Adaptive illumination control method reduces photo damage in live cell microscopy

Scientists from the New York State Department of Health/Health Research, Inc. and Rensselaer Polytechnic Institute created a less expensive, software-based Adaptive Illumination Control for Fluorescence Microscopy (AIFM) technique.

Scientists from the New York State Department of Health/Health Research, Inc. and Rensselaer Polytechnic Institute (RPI), both based in Troy, NY, created a less expensive, software-based Adaptive Illumination Control for Fluorescence Microscopy (AIFM) technique. Easy to implement on standard microscopes without hardware modifications, the technique results in a significant decrease in the number of exposures required for typical 3-D time lapse recordings.

Current, advantageous approaches for decreasing photo damage in 3-D imaging are selective plane illumination microscopy (SPIM) and controlled light illumination microscopy (CLEM). However, they have not gained popularity due to their required sophisticated and expensive hardware modifications.

The new AIFM detection algorithm comprises three principal steps: filtering, response scaling and localization refinement. A single full series of all focal planes spanning the entire volume of the cell is initially collected.

The user chooses “object of interest” (OOI) and the software predicts the new location for each chosen OOI. AIFM restricts the range of Z-scanning to the focal planes immediately adjacent to each OOI. The strongest response after scaling is considered the new location estimate for each chosen OOI, subsequently suppressing the former in order to eliminate subsequent interference detections of the same image. Lastly, precise localization of each OOI is determined by using estimated coordinates as the initial guess for the 3-D Gaussian fit. The final location of the OOI is stored and used to determine the Z-range for the next time point. The distinct advantage of this process is modulation of illumination intensity to limit photo damage.

The scientists cite the applications for the AIFM detection algorithm, which include increased number of useful images before the onset of phototoxicity; rapid object detection completed between time points; optimal mechanistic understanding of cellular processes; determination of structural biological states at the highest resolution; and tracking few small objects in a relatively large space.

The AIFM detection algorithm is currently under review for publication in Nature Methods.

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Posted by Lee Mather

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