Super Resolution Method For Flurescent Deconvolution

MICAELA TOSCANI; SANDRA MARTINEZ; OSCAR MARTINEZ

San Francisco

Conferencia; 62 Annual Meeting of the Biophysical Society; 2018

A new algorithm for microscope fluorescent images deconvolution is presented that retrieves the informationwith super resolution. The method is particularly fit for sparse data, such as encountered in typicalintracellular experiments, where only a minor fraction of the explored volume has fluorescent markers. Thetechnique retrieves by construction only positive values for the spatial density, avoiding the need for nonlinearconstrains found in prior deconvolution techniques such as Tikhonov-Miller and Richardson-Lucy . A geneticalgorithm is performed to optimize the solution. The method automatically subtracts the background fromthe image. Based on the measured point spread function with the information of the quality of the fit and theinformation on the noise figure of the camera as a function of the read signal, the method provides apredictor of the uncertainty in the reconstruction both in lateral resolution and amplitude. Artificiallysynthesized images including noise were used to test the method, showing the reconstruction below the50nm spatial resolution when analyzing arrays of molecules or fluorescent lines. Images of 100nm and 200nmfluorescent beads and bead clusters are also efficiently and precisely deconvolved. For the images obtainedwith an Andor Zyla5.5 camera with a measured noise given by Noise= 23+sqrt(N) (where N is the count of thepixel) the theoretical predictor mentioned before gave a lateral resolution of 44nm consistent with theexperimental and simulated results. Examples of intracellular fluorescent image reconstruction are presentedusing bovine pulmonary artery endothelial cells with fluorescent labels for the F-actin, microtubules andmitochondria . The extension of the technique to 3D deconvolution will also be discussed.