Fluctuations of cell geometry and their nonequilibrium thermodynamics in living epithelial tissue

Olenik, Mark; Turley, Jake; Cross, Stephen; Weavers, Helen; Martin, Paul; Chenchiah, Isaac V.; Liverpool, Tanniemola B.

doi:10.1103/physreve.107.014403

Cited by 11 publications

(12 citation statements)

References 47 publications

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“…We initially labelled the cell division orientations by hand and then trained the new Deep Learning Model to extract θ i.e., the orientation of the division (see Fig. S2A) (Olenik et al, 2022). After training, we tested the model’s accuracy and found that the median error is 4° ( π /45 radians ) (Fig.…”

Section: Resultsmentioning

confidence: 99%

“…To determine the orientation of the oval shape produced by the U-NetOrientation deep learning model, we calculated a second-rank tensor (which we call the q-tensor) for the output image that stores information about the orientation of the oval shapes (Olenik et al, 2022). …”

Section: Methodsmentioning

confidence: 99%

“…In parallel with the development of these imaging technologies, new methodologies are required in order to efficiently analyse these movies and extract detailed information about how the various cell behaviours (e.g. cell movements, divisions etc) contribute to tissue development and expansion, and how they enable repair responses following tissue damage (Etournay et al, 2015; Nestor-Bergmann et al, 2019; Olenik et al, 2023; Park et al, 2017; Scarpa et al, 2018; Tetley et al, 2019; Turley et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

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Deep learning for rapid analysis of cell divisionsin vivoduring epithelial morphogenesis and repair

Turley

Chenchiah

Martin

et al. 2023

Preprint

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Cell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation), over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be much enabled using Deep Learning. Hence, here we have developed a pipeline of Deep Learning Models that accurately identify dividing cells in time- lapse movies of epithelial tissues in vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy has enabled us to analyse the dynamic profile of cell divisions within the Drosophila pupal wing epithelium, both as it undergoes developmental morphogenesis, and as it repairs following laser wounding. We show that the axis of division is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) wave of cell divisions back from the leading edge.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep learning for rapid analysis of cell divisionsin vivoduring epithelial morphogenesis and repair

Turley

Chenchiah

Martin

et al. 2023

Preprint

Self Cite

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“…Our results show that in both cases, the orientational elasticity of nematogens plays a significant role in determining the stress localization patterns around defects. Due to the generic presence of fluctuations in active nematic systems, from subcellular fluctuations in focal adhesion 52 or stress fibers 53 to cell level shape fluctuations in MDCK layers 54 or Drosophila , 55 we expect that understanding the interplay between activity and fluctuations will play an important role in understanding cell behavior in the future.…”

Section: Discussionmentioning

confidence: 99%

“…Each point is averaged over 5 realizations. or Drosophila, 55 we expect that understanding the interplay between activity and fluctuations will play an important role in understanding cell behavior in the future.…”

Section: Discussionmentioning

confidence: 99%

Elasticity tunes mechanical stress localization around active topological defects

Bonn,

Ardaševa,

Doostmohammadi

2024

Soft Matter

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Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis

Turley,

Leong,

Chan

2024

Biophys Rev

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A key developmental stage in mammalian folliculogenesis is the formation of a fluid-filled lumen (antrum) prior to ovulation. While it has long been speculated that the follicular fluid is essential for oocyte maturation and ovulation, little is known about the morphogenesis and the mechanisms driving the antrum formation and ovulation, potentially due to challenges in imaging tissue dynamics in large tissues. Misregulation of such processes leads to anovulation, a hallmark of infertility in ageing and diseases such as the polycystic ovary syndrome (PCOS). In this review, we discuss recent advances in deep tissue imaging techniques, machine learning and theoretical approaches that have been applied to study development and diseases. We propose that an integrative approach combining these techniques is essential for understanding the physics of hydraulics in follicle development and ovarian functions.

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Fluctuations of cell geometry and their nonequilibrium thermodynamics in living epithelial tissue

Cited by 11 publications

References 47 publications

Deep learning for rapid analysis of cell divisionsin vivoduring epithelial morphogenesis and repair

Deep learning for rapid analysis of cell divisionsin vivoduring epithelial morphogenesis and repair

Elasticity tunes mechanical stress localization around active topological defects

Novel imaging and biophysical approaches to study tissue hydraulics in mammalian folliculogenesis

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