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An image processing application for quantitative cross-correlative microscopy for large cell-populations: a gold nanoparticle radiosensitisation study

Published online by Cambridge University Press:  02 August 2017

Tyron Turnbull ,
Michael Douglass ,
Eva Bezak ,
Benjamin Thierry  and
Ivan Kempson
Tyron Turnbull
Affiliation:
Future Industries Institute, University of South Australia, Mawson Lakes Campus, Mawson Lakes, Adelaide, SA 5095, Australia
Michael Douglass
Affiliation:
Department of Medical Physics, Royal Adelaide Hospital, North Terrace, Adelaide, SA 5000, Australia School of Physical Sciences, University of Adelaide, Adelaide, SA 5005, Australia
Eva Bezak
Affiliation:
School of Physical Sciences, University of Adelaide, Adelaide, SA 5005, Australia International Centre for Allied Health Evidence and Sansom Insitute for Health Research, University of South Australia, City East Campus, North Terrace, Adelaide, SA 5001, Australia
Benjamin Thierry
Affiliation:
Future Industries Institute, University of South Australia, Mawson Lakes Campus, Mawson Lakes, Adelaide, SA 5095, Australia
Ivan Kempson*
Affiliation:
Future Industries Institute, University of South Australia, Mawson Lakes Campus, Mawson Lakes, Adelaide, SA 5095, Australia
*
a) Author to whom correspondence should be addressed. Electronic mail: Ivan.Kempson@unisa.edu.au
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Abstract

A robust analysis script was developed in MATLAB for cross-correlative quantification of internalised gold nanoparticle (AuNP) uptake in a large number of individual cells with the corresponding number of DNA double-strand breaks (DSBs) in the same cells. The correlation of inorganic NP content with a biological marker at the single-cell level will aid in the elucidation of mechanisms of NP radiosensitisation. PC-3 cells were co-cultured with AuNPs and irradiated using an iridium-192 source. AuNP uptake was measured using synchrotron X-ray fluorescence (XRF) and DSBs imaged via confocal microscopy. MATLAB 2016a was used to develop a script to cross-correlate the two imaging modalities and quantify both DSBs and internalised AuNP content in the same cell. Various user-defined options written into the script give a high degree of versatility, which can account for a large number of variables in experimental parameters and data acquisition. The analysis procedure is flexible and robust, which gives consistent consideration to the wide spectrum of potential input image/data sets. Quantitative correlative microscopy was achieved with a custom MATLAB script used to correlate γH2AX foci (a marker of DNA DSBs) from confocal microscopy with AuNP content acquired using synchrotron XRF at the single-cell level. The script can be extended to a broad range of multi-modality imaging spectroscopies.

Keywords

radiation damage evaluation methodsimage processingX-ray fluorescence (XRF) systemsimaging spectroscopy

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Type
Technical Articles
Information
Powder Diffraction , Volume 32 , Supplement S2 , December 2017 , pp. S33 - S37
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Copyright
Copyright © International Centre for Diffraction Data 2017 

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References

Brun, E. and Sicard-Roselli, C. (2016). “Actual questions raised by nanoparticle radiosensitization,” Radiat. Phys. Chem. 128, 134–142.Google Scholar
Cruje, C. and Chithrani, B. D. (2015). “Integration of peptides for enhanced uptake of PEGylayed gold nanoparticles,” J. Nanosci. Nanotechnol. 15(3), 2125–2131.Google Scholar
Douglass, M., Bezak, E., and Penfold, S. (2013). “Monte Carlo investigation of the increased radiation deposition due to gold nanoparticles using kilovoltage and megavoltage photons in a 3D randomized cell model,” Med. Phys. 40(7), 071710.CrossRefGoogle Scholar
Drescher, D., Giesen, C., Traub, H., Panne, U., Kneipp, J., and Jakubowski, N. (2012). “Quantitative imaging of gold and silver nanoparticles in single eukaryotic cells by laser ablation ICP-MS,” Anal. Chem. 84(22), 9684–9688.Google Scholar
Enüstün, B. V. and Turkevich, J. (1963). “Coagulation of colloidal gold,” J. Am. Chem. Soc. 85(21), 3317–3328.Google Scholar
Hainfeld, J. F., Slatkin, D. N., and Smilowitz, H. M. (2004). “The use of gold nanoparticles to enhance radiotherapy in mice,” Phys. Med. Biol. 49(18), N309–N315.Google Scholar
Jain, S., Hirst, D. G., and O'Sullivan, J. M. (2012) “Gold nanoparticles as novel agents for cancer therapy,” Br. J. Radiol. 85(1010), 101–113.Google Scholar
Kempson, I., Smith, E., Gao, M., Jonge, M. D., and Thierry, B. (2014). “Large area synchrotron X-ray fluorescence mapping of biological samples,” J. Instrum. 9(12), C12040.CrossRefGoogle Scholar
Liu, C. J., Wang, C. H., Chen, S. T., Chen, H. H., Leng, W. H., Chien, C. C., Wang, C. L., Kempson, I. M., Hwu, Y., Lai, T. C., Hsiao, M., Yang, C. S., Chen, Y. J., and Margaritondo, G. (2010). “Enhancement of cell radiation sensitivity by pegylated gold nanoparticles,” Phys. Med. Biol. 55(4), 931–945.Google Scholar
Liu, T. and Thierry, B. (2012). “A solution to the PEG dilemma: efficient bioconjugation of large gold nanoparticles for biodiagnostic applications using mixed layers,” Langmuir 28(44), 15634–15642.CrossRefGoogle Scholar
Liu, T., Kempson, I., De Jonge, M., Howard, D. L., and Thierry, B. (2014). “Quantitative synchrotron X-ray fluorescence study of the penetration of transferrin-conjugated gold nanoparticles inside model tumour tissues,” Nanoscale 6(16), 9774–9782.CrossRefGoogle ScholarPubMed
Liu, Y., Liu, X., Jin, X., He, P., Zheng, X., Dai, Z., Ye, F., Zhao, T., Chen, W., and Li, Q. (2015). “The dependence of radiation enhancement effect on the concentration of gold nanoparticles exposed to low- and high-LET radiations,” Phys. Med. 31(3), 210–218.Google Scholar
McMahon, S. J., Hyland, W. B., Muir, M. F., Coulter, J. A., Jain, S., Butterworth, K. T., Schettino, G., Dickson, G. R., Hounsell, A. R., O'Sullivan, J. M., Prise, K. M., Hirst, D. G., and Currell, F. J. (2011a). “Biological consequences of nanoscale energy deposition near irradiated heavy atom nanoparticles,” Sci. Rep., 1, 18.Google Scholar
McMahon, S. J., Hyland, W. B., Muir, M. F., Coulter, J. A., Jain, S., Butterworth, K. T., Schettino, G., Dickson, G. R., Hounsell, A. R., O'Sullivan, J. M., Prise, K. M., Hirst, D. G., and Currell, F. J. (2011b). “Nanodosimetric effects of gold nanoparticles in megavoltage radiation therapy,” Radiother. Oncol. 100(3), 412–416.CrossRefGoogle ScholarPubMed
Paterson, D., de Jonge, M. D., McKinlay, J., Starritt, A., Kusel, M., Ryan, C. G., Kirkham, R., Moorhead, G., and Siddons, D. P. (2011). “The x-ray fluorescence microscopy beamline at the Australian synchrotron,” AIP Conf. Proc, 1365, 219–222.CrossRefGoogle Scholar
Peng, H. (2008). “Bioimage informatics: a new area of engineering biology,” Bioinformatics 24(17), 1827–1836.Google Scholar
Ryan, C. G. (2000). “Quantitative trace element imaging using PIXE and the nuclear microprobe,” Int. J. Imaging Syst. Technol. 11(4), 219–230.Google Scholar
Subiel, A., Ashmore, R., and Schettino, G. (2016). “Standards and methodologies for characterizing radiobiological impact of high-Z nanoparticles,” Theranostics 6(10), 1651–1671.Google Scholar
Turnbull, T., Douglass, M., Paterson, D., Bezak, E., Thierry, B., and Kempson, I. (2015). “Relating intercellular variability in nanoparticle uptake with biological consequence: a quantitative X-ray fluorescence study for radiosensitization of cells,” Anal. Chem. 87(21), 10693–10697.Google Scholar
Vogt, S. and Lanzirotti, A. (2013). “Trends in X-ray fluorescence microscopy,” Synchrotron Radiat. News 26(2), 32–38.CrossRefGoogle Scholar