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.

Cancer clocks in tumourigenesis: the p53 pathway and beyond.

Circadian rhythms regulate a vast array of physiological and cellular processes, as well as the hormonal milieu, to keep our cells synchronised to the light-darkness cycle. Epidemiologic studies have implicated circadian disruption in the development of breast and other cancers, and numerous clock genes are dysregulated in human tumours. Here we review the evidence that circadian rhythms, when altered at the molecular level, influence cancer growth. We also note some common pitfalls in circadian-cancer research and how they might be avoided to maximise comparable results and minimise misleading data. Studies of circadian gene mutant mice, and human cancer models in vitro and in vivo, demonstrate that clock genes can impact tumourigenesis. Clock genes influence important cancer-related pathways, ranging from p53-mediated apoptosis to cell cycle progression. Confusingly, clock dysfunction can be both pro- or anti-tumourigenic in a model and cell type-specific manner. Due to this duality, there is no canonical mechanism for clock interaction with tumourigenic pathways. To understand the role of the circadian clock in patients' tumours requires analysis of the molecular clock status compared to healthy tissue. Novel mathematical approaches are under development, but this remains largely aspirational, and is hampered by a lack of temporal information in publicly available datasets. Current evidence broadly supports the notion that the circadian clock is important for cancer biology. More work is necessary to develop an overarching model of this connection. Future studies would do well to analyse the clock network in addition to alterations in single clock genes.

A flexible high content imaging assay for profiling macrophage efferocytosis.

Macrophages are a diverse population of cells originating from the myeloid lineage, which form an important component of the innate immune system, helping to regulate immune response through secretion of pro/anti-inflammatory cytokines. However they also have an important homeostatic role - helping to remove cellular debris and apoptotic cells from the body (a phagocytic process known as efferocytosis). Here we describe a robust 384 well microplate based imaging assay, using apoptotic target cells for the specific quantification of efferocytosis in human primary monocyte derived macrophages. The methodology described allows for the assay to run in either fixed end-point or live-cell format (the former offering multiple morphological and intensity-based readouts, whilst the latter opens the possibility for future expansion of the methodology to encompass kinetic profiling). Within the methodology described we couple high content image acquisition (on the Cell Voyager 7000S) with multi-parametric image analysis - using Perkin Elmer Columbus combined with GeneData Screener.