Bayesian inference for improved single molecule fluorescence tracking

Won Yoon Ji, Andreas Bruckbauer, William J. Fitzgerald, David Klenerman

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Single molecule tracking is widely used to monitor the change in position of lipids and proteins in living cells. In many experiments in which molecules are tagged with a single or small number of fluorophores, the signal/noise ratio may be limiting, the number of molecules is not known, and fluorophore blinking and photobleaching can occur. All these factors make accurate tracking over long trajectories difficult and hence there is still a pressing need to develop better algorithms to extract the maximum information from a sequence of fluorescence images. We describe here a Bayesian-based inference approach, based on a transdimensional sequential Monte Carlo method that utilizes both the spatial and temporal information present in the image sequences. We show, using model data, where the real trajectory of the molecule is known, that our method allows accurate tracking of molecules over long trajectories even with low signal/noise ratio and in the presence of fluorescence blinking and photobleaching. The method is then applied to real experimental data.

Original languageEnglish
Pages (from-to)4932-4947
Number of pages16
JournalBiophysical Journal
Issue number12
Publication statusPublished - 2008 Jun 15
Externally publishedYes

Bibliographical note

Funding Information:
Authors and this project are supported by Biotechnology and Biological Sciences Research Council funds.

ASJC Scopus subject areas

  • Biophysics


Dive into the research topics of 'Bayesian inference for improved single molecule fluorescence tracking'. Together they form a unique fingerprint.

Cite this