PANDORA-Seq unveils the hidden small noncoding RNA landscape in atherosclerosis of LDL receptor-deficient mice.

Small noncoding RNAs (sncRNAs) play diverse roles in numerous biological processes. While the widely used RNA sequencing (RNA-Seq) method has advanced sncRNA discovery, RNA modifications can interfere with the complementary DNA library construction process, preventing the discovery of highly modified sncRNAs including transfer RNA-derived small RNAs (tsRNAs) and ribosomal RNA-derived small RNAs (rsRNAs) that may have important functions in disease development. To address this technical obstacle, we recently developed a novel PANDORA-Seq (Panoramic RNA Display by Overcoming RNA Modification Aborted Sequencing) method to overcome RNA modification-elicited sequence interferences. To identify novel sncRNAs associated with atherosclerosis development, LDL receptor-deficient (LDLR-/-) mice were fed a low-cholesterol diet or high-cholesterol diet (HCD) for 9 weeks. Total RNAs isolated from the intima were subjected to PANDORA-Seq and traditional RNA-Seq. By overcoming RNA modification-elicited limitations, PANDORA-Seq unveiled an rsRNA/tsRNA-enriched sncRNA landscape in the atherosclerotic intima of LDLR-/- mice, which was strikingly different from that detected by traditional RNA-Seq. While microRNAs were the dominant sncRNAs detected by traditional RNA-Seq, PANDORA-Seq substantially increased the reads of rsRNAs and tsRNAs. PANDORA-Seq also detected 1,383 differentially expressed sncRNAs induced by HCD feeding, including 1,160 rsRNAs and 195 tsRNAs. One of HCD-induced intimal tsRNAs, tsRNA-Arg-CCG, may contribute to atherosclerosis development by regulating the proatherogenic gene expression in endothelial cells. Overall, PANDORA-Seq revealed a hidden rsRNA and tsRNA population associated with atherosclerosis development. These understudied tsRNAs and rsRNAs, which are much more abundant than microRNAs in the atherosclerotic intima of LDLR-/- mice, warrant further investigations.

Modeling the ACVR1R206H mutation in human skeletal muscle stem cells.

Abnormalities in skeletal muscle repair can lead to poor function and complications such as scarring or heterotopic ossification (HO). Here, we use fibrodysplasia ossificans progressiva (FOP), a disease of progressive HO caused by ACVR1R206H (Activin receptor type-1 receptor) mutation, to elucidate how ACVR1 affects skeletal muscle repair. Rare and unique primary FOP human muscle stem cells (Hu-MuSCs) isolated from cadaveric skeletal muscle demonstrated increased extracellular matric (ECM) marker expression, showed skeletal muscle-specific impaired engraftment and regeneration ability. Human induced pluripotent stem cell (iPSC)-derived muscle stem/progenitor cells (iMPCs) single-cell transcriptome analyses from FOP also revealed unusually increased ECM and osteogenic marker expression compared to control iMPCs. These results show that iMPCs can recapitulate many aspects of Hu-MuSCs for detailed in vitro study; that ACVR1 is a key regulator of Hu-MuSC function and skeletal muscle repair; and that ACVR1 activation in iMPCs or Hu-MuSCs may contribute to HO by changing the local tissue environment.

Functionally heterogeneous human satellite cells identified by single cell RNA sequencing.

Although heterogeneity is recognized within the murine satellite cell pool, a comprehensive understanding of distinct subpopulations and their functional relevance in human satellite cells is lacking. We used a combination of single cell RNA sequencing and flow cytometry to identify, distinguish, and physically separate novel subpopulations of human PAX7+ satellite cells (Hu-MuSCs) from normal muscles. We found that, although relatively homogeneous compared to activated satellite cells and committed progenitors, the Hu-MuSC pool contains clusters of transcriptionally distinct cells with consistency across human individuals. New surface marker combinations were enriched in transcriptional subclusters, including a subpopulation of Hu-MuSCs marked by CXCR4/CD29/CD56/CAV1 (CAV1+). In vitro, CAV1+ Hu-MuSCs are morphologically distinct, and characterized by resistance to activation compared to CAV1- Hu-MuSCs. In vivo, CAV1+ Hu-MuSCs demonstrated increased engraftment after transplantation. Our findings provide a comprehensive transcriptional view of normal Hu-MuSCs and describe new heterogeneity, enabling separation of functionally distinct human satellite cell subpopulations.

CaliBayes and BASIS: integrated tools for the calibration, simulation and storage of biological simulation models.

Dynamic simulation modelling of complex biological processes forms the backbone of systems biology. Discrete stochastic models are particularly appropriate for describing sub-cellular molecular interactions, especially when critical molecular species are thought to be present at low copy-numbers. For example, these stochastic effects play an important role in models of human ageing, where ageing results from the long-term accumulation of random damage at various biological scales. Unfortunately, realistic stochastic simulation of discrete biological processes is highly computationally intensive, requiring specialist hardware, and can benefit greatly from parallel and distributed approaches to computation and analysis. For these reasons, we have developed the BASIS system for the simulation and storage of stochastic SBML models together with associated simulation results. This system is exposed as a set of web services to allow users to incorporate its simulation tools into their workflows. Parameter inference for stochastic models is also difficult and computationally expensive. The CaliBayes system provides a set of web services (together with an R package for consuming these and formatting data) which addresses this problem for SBML models. It uses a sequential Bayesian MCMC method, which is powerful and flexible, providing very rich information. However this approach is exceptionally computationally intensive and requires the use of a carefully designed architecture. Again, these tools are exposed as web services to allow users to take advantage of this system. In this article, we describe these two systems and demonstrate their integrated use with an example workflow to estimate the parameters of a simple model of Saccharomyces cerevisiae growth on agar plates.