Supplementary MaterialsAdditional file 1: Supplemental experimental procedures. cells. Figure S5. Effects of miRNAs on cytoplasmic cyclin B1 levels in NT2 cells and H1 hESCs cells. (DOCX 3295 kb) 13287_2019_1318_MOESM3_ESM.docx (3.2M) GUID:?5D673AFF-6DAD-420C-89B3-FA88FEE9EEE4 Additional file 4: DAVID pathways analysis. Excel file with all results from the enrichment pathway analyses carried using DAVID. (XLSX 163 kb) 13287_2019_1318_MOESM4_ESM.xlsx (164K) GUID:?9450867B-6143-4E50-9CFA-10AA0A696F70 Additional file 5: Pathways comparisons. Excel file with all comparisons between the pathways identified by DAVID. (XLSX 31 kb) 13287_2019_1318_MOESM5_ESM.xlsx (31K) GUID:?AE5CEB56-C8C3-4834-B689-64C192C078AA Data Availability StatementPart of the data generated or analyzed during this study are included in this published article [and its supplementary information files]. The remaining datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. Abstract Background By post-transcriptionally regulating multiple target transcripts, microRNAs (miRNAs or miR) play important biological functions. H1 embryonic stem cells (hESCs) and NTera-2 embryonal carcinoma cells (ECCs) are two of the most widely used human pluripotent model cell lines, sharing several Tranylcypromine hydrochloride characteristics, including the expression of miRNAs associated to the pluripotent state or with differentiation. However, how each of these miRNAs functionally impacts the biological properties of these cells has not been systematically evaluated. Methods We investigated the effects of 31 miRNAs on NTera-2 and H1 hESCs, by transfecting miRNA mimics. Following 3C4?days of culture, cells were stained Tranylcypromine hydrochloride for the pluripotency marker OCT4 and the G2 cell-cycle marker Cyclin B1, and nuclei and cytoplasm were co-stained with Hoechst and Cell Mask Blue, respectively. By using automated quantitative fluorescence microscopy (i.e., high-content screening (HCS)), we obtained several morphological and marker intensity measurements, in both cell compartments, allowing the generation of a multiparametric miR-induced phenotypic profile describing changes related to proliferation, cell cycle, pluripotency, and differentiation. Results Despite the overall similarities between both cell types, some miRNAs elicited cell-specific effects, while some related miRNAs induced contrasting effects in the same cell. By identifying transcripts predicted to be commonly targeted by miRNAs inducing similar effects (profiles grouped by hierarchical clustering), we were able to uncover potentially modulated signaling pathways and biological processes, likely mediating the effects of the microRNAs on the distinct groups identified. Specifically, we show that miR-363 contributes to pluripotency maintenance, at least in part, by targeting NOTCH1 and Tranylcypromine hydrochloride PSEN1 and inhibiting Notch-induced differentiation, a mechanism that could be implicated in na?ve and primed pluripotent states. Conclusions We present the first multiparametric high-content microRNA functional screening in human pluripotent cells. Integration of this type of data with similar data obtained from siRNA screenings (using the same HCS assay) could provide a large-scale functional approach to identify and validate microRNA-mediated regulatory mechanisms controlling pluripotency and differentiation. Electronic supplementary material The online version of this Tranylcypromine hydrochloride article (10.1186/s13287-019-1318-6) Tranylcypromine hydrochloride contains supplementary material, which is available to authorized users. (POC), allowing a direct comparison of all treatment conditions in both plates Rabbit polyclonal to AnnexinA10 of each screening [45]. Median values from each quantified parameter were combined in a multiparametric phenotypic profile representing the effect of each miR in the whole population. Details are provided in the supplemental experimental procedures (see Additional?file?1). Phenotypic clustering of miRs, identification of shared predicted targets, and pathway analysis In order to obtain a less redundant and more naturally interpretable set of biologically relevant phenotypic parameters, the following features were selected to compose multiparametric phenotypic profiles: cell count, solidity (a feature varying from 0, for complex shapes with reentrances, up to 1 1, for solid shapes), eccentricity (varying from 0 to 1 1, from round to increasingly elliptical shapes), nuclear and cellular areas, nuclear and cellular perimeter, and nuclear and cytoplasmic OCT4 and CCNB1fluorescence intensities. The phenotypic profiles obtained for all miR treatments were submitted to hierarchical clustering using the software Cluster 3.0, using centered correlation metrics and average linkage [46],.