Microarray analysis at single-molecule resolution

Jaroslaw Jacak, Leila Muresan, Jan Hesse, Erich Peter Klement, Gerhard Schütz

Publikation: Beitrag in FachzeitschriftArtikelBegutachtung

13 Zitate (Scopus)


Bioanalytical chip-based assays have been enormously improved in sensitivity in the recent years; detection of trace amounts of substances down to the level of individual fluorescent molecules has become state-of-the-art technology. The impact of such detection methods, however, has yet not fully been exploited, mainly due to a lack of appropriate mathematical tools for robust data analysis. One particular example relates to the analysis of microarray data. While classical microarray analysis works at resolutions of 220 μm and quantifies the abundance of target molecules by determining average pixel intensities, a novel high-resolution approach directly visualizes individual bound molecules as diffraction-limited peaks. The now possible quantification via counting is less susceptible to labeling artifacts and background noise. We have developed an approach for the analysis of high-resolution microarray images. First, it consists of a single-molecule detection step, based on undecimated wavelet transforms, and second, a spot identification step via spatial statistics approach (corresponding to the segmentation step in the classical microarray analysis). The detection method was tested on simulated images with a concentration range of 0.001 to 0.5 molecules per square micrometer and signal-to-noise ratio (SNR) between 0.9 and 31.6. For SNR above 15, the false negatives relative error was below 15%. Separation of foreground/background is proved reliable, in case foreground density exceeds background by a factor of 2. The method has also been applied to real data from high-resolution microarray measurements.

Seiten (von - bis)51-58
FachzeitschriftIEEE transactions on nanobioscience
PublikationsstatusVeröffentlicht - März 2010


Untersuchen Sie die Forschungsthemen von „Microarray analysis at single-molecule resolution“. Zusammen bilden sie einen einzigartigen Fingerprint.