Frustration-induced phases in migrating cell clusters

Katherine Copenhagen; Gema Malet-Engra; Weimiao Yu; Giorgio Scita; Nir Gov; Ajay Gopinathan

doi:10.1126/sciadv.aar8483

Frustration-induced phases in migrating cell clusters

Science Advances ◽

10.1126/sciadv.aar8483 ◽

2018 ◽

Vol 4 (9) ◽

pp. eaar8483 ◽

Cited By ~ 12

Author(s):

Katherine Copenhagen ◽

Gema Malet-Engra ◽

Weimiao Yu ◽

Giorgio Scita ◽

Nir Gov ◽

...

Keyword(s):

Experimental Data ◽

Velocity Field ◽

Cancer Cells ◽

Collective Motion ◽

Topological Defects ◽

Local Environment ◽

Two Dimensional ◽

Agent Based ◽

Heterogeneous Behavior

Certain malignant cancer cells form clusters in a chemoattractant gradient, which can spontaneously show three different phases of motion: translational, rotational, and random. Guided by our experiments on the motion of two-dimensional clusters in vitro, we developed an agent-based model in which the cells form a cohesive cluster due to attractive and alignment interactions. We find that when cells at the cluster rim are more motile, all three phases of motion coexist, in agreement with our observations. Using the model, we show that the transitions between different phases are driven by competition between an ordered rim and a disordered core accompanied by the creation and annihilation of topological defects in the velocity field. The model makes specific predictions, which we verify with our experimental data. Our results suggest that heterogeneous behavior of individuals, based on local environment, can lead to novel, experimentally observed phases of collective motion.

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Resolving Filament Level Mechanics in Collagen Networks using Activity Microscopy

10.1101/382903 ◽

2018 ◽

Cited By ~ 1

Author(s):

Emanuel N. Lissek ◽

Tobias F. Bartsch ◽

Ernst-Ludwig Florin

Keyword(s):

Mechanical Properties ◽

Local Environment ◽

Thermal Fluctuations ◽

Primary Component ◽

Local Network ◽

Two Dimensional ◽

Single Filament ◽

Matrix Interaction ◽

Cell Matrix

AbstractCollagen is the most abundant protein in humans and the primary component of the extracellular matrix, a meshwork of biopolymer networks, which provides structure and integrity to tissues. Its mechanical properties profoundly influence the fate of cells. The cell-matrix interaction, however, is not well understood due to a lack of experimental techniques to study the mechanical interplay between cells and their local environment. Here we introduce Activity Microscopy, a new way to visualize local network mechanics with single filament resolution. Using collagen I networks in vitro, we localize fibril positions in two-dimensional slices through the network with nanometer precision and quantify the fibrils’ transverse thermal fluctuations with megahertz bandwidth. Using a fibril’s thermal fluctuations as an indicator for its tension, we find a heterogeneous stress distribution, where “cold” fibrils with small thermal fluctuations surround regions of highly fluctuating “hot” fibrils. We seed HeLa cells into collagen networks and quantify the anisotropy in the propagation of their forces.

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Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

10.1101/2021.03.03.433731 ◽

2021 ◽

Author(s):

Ernesto A. B. F. Lima ◽

Danial Faghihi ◽

Russel Philley ◽

Jianchen Yang ◽

John Virostko ◽

...

Keyword(s):

Experimental Data ◽

Cancer Cells ◽

Temporal Evolution ◽

Initial Conditions ◽

Individual Cell ◽

Computational Cost ◽

Coarse Grained ◽

Model Parameters ◽

Dead Cell ◽

Agent Based

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity due to probabilistic phenotypic transitions, and numerous model parameters that are difficult to measure directly are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) calibrated with a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model and data, we perform a global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM with a reduced parameter space is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties in model parameters and observational data. The results indicate that the cgABM can reliably predict the spatiotemporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61 [[EQUATION]] 2.01 and 5.78 [[EQUATION]] 1.13, respectively.

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In-depth characterization and comparison of the N-glycosylated proteome of two-dimensional- and three-dimensional-cultured breast cancer cells and xenografted tumors

PLoS ONE ◽

10.1371/journal.pone.0243789 ◽

2020 ◽

Vol 15 (12) ◽

pp. e0243789

Author(s):

Yonghong Mao ◽

Yang Zhao ◽

Yong Zhang ◽

Hao Yang

Keyword(s):

Breast Cancer ◽

Cancer Cells ◽

Breast Cancer Cells ◽

Drug Evaluation ◽

Tumor Biology ◽

Three Dimensional ◽

Growth Conditions ◽

Two Dimensional ◽

Cancer Cell Growth

Native intact N-glycopeptide analysis can provide access to the comprehensive characteristics of N-glycan occupancy, including N-glycosites, N-glycan compositions, and N-glycoproteins for complex samples. The sample pre-processing method used for the analysis of intact N-glycopeptides usually depends on the enrichment of low abundance N-glycopeptides from a tryptic peptide mixture using hydrophilic substances before LC-MS/MS detection. However, the number of identified intact N-glycopeptides remains inadequate to achieve an in-depth profile of the N-glycosylation landscape. Here, we optimized the sample preparation workflow prior to LC-MS/MS analysis by systematically comparing different analytical methods, including the use of different sources of trypsin, combinations of different proteases, and different enrichment materials. Finally, we found that the combination of Trypsin (B)/Lys-C digestion and zwitterionic HILIC (Zic-HILIC) enrichment significantly improved the mass spectrometric characterization of intact N-glycopeptides, increasing the number of identified intact N-glycopeptides and offering better analytical reproducibility. Furthermore, the optimized workflow was applied to the analysis of intact N-glycopeptides in two-dimensional (2D) and three-dimensional (3D)-cultured breast cancer cells in vitro and xenografted tumors in mice. These results indicated that the same breast cancer cells, when cultured in different microenvironments, can show different N-glycosylation patterns. This study also provides an interesting comparison of the N-glycoproteome of breast cancer cells cultured in different growth conditions, indicating the important role of N-glycosylated proteins in cancer cell growth and the choice of the cell culture model for studies in tumor biology and drug evaluation.

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SUPPRESSION OF GLIOBLASTOMA CELLULAR TRANSPORTERS SENSITIZES TARGET CELLS TO INFECTION WITH ONCOLYTIC VIRUS IN THE PRESENCE OF IONIZING RADIATION

Russian Journal of Biotherapy ◽

10.17650/1726-9784-2015-14-2-65-70 ◽

2015 ◽

Vol 14 (2) ◽

pp. 65-70

Author(s):

I. V. Ulasov ◽

N. V. Kaverina ◽

Z. G. Kadagidze ◽

A. Yu. Baryshnikov

Keyword(s):

Experimental Data ◽

Ionizing Radiation ◽

Cancer Cells ◽

Adenoviral Vector ◽

Oncolytic Virus ◽

Target Cells ◽

Glioblastoma Cell ◽

The Brain ◽

Glioblastoma Cell Lines

It is already established that verapamil inhibits Pgp expression in the brain tumor cancer cells such as medulloblastoma. The aim of this study is to investigate the sensitivity of glioblastoma cancer cells to verapamil and its combination with oncolytic adenoviral victor CRAd-S-pK7. In vitro using U87 and U251 human glioblastoma cell lines, we obtained experimental data suggesting a therapeutic effect of verapamil and CRAd-S-pK7. Moreover, we established that verapamil improves anti-glioma effect of oncolytic adenoviral vector in the presence of ionizing radiation, which results into more suppression of U251 cancer cells via inhibition of their proliferation.

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Bayesian calibration of a stochastic, multiscale agent-based model for predicting in vitro tumor growth

PLoS Computational Biology ◽

10.1371/journal.pcbi.1008845 ◽

2021 ◽

Vol 17 (11) ◽

pp. e1008845

Author(s):

Ernesto A. B. F. Lima ◽

Danial Faghihi ◽

Russell Philley ◽

Jianchen Yang ◽

John Virostko ◽

...

Keyword(s):

Experimental Data ◽

Cancer Cells ◽

Temporal Evolution ◽

Individual Cell ◽

Coarse Grained ◽

Computational Time ◽

Model Parameters ◽

Dead Cell ◽

Cell Dynamics ◽

Agent Based

Hybrid multiscale agent-based models (ABMs) are unique in their ability to simulate individual cell interactions and microenvironmental dynamics. Unfortunately, the high computational cost of modeling individual cells, the inherent stochasticity of cell dynamics, and numerous model parameters are fundamental limitations of applying such models to predict tumor dynamics. To overcome these challenges, we have developed a coarse-grained two-scale ABM (cgABM) with a reduced parameter space that allows for an accurate and efficient calibration using a set of time-resolved microscopy measurements of cancer cells grown with different initial conditions. The multiscale model consists of a reaction-diffusion type model capturing the spatio-temporal evolution of glucose and growth factors in the tumor microenvironment (at tissue scale), coupled with a lattice-free ABM to simulate individual cell dynamics (at cellular scale). The experimental data consists of BT474 human breast carcinoma cells initialized with different glucose concentrations and tumor cell confluences. The confluence of live and dead cells was measured every three hours over four days. Given this model, we perform a time-dependent global sensitivity analysis to identify the relative importance of the model parameters. The subsequent cgABM is calibrated within a Bayesian framework to the experimental data to estimate model parameters, which are then used to predict the temporal evolution of the living and dead cell populations. To this end, a moment-based Bayesian inference is proposed to account for the stochasticity of the cgABM while quantifying uncertainties due to limited temporal observational data. The cgABM reduces the computational time of ABM simulations by 93% to 97% while staying within a 3% difference in prediction compared to ABM. Additionally, the cgABM can reliably predict the temporal evolution of breast cancer cells observed by the microscopy data with an average error and standard deviation for live and dead cells being 7.61±2.01 and 5.78±1.13, respectively.

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Integrating experimental data to calibrate quantitative cancer models

10.1101/032102 ◽

2015 ◽

Author(s):

Heiko Enderling

Keyword(s):

Differential Equation ◽

Experimental Data ◽

Cell Cycle ◽

Cell Proliferation ◽

Cancer Cells ◽

Unknown Parameters ◽

Agent Based ◽

Cancer Models ◽

Differential Equation Models ◽

Model Dynamics

For quantitative cancer models to be meaningful and interpretable the number of unknown parameters must be kept minimal. Experimental data can be utilized to calibrate model dynamics rates or rate constants. Proper integration of experimental data, however, depends on the chosen theoretical framework. Using live imaging of cell proliferation as an example, we show how to derive cell cycle distributions in agent-based models and averaged proliferation rates in differential equation models. We focus on a tumor hierarchy of cancer stem and progenitor non-stem cancer cells.

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Specific Labeling of Protein Domains with Antibody Fragments

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100098587 ◽

1981 ◽

Vol 39 ◽

pp. 416-417

Author(s):

U. Aebi ◽

L.E. Buhle ◽

W.E. Fowler

Keyword(s):

Image Processing ◽

Specific Protein ◽

Antibody Fragments ◽

Supramolecular Organization ◽

Two Dimensional ◽

Ordered Structures ◽

Virus Capsids ◽

Processing Techniques

Many important supramolecular structures such as filaments, microtubules, virus capsids and certain membrane proteins and bacterial cell walls exist as ordered polymers or two-dimensional crystalline arrays in vivo. In several instances it has been possible to induce soluble proteins to form ordered polymers or two-dimensional crystalline arrays in vitro. In both cases a combination of electron microscopy of negatively stained specimens with analog or digital image processing techniques has proven extremely useful for elucidating the molecular and supramolecular organization of the constituent proteins. However from the reconstructed stain exclusion patterns it is often difficult to identify distinct stain excluding regions with specific protein subunits. To this end it has been demonstrated that in some cases this ambiguity can be resolved by a combination of stoichiometric labeling of the ordered structures with subunit-specific antibody fragments (e.g. Fab) and image processing of the electron micrographs recorded from labeled and unlabeled structures.

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