Users are frequently frustrated by trying to balance obtaining the right signal to noise ratio with photobleaching and phototoxicity when doing timelapse imaging. The introduction of singly-tagged fluorophores into endogenous loci increases this challenge. One solution for overcoming this challenge is to acquire images that are on the edge of usability (i.e. using the lowest possible laser power and short camera exposures) and then use post-processing steps to enhance the contrast and bring out the structural information that is detectable. In the image below from our spinning disk confocal, I have some neurons expressing a CRISPR GFP-tagged protein at endogenous levels that were imaged for two minutes with low laser power. These cells were also four weeks old at the time of imaging and not terribly healthy, adding to the degree of difficulty. To improve the original image (left), I first applied a local contrast filter before subtracting the background (middle image). Finally, I ran a 20 iteration blind deconvolution with the noise level set to “Grainy” to produce the image on the right. The resulting image, although non-quantitavive, has significantly higher contrast and moving particles are easy to detect during the timelapse. With NIS Elements it’s easy to write these steps into a GA3 recipe that can be applied in batch to all of the images from a given session.