The aim of this mRNA expression profiling experiment was to screen for ecotoxicogenomic fingerprints in juvenile daphnids (daphnia magna) as aquatic vertebrate non-target model exposed to sub lethal concentrations of Imidacloprid. Imidacloprid is is a popular insecticide widely applied in agriculture and by veterinarians against ectoparasites.The Insecticide Resistance Action Committee (IRAC) classified Imidacloprid after its mode of Action (MoA) in the target organism as a nicotinic acetylcholine receptor (nAChR) competitive modulator (Group 4). The goal is to identify toxicogenomic profiles with predictive character and identify potential molecular key events (KE) explaining upstream adverse effects. This will provide useful information to refine and improve existing adverse outcome pathways (AOP). Furthermore, integrating the obtained profiles for this and other tested chemicals in a collective database will enable us in the future to derive predictions about the ecotoxicological hazard for chemcials with unknown apical effects, based on similarly altered transcriptomic and proteomic profiles. In a modified version of the acute immobilisation test (OECD 202), 50 juvenile Daphnids were exposed to two sub lethal concentrations of Imidacloprid for 48 hours under static conditions. Each test comprised of a low exposure (LE), high exposure (HE) and a negative control (NC) group and was performed in triplicates. At 48 hours after introducing the daphnids into the test solutions, RNA and protein was extracted from living daphnids with NucleoSpin RNA/Protein kit (Macherey-Nagel). RNA quality was assessed with a 2100 Bioanalyzer system (Agilent) before messenger RNA was purified (PolyA selection with TruSeq RNA Library Prep Kit v2) and sequenced on an Illumina HiSeq 4000 System (Illumina) in 50 bp single read mode, producing roughly 30 million reads per sample. Adapter sequences were removed with trimmomatic and sequences were aligned to the D. magna reference genome (daphmag2.4) using STAR. Counting of feature mapped reads was performed through featureCounts. Library gene count tables were then merged to a single count matrix as input for count normalization and differential gene expression analysis with DESeq2.