Drawitsch, F. | Search bioRxiv

bioRxiv2019

Cell-type specific innervation of cortical pyramidal cells at their apical tufts

We investigated the synaptic innervation of apical tufts of cortical pyramidal cells in a region between layers 1 and 2 using 3-D electron microscopy (3D-EM) applied to four cortical regions in mouse. Across all cortices, we found the relative inhibitory input at the apical dendrites main bifurcation to be more than 3-fold stronger for layer 2 pyramidal cells than for all other pyramidal cells. Towards the distal tuft dendrites in upper layer 1, however, the relative inhibitory input was about 2-fold stronger for L5 pyramidal cells than for all others. Only L3 pyramidal cells showed homogeneous inhibitory input density. The inhibitory to excitatory synaptic balance is thus specific for the types of pyramidal cells. Inhibitory axons preferentially innervated either layer 2 or L3/5 apical dendrites, but not both. These findings describe connectomic principles for the control of pyramidal cells at their apical dendrites in the upper layers of the cerebral cortex and point to differential computational properties of layer 2, layer 3 and layer 5 pyramidal cells in cortex.

neuroscience↗

bioRxiv2018

FluoEM: Virtual labeling of axons in 3-dimensional electron microscopy data for long-range connectomics

Volume electron microscopy (3D EM) has enabled the dense reconstruction of neuronal circuits in datasets that are so far about a few hundred micrometers in extent. In mammalian brains, most neuronal circuits are however highly non-local, such that a large fraction of the synapses in such a volume of neuropil originates from distant projection sources. The labeling and identification of such long-range axonal inputs from multiple sources within a densely reconstructed EM dataset has been notoriously difficult because of the limited color label space of EM. Here, we present FluoEM, a set of experimental and computational methods that allows the identification of multi-color fluorescently labeled axons in dense EM data without the need for artificially introduced fiducial marks or direct label conversion for EM. The approach is based on correlated imaging of the tissue and computational matching of neurite reconstructions, amounting to a virtual color labeling of axons in dense EM circuit data. We show that the identification of fluorescent light-microscopically (LM) imaged axons in 3D EM data from mouse cortex is faithfully possible as soon as the EM dataset is about 40-50 m in extent, relying on the unique trajectories of axons in dense mammalian neuropil. The method is exemplified for the identification of longdistance axonal input into layer 1 of the mouse cerebral cortex.

neuroscience↗