Predicting the clinical outcome of infectious and non-infectious disease patients using whole blood transcriptomics

High clinical variability between individuals with the same (infectious or non-infectious) disease is the medical reality of the vast majority of diseases. These clinical manifestations can range from asymptomatic to lethal with a range of clinical complications in between. For example, malaria infected patients are generally divided into those with either uncomplicated or severe infections. The severe and deadlier form of the disease is almost exclusively caused by Plasmodium falciparum and is characterised by the presence of one or more clinical complications. These can include: cerebral malaria, severe anaemia, kidney failure, hypoglycaemia, lactic acidosis, pulmonary edema, respiratory distress syndrome and liver dysfunction. Consequently, there is high variability in the severity of this disease between individuals, but >70% of malarial deaths occur in children under 5. Hence, transcriptomic, primarily differential expression-based, methods have been utilised in the study of this and other diseases to identify transcriptomic prognostic biomarkers. This has resulted in the identification of a few pathways, including toll-like receptor pathways, and genes that are associated with malarial severity but the progression from an uncomplicated to severe infection remains poorly understood. Hence, we cannot predict the clinical complications a patient is likely to develop, as the result of a P. falciparum infection, based on what we can measure at clinical presentation. Thus, new approaches may be required to model the natural history of a patient's disease. One such approach that could be adapted for this purpose is fate mapping. Fate-mapping, which is also called cell lineage tracing when performed at the single cell level, uses a cell's transcriptomic profile to trace its development through a process, commonly differentiation. This has recently been combined with a new method, RNA velocity, to place a directionality onto the transcriptome of each cell within the map (the velocyto algorithm). It is this method I am using in this study.