New statistical methods enhance imaging of cameleon fluorescence resonance energy transfer in cultured zebrafish spinal neurons.

Cameleons are genetically encoded fluorescence resonance energy transfer (FRET)-based Ca(2+) indicators. Attempts to use cameleons to detect neural activity in vertebrate systems have been largely frustrated by the small FRET signal, in contradistinction to the higher signals seen in Drosophila and Caenorhabditis elegans. We have developed a statistical optimization method capable of detecting small ratiometric signals in noisy imaging data, called statistical optimization for the analysis of ratiometric signals. Using this method, we can detect and estimate anticorrelated ratiometric signals with subcellular resolution in cultured, dissociated zebrafish spinal neurons expressing cameleon or loaded with fluo-4 and fura-red. This method may make it possible to use yellow cameleons for measuring neural activity at high resolution in transgenic animals.

Estimating weak ratiometric signals in imaging data. II. Meta-analysis with multiple, dual-channel datasets.

Ratiometric fluorescent indicators are used for making quantitative measurements of a variety of physiological variables. Their utility is often limited by noise. This is the second in a series of papers describing statistical methods for denoising ratiometric data with the aim of obtaining improved quantitative estimates of variables of interest. Here, we outline a statistical optimization method that is designed for the analysis of ratiometric imaging data in which multiple measurements have been taken of systems responding to the same stimulation protocol. This method takes advantage of correlated information across multiple datasets for objectively detecting and estimating ratiometric signals. We demonstrate our method by showing results of its application on multiple, ratiometric calcium imaging experiments.