Numerous software tools exist for data-independent acquisition (DIA) analysis of clinical samples, necessitating their comprehensive benchmarking. We present a benchmark dataset comprising real-world inter-patient heterogeneity, which we use for in-depth benchmarking of DIA data analysis workflows for clinical settings. Combining spectral libraries, DIA software, sparsity reduction, normalization, and statistical tests results in 1428 distinct data analysis workflows, which we evaluate based on their ability to correctly identify differentially abundant proteins. From our dataset, we derive bootstrap datasets of varying sample sizes and use the whole range of bootstrap datasets to robustly evaluate each workflow. We find that all DIA software suites benefit from using a gas-phase fractionated spectral library, irrespective of the library refinement used. Gas-phase fractionation-based libraries perform best against two out of three reference protein lists. Among all investigated statistical tests non-parametric permutation-based statistical tests consistently perform best.

Autism spectrum disorders (ASD) are a heterogeneous group of disorders which have complex behavioural phenotypes. Although ASD is a highly heritable neuropsychiatric disorder, genetic research alone has not provided a profound understanding of the underlying causes. Recent developments using biochemical tools such as transcriptomics, proteomics and cellular models, will pave the way to gain new insights into the underlying pathological pathways. This review addresses the state-of-the-art in the search for molecular biomarkers for ASD. In particular, the most important findings in the biochemical field are highlighted and the need for establishing streamlined interaction between behavioural studies, genetics and proteomics is stressed. Eventually, these approaches will lead to suitable translational ASD models and, therefore, a better disease understanding which may facilitate novel drug discovery efforts in this challenging field.

This study reports on the development and application of a fish-specific estrogen-responsive reporter gene assay. The assay is based on the rainbow trout (Oncorhynchus mykiss) gonad cell line RTG-2 in which an acute estrogenic response is created by cotransfecting cultures with an expression vector containing rainbow trout estrogen receptor a complementary DNA (rtERalpha cDNA) in the presence of an estrogen-dependent reporter plasmid and an estrogen receptor (ER) agonist. In a further approach, RTG-2 cells were stably transfected with the rtERalpha cDNA expression vector, and clones responsive to 17beta-estradiol (E2) were selected. The estrogenic activity of E2, 17alpha-ethinylestradiol, 4-nonylphenol, nonylphenoxy acetic acid, 4-tert-octylphenol, bisphenol A, o,p'-DDT, p,p'-DDT, o,p'-2,2-bis(chlorophenyl)-1,1-dichloroethylene (o,p'-DDE), p,p'-DDE, o,p'-2,2-bis(chlorophenyl)-1,1-di-chloroethane (o,p'-DDD), p,p'-DDD, and p,p'-2,2-bis(chlorophenyl)acetic acid (p,p'-DDA) was assessed at increasing concentrations. All compounds except o,p'-DDT, p,p'-DDE, and p,p'-DDA showed logistic dose-response curves, which allowed the calculation of lowest-observed-effect concentrations and the concentrations at which half-maximal reporter gene activities were reached. To check whether estrogen-responsive RTG-2 cells may be used to detect the estrogenic activity of environmental samples, an extract from a sewage treatment plant (STP) effluent was assessed and found to have estrogenic activity corresponding to the transcriptional activity elicited by 0.05 nM of E2. Dose-response curves of nonylphenol, octylphenol, bisphenol A, and o,p'-DDD revealed that the RTG-2 reporter gene assay is more sensitive for these compounds when compared to transfection systems recombinant for mammalian ERs. These differences may have an effect on the calculation of E2 equivalents when estrogenic mixtures of known constitution, or environmental samples, such as STP effluents, are assessed.

Imputation is a prominent strategy when dealing with missing values (MVs) in proteomics data analysis pipelines. However, it is difficult to assess the performance of different imputation methods and varies strongly depending on data characteristics. To overcome this issue, we present the concept of a data-driven selection of an imputation algorithm (DIMA). The performance and broad applicability of DIMA are demonstrated on 142 quantitative proteomics data sets from the PRoteomics IDEntifications (PRIDE) database and on simulated data consisting of 5–50% MVs with different proportions of missing not at random and missing completely at random values. DIMA reliably suggests a high-performing imputation algorithm, which is always among the three best algorithms and results in a root mean square error difference (ΔRMSE) ≤ 10% in 80% of the cases. DIMA implementation is available in MATLAB at and in R at .