A mathematical model of immunohistochemical preparations, which provides quantitative predictions

Bayesian model for detection and classification of meningioma nuclei in microscopic images

Image segmentation aims to determine structures of interest inside a digital picture in biomedical sciences. State-of-the art automatic methods however still fail to provide the segmentation quality achievable by humans who employ expert knowledge and use software to mark target structures on an image. Manual segmentation is time-consuming, tedious and suffers from interoperator variability, thus not serving the requirements of daily use well. Therefore, the approach presented here abandons the goal of full-fledged segmentation and settles for the localization of circular objects in photographs (10 training images and 20 testing images with several hundreds of nuclei each). A fully trainable softcore interaction point process model was hence fit to the most likely locations of nuclei of meningioma cells. The Broad Bioimage Benchmark Collection/SIMCEP data set of virtual cells served as controls. A 'colour deconvolution' algorithm was integrated to determine (based on anti-Ki67 immunohistochemistry) which real cells might have the potential to proliferate. In addition, a density parameter of the underlying Bayesian model was estimated. Immunohistochemistry results were 'simulated'for the virtual cells. The system yielded true positive (TP) rates in the detection and classification of real nuclei and their virtual counterparts. These hits outnumbered those obtained from the public domain image processing software ImageJ by 10%. The method introduced here can be trained to function not only in medicine and morphology-based systems biology but in other application domains as well. The algorithm lends itself to an automated approach that constitutes a valuable tool which is easy to use and generates acceptable results quickly.

Improved semiquantitative Western blot technique with increased quantification range

With the development of new interdisciplinary fields such as systems biology, the quantitative analysis of protein expression in biological samples gains more and more importance. Although the most common method for this is ELISA, Western blot also has advantages: The separation of proteins by size allows the evaluation of only specifically bound protein. This work examines the Western blot signal chain, determines some of the parameters relevant for quantitative analysis and proposes a mathematical model of the reaction kinetics. Using this model, a semiquantitative Western blot method for simultaneous quantification of different proteins using a hyperbolic calibration curve was developed. A program was written for the purpose of hyperbolic regression that allows quick determination of the calibration curve coefficients. This program can be used also for approximation of calibration curves in other applications such as ELISA, BCA or Bradford assays.

Quantifying specific antibody concentrations by enzyme-linked immunosorbent assay using slope correction

Assessing the magnitude of an antibody response is important to many research and clinical endeavors; however, there are considerable differences in the experimental approaches used to achieve this end. Although the time-honored approach of end point titration has merit, the titer can often be misleading due to differences in how it is calculated or when samples contain high concentrations of low-avidity antibodies. One frequently employed alternative is to adapt commercially available enzyme-linked immunosorbent assay kits, designed to measure total antibody concentrations, to estimate antigen-specific antibody concentrations. This is accomplished by coating the specific antigen of interest in place of the capture antibody provided with the kit and then using the kit's standard curve to quantify the specific antibody concentration. This approach introduces considerable imprecision, due primarily to its reliance on a single sample dilution. This "single-point" approach fails to address differences in the slope of the sample titration curve compared to that of the standard curve. Here, we describe a general approach for estimating the effective concentration of specific antibodies, using antisera against foot-and-mouth disease virus VP1 peptide. This was accomplished by initially calculating the slope of the sample titration curve and then mathematically correcting the slope to that of a corresponding standard curve. A significantly higher degree of precision was attained using this approach rather than the single-point method.