@article {65902, title = {Directed Evolution of a Selective and Sensitive Serotonin Sensor via Machine Learning.}, journal = {Cell}, volume = {183}, year = {2020}, month = {2020 Dec 23}, pages = {1986-2002.e26}, abstract = {

Serotonin plays a central role in cognition and is the target of most pharmaceuticals for psychiatric disorders. Existing drugs have limited efficacy; creation of improved versions will require better understanding of serotonergic circuitry, which has been hampered by our inability to monitor serotonin release and transport with high spatial and temporal resolution. We developed and applied a binding-pocket redesign strategy, guided by machine learning, to create a high-performance, soluble, fluorescent serotonin sensor (iSeroSnFR), enabling optical detection of millisecond-scale serotonin transients. We demonstrate that iSeroSnFR can be used to detect serotonin release in freely behaving mice during fear conditioning, social interaction, and sleep/wake transitions. We also developed a robust assay of serotonin transporter function and modulation by drugs. We expect that both machine-learning-guided binding-pocket redesign and iSeroSnFR will have broad utility for the development of other sensors and in\ vitro and in\ vivo serotonin detection, respectively.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2020.11.040}, author = {Unger, Elizabeth K and Keller, Jacob P and Altermatt, Michael and Liang, Ruqiang and Matsui, Aya and Dong, Chunyang and Hon, Olivia J and Yao, Zi and Sun, Junqing and Banala, Samba and Flanigan, Meghan E and Jaffe, David A and Hartanto, Samantha and Carlen, Jane and Mizuno, Grace O and Borden, Phillip M and Shivange, Amol V and Cameron, Lindsay P and Sinning, Steffen and Underhill, Suzanne M and Olson, David E and Amara, Susan G and Temple Lang, Duncan and Rudnick, Gary and Marvin, Jonathan S and Lavis, Luke D and Lester, Henry A and Alvarez, Veronica A and Fisher, Andrew J and Prescher, Jennifer A and Kash, Thomas L and Yarov-Yarovoy, Vladimir and Gradinaru, Viviana and Looger, Loren L and Tian, Lin} } @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} }