The labeling and identification of long-range axonal inputs from multiple sources within
densely reconstructed electron microscopy (EM) datasets from mammalian brains has been
notoriously difficult because of the limited color label space of EM. Here, we report FluoEM
for the identification of multi-color fluorescently labeled axons in dense EM data without the
need for artificial fiducial marks or chemical label conversion. The approach is based on
correlated tissue imaging and computational matching of neurite reconstructions, amounting …

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 …