scholarly journals Learning the dynamics of cell–cell interactions in confined cell migration

2021 ◽  
Vol 118 (7) ◽  
pp. e2016602118 ◽  
Author(s):  
David B. Brückner ◽  
Nicolas Arlt ◽  
Alexandra Fink ◽  
Pierre Ronceray ◽  
Joachim O. Rädler ◽  
...  
Keyword(s):  
Cancer Metastasis ◽  
Single Cells ◽  
Cell Types ◽  
Theoretical Description ◽  
Cell Interactions ◽  
Cellular Interaction ◽  
Migration Dynamics ◽  
Interaction Behaviors ◽  
Cell Trajectories ◽  
Cell Cell

The migratory dynamics of cells in physiological processes, ranging from wound healing to cancer metastasis, rely on contact-mediated cell–cell interactions. These interactions play a key role in shaping the stochastic trajectories of migrating cells. While data-driven physical formalisms for the stochastic migration dynamics of single cells have been developed, such a framework for the behavioral dynamics of interacting cells still remains elusive. Here, we monitor stochastic cell trajectories in a minimal experimental cell collider: a dumbbell-shaped micropattern on which pairs of cells perform repeated cellular collisions. We observe different characteristic behaviors, including cells reversing, following, and sliding past each other upon collision. Capitalizing on this large experimental dataset of coupled cell trajectories, we infer an interacting stochastic equation of motion that accurately predicts the observed interaction behaviors. Our approach reveals that interacting noncancerous MCF10A cells can be described by repulsion and friction interactions. In contrast, cancerous MDA-MB-231 cells exhibit attraction and antifriction interactions, promoting the predominant relative sliding behavior observed for these cells. Based on these experimentally inferred interactions, we show how this framework may generalize to provide a unifying theoretical description of the diverse cellular interaction behaviors of distinct cell types.

scholarly journals Predicting global dynamics of spatial microbial communities from local interaction rules

2020 ◽  
Author(s):  
Simon van Vliet ◽  
Christoph Hauert ◽  
Martin Ackermann ◽  
Alma Dal Co
Keyword(s):  
Growth Rate ◽  
Microbial Communities ◽  
Molecular Mechanisms ◽  
Single Cells ◽  
Cell Types ◽  
Cell Interactions ◽  
Community Level ◽  
Global Dynamics ◽  
Mathematical Framework ◽  
Cell Cell

AbstractInteractions between cells drive biological processes across all of life, from microbes in the environment to cells in multicellular organisms. Interactions often arise in spatially structured settings, where cells mostly interact with their neighbors. A central question is how the properties of biological systems emerge from local interactions. This question is very relevant in the context of microbial communities, such as biofilms, where cells live close by in space and are connected via a dense network of biochemical interactions. To understand and control the functioning of these communities, it is essential to uncover how community-level properties, such as the community composition, spatial arrangement, and growth rate, arise from these interactions. Here, we develop a mathematical framework that can predict community-level properties from the molecular mechanisms underlying the cell-cell interactions for systems consisting of two cell types. Our predictions match quantitative measurements from an experimental cross-feeding community. For these cross-feeding communities, the community growth rate is reduced when cells interact only with few neighbors; as a result, some communities can co-exist in a well-mixed system, but not in a spatial one. In general, our framework shows that key molecular parameters underlying the cell-cell interactions (e.g. the uptake and leakage rates of molecules) determine community level properties. Our framework can be extended to a variety of systems of two interacting cell types, within and beyond the microbial world, and contributes to our understanding of how biological functions arise from interactions between single cells.

scholarly journals CCPLS reveals cell-type-specific spatial dependence of transcriptomes in single cells

2022 ◽  
Author(s):  
Takaho Tsuchiya ◽  
Hiroki Hori ◽  
Haruka Ozaki
Keyword(s):  
Gene Expression ◽  
Single Cells ◽  
Cell Types ◽  
Regression Modeling ◽  
Transcriptome Data ◽  
Cell Type ◽  
Neighboring Cell ◽  
Expression Variability ◽  
Cell Expression ◽  
Cell Cell

Motivation: Cell-cell communications regulate internal cellular states of the cell, e.g., gene expression and cell functions, and play pivotal roles in normal development and disease states. Furthermore, single-cell RNA sequencing methods have revealed cell-to-cell expression variability of highly variable genes (HVGs), which is also crucial. Nevertheless, the regulation on cell-to-cell expression variability of HVGs via cell-cell communications is still unexplored. The recent advent of spatial transcriptome measurement methods has linked gene expression profiles to the spatial context of single cells, which has provided opportunities to reveal those regulations. The existing computational methods extract genes with expression levels that are influenced by neighboring cell types based on the spatial transcriptome data. However, limitations remain in the quantitativeness and interpretability: it neither focuses on HVGs, considers cooperation of neighboring cell types, nor quantifies the degree of regulation with each neighboring cell type. Results: Here, we propose CCPLS (Cell-Cell communications analysis by Partial Least Square regression modeling), which is a statistical framework for identifying cell-cell communications as the effects of multiple neighboring cell types on cell-to-cell expression variability of HVGs, based on the spatial transcriptome data. For each cell type, CCPLS performs PLS regression modeling and reports coefficients as the quantitative index of the cell-cell communications. Evaluation using simulated data showed our method accurately estimated effects of multiple neighboring cell types on HVGs. Furthermore, by applying CCPLS to the two real datasets, we demonstrate CCPLS can be used to extract biologically interpretable insights from the inferred cell-cell communications.

oko meduzy mutations affect neuronal patterning in the zebrafish retina and reveal cell-cell interactions of the retinal neuroepithelial sheet

Development ◽  
1999 ◽  
Vol 126 (6) ◽  
pp. 1235-1246 ◽  
Author(s):  
J. Malicki ◽  
W. Driever
Keyword(s):  
Critical Role ◽  
Cell Types ◽  
Cell Interactions ◽  
Retinal Cell ◽  
Wild Type ◽  
Vertebrate Retina ◽  
Neuroepithelial Cells ◽  
Patterning Defect ◽  
Cell Cell

Mutations of the oko meduzy (ome) locus cause drastic neuronal patterning defect in the zebrafish retina. The precise, stratified appearance of the wild-type retina is absent in the mutants. Despite the lack of lamination, at least seven retinal cell types differentiate in oko meduzy. The ome phenotype is already expressed in the retinal neuroepithelium affecting morphology of the neuroepithelial cells. Our experiments indicate that previously unknown cell-cell interactions are involved in development of the retinal neuroepithelial sheet. In genetically mosaic animals, cell-cell interactions are sufficient to rescue the phenotype of oko meduzy retinal neuroepithelial cells. These cell-cell interactions may play a critical role in the patterning events that lead to differentiation of distinct neuronal laminae in the vertebrate retina.

scholarly journals Classical cadherins control nucleus and centrosome position and cell polarity

2009 ◽  
Vol 185 (5) ◽  
pp. 779-786 ◽  
Author(s):  
Isabelle Dupin ◽  
Emeline Camand ◽  
Sandrine Etienne-Manneville
Keyword(s):  
Cell Polarity ◽  
Control Cell ◽  
Cell Types ◽  
Free Cell ◽  
Cell Polarization ◽  
Cell Interactions ◽  
Spatial Cues ◽  
Cell Contacts ◽  
Classical Cadherins ◽  
Cell Cell

Control of cell polarity is crucial during tissue morphogenesis and renewal, and depends on spatial cues provided by the extracellular environment. Using micropatterned substrates to impose reproducible cell–cell interactions, we show that in the absence of other polarizing cues, cell–cell contacts are the main regulator of nucleus and centrosome positioning, and intracellular polarized organization. In a variety of cell types, including astrocytes, epithelial cells, and endothelial cells, calcium-dependent cadherin-mediated cell–cell interactions induce nucleus and centrosome off-centering toward cell–cell contacts, and promote orientation of the nucleus–centrosome axis toward free cell edges. Nucleus and centrosome off-centering is controlled by N-cadherin through the regulation of cell interactions with the extracellular matrix, whereas the orientation of the nucleus–centrosome axis is determined by the geometry of N-cadherin–mediated contacts. Our results demonstrate that in addition to the specific function of E-cadherin in regulating baso-apical epithelial polarity, classical cadherins control cell polarization in otherwise nonpolarized cells.

scholarly journals Substrate mediated elastic coupling between motile cells modulates inter–cell interactions and enhances cell–cell contact

2021 ◽  
Author(s):  
Subhaya Bose ◽  
Kinjal Dasbiswas ◽  
Arvind Gopinath
Keyword(s):  
Cell Motility ◽  
Single Cells ◽  
Cell Structure ◽  
Control Cell ◽  
Cell Interactions ◽  
Biophysical Model ◽  
Cell Contact ◽  
Motile Cells ◽  
Key Aspects ◽  
Cell Cell

AbstractThe mechanical micro–environment of cells and tissues influences key aspects of cell structure and function including cell motility. For proper tissue development, cells need to migrate, interact with other neighbouring cells and form contacts, each of which require the cell to exert physical forces. Cells are known to exert contractile forces on underlying soft substrates. These stresses result in substrate deformation that can affect migratory behavior of cells as well as provide an avenue for cells to sense each other and coordinate their motion. The role of substrate mechanics, particularly its stiffness, in such biological processesis therefore a subject of active investigation. Recent progress in experimental techniques have enabled key insights into pairwise mechanical interactions that control cell motility when they move on compliant soft substrates. Analysis and modeling of such systemsis however still in its nascent stages. Motivated by the role modeling is expected to play in interpreting, informing and guiding experiments, we build a biophysical model for cell migration and cell–cell interactions. Our focus is on situations highly relevant to tissue engineering and regenerative medicine –when substrate traction stresses induced by motile cells enable substrate deformation and serve as a medium of communication. Using a generalizable agent–basedmodel, we compute key metrics of cell motile behavior such as the number of cell–cell contacts over a given time, dispersion of cell trajectories, and probability of permanent cell contact, and analyze how these depend on a cell motility parameter and on substrate stiffness. Our results provide a framework towards modeling the manner in which cells may sense each other mechanically via the substrate and use this information to generate coordinated movements across much longer length scales. Our results also provide a foundation to analyze experiments on the phenomenon known as durotaxis where single cells move preferentially towards regions of high stiffness on patterned substrates.

Cell Interaction by Multiplet sequencing (CIM-seq) v2

2021 ◽  
Author(s):  
Nathanael Andrews ◽  
Jason T. Serviss ◽  
Natalie Geyer (Karolinska Institute Stockholm) ◽  
Agneta B. Andersson ◽  
Ewa Dzwonkowska ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cell Types ◽  
Cell Interaction ◽  
Cell Interactions ◽  
Cell Type ◽  
Single Cell Sequencing ◽  
High Throughput Method ◽  
Dissociated Cells ◽  
Specific Tissue ◽  
Cell Cell

Single cell sequencing methods facilitate the study of tissues at high resolution, revealing rare cell types with varying transcriptomes or genomes, but so far have been lacking the capacity to investigate cell-cell interactions. Here, we introduce CIM-seq, an unsupervised and high-throughput method to analyze direct physical cell-cell interactions between every cell type in a given tissue. CIM-seq is based on RNA sequencing of incompletely dissociated cells, followed by computational deconvolution of these into their constituent cell types using machine learning. CIM-seq is broadly applicable to studies that aim to simultaneously investigate the constituent cell types and the global interaction profile in a specific tissue.

scholarly journals Regulation of C-cadherin function during activin induced morphogenesis of Xenopus animal caps.

1994 ◽  
Vol 126 (2) ◽  
pp. 519-527 ◽  
Author(s):  
W M Brieher ◽  
B M Gumbiner
Keyword(s):  
Cell Adhesion ◽  
Single Cells ◽  
Cell Types ◽  
Aggregation Assay ◽  
Animal Pole ◽  
Mesodermal Cell ◽  
Calcium Dependent ◽  
L Cells ◽  
Steady State Levels ◽  
Cell Cell

Treatment of Xenopus animal pole tissue with activin results in the induction of mesodermal cell types and a dramatic elongation of the tissue. The morphogenetic movements involved in the elongation appear similar to those in normal gastrulation, which is driven by cell rearrangement and cell intercalations. We have used this system to explore the potential regulation of cell-cell adhesion and cadherin function during morphogenesis. Quantitative blastomere aggregation assays revealed that activin induction reduced the calcium-dependent adhesion between blastomeres. Activin-induced blastomeres formed smaller aggregates, and a greater proportion of the population remained as single cells compared to uninduced blastomeres. The aggregation was mediated by C-cadherin because C-cadherin was present in the blastomeres during the aggregation assay, and monoclonal antibodies against C-cadherin inhibited the calcium-dependent aggregation of blastomeres. E-cadherin was not detectable until after the completion of the assay and, therefore, does not explain the adhesive differences between induced and uninduced blastomeres. L cells stably expressing C-cadherin (LC cells) were used to demonstrate that C-cadherin activity was specifically altered after activin induction. Blastomeres induced with activin bound fewer LC cells than uninduced blastomers. L cells not expressing C-cadherin did not adhere to blastomeres. The changes in C-cadherin-mediated adhesion occurred without detectable changes in the steady-state levels of C-cadherin or the amount of C-cadherin present on the surface of the cell. Immunoprecipitation of C-cadherin and its associated catenins revealed that the ratio of C-cadherin and the catenins was not altered by activin induction. These results demonstrate that activin decreases the adhesive function of existing C-cadherin molecules on the surface of blastomeres and suggest that decreased cadherin mediated cell-cell adhesion is associated with increased morphogenetic movement.

scholarly journals A modular microfluidic bioreactor to investigate plant cell–cell interactions

PROTOPLASMA ◽  
2021 ◽  
Author(s):  
T. Finkbeiner ◽  
C. Manz ◽  
M. L. Raorane ◽  
C. Metzger ◽  
L. Schmidt-Speicher ◽  
...  
Keyword(s):  
Plant Cell ◽  
Cell Biology ◽  
Cultured Cells ◽  
Abiotic Factors ◽  
Welding Process ◽  
Cell Types ◽  
Cell Interactions ◽  
Chemical Signalling ◽  
Different Cell Types ◽  
Cell Cell

AbstractPlants produce a wide variety of secondary metabolites, which often are of interest to pharmaceutical and nutraceutical industry. Plant-cell cultures allow producing these metabolites in a standardised manner, independently from various biotic and abiotic factors difficult to control during conventional cultivation. However, plant-cell fermentation proves to be very difficult, since these chemically complex compounds often result from the interaction of different biosynthetic pathways operating in different cell types. To simulate such interactions in cultured cells is a challenge. Here, we present a microfluidic bioreactor for plant-cell cultivation to mimic the cell–cell interactions occurring in real plant tissues. In a modular set-up of several microfluidic bioreactors, different cell types can connect through a flow that transports signals or metabolites from module to module. The fabrication of the chip includes hot embossing of a polycarbonate housing and subsequent integration of a porous membrane and in-plane tube fittings in a two-step ultrasonic welding process. The resulting microfluidic chip is biocompatible and transparent. Simulation of mass transfer for the nutrient sucrose predicts a sufficient nutrient supply through the membrane. We demonstrate the potential of this chip for plant cell biology in three proof-of-concept applications. First, we use the chip to show that tobacco BY-2 cells in suspension divide depending on a “quorum-sensing factor” secreted by proliferating cells. Second, we show that a combination of two Catharanthus roseus cell strains with complementary metabolic potency allows obtaining vindoline, a precursor of the anti-tumour compound vincristine. Third, we extend the approach to operationalise secretion of phytotoxins by the fungus Neofusicoccum parvum as a step towards systems to screen for interorganismal chemical signalling.

scholarly journals Growth kinetics and power laws indicate distinct mechanisms of cell-cell interactions in the aggregation process

2021 ◽  
Author(s):  
Debangana Mukhopadhyay ◽  
Rumi De
Keyword(s):  
Growth Kinetics ◽  
Single Cells ◽  
Domain Size ◽  
Power Laws ◽  
Theoretical Work ◽  
Cell Interactions ◽  
Specific Cell ◽  
Aggregation Process ◽  
Chemical Signalling ◽  
Cell Cell

Cellular aggregation is a complex process orchestrated by various kinds of interactions depending on its environments. Different interactions give rise to different pathways of cellular rearrangement and the development of specialized tissues. To distinguish the underlying mechanisms, in this theoretical work, we investigate the spontaneous emergence of tissue patterns from an ensemble of single cells on a substrate following three leading pathways of cell-cell interactions, namely, direct cell adhesion contacts, matrix mediated mechanical interaction, and chemical signalling. Our analysis shows that the growth kinetics of the aggregation process is distinctly different for each pathway and bears the signature of the specific cell-cell interactions. Interestingly, we find that the average domain size and the mass of the clusters exhibit a power law growth in time under certain interaction mechanisms hitherto unexplored. Further, as observed in experiments, the cluster size distribution can be characterized by stretched exponential functions showing distinct cellular organization processes.

scholarly journals Microfluidic system-based time-course tracking of physical proximity between cells and its effect on gene expression for elucidating live single cancer-immune cell interactions

2021 ◽  
Author(s):  
Bianca C.T Flores ◽  
Smriti Chawla ◽  
Ning Ma ◽  
Chad Sanada ◽  
Praveen Kumar Kujur ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Immune Cell ◽  
Cell Communication ◽  
Time Lapse ◽  
Cell Types ◽  
Temporal Variations ◽  
Microfluidic System ◽  
Cell Interactions ◽  
Cell Cell

Cell-cell communication and physical interactions play a vital role in cancer initiation, homeostasis, progression, and immune response. Here, we report a system that combines live capture of different cell types, co-incubation, time-lapse imaging, and gene expression profiling of doublets using a microfluidic integrated fluidic circuit (IFC) that enables measurement of physical distances between cells and the associated transcriptional profiles due to cell-cell interactions. The temporal variations in natural killer (NK) - triple-negative breast cancer (TNBC) cell distances were tracked and compared with terminally profiled cellular transcriptomes. The results showed the time-bound activities of regulatory modules and alluded to the existence of transcriptional memory. Our experimental and bioinformatic approaches serve as a proof of concept for interrogating live cell interactions at doublet resolution, which can be applied across different cancers and cell types.

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