Fabio Pezzano’s research while affiliated with Centre for Genomic Regulation and other places

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Publications (8)


TimSOM
a, Schematic of TimSOM with direct light momentum sensing of optical forces. A single laser beam is time shared at 25 kHz between a driving (1) and a static detection (2) trap. The driving trap (orange) oscillates around the trapped particle, whereas the static trap (light orange, dashed line) monitors the particle position as xp = F2/k. For clarity, only the spring for the driving trap was indicated, noting that both traps have the same spring constant. The optical force acting onto the probe particle corresponds to the addition of forces exerted by the two traps: F = F1 + F2, which are obtained as F1,2 = αV1,2, where α is the volt-to-piconewton conversion factor of a single, direct light momentum force sensor. b, Time-sharing position and force measurement sequence. Although trap 2 remains motionless at the optical axis to detect bead displacements through the BFP interferometry, trap 1 applies an active sinusoidal perturbation with amplitude A at the time-sharing frequency fTS. The schematic on the bottom represents the deflection of the laser beam by the trapped particle for the driving (orange) and static (grey) traps. The orange (black) arrow indicates the optical (material) force acting onto the bead for the driving trap. c, Force profile acquired by sweeping the trap across a 1 µm polystyrene microsphere embedded in the cytoplasm of a zebrafish cell. The shaded area indicates that the force is linear with displacement over the amplitude of the rheology routine. The blue dotted line is F = kx. d–g, Quantitative description of bead motion in TimSOM. Simulation of the instantaneous position (i) of the probe particle in water using the FDE method (A = 100 nm; f = 625 Hz; k = 50 pN µm–1; water viscosity, η = 10⁻³ Pa s) and the resulting instantaneous optical force acting onto the probe (ii, dashed line) (d). Interleaved force values for the static and driving traps, sampled at fTS/2 = 12.5 kHz with a delay of 33 µs (Supplementary Text 2), are indicated as the orange and grey circles, respectively. The inset in (i) shows the time-sharing properties of the driving (trap 1) and static (trap 2) traps with the rise time of 10 µs. In (iii), the probe position and total force are shown. Response function derived from the FDE simulation (orange circles) with the parameters indicated in the top left and experimental data acquired in a zebrafish progenitor cell (green circles) using the time-shared microrheology routine (e). The solid lines show the expected, ideal behaviour of a fractional Kelvin–Voigt material. The inset shows the complex shear modulus. The dashed box indicates high frequencies with expected deviations due to the non-simultaneous measurement of stress and strain. Analytical pipeline to retrieve G modulus from the deviated measurements and/or FDE simulations (f). Response function (χ′, storage; χ″, loss) of the ideal scenario (theoretical), the time-shared simulations (FDE) and the compensated data points (FHA) for a single springpot (i), fractional Maxwell model (ii) and fractional Kelvin–Voigt (iii) model (g). The parameters used for the simulation are indicated in each panel and the legend is indicated on the right.
Source data
TimSOM correctly measures known viscoelastic materials
a, Representative experimental data of the G* modulus for water obtained from TimSOM compensated using Supplementary Equation (46). The dashed line indicates the fit to the data. b, Viscosity of different glycerol mixtures extracted from TimSOM (orange circles) and the classical drag force method (black circle). Viscosity was obtained from a linear fit to the averaged force values obtained for a series of triangles with different velocities (Extended Data Fig. 2a). The closed circles are the known references taken from ref. ²⁴. c, Rheological spectra of different PAA gels. Real (solid circles) and imaginary part (open circles) of the G modulus derived from the creep compliance force-clamp measurement (J(t)→Ĝ(ω))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$(J(t)\rightarrow\hat{G}(\omega))$$\end{document} on a 2% PAA gel (i). The solid or dashed line represents a fit of the data to the Fractional Kelvin–Voigt model with a low-frequency elastic modulus of Cα = 21.1 ± 8.83 Pa (mean ± confidence interval of 95%). TimSOM measurements on two different gels with varying stiffness values (ii). The orange gel is the exact same one as that in panel (i). The filled (open) circles correspond to the real (imaginary) part. The solid (dashed) lines are the real (imaginary) part of the fractional Kelvin–Voigt model, as specified in the legends. For the PAA gel, using the TimSOM method, we get Cα = 24.3 ± 1.28 Pa (confidence interval of 95%). The mint-coloured dots and lines correspond to a 20% acrylamide gel. The modulus is indicated in the figure, together with the trap stiffness G0 that was used to measure each gel. d, Representative raw data of the G modulus for PDMS obtained through equations (1a) and (1b), compensated using the FHA method (Supplementary Equation (46)). The filled (open) circles correspond to the real (imaginary) part. The solid (dashed) lines are the real (imaginary) part of the model specified in the legends.
Source data
Viscoelastic properties of BMCs
a–d, Schematic (a) and snapshot of an MEC-2/UNC-89 protein droplet measured with a pair of optically trapped polyethylene-glycol-terminated microspheres in a dual optical trap. Scale bar, 10 µm. Pin and Pout define the light momentum before and after interacting with the trapped microsphere (Supplementary Video 1). b,c, Storage (G′(ω), filled circles) and loss (G″(ω), open circles) moduli measured in the dual optical trap at t = 0 h (b) and t = 24 h (c) after condensate formation. The solid and dashed lines are the real and imaginary parts of the G modulus, derived from a fit of the Maxwell model to the acquired data. The circles and shadows are the median and ±25% quantiles measured for N = 10 (b) and N = 9 (c) droplets. d, Variation in the fitting parameters showing the changes in dynamic viscosity η (Pa s), stiffness E (Pa), time constant τ = η/E (s) and crossover frequency ωc = 1/τ (Hz) over 24 h condensate maturation in the dual optical trap. The mean and standard deviation derived from the fits in b and c. e–h, Schematic (e) and snapshot of a TimSOM experiment on MEC-2/UNC-89 protein droplet with an embedded carboxylated microbead (Supplementary Video 2). f,g, Storage (G′(ω), filled squares) and loss (G″(ω), open squares) moduli measured with TimSOM at t = 0 h (f) and t = 24 h (g) after condensate formation. The solid and dashed lines are the real and imaginary parts of the G modulus, derived from a fit of the Maxwell model to the acquired data. The squares and shadows are the median and ±25% quantiles measured for N = 17 (f) and N = 14 (g) droplets. h, Variation in the fitting parameters (dynamic viscosity η (Pa s), stiffness E (Pa), time constant τ = η/E (s) and crossover frequency ωc = 1/τ (Hz)) over 24 h extracted from the TimSOM routine. The mean and standard deviation derived from the fits in f and g.
Source data
Cytoplasm versus nuclear rheology
a–c, Representative bright-field (i) and confocal (ii) images of a zebrafish progenitor cell stained with Hoechst (blue) to label the nucleus and expressing Lap2β-GFP (green) with a microsphere in its cytoplasm (cyto) (a), nuclear interface (i/f) (b) and inside the nucleus (nuc) (c). Frequency spectrum of the complex G modulus, indicating the storage (closed symbols) and loss (open symbols) moduli of the three corresponding compartments (iii). Scale bars, 10 µm. Supplementary Video 3 shows the complete routine. d, Stiffness (Cα) of the cytoplasm, nuclear interface and nucleoplasm for controlm F-actin depolymerization (LatA) and LMNA overexpression conditions as extracted from the fit of a fractional Kelvin–Voigt model to the rheological spectrum. The lines connect paired data points that were acquired in the same cell with the same microsphere. For control cells, the experiments were independently repeated n = 9, 9 and 3 times for cyto, i/f and nuc, respectively. For LatA, n = 7, 7 and 3 and for lamin A, n = 4, 4 and 1, respectively. P values above the brackets derived from a paired t-test. Extended Data Fig. 5 and Supplementary Table 1 show a comparison of all the other fit parameters and their P values. N is the number of cells used in the measurement. e, P values of the indicated pairwise comparison using a two-sided Mann–Whitney U-test for Cα of the cytoplasm, nuclear interface and nucleoplasm in control, LatA treatment and lamin A (LMNA) overexpression.
Source data
Longitudinal tissue microrheology in vivo
a, Sketch of an animal with the intestinal tissue highlighted in green and the pharynx in red. The close-up sketch shows a pair of posterior intestinal cells with lipid droplets in blue. The lipid droplets were isolated from adult animals, purified and tested under various conditions for their suitability as optical tweezer probes (b and c; Methods). For in vivo application, individual droplets were trapped to measure the rheological response of the material in its vicinity (d–g). b, Refractive-index matching with varying concentrations of iodixanol. Bright-field micrograph of a lipid droplet in buffer B on the left (Methods; representative for N = 6 droplets) and in 48% of iodixanol (right; N = 12) (i). The graph shows the intensity profile along the dotted line indicated in the photograph. Scale bar, 2 µm. Force profile on a droplet in the matched conditions, for 0%, 48% and 54% of iodixanol (ii). c, Force scan across the lipid droplet for particle radius estimation (Methods). The lipid droplets vary in size from the trapping-force Rayleigh (dark red) and Mie (light red) limits. N = 2 droplets, representative for all the measurements. d, Fluorescence of GFP::lmn-1 and bright-field images demonstrating nuclear deformation with a trapped lipid droplet on contact during a tweezer experiment (i). δ indicates the deformation of the nucleus during the test, and the arrowhead points to the trapped lipid droplet. Scale bar, 2 µm. Kymograph of two consecutive step indentations of an intestinal nucleus using a lipid droplet as the force probe (ii). Force and displacement during the same step of the indentation protocol (iii). e,f, Frequency-dependent shear modulus for two different ages of wild-type (e) and age-matched (f) lem-2 mutants. The median and ±25% quantiles are represented by lines and shadows, respectively. g, Viscosity (Cβ) of the cytoplasm as extracted from the high-frequency component derived from the fit of the fractional Kelvin–Voigt model to the rheological spectrum of day 1 and day 8 adults for four different genotypes, as indicated. The red circle indicates median ± bootstrapped 95% confidence interval. P = 0.003 derived from a non-parametric Kruskal–Wallis test, followed by a pairwise comparison using a one-sided Dunn test without adjustment, as indicated above the horizontal brackets (for details on statistics and number of measurements, see Supplementary Data Table 2 and Extended Data Fig. 10). For wild type in D1, N = 35, n = 3, m = 10; in D8, N = 35, n = 3, m = 7. For GFP::lmn-1 in D1, N = 32, n = 4, m = 9; in D8, N = 24, n = 4, m = 8. For lem-2 in D1, N = 28, n = 3, m = 8; in D8, N = 20, n = 2, m = 6. For emr-1 in D1, N = 25, n = 3, m = 10; in D8, N = 21, n = 2, m = 6.
Source data
Measuring age-dependent viscoelasticity of organelles, cells and organisms with time-shared optical tweezer microrheology
  • Article
  • Full-text available

January 2025

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60 Reads

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2 Citations

Nature Nanotechnology

Frederic Català-Castro

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Santiago Ortiz-Vásquez

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Carmen Martínez-Fernández

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[...]

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Michael Krieg

Quantifying the mechanical response of the biological milieu (such as the cell’s interior) and complex fluids (such as biomolecular condensates) would enable a better understanding of cellular differentiation and aging and accelerate drug discovery. Here we present time-shared optical tweezer microrheology to determine the frequency- and age-dependent viscoelastic properties of biological materials. Our approach involves splitting a single laser beam into two near-instantaneous time-shared optical traps to carry out simultaneous force and displacement measurements and quantify the mechanical properties ranging from millipascals to kilopascals across five decades of frequency. To create a practical and robust nanorheometer, we leverage both numerical and analytical models to analyse typical deviations from the ideal behaviour and offer solutions to account for these discrepancies. We demonstrate the versatility of the technique by measuring the liquid–solid phase transitions of MEC-2 stomatin and CPEB4 biomolecular condensates, and quantify the complex viscoelastic properties of intracellular compartments of zebrafish progenitor cells. In Caenorhabditis elegans, we uncover how mutations in the nuclear envelope proteins LMN-1 lamin A, EMR-1 emerin and LEM-2 LEMD2, which cause premature aging disorders in humans, soften the cytosol of intestinal cells during organismal age. We demonstrate that time-shared optical tweezer microrheology offers the rapid phenotyping of material properties inside cells and protein blends, which can be used for biomedical and drug-screening applications.

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The structure and mechanics of the cell cortex depend on the location and adhesion state

July 2024

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73 Reads

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5 Citations

Proceedings of the National Academy of Sciences

Cells exist in different phenotypes and can transition between them. A phenotype may be characterized by many different aspects. Here, we focus on the example of whether the cell is adhered or suspended and choose particular parameters related to the structure and mechanics of the actin cortex. The cortex is essential to cell mechanics, morphology, and function, such as for adhesion, migration, and division of animal cells. To predict and control cellular functions and prevent malfunctioning, it is necessary to understand the actin cortex. The structure of the cortex governs cell mechanics; however, the relationship between the architecture and mechanics of the cortex is not yet well enough understood to be able to predict one from the other. Therefore, we quantitatively measured structural and mechanical cortex parameters, including cortical thickness, cortex mesh size, actin bundling, and cortex stiffness. These measurements required developing a combination of measurement techniques in scanning electron, expansion, confocal, and atomic force microscopy. We found that the structure and mechanics of the cortex of cells in interphase are different depending on whether the cell is suspended or adhered. We deduced general correlations between structural and mechanical properties and show how these findings can be explained within the framework of semiflexible polymer network theory. We tested the model predictions by perturbing the properties of the actin within the cortex using compounds. Our work provides an important step toward predictions of cell mechanics from cortical structures and suggests how cortex remodeling between different phenotypes impacts the mechanical properties of cells.


Mitochondria-derived nuclear ATP surge protects against confinement-induced proliferation defects

December 2023

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70 Reads

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1 Citation

The physical microenvironment regulates cell behaviour. However, whether physical confinement rewires the subcellular localisation of organelles and affect metabolism is unknown. Proteomics analysis revealed that cellular confinement induces a strong enrichment of mitochondrial proteins within the nuclear compartment. High-resolution microscopy confirmed that mechanical cell confinement leads to a rapid re-localisation of mitochondria to the nuclear periphery. This nuclear-mitochondrial proximity is mediated by an endoplasmic reticulum-based net that entraps the mitochondria in an actin-dependent manner. Functionally, the mitochondrial proximity results in a nuclear ATP surge, which can be reverted by the pharmacological inhibition of mitochondrial ATP production or via actin depolymerisation. Inhibition of the confinement-derived nuclear ATP surge reveals long-term effects on cell fitness which arise from alterations of chromatin states, delayed DNA damage repair, and impaired cell cycle progression. Together, our data describe a confinement-induced metabolic adaptation that is required to enable prompt DNA damage repair and cell cycle progression by allowing chromatin state transitions.


Adaptation to ARF6-depletion in KRAS-driven PDAC is abolished by targeting TLR2

December 2023

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39 Reads

Metastasis is responsible for nearly 90% of all cancer-related deaths. Despite global efforts to prevent aggressive tumours, cancers such as pancreatic ductal adenocarcinoma (PDAC) are poorly diagnosed in the primary stage, resulting in lethal metastatic disease. RAS mutations are known to promote tumour spread, with mutant KRAS present in up to 90% of cases. Until recently, mutant KRAS remained untargeted and, despite the recent development of inhibitors, results show that tumour cells develop resistance. Another strategy for targeting mutant KRAS-dependent PDAC proliferation and metastasis may come from targeting the downstream effectors of KRAS. One such axis, which controls tumour proliferation, invasiveness and immune evasion, is represented by ARF6-ASAP1. Here we show that targeting ARF6 results in adaptive rewiring that can restore proliferation and invasion potential over time. Using time-series RNA and ATAC sequencing approaches, we identified TLR-dependent NFκB, TNFα and hypoxia signalling as key drivers of adaptation in ARF6-depleted KRAS-dependent PDAC. Using in vitro and in vivo assays, we show that knocking down TLR2 with ARF6 significantly reduces proliferation, migration and invasion. Taken together, our data shed light on a novel co-targeting strategy with the therapeutic potential to counteract PDAC proliferation and metastasis. GRAPHICAL SUMMARY


Active microrheology with a single, time-shared laser trap

October 2023

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65 Reads

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1 Citation

Recording the mechanical response of biological samples, the cell's interior and complex fluids in general, would enable deeper understanding of cellular differentiation, ageing and drug discovery. Here, we present a time-shared optical tweezer microrheology (TimSOM) pipeline to determine the frequency- and age-dependent viscoelastic properties of biological materials. Our approach consists in splitting a single laser beam into two near-instantaneous time-shared optical traps to carry out simultaneous force and displacement measurements with sub-nanometer and sub-picoNewton accuracy during sinusoidal perturbations. Leveraging numerical and analytical models, we find solutions to commonly encountered deviations, to build an artefact-free nanorheometer. We demonstrate the versatility of the technique by 1) measuring the phase transitions of an ageing biomolecular condensate, 2) quantifying the complex viscoelastic properties of three intracellular compartments of zebrafish progenitor cells, and, using Caenorhabditis elegans, we uncover how mutations causing nuclear envelopathies soften the cytosol of intestinal cells during organismal age. Together, our advances afford rapid phenotyping of material properties inside cells and proteins blends, opening avenues for biomedical and drug screening applications.



The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior

October 2020

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275 Reads

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312 Citations

Science

The physical microenvironment regulates cell behavior during tissue development and homeostasis. How single cells decode information about their geometrical shape under mechanical stress and physical space constraints within tissues remains largely unknown. Here, using a zebrafish model, we show that the nucleus, the biggest cellular organelle, functions as an elastic deformation gauge that enables cells to measure cell shape deformations. Inner nuclear membrane unfolding upon nucleus stretching provides physical information on cellular shape changes and adaptively activates a calcium-dependent mechanotransduction pathway, controlling actomyosin contractility and migration plasticity. Our data support that the nucleus establishes a functional module for cellular proprioception that enables cells to sense shape variations for adapting cellular behavior to their microenvironment.


Fig. 1. Cell deformation in confined environments defines cortical contractility, polarization and fast amoeboid cell migration. (A) Relative cortical myosin II enrichment for decreasing confinement height in un-polarized progenitor cells (n=477 (suspension, unconfined); n=56 (18 µm); n=35 (16 µm); n=103 (13 µm); n=131 (10 µm); n=49 (8.5 µm); n=348 (7 µm)). Significance
Fig. 2. Nuclear envelop unfolding is associated with increasing cortical contractility. (A) Double boxplot of relative cortical myosin II enrichment (left axis, green) and nuclear size increase (right axis, grey) for decreasing confinement height. (B) Exemplary confocal top views (x-y) and side views (y-z) of progenitor stem cells expressing Myl12.1-eGFP stained with DNA-Hoechst and
Fig. 3. Nucleus deformation activates a mechanosensitive lipase signaling pathway regulating myosin II activity. (A) Relative cortical myosin II intensity for progenitor cells cultured in suspension versus 7 µm confinement conditions for control cells (DMEM), with cPLA 2 inhibitor, or injected with cPLA 2 MO and cPLA2 morpholino+cPLA 2 mRNA. (B) Exemplary confocal
Fig. 4. Nucleus unfolding and intracellular positioning enable adaptive cellular response to different types of physical cell deformation. (A) Relative cortical myosin II enrichment for progenitor cells cultured under different osmolarity conditions. (B) Normalized Ca 2+ (Calbryte520) intensity for control (Ctrl) and hypotonic (0.5x) conditions and mechanical
The nucleus measures shape deformation for cellular proprioception and regulates adaptive morphodynamics

December 2019

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415 Reads

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5 Citations

The physical microenvironment regulates cell behavior during tissue development and homeostasis. How single cells decode information about their geometrical shape under mechanical stress and physical space constraints within their local environment remains largely unknown. Here we show that the nucleus, the biggest cellular organelle, functions as a non-dissipative cellular shape deformation gauge that enables cells to continuously measure shape variations on the time scale of seconds. Inner nuclear membrane unfolding together with the relative spatial intracellular positioning of the nucleus provides physical information on the amplitude and type of cellular shape deformation. This adaptively activates a calcium-dependent mechano-transduction pathway, controlling the level of actomyosin contractility and migration plasticity. Our data support that the nucleus establishes a functional module for cellular proprioception that enables cells to sense shape variations for adapting cellular behaviour to their microenvironment. One Sentence Summary The nucleus functions as an active deformation sensor that enables cells to adapt their behavior to the tissue microenvironment.

Citations (5)


... For instance, in blastoderm during early cellularization, it limits the extensive tissue deformations required for drosophila morphogenesis [145]. Additionally, softening and fluidification of the cytoplasm was found to be a sign of ageing in C. elegans, which is enhanced by nuclear envelope protein defects [146]. ...

Reference:

Active Intracellular Mechanics: A Key to Cellular Function and Organization
Measuring age-dependent viscoelasticity of organelles, cells and organisms with time-shared optical tweezer microrheology

Nature Nanotechnology

... ; To establish whether our approach is also able to quantify the eAect of mechanical perturbations when cells are in suspension, we trypsinized and resuspended cells in medium containing 1 µM Latrunculin-A, an inhibitor that prevents actin polymerization by sequestering monomeric G-actin 33 . When cells are brought in the suspended state, actin forms a thick cortical layer beneath the cell membrane known as the actin cortex, which provides structural and mechanical support 22,34 . Depolymerization of actin filaments . ...

The structure and mechanics of the cell cortex depend on the location and adhesion state
  • Citing Article
  • July 2024

Proceedings of the National Academy of Sciences

... For a more extensive review on cell migration in confined 3D environments, we direct the reader to other recent reviews on cell migration and cancer cell metastasis ( Cells under high levels of confinement show signs of adapting to it. Acute confinement of HeLa cells to 3-7 µm height led to an accumulation and immobilization of mitochondria and the endoplasmic reticulum within nuclear indentations (Ghose et al. 2023;Liu et al. 2024b). The functional consequences were a surge in nuclear ATP production, which in turn led to an increase in chromatin accessibility and faster repair of DNA damage (Ghose et al. 2023). ...

Mitochondria-derived nuclear ATP surge protects against confinement-induced proliferation defects

... In practice, the ability to extract the "correct" features has been a bottleneck 16 . Traits commonly used by pathologists (e.g., cell type) are non-trivial to engineer, while histological presentation of novel discriminative features in cancers are often unintuitive due to tissue complexity and tumor variability, making them hard to formulate [17][18][19][20][21][22][23][24][25][26][27][28] . In contrast, deep learning (DL) algorithms, which bypass the task of feature engineering, have excelled on a wide range 9, 29-33 of histopathology-based predictions, matching human performance on traditional classification tasks and enabled predictions of mutation status, gene expression, molecular subtypes and treatment response [34][35][36] . ...

The nucleus measures shape changes for cellular proprioception to control dynamic cell behavior
  • Citing Article
  • October 2020

Science

... More globally, recent work by our lab and others (Lomakin et al., 2019;Venturini et al., 2019) showed that contractility is activated upon confinement by a mechanoresponse pathway mediated by the release of calcium and the activation of the enzyme cPLA2 (Enyedi et al., 2016). These works proposed that the contractility activation happens under 5µm compression due to nuclear stretching and that the stretch depended on the cell cycle stage or the state of the nuclear lamina. ...

The nucleus measures shape deformation for cellular proprioception and regulates adaptive morphodynamics