Palpant, N. J. | Search bioRxiv

bioRxiv2018

Evidence for minimal cardiogenic potential of Sca-1 positive cells in the adult mouse heart

BackgroundDespite modern pharmacotherapy, heart failure remains a major medical burden. The heart has a limited regenerative capacity, and bolstering regeneration might represent new therapeutic approaches for heart failure patients. Various progenitor cells in the heart have been proposed to have cardiomyogenic properties, but this evidence is based mostly on cell culture and transplantation studies. One population of interest is characterized by the expression of Stem Cell Antigen-1 (Sca-1). Here we tested the hypothesis that Sca-1+ cells are endogenous progenitors for cardiomyocytes in the adult heart.\n\nMethodsWe evaluated the innate cardiogenic potential of Sca-1+ cells in vivo by generating a novel mouse model to genetically lineage-trace the fate of Sca-1 expressing cells. This was accomplished by introducing a tamoxifen-inducible Cre-recombinase into the Sca-1 locus (Sca-1mCm/+). Crossing this mouse line to a Cre-dependent tdTomato reporter line allowed for genetic lineage-tracing of endogenous Sca-1+ cells (Sca-1mCmR26tdTomato). The frequency of Sca-1+ cardiomyocytes was quantified from dispersed cell preparations and confirmed by in situ histology.\n\nResultsWe validated the genetic lineage tracing mouse model in bone marrow and heart. Unlike previous publications suggesting significant cardiogenic potential, we found that less than 0.02% of cardiomyocytes per year were derived from Sca-1+ cells in the adult heart under homeostatic conditions. At six months after myocardial infarction, we found less than 0.01% of cardiomyocytes were derived from Sca-1+ cells.\n\nConclusionOur results show that Sca-1+ cells in the adult heart have minimal cardiogenic potential under homeostatic conditions or in response to myocardial infarction.

cell biology↗

bioRxiv2016

Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experiments

While RNA-Seq has enabled great progress towards the goal of wide-scale isoform-level mRNA quantification, short reads have limitations when resolving complex or similar sets of isoforms. As a result, estimates of isoform abundance carry far more uncertainty than those made at the gene level. When confronted with this uncertainty, commonly used methods produce estimates that are often high-variance--small perturbations in the data often produce dramatically different results, confounding downstream analysis. We introduce a new method, Isolator, which analyzes all samples in an experiment in unison using a simple Bayesian hierarchical model. Combined with aggressive bias correction, it produces estimates that are simultaneously accurate and show high agreement between samples. In a comprehensive comparison of accuracy and variance, we show that this property is unique to Isolator. We further demonstrate that the approach of modeling an entire experiment enables new analyses, which we demonstrate by examining splicing monotonicity across several time points in the development of human cardiomyocyte cells.

bioinformatics↗