Paper List
-
SSDLabeler: Realistic semi-synthetic data generation for multi-label artifact classification in EEG
This paper addresses the core challenge of training robust multi-label EEG artifact classifiers by overcoming the scarcity and limited diversity of ma...
-
Decoding Selective Auditory Attention to Musical Elements in Ecologically Valid Music Listening
This paper addresses the core challenge of objectively quantifying listeners' selective attention to specific musical components (e.g., vocals, drums,...
-
Physics-Guided Surrogate Modeling for Machine Learning–Driven DLD Design Optimization
This paper addresses the core bottleneck of translating microfluidic DLD devices from research prototypes to clinical applications by replacing weeks-...
-
Mechanistic Interpretability of Antibody Language Models Using SAEs
This work addresses the core challenge of achieving both interpretability and controllable generation in domain-specific protein language models, spec...
-
The Effective Reproduction Number in the Kermack-McKendrick model with age of infection and reinfection
This paper addresses the challenge of accurately estimating the time-varying effective reproduction number ℛ(t) in epidemics by incorporating two crit...
-
Fluctuating Environments Favor Extreme Dormancy Strategies and Penalize Intermediate Ones
This paper addresses the core challenge of determining how organisms should tune dormancy duration to match the temporal autocorrelation of their envi...
-
Covering Relations in the Poset of Combinatorial Neural Codes
This work addresses the core challenge of navigating the complex poset structure of neural codes to systematically test the conjecture linking convex ...
-
Revealing stimulus-dependent dynamics through statistical complexity
This paper addresses the core challenge of detecting stimulus-specific patterns in neural population dynamics that remain hidden to traditional variab...
Tissue stress measurements with Bayesian Inversion Stress Microscopy
Institut Jacques Monod, CNRS, Université Paris Cité | Institut Curie, Paris Université Sciences et Lettres | Friedrich-Alexander Universität Erlangen-Nürnberg | Max-Planck-Zentrum für Physik und Medizin | Physique et Mécanique des Milieux Hétérogènes, CNRS, ESPCI Paris
The 30-Second View
IN SHORT: This paper addresses the core challenge of measuring absolute, tissue-scale mechanical stress without making assumptions about tissue rheology, which is crucial for understanding mechanobiology in complex, heterogeneous tissues.
Innovation (TL;DR)
- Methodology Introduces Bayesian Inversion Stress Microscopy (BISM), a method that infers the complete 2D stress tensor (σ_xx, σ_yy, σ_xy) from traction force data by solving an underdetermined inverse problem using Bayesian inference, without requiring rheological assumptions.
- Methodology Demonstrates robust applicability across diverse experimental geometries and boundary conditions, including confined tissues of arbitrary shape (e.g., star-shaped, elliptic) and systems with free boundaries (e.g., wound healing assays).
- Biology Provides absolute stress measurements, enabling the testing of fundamental assumptions in tissue mechanics. For example, it shows that a fourfold increase in cell density does not necessarily lead to compressive stress (mean tension decreased by a factor of three but remained positive), challenging the simple density-stress paradigm.
Key conclusions
- BISM provides absolute stress measurements validated against traction force moments. In a confined square MDCK monolayer, inferred mean isotropic stress (⟨σ_iso_inf⟩ = 7.76 kPa·μm) closely matched the calculated true value (⟨σ_iso_true⟩ = 7.77 kPa·μm), with a coefficient of determination R_t² = 1.0.
- The method is geometry-agnostic. Applied to a star-shaped MDCK island, BISM inferred stresses (e.g., ⟨σ_iso_inf⟩ = 1.57 kPa·μm) that excellently agreed with traction force moments (⟨σ_iso_true⟩ = 1.56 kPa·μm), demonstrating reliability in arbitrary confined shapes.
- BISM reveals a linear relationship between mean tissue tension and mean traction force amplitude (slope ~15.5 μm, on the order of a cell diameter), providing a quantitative link between external cell-substrate forces and internal tissue stress.
Abstract: Cells within biological tissue are constantly subjected to dynamic mechanical forces. Measuring the internal stress of tissues has proven crucial for our understanding of the role of mechanical forces in fundamental biological processes like morphogenesis, collective migration, cell division or cell elimination and death. Previously, we have introduced Bayesian Inversion Stress Microscopy (BISM), which is relying on measuring cell-generated traction forces in vitro and has proven particularly useful to measure absolute stresses in confined cell monolayers. We further demonstrate the applicability and robustness of BISM across various experimental settings with different boundary conditions, ranging from confined tissues of arbitrary shape to monolayers composed of different cell types. Importantly, BISM does not require assumptions on cell rheology. Therefore, it can be applied to complex heterogeneous tissues consisting of different cell types, as long as they can be grown on a flat substrate. Finally, we compare BISM to other common stress measurement techniques using a coherent experimental setup, followed by a discussion on its limitations and further perspectives.