Literature DB >> 12235000

Fully automated three-dimensional tracking of cancer cells in collagen gels: determination of motility phenotypes at the cellular level.

Zoe N Demou1, Larry V McIntire.   

Abstract

We developed a fully automated three-dimensional cell tracking system that quantified the effect of extracellular matrix components on the infiltration and migration of tumor cells. The three-dimensional trajectories of two highly invasive cell lines, the human HT-1080 fibrosarcoma and the human MDA-MB-231 adenocarcinoma, were determined for long-term infiltration in plain or Matrigel-containing collagen type I gels. We modeled the trajectories with a novel formulation of the continuous Markov chain model that can distinguish between the tendencies for infiltration or lateral motion. Parameters such as the speed of subpopulations, the persistence of motion in certain directions, the turning frequency of the cells, the ultimate direction of motion, and the cell distribution with the infiltration depth were obtained to quantify the migration and infiltration at the cellular level. Distinct migratory and infiltration phenotypes were identified for the two cell types that were significantly dependent on gel composition. The HT-1080 cell line expressed a high motility phenotype on the plain collagen gel surface. The Matrigel-containing gel significantly enhanced the infiltration and the turning frequency of the HT-1080 cells. This study shows that tumor cell infiltration and migration are dynamic processes that depend significantly on the cell type and the microenvironment.

Entities:  

Keywords:  Non-programmatic

Mesh:

Substances:

Year:  2002        PMID: 12235000

Source DB:  PubMed          Journal:  Cancer Res        ISSN: 0008-5472            Impact factor:   12.701


  9 in total

1.  New method for modeling connective-tissue cell migration: improved accuracy on motility parameters.

Authors:  Matt J Kipper; Hynda K Kleinman; Francis W Wang
Journal:  Biophys J       Date:  2007-05-04       Impact factor: 4.033

Review 2.  Cellular modeling of cancer invasion: integration of in silico and in vitro approaches.

Authors:  Yoonseok Kam; Katarzyna A Rejniak; Alexander R A Anderson
Journal:  J Cell Physiol       Date:  2012-02       Impact factor: 6.384

3.  NO mediates mural cell recruitment and vessel morphogenesis in murine melanomas and tissue-engineered blood vessels.

Authors:  Satoshi Kashiwagi; Yotaro Izumi; Takeshi Gohongi; Zoe N Demou; Lei Xu; Paul L Huang; Donald G Buerk; Lance L Munn; Rakesh K Jain; Dai Fukumura
Journal:  J Clin Invest       Date:  2005-06-09       Impact factor: 14.808

4.  Spontaneous migration of cancer cells under conditions of mechanical confinement.

Authors:  Daniel Irimia; Mehmet Toner
Journal:  Integr Biol (Camb)       Date:  2009-07-16       Impact factor: 2.192

5.  A new method to address unmet needs for extracting individual cell migration features from a large number of cells embedded in 3D volumes.

Authors:  Ivan Adanja; Véronique Megalizzi; Olivier Debeir; Christine Decaestecker
Journal:  PLoS One       Date:  2011-07-15       Impact factor: 3.240

6.  Superstatistical analysis and modelling of heterogeneous random walks.

Authors:  Claus Metzner; Christoph Mark; Julian Steinwachs; Lena Lautscham; Franz Stadler; Ben Fabry
Journal:  Nat Commun       Date:  2015-06-25       Impact factor: 14.919

7.  ROCK-generated contractility regulates breast epithelial cell differentiation in response to the physical properties of a three-dimensional collagen matrix.

Authors:  Michele A Wozniak; Radhika Desai; Patricia A Solski; Channing J Der; Patricia J Keely
Journal:  J Cell Biol       Date:  2003-11-10       Impact factor: 10.539

8.  A novel circular invasion assay mimics in vivo invasive behavior of cancer cell lines and distinguishes single-cell motility in vitro.

Authors:  Yoonseok Kam; Cherise Guess; Lourdes Estrada; Brandy Weidow; Vito Quaranta
Journal:  BMC Cancer       Date:  2008-07-14       Impact factor: 4.430

9.  Multicell migration tracking within angiogenic networks by deep learning-based segmentation and augmented Bayesian filtering.

Authors:  Mengmeng Wang; Lee-Ling Sharon Ong; Justin Dauwels; H Harry Asada
Journal:  J Med Imaging (Bellingham)       Date:  2018-06-13
  9 in total

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