A new graph-based clustering method with application to single-cell RNA-seq data from human pancreatic islets.

Traditional bulk RNA-sequencing of human pancreatic islets mainly reflects transcriptional response of major cell types. Single-cell RNA sequencing technology enables transcriptional characterization of individual cells, and thus makes it possible to detect cell types and subtypes. To tackle the heterogeneity of single-cell RNA-seq data, powerful and appropriate clustering is required to facilitate the discovery of cell types. In this paper, we propose a new clustering framework based on a graph-based model with various types of dissimilarity measures. We take the compositional nature of single-cell RNA-seq data into account and employ log-ratio transformations. The practical merit of the proposed method is demonstrated through the application to the centered log-ratio-transformed single-cell RNA-seq data for human pancreatic islets. The practical merit is also demonstrated through comparisons with existing single-cell clustering methods. The R-package for the proposed method can be found at https://github.com/Zhang-Data-Science-Research-Lab/LrSClust

Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data.

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues

Inhibition of P2X7 receptors improves outcomes after traumatic brain injury in rats

Traumatic brain injury (TBI) is the leading cause of death and disability for people under the age of 45 years worldwide. Neuropathology after TBI is the result of both the immediate impact injury and secondary injury mechanisms. Secondary injury is the result of cascade events, including glutamate excitotoxicity, calcium overloading, free radical generation, and neuroinflammation, ultimately leading to brain cell death. In this study, the P2X7 receptor (P2X7R) was detected predominately in microglia of the cerebral cortex and was up-regulated on microglial cells after TBI. The microglia transformed into amoeba-like and discharged many microvesicle (MV)-like particles in the injured and adjacent regions. A P2X7R antagonist (A804598) and an immune inhibitor (FTY720) reduced significantly the number of MV-like particles in the injured/adjacent regions and in cerebrospinal fluid, reduced the number of neurons undergoing apoptotic cell death, and increased the survival of neurons in the cerebral cortex injured and adjacent regions. Blockade of the P2X7R and FTY720 reduced interleukin-1βexpression, P38 phosphorylation, and glial activation in the cerebral cortex and improved neurobehavioral outcomes after TBI. These data indicate that MV-like particles discharged by microglia after TBI may be involved in the development of local inflammation and secondary nerve cell injury

DNA methylomes of bovine gametes and in vivo produced preimplantation embryos.

DNA methylation is an important epigenetic modification that undergoes dynamic changes in mammalian embryogenesis, during which both parental genomes are reprogrammed. Despite the many immunostaining studies that have assessed global methylation, the gene-specific DNA methylation patterns in bovine preimplantation embryos are unknown. Using reduced representation bisulfite sequencing, we determined genome-scale DNA methylation of bovine sperm and individual in vivo developed oocytes and preimplantation embryos. We show that (1) the major wave of genome-wide demethylation was completed by the 8-cell stage; (2) promoter methylation was significantly and inversely correlated with gene expression at the 8-cell and blastocyst stages; (3) sperm and oocytes have numerous differentially methylated regions (DMRs)-DMRs specific for sperm were strongly enriched in long terminal repeats and rapidly lost methylation in embryos; while the oocyte-specific DMRs were more frequently localized in exons and CpG islands (CGIs) and demethylated gradually across cleavage stages; (4) DMRs were also found between in vivo and in vitro matured oocytes; and (5) differential methylation between bovine gametes was confirmed in some but not all known imprinted genes. Our data provide insights into the complex epigenetic reprogramming of bovine early embryos, which serve as an important model for human preimplantation development