Abstract

In recent years, single particle tracking of quantum dot (QD) labeled membrane proteins has yielded significant biological insights. In conventional single particle tracking, the experimentalist is limited to 2-3 colors of quantum dots and sparse labeling density. In order to circumvent these limitations, we have developed a hyperspectral line scanning microscope with the ability to acquire hyperspectral images (128 spectral channels over 500 to 800 nm) at a rate of 30 frames per second. Hyperspectral images possess a wealth of information that can be used to improve multicolor single particle localization and tracking. Here we describe new algorithms for the analysis of hyperspectral images for single particle localization and trajectory building. In conventional single particle localization, a two dimensional Gaussian is often used to estimate the point spread function of the microscope. In hyperspectral images, the spectral characteristics of single QDs can be estimated using a Gaussian distribution in the wavelength dimension. Combining this with a two dimensional Gaussian estimate for the microscope point spread function gives a three dimensional Gaussian estimate for the position of a single quantum dot in both spatial dimensions and the spectral dimension. We extend our high speed two dimensional [1] fitting to three dimensions and implement the routine on GPU architecture giving more than an order of magnitude increase in analysis speed over an equivalent CPU based implementation. Analysis of hyperspectral data allows the relatively unique spectral signature of each QD to be used to help resolve indeterminacy during connection of points into trajectories, allowing for single particle tracking in relatively densely labeled samples. The hyperspectral line scanning microscope and single particle tracking algorithms are demonstrated with experiments of QD- IgE bound to Fc(epsilon)RI on live cells. Smith, Nat. Methods 7, 373-375 2010.

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