In this paper, we describe an algorithm that performs automatic detection and tracking of astral microtubules in fluorescence confocal images. This sub-population of microtubules only exists during and immediately before mitosis and aids in the spindle orientation by connecting it to the cell cortex. Anomalies in their dynamic behaviour play a causal role in many diseases, such as development disorders and cancer. The main novelty of the proposed algorithm lies in the fact it provides a fully automated estimation of parameters related to microtubule dynamic instability (growth velocity, track length and track lifetime), and helps in understanding the effects of intermediate drug concentrations. Its performance has been objectively assessed using publicly available synthetic data and largely employed metrics. Moreover, we present experiments addressing cell cultures doped with different concentrations of taxol and nocodazole. Such drugs are known to suppress the microtubule dynamic instability, but their effects at intermediate concentrations are not completely assessed. The algorithm has been compared with other state-of-the-art approaches, tested on consistent real datasets. The results are encouraging in terms of performance, robustness and simplicity of use, and the algorithm is now routinely employed in our Department of Molecular Biotechnology.
Prosser, S.L. and Pelletier, L. (2017) Mitotic Spindle Assembly in Animal Cells: A fine Balancing Act. Nature Reviews Molecular Cell Biology, 18, 187-201. https://doi.org/10.1038/nrm.2016.162
Cooper, G.M. (2000) The Cell: A Molecular Approach, Sinauer Associates, Inc., Sunderland, MA.
Ito, A. and Goshima, G. (2015) Microcephaly Protein ASP Focuses the Minus Ends of Spindle Microtubules at the Pole and within the Spindle. Journal of Cell Biology, 211, 999-1009. https://doi.org/10.1083/jcb.201507001
White, D., Honoré, S. and Hubert, F. (2017) Exploring the Effect of End-Binding Proteins and Microtubule Targeting Chemotherapy Drugs on Microtubule Dynamic Instability. Journal of Theoretical Biology, 429, 18-34. https://doi.org/10.1016/j.jtbi.2017.06.014
Vonesh, C., Aguet, F., Vonesh, J.L. and Unser, M. (2006) The Colored Revolution of Bioimaging. IEEE Signal Processing Magazine, 23, 20-31. https://doi.org/10.1109/MSP.2006.1628875
E. Meijering, I. Smal, and G. Danuser (2006) Tracking in Molecular Bioimaging. IEEE Signal Processing Magazine, 23, 46-53. https://doi.org/10.1109/MSP.2006.1628877
Matov, A., Applegate, K., Kumar, P., Thoma, C., Krek, W., Danuser, G. and Wittmann, T. (2010) Analysis of Microtubule Dynamic Instability Using a Plus End Growth Marker. Nature Methods, 7, 761-768. https://doi.org/10.1038/nmeth.1493
Chenouard, N., Smal, I., De Chaumont, F., Maska, M., Sbalzarini, I.F., Gong, Y., Cardinale, J., Carthel, C., Coraluppi, S., Winter, M., Cohen, A.R., Godinez, W.J., Rohr, K., Kalaidzidis, Y., Liang, L., Duncan, J., Shen, H., Xu, Y., Magnusson, K.E.G., Jalden, J., Blau, H.M., Paul-Gilloteaux, P., Roudot, P., Kervrann, C., Waharte, F., Tinevez, J.Y., Shorte, S.L., Willemse, J., Celler, K., Van Wezel, G.P., Dan, H.W., Tsai, Y.S., De Solórzano, C.O., Olivo-Marin, J.C. and Meijering, E. (2014) Objective Comparison of Particle Tracking Methods. Nature Methods, 11, 281-289. https://doi.org/10.1038/nmeth.2808
Dzyubachyk, O., Meijering, E. and Smal, I. (2012) Chapter Nine-Methods for Cell and Particle Tracking. In: Michael Conn, P., Ed., Imaging and Spectroscopic Analysis of Living Cells, Methods in Enzymology, Academic Press, London, 183-200.
Godinez, W.J., Lampe, M., Wörz, S., Müller, B., Eils, R. and Rohr, K. (2009) Deterministic and Probabilistic Approaches for Tracking Virus Particles in Time-Lapse Fluorescence Microscopy Image Sequences. Medical Image Analysis, 13, 325-342. https://doi.org/10.1016/j.media.2008.12.004
A Method for Astral Microtubule Tracking in Fluorescence Images of Cells Doped with Taxol and Nocodazole — Oak Academic Publishing
Roudot, P., Ding, L., Jaqaman, K., Kervrann, C. and Danuser, G. (2017) Piecewise-Stationary Motion Modeling and Iterative Smoothing to Track Heterogeneous Particle Motions in Dense Environments. IEEE Transactions on Image Processing, 26, 5395-5410. https://doi.org/10.1109/TIP.2017.2707803
Smal, I., Loog, M., Niessen, W. and Meijering, E. (2010) Quantitative Comparison of Spot Detection Methods in Fluorescence Microscopy. IEEE Transactions on Medical Imaging, 29, 282-301. https://doi.org/10.1109/TMI.2009.2025127
Boulanger, J., Kervrann, C., Bouthemy, P., Elbau, P., Sibarita, J. and Salamero, J. (2010) Patch-Based Nonlocal Functional for Denoising Fluorescence Microscopy Image Sequences. IEEE Transactions on Medical Imaging, 29, 442-454. https://doi.org/10.1109/TMI.2009.2033991
Sbalzarini, I.F. and Koumoutsakos, P. (2005) Feature Point Tracking and Trajectory Analysis for Video Imaging in Cell Biology. Journal of Structural Biology, 151, 182-195. https://doi.org/10.1016/j.jsb.2005.06.002
Smal, I. and Meijering, E. (2015) Quantitative Comparison of Multiframe Data Association Techniques for Particle Tracking in Time-Lapse Fluorescence Microscopy. Medical Image Analysis, 24, 163-189. https://doi.org/10.1016/j.media.2015.06.006
Sironi, L., Solon, J., Conrad, C., Mayer, T.U., Brunner, D. and Ellen-berg, J. (2011) Automatic Quantification of Microtubule Dynamics Enables Screening of New Mitotic Spindle Regulators. Cytoskeleton, 68, 266-278. https://doi.org/10.1002/cm.20510
Mahemuti, B., Inoue, D., Kakugo, A. and Konagaya, A. (2016) Investigation of the Microtubule Dynamics with Probabilistic Data Association Filter. 2016 IEEE 11th Annual International Conference on Nano/Micro Engineered and Molecular Systems, Sendai, Japan, 17-20 April 2016, 101-106. https://doi.org/10.1109/NEMS.2016.7758209
Levine, J., Grangetto, M., Varrecchia, M. and Olmo, G. (2018) Detection and Tracking of Astral Microtubules in Fluorescence Microscopy Images. 2018 25th IEEE International Conference on Image Processing, Athens, 7-10 October 2018, 361-365. https://doi.org/10.1109/ICIP.2018.8451383
Smal, I., Basu, S., Sayas, L., Galjart, N. and Meijering, E. (2017) Gaussian Processes for Trajectory Analysis in Microtubule Tracking Applications. 2017 IEEE 14th International Symposium on Biomedical Imaging, Melbourne, 18-21 April 2017, 206-209. https://doi.org/10.1109/ISBI.2017.7950502
Smal, I., Galjart, N. and Meijering, E. (2018) Accurate Estimation of Intracellular Dynamics and Underlying Spatial Structures Using Hierarchical Trajectory Smoothing. 2018 IEEE 15th International Symposium on Biomedical Imaging, Washington DC, 4-7 April 2018, 973-976. https://doi.org/10.1109/ISBI.2018.8363733
Samuylov, D.K., Szekely, G. and Paul, G. (2017) Tracking Microtubule Ends Is More Than Point Tracking. 2017 IEEE 14th International Symposium on Biomedical Imaging, Melbourne, 18-21 April 2017, 808-812. https://doi.org/10.1109/ISBI.2017.7950641
Newby, J.M., Schaefer, A.M., Lee, P.T., Forest, M.G. and Lai, S.K. (2018) Convolutional Neural Networks Automate Detection for Tracking of Submicron-Scale Particles in 2D and 3D. Proceedings of the National Academy of Sciences of the United States of America, 115, 9026-9031. https://doi.org/10.1073/pnas.1804420115
Sorokin, D.V., Peterlk, I., Ulman, V., Svoboda, D., Neasov, T., Morgaenko, K., Eiselleov, L., Tesaov, L. and Maka, M. (2018) Filogen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells with Growing and Branching Filopodia. IEEE Transactions on Medical Imaging, 37, 2630-2641. https://doi.org/10.1109/TMI.2018.2845884
Svoboda, D. and Ulman, V. (2017) Mitogen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy. IEEE Transactions on Medical Imaging, 36, 310-321. https://doi.org/10.1109/TMI.2016.2606545
Yao, Y., Smal, I. and Meijering, E. (2018) Deep Neural Networks for Data Association in Particle Tracking. 2018 IEEE 15th International Symposium on Biomedical Imaging, Washington DC, 4-7 April 2018, 458-461. https://doi.org/10.1109/ISBI.2018.8363615
Lucey, B.P., Nelson-Rees, W.A. and Hutchins, G.M. (2009) Henrietta Lacks, Hela Cells, and Cell Culture Contamination. Archives of Pathology & Laboratory Medicine, 133, 1463-1467.
Garg, R. and Kumar, A. (2012) Comparison of Various Noise Removals Using Bayesian Framework. International Journal of Modern Engineering Research, 2, 265-270.
Applegate, K.T., Besson, S., Matov, A., Bagonis, M.H., Jaqaman, K. and Danuser, G. (2011) Plustiptracker: Quantitative Image Analysis Software for the Measurement of Microtubule Dynamics. Journal of Structural Biology, 176, 168-184. https://doi.org/10.1016/j.jsb.2011.07.009
Soper, H.E., Young, A.W., Cave, B.M., Lee, A. and Pearson, K. (1917) On the Distribution of the Correlation Coefficient in Small Samples. Biometrika, 11, 328-413. https://doi.org/10.1093/biomet/11.4.328