HIGH-THROUGHPUT MAPPING OF DEFINED POPULATIONS ACROSS THE WHOLE MOUSE BRAIN USING IDISCO+ CLEARING AND AUTOMATED SPOT DETECTION

Understanding the spatial organization of specific neuronal populations requires whole-brain analysis at cellular resolution. We present an integrated experimental and computational pipeline for mapping fluorescently labeled cells across intact mouse brains.
We labeled neurons by crossing Cre driver lines (inhibitory subtypes and layer-specific markers) or TRAP mice (DeNardo, L.A., et al., 2019) with a nuclear tdTomato reporter line. Following perfusion, brains were cleared using a modified iDISCO+ protocol optimized for direct immunofluorescence (Renier N., et al., 2014) against tdTomato. Samples were imaged using a Zeiss Z1 lightsheet microscope with two channels, a background channel and a signal channel. We developed a computational pipeline for tile alignment and fusion, registration using Brainreg (Tyson, A.L, et al., 2022) and automated cell detection using Spotiflow (Dominguez Mantes, A., et al., 2025). Furthermore, implementation on High-Performance Computing platforms enabled high-throughput analysis of large datasets.
Our pipeline successfully maps cellular distributions across the entire mouse brain with high specificity. We are currently testing registration accuracy and assessing layer-specific Cre line fidelity by quantifying labeled cell distributions relative to atlas annotations. We are also exploring whole brain distribution patterns of different inhibitory subtypes (PV, VIP and SST). Finally, application to a dataset of TRAP-labeled activity-dependent cells suggests the involvement of many brain areas in the single session learning of a new sensorimotor association.
This pipeline enables rapid, unbiased quantification of genetically-defined and activity-dependent neuronal populations across the whole mouse brain, providing a powerful tool for systems neuroscience investigations.