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Andreeva, V.

Publications and source records attributed to Andreeva, V..

2 recordsLinked to original sources

Simulation of neurotransmitter release and its imaging by fluorescent sensors

Cells release signaling molecules such as neurotransmitters that diffuse through the extracellular space and bind to receptors. These signaling molecules can be detected by fluorescent sensors/probes to provide images of the signaling process. Such images are not equivalent to a concentration because diffusion and sensor kinetics affect (convolute) them. Therefore, computational approaches are necessary to disentangle these contributions and allow interpretation of fluorescent sensor-based images. Here, we present a kinetic Monte Carlo framework (FLuorescence Imaging Kinetic Simulation, FLIKS) that simulates signaling molecules undergoing cellular release, stochastic diffusion and reversible binding to sensors in realistic cellular (2D or 3D) geometries. We apply it to model neurotransmitter (dopamine) release in synaptic clefts and for paracrine signaling by immune cells. We also show how sensor location, sensor kinetics and release location affect fluorescence images. For example, we show how sensor sensitivity depends on the distance from the synaptic cleft and changes when dopamine transporters (DAT) clear dopamine. The approach also allows to compare the performance of membrane bound (genetically encoded) sensors versus artificial sensors such as nanosensors placed outside under or around the cells. As an example, we also demonstrate how the images of catecholamine release by immune cells can be modeled and compared to experimental data to better understand the release pattern. This framework provides a quantitative basis for analyzing and interpreting fluorescent sensor imaging data.

neuroscience↗

Expression analysis of Huntington disease mouse models reveals robust striatum disease signatures

Huntingtons disease is caused by expanded trinucleotide repeats in the huntingtin gene (HTT), and a higher number of repeats is associated with an earlier age of disease onset. Although the causative gene has been identified, its connections to the observed disease phenotypes is still unclear. Mouse models engineered to contain increasing numbers of trinucleotide repeats were sacrificed at different ages to detect RNA-level and protein-level changes specific to each repeat length and age in order to examine the transcriptional and translational characteristics of the disease. RNA-seq and quantitative proteomics data were collected on 14 types of tissues at up to 8 repeat lengths and in up to 3 different ages, and differential gene and protein expression were examined. A modified method of imputing missing proteomics data was employed and is described here. The most dysregulated tissue at both the RNA and protein levels was the striatum, and a strong gender effect was observed in all of the liver experiments. The full differential expression results are available to the research community on the HDinHD.org website. The results of multiple expression tests in the striatum were combined to generate an RNA disease signature and a protein disease signature, and the signatures were validated in external data sets. These signatures represent molecular readouts of disease progression in HD mice, which further characterizes their HD-related phenotype and can be useful in the preclinical evaluation of candidate therapeutic interventions. Author SummaryMouse models of Huntingtons disease were engineered to allow a detailed examination of how the disease causes changes in gene activity in a variety of tissues. Among the 14 tissues studied, the one most affected by the disease in our experiments was the striatum, a brain region involved in voluntary movement. The liver results showed large differences in gene activity between the male and female mice. In our analysis, we propose a minor change in how proteomics data is typically analyzed in order to improve the ranking of significant results. Using the striatum data in this study and in others, we identified robust genetic signatures of disease at both the RNA and protein levels.

neuroscience↗