Paul Robson | Search bioRxiv

bioRxiv2016

Dynamic changes in Sox2 spatio-temporal expression direct the second cell fate decision through Fgf4/Fgfr2 signaling in preimplantation mouse embryos

Oct4 and Sox2 regulate the expression of target genes such as Nanog, Fgf4 and Utf1, by binding to their respective regulatory motifs. Their functional cooperation is reflected in their ability to heterodimerise on adjacent cis regulatory elements, the composite Sox/Oct motif. Given that Oct4 and Sox2 regulate many developmental genes, a quantitative analysis of their synergistic action on different Sox/Oct motifs would yield valuable insights into the mechanisms of early embryonic development. In this study, we measured binding affinities of Oct4 and Sox2 to different Sox/Oct motifs using fluorescence correlation spectroscopy (FCS). We found that the synergistic binding interaction is driven mainly by the level of Sox2 in the case of the Fgf4s Sox/Oct motif. Taking into account that Sox2 expression levels fluctuate more than Oct4, our finding could explain how Sox2 controls the segregation of the cells of the inner cell mass (ICM) into the epiblast (EPI) and the primitive endoderm (PE) populations within the developing rodent blastocyst.

Developmental Biology↗

bioRxiv2014

The importance of study design for detecting differentially abundant features in high-throughput experiments

The use of high-throughput experiments, such as RNA-seq, to simultaneously identify differentially abundant entities across conditions has become widespread, but the systematic planning of such studies is currently hampered by the lack of general-purpose tools to do so. Here we demonstrate that there is substantial variability in performance across statistical tests, normalization techniques and study conditions, potentially leading to significant wastage of resources and/or missing information in the absence of careful study design. We present a broadly applicable experimental design tool called EDDA, and the first for single-cell RNA-seq, Nanostring and Metagenomic studies, that can be used to i) rationally choose from a panel of statistical tests, ii) measure expected performance for a study and iii) plan experiments to minimize mis-utilization of valuable resources. Using case studies from recent single-cell RNA-seq, Nanostring and Metagenomics studies, we highlight its general utility and, in particular, show a) the ability to correctly model single-cell RNA-seq data and do comparisons with 1/5th the amount of sequencing currently used and b) that the selection of suitable statistical tests strongly impacts the ability to detect biomarkers in Metagenomic studies. Furthermore, we demonstrate that a novel mode-based normalization employed in EDDA uniformly improves in robustness over existing approaches (10-20%) and increases precision to detect differential abundance by up to 140%.

Bioinformatics↗