Motility Enhancement through Surface Modification Is Sufficient for Cyanobacterial Community Organization during Phototaxis

The emergent behaviors of communities of genotypically identical cells cannot be easily predicted from the behaviors of individual cells. In many cases, it is thought that direct cell-cell communication plays a critical role in the transition from individual to community behaviors. In the unicellular photosynthetic cyanobacterium Synechocystis sp. PCC 6803, individual cells exhibit light-directed motility (“phototaxis”) over surfaces, resulting in the emergence of dynamic spatial organization of multicellular communities. To probe this striking community behavior, we carried out time-lapse video microscopy coupled with quantitative analysis of single-cell dynamics under varying light conditions. These analyses suggest that cells secrete an extracellular substance that modifies the physical properties of the substrate, leading to enhanced motility and the ability for groups of cells to passively guide one another. We developed a biophysical model that demonstrates that this form of indirect, surface-based communication is sufficient to create distinct motile groups whose shape, velocity, and dynamics qualitatively match our experimental observations, even in the absence of direct cellular interactions or changes in single-cell behavior. Our computational analysis of the predicted community behavior, across a matrix of cellular concentrations and light biases, demonstrates that spatial patterning follows robust scaling laws and provides a useful resource for the generation of testable hypotheses regarding phototactic behavior. In addition, we predict that degradation of the surface modification may account for the secondary patterns occasionally observed after the initial formation of a community structure. Taken together, our modeling and experiments provide a framework to show that the emergent spatial organization of phototactic communities requires modification of the substrate, and this form of surface-based communication could provide insight into the behavior of a wide array of biological communities.     

Multiscale feature analysis of salivary gland branching morphogenesis

Pattern formation in developing tissues involves dynamic spatio-temporal changes in cellular organization and subsequent evolution of functional adult structures. Branching morphogenesis is a developmental mechanism by which patterns are generated in many developing organs, which is controlled by underlying molecular pathways. Understanding the relationship between molecular signaling, cellular behavior and resulting morphological change requires quantification and categorization of the cellular behavior. In this study, tissue-level and cellular changes in developing salivary gland in response to disruption of ROCK-mediated signaling by are modeled by building cell-graphs to compute mathematical features capturing structural properties at multiple scales. These features were used to generate multiscale cell-graph signatures of untreated and ROCK signaling disrupted salivary gland organ explants. From confocal images of mouse submandibular salivary gland organ explants in which epithelial and mesenchymal nuclei were marked, a multiscale feature set capturing global structural properties, local structural properties, spectral, and morphological properties of the tissues was derived. Six feature selection algorithms and multiway modeling of the data was performed to identify distinct subsets of cell graph features that can uniquely classify and differentiate between different cell populations. Multiscale cell-graph analysis was most effective in classification of the tissue state. Cellular and tissue organization, as defined by a multiscale subset of cell-graph features, are both quantitatively distinct in epithelial and mesenchymal cell types both in the presence and absence of ROCK inhibitors. Whereas tensor analysis demonstrate that epithelial tissue was affected the most by inhibition of ROCK signaling, significant multiscale changes in mesenchymal tissue organization were identified with this analysis that were not identified in previous biological studies. We here show how to define and calculate a multiscale feature set as an effective computational approach to identify and quantify changes at multiple biological scales and to distinguish between different states in developing tissues.     

Seeing the Whole Elephant: Imaging Flow Cytometry Reveals Extensive Morphological Diversity within Blastocystis Isolates

Blastocystis is a common protist isolated in humans and many animals. The parasite is a species complex composed of 19 subtypes, 9 of which have been found in humans. There are biological and molecular differences between Blastocystis subtypes although microscopy alone is unable to distinguish between these subtypes. Blastocystis isolates also display various morphological forms. Several of these forms, however, have not been properly evaluated on whether or not these play significant functions in the organism''s biology. In this study, we used imaging flow cytometry to analyze morphological features of Blastocystis isolates representing 3 subtypes (ST1, ST4 and ST7). We also employed fluorescence dyes to discover new cellular features. The profiles from each of the subtypes exhibit considerable differences with the others in terms of shape, size and granularity. We confirmed that the classical vacuolar form comprises the majority in all three subtypes. We have also evaluated other morphotypes on whether these represent distinct life stages in the parasite. Irregularly-shaped cells were identified but all of them were found to be dying cells in one isolate. Granular forms were present as a continuum in both viable and non-viable populations, with non-viable forms displaying higher granularity. By analyzing the images, rare morphotypes such as multinucleated cells could be easily observed and quantified. These cells had low granularity and lower DNA content. Small structures containing nucleic acid were also identified. We discuss the possible biological implications of these unusual forms.     

Self-organization versus Watchmaker: ambiguity of molecular recognition and design charts of cellular circuitry

A large body of experimental evidence indicates that the specific molecular interactions and/or chemical conversions depicted as links in the conventional diagrams of cellular signal transduction and metabolic pathways are inherently probabilistic, ambiguous and context-dependent. Being the inevitable consequence of the dynamic nature of protein structure in solution, the ambiguity of protein-mediated interactions and conversions challenges the conceptual adequacy and practical usefulness of the mechanistic assumptions and inferences embodied in the design charts of cellular circuitry. It is argued that the reconceptualization of molecular recognition and cellular organization within the emerging interpretational framework of self-organization, which is expanded here to include such concepts as bounded stochasticity, evolutionary memory, and adaptive plasticity offers a significantly more adequate representation of experimental reality than conventional mechanistic conceptions do. Importantly, the expanded framework of self-organization appears to be universal and scale-invariant, providing conceptual continuity across multiple scales of biological organization, from molecules to societies. This new conceptualization of biological phenomena suggests that such attributes of intelligence as adaptive plasticity, decision-making, and memory are enforced by evolution at different scales of biological organization and may represent inherent properties of living matter.     

Biomechanics of the lung parenchyma: critical roles of collagen and mechanical forces.

The biomechanical properties of connective tissues play fundamental roles in how mechanical interactions of the body with its environment produce physical forces at the cellular level. It is now recognized that mechanical interactions between cells and the extracellular matrix (ECM) have major regulatory effects on cellular physiology and cell-cycle kinetics that can lead to the reorganization and remodeling of the ECM. The connective tissues are composed of cells and the ECM, which includes water and a variety of biological macromolecules. The macromolecules that are most important in determining the mechanical properties of these tissues are collagen, elastin, and proteoglycans. Among these macromolecules, the most abundant and perhaps most critical for structural integrity is collagen. In this review, we examine how mechanical forces affect the physiological functioning of the lung parenchyma, with special emphasis on the role of collagen. First, we overview the composition of the connective tissue of the lung and their complex structural organization. We then describe how mechanical properties of the parenchyma arise from its composition as well as from the architectural organization of the connective tissue. We argue that, because collagen is the most important load-bearing component of the parenchymal connective tissue, it is also critical in determining the homeostasis and cellular responses to injury. Finally, we overview the interactions between the parenchymal collagen network and cellular remodeling and speculate how mechanotransduction might contribute to disease propagation and the development of small- and large-scale heterogeneities with implications to impaired lung function in emphysema.     

Cell communities and robustness in development

The robustness of patterning events in development is a key feature that must be accounted for in proposed models of these events. When considering explicitly cellular systems, robustness can be exhibited at different levels of organization. Consideration of two widespread patterning mechanisms suggests that robustness at the level of cell communities can result from variable development at the level of individual cells; models of these mechanisms show how interactions between participating cells guarantee community-level robustness. Cooperative interactions enhance homogeneity within communities of like cells and the sharpness of boundaries between communities of distinct cells, while competitive interactions amplify small inhomogeneities within communities of initially equivalent cells, resulting in fine-grained patterns of cell specialization.     

AgentCell: a digital single-cell assay for bacterial chemotaxis

MOTIVATION: In recent years, single-cell biology has focused on the relationship between the stochastic nature of molecular interactions and variability of cellular behavior. To describe this relationship, it is necessary to develop new computational approaches at the single-cell level. RESULTS: We have developed AgentCell, a model using agent-based technology to study the relationship between stochastic intracellular processes and behavior of individual cells. As a test-bed for our approach we use bacterial chemotaxis, one of the best characterized biological systems. In this model, each bacterium is an agent equipped with its own chemotaxis network, motors and flagella. Swimming cells are free to move in a 3D environment. Digital chemotaxis assays reproduce experimental data obtained from both single cells and bacterial populations.     

Electrifying rhythms in plant cells

Physiological oscillations (or rhythms) pervade all spatiotemporal scales of biological organization, either because they perform critical functions or simply because they can arise spontaneously and may be difficult to prevent. Regardless of the case, they reflect regulatory relationships between control points of a given system and offer insights as read-outs of the concerted regulation of a myriad of biological processes. Here we review recent advances in understanding ultradian oscillations (period < 24h) in plant cells, with a special focus on single-cell oscillations. Ion channels are at the center stage due to their involvement in electrical/excitabile phenomena associated with oscillations and cell-cell communication. We highlight the importance of quantitative approaches to measure oscillations in appropriate physiological conditions, which are essential strategies to deal with the complexity of biological rhythms. Future development of optogenetics techniques in plants will further boost research on the role of membrane potential in oscillations and waves across multiple cell types.     

Influence of cell adhesion and spreading on impedance characteristics of cell-based sensors

Impedance measurements of cell-based sensors are a primary characterization route for detection and analysis of cellular responses to chemical and biological agents in real time. The detection sensitivity and limitation depend on sensor impedance characteristics and thus on cell patterning techniques. This study introduces a cell patterning approach to bind cells on microarrays of gold electrodes and demonstrates that single-cell patterning can substantially improve impedance characteristics of cell-based sensors. Mouse fibroblast cells (NIH3T3) are immobilized on electrodes through a lysine-arginine-glycine-aspartic acid (KRGD) peptide-mediated natural cell adhesion process. Electrodes are made of three sizes and immobilized with either covalently bound or physically adsorbed KRGD (c-electrodes or p-electrodes). Cells attached to c-electrodes increase the measurable electrical signal strength by 48.4%, 24.2%, and 19.0% for three electrode sizes, respectively, as compared to cells attached to p-electrodes, demonstrating that both the electrode size and surface chemistry play a key role in cell adhesion and spreading and thus the impedance characteristics of cell-based sensors. Single cells patterned on c-electrodes with dimensions comparable to cell size exhibit well-spread cell morphology and substantially outperform cells patterned on electrodes of other configurations.     

Robust Inference of Cell-to-Cell Expression Variations from Single- and K-Cell Profiling

Quantifying heterogeneity in gene expression among single cells can reveal information inaccessible to cell-population averaged measurements. However, the expression level of many genes in single cells fall below the detection limit of even the most sensitive technologies currently available. One proposed approach to overcome this challenge is to measure random pools of k cells (e.g., 10) to increase sensitivity, followed by computational “deconvolution” of cellular heterogeneity parameters (CHPs), such as the biological variance of single-cell expression levels. Existing approaches infer CHPs using either single-cell or k-cell data alone, and typically within a single population of cells. However, integrating both single- and k-cell data may reap additional benefits, and quantifying differences in CHPs across cell populations or conditions could reveal novel biological information. Here we present a Bayesian approach that can utilize single-cell, k-cell, or both simultaneously to infer CHPs within a single condition or their differences across two conditions. Using simulated as well as experimentally generated single- and k-cell data, we found situations where each data type would offer advantages, but using both together can improve precision and better reconcile CHP information contained in single- and k-cell data. We illustrate the utility of our approach by applying it to jointly generated single- and k-cell data to reveal CHP differences in several key inflammatory genes between resting and inflammatory cytokine-activated human macrophages, delineating differences in the distribution of ‘ON’ versus ‘OFF’ cells and in continuous variation of expression level among cells. Our approach thus offers a practical and robust framework to assess and compare cellular heterogeneity within and across biological conditions using modern multiplexed technologies.     

Tissue scaffold surface patterning for clinical applications

Patterned scaffold surfaces provide a platform for highly defined cellular interactions, and have recently taken precedence in tissue engineering. Despite advances in patterning techniques and improved tissue growth, no clinical studies have been conducted for implantation of patterned biomaterials. Four major clinical application fields where patterned materials hold great promise are antimicrobial surfaces, cardiac constructs, neurite outgrowth, and stem cell differentiation. Specific examples include applications of patterned materials to (i) counter infection by antibiotic resistant bacteria, (ii) establish proper alignment and contractile force of regrown cardiac cells for repairing tissue damaged by cardiac infarction, (iii) increase neurite outgrowth for central nervous system wound repair, and (iv) host differentiated stem cells while preventing reversion to a pluripotent state. Moreover, patterned materials offer unique advantages for artificial implants which other constructs cannot. For example, by inducing selective cell adhesion using topographical cues, patterned surfaces present cellular orientation signals that lead to functional tissue architectures. Mechanical stimuli such as modulus, tension, and material roughness are known to influence tissue growth, as are chemical stimuli for cell adhesion. Scaffold surface patterns allow for control of these mechanical and chemical factors. This review identifies research advances in scaffold surface patterning, in light of pressing clinical needs requiring organization of cellular interactions.     

Membrane lipids: where they are and how they behave

Throughout the biological world, a 30 A hydrophobic film typically delimits the environments that serve as the margin between life and death for individual cells. Biochemical and biophysical findings have provided a detailed model of the composition and structure of membranes, which includes levels of dynamic organization both across the lipid bilayer (lipid asymmetry) and in the lateral dimension (lipid domains) of membranes. How do cells apply anabolic and catabolic enzymes, translocases and transporters, plus the intrinsic physical phase behaviour of lipids and their interactions with membrane proteins, to create the unique compositions and multiple functionalities of their individual membranes?     

Measuring enzyme activity in single cells

Seemingly identical cells can differ in their biochemical state, function and fate, and this variability plays an increasingly recognized role in organism-level outcomes. Cellular heterogeneity arises in part from variation in enzyme activity, which results from interplay between biological noise and multiple cellular processes. As a result, single-cell assays of enzyme activity, particularly those that measure product formation directly, are crucial. Recent innovations have yielded a range of techniques to obtain these data, including image-, flow- and separation-based assays. Research to date has focused on easy-to-measure glycosylases and clinically-relevant kinases. Expansion of these techniques to a wider range and larger number of enzymes will answer contemporary questions in proteomics and glycomics, specifically with respect to biological noise and cellular heterogeneity.     

Single-cell patterning and axis characterization in the murine and human definitive endoderm

Defining the precise regionalization of specified definitive endoderm progenitors is critical for understanding the mechanisms underlying the generation and regeneration of respiratory and digestive organs, yet the patterning of endoderm progenitors remains unresolved, particularly in humans. We performed single-cell RNA sequencing on endoderm cells during the early somitogenesis stages in mice and humans. We developed molecular criteria to define four major endoderm regions (foregut, lip of anterior intestinal portal, midgut, and hindgut) and their developmental pathways. We identified the cell subpopulations in each region and their spatial distributions and characterized key molecular features along the body axes. Dorsal and ventral pancreatic progenitors appear to originate from the midgut population and follow distinct pathways to develop into an identical cell type. Finally, we described the generally conserved endoderm patterning in humans and clear differences in dorsal cell distribution between species. Our study comprehensively defines single-cell endoderm patterning and provides novel insights into the spatiotemporal process that drives establishment of early endoderm domains.Subject terms: Developmental biology, Stem cells     

Natural intracellular peptides can modulate the interactions of mouse brain proteins and thimet oligopeptidase with 14-3-3ε and calmodulin

Protein interactions are crucial for most cellular process. Thus, rationally designed peptides that act as competitive assembly inhibitors of protein interactions by mimicking specific, determined structural elements have been extensively used in clinical and basic research. Recently, mammalian cells have been shown to contain a large number of intracellular peptides of unknown function. Here, we investigate the role of several of these natural intracellular peptides as putative modulators of protein interactions that are related to Ca(2+) -calmodulin (CaM) and 14-3-3ε, which are proteins that are related to the spatial organization of signal transduction within cells. At concentrations of 1-50 μM, most of the peptides that are investigated in this study modulate the interactions of CaM and 14-3-3ε with proteins from the mouse brain cytoplasm or recombinant thimet oligopeptidase (EP24.15) in vitro, as measured by surface plasmon resonance. One of these peptides (VFDVELL; VFD-7) increases the cytosolic Ca(2+) concentration in a dose-dependent manner but only if introduced into HEK293 cells, which suggests a wide biological function of this peptide. Therefore, it is exciting to suggest that natural intracellular peptides are novel modulators of protein interactions and have biological functions within cells.     

Capabilities and Limitations of Tissue Size Control through Passive Mechanical Forces

Embryogenesis is an extraordinarily robust process, exhibiting the ability to control tissue size and repair patterning defects in the face of environmental and genetic perturbations. The size and shape of a developing tissue is a function of the number and size of its constituent cells as well as their geometric packing. How these cellular properties are coordinated at the tissue level to ensure developmental robustness remains a mystery; understanding this process requires studying multiple concurrent processes that make up morphogenesis, including the spatial patterning of cell fates and apoptosis, as well as cell intercalations. In this work, we develop a computational model that aims to understand aspects of the robust pattern repair mechanisms of the Drosophila embryonic epidermal tissues. Size control in this system has previously been shown to rely on the regulation of apoptosis rather than proliferation; however, to date little work has been done to understand the role of cellular mechanics in this process. We employ a vertex model of an embryonic segment to test hypotheses about the emergence of this size control. Comparing the model to previously published data across wild type and genetic perturbations, we show that passive mechanical forces suffice to explain the observed size control in the posterior (P) compartment of a segment. However, observed asymmetries in cell death frequencies across the segment are demonstrated to require patterning of cellular properties in the model. Finally, we show that distinct forms of mechanical regulation in the model may be distinguished by differences in cell shapes in the P compartment, as quantified through experimentally accessible summary statistics, as well as by the tissue recoil after laser ablation experiments.