Inferring cell state by quantitative motility analysis reveals a dynamic state system and broken detailed balance
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Abstract
Cell populations display heterogeneous and dynamic phenotypic states at multiple scales. Similar to molecular features commonly used to explore cell heterogeneity, cell behavior is a rich phenotypic space that may allow for identification of relevant cell states. Inference of cell state from cell behavior across a time course may enable the investigation of dynamics of transitions between heterogeneous cell states, a task difficult to perform with destructive molecular observations. Cell motility is one such easily observed cell behavior with known biomedical relevance. To investigate heterogenous cell states and their dynamics through the lens of cell behavior, we developed Heteromotility, a software tool to extract quantitative motility features from timelapse cell images. In mouse embryonic fibroblasts (MEFs), myoblasts, and muscle stem cells (MuSCs), Heteromotility analysis identifies multiple motility phenotypes within the population. In all three systems, the motility state identity of individual cells is dynamic. Quantification of state transitions reveals that MuSCs undergoing activation transition through progressive motility states toward the myoblast phenotype. Transition rates during MuSC activation suggest non-linear kinetics. By probability flux analysis, we find that this MuSC motility state system breaks detailed balance, while the MEF and myoblast systems do not. Balanced behavior state transitions can be captured by equilibrium formalisms, while unbalanced switching between states violates equilibrium conditions and would require an external driving force. Our data indicate that the system regulating cell behavior can be decomposed into a set of attractor states which depend on the identity of the cell, together with a set of transitions between states. These results support a conceptual view of cell populations as dynamical systems, responding to inputs from signaling pathways and generating outputs in the form of state transitions and observable motile behaviors.
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Quantitative measurements of cell motility behavior from time-lapse imaging data are sufficient to resolve states of MuSC activation and state transitions.
“Previously, we demonstrated that quantitative measurements of cell motility behavior from time-lapse imaging data are sufficient to resolve states of MuSC activation and state transitions (Kimmel et al., 2018).”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Kimmel et al., 2018
Visualization of cell behavior state space with t-SNE revealed heterogeneous cell behavior states, previously shown to reflect different states of MuSC activation.
“The visualization of cell behavior state space with t-SNE (van der Maaten and Hinton, 2008) revealed heterogeneous cell behavior states, previously shown to reflect different states of MuSC activation (Fig. 1C) (Kimmel et al., 2018).”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Kimmel et al., 2018
Concave transition rates are consistent with a model in which the peak transition rate represents a 'switch' between two states.
“Concave transition rates are consistent with a model in which the peak transition rate represents a ‘switch’ between two states, corroborating our earlier observations made by cell behavior analysis (Kimmel et al., 2018).”
From Aging induces aberrant state transition kinetics in murine muscle stem cells (2020) · cites this paper as Kimmel et al., 2018
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BackgroundChakkalakal, J. V., Christensen, J., Xiang, W., Tierney, M. T., Boscolo, F. S., Sacco, A., & Brack, A. S. (2014) · Development · doi:10.1242/dev.100842 · Cited by 148 · see citing works
Tuberculosis Immunopathology: The Neglected Role of Extracellular Matrix Destruction
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Equilibrium Regained: From Nonequilibrium Chaos to Statistical Mechanics
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“A cell system governed by this model would display detailed balance in the absence of an external force or signal, and break detailed balance in the presence of such an external input [16,17].”
BackgroundComputational prediction of neural progenitor cell fates
Cohen, A. R., Gomes, F. L. A. F., Roysam, B., & Cayouette, M. (2010) · Nature Methods · doi:10.1038/nmeth.1424 · Cited by 109 · cited 1× · see citing works
“Recent approaches to timelapse motility analysis have expanded upon these techniques to extract multidimensional quantitative information from individual cells [28-33].”
BackgroundCellTrack: an open-source software for cell tracking and motility analysis
Saçan, A., Ferhatosmanoğlu, H., & Coşkun, H. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn247 · Cited by 96 · see citing works
Epithelial and mesenchymal phenotypic switchings modulate cell motility in metastasis
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PhenoRipper: software for rapidly profiling microscopy images
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Time series modeling of live-cell shape dynamics for image-based phenotypic profiling
Gordonov, S., Hwang, M. K., Wells, A. H., Gertler, F. B., Lauffenburger, D. A., & Bathe, M. (2015) · Integrative Biology · doi:10.1039/c5ib00283d · Cited by 78 · cited 2× · see citing works
“Previous work to quanitfy morphological dynamics has allowed for quantification of cell state transitions in this manner [20, 24, 31].”
BackgroundThe ameboid motility of human and animal neoplastic cells
Enterline, H. T., & Coman, D. R. (1950) · Cancer · doi:10.1002/1097-0142(1950)3:6 · Cited by 76 · see citing works
Regulation of Myoblast Motility and Fusion by the CXCR4-associated Sialomucin, CD164
Bae, G., Gaio, U., Yang, Y.-J., Lee, H., Kang, J., & Krauss, R. S. (2008) · Journal of Biological Chemistry · doi:10.1074/jbc.m706730200 · Cited by 74 · cited 1× · see citing works
“Mouse myogenic progenitor cells provide a well-characterized system in which chemical signals alter the behaviors of a mechanically-active cell type [56-59], and dynamic activation and lineage commitment processes can be manipulated [60,61].”
Backgroundc-MET Regulates Myoblast Motility and Myocyte Fusion during Adult Skeletal Muscle Regeneration
Webster, M. T., & Fan, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0081757 · Cited by 70 · cited 1× · see citing works
“Myoblast motility has direct functional relevance, as muscle progenitors translocate along the muscle fiber to sites of injury during muscle regeneration [58,62] and transverse fibers during development [63].”
BackgroundMigration Phenotype of Brain-Cancer Cells Predicts Patient Outcomes
Smith, C. L., Kilic, O., Schiapparelli, P. V., Guerrero‐Cazares, H., Kim, D., Sedora-Roman, N. I., Gupta, S., O'Donnell, T. J., Chaichana, K. L., Rodríguez, F. J., et al. (2016) · Cell Reports · doi:10.1016/j.celrep.2016.05.042 · Cited by 70 · see citing works
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Gene therapy using SV40-derived vectors: What does the future hold?
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“Recent approaches to timelapse motility analysis have expanded upon these techniques to extract multidimensional quantitative information from individual cells [28-33].”
BackgroundVerschueren, H., der Taelen, I. V., Dewit, J., Braekeleer, J. D., & BAETSELIER, P. D. (1994) · Journal of Leukocyte Biology · doi:10.1002/jlb.55.4.552 · Cited by 51 · cited 1× · see citing works
“Functionally, the increased motility of neoplastic cells may be related to their potential for tissue invasion [67, 68].”
BackgroundMeasurement of cell motility and morphology with an automated microscope system
Thurston, G., Jaggi, B., & Palcic, B. (1988) · Cytometry · doi:10.1002/cyto.990090502 · Cited by 46 · see citing works
CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data
Bray, M., & Carpenter, A. E. (2015) · BMC Bioinformatics · doi:10.1186/s12859-015-0759-x · Cited by 43 · see citing works
Deposition of extracellular matrix along the pathways of migrating fibroblasts
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“For example, extracellular matrix remodeling and deposition by fibroblasts is dictated by their motion [21,22], and stem cell and progenitor migration is critical for organismal development [23].”
BackgroundDo, M. Q., Suzuki, T., Borjigin, G., Sato, Y., Mizunoya, W., Nakamura, M., Ikeuchi, Y., Anderson, J. E., & Tatsumi, R. (2012) · Animal Science Journal · doi:10.1111/j.1740-0929.2012.01057.x · Cited by 35 · cited 1× · see citing works
“In vivo, FGF2 is released during muscle injury [67,68], promoting expansion of the myogenic progenitor pool and possibly migration to sites of injury.”
BackgroundJoint modeling of cell and nuclear shape variation
Johnson, G. R., Buck, T. E., Sullivan, D. P., Rohde, G. K., & Murphy, R. F. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-06-0370 · Cited by 34 · cited 2× · see citing works
“Previous work to quanitfy morphological dynamics has allowed for quantification of cell state transitions in this manner [20, 24, 31].”
BackgroundCooper, S., Sadok, A., Bousgouni, V., & Bakal, C. (2015) · Molecular Biology of the Cell · doi:10.1091/mbc.e15-06-0382 · Cited by 28 · see citing works
Plasticity in the Macromolecular-Scale Causal Networks of Cell Migration
Lock, J. G., Jafari‐Mamaghani, M., Shafqat-Abbasi, H., Gong, X., Tyrcha, J., & Strömblad, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0090593 · Cited by 23 · cited 1× · see citing works
“Recent work from several groups has elucidated the regulatory priciples governing cell shape definition [24, 25] and the relationships between morphological variables [26] utilizing quantitative, timelapse imaging.”
BackgroundCell_motility: a cross-platform, open source application for the study of cell motion paths
Martens, L., Monsieur, G., Ampè, C., Gevaert, K., & Vandekerckhove, J. S. (2006) · BMC Bioinformatics · doi:10.1186/1471-2105-7-289 · Cited by 22 · see citing works
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“Recent approaches to timelapse motility analysis have expanded upon these techniques to extract multidimensional quantitative information from individual cells [28-33].”
BackgroundAudit of the change in the on-call practices in neuroradiology and factors affecting it
Mukerji, N., Wallace, D., & Mitra, D. (2006) · BMC Medical Imaging · doi:10.1186/1471-2342-6-13 · Cited by 17 · see citing works
Doyle, G. M., Sharief, Y., & Mohler, J. L. (1992) · The Journal of Urology · doi:10.1016/s0022-5347(17)37291-9 · Cited by 14 · see citing works
Flagged first, then by citations
1 of the 103 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [35]Cooper S, Sadok A, Bousgouni V, Bakal C. Apolar and polar transitions drive the conversion between amoeboid and mesenchymal shapes in melanoma cells. Molecular Biology of the Cell. 2015; 26(22): 4163– 4170. doi: 10.1091/mbc.E15-06-0382 26310441 PMC4710245 · DOI
Cited by
Sorted by citations
Deep learning for cellular image analysis
Moen, E., Bannon, D., Kudo, T., Graf, W., Covert, M., & Van Valen, D. (2019) · Nature Methods · doi:10.1038/s41592-019-0403-1 · Cited by 1,225 · see citing works
“Although object tracking can be challenging, successful solutions have made it useful in a variety of biological analyses, including quantification of signaling dynamics 109 , efforts to understand cell motility 110 , and attempts to unravel the laws of bacterial cell growth 111 .”
BackgroundBroken detailed balance and non-equilibrium dynamics in living systems: a review
Gnesotto, F. S., Mura, F., Gladrow, J., & Broedersz, C. P. (2018) · Reports on Progress in Physics · doi:10.1088/1361-6633/aab3ed · Cited by 318 · see citing works
“This approach is now gaining traction in variety of systems, ranging from the post translation Kai circadian clock [243] to motility phenotypes [244].”
BackgroundWnt4 from the Niche Controls the Mechano-Properties and Quiescent State of Muscle Stem Cells
Eliazer, S., Muncie, J. M., Christensen, J., Sun, X., D’Urso, R. S., Weaver, V. M., & Brack, A. S. (2019) · Cell Stem Cell · doi:10.1016/j.stem.2019.08.007 · Cited by 171 · see citing works
“Using time-lapse imaging we recently demonstrated that SC motility occurs early in the activation response (Kimmel et al., 2018).”
Of Cell Shapes and Motion: The Physical Basis of Animal Cell Migration
Bodor, D. L., Pönisch, W., Endres, R. G., & Paluch, E. K. (2020) · Developmental Cell · doi:10.1016/j.devcel.2020.02.013 · Cited by 154 · see citing works
“, 2018) and even to characterize the motile dynamics of cells (Kimmel et al., 2018).”
BackgroundAging induces aberrant state transition kinetics in murine muscle stem cells
Kimmel, J. C., Hwang, A. B., Scaramozza, A., Marshall, W. F., & Brack, A. S. (2020) · Development · doi:10.1242/dev.183855 · Cited by 95 · see citing works
“To measure rates of change, we leverage our recently developed cell behavior analy-sis platform “Heteromotility” [28] to quantify phenotypic state dynamics during MuSC activation in aged and young MuSCs.”
InfluentialMachine Learning in Tissue Engineering
Guo, J. L., Januszyk, M., & Longaker, M. T. (2022) · Tissue Engineering Part A · doi:10.1089/ten.tea.2022.0128 · Cited by 79 · see citing works
“86 In another example, Cohen et al used time-lapse videos of cell morphology and movement to predict cell fate in cultured retinal progenitor cells.”
Time Irreversibility and Criticality in the Motility of a Flagellate Microorganism
Wan, K. Y., & Goldstein, R. E. (2018) · Physical Review Letters · doi:10.1103/physrevlett.121.058103 · Cited by 53 · see citing works
Deep Convolutional and Recurrent Neural Networks for Cell Motility Discrimination and Prediction
Kimmel, J. C., Brack, A. S., & Marshall, W. F. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2919307 · Cited by 49 · see citing works
“Mouse embryonic fibroblasts (MEFs) are commonly used for in vitro cell culture assays, and neoplastic transformation of these cells has been demonstrated to alter their motility behaviors [15].”
BackgroundMethodInfluentialmotilitAI: A machine learning framework for automatic prediction of human sperm motility
Ottl, S., Amiriparian, S., Gerczuk, M., & Schuller, B. W. (2022) · iScience · doi:10.1016/j.isci.2022.104644 · Cited by 39 · see citing works
“Furthermore, applications that solve individual parts of the automatic sperm analysis task can be found with particle tracking software, such as Trackpy (Allan et al., 2019), or motility analysis toolkits for inference of cell state (Kimmel et al., 2018).”
Morphological State Transition Dynamics in EGF-Induced Epithelial to Mesenchymal Transition
Devaraj, V., & Bose, B. (2019) · Journal of Clinical Medicine · doi:10.3390/jcm8070911 · Cited by 44 · see citing works
“[24] used cell motility to define phenotypic states and investigated state transition behaviors in mouse cells.”
MethodDynaMorph: self-supervised learning of morphodynamic states of live cells
Wu, Z., Chhun, B. B., Popova, G., Guo, S.-M., Kim, C. N., Yeh, L.-H., Nowakowski, T. J., Zou, J., & Mehta, S. B. (2022) · Molecular Biology of the Cell · doi:10.1091/mbc.e21-11-0561 · Cited by 42 · see citing works
“DynaMorph: self-supervised learning of morphodynamic states of live cells”
Distribution of the time of the maximum for stationary processes
Mori, F., Majumdar, S. N., & Schehr, G. (2021) · Europhysics Letters (EPL) · doi:10.1209/0295-5075/ac19ee · Cited by 45 · see citing works
Differentiation reveals latent features of aging and an energy barrier in murine myogenesis
Kimmel, J. C., Yi, N., Roy, M., Hendrickson, D. G., & Kelley, D. R. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.109046 · Cited by 42 · see citing works
Wang, W., Poe, D., Yang, Y., Hyatt, T., & Xing, J. (2022) · eLife · doi:10.7554/elife.74866 · Cited by 39 · see citing works
Alieva, M., Román, M. B., de Blank, S., Petcu, D., Zeeman, A. L., Dautzenberg, N. M. M., Cornel, A. M., van de Ven, C., Pieters, R., den Boer, M. L., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-00972-6 · Cited by 37 · see citing works
Learning the non-equilibrium dynamics of Brownian movies
Gnesotto, F. S., Gradziuk, G., Ronceray, P., & Broedersz, C. P. (2020) · Nature Communications · doi:10.1038/s41467-020-18796-9 · Cited by 33 · see citing works
“[35] Kimmel, J.”
BackgroundA unicellular walker controlled by a microtubule-based finite-state machine
Larson, B. T., Garbus, J., Pollack, J. B., & Marshall, W. F. (2022) · Current Biology · doi:10.1016/j.cub.2022.07.034 · Cited by 30 · see citing works
Optofluidic imaging meets deep learning: from merging to emerging
Siu, D. M. D., Lee, K. C. M., Chung, B. M. F., Wong, J. S. J., Zheng, G., & Tsia, K. K. (2023) · Lab on a Chip · doi:10.1039/d2lc00813k · Cited by 30 · see citing works
Shannon, M. J., Eisman, S. E., Lowe, A. R., Sloan, T. F. W., & Mace, E. M. (2024) · Journal of Cell Science · doi:10.1242/jcs.261887 · Cited by 28 · see citing works
Emerging machine learning approaches to phenotyping cellular motility and morphodynamics
Choi, H. J., Wang, C., Pan, X., Jang, J., Cao, M., Brazzo, J. A., Bae, Y., & Lee, K. (2021) · Physical Biology · doi:10.1088/1478-3975/abffbe · Cited by 27 · see citing works
“Kimmel et al [59] took a similar approach, termed heteromotility, that extracts motility features”
BackgroundInfluentialDeep Learning in Cell Image Analysis
Xu, J., Zhou, D., Deng, D., Li, J., Chen, C., Liao, X., Chen, G., & Heng, P. (2022) · Intelligent Computing · doi:10.34133/2022/9861263 · Cited by 27 · see citing works
“Monitoring cell behaviors throughout the lineage can provide useful information for drug discovery, including quanti fi cation of signaling dynamics [65], e ff orts to understand cell motility [66], and attempts to unravel the laws of bacterial cell growth [67].”
BackgroundLearning the non-equilibrium dynamics of Brownian movies
Gnesotto, F. S., Gradziuk, G., Ronceray, P., & Broedersz, C. P. (2020) · RePEc: Research Papers in Economics · Cited by 26 · see citing works
Dynamics of living cells in a cytomorphological state space
Chang, A. Y., & Marshall, W. F. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902849116 · Cited by 24 · see citing works
Fractional re-distribution among cell motility states during ageing
Phillip, J. M., Zamponi, N., Phillip, M. P., Daya, J., McGovern, S., Williams, W. A., Tschudi, K., Jayatilaka, H., Wu, P.-H., Walston, J. D., et al. (2021) · Communications Biology · doi:10.1038/s42003-020-01605-w · Cited by 23 · see citing works
Xing, J. (2022) · Physical Biology · doi:10.1088/1478-3975/ac8c16 · Cited by 22 · see citing works
“In a series of studies [40, 41], Marshall and co-workers examined multiple types of cells in cell motility and morphology state space.”
Profiling Dynamic Patterns of Single‐Cell Motility
Maity, D., Sivakumar, N., Kamat, P., Zamponi, N., Min, C., Du, W., Jayatilaka, H., Johnston, A. C., Starich, B., Agrawal, A., et al. (2024) · Advanced Science · doi:10.1002/advs.202400918 · Cited by 15 · see citing works
“We found that the motility information captured by the eight APRW parameters (communality = 0.53) was greater than that of the >30 parameters from Heteromotility (communality = 0.39).”
MethodInfluentialChoi, S., Ferrari, G., Moyle, L. A., Mackinlay, K. M., Naouar, N., Jalal, S., Benedetti, S., Wells, C. A., Francesco Muntoni, A., & Tedesco, F. S. (2022) · EMBO Molecular Medicine · doi:10.15252/emmm.202114526 · Cited by 15 · see citing works
“For more information i Lab website: www.tedescolab.org ii Heteromotility source code: https://github.com/cellgeometry/heteromotility iii Stemformatics: https://www.stemformatics.org iv Functional protein association network analysis: https://string-db.org v Relevant patient associations (examples): https://www.muscula…”
InfluentialShakarchy, A., Zarfati, G., Hazak, A., Mealem, R., Huk, K., Ziv, T., Avinoam, O., & Zaritsky, A. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00010-3 · Cited by 14 · see citing works
“…identi fi cation of sub-populations and discrimination of perturbations (Choi et al, 2021; Goglia et al, 2020; Jacques et al, 2021; Jena et al, 2022; Kimmel et al, 2018; Valls and Esposito, 2022), that cannot be inferred from static snapshot images (Copperman et al, 2021; Wang et al, 2020; Wu et…”
Nordin, A., Chowdhury, S. R., Saim, A. B., & Idrus, R. B. H. (2020) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph17093229 · Cited by 14 · see citing works
“These parameters can be used to describe the step-wise transitional dynamic of the different cell state, instead of a singular endpoint achieved with destructive molecular evaluation [7].”
BackgroundMethods and Measures for Investigating Microscale Motility
Bondoc, K. G. V., Laeverenz-Schlogelhofer, H., Poon, R. N., Boggon, A. K., Bentley, S. A., Cortese, D., & Wan, K. Y. (2023) · Integrative and Comparative Biology · doi:10.1093/icb/icad075 · Cited by 12 · see citing works
Dynamics of macrophage tumor infiltration
Adebowale, K., Guerriero, J. L., & Mitragotri, S. (2023) · Applied Physics Reviews · doi:10.1063/5.0160924 · Cited by 13 · see citing works
Song, T., Choi, Y., Jeon, J., & Cho, Y. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1129600 · Cited by 11 · see citing works
Mitochondrial networks through the lens of mathematics
Lewis, G. R., & Marshall, W. F. (2023) · Physical Biology · doi:10.1088/1478-3975/acdcdb · Cited by 10 · see citing works
Free-energy-based framework for early forecasting of stem cell differentiation
Suresh, H., Shishvan, S. S., Vigliotti, A., & Deshpande, V. S. (2019) · Journal of The Royal Society Interface · doi:10.1098/rsif.2019.0571 · Cited by 10 · see citing works
Kang, M., Min, C., Devarasou, S., & Shin, J. H. (2025) · APL Bioengineering · doi:10.1063/5.0250502 · Cited by 8 · see citing works
From images to understanding: Advances in deep learning for cellular dynamics analysis
Woodhams, B., & Uhlmann, V. (2025) · Current Opinion in Cell Biology · doi:10.1016/j.ceb.2025.102585 · Cited by 7 · see citing works
Methods to analyze cell migration data: fundamentals and practical guidelines
Wu, P.-H., Phillip, J. M., Du, W., Forjaz, A., Nair, P. R., & Wirtz, D. (2025) · Nature Methods · doi:10.1038/s41592-025-02935-5 · Cited by 7 · see citing works
“However, recent advances in single-cell analyses are introducing new ways to measure and interpret cell migration patterns at single-cell resolution 45,60,72,93 .”
Copperman, J., McLean, I. C., Gross, S. M., Singh, J., Murthy, V., Chang, Y. H., Davies, A. E., Zuckerman, D. M., & Heiser, L. M. (2026) · Cell Systems · doi:10.1016/j.cels.2026.101567 · Cited by 6 · see citing works
The Mathematics of Phenotypic State Transition: Paths and Potential
Devaraj, V., & Bose, B. (2020) · Journal of the Indian Institute of Science · doi:10.1007/s41745-020-00173-6 · Cited by 6 · see citing works
Interpretable Fine‐Grained Phenotypes of Subcellular Dynamics via Unsupervised Deep Learning
Wang, C., Choi, H. J., Woodbury, L. S., & Lee, K. (2024) · Advanced Science · doi:10.1002/advs.202403547 · Cited by 4 · see citing works
Deep learning for biomedical videos: perspective and recommendations
Ouyang, D., Wu, Z., He, B. D., & Zou, J. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00003-x · Cited by 4 · see citing works
“There is increasing recognition that cellular systems are incredibly dynamic 40 throughout the cell cycle 41 and further information can be obtained in dynamic analysis of the cell morphology.”
Wang, C., Choi, H. J., Woodbury, L. S., & Lee, K. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.25.445699 · Cited by 4 · see citing works
“The investigation into phenotypic heterogeneity in live cell images is still in its nascent stages 4,6,10-13 ,necessitating the development of novel features that accurately represent subcellular and cellular temporal dynamics for effective deconvolution of heterogeneous phenotypes.”
BackgroundWhere physics and biology meet
Marshall, W. F., Baum, B., Fairhall, A. L., Heisenberg, C., Koslover, E. F., Liu, A. J., Mao, Y., Mogilner, A., Nelson, C. M., Paluch, E. K., et al. (2024) · Current Biology · doi:10.1016/j.cub.2024.08.022 · Cited by 2 · see citing works
Erdem, Y. S., Ayanzadeh, A., Mayalı, B., Balıkçi, M., Belli, Ö. N., Uçar, M., Özyusal, Ö. Y., Pesen‐Okvur, D., Önal, S., Morani, K., et al. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96129-5.00013-5 · Cited by 2 · see citing works
Dance, Y. W., Amitrano, A., Saffron, A. M., Min, C., Thompson, L. T., Ge, Z., Smith, I. M., Macaluso, N. C., Ezenwanne, C., Milcik, N., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.13.699374 · Cited by 2 · see citing works
“Recent advances combining single-cell imaging with machine learning now provide unprecedented tools for profiling migration behaviors of cells and simultaneously capturing the range of single-cell heterogeneities 14–18 .”
Coarse-graining nonequilibrium diffusions with Markov chains
Nartallo-Kaluarachchi, R., Lambiotte, R., & Goriely, A. (2026) · Journal of Statistical Mechanics Theory and Experiment · doi:10.1088/1742-5468/ae4f7d · Cited by 1 · see citing works
“We find that using longer trajectories and smaller grids leads to higher accuracy, but that the inferred EPR is substantially lower than the true value.”
Devaraj, V., & Bose, B. (2020) · Heliyon · doi:10.1016/j.heliyon.2020.e04489 · Cited by 1 · see citing works
A Machine Learning Framework for Automatic Prediction of Human Semen MotilityPreprint
Ottl, S., Amiriparian, S., Gerczuk, M., & Schuller, B. W. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2109.08049 · Cited by 1 · see citing works
“Furthermore, applications which solve individual parts of the automatic sperm analysis task can be found with particle tracking software, such as Trackpy [26], or motility analysis toolkits for inference of cell state [27].”
Brown, A. D., Scaramozza, A., Zhang, H., Mahin, S., Suresh, N., Tsusaka, T., Eliazer, S., Liu, X., Feeley, B. T., & Brack, A. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.09.26.678822 · Cited by 1 · see citing works
“Because activation was improved in aged MuSCs with ISR-a, we tested whether ISR-a altered aged MuSC migration behavior within the first 48 hours using our heteromotility assay (Figure 4A: (Kimmel et al , 2018)).”
InfluentialFree-energy-based framework for early forecasting of stem cell differentiationPreprint
Suresh, H., Shishvan, S. S., Vigliotti, A., & Deshpande, V. S. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/692285
DLL4 and PDGF-BB regulate migration of human iPSC-derived skeletal myogenic progenitorsPreprint
Ferrari, G., Choi, S., Moyle, L. A., Mackinlay, K. M., Naouar, N., Wells, C. A., Francesco Muntoni, A., & Tedesco, F. S. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.28.431778
Martin, A., Godefroy, G., Lemarchand, F., Neri, J., Cioni, O., Padmanabhan, K., & Paviolo, C. (2025) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2025.3590564
“He2Cl is then applied on these multivariate time-series to obtain cell clusters with specific morpho-dynamic behaviors. cell morphology [24] or motility [25].”
PMC-Vid: A Large-Scale Biomedical Video Captioning Dataset
Yamagishi, Y., Saito, K., Hashimoto, A., & Ushiku, Y. (2025) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw69036.2025.00135
“…medical videos—including cardiac ultrasound to assess heart motion [2], endoscopies for cancer screening [10], and microscopy for cellular interactions—provide critical insights for disease diagnosis and scientific understanding [22, 38] that would be impossible with still images alone [39].”
Monocytes are biological sensors of aging and frailty in humansPreprint
Min, C., Ezenwanne, C., Dance, Y. W., Macaluso, N. C., Thompson, L. T., Nidadavolu, L. S., Katuri, A., Szczesny, C., Langdon, J. M., Pearce, E. J., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.10.710601
- Authors
- Kimmel, J. C., Chang, A. Y., Brack, A. S., & Marshall, W. F.
- Affiliations
- University of California, San Francisco (US)
- Corresponding author
- Kimmel, Jacob C.
- Chang, Amy Y.
- Brack, Andrew S.
- Marshall, Wallace F.
- Venue
- PLoS Computational Biology
- Publisher
- Public Library of Science (PLoS)
- Published
- 2018-01-16
- Volume
- 14
- Issue
- 1
- Pages
- e1005927
- ISSN
- 1553-734X (ISSN-L)
- 1553-7358
- PMC
- PMC5786322
- OpenAlex
- W2950070192
- Cited by
- 54
- References
- 103
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 6 · 2022: 8 · 2021: 7 · 2020: 8 · 2019: 7 · 2018: 2
- FWCI
- 2.13
- Type
- journal article
- Language
- en
- Update notices
- new_version (2018-01-26)
- Open access
- gold
- doi.org
- Licence
- Locations
- PLoS Computational Biology (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- eScholarship (California Digital Library) (submittedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Institutes of Health (AR060868, AR061002)
- National Science Foundation (MCB-1515456, 1650113, 1548297)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular Mechanics and Interactions
- Keywords
- cell motility, cellular heterogeneity, state transitions, detailed balance, attractor states, muscle stem cells, myogenesis, mouse embryonic fibroblasts, nonequilibrium dynamics
- MeSH terms
- Algorithms, Animals, Cell Movement, Female, Fibroblasts, Kinetics, Leukocytes, Mononuclear, Male, Mice, Inbred C57BL, Muscles, Phenotype, Probability, Stem Cells, Signal Transduction, Cluster Analysis, Nonlinear Dynamics, Computational Biology, Mice
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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