Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2017

AUTHORS

Veronica Biga , Olívia M. Alves Coelho , Paul J. Gokhale , James E. Mason , Eduardo M. A. M. Mendes , Peter W. Andrews , Daniel Coca

ABSTRACT

Timelapse microscopy enables long term monitoring of biological processes, however a major bottleneck in assesing experimental outcome is the need for an automated analysis framework to extract statistics and evaluate results. In this study, we use Gabor energy texture descriptors to generate a high dimensional feature space which is analysed with principal component analysis to provide unsupervised characterisation of texture differences between pairs of images. We apply this technique to differentiation of human embryonic carcinoma cells in the presence of all-trans retinoic acid (RA) and show that differentiation outcome can be predicted directly from texture information. A microfluidic environment is used to deliver pulses of RA stimulation over five days in culture. Results provide insight into the dynamics of cell response to differentiation signals over time. More... »

PAGES

93-106

Book

TITLE

Computational Intelligence Methods for Bioinformatics and Biostatistics

ISBN

978-3-319-67833-7
978-3-319-67834-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-67834-4_8

DOI

http://dx.doi.org/10.1007/978-3-319-67834-4_8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092236066


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