Imaging fluorescence (cross-) correlation spectroscopy in live cells and organisms


Single-plane illumination (SPIM) or total internal reflection fluorescence (TIRF) microscopes can be combined with fast and single-molecule-sensitive cameras to allow spatially resolved fluorescence (cross-) correlation spectroscopy (FCS or FCCS, hereafter referred to FCS/FCCS). This creates a powerful quantitative bioimaging tool that can generate spatially resolved mobility and interaction maps with hundreds to thousands of pixels per sample. These massively parallel imaging schemes also cause less photodamage than conventional single-point confocal microscopy–based FCS/FCCS. Here we provide guidelines for imaging FCS/FCCS measurements on commercial and custom-built microscopes (including sample preparation, setup calibration, data acquisition and evaluation), as well as anticipated results for a variety of in vitro and in vivo samples. For a skilled user of an available SPIM or TIRF setup, sample preparation, microscope alignment, data acquisition and data fitting, as described in this protocol, will take 1 d, depending on the sample and the mode of imaging.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Principle of FCS and imaging FCS.
Figure 2: Principle of FCCS.
Figure 3: Schematic diagrams of microscopes typically used for FCS.
Figure 4: SPIM sample mounting procedures.
Figure 5: Visualization of a light sheet, using a 45° mirror.
Figure 6: The volume overlap optimization of the observation plane and the light sheet in a SPIM.
Figure 7: Illustration of the dual-channel image splitter alignment process used to optimize the overlap between the two image halves for imaging FCCS measurements.
Figure 8: Results of an imaging FCS calibration with increasing pixel size.
Figure 9: Examples of typical SPIM-FCS measurements.
Figure 10: Phase separated supported lipid bilayer imaged on a TIRF microscope.
Figure 11: Example results for an in vivo, two-color SPIM-FCCS measurement of the AT-1 transcription factor system (c-Fos-GFP + c-Jun-mRFP).


  1. 1

    Bag, N., Sankaran, J., Paul, A., Kraut, R.S. & Wohland, T. Calibration and limits of camera-based fluorescence correlation spectroscopy: a supported lipid bilayer study. Chemphyschem 13, 2784–2794 (2012).

    CAS  Article  Google Scholar 

  2. 2

    Singh, A.P. et al. The performance of 2D array detectors for light sheet based fluorescence correlation spectroscopy. Opt. Express 21, 8652–8668 (2013).

    CAS  Article  Google Scholar 

  3. 3

    Krieger, J.W., Singh, A.P., Garbe, C.S., Wohland, T. & Langowski, J. Dual-color fluorescence cross-correlation spectroscopy on a single plane illumination microscope (SPIM-FCCS). Opt. Express 22, 2358–2375 (2014).

    Article  Google Scholar 

  4. 4

    Magde, D., Elson, E. & Webb, W.W. Thermodynamic fluctuations in a reacting system--measurement by fluorescence correlation spectroscopy. Phys. Rev. Lett. 29, 705–708 (1972).

    CAS  Article  Google Scholar 

  5. 5

    Sankaran, J., Bag, N., Kraut, R.S. & Wohland, T. Accuracy and precision in camera-based fluorescence correlation spectroscopy measurements. Anal. Chem. 85, 3948–3954 (2013).

    CAS  Article  Google Scholar 

  6. 6

    Koppel, D.E. Statistical accuracy in fluorescence correlation spectroscopy. Phys. Rev. A 10, 1938–1945 (1974).

    Article  Google Scholar 

  7. 7

    Krichevsky, O. & Bonnet, G. Fluorescence correlation spectroscopy: the technique and its applications. Rep. Prog. Phys. 65, 251 (2002).

    CAS  Article  Google Scholar 

  8. 8

    Haustein, E. & Schwille, P. Fluorescence correlation spectroscopy: novel variations of an established technique. Annu. Rev. Biophys. Biomol. Struct. 36, 151–169 (2007).

    CAS  Article  Google Scholar 

  9. 9

    Ries, J., Weidemann, T. & Schwille, P. Fluorescence correlation spectroscopy. in Comprehensive Biophysics. Vol. 2 (ed. Egelman, Edward H.) 210–245 (Elsevier, 2012).

  10. 10

    Wood, C. et al. Fluorescence correlation spectroscopy as tool for high-content-screening in yeast (HCS-FCS). Proc. SPIE 7905 doi:10.1117/12.873947 (2011).

  11. 11

    Lange, J.J. et al. Correction of bleaching artifacts in high content fluorescence correlation spectroscopy (HCS-FCS) data. Proc. SPIE 8590 doi:10.1117/12.2005205 (2013).

  12. 12

    Wachsmuth, M. et al. High-throughput fluorescence correlation spectroscopy enables analysis of proteome dynamics in living cells. Nat. Biotechnol. 33, 384–389 (2015).

    CAS  Article  Google Scholar 

  13. 13

    Petersen, N.O., Hoddelius, P.L., Wiseman, P.W., Seger, O. & Magnusson, K.E. Quantitation of membrane receptor distributions by image correlation spectroscopy: concept and application. Biophys. J. 65, 1135–1146 (1993).

    CAS  Article  Google Scholar 

  14. 14

    Kapusta, P., Wahl, M., Benda, A., Hof, M. & Enderlein, J. Fluorescence lifetime correlation spectroscopy. J. Fluoresc. 17, 43–48 (2007).

    CAS  Article  Google Scholar 

  15. 15

    Burkhardt, M., Heinze, K.G. & Schwille, P. Four-color fluorescence correlation spectroscopy realized in a grating-based detection platform. Opt. Lett. 30, 2266–2268 (2005).

    Article  Google Scholar 

  16. 16

    Hwang, L.C. et al. Prism-based multicolor fluorescence correlation spectrometer. Opt. Lett. 31, 1310–1312 (2006).

    Article  Google Scholar 

  17. 17

    Previte, M.J.R., Pelet, S., Kim, K.H., Buehler, C. & So, P.T.C. Spectrally resolved fluorescence correlation spectroscopy based on global analysis. Anal. Chem. 80, 3277–3284 (2008).

    CAS  Article  Google Scholar 

  18. 18

    Hebert, B., Costantino, S. & Wiseman, P.W. Spatiotemporal image correlation spectroscopy (STICS) theory, verification, and application to protein velocity mapping in living CHO cells. Biophys. J. 88, 3601–3614 (2005).

    CAS  Article  Google Scholar 

  19. 19

    Kolin, D. & Wiseman, P. Advances in image correlation spectroscopy: measuring number densities, aggregation states, and dynamics of fluorescently labeled macromolecules in cells. Cell Biochem. Biophys. 49, 141–164 (2007).

    CAS  Article  Google Scholar 

  20. 20

    Digman, M.A. et al. Measuring fast dynamics in solutions and cells with a laser scanning microscope. Biophys. J. 89, 1317–1327 (2005).

    CAS  Article  Google Scholar 

  21. 21

    Rossow, M.J., Sasaki, J.M., Digman, M.A. & Gratton, E. Raster image correlation spectroscopy in live cells. Nat. Protoc. 5, 1761–1774 (2010).

    CAS  Article  Google Scholar 

  22. 22

    Digman, M.A. & Gratton, E. Lessons in fluctuation correlation spectroscopy. Annu. Rev. Phys. Chem. 62, 645–668 (2011).

    CAS  Article  Google Scholar 

  23. 23

    Rička, J. & Binkert, T. Direct measurement of a distinct correlation function by fluorescence cross correlation. Phys. Rev. A 39, 2646–2652 (1989).

    Article  Google Scholar 

  24. 24

    Schwille, P., Meyer-Almes, F.J. & Rigler, R. Dual-color fluorescence cross-correlation spectroscopy for multicomponent diffusional analysis in solution. Biophys. J. 72, 1878–1886 (1997).

    CAS  Article  Google Scholar 

  25. 25

    Bacia, K., Petrasek, Z. & Schwille, P. Correcting for spectral cross-talk in dual-color fluorescence cross-correlation spectroscopy. Chemphyschem 13, 1221–1231 (2012).

    CAS  Article  Google Scholar 

  26. 26

    Bag, N. & Wohland, T. Imaging fluorescence fluctuation spectroscopy: new tools for quantitative bioimaging. Annu. Rev. Phys. Chem. 65, 225–248 (2014).

    CAS  Article  Google Scholar 

  27. 27

    Singh, A.P. & Wohland, T. Applications of imaging fluorescence correlation spectroscopy. Curr. Opin. Chem. Biol. 20, 29–35 (2014).

    CAS  Article  Google Scholar 

  28. 28

    Kannan, B. et al. Electron multiplying charge-coupled device camera based fluorescence correlation spectroscopy. Anal. Chem. 78, 3444–3451 (2006).

    CAS  Article  Google Scholar 

  29. 29

    Burkhardt, M. & Schwille, P. Electron multiplying CCD based detection for spatially resolved fluorescence correlation spectroscopy. Opt. Express 14, 5013–5020 (2006).

    Article  Google Scholar 

  30. 30

    Sisan, D.R., Arevalo, R., Graves, C., McAllister, R. & Urbach, J.S. Spatially resolved fluorescence correlation spectroscopy using a spinning disk confocal microscope. Biophys. J. 91, 4241–4252 (2006).

    CAS  Article  Google Scholar 

  31. 31

    Kannan, B. et al. Spatially resolved total internal reflection fluorescence correlation microscopy using an electron multiplying charge-coupled device camera. Anal. Chem. 79, 4463–4470 (2007).

    CAS  Article  Google Scholar 

  32. 32

    Matsumoto, M., Sugiura, T. & Minato, K. Spatially resolved fluorescence correlation spectroscopy based on electron-multiplying CCD. Proc. SPIE 6630 doi:10.1117/12.728399 (2013).

  33. 33

    Wohland, T., Shi, X., Sankaran, J. & Stelzer, E.H.K. Single plane illumination fluorescence correlation spectroscopy (SPIM-FCS) probes inhomogeneous three-dimensional environments. Opt. Express 18, 10627–10641 (2010).

    CAS  Article  Google Scholar 

  34. 34

    Schätzel, K. New concepts in correlator design. Inst. Phys. Conf. Ser. 77, 175–184 (1985).

    Google Scholar 

  35. 35

    Sankaran, J., Manna, M., Guo, L., Kraut, R. & Wohland, T. Diffusion, transport, and cell membrane organization investigated by imaging fluorescence cross-correlation spectroscopy. Biophys. J. 97, 2630–2639 (2009).

    CAS  Article  Google Scholar 

  36. 36

    Baum, M., Erdel, F., Wachsmuth, M. & Rippe, K. Retrieving the intracellular topology from multi-scale protein mobility mapping in living cells. Nat. Commun. 5, 4494 (2014).

    CAS  Article  Google Scholar 

  37. 37

    Bacia, K. & Schwille, P. Practical guidelines for dual-color fluorescence cross-correlation spectroscopy. Nat. Protoc. 2, 2842–2856 (2007).

    CAS  Article  Google Scholar 

  38. 38

    Capoulade, J., Wachsmuth, M., Hufnagel, L. & Knop, M. Quantitative fluorescence imaging of protein diffusion and interaction in living cells. Nat. Biotechnol. 29, 835–839 (2011).

    CAS  Article  Google Scholar 

  39. 39

    Sankaran, J., Shi, X., Ho, L.Y., Stelzer, E.H. & Wohland, T. ImFCS: a software for imaging FCS data analysis and visualization. Opt. Express 18, 25468–25481 (2010).

    Article  Google Scholar 

  40. 40

    Szaloki, N., Krieger, J.W., Komaromi, I., Toth, K. & Vamosi, G. Evidence for homodimerization of the c-Fos transcription factor in live cells revealed by FRET, SPIM-FCCS and MD-modeling. Mol. Cell. Biol. doi:10.1128/MCB.00346-15 (24 August 2015).

  41. 41

    Brazda, P. et al. Ligand binding shifts highly mobile retinoid X receptor to the chromatin-bound state in a coactivator-dependent manner, as revealed by single-cell imaging. Mol. Cell. Biol. 34, 1234–1245 (2014).

    Article  Google Scholar 

  42. 42

    Bag, N., Ali, A., Chauhan, V.S., Wohland, T. & Mishra, A. Membrane destabilization by monomeric hIAPP observed by imaging fluorescence correlation spectroscopy. Chem. Commun. 49, 9155–9157 (2013).

    CAS  Article  Google Scholar 

  43. 43

    Müller, P., Rogers, K.W., Yu, S.R., Brand, M. & Schier, A.F. Morphogen transport. Development 140, 1621–1638 (2013).

    Article  Google Scholar 

  44. 44

    Lenne, P.-F. et al. Dynamic molecular confinement in the plasma membrane by microdomains and the cytoskeleton meshwork. EMBO J. 25, 3245–3256 (2006).

    CAS  Article  Google Scholar 

  45. 45

    Wawrezinieck, L., Rigneault, H., Marguet, D. & Lenne, P.-F. Fluorescence correlation spectroscopy diffusion laws to probe the submicron cell membrane organization. Biophys. J. 89, 4029–4042 (2005).

    CAS  Article  Google Scholar 

  46. 46

    Unruh, J.R. & Gratton, E. Analysis of molecular concentration and brightness from fluorescence fluctuation data with an electron multiplied CCD camera. Biophys. J. 95, 5385–5398 (2008).

    CAS  Article  Google Scholar 

  47. 47

    Bestvater, F. et al. EMCCD-based spectrally resolved fluorescence correlation spectroscopy. Opt. Express 18, 23818–23828 (2010).

    CAS  Article  Google Scholar 

  48. 48

    Buchholz, J. et al. FPGA implementation of a 32×32 autocorrelator array for analysis of fast image series. Opt. Express 20, 17767–17782 (2012).

    Article  Google Scholar 

  49. 49

    Burri, S. et al. Architecture and applications of a high resolution gated SPAD image sensor. Opt. Express 22, 17573–17589 (2014).

    Article  Google Scholar 

  50. 50

    Greger, K., Neetz, M.J., Reynaud, E.G. & Stelzer, E.H. Three-dimensional fluorescence lifetime imaging with a single plane illumination microscope provides an improved signal to noise ratio. Opt. Express 19, 20743–20750 (2011).

    CAS  Article  Google Scholar 

  51. 51

    Pitrone, P.G. et al. OpenSPIM: an open-access light-sheet microscopy platform. Nat. Methods 10, 598–599 (2013).

    CAS  Article  Google Scholar 

  52. 52

    Wu, Y. et al. Inverted selective plane illumination microscopy (iSPIM) enables coupled cell identity lineaging and neurodevelopmental imaging in Caenorhabditis elegans. Proc. Natl. Acad. Sci. USA 108, 17708–17713 (2011).

    CAS  Article  Google Scholar 

  53. 53

    Wu, Y. et al. Spatially isotropic four-dimensional imaging with dual-view plane illumination microscopy. Nat. Biotechnol. 31, 1032–1038 (2013).

    CAS  Article  Google Scholar 

  54. 54

    Ries, J., Chiantia, S. & Schwille, P. Accurate determination of membrane dynamics with line-scan FCS. Biophys. J. 96, 1999–2008 (2009).

    CAS  Article  Google Scholar 

  55. 55

    Krieger, J.W. Mapping Diffusion Properties in Living Cells dissertation thesis, University of Heidelberg (2014).

  56. 56

    Singh, A.P. Light Sheet–Based Fluorescence Correlation and Cross-Correlation Spectroscopy for Quantitative Measurements of Bio-Molecules in Live Cells dissertation thesis, National University of Singapore (2014).

  57. 57

    Hwang, L.C. & Wohland, T. Single wavelength excitation fluorescence cross-correlation spectroscopy with spectrally similar fluorophores: resolution for binding studies. J. Chem. Phys. 122, 114708 (2005).

    Article  Google Scholar 

  58. 58

    Edelstein, A., Amodaj, N., Hoover, K., Vale, R. & Stuurman, N. Computer control of microscopes using μManager. Curr. Protoc. Mol. Biol. 2, 14.20.1–14.20.17 (2010).

    Google Scholar 

  59. 59

    Reynaud, E.G., Krzic, U., Greger, K. & Stelzer, E.H. Light sheet-based fluorescence microscopy: more dimensions, more photons, and less photodamage. HFSP J. 2, 266–275 (2008).

    Article  Google Scholar 

  60. 60

    Maizel, A., von Wangenheim, D., Federici, F., Haseloff, J. & Stelzer, E.H. High-resolution live imaging of plant growth in near physiological bright conditions using light sheet fluorescence microscopy. Plant. J. 68, 377–385 (2011).

    CAS  Article  Google Scholar 

  61. 61

    Weber, M., Mickoleit, M. & Huisken, J. Multilayer mounting for long-term light sheet microscopy of zebrafish. J. Vis. Exp. doi:10.3791/51119 (27 February 2014).

  62. 62

    Schmied, C., Stamataki, E. & Tomancak, P. Open-source solutions for SPIMage processing. Methods Cell Biol. 123, 505–529 (2014).

    Article  Google Scholar 

  63. 63

    Audiffred, J.F., De Leo, S.E., Brown, P.K., Hale-Donze, H. & Monroe, W.T. Characterization and applications of serum-free induced adhesion in Jurkat suspension cells. Biotechnol. Bioeng. 106, 784–793 (2010).

    CAS  Article  Google Scholar 

  64. 64

    Krzic, U., Gunther, S., Saunders, T.E., Streichan, S.J. & Hufnagel, L. Multiview light-sheet microscope for rapid in toto imaging. Nat. Methods 9, 730–733 (2012).

    CAS  Article  Google Scholar 

  65. 65

    Manders, E.M.M., Verbeek, F.J. & Aten, J.A. Measurement of co-localization of objects in dual-colour confocal images. J. Microsc. 169, 375–382 (1993).

    Article  Google Scholar 

  66. 66

    Cole, R.W., Jinadasa, T. & Brown, C.M. Measuring and interpreting point spread functions to determine confocal microscope resolution and ensure quality control. Nat. Protoc. 6, 1929–1941 (2011).

    CAS  Article  Google Scholar 

  67. 67

    Guo, S.M. et al. Bayesian approach to the analysis of fluorescence correlation spectroscopy data II: application to simulated and in vitro data. Anal. Chem. 84, 3880–3888 (2012).

    CAS  Article  Google Scholar 

  68. 68

    Akaike, H. A new look at the statistical-model identification. IEEE Transactions on Automatic Control 19, 716–723 (1974).

    Article  Google Scholar 

  69. 69

    Levin, M.K. & Carson, J.H. Fluorescence correlation spectroscopy and quantitative cell biology. Differentiation 72, 1–10 (2004).

    CAS  Article  Google Scholar 

  70. 70

    He, J., Guo, S.M. & Bathe, M. Bayesian approach to the analysis of fluorescence correlation spectroscopy data I: theory. Anal. Chem. 84, 3871–3879 (2012).

    CAS  Article  Google Scholar 

  71. 71

    Guo, S.M., Bag, N., Mishra, A., Wohland, T. & Bathe, M. Bayesian total internal reflection fluorescence correlation spectroscopy reveals hIAPP-induced plasma membrane domain organization in live cells. Biophys. J. 106, 190–200 (2014).

    CAS  Article  Google Scholar 

  72. 72

    Sengupta, P., Garai, K., Balaji, J., Periasamy, N. & Maiti, S. Measuring size distribution in highly heterogeneous systems with fluorescence correlation spectroscopy. Biophys. J. 84, 1977–1984 (2003).

    CAS  Article  Google Scholar 

  73. 73

    Modos, K. et al. Maximum-entropy decomposition of fluorescence correlation spectroscopy data: application to liposome-human serum albumin association. Eur. Biophys. J. 33, 59–67 (2004).

    CAS  Article  Google Scholar 

  74. 74

    Press, W.H., Flannery, B.P., Teukolsky, S.A. & Vetterling W.T . Numerical Recipes in C: The Art of Scientific Computing 2nd edn. (Cambridge University Press, 1993).

  75. 75

    Rao, R. et al. Stochastic approach to data analysis in fluorescence correlation spectroscopy. J. Phys. Chem. A 110, 10674–10682 (2006).

    CAS  Article  Google Scholar 

  76. 76

    Bag, N., Yap, D.H.X. & Wohland, T. Temperature dependence of diffusion in model and live cell membranes characterized by imaging fluorescence correlation spectroscopy. Biochim. Biophys. Acta-Biomembranes 1838, 802–813 (2014).

    CAS  Article  Google Scholar 

  77. 77

    Hirsch, M., Wareham, R.J., Martin-Fernandez, M.L., Hobson, M.P. & Rolfe, D.J. A stochastic model for electron multiplication charge-coupled devices—from theory to practice. PLoS ONE 8, e53671 (2013).

    CAS  Article  Google Scholar 

  78. 78

    Bag, N. Imaging Fluorescence Correlation Spectroscopy Studies Lipid Membrane Dynamics and Organization: Applications to the Investigation of Lipid Bilayers, Cell Membranes, and Membrane Active Peptides dissertation thesis, National University of Singapore (2014).

  79. 79

    Liu, P. et al. Investigation of the dimerization of proteins from the epidermal growth factor receptor family by single wavelength fluorescence cross-correlation spectroscopy. Biophys. J. 93, 684–698 (2007).

    CAS  Article  Google Scholar 

  80. 80

    Ries, J., Petrasek, Z., Garcia-Saez, A.J. & Schwille, P. A comprehensive framework for fluorescence cross-correlation spectroscopy. New J. Phys. 12 doi:10.1088/1367-2630/12/11/113009 (4 November 2010).

Download references


The project was supported by a NUS-BW (National University of Singapore/Baden-Württemberg BW2010-2) joint grant to T.W. and J.L., a doctoral and postdoc fellowship of the Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences to J.W.K., a doctoral scholarship by the National University of Singapore to A.P.S., a Singapore National Research Foundation funding for T.E.S. and A.P.S. and a doctoral scholarship of the National University of Singapore and a post-doctoral fellowship by a grant from the Singapore Ministry of Education (MOE2012-T3-1-008) to N.B. The authors thank G. Müller for preparing many of the cells that were measured for this paper. We also thank M. Suresh Sawant for providing the protocol for collagen coating. We thank P. Ingham for providing access to his light-sheet microscope.

Author information




T.W., J.L. and C.S.G. conceived the project; J.W.K., A.P.S. and N.B. performed the experiments and worked out major parts of the protocol; C.S.G. helped with the necessary image processing algorithms; T.E.S. and A.P.S. were responsible for the Drosophila measurements; J.W.K. and T.W. developed the software for data evaluation; all authors contributed to the writing of the paper.

Corresponding author

Correspondence to Thorsten Wohland.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Effect of pixel size on the measured autocorrelation functions.

(a-c) Autocorrelation curves from a HeLa cell, expressing eGFP-4x, acquired on a pco.edge sCMOS camera. (d-f) Intensity images of the same cell at different binning stages. The plots show different pixel binning settings. 2×2-binning was done during acquisition and additional binning was imposed during the correlation step. Minimum lag time and frame repetition time were τmin = 761.5 μs, exposure time was Δtexp = 500 μs, the pixel size (a) is given above the plots.

Supplementary Figure 2 Baseline clamping in Andor EMCCD cameras.

The upper two plots show the background intensity over time with (a) and without (b) the baseline clamp function activated. Subfigures (c) and (d) show typical ACFs calculated from data with and without the baseline clamping applied. For the latter case, there is a significant decay of the ACF on long timescales. As can be seen, this function significantly improves the stability of the signal. The camera was an Andor iXon 860, operated at 1 ms frame time and an EM-gain setting of 300.

Supplementary Figure 3 Preparation of sample bags for SPIM.

(a ) Shows the actual process from a sheet of LumoxFolie, which is heat-sealed to form a sheath. Then small sample bags are formed which may be filled and held by a self-closing tweezer. (b) Shows the controller built to control the temperature of a modified soldering tweezer, as shown in (c).

Supplementary Figure 4 Schematic of a dual-color SPIM with a multichannel imaging systems (DualView DV2).

The upper part of the image shows a top view of the instrument. The lower, shaded part shows a side-view of the illumination beam path. Optical components are labeled with abbreviations that are explained in the legend. Red lines are signal connection to the control computer. The table lists the parts we use in this SPIM.

Supplementary Figure 5 Stability over several years of the PSF-parameters determined with an imaging FCS focal volume calibration on a TIRF microscope and SPIM microscopes.

(a, b) Independent measurements on different RhoPE labelled DOPC bilayer preparations were performed across several years. The experimental conditions were kept identical. The measurements were taken at 1000 fps and an EM-gain of 300 (scale 6-300) with an Andor iXon 860 camera. The variation of (a) diffusion coefficient and (b) point spread function, which were determined following the protocol described in the main text, are shown. The solid blue line is the average and the dotted lines are the standard deviations of all the measurements. (c) Mean and standard deviation of the largest and (d) of the smallest width determined in a 3-dimensional Gaussian fit. The violet bands and numbers in the graphs are average and standard deviation over all mean values. All PSF-widths are given as 1/e² half-widths. For the TIRF with an NA of 1.45 this is significantly smaller than for the SPIM with a detection objective NA of only 1.0.

Supplementary Figure 6 Examples of the most often occurring artifacts in SPIM-FCS measurements.

(a) The effect of aggregates in a microspheres solution (as often used for calibration) on the autocorrelation curves (green: no aggregates, red: aggregates) and the maps of diffusion coefficient and particle number. (b) Typical distortions of autocorrelation curves by air bubbles and dirt in the buffer inside the sample chamber. If these swim through the light sheet, small dips in the count rate trace occur (see grey marked regions in the non-bleach corrected count rate curves on the right), that lead to long-term components and distortions of the correlation curves. The measurements were taken on HeLa cells expressing a fluorescent protein. (c) Distortions of the autocorrelation curves (EGFP-4x in HeLa cells) at moving intensity steps (green) and in unperturbed regions (red) inside a sample. The inset shows the bleach-corrected intensity traces for the two pixels. (d) Artifacts in a measurement of a membrane-bound protein in living cells, due to oscillations of the microscope setup (bad vibrational isolation of the optical table). All measurements were acquired on a home-built SPIM-FCS setup, using an EMCCD camera.

Supplementary Figure 7 Example results of SPIM-FCCS calibration measurements.

(a) Typical calibration measurements with 100 nm TetraSpec beads. The first graph shows auto- (green/red) and cross-correlation curves (blue) from a single pixel. The histograms (with parameter images as insets) show the distribution of three important fit results in a single measurement (128x20 pixels). (b) Shows single-pixel auto-and cross-correlation curves of a sample of 170bp dsDNA that is labeled on opposing ends with the dyes Alexa-488 and Alexa-594. (c) Shows example measurement of an eGFP-P30-mRFP dimer and separate eGFP and mRFP monomers, transfected into HeLa cells. For each sample, typical single-pixel auto- and cross-correlation curves are given. The images show the fluorescence intensity and a map of the relative dimer concentration CAB/Call. The histogram shows the distribution of CAB/Call in the images. All data was acquired on an Andor EMCCD camera with EM-Gain 300 (except beads: 100) and 128x20 pixels (except 170bp DNA: 128x6 pixels). The dark blue dashed line gives the level of cross-correlation, which can be explained by crosstalk. Scale bar lengths differ between subfigures and are indicated in the figure.

Supplementary Figure 8 Example raw data from a SPIM-FCS measurement.

(a,b) Single transmitted and fluorescence image of a cell to select the region-of-interest (shown in blue color), (c) Time image series of a region-of-interest (~60,000 frames of 20x64 pixels at ~2,000 frames per second, here just shown three representative images), (d) Background time image series for background correction, (e) ACF and/or CCF parameter fits and statistical analysis; in this example measurement, the pixelated image on left shows the diffusion coefficient of membrane protein and the right side is the cytosolic protein diffusion coefficient.

Supplementary Figure 9 Bleach correction in FCS.

This figure demonstrates the effect of fluorophore reservoir depletion on the fluorescence intensity signal (a) and on the ACFs calculated from it (b) in the first row. (c,d) The second row shows the result of the bleach correction, described in the main text both on the count rate and the ACF curves. Note, how also the intensity signal variance (height of the intensity fluctuations) is recovered by the proposed transformation, which can be seen especially well for the light blue curve. As can be seen in (d), the bleach correction cannot recover the correct ACF amplitude, if too much bleaching occurs (typically >50%), but this effect usually only influence the particle number (i.e. amplitude of G(τ)) and with little effect on the diffusion coefficient (decay time of G(τ)).

Supplementary Figure 10 FCS autocorrelation curves with vibrations present in the microscope.

Typical correlation plots in presence and absence of vibration present due to either laser instability, cooling fan vibration (laser and camera), drift in the sample bag/mount or drift in sample stages.

Supplementary Figure 11 Effect of the background correction on fit results.

(a-c) Autocorrelation curves from a HeLa cell, expressing eGFP-4x, acquired on a pco.edge sCMOS camera. (d-f) Intensity images of the same cell at different binning stages. The plots show different pixel binning settings. 2 × 2-binning was done during acquisition and additional binning was imposed during the correlation step. Minimum lag time and frame repetition time were τmin = 761.5 μs, exposure time was Δtexp = 500 μs, the pixel size a is given above the plots.

Supplementary Figure 12 Typical stripe artifacts in SPIM-FCS measurements.

(a,b) Fluorescence intensity images with dark stripes due to dirt on the sample bag. (c) Map of the diffusion coefficient D. (d) Map of the concentration c. (e) Plot of D vs. x-coordinate. (f) Plot of c vs. x-coordinate. The color bars for (b,d) are placed on the right of (c,e). (g) Fluorescence intensity image of the cell. (h) Map of the diffusion coefficient D in the cell. (i) Map of the concentration c in the cell. A 1-component normal diffusion fit was used for all samples.

Supplementary Figure 13 Example result of SPIM-FCS experiment on a giant unilamellar vesicle.

(a) The figure shows the cross-section fluorescence image of a giant unilamellar vesicle embedded in agar (GUVs, POPC 89%, POPG 10% and PI(4,5)P2 1% and TopFluor PI(4,5)P2), (b) the ACF and (c-d) diffusion and concentration maps.

Supplementary Figure 14 Example results of SPIM-FCS experiments on a membrane-bound protein

The figure shows the results of a SPIM-FCS measurement (using an EMCCD camera) of eGFP fluorescent proteins, which carry a plasma membrane targeting sequence (PMT) expressed in a CHO cell. (a) Shows the autocorrelation functions for a pixel in the cytosol (red) and a pixel in the membrane (blue). (b) Shows a fluorescence intensity image, which shows that the fusion protein is enriched in the cell membrane. (c,d) Show maps of the fast diffusion coefficient and of the fraction of the slow diffusing component from a 2-component normal diffusion fit. The fraction of the slow component is significantly increased for the membrane pixels.

Supplementary Figure 15 Fluorescence fluctuations and ACFs extracted from the same lipid bilayer sample at different EM-gain settings of the used EM-CCD camera.

This figure shows the improvement of the quality of ACF as the EM gain is raised. The first column shows the single-pixel intensity signal (red) and non-illuminated/background signal (black) of a labeled supported bilayer acquired on a TIRF microscope at different EM-gain settings. The second column shows the ACFs of the background signal (no illumination) and the third column the ACFs of the illuminated sample. The sample was a DOPC bilayer labeled with 30 nM Rho-PE. Measurements were performed on a TIRF microscope. The camera was an Andor iXon 860, operated at 1ms frame time.

Supplementary Figure 16 Dependence of the diffusion coefficient D and particle number N in dependence of the EM gain setting of the EM-CCD camera, used for the measurements.

This figure shows how the evaluated D and N of a sample changes with EM-gain. In the inset the change of the coefficient of variation (SD/mean) of the two parameters is shown. The sample was a DOPC bilayer labeled with 30 nM Rho-PE. Measurements were performed on a TIRF microscope. The camera was an Andor iXon 860, operated at 1 ms frame time.

Supplementary Figure 17 Example results of a 2-pixel imaging FCCS measurement with a directed flow.

The figure shows the results of a 2-pixel SPIM-FCCS measurement of a sample of fluorescent microspheres (100 nm), which show a directed flowing motion. (a) Shows the auto- G(τ;0,0) and cross-correlation functions G(τ; Δx, Δy) to the direct neighbor pixels of a single pixel (pixel size: a). The lines are the results of a global imaging FCCS fit. (b) Shows a map of retrieved 2D flow vectors and (c) the histogram of the two flow velocity vector components. (d) Shows a long-exposure overview image (100 ms) of the sample. The white stripes are tracks of larger aggregates.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–17, Supplementary Discussion, Supplementary Note, Supplementary Methods 1 and 2, and Supplementary Table 1 (PDF 3316 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Krieger, J., Singh, A., Bag, N. et al. Imaging fluorescence (cross-) correlation spectroscopy in live cells and organisms. Nat Protoc 10, 1948–1974 (2015).

Download citation

Further reading


By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing