Analysis of Individual Cell Trajectories in Lattice-Gas Cellular Automaton Models for Migrating Cell Populations

2015 ◽  
Vol 77 (4) ◽  
pp. 660-697 ◽  
Author(s):  
Carsten Mente ◽  
Anja Voss-Böhme ◽  
Andreas Deutsch
Keyword(s):  
Cellular Automaton ◽  
Lattice Gas ◽  
Individual Cell ◽  
Cell Populations ◽  
Cell Trajectories

scholarly journals Cancer Cell Invasion of Brain Tissue: Guided by a Prepattern?

2005 ◽  
Vol 6 (1) ◽  
pp. 21-31 ◽  
Author(s):  
Michael Wurzel ◽  
Carlo Schaller ◽  
Matthias Simon ◽  
Andreas Deutsch
Keyword(s):  
Lattice Gas ◽  
Individual Cell ◽  
Physical Structure ◽  
Tumour Cells ◽  
Malignant Cells ◽  
Malignant Brain Tumour ◽  
Tumour Bulk ◽  
Cell Motion ◽  
The Brain ◽  
Global Growth

The malignant brain tumourGlioblastoma multiforme(GBM) displays a highly invasive behaviour. Spreading of the malignant cells appears to be guided by the white matter fibre tracts within the brain. In order to understand the global growth process we introduce a lattice-gas cellular automaton model which describes the local interaction between individual malignant cells and their neighbourhood. We consider interactions between cells (brain cells and tumour cells) and between malignant cells and the fibre tracts in the brain, which are considered as a prepattern. The prepattern implies persistent individual cell motion along the fibre structure. Simulations with the model show that only the inclusion of the prepattern results in invading tumour and growing tumour islets in front of the expanding tumour bulk (i.e. the growth pattern observed in clinical practice). Our results imply that the infiltrative growth of GBMs is, in part, determined by the physical structure of the surrounding brain rather than by intrinsic properties of the tumour cells.

Diffusion in tight confinement: A lattice-gas cellular automaton approach. II. Transport properties

2007 ◽  
Vol 126 (19) ◽  
pp. 194710 ◽  
Author(s):  
Pierfranco Demontis ◽  
Federico G. Pazzona ◽  
Giuseppe B. Suffritti
Keyword(s):  
Cellular Automaton ◽  
Transport Properties ◽  
Lattice Gas

A Lattice-Gas Cellular Automaton to Model Diffusion in Restricted Geometries

2006 ◽  
Vol 110 (27) ◽  
pp. 13554-13559 ◽  
Author(s):  
P. Demontis ◽  
F. G. Pazzona ◽  
G. B. Suffritti
Keyword(s):  
Cellular Automaton ◽  
Lattice Gas ◽  
Model Diffusion

scholarly journals Methods to Determine and Analyze the Cellular Spatial Distribution Extracted From Multiplex Immunofluorescence Data to Understand the Tumor Microenvironment

2021 ◽  
Vol 8 ◽  
Author(s):  
Edwin Roger Parra
Keyword(s):  
Image Analysis ◽  
Spatial Distribution ◽  
Spatial Analysis ◽  
Tumor Microenvironment ◽  
Individual Cell ◽  
Clinical Information ◽  
Cell Populations ◽  
Cellular Distribution ◽  
Main Challenge ◽  
Cell Phenotypes

Image analysis using multiplex immunofluorescence (mIF) to detect different proteins in a single tissue section has revolutionized immunohistochemical methods in recent years. With mIF, individual cell phenotypes, as well as different cell subpopulations and even rare cell populations, can be identified with extraordinary fidelity according to the expression of antibodies in an mIF panel. This technology therefore has an important role in translational oncology studies and probably will be incorporated in the clinic. The expression of different biomarkers of interest can be examined at the tissue or individual cell level using mIF, providing information about cell phenotypes, distribution of cells, and cell biological processes in tumor samples. At present, the main challenge in spatial analysis is choosing the most appropriate method for extracting meaningful information about cell distribution from mIF images for analysis. Thus, knowing how the spatial interaction between cells in the tumor encodes clinical information is important. Exploratory analysis of the location of the cell phenotypes using point patterns of distribution is used to calculate metrics summarizing the distances at which cells are processed and the interpretation of those distances. Various methods can be used to analyze cellular distribution in an mIF image, and several mathematical functions can be applied to identify the most elemental relationships between the spatial analysis of cells in the image and established patterns of cellular distribution in tumor samples. The aim of this review is to describe the characteristics of mIF image analysis at different levels, including spatial distribution of cell populations and cellular distribution patterns, that can increase understanding of the tumor microenvironment.

scholarly journals P-selectin glycoprotein ligand-1 (PSGL-1) is a ligand for L-selectin in neutrophil aggregation

Blood ◽  
1996 ◽  
Vol 88 (7) ◽  
pp. 2415-2421 ◽  
Author(s):  
DA Guyer ◽  
KL Moore ◽  
EB Lynam ◽  
CM Schammel ◽  
S Rogelj ◽  
...  
Keyword(s):  
Monoclonal Antibody ◽  
Flow Cytometry ◽  
Individual Cell ◽  
Cell Interaction ◽  
Cell Populations ◽  
Step Process ◽  
Activated Neutrophils ◽  
Neutrophil Aggregation ◽  
Beta 2 ◽  
Dependent Interaction

In inflammation, activated neutrophils adhere to endothelial cells and aggregate with one another. While beta 2-integrin and L-selectin are essential for aggregation, their ligands remain to be identified. We have previously shown that L-selectin mediates a carbohydrate-dependent interaction in aggregation (Simon et al: J Immunol 149:2765, 1992; Rochon et al: J Immunol 152:1385, 1994). We have suggested that the L-selectin counter-structure is a mucinlike protein and proposed that aggregation occurs through a two-step process involving L-selectin, beta 2-integrin, and their distinct counter-structures (Bennett et al: J Leuk Biol 58:510, 1995). A candidate ligand for L-selectin is P-selectin glycoprotein ligand-1 (PSGL-1), a mucinlike protein on neutrophils that binds P-and E-selectin. Using flow cytometry we show that the number and size of neutrophil aggregates is reduced with Fab fragments of PL1, an anti-PSGL-1 monoclonal antibody that blocks the interaction between P-selectin and PSGL-1 (Moore et al: J Cell Biol 128:661, 1995). In addition, monoclonal antibodies to L-selectin and PSGL-1 were used simultaneously to modulate the availability of these adhesion molecules on individual cell populations. The inhibition of aggregation by these antibodies is consistent with L-selectin and PSGL-1 being counter-structures. We suggest that L-selectin and PSGL-1 support a collisional cell-cell interaction that represents the first step in neutrophil aggregation.

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