Insights from mathematical modelling for T cell migration into the central nervous system.

The migration of immune cells from peripheral immune organs into the central nervous system (CNS) through the blood-brain barrier (BBB) is a tightly regulated process. The complex interplay between cells of the BBB and immune cells coordinates cell migration as a part of normal immune surveillance while its dysregulation is critically involved in the pathogenesis of various CNS diseases. To develop tools for a deeper understanding of distribution and migratory pattern of immune cells regulated by the BBB, we made use of a mathematical modelling approach derived from Markov chain theory. We present a data-driven model using a derivation of kinetic differential equations from a particle game. According to the theory of gases, these equations allow one to predict the mean behaviour of a large class of cells by modelling cell-cell interactions. We used this model to assess the distribution of naive, central memory and effector memory T lymphocytes in the peripheral blood and cerebrospinal fluid. Our model allows us to evaluate the impact of activation status, migratory capacity and cell death for cell distribution in the peripheral blood and the CNS.

Standardizing the analysis of conditioned fear in rodents: a multidimensional software approach.

Data comparability between different laboratories strongly depends on the individually applied analysis method. This factor is often a critical source of variation in rodent phenotyping and has never been systematically investigated in Pavlovian fear conditioning paradigms. In rodents, fear is typically quantified in terms of freezing duration via manual observation or automated systems. While manual analysis includes biases such as tiredness or inter-personal scoring variability, computer-assisted systems are unable to distinguish between freezing and immobility. Consequently, the novel software called MOVE follows a semi-automatized approach that prefilters video sequences of interest for the final human judgment. Furthermore, MOVE allows integrating additional data sources (e.g. force-sensitive platform, EEG) to reach the most accurate and precise results. MOVE directly supports multi-angle video recordings with webcams or standard laboratory equipment. The integrated manual key logger and internal video player complement this all-in-one software solution. Calculating the interlaboratory variability of manual freezing evaluation revealed significantly different freezing scores in two out of six laboratories. This difference was minimized when all experiments were analyzed with MOVE. Applied to a genetically modified mouse model, MOVE revealed higher fear responses of CB1 deficient mice compared to their wild-type littermates after foreground context fear conditioning. Multi-angle video analysis compared to the single-camera approach reached up to 15% higher accuracy and two fold higher precision. Multidimensional analysis provided by integration of additional data sources further improved the overall result. We conclude that the widespread usage of MOVE could substantially improve the comparability of results from different laboratories.