Tracking single particles: a user-friendly quantitative evaluation.
As our knowledge of biological processes advances, we are increasingly aware that cells actively position sub-cellular organelles and other constituents to control a wide range of biological processes. Many studies quantify the position and motion of, for example, fluorescently labeled proteins, protein aggregates, mRNA particles or virus particles. Both differential interference contrast (DIC) and fluorescence microscopy can visualize vesicles, nuclei or other small organelles moving inside cells. While such studies are increasingly important, there has been no complete analysis of the different tracking methods in use, especially from the practical point of view. Here we investigate these methods and clarify how well different algorithms work and also which factors play a role in assessing how accurately the position of an object can be determined. Specifically, we consider how ultimate performance is affected by magnification, by camera type (analog versus digital), by recording medium (VHS and SVHS tape versus direct tracking from camera), by image compression, by type of imaging used (fluorescence versus DIC images) and by a variety of sources of noise. We show that most methods are capable of nanometer scale accuracy under realistic conditions; tracking accuracy decreases with increasing noise. Surprisingly, accuracy is found to be insensitive to the numerical aperture, but, as expected, it scales with magnification, with higher magnification yielding improved accuracy (within limits of signal-to-noise). When noise is present at reasonable levels, the effect of image compression is in most cases small. Finally, we provide a free, robust implementation of a tracking algorithm that is easily downloaded and installed.
Carter
Brian C
BC
Department of Physics and Astronomy, University of California Irvine, Irvine, CA 92612, USA.
Shubeita
George T
GT
Gross
Steven P
SP
D000465:Algorithms; D001703:Biophysics / Q000379:methods*; D003198:Computer Simulation; D005453:Fluorescence; D007091:Image Processing, Computer-Assisted; D008856:Microscopy, Fluorescence; D018715:Microscopy, Video / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D010316:Particle Size; D010363:Pattern Recognition, Automated; D012333:RNA, Messenger / Q000378:metabolism; D013223:Statistics as Topic
D012333:RNA, Messenger