Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Comparison of two automatic cell-counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell countinghavebeendeveloped sofar,butmostof them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the CELLCOUNTER and LEARN123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although CELLCOUNTER is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, LEARN123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. CELLCOUNTER also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R2 < 0.9), although CELLCOUNTER had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours tominutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, CELLCOUNTER overlay extension also enables fast analysis of related images thatwouldotherwise require imagemergingforaccurateanalysis, whereas LEARN123’s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings

Analysis of the Direct and Indirect Effects of Nanoparticle Exposure on Microglial and Neuronal Cells In Vitro

Environmental or biomedical exposure to nanoparticles (NPs) can results in translocation and accumulation of NPs in the brain, which can lead to health-related problems. NPs have been shown to induce toxicity to neuronal cells through several direct mechanisms, but only a few studies have also explored the indirect effects of NPs, through consequences due to the exposure of neighboring cells to NPs. In this study, we analysed possible direct and indirect effects of NPs (polyacrylic acid (PAA) coated cobalt ferrite NP, TiO2 P25 and maghemite NPs) on immortalized mouse microglial cells and differentiated CAD mouse neuronal cells in monoculture (direct toxicity) or in transwell co-culture system (indirect toxicity). We showed that although the low NP concentrations (2&ndash;25 &micro;g/mL) did not induce changes in cell viability, cytokine secretion or NF-&kappa;B activation of microglial cells, even low NP concentrations of 10 &micro;g/mL can affect the cells and change their secretion of protein stress mediators. These can in turn influence neuronal cells in indirect exposure model. Indirect toxicity of NPs is an important and not adequately assessed mechanism of NP toxicity, since it not only affects cells on the exposure sites, but through secretion of signaling mediators, can also affect cells that do not come in direct contact with NPs

Roles of Non-Canonical Wnt Signalling Pathways in Bone Biology

The Wnt signalling pathway is one of the central signalling pathways in bone development, homeostasis and regulation of bone mineral density. It consists of numerous Wnt ligands, receptors and co-receptors, which ensure tight spatiotemporal regulation of Wnt signalling pathway activity and thus tight regulation of bone tissue homeostasis. This enables maintenance of optimal mineral density, tissue healing and adaptation to changes in bone loading. While the role of the canonical/β-catenin Wnt signalling pathway in bone homeostasis is relatively well researched, Wnt ligands can also activate several non-canonical, β-catenin independent signalling pathways with important effects on bone tissue. In this review, we will provide a thorough overview of the current knowledge on different non-canonical Wnt signalling pathways involved in bone biology, focusing especially on the pathways that affect bone cell differentiation, maturation and function, processes involved in bone tissue structure regulation. We will describe the role of the two most known non-canonical pathways (Wnt/planar cell polarity pathways and Wnt/Ca2+ pathway), as well as other signalling pathways with a strong role in bone biology that communicate with the Wnt signalling pathway through non-canonical Wnt signalling. Our goal is to bring additional attention to these still not well researched but important pathways in the regulation of bone biology in the hope of prompting additional research in the area of non-canonical Wnt signalling pathways

Roles of non-canonical Wnt signalling pathways in bone biology

The Wnt signalling pathway is one of the central signalling pathways in bone development, homeostasis and regulation of bone mineral density. It consists of numerous Wnt ligands, receptors and co-receptors, which ensure tight spatiotemporal regulation of Wnt signalling pathway activity and thus tight regulation of bone tissue homeostasis. This enables maintenance of optimal mineral density, tissue healing and adaptation to changes in bone loading. While the role of the canonical/β-catenin Wnt signalling pathway in bone homeostasis is relatively well researched, Wnt ligands can also activate several non-canonical, β-catenin independent signalling pathways with important effects on bone tissue. In this review, we will provide a thorough overview of the current knowledge on different non-canonical Wnt signalling pathways involved in bone biology, focusing especially on the pathways that affect bone cell differentiation, maturation and function, processes involved in bone tissue structure regulation. We will describe the role of the two most known non-canonical pathways (Wnt/planar cell polarity pathways and Wnt/Ca$^{2+}$ pathway), as well as other signalling pathways with a strong role in bone biology that communicate with the Wnt signalling pathway through non-canonical Wnt signalling. Our goal is to bring additional attention to these still not well researched but important pathways in the regulation of bone biology in the hope of prompting additional research in the area of non-canonical Wnt signalling pathways

Comparison of two automatic cell-counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell countinghavebeendeveloped sofar,butmostof them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the CELLCOUNTER and LEARN123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although CELLCOUNTER is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, LEARN123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. CELLCOUNTER also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R2 < 0.9), although CELLCOUNTER had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours tominutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, CELLCOUNTER overlay extension also enables fast analysis of related images thatwouldotherwise require imagemergingforaccurateanalysis, whereas LEARN123’s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings