Bridging Function, Connectivity, and Transcriptomics of Mouse Cortical Neurons

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Bridging Function, Connectivity, and Transcriptomics of Mouse Cortical Neurons The versatile and powerful functional properties of the brain are reflected in the neuronal activity patterns and computations, and their evolution over time due to learning, homeostatic plasticity, and other processes. The composition of brain circuits out of a large number of cell types, which may be defined by the characteristic patterns of gene expression, and the intricate connectivity of these circuits are expected to be intimately related to their functional properties. However, the exact nature of these relationships is far from clear. The concept of a cell type itself, especially when considered at a fine-grained level, with a hundred or more cell types in any given brain area, is under active research in the community. A central question is whether and how transcriptomically-defined cell types provide specific underpinnings for broader circuit properties, such as those expressed in anatomy – defined by neuron’s location, morphology, connectivity – or in the functional types of neuronal activity in vivo. The proposed project will address this question by investigating the links between molecular and anatomical cell types to circuits and function in the mouse primary visual cortex (V1). We will connect the types of functional visual responses in vivo with transcriptomic types via multiplexed fluorescence in-situ hybridization (mFISH). Calcium imaging of neural activity will be carried out across the full cortical depth in V1, co-registered with mFISH imaging of that tissue, and the transcriptomic types of the neurons will be determined, establishing links between each neuron’s function and its type. In parallel, we will use a unique functional connectomics dataset already obtained at the Allen Institute, in which Electron Microscopy (EM) images are co-registered with in vivo imaging data from V1. These data will permit us to map the functional properties of each neuron to its morphological type and connectivity characteristics, resolved in the EM volume. The morphological type will, in turn, allow us to compare this dataset with the transcriptomic types, using our earlier PatchSeq dataset, where triple-modality data of morphology, intrinsic electrophysiology, and transcriptomics was obtained for individual neurons. These data and analyses will be freely shared with the scientific community. We will provide a web-based resource through the Allen Institute Cell Type Cards portal, linking across transcriptomic, morphological, connectivity, and functional types in these datasets. Thus, this project will uncover the relations between transcriptomic types, cortical circuit structure, and its function, while providing a major resource for a broad spectrum of future studies in this area.