Interindividual variability in the epigenome has gained tremendous attention for its potential in pathophysiological investigation, disease diagnosis, and evaluation of clinical intervention. DNA methylation is the most studied epigenetic mark in epigenome-wide association studies (EWAS) as it can be detected from limited starting material. Infinium 450K methylation array is the most popular platform for high-throughput profiling of this mark in clinical samples, as it is cost-effective and requires small amounts of DNA. However, this method suffers from low genome coverage and errors introduced by probe cross-hybridization. Whole-genome bisulfite sequencing can overcome these limitations but elevates the costs tremendously. Methyl-Capture Sequencing (MC Seq) is an attractive intermediate solution to increase the methylome coverage in large sample sets. Here we first demonstrate that MC Seq can be employed using DNA amounts comparable to the amounts used for Infinium 450K. Second, to provide guidance when choosing between the 2 platforms for EWAS, we evaluate and compare MC Seq and Infinium 450K in terms of coverage, technical variation, and concordance of methylation calls in clinical samples. Last, since the focus in EWAS is to study interindividual variation, we demonstrate the utility of MC Seq in studying interindividual variation in subjects from different ethnicities.

The proliferation and differentiation of neural progenitor lay the foundation for brain development. In neural progenitors, activation of Signal Transducer and Activator of Transcription 3 (STAT3) has been found to promote proliferation and astrocytogenesis while suppressing neurogenesis. However, our study found that Stat3 conditional knockout in neural progenitors (Stat3 cKO) also results in increased proliferation and suppressed neurogenesis. To investigate how STAT3 regulates these processes, we attempted to identify potential STAT3 target genes by RNA-seq profiling of the control (CTL) and Stat3 cKO neural progenitors. We found that STAT3 promotes the expression of genes involved in the mitochondrial oxidative phosphorylation (OXPHOS), and thereby promotes mitochondrial respiration and negatively regulates reactive oxygen species (ROS) production. In addition, we demonstrated that Stat3 loss-of-function promotes proliferation via regulation of mitochondrial metabolism and downstream signaling pathways. Our study provides novel insights into the relation between STAT3, mitochondrial metabolism and the process of embryonic neurogenesis.

Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.