@article {65253, title = {Tissue clearing and its applications in neuroscience}, journal = {Nature Reviews Neuroscience}, year = {2020}, month = {2020 Feb 1}, abstract = {

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

}, issn = {1471-003X}, doi = {10.1038/s41583-019-0250-1}, url = {http://www.nature.com/articles/s41583-019-0250-1}, author = {Ueda, Hiroki R. and Ert{\"u}rk, Ali and Chung, Kwanghun and Gradinaru, Viviana and Ch{\'e}dotal, Alain and Tomancak, Pavel and Keller, Philipp J.} } @article {65264, title = {Tissue clearing and its applications in~neuroscience.}, journal = {Nature Reviews Neuroscience}, volume = {21}, year = {2020}, month = {2020 Jan 02}, pages = {61-79}, abstract = {

State-of-the-art tissue-clearing methods provide subcellular-level optical access to intact tissues from individual organs and even to some entire mammals. When combined with light-sheet microscopy and automated approaches to image analysis, existing tissue-clearing methods can speed up and may reduce the cost of conventional histology by several orders of magnitude. In addition, tissue-clearing chemistry allows whole-organ antibody labelling, which can be applied even to thick human tissues. By combining the most powerful labelling, clearing, imaging and data-analysis tools, scientists are extracting structural and functional cellular and subcellular information on complex mammalian bodies and large human specimens at an accelerated pace. The rapid generation of terabyte-scale imaging data furthermore creates a high demand for efficient computational approaches that tackle challenges in large-scale data analysis and management. In this Review, we discuss how tissue-clearing methods could provide an unbiased, system-level view of mammalian bodies and human specimens and discuss future opportunities for the use of these methods in human neuroscience.

}, issn = {1471-0048}, doi = {10.1038/s41583-019-0250-1}, author = {Ueda, Hiroki R and Ert{\"u}rk, Ali and Chung, Kwanghun and Gradinaru, Viviana and Ch{\'e}dotal, Alain and Tomancak, Pavel and Keller, Philipp J} } @article {5684, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

}, keywords = {Animals, Models, Nerve Net, Neuroimaging, Neurological, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} } @article {46785, title = {TrakEM2 software for neural circuit reconstruction.}, journal = {PLoS One}, volume = {7}, year = {2012}, month = {2012}, pages = {e38011}, abstract = {

A key challenge in neuroscience is the expeditious reconstruction of neuronal circuits. For model systems such as Drosophila and C. elegans, the limiting step is no longer the acquisition of imagery but the extraction of the circuit from images. For this purpose, we designed a software application, TrakEM2, that addresses the systematic reconstruction of neuronal circuits from large electron microscopical and optical image volumes. We address the challenges of image volume composition from individual, deformed images; of the reconstruction of neuronal arbors and annotation of synapses with fast manual and semi-automatic methods; and the management of large collections of both images and annotations. The output is a neural circuit of 3d arbors and synapses, encoded in NeuroML and other formats, ready for analysis.

}, keywords = {Animals, Models, Neurological, Nerve Net, Neuroimaging, Neurons, Software, Synapses, Synaptic Transmission}, issn = {1932-6203}, doi = {10.1371/journal.pone.0038011}, author = {Cardona, Albert and Saalfeld, Stephan and Schindelin, Johannes and Arganda-Carreras, Ignacio and Preibisch, Stephan and Longair, Mark and Tomancak, Pavel and Hartenstein, Volker and Douglas, Rodney J} }