Bright Field Microscopy as an Alternative to Whole Cell Fluorescence in Automated Analysis of Macrophage Images

Fluorescence microscopy is the standard tool for detection and analysis of cellular phenomena. This technique, however, has a number of drawbacks such as the limited number of available fluorescent channels in microscopes, overlapping excitation and emission spectra of the stains, and phototoxicity.We here present and validate a method to automatically detect cell population outlines directly from bright field images. By imaging samples with several focus levels forming a bright field -stack, and by measuring the intensity variations of this stack over the -dimension, we construct a new two dimensional projection image of increased contrast. With additional information for locations of each cell, such as stained nuclei, this bright field projection image can be used instead of whole cell fluorescence to locate borders of individual cells, separating touching cells, and enabling single cell analysis. Using the popular CellProfiler freeware cell image analysis software mainly targeted for fluorescence microscopy, we validate our method by automatically segmenting low contrast and rather complex shaped murine macrophage cells.The proposed approach frees up a fluorescence channel, which can be used for subcellular studies. It also facilitates cell shape measurement in experiments where whole cell fluorescent staining is either not available, or is dependent on a particular experimental condition. We show that whole cell area detection results using our projected bright field images match closely to the standard approach where cell areas are localized using fluorescence, and conclude that the high contrast bright field projection image can directly replace one fluorescent channel in whole cell quantification. Matlab code for calculating the projections can be downloaded from the supplementary site: http://sites.google.com/site/brightfieldorstaining

Evaluation of methods for detection of fluorescence labeled subcellular objects in microscope images

<p>Abstract</p> <p>Background</p> <p>Several algorithms have been proposed for detecting fluorescently labeled subcellular objects in microscope images. Many of these algorithms have been designed for specific tasks and validated with limited image data. But despite the potential of using extensive comparisons between algorithms to provide useful information to guide method selection and thus more accurate results, relatively few studies have been performed.</p> <p>Results</p> <p>To better understand algorithm performance under different conditions, we have carried out a comparative study including eleven spot detection or segmentation algorithms from various application fields. We used microscope images from well plate experiments with a human osteosarcoma cell line and frames from image stacks of yeast cells in different focal planes. These experimentally derived images permit a comparison of method performance in realistic situations where the number of objects varies within image set. We also used simulated microscope images in order to compare the methods and validate them against a ground truth reference result. Our study finds major differences in the performance of different algorithms, in terms of both object counts and segmentation accuracies.</p> <p>Conclusions</p> <p>These results suggest that the selection of detection algorithms for image based screens should be done carefully and take into account different conditions, such as the possibility of acquiring empty images or images with very few spots. Our inclusion of methods that have not been used before in this context broadens the set of available detection methods and compares them against the current state-of-the-art methods for subcellular particle detection.</p

On Algorithms for Two and Three Dimensional High Throughput Light Microscopy

In biomedical research, it is often necessary to study cell population characteristics, and quantify changes in cell phenotypes on a cell-by-cell basis. Traditionally, this work has been performed by interactive manual use of a microscope. In disciplines like systems biology, studying topologies and dynamics of complex functional networks of cells, massive systematical screens for phenotypic changes in cell populations are required. Also in drug discovery, effects of pharmacological agents on the populations must be tested automatically in a high throughput fashion. The development of robotic arrayers and automated microscopes, together with increasing computing power and storage space have enabled the automated screening of cell populations, resulting in a revolution of microscopy imaging. Currently, imaging of hundreds of populations in parallel is common practice in a single experiment. During the screen, images of each of the cell populations are stored for subsequent analysis. The amount of image data renders manual visual analysis impossible, requiring automated image analysis systems, and software. Current procedures of automated analysis in high throughput microscopy, however, have several drawbacks. Standard practices exist for a number of analysis approaches, but especially three dimensional studies are generally performed manually, or semi-automatically. Furthermore, greater care must be taken on practical issues, such as low computational cost and easy implementation to advance routine high throughput screening studies by bioscientist. This thesis considers fully automated methods ranging from cell enumeration, to subcellular analysis in two and three dimensions, concentrating on the applicability of the algorithms for high throughput microscopy

On Algorithms for Two and Three Dimensional High Throughput Light Microscopy

In biomedical research, it is often necessary to study cell population characteristics, and quantify changes in cell phenotypes on a cell-by-cell basis. Traditionally, this work has been performed by interactive manual use of a microscope. In disciplines like systems biology, studying topologies and dynamics of complex functional networks of cells, massive systematical screens for phenotypic changes in cell populations are required. Also in drug discovery, effects of pharmacological agents on the populations must be tested automatically in a high throughput fashion. The development of robotic arrayers and automated microscopes, together with increasing computing power and storage space have enabled the automated screening of cell populations, resulting in a revolution of microscopy imaging. Currently, imaging of hundreds of populations in parallel is common practice in a single experiment. During the screen, images of each of the cell populations are stored for subsequent analysis. The amount of image data renders manual visual analysis impossible, requiring automated image analysis systems, and software. Current procedures of automated analysis in high throughput microscopy, however, have several drawbacks. Standard practices exist for a number of analysis approaches, but especially three dimensional studies are generally performed manually, or semi-automatically. Furthermore, greater care must be taken on practical issues, such as low computational cost and easy implementation to advance routine high throughput screening studies by bioscientist. This thesis considers fully automated methods ranging from cell enumeration, to subcellular analysis in two and three dimensions, concentrating on the applicability of the algorithms for high throughput microscopy

BENCHMARK SET OF SYNTHETIC IMAGES FOR VALIDATING CELL IMAGE ANALYSIS ALGORITHMS

This article presents a synthetic image set for validation of cell image analysis algorithms. To address the problem of validation, we have previously developed a simulation framework for cell population images. Here, we apply the simulation for generating a benchmark set of cell images with varying characteristics. The value of simulation is in the ground truth information known for the generated images. Traditionally, the ground-truth has been obtained through tedious and error-prone manual segmentation of the images. While such approach cannot be fully replaced, we propose to use the simulated images for benchmarking along with manually labeled images, and present case studies of tuning and testing a cell image analysis algorithm based on simulated images. 1

Pixel-by-pixel comparison of whole cell segmentation using bright field projections against fluorescence ground truth.

<p>(A) Median F-scores over all cells for each image group, with all the projection methods. (B) Median F-scores for cell segmentation using standard deviation projection images, each projected from three randomly selected slices.</p