Shifts in Microglial Activation with Disease Progression

Table of Contents

Abstract

Presentation

01. Introduction 02. Microglial Classification 03. Machine Learning Model 04. Activation Score 05. Alpha-Synuclein Mouse Model 06. Microglia Density 07. Microglia Activation 08. Activation vs. Density 09. Mouse Model Activation Score 10. Clustering 11. Summary

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Histograms showing characterization of the distribution of activation score in the entorhinal cortex. These histograms show similar trend to the temporal changes in microglia density, but also show an increase in the number of microglia in the early stage of activation (e.g. activation score = 0.2).

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The cumulative distribution function for the entorhinal cortex for the different groups. The shaded regions around the curves show the 95% confidence intervals (obtained by bootstrapping) to demonstrate the inter-animal variability.

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We can characterize the quantity of cells and their activation scores for each brain region at the different time points by plotting their histograms. The histograms in the top figure show similar patterns in activation score density for PBS-injected control mice at 2 ,3, and 4 months. In contrast, we see evolving microglial activation in PFF-injected mice over time.

To get a better look at activation scores independently of microglial cell counts, we can plot the distribution of activation scores. The plots in the bottom figure show the percentage of cells with an activation score less than the value on the x-axis. For example, the contralateral entorhinal cortex for PBS-injected control mice at 4 months (indicated by the blue arrow) has approximately 99% of microglia at an activation state of less than 0.25, whereas the PFF-injected mice at 4 months (indicated by the red arrow) have approximately 85% of microglia at an activation state of 0.25 or lower. This shift in the distribution of activation score is statistically significant.