, Volume 2, Issue 2, pp 94–103 | Cite as

Simulated Biological Cells for Receptor Counting in Fluorescence Imaging

  • Julien Ghaye
  • Giovanni De Micheli
  • Sandro Carrara


Digital image processing and epifluorescence microscopy provide one of the main and basic tools for living biological cell analysis and studying. Developing, testing, and comparing those image processing methods properly is eased by the use of a controlled environment. Taking advantage of an existing database of verified and trustworthy images and meta-data helps controlling the validity of the processing results. Manually generating that golden database is a long process involving specialists being able to apprehend and extract useful data out of fluorescent images. Having enough cases in the database to challenge the processing methods and gain trust in them can only be achieved manually through time-consuming, prone to human-error processes. More and more we need to automate this process. This paper presents a framework implementing a novel approach to generate synthetic fluorescent images of fluorescently stained cell populations by simulating the imaging process of fluorescent molecules. Ultimately, the proposed simulator allows us to generate images and golden data to populate the database, thus providing tools for the development, evaluation, and testing of processing algorithms meant to be used in automated systems.


Biological image processing Fluorescence imaging Cell simulation Synthetic image Simulator Deconvolution 



The authors would like to acknowledge Paolo Scilacci and Linda Corbino-Giunta for the Caco-2 cell samples preparation. (Fig. 1). This work is supported by the Nutri-CHIP project, which is financed with a grant form the Swiss initiative and evaluated by the Swiss National Science Foundation. The research was also partially supported by the NanoSys project, within the program ERC-2009-AdG-246810.


  1. 1.
    Mascetti, G., Vergani, L., Diaspro, A., Carrara, S., Radicchi, G., Nicolini, C. (1996). Effect of fixatives on calf thymocytes chromatin as analyzed by 3D high-resolution fluorescence microscopy. Cytometry, 23(2), 110–119.CrossRefGoogle Scholar
  2. 2.
    Anderson, C. M., Georgiou, G. N., Morrison, I. E., Stevenson, G. V., Cherry, R. J. (1992). Tracking of cell surface receptors by fluorescence digital imaging microscopy using a charge-coupled device camera. Low-density lipoprotein and influenza virus receptor mobility at 4 degrees C. Journal of Cell Science, 101(2), 415–425.Google Scholar
  3. 3.
    Mascetti, G., Carrara, S., Vergani, L. (2001). Relationship between chromatin compactness and dye uptake for in situ chromatin stained with DAPI. Cytometry, 44(2), 113–119.CrossRefGoogle Scholar
  4. 4.
    Nicolini, C., Carrara, S., Mascetti, G. (1997). High order DNA structure as inferred by optical fluorimetry and scanning calorimetry. Molecular Biology Reports, 24(4), 235–246.CrossRefGoogle Scholar
  5. 5.
    Lehmussola, A., Ruusuvuori, P., Selinummi, J., Huttunen, H., Yli-Harja, O. (2007). Computational framework for simulating fluorescence microscope images with cell populations. IEEE Transactions on Medical Imaging, 26(7), 1010–1016.CrossRefGoogle Scholar
  6. 6.
    Lehmussola, A., Selinummi, J., Ruusuvuori, P., Niemisto, A., Yli-Harja, O. (2005). Simulating fluorescent microscope images of cell populations. In 27th annual international conference of the engineering in medicine and biology society, 2005. IEEE-EMBS 2005 (pp. 3153–3156).Google Scholar
  7. 7.
    Svoboda, D., Kašík, M., Maška, M., Hubenỳ, J., Stejskal, S., Zimmermann, M. (2007). On simulating 3D fluorescent microscope images. In Computer analysis of images and patterns (pp. 309–316).Google Scholar
  8. 8.
    Perlin, K. (1985). An image synthesizer. ACM SIGGRAPH Computer Graphics, 19(3), 287–296.CrossRefGoogle Scholar
  9. 9.
    Yaqoob, P. (2009). The nutritional significance of lipid rafts. Annual Review of Nutrition, 29, 257–282.CrossRefGoogle Scholar
  10. 10.
    Haeberlé, O. (2003). Focusing of light through a stratified medium: A practical approach for computing microscope point spread functions. Part I: Conventional microscopy. Optics Communications, 216(1–3), 55–63.CrossRefGoogle Scholar
  11. 11.
    Jamur, M. C., & Oliver, C. (2010). Permeabilization of cell membranes. Methods in Molecular Biology, 588, 63–66.CrossRefGoogle Scholar
  12. 12.
    Klein, A., van den Doel, R., Young, I. T., Ellenberger, S., van Vliet, L. (1998). Quantitative evaluation and comparison of light microscopes. In Proc. SPIE, progress in biomedical optics, optical investigation of cells in vitro and in vivo (Vol. 3260, pp. 162–173).Google Scholar
  13. 13.
    Frisken-Gibson, S., & Lanni, F. (1991). Experimental test of an analytical model of aberration in an oil-immersion objective lens used in three-dimensional light microscopy. Journal of the Optical Society of America A, 8(10), 1601–1613.CrossRefGoogle Scholar
  14. 14.
    Mullikin, J. C., van Vliet, L. J., Netten, H., Boddeke, F. R., Van der Feltz, G., Young, I. T. (1994). Methods for CCD camera characterization. In Proceedings of the SPIE image acquisition and scientific imaging systems (Vol. 2173, pp 73–84).Google Scholar
  15. 15.
    Zhang, B., Zerubia, J., Olivo-Marin, J. C. (2007). Gaussian approximations of fluorescence microscope point-spread function models. Applied Optics, 46(10), 1819–1829.CrossRefGoogle Scholar
  16. 16.
    Bigas, M., Cabruja, E., Forest, J., Salvi, J. (2006). Review of CMOS image sensors. Microelectronics Journal, 37(5), 433–451.CrossRefGoogle Scholar
  17. 17.
    Mutch, S. A., Fujimoto, B. S., Kuyper, C. L., Kuo, J. S., Bajjalieh, S. M., Chiu, D. T. (2007) Deconvolving single-molecule intensity distributions for quantitative microscopy measurements. Biophysical Journal, 92(8) 2926–2943.CrossRefGoogle Scholar
  18. 18.
    Mutch, S. A., Kensel-Hammes, P., Gadd, J. C., Fujimoto, B. S., Allen, R. W., Schiro, P. G., et al. (2011). Protein quantification at the single vesicle level reveals that a subset of synaptic vesicle proteins are trafficked with high precision. The Journal of Neuroscience, 31(41), 1461–1470.CrossRefGoogle Scholar
  19. 19.
    Cronin, B., de Wet, B., Wallace, N. I. (2009). Lucky imaging: Improved localization accuracy for single molecule imaging. Biophysical Journal, 96, 2912–2917.CrossRefGoogle Scholar
  20. 20.
    Huang, B., Bates, M., Zhuang, Z. (2009). Super-resolution fluorescence microscopy. Annual Review of Biochemistry, 78, 993–1016.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Julien Ghaye
    • 1
  • Giovanni De Micheli
    • 2
  • Sandro Carrara
    • 3
  1. 1.Integrated Systems LaboratoryEPFL IC ISIM LSI1LausanneSwitzerland
  2. 2.Integrated Systems LaboratoryEPFL IC ISIM LSI1LausanneSwitzerland
  3. 3.Integrated Systems LaboratoryEPFL IC ISIM LSI1LausanneSwitzerland

Personalised recommendations