Abstract

Multifocal two-photon microscopy (MTPM) increases imaging speed over single-focus scanning by parallelizing fluorescence excitation. The imaged fluorescence’s susceptibility to crosstalk, however, severely degrades contrast in scattering tissue. Here we present a source-localized MTPM scheme optimized for high speed functional fluorescence imaging in scattering mammalian brain tissue. A rastered line array of beamlets excites fluorescence imaged with a complementary metal-oxide-semiconductor (CMOS) camera. We mitigate scattering-induced crosstalk by temporally oversampling the rastered image, generating grouped images with structured illumination, and applying Richardson-Lucy deconvolution to reassign scattered photons. Single images are then retrieved with a maximum intensity projection through the deconvolved image groups. This method increased image contrast at depths up to 112 μm in scattering brain tissue and reduced functional crosstalk between pixels during neuronal calcium imaging. Source-localization did not affect signal-to-noise ratio (SNR) in densely labeled tissue under our experimental conditions. SNR decreased at low frame rates in sparsely labeled tissue, with no effect at frame rates above 50 Hz. Our non-descanned source-localized MTPM system enables high SNR, 100 Hz capture of fluorescence transients in scattering brain, increasing the scope of MTPM to faster and smaller functional signals.

Published by The Optical Society under the terms of the Creative Commons Attribution 4.0 License. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI.

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References

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2018 (1)

R. Schuck, M. A. Go, S. Garasto, S. Reynolds, P. L. Dragotti, and S. R. Schultz, “Multiphoton minimal inertia scanning for fast acquisition of neural activity signals,” J. Neural Eng. 15, 025003 (2018).
[Crossref]

2017 (3)

S. R. Schultz, C. S. Copeland, A. J. Foust, P. Quicke, and R. Schuck, “Advances in Two-Photon Scanning and Scanless Microscopy Technologies for Functional Neural Circuit Imaging,” Proc. IEEE 105, 139–157 (2017).
[Crossref]

S. Bovetti, C. Moretti, S. Zucca, M. Dal Maschio, P. Bonifazi, and T. Fellin, “Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain,” Sci. Rep. 7, 40041 (2017).
[Crossref] [PubMed]

D. Tanese, J.-Y. Weng, V. Zampini, V. de Sars, M. Canepari, B. Rózsa, v. Emiliani, and D. Zecevic, “Imaging membrane potential changes from dendritic spines using computer-generated holography,” Neurophotonics 4, 031211 (2017).
[Crossref] [PubMed]

2016 (2)

M. L. Castanares, V. Gautam, J. Drury, H. Bachor, and V. R. Daria, “Efficient multi-site two-photon functional imaging of neuronal circuits,” Biomed. Opt. Express 7, 5325–5334 (2016).
[Crossref] [PubMed]

R. Prevedel, A. J. Verhoef, A. J. Pernía-Andrade, S. Weisenburger, B. S. Huang, T. Nöbauer, A. Fernández, J. E. Delcour, P. Golshani, A. Baltuska, and A. Vaziri, “Fast volumetric calcium imaging across multiple cortical layers using sculpted light,” Nat. Methods 13, 1021–1028 (2016).
[Crossref] [PubMed]

2015 (6)

A. Luczak, B. L. McNaughton, and K. D. Harris, “Packet-based communication in the cortex,” Nat. Rev. Neurosci. 16, 745–755 (2015).
[Crossref] [PubMed]

A. J. Foust, V. Zampini, D. Tanese, E. Papagiakoumou, and V. Emiliani, “Computer-generated holography enhances voltage dye fluorescence discrimination in adjacent neuronal structures,” Neurophotonics 2, 021007 (2015).
[Crossref] [PubMed]

P. Pozzi, D. Gandolfi, M. Tognolina, G. Chirico, J. Mapelli, and E. D’Angelo, “High-throughput spatial light modulation two-photon microscopy for fast functional imaging,” Neurophotonics 2, 015005 (2015).
[Crossref] [PubMed]

S. J. Yang, W. E. Allen, I. Kauvar, A. S. Andalman, N. P. Young, C. K. Kim, J. H. Marshel, G. Wetzstein, and K. Deisseroth, “Extended field-of-view and increased-signal 3d holographic illumination with time-division multiplexing,” Opt. Express 23, 32573–32581 (2015).
[Crossref] [PubMed]

J. W. Cha, E. Y. S. Yew, D. Kim, J. Subramanian, E. Nedivi, and P. T. C. So, “Non-descanned multifocal multiphoton microscopy with a multianode photomultiplier tube,” AIP Adv. 5, 084802 (2015).
[Crossref] [PubMed]

P. Rupprecht, R. Prevedel, F. Groessl, W. E. Haubensak, and A. Vaziri, “Optimizing and extending light-sculpting microscopy for fast functional imaging in neuroscience,” Biomed. Opt. Express 6, 353–368 (2015).
[Crossref] [PubMed]

2014 (5)

S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and S. image contributors, “scikit-image: image processing in Python,” PeerJ 2, e453 (2014).
[Crossref] [PubMed]

J. T. Ting, T. L. Daigle, Q. Chen, and G. Feng, “Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics,” Methods Mol. Biol. 1183, 221–242 (2014).
[Crossref] [PubMed]

M. Tada, A. Takeuchi, M. Hashizume, K. Kitamura, and M. Kano, “A highly sensitive fluorescent indicator dye for calcium imaging of neural activity in vitro and in vivo,” Eur. J. Neurosci. 39, 1720–1728 (2014).
[Crossref] [PubMed]

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1, e10 (2014).
[Crossref]

J. W. Cha, V. R. Singh, K. H. Kim, J. Subramanian, Q. Peng, H. Yu, E. Nedivi, and P. T. C. So, “Reassignment of scattered emission photons in multifocal multiphoton microscopy,” Sci. Rep. 4, 5153 (2014).
[Crossref] [PubMed]

2013 (1)

M. Ducros, Y. Goulam Houssen, J. Bradley, V. de Sars, and S. Charpak, “Encoded multisite two-photon microscopy,” Proc. Natl. Acad. Sci. U. S. A. 110, 13138–13143 (2013).
[Crossref] [PubMed]

2012 (1)

Y. Shao, W. Qin, H. Liu, J. Qu, X. Peng, H. Niu, and B. Z. Gao, “Addressable multiregional and multifocal multiphoton microscopy based on a spatial light modulator,” J. Biomed. Opt. 17, 030505 (2012).
[Crossref] [PubMed]

2011 (2)

R. Franconville, G. Revet, G. Astorga, B. Schwaller, and I. Llano, “Somatic calcium level reports integrated spiking activity of cerebellar interneurons in vitro and in vivo,” J. Neurophysiol. 106, 1793–1805 (2011).
[Crossref] [PubMed]

A. Cheng, J. T. Gonçalves, P. Golshani, K. Arisaka, and C. Portera-Cailliau, “Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing,” Nat. Methods 8, 139–142 (2011).
[Crossref] [PubMed]

2009 (1)

B. O. Watson, V. Nikolenko, and R. Yuste, “Two-photon imaging with diffractive optical elements,” Front. Neural Circuits 3, 6 (2009).
[PubMed]

2008 (1)

V. Nikolenko, B. O. Watson, R. Araya, A. Woodruff, D. S. Peterka, and R. Yuste, “SLM Microscopy: Scanless Two-Photon Imaging and Photostimulation with Spatial Light Modulators,” Front. Neural Circuits 2, 5 (2008).
[Crossref]

2007 (2)

Q. Yuan, D.-L. Qiu, J. T. Weber, C. Hansel, and T. Knöpfel, “Climbing fiber-triggered metabotropic slow potentials enhance dendritic calcium transients and simple spike firing in cerebellar Purkinje cells,” Mol. Cell. Neurosci. 35, 596–603 (2007).
[Crossref] [PubMed]

K. H. Kim, C. Buehler, K. Bahlmann, T. Ragan, W.-C. A. Lee, E. Nedivi, E. L. Heffer, S. Fantini, and P. T. C. So, “Multifocal multiphoton microscopy based on multianode photomultiplier tubes,” Opt. Express 15, 11658–11678 (2007).
[Crossref] [PubMed]

2006 (1)

R. Kurtz, M. Fricke, J. Kalb, P. Tinnefeld, and M. Sauer, “Application of multiline two-photon microscopy to functional in vivo imaging,” J. Neurosci. Methods 151, 276–286 (2006).
[Crossref] [PubMed]

2003 (1)

2001 (1)

T. Nielsen, M. Fricke, D. Hellweg, and P. Andresen, “High efficiency beam splitter for multifocal multiphoton microscopy,” J. Microsc. 201, 368–376 (2001).
[Crossref] [PubMed]

2000 (1)

1998 (1)

1995 (2)

J. C. Yen, F. J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding,” IEEE Trans. Image Process. 4, 370–378 (1995).
[Crossref] [PubMed]

D. A. Fish, J. G. Walker, A. M. Brinicombe, and E. R. Pike, “Blind deconvolution by means of the Richardson-Lucy algorithm,” J. Opt. Soc. Am. A 12, 58–65 (1995).
[Crossref]

1994 (1)

R. L. White, “Image restoration using the damped richardson-lucy method,” Proc. SPIE 2198, 1342–1349 (1994).
[Crossref]

1990 (1)

W. Denk, J. Strickler, and W. Webb, “Two-photon laser scanning fluorescence microscopy,” Science 248, 73–76 (1990).
[Crossref] [PubMed]

1974 (1)

L. B. Lucy, “An iterative technique for the rectification of observed distributions,” Astron. J. 79, 745–754 (1974).
[Crossref]

1972 (1)

1941 (1)

L. C. Henyey and J. L. Greenstein, “Diffuse radiation in the Galaxy,” Astrophys. J. 93, 70–83 (1941).
[Crossref]

Abrahamsson, T.

S. Reynolds, T. Abrahamsson, P. J. Sjöström, S. R. Schultz, and P. L. Dragotti, “Cosmic: A consistent metric for spike inference from calcium imaging,” Neural Computation (In press).

Allen, W. E.

Amodaj, N.

A. D. Edelstein, M. A. Tsuchida, N. Amodaj, H. Pinkard, R. D. Vale, and N. Stuurman, “Advanced methods of microscope control using μManager software,” J. Biol. Methods 1, e10 (2014).
[Crossref]

Andalman, A. S.

Andresen, P.

T. Nielsen, M. Fricke, D. Hellweg, and P. Andresen, “High efficiency beam splitter for multifocal multiphoton microscopy,” J. Microsc. 201, 368–376 (2001).
[Crossref] [PubMed]

Antolini, R.

Araya, R.

V. Nikolenko, B. O. Watson, R. Araya, A. Woodruff, D. S. Peterka, and R. Yuste, “SLM Microscopy: Scanless Two-Photon Imaging and Photostimulation with Spatial Light Modulators,” Front. Neural Circuits 2, 5 (2008).
[Crossref]

Arisaka, K.

A. Cheng, J. T. Gonçalves, P. Golshani, K. Arisaka, and C. Portera-Cailliau, “Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing,” Nat. Methods 8, 139–142 (2011).
[Crossref] [PubMed]

Astorga, G.

R. Franconville, G. Revet, G. Astorga, B. Schwaller, and I. Llano, “Somatic calcium level reports integrated spiking activity of cerebellar interneurons in vitro and in vivo,” J. Neurophysiol. 106, 1793–1805 (2011).
[Crossref] [PubMed]

Bachor, H.

Bahlmann, K.

Baltuska, A.

R. Prevedel, A. J. Verhoef, A. J. Pernía-Andrade, S. Weisenburger, B. S. Huang, T. Nöbauer, A. Fernández, J. E. Delcour, P. Golshani, A. Baltuska, and A. Vaziri, “Fast volumetric calcium imaging across multiple cortical layers using sculpted light,” Nat. Methods 13, 1021–1028 (2016).
[Crossref] [PubMed]

Bewersdorf, J.

Bonifazi, P.

S. Bovetti, C. Moretti, S. Zucca, M. Dal Maschio, P. Bonifazi, and T. Fellin, “Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain,” Sci. Rep. 7, 40041 (2017).
[Crossref] [PubMed]

Boulogne, F.

S. van der Walt, J. L. Schönberger, J. Nunez-Iglesias, F. Boulogne, J. D. Warner, N. Yager, E. Gouillart, T. Yu, and S. image contributors, “scikit-image: image processing in Python,” PeerJ 2, e453 (2014).
[Crossref] [PubMed]

Bovetti, S.

S. Bovetti, C. Moretti, S. Zucca, M. Dal Maschio, P. Bonifazi, and T. Fellin, “Simultaneous high-speed imaging and optogenetic inhibition in the intact mouse brain,” Sci. Rep. 7, 40041 (2017).
[Crossref] [PubMed]

Bradley, J.

M. Ducros, Y. Goulam Houssen, J. Bradley, V. de Sars, and S. Charpak, “Encoded multisite two-photon microscopy,” Proc. Natl. Acad. Sci. U. S. A. 110, 13138–13143 (2013).
[Crossref] [PubMed]

Brinicombe, A. M.

Buehler, C.

Canepari, M.

D. Tanese, J.-Y. Weng, V. Zampini, V. de Sars, M. Canepari, B. Rózsa, v. Emiliani, and D. Zecevic, “Imaging membrane potential changes from dendritic spines using computer-generated holography,” Neurophotonics 4, 031211 (2017).
[Crossref] [PubMed]

Castanares, M. L.

Cha, J. W.

J. W. Cha, E. Y. S. Yew, D. Kim, J. Subramanian, E. Nedivi, and P. T. C. So, “Non-descanned multifocal multiphoton microscopy with a multianode photomultiplier tube,” AIP Adv. 5, 084802 (2015).
[Crossref] [PubMed]

J. W. Cha, V. R. Singh, K. H. Kim, J. Subramanian, Q. Peng, H. Yu, E. Nedivi, and P. T. C. So, “Reassignment of scattered emission photons in multifocal multiphoton microscopy,” Sci. Rep. 4, 5153 (2014).
[Crossref] [PubMed]

Chang, F. J.

J. C. Yen, F. J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding,” IEEE Trans. Image Process. 4, 370–378 (1995).
[Crossref] [PubMed]

Chang, S.

J. C. Yen, F. J. Chang, and S. Chang, “A new criterion for automatic multilevel thresholding,” IEEE Trans. Image Process. 4, 370–378 (1995).
[Crossref] [PubMed]

Charpak, S.

M. Ducros, Y. Goulam Houssen, J. Bradley, V. de Sars, and S. Charpak, “Encoded multisite two-photon microscopy,” Proc. Natl. Acad. Sci. U. S. A. 110, 13138–13143 (2013).
[Crossref] [PubMed]

Chen, Q.

J. T. Ting, T. L. Daigle, Q. Chen, and G. Feng, “Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics,” Methods Mol. Biol. 1183, 221–242 (2014).
[Crossref] [PubMed]

Cheng, A.

A. Cheng, J. T. Gonçalves, P. Golshani, K. Arisaka, and C. Portera-Cailliau, “Simultaneous two-photon calcium imaging at different depths with spatiotemporal multiplexing,” Nat. Methods 8, 139–142 (2011).
[Crossref] [PubMed]

Chirico, G.

P. Pozzi, D. Gandolfi, M. Tognolina, G. Chirico, J. Mapelli, and E. D’Angelo, “High-throughput spatial light modulation two-photon microscopy for fast functional imaging,” Neurophotonics 2, 015005 (2015).
[Crossref] [PubMed]

Choudhury, A.

Copeland, C. S.

S. R. Schultz, C. S. Copeland, A. J. Foust, P. Quicke, and R. Schuck, “Advances in Two-Photon Scanning and Scanless Microscopy Technologies for Functional Neural Circuit Imaging,” Proc. IEEE 105, 139–157 (2017).
[Crossref]

D’Angelo, E.

P. Pozzi, D. Gandolfi, M. Tognolina, G. Chirico, J. Mapelli, and E. D’Angelo, “High-throughput spatial light modulation two-photon microscopy for fast functional imaging,” Neurophotonics 2, 015005 (2015).
[Crossref] [PubMed]

Daigle, T. L.

J. T. Ting, T. L. Daigle, Q. Chen, and G. Feng, “Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics,” Methods Mol. Biol. 1183, 221–242 (2014).
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Supplementary Material (1)

NameDescription
» Visualization 1       A source localized (top) and non-source localized (bottom) image of a GFP-expressing neuron's apical dendrite acquired with the multifocal two-photon system. Complete frames are maximum intensity projections of 8 streak images.

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Figures (7)

Fig. 1
Fig. 1 The multifocal apparatus. The laser beam is shaped into a line by an asymmetric telescope before passing through a microlens array which splits it into multiple beamlets. Individual beamlets are collimated and directed onto galvanometer mirrors which are conjugate with the back focal plane of the objective lens. The beamlets are raster scanned in the sample forming an image which is collected onto a CMOS camera synchronised with the galvanometers. A wide-field LED excitation path is used for comparison with the multifocal excitation.
Fig. 2
Fig. 2 Optical characteristics of the multifocal array. a) Example image of a line beamlet array on a thin fluorescent slide. b) Lateral and c) axial cross sections of a single beamlet scanned through a thin fluorescent layer. d) An illustration of the scanning strategy used. Each beamlet is rastered in a rectangle orthogonal to the beamlet axis to build up the image. The temporally oversampled images used in the source localization process consist of one image per vertical raster line, in this illustration, four images.
Fig. 3
Fig. 3 The source localization process. a) To generate a single source-localized frame, eight temporally oversampled images are acquired, corresponding to a single line scan from each beamlet. A subset of these are shown in the leftmost column of ‘streak’ images. Any illumination arriving onto the camera not on the line has arisen from scattering of the emission. We use Richardson-Lucy deconvolution to reassign the light back to its probable origin. The right column of ‘streak’ images shows the deconvolved images. Below these columns is a maximum intensity projection in y of the bottom ‘streak’ image showing the increase in contrast due to source localization. Once the light is reassigned, we take a maximum intensity projection through the temporally oversampled frames to recover a single frame. The two right-most images show a comparison between a source localized frame and an unprocessed image and below them a maximum intensity projection in y to show the increased contrast from source localization. Scale bar 20 μm. b) A diagram of the Monte Carlo method used to generate a deconvolution kernel for the Richardson-Lucy deconvolution. Photons from a point source (green dot) were propagated through a scattering medium and backprojected when they reached the surface. Red rays were scattered and the dotted lines show backprojection to their apparent origin. Black rays are unscattered light. c) A cross section through the scattering PSF used for the deconvolution. The sampled PSF was approximately radially symmetric. d) A source localized (top) and non-source localized (bottom) image of a layer 2/3 pyramidal cell’s apical dendrite labeled with membrane-targeted GFP. 12 lines per frame, scale bar 50 μm, see Visualization 1.)
Fig. 4
Fig. 4 Characterization of the increased depth penetration from source localization. a) Image contrast plotted against depth from the tissue surface for one-photon wide-field LED illumination, MTPM, source localized MTPM and two-photon point scanning. Plotted as mean ± s.e.m. b) Example frames taken at different depths in fixed brain tissue without (i – iv) and with (v – viii) source localization. To the right ix) – xii) are the histograms of the normalized images, in red without source localization and blue with source localization. Scale bar 50 μm
Fig. 5
Fig. 5 The predicted effect of changing foci separation on depth penetration. a) False color plots of relative contrast modeled at different depths for varying line spacing for standard MTPM and source localized MTPM for three different scattering coefficients b) A plot of the depths corresponding to a 50% decrease in relative contrast for MTPM (black) and source localized MTPM (red) for different line spacings. Solid lines show the data for the scattering coefficient used in the reconstructions of the experimental data, while dashed lines were modeled using different scattering coefficients. Source localization increases maximum imaging depth compared to standard MTPM, improving contrast more at larger line separations. Increased depth penetration must be traded off against increased imaging speed or SNR.
Fig. 6
Fig. 6 Source localization decreases the functional crosstalk between adjacent neuronal structures. a) Example ROIs and fluorescence time courses of multi-cell labeled neuronal tissue with a single responsive cell using i) wide-field LED, ii) MTPM and iii) source localized MTPM. Red traces are the average of the red intracellular region and black traces of the entire region outside the red area. In both the wide-field LED and MTPM case there is a significant component of the intracellular time course contaminating the extracellular pixels. This leads to difficulty discriminating between cells in areas with multiple active cells. Source localization drastically reduces the relative contribution of the intracellular time course to the extracellular region. b) Summary data showing the ratio of peak intracellular to extracellular response to excitatory stimulus (the signal localization ratio) over 20 trials. This is significantly higher for source localized MTPM compared to MTPM or wide-field LED illumination. Scale bar 10 μm.
Fig. 7
Fig. 7 The effect of source localization on PSNR is labeling and frame rate dependent. a) Example single-cell labeled traces acquired at different frame rates. Traces are source localized for all but 200 Hz where no temporal oversampling or source localization was used. A binomial filter was applied to 100 and 200 Hz traces, and the raw trace is shown in gray. b & c) Comparisons of PSNR with and without source localization for all frame rates for multi-cell labeling (b) and single-cell labeling (c). Single-cell labeling shows a significant decrease in SNR when source localization is applied due to noise introduced in the deconvolution. This is not seen when imaging with densely labeled samples as the signal increase is much larger due to the reduction in non-signal-containing scattered light collected in the intracellular ROI. c) The ratio of source localized PSNR to non source-localized PSNR plotted against frame rate. This increases at higher frame rates as Poisson noise dominates over noise introduced in the deconvolution. Red area indicates worse performance for source localized videos.

Equations (3)

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u t + 1 = u t ( d u t p p ^ ) ,
C = I max I min I max + I min ,
f ( t ) = k = 1 K A c α , γ ( e α ( t t k ) e γ ( t t k ) ) u ( t t k ) ,