Influence of cell-to-cell variability on spatial pattern formation.

Many spatial patterns in biology arise through differentiation of selected cells within a tissue, which is regulated by a genetic network. This is specified by its structure, parameterisation and the noise on its components and reactions. The latter, in particular, is not well examined because it is rather difficult to trace. The authors use suitable local mathematical measures based on the Voronoi diagram of experimentally determined positions of epidermal plant hairs (trichomes) to examine the variability or noise in pattern formation. Although trichome initiation is a highly regulated process, the authors show that the experimentally observed trichome pattern is substantially disturbed by cell-to-cell variations. Using computer simulations, they find that the rates concerning the availability of the protein complex that triggers trichome formation plays a significant role in noise-induced variations of the pattern. The focus on the effects of cell noise yields further insights into pattern formation of trichomes. The authors expect that similar strategies can contribute to the understanding of other differentiation processes by elucidating the role of naturally occurring fluctuations in the concentration of cellular components or their properties.

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.

Spatial quantitative analysis of fluorescently labeled nuclear structures: problems, methods, pitfalls.

The vast majority of microscopic data in biology of the cell nucleus is currently collected using fluorescence microscopy, and most of these data are subsequently subjected to quantitative analysis. The analysis process unites a number of steps, from image acquisition to statistics, and at each of these steps decisions must be made that may crucially affect the conclusions of the whole study. This often presents a really serious problem because the researcher is typically a biologist, while the decisions to be taken require expertise in the fields of physics, computer image analysis, and statistics. The researcher has to choose between multiple options for data collection, numerous programs for preprocessing and processing of images, and a number of statistical approaches. Written for biologists, this article discusses some of the typical problems and errors that should be avoided. The article was prepared by a team uniting expertise in biology, microscopy, image analysis, and statistics. It considers the options a researcher has at the stages of data acquisition (choice of the microscope and acquisition settings), preprocessing (filtering, intensity normalization, deconvolution), image processing (radial distribution, clustering, co-localization, shape and orientation of objects), and statistical analysis.

Classification of lactic acid bacteria with UV-resonance Raman spectroscopy.

UV-resonance Raman spectroscopy is applied as a method for the identification of lactic acid bacteria from yogurt. Eight different strains of bacteria from Lactobacillus acidophilus, L. delbrueckii ssp. bulgaricus, and Streptococcus thermophilus were investigated. At an excitation wavelength of 244 nm signals from nucleic acids and proteins are selectively enhanced. Classification was accomplished using different chemometric methods. In a first attempt, the unsupervised methods hierarchical cluster analysis and principal component analysis were applied to investigate natural grouping in the data. In a second step the spectra were analyzed using several supervised methods: K-nearest neighbor classifier, nearest mean classifier, linear discriminant analysis, and support vector machines.

Identification of single eukaryotic cells with micro-Raman spectroscopy.

For a fast identification of eukaryotic cells such as yeast species without a cultivation step it should be possible to perform the investigation on only one single cell. Since yeasts as eukaryotes are heterogeneous and their Raman spectra are therefore dependent on the measuring position, one Raman spectra is not representative of the whole cell. In this contribution we demonstrate the application of average Raman spectra of a line scan over single yeast cells. These average spectra are used for classification with the help of a support vector machine.

Micro-Raman spectroscopic identification of bacterial cells of the genus Staphylococcus and dependence on their cultivation conditions.

Microbial contamination is not only a medical problem, but also plays a large role in pharmaceutical clean room production and food processing technology. Therefore many techniques were developed to achieve differentiation and identification of microorganisms. Among these methods vibrational spectroscopic techniques (IR, Raman and SERS) are useful tools because of their rapidity and sensitivity. Recently we have shown that micro-Raman spectroscopy in combination with a support vector machine is an extremely capable approach for a fast and reliable, non-destructive online identification of single bacteria belonging to different genera. In order to simulate different environmental conditions we analyzed in this contribution different Staphylococcus strains with varying cultivation conditions in order to evaluate our method with a reliable dataset. First, micro-Raman spectra of the bulk material and single bacterial cells that were grown under the same conditions were recorded and used separately for a distinct chemotaxonomic classification of the strains. Furthermore Raman spectra were recorded from single bacterial cells that were cultured under various conditions to study the influence of cultivation on the discrimination ability. This dataset was analyzed both with a hierarchical cluster analysis (HCA) and a support vector machine (SVM).