Hostname: page-component-586b7cd67f-vdxz6 Total loading time: 0 Render date: 2024-11-22T19:37:09.109Z Has data issue: false hasContentIssue false
  • English
  • Français

Accurate Detection of Low Levels of Fluorescence Emission in Autofluorescent Background: Francisella-Infected Macrophage Cells

Published online by Cambridge University Press:  22 June 2010

Ryan W. Davis ,
Jerilyn A. Timlin ,
Julia N. Kaiser ,
Michael B. Sinclair ,
Howland D.T. Jones  and
Todd W. Lane
Ryan W. Davis*
Affiliation:
Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94550, USA
Jerilyn A. Timlin
Affiliation:
Sandia National Laboratories, 1515 Eubank Blvd. SE, Albuquerque, NM 87123, USA
Julia N. Kaiser
Affiliation:
Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94550, USA
Michael B. Sinclair
Affiliation:
Sandia National Laboratories, 1515 Eubank Blvd. SE, Albuquerque, NM 87123, USA
Howland D.T. Jones
Affiliation:
Sandia National Laboratories, 1515 Eubank Blvd. SE, Albuquerque, NM 87123, USA
Todd W. Lane
Affiliation:
Sandia National Laboratories, 7011 East Avenue, Livermore, CA 94550, USA
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Cellular autofluorescence, though ubiquitous when imaging cells and tissues, is often assumed to be small in comparison to the signal of interest. Uniform estimates of autofluorescence intensity obtained from separate control specimens are commonly employed to correct for autofluorescence. While these may be sufficient for high signal-to-background applications, improvements in detector and probe technologies and introduction of spectral imaging microscopes have increased the sensitivity of fluorescence imaging methods, exposing the possibility of effectively probing the low signal-to-background regime. With spectral imaging, reliable monitoring of signals near or even below the noise levels of the microscope is possible if compensation for autofluorescence and background signals can be performed accurately. We demonstrate the importance of accurate autofluorescence modeling and the utility of spectral imaging and multivariate analysis methods using a case study focusing on fluorescence confocal spectral imaging of host-pathogen interactions. In this application fluorescent proteins are produced when Francisella novicida invade host macrophage cells. The resulting analyte signal is spectrally overlapped and typically weaker than the cellular autofluorescence. In addition to discussing the advantages of spectral imaging for following pathogen invasion, we present the spectral properties and cellular origin of macrophage autofluorescence.

Keywords

spectral imagingfluorescence imagingFrancisellaautofluorescencespectral crosstalkmultivariate curve resolutionmacrophagefluorescent proteins
Type
Biological Applications
Information
Microscopy and Microanalysis , Volume 16 , Issue 4 , August 2010 , pp. 478 - 487
Copyright
Copyright © Microscopy Society of America 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Andersson, H., Baechi, T., Hoechl, M. & Richter, C. (1998). Autofluorescence in living cells. J Microsc 191, 1–7.CrossRefGoogle ScholarPubMed
Bro, R. & DeJong, S.A. (1997). A fast non-negativity constrained least squares algorithm. J Chemom 11, 393–401.3.0.CO;2-L>CrossRefGoogle Scholar
Davis, R.W., Arango, D.C., Jones, H.D.T., Van Benthem, M.H., Haaland, D.M., Brozik, S.M. & Sinclair, M.B. (2009). Antimicrobial peptide interactions with silica bead supported bilayers and E. coli: Buforin II, magainin II, and arenicin. J Pept Sci 15, 511–522.CrossRefGoogle Scholar
de Bruin, O.M., Ludu, J.S. & Nano, F.E. (2007). The Francisella pathogenicity island protein IglA localizes to the bacterial cytoplasm and is needed for intracellular growth. BMC Microbiol 7, 1471.Google Scholar
Dickinson, M.E., Bearman, G.H., Tille, S., Lansford, R. & Fraser, S.E. (2001). Multi-spectral imaging and linear unmixing add a whole new dimension to laser scanning fluorescence microscopy. Biotechniques 31, 1272–1278.CrossRefGoogle ScholarPubMed
Golub, G.H. & Reinsch, C. (1970). Singular value decomposition and least squares solutions. Numer Math 14, 403–420.CrossRefGoogle Scholar
Haaland, D.M., Easterling, R.G. & Vopicka, D.A. (1985). Multivariate least-squares methods applied to the quantitative spectral analysis of multicomponent samples. Appl Spectrosc 39, 73–83.CrossRefGoogle Scholar
Haaland, D.M., Jones, H.D.T., Sinclair, M.B., Carson, B., Branda, C., Poschet, J.F., Rebeil, R., Tian, B., Liu, P. & Brasier, A.R. (2007). Hyperspectral confocal fluorescence imaging of cells. In Next-Generation Spectroscopic Technologies, Brown, C.D., Druy, M.A., Coates, J.P. (Eds.), 6765. Boston, MA: SPIE.CrossRefGoogle Scholar
Haaland, D.M., Timlin, J.A., Sinclair, M.B., Van Benthem, M.H., Martinez, M.J., Aragon, A.D. & Werner-Washburne, M. (2003). Multivariate curve resolution for hyperspectral image analysis: Applications to microarray technology. In Spectral Imaging: Instrumentation, Applications, and Analysis, Levenson, R.M., Bearman, G.H. & Mahadevan-Jansen, A. (Eds.). San Jose, CA: SPIE.Google Scholar
Heikel, A.A., Hess, S.T., Baird, G.S., Tsien, R.Y. & Webb, W.W. (2001). Molecular spectroscopy and dynamics of intrinsically fluorescent proteins: Coral red (dsRed) and yellow (Citrine). Proc Nat Acad Sci USA 97, 11996–12001.CrossRefGoogle Scholar
Hing, P. & Muller, H.W. (2003). CCD cameras simplify biology. Biophotonics 9, 52–58.Google Scholar
Jones, H.D.T, Haaland, D.M., Sinclair, M.B., Melgaard, D.K., Van Benthem, M.H. & Pedroso, M.C. (2008). Weighting hyperspectral image data for improved multivariate curve resolution results. J Chemom 22, 482–490.CrossRefGoogle Scholar
Keshava, N. & Mustard, J.F. (2002). Spectral unmixing. IEEE Signal Proc Mag 19, 44–57.CrossRefGoogle Scholar
Kotula, P.G., Keenan, M.R. & Michael, J.R. (2003). Automated analysis of SEM X-Ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9, 1–17.CrossRefGoogle ScholarPubMed
Lawton, W.H. & Sylvestre, E.A. (1971). Self modeling curve resolution. Technometrics 13, 617–633.CrossRefGoogle Scholar
Ludu, J.S., Nix, E.B., Duplantis, B.N., de Bruin, O.M., Gallagher, L.A., Hawley, L.M. & Nano, F.E. (2007). Genetic elements for selection, deletion mutagenesis and complementation in Francisella spp. FEMS Microbiol Lett 278, 86–93.Google Scholar
Maier, T.M., Havig, A., Casey, M., Nano, F.E., Frank, D.W. & Zahrt, T.C. (2004). Construction and characterization of a highly efficient Francisella shuttle plasmid. Appl Environ Microbiol 70, 7511–7519.CrossRefGoogle ScholarPubMed
Manders, E.M.M., Verbeek, F.J. & Aten, J.A. (1993). Measurement of co-localization of objects in dual-color confocal images. J Microsc 169, 375–382.Google Scholar
Mansfield, J.R., Gossage, K.W., Hoyt, C.C. & Levenson, R.M. (2005). Autofluorescence removal, multiplexing, and automated analysis methods for in-vivo fluorescence imaging. J Biomed Opt 10, 41207.Google Scholar
Martinez, M.J., Aragon, A.D., Rodriguez, A.L., Weber, J.M., Timlin, J.A., Sinclair, M.B., Haaland, D.M. & Werner-Washburne, M. (2003). Identification and removal of contaminating fluorescence from commercial and in-house printed DNA microarrays. Nucleic Acids Res 31, e18.CrossRefGoogle ScholarPubMed
Nieman, L.T., Sinclair, M.B., Timlin, J.A., Jones, H.D.T. & Haaland, D.M. (2006). Hyperspectral imaging system for quantitative identification and discrimination of fluorescent labels in the presence of autofluorescence. In IEEE International Symposium on Biomedical Imaging, Kovacevic, J. & Meijering, E. (Eds.), pp. 1703–1706. Arlington, VA: IEEE.Google Scholar
Periasamy, A. & Day, R.N. (2005). Molecular Imaging. New York: Oxford University Press.Google Scholar
Schoonover, J.R., Marx, R. & Zhang, S.L. (2003). Multivariate curve resolution in the analysis of vibrational spectroscopy data files. Appl Spectrosc 57, 154A–170A.CrossRefGoogle ScholarPubMed
Schultz, R.A., Nielsen, T., Zavaleta, J.R., Ruch, R., Wyatt, R. & Garner, H. (2001). Hyperspectral imaging: A novel approach for microscopic analysis. Cytometry 43, 239–247.3.0.CO;2-Z>CrossRefGoogle ScholarPubMed
Shaner, N.C., Campbell, R.E., Steinbach, P.A., Giepmans, B.N., Palmer, A.E. & Tsien, R.Y. (2004). Improved monomeric red, orange, and yellow fluorescent proteins derived from Dicosoma sp. red fluorescent protein. Nat Biotechnol 22, 1567–1572.Google Scholar
Sinclair, M.B., Haaland, D.M., Timlin, J.A. & Jones, H.D.T. (2006). Hyperspectral confocal microscope. Appl Opt 45, 6283–6291.CrossRefGoogle ScholarPubMed
Sutherland, V., Timlin, J.A., Nieman, L.T., Guzowski, J.F., Chawla, M.K., Roysam, B., Worley, P.F., McNaughton, B.L., Sinclair, M.B. & Barnes, C.A. (2007). Advanced imaging of multiple mRNAs in brain tissue using a custom hyperspectral imager and multivariate curve resolution. J Neurosci Meth 160, 144–148.Google Scholar
Tauler, R., Smilde, A. & Kowalski, B. (1995). Selectivity, local rank, three-way data analysis and ambiguity in multivariate curve resolution. J Chemom 9, 31–58.CrossRefGoogle Scholar
Timlin, J.A., Haaland, D.M., Sinclair, M.B., Aragon, A.D., Martinez, M.J. & Werner-Washburne, M. (2005). Hyperspectral microarray scanning: Impact and reliability of gene expression data. BMC Genomics 6, 72.CrossRefGoogle ScholarPubMed
Timlin, J.A., Nieman, L.T., Jones, H.D.T., Sinclair, M.B., Haaland, D.M. & Guzowski, J.F. (2006). Imaging multiple endogenous and exogenous fluorescent species in cells and tissues. In Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues IV, Farkas, D.L., Nicolau, D.V. & Leif, R.C. (Eds.). San Jose, CA: SPIE.Google Scholar
Timlin, J.A., Sinclair, M.B., Haaland, D.M, Martinez, M.J., Manginell, M., Brozik, S.M., Guzowski, J.F. & Werner-Washburne, M. (2004). Hyperspectral imaging of biological targets: The difference a high resolution spectral dimension and multivariate analysis can make. In IEEE International Symposium on Biomedical Imaging, Leahy, R.M. & Roux, C. (Eds.), pp. 1529–1532. Arlington, VA: IEEE.Google Scholar
Van Benthem, M.H. & Keenan, M.R. (2004). Fast algorithm for the solution of large scale non-negativity constrained least squares problems. J Chemom 18, 441–450.Google Scholar
Vermaas, W.F.J., Timlin, J.A., Jones, H.D.T., Sinclair, M.B., Nieman, L.T., Hamad, S.W., Melgaard, D.K. & Haaland, D.M. (2008). In vivo hyperspectral confocal fluorescence imaging to determine pigment localization and distribution in cyanobacterial cells. Proc Natl Acad Sci USA 105, 4050–4055.CrossRefGoogle ScholarPubMed
Xu, H. & Rice, B.W. (2009). In-vivo fluorescence imaging with a multivariate curve resolution spectral unmixing technique. J Biomed Opt 14, 064011.CrossRefGoogle ScholarPubMed
Zimmerman, T., Rietdorf, J. & Pepperkok, R. (2003). Spectral imaging and its applications in live cell microscopy. FEBS Lett 546, 87–92.CrossRefGoogle Scholar