ABSTRACT
Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualizer), a new machine learning pipeline that incrementally learns and visualizes a progression trajectory representing the longitudinal changes in longitudinal studies. At each sampling time point in an experiment, IL-VIS generates a snapshot of the longitudinal process on the data observed thus far, a new feature that is beyond the reach of classical static models. We first verify the utility and correctness of IL-VIS using simulated data, for which the true progression trajectories are known. We find that it accurately captures and visualizes the trends and (dis)similarities between high-dimensional progression trajectories. We then apply IL-VIS to longitudinal multi-electrode array data from brain cortical organoids when exposed to different levels of quinolinic acid, a metabolite contributing to many neuroinflammatory diseases including Alzheimer's disease, and its blocking antibody. We uncover valuable insights into the organoids' electrophysiological maturation and response patterns over time under these conditions.
Subject(s)
Machine Learning , Longitudinal Studies , Humans , Organoids , Alzheimer Disease/metabolism , Brain/physiologyABSTRACT
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the highly contagious respiratory disease Corona Virus Disease 2019 (COVID-19) that may lead to various neurological and psychological disorders that can be acute, lasting days to weeks or months and possibly longer. The latter is known as long-COVID or more recently post-acute sequelae of COVID (PASC). During acute COVID-19 infection, a strong inflammatory response, known as the cytokine storm, occurs in some patients. The levels of interferon-γ (IFN-γ), interferon-ß (IFN-ß), interleukin-6 (IL-6) and tumour necrosis factor-alpha (TNF-α) are particularly increased. These cytokines are known to activate the enzyme indoleamine 2,3-dioxygenase 1 (IDO-1), catalysing the first step of tryptophan (Trp) catabolism through the kynurenine pathway (KP) leading to the production of several neurotoxic and immunosuppressive metabolites. There is already data showing elevation in KP metabolites both acutely and in PASC, especially regarding cognitive impairment. Thus, it is likely that KP involvement is significant in SARS-CoV-2 pathogenesis especially neurologically.
ABSTRACT
The kynurenine pathway (KP) is the main pathway of tryptophan (TRP) metabolism that generates energy for multiple cellular processes. The activity of this pathway has been shown to be dysregulated in multiple human diseases. The resultant modulation of metabolites has been suggested to comprise biomarkers to track disease progression or could identify new therapeutic targets. While metabolite changes can be measured readily in blood, there is limited knowledge on the effect of blood matrices and sample processing time may have on the stability of KP metabolites. Understanding the stability of KP metabolites in blood is integral to obtaining accurate KP data to correlate with clinical pathology. Hence, the aim of this study was to assess the concentration of KP metabolites in matched whole blood, plasma and serum. The impact of pre-analytical sample processing time in the various blood matrices was also analysed. Serum and plasma had the higher concentration of KP metabolites compared to whole blood. Furthermore, concentrations of KP metabolites declined when the collected blood was processed after 24 hours storage at 4°C. Our study shows that that type of blood matrix and the time to processing have an impact on the stability of the KP metabolites. Serum or plasma are the preferred choice of matrix and the isolation of these matrices from whole blood is best performed immediately after collection for optimal analytical KP data.