Cytomics and location proteomics: Automated interpretation of subcellular patterns in fluorescence microscope images

Murphy, Robert F.

doi:10.1002/cyto.a.20179

Cited by 24 publications

(18 citation statements)

References 15 publications

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“…Currently, subcellular location patterns are routinely determined by visual inspection of fluorescence microscope images. In the future, however, automated interpretation of subcellular patterns in fluorescence microscope images for location proteomics (Murphy 2005) and toponomics (Schubert 2003) will become available for mapping and for deciphering functional molecular networks of proteins directly in a cell or a tissue. Proteomics, the large-scale identification and characterization of one or more signalling cascades expressed in a given cell type, has become a major area of cancer research.…”

Section: Concepts In Systemic Tumour Cell Analysismentioning

confidence: 99%

“…Typical investigations use hypothesis‐driven parameter selection in combination with hypothesis‐free exhaustive knowledge extraction, that is, cytomics (Kitano 2002; Hood et al . 2004; Kriete 2005; Murphy 2005; Valet 2005b). Although a given hypothesis can be proved or disproved by a given experiment, the evaluation of all cell data in a hypothesis‐free fashion (discovery science) enables the exploration of unknown multiparametric data spaces.…”

Section: Introductionmentioning

confidence: 99%

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Cytomics – importance of multimodal analysis of cell function and proliferation in oncology

2006

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Cancer is a highly complex and heterogeneous disease involving a succession of genetic changes (frequently caused or accompanied by exogenous trauma), and resulting in a molecular phenotype that in turn results in a malignant specification. The development of malignancy has been described as a multistep process involving self-sufficiency in growth signals, insensitivity to antigrowth signals, evasion of apoptosis, limitless replicative potential, sustained angiogenesis, and finally tissue invasion and metastasis. The quantitative analysis of networking molecules within the cells might be applied to understand native-state tissue signalling biology, complex drug actions and dysfunctional signalling in transformed cells, that is, in cancer cells. High-content and high-throughput single-cell analysis can lead to systems biology and cytomics. The application of cytomics in cancer research and diagnostics is very broad, ranging from the better understanding of the tumour cell biology to the identification of residual tumour cells after treatment, to drug discovery. The ultimate goal is to pinpoint in detail these processes on the molecular, cellular and tissue level. A comprehensive knowledge of these will require tissue analysis, which is multiplex and functional; thus, vast amounts of data are being collected from current genomic and proteomic platforms for integration and interpretation as well as for new varieties of updated cytomics technology. This overview will briefly highlight the most important aspects of this continuously developing field.

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Section: Concepts In Systemic Tumour Cell Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cytomics – importance of multimodal analysis of cell function and proliferation in oncology

2006

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“…6,7 To the best of our knowledge, the only successful use of these moments for classification of biological images is in the area of location proteomics. [8][9][10] The advantages of the quantitative approach in this research are reproducibility, objectivity, robustness, and reliability of measurements and analysis. The identification of bacterial scatterograms was performed via shape analysis implemented using digital image-processing techniques.…”

Section: Introductionmentioning

confidence: 99%

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

et al. 2006

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Bacterial contamination by Listeria monocytogenes not only puts the public at risk, but also is costly for the food-processing industry. Traditional biochemical methods for pathogen identification require complicated sample preparation for reliable results. Optical scattering technology has been used for identification of bacterial cells in suspension, but with only limited success. Therefore, to improve the efficacy of the identification process using our novel imaging approach, we analyze bacterial colonies grown on solid surfaces. The work presented here demonstrates an application of computer-vision and pattern-recognition techniques to classify scatter patterns formed by Listeria colonies. Bacterial colonies are analyzed with a laser scatterometer. Features of circular scatter patterns formed by bacterial colonies illuminated by laser light are characterized using Zernike moment invariants. Principal component analysis and hierarchical clustering are performed on the results of feature extraction. Classification using linear discriminant analysis, partial least squares, and neural networks is capable of separating different strains of Listeria with a low error rate. The demonstrated system is also able to determine automatically the pathogenicity of bacteria on the basis of colony scatter patterns. We conclude that the obtained results are encouraging, and strongly suggest the feasibility of image-based biodetection systems.

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“…Integrating advances in the cognate fields of cheminformatics and bioimage informatics [15,21-23], our research group has been developing computational tools to address the data mining challenges inherent to complex phenotypic screening experiments, such as bioimaging probe discovery and development efforts [6,24-27]. In 2003, we began cell-based screening experiments involving a combinatorial libraries of cell permeant, organelle-targeted fluorescent compounds [25].…”

Section: Introductionmentioning

confidence: 99%

Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport

et al. 2013

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BackgroundTo study the chemical determinants of small molecule transport inside cells, it is crucial to visualize relationships between the chemical structure of small molecules and their associated subcellular distribution patterns. For this purpose, we experimented with cells incubated with a synthetic combinatorial library of fluorescent, membrane-permeant small molecule chemical agents. With an automated high content screening instrument, the intracellular distribution patterns of these chemical agents were microscopically captured in image data sets, and analyzed off-line with machine vision and cheminformatics algorithms. Nevertheless, it remained challenging to interpret correlations linking the structure and properties of chemical agents to their subcellular localization patterns in large numbers of cells, captured across large number of images.ResultsTo address this challenge, we constructed a Multidimensional Online Virtual Image Display (MOVID) visualization platform using off-the-shelf hardware and software components. For analysis, the image data set acquired from cells incubated with a combinatorial library of fluorescent molecular probes was sorted based on quantitative relationships between the chemical structures, physicochemical properties or predicted subcellular distribution patterns. MOVID enabled visual inspection of the sorted, multidimensional image arrays: Using a multipanel desktop liquid crystal display (LCD) and an avatar as a graphical user interface, the resolution of the images was automatically adjusted to the avatar’s distance, allowing the viewer to rapidly navigate through high resolution image arrays, zooming in and out of the images to inspect and annotate individual cells exhibiting interesting staining patterns. In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies. MOVID also facilitated direct, intuitive exploration of the relationship between the chemical structures of the probes and their microscopic, subcellular staining patterns.ConclusionMOVID can provide a practical, graphical user interface and computer-assisted image data visualization platform to facilitate bioimage data mining and cheminformatics analysis of high content, phenotypic screening experiments.

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Cytomics and location proteomics: Automated interpretation of subcellular patterns in fluorescence microscope images

Cited by 24 publications

References 15 publications

Cytomics – importance of multimodal analysis of cell function and proliferation in oncology

Cytomics – importance of multimodal analysis of cell function and proliferation in oncology

Feature extraction from light-scatter patterns of Listeria colonies for identification and classification

Visualizing chemical structure-subcellular localization relationships using fluorescent small molecules as probes of cellular transport

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