Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow

Veronica Biga, Olivia Alves Coelho, Paul J. Gokhale, James Mason, Eduardo Mendes, Peter W. Andrews, Daniel Coca

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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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.
Original languageEnglish
Title of host publicationComputational intelligence methods for bioinformatics and biostatistics
Subtitle of host publication13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers
EditorsAndrea Bracciali, Giulio Caravagna, David Gilbert, Roberto Tagliaferri
PublisherSpringer Nature
ISBN (Electronic)978-3-319-67834-4
Publication statusPublished - 2017

Publication series

NameLecture Notes in Bioinformatics


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