Current methods for characterisation of extracellular vesicles (EVs) need further standardisation in order to obtain an acceptable level of data comparability. Size and concentration of EVs can be determined by nanoparticle tracking analysis (NTA). However, both the heterogeneity of EVs and the choice of instrument settings may cause an appreciable analytical variation. Intra-assay (within-day, n = 6) and inter-assay (day-to-day, n = 6) variations (coefficient of variation, % CV) of different preparations of EVs and artificial vesicles or beads were determined using two NanoSight NS500 instruments, located at different laboratories. All analyses were performed by the same operator. The effect of applying identical software settings or instrument-optimised settings for each sample type and instrument was also evaluated. Finally, the impact of different operators and the use of two different software versions were investigated. The intra-assay CVs were 1–12% for both EVs and artificial samples, measured on the same instrument. The overall day-to-day variation was similar for both instruments, ranging from 2% to 25%. However, significantly different results were observed between the two instruments using identical software settings. The effect of applying instrument-optimised settings reduced the mismatch between the instruments, resulting in little to no significant divergences. The impact of using different operators and software versions when analysing silica microspheres and microvesicles from monocytes using instrument-optimised settings on the same instrument did not contribute to significant variation compared to the overall day-to-day variation of one operator. Performance differences between two similar NTA instruments may display significant divergences in size and concentration measurements when analysing EVs, depending on applied instrument settings and technical conditions. The importance of developing a streamlined and standardised execution of analysis, as well as monitoring longitudinal variation parameters on both biological and synthetic samples, should be highlighted.

BackgroundImproved insight into the molecular characteristics of the different ovarian cancer subgroups is needed for developing a more individualized and optimized treatment regimen. The aim of this study was to a) identify differentially expressed miRNAs in high-grade serous ovarian carcinoma (HGSC), clear cell ovarian carcinoma (CCC) and ovarian surface epithelium (OSE), b) evaluate selected miRNAs for association with clinical parameters including survival and c) map miRNA-mRNA interactions.MethodsDifferences in miRNA expression between HGSC, CCC and OSE were analyzed by global miRNA expression profiling (Affymetrix GeneChip miRNA 2.0 Arrays, n = 12, 9 and 9, respectively), validated by RT-qPCR (n = 35, 19 and 9, respectively), and evaluated for associations with clinical parameters. For HGSC, differentially expressed miRNAs were linked to differentially expressed mRNAs identified previously.ResultsDifferentially expressed miRNAs (n = 78) between HGSC, CCC and OSE were identified (FDR < 0.01%), of which 18 were validated (p < 0.01) using RT-qPCR in an extended cohort. Compared with OSE, miR-205-5p was the most overexpressed miRNA in HGSC. miR-200 family members and miR-182-5p were the most overexpressed in HGSC and CCC compared with OSE, whereas miR-383 was the most underexpressed. miR-205-5p and miR-200 members target epithelial-mesenchymal transition (EMT) regulators, apparently being important in tumor progression. miR-509-3-5p, miR-509-5p, miR-509-3p and miR-510 were among the strongest differentiators between HGSC and CCC, all being significantly overexpressed in CCC compared with HGSC. High miR-200c-3p expression was associated with poor progression-free (p = 0.031) and overall (p = 0.026) survival in HGSC patients. Interacting miRNA and mRNA targets, including those of a TP53-related pathway presented previously, were identified in HGSC.ConclusionsSeveral miRNAs differentially expressed between HGSC, CCC and OSE have been identified, suggesting a carcinogenetic role for these miRNAs. miR-200 family members, targeting EMT drivers, were mostly overexpressed in both subgroups, among which miR-200c-3p was associated with survival in HGSC patients. A set of miRNAs differentiates CCC from HGSC, of which miR-509-3-5p and miR-509-5p are the strongest classifiers. Several interactions between miRNAs and mRNAs in HGSC were mapped.

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The present study, using robotized DNA isolation and quantitative PCR based on the Neisseria meningitidisspecific capsular transport A gene, demonstrates the ease, rapidity, specificity, and sensitivity of quantifying neisserial DNA in plasma (n ‫؍‬ 65) and cerebrospinal fluid (CSF) (n ‫؍‬ 12) from patients with systemic meningococcal disease. We found a close correlation between the levels of neisserial DNA and lipopolysaccharides in plasma (r ‫؍‬ 0.905) and in CSF (r ‫؍‬ 0.964). The median concentration of neisserial DNA in plasma in 23 patients with persistent shock was 2 ؋ 10 7 copies/ml, versus <10 3 copies/ml in 42 nonshock patients. Furthermore, quantitative PCR made possible estimates of the total number of meningococci in plasma, as opposed to conventional blood cultures, suggesting about 1,000 dead meningococci for every viable bacterium. Finally, with logistic regression analyses, neisserial DNA may predict a patient's disease severity and outcome at hospital admission. The number of meningococci in plasma and CSF appears to be the main determinant of the lipopolysaccharide levels, clinical presentation, and outcome.