Abstract

Confocal microscopy is an oft-used technique in biology. Deconvolution of 3D images reduces blurring from out-of-focus light and enables quantitative analyses, but existing software for deconvolution is slow and expensive. We present a parallelized software method that runs within ImageJ and deconvolves 3D images ~100 times faster than conventional software (few seconds per image) by running on a low-cost graphics processor board (GPU). We demonstrate the utility of this software by analyzing microclusters of T cell receptors in the immunological synapse of a CD4 + T cell and dendritic cell. This software provides a low-cost and rapid way to improve the accuracy of 3D microscopic images obtained by any method.

© 2013 OSA

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  1. F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
    [CrossRef] [PubMed]
  2. B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
    [CrossRef] [PubMed]
  3. K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
    [CrossRef] [PubMed]
  4. A. Edelstein, N. Amodaj, K. Hoover, R. Vale, and N. Stuurman, Computer Control of Microscopes Using µManager, Current Protocols in Molecular Biology (John Wiley & Sons, Inc., 2010).
  5. J. Hoberock and N. Bell, “Thrust: A Parallel Template Library,” (2010), retrieved http://www.meganewtons.com/ .
  6. J. B. Pawley, Handbook of Biological Confocal Microscopy (Kluwer Academic Publishers, 1995).
  7. J. B. Sibarita, “Deconvolution microscopy,” Microscopy Techniques, 1288–1291 (2005).
  8. Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
    [CrossRef] [PubMed]
  9. J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
    [CrossRef] [PubMed]
  10. R. P. Dougherty, “Diffraction PSF 3D,” (2005), retrieved http://www.optinav.com/Diffraction-PSF-3D.htm .
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    [CrossRef]
  12. W. Richardson, “Bayesian-based iterative method of image reconstruction,” J. Opt. Soc. Am.62(1), 55–59 (1972).
    [CrossRef]
  13. D. S. Biggs, “3D Deconvolution Microscopy,” in Current Protocols in Cytometry (Wiley Online Library, 2010), Chap. 12, Unit 12 19, pp. 11–20.
  14. A. Griffa, N. Garin, and D. Sage, “Hollow Bars,” retrieved http://bigwww.epfl.ch/deconvolution/?p=bars .
  15. A. Griffa, N. Garin, and D. Sage, “C. elegans embryo” retrieved http://bigwww.epfl.ch/deconvolution/?p=bio .
  16. S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
    [CrossRef] [PubMed]
  17. P. J. Lu, P. A. Sims, H. Oki, J. B. Macarthur, and D. A. Weitz, “Target-locking acquisition with real-time confocal (TARC) microscopy,” Opt. Express15(14), 8702–8712 (2007).
    [CrossRef] [PubMed]

2009 (1)

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

2008 (1)

B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
[CrossRef] [PubMed]

2007 (1)

2005 (1)

J. B. Sibarita, “Deconvolution microscopy,” Microscopy Techniques, 1288–1291 (2005).

2004 (1)

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

2002 (1)

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

1999 (1)

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

1990 (1)

Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
[CrossRef] [PubMed]

1974 (1)

L. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J.79, 745 (1974).
[CrossRef]

1972 (1)

Agard, D. A.

Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
[CrossRef] [PubMed]

Barr, V. A.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Bunnell, S. C.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Conchello, J. A.

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

Cooper, J.

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

Forsten-Williams, K.

B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
[CrossRef] [PubMed]

Hiraoka, Y.

Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
[CrossRef] [PubMed]

Hong, D. I.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Hoppe, A. D.

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Kainkaryam, R.

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Kardon, J. R.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Karpova, T.

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

Lin, E.

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

Linderman, J. J.

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Lu, P. J.

Lucy, L.

L. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J.79, 745 (1974).
[CrossRef]

Macarthur, J. B.

McGlade, C. J.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

McNally, J. G.

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

Mehta, K.

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Moore, E. D. W.

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

Oki, H.

Richardson, W.

Samelson, L. E.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Sedarat, F.

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

Sedat, J. W.

Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
[CrossRef] [PubMed]

Sibarita, J. B.

J. B. Sibarita, “Deconvolution microscopy,” Microscopy Techniques, 1288–1291 (2005).

Sims, P. A.

Starr, T.

B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
[CrossRef] [PubMed]

Storrie, B.

B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
[CrossRef] [PubMed]

Tibbits, G. F.

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

Weitz, D. A.

Woolf, P. J.

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Yamazaki, T.

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

Astron. J. (1)

L. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J.79, 745 (1974).
[CrossRef]

Biophys. J. (1)

Y. Hiraoka, J. W. Sedat, and D. A. Agard, “Determination of three-dimensional imaging properties of a light microscope system. Partial confocal behavior in epifluorescence microscopy,” Biophys. J.57(2), 325–333 (1990).
[CrossRef] [PubMed]

J. Appl. Physiol. (1)

F. Sedarat, E. Lin, E. D. W. Moore, and G. F. Tibbits, “Deconvolution of confocal images of dihydropyridine and ryanodine receptors in developing cardiomyocytes,” J. Appl. Physiol.97(3), 1098–1103 (2004).
[CrossRef] [PubMed]

J. Cell Biol. (1)

S. C. Bunnell, D. I. Hong, J. R. Kardon, T. Yamazaki, C. J. McGlade, V. A. Barr, and L. E. Samelson, “T cell receptor ligation induces the formation of dynamically regulated signaling assemblies,” J. Cell Biol.158(7), 1263–1275 (2002).
[CrossRef] [PubMed]

J. Opt. Soc. Am. (1)

Methods (1)

J. G. McNally, T. Karpova, J. Cooper, and J. A. Conchello, “Three-dimensional imaging by deconvolution microscopy,” Methods19(3), 373–385 (1999).
[CrossRef] [PubMed]

Methods Mol. Biol. (1)

B. Storrie, T. Starr, and K. Forsten-Williams, “Using quantitative fluorescence microscopy to probe organelle assembly and membrane trafficking,” Methods Mol. Biol.457, 179–192 (2008).
[CrossRef] [PubMed]

Microscopy Techniques (1)

J. B. Sibarita, “Deconvolution microscopy,” Microscopy Techniques, 1288–1291 (2005).

Opt. Express (1)

Proteomics (1)

K. Mehta, A. D. Hoppe, R. Kainkaryam, P. J. Woolf, and J. J. Linderman, “A computational approach to inferring cellular protein-binding affinities from quantitative fluorescence resonance energy transfer imaging,” Proteomics9(23), 5371–5383 (2009).
[CrossRef] [PubMed]

Other (7)

A. Edelstein, N. Amodaj, K. Hoover, R. Vale, and N. Stuurman, Computer Control of Microscopes Using µManager, Current Protocols in Molecular Biology (John Wiley & Sons, Inc., 2010).

J. Hoberock and N. Bell, “Thrust: A Parallel Template Library,” (2010), retrieved http://www.meganewtons.com/ .

J. B. Pawley, Handbook of Biological Confocal Microscopy (Kluwer Academic Publishers, 1995).

D. S. Biggs, “3D Deconvolution Microscopy,” in Current Protocols in Cytometry (Wiley Online Library, 2010), Chap. 12, Unit 12 19, pp. 11–20.

A. Griffa, N. Garin, and D. Sage, “Hollow Bars,” retrieved http://bigwww.epfl.ch/deconvolution/?p=bars .

A. Griffa, N. Garin, and D. Sage, “C. elegans embryo” retrieved http://bigwww.epfl.ch/deconvolution/?p=bio .

R. P. Dougherty, “Diffraction PSF 3D,” (2005), retrieved http://www.optinav.com/Diffraction-PSF-3D.htm .

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Figures (4)

Fig. 1
Fig. 1

(a) Parallel algorithm for calculation of the point spread function (PSF). The PSF has radial symmetry, so we start by calculating one radial slice of the PSF at integer radii using cylindrical coordinates. Each point of the radius-depth slice grid entails numerical integration of the Airy function, which is performed in parallel. The inset cube is the final PSF, and the points of one octant are calculated by interpolating from the cylindrical coordinates. Then the octants are mapped using symmetry operations. (b) Timing to calculate the point spread function (PSF) for a variety of image sizes and using our GPU-based parallel, interpolation algorithm; using a GPU-based direct calculation for each point in the PSF; and using a CPU-based publically available package, Diffraction PSF 3D [10], to calculate.

Fig. 2
Fig. 2

Deconvolution of test data. (a) Richardson-Lucy deconvolution is applied to the noisy bars data set (left) for 25 iterations (middle), compared to the underlying true image (right). (b) Timing of deconvolution for given number of iterations to deconvolve the bars data set by CPU-based and GPU-based software.

Fig. 3
Fig. 3

Deconvolution of T cell-Dendritic cell immunological synapse. Deconvolved image shows two T cells, where right-most one is interacting with antigen presenting cell, fixed, permeabilized and stained with AlexaFluor 568-labeled phalloidin to reveal the distribution of intracellular actin. (a) Shows 3D rendering of cells, where red portrays TCR staining, green I-Ab (MHC class II), and blue phalloidin. (b) Shows a slice at the synapse, revealing the improvement in visualization of structures in the immunological synapse. (c) Line scan from (b) shows improved visualization of TCR microclusters in the interface.

Fig. 4
Fig. 4

(a) Times to run various numbers of iterations of Richardson-Lucy deconvolution for various sizes of images using our GPU-based software. Even quite large images can undergo 100 iteration cycles in less than 5 s. (b) Software interface runs as a plug-in in ImageJ. User can specify the GPU device to run on (if more than one is present), which image to deconvolve, and the number of iterations to run. To have the software stop iterating automatically, specify “-1” as the number of iterations. If generate PSF is unchecked, the user can specify the PSF to use. Otherwise, the user can specify the emission wavelength, spacing, objective numerical aperture (NA), and refractive index.

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