SummaryReproducibility in molecular and cellular studies is fundamental to scientific discovery. To establish the reproducibility of a well-defined long-term neuronal differentiation protocol, we repeated the cellular and molecular comparison of the same two iPSC lines across five distinct laboratories. Despite uncovering acceptable variability within individual laboratories, we detect poor cross-site reproducibility of the differential gene expression signature between these two lines. Factor analysis identifies the laboratory as the largest source of variation along with several variation-inflating confounders such as passaging effects and progenitor storage. Single-cell transcriptomics shows substantial cellular heterogeneity underlying inter-laboratory variability and being responsible for biases in differential gene expression inference. Factor analysis-based normalization of the combined dataset can remove the nuisance technical effects, enabling the execution of robust hypothesis-generating studies. Our study shows that multi-center collaborations can expose systematic biases and identify critical factors to be standardized when publishing novel protocols, contributing to increased cross-site reproducibility.

Pathways in Escherichia coli show large differences in the extent to which enzymes from the same pathway are expressed in a coordinated manner.Using dynamic optimization, we show that regulation of the initial and terminal reactions of a pathway is the minimum requirement for a precise control of flux.We find that in E. coli a regulation of initial and terminal reactions is predominantly used to control pathways with low costs of enzymes while a regulation of all enzymes occurs if protein costs are high.A trade-off between minimization of protein investment and minimization of response time can explain the preference for transcriptional regulation at key positions (leading to high protein costs, but low response time) or coordinated transcriptional regulation of all enzymes (leading to low protein costs, but high response time).

DNA methylation is one of the several epigenetic modifications that together with genetic aberrations are hallmarks of tumorigenesis including those emanating from the pituitary gland. In this study, we examined DNA methylation across 27 578 CpG sites spanning more than 14 000 genes in the major pituitary adenoma subtypes. Genome-wide changes were first determined in a discovery cohort comprising non-functioning (NF), growth hormone (GH), prolactin (PRL)-secreting and corticotroph (CT) adenoma relative to post-mortem pituitaries. Using stringent cut-off criteria, we validated increased methylation by pyrosequencing in 12 of 16 (75%) genes. Overall, these criteria identified 40 genes in NF, 21 in GH, six in PRL and two in CT that were differentially methylated relative to controls. In a larger independent cohort of adenomas, for genes in which hypermethylation had been validated, different frequencies of hypermethylation were apparent, where the KIAA1822 (HHIPL1) and TFAP2E genes were hypermethylated in 12 of 13 NF adenomas whereas the COL1A2 gene showed an increase in two of 13 adenomas. For genes showing differential methylation across and between adenoma subtypes, pyrosequencing confirmed these findings. In three of 12 genes investigated, an inverse relationship between methylation and transcript expression was observed where increased methylation of EML2, RHOD and HOXB1 is associated with significantly reduced transcript expression. This study provides the first genome-wide survey of adenoma, subtype-specific epigenomic changes and will prove useful for identification of biomarkers that perhaps predict or characterise growth patterns. The functional characterisation of identified genes will also provide insight of tumour aetiology and identification of new therapeutic targets.

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