#PAGE_PARAMS# #ADS_HEAD_SCRIPTS# #MICRODATA#

A global sampler of single particle tracking solutions for single molecule microscopy


Autoři: Michael Hirsch aff001;  Richard Wareham aff002;  Ji W. Yoon aff003;  Daniel J. Rolfe aff001;  Laura C. Zanetti-Domingues aff001;  Michael P. Hobson aff004;  Peter J. Parker aff005;  Marisa L. Martin-Fernandez aff001;  Sumeetpal S. Singh aff002
Působiště autorů: Central Laser Facility, Science and Technologies Facilities Council, UK Research and Innovation, Didcot, Oxfordshire, United Kingdom aff001;  Department of Engineering, University of Cambridge, Cambridge, United Kingdom aff002;  Center for Information Security Technology, Korea University, Seoul, South Korea aff003;  Department of Physics, University of Cambridge, Cambridge, United Kingdom aff004;  School of Cancer and Pharmaceutical Sciences, King’s College London, London, United Kingdom aff005;  Protein Phosphorylation Laboratory, The Francis Crick Institute, London, United Kingdom aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
prolekare.web.journal.doi_sk: https://doi.org/10.1371/journal.pone.0221865

Souhrn

The dependence on model-fitting to evaluate particle trajectories makes it difficult for single particle tracking (SPT) to resolve the heterogeneous molecular motions typical of cells. We present here a global spatiotemporal sampler for SPT solutions using a Metropolis-Hastings algorithm. The sampler does not find just the most likely solution but also assesses its likelihood and presents alternative solutions. This enables the estimation of the tracking error. Furthermore the algorithm samples the parameters that govern the tracking process and therefore does not require any tweaking by the user. We demonstrate the algorithm on synthetic and single molecule data sets. Metrics for the comparison of SPT are generalised to be applied to a SPT sampler. We illustrate using the example of the diffusion coefficient how the distribution of the tracking solutions can be propagated into a distribution of derived quantities. We also discuss the major challenges that are posed by the realisation of a SPT sampler.

Klíčová slova:

Algorithms – Probability distribution – Motion – Kalman filter – Mass diffusivity – Probability density – Random walk – Birth rates


Zdroje

1. Mortensen KI, Churchman LS, Spudich JA, Flyvbjerg H. Optimized localization analysis for single-molecule tracking and super-resolution microscopy. Nat Methods. 2010;7:377–359. doi: 10.1038/nmeth.1447 20364147

2. Manley S, Gillette JM, Patterson GH, Shroff H, Hess HF, Betzig E, et al. High-density mapping of single-molecule trajectories with photoactivated localization microscopy. Nat Methods. 2008;5:155–157. doi: 10.1038/nmeth.1176 18193054

3. Low-Nam ST, Lidke KA, Cutler PJ, Roovers RC, van Bergen en Henegouwen PMP, Wilson BS, et al. ErbB1 dimerization is promoted by domain co-confinement and stabilized by ligand binding. Nat Struct Mol Biol. 2011;18:1244–1288. doi: 10.1038/nsmb.2135 22020299

4. Cutler PJ, Malik MD, Liu S, Byars JM, Lidke DS, Lidke KA. Multi-Color Quantum Dot Tracking Using a High-Speed Hyperspectral Line-Scanning Microscope. PLoS One. 2013;8. doi: 10.1371/journal.pone.0064320

5. Lidke DS, Lidke KA, Rieger B, Jovin TM, Arndt-Jovin DJ. Reaching out for signals: filopodia sense EGF and respond by directed retrograde transport of activated receptors. J Cell Biol. 2005;170:619–626. doi: 10.1083/jcb.200503140 16103229

6. Saxton MJ. Single-particle tracking: The distribution of diffusion coefficients. Biophys J. 1997;72:1744–1753. doi: 10.1016/S0006-3495(97)78820-9 9083678

7. Kusumi A, Sako Y, Yamamoto M. Confined lateral diffusion of membrane receptors as studied by single particle tracking (nanovid microscopy). Effects of calcium-induced differentiation in cultured epithelial cells. Biophys J. 1993;65:2021–2040. doi: 10.1016/S0006-3495(93)81253-0 8298032

8. Bouzigues C, Dahan M. Transient directed motions of GABA(A) receptors in growth cones detected by a speed correlation index. Biophys J. 2007;92:654–660. doi: 10.1529/biophysj.106.094524 17071660

9. Dietrich C, Yang B, Fujiwara T, Kusumi A, Jacobson K. Relationship of lipid rafts to transient confinement zones detected by single particle tracking. Biophys J. 2002;82:274–284. doi: 10.1016/S0006-3495(02)75393-9 11751315

10. Das R, Cairo CW, Coombs DA. Hidden Markov Model for Single Particle Tracks Quantifies Dynamic Interactions between LFA-1 and the Actin Cytoskeleton. PLOS Comput Biol. 2009;5. doi: 10.1371/journal.pcbi.1000556

11. Saxton MJ. Single-particle tracking: connecting the dots. Nat Methods. 2008;5:671–672. doi: 10.1038/nmeth0808-671 18668034

12. Smal I, Meijering E. Quantative comparison of multiframe data assoication techniques for particle tracking in time-lapse fluorescence microscopy. Med Image Anal. 2015;24:163–189. doi: 10.1016/j.media.2015.06.006 26176413

13. Chetverikov D, Verestoy J. Feature point tracking for incomplete trajectories. Computing. 1999;62:321–338. doi: 10.1007/s006070050027

14. Shafique K, Shah M. A noniterative greedy algorithm for multiframe point correspondence. IEEE Trans Pattern Anal Mach Intell. 2005;27:51–65. doi: 10.1109/TPAMI.2005.1 15628268

15. Sbalzarini IF, Koumoutsakos P. Feature point tracking and trajectory analysis for video imaging in cell biology. J Struct Biol. 2005;151:182–195. doi: 10.1016/j.jsb.2005.06.002 16043363

16. Feng L, Xu Y, Yang Y, Zheng X. Multiple dense particle tracking in fluorescence microscopy images based on multidimensional assignment. J Struct Biol. 2001;173:219–228. doi: 10.1016/j.jsb.2010.11.001

17. Bonneau S, Dahan M, Cohen LD. Single quantum dot tracking based on perceptual grouping using minimal paths in a spatiotemporal volume. IEEE Trans Image Process. 2005;14:1384–1395. doi: 10.1109/TIP.2005.852794 16190473

18. Sage D, Neumann FR, Hediger F, Gasser SM, Unser M. Automatic tracking of individual fluorescence particles: Application to the study of chromosome dynamics. IEEE Trans Image Process. 2005;14:1372–1383. doi: 10.1109/TIP.2005.852787 16190472

19. Serge A, Bertaux N, Rigneault H, Marguet D. Dynamic multiple-target tracing to probe spatiotemporal cartography of cell membranes. Nat Methods. 2008;5:687–694. doi: 10.1038/nmeth.1233 18604216

20. Jaqaman K, Loerke D, Mettlen M, Kuwata H, Grinstein S, Schmid SL, et al. Robust single-particle tracking in live-cell time-lapse sequences. Nat Methods. 2008;5:695–702. doi: 10.1038/nmeth.1237 18641657

21. Hughes BD. Random walks and random environments. vol. 1: random walks. Clarendon Press; 1995.

22. Yoon JW, Bruckbauer A, Fitzgerald WJ, Klenerman D. Bayesian Inference for Improved Single Molecule Fluorescence Tracking. Biophys J. 2008;12:4932–4947. doi: 10.1529/biophysj.107.116285

23. Geman S, Geman D. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. IEEE Trans Pattern Anal Mach Intell. 1984;6:721–741. doi: 10.1109/TPAMI.1984.4767596 22499653

24. Metropolis N, Rosenbluth AW, Rosenbluth MN, Teller AH, Teller E. Equation of state calculations by fast computing machines. J Chem Phys. 1953;21:1087–1092. doi: 10.1063/1.1699114

25. Durbin J, Koopman SJ. Time series analysis by state space methods. Oxford University Press; 2001.

26. Shumway RH, Stoffer DS. Time Series Analysis and Its Applications. 4th ed. Springer International Publishing; 2017.

27. Rauch HE, Tung F, Striebel CT. Maximum likelihood estimates of linear dynamic systems. AIAA J. 1965;3(8):1445–1450. doi: 10.2514/3.3166

28. Gao X, Xiao B, Tao D, Li X. A survey of graph edit distance. Pattern Anal Appl. 2010;13:113–129. doi: 10.1007/s10044-008-0141-y

29. Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian data analysis. 3rd ed. CRC press; 2013.

30. Givens GH, Hoeting JA. Computational Statistics, 2nd edition. Wiley; 2013.

31. Chenouard N, Smal I, de Chaumont F, Maška M, Sbalzarini IF, Gong Y, et al. Objective comparison of particle tracking methods. Nat Methods. 2014;11:281–289. doi: 10.1038/nmeth.2808 24441936

32. Bräuchle C, Lamb DC, Michaelis J, editors. Single Particle Tracking and Single Molecule Energy Transfer. Wiley-VCH; 2009.

33. Clarke DT, Botchway SW, Coles BC, Needham SR, Roberts SK, Rolfe DJ, et al. Optics clustered to output unique solutions: A multi-laser facility for combined single molecule and ensemble microscopy. Rev Sci Instrum. 2011;82. doi: 10.1063/1.3635536

34. Rolfe DJ, McLachlan CI, Hirsch M, Needham SR, Tynan CJ, Webb SED, et al. Automated multidimensional single molecule fluorescence microscopy feature detection and tracking. European Biophysics Journal. 2011;40(10):1167–1186. doi: 10.1007/s00249-011-0747-7 21928120


Článok vyšiel v časopise

PLOS One


2019 Číslo 10
Najčítanejšie tento týždeň
Najčítanejšie v tomto čísle
Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

Aktuální možnosti diagnostiky a léčby litiáz
nový kurz
Autori: MUDr. Tomáš Ürge, PhD.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa

#ADS_BOTTOM_SCRIPTS#