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Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow

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Computational Intelligence Methods for Bioinformatics and Biostatistics (CIBB 2016)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 10477))

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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.

V. Biga and O.M. Alves Coelho have contributed equally to this work.

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Acknowledgments

This work was funded by a Human Frontier Science Program grant. OC was funded by Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brazil.

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Authors and Affiliations

  1. Faculty of Biology, Medicine and Health, The University of Manchester, Oxford Road, Manchester, M13 9PL, UK

    Veronica Biga

  2. Instiuto Tecnológico de Aeronáutica, São José dos Campos, SP, 12228-900, Brazil

    Olívia M. Alves Coelho

  3. Centre for Stem Cell Biology, The University of Sheffield, Western Bank, Sheffield, S10 2TN, UK

    Paul J. Gokhale, James E. Mason & Peter W. Andrews

  4. Universidade Federal de Minas Gerais, Antonio Carlos 6627, Belo Horizonte, Brazil

    Eduardo M. A. M. Mendes

  5. Automatic Control and Systems Engineering Department, The University of Sheffield, Mappin Street, Sheffield, S1 3JD, UK

    Daniel Coca

Authors
  1. Veronica Biga
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  2. Olívia M. Alves Coelho
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  3. Paul J. Gokhale
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  4. James E. Mason
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  5. Eduardo M. A. M. Mendes
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  6. Peter W. Andrews
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  7. Daniel Coca
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Corresponding author

Correspondence to Veronica Biga .

Editor information

Editors and Affiliations

  1. Computing Science and Mathematics, University of Stirling, Stirling, United Kingdom

    Andrea Bracciali

  2. School of Informatics, University of Edinburgh, Edinburgh, United Kingdom

    Giulio Caravagna

  3. Department of Computer Science, Brunel University London, Uxbridge, Middlesex, United Kingdom

    David Gilbert

  4. Department of Management and Innovation Systems DISA-MIS, University of Salerno, Fisciano, Italy

    Roberto Tagliaferri

Appendix

Appendix

Microfluidic experiments on the M04S mammalian plate were prepared according to the diagram in Fig. 6 and in addition the waste wells 7 were filled with 300 \(\upmu \)l media per well to prevent gravity flow. The protocol for the microfluidic system is summarised in Algorithm 1.

Fig. 6.
figure 6

Preparation of differentiation experiments in microfluidic M04S plate. Legends denote: (M) DMEM-F12+20% FBS media; (RA) all-trans retinoic acid at \(10^-7\) in media M; (RA/3) RA diluted at 1:3 in media M.

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Biga, V. et al. (2017). Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. In: Bracciali, A., Caravagna, G., Gilbert, D., Tagliaferri, R. (eds) Computational Intelligence Methods for Bioinformatics and Biostatistics. CIBB 2016. Lecture Notes in Computer Science(), vol 10477. Springer, Cham. https://doi.org/10.1007/978-3-319-67834-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-67834-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67833-7

  • Online ISBN: 978-3-319-67834-4

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