BioSig : High-Content Representation and Association of Three-Dimensional Cell Culture Models

Background: Three-dimensional cell culture models have emerged as effective systems to study tissue differentiation and cancer behavior. If cancer is fundamentally a disease of aberrant multicellular organization, then understanding the effects of the tissue microenvironment, cellular and molecular variables, and possible therapeutic interventions on the oncogenic phenotype requires the development and use of more sophisticated models that can approximate cell-cell and cell-matrix interactions in vivo. Recent advances in 3D cell culture technology have made it possible to model key aspects of the ECM environment and cell and tissue organization that cannot be modeled using conventional two-dimensional methods. Obtaining quanti´Čüable data from 3D models with substantial throughput and relating such data to other cellular and molecular parameters entail a unique set of problems that we will solve. Routine application and utility of these engineered 3D cell culture models will have profound implications for the systematic analysis of cellular biology.
We are developing a platform for morphometric profiling of three-dimensional (3D) cell culture models. Multicellular systems will be imaged with confocal microscopy in full 3D; cellular organization and a number of other end points will be computed; and multidimensional phenotypic signatures will be associated with genomic data. The potential results of this initiative are (i) a basic understanding of the biological processes in a model system that is a better predictor of in vivo models, (ii) a template for drug screening against tumor lines with desirable reversion properties, and (iii) a template for hypothesis generation and validation through associations of genomic and phenotypic data. More importantly, we are designing experiments that involve manipulation of the microenvironment to elicit malignant behavior, which is manifested in disorganized phenotypic signature.


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Email: biosig@lists.lbl.gov

LBNL Imaging and Bioinformatics Group website

BioSig (last edited 2014-01-03 01:59:29 by gvf)