Project description:MicroRNAs are important negative regulators of protein coding gene expression, and have been studied intensively over the last few years. To this purpose, different measurement platforms to determine their RNA abundance levels in biological samples have been developed. In this study, we have systematically compared 12 commercially available microRNA expression platforms by measuring an identical set of 20 standardized positive and negative control samples, including human universal reference RNA, human brain RNA and titrations thereof, human serum samples, and synthetic spikes from homologous microRNA family members. We developed novel quality metrics in order to objectively assess platform performance of very different technologies such as small RNA sequencing, RT-qPCR and (microarray) hybridization. We assessed reproducibility, sensitivity, quantitative performance, and specificity. The results indicate that each method has its strengths and weaknesses, which helps guiding informed selection of a quantitative microRNA gene expression platform in function of particular study goals.
Project description:Environmental exposures across critical developmental windows can significantly influence brain development and contribute to the risk of neurodevelopmental disorders (NDDs). Importantly, emerging clinical evidence suggests that multiple environmental factors during early development result in more pronounced disease phenotypes in offspring. To expand upon this existing notion, we developed a ‘triple-hit’ mouse model to examine the combined effects of maternal social stress, chronic high-fat diet consumption, and early life poly(I:C) exposure on long-term developmental outcomes in offspring. We observed that ‘triple-hit’ male offspring displayed autism-like social deficits and an overall increased susceptibility to NDD-like behavioural alterations in adulthood. Single-cell RNA (scRNA) transcriptomic and bulk proteomic analyses were performed in male triple hit offspring in brain tissue. The results are discussed in the associated publication.
Project description:We employed single-cell combinatorial indexing RNA-seq (sci-RNA-seq), a scRNA-seq technology with high throughput, high sample multiplexing capacity and low costs, to decipher the molecular events involved in mouse kidney fibrogenesis. With the hypothesis that different types of kidney insults may lead to distinct cellular injury responses, we leveraged sci-RNA-seq to profile mouse kidneys collected from two mouse kidney fibrogenesis models, unilateral ischemia-reperfusion injury (uni-IRI) and unilateral ureteral obstruction (UUO), at multiple stages. We described an atlas of kidney fibrogenesis (available at http://humphreyslab.com/SingleCell/) with a total of 309,666 cells profiled from 11 biological conditions and 24 samples in one experiment. We discovered that uni-IRI and UUO produced two types of early-stage injured PT cells with different transcriptomic signature. Further investigation on the two cell states highlighted their distinct mechanisms of metabolic regulation. Analysis of other structures of TECs revealed a common cellular response to injury and repair. In addition, we described the heterogeneity within kidney stroma and the dynamics of cell-cell communications in kidney fibrogenesis.
Project description:We present the adaptability of Mascot search engine for automated identification of intact glycopeptide mass spectra. The steps involved in adopting Mascot for intact glycopeptide analysis include: i) assigning unique one letter codes for monosaccharides, ii) linearizing glycan sequences and iii) preparing custom glycoprotein databases. Stepped normalized collision energy (NCE) for HCD mostly provided both the peptide and glycan information in a single MS2 spectrum. Using standard glycoproteins, we showed that Mascot can be adopted for automated annotation of both N- and O-linked glycopeptides. In a large scale validation study, a total of 257 glycoproteins containing 970 unique glycosylation sites and 3447 non-redundant N-linked glycopeptide variants were identified in serum samples. This represent a single tool that collectively allows the i) elucidation of N- and O-linked glycopeptide spectra, ii) matching glycopeptides to known protein sequences, and iii) high-throughput, batch wise analysis of large scale glycoproteomics data sets.