Eric Wu

Autonomous sweat extraction and analysis applied to cystic fibrosis and glucose monitoring using a fully integrated wearable platform

Significance The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. Through devising an electrochemically enhanced, programmable, and miniaturized iontophoresis interface, integrated in a wearable sensing platform, we demonstrated a method for periodic sweat extraction and in situ analysis. The system can be programmed to induce sweat with various secretion profiles, which in combination with the in situ analysis capability allow us to gain real-time insight into the sweat-secretion and gland physiology. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. Perspiration-based wearable biosensors facilitate continuous monitoring of individuals’ health states with real-time and molecular-level insight. The inherent inaccessibility of sweat in sedentary individuals in large volume (≥10 µL) for on-demand and in situ analysis has limited our ability to capitalize on this noninvasive and rich source of information. A wearable and miniaturized iontophoresis interface is an excellent solution to overcome this barrier. The iontophoresis process involves delivery of stimulating agonists to the sweat glands with the aid of an electrical current. The challenge remains in devising an iontophoresis interface that can extract sufficient amount of sweat for robust sensing, without electrode corrosion and burning/causing discomfort in subjects. Here, we overcame this challenge through realizing an electrochemically enhanced iontophoresis interface, integrated in a wearable sweat analysis platform. This interface can be programmed to induce sweat with various secretion profiles for real-time analysis, a capability which can be exploited to advance our knowledge of the sweat gland physiology and the secretion process. To demonstrate the clinical value of our platform, human subject studies were performed in the context of the cystic fibrosis diagnosis and preliminary investigation of the blood/sweat glucose correlation. With our platform, we detected the elevated sweat electrolyte content of cystic fibrosis patients compared with that of healthy control subjects. Furthermore, our results indicate that oral glucose consumption in the fasting state is followed by increased glucose levels in both sweat and blood. Our solution opens the possibility for a broad range of noninvasive diagnostic and general population health monitoring applications.

Transcriptome Sequencing of a Large Human Family Identifies the Impact of Rare Noncoding Variants

Recent and rapid human population growth has led to an excess of rare genetic variants that are expected to contribute to an individual’s genetic burden of disease risk. To date, much of the focus has been on rare protein-coding variants, for which potential impact can be estimated from the genetic code, but determining the impact of rare noncoding variants has been more challenging. To improve our understanding of such variants, we combined high-quality genome sequencing and RNA sequencing data from a 17-individual, three-generation family to contrast expression quantitative trait loci (eQTLs) and splicing quantitative trait loci (sQTLs) within this family to eQTLs and sQTLs within a population sample. Using this design, we found that eQTLs and sQTLs with large effects in the family were enriched with rare regulatory and splicing variants (minor allele frequency < 0.01). They were also more likely to influence essential genes and genes involved in complex disease. In addition, we tested the capacity of diverse noncoding annotation to predict the impact of rare noncoding variants. We found that distance to the transcription start site, evolutionary constraint, and epigenetic annotation were considerably more informative for predicting the impact of rare variants than for predicting the impact of common variants. These results highlight that rare noncoding variants are important contributors to individual gene-expression profiles and further demonstrate a significant capability for genomic annotation to predict the impact of rare noncoding variants.

SplicePlot: a utility for visualizing splicing quantitative trait loci

Summary: RNA sequencing has provided unprecedented resolution of alternative splicing and splicing quantitative trait loci (sQTL). However, there are few tools available for visualizing the genotype-dependent effects of splicing at a population level. SplicePlot is a simple command line utility that produces intuitive visualization of sQTLs and their effects. SplicePlot takes mapped RNA sequencing reads in BAM format and genotype data in VCF format as input and outputs publication-quality Sashimi plots, hive plots and structure plots, enabling better investigation and understanding of the role of genetics on alternative splicing and transcript structure. Availability and implementation: Source code and detailed documentation are available at http://montgomerylab.stanford.edu/spliceplot/index.html under Resources and at Github. SplicePlot is implemented in Python and is supported on Linux and Mac OS. A VirtualBox virtual machine running Ubuntu with SplicePlot already installed is also available. Contact: wu.eric.g@gmail.com or smontgom@stanford.edu

Wearable sweat biosensors

Wearable perspiration biosensors enable real-time analysis of the sweat composition and can provide insightful information about health conditions. In this review, we discuss the recent developments in wearable sweat sensing platforms and detection techniques. Specifically, on-body monitoring of a wide spectrum of sweat biomarkers are illustrated. Opportunities and challenges in the field are discussed. Although still in an early research stage, wearable sweat biosensors may enable a wide range of personalized diagnostic and physiological monitoring applications.