Abstract

All-optical interrogation of population neuron activity is a promising approach to deciphering the neural circuit mechanisms supporting brain functions. However, this interrogation is currently limited to local brain areas. Here, we incorporate patterned photo-stimulation into light-sheet microscopy, allowing simultaneous targeted optogenetic manipulation and brain-wide monitoring of the neuronal activities of head-restrained behaving larval zebrafish. Using this system, we photo-stimulate arbitrarily selected neurons (regions as small as ~10-20 neurons in 3D) in zebrafish larvae with pan-neuronal expression of a spectrally separated calcium indicator, GCaMP6f, and an activity actuator, ChrimsonR, and observe downstream neural circuit activation and behavior generation. This approach allows us to dissect the causal role of neural circuits in brain functions and behavior generation.

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

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2017 (8)

T. W. Chen, N. Li, K. Daie, and K. Svoboda, “A map of anticipatory activity in mouse motor cortex,” Neuron 94(4), 866–879 (2017).
[Crossref] [PubMed]

H. Makino, C. Ren, H. Liu, A. N. Kim, N. Kondapaneni, X. Liu, D. Kuzum, and T. Komiyama, “Transformation of cortex-wide emergent properties during motor learning,” Neuron 94(4), 880–890 (2017).
[Crossref] [PubMed]

W. E. Allen, I. V. Kauvar, M. Z. Chen, E. B. Richman, S. J. Yang, K. Chan, V. Gradinaru, B. E. Deverman, L. Luo, and K. Deisseroth, “Global representations of goal-directed behavior in distinct cell types of mouse neocortex,” Neuron 94(4), 891–907 (2017).
[Crossref] [PubMed]

M. dal Maschio, J. C. Donovan, T. O. Helmbrecht, and H. Baier, “Linking neurons to network function and behavior by two-photon holographic optogenetics and volumetric imaging,” Neuron 94(4), 774–789 (2017).
[Crossref] [PubMed]

B. B. Zhang, Y. Y. Yao, H. F. Zhang, K. Kawakami, and J. L. Du, “Left habenula mediates light-preference behavior in zebrafish via an asymmetrical visual pathway,” Neuron 93(4), 914–928 (2017).
[Crossref] [PubMed]

L. Cong, Z. Wang, Y. Chai, W. Hang, C. Shang, W. Yang, L. Bai, J. Du, K. Wang, and Q. Wen, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (Danio rerio),” eLife 6, e28158 (2017).
[Crossref] [PubMed]

D. H. Kim, J. Kim, J. C. Marques, A. Grama, D. G. C. Hildebrand, W. Gu, J. M. Li, and D. N. Robson, “Pan-neuronal calcium imaging with cellular resolution in freely swimming zebrafish,” Nat. Methods 14(11), 1107–1114 (2017).
[Crossref] [PubMed]

D. Förster, M. Dal Maschio, E. Laurell, and H. Baier, “An optogenetic toolbox for unbiased discovery of functionally connected cells in neural circuits,” Nat. Commun. 8(1), 116 (2017).
[Crossref] [PubMed]

2016 (1)

E. A. Naumann, J. E. Fitzgerald, T. W. Dunn, J. Rihel, H. Sompolinsky, and F. Engert, “From whole-brain data to functional circuit models: the zebrafish optomotor response,” Cell 167(4), 947–960 (2016).
[Crossref] [PubMed]

2015 (9)

L. Grosenick, J. H. Marshel, and K. Deisseroth, “Closed-loop and activity-guided optogenetic control,” Neuron 86(1), 106–139 (2015).
[Crossref] [PubMed]

A. M. B. Lacoste, D. Schoppik, D. N. Robson, M. Haesemeyer, R. Portugues, J. M. Li, O. Randlett, C. L. Wee, F. Engert, and A. F. Schier, “A convergent and essential interneuron pathway for Mauthner-cell-mediated escapes,” Curr. Biol. 25(11), 1526–1534 (2015).
[Crossref] [PubMed]

Y. Gong, C. Huang, J. Z. Li, B. F. Grewe, Y. Zhang, S. Eismann, and M. J. Schnitzer, “High-speed recording of neural spikes in awake mice and flies with a fluorescent voltage sensor,” Science 350(6266), 1361–1366 (2015).
[Crossref] [PubMed]

P. J. Keller and M. B. Ahrens, “Visualizing whole-brain activity and development at the single-cell level using light-sheet microscopy,” Neuron 85(3), 462–483 (2015).
[Crossref] [PubMed]

Z. Yang, L. Mei, F. Xia, Q. Luo, L. Fu, and H. Gong, “Dual-slit confocal light sheet microscopy for in vivo whole-brain imaging of zebrafish,” Biomed. Opt. Express 6(5), 1797–1811 (2015).
[Crossref] [PubMed]

K. Deisseroth, “Optogenetics: 10 years of microbial opsins in neuroscience,” Nat. Neurosci. 18(9), 1213–1225 (2015).
[Crossref] [PubMed]

V. Emiliani, A. E. Cohen, K. Deisseroth, and M. Häusser, “All-optical interrogation of neural circuits,” J. Neurosci. 35(41), 13917–13926 (2015).
[Crossref] [PubMed]

A. M. Packer, L. E. Russell, H. W. Dalgleish, and M. Häusser, “Simultaneous all-optical manipulation and recording of neural circuit activity with cellular resolution in vivo,” Nat. Methods 12(2), 140–146 (2015).
[Crossref] [PubMed]

M. Siegel, T. J. Buschman, and E. K. Miller, “Cortical information flow during flexible sensorimotor decisions,” Science 348(6241), 1352–1355 (2015).
[Crossref] [PubMed]

2014 (9)

R. Portugues, C. E. Feierstein, F. Engert, and M. B. Orger, “Whole-brain activity maps reveal stereotyped, distributed networks for visuomotor behavior,” Neuron 81(6), 1328–1343 (2014).
[Crossref] [PubMed]

J. L. Semmelhack, J. C. Donovan, T. R. Thiele, E. Kuehn, E. Laurell, and H. Baier, “A dedicated visual pathway for prey detection in larval zebrafish,” eLife 3, e04878 (2014).
[Crossref] [PubMed]

N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
[Crossref] [PubMed]

J. P. Rickgauer, K. Deisseroth, and D. W. Tank, “Simultaneous cellular-resolution optical perturbation and imaging of place cell firing fields,” Nat. Neurosci. 17(12), 1816–1824 (2014).
[Crossref] [PubMed]

N. Vladimirov, Y. Mu, T. Kawashima, D. V. Bennett, C.-T. Yang, L. L. Looger, P. J. Keller, J. Freeman, and M. B. Ahrens, “Light-sheet functional imaging in fictively behaving zebrafish,” Nat. Methods 11(9), 883–884 (2014).
[Crossref] [PubMed]

F. St-Pierre, J. D. Marshall, Y. Yang, Y. Gong, M. J. Schnitzer, and M. Z. Lin, “High-fidelity optical reporting of neuronal electrical activity with an ultrafast fluorescent voltage sensor,” Nat. Neurosci. 17(6), 884–889 (2014).
[Crossref] [PubMed]

R. Prevedel, Y.-G. Yoon, M. Hoffmann, N. Pak, G. Wetzstein, S. Kato, T. Schrödel, R. Raskar, M. Zimmer, E. S. Boyden, and A. Vaziri, “Simultaneous whole-animal 3D imaging of neuronal activity using light-field microscopy,” Nat. Methods 11(7), 727–730 (2014).
[Crossref] [PubMed]

V. Szabo, C. Ventalon, V. De Sars, J. Bradley, and V. Emiliani, “Spatially selective holographic photoactivation and functional fluorescence imaging in freely behaving mice with a fiberscope,” Neuron 84(6), 1157–1169 (2014).
[Crossref] [PubMed]

W. C. Wang and D. L. McLean, “Selective responses to tonic descending commands by temporal summation in a spinal motor pool,” Neuron 83(3), 708–721 (2014).
[Crossref] [PubMed]

2013 (6)

A. M. Packer, B. Roska, and M. Häusser, “Targeting neurons and photons for optogenetics,” Nat. Neurosci. 16(7), 805–815 (2013).
[Crossref] [PubMed]

M. B. Ahrens, M. B. Orger, D. N. Robson, J. M. Li, and P. J. Keller, “Whole-brain functional imaging at cellular resolution using light-sheet microscopy,” Nat. Methods 10(5), 413–420 (2013).
[Crossref] [PubMed]

T. Panier, S. A. Romano, R. Olive, T. Pietri, G. Sumbre, R. Candelier, and G. Debrégeas, “Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy,” Front. Neural Circuits 7, 65 (2013).
[Crossref] [PubMed]

T. N. deCarvalho, C. M. Akitake, C. Thisse, B. Thisse, and M. E. Halpern, “Aversive cues fail to activate fos expression in the asymmetric olfactory-habenula pathway of zebrafish,” Front. Neural Circuits 7, 98 (2013).
[Crossref] [PubMed]

T. W. Chen, T. J. Wardill, Y. Sun, S. R. Pulver, S. L. Renninger, A. Baohan, E. R. Schreiter, R. A. Kerr, M. B. Orger, V. Jayaraman, L. L. Looger, K. Svoboda, and D. S. Kim, “Ultrasensitive fluorescent proteins for imaging neuronal activity,” Nature 499(7458), 295–300 (2013).
[Crossref] [PubMed]

A. Muto, M. Ohkura, G. Abe, J. Nakai, and K. Kawakami, “Real-time visualization of neuronal activity during perception,” Curr. Biol. 23(4), 307–311 (2013).
[Crossref] [PubMed]

2012 (4)

M. B. Ahrens, J. M. Li, M. B. Orger, D. N. Robson, A. F. Schier, F. Engert, and R. Portugues, “Brain-wide neuronal dynamics during motor adaptation in zebrafish,” Nature 485(7399), 471–477 (2012).
[Crossref] [PubMed]

E. Warp, G. Agarwal, C. Wyart, D. Friedmann, C. S. Oldfield, A. Conner, F. Del Bene, A. B. Arrenberg, H. Baier, and E. Y. Isacoff, “Emergence of patterned activity in the developing zebrafish spinal cord,” Curr. Biol. 22(2), 93–102 (2012).
[Crossref] [PubMed]

P. Zhu, O. Fajardo, J. Shum, Y. P. Zhang Schärer, and R. W. Friedrich, “High-resolution optical control of spatiotemporal neuronal activity patterns in zebrafish using a digital micromirror device,” Nat. Protoc. 7(7), 1410–1425 (2012).
[Crossref] [PubMed]

Y. Mu, X. Q. Li, B. Zhang, and J. L. Du, “Visual input modulates audiomotor function via hypothalamic dopaminergic neurons through a cooperative mechanism,” Neuron 75(4), 688–699 (2012).
[Crossref] [PubMed]

2011 (1)

S. Peron and K. Svoboda, “From cudgel to scalpel: toward precise neural control with optogenetics,” Nat. Methods 8(1), 30–34 (2011).
[Crossref] [PubMed]

2010 (2)

C. M. Niell and M. P. Stryker, “Modulation of visual responses by behavioral state in mouse visual cortex,” Neuron 65(4), 472–479 (2010).
[Crossref] [PubMed]

R. Amo, H. Aizawa, M. Takahoko, M. Kobayashi, R. Takahashi, T. Aoki, and H. Okamoto, “Identification of the zebrafish ventral habenula as a homolog of the mammalian lateral habenula,” J. Neurosci. 30(4), 1566–1574 (2010).
[Crossref] [PubMed]

2009 (1)

Z. V. Guo, A. C. Hart, and S. Ramanathan, “Optical interrogation of neural circuits in Caenorhabditis elegans,” Nat. Methods 6(12), 891–896 (2009).
[Crossref] [PubMed]

2005 (1)

E. Gahtan, P. Tanger, and H. Baier, “Visual prey capture in larval zebrafish is controlled by identified reticulospinal neurons downstream of the tectum,” J. Neurosci. 25(40), 9294–9303 (2005).
[Crossref] [PubMed]

2004 (1)

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2002 (1)

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2000 (1)

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1987 (1)

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1984 (1)

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1982 (1)

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Akitake, C. M.

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N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
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Chai, Y.

L. Cong, Z. Wang, Y. Chai, W. Hang, C. Shang, W. Yang, L. Bai, J. Du, K. Wang, and Q. Wen, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (Danio rerio),” eLife 6, e28158 (2017).
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T. W. Chen, N. Li, K. Daie, and K. Svoboda, “A map of anticipatory activity in mouse motor cortex,” Neuron 94(4), 866–879 (2017).
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N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
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Chow, B. Y.

N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
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N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
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V. Emiliani, A. E. Cohen, K. Deisseroth, and M. Häusser, “All-optical interrogation of neural circuits,” J. Neurosci. 35(41), 13917–13926 (2015).
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N. C. Klapoetke, Y. Murata, S. S. Kim, S. R. Pulver, A. Birdsey-Benson, Y. K. Cho, T. K. Morimoto, A. S. Chuong, E. J. Carpenter, Z. Tian, J. Wang, Y. Xie, Z. Yan, Y. Zhang, B. Y. Chow, B. Surek, M. Melkonian, V. Jayaraman, M. Constantine-Paton, G. K. Wong, and E. S. Boyden, “Independent optical excitation of distinct neural populations,” Nat. Methods 11(3), 338–346 (2014).
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J. Cornwall, J. D. Cooper, and O. T. Phillipson, “Afferent and efferent connections of the laterodorsal tegmental nucleus in the rat,” Brain Res. Bull. 25(2), 271–284 (1990).
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J. Cornwall, J. D. Cooper, and O. T. Phillipson, “Afferent and efferent connections of the laterodorsal tegmental nucleus in the rat,” Brain Res. Bull. 25(2), 271–284 (1990).
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T. W. Chen, N. Li, K. Daie, and K. Svoboda, “A map of anticipatory activity in mouse motor cortex,” Neuron 94(4), 866–879 (2017).
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M. dal Maschio, J. C. Donovan, T. O. Helmbrecht, and H. Baier, “Linking neurons to network function and behavior by two-photon holographic optogenetics and volumetric imaging,” Neuron 94(4), 774–789 (2017).
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D. Förster, M. Dal Maschio, E. Laurell, and H. Baier, “An optogenetic toolbox for unbiased discovery of functionally connected cells in neural circuits,” Nat. Commun. 8(1), 116 (2017).
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V. Szabo, C. Ventalon, V. De Sars, J. Bradley, and V. Emiliani, “Spatially selective holographic photoactivation and functional fluorescence imaging in freely behaving mice with a fiberscope,” Neuron 84(6), 1157–1169 (2014).
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T. Panier, S. A. Romano, R. Olive, T. Pietri, G. Sumbre, R. Candelier, and G. Debrégeas, “Fast functional imaging of multiple brain regions in intact zebrafish larvae using Selective Plane Illumination Microscopy,” Front. Neural Circuits 7, 65 (2013).
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T. N. deCarvalho, C. M. Akitake, C. Thisse, B. Thisse, and M. E. Halpern, “Aversive cues fail to activate fos expression in the asymmetric olfactory-habenula pathway of zebrafish,” Front. Neural Circuits 7, 98 (2013).
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W. E. Allen, I. V. Kauvar, M. Z. Chen, E. B. Richman, S. J. Yang, K. Chan, V. Gradinaru, B. E. Deverman, L. Luo, and K. Deisseroth, “Global representations of goal-directed behavior in distinct cell types of mouse neocortex,” Neuron 94(4), 891–907 (2017).
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W. E. Allen, I. V. Kauvar, M. Z. Chen, E. B. Richman, S. J. Yang, K. Chan, V. Gradinaru, B. E. Deverman, L. Luo, and K. Deisseroth, “Global representations of goal-directed behavior in distinct cell types of mouse neocortex,” Neuron 94(4), 891–907 (2017).
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G. V. Di Prisco, E. Pearlstein, D. Le Ray, R. Robitaille, and R. Dubuc, “A cellular mechanism for the transformation of a sensory input into a motor command,” J. Neurosci. 20(21), 8169–8176 (2000).
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M. dal Maschio, J. C. Donovan, T. O. Helmbrecht, and H. Baier, “Linking neurons to network function and behavior by two-photon holographic optogenetics and volumetric imaging,” Neuron 94(4), 774–789 (2017).
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L. Cong, Z. Wang, Y. Chai, W. Hang, C. Shang, W. Yang, L. Bai, J. Du, K. Wang, and Q. Wen, “Rapid whole brain imaging of neural activity in freely behaving larval zebrafish (Danio rerio),” eLife 6, e28158 (2017).
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B. B. Zhang, Y. Y. Yao, H. F. Zhang, K. Kawakami, and J. L. Du, “Left habenula mediates light-preference behavior in zebrafish via an asymmetrical visual pathway,” Neuron 93(4), 914–928 (2017).
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G. V. Di Prisco, E. Pearlstein, D. Le Ray, R. Robitaille, and R. Dubuc, “A cellular mechanism for the transformation of a sensory input into a motor command,” J. Neurosci. 20(21), 8169–8176 (2000).
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E. A. Naumann, J. E. Fitzgerald, T. W. Dunn, J. Rihel, H. Sompolinsky, and F. Engert, “From whole-brain data to functional circuit models: the zebrafish optomotor response,” Cell 167(4), 947–960 (2016).
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Figures (10)

Fig. 1
Fig. 1 Simultaneous whole-brain imaging and targeted manipulation of neuronal activity of behaving larval zebrafish. (a) Schematic diagram of setup. Patterned photo-stimulation was incorporated into a light-sheet microscope through the imaging light path, and a near-infrared light path was used for behavior monitoring. (bottom left) GCaMP6f, ChrimsonR, and filter combination spectra. The separated GCaMP6f and ChrimsonR spectra allowed simultaneous light-sheet imaging and photo-stimulation without spectral bleeding. ChrimsonR Ex: ChrimsonR excitation spectrum; GCaMP6f Ex and Em: GCaMP6f excitation and fluorescence emission spectra, respectively. Blue and yellow dashed lines: GCaMP6f (488 nm) and ChrimsonR (589 nm) excitation wavelengths, respectively. (b) Simultaneous targeted manipulation and whole-brain imaging of neuronal activity (top) and behavior monitoring (bottom). Distributed neurons were targeted with photo-stimulation (red dots) during whole-brain neuronal activity imaging and fish behavior monitoring. (DMD: digital micromirror device; L1−L4: lenses 1−4; OD: optical diffuser; (s)CMOS: (scientific) complementary metal-oxide semiconductor; F1, F2: green and red band-pass filters, respectively; F3: infrared long-pass filter; DM: multi-band dichroic mirror; TL1, TL2: tube lenses 1 and 2; Obj3, Obj4: fluorescence detection and behavior monitoring objectives, respectively; M, reflective mirror.)
Fig. 2
Fig. 2 Axial resolution of OD-enhanced photo-stimulation. (a and b, left) Optical layout of DMD integration into light-sheet microscope without (a) or with (b) OD. An intensity-modulated beam was relayed by a lens pair (L3−L4) arranged in 4f to the dashed plane or the OD after L4. The beam from the dashed plane or OD was reflected by the DM to the TL1 and Obj3 of the light-sheet microscope. The Obj3 focal plane was conjugated with the dashed plane or OD. (a and b, right) Magnified image of DMD, dashed plane or OD before TL1, and focal plane after Obj3. (c) Axial section of photo-stimulation beam measured without (left) or with OD (right) using behavior-monitoring light path. (d) Axial resolution of photo-stimulation without (black curve) and with (red curve) an OD, being 72.6 or 19.6 μm, respectively.
Fig. 3
Fig. 3 Pipeline for photo-stimulation of targeted neurons. From the captured fluorescence image of the brain of a 5-dpf zebrafish larva pan-neuronally expressing ChrimsonR-tdTomato (a), we arbitrarily selected single neurons (red circles in (b)) as photo-stimulation targets. Based on these neurons' positions and soma sizes, we calculated the photo-stimulation pattern and loaded it onto the DMD (c) to activate the target-corresponding micromirrors. Upon this targeted photo-stimulation, tdTomato fluorescence was imaged from the same field of view as for (a), and fluorescence was observed on the target neurons. (a and d, bottom) Enlarged view of region of interest (white dashed square) in each panel.
Fig. 4
Fig. 4 Timing diagram of control waveforms for all instruments during simultaneous volumetric imaging and targeted photo-stimulation. All signals were generated by a PCI-6733 data acquisition (DAQ) card programmed by a LabView controller. (Galvo XY and Z: horizontal and vertical galvano mirrors, respectively.)
Fig. 5
Fig. 5 Spatial resolution of light-sheet microscope. (a, b) Lateral spatial resolution of light-sheet microscope, examined by measuring point-spread-function full width at half maximum (FWHM) and Gaussian fitting (green line in (b)). Fluorescent beads with 20-nm diameter (F8787, ThermoFisher) were illuminated by the light sheets and employed as a point light source. The fluorescence intensity profile showed an FWHM of 0.8 μm. This lateral resolution was sufficient to discriminate the cell contour. (c, d) Axial resolution of light-sheet microscope, measured by axially scanning the fluorescent beads and capturing images at various axial distances. The measured axial resolution was approximately 5.0 μm. This is consistent with the calculated light-sheet thickness (4.8-μm FWHM at waist) and the calculated Obj3 axial resolution (5.0 μm).
Fig. 6
Fig. 6 Spectra and filter combinations for tdTomato fluorescence examination. (a) Excitation (tdTomato Ex) and fluorescence emission (tdTomato Em) spectra of tdTomato. The green and yellow dashed lines indicate 561 and 589 nm, respectively. (b) Filter combination for examining tdTomato fluorescence to assess ChrimsonR expression. As ChrimsonR does not emit fluorescence, we assessed its expression in targeted neurons by examining the tdTomato expression, which was fused with ChrimsonR. Light sheets with 561-nm wavelength (green dashed line in (a)) were employed to excite tdTomato and the emitted fluorescence was filtered by DM and F2 before being captured by the sCMOS. (c) Filter combination for examining tdTomato to characterize photo-stimulation performance. Patterned photo-stimulation of 589 nm (yellow dashed line in (a)) was targeted towards single neurons expressing tdTomato. The emitted fluorescence was filtered by DM and F4, before being captured by the sCMOS. A filter wheel was used to switch filters in experiment.
Fig. 7
Fig. 7 Targeting performance of patterned photo-stimulation in zebrafish brain. (a) Characterization of photo-stimulation precision in 6-dpf Tg(elavl3:ChrimsonR-tdTomato) larva. Single neuron-shaped laser beams (589 nm, yellow triangles) were projected into the larval brain to excite tdTomato and the emitted fluorescence was detected (red, collected at 600 nm and above). Inset: Imaging area position in brain. (b) Photo-stimulation deviation and scattering. Results from 608 cells in 5 larvae were averaged. The shaded box marks the photo-stimulation target range. The tdTomato-emitted fluorescence pattern was fitted with a 2-D Gaussian function. The center position of the fitted fluorescence distribution was compared with that of the target to determine the targeting error, which on average was 0.3 μm (first quartile −0.2 μm, third quartile 0.6 μm). The FWHM of the intensity profile indicates the photo-stimulation beam diameter. (c) Relationship between FWHM and axial depth. The FWHM was slightly correlated with the penetration depth (n = 608, ρ = 0.2, P < 0.001, Spearman's correlation). Yellow line: original photo-stimulation beam diameter; shaded area: mean ± standard error of mean (SEM). (d−f) Example of targeted photo-stimulation precision. Photo-stimulation (10-ms pulses, 0.5-s duration, 50 Hz; yellow triangle in (d) and yellow dashed line in (e)) was targeted towards a single optic tectum (OT) neuron and activated both the target and 5 nearby cells in a 9-dpf Tg(elavl3:H2B-GCaMP6f;elavl3:ChrimsonR-tdTomato) larva with bilateral enucleation. Bath application of 6-cyano-7-nitroquinoxaline-2,3-dione (CNQX; 50 μM) and DL-2-amino-5-phosphonovaleric acid (APV; 50 μM) was used to block glutamatergic transmission. The mean optogenetic responses of the target neuron (1), a neighbor neuron (2), and a distant neuron (3, 25 μm from the target) are shown in (e). (f) For the activated neurons, the activation amplitude was lower for the neurons farther from the target. Inset in (d): Imaging area position, the tectal paraventricular zone, in brain. Color bar: change in fluorescence ΔF/F0. Shaded area: mean ± SEM.
Fig. 8
Fig. 8 Targeted photo-stimulation of habenulae evokes synaptic transmission. (a) Postsynaptic responses evoked by targeted photo-stimulation. Nine neurons (green) in the left dorsal habenula (Hb-l) and activated neurons (color dots) in bilateral Hb (55 Hb-l and 11 Hb-r (the right dorsal habenula)) in a 6-dpf Tg(elavl3:H2B-GCaMP6f;elavl3:ChrimsonR-tdTomato) larva were targeted for photo-stimulation. The neuronal activities were measured based on the amplitude of the GCaMP6f fluorescence change (ΔF/F0) and superimposed on reference anatomy (grey) in dorsal (x-y), sagittal (y-z) and coronal (x-z) projections. (b) Representative neuronal responses evoked by targeted photo-stimulation before (left) and after (right) blockade of glutamatergic transmission through bath application of CNQX (50 μM) and APV (50 μM). The green traces indicate the response of a targeted neuron in Hb-l, and the blue traces display the postsynaptic responses of three activated neurons in Hb-r. The yellow arrows indicate the targeted photo-stimulation events.
Fig. 9
Fig. 9 Targeted photo-stimulation of tegmentum activates brain-wide functional connections. Representative images (a) and traces (b) showing activation of brain-wide downstream neurons evoked by targeted photo-stimulation. The left dorsal tegmentum (Teg-l, green area) in a 6-dpf Tg(elavl3:H2B-GCaMP6f;elavl3:ChrimsonR-tdTomato) larva was targeted for photo-stimulation, which activated widely distributed neurons in brain regions including Teg-l, Pa-l, Pa-r, Hb-l, Hb-r, Th-l, Th-r, OT-l, OT-r, Cb-l, and Hind-l (Pa: bilateral pallium; Th: thalmus; CB: cerebellum; Hind: hindbrain). The stimulation pattern designed based on the anatomical position of Teg-l on each depth. (b, right) Average responses expanded in time. The yellow arrows (b, left) and shaded area (b, right) indicate the targeted photo-stimulation events. The activities of the representative Th-l, Th-r, and Cb-l neurons were not recorded during the stimulation with the current experiment paradigm. (c) Activated neuron distribution (left) and differences in optogenetic response amplitude (middle) and latency (right) among regions across the whole brain. Neurons showing significant responses to three or four of four photo-stimulation repetitions were considered reliably responsive and included in the distribution. Box-and-whisker plots of the amplitude and latency represent the 25th, 50th, and 75th quartiles (box lines) and the extreme values (by whiskers). (d) Schematic showing brain-wide functional neural networks downstream of Teg-l. The red arrows represent the functional connections between Teg-l and the indicated brain areas.
Fig. 10
Fig. 10 Targeted photo-stimulation of Mauthner cells evokes tail curling in larval zebrafish. (a) Tail movement of head-restrained larval zebrafish measured in terms of angle between current tail direction (blue) and resting tail position (red). (b, c) Example showing that targeted photo-stimulation (b, yellow triangle) of ChrimsonR-expressing Mauthner cell (b, red) reliably evoked tail curling in five consecutive trials (c). White dashed lines in (b): eye outlines; yellow shaded areas in (c), photo-stimulation periods. (d, e) Example showing that targeted photo-stimulation (d, yellow triangle) of contralateral Mauthner cell (d, red dash circle) with expression of GFP but not ChrimsonR could not evoke tail movement (e). The data shown in (b)−(e) were obtained from the same larva. (f) Success rates of tail movements evoked by targeted photo-stimulation of unilateral Mauthner cells with (red) and without (green) ChrimsonR expression. The numbers indicate the number of examined cells.

Equations (5)

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M= f TL1 f Obj3 ,
d= D M ,
tan(β)=Mtan(α),
l= 2 2 d.
h= (l d 2 ) tan(β) = (l d 2 ) Mtan(α) .

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