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

doi:10.1007/978-3-319-67834-4_8

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

Division of Developmental Biology & Medicine (L5)

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

223 Downloads (Pure)

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.

Original language	English
Title of host publication	Computational intelligence methods for bioinformatics and biostatistics
Subtitle of host publication	13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers
Editors	Andrea Bracciali, Giulio Caravagna, David Gilbert, Roberto Tagliaferri
Publisher	Springer Nature
Pages	93-106
ISBN (Electronic)	978-3-319-67834-4
DOIs	https://doi.org/10.1007/978-3-319-67834-4_8
Publication status	Published - 2017

Publication series

Name	Lecture Notes in Bioinformatics
Volume	10477

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Access to Document

10.1007/978-3-319-67834-4_8

StatisticalTextureMappingOfDifferentiationBigaCoelho2017Accepted author manuscript, 1.51 MB
StatisticalTextureMappingOfDifferentiationBigaCoelho2017Accepted author manuscript, 1.53 MB

Cite this

Biga, V., Alves Coelho, O., Gokhale, P. J., Mason, J., Mendes, E., Andrews, P. W., & Coca, D. (2017). Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. In A. Bracciali, G. Caravagna, D. Gilbert, & R. Tagliaferri (Eds.), Computational intelligence methods for bioinformatics and biostatistics : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers (pp. 93-106). (Lecture Notes in Bioinformatics; Vol. 10477). Springer Nature. https://doi.org/10.1007/978-3-319-67834-4_8

Biga, Veronica ; Alves Coelho, Olivia ; Gokhale, Paul J. et al. / Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. Computational intelligence methods for bioinformatics and biostatistics : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers . editor / Andrea Bracciali ; Giulio Caravagna ; David Gilbert ; Roberto Tagliaferri. Springer Nature, 2017. pp. 93-106 (Lecture Notes in Bioinformatics).

@inproceedings{0c64caa362de433f88650373e0891425,

title = "Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow",

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.",

author = "Veronica Biga and \{Alves Coelho\}, Olivia and Gokhale, \{Paul J.\} and James Mason and Eduardo Mendes and Andrews, \{Peter W.\} and Daniel Coca",

year = "2017",

doi = "10.1007/978-3-319-67834-4\_8",

language = "English",

series = "Lecture Notes in Bioinformatics",

publisher = "Springer Nature",

pages = "93--106",

editor = "Andrea Bracciali and Giulio Caravagna and David Gilbert and Roberto Tagliaferri",

booktitle = "Computational intelligence methods for bioinformatics and biostatistics",

address = "United States",

}

Biga, V, Alves Coelho, O, Gokhale, PJ, Mason, J, Mendes, E, Andrews, PW & Coca, D 2017, Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. in A Bracciali, G Caravagna, D Gilbert & R Tagliaferri (eds), Computational intelligence methods for bioinformatics and biostatistics : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers . Lecture Notes in Bioinformatics, vol. 10477, Springer Nature, pp. 93-106. https://doi.org/10.1007/978-3-319-67834-4_8

Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. / Biga, Veronica; Alves Coelho, Olivia; Gokhale, Paul J. et al.
Computational intelligence methods for bioinformatics and biostatistics : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers . ed. / Andrea Bracciali; Giulio Caravagna; David Gilbert; Roberto Tagliaferri. Springer Nature, 2017. p. 93-106 (Lecture Notes in Bioinformatics; Vol. 10477).

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow

AU - Biga, Veronica

AU - Alves Coelho, Olivia

AU - Gokhale, Paul J.

AU - Mason, James

AU - Mendes, Eduardo

AU - Andrews, Peter W.

AU - Coca, Daniel

PY - 2017

Y1 - 2017

N2 - 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.

AB - 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.

U2 - 10.1007/978-3-319-67834-4_8

DO - 10.1007/978-3-319-67834-4_8

M3 - Conference contribution

T3 - Lecture Notes in Bioinformatics

SP - 93

EP - 106

BT - Computational intelligence methods for bioinformatics and biostatistics

A2 - Bracciali, Andrea

A2 - Caravagna, Giulio

A2 - Gilbert, David

A2 - Tagliaferri, Roberto

PB - Springer Nature

ER -

Biga V, Alves Coelho O, Gokhale PJ, Mason J, Mendes E, Andrews PW et al. Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow. In Bracciali A, Caravagna G, Gilbert D, Tagliaferri R, editors, Computational intelligence methods for bioinformatics and biostatistics : 13th International Meeting, CIBB 2016, Stirling, UK, September 1-3, 2016, revised selected papers . Springer Nature. 2017. p. 93-106. (Lecture Notes in Bioinformatics). Epub 2017 Oct 17. doi: 10.1007/978-3-319-67834-4_8

Statistical Texture-Based Mapping of Cell Differentiation Under Microfluidic Flow

Abstract

Publication series

UN SDGs

Access to Document

Fingerprint

Cite this