Peeking into the future: inferring mechanics in dynamical tissues DOI Creative Commons
Augusto Borges, Osvaldo Chara

Biochemical Society Transactions, Journal Year: 2024, Volume and Issue: 52(6), P. 2579 - 2592

Published: Dec. 10, 2024

Cells exert forces on each other and their environment, shaping the tissue. The resulting mechanical stresses can be determined experimentally or estimated computationally using stress inference methods. Over years, has become a non-invasive, low-cost computational method for estimating relative intercellular intracellular pressures of tissues. This mini-review introduces compares static dynamic modalities inference, considering advantages limitations. To date, most software focused which requires only single microscopy image as input. Although applicable in quasi-equilibrium states, this approach neglects influence that cell rearrangements might have inference. In contrast, relies time series images to estimate pressures. Here, we discuss both terms physical, mathematical, foundations then outline what believe are promising avenues silico states

Language: Английский

Establishing a conceptual framework for holistic cell states and state transitions DOI Creative Commons
Susanne M. Rafelski, Julie A. Theriot

Cell, Journal Year: 2024, Volume and Issue: 187(11), P. 2633 - 2651

Published: May 1, 2024

Cell states were traditionally defined by how they looked, where located, and what functions performed. In this post-genomic era, the field is largely focused on a molecular view of cell state. Moving forward, we anticipate that observables used to define will evolve again as single-cell imaging analytics are advancing at breakneck pace via collection large-scale, systematic image datasets application quantitative image-based data science methods. This is, therefore, key moment in arc biological research develop approaches integrate spatiotemporal physical structure organization with toward concept holistic perspective, propose conceptual framework for state transitions data-driven, practical, useful enable integrative analyses modeling across many types.

Language: Английский

Citations

24
Learning dynamical models of single and collective cell migration: a review DOI Creative Commons
David B. Brückner,

Chase P. Broedersz

Reports on Progress in Physics, Journal Year: 2024, Volume and Issue: 87(5), P. 056601 - 056601

Published: March 22, 2024

Single and collective cell migration are fundamental processes critical for physiological phenomena ranging from embryonic development immune response to wound healing cancer metastasis. To understand a physical perspective, broad variety of models the underlying mechanisms that govern motility have been developed. A key challenge in such is how connect them experimental observations, which often exhibit complex stochastic behaviours. In this review, we discuss recent advances data-driven theoretical approaches directly with data infer dynamical migration. Leveraging nanofabrication, image analysis, tracking technology, studies now provide unprecedented large datasets on cellular dynamics. parallel, efforts directed towards integrating into single tissue scale aim conceptualising emergent behaviour cells. We first review inference problem has addressed both freely migrating confined Next, why these dynamics typically take form underdamped equations motion, can be inferred data. then applications machine learning heterogeneity behaviour, subcellular degrees freedom, multicellular systems. Across applications, emphasise methods integrated active matter cells, help reveal molecular control behaviour. Together, promising avenue building data, providing conceptual links between different length-scales description.

Language: Английский

Citations

11
The evolution of developmental biology through conceptual and technological revolutions DOI Creative Commons
Prisca Liberali, Alexander F. Schier

Cell, Journal Year: 2024, Volume and Issue: 187(14), P. 3461 - 3495

Published: June 20, 2024

Developmental biology-the study of the processes by which cells, tissues, and organisms develop change over time-has entered a new golden age. After molecular genetics revolution in 80s 90s diversification field early 21st century, we have phase when powerful technologies provide approaches open unexplored avenues. Progress has been accelerated advances genomics, imaging, engineering, computational biology emerging model systems ranging from tardigrades to organoids. We summarize how revolutionary led remarkable progress understanding animal development. describe classic questions gene regulation, pattern formation, morphogenesis, organogenesis, stem cell are being revisited. discuss connections development with evolution, self-organization, metabolism, time, ecology. speculate developmental might evolve an era synthetic biology, artificial intelligence, human engineering.

Language: Английский

Citations

11
Biomechanics in the tumor microenvironment: from biological functions to potential clinical applications DOI Creative Commons
Hao Peng,

Zheng Chao,

Zefeng Wang

et al.

Experimental Hematology and Oncology, Journal Year: 2025, Volume and Issue: 14(1)

Published: Jan. 11, 2025

Abstract Immune checkpoint therapies have spearheaded drug innovation over the last decade, propelling cancer treatments toward a new era of precision therapies. Nonetheless, challenges low response rates and prevalent resistance underscore imperative for deeper understanding tumor microenvironment (TME) pursuit novel targets. Recent findings revealed profound impacts biomechanical forces within on immune surveillance progression in both murine models clinical settings. Furthermore, pharmacological or genetic manipulation mechanical checkpoints, such as PIEZO1, DDR1, YAP/TAZ, TRPV4, has shown remarkable potential activation eradication tumors. In this review, we delved into underlying mechanisms resulting intricate biological meaning TME, focusing mainly extracellular matrix, stiffness cells, synapses. We also summarized methodologies employed research translation derived from current evidence. This comprehensive review biomechanics will enhance functional role provide basic knowledge discovery therapeutic

Language: Английский

Citations

1
T cells use focal adhesions to pull themselves through confined environments DOI
Alexia Caillier,

David Oleksyn,

Deborah J. Fowell

et al.

The Journal of Cell Biology, Journal Year: 2024, Volume and Issue: 223(10)

Published: June 18, 2024

Immune cells are highly dynamic and able to migrate through environments with diverse biochemical mechanical compositions. Their migration has classically been defined as amoeboid under the assumption that it is integrin independent. Here, we show activated primary Th1 T require both confinement extracellular matrix proteins efficiently. This mediated small focal adhesions composed of same associated canonical mesenchymal cell adhesions, such integrins, talin, vinculin. These furthermore, localize sites contractile traction stresses, enabling pull themselves confined spaces. Finally, preferentially follow tracks other cells, suggesting these modify provide additional environmental guidance cues. results demonstrate not only boundaries between modes ambiguous, but integrin-mediated play a key role in motility.

Language: Английский

Citations

7
Visual interpretability of bioimaging deep learning models DOI
Oded Rotem, Assaf Zaritsky

Nature Methods, Journal Year: 2024, Volume and Issue: 21(8), P. 1394 - 1397

Published: Aug. 1, 2024

Language: Английский

Citations

7
Myosin forces elicit an F-actin structural landscape that mediates mechanosensitive protein recognition DOI Creative Commons

Ayala G. Carl,

Matthew J. Reynolds, Pinar S. Gurel

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 17, 2024

Abstract Cells mechanically interface with their surroundings through cytoskeleton-linked adhesions, allowing them to sense physical cues that instruct development and drive diseases such as cancer. Contractile forces generated by myosin motor proteins mediate these mechanical signal transduction processes unclear protein structural mechanisms. Here, we show elicit changes in actin filaments (F-actin) modulate binding the mechanosensitive adhesion α-catenin. Using correlative cryo-fluorescence microscopy cryo-electron tomography, identify F-actin featuring domains of nanoscale oscillating curvature at cytoskeleton-adhesion interfaces enriched zyxin, a marker actin-myosin traction forces. We next introduce reconstitution system for visualizing presence microscopy, which reveals morphologically similar superhelical spirals. In simulations, transient mimicking tugging release motors produce spirals, supporting mechanistic link myosin’s ATPase mechanochemical cycle. Three-dimensional reconstruction spirals uncovers extensive asymmetric remodeling F-actin’s helical lattice. This is recognized α-catenin, cooperatively binds along individual strands, preferentially engaging extended inter-subunit distances while simultaneously suppressing rotational deviations regularize Collectively, find can deform F-actin, generating conformational landscape detected reciprocally modulated protein, providing direct glimpse active force cytoskeleton.

Language: Английский

Citations

4
Data-driven discovery and parameter estimation of mathematical models in biological pattern formation DOI Creative Commons
Hidekazu Hishinuma, Hisako Takigawa-Imamura, Takashi Miura

et al.

PLoS Computational Biology, Journal Year: 2025, Volume and Issue: 21(1), P. e1012689 - e1012689

Published: Jan. 23, 2025

Mathematical modeling has been utilized to explain biological pattern formation, but the selections of models and parameters have made empirically. In present study, we propose a data-driven approach validate applicability mathematical models. Specifically, developed methods automatically select appropriate based on patterns interest estimate model parameters. For selection, employed Contrastive Language-Image Pre-training (CLIP) for zero-shot feature extraction, mapping given images latent space specifying model. parameter estimation, novel technique that rapidly performs approximate Bayesian inference Natural Gradient Boosting (NGBoost). This method allows estimation under minimal constraints; i.e., it does not require time-series data or initial conditions is applicable various types We tested with Turing demonstrated its high accuracy correspondence analytical features. Our strategy enables efficient validation using spatial patterns.

Language: Английский

Citations

0
Unveiling fundamental principles: visualizing T cell immunity with explainable artificial intelligence DOI Creative Commons
Liyun Tu,

Aoyu Xu,

Hantao Lou

et al.

Medicine Plus, Journal Year: 2025, Volume and Issue: unknown, P. 100072 - 100072

Published: Jan. 1, 2025

Language: Английский

Citations

0
Exploring the intersection of mechanobiology and artificial intelligence DOI Creative Commons
Roger Oria, Kashish Jain, Valerie M. Weaver

et al.

npj Biological Physics and Mechanics., Journal Year: 2025, Volume and Issue: 2(1)

Published: March 26, 2025

Language: Английский

Citations

0