Biology subjects

Irene Gallego Romero

Publications and source records attributed to Irene Gallego Romero.

3 recordsLinked to original sources

bioRxiv2015

Genetic Variation, Not Cell Type of Origin, Underlies Regulatory Differences in iPSCs

The advent of induced pluripotent stem cells (iPSCs)1 revolutionized Human Genetics by allowing us to generate pluripotent cells from easily accessible somatic tissues. This technology can have immense implications for regenerative medicine, but iPSCs also represent a paradigm shift in the study of complex human phenotypes, including gene regulation and disease2-5. Yet, an unresolved caveat of the iPSC model system is the extent to which reprogrammed iPSCs retain residual phenotypes from their precursor somatic cells. To directly address this issue, we used an effective study design to compare regulatory phenotypes between iPSCs derived from two types of commonly used somatic precursor cells. We show that the cell type of origin only minimally affects gene expression levels and DNA methylation in iPSCs. Instead, genetic variation is the main driver of regulatory differences between iPSCs of different donors.

Genetics↗

bioRxiv2014

Generation of a Panel of Induced Pluripotent Stem Cells From Chimpanzees: a Resource for Comparative Functional Genomics

Comparative genomics studies in primates are extremely restricted because we only have access to a few types of cell lines from non-human apes and to a limited collection of frozen tissues. In order to gain better insight into regulatory processes that underlie variation in complex phenotypes, we must have access to faithful model systems for a wide range of tissues and cell types. To facilitate this, we have generated a panel of 7 fully characterized chimpanzee (Pan troglodytes) induced pluripotent stem cell (iPSC) lines derived from fibroblasts of healthy donors. All lines appear to be free of integration from exogenous reprogramming vectors, can be maintained using standard iPSC culture techniques, and have proliferative and differentiation potential similar to human and mouse lines. To begin demonstrating the utility of comparative iPSC panels, we collected RNA sequencing data and methylation profiles from the chimpanzee iPSCs and their corresponding fibroblast precursors, as well as from 7 human iPSCs and their precursors, which were of multiple cell type and population origins. Overall, we observed much less regulatory variation within species in the iPSCs than in the somatic precursors, indicating that the reprogramming process has erased many of the differences observed between somatic cells of different origins. We identified 4,918 differentially expressed genes and 3,598 differentially methylated regions between iPSCs of the two species, many of which are novel inter-species differences that were not observed between the somatic cells of the two species. Our panel will help realise the potential of iPSCs in primate studies, and in combination with genomic technologies, transform studies of comparative evolution.

Evolutionary Biology↗

bioRxiv2014

Impact of RNA degradation on measurements of gene expression

The use of low quality RNA samples in whole-genome gene expression profiling remains controversial. It is unclear if transcript degradation in low quality RNA samples occurs uniformly, in which case the effects of degradation can be normalized, or whether different transcripts are degraded at different rates, potentially biasing measurements of expression levels. This concern has rendered the use of low quality RNA samples in whole-genome expression profiling problematic. Yet, low quality samples are at times the sole means of addressing specific questions - e.g., samples collected in the course of fieldwork. We sought to quantify the impact of variation in RNA quality on estimates of gene expression levels based on RNA-seq data. To do so, we collected expression data from tissue samples that were allowed to decay for varying amounts of time prior to RNA extraction. The RNA samples we collected spanned the entire range of RNA Integrity Number (RIN) values (a quality metric commonly used to assess RNA quality). We observed widespread effects of RNA quality on measurements of gene expression levels, as well as a slight but significant loss of library complexity in more degraded samples. While standard normalizations failed to account for the effects of degradation, we found that a simple linear model that controls for the effects of RIN can correct for the majority of these effects. We conclude that in instances where RIN and the effect of interest are not associated, this approach can help recover biologically meaningful signals in data from degraded RNA samples.

Genomics↗