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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

Histogram plots comparing the calculated physicochemical properties of a reference dataset of compounds with known subcellular localization features (top row) in relation to the library of 1,344 styryl molecules analyzed in this study (bottom row). A) Molecular weight; B) Radius of gyration; C) Logarithm of the octanol:water partition coefficients (logP); D) Fraction of rotatable bonds; E) Number of hydrogen bond acceptors.
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Figure 2: Histogram plots comparing the calculated physicochemical properties of a reference dataset of compounds with known subcellular localization features (top row) in relation to the library of 1,344 styryl molecules analyzed in this study (bottom row). A) Molecular weight; B) Radius of gyration; C) Logarithm of the octanol:water partition coefficients (logP); D) Fraction of rotatable bonds; E) Number of hydrogen bond acceptors.

Mentions: All styryl compounds in the particular library analyzed in this study shared the same molecular scaffold and therefore possessed many features in common (Figure 2). Compared to a reference set of molecules with known subcellular localization features[31,32], cheminformatics analysis revealed that the styryl compounds were less diverse: they possessed a narrower range of molecular weights (Figure 2A); radius of gyration (Figure 2B); logP (Figure 2C); fraction of rotatable bonds (Figure 2D) and number of hydrogen bond acceptors (Figure 2E). More specifically, styryl compounds were < 500 Daltons in molecular weight; their radius of gyration ranged from 4 to 6 Armstrongs; their logP was generally between 2 and 6; their fraction of rotatable bonds tended to be < 0.2; and their number of hydrogen bond acceptors generally was <2. Among the 1344 styryl molecules, 872 compounds had one fixed positive charge and lacked additional ionizable groups. The rest of the compounds had a fixed positive charge plus one or two additional ionizable groups. By covering a smaller fraction of chemical space with many molecules of similar size, shape and chemical features, this focused combinatorial library of compounds allowed us to explore how small variations in topological features and chemical structures influenced cellular staining patterns[27].


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)

Histogram plots comparing the calculated physicochemical properties of a reference dataset of compounds with known subcellular localization features (top row) in relation to the library of 1,344 styryl molecules analyzed in this study (bottom row). A) Molecular weight; B) Radius of gyration; C) Logarithm of the octanol:water partition coefficients (logP); D) Fraction of rotatable bonds; E) Number of hydrogen bond acceptors.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Histogram plots comparing the calculated physicochemical properties of a reference dataset of compounds with known subcellular localization features (top row) in relation to the library of 1,344 styryl molecules analyzed in this study (bottom row). A) Molecular weight; B) Radius of gyration; C) Logarithm of the octanol:water partition coefficients (logP); D) Fraction of rotatable bonds; E) Number of hydrogen bond acceptors.
Mentions: All styryl compounds in the particular library analyzed in this study shared the same molecular scaffold and therefore possessed many features in common (Figure 2). Compared to a reference set of molecules with known subcellular localization features[31,32], cheminformatics analysis revealed that the styryl compounds were less diverse: they possessed a narrower range of molecular weights (Figure 2A); radius of gyration (Figure 2B); logP (Figure 2C); fraction of rotatable bonds (Figure 2D) and number of hydrogen bond acceptors (Figure 2E). More specifically, styryl compounds were < 500 Daltons in molecular weight; their radius of gyration ranged from 4 to 6 Armstrongs; their logP was generally between 2 and 6; their fraction of rotatable bonds tended to be < 0.2; and their number of hydrogen bond acceptors generally was <2. Among the 1344 styryl molecules, 872 compounds had one fixed positive charge and lacked additional ionizable groups. The rest of the compounds had a fixed positive charge plus one or two additional ionizable groups. By covering a smaller fraction of chemical space with many molecules of similar size, shape and chemical features, this focused combinatorial library of compounds allowed us to explore how small variations in topological features and chemical structures influenced cellular staining patterns[27].

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