Human pluripotent stem cell-derived cardiomyocytes as a target platform for paracrine protection by cardiac mesenchymal stromal cells.

Ischemic heart disease remains the foremost cause of death globally, with survivors at risk for subsequent heart failure. Paradoxically, cell therapies to offset cardiomyocyte loss after ischemic injury improve long-term cardiac function despite a lack of durable engraftment. An evolving consensus, inferred preponderantly from non-human models, is that transplanted cells benefit the heart via early paracrine signals. Here, we tested the impact of paracrine signals on human cardiomyocytes, using human pluripotent stem cell-derived cardiomyocytes (hPSC-CMs) as the target of mouse and human cardiac mesenchymal stromal cells (cMSC) with progenitor-like features. In co-culture and conditioned medium studies, cMSCs markedly inhibited human cardiomyocyte death. Little or no protection was conferred by mouse tail tip or human skin fibroblasts. Consistent with the results of transcriptomic profiling, functional analyses showed that the cMSC secretome suppressed apoptosis and preserved cardiac mitochondrial transmembrane potential. Protection was independent of exosomes under the conditions tested. In mice, injecting cMSC-conditioned media into the infarct border zone reduced apoptotic cardiomyocytes > 70% locally. Thus, hPSC-CMs provide an auspicious, relevant human platform to investigate extracellular signals for cardiac muscle survival, substantiating human cardioprotection by cMSCs, and suggesting the cMSC secretome or its components as potential cell-free therapeutic products.

Influential factors in patient uptake of influenza vaccination during pregnancy; a survey-based audit in a tertiary hospital setting.

AIM: The aim of this study was to determine the key influential factors for pregnant or recently pregnant women in deciding on influenza vaccination. METHOD: This study was conducted in a single tertiary hospital in New Zealand using an anonymous and voluntary patient survey. Ethnicity, age and stage of pregnancy along with self-reported data on factors that influenced the decision to vaccinate against influenza during pregnancy were recorded. RESULTS: We included 101 participants over the one-week study period, 76% of whom had received the influenza vaccination. The most commonly reported reason for vaccination was the desire for neonatal protection, the common reasons for not being vaccinated were not receiving information on vaccination or safety concerns. CONCLUSION: There are a variety of factors influencing women when deciding on antenatal influenza vaccination. Further studies are needed to expand on the findings of this small local study in order to be able to improve vaccination uptake through empathetic delivery of evidence-based recommendations.

A powerful statistical approach for large-scale differential transcription analysis.

Next generation sequencing (NGS) is increasingly being used for transcriptome-wide analysis of differential gene expression. The NGS data are multidimensional count data. Therefore, most of the statistical methods developed well for microarray data analysis are not applicable to transcriptomic data. For this reason, a variety of new statistical methods based on count data of transcript reads have been correspondingly proposed. But due to high cost and limitation of biological resources, current NGS data are still generated from a few replicate libraries. Some of these existing methods do not always have desirable performances on count data. We here developed a very powerful and robust statistical method based on beta and binomial distributions. Our method (mBeta t-test) is specifically applicable to sequence count data from small samples. Both simulated and real transcriptomic data showed mBeta t-test significantly outperformed the existing top statistical methods chosen in all 12 given scenarios and performed with high efficiency and high stability. The differentially expressed genes found by our method from real transcriptomic data were validated by qPCR experiments. Our method shows high power in finding truly differential expression, conservatively estimating FDR and high stability in RNA sequence count data derived from small samples. Our method can also be extended to genome-wide detection of differential splicing events.