on pre-prophase bands and on the substructure of cortical arrays lying against the faces of cells, to develop an hypothesis on the development of cortical microtubules, summarised below: Microtubules are nucleated along the edges of cells, at first growing in unspecified orientations and then becoming bridged to the plasma membrane. Parallelism of microtubules in the arrays arises by inter-tubule cross-bridging. Lengths of microtubule are released from, or break off, the nucleating centres and are moved out onto the face of the cell by intertubule and tubule-membrane sliding, thus accounting for the presence there of short tubules with randomly placed terminations. The nucleating zones along cell edges might have vectorial properties, and thus be able to control the orientation of the microtubules on the different faces of the cell. Also, localised activation could generate localised arrays, especially pre-prophase bands in specified sites and planes. Two possible reasons for the spatial restriction of nucleation to cell edges are considered. One is that the geometry of an edge is itself important; the other is that along most cell edges there is a persistent specialised zone, inherited at cytokinesis by the daughter cells when the cell plate bisects the former pre-prophase-band zone.     

A unique role for heat shock protein 70 and its binding partner tissue transglutaminase in cancer cell migration

Cell migration is essential for several important biological outcomes and is involved in various developmental disorders and disease states including cancer cell invasiveness and metastasis. A fundamental step in cell migration is the development of a leading edge. By using HeLa carcinoma cells as an initial model system, we uncovered a surprising role for the heat shock protein 70 (Hsp70) and its ability to bind the protein cross-linking enzyme, tissue transglutaminase (tTG), in cancer cell migration. Treatment of HeLa cells with EGF results in the activation of a plasma membrane-associated pool of tTG and its redistribution to the leading edges of these cells, which are essential events for EGF-stimulated HeLa cell migration. However, we then found that the ability of tTG to be localized to the leading edge is dependent on Hsp70. Similarly, the localization of tTG to the leading edges of MDAMB231 breast carcinoma cells, where it also plays an essential role in their migration, has a strict requirement for Hsp70. Treatment of these different cell lines with inhibitors against the ATP hydrolytic activity of Hsp70 prevented tTG from localizing to their leading edges and thereby blocked EGF-stimulated HeLa cell migration, as well as the constitutive migration normally exhibited by MDAMB231 cells. These findings highlight a new and unconventional role for the chaperonin activity of Hsp70 in the localization of a key regulatory protein (tTG) at the leading edges of cancer cells and the important consequences that this holds for their ability to migrate.     

The Role of the Microenvironment in Mammary Gland Development and Cancer

The mammary gland is composed of a diverse array of cell types that form intricate interaction networks essential for its normal development and physiologic function. Abnormalities in these interactions play an important role throughout different stages of tumorigenesis. Branching ducts and alveoli are lined by an inner layer of secretory luminal epithelial cells that produce milk during lactation and are surrounded by contractile myoepithelial cells and basement membrane. The surrounding stroma comprised of extracellular matrix and various cell types including fibroblasts, endothelial cells, and infiltrating leukocytes not only provides a scaffold for the organ, but also regulates mammary epithelial cell function via paracrine, physical, and hormonal interactions. With rare exceptions breast tumors initiate in the epithelial compartment and in their initial phases are confined to the ducts but this barrier brakes down with invasive progression because of a combination of signals emitted by tumor epithelial and various stromal cells. In this article, we overview the importance of cellular interactions and microenvironmental signals in mammary gland development and cancer.The mammary gland is composed of a combination of multiple cell types that together form complex interaction networks required for the proper development and functioning of the organ. The branching milk ducts are formed by an outer myoepithelial cell layer producing the basement membrane (BM) and an inner luminal epithelial cell layer producing milk during lactation. The ducts are surrounded by the microenvironment composed of extracellular matrix (ECM) and various stromal cell types (e.g., endothelial cells, fibroblasts, myofibroblasts, and leukocytes). Large amount of data suggest that cell-cell and cell-microenvironment interactions modify the proliferation, survival, polarity, differentiation, and invasive capacity of mammary epithelial cells. However, the molecular mechanisms underlying these effects are poorly understood. The purification and comprehensive characterization of each cell type comprising normal and neoplastic human breast tissue combined with hypothesis testing in cell culture and animal models are likely to improve our understanding of the role these cells play in the normal functioning of the mammary gland and in breast tumorigenesis. In this article, we overview cellular and microenvironmental interactions that play important roles in the normal functioning of the mammary gland and their abnormalities in breast cancer.     

Single-Cell Gene Expression Analysis: Implications for Neurodegenerative and Neuropsychiatric Disorders

Technical and experimental advances in microaspiration techniques, RNA amplification, quantitative real-time polymerase chain reaction (qPCR), and cDNA microarray analysis have led to an increase in the number of studies of single-cell gene expression. In particular, the central nervous system (CNS) is an ideal structure to apply single-cell gene expression paradigms. Unlike an organ that is composed of one principal cell type, the brain contains a constellation of neuronal and noneuronal populations of cells. A goal is to sample gene expression from similar cell types within a defined region without potential contamination by expression profiles of adjacent neuronal subpopulations and noneuronal cells. The unprecedented resolution afforded by single-cell RNA analysis in combination with cDNA microarrays and qPCR-based analyses allows for relative gene expression level comparisons across cell types under different experimental conditions and disease states. The ability to analyze single cells is an important distinction from global and regional assessments of mRNA expression and can be applied to optimally prepared tissues from animal models as well as postmortem human brain tissues. This focused review illustrates the potential power of single-cell gene expression studies within the CNS in relation to neurodegenerative and neuropsychiatric disorders such as Alzheimer's disease (AD) and schizophrenia, respectively.     

STRUCTURE OF THE LEAF OF PYROSSIA LONGIFOLIA—A FERN EXHIBITING CRASSULACEAN ACID METABOLISM

The leaf of Pyrossia longifolia (Burm.) Morton, an epiphytic fern known to exhibit CAM, was examined by light and electron microscopy. The relatively thick leaf contains a single-layered epidermis, “water-storage” tissue, and a reticulate vascular system embedded in mesophyll tissue not differentiated into palisade and spongy layers. Mesophyll is composed of large, slightly elongate cells each with a thin, parietal layer of cytoplasm and a large central vacuole. The chloroplast-microbody ratio in mesophyll cells indicates that Pyrossia may be a high photorespirer and thus similar in that sense to C₃ plants. Mesophyll is separated from the vascular tissue by a tightly-arranged layer of endodermal cells with Casparian strips. The inner layer of mesophyll cells and the endodermal cells lack suberin lamellae. The collateral veins contain sieve elements, tracheary elements, pericycle and vascular parenchyma cells, the latter conspicuously larger than the sieve elements. The vascular parenchyma is the only cell type in the leaf which contains plastids with a peripheral reticulum. The parenchymatic elements of the leaf are connected by plasmodesmata, all of which lack neck constrictions and sphincters, or sphincter-like structures. The connections between sieve elements and adjacent parenchymatic elements are pore-plasmodesmata characterized by prominent wall thickenings on the parenchymatic-element side of the wall. The distribution and relative frequencies of plasmodesmata between the various cell types of the leaf indicate photoassimilates may move either symplastically or by a combination of symplast and apoplast from the mesophyll to the site of phloem loading in the veins.     

Structural cell-wall proteins in protoxylem development: evidence for a repair process mediated by a glycine-rich protein

Polyclonal antibodies were used to localize structural cell-wall proteins in differentiating protoxylem elements in etiolated bean and soybean hypocotyls at the light- and electron-microscopic level. A proline-rich protein was localized in the lignified secondary walls, but not in the primary walls of protoxylem elements, which remain unlignified, as shown with lignin-specific antibodies. Secretion of the proline-rich protein was observed during lignification in different cell types. A glycine-rich protein (GRP1.8) was specifically localized in the modified primary walls of mature protoxylem elements and in cell corners between xylem elements and xylem parenchyma cells. The protein was secreted by Golgi bodies both in protoxylem cells after the lignification of their secondary walls and in the surrounding xylem parenchyma cells. The modified primary walls of protoxylem elements were visualized under the light microscope as filaments or sheets staining distinctly with the protein stain Coomassie blue. Electron micrographs of these walls show that they are composed of an amorphous material of moderate electron-density and of polysaccharide microfibrils. These materials form a three-dimensional network, interconnecting the ring- or spiral-shaped secondary wall thickenings of protoxylem elements and xylem parenchyma cells. The results demonstrate that the modified primary walls of protoxylem cells are not simply breakdown products due to partial hydrolysis and passive elongation, as believed until now. Extensive repair processes produce cell walls with unique staining properties. It is concluded that these walls are unusually rich in protein and therefore have special chemical and physical properties.     

Analysis of soft tissue tumors by an attributed minimum spanning tree.

Histologic slides of 22 soft tissue tumors (9 malignant fibrous histiocytoma, 8 fibrosarcoma, 2 rhabdomyosarcoma, 2 osteosarcoma, 1 Askin tumor) were Feulgen stained. Using an automated image analyzing system (Cambridge 570) at low magnification (25x), the tumor cell nuclei were segmented. The geometrical center of the nuclei was considered the vertex. A basic graph was constructed according to the neighborhood condition of O'Callaghan. Neighboring tumor cell nuclei were visualized by connecting edges. Several features of tumor cell nuclei were measured, including area, surface, major and minor axis of best fitting ellipsis and extinction (DNA content). Nuclear features are attributed to the vertices. The differences, or "distances," between features of connected vertices are attributed to the corresponding edges, which are dependent on the attributes. Thus, different minimum spanning trees (MST) result. Each MST can be decomposed into clusters using a suitable decomposition function on the edges, which rejects an edge if its attributes differ from the mean of the attributed values of surrounding edges more than a neighbor dependent bound (lower limit). Taking into account the length and other attributes of edges (e.g., differences in orientation of the major axis), clusters of different nuclear orientation can be detected. A cluster tree can be constructed by defining the geometric center of a cluster as a new vertex, and by computing the neighborhood of the cluster vertices. The result is an attributed MST containing characteristic structural properties of the image (in cases of sarcomatous tumors, local orientation of tumor cell nuclei and local DNA abnormalities).     

The impacts of network topology on disease spread

Individuals in a population susceptible to a disease may be represented as vertices in a network, with the edges that connect vertices representing social and/or spatial contact between individuals. Networks, which explicitly included six different patterns of connection between vertices, were created. Both scale-free networks and random graphs showed a different response in path level to increasing levels of clustering than regular lattices. Clustering promoted short path lengths in all network types, but randomly assembled networks displayed a logarithmic relationship between degree and path length; whereas this response was linear in regular lattices. In all cases, small-world models, generated by rewiring the connections of a regular lattice, displayed properties, which spanned the gap between random and regular networks.Simulation of a disease in these networks showed a strong response to connectance pattern, even when the number of edges and vertices were approximately equal. Epidemic spread was fastest, and reached the largest size, in scale-free networks, then in random graphs. Regular lattices were the slowest to be infected, and rewired lattices were intermediate between these two extremes. Scale-free networks displayed the capacity to produce an epidemic even at a likelihood of infection, which was too low to produce an epidemic for the other network types. The interaction between the statistical properties of the network and the results of epidemic spread provides a useful tool for assessing the risk of disease spread in more realistic networks.     

Arrangement of Cellulose Microfibrils in Walls of Elongating Parenchyma Cells

The arrangement of cellulose microfibrils in walls of elongating parenchyma cells of Avena coleoptiles, onion roots, and celery petioles was studied in polarizing and electron microscopes by examining whole cell walls and sections. Walls of these cells consist firstly of regions containing the primary pit fields and composed of microfibrils oriented predominantly transversely. The transverse microfibrils show a progressive disorientation from the inside to the outside of the wall which is consistent with the multinet model of wall growth. Between the pit-field regions and running the length of the cells are ribs composed of longitudinally oriented microfibrils. Two types of rib have been found at all stages of cell elongation. In some regions, the wall appears to consist entirely of longitudinal microfibrils so that the rib forms an integral part of the wall. At the edges of such ribs the microfibrils can be seen to change direction from longitudinal in the rib to transverse in the pit-field region. Often, however, the rib appears to consist of an extra separate layer of longitudinal microfibrils outside a continuous wall of transverse microfibrils. These ribs are quite distinct from secondary wall, which consists of longitudinal microfibrils deposited within the primary wall after elongation has ceased. It is evident that the arrangement of cellulose microfibrils in a primary wall can be complex and is probably an expression of specific cellular differentiation.     

On a phase diagram for random neural networks with embedded spike timing dependent plasticity

This paper presents an original mathematical framework based on graph theory which is a first attempt to investigate the dynamics of a model of neural networks with embedded spike timing dependent plasticity. The neurons correspond to integrate-and-fire units located at the vertices of a finite subset of 2D lattice. There are two types of vertices, corresponding to the inhibitory and the excitatory neurons. The edges are directed and labelled by the discrete values of the synaptic strength. We assume that there is an initial firing pattern corresponding to a subset of units that generate a spike. The number of activated externally vertices is a small fraction of the entire network. The model presented here describes how such pattern propagates throughout the network as a random walk on graph. Several results are compared with computational simulations and new data are presented for identifying critical parameters of the model.     

Identifying Cognitive States Using Regularity Partitions

Functional Magnetic Resonance (fMRI) data can be used to depict functional connectivity of the brain. Standard techniques have been developed to construct brain networks from this data; typically nodes are considered as voxels or sets of voxels with weighted edges between them representing measures of correlation. Identifying cognitive states based on fMRI data is connected with recording voxel activity over a certain time interval. Using this information, network and machine learning techniques can be applied to discriminate the cognitive states of the subjects by exploring different features of data. In this work we wish to describe and understand the organization of brain connectivity networks under cognitive tasks. In particular, we use a regularity partitioning algorithm that finds clusters of vertices such that they all behave with each other almost like random bipartite graphs. Based on the random approximation of the graph, we calculate a lower bound on the number of triangles as well as the expectation of the distribution of the edges in each subject and state. We investigate the results by comparing them to the state of the art algorithms for exploring connectivity and we argue that during epochs that the subject is exposed to stimulus, the inspected part of the brain is organized in an efficient way that enables enhanced functionality.     

A plant cell division algorithm based on cell biomechanics and ellipse-fitting

Background and Aims

The importance of cell division models in cellular pattern studies has been acknowledged since the 19th century. Most of the available models developed to date are limited to symmetric cell division with isotropic growth. Often, the actual growth of the cell wall is either not considered or is updated intermittently on a separate time scale to the mechanics. This study presents a generic algorithm that accounts for both symmetrically and asymmetrically dividing cells with isotropic and anisotropic growth. Actual growth of the cell wall is simulated simultaneously with the mechanics.

Methods

The cell is considered as a closed, thin-walled structure, maintained in tension by turgor pressure. The cell walls are represented as linear elastic elements that obey Hooke''s law. Cell expansion is induced by turgor pressure acting on the yielding cell-wall material. A system of differential equations for the positions and velocities of the cell vertices as well as for the actual growth of the cell wall is established. Readiness to divide is determined based on cell size. An ellipse-fitting algorithm is used to determine the position and orientation of the dividing wall. The cell vertices, walls and cell connectivity are then updated and cell expansion resumes. Comparisons are made with experimental data from the literature.

Key Results

The generic plant cell division algorithm has been implemented successfully. It can handle both symmetrically and asymmetrically dividing cells coupled with isotropic and anisotropic growth modes. Development of the algorithm highlighted the importance of ellipse-fitting to produce randomness (biological variability) even in symmetrically dividing cells. Unlike previous models, a differential equation is formulated for the resting length of the cell wall to simulate actual biological growth and is solved simultaneously with the position and velocity of the vertices.

Conclusions

The algorithm presented can produce different tissues varying in topological and geometrical properties. This flexibility to produce different tissue types gives the model great potential for use in investigations of plant cell division and growth in silico.     

Drebrin particles: components in the ensemble of proteins regulating actin dynamics of lamellipodia and filopodia

Drebrin, an actin-binding 70-kDa protein with an unusually slow SDS-PAGE mobility corresponding to approximately 120 kDa, containing a proline-rich, profilin-binding motif, had originally been reported from neuronal cells, but recently has also been found in diverse other kinds of tissues and cell lines. In biochemical analyses of various cells and tissues, employing gel filtration, sucrose gradient centrifugation, immunoprecipitation and -blotting, we have identified distinct states of soluble drebrin: a approximately 4S monomer, an 8S, ca. 217-kDa putative trimer, a 13S and a > 20S oligomer. In the 8S particles only [35S]methionine-labelled drebrin but no other actin-binding protein has been detected in stoichiometric amounts. By immunofluorescence and immunoelectron microscopy, drebrin-positive material often appeared as "granules" up to 400 nm in diameter, in some cell types clustered near the Golgi apparatus or in lamellipodia, particularly at leading edges, or in dense-packed submembranous masses at tips (acropodia) or ruffles of leading edges, in filopodia and at plaques of adhering junctions. We conclude that these drebrin complexes and drebrin-rich structures allow the build-up and maintenance of high local drebrin concentrations in strategic positions for the regulation of actin filament assembly, thereby contributing to cell motility and morphology, in particular local changes of plasticity and the formation of protrusions.     

Isolation of epiblast stem cells from preimplantation mouse embryos

Pluripotent stem cells provide a platform to interrogate control elements that function to generate all cell types of the body. Despite their utility for modeling development and disease, the relationship of mouse and human pluripotent stem cell states to one another remains largely undefined. We have shown that mouse embryonic stem (ES) cells and epiblast stem cells (EpiSCs) are distinct, pluripotent states isolated from pre- and post-implantation embryos respectively. Human ES cells are different than mouse ES cells and share defining features with EpiSCs, yet are derived from pre-implantation human embryos. Here we show that EpiSCs can be routinely derived from pre-implantation mouse embryos. The preimplantation-derived EpiSCs exhibit molecular features and functional properties consistent with bona fide EpiSCs. These results provide a simple method for isolating EpiSCs and offer direct insight into the intrinsic and extrinsic mechanisms that regulate the acquisition of distinct pluripotent states.