Deep learning-assisted analysis of single-particle tracking for automated correlation between diffusion and function
Open accessPublished version of a preprint
Abstract
Abstract Sub-cellular diffusion in living systems reflects cellular processes and interactions. Recent advances in optical microscopy allow the tracking of this nanoscale diffusion of individual objects with an unprecedented level of precision. However, the agnostic and automated extraction of functional information from the diffusion of molecules and organelles within the sub-cellular environment, is labor-intensive and poses a significant challenge. Here we introduce DeepSPT, a deep learning framework to interpret the diffusional 2D or 3D temporal behavior of objects in a rapid and efficient manner, agnostically. Demonstrating its versatility, we have applied DeepSPT to automated mapping of the early events of viral infections, identifying distinct types of endosomal organelles, and clathrin-coated pits and vesicles with up to 95% accuracy and within seconds instead of weeks. The fact that DeepSPT effectively extracts biological information from diffusion alone illustrates that besides structure, motion encodes function at the molecular and subcellular level.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
1 retracted
U-Net-Based Medical Image Segmentation
Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/4189781 · Cited by 318 · see citing works
RetractedU-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016 · see citing works
Highly accurate protein structure prediction with AlphaFold
Jumper, J., Evans, R., Pritzel, A., Green, T., Figurnov, M., Ronneberger, O., Tunyasuvunakool, K., Bates, R., Žídek, A., Potapenko, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03819-2 · Cited by 43,764 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0686-2 · Cited by 38,430 · see citing works
SciPy 1.0: fundamental algorithms for scientific computing in Python
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., et al. (2019) · Apollo - University of Cambridge Repository · doi:10.17863/cam.64134 · Cited by 10,460 · see citing works
Larian Studios Dev Team (2024) · doi:10.1145/3292500.3330701 · Cited by 8,618 · see citing works
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,542 · see citing works
Cellpose: a generalist algorithm for cellular segmentation
Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021) · Nature Methods · doi:10.1038/s41592-020-01018-x · Cited by 4,369 · see citing works
On calibration of modern neural networks
Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017) · arXiv · doi:10.48550/arxiv.1706.04599 · Cited by 3,435 · see citing works
Robust single particle tracking in live cell time-lapse sequences
Jaqaman, K., Loerke, D., Mettlen, M., Kuwata, H., Grinstein, S., Schmid, S., & Danuser, G. (2008) · Nature Methods · doi:10.1038/nmeth.1237 · Cited by 2,045 · see citing works
A practical guide to evaluating colocalization in biological microscopy.
Dunn, K., Kamocka, M., & McDonald, J. (2011) · American Journal of Physiology - Cell Physiology · doi:10.1152/ajpcell.00462.2010 · Cited by 1,998 · see citing works
Lattice light-sheet microscopy: Imaging molecules to embryos at high spatiotemporal resolution
Chen, B., Legant, W. R., Wang, K., Shao, L., Milkie, D. E., Davidson, M. W., Janetopoulos, C., Wu, X., Hammer, J. A., Liu, Z. J., et al. (2014) · Science · doi:10.1126/science.1257998 · Cited by 1,932 · see citing works
SINGLE-PARTICLE TRACKING:Applications to Membrane Dynamics
Saxton, M. J., & Jacobson, K. (1997) · Annual Review of Biophysics and Biomolecular Structure · doi:10.1146/annurev.biophys.26.1.373 · Cited by 1,810 · see citing works
Rab Conversion as a Mechanism of Progression from Early to Late Endosomes
Rink, J. C., Ghigo, É., Kalaidzidis, Y. L., & Zerial, M. (2005) · Cell · doi:10.1016/j.cell.2005.06.043 · Cited by 1,680 · see citing works
Deep learning enables rapid identification of potent DDR1 kinase inhibitors
Zhavoronkov, A., Ivanenkov, Y. A., Aliper, A., Veselov, M. S., Aladinskiy, V. A., Aladinskaya, A. V., Terentiev, V. A., Polykovskiy, D. A., Kuznetsov, M. D., Asadulaev, A., et al. (2019) · Nature Biotechnology · doi:10.1038/s41587-019-0224-x · Cited by 1,573 · see citing works
Diffusion Dynamics of Glycine Receptors Revealed by Single-Quantum Dot Tracking
Dahan, M., Lévi, S., Luccardini, C., Rostaing, P., Riveau, B., & Triller, A. (2003) · Science · doi:10.1126/science.1088525 · Cited by 1,465 · see citing works
Content-aware image restoration: pushing the limits of fluorescence microscopy
WEIGERT, M. G., Schmidt, U., Boothe, T., Müller, A., Dibrov, A., Jain, A., Wilhelm, B. G., Schmidt, D., Broaddus, C., Culley, S., et al. (2018) · Nature Methods · doi:10.1038/s41592-018-0216-7 · Cited by 1,210 · see citing works
Objective comparison of particle tracking methods
Chenouard, N., Smal, I., de Chaumont, F., Maška, M., Sbalzarini, I. F., Gong, Y., Cardinale, J., Carthel, C. A., Coraluppi, S. P., Winter, M. R., et al. (2014) · Nature Methods · doi:10.1038/nmeth.2808 · Cited by 894 · see citing works
Biogenesis and Function of Multivesicular Bodies
Piper, R. C., & Katzmann, D. J. (2007) · Annual Review of Cell and Developmental Biology · doi:10.1146/annurev.cellbio.23.090506.123319 · Cited by 771 · see citing works
Michalet, X. (2010) · Physical Review E · doi:10.1103/physreve.82.041914 · Cited by 730 · see citing works
DeepCRISPR: Optimized CRISPR guide RNA design by deep learning
Chuai, G., Ma, H., Yan, J., Chen, M., Hong, N., Xue, D., Zhou, C., Zhu, C., Chen, K., Duan, B., et al. (2018) · Genome biology · doi:10.1186/s13059-018-1459-4 · Cited by 641 · see citing works
Regulation of membrane protein structure and function by their lipid nano-environment
Levental, I., & Lyman, E. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-022-00524-4 · Cited by 590 · see citing works
Observing the cell in its native state: Imaging subcellular dynamics in multicellular organisms
Liu, T., Upadhyayula, S., Milkie, D. E., Singh, V. P., Wang, K., Swinburne, I. A., Mosaliganti, K. R., Collins, Z. M., Hiscock, T. W., Shea, J., et al. (2018) · Science · doi:10.1126/science.aaq1392 · Cited by 567 · see citing works
Self-citationDynamics of CTCF- and cohesin-mediated chromatin looping revealed by live-cell imaging
Gabriele, M., Brandão, H. B., Grosse-Holz, S., Jha, A., Dailey, G. M., Cattoglio, C., Hsieh, T.-H. S., Mirny, L., Zechner, C., & Hansen, A. S. (2022) · Science · doi:10.1126/science.abn6583 · Cited by 506 · see citing works
SR-Tesseler: a method to segment and quantify localization-based super-resolution microscopy data
Levet, F., Hosy, E., Kechkar, A., Butler, C., Beghin, A., Choquet, D., & Sibarita, J.-B. (2015) · Nature Methods · doi:10.1038/nmeth.3579 · Cited by 502 · see citing works
Label-free prediction of three-dimensional fluorescence images from transmitted-light microscopy
Ounkomol, C., Seshamani, S., Maleckar, M. M., Collman, F., & Johnson, G. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0111-2 · Cited by 495 · see citing works
Discovery of a structural class of antibiotics with explainable deep learning
Wong, F. J., Zheng, E. J., Valeri, J. A., Donghia, N. M., Anahtar, M. N., Omori, S., Li, A., Cubillos-Ruiz, A., Krishnan, A., Jin, W., et al. (2023) · Nature · doi:10.1038/s41586-023-06887-8 · Cited by 481 · see citing works
DNA Repair Profiling Reveals Nonrandom Outcomes at Cas9-Mediated Breaks
van Overbeek, M., Capurso, D., Carter, M. M., Thompson, M. S., Frias, E., Russ, C., Reece-Hoyes, J. S., Nye, C., Gradia, S., Vidal, B., et al. (2016) · Molecular Cell · doi:10.1016/j.molcel.2016.06.037 · Cited by 454 · see citing works
Systems survey of endocytosis by multiparametric image analysis
Collinet, C., Stöter, M., Bradshaw, C. R., Samusik, N., Rink, J. C., Kenski, D. M., Habermann, B. H., Buchholz, F., Henschel, R., Mueller, M., et al. (2010) · Nature · doi:10.1038/nature08779 · Cited by 446 · see citing works
Illuminating protein space with a programmable generative model
Ingraham, J. B., Baranov, M., Costello, Z., Barber, K. W., Wang, W., Ismail, A., Frappier, V., Lord, D. M., Ng-Thow-Hing, C., Van Vlack, E. R., et al. (2023) · Nature · doi:10.1038/s41586-023-06728-8 · Cited by 444 · see citing works
Extracting intracellular diffusive states and transition rates from single-molecule tracking data
Persson, F., Lindén, M., Unoson, C., & Elf, J. (2013) · Nature Methods · doi:10.1038/nmeth.2367 · Cited by 406 · see citing works
Allan, D. B., Caswell, T., Keim, N. C., van der Wel, C., & Verweij, R. W. (2015) · Figshare · doi:10.5281/zenodo.1213240 · Cited by 392 · see citing works
The First Five Seconds in the Life of a Clathrin-Coated Pit
Cocucci, E., Aguet, F., Boulant, S., & Kirchhausen, T. (2012) · Cell · doi:10.1016/j.cell.2012.05.047 · Cited by 391 · cited 3× · see citing works
“Importantly, DeepSPT outputs these predictions, in 500 ms per trajectory, based solely on the motion captured in the DLP trajectories, rendering the secondary VP7 channel redundant (Supplementary Videos 1 and 2).”
Self-citationMechanisms of pathogen entry through the endosomal compartments
Gruenberg, J., & van der Goot, F. G. (2006) · Nature reviews. Molecular cell biology · doi:10.1038/nrm1959 · Cited by 360 · see citing works
Single-molecule imaging reveals receptor–G protein interactions at cell surface hot spots
Sungkaworn, T., Jobin, M., Burnecki, K., Weron, A., Lohse, M. J., & Calebiro, D. (2017) · Nature · doi:10.1038/nature24264 · Cited by 320 · see citing works
Robust model-based analysis of single-particle tracking experiments with Spot-On
Hansen, A. S., Woringer, M., Grimm, J. B., Lavis, L. D., Tjian, R., & Darzacq, X. (2018) · eLife · doi:10.7554/elife.33125 · Cited by 283 · see citing works
Ruthardt, N., Lamb, D. C., & Bräuchle, C. (2011) · Molecular Therapy · doi:10.1038/mt.2011.102 · Cited by 258 · see citing works
Kim, H. K., Kim, Y., Lee, S., Min, S., Bae, J. Y., Choi, J. W., Park, J., Jung, D., Yoon, S., & Kim, S. (2019) · Science Advances · doi:10.1126/sciadv.aax9249 · Cited by 235 · see citing works
Temporal Analysis of Active and Passive Transport in Living Cells
Arcizet, D., Meier, B., Sackmann, E., Rädler, J. O., & Heinrich, D. (2008) · Physical Review Letters · doi:10.1103/physrevlett.101.248103 · Cited by 226 · see citing works
Single-Particle Diffusion Characterization by Deep Learning
Granik, N., Weiss, L. E., Nehme, E., Levin, M., Chein, M., Perlson, E., Roichman, Y., & Shechtman, Y. (2019) · Biophysical Journal · doi:10.1016/j.bpj.2019.06.015 · Cited by 202 · see citing works
Inhibition of PIKfyve kinase prevents infection by Zaire ebolavirus and SARS-CoV-2
Kang, Y.-L., Chou, Y.-Y., Rothlauf, P. W., Liu, Z., Soh, T. K., Cureton, D. K., Case, J. B., Chen, R. E., Diamond, M., Whelan, S. P. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2007837117 · Cited by 192 · see citing works
Self-citationParticle tracking in living cells: a review of the mean square displacement method and beyond
Gal, N., Lechtman-Goldstein, D., & Weihs, D. (2013) · Rheologica Acta · doi:10.1007/s00397-013-0694-6 · Cited by 179 · see citing works
A large size-selective DNA nanopore with sensing applications
Thomsen, R. P., Malle, M. G., Okholm, A. H., Krishnan, S., Bohr, S. S.-R., Sørensen, R. S., Ries, O., Vogel, S., Simmel, F. C., Hatzakis, N. S., et al. (2019) · Nature Communications · doi:10.1038/s41467-019-13284-1 · Cited by 175 · see citing works
Self-citationInferring transient particle transport dynamics in live cells
Ioannidis, N. M., Barry, Z. T., Park, H. Y., Su, K.-C., Katz, Z., English, B. P., Dey, A., Pan, K., Cheeseman, I. M., Singer, R. H., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3483 · Cited by 173 · see citing works
Dynamin recruitment and membrane scission at the neck of a clathrin-coated pit
Cocucci, E., Gaudin, R., & Kirchhausen, T. (2014) · Molecular Biology of the Cell · doi:10.1091/mbc.e14-07-1240 · Cited by 156 · see citing works
Self-citationDynamics of phosphoinositide conversion in clathrin-mediated endocytic traffic
He, K., Marsland, R., III, Upadhyayula, S., Song, E., Dang, S., Capraro, B. R., Wang, W., Skillern, W., Gaudin, R., Ma, M., et al. (2017) · Nature · doi:10.1038/nature25146 · Cited by 156 · see citing works
Self-citationShen, W. X., Zeng, X. L., Zhu, F., Wang, Y. L., Chu, Q., Tan, Y. C., Jiang, Y. Y., & Chen, Y. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00301-6 · Cited by 156 · see citing works
Membrane dynamics of dividing cells imaged by lattice light-sheet microscopy
Aguet, F., Upadhyayula, S., Gaudin, R., Chou, Y.-Y., Cocucci, E., He, K., Chen, B., Mosaliganti, K. R., Pasham, M. R., Skillern, W., et al. (2016) · Molecular Biology of the Cell · doi:10.1091/mbc.e16-03-0164 · Cited by 154 · see citing works
Self-citationBayesian Approach to MSD-Based Analysis of Particle Motion in Live Cells
Ioannidis, N. M., Guo, S.-M., Mori, M., He, J., Lénárt, P., & Bathe, M. (2012) · Biophysical Journal · doi:10.1016/j.bpj.2012.06.029 · Cited by 140 · see citing works
Kowalek, P., Loch-Olszewska, H., & Szwabiński, J. (2019) · Physical review. E · doi:10.1103/physreve.100.032410 · Cited by 139 · see citing works
Wagner, T., Kröll, A., Haramagatti, C. R., Lipinski, H., & Wiemann, M. (2017) · PLoS ONE · doi:10.1371/journal.pone.0170165 · Cited by 136 · see citing works
Helmuth, J. A., Burckhardt, C. J., Koumoutsakos, P., Greber, U. F., & Sbalzarini, I. F. (2007) · Journal of Structural Biology · doi:10.1016/j.jsb.2007.04.003 · Cited by 112 · see citing works
Visualisation of dCas9 target search in vivo using an open-microscopy framework
Martens, K. J. A., van Beljouw, S. P. B., van der Els, S., Vink, J. N. A., Baas, S., Vogelaar, G., Brouns, S. J. J., van Baarlen, P., Kleerebezem, M., & Hohlbein, J. (2019) · Nature Communications · doi:10.1038/s41467-019-11514-0 · Cited by 109 · see citing works
Objective comparison of methods to decode anomalous diffusion
Muñoz-Gil, G., Volpe, G., García-March, M., Aghion, E., Argun, A., Hong, C., Bland, T., Bo, S., Conejero, J. A., Firbas, N., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26320-w · Cited by 102 · see citing works
Multistep Track Segmentation and Motion Classification for Transient Mobility Analysis
Vega, A. R., Freeman, S. A., Grinstein, S., & Jaqaman, K. (2018) · Biophysical Journal · doi:10.1016/j.bpj.2018.01.012 · Cited by 88 · see citing works
Wan, F., Bohr, S. S.-R., Kłodzińska, S. N., Jumaa, H., Huang, Z., Nylander, T., Thygesen, M. B., Sørensen, K. K., Jensen, K. J., Sternberg, C., et al. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b19644 · Cited by 87 · see citing works
Self-citationPinholt, H. D., Bohr, S. S.-R., Iversen, J. F., Boomsma, W., & Hatzakis, N. S. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2104624118 · Cited by 84 · see citing works
Self-citationThomsen, J., Sletfjerding, M. B., Stella, S., Paul, B., Jensen, S. B., Malle, M. G., Montoya, G., Petersen, T. C., & Hatzakis, N. S. (2020) · eLife · doi:10.7554/elife.60404 · Cited by 81 · see citing works
Self-citationSMAUG: Analyzing single-molecule tracks with nonparametric Bayesian statistics
Karslake, J. D., Donarski, E. D., Shelby, S. A., Demey, L. M., DiRita, V. J., Veatch, S. L., & Biteen, J. S. (2019) · Methods · doi:10.1016/j.ymeth.2020.03.008 · Cited by 71 · see citing works
Dosset, P., Rassam, P., Fernandez, L., Espenel, C., Rubinstein, E., Margeat, E., & Milhiet, P. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1064-z · Cited by 71 · see citing works
A guide to single-particle tracking
Simon, F., Weiss, L. E., & van Teeffelen, S. (2024) · Nature Reviews Methods Primers · doi:10.1038/s43586-024-00341-3 · Cited by 65 · see citing works
Single-particle combinatorial multiplexed liposome fusion mediated by DNA
Malle, M. G., Löffler, P. M. G., Bohr, S. S.-R., Sletfjerding, M. B., Risgaard, N. A., Jensen, S. B., Zhang, M., Hedegård, P., Vogel, S., & Hatzakis, N. S. (2022) · Nature Chemistry · doi:10.1038/s41557-022-00912-5 · Cited by 64 · see citing works
Self-citationBiased cytochrome P450-mediated metabolism via small-molecule ligands binding P450 oxidoreductase
Jensen, S. B., Thodberg, S., Parween, S., Moses, M. E., Hansen, C. C., Thomsen, J., Sletfjerding, M. B., Knudsen, C., Del Giudice, R., Lund, P. M., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-22562-w · Cited by 64 · see citing works
Self-citationParticle Mobility Analysis Using Deep Learning and the Moment Scaling Spectrum
Arts, M., Smal, I., Paul, M. W., Wyman, C., & Meijering, E. H. W. (2019) · Scientific Reports · doi:10.1038/s41598-019-53663-8 · Cited by 56 · see citing works
Structural Correlates of Rotavirus Cell Entry
Abdelhakim, A. H., Salgado, E. N., Fu, X., Pasham, M. R., Nicastro, D., Kirchhausen, T. L., & Harrison, S. C. (2014) · PLoS Pathogens · doi:10.1371/journal.ppat.1004355 · Cited by 53 · see citing works
Self-citationBohr, S. S.-R., Lund, P. M., Kallenbach, A. S., Pinholt, H. D., Thomsen, J., Iversen, L., Svendsen, A., Christensen, S. M., & Hatzakis, N. S. (2019) · Scientific Reports · doi:10.1038/s41598-019-52539-1 · Cited by 48 · see citing works
Self-citationCapturing the start point of the virus–cell interaction with high-speed 3D single-virus tracking
Johnson, C. C., Exell, J. C., Lin, Y., Aguilar, J., & Welsher, K. (2022) · Nature Methods · doi:10.1038/s41592-022-01672-3 · Cited by 48 · see citing works
Slator, P. J., Cairo, C. W., & Burroughs, N. J. (2015) · PLoS ONE · doi:10.1371/journal.pone.0140759 · Cited by 46 · see citing works
The ABC of Insulin: The Organic Chemistry of a Small Protein
Østergaard, M., Mishra, N. K., & Jensen, K. J. (2020) · Chemistry - A European Journal · doi:10.1002/chem.202000337 · Cited by 37 · see citing works
Visualization of Calcium Ion Loss from Rotavirus during Cell Entry
Salgado, E. N., Rodriguez, B. G., Narayanaswamy, N., Krishnan, Y., & Harrison, S. C. (2018) · Journal of Virology · doi:10.1128/jvi.01327-18 · Cited by 36 · see citing works
ExTrack characterizes transition kinetics and diffusion in noisy single-particle tracks
Simon, F., Tinévez, J.-Y., & van Teeffelen, S. (2023) · The Journal of Cell Biology · doi:10.1083/jcb.202208059 · Cited by 35 · see citing works
Cross-Linking of Rotavirus Outer Capsid Protein VP7 by Antibodies or Disulfides Inhibits Viral Entry
Aoki, S. T., Trask, S. D., Coulson, B. S., Greenberg, H. B., Dormitzer, P. R., & Harrison, S. C. (2011) · Journal of Virology · doi:10.1128/jvi.00234-11 · Cited by 31 · see citing works
Chen, Z., Geffroy, L., & Biteen, J. S. (2021) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2021.742073 · Cited by 29 · see citing works
Matsuda, Y., Hanasaki, I., Iwao, R., Yamaguchi, H., & NIIMI, T. (2018) · Physical Chemistry Chemical Physics · doi:10.1039/c8cp02566e · Cited by 28 · see citing works
Extracting Transition Rates in Particle Tracking Using Analytical Diffusion Distribution Analysis
Vink, J. N. A., Brouns, S. J. J., & Hohlbein, J. (2020) · Biophysical Journal · doi:10.1016/j.bpj.2020.09.033 · Cited by 28 · see citing works
Semantic segmentation of anomalous diffusion using deep convolutional networks
Qu, X., Hu, Y. T., Cai, W., Xu, Y., Ke, H., Zhu, G., & Huang, Z. (2024) · Physical Review Research · doi:10.1103/physrevresearch.6.013054 · Cited by 27 · see citing works
Momboisse, F., Nardi, G., Colin, P., Hery, M., Cordeiro, N., Blachier, S., Schwartz, O., Arenzana‐Seisdedos, F., Sauvonnet, N., Olivo‐Marín, J., et al. (2022) · eLife · doi:10.7554/elife.76281 · Cited by 23 · see citing works
Moses, M. E., Lund, P. M., Bohr, S. S.-R., Iversen, J. F., Kæstel‐Hansen, J., Kallenbach, A. S., Iversen, L., Christensen, S. M., & Hatzakis, N. S. (2021) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.1c08809 · Cited by 20 · see citing works
Self-citationMartens, K. J. A., Turkowyd, B., Hohlbein, J., & Endesfelder, U. (2024) · Nature Methods · doi:10.1038/s41592-023-02149-7 · Cited by 20 · see citing works
Bohr, F., Bohr, S. S.-R., Mishra, N. K., Foutel, N. S. G., Pinholt, H. D., Wu, S., Nielsen, E. M., Zhang, M., Kjærgaard, M., Jensen, K. J., et al. (2023) · Communications Biology · doi:10.1038/s42003-022-04386-6 · Cited by 14 · see citing works
Self-citationEjdrup, A. L., Lycas, M. D., Lorenzen, N., Konomi-Pilkati, A., Herborg, F., Madsen, K. L., & Gether, U. (2022) · Nature Communications · doi:10.1038/s41467-022-32064-y · Cited by 14 · see citing works
Mizrak, A., Kæstel‐Hansen, J., Matthias, J., Harper, J. W., Hatzakis, N. S., Farese, R. V., & Walther, T. C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.610018 · Cited by 9 · see citing works
Self-citationClassification of trajectories—Extracting invariants with a neural network
Kinder, M., & Brauer, W. (1993) · Neural Networks · doi:10.1016/s0893-6080(09)80010-5 · Cited by 8 · see citing works
Benning, N. A., Kæstel‐Hansen, J., Rashid, F., Park, S., Merino‐Urteaga, R., Liao, T.-W., Hao, J., Berger, J. M., Hatzakis, N. S., & Ha, T. (2023) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c07011 · Cited by 5 · see citing works
Self-citationMind your tag in single-molecule measurements
Merino‐Urteaga, R., & Ha, T. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100623 · Cited by 4 · see citing works
Attention-based LSTM for Motion Switching Detection of Particles in Living Cells
You, B., & Yang, G. (2021) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn52387.2021.9533629 · Cited by 3 · see citing works
Kæstel‐Hansen, J. (2024) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.17894/ucph.25800387-29f5-4815-ae49-9a9d4d063bc4 · Cited by 1 · see citing works
Self-citationKæstel‐Hansen, J. (2024) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.17894/ucph.75da99a5-f7f1-44e7-bb6e-3fcc97bf0a15 · Cited by 1 · see citing works
Self-citationKæstel‐Hansen, J. (2024) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.17894/ucph.927973c8-6821-49c8-abae-6cd5996f1c47 · Cited by 1 · see citing works
Self-citationKæstel‐Hansen, J., & Hatzakis, N. (2024) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.17894/ucph.8d6d81d4-c155-4686-a381-50409e5a048c · Cited by 1 · see citing works
Self-citationKæstel‐Hansen, J., & Hatzakis, N. (2024) · Research at the University of Copenhagen (University of Copenhagen) · doi:10.17894/ucph.2361455c-daeb-4cef-93b6-7451130b19ba · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Mizrak, A., Kæstel‐Hansen, J., Matthias, J., Harper, J. W., Hatzakis, N. S., Farese, R. V., & Walther, T. C. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.08.27.610018 · Cited by 9 · see citing works
Self-citationZhang, C., Liu, R., Ding, Z., Lu, P., Tian, W., Zhao, Y., He, J., & Hou, S. (2025) · Chemical & Biomedical Imaging · doi:10.1021/cbmi.5c00146 · Cited by 6 · see citing works
Samanta, A., Malle, M. G., Tsang, E., Omer, M., Skaanning, M. K., Youssef, S., Kjems, J., & Gothelf, K. V. (2026) · Advanced Science · doi:10.1002/advs.202512844 · Cited by 6 · see citing works
Tollemeto, M., Thamdrup, L. H. E., Hatzakis, N. S., Lehr, C., van Hest, J. C. M., & Boisen, A. (2025) · Journal of Controlled Release · doi:10.1016/j.jconrel.2025.114268 · Cited by 5 · see citing works
Self-citationDeep learning in chromatin organization: from super-resolution microscopy to clinical applications
Rotkevich, M., Viana, C., Neguembor, M. V., & Cosma, M. P. (2025) · Cellular and Molecular Life Sciences · doi:10.1007/s00018-025-05837-z · Cited by 4 · see citing works
Membrane bridges and nanodomain partitioning govern membrane protein targeting to lipid droplets
Mizrak, A., Kæstel‐Hansen, J., Matthias, J., Harper, J. W., Hatzakis, N. S., Farese, R. V., & Walther, T. C. (2026) · Nature Cell Biology · doi:10.1038/s41556-026-01963-3 · Cited by 4 · see citing works
Self-citationHigh-fidelity predictions of diffusion in the brain microenvironment
Schimek, N., Wood, T. R., Beck, D. A. C., McKenna, M., Toghani, A., & Nance, E. A. (2024) · Biophysical Journal · doi:10.1016/j.bpj.2024.10.005 · Cited by 3 · see citing works
Yoshida, S. R., Zhong, Y., Banh, J., Guo, J., & Chong, S. (2025) · Journal of Molecular Biology · doi:10.1016/j.jmb.2025.169343 · Cited by 3 · see citing works
Ahsini, Y., Escoto, M., & Conejero, J. A. (2025) · Journal of Physics Photonics · doi:10.1088/2515-7647/ae0120 · Cited by 2 · see citing works
Sumrall, E. R., Husna, S. M. A., Halder, B., Ahnoud, A., & Walter, N. G. (2026) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2026.103271 · Cited by 2 · see citing works
Kumar, V., Bryan, J. S., Rojewski, A., Manzo, C., & Pressé, S. (2024) · Biophysical Reports · doi:10.1016/j.bpr.2024.100194 · Cited by 2 · see citing works
Purohit, A., Chhatre, N., Qazi, B., Kumar, V. R., Haldar, S., & Bhattacharya, A. (2025) · Small · doi:10.1002/smll.202412218 · Cited by 2 · see citing works
Bleshøy, S. V., Kæstel‐Hansen, J., Nielsen, A. J., Mishra, N. K., Jensen, K. J., & Hatzakis, N. S. (2026) · iScience · doi:10.1016/j.isci.2025.114516 · Cited by 1 · see citing works
Self-citationKovtun, O. (2025) · Receptors · doi:10.3390/receptors4010006 · Cited by 1 · see citing works
Voutyritsa, E., Oikonomou, A., Bolis, G., Papageorgiou, V., Vougiatzi, A. S., & Hatzakis, N. S. (2025) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.5c16748 · Cited by 2 · see citing works
Self-citationDeepTRACE brings flexible machine learning to single-molecule track analysis
Pambos, O. J., Wright, J. A. R., & Kapanidis, A. N. (2026) · Communications Biology · doi:10.1038/s42003-026-09899-y · Cited by 1 · see citing works
Shen, C., Ye, Z., Liu, X., He, D., Zhang, Y., Fu, Y., Guo, J., & Ma, X. (2026) · Nano Letters · doi:10.1021/acs.nanolett.6c01373 · Cited by 1 · see citing works
Deep Learning-Based Micro-Volume Viscosity Prediction Using the Blinking Dynamics of Janus Particles
Podder, S., Sakamoto, H., Hasegawa, T., Sakamoto, C., & Chuang, H.-S. (2026) · IEEE Sensors Journal · doi:10.1109/jsen.2026.3697410 · Cited by 1 · see citing works
Sequence-defined polymers for predictable gene delivery
Evans, C. W., Kyrwood, C. M., Schaefer, L. J., Wood, J. L., Chen, K., Bell, C. A., Thurecht, K. J., Qi, X., Jiang, H., Alexander, C., et al. (2026) · Nature Reviews Chemistry · doi:10.1038/s41570-026-00850-7 · Cited by 1 · see citing works
Müller, T., Meiser, E., & Engstler, M. (2024) · Communications Biology · doi:10.1038/s42003-024-07378-w · Cited by 1 · see citing works
Subcellular dynamics in unicellular parasites
Müller, T., Krüger, T., & Engstler, M. (2025) · Trends in Parasitology · doi:10.1016/j.pt.2025.01.007 · Cited by 1 · see citing works
Kobayashi, A., Park, J., Miné-Hattab, J., & Fernández, F. G. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.03.647105 · Cited by 1 · see citing works
Schulz, F., Dreisler, M. W., Koylyu, D., Valero, J., Malle, M. G., Civit, L., Kjems, J., & Hatzakis, N. S. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2025.12.18.695048 · Cited by 1 · see citing works
Self-citationTuning siRNA packing order in lipid nanoparticles modulates oligonucleotide functional deliveryPreprint
Breuer, A., Kyriakakis, G., Dreisler, M. W., Schulz, F., Bolis, G., Margaritaki, S., Papageorgiou, V., Spacho, N., & Hatzakis, N. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.06.704289 · Cited by 1 · see citing works
Self-citationDeep learning for microscopy without machine learning expertisePreprint
Zhou, X., & Wang, S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.04.03.716415 · Cited by 1 · see citing works
P-variation statistics of random diffusivity processes
Chen, K., Fu, Y., Xue, Y., Liang, X., Gao, Y., Cheng, L., & Wang, X. (2025) · Journal of Physics A Mathematical and Theoretical · doi:10.1088/1751-8121/ae0669
Fuel-Free Rolosense: Viral Sensing Using Diffusional Particle Tracking
Piranej, S., Jackson, K., Zhang, L., Kæstel‐Hansen, J., Sommerhage, F., DeRoo, D., Hatzakis, N. S., & Salaita, K. S. (2025) · ACS Sensors · doi:10.1021/acssensors.5c02311
Self-citationSimon, F., Wiggins, C. H., & Weiss, L. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.22.700663
Single-molecule microscopy for precision medicine and diagnostics
Ying, Y., Liu, Y., Liu, S., Li, L., Ma, B., Yuan, L., Dou, X., Shentu, Q., Ai, R., Lei, J., et al. (2026) · TrAC Trends in Analytical Chemistry · doi:10.1016/j.trac.2026.118723
Chen, C., Du, X., Yang, J., Zeng, Y., Xie, H., Wang, D., Qi, P., & Zeng, Y. (2026) · Frontiers in Pharmacology · doi:10.3389/fphar.2026.1796070
Bohr, F., Oikonomou, A., Nielsen, E. M., Konstantinidis, G., Mortensen, J. S., Tavernarakis, N., Nielsen, H. M., & Hatzakis, N. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.24.713910
Self-citationWang, F., Tan, Y., Gu, J., Li, J., Zheng, H., Xia, X., Zhang, Y., Wen, G., & Xiao, Y. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.114620
Optical characterization of proteins at single-molecule level
Yousefi, A., Assadipapari, M., Zargarbashi, S., Xu, L., Benesch, J. L. P., Rahmani, M., & Ying, C. (2026) · Newton · doi:10.1016/j.newton.2026.100482
Lin, Y., Lin, H., Yu, D., & Welsher, K. (2026) · Viruses · doi:10.3390/v18050521
Atomic transport in metallic systems: mechanisms, characterization and multiscale modelling
Zhou, Y., Dong, B., Ouyang, B., Wang, Y. Q., Huang, X., Nie, Y., Zhan, H., Zhang, G., & Lü, C. (2026) · Materials & Design · doi:10.1016/j.matdes.2026.116174
A light-weight, data-driven segmentation method for multi-state Brownian trajectories
Korde, I. E., Lewis, J. M., Clarkson, E., Dam, T., Jönsson, P., Ambjörnsson, T., & Stenhammar, J. (2026) · npj Biological Physics and Mechanics. · doi:10.1038/s44341-026-00037-7
Dynamics Segmentation of Single Particle Trajectories Based on Recurrent Learning
Shikhli, N., Brelot, A., Olivo‐Marín, J., Lagache, T., & Nardi, G. (2026) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi61048.2026.11515761
Brøchner, B. V., Song, P., Yan, Y., Weissinger, H., Sørensen, E. A., Kjems, J., & Malle, M. G. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.03.729830
BioBrain: A Multi-Agent Framework for Natural Language Driven Quantitative Microscopy Data AnalysisPreprint
Tsolakidis, K., Breuer, A., Bender, S. W. B., Margaritaki, S., Dreisler, M. W., Oikonomou, A., & Hatzakis, N. S. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.17.732700
Self-citationTesta, F., Banfi, A., Giustra, M., Tomaino, G., Fiandra, L., Verkade, P., Bravi, M. G., Vanacore, G. M., Salvioni, L., & Prosperi, D. (2026) · ACS Applied Nano Materials · doi:10.1021/acsanm.6c01115
Schulz, F., Dreisler, M. W., Koylyu, D., Valero, J., Malle, M. G., Civit, L., Kjems, J., & Hatzakis, N. S. (2026) · ACS Applied Bio Materials · doi:10.1021/acsabm.6c00883
Self-citationRecovering membrane interaction kinetics of single molecules from 3D tracking data
Lundin, E., Volkov, I. L., & Johansson, M. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.07.029
Deep Learning Tools for Single Quantum Dot Tracking
Kovtun, O. (2026) · Methods in molecular biology · doi:10.1007/978-1-0716-5210-7_6
Artificial intelligence in nanotechnology: current applications and future perspectives
Majedi, A. (2026) · Health Nanotechnology · doi:10.1186/s44301-026-00038-4
Tuning siRNA packing order in lipid nanoparticles modulates oligonucleotide functional deliveryPreprint
Hatzakis, N. S., Breuer, A., Kyriakakis, G., Dreisler, M. W., Schulz, F., Bolis, G., Margaritaki, S., Papageorgiou, V., & Spacho, N. (2026) · Research Square · doi:10.21203/rs.3.rs-9922590/v1
Self-citationShyeed, M. A., Lee, D., Hosen, F., Lee, S., & Kang, S. H. (2026) · ACS Sensors · doi:10.1021/acssensors.6c02138
Recent advances in deep learning for biological microscopy image analysis beyond segmentation
Cao, K., Gao, C., Zhang, X., Pan, X., Chen, J., & Wang, H. (2026) · BioTechniques · doi:10.1080/07366205.2026.2730974
A single-particle tracking framework for quantifying intracellular partition free energies
Nordentoft, M. S., Jessen, E., Corsin, E., Miné-Hattab, J., Jensen, M. H., & Heltberg, M. L. (2026) · Biophysical Journal · doi:10.1016/j.bpj.2026.09.023
Müller, C., Klompmaker, K. K., Zhang, Y., Zhang, J., Kalatanova, A., Li, P., Meng, L., Madsen, J. G., Jakobsen, T. S., Jørgensen, A. C., et al. (2026) · Nature Biomedical Engineering · doi:10.1038/s41551-026-01773-w
Self-citationAI-enhanced imaging, monitoring, and diagnostics in precision nanomedicine
Suruthi, G. S., Puzhakkal, A., Palanisamy, S. K., Gunasekaran, A., Asirvatham, A. R., Shanmugam, V., Philomenathan, A. P. J., & Arumugam, V. A. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-45321-2.00010-2
Lipid diffusion enhanced by molecular motorsPreprint
Guérin, G., Sorensen, E. W., Chen, C., Manzi, J., Berro, J., Hatzakis, N. S., Chen, H., & Tsai, F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.10.10.758165
Self-citation
- Authors
- Kæstel-Hansen, J., de Sautu, M., Saminathan, A., Scanavachi, G., Bango Da Cunha Correia, R. F., Juma Nielsen, A., Bleshøy, S. V., Tsolakidis, K., Boomsma, W., Kirchhausen, T., & Hatzakis, N. S.
- Affiliations
- Boston Children's Hospital (US)
- University of Copenhagen (DK)
- Corresponding author
- Kirchhausen, Tomas
- Hatzakis, Nikos S.
- ORCID
- Venue
- Nature Methods
- Published
- 2025-05
- Volume
- 22
- Issue
- 5
- Pages
- 1091-1100
- ISSN
- 1548-7091 (ISSN-L)
- 1548-7105
- PMC
- PMC12272723
- OpenAlex
- W4410251380
- Cited by
- 51
- References
- 92
- Citations by year
- 2026: 34 · 2025: 13 · 2024: 4
- FWCI
- 17.72
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature Methods (publishedVersion, cc-by-nc-nd)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Research Square (acceptedVersion, cc-by)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- PubMed Central (acceptedVersion)
- PubMed Central (submittedVersion, cc-by-nc-nd)
- PubMed Central (submittedVersion, cc-by)
- Funders
- U.S. Department of Health and Human Services (CA13202)
- Velux Fonden (18333, 40516)
- Villum Fonden (18333, 40516)
- Ionis Pharmaceuticals
- Novo Nordisk (NNF14CC0001)
- Novo Nordisk Fonden (NNF 14CC0001, NNF22OC0075851, NNF23OC0081287, NNF20OC0062606)
- National Institutes of Health (CA-13202)
- National Cancer Institute (CA-13202, R01CA013202, R37CA013202)
- National Institute of General Medical Sciences (R35GM130386)
- National Institute of Allergy and Infectious Diseases (R01AI163019)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Advanced Fluorescence Microscopy Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cell Biology › Cellular transport and secretion
- Keywords
- single-particle tracking, clathrin-coated pits, 3D tracking, organelle tracking
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review