Belinda Phipson

Bio: Belinda Phipson is an academic researcher from Royal Children's Hospital. The author has contributed to research in topics: Bioconductor & Progenitor cell. The author has an hindex of 27, co-authored 51 publications receiving 18485 citations. Previous affiliations of Belinda Phipson include University of Melbourne & Walter and Eliza Hall Institute of Medical Research.

Single cell analysis of the developing mouse kidney provides deeper insight into marker gene expression and ligand-receptor crosstalk

Abstract: Recent advances in the generation of kidney organoids and the culture of primary nephron progenitors from mouse and human have been based on knowledge of the molecular basis of kidney development in mice. Although gene expression during kidney development has been intensely investigated, single cell profiling provides new opportunities to further subsect component cell types and the signalling networks at play. Here, we describe the generation and analysis of 6732 single cell transcriptomes from the fetal mouse kidney [embryonic day (E)18.5] and 7853 sorted nephron progenitor cells (E14.5). These datasets provide improved resolution of cell types and specific markers, including subdivision of the renal stroma and heterogeneity within the nephron progenitor population. Ligand-receptor interaction and pathway analysis reveals novel crosstalk between cellular compartments and associates new pathways with differentiation of nephron and ureteric epithelium cell types. We identify transcriptional congruence between the distal nephron and ureteric epithelium, showing that most markers previously used to identify ureteric epithelium are not specific. Together, this work improves our understanding of metanephric kidney development and provides a template to guide the regeneration of renal tissue.

DiffVar: a new method for detecting differential variability with application to methylation in cancer and aging

Abstract: Methylation of DNA is known to be essential to development and dramatically altered in cancers. The Illumina HumanMethylation450 BeadChip has been used extensively as a cost-effective way to profile nearly half a million CpG sites across the human genome. Here we present DiffVar, a novel method to test for differential variability between sample groups. DiffVar employs an empirical Bayes model framework that can take into account any experimental design and is robust to outliers. We applied DiffVar to several datasets from The Cancer Genome Atlas, as well as an aging dataset. DiffVar is available in the missMethyl Bioconductor R package.

MOZ (MYST3, KAT6A) inhibits senescence via the INK4A-ARF pathway

Abstract: Cellular senescence is an important mechanism that restricts tumour growth The Ink4a-Arf locus (also known as Cdkn2a), which encodes p16(INK4A) and p19(ARF), has a central role in inducing and maintaining senescence Given the importance of cellular senescence in restraining tumour growth, great emphasis is being placed on the identification of novel factors that can modulate senescence The MYST-family histone acetyltransferase MOZ (MYST3, KAT6A), first identified in recurrent translocations in acute myeloid leukaemia, has been implicated in both the promotion and inhibition of senescence In this study, we investigate the role of MOZ in cellular senescence and show that MOZ is a potent inhibitor of senescence via the INK4A-ARF pathway Primary mouse embryonic fibroblasts (MEFs) isolated from Moz-deficient embryos exhibit premature senescence, which was rescued on the Ink4a-Arf(-/-) background Importantly, senescence resulting from the absence of MOZ was not accompanied by DNA damage, suggesting that MOZ acts independently of the DNA damage response Consistent with the importance of senescence in cancer, expression profiling revealed that genes overexpressed in aggressive and highly proliferative cancers are expressed at low levels in Moz-deficient MEFs We show that MOZ is required to maintain normal levels of histone 3 lysine 9 (H3K9) and H3K27 acetylation at the transcriptional start sites of at least four genes, Cdc6, Ezh2, E2f2 and Melk, and normal mRNA levels of these genes CDC6, EZH2 and E2F2 are known inhibitors of the INK4A-ARF pathway Using chromatin immunoprecipitation, we show that MOZ occupies the Cdc6, Ezh2 and Melk loci, thereby providing a direct link between MOZ, H3K9 and H3K27 acetylation, and normal transcriptional levels at these loci This work establishes that MOZ is an upstream inhibitor of the INK4A-ARF pathway, and suggests that inhibiting MOZ may be one way to induce senescence in proliferative tumour cells

limma powers differential expression analyses for RNA-sequencing and microarray studies

Abstract: limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.

Integrating single-cell transcriptomic data across different conditions, technologies, and species.

Abstract: Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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voom: precision weights unlock linear model analysis tools for RNA-seq read counts

Abstract: New normal linear modeling strategies are presented for analyzing read counts from RNA-seq experiments. The voom method estimates the mean-variance relationship of the log-counts, generates a precision weight for each observation and enters these into the limma empirical Bayes analysis pipeline. This opens access for RNA-seq analysts to a large body of methodology developed for microarrays. Simulation studies show that voom performs as well or better than count-based RNA-seq methods even when the data are generated according to the assumptions of the earlier methods. Two case studies illustrate the use of linear modeling and gene set testing methods.

Transcript-level expression analysis of RNA-seq experiments with HISAT, StringTie and Ballgown

Abstract: High-throughput sequencing of mRNA (RNA-seq) has become the standard method for measuring and comparing the levels of gene expression in a wide variety of species and conditions. RNA-seq experiments generate very large, complex data sets that demand fast, accurate and flexible software to reduce the raw read data to comprehensible results. HISAT (hierarchical indexing for spliced alignment of transcripts), StringTie and Ballgown are free, open-source software tools for comprehensive analysis of RNA-seq experiments. Together, they allow scientists to align reads to a genome, assemble transcripts including novel splice variants, compute the abundance of these transcripts in each sample and compare experiments to identify differentially expressed genes and transcripts. This protocol describes all the steps necessary to process a large set of raw sequencing reads and create lists of gene transcripts, expression levels, and differentially expressed genes and transcripts. The protocol's execution time depends on the computing resources, but it typically takes under 45 min of computer time. HISAT, StringTie and Ballgown are available from http://ccb.jhu.edu/software.shtml.