Skip to main content
SpringerLink
Log in

Pattern-Based Linear Unmixing for Efficient and Reliable Analysis of Multicomponent TCSPC Data

  • Chapter
  • First Online:
Book cover Advanced Photon Counting

Part of the book series: Springer Series on Fluorescence ((SS FLUOR,volume 15))

Abstract

A method for a reliable quantitative analysis of fluorescence lifetime imaging microscopy (FLIM) data is presented. It is based on the linear unmixing of the intensity decay on the basis of selected reference patterns. This approach allows to use decays that are not mono-exponential without increasing the complexity of the analysis. This is a major benefit when working with labeled biomolecules or using autofluorescent cellular chromophores.

The method can be used intuitively and is fast. Furthermore, based on the reference patterns and the amount of recorded photons, one can easily determine confidence levels of the obtained results. We demonstrate that for a decomposition to three patterns of common chromophores, one achieves a standard deviation of better than 10% for as few as 1,000 photons per pixel, where the total amplitude of such a signal will show an error of 3% due to shot noise. Indeed, the accuracy of the results is very close to a maximum-likelihood estimator that defines the absolute limit for this kind of problem.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 349.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 449.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    http://www.picoquant.com/products/category/fluorescence-microscopes/lsm-upgrade-kit-compact-flim-and-fcs-upgrade-kit-for-lsms

References

  1. Harvey CD, Yasuda R, Zhong H, Svoboda K (2008) The spread of Ras activity triggered by activation of a single dendritic spine. Science 321(5885):136–140

    Article  CAS  Google Scholar 

  2. Verveer PJ, Wouters FS, Reynolds AR, Bastiaens PIH (2000) Quantitative imaging of lateral ErbB1 receptor signal propagation in the plasma membrane. Science 290:1567–1570

    Article  CAS  Google Scholar 

  3. Wouters FS, Bastiaens PIH (1999) Fluorescence lifetime imaging of receptor tyrosine kinase activity in cells. Curr Biol 9(19):1127–1130

    Article  CAS  Google Scholar 

  4. Yasuda R (2006) Imaging spatiotemporal dynamics of neuronal signaling using fluorescence resonance energy transfer and fluorescence lifetime imaging microscopy. Curr Opin Neurobiol 16(5):551–561

    Article  CAS  Google Scholar 

  5. Clayton AHA, Hanley QS, Verveer PJ (2004) Graphical representation and multicomponent analysis of single-frequency fluorescence lifetime imaging microscopy data. J Microsc 213(1):1–5

    Article  CAS  Google Scholar 

  6. Digman MA, Caiolfa VR, Zamai M, Gratton E (2008) The phasor approach to fluorescence lifetime imaging analysis. Biophys J 94(2):L14–L16

    Article  CAS  Google Scholar 

  7. Redford GI, Clegg RM (2005) Polar plot representation for frequency-domain analysis of fluorescence lifetimes. J Fluoresc 15(5):805–815

    Article  CAS  Google Scholar 

  8. Lakowicz JR (2006) Principles of fluorescence spectroscopy, 3rd edn. Springer, New York

    Book  Google Scholar 

  9. Walther KA, Papke B, Sinn MB, Michel K, Kinkhabwala A (2011) Precise measurement of protein interacting fractions with fluorescence lifetime imaging microscopy. Mol Biosyst 7(2):322–336

    Article  CAS  Google Scholar 

  10. Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):79–86

    Article  Google Scholar 

  11. Lee DD, Seung HS (2000) Algorithms for non-negative matrix factorization. In: Leen TK, Dietterich TG, Tresp V (eds) Advances in neuronal image processing systems 13. MIT, Cambridge, pp 556–562

    Google Scholar 

  12. Neher RA, Neher E (2004) Applying spectral fingerprinting to the analysis of FRET images. Microsc Res Tech 64(2):185–195

    Article  Google Scholar 

  13. Neher RA, Neher E (2004) Optimizing imaging parameters for the separation of multiple labels in a fluorescence image. J Microsc 213:46–62

    Article  CAS  Google Scholar 

  14. Enderlein J, Sauer M (2001) Optimal algorithm for single-molecule identification with time-correlated single-photon counting. J Phys Chem A 105(1):48–53

    Article  CAS  Google Scholar 

  15. Lawson CL, Hanson RJ (1974) Solving least squares problems. Prentice Hall, Englewood Cliffs

    Google Scholar 

  16. Isenberg I, Dyson RD (1969) The analysis of fluorescence decay by a method of moments. Biophys J 9:1337–1350

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank Jörg Enderlein, Fred Wouters, Gertrude Bunt, and Benedikt Krämer for valuable discussions. The funding of the German Federal Ministry for Education and Research (BMBF) is gratefully acknowledged.

Author information

Authors and Affiliations

  1. Third Institute of Physics, Georg-August-University, Göttingen, Germany

    Ingo Gregor

  2. PicoQuant GmbH, Berlin, Germany

    Matthias Patting

Authors
  1. Ingo Gregor
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthias Patting
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ingo Gregor .

Editor information

Editors and Affiliations

  1. J. Heyrovsky Institute of Physical Chemistry, Academy of Sciences of the Czech Republic, Prague 8, Czech Republic

    Peter Kapusta

  2. PicoQuant GmbH, Berlin, Germany

    Michael Wahl

  3. PicoQuant GmbH, Berlin, Germany

    Rainer Erdmann

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Gregor, I., Patting, M. (2014). Pattern-Based Linear Unmixing for Efficient and Reliable Analysis of Multicomponent TCSPC Data. In: Kapusta, P., Wahl, M., Erdmann, R. (eds) Advanced Photon Counting. Springer Series on Fluorescence, vol 15. Springer, Cham. https://doi.org/10.1007/4243_2014_70

Download citation

Publish with us

Policies and ethics

Search

Navigation