Decoding the Heterogeneity of Interneuron Vulnerability in Aging and Alzheimer's Disease: An Ultraplex CLEM Approach

University Of Texas At Austin, Austin TX

Investigators

Abstract

ABSTRACT Age-related cognitive decline and Alzheimer's disease are associated with hippocampal interneuron loss and hyperexcitability, which reflects the selective vulnerability of inhibitory neurons. Our preliminary data shows a ubiquitous reduction of inhibitory synapses onto hippocampal CA1 stratum radiatum dendrites in aged rats compared to young adults, regardless of their cognitive status. Aged rats that showed spatial learning deficits had enlarged excitatory synapses on these same dendrites, disrupting local E/I balance, which is important for learning and memory. In aged animals that maintained learning capacity, the remaining inhibitory synapses were larger, thus preserving local dendritic E/I balance. These data suggest that while some interneurons are vulnerable in the aging process, others are resilient and provide a potential compensatory role for maintaining local excitation and inhibition. The broad heterogeneity of interneurons reflects subtypes with various molecular profiles, firing rates, and differential targeting to specific CA1 dendrites. We hypothesize that specific interneuron subtypes are more susceptible to age-related changes, while others are resilient and contribute to compensatory learning mechanisms by maintaining local E/I balance. Studies that previously identified vulnerable interneuron subtypes in the aging brain typically used techniques to isolate one interneuron subtype at a time, limiting our understanding of how diverse interneuron populations interact and contribute to circuit function in the aging brain. Furthermore, the subcellular organelles and local resources crucial for ultrastructural plasticity, such as mitochondria, endosomes, smooth endoplasmic reticulum, and polyribosomes, remain largely uncharacterized in these interneuron subtypes during aging. Consequently, we lack a comprehensive understanding of how interneuron interactions and age-related alterations in their ultrastructural resources contribute to cognitive decline or resilience. This knowledge gap is exacerbated by limitations in traditional techniques for identifying interneuron subtypes that often compromise tissue quality, hindering ultrastructural analysis via serial section electron microscopy (3DEM). We propose using a novel ultraplex correlative light and electron microscopy (CLEM) method that allows for simultaneous identification of multiple interneuron subtypes via RNA and protein markers in tissue optimized for 3DEM. In Aim 1, we will use ultraplex CLEM to quantify interneuron subtypes and their subcellular structures in hippocampal tissue from behaviorally characterized aged rats, capturing any intrinsic ultrastructural changes that occur in these cell populations. In Aim 2, we will use our new machine learning tools to segment and reconstruct local interneuron circuits and synapses in distinct CA1 layers to identify alterations associated with age-related cognitive decline and resilience. This approach will provide valuable insights into the role of specific interneuron subtypes in maintaining E/I balance and cognitive function during aging, ultimately contributing to our understanding of selective neuronal vulnerability in Alzheimer's disease.