The analysis of real-time biomolecular interactions (observation is performed as the biological interaction occurs)
provides information on the formation of target/probe complexes, particularly on their dynamic behaviours.
Namely, it allows the determination of the affinity constant, a static value that characterizes the interaction
properties, using two dynamic values, the association and dissociation constants. Such dynamic behaviour can
be assessed either with surface plasmon resonance (SPR) or
uorescence-based biosensors. The challenging issue
is the automatic extraction and analysis of the interaction signal for each spotted probe on the biosensor in a highthroughput
framework (hundreds of probes). This paper addresses such issue and develops a uniffied approach
for analyzing the image data provided by the above-mentioned technologies. A mathematical modelling of the
image data allowed building-up a virtual biosensor able to simulate biologic experiences related to various possible
parameters (level of signal and noise, presence of artefacts, surface functionalization, spotting heterogeneity).
Based on such simulation, a generic and automated approach combining 3D mathematical morphology and
spatio-temporal classiffication is proposed for detecting the interacting probes, segmenting the regions of effective
signal, and characterizing the associated affinity constants. The developed method has been assessed both
qualitatively and quantitatively on simulated and experimental datasets and showed accurate results (maximum
error of 7% for the most difficult cases in terms of noise and surface functionalization).
Real time imaging of macro molecular interactions is a challenging issue in life science. Among different developed techniques, the emerging SPR (Surface Plasmon Resonance) approach is one of the most promising: no molecular labeling is necessary to reveal molecular interactions, especially since extension of microarray allow multiple
interactions to be observed at the same time. Such a real time monitoring of various biomolecular interactions raises several challenges in terms of image segmentation of the spotted material, extraction and interpretation of the mean hybridization signal of each spot. This paper develops an automated approach for SPR image analysis tackling the above-mentioned issues. First, a
spatio-temporal anisotropic filtering removes the random noise of
large amplitude present in the SPR data. The pre-filtered signal supplies an image segmentation module in charge with the automated detection of the material deposited on the spots. Microarray spot segmentation is performed by combining advanced gray-level morphological operators and a priori knowledge on the spotting geometry. Non-uniform hybridization within a spot can thus be detected and the spot excluded from analysis. In the same manner, the presence of image artefacts (chip scratch, deposit leakage) can be notified during experimentation. The mean signal over each valid spot is then extracted and its temporal behavior provides the kinetic parameters characterizing the biological interaction hybridization/dehybridization speed, end-point state). The preliminary
results obtained on test biomolecular interactions confirmed our expectations in sensitivity improvement with respect to
fluorescence-based techniques. A larger validation of the proposed approach in terms of maximum sensitivity allowed for biological interactions discrimination is currently in progress.