Abstract

Imaging increasingly large neuronal populations at high rates pushed multi-photon microscopy into the photon-deprived regime. We present PySight, an add-on hardware and software solution tailored for photon-deprived imaging conditions. PySight more than triples the measured median amplitude of neuronal calcium transients in awake mice and facilitates single-trial intravital voltage imaging in fruit flies. Its unique data streaming architecture allowed us to image a fruit fly’s brain olfactory response over 234  μm×600  μm×330  μm at 73 volumes per second, while retaining over 200 times lower data rates than those of a conventional data acquisition system with comparable voxel sizes (1.2  μm×1.2  μm×2.2  μm). PySight requires no electronics expertise or custom synchronization boards, and its open-source software is extensible to any imaging method based on single-pixel (bucket) detectors. PySight offers an optimal data acquisition scheme for ever increasing imaging volumes of turbid living tissue.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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2018 (3)

D. B. Papkovsky and R. I. Dmitriev, “Imaging of oxygen and hypoxia in cell and tissue samples,” Cell Mol. Life Sci. 75, 2963–2980 (2018).
[Crossref]

M. Patting, P. Reisch, M. Sackrow, R. Dowler, M. Koenig, and M. Wahl, “Fluorescence decay data analysis correcting for detector pulse pile-up at very high count rates,” Opt. Eng. 57, 031305 (2018).
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J. Ryu, U. Kang, J. Kim, H. Kim, J. H. Kang, H. Kim, D. K. Sohn, J.-H. Jeong, H. Yoo, and B. Gweon, “Real-time visualization of two-photon fluorescence lifetime imaging microscopy using a wavelength-tunable femtosecond pulsed laser,” Biomed. Opt. Express 9, 3449–3463 (2018).
[Crossref]

2017 (11)

M. Eibl, S. Karpf, D. Weng, H. Hakert, T. Pfeiffer, J. P. Kolb, and R. Huber, “Single pulse two photon fluorescence lifetime imaging (SP-FLIM) with MHz pixel rate,” Biomed. Opt. Express 8, 3132–3142 (2017).
[Crossref]

M. Eibl, S. Karpf, H. Hakert, T. Blömker, J. P. Kolb, C. Jirauschek, and R. Huber, “Pulse-to-pulse wavelength switching of a nanosecond fiber laser by four-wave mixing seeded stimulated Raman amplification,” Opt. Lett. 42, 4406–4409 (2017).
[Crossref]

Z. Li, J. Suo, X. Hu, C. Deng, J. Fan, and Q. Dai, “Efficient single-pixel multispectral imaging via non-mechanical spatio-spectral modulation,” Sci. Rep. 7, 41435 (2017).
[Crossref]

Q. Guo, H. Chen, Y. Wang, M. Chen, S. Yang, and S. Xie, “High-speed real-time image compression based on all-optical discrete cosine transformation,” Proc. SPIE 10076, 100760E (2017).
[Crossref]

S. Piazza, P. Bianchini, C. Sheppard, A. Diaspro, and M. Duocastella, “Enhanced volumetric imaging in 2-photon microscopy via acoustic lens beam shaping,” J. Biophoton. 11, e201700050 (2017).
[Crossref]

R. Lu, W. Sun, Y. Liang, A. Kerlin, J. Bierfeld, J. D. Seelig, D. E. Wilson, B. Scholl, B. Mohar, M. Tanimoto, M. Koyama, D. Fitzpatrick, M. B. Orger, and N. Ji, “Video-rate volumetric functional imaging of the brain at synaptic resolution,” Nat. Neurosci. 20, 620–628 (2017).
[Crossref]

A. C. Geiger, J. A. Newman, S. Sreehari, S. Z. Sullivan, C. A. Bouman, and G. J. Simpson, “Sparse sampling image reconstruction in Lissajous trajectory beam-scanning multiphoton microscopy,” Proc. SPIE 10076, 1007606 (2017).
[Crossref]

B. Simsek and S. Iyengar, “On the distribution of photon counts with censoring in two-photon laser scanning microscopy,” J. Math. Imaging Vis. 58, 47–56 (2017).
[Crossref]

W. Yang and R. Yuste, “In vivo imaging of neural activity,” Nat. Methods 14, 349–359 (2017).
[Crossref]

A. Urban, L. Golgher, C. Brunner, A. Gdalyahu, H. Har-Gil, D. Kain, G. Montaldo, L. Sironi, and P. Blinder, “Understanding the neurovascular unit at multiple scales: advantages and limitations of multi-photon and functional ultrasound imaging,” Adv. Drug Delivery Rev. 119, 73–100 (2017).
[Crossref]

R. Blau, M. Neeman, and R. Satchi-Fainaro, “Emerging nanomedical solutions for angiogenesis regulation,” Adv. Drug Delivery Rev. 119, 1–2 (2017).
[Crossref]

2016 (6)

N. J. Sofroniew, D. Flickinger, J. King, and K. Svoboda, “A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging,” eLife 5, e14472 (2016).
[Crossref]

J. N. Stirman, I. T. Smith, M. W. Kudenov, and S. L. Smith, “Wide field-of-view, multi-region, two-photon imaging of neuronal activity in the mammalian brain,” Nat. Biotechnol. 34, 857–862 (2016).
[Crossref]

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]

E. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. Peterka, R. Yuste, and L. Paninski, “Simultaneous denoising, deconvolution, and demixing of calcium imaging data,” Neuron 89, 285–299 (2016).
[Crossref]

H. H. Yang, F. St-Pierre, X. Sun, X. Ding, M. Z. Lin, and T. R. Clandinin, “Subcellular imaging of voltage and calcium signals reveals neural processing in vivo,” Cell 166, 245–257 (2016).
[Crossref]

S. Isbaner, N. Karedla, D. Ruhlandt, S. C. Stein, A. Chizhik, I. Gregor, and J. Enderlein, “Dead-time correction of fluorescence lifetime measurements and fluorescence lifetime imaging,” Opt. Express 24, 9429–9445 (2016).
[Crossref]

2015 (9)

P. S. Tsai, C. Mateo, J. J. Field, C. B. Schaffer, M. E. Anderson, and D. Kleinfeld, “Ultra-large field-of-view two-photon microscopy,” Opt. Express 23, 13833–13847 (2015).
[Crossref]

X. Y. Dow, S. Z. Sullivan, R. D. Muir, and G. J. Simpson, “Video-rate two-photon excited fluorescence lifetime imaging system with interleaved digitization,” Opt. Lett. 40, 3296–3299 (2015).
[Crossref]

M. G. Giacomelli, Y. Sheikine, H. Vardeh, J. L. Connolly, and J. G. Fujimoto, “Rapid imaging of surgical breast excisions using direct temporal sampling two photon fluorescent lifetime imaging,” Biomed. Opt. Express 6, 4317–4325 (2015).
[Crossref]

J. Schindelin, C. T. Rueden, M. C. Hiner, and K. W. Eliceiri, “The ImageJ ecosystem: an open platform for biomedical image analysis,” Mol. Reprod. Dev. 82, 518–529 (2015).
[Crossref]

X. Wu, L. Toro, E. Stefani, and Y. Wu, “Ultrafast photon counting applied to resonant scanning STED microscopy,” J. Microsc. 257, 31–38 (2015).
[Crossref]

Y. Wu, X. Wu, L. Toro, and E. Stefani, “Resonant-scanning dual-color STED microscopy with ultrafast photon counting: a concise guide,” Methods 88, 48–56 (2015).
[Crossref]

D. Brinks, A. J. Klein, and A. E. Cohen, “Two-photon lifetime imaging of voltage indicating proteins as a probe of absolute membrane voltage,” Biophys. J. 109, 914–921 (2015).
[Crossref]

L. Kong, J. Tang, J. P. Little, Y. Yu, T. Lämmermann, C. P. Lin, R. N. Germain, and M. Cui, “Continuous volumetric imaging via an optical phase-locked ultrasound lens,” Nat. Methods 12, 759–762 (2015).
[Crossref]

E. J. O. Hamel, B. F. Grewe, J. G. Parker, and M. J. Schnitzer, “Cellular level brain imaging in behaving mammals: an engineering approach,” Neuron 86, 140–159 (2015).
[Crossref]

2014 (1)

G. Buzsáki and K. Mizuseki, “The log-dynamic brain: how skewed distributions affect network operations,” Nat. Rev. Neurosci. 15, 264–278 (2014).
[Crossref]

2013 (1)

B. A. Wilt, J. E. Fitzgerald, and M. J. Schnitzer, “Photon shot noise limits on optical detection of neuronal spikes and estimation of spike timing,” Biophys. J. 104, 51–62 (2013).
[Crossref]

2012 (3)

E. E. Hoover, J. J. Field, D. G. Winters, M. D. Young, E. V. Chandler, J. C. Speirs, J. T. Lapenna, S. M. Kim, S.-Y. Ding, R. A. Bartels, J. W. Wang, and J. A. Squier, “Eliminating the scattering ambiguity in multifocal, multimodal, multiphoton imaging systems,” J. Biophoton. 5, 425–436 (2012).
[Crossref]

E. J. Botcherby, C. W. Smith, M. M. Kohl, D. Débarre, M. J. Booth, R. Juškaitis, O. Paulsen, and T. Wilson, “Aberration-free three-dimensional multiphoton imaging of neuronal activity at KHz rates,” Proc. Natl. Acad. Sci. USA 109, 2919–2924 (2012).
[Crossref]

R. D. Muir, D. J. Kissick, and G. J. Simpson, “Statistical connection of binomial photon counting and photon averaging in high dynamic range beam-scanning microscopy,” Opt. Express 20, 10406–10415 (2012).
[Crossref]

2011 (2)

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]

J. D. Driscoll, A. Y. Shih, S. Iyengar, J. J. Field, G. A. White, J. A. Squier, G. Cauwenberghs, and D. Kleinfeld, “Photon counting, censor corrections, and lifetime imaging for improved detection in two-photon microscopy,” J. Neurophysiol. 105, 3106–3113 (2011).
[Crossref]

2010 (1)

D. J. Kissick, R. D. Muir, and G. J. Simpson, “Statistical treatment of photon/electron counting: extending the linear dynamic range from the dark count rate to saturation,” Anal. Chem. 82, 10129–10134 (2010).
[Crossref]

2009 (1)

2008 (4)

2007 (2)

2004 (1)

W. Becker, A. Bergmann, M. A. Hink, K. König, K. Benndorf, and C. Biskup, “Fluorescence lifetime imaging by time-correlated single-photon counting,” Microsc. Res. Tech. 63, 58–66 (2004).
[Crossref]

2003 (1)

W. R. Zipfel, R. M. Williams, and W. W. Webb, “Nonlinear magic: multiphoton microscopy in the biosciences,” Nat. Biotechnol. 21, 1369–1377 (2003).
[Crossref]

1990 (1)

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

Ahrens, M.

E. Pnevmatikakis, D. Soudry, Y. Gao, T. A. Machado, J. Merel, D. Pfau, T. Reardon, Y. Mu, C. Lacefield, W. Yang, M. Ahrens, R. Bruno, T. M. Jessell, D. Peterka, R. Yuste, and L. Paninski, “Simultaneous denoising, deconvolution, and demixing of calcium imaging data,” Neuron 89, 285–299 (2016).
[Crossref]

Amir, W.

Anderson, M. E.

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).
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Supplementary Material (1)

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Figures (4)

Fig. 1.
Fig. 1. Imaging setup of the proposed system and representative in vivo images taken in an awake Thy1-GCaMP6f mouse. (a) A typical two-photon imaging setup, depicted in gray, can be easily upgraded to encompass the multiscaler and enable photon-counting acquisition. The output of the PMTs, after optional amplification by fast preamplifiers, is relayed through the multiscaler’s analog inputs (STOP1 and STOP2) to generate an image using PySight, the software suite provided alongside this paper. The multiscaler’s SYNC port can output the discriminated signal for a specific PMT, enabling simultaneous digital acquisition and online monitoring of the discriminated signal through the analog imaging setup. DM, dichroic mirror; PMT, photomultiplier tube; Preamp, preamplifier; ADC, analog to digital converter. The full optical path can be found in Fig. S6 in Supplement 1. (b) Intravital calcium imaging with and without PySight. Images shown were summed over 100 frames taken at 15 Hz with 44 ns pixel dwell time. The offset of the analog images (“Analog” and “Online monitor”) was subtracted, and the mean of the image was matched to that of PySight’s for comparable presentation. The intensity scale represents photons (in PySight’s case) or normalized grayscale units in the analog case. (c) Single frames acquired from the same mouse, normalized to the same intensity level as explained in (b). The insets depict a single, bright neuron. (d) Histogram of pixel intensity values in a single frame parsed through PySight (blue) or analog integration (orange). The mean value is 0.03 counts per pixel. The inset depicts the analog pixel brightness distribution, along with its mean value. Comparing the two histograms, photon counting provided better performance (see also Fig. S4 in Supplement 1 for a comparison with simulated longer pixel dwell times). Scale bars equal to 50 μm.
Fig. 2.
Fig. 2. Intravital calcium imaging. (a)–(d) Intravital calcium imaging with (a),(c) analog imaging and (b),(d) PySight. The images were summed over 500 frames to capture the sparse activity of cells and normalized by subtracting the analog offset and equalizing the images’ mean values. Notice the larger Δ F / F amplitude for PySight-detected calcium transients (b) and (d). (e) Distribution and median of the Δ F / F values for spike-like events. Median of PySight-generated calcium transients are 3.6 higher than those in analog imaging (57% for 324 cells, 16% for 311 cells, p < 0.0001 , Mann–Whitney test). Scale bar for (a) and (b) equals 50 μm. Both time lapse recordings were acquired with a 44 ns pixel dwell time.
Fig. 3.
Fig. 3. Intravital volumetric imaging of a live GCaMP6f-expressing fruit fly brain at a rate of 73.4 volumes per second while the fly was exposed to two different odors. (a) Multiview projections of the imaged volume to the XY (left), XZ (top), and YZ (right) planes summed across the 234    μm × 600    μm × 330    μm volume over 33.5 s. (b) and (c) Single-trial fluorescence variations over 25 s in (b) the left lateral horn (brown) and (c) the right antennal lobe (orange). The fruit fly was exposed to odor puffs during seconds 5–10 and 15–20. While the lateral horn and antennal lobe responded similarly to the 2-Pentanone puffs with prolonged onset responses, their responses consistently diverged for isoamyl acetate puffs. (d)–(f) Glomeruli-specific odor response dynamics within the antennal lobes. (d) Volume rendering of the antennal lobes with artificial coloring of glomeruli A (magenta), B (blue), and C (green). Scale bar equals 100 μm. (e) Volumetric calcium transients ( Δ F / F ) from distinct glomeruli in the fly’s antennal lobes were acquired at 67.2 volumes per second over a volume of 110    μm × 257    μm × 330    μm . The mean response to isoamyl acetate and 2-Pentanone odor puffs is traced in colorful and gray traces, respectively. The pink rectangles mark the duration of the odor puffs. Glomerulus A exhibits a graduated weakly adapting response to 2-Pentanone contrasted by a weak response to isoamyl acetate, whereas glomerulus B exhibits a sustained response to isoamyl acetate that peaks well after the odor puff has ceased. While glomeruli A and B were identified according to their morphological structure, glomeruli C were identified according to their preferential response to isoamyl acetate, which was consistently stronger than their response to 2-Pentanone. (f) Montage of axial slices of the antennal lobes, highlighting the localization of the glomeruli rendered in (d) and traced in (e) within the antennal lobes. Each slice spans 6.6 μm in z and 5 s in time axially spaced 40 μm apart. The glomeruli are separable laterally, axially, and by their response dynamics to both odors and are marked by colorful arrows. Voxel sizes are ( 1.2    μm × 1.2    μm × 2.2    μm ), while the voxel dwell time is 17.63 ns; a discussion of its calculation is included in Section 4 of Supplement 1.
Fig. 4.
Fig. 4. Odor responses of a GEVI-marked antennal lobe. (a) Representative image of baseline GEVI signal with a ROI marked over the area used to measure responses over the antennal lobe. (b) Examples of twelve different single-trial responses to a 5 s odor stimuli (isoamyl acetate). The red bar marks the odor puff duration. (c) Zoomed-in trace marked by the blue rectangle in (b). (d) Overlaid traces (gray) with their mean (black) and the puff duration (red). Scale bar equals 50 μm. Pixel dwell time is 123 ns.