Chloride channels regulate differentiation and barrier functions of the mammalian airway.

The conducting airway forms a protective mucosal barrier and is the primary target of airway disorders. The molecular events required for the formation and function of the airway mucosal barrier, as well as the mechanisms by which barrier dysfunction leads to early onset airway diseases, remain unclear. In this study, we systematically characterized the developmental landscape of the mouse airway using single-cell RNA sequencing and identified remarkably conserved cellular programs operating during human fetal development. We demonstrated that in mouse, genetic inactivation of chloride channel Ano1/Tmem16a compromises airway barrier function, results in early signs of inflammation, and alters the airway cellular landscape by depleting epithelial progenitors. Mouse Ano1-/-mutants exhibited mucus obstruction and abnormal mucociliary clearance that resemble the airway defects associated with cystic fibrosis. The data reveal critical and non-redundant roles for Ano1 in organogenesis, and show that chloride channels are essential for mammalian airway formation and function

Persistent features of intermittent transcription

Single-cell RNA sequencing is a powerful tool for exploring gene expression heterogeneity, but the results may be obscured by technical noise inherent in the experimental procedure. Here we introduce a novel parametrisation of sc-RNA data, giving estimates of the probability of activation of a gene and its peak transcription rate, which are agnostic about the mechanism underlying the fluctuations in the counts. Applying this approach to single cell mRNA counts across different tissues of adult mice, we find that peak transcription levels are approximately constant across different tissue types, in contrast to the gene expression probabilities which are, for many genes, markedly different. Many genes are only observed in a small fraction of cells. An investigation of correlation between genes activities shows that this is primarily due to temporal intermittency of transcription, rather than some genes being expressed in specialised cell types. Both the probability of activation and the peak transcription rate have a very wide ranges of values, with a probability density function well approximated by a power law. Taken together, our results indicate that the peak rate of transcription is a persistent property of a gene, and that differences in gene expression are modulated by temporal intermittency of the transcription

Genome, transcriptome and proteome: the rise of omics data and their integration in biomedical sciences

Advances in the technologies and informatics used to generate and process large biological data sets (omics data) are promoting a critical shift in the study of biomedical sciences. While genomics, transcriptomics and proteinomics, coupled with bioinformatics and biostatistics, are gaining momentum, they are still, for the most part, assessed individually with distinct approaches generating monothematic rather than integrated knowledge. As other areas of biomedical sciences, including metabolomics, epigenomics and pharmacogenomics, are moving towards the omics scale, we are witnessing the rise of inter-disciplinary data integration strategies to support a better understanding of biological systems and eventually the development of successful precision medicine. This review cuts across the boundaries between genomics, transcriptomics and proteomics, summarizing how omics data are generated, analysed and shared, and provides an overview of the current strengths and weaknesses of this global approach. This work intends to target students and researchers seeking knowledge outside of their field of expertise and fosters a leap from the reductionist to the global-integrative analytical approach in research

Sensitive detection of Aβ protofibrils by proximity ligation - relevance for Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Protein aggregation plays important roles in several neurodegenerative disorders. For instance, insoluble aggregates of phosphorylated tau and of Aβ peptides are cornerstones in the pathology of Alzheimer's disease. Soluble protein aggregates are therefore potential diagnostic and prognostic biomarkers for their cognate disorders. Detection of the aggregated species requires sensitive tools that efficiently discriminate them from monomers of the same proteins. Here we have established a proximity ligation assay (PLA) for specific and sensitive detection of Aβ protofibrils via simultaneous recognition of three identical determinants present in the aggregates. PLA is a versatile technology in which the requirement for multiple target recognitions is combined with the ability to translate signals from detected target molecules to amplifiable DNA strands, providing very high specificity and sensitivity.</p> <p>Results</p> <p>For specific detection of Aβ protofibrils we have used a monoclonal antibody, mAb158, selective for Aβ protofibrils in a modified PLA, where the same monoclonal antibody was used for the three classes of affinity reagents required in the assay. These reagents were used for detection of soluble Aβ aggregates in solid-phase reactions, allowing detection of just 0.1 pg/ml Aβ protofibrils, and with a dynamic range greater than six orders of magnitude. Compared to a sandwich ELISA setup of the same antibody the PLA increases the sensitivity of the Aβ protofibril detection by up to 25-fold. The assay was used to measure soluble Aβ aggregates in brain homogenates from mice transgenic for a human allele predisposing to Aβ aggregation.</p> <p>Conclusions</p> <p>The proximity ligation assay is a versatile analytical technology for proteins, which can provide highly sensitive and specific detection of Aβ aggregates - and by implication other protein aggregates of relevance in Alzheimer's disease and other neurodegenerative disorders.</p

Tuning MPL signaling to influence hematopoietic stem cell differentiation and inhibit essential thrombocythemia progenitors

Thrombopoietin (TPO) and the TPO-receptor (TPO-R, or c-MPL) are essential for hematopoietic stem cell (HSC) maintenance and megakaryocyte differentiation. Agents that can modulate TPO-R signaling are highly desirable for both basic research and clinical utility. We developed a series of surrogate protein ligands for TPO-R, in the form of diabodies (DBs), that homodimerize TPO-R on the cell surface in geometries that are dictated by the DB receptor binding epitope, in effect "tuning" downstream signaling responses. These surrogate ligands exhibit diverse pharmacological properties, inducing graded signaling outputs, from full to partial TPO agonism, thus decoupling the dual functions of TPO/TPO-R. Using single-cell RNA sequencing and HSC self-renewal assays we find that partial agonistic diabodies preserved the stem-like properties of cultured HSCs, but also blocked oncogenic colony formation in essential thrombocythemia (ET) through inverse agonism. Our data suggest that dampening downstream TPO signaling is a powerful approach not only for HSC preservation in culture, but also for inhibiting oncogenic signaling through the TPO-R

Benchmark and Parameter Sensitivity Analysis of Single-Cell RNA Sequencing Clustering Methods

Single-cell RNA-seq (scRNAseq) is a powerful tool to study heterogeneity of cells. Recently, several clustering based methods have been proposed to identify distinct cell populations. These methods are based on different statistical models and usually require to perform several additional steps, such as preprocessing or dimension reduction, before applying the clustering algorithm. Individual steps are often controlled by method-specific parameters, permitting the method to be used in different modes on the same datasets, depending on the user choices. The large number of possibilities that these methods provide can intimidate non-expert users, since the available choices are not always clearly documented. In addition, to date, no large studies have invistigated the role and the impact that these choices can have in different experimental contexts. This work aims to provide new insights into the advantages and drawbacks of scRNAseq clustering methods and describe the ranges of possibilities that are offered to users. In particular, we provide an extensive evaluation of several methods with respect to different modes of usage and parameter settings by applying them to real and simulated datasets that vary in terms of dimensionality, number of cell populations or levels of noise. Remarkably, the results presented here show that great variability in the performance of the models is strongly attributed to the choice of the user-specific parameter settings. We describe several tendencies in the performance attributed to their modes of usage and different types of datasets, and identify which methods are strongly affected by data dimensionality in terms of computational time. Finally, we highlight some open challenges in scRNAseq data clustering, such as those related to the identification of the number of clusters

ProteinSeq: High-Performance Proteomic Analyses by Proximity Ligation and Next Generation Sequencing

Despite intense interest, methods that provide enhanced sensitivity and specificity in parallel measurements of candidate protein biomarkers in numerous samples have been lacking. We present herein a multiplex proximity ligation assay with readout via realtime PCR or DNA sequencing (ProteinSeq). We demonstrate improved sensitivity over conventional sandwich assays for simultaneous analysis of sets of 35 proteins in 5 µl of blood plasma. Importantly, we observe a minimal tendency to increased background with multiplexing, compared to a sandwich assay, suggesting that higher levels of multiplexing are possible. We used ProteinSeq to analyze proteins in plasma samples from cardiovascular disease (CVD) patient cohorts and matched controls. Three proteins, namely P-selectin, Cystatin-B and Kallikrein-6, were identified as putative diagnostic biomarkers for CVD. The latter two have not been previously reported in the literature and their potential roles must be validated in larger patient cohorts. We conclude that ProteinSeq is promising for screening large numbers of proteins and samples while the technology can provide a much-needed platform for validation of diagnostic markers in biobank samples and in clinical use

Single cell analysis of human foetal liver captures the transcriptional profile of hepatobiliary hybrid progenitors

The liver parenchyma is composed of hepatocytes and bile duct epithelial cells (BECs). Controversy exists regarding the cellular origin of human liver parenchymal tissue generation during embryonic development, homeostasis or repair. Here we report the existence of a hepatobiliary hybrid progenitor (HHyP) population in human foetal liver using single-cell RNA sequencing. HHyPs are anatomically restricted to the ductal plate of foetal liver and maintain a transcriptional profile distinct from foetal hepatocytes, mature hepatocytes and mature BECs. In addition, molecular heterogeneity within the EpCAM+ population of freshly isolated foetal and adult human liver identifies diverse gene expression signatures of hepatic and biliary lineage potential. Finally, we FACS isolate foetal HHyPs and confirm their hybrid progenitor phenotype in vivo. Our study suggests that hepatobiliary progenitor cells previously identified in mice also exist in humans, and can be distinguished from other parenchymal populations, including mature BECs, by distinct gene expression profiles