A MULTIMODAL RAT BRAIN ATLAS AND WHOLE-BRAIN LIGHT-SHEET IMAGING PLATFORM FOR MAPPING AND QUANTIFICATION OF CNS DATASETS

Light-sheet fluorescence microscopy (LSFM) of intact cleared mouse brains enables single-cell–resolution imaging and, when combined with computational registration to a digital reference atlas, provides a powerful approach for characterizing molecular and cellular brain organization and its responses to physiological and pharmacological stimuli. Lack of a similar digital rat brain atlas together with inadequate methods for protein and mRNA imaging have limited similar studies in rats. To establish a unified anatomical framework, we integrated in-skull micro-computed tomography (µCT) and magnetic resonance imaging (MRI) with out-of-skull LSFM imaging of cleared tissue. We generated population-averaged MRI and LSFM templates, mapping anatomical annotations from the Waxholm Space atlas directly into both modalities. This dual-template approach enables standardized 3D regional analysis and flexible cross-modal registration while preserving morphological consistency across datasets. We optimized and validated whole rat brain immunohistochemistry and mRNA in situ hybridization protocols using cell-type specific markers. The stereotaxic coordinate system was validated by 6-hydroxydopamine lesions targeting the medial forebrain bundle, followed by quantitative analysis of tyrosine hydroxylase-positive neurons, which confirmed accurate ablation of the targeted region. To demonstrate the utility of the atlas for translational pharmacology, we mapped brain-wide neuronal activation following systemic semaglutide administration by quantifying c-Fos expression across brain regions, revealing drug-induced activation signatures on appetite-regulating circuits. The platform enables single-cell–resolved mapping and quantification of protein expression, mRNA localization, connectivity and neuronal activation across more than 200 brain regions, establishing a powerful and versatile reference framework for whole-brain datasets across disease models and therapeutic interventions.