Strategies and Solutions to Maintain and Retain Data from High Content Imaging, Analysis, and Screening Assays.

Data analysis and management in high content screening (HCS) has progressed significantly in the past 10 years. The analysis of the large volume of data generated in HCS experiments represents a significant challenge and is currently a bottleneck in many screening projects. In most screening laboratories, HCS has become a standard technology applied routinely to various applications from target identification to hit identification to lead optimization. An HCS data management and analysis infrastructure shared by several research groups can allow efficient use of existing IT resources and ensures company-wide standards for data quality and result generation. This chapter outlines typical HCS workflows and presents IT infrastructure requirements for multi-well plate-based HCS.

XuvTools: free, fast and reliable stitching of large 3D datasets.

Current biomedical research increasingly requires imaging large and thick 3D structures at high resolution. Prominent examples are the tracking of fine filaments over long distances in brain slices, or the localization of gene expression or cell migration in whole animals like Caenorhabditis elegans or zebrafish. To obtain both high resolution and a large field of view (FOV), a combination of multiple recordings ('tiles') is one of the options. Although hardware solutions exist for fast and reproducible acquisition of multiple 3D tiles, generic software solutions are missing to assemble ('stitch') these tiles quickly and accurately. In this paper, we present a framework that achieves fully automated recombination of tiles recorded at arbitrary positions in 3D space, as long as some small overlap between tiles is provided. A fully automated 3D correlation between all tiles is achieved such that no manual interaction or prior knowledge about tile positions is needed. We use (1) phase-only correlation in a multi-scale approach to estimate the coarse positions, (2) normalized cross-correlation of small patches extracted at salient points to obtain the precise matches, (3) find the globally optimal placement for all tiles by a singular value decomposition and (4) accomplish a nearly seamless stitching by a bleaching correction at the tile borders. If the dataset contains multiple channels, all channels are used to obtain the best matches between tiles. For speedup we employ a heuristic method to prune unneeded correlations, and compute all correlations via the fast Fourier transform (FFT), thereby achieving very good runtime performance. We demonstrate the successful application of the proposed framework to a wide range of different datasets from whole zebrafish embryos and C. elegans, mouse and rat brain slices and fine plant hairs (trichome). Further, we compare our stitching results to those of other commercially and freely available software solutions. The algorithms presented are being made available freely as an open source toolset 'XuvTools' at the corresponding author's website (http://lmb.informatik.uni-freiburg.de/people/ronneber), licensed under the GNU General Public License (GPL) v2. Binaries are provided for Linux and Microsoft Windows. The toolset is written in templated C++, such that it can operate on datasets with any bit-depth. Due to the consequent use of 64bit addressing, stacks of arbitrary size (i.e. larger than 4 GB) can be stitched. The runtime on a standard desktop computer is in the range of a few minutes. A user friendly interface for advanced manual interaction and visualization is also available.