Abstract
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 language | English |
|---|---|
| Pages (from-to) | 4932-4947 |
| Number of pages | 16 |
| Journal | Biophysical Journal |
| Volume | 94 |
| Issue number | 12 |
| DOIs | |
| Publication status | Published - 2008 Jun 15 |
| Externally published | Yes |
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ASJC Scopus subject areas
- Biophysics
Cite this
Bayesian inference for improved single molecule fluorescence tracking. / Yoon, Ji Won; Bruckbauer, Andreas; Fitzgerald, William J.; Klenerman, David.
In: Biophysical Journal, Vol. 94, No. 12, 15.06.2008, p. 4932-4947.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Bayesian inference for improved single molecule fluorescence tracking
AU - Yoon, Ji Won
AU - Bruckbauer, Andreas
AU - Fitzgerald, William J.
AU - Klenerman, David
PY - 2008/6/15
Y1 - 2008/6/15
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=45849092940&partnerID=8YFLogxK
U2 - 10.1529/biophysj.107.116285
DO - 10.1529/biophysj.107.116285
M3 - Article
C2 - 18339757
AN - SCOPUS:45849092940
VL - 94
SP - 4932
EP - 4947
JO - Biophysical Journal
JF - Biophysical Journal
SN - 0006-3495
IS - 12
ER -