Destabilization/reconsolidation process and hippocampal spine remodeling-Comas Mutis et al 2021
Description
In the present work we studied the interaction between two key neural structures involved in the reconsolidation process: the basolateral complex of the amygdala (BLA) and the dorsal hippocampus (DH). Our results show changes in the structural plasticity of the CA1 region of the DH in the form of dendritic spines density changes associated with the destabilization/reconsolidation process. Furthermore, we demonstrate a modulatory role of BLA over said plasticity infusing different drugs such as ifenprodil, a destabilization blocker, and propranolol, a reconsolidation disruptor, in this structure. Altogether our work shows a particular temporal dynamic in the CA1 region of DH that accompanies the destabilization/reconsolidation process and aims to provide new information on the underlying mechanisms of this process that may help create a better understanding of memory storage, maintenance, expression and updating and its potential medical applications.
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Dendritic spine density was analyzed following the protocols detailed in previous works (Bender et al., 2018a; Calfa et al., 2012; Giachero et al., 2013b, 2015). Animals were anesthetized using urethane (Sigma-Aldrich, Missouri, USA; diluted 50% in water; i.p.) in order to be perfused transcardially first by ice-cold PB (0.1 M, pH 7.4) and then fixed using ice-cold 4% para-formaldehyde (dissolved in 0.1M PB, pH 7.4). The brain was removed, post-fixed in the same fixative for 24h at 4ºC and 200µm slices containing DH were sectioned with a vibratome and collected in PBS 0.1%. The slices were injected in the stratum radiatum of the CA1 region of the DH with a saturated solution of the lipophilic dye 1,1′-dioctadecyl-3,3,3′,3′-tetramethyl indocarbocyanine perchlorate (DiI, InVitrogen; Carlsbad, CA) in fish oil (Pozzo-Miller et al., 1999). Z-section images from dyed dendritic segments were collected using a Fluoview FV-1000 laser-scanning confocal microscope (Olympus IX81 inverted microscope) with an oil immersion (NA 1.42) objective lens (PlanApo) from the Centro de Microscopía Óptica y Confocal de Avanzada, (CEMINCO, Córdoba, Argentina). The images were deconvoluted using the “advanced maximum likelihood estimation algorithm” for Cell R software (Olympus Soft Imaging Solutions, Munchen, Germany), version 3.3, set with 15 iterations, and an overlay subvolume of 10 pixels. A theoretical point spread function was used. The dendritic spine analysis was performed manually using Fiji ImageJ software. Dendritic protrusions less than 3 µm length and contacting with the parent dendrite were considered for the analysis (Calfa et al., 2012; Chapleau et al., 2009; Murphy and Segal, 1996). Spines from each dendritic section were counted and classified in “Stubby” (type I), “Mushroom” (type II) and “thin” (type III) considering the length (dimension from the base at the dendrite to the tip of its head, L), the diameter of the neck (measured as the maximum neck diameter, dn), and the diameter of the head (measured as the maximum head diameter, dh). Thus, individual spines were included in each category based on the specific ratios L/dn and dh/dn (Koh et al., 2002) and normalized to 10µm of the dendritic segment length. We considered “mushroom” and “stubby” spines as mature spines due to the strength of the excitatory synapses formed on these spines (Harris, 1999; Kasai et al., 2003; Nimchinsky et al., 2002; Segal and Andersen, 2000; Yuste et al., 2000). Statistical Analysis. All data was collected in a blinded manner and analyzed using the STATISTICA 7.0 software. Data was expressed as mean ± SD. For dendritic spines we used a two-way ANOVA analysis. We choose such analysis due to the possibility of finding a significant effect due to the stress or the conditioning or the treatment counted as a separate significant effect, for example. A p<0.05 was considered statistically significant.