Biology subjects

Moutsopoulos, I.

Publications and source records attributed to Moutsopoulos, I..

3 recordsLinked to original sources

bioRxiv2021

bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk sequencing results

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks. bulkAnalyseR is available on CRAN and GitHub, with extensive documentation and usage examples (https://github.com/Core-Bioinformatics/bulkAnalyseR, https://cran.r-project.org/web/packages/bulkAnalyseR/) ContactIrina Mohorianu iim22@cam.ac.uk

bioinformatics↗

bioRxiv2021

Defining the transcriptional signature of esophageal-to-skin lineage conversion

The ability of epithelial cells to rewire their cell fate program beyond their physiological repertoire has become a new paradigm in stem cell biology. This plasticity leaves behind the concept of strict stem cell hierarchies, opening up new exciting questions about its limits and underlying regulation. Here we developed a heterotypic 3D culture system to study the mechanisms modulating changes in the identity of adult esophageal epithelial cells. We demonstrate that, when exposed to the foreign stroma of adult skin, esophageal cells transition towards hair follicle identity and architecture. Heterotypic transplantation experiments recapitulated this cell fate conversion process in vivo. Single-cell RNA sequencing and histological analysis, capturing the temporality of this process, reveal that most esophageal cells switching towards skin identity remain in an intermediate state marked by a transient regenerative profile and a particularly strong hypoxic signature. Inhibition of HIF1a establishes the central role of this pathway in regulating epithelial cell plasticity, driving cells away from their transition state in favor of cell fate conversion.

cell biology↗

bioRxiv2021

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches. TeaserNoise removal from sequencing quantification improves the convergence of downstream tools and robustness of conclusions.

bioinformatics↗