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

Rosania GR, Shedden K, Zheng N, Zhang X - J Cheminform (2013)

Bottom Line: To 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.In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies.MOVID 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmaceutical Sciences, University of Michigan College of Pharmacy, 428 Church Street, Ann Arbor, MI 48109, USA. grosania@umich.edu.

ABSTRACT

Background: To 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.

Results: To 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.

Conclusion: MOVID 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.

No MeSH data available.


Related in: MedlinePlus

Visualizing the relationship between the staining patterns of a reference probe (located at the upper left corner of the array; probe structure indicated by arrow) and the staining patterns of compounds with different chemical structures. Different aldehyde building blocks were plotted in rows (left) and pyridinium or quinolinium building blocks were plotted in columns (top). Numbers correspond to the Tanimoto coefficients between the different building blocks and the building blocks of the reference compound at the upper-left most corner of the array. As in Figure 6, individual cells representing the staining patterns observed in each image were manually cut from the images, and labeled based on their apparent organelle (o), membrane (m) or nuclear (n) staining patterns. Cells from images lacking significant signal or ambiguous in localization patterns were not labeled.
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Figure 7: Visualizing the relationship between the staining patterns of a reference probe (located at the upper left corner of the array; probe structure indicated by arrow) and the staining patterns of compounds with different chemical structures. Different aldehyde building blocks were plotted in rows (left) and pyridinium or quinolinium building blocks were plotted in columns (top). Numbers correspond to the Tanimoto coefficients between the different building blocks and the building blocks of the reference compound at the upper-left most corner of the array. As in Figure 6, individual cells representing the staining patterns observed in each image were manually cut from the images, and labeled based on their apparent organelle (o), membrane (m) or nuclear (n) staining patterns. Cells from images lacking significant signal or ambiguous in localization patterns were not labeled.

Mentions: A chemical fingerprinting approach was used to relate the structure of different pairs of probes based on shared similarities in the specific atoms and sub-fragments captured by the 2-dimensional connectivity of the chemical structure of the molecules. Intuitively, we expected that similar molecules would share similar staining patterns. Many styryl isomers in the library shared the same molecular formula and only varied in the ortho-, meta- and para- positions of attached functional groups. Therefore, we proceeded to visualize how staining patterns of styryl molecules were related to the staining patterns of specific reference compounds. For this purpose, two dimensional image arrays were constructed. With the reference probe in the upper-left corner of the array, aldehyde and pyridinium or quinolinium building blocks were sorted based on their Tanimoto coefficient with respect to the building blocks of the reference probe (Figures 6 and7). Images were scored based on the most clearly discernible staining patterns: mitochondrial or lysosomal (o; Figure 4A); cytoplasmic (or plasma) membrane (m; Figure 4D); or, nuclear or nucleolar (n; Figure 4G).


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

Rosania GR, Shedden K, Zheng N, Zhang X - J Cheminform (2013)

Visualizing the relationship between the staining patterns of a reference probe (located at the upper left corner of the array; probe structure indicated by arrow) and the staining patterns of compounds with different chemical structures. Different aldehyde building blocks were plotted in rows (left) and pyridinium or quinolinium building blocks were plotted in columns (top). Numbers correspond to the Tanimoto coefficients between the different building blocks and the building blocks of the reference compound at the upper-left most corner of the array. As in Figure 6, individual cells representing the staining patterns observed in each image were manually cut from the images, and labeled based on their apparent organelle (o), membrane (m) or nuclear (n) staining patterns. Cells from images lacking significant signal or ambiguous in localization patterns were not labeled.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852740&req=5

Figure 7: Visualizing the relationship between the staining patterns of a reference probe (located at the upper left corner of the array; probe structure indicated by arrow) and the staining patterns of compounds with different chemical structures. Different aldehyde building blocks were plotted in rows (left) and pyridinium or quinolinium building blocks were plotted in columns (top). Numbers correspond to the Tanimoto coefficients between the different building blocks and the building blocks of the reference compound at the upper-left most corner of the array. As in Figure 6, individual cells representing the staining patterns observed in each image were manually cut from the images, and labeled based on their apparent organelle (o), membrane (m) or nuclear (n) staining patterns. Cells from images lacking significant signal or ambiguous in localization patterns were not labeled.
Mentions: A chemical fingerprinting approach was used to relate the structure of different pairs of probes based on shared similarities in the specific atoms and sub-fragments captured by the 2-dimensional connectivity of the chemical structure of the molecules. Intuitively, we expected that similar molecules would share similar staining patterns. Many styryl isomers in the library shared the same molecular formula and only varied in the ortho-, meta- and para- positions of attached functional groups. Therefore, we proceeded to visualize how staining patterns of styryl molecules were related to the staining patterns of specific reference compounds. For this purpose, two dimensional image arrays were constructed. With the reference probe in the upper-left corner of the array, aldehyde and pyridinium or quinolinium building blocks were sorted based on their Tanimoto coefficient with respect to the building blocks of the reference probe (Figures 6 and7). Images were scored based on the most clearly discernible staining patterns: mitochondrial or lysosomal (o; Figure 4A); cytoplasmic (or plasma) membrane (m; Figure 4D); or, nuclear or nucleolar (n; Figure 4G).

Bottom Line: To 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.In this manner, MOVID facilitated visualization and interpretation of quantitative structure-localization relationship studies.MOVID 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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Pharmaceutical Sciences, University of Michigan College of Pharmacy, 428 Church Street, Ann Arbor, MI 48109, USA. grosania@umich.edu.

ABSTRACT

Background: To 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.

Results: To 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.

Conclusion: MOVID 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.

No MeSH data available.


Related in: MedlinePlus