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There is growing evidence for the key role of microglial activation in brain pathophysiology. Consequently, there is a need for efficient automated methods to measure the morphological changes distinctive of microglia functional states in research settings. Currently, many commonly used automated methods can be subject to sample representation bias, time consuming imaging, specific hardware requirements, and difficulty in maintaining an accurate comparison across research environments. To overcome these issues, we use commercially available deep learning tools (Aiforia® Cloud (Aifoira Inc., Cambridge, United States) to quantify microglial morphology and cell counts from histopathological slides of Iba1 stained tissue sections. We provide evidence for the effective application of this method across a range of independently collected datasets in mouse models of viral infection and Parkinson’s disease. Additionally, we provide a comprehensive workflow with training details and annotation strategies by feature layer that can be used as a guide to generate new models. In addition, all models described in this work are shared within the Aiforia® platform and are available for study-specific adaptation and validation.

Odors may elicit strong emotional and behavioral responses which are known to be strengthened through learning. Presumably subserving this, olfactory structures are well-known for their experience-dependent plasticity, yet the specific cellular systems involved and the actual influence of these systems on behavior are less understood. Here we investigated the transformation of odor information throughout the association of odors with learned-rewarded outcomes and also sought to link those neural systems with displays of reinforcement-based approach behaviors. First, we investigated the representation of odor-reward associations, also known as valence, within two areas recipient of dense olfactory bulb input, the posterior piriform cortex (pPCX) and olfactory tubercle (OT), using simultaneous multi-site electrophysiological recordings from mice engaged in a reward-based olfactory learning task. As expected, neurons in both regions represented the reward-associated valence of odors, yet both the proportion of neurons recruited by conditioned rewarded odors and the magnitudes of their recruitment were distinctly pronounced in the OT. Using fiber photometry we found that odor valence coding in the OT occurs among D1- but not D2-type dopamine receptor expressing neurons. In both the recordings and imaging, statistically meaningful changes in activity occurred prior to instrumental behavior responses. Finally, using optogenetics we show that OT D1-receptor expressing neurons strongly influence reward-motivated approach behavior. Together our results contribute to a model whereby OT D1 neurons support olfactory valence and ultimately hedonic-based behavioral responses.

International audience; Animals choose between sensory stimuli, a highly complex behavior which includes detection, discrimination, preference, and memory processes. Rodents are reported to display robust preferences for some odors, for instance in the context of choosing among possible mates or food items. In contrast to the apparent robustness of responses towards these and other 'ethologically relevant' odors, little is known about the robustness of behaviors towards odors which have no overt role in the rodent ecological niche, so-called 'non-ethologically relevant' odors. We developed an apparatus for monitoring the nose-poking behavior of mice and used this apparatus to explore the prevalence and stability of choices among different odors both across mice, and within mice over successive days. Mice were tested to either a panel of ethologically relevant or non-ethologically relevant odors in an olfactory multiple choice test. Significant preferences to non-ethologically relevant odors were observed across the population of mice, with longer investigation durations to some odors more than to others. However, we found substantial inter-mouse variability in these responses, and that responses to these odors even varied within mice across days of testing. Tests with ethologically-relevant odors revealed that responses towards these odors were also variable across mice, but within individual mice responses were somewhat stable. This work establishes an olfactory multiple choice test for monitoring odor investigation, choice, and preference behaviors and the application of this apparatus to assess across and within-mouse odor preference choice stability. These results highlight that odor preferences, as assayed by measuring choice behaviors, are variable.