Transcriptomic Analyses of Neurotoxic Astrocytes Derived from Adult Triple Transgenic Alzheimer's Disease Mice

Prenylated Flavanone Isolated from Dalea Species as a Potential Multitarget-Neuroprotector in an In Vitro Alzheimer's Disease Mice Model

Alzheimer's disease (AD) involves a neurodegenerative process that has not yet been prevented, reversed, or stopped. Continuing with the search for natural pharmacological treatments, flavonoids are a family of compounds with proven neuroprotective effects and multi-targeting behavior. The American genus Dalea L. (Fabaceae) is an important source of bioactive flavonoids. In this opportunity, we tested the neuroprotective potential of three prenylated flavanones isolated from Dalea species in a new in vitro pre-clinical AD model previously developed by us. Our approach consisted in exposing neural cells to conditioned media (3xTg-AD ACM) from neurotoxic astrocytes derived from hippocampi and cortices of old 3xTg-AD mice, mimicking a local neurodegenerative microenvironment. Flavanone 1 and 3 showed a neuroprotective effect against 3xTg-AD ACM, being 1 more active than 3. The structural requirements to afford neuroprotective activity in this model are a 5'-dimethylallyl and 4'-hydroxy at the B ring. In order to search the mechanistic performance of the most active flavanone, we focus on the flavonoid-mediated regulation of GSK-3β-mediated tau phosphorylation previously reported. Flavanone 1 treatment decreased the rise of hyperphosphorylated tau protein neuronal levels induced after 3xTg-AD ACM exposure and inhibited the activity of GSK-3β. Finally, direct exposure of these neurotoxic 3xTg-AD astrocytes to flavanone 1 resulted in toxicity to these cells and reduced the neurotoxicity of 3xTg-AD ACM as well. Our results allow us to present compound 1 as a natural prenylated flavanone that could be used as a precursor to development and design of future drug therapies for AD.

RECCIPE: A new framework assessing localized cell-cell interaction on gene expression in multicellular ST data

Cell-cell interaction (CCI) plays a pivotal role in cellular communication within the tissue microenvironment. The recent development of spatial transcriptomics (ST) technology and associated data analysis methods has empowered researchers to systematically investigate CCI. However, existing methods are tailored to single-cell resolution datasets, whereas the majority of ST platforms lack such resolution. Additionally, the detection of CCI through association screening based on ST data, which has complicated dependence structure, necessitates proper control of false discovery rates due to the multiple hypothesis testing issue in high dimensional spaces. To address these challenges, we introduce RECCIPE, a novel method designed for identifying cell signaling interactions across multiple cell types in spatial transcriptomic data. RECCIPE integrates gene expression data, spatial information and cell type composition in a multivariate regression framework, enabling genome-wide screening for changes in gene expression levels attributed to CCIs. We show that RECCIPE not only achieves high accuracy in simulated datasets but also provides new biological insights from real data obtained from a mouse model of Alzheimer's disease (AD). Overall, our framework provides a useful tool for studying impact of cell-cell interactions on gene expression in multicellular systems.