Structured Sampling of Molecularly Classified Mossy Fiber Inputs by Cerebellar Granule Cells

Abstract

The cerebellar granule cell layer receives mossy fiber inputs from diverse brain regions, yet the principles governing how individual granule cells sample distinct types of inputs remain poorly understood. Using a volumetric correlated light and electron microscopy (vCLEM) dataset from an adult female mouse cerebellum, in which VGluT1-positive and VGluT1-negative mossy fiber terminals are molecularly distinguished, we reconstructed granule cell and mossy fiber connectivity to examine input selection rules. We constructed spatially constrained null models to simulate sampling during adulthood and development. Granule cell-centered analysis showed that granule cells shared less innervation from the same mossy fiber than expected by chance. Moreover, subpopulations of granule cells preferentially sample either VGluT1-positive or VGluT1-negative mossy fibers. In contrast, mossy fiber-centered analysis showed that individual terminals distributed their outputs across granule cells in a pattern consistent with random sampling. However, sampling in the adult state was more selective than in developmental simulations. Together, our findings demonstrated structured, non-random sampling of cerebellar VGluT1-positive and VGluT1-negative mossy fiber inputs and provide a framework for understanding how granule cells integrate molecularly distinct inputs to support cerebellar computation.