Paper List
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STAR-GO: Improving Protein Function Prediction by Learning to Hierarchically Integrate Ontology-Informed Semantic Embeddings
This paper addresses the core challenge of generalizing protein function prediction to unseen or newly introduced Gene Ontology (GO) terms by overcomi...
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Incorporating indel channels into average-case analysis of seed-chain-extend
This paper addresses the core pain point of bridging the theoretical gap for the widely used seed-chain-extend heuristic by providing the first rigoro...
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Competition, stability, and functionality in excitatory-inhibitory neural circuits
This paper addresses the core challenge of extending interpretable energy-based frameworks to biologically realistic asymmetric neural networks, where...
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Enhancing Clinical Note Generation with ICD-10, Clinical Ontology Knowledge Graphs, and Chain-of-Thought Prompting Using GPT-4
This paper addresses the core challenge of generating accurate and clinically relevant patient notes from sparse inputs (ICD codes and basic demograph...
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Hypothesis-Based Particle Detection for Accurate Nanoparticle Counting and Digital Diagnostics
This paper addresses the core challenge of achieving accurate, interpretable, and training-free nanoparticle counting in digital diagnostic assays, wh...
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MCP-AI: Protocol-Driven Intelligence Framework for Autonomous Reasoning in Healthcare
This paper addresses the critical gap in healthcare AI systems that lack contextual reasoning, long-term state management, and verifiable workflows by...
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Model Gateway: Model Management Platform for Model-Driven Drug Discovery
This paper addresses the critical bottleneck of fragmented, ad-hoc model management in pharmaceutical research by providing a centralized, scalable ML...
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Tree Thinking in the Genomic Era: Unifying Models Across Cells, Populations, and Species
This paper addresses the fragmentation of tree-based inference methods across biological scales by identifying shared algorithmic principles and stati...
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.