IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v16y2025i1d10.1038_s41467-025-61949-x.html
   My bibliography  Save this article

Quantitative evaluation of methods to analyze motion changes in single-particle experiments

Author

Listed:
  • Gorka Muñoz-Gil

    (University of Innsbruck)

  • Harshith Bachimanchi

    (University of Gothenburg)

  • Jesús Pineda

    (University of Gothenburg)

  • Benjamin Midtvedt

    (University of Gothenburg)

  • Gabriel Fernández-Fernández

    (The Barcelona Institute of Science and Technology)

  • Borja Requena

    (The Barcelona Institute of Science and Technology)

  • Yusef Ahsini

    (Universitat Politècnica de València)

  • Solomon Asghar

    (University College London)

  • Jaeyong Bae

    (Korea Advanced Institute of Science and Technology)

  • Francisco J. Barrantes

    (BIOMED UCA-CONICET)

  • Steen W. B. Bender

    (University of Copenhagen
    University of Copenhagen)

  • Clément Cabriel

    (Université PSL, CNRS)

  • J. Alberto Conejero

    (Universitat Politècnica de València)

  • Marc Escoto

    (Universitat Politècnica de València)

  • Xiaochen Feng

    (Harbin Institute of Technology (Shenzhen))

  • Rasched Haidari

    (University of Oxford
    University of Oxford)

  • Nikos S. Hatzakis

    (University of Copenhagen
    University of Copenhagen)

  • Zihan Huang

    (Hunan University)

  • Ignacio Izeddin

    (Université PSL, CNRS)

  • Hawoong Jeong

    (Korea Advanced Institute of Science and Technology
    Korea Advanced Institute of Science and Technology)

  • Yuan Jiang

    (Harbin Institute of Technology (Shenzhen))

  • Jacob Kæstel-Hansen

    (University of Copenhagen
    University of Copenhagen)

  • Judith Miné-Hattab

    (Sorbonne Université, CNRS)

  • Ran Ni

    (Nanyang Technological University)

  • Junwoo Park

    (Sorbonne Université, CNRS)

  • Xiang Qu

    (Hunan University)

  • Lucas A. Saavedra

    (BIOMED UCA-CONICET)

  • Hao Sha

    (Harbin Institute of Technology (Shenzhen))

  • Nataliya Sokolovska

    (Sorbonne Université, CNRS)

  • Yongbing Zhang

    (Harbin Institute of Technology (Shenzhen))

  • Giorgio Volpe

    (University College London)

  • Maciej Lewenstein

    (The Barcelona Institute of Science and Technology
    ICREA)

  • Ralf Metzler

    (University of Potsdam
    Asia Pacific Centre for Theoretical Physics)

  • Diego Krapf

    (Colorado State University)

  • Giovanni Volpe

    (University of Gothenburg
    University of Gothenburg)

  • Carlo Manzo

    (Universitat de Vic—Universitat Central de Catalunya (UVic-UCC)
    Institut de Recerca i Innovació en Ciències de la Vida i de la Salut a la Catalunya Central (IRIS-CC))

Abstract

The analysis of live-cell single-molecule imaging experiments can reveal valuable information about the heterogeneity of transport processes and interactions between cell components. These characteristics are seen as motion changes in the particle trajectories. Despite the existence of multiple approaches to carry out this type of analysis, no objective assessment of these methods has been performed so far. Here, we report the results of a competition to characterize and rank the performance of these methods when analyzing the dynamic behavior of single molecules. To run this competition, we implemented a software library that simulates realistic data corresponding to widespread diffusion and interaction models, both in the form of trajectories and videos obtained in typical experimental conditions. The competition constitutes the first assessment of these methods, providing insights into the current limitations of the field, fostering the development of new approaches, and guiding researchers to identify optimal tools for analyzing their experiments.

Suggested Citation

  • Gorka Muñoz-Gil & Harshith Bachimanchi & Jesús Pineda & Benjamin Midtvedt & Gabriel Fernández-Fernández & Borja Requena & Yusef Ahsini & Solomon Asghar & Jaeyong Bae & Francisco J. Barrantes & Steen W, 2025. "Quantitative evaluation of methods to analyze motion changes in single-particle experiments," Nature Communications, Nature, vol. 16(1), pages 1-17, December.
  • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61949-x
    DOI: 10.1038/s41467-025-61949-x
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-025-61949-x
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-025-61949-x?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Minho S. Song & Hyungseok C. Moon & Jae-Hyung Jeon & Hye Yoon Park, 2018. "Neuronal messenger ribonucleoprotein transport follows an aging Lévy walk," Nature Communications, Nature, vol. 9(1), pages 1-8, December.
    2. Titiwat Sungkaworn & Marie-Lise Jobin & Krzysztof Burnecki & Aleksander Weron & Martin J. Lohse & Davide Calebiro, 2017. "Single-molecule imaging reveals receptor–G protein interactions at cell surface hot spots," Nature, Nature, vol. 550(7677), pages 543-547, October.
    3. I. Bronshtein & E. Kepten & I. Kanter & S. Berezin & M. Lindner & Abena B. Redwood & S Mai & S. Gonzalo & R. Foisner & Y. Shav-Tal & Y. Garini, 2015. "Loss of lamin A function increases chromatin dynamics in the nuclear interior," Nature Communications, Nature, vol. 6(1), pages 1-9, November.
    4. Christian Eggeling & Christian Ringemann & Rebecca Medda & Günter Schwarzmann & Konrad Sandhoff & Svetlana Polyakova & Vladimir N. Belov & Birka Hein & Claas von Middendorff & Andreas Schönle & Stefan, 2009. "Direct observation of the nanoscale dynamics of membrane lipids in a living cell," Nature, Nature, vol. 457(7233), pages 1159-1162, February.
    5. Lux, Thomas & Morales-Arias, Leonardo, 2010. "Forecasting volatility under fractality, regime-switching, long memory and student-t innovations," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2676-2692, November.
    6. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    7. Alf Honigmann & Veronika Mueller & Haisen Ta & Andreas Schoenle & Erdinc Sezgin & Stefan W. Hell & Christian Eggeling, 2014. "Scanning STED-FCS reveals spatiotemporal heterogeneity of lipid interaction in the plasma membrane of living cells," Nature Communications, Nature, vol. 5(1), pages 1-12, December.
    8. Valerii M Sukhorukov & Jürgen Bereiter-Hahn, 2009. "Anomalous Diffusion Induced by Cristae Geometry in the Inner Mitochondrial Membrane," PLOS ONE, Public Library of Science, vol. 4(2), pages 1-14, February.
    9. Henrik Seckler & Ralf Metzler, 2022. "Bayesian deep learning for error estimation in the analysis of anomalous diffusion," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    10. Jan Bulla & Andreas Berzel, 2008. "Computational issues in parameter estimation for stationary hidden Markov models," Computational Statistics, Springer, vol. 23(1), pages 1-18, January.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Pauline Formaglio & Marina E. Wosniack & Raphael M. Tromer & Jaderson G. Polli & Yuri B. Matos & Hang Zhong & Ernesto P. Raposo & Marcos G. E. Luz & Rogerio Amino, 2023. "Plasmodium sporozoite search strategy to locate hotspots of blood vessel invasion," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    2. Janczura, Joanna & Weron, Rafal, 2010. "Goodness-of-fit testing for regime-switching models," MPRA Paper 22871, University Library of Munich, Germany.
    3. Joanna Janczura & Rafał Weron, 2013. "Goodness-of-fit testing for the marginal distribution of regime-switching models with an application to electricity spot prices," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 97(3), pages 239-270, July.
    4. De Angelis Luca & Viroli Cinzia, 2017. "A Markov-switching regression model with non-Gaussian innovations: estimation and testing," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 21(2), pages 1-22, April.
    5. Raggi, Davide & Bordignon, Silvano, 2012. "Long memory and nonlinearities in realized volatility: A Markov switching approach," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3730-3742.
    6. Asaf Ashkenazy-Titelman & Mohammad Khaled Atrash & Alon Boocholez & Noa Kinor & Yaron Shav-Tal, 2022. "RNA export through the nuclear pore complex is directional," Nature Communications, Nature, vol. 13(1), pages 1-14, December.
    7. Čukić, Milena & Galovic, Slobodanka, 2023. "Mathematical modeling of anomalous diffusive behavior in transdermal drug-delivery including time-delayed flux concept," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    8. Yong-Seok Kim & Jun-Hee Yeon & Woori Ko & Byung-Chang Suh, 2023. "Two-step structural changes in M3 muscarinic receptor activation rely on the coupled Gq protein cycle," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
    9. Loch-Olszewska, Hanna, 2019. "Properties and distribution of the dynamical functional for the fractional Gaussian noise," Applied Mathematics and Computation, Elsevier, vol. 356(C), pages 252-271.
    10. Marie-Lise Jobin & Sana Siddig & Zsombor Koszegi & Yann Lanoiselée & Vladimir Khayenko & Titiwat Sungkaworn & Christian Werner & Kerstin Seier & Christin Misigaiski & Giovanna Mantovani & Markus Sauer, 2023. "Filamin A organizes γ‑aminobutyric acid type B receptors at the plasma membrane," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    11. Yifan Wu & Yang Song & Jennifer Soto & Tyler Hoffman & Xiao Lin & Aaron Zhang & Siyu Chen & Ramzi N. Massad & Xiao Han & Dongping Qi & Kun-Wei Yeh & Zhiwei Fang & Joon Eoh & Luo Gu & Amy C. Rowat & Zh, 2025. "Viscoelastic extracellular matrix enhances epigenetic remodeling and cellular plasticity," Nature Communications, Nature, vol. 16(1), pages 1-19, December.
    12. repec:plo:pcbi00:1003911 is not listed on IDEAS
    13. Bulla, Jan & Mergner, Sascha & Bulla, Ingo & Sesboüé, André & Chesneau, Christophe, 2010. "Markov-switching Asset Allocation: Do Profitable Strategies Exist?," MPRA Paper 21154, University Library of Munich, Germany.
    14. Jan Bulla, 2010. "Hidden Markov models with t components. Increased persistence and other aspects," Quantitative Finance, Taylor & Francis Journals, vol. 11(3), pages 459-475.
    15. Marta Ukleja & Lara Kricks & Gabriel Torrens & Ilaria Peschiera & Ines Rodrigues-Lopes & Marcin Krupka & Julia García-Fernández & Roberto Melero & Rosa Campo & Ana Eulalio & André Mateus & María López, 2024. "Flotillin-mediated stabilization of unfolded proteins in bacterial membrane microdomains," Nature Communications, Nature, vol. 15(1), pages 1-21, December.
    16. Mohamed CHIKHI & Ali BENDOB & Ahmed Ramzi SIAGH, 2019. "Day-of-the-week and month-of-the-year effects on French Small-Cap Volatility: the role of asymmetry and long memory," Eastern Journal of European Studies, Centre for European Studies, Alexandru Ioan Cuza University, vol. 10, pages 221-248, December.
    17. Muszkieta, Monika & Janczura, Joanna, 2023. "A compressed sensing approach to interpolation of fractional Brownian trajectories for a single particle tracking experiment," Applied Mathematics and Computation, Elsevier, vol. 446(C).
    18. Gao, Guangyuan & Ho, Kin-Yip & Shi, Yanlin, 2020. "Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices," Pacific-Basin Finance Journal, Elsevier, vol. 61(C).
    19. Ke Yang & Langnan Chen, 2014. "Realized Volatility Forecast: Structural Breaks, Long Memory, Asymmetry, and Day-of-the-Week Effect," International Review of Finance, International Review of Finance Ltd., vol. 14(3), pages 345-392, September.
    20. Lucas J. Handlin & Gucan Dai, 2023. "Direct regulation of the voltage sensor of HCN channels by membrane lipid compartmentalization," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    21. Mark, Tanya & Bulla, Jan & Niraj, Rakesh & Bulla, Ingo & Schwarzwäller, Wolfgang, 2019. "Catalogue as a tool for reinforcing habits: Empirical evidence from a multichannel retailer," International Journal of Research in Marketing, Elsevier, vol. 36(4), pages 528-541.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-61949-x. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.