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Refining the Statistical Model for Quantitative Immunostaining of Surface-Functionalized Nanoparticles by AFM

Published

Author(s)

Robert I. MacCuspie, Danielle E. Gorka

Abstract

Recently, an AFM-based approach for quantifying the number of biological molecules conjugated to a nanoparticle surface at low number densities was reported. By using antibody-mediated self-limiting self-assembly to decorate the analyte nanoparticles with antibody-functionalized probe nanoparticles (i.e., immunostaining), the number of target molecules on the analyte nanoparticle can be determined (i.e., quantitative immunostaining). This work aims to refinerefines the statistical models used to interpret the number of observed molecules conjugated per nanoparticle, with single nanoparticle fidelity, when atomic force microscopy (AFM) is used to image the resulting structures. This work improves the previous statistical models used to interpret the observed AFM data by adding terms to The refinements add terms to the previous statistical models to account for the physical sizes of the analyte nanoparticles, conjugated molecules, antibodies, and probe nanoparticles. Thus, a more physically realistic statistical computation can be implemented for a given sample of known qualitative composition, using the software scripts provided. Example AFM data sets, using horseradish peroxidase conjugated to gold nanoparticles, are presented to show examples ofillustrate how to implement this method successfully.
Citation
Journal of Nanoparticle Research
Volume
405
Issue
25

Keywords

gold nanoparticles, atomic force microscopy, quantitative immunostaining, functionalized AuNPs

Citation

MacCuspie, R. and Gorka, D. (2013), Refining the Statistical Model for Quantitative Immunostaining of Surface-Functionalized Nanoparticles by AFM, Journal of Nanoparticle Research, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=912885 (Accessed December 10, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created July 31, 2013, Updated February 19, 2017