Interactive, Graph-based Visual Analysis of High-dimensional, Multi-parameter Fluorescence Microscopy Data in Toponomics

Abstract

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

Systematic, spatial imaging of large multimolecular assemblies and the emerging principles of supramolecular order in biological systems

“…An example of a prostate cancer CMP data set comprising more than 2000 distinct CMPs present in a single tissue section is available (Schubert et al ., , supplementary material available at: http://pubs.acs.org/doi/suppl/10.1021/pr800944f). Similar data sets have been published (Friedenberger et al ., ; Oeltze et al ., ). They show (i) the 30 most frequent CMPs (out of 9646 CMPs in total of simultaneously measured peripheral blood mononuclear cells) (http://www.nature.com/nprot/journal/v2/n9/extref/nprot.2007.320-S8.mov); and (ii) various real time visualizations of CMPs in 3D (http://www.computer.org/csdl/trans/tg/2011/12/ttg2011121882-abs.html).…”

A Systematic Review of Experimental Studies on Data Glyphs

“…Many cells remain empty, and there are several sparsely populated ones. One of the first things to notice is that there is no single study on circular orientation encodings, although they are used in visualization applications: representatives of this category are the compound glyph used in network graphs [12], pie chart glyphs for analyzing multi-dimensional data (e.g., global material composition [101], or biological binding properties [102] ), or as provided in visualization toolkits (e.g., JIT 2 ). Perhaps this type of encoding is a-priori deemed inferior based on Cleveland and McGill's [98] work that ranks orientation low for quantitative data representation.…”

Graphs in Scientific Visualization: A Survey

“…Oeltze et al . [OFH*11] applied graph visualization techniques for visual analysis of high‐dimensional, multi‐parameter fluorescence microscopy data. They drew a circular graph where each node represents an affinity reagent, while each edge represents two co‐occurring affinity reagent bindings.…”