Failure of microtubule-mediated peroxisome division and trafficking in disorders with reduced peroxisome abundance

In contrast to peroxisomes in normal cells, remnant peroxisomes in cultured skin fibroblasts from a subset of the clinically severe peroxisomal disorders that includes the biogenesis disorder Zellweger syndrome and the single-enzyme defect D-bifunctional protein (D-BP) deficiency, are enlarged and significantly less abundant. We tested whether these features could be related to the known role of microtubules in peroxisome trafficking in mammalian cells. We found that remnant peroxisomes in fibroblasts from patients with PEX1-null Zellweger syndrome or D-BP deficiency exhibited clustering and loss of alignment along peripheral microtubules. Similar effects were observed for both cultured embryonic fibroblasts and brain neurons from a PEX13-null mouse with a Zellweger-syndrome-like phenotype, and a less-pronounced effect was observed for fibroblasts from an infantile Refsum patient who was homozygous for a milder PEX1 mutation. By contrast, such changes were not seen for patients with peroxisomal disorders characterized by normal peroxisome abundance and size. Stable overexpression of PEX11ß to induce peroxisome proliferation largely re-established the alignment of peroxisomal structures along peripheral microtubules in both PEX1-null and D-BP-deficient cells. In D-BP-deficient cells, peroxisome division was apparently driven to completion, as induced peroxisomal structures were similar to the spherical parental structures. By contrast, in PEX1-null cells the majority of induced peroxisomal structures were elongated and tubular. These structures were apparently blocked at the division step, despite having recruited DLP1, a protein necessary for peroxisome fission. These findings indicate that the increased size, reduced abundance, and disturbed cytoplasmic distribution of peroxisomal structures in PEX1-null and D-BP-deficient cells reflect defects at different stages in peroxisome proliferation and division, processes that require association of these structures with, and dispersal along, microtubules.Tam Nguyen, Jonas Bjorkman, Barbara C. Paton and Denis I. Cran

Image thresholding techniques for localization of sub-resolution fluorescent biomarkers

In this article, we explore adaptive global and local segmentation techniques for a lab-on-chip nutrition monitoring system (NutriChip). The experimental setup consists of Caco-2 intestinal cells that can be artificially stimulated to trigger an immune response. The eventual response is optically monitored using immunofluoresence techniques targeting toll-like receptor 2 (TLR2). Two problems of interest need to be addressed by means of image processing. First, a new cell sample must be properly classified as stimulated or not. Second, the location of the stained TLR2 must be recovered in case the sample has been stimulated. The algorithmic approach to solving these problems is based on the ability of a segmentation technique to properly segment fluorescent spots. The sample classification is based on the amount and intensity of the segmented pixels, while the various segmenting blobs provide an approximate localization of TLR2. A novel local thresholding algorithm and three well-known spot segmentation techniques are compared in this study. Quantitative assessment of these techniques based on real and synthesized data demonstrates the improved segmentation capabilities of the proposed algorithm

Towards increased efficiency and automation in fluorescence micrograph analysis based on hand-labeled data

Held CH. Towards increased efficiency and automation in fluorescence micrograph analysis based on hand-labeled data. Bielefeld: Universität Bielefeld; 2013.In the past decade, automation in fluorescence microscopy has strongly increased, particularly in regards to image acquisition and sample preparation, which results in a huge volume of data. The amount of time required for manual assessment of an experiment is hence mainly determined by the amount of time required for data analysis. In addition, manual data analysis is often a task with poor reproducibility and lack of objectivity. Using automated image analysis software, the time required for data analysis can be reduced while quality and reproducibility of the evaluation are improved. Most image analysis approaches are based on a segmentation of the image. By arranging several image processing methods in a so-called segmentation pipeline, and by adjusting all parameters, a broad range of fluorescence image data can be segmented. The drawback of available software tools is the long time required to calibrate the segmentation pipeline for an experiment, particularly for researchers with little knowledge of image processing. As a result, many experiments that could benefit from automated image analysis are still evaluated manually. In order to reduce the amount of time users have to spend in adapting automated image analysis software to their data, research was carried out on a novel image analysis concept based on hand-labeled data. Using this concept, the user is required to provide hand-labeled cells, based on which an efficient combination of image processing methods and their parameterization is automatically calibrated, without further user input. The development of a segmentation pipeline that allows high-quality segmentation of a broad range of fluorescence micrographs in short time poses a challenge. In this work, a three-stage segmentation pipeline consisting of exchangeable preprocessing, figure-ground separation and cell-splitting methods was developed. These methods are mainly based on the state of the art, whereas some of them represent contributions to this status. Discretization of parameters must be performed carefully, as a broad range of fluorescence image data shall be supported. In order to allow calibration of the segmentation pipeline in a short time, discretization with equidistant as well as nonlinear step sizes was implemented. Apart from parameter discretization, quality of the calibration strongly depends on choice of the parameter optimization technique. In order to reduce calibration runtime, exploratory parameter space analysis was performed for different segmentation methods. This experiment showed that parameter spaces are mostly monotonous, but also show several local performance maxima. The comparison of different parameter optimization techniques indicated that the coordinate descent method results in a good parameterization of the segmentation pipeline in a small amount of time. In order to minimize the amount of time spent by the user in calibration of the system, correlation between the number of hand-labeled reference samples and the resulting segmentation performance was investigated. This experiment demonstrates that as few as ten reference samples often result in a good parameterization of the segmentation pipeline. Due to the low number of cells required for automatic calibration of the segmentation pipeline, as well as its short runtime, it can be concluded that the investigated method improves automation and efficiency in fluorescence micrograph analysis

Extraction of fluorescent cell puncta by adaptive fuzzy segmentation

Motivation: The discrimination and measurement of fluorescent-labeled vesicles using microscopic analysis of fixed cells presents a challenge for biologists interested in quantifying the abundance, size and distribution of such ves-icles in normal and abnormal cellular situations. In the specific application reported here, we were interested in quantifying changes to the population of a major organelle, the peroxi-some, in cells from normal control patients and from patients with a defect in peroxisome biogenesis. In the latter, peroxi-somes are present as larger vesicular structures with a more restricted cytoplasmic distribution. Existing image processing methods for extracting fluorescent cell puncta do not provide useful results and therefore, there is a need to develop some new approaches for dealing with such a task effectively. Results: We present an effective implementation of the fuzzy c-means algorithm for extracting puncta (spots), represent-ing fluorescent-labeled peroxisomes, which are subject to low contrast.We make use of the quadtree partition to enhance the fuzzy c-means based segmentation and to disregard regions which contain no target objects (peroxisomes) in order to min-imize considerable time taken by the iterative process of the fuzzy c-means algorithm. We finally isolate touching peroxi-somes by an aspect-ratio criterion. The proposed approach has been applied to extract peroxisomes contained in sev-eral sets of color images and the results are superior to those obtained from a number of standard techniques for spot extraction. Availability: Image data and computer codes written in Matlab are available upon request from the first author. Contact

Image processing on reconfigurable hardware for continuous monitoring of fluorescent biomarkers in cell cultures

Fluorescence microscopy is a widespread tool in biological research. It is the primary modality for bioimaging and empowers the study and analysis of multitudes of biological processes. It can be applied to fixed biosamples, that is samples with frozen biological features by mean of chemical linkers, or live biosamples providing useful insights on the spatio-temporal behavior of fluorescently stained biomarkers. Current fluorescent microscopy techniques use digital image sensors which are used to leverage quantitative studies instead qualitative outcomes. However, state-of-the-art techniques are not suitable for integration in small, contained and (semi-)autonomous systems. They remain costly, bulky and rather quantitatively inefficient methods for monitoring fluorescent biomarkers, which is not on par with the design constraints found in modern Lab-on-a-Chip or Point-of-Use systems requiring the use of miniaturized and integrated fluroscence microscopy. In this thesis, I summarize my research and engineering efforts in bringing an embedded image processing system capable of monitoring fluorescent biomarkers in cell cultures in a continuous and real-time manner. Three main areas related to the problem at hand were explored in the course of this work: simulation, segmentation algorithms and embedded image processing. n the area of simulation, a novel approach for generating synthetic fluorescent 2D images of cell cultures is presented. This approach is dichotomized in a first part focusing on the modeling and generation of synthetic populations of cells (i.e. cell cultures) at the level of single fluorescent biomarkers and in a second part simulating the imaging process occurring in a traditional digital fluorescent microscope to produce realistic images of the synthetic cell cultures. The objective of the proposed approach aims at providing synthetic data at will in order to test and validate image processing systems and algorithms. Various image segmentation algorithms are considered and compared for the purpose of segmenting fluorescent spots in microscopic images. The study presented in this thesis includes a novel image thresholding technique for spot extraction along with three well-known spot segmentation techniques. The comparison is undertaken on two aspects. The segmentation masks provided by the methods are used to extract further metrics related to the fluorescent signals in order to (i) evaluate how well the segmentation masks can provide data for classifying real fluorescent biological samples from negative control samples and (ii) quantitatively compare the segmentations masks based on simulated data from the previously stated simulation tool. Finally, the design of an embedded image processing system based on FPGA technologies is showcased. A semi-autonomous smart camera is conceived for the continuous monitoring of fluorescent biomarkers based on one of the segmentation methods incorporated in the previously stated comparison. Keeping the focus on the need for integration in fluorescence microscopy, the image processing core at the heart of the smart camera results from the use of a novel image processing suite; a suite of IP cores developed under the constraints dictated by the bioimaging needs of fluorescence microscopy for use in FPGA and SoC technologies. As a proof of concept, the smart camera is applied to the monitoring of the kinetics of the uptake of fluorescent silica nano-particles in cell cultures