Please use this identifier to cite or link to this item: https://doi.org/10.1002/cyto.a.20713
Title: Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes
Authors: Shedden, K.
Li, Q.
Liu, F.
Young, T.C. 
Rosania, G.R.
Keywords: Bioimaging
Cheminformatics
Combinatorial chemistry
Fluorescence
High content screening
Image cytometry
Machine vision
Styryl
Issue Date: Jun-2009
Citation: Shedden, K., Li, Q., Liu, F., Young, T.C., Rosania, G.R. (2009-06). Machine vision-assisted analysis of structure-localization relationships in a combinatorial library of prospective bioimaging probes. Cytometry Part A 75 (6) : 482-493. ScholarBank@NUS Repository. https://doi.org/10.1002/cyto.a.20713
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. © 2009 International Society for Advancement of Cytometry.
Source Title: Cytometry Part A
URI: http://scholarbank.nus.edu.sg/handle/10635/76462
ISSN: 15524922
DOI: 10.1002/cyto.a.20713
Appears in Collections:Staff Publications

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