Mechanical regulation of cell-cycle progression and division

Gupta, Vivek; Chaudhuri, Ovijit

doi:10.1016/j.tcb.2022.03.010

Cited by 26 publications

(17 citation statements)

References 73 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is well recognized that the process of cell growth and division is highly mechanically-driven [ 26 ] and that the shape and elasticity of single cells change quite dramatically in different moments of the cycle [ 27 ]. To characterize the impact of this biological process, we evaluated the distribution of mechanical properties on cell populations subjected to a starvation protocol (see ‘Materials and methods’).…”

Section: Resultsmentioning

confidence: 99%

The Impact of Experimental Conditions on Cell Mechanics as Measured with Nanoindentation

et al. 2023

View full text Add to dashboard Cite

The evaluation of cell elasticity is becoming increasingly significant, since it is now known that it impacts physiological mechanisms, such as stem cell differentiation and embryogenesis, as well as pathological processes, such as cancer invasiveness and endothelial senescence. However, the results of single-cell mechanical measurements vary considerably, not only due to systematic instrumental errors but also due to the dynamic and non-homogenous nature of the sample. In this work, relying on Chiaro nanoindenter (Optics11Life), we characterized in depth the nanoindentation experimental procedure, in order to highlight whether and how experimental conditions could affect measurements of living cell stiffness. We demonstrated that the procedure can be quite insensitive to technical replicates and that several biological conditions, such as cell confluency, starvation and passage, significantly impact the results. Experiments should be designed to maximally avoid inhomogeneous scenarios to avoid divergences in the measured phenotype.

show abstract

Section: Resultsmentioning

confidence: 99%

The Impact of Experimental Conditions on Cell Mechanics as Measured with Nanoindentation

et al. 2023

View full text Add to dashboard Cite

show abstract

“…Moreover, one can show (see SI) that Eqs. (3,4,5) constitute a minimal model in order to capture the shape of the experimental R-curve and the shape of the α-curve (32). In other words, simpler division protocols, such as a deterministic cell division above a given threshold (23-26, 34, 35) or a constant probability to enter division above a given threshold (36), do not reproduce the features of the experiments (Fig.…”

Section: Microscopic Model For Cell Proliferationmentioning

confidence: 99%

“…For instance, small cells in tissue sense local higher cell density and therefore larger tissue inner pressure. This leads to a longer lag time in the growing phase which therefore delays proliferation (5).…”

mentioning

confidence: 99%

Capturing the mechanosensitivity of cell proliferation in models of epithelium

Höllring¹,

Nuic

Rogic

et al. 2023

Preprint

View full text Add to dashboard Cite

Despite the primary role of cell proliferation in tissue development and homeostatic maintenance, the interplay between cell density, cell mechanoresponse, and cell growth and division is poorly described in theoretical models of tissues. As a consequence, the predictive power of such models is largely undermined, and the lack of systematic statistical and experimental analysis in controlled experiments does not allow to calibrate and validate already existing tools. In this article, we first report an experimental investigation of cell proliferation on all time- and length-scales and quantify the role of cell-cell and cell-matrix interactions. Based on these results, we build a minimal density-driven mean-field model of cell proliferation within 2D epithelia which can account for mechanoresponse and which is based on a separation of cell population into growing and dividing cells. We show that the model can capture the in-vitro experimental results and is further validated by in-silico experiments, and we highlight the importance of the separation of time scale and populations in the description of the cell life cycle. Additionally, we show that the mechanoresponse observed in the proliferation patterns is responsible of large-scale density structures across the tissues that we can capture by nonlinear delayed Fisher-Kolmogorov-like equations. This work sets a first building stone in a theoretical description of tissues on long time- and length-scale while accounting for proliferation.

show abstract

“…It is becoming abundantly clear that the mechanical environment encountered by cells, tissues and organisms plays an important regulatory role intertwined with chemical or metabolic regulations. At the cell level, mechanical cues can influence processes as important as cell division [ 1 , 2 ], individual and collective migration [ 3 – 5 ], gene expression [ 6 , 7 ] or even cell fate [ 8 , 9 ]. The mechanical regulation of biological processes can be mediated by the rheology of the substrate and tissues [ 10 , 11 ], tissue scale stresses [ 12 ], nematic defects [ 13 , 14 ] or cellular strains and deformations [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues

et al. 2023

View full text Add to dashboard Cite

Mechanical cues such as stresses and strains are now recognized as essential regulators in many biological processes like cell division, gene expression or morphogenesis. Studying the interplay between these mechanical cues and biological responses requires experimental tools to measure these cues. In the context of large scale tissues, this can be achieved by segmenting individual cells to extract their shapes and deformations which in turn inform on their mechanical environment. Historically, this has been done by segmentation methods which are well known to be time consuming and error prone. In this context however, one doesn’t necessarily require a cell-level description and a coarse-grained approach can be more efficient while using tools different from segmentation. The advent of machine learning and deep neural networks has revolutionized the field of image analysis in recent years, including in biomedical research. With the democratization of these techniques, more and more researchers are trying to apply them to their own biological systems. In this paper, we tackle a problem of cell shape measurement thanks to a large annotated dataset. We develop simple Convolutional Neural Networks (CNNs) which we thoroughly optimize in terms of architecture and complexity to question construction rules usually applied. We find that increasing the complexity of the networks rapidly no longer yields improvements in performance and that the number of kernels in each convolutional layer is the most important parameter to achieve good results. In addition, we compare our step-by-step approach with transfer learning and find that our simple, optimized CNNs give better predictions, are faster in training and analysis and don’t require more technical knowledge to be implemented. Overall, we offer a roadmap to develop optimized models and argue that we should limit the complexity of such models. We conclude by illustrating this strategy on a similar problem and dataset.

show abstract

Mechanical regulation of cell-cycle progression and division

Cited by 26 publications

References 73 publications

The Impact of Experimental Conditions on Cell Mechanics as Measured with Nanoindentation

The Impact of Experimental Conditions on Cell Mechanics as Measured with Nanoindentation

Capturing the mechanosensitivity of cell proliferation in models of epithelium

Small hand-designed convolutional neural networks outperform transfer learning in automated cell shape detection in confluent tissues

Contact Info

Product

Resources

About