Scalable integration of cell types and connectivity in the motor cortex of rodents and non-human primates

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Project Summary The BICCN has recently completed a broad survey of the cellular components of motor cortex, including transcriptomic profiling, patch-seq, multiplexed FISH, inter-areal connectivity, and single neuron morphology. Missing from this view is a detailed picture of how individual neurons and neuronal types interconnect, in large part because acquiring a comprehensive picture of individual neuronal connections is best achieved with the difficult methods of large-scale electron microscopic reconstructions. Further, there are no scalable methods to assign transcriptomic cell classes to EM reconstructions, particularly when considering the myriad set of non- local inputs. In order to fulfill the BICCN mandate, we need scalable methods for measuring connectivity that can be integrated with cross-modal data, such as from Patch-seq, or with tools that target specific cell classes in transgenic and non-transgenic species, such as non-human primate. Here, we propose to exploit both approaches—retrospectively linking EM data with Patch-seq datasets or the prospective targeting of cell classes labeling in EM reconstructions—with our large-scale electron microscopy pipeline, while continuing to improve throughput and lower its overall cost. We propose to deploy these tools to examine the motor cortex of mouse and non-human primate, to demonstrate a scalable approach to delivering data that integrate local cellular morphology, ultrastructural detail, and specific local connectivity with transcriptomic information, from Patch-seq, while identifying the source and cell type of individual afferents, with viral genetic tools.