A spectral graph theoretic approach to quantification and calibration of collective morphological differences in cell images.

MOTIVATION: High-throughput image-based assay technologies can rapidly produce a large number of cell images for drug screening, but data analysis is still a major bottleneck that limits their utility. Quantifying a wide variety of morphological differences observed in cell images under different drug influences is still a challenging task because the result can be highly sensitive to sampling and noise. RESULTS: We propose a graph-based approach to cell image analysis. We define graph transition energy to quantify morphological differences between image sets. A spectral graph theoretic regularization is applied to transform the feature space based on training examples of extremely different images to calibrate the quantification. Calibration is essential for a practical quantification method because we need to measure the confidence of the quantification. We applied our method to quantify the degree of partial fragmentation of mitochondria in collections of fluorescent cell images. We show that with transformation, the quantification can be more accurate and sensitive than that without transformation. We also show that our method outperforms competing methods, including neighbourhood component analysis and the multi-variate drug profiling method by Loo et al. We illustrate its utility with a study of Annonaceous acetogenins, a family of compounds with drug potential. Our result reveals that squamocin induces more fragmented mitochondria than muricin A. AVAILABILITY: Mitochondrial cell images, their corresponding feature sets (SSLF and WSLF) and the source code of our proposed method are available at http://aiia.iis.sinica.edu.tw/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Morphological filter improve the efficiency of automated tracking of secretory vesicles with various dynamic properties.

Membrane trafficking is a very important physiological process involved in protein transport, endocytosis, and exocytosis. The functions of vesicles are strongly correlated with various spatial dynamic properties of vesicles, including their types of movements and morphology. Several methods are used to quantify such dynamic properties, but most of them are specific to particular populations of vesicles. We previously developed the so-called PTrack system for quantifying the dynamics of secretory vesicles near the cell surface, which are small and move slowly. To improve the system performance in quantifying large and fast-moving vesicles, we firstly combined morphological filter with two-threshold image processing techniques to locate granules of various sizes. Next, Kalman filtering was used to improve the performance in tracking fast-moving and large granules. Performance evaluation by using simulation image sequences shown that the new system, called PTrack II, yields better tracking accuracy. The tracking system was validated using time-lapse images of insulin granules in betaTC3 cells, which revealed that PTrack II could track better than PTrack, averaged accuracy up to 56%. The overall tracking results indicate that PTrack II is better at tracking vesicles with various dynamic properties, which will facilitate the acquisition of more-complete information on vesicle dynamics.

An automated tracking system to measure the dynamic properties of vesicles in living cells.

Recent technological improvements have made it possible to examine the dynamics of individual vesicles at a very high temporal and spatial resolution. Quantification of the dynamic properties of secretory vesicles is labor-intensive and therefore it is crucial to develop software to automate the process of analyzing vesicle dynamics. Dual-threshold and binary image conversion were applied to enhance images and define the areas of objects of interest that were to be tracked. The movements, changes in fluorescence intensity, and changes in the area of each tracked object were measured using a new software system named the Protein Tracking system (PTrack). Simulations revealed that the system accurately recognized tracked objects and measured their dynamic properties. Comparison of the results from tracking real time-lapsed images manually with those automatically obtained using PTrack revealed similar patterns for changes in fluorescence intensity and a high accuracy (<89%). According to tracking results, PTrack can distinguish different vesicular organelles that are similar in shape, based on their unique dynamic properties. In conclusion, the novel tracking system, PTrack, should facilitate automated quantification of the dynamic properties of vesicles that are important when classifying vesicular protein locations.