Fat accumulation is a complex phenotype affected by factors such as neuroendocrine signaling feeding activity and reproductive output. worm lipid-droplet fat depots would make the only metazoan in which genes affecting not only fat mass but also body fat distribution could be assessed at a genome-wide scale. Here we present a radical improvement in oil red O worm staining together with high-throughput image-based phenotyping. The three-step sample preparation method is robust formaldehyde-free and inexpensive and requires only 15 minutes of hands-on time to process Bay 65-1942 R form a 96-well plate. Together with our free and user-friendly automated image analysis package this method enables sample preparation and phenotype scoring at a scale that is compatible with genome-wide screens. Thus we present a feasible approach to small-scale phenotyping and large-scale screening for genetic and/or chemical perturbations that lead to alterations in fat quantity and distribution in whole animals. is not straightforward. Depending on the experimental conditions feeding worms with the vital dye Nile red leads to exclusive staining of the lysosomal-related organelle compartment or staining of this compartment in addition to the lipid droplet compartment[3-7]. Similarly live staining with BODIPY-labeled fatty-acids although robust leads to staining of both the lysosomal-related organelle compartment and the lipid droplet compartment[4 8 Having two vesicular compartments stained with the same fluorophore complicates the use of automated Bay 65-1942 R form scoring for lipid-droplet fats only. Feeding high concentrations of Nile red (2-10μM) improves lipid droplet Bay 65-1942 R form staining with this dye. However live high-concentration Nile red staining leads to heterogenic signal within and among samples. Nile red exclusively stains the lipid droplet compartment in paraformaldehyde-fixed worms however paraformaldehyde fixation leads to variable staining and broken animals which together preclude the use of automated scoring for lipid-droplet fats. Sudan Black stains lipid-vesicle fats only but it is highly error-prone due to a final alcohol-based wash that introduces enormous variability. Therefore Sudan Black requires mixing of the control and the test samples in the same tube after marking or labeling them in a way that the original populations can be distinguished after imaging (e.g. an additional fluorescent dye or an independent phenotypic distinction such as MGC33310 sterility or size that enables distinguishing control worms and sample worms). This requirement makes Sudan black incompatible with large-scale studies. Also Stimulated Raman-Scattering (SRS) and Coherent Anti-Stokes Raman Scattering (CARS) have been successfully used to assess fat levels in fat stores contained only in lipid droplets. Second because ORO does not require alcohol-based de-staining it limits the variability introduced by de-staining timing which is the major caveat of Sudan black. Our ORO staining protocol is robust and correlates well with biochemically-measured lipids (total fatty-acid methyl esters by GCMS). Nevertheless this protocol as well as the alternative fixative-based Nile red staining protocol includes paraformaldehyde-based fixation. Paraformaldehyde is a carcinogen requiring the user to perform the protocol in a fume hood and increasing the cost of the procedure by generating toxic waste. Additionally paraformaldehyde-based fixation of generates a large proportion of broken animals which affects staining and makes automated image-based phenotype scoring difficult. Here we describe a radically improved whole-animal fat screening protocol which allows the user to phenotype a 96-well plate of RNAi- or compound-treated animals in 15 minutes of hands-on time. This method named “quick oil red O” (qORO) does not use paraformaldehyde or other toxic fixatives. Instead fixation is achieved with a mixture of water and isopropanol. This qORO method yields almost 100% intact worms making it possible Bay 65-1942 R form to quantify fat storage patterns in relation to the worm’s anatomy. We also present a set of digital image processing and analysis tools for high- and low-throughput quantitative qORO phenotype scoring. We have previously presented image-analysis methods for scoring of ORO stained worms;.