Assessment of three-dimensional morphological structure and synaptic connectivity is essential for a comprehensive understanding of neural processes controlling behavior. Different microscopy approaches have been proposed based on light microcopy (LM), electron microscopy (EM), or a combination of both. Correlative array tomography (CAT) is a technique in which arrays of ultrathin serial sections are repeatedly stained with fluorescent antibodies against synaptic molecules and neurotransmitters and imaged with LM and EM (Micheva and Smith, 2007). The utility of this correlative approach is limited by the ability to preserve fluorescence and antigenicity on the one hand, and EM tissue ultrastructure on the other. We demonstrate tissue staining and fixation protocols and a workflow that yield an excellent compromise between these multimodal imaging constraints. We adapt CAT for the study of projection neurons between different vocal brain regions in the songbird. We inject fluorescent tracers of different colors into afferent and efferent areas of HVC in zebra finches. Fluorescence of some tracers is lost during tissue preparation but recovered using anti-dye antibodies. Synapses are identified in EM imagery based on their morphology and ultrastructure and classified into projection neuron type based on fluorescence signal. Our adaptation of array tomography, involving the use of fluorescent tracers and heavy-metal rich staining and embedding protocols for high membrane contrast in EM will be useful for research aimed at statistically describing connectivity between different projection neuron types and for elucidating how sensory signals are routed in the brain and transformed into a meaningful motor output.
Keywords: correlative microscopy, projectomics, songbird, array tomography, neural tracers
Citation: Oberti D, Kirschmann MA and Hahnloser RH, (2011) Projection neuron circuits resolved using correlative array tomography. Front. Neurosci. 5:50. doi:10.3389/fnins.2011.00050
Received: 21 January 2011; Paper pending published: 01 March 2011;
Accepted: 28 March 2011; Published online: 12 April 2011.
Edited by:Kristen M. Harris, The University of Texas at Austin, USA
Reviewed by:Brian Antonsen, Marshall University, USA
Copyright: © 2011 Oberti, Kirschmann and Hahnloser. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.
*Correspondence: Richard H. R. Hahnloser, University of Zurich/ETH Zurich, Institute of Neuroinformatics, Zurich, Switzerland, email@example.com