Quantitative analysis of fibroblast migration reveals migratory states characterized by force generation, cell shape and motion

bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-11

ABSTRACT Cell migration depends on coordinating cell shape changes with force generation, yet how these processes are integrated remains unclear. Here, we combine live-cell imaging with traction force microscopy and computational analysis to quantify cell morphology, motility and force generation in migrating fibroblasts. We find that traction force magnitudes display a multimodal distribution, suggesting discrete migratory regimes. Using a Hidden Markov Model, we identify distinct force states that exhibit differences in shape and motion metrics, and show that individual cells transition between force states over time. To test the role of cytoskeletal organization in establishing the identified states, we analyzed cells lacking Arpc 2, which disrupts branched actin assembly. Despite reduced forces and altered morphology, these cells also exhibit three migratory states. State transitions occur more frequently in cells lacking Arpc2 and unlike normal cells their protrusion geometry is force dependent. Together, our findings show that cell migration is organized into discrete mechanical states that couple morphology, motility and force generation. SUMMARY STATEMENT Fibroblast motility involves distinct migratory states. These states exist independent of branched actin. However, state transition frequencies, traction force magnitudes and protrusion geometry are branched actin dependent.

Sparse and nonparametric estimation of equations governing dynamical systems with applications to biology

Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · 2026-04-15

Abstract Data-driven discovery of model equations is a powerful approach to understanding the behaviour of dynamical systems in many scientific fields. In particular, the ability to learn mathematical models from data would benefit systems biology, where the complex nature of these systems often makes a bottom up approach to modelling unfeasible. In recent years, sparse estimation techniques have gained prominence in system identification, primarily using parametric paradigms to efficiently capture system dynamics with minimal model complexity. In particular, the Sindy algorithm has successfully used sparsity to estimate nonlinear systems by extracting from a library of functions only a few key terms needed to capture the dynamics of these systems. However, parametric models often fall short in accurately representing certain nonlinearities inherent in complex systems. To address this limitation, we introduce a novel framework that integrates sparse parametric estimation with nonparametric techniques. It captures nonlinearities that Sindy cannot describe without requiring a priori information about their functional form. That is, without expanding the library of functions to include the one that is trying to be discovered. We illustrate our approach on several examples related to estimation of complex biological phenomena.

BPS2026 – Conformational dynamics of individual GTPase molecules probed in living cells: Localized regulation of specific steps in the GTPase cycle

Biophysical Journal · 2026-02-01

Abstract 6796: AI enabled imaging and single cell multiomics reveal how gene amplification architecture shapes gene expression

Cancer Research · 2026-04-03

Abstract Cancer cells often carry large gene amplifications that can arise through very different structural mechanisms. In some tumors, amplified oncogenes reside on circular extrachromosomal DNA, or ecDNA. In others, the same oncogenes reside within chromosomes in homogeneously staining regions, or HSRs. While HSRs propagate through conventional chromosomal segregation, ecDNA replicates and divides unevenly, creating extreme variation in oncogene dosage from cell to cell. These architectural differences are increasingly linked to aggressive tumor behavior, therapeutic resistance, and rapid evolution, yet their functional and molecular consequences remain poorly understood. Traditional bulk sequencing cannot distinguish ecDNA from HSRs, and imaging based detection has relied on manual, low throughput workflows, limiting our ability to assess amplification architecture across cell populations. To address this challenge, we developed AI based imaging tools that automatically identify ecDNA and HSRs in fluorescence microscopy images and classify amplification architecture across hundreds of nuclei per experiment. In parallel, we created computational approaches that infer amplification architecture directly from single cell sequencing data, using the characteristic copy number patterns generated by ecDNA versus HSR based amplification. These automated tools provide scalable, reproducible structural profiling of cancer cells. We integrated these structural assignments with 10x Genomics single cell multiome data and BioSkryb ResolveOME, which provides whole genome and whole transcriptome profiles from the same individual cells. We applied this framework to six human cancer cell lines, three dominated by ecDNA and three dominated by HSR amplifications. Across these models, the frequently amplified oncogenic locus PVT1 offered a shared point of comparison. The data revealed striking differences between the two amplification types: Genes amplified on ecDNA showed broad spreads of expression and distinct isoform usage, including a consistent PVT1 isoform whereas HSR amplifications produced tighter, more predictable transcriptional profiles. These findings suggest that ecDNA enables tumors to access a wider range of transcriptional states that may support bet hedging and drug resistant phenotypes. HSRs may stabilize expression programs, emerging through ecDNA reintegration under selective pressure. Together, these results demonstrate how AI enabled imaging and computational inference, combined with single cell multiomics, can uncover the hidden architecture of oncogene amplification in cancer and link it directly to transcriptional output. This framework provides a scalable path for understanding how genome organization drives tumor evolution, therapeutic resistance, and cancer aggressiveness. Citation Format: Yue Wang, Jingting Chen, Oliver Cope, Aarav Mehta, Dalia Fleifel, Christina Gutierrez-Ford, Poorya Behnamie, Santiago Haase, Saygin Gulec, Timothy C. Elston, Philip M. Spanheimer, Caroline Tomblin, Alison Rojas, Tia Tate, Jeremy E. Purvis, Jeremy Wang, Joseph M. Dahl, Sam Wolff, Jean Cook, Elizabeth C. Brunk. AI enabled imaging and single cell multiomics reveal how gene amplification architecture shapes gene expression [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 6796.

RhoG, Rac1 and Cdc42 cooperation in cell protrusion revealed by multiplexed optogenetics and biosensor imaging

bioRxiv (Cold Spring Harbor Laboratory) · 2026-05-13

Abstract The small GTPase Rac1 controls cell protrusion for a wide variety of critical cell functions. Its regulation by upstream guanine exchange factors (GEFs) has been the focus of multiple studies, but regulation by the GTPase RhoG remains poorly understood. RhoG is known to activate the ELMO/DOCK180 GEF complex, which in turn interacts with Rac1. It is unclear which aspects of protrusion are controlled by RhoG, and which of RhoG’s effects on protrusion are mediated by Rac1. To address these questions, we developed biosensors and optogenetic tools to activate one GTPase while observing another, and to simultaneously visualize the activity of two GTPases. New tools included a photoactivable RhoG, a RhoG biosensor, and red shifted biosensors of RhoG and Rac1. RhoG and Rac1 activation events in protrusions were spatio-temporally correlated with one another and with protrusion velocity. Causal inference indicated that RhoG indeed unidirectionally activated Rac1. Photoactivation of RhoG and Rac1 indicated that specific aspects of protrusion behavior were controlled by RhoG, and only some via Rac1. Further dissection of RhoG to Rac1 signaling through simultaneous GTPase activation and biosensor visualization showed that PA-RhoG activates Rac1 predominantly through DOCK180 and that PA-RhoG can activate Cdc42 independently of Rac1.

Long‐distance communication in Arabidopsis involving a self‐activating G protein

UNC Libraries · 2026-04-14

In plant cells, heterotrimeric G protein signaling mediates development, biotic/abiotic stress responsiveness, hormone signaling, and extracellular sugar sensing. The amount of sugar in plant cells fluctuates from nanomolar to high millimolar concentrations over time depending on changes in the light environment. <em>Arabidopsis thaliana</em> Regulator of G Signaling protein 1 (AtRGS1) modulates G protein activation and detects the concentration and the exposure time of sugars. This is called dose-duration reciprocity in sugar sensing and occurs through AtRGS1 internalization which is directly proportional to G protein activation. One source of sugars is from CO ₂ fixation by photosynthesis. Through a simple set of experiments, we show that sugars made in cotyledons that are undergoing photomorphogenesis activate G signaling in cells distal to the nascent photosynthesis center. This occurs with sufficient speed to enable distal cells to monitor changes in photosynthetic activity in the leaves.

BPS2025 - A model for random X chromosome inactivation

Biophysical Journal · 2025-02-01

Negative Feedback Equalizes Polarity Sites in a Multi-Budding Yeast

SSRN Electronic Journal · 2025-01-01

Ratiometric signaling produces robust temporal integration for accurate cellular gradient sensing

bioRxiv (Cold Spring Harbor Laboratory) · 2025-04-24 · 2 citations

Cells excel at interpreting noisy chemical gradients to guide fertilization, development, and immune responses, but the mechanisms underlying this remarkable ability remain poorly understood. Previous work showed that some G protein signaling pathways can overcome challenges from uneven receptor distribution by using a ratiometric signaling strategy. In this mechanism, G proteins receive information from both bound and unbound receptors, unlike classical signaling where only bound receptors contribute. Here, we show that ratiometric signaling also provides an unexpected ability to suppress noise from low receptor numbers. The benefit stems from each G protein remembering the last receptor state it encountered, so that at any instant, ratiometric G protein collectives reflect time-averaged receptor activity. Unlike classical signaling, this averaging remains unbiased and accurate across the varying ligand concentrations present in a spatial gradient. Using theory and simulations, we demonstrate that this averaging mechanism allows cells to surpass theoretical limits for gradient detection from instantaneous receptor information alone. Our findings reveal how ratiometric biochemical architectures enable robust temporal integration across spatially varying signals, providing cells with enhanced directional accuracy under noisy conditions.

Deep learning enables structured illumination microscopy with low light levels and enhanced speed

UNC Libraries · 2025-07-10