Machine Vision-assisted Analysis of Structure-localization Relationships in a Combinatorial Library of Prospective Bioimaging Probes

Overview

Journal Cytometry A

Specialties Cell Biology
Radiology

Date 2009 Feb 27

PMID 19243023

Citations 7

Authors

Kerby Shedden

Qian Li

Fangyi Liu

Young Tae Chang

Gus R Rosania

Affiliations

Soon will be listed here.

Abstract

With a combinatorial library of bioimaging probes, it is now possible to use machine vision to analyze the contribution of different building blocks of the molecules to their cell-associated visual signals. For this purpose, cell-permeant, fluorescent styryl molecules were synthesized by condensation of 168 aldehyde with 8 pyridinium/quinolinium building blocks. Images of cells incubated with fluorescent molecules were acquired with a high content screening instrument. Chemical and image feature analysis revealed how variation in one or the other building block of the styryl molecules led to variations in the molecules' visual signals. Across each pair of probes in the library, chemical similarity was significantly associated with spectral and total signal intensity similarity. However, chemical similarity was much less associated with similarity in subcellular probe fluorescence patterns. Quantitative analysis and visual inspection of pairs of images acquired from pairs of styryl isomers confirm that many closely-related probes exhibit different subcellular localization patterns. Therefore, idiosyncratic interactions between styryl molecules and specific cellular components greatly contribute to the subcellular distribution of the styryl probes' fluorescence signal. These results demonstrate how machine vision and cheminformatics can be combined to analyze the targeting properties of bioimaging probes, using large image data sets acquired with automated screening systems.

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References

Svetnik V, Liaw A, Tong C, Culberson J, Sheridan R, Feuston B . Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci. 2003; 43(6):1947-58. DOI: 10.1021/ci034160g. View

Horobin R, Flemming L . Structure-staining relationships in histochemistry and biological staining. II. Mechanistic and practical aspects of the staining of elastic fibres. J Microsc. 1980; 119(3):357-72. DOI: 10.1111/j.1365-2818.1980.tb04107.x. View

Wang M, Gao M, Miller K, Sledge G, Hutchins G, Zheng Q . Simple synthesis of carbon-11 labeled styryl dyes as new potential PET RNA-specific, living cell imaging probes. Eur J Med Chem. 2008; 44(5):2300-6. DOI: 10.1016/j.ejmech.2008.02.033. View

Ponce Y, Cabrera Perez M, Zaldivar V, Diaz H, Torrens F . A new topological descriptors based model for predicting intestinal epithelial transport of drugs in Caco-2 cell culture. J Pharm Pharm Sci. 2004; 7(2):186-99. View

Ghose A, Crippen G . Use of physicochemical parameters in distance geometry and related three-dimensional quantitative structure-activity relationships: a demonstration using Escherichia coli dihydrofolate reductase inhibitors. J Med Chem. 1985; 28(3):333-46. DOI: 10.1021/jm00381a013. View

Murphy R, Boland M, Velliste M . Towards a systematics for protein subcelluar location: quantitative description of protein localization patterns and automated analysis of fluorescence microscope images. Proc Int Conf Intell Syst Mol Biol. 2000; 8:251-9. View

Ghose A, Crippen G . Modeling the benzodiazepine receptor binding site by the general three-dimensional structure-directed quantitative structure-activity relationship method REMOTEDISC. Mol Pharmacol. 1990; 37(5):725-34. View

Hu Y, Murphy R . Automated interpretation of subcellular patterns from immunofluorescence microscopy. J Immunol Methods. 2004; 290(1-2):93-105. DOI: 10.1016/j.jim.2004.04.011. View

Giuliano K, Haskins J, Taylor D . Advances in high content screening for drug discovery. Assay Drug Dev Technol. 2004; 1(4):565-77. DOI: 10.1089/154065803322302826. View

10.

Rashid F, Horobin R, Williams M . Predicting the behaviour and selectivity of fluorescent probes for lysosomes and related structures by means of structure-activity models. Histochem J. 1991; 23(10):450-9. DOI: 10.1007/BF01041375. View

11.

Boland M, Murphy R . A neural network classifier capable of recognizing the patterns of all major subcellular structures in fluorescence microscope images of HeLa cells. Bioinformatics. 2001; 17(12):1213-23. DOI: 10.1093/bioinformatics/17.12.1213. View

12.

Rashid F, Horobin R . Interaction of molecular probes with living cells and tissues. Part 2. A structure-activity analysis of mitochondrial staining by cationic probes, and a discussion of the synergistic nature of image-based and biochemical approaches. Histochemistry. 1990; 94(3):303-8. DOI: 10.1007/BF00266632. View

13.

Giuliano K . High-content profiling of drug-drug interactions: cellular targets involved in the modulation of microtubule drug action by the antifungal ketoconazole. J Biomol Screen. 2003; 8(2):125-35. DOI: 10.1177/1087057103252616. View

14.

Giuliano K, Chen Y, Taylor D . High-content screening with siRNA optimizes a cell biological approach to drug discovery: defining the role of P53 activation in the cellular response to anticancer drugs. J Biomol Screen. 2004; 9(7):557-68. DOI: 10.1177/1087057104265387. View

15.

Rashid F, Horobin R . Accumulation of fluorescent non-cationic probes in mitochondria of cultured cells: observations, a proposed mechanism, and some implications. J Microsc. 1991; 163(Pt 2):233-41. DOI: 10.1111/j.1365-2818.1991.tb03175.x. View

16.

Ghose A, Crippen G . Quantitative structure-activity relationship by distance geometry: quinazolines as dihydrofolate reductase inhibitors. J Med Chem. 1982; 25(8):892-9. DOI: 10.1021/jm00350a003. View

17.

Li Q, Min J, Ahn Y, Namm J, Kim E, Lui R . Styryl-based compounds as potential in vivo imaging agents for beta-amyloid plaques. Chembiochem. 2007; 8(14):1679-87. DOI: 10.1002/cbic.200700154. View

18.

Shedden K, Posada M, Chang Y, Li Q, Rosania G . Prospecting for Live Cell BioImaging Probes With Cheminformatic Assisted Image Arrays (CAIA). Proc IEEE Int Symp Biomed Imaging. 2013; :1108-1111. PMC: 3592986. DOI: 10.1109/ISBI.2007.357050. View

19.

Lee J, Jung M, Rosania G, Chang Y . Development of novel cell-permeable DNA sensitive dyes using combinatorial synthesis and cell-based screening. Chem Commun (Camb). 2003; (15):1852-3. DOI: 10.1039/b303960a. View

20.

Huang K, Murphy R . From quantitative microscopy to automated image understanding. J Biomed Opt. 2004; 9(5):893-912. PMC: 1458526. DOI: 10.1117/1.1779233. View