@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 {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 {47133, title = {Stochastic electrotransport selectively enhances the transport of highly electromobile molecules.}, journal = {Proceedings of the National Academy of Sciences of the United States of America}, volume = {112}, year = {2015}, month = {2015 Nov 2}, pages = {E6274-83}, abstract = {

Nondestructive chemical processing of porous samples such as fixed biological tissues typically relies on molecular diffusion. Diffusion into a porous structure is a slow process that significantly delays completion of chemical processing. Here, we present a novel electrokinetic method termed stochastic electrotransport for rapid nondestructive processing of porous samples. This method uses a rotational electric field to selectively disperse highly electromobile molecules throughout a porous sample without displacing the low-electromobility molecules that constitute the sample. Using computational models, we show that stochastic electrotransport can rapidly disperse electromobile molecules in a porous medium. We apply this method to completely clear mouse organs within 1{\textendash}3 days and to stain them with nuclear dyes, proteins, and antibodies within 1 day. Our results demonstrate the potential of stochastic electrotransport to process large and dense tissue samples that were previously infeasible in time when relying on diffusion.

}, doi = {10.1073/pnas.1510133112}, author = {Kim, Sung-Yon and Cho, Jae Hun and Murray, Evan and Bakh, Naveed and Choi, Heejin and Ohn, Kimberly and Ruelas, Luzdary and Hubbert, Austin and McCue, Meg and Vassallo, Sara L and Keller, Philipp J and Chung, Kwanghun} } @article {6904, title = {The Open Connectome Project Data Cluster: Scalable analysis and vision for high-throughput neuroscience.}, journal = {Scientific and Statistical Database Management: International Conference, SSDBM ... : Proceedings. International Conference on Scientific and Statistical Database Management}, year = {2013}, month = {2013 Jun 18}, abstract = {

We describe a scalable database cluster for the spatial analysis and annotation of high-throughput brain imaging data, initially for 3-d electron microscopy image stacks, but for time-series and multi-channel data as well. The system was designed primarily for workloads that build connectomes- neural connectivity maps of the brain-using the parallel execution of computer vision algorithms on high-performance compute clusters. These services and open-science data sets are publicly available at openconnecto.me. The system design inherits much from NoSQL scale-out and data-intensive computing architectures. We distribute data to cluster nodes by partitioning a spatial index. We direct I/O to different systems-reads to parallel disk arrays and writes to solid-state storage-to avoid I/O interference and maximize throughput. All programming interfaces are RESTful Web services, which are simple and stateless, improving scalability and usability. We include a performance evaluation of the production system, highlighting the effec-tiveness of spatial data organization.

}, doi = {10.1145/2484838.2484870}, author = {Burns, Randal and Roncal, William Gray and Kleissas, Dean and Lillaney, Kunal and Manavalan, Priya and Perlman, Eric and Berger, Daniel R. and Bock, Davi D and Chung, Kwanghun and Grosenick, Logan and Kasthuri, Narayanan and Weiler, Nicholas C and Deisseroth, Karl and Kazhdan, Michael and Lichtman, Jeff and Reid, R Clay and Smith, Stephen J and Szalay, Alexander S and Vogelstein, Joshua T and Vogelstein, R Jacob} } @article {4070, title = {A computational statistics approach for estimating the spatial range of morphogen gradients.}, journal = {Development}, volume = {138}, year = {2011}, month = {2011 Nov}, pages = {4867-74}, abstract = {

A crucial issue in studies of morphogen gradients relates to their range: the distance over which they can act as direct regulators of cell signaling, gene expression and cell differentiation. To address this, we present a straightforward statistical framework that can be used in multiple developmental systems. We illustrate the developed approach by providing a point estimate and confidence interval for the spatial range of the graded distribution of nuclear Dorsal, a transcription factor that controls the dorsoventral pattern of the Drosophila embryo.

}, issn = {1477-9129}, doi = {10.1242/dev.071571}, author = {Kanodia, Jitendra S and Kim, Yoosik and Tomer, Raju and Khan, Zia and Chung, Kwanghun and Storey, John D and Lu, Hang and Keller, Philipp J and Shvartsman, Stansilas Y} }