The labeling and identification of

The labeling and identification of long-range axonal inputs from multiple sourceswithin densely reconstructed electron microscopy (EM) datasets from mammalian brains has beennotoriously difficult because of the limited color label space of EM. Here, we report FluoEM for theidentification of multi-color fluorescently labeled axons in dense EM data without the need forartificial fiducial marks or chemical label conversion. The approach is based on correlated tissueimaging and computational matching of neurite reconstructions, amounting to a virtual colorlabeling 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 soonas the EM dataset is about 40–50 mm in extent, relying on the unique trajectories of axons in densemammalian neuropil. The method is exemplified for the identification of long-distance axonal inputinto layer 1 of the mouse cerebral cortex.