Simulated Biological Cells for Receptor Counting in Fluorescence Imaging

upU-Net Approaches for Background Emission Removal in Fluorescence Microscopy

The physical process underlying microscopy imaging suffers from several issues: some of them include the blurring effect due to the Point Spread Function, the presence of Gaussian or Poisson noise, or even a mixture of these two types of perturbation. Among them, auto-fluorescence presents other artifacts in the registered image, and such fluorescence may be an important obstacle in correctly recognizing objects and organisms in the image. For example, particle tracking may suffer from the presence of this kind of perturbation. The objective of this work is to employ Deep Learning techniques, in the form of U-Nets like architectures, for background emission removal. Such fluorescence is modeled by Perlin noise, which reveals to be a suitable candidate for simulating such a phenomenon. The proposed architecture succeeds in removing the fluorescence, and at the same time, it acts as a denoiser for both Gaussian and Poisson noise. The performance of this approach is furthermore assessed on actual microscopy images and by employing the restored images for particle recognition.

FiloGen: A Model-Based Generator of Synthetic 3-D Time-Lapse Sequences of Single Motile Cells With Growing and Branching Filopodia

The existence of diverse image datasets accompanied by reference annotations is a crucial prerequisite for an objective benchmarking of bioimage analysis methods. Nevertheless, such a prerequisite is hard to satisfy for time lapse, multidimensional fluorescence microscopy image data, manual annotations of which are laborious and often impracticable. In this paper, we present a simulation system capable of generating 3-D time-lapse sequences of single motile cells with filopodial protrusions of user-controlled structural and temporal attributes, such as the number, thickness, length, level of branching, and lifetime of filopodia, accompanied by inherently generated reference annotations. The proposed simulation system involves three globally synchronized modules, each being responsible for a separate task: the evolution of filopodia on a molecular level, linear elastic deformation of the entire cell with filopodia, and the synthesis of realistic, time-coherent cell texture. Its flexibility is demonstrated by generating multiple synthetic 3-D time-lapse sequences of single lung cancer cells of two different phenotypes, qualitatively and quantitatively resembling their real counterparts acquired using a confocal fluorescence microscope.

Using simulated fluorescence cell micrographs for the evaluation of cell image segmentation algorithms

Background

Manual assessment and evaluation of fluorescent micrograph cell experiments is time-consuming and tedious. Automated segmentation pipelines can ensure efficient and reproducible evaluation and analysis with constant high quality for all images of an experiment. Such cell segmentation approaches are usually validated and rated in comparison to manually annotated micrographs. Nevertheless, manual annotations are prone to errors and display inter- and intra-observer variability which influence the validation results of automated cell segmentation pipelines.

Results

We present a new approach to simulate fluorescent cell micrographs that provides an objective ground truth for the validation of cell segmentation methods. The cell simulation was evaluated twofold: (1) An expert observer study shows that the proposed approach generates realistic fluorescent cell micrograph simulations. (2) An automated segmentation pipeline on the simulated fluorescent cell micrographs reproduces segmentation performances of that pipeline on real fluorescent cell micrographs.

Conclusion

The proposed simulation approach produces realistic fluorescent cell micrographs with corresponding ground truth. The simulated data is suited to evaluate image segmentation pipelines more efficiently and reproducibly than it is possible on manually annotated real micrographs.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-017-1591-2) contains supplementary material, which is available to authorized users.

Quantitative Co-Localization and Pattern Analysis of Endo-Lysosomal Cargo in Subcellular Image Cytometry and Validation on Synthetic Image Sets

Late endosomes and lysosomes (LE/LYSs) play a central role in trafficking of endocytic cargo, secretion of exosomes, and hydrolysis of ingested proteins and lipids. Failure in such processes can lead to lysosomal storage disorders in which a particular metabolite accumulates within LE/LYSs. Analysis of endocytic trafficking relies heavily on quantitative fluorescence microscopy, but evaluation of the huge image data sets is challenging and demands computer-assisted statistical tools. Here, we describe how to use SpatTrack ( www.sdu.dk/bmb/spattrack ), an imaging toolbox, which we developed for quantification of the distribution and dynamics of endo-lysosomal cargo from fluorescence images of living cells. First, we explain how to analyze experimental images of endocytic processes in Niemann Pick C2 disease fibroblasts using SpatTrack. We demonstrate how to quantify the location of the sterol-binding protein NPC2 in LE/LYSs relative to cholesterol -rich lysosomal storage organelles (LSOs) stained with filipin. Second, we show how to simulate realistic vesicle patterns in the cell geometry using Markov Chain Monte Carlo and suitable inter-vesicle and cell-vesicle interaction potentials. Finally, we use such synthetic vesicle patterns as "ground truth" for validation of two-channel analysis tools in SpatTrack, revealing their high reliability. An improved version of SpatTrack for microscopy-based quantification of cargo transport through the endo-lysosomal system accompanies this protocol.

MitoGen: A Framework for Generating 3D Synthetic Time-Lapse Sequences of Cell Populations in Fluorescence Microscopy

The proper analysis of biological microscopy images is an important and complex task. Therefore, it requires verification of all steps involved in the process, including image segmentation and tracking algorithms. It is generally better to verify algorithms with computer-generated ground truth datasets, which, compared to manually annotated data, nowadays have reached high quality and can be produced in large quantities even for 3D time-lapse image sequences. Here, we propose a novel framework, called MitoGen, which is capable of generating ground truth datasets with fully 3D time-lapse sequences of synthetic fluorescence-stained cell populations. MitoGen shows biologically justified cell motility, shape and texture changes as well as cell divisions. Standard fluorescence microscopy phenomena such as photobleaching, blur with real point spread function (PSF), and several types of noise, are simulated to obtain realistic images. The MitoGen framework is scalable in both space and time. MitoGen generates visually plausible data that shows good agreement with real data in terms of image descriptors and mean square displacement (MSD) trajectory analysis. Additionally, it is also shown in this paper that four publicly available segmentation and tracking algorithms exhibit similar performance on both real and MitoGen-generated data. The implementation of MitoGen is freely available.

Virtual cell imaging: A review on simulation methods employed in image cytometry

The simulations of cells and microscope images thereof have been used to facilitate the development, selection, and validation of image analysis algorithms employed in cytometry as well as for modeling and understanding cell structure and dynamics beyond what is visible in the eyepiece. The simulation approaches vary from simple parametric models of specific cell components-especially shapes of cells and cell nuclei-to learning-based synthesis and multi-stage simulation models for complex scenes that simultaneously visualize multiple object types and incorporate various properties of the imaged objects and laws of image formation. This review covers advances in artificial digital cell generation at scales ranging from particles up to tissue synthesis and microscope image simulation methods, provides examples of the use of simulated images for various purposes ranging from subcellular object detection to cell tracking, and discusses how such simulators have been validated. Finally, the future possibilities and limitations of simulation-based validation are considered. © 2016 International Society for Advancement of Cytometry.

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.

NutriChip: nutrition analysis meets microfluidics

This focus article introduces the concept of NutriChip, an integrated microfluidic platform for investigating the potential of the immuno-modulatory function of dairy food. The core component of the NutriChip is a miniaturized artificial human gastrointestinal tract (GIT), which consists of a confluent layer of epithelial cells separated from a co-culture of immune cells by a permeable membrane. This setting creates conditions mimicking the human GIT and allows studying processes that characterize the passage of nutrients though the human GIT, including the activation of immune cells in response to the transfer of nutrients across the epithelial layer. The NutriChip project started by developing a biologically active in vitro cellular system in a commercial Transwell co-culture system. This Transwell system serves as a reference for the micro-scale device which is being developed. The microfluidic setup of NutriChip allows monitoring of the response of immune cells to pro-inflammatory stimuli, such as lipid polysaccharide (LPS), and to the application of potentially anti-inflammatory dairy food. This differential response will be quantified by measuring the variation in expression of pro-inflammatory cytokines, including interleukin 1 (IL-1) and interleukin 6 (IL-6), secreted by the immune cells, and this is achieved by using a dedicated optical imager. A series of dairy products will be screened for their anti-inflammatory properties using the NutriChip system and, finally, the outcome of the NutriChip will be validated by a human nutrition trial. Therefore, the NutriChip platform offers a new option to evaluate the influence of food quality on health, by monitoring the expression of relevant immune cell biomarkers.

The NutriChip project – translating technology into nutritional knowledge

Advances in food transformation have dramatically increased the diversity of products on the market and, consequently, exposed consumers to a complex spectrum of bioactive nutrients whose potential risks and benefits have mostly not been confidently demonstrated. Therefore, tools are needed to efficiently screen products for selected physiological properties before they enter the market. NutriChip is an interdisciplinary modular project funded by the Swiss programme Nano-Tera, which groups scientists from several areas of research with the aim of developing analytical strategies that will enable functional screening of foods. The project focuses on postprandial inflammatory stress, which potentially contributes to the development of chronic inflammatory diseases. The first module of the NutriChip project is composed of three in vitro biochemical steps that mimic the digestion process, intestinal absorption, and subsequent modulation of immune cells by the bioavailable nutrients. The second module is a miniaturised form of the first module (gut-on-a-chip) that integrates a microfluidic-based cell co-culture system and super-resolution imaging technologies to provide a physiologically relevant fluid flow environment and allows sensitive real-time analysis of the products screened in vitro. The third module aims at validating the in vitro screening model by assessing the nutritional properties of selected food products in humans. Because of the immunomodulatory properties of milk as well as its amenability to technological transformation, dairy products have been selected as model foods. The NutriChip project reflects the opening of food and nutrition sciences to state-of-the-art technologies, a key step in the translation of transdisciplinary knowledge into nutritional advice.