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.

Recent studies have established that the circadian clock influences onset, progression and therapeutic outcomes in a number of diseases including cancer and heart disease. Therefore, there is a need for tools to measure the functional state of the circadian clock and its downstream targets in patients. We provide such a tool and demonstrate its clinical relevance by an application to breast cancer where we find a strong link between survival and our measure of clock dysfunction. We use a machine-learning approach and construct an algorithm called TimeTeller which uses the multi-dimensional state of the genes in a transcriptomics analysis of a single biological sample to assess the level of circadian clock dysfunction. We demonstrate how this can distinguish healthy from malignant tissues and demonstrate that the molecular clock dysfunction metric is a potentially new prognostic and predictive breast cancer biomarker that is independent of the main established prognostic factors.

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 system. In this paper we consider the methodology behind TimeTeller, a machine learning tool that analyses the clock as a 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.