TimeTeller: A tool to probe the circadian clock as a multigene dynamical system.

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart diseases. Therefore, there is a need for tools to measure the functional state of the molecular circadian clock and its downstream targets in patients. Moreover, the clock is a multi-dimensional stochastic oscillator and there are few tools for analysing it as a noisy multigene dynamical system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a noisy multigene dynamical system and aims to estimate circadian clock function from a single transcriptome by modelling the multi-dimensional state of the clock. We demonstrate its potential for clock systems assessment by applying it to mouse, baboon and human microarray and RNA-seq data and show how to visualise and quantify the global structure of the clock, quantitatively stratify individual transcriptomic samples by clock dysfunction and globally compare clocks across individuals, conditions and tissues thus highlighting its potential relevance for advancing circadian medicine.

Evaluation of a Three-Dimensional Primary Human Hepatocyte Spheroid Model: Adoption and Industrialization for the Enhanced Detection of Drug-Induced Liver Injury.

Drug-induced liver injury is a leading cause of compound attrition during both preclinical and clinical drug development, and early strategies are in place to tackle this recurring problem. Human-relevant in vitro models that are more predictive of hepatotoxicity hazard identification, and that could be employed earlier in the drug discovery process, would improve the quality of drug candidate selection and help reduce attrition. We present an evaluation of four human hepatocyte in vitro models of increasing culture complexity (i.e., two-dimensional (2D) HepG2 monolayers, hepatocyte sandwich cultures, three-dimensional (3D) hepatocyte spheroids, and precision-cut liver slices), using the same tool compounds, viability end points, and culture time points. Having established the improved prediction potential of the 3D hepatocyte spheroid model, we describe implementing this model into an industrial screening setting, where the challenge was matching the complexity of the culture system with the scale and throughput required. Following further qualification and miniaturization into a 384-well, high-throughput screening format, data was generated on 199 compounds. This clearly demonstrated the ability to capture a greater number of severe hepatotoxins versus the current routine 2D HepG2 monolayer assay while continuing to flag no false-positive compounds. The industrialization and miniaturization of the 3D hepatocyte spheroid complex in vitro model demonstrates a significant step toward reducing drug attrition and improving the quality and safety of drugs, while retaining the flexibility for future improvements, and has replaced the routine use of the 2D HepG2 monolayer assay at GlaxoSmithKline.

Glycan heterogeneity on gold nanoparticles increases lectin discrimination capacity in label-free multiplexed bioassays.

The development of new analytical tools as point-of-care biosensors is crucial to combat the spread of infectious diseases, especially in the context of drug-resistant organisms, or to detect biological warfare agents. Glycan/lectin interactions drive a wide range of recognition and signal transduction processes within nature and are often the first site of adhesion/recognition during infection making them appealing targets for biosensors. Glycosylated gold nanoparticles have been developed that change colour from red to blue upon interaction with carbohydrate-binding proteins and may find use as biosensors, but are limited by the inherent promiscuity of some of these interactions. Here we mimic the natural heterogeneity of cell-surface glycans by displaying mixed monolayers of glycans on the surface of gold nanoparticles. These are then used in a multiplexed, label-free bioassay to create 'barcodes' which describe the lectin based on its binding profile. The increased information content encoded by using complex mixtures of a few sugars, rather than increased numbers of different sugars makes this approach both scalable and accessible. These nanoparticles show increased lectin identification power at a range of lectin concentrations, relative to single-channel sensors. It was also found that some information about the concentration of the lectins can be extracted, all from just a simple colour change, taking this technology closer to being a realistic biosensor.