Single Cells In Isolation Research Articles

Tumorigenic mesenchymal clusters are less sensitive to moderate osmotic stresses due to low amounts of junctional E-cadherin

The migration of tumorigenic cells is a critical step for metastatic breast cancer progression. Although the role of the extracellular matrix in breast cancer cell migration has been extensively described, the effect of osmotic stress on the migration of tumor breast cohorts remains unclear. Most of our understanding on the effect of osmotic stresses on cell migration comes from studies at the level of the single cell in isolation and does not take cell–cell interactions into account. Here, we study the impact of moderate osmotic stress on the migration of cell clusters composed of either non-tumorigenic or tumorigenic cells. We observe a decrease in migration distance and speed for non-tumorigenic cells but not for tumorigenic ones. To explain these differences, we investigate how osmotic stress impacts the mechanical properties of cell clusters and affects their volumes. Our findings show that tumorigenic mesenchymal cells are less sensitive to osmotic stress than non-tumorigenic cells and suggest that this difference is associated with a lower expression of E-cadherin. Using EGTA treatments, we confirm that the establishment of cell–cell adhesive interactions is a key component of the behavior of cell clusters in response to osmotic stress. This study provides evidence on the low sensitivity of mesenchymal tumorigenic clusters to moderate osmotic stress and highlights the importance of cadherin-based junctions in the response to osmotic stress.

Analysis of Cell Size Homeostasis at the Single-Cell and Population Level

Growth pervades all areas of life from single cells to cell populations to tissues. However, cell size often fluctuates significantly from cell to cell and from generation to generation. Here we present a unified framework to predict the statistics of cell size variations within a lineage tree of a proliferating population. We analytically characterise (i) the distributions of cell size snapshots, (ii) the distribution within a population tree, and (iii) the distribution of lineages across the tree. Surprisingly, these size distributions differ significantly from observing single cells in isolation. In populations, cells seemingly grow to different sizes, typically exhibit less cell-to-cell variability and often display qualitatively different sensitivities to cell cycle noise and division errors. We demonstrate the key findings using recent single-cell data and elaborate on the implications for the ability of cells to maintain a narrow size distribution and the emergence of different power laws in these distributions.

Morphometrics of complex cell shapes: lobe contribution elliptic Fourier analysis (LOCO-EFA).

ABSTRACTQuantifying cell morphology is fundamental to the statistical study of cell populations, and can help unravel mechanisms underlying cell and tissue morphogenesis. Current methods, however, require extensive human intervention, are highly parameter sensitive, or produce metrics that are difficult to interpret biologically. We therefore developed a method, lobe contribution elliptical Fourier analysis (LOCO-EFA), which generates from digitalised two-dimensional cell outlines meaningful descriptors that can be directly matched to morphological features. This is shown by studying well-defined geometric shapes as well as actual biological cells from plant and animal tissues. LOCO-EFA provides a tool to phenotype efficiently and objectively populations of cells, here demonstrated by applying it to the complex shaped pavement cells of Arabidopsis thaliana wild-type and speechless leaves, and Drosophila amnioserosa cells. To validate our method's applicability to large populations, we analysed computer-generated tissues. By controlling in silico cell shape, we explored the potential impact of cell packing on individual cell shape, quantifying through LOCO-EFA deviations between the specified shape of single cells in isolation and the resultant shape when they interact within a confluent tissue.

Index sorting resolves heterogeneous murine hematopoietic stem cell populations

Recent advances in the cellular and molecular biology of single stem cells have uncovered significant heterogeneity in the functional properties of stem cell populations. This has prompted the development of approaches to study single cells in isolation, often performed using multiparameter flow cytometry. However, many stem cell populations are too rare to test all possible cell surface marker combinations, and virtually nothing is known about functional differences associated with varying intensities of such markers. Here we describe the use of index sorting for further resolution of the flow cytometric isolation of single murine hematopoietic stem cells (HSCs). Specifically, we associate single-cell functional assay outcomes with distinct cell surface marker expression intensities. High levels of both CD150 and EPCR associate with delayed kinetics of cell division and low levels of differentiation. Moreover, cells that do not form single HSC-derived clones appear in the 7AADdim fraction, suggesting that even low levels of 7AAD staining are indicative of less healthy cell populations. These data indicate that when used in combination with single-cell functional assays, index sorting is a powerful tool for refining cell isolation strategies. This approach can be broadly applied to other single-cell systems, both to improve isolation and to acquire additional cell surface marker information.

Clonal colony formation from spiral ganglion stem cells.

Neural stem cells from the central nervous system have the distinct capacity to give rise to clonal neurospheres. These clonal spheres are derived from a single clone-forming cell and represent homogenous, pure cell colonies. Recently, stem/progenitor cells have been isolated from the spiral ganglion of the inner ear using sphere-forming assays. However, the clonality of spiral ganglion-derived spheres has not yet been addressed in detail. Here, we report the isolation of clonal colonies from the spiral ganglion of early postnatal mice. We analyze sphere clonality using coculture experiments with transgenic cells, a semisolid assay, and culture of single cells in isolation. Our data show that sphere clonality differs in primary and secondary cultures and indicate that clonal sphere formation is dependent on specific culture parameters. We also show that the initiation of clonal colony formation does not require cell-to-cell interactions or paracrine signaling from surrounding cells. Generation of clonal colonies from spiral ganglion stem/progenitor cells might be crucial for future clinical applications because pure cell populations are considered to be more efficient and safe for therapeutic use than chimeric, heterogeneous spheres.

A novel cell traction force microscopy to study multi-cellular system.

Traction forces exerted by adherent cells on their microenvironment can mediate many critical cellular functions. Accurate quantification of these forces is essential for mechanistic understanding of mechanotransduction. However, most existing methods of quantifying cellular forces are limited to single cells in isolation, whereas most physiological processes are inherently multi-cellular in nature where cell-cell and cell-microenvironment interactions determine the emergent properties of cell clusters. In the present study, a robust finite-element-method-based cell traction force microscopy technique is developed to estimate the traction forces produced by multiple isolated cells as well as cell clusters on soft substrates. The method accounts for the finite thickness of the substrate. Hence, cell cluster size can be larger than substrate thickness. The method allows computing the traction field from the substrate displacements within the cells' and clusters' boundaries. The displacement data outside these boundaries are not necessary. The utility of the method is demonstrated by computing the traction generated by multiple monkey kidney fibroblasts (MKF) and human colon cancerous (HCT-8) cells in close proximity, as well as by large clusters. It is found that cells act as individual contractile groups within clusters for generating traction. There may be multiple of such groups in the cluster, or the entire cluster may behave a single group. Individual cells do not form dipoles, but serve as a conduit of force (transmission lines) over long distances in the cluster. The cell-cell force can be either tensile or compressive depending on the cell-microenvironment interactions.

N-cadherin-mediated cell–cell adhesion promotes cell migration in a three-dimensional matrix

Cancer cells that originate from epithelial tissues typically lose epithelial specific cell-cell junctions, but these transformed cells are not devoid of cell-cell adhesion proteins. Using hepatocyte-growth-factor-treated MDCK cells that underwent a complete epithelial-to-mesenchymal transition, we analyzed cell-cell adhesion between these highly invasive transformed epithelial cells in a three-dimensional (3D) collagen matrix. In a 3D matrix, these transformed cells formed elongated multicellular chains, and migrated faster and more persistently than single cells in isolation. In addition, the cell clusters were enriched with stress-fiber-like actin bundles that provided contractile forces. N-cadherin-knockdown cells failed to form cell-cell junctions or migrate, and the expression of the N-cadherin cytoplasmic or extracellular domain partially rescued the knockdown phenotype. By contrast, the expression of N-cadherin-α-catenin chimera rescued the knockdown phenotype, but individual cells within the cell clusters were less mobile. Together, our findings suggest that a dynamic N-cadherin and actin linkage is required for efficient 3D collective migration.

From pathway to population – a multiscale model of juxtacrine EGFR-MAPK signalling

BackgroundMost mathematical models of biochemical pathways consider either signalling events that take place within a single cell in isolation, or an 'average' cell which is considered to be representative of a cell population. Likewise, experimental measurements are often averaged over populations consisting of hundreds of thousands of cells. This approach ignores the fact that even within a genetically-homogeneous population, local conditions may influence cell signalling and result in phenotypic heterogeneity.We have developed a multi-scale computational model that accounts for emergent heterogeneity arising from the influences of intercellular signalling on individual cells within a population. Our approach was to develop an ODE model of juxtacrine EGFR-ligand activation of the MAPK intracellular pathway and to couple this to an agent-based representation of individual cells in an expanding epithelial cell culture population. This multi-scale, multi-paradigm approach has enabled us to simulate Extracellular signal-regulated kinase (Erk) activation in a population of cells and to examine the consequences of interpretation at a single cell or population-based level using virtual assays.ResultsA model consisting of a single pair of interacting agents predicted very different Erk activation (phosphorylation) profiles, depending on the formation rate and stability of intercellular contacts, with the slow formation of stable contacts resulting in low but sustained activation of Erk, and transient contacts resulting in a transient Erk signal. Extension of this model to a population consisting of hundreds to thousands of interacting virtual cells revealed that the activated Erk profile measured across the entire cell population was very different and may appear to contradict individual cell findings, reflecting heterogeneity in population density across the culture. This prediction was supported by immunolabelling of an epithelial cell population grown in vitro, which confirmed heterogeneity of Erk activation.ConclusionThese results illustrate that mean experimental data obtained from analysing entire cell populations is an oversimplification, and should not be extrapolated to deduce the signal:response paradigm of individual cells. This multi-scale, multi-paradigm approach to biological simulation provides an important conceptual tool in addressing how information may be integrated over multiple scales to predict the behaviour of a biological system.

Analysis of a Kanban Discipline for Cell Coordination in Production Lines, II: Stochastic Demands

We describe and analyze a discipline called kanban for the control and coordination of cells in large-scale manufacturing facilities. There are many cells in tandem and also a process of arriving consumers; each consumer queues as long as necessary before departing with a product finished by the last cell. The coordination scheme relies on the circulation of a fixed number of cards (or kanbans) in each cell. This paper differs from our earlier paper in that here the process of consumers modulates the production process. Our results are in three parts. First, the kanban is compared to the classical production discipline and its (sample path) dominance in terms of the consumers' waiting time is proven. Second, we give an analytic scheme for approximating the performance of the stochastic kanban system by examining a single cell in isolation and then combining these through fixed-point equations. Ergodicity is equivalent in our approximation to the existence and uniqueness of a solution to the fixed-point equations and a complete, finite procedure for its resolution is obtained. Third, we report on experiments involving simulations and our method of analysis.

Analysis of a Kanban Discipline for Cell Coordination in Production Lines. I

We describe and analyze a discipline called kanban for the coordination of cells in a large-scale manufacturing facility. There are many cells in a tandem configuration and a fixed number of cards (or kanbans in Japanese usage) in each cell. The cards circulate within the cell and their presence at designated positions signals to the neighboring cells the status of the cell's inventory; rules specify the timing of the movement of parts from cell to cell. We construct a stochastic model of the whole manufacturing facility and give our results in three parts. First, the kanban discipline is compared to the classical production discipline and it is shown to dominate the classical discipline in terms of throughput. Secondly, we give a scheme for analyzing approximately the performance of the kanban system by examining first a single cell in isolation and then combining the isolated cells through fixed-point equations. Thirdly, we report on extensive experiments and on their numerical results which are obtained from simulations and our method of analysis. The throughput-inventory relationship of the kanban scheme is observed to be superior to that of the classical discipline.

Control and coordination policies for systems with buffers

We study systems consisting of a number of service cells in tandem, each containing a finite buffer. Several policies governing the operation of such systems are described and compared. These include traditional and novel blocking schemes, with applications to computer communications and production lines. In particular, it is shown that kanban , a novel discipline for coordinating cells in a manufacturing context, is obtained by combining two, more basic, concepts: a blocking policy introduced here as minimal blocking , and shared buffers . The Kanban discipline is superior in terms of throughput to the ordinary transfer blocking policy. A method for analyzing approximately the performance of the Kanban system is also presented. This is based on examining first a single cell in isolation and then combining the isolated cells through fixed-point equations. Some numerical results and comparisons with simulations are included.