Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Multiple convergent hypothalamus–brainstem circuits drive defensive behavior

Nature Neuroscience volume 23, pages 959–967 (2020)Cite this article

Subjects

Abstract

The hypothalamus is composed of many neuropeptidergic cell populations and directs multiple survival behaviors, including defensive responses to threats. However, the relationship between the peptidergic identity of neurons and their roles in behavior remains unclear. Here, we address this issue by studying the function of multiple neuronal populations in the zebrafish hypothalamus during defensive responses to a variety of homeostatic threats. Cellular registration of large-scale neural activity imaging to multiplexed in situ gene expression revealed that neuronal populations encoding behavioral features encompass multiple overlapping sets of neuropeptidergic cell classes. Manipulations of different cell populations showed that multiple sets of peptidergic neurons play similar behavioral roles in this fast-timescale behavior through glutamate co-release and convergent output to spinal-projecting premotor neurons in the brainstem. Our findings demonstrate that homeostatic threats recruit neurons across multiple hypothalamic cell populations, which cooperatively drive robust defensive behaviors.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Rent or buy this article

Get just this article for as long as you need it

$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Hypothalamic population dynamics distinguish between avoidance-promoting threats.
Fig. 2: Large-scale cellular registration of neural dynamics and neuropeptide gene expression across the hypothalamus.
Fig. 3: Behavior-specific neural ensembles span multiple neuropeptidergic hypothalamic populations.
Fig. 4: Overlapping functional properties of multiple populations of hypothalamic neurons.
Fig. 5: Hypothalamic outputs are glutamatergic and converge on brainstem neurons required for defensive behavior.

Data availability

Data will be made available from the corresponding author upon reasonable request.

Code availability

The analysis code will be made available from the corresponding author upon reasonable request.

References

  1. Saper, C. B. & Lowell, B. B. The hypothalamus. Curr. Biol. 24, R1111–R1116 (2014).

    CAS  PubMed  Google Scholar 

  2. Sternson, S. M. Hypothalamic survival circuits: blueprints for purposive behaviors. Neuron 77, 810–824 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Aponte, Y., Atasoy, D. & Sternson, S. M. AGRP neurons are sufficient to orchestrate feeding behavior rapidly and without training. Nat. Neurosci. 14, 351–355 (2011).

    CAS  PubMed  Google Scholar 

  4. Allen, W. E. et al. Thirst-associated preoptic neurons encode an aversive motivational drive. Science 357, 1149–1155 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Lee, H. et al. Scalable control of mounting and attack by Esr1+ neurons in the ventromedial hypothalamus. Nature 509, 627–632 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Tan, C. L. et al. Warm-sensitive neurons that control body temperature. Cell 167, 47–59 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Wu, Z., Autry, A. E., Bergan, J. F., Watabe-Uchida, M. & Dulac, C. G. Galanin neurons in the medial preoptic area govern parental behaviour. Nature 509, 325–330 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Kunwar, P. S. et al. Ventromedial hypothalamic neurons control a defensive emotion state. eLife 4, e06633 (2015).

    PubMed Central  Google Scholar 

  9. Mangieri, L. R. et al. Defensive behaviors driven by a hypothalamic–ventral midbrain circuit. eNeuro 6, ENEURO.0156-19.2019 (2019).

    PubMed  PubMed Central  Google Scholar 

  10. Romanov, R. A., Alpár, A., Hökfelt, T. & Harkany, T. Unified classification of molecular, network, and endocrine features of hypothalamic neurons. Annu. Rev. Neurosci. 42, 1–26 (2019).

    CAS  PubMed  Google Scholar 

  11. Moffitt, J. R. et al. Molecular, spatial, and functional single-cell profiling of the hypothalamic preoptic region. Science 362, eaau5324 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. Kim, D. W. et al. Multimodal analysis of cell types in a hypothalamic node controlling social behavior. Cell 179, 713–728 (2019).

    CAS  PubMed  Google Scholar 

  13. Biran, J., Blechman, J., Wircer, E., & Levkowitz, G. In Model Animals in Neuroendocrinology: From Worm to Mouse to Man (eds Ludwig, M. & Levkowitz, G.) 101–131 (Wiley–Blackwell, 2018).

  14. Ahrens, M. B. et al. Brain-wide neuronal dynamics during motor adaptation in zebrafish. Nature 485, 471–477 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Ahrens, M. B. & Engert, F. Large-scale imaging in small brains. Curr. Opin. Neurobiol. 32, 78–86 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Vladimirov, N. et al. Light-sheet functional imaging in fictively behaving zebrafish. Nat. Methods 11, 883–884 (2014).

    CAS  PubMed  Google Scholar 

  17. De Marco, R. J., Groneberg, A. H., Yeh, C. M., Treviño, M. & Ryu, S. The behavior of larval zebrafish reveals stressor-mediated anorexia during early vertebrate development. Front. Behav. Neurosci. 8, 367 (2014).

    PubMed  PubMed Central  Google Scholar 

  18. vom Berg-Maurer, C. M., Trivedi, C. A., Bollmann, J. H., De Marco, R. J. & Ryu, S. The severity of acute stress is represented by increased synchronous activity and recruitment of hypothalamic CRH neurons. J. Neurosci. 36, 3350–3362 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Liu, N. A. et al. Pituitary corticotroph ontogeny and regulation in transgenic zebrafish. Mol. Endocrinol. 17, 959–966 (2003).

    CAS  PubMed  Google Scholar 

  20. Curado, S., Stainier, D. Y. & Anderson, R. M. Nitroreductase-mediated cell/tissue ablation in zebrafish: a spatially and temporally controlled ablation method with applications in developmental and regeneration studies. Nat. Protoc. 3, 948–954 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Temizer, I., Donovan, J. C., Baier, H. & Semmelhack, J. L. A visual pathway for looming-evoked escape in larval zebrafish. Curr. Biol. 25, 1823–1834 (2015).

    CAS  PubMed  Google Scholar 

  22. Dunn, T. W. et al. Neural circuits underlying visually evoked escapes in larval zebrafish. Neuron 89, 613–628 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Randlett, O. et al. Whole-brain activity mapping onto a zebrafish brain atlas. Nat. Methods 12, 1039–1046 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Herget, U., Wolf, A., Wullimann, M. F. & Ryu, S. Molecular neuroanatomy and chemoarchitecture of the neurosecretory preoptic‐hypothalamic area in zebrafish larvae. J. Comp. Neurol. 522, 1542–1564 (2014).

    CAS  PubMed  Google Scholar 

  25. Joëls, M. & Baram, T. Z. The neuro-symphony of stress. Nat. Rev. Neurosci. 10, 459–466 (2009).

    PubMed  PubMed Central  Google Scholar 

  26. Lovett-Barron, M. et al. Ancestral circuits for the coordinated modulation of brain state. Cell 171, 1411–1423 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Choi, H. M. et al. Third-generation in situ hybridization chain reaction: multiplexed, quantitative, sensitive, versatile, robust. Development 145, dev165753 (2018).

    PubMed  PubMed Central  Google Scholar 

  28. Rohlfing, T. & Maurer, C. R. Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Inf. Technol. Biomed. 7, 16–25 (2003).

    PubMed  Google Scholar 

  29. Herget, U. & Ryu, S. Coexpression analysis of nine neuropeptides in the neurosecretory preoptic area of larval zebrafish. Front. Neuroanat.9, 2 (2015).

    PubMed  PubMed Central  Google Scholar 

  30. Musall, S., Kaufman, M. T., Juavinett, A. L., Gluf, S. & Churchland, A. K. Movement-related activity dominates cortex during sensory-guided decision making. Nat. Neurosci. 22, 1677–1686 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. Wee, C. L. et al. Zebrafish oxytocin neurons drive nocifensive behavior via brainstem premotor targets. Nat. Neurosci. 22, 1477–1492 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  32. Förster, D. et al. Genetic targeting and anatomical registration of neuronal populations in the zebrafish brain with a new set of BAC transgenic tools. Sci. Rep. 7, 5230 (2017).

    PubMed  PubMed Central  Google Scholar 

  33. Fujimoto, E., Stevenson, T. J., Chien, C. B. & Bonkowsky, J. L. Identification of a dopaminergic enhancer indicates complexity in vertebrate dopamine neuron phenotype specification. Dev. Biol. 352, 393–404 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Li, J. et al. Intron targeting-mediated and endogenous gene integrity-maintaining knockin in zebrafish using the CRISPR/Cas9 system. Cell Res. 25, 634–637 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Kimmel, C. B., Powell, S. L. & Metcalfe, W. K. Brain neurons which project to the spinal cord in young larvae of the zebrafish. J. Comp. Neurol. 205, 112–127 (1982).

    CAS  PubMed  Google Scholar 

  36. Orger, M. B., Kampff, A. R., Severi, K. E., Bollmann, J. H. & Engert, F. Control of visually guided behavior by distinct populations of spinal projection neurons. Nat. Neurosci. 11, 327–333 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Korn, H. & Faber, D. S. The Mauthner cell half a century later: a neurobiological model for decision-making? Neuron 47, 13–28 (2005).

    CAS  PubMed  Google Scholar 

  38. Schöne, C. & Burdakov, D. Glutamate and GABA as rapid effectors of hypothalamic “peptidergic” neurons. Front. Behav. Neurosci. 6, 81 (2012).

    PubMed  PubMed Central  Google Scholar 

  39. Haesemeyer, M., Robson, D. N., Li, J. M., Schier, A. F. & Engert, F. A brain-wide circuit model of heat-evoked swimming behavior in larval zebrafish. Neuron 98, 817–831 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Condés‐Lara, M., Rojas‐Piloni, G., Martínez‐Lorenzana, G. & Rodríguez‐Jiménez, J. Paraventricular hypothalamic oxytocinergic cells responding to noxious stimulation and projecting to the spinal dorsal horn represent a homeostatic analgesic mechanism. Eur. J. Neurosci.30, 1056–1063 (2009).

    PubMed  Google Scholar 

  41. Kim, J. et al. Rapid, biphasic CRF neuronal responses encode positive and negative valence. Nat. Neurosci. 22, 576–585 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Ziegler, D. R., Cullinan, W. E. & Herman, J. P. Distribution of vesicular glutamate transporter mRNA in rat hypothalamus. J. Comp. Neurol. 448, 217–229 (2002).

    CAS  PubMed  Google Scholar 

  43. Ponzio, T. A., Ni, Y., Montana, V., Parpura, V. & Hatton, G. I. Vesicular glutamate transporter expression in supraoptic neurones suggests a glutamatergic phenotype. J. Neuroendocrinol. 18, 253–265 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Geerling, J. C., Shin, J. W., Chimenti, P. C. & Loewy, A. D. Paraventricular hypothalamic nucleus: axonal projections to the brainstem. J. Comp. Neurol. 518, 1460–1499 (2010).

    PubMed  PubMed Central  Google Scholar 

  45. Remedios, R. et al. Social behaviour shapes hypothalamic neural ensemble representations of conspecific sex. Nature 550, 388–392 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Edelman, G. M. & Gally, J. A. Degeneracy and complexity in biological systems. Proc. Natl Acad. Sci USA. 98, 13763–13768 (2001).

    CAS  PubMed  Google Scholar 

  47. Palmiter, R. D. The parabrachial nucleus: CGRP neurons function as a general alarm. Trends Neurosci. 41, 280–293 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Hartenstein, V. The neuroendocrine system of invertebrates: a developmental and evolutionary perspective. J. Endocrinol. 190, 555–570 (2006).

    CAS  PubMed  Google Scholar 

  49. Tessmar-Raible, K. et al. Conserved sensory-neurosecretory cell types in annelid and fish forebrain: insights into hypothalamus evolution. Cell 129, 1389–1400 (2007).

    CAS  PubMed  Google Scholar 

  50. Kimura, Y., Satou, C. & Higashijima, S. I. V2a and V2b neurons are generated by the final divisions of pair-producing progenitors in the zebrafish spinal cord. Development 135, 3001–3005 (2008).

    CAS  PubMed  Google Scholar 

  51. Kimura, Y. et al. Hindbrain V2a neurons in the excitation of spinal locomotor circuits during zebrafish swimming. Curr. Biol. 23, 843–849 (2013).

    CAS  PubMed  Google Scholar 

  52. Dal Maschio, M., Donovan, J. C., Helmbrecht, T. O. & Baier, H. Linking neurons to network function and behavior by two-photon holographic optogenetics and volumetric imaging. Neuron 94, 774–789 (2017).

    CAS  PubMed  Google Scholar 

  53. Mohamed, G. A. et al. Optical inhibition of larval zebrafish behaviour with anion channelrhodopsins. BMC Biol. 15, 103 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. Davison, J. M. et al. Transactivation from Gal4-VP16 transgenic insertions for tissue-specific cell labeling and ablation in zebrafish. Dev. Biol. 304, 811–824 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. Hatta, K., Tsujii, H. & Omura, T. Cell tracking using a photoconvertible fluorescent protein. Nat. Protoc. 1, 960–967 (2006).

    CAS  PubMed  Google Scholar 

  56. Wen, L. et al. Visualization of monoaminergic neurons and neurotoxicity of MPTP in live transgenic zebrafish. Dev. Biol. 314, 84–92 (2008).

    CAS  PubMed  Google Scholar 

  57. Lambert, A. M., Bonkowsky, J. L. & Masino, M. A. The conserved dopaminergic diencephalospinal tract mediates vertebrate locomotor development in zebrafish larvae. J. Neurosci. 32, 13488–13500 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Andalman, A. S. et al. Neuronal dynamics regulating brain and behavioral state transitions. Cell 177, 970–985 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Giovannucci, A. et al. CaImAn an open source tool for scalable calcium imaging data analysis. eLife 8, e38173 (2019).

    PubMed  PubMed Central  Google Scholar 

  60. Engelhard, B. et al. Specialized coding of sensory, motor and cognitive variables in VTA dopamine neurons. Nature 570, 509–513 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Jones, E., Oliphant, T., & Peterson, P. SciPy: Open Source Scientific Tools for Python (2001).

  62. Hunter, J. D. Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9, 90–95 (2007).

    Google Scholar 

  63. McKinney, W. Data structures for statistical computing in Python. In Proc. 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 56–61 (SCIPY, 2010).

  64. Kluyver, T. et al. Jupyter Notebooks—a publishing format for reproducible computational workflows. In Proc. 20th International Conference on Electronic Publishing (eds Loizides, F. & Schmidt, B.) 87–90 (IOS Press, 2016).

  65. Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with Python. In Proc. 9th Python in Science Conference (eds van der Walt, S. & Millman, J.) 92–96 (SCIPY, 2010).

  66. Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res.12, 2825–2830 (2011).

    Google Scholar 

  67. Van Der Walt, S., Colbert, S. C. & Varoquaux, G. The NumPy array: a structure for efficient numerical computation. Comput. Sci. Eng. 13, 22–30 (2011).

    Google Scholar 

  68. Van der Walt, S. et al. Scikit-image: image processing in Python. PeerJ 2, e453 (2014).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank our colleagues for generously sharing transgenic zebrafish lines: A. Douglass, H. Baier, M. Ahrens, S. Jesuthasan, J. Bonkowsky and J.-l. Du. We thank C. Constantinople, F. Gore, T. Machado, W. Allen, R. Nath and C. Bedbrook for feedback on an earlier version of the manuscript, and the entire Deisseroth Lab for advice and discussions. This research was supported by Helen Hay Whitney Foundation Postdoctoral Fellowships (to M.L.-B. and A.S.A.), Brain and Behavior Research Foundation NARSAD Young Investigator Awards (to M.L.-B. and A.S.A.), the NIMH (to M.L.-B.: K99MH11284002), the NIDA (to S.G.: R01DA035680 and R21DA038447), and grants from the NIH, NSF, Gatsby, Fresenius, and NOMIS Foundations (to K.D.).

Author information

Authors and Affiliations

  1. Department of Bioengineering, Stanford University, Stanford, CA, USA

    Matthew Lovett-Barron, Ritchie Chen, Susanna Bradbury, Aaron S. Andalman & Karl Deisseroth

  2. Department of Bioengineering and Therapeutic Sciences, Programs in Human Genetics and Biological Sciences, University of California, San Francisco, San Francisco, CA, USA

    Mahendra Wagle & Su Guo

  3. Department of Psychiatry & Behavioral Sciences, Stanford University, Stanford, CA, USA

    Karl Deisseroth

  4. Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA

    Karl Deisseroth

Authors
  1. Matthew Lovett-Barron
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ritchie Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Susanna Bradbury
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Aaron S. Andalman
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mahendra Wagle
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Su Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karl Deisseroth
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.L.-B. and K.D. designed the experiments. M.L.-B., R.C. and S.B. performed the experiments. M.L.-B. analyzed data. R.C. developed the in situ hybridization protocols. A.S.A. contributed software. M.W. and S.G. contributed transgenic zebrafish. M.L.-B. and K.D. wrote the paper with input from all authors. K.D. supervised all aspects of the project.

Corresponding author

Correspondence to Karl Deisseroth.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Neuroscience thanks Florian Engert and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Responses to rapid environmental changes do not influence forward swimming, do not habituate over trials, and are not influenced by ablation of pomc+ cells in the pituitary.

a, Schematic of behavior (left), and measurement of threat onset/offset kinetics using a thermocouple (heat) or two-photon imaging of fluorescence in flow solution (flow - salinity, acidity) (right). b, Threat onset does not influence forward swimming (mean ± s.e.m., 1 s bins, N = 57 fish (5 trials each), same fish as shown in Fig. 1c). c, Fraction of trials with turning movements during threat presentation, across all 5 trials (mean ± s.e.m., N = 57 fish). Comparison of first two trials vs last two trials. All p > 0.1, Two-sided Wilcoxon signed-rank test. d, Ventral sections of Tg(pomc:Gal4;UAS:NTR-mCherry) fish, showing ablation of pomc+ cells in the pituitary (red) upon treatment with metronidazole (MTZ; right). Similar ablation observed in N = 3 fish examined. e, Summary data from control and ablated fish. N = 9 fish per group, mean ± s.e.m., individual fish are points. One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons. NS = ‘not significant’, all p > 0.4. f, Maximum tail angle of responsive movements - summary data from control and ablated fish. Note that control fish have lower response magnitude on salinity trials. N = 9 fish per group, mean ± s.e.m., individual fish are points. One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons. *p < 0.05. NS = ‘not significant’, all p > 0.4. See Supplementary Table 2 for test statistics.

Extended Data Fig. 2 Coverage, registration, and anatomical segregation of brain-wide imaging data.

a, All z-planes functionally imaged in a single Tg(elavl3:H2B-GCaMP6s) fish - 14 z-planes, 15 µm separation, 2 volumes/second. All N = 8 fish were imaged with the same x/y/z-sampling. b, Example z-planes from N = 6 fish, after registration to a common volume. Fish were registered to a common ‘bridge’ brain; the standard Tg(elavl3:H2B-RFP) brain from the Z-Brain atlas (along with atlas masks) were registered to this bridge brain, to allow for anatomical region analysis. c, Example z-planes from a single fish, with a subset of overlaid anatomical regions from the registered Z-Brain atlas. The same regions were aligned to each of the N = 8 fish. d, Step-by-step protocol for registering each recorded brain to a common volume, and assigning recorded neurons to brain regions in the Z-brain atlas.

Extended Data Fig. 3 Brain-wide imaging and linear models of single neurons reveal feature-selective neurons across brain regions.

a, Schematic of experiment, and example tail angle over threat presentation. b, Schematic of single-neuron analyses, and data from an example neuron. A linear model of each cell’s denoised fluorescence time series is composed from behavioral events (convolved with calcium indicator decay), and the importance of each event is determined by the change in model performance (ΔR2) upon shuffling of each event time-series (unique model contributions, UMC). UMC plot for the example neuron (right). This example neuron preferentially encodes salinity. c, Spatial distribution of UMC for each variable, shown in side projection of the medial 40 µm of whole-brain volume. Values are displayed as the percentile within the distribution of UMC for that task component across all cells. Brain regions are outlined in brown. d, Violin plots showing the distribution of unique model contributions (UMC, ΔR2) for brain regions denoted by the Z-brain atlas. Density does not extend beyond the minima and maxima of the data (0-100th percentile). Quartiles are not shown. The number of neurons in each group are noted at bottom, along with the region name.

Extended Data Fig. 4 Details of MultiMAP experimental protocol for registration of live functional imaging to multi-color fluorescent in situ hybridization.

a, Step-by-step protocol for performing MultiMAP procedure, and assigning recorded neurons to molecularly-defined populations. b, Overlay of single live-imaging z-plane (mean of all time-points in motion-corrected plane) with the associated z-plane from registered volumes. Similar results were obtained from all N = 7 fish. c, Validation of registration in the hypothalamus, using the red fluorescent protein (RFP, mCherry in this case) as a “held out” marker in both live and fixed volumes to assess accuracy of fixed-to-live registration of H2B-GCaMP (in Tg(oxt:Gal4; UAS:NTR-mCherry; elavl3:H2B-GCaMP6s) fish (N = 4). Note that after registration, the held-out RFP signals precisely overlap. Scale bar = 10 μm.

Extended Data Fig. 5 Cellular-resolution registration of live functional imaging with up to three rounds of triple fluorescent in situ hybridization.

a, All z-planes functionally imaged in a single Tg(elavl3:H2B-GCaMP6s) fish - 5 z-planes, 15 µm separation, 4 volumes/second. Showing live GCaMP z-plane (grey), and fixed GCaMP z-planes from round 1 and round 2 of in situ hybridization (green and red, respectively), before (left) and after (middle) volume registration. Overlap of live GCaMP and the six registered in situ hybridization labels from two rounds of multicolor fluorescent in situ hybridization (right). b, Example z-planes from all N = 7 fish used in Figs. 2 and 3. c, Overlap of molecular labels in cell types identified (N = 7 fish, Pearson’s R). Note the co-expression of avp with the medial crf population. d, Example z-planes functionally imaged in a single Tg(elavl3:H2B-GCaMP6s) fish, showing live GCaMP z-plane (grey), and fixed GCaMP z-planes from round 1, 2, and 3 of fluorescent in situ hybridization (green, red, and cyan, respectively), before and after volume registration. Overlap of live GCaMP and the nine registered in situ hybridization labels from three rounds of triple fluorescent in situ hybridization (right). Similar expression patterns are obtained from each of the N = 3 fish tested.

Extended Data Fig. 6 Functional clusters of hypothalamic neurons.

Stimulus-aligned activity averaged across all recorded neurons and trials, and unique model contribution (UMC) values, for all functional clusters identified in Fig. 3c. These plots include all recorded neurons in Tg(elavl3:H2B-GCaMP6s) fish, rather than just the identified peptidergic neurons.

Extended Data Fig. 7 Characterization of Gal4 lines, optical excitation, and electrophysiological validation of ChR2 expression.

a, Overlap of GFP expression in the preoptic area of Tg(oxt:Gal4; UAS:GFP) and Tg(sst3:Gal4; UAS:GFP) with in situ hybridization labels, and overlap of GFP expression in Tg(otpba:Gal4;UAS:GFP) line with fluorescent in situ hybridization for oxt, crf, and avp. Similar expression for N = 3 fish per genotype. Crf and avp are co-localized in the same cells. b, Co-localization of GFP and crf in Tg(crf:Gal4; UAS:GFP) fish. Note that GFP expression is specific to crf+ neurons, but that only 29% of crf+ neurons are GFP+ (n = 2 fish). c, Imaging oxt+ and crf+ neurons in transgenic lines. Image of GCaMP expressing neurons and event-averaged responses of neurons recorded from Tg(oxt:Gal4; UAS:GCaMP6s) and Tg(crf:Gal4; UAS:GCaMP6s) fish (top and bottom, respectively). Neurons pooled from N = 2 fish per genotype. d, Localization of optical stimulation for optogenetics experiments. 405 nm light was delivered through the same optic fiber used for ChR2 or GtACR1 stimulation, and converts kaede from green to red. The increased green-to-red (pseudocolored magenta) change in the preoptic area demonstrates limited spatial extent of optogenetic stimulation. Similar results obtained for N = 3 fish tested. e, in vivo cell-attached recordings from single ChR2-mCherry+ and neighboring ChR2-mCherry-negative neurons in the preoptic hypothalamus of a Tg(oxt:Gal4; UAS:ChR2-mCherry) fish, during stimulation with the same parameters used for behavioral experiments. 5 Hz blue light trains evoke spiking in the opsin-expressing neuron. Similar results were obtained from 2 other neurons.

Extended Data Fig. 8 Controls related to cellular ablation.

a, Expression of UAS:NTR-mCherry in the preoptic hypothalamus of each Gal4 line, and neuronal ablation upon treatment with MTZ (related to Fig. 4e, f). A similar extent of ablation was observed in all fish examined. b, Maximum tail angle of responsive movements - summary data from control and ablated fish. Same fish as Fig. 4f. Mean ± s.e.m., individual fish are points. One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons within genotype. NS = “not significant”, all p > 0.1. c, Inactivation of preoptic neurons in Tg(otpba:Gal4; UAS:GtACR1-eYFP) fish. Schematic of experiment (left), image of GtACR1-eYFP expression (middle; similar expression observed in all N = 8 fish.), and behavioral results (right). N = 8 fish per group, mean ± s.e.m., individual fish are points. One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons. NS = “not significant”, all p > 0.1. *p < 0.05. While light ON trials increases total movement in control and opsin fish, GtACR1-expressing fish move less under these conditions. d, Ablation of neurons in Tg(th:Gal4) line, including the dopaminergic posterior tuberculum neurons also labeled in the Tg(otpba:Gal4) line, does not influence behavior in this assay. A similar extent of ablation was observed in all fish examined. N = 8 fish per group, mean ± s.e.m., individual fish are points. One-tailed Mann-Whitney U test, Benjamini-Hochberg false discovery rate correction for multiple comparisons. NS = “not significant”, all p > 0.4. See Supplementary Table 2 for test statistics.

Extended Data Fig. 9 Details of RoL1 location, two-photon single-cell ablation of spinal projection neurons, and local injection of NBQX.

a, Location of spinal projection neurons (SPNs), with cell names, and axonal projections in Tg(oxt:Gal4; UAS:GFP) fish. b, Location of SPNs relative to monoaminergic cells, labeled with spinal backfill in a Tg(vmat2:eGFP) line. RoL1 neurons reside slightly anterior and medial to the noradrenergic neurons of the locus coeruleus. c, Example images showing sequential two-photon single-cell ablation of three RoL1 neurons in a Tg(elavl3:H2B-GCaMP6s) fish backfilled with Texas red dextran. This specificity and extent of ablation was confirmed across all N = 10 fish. d, Images of an example fish, backfilled with Texas red dextran, before and after bilateral ablation of the Mauthner neurons and segmental homologues (“M-array” ablation). This specificity and extent of ablation was confirmed across all N = 11 fish. e, Maximum tail angle of responsive movements - summary data from control and ablated fish. Mean ± s.e.m., individual fish are points. N = (11,11,10) – (sham, M-array, RoL1), left to right. Note that RoL1 neuron ablation reduced the maximum tail angle in response to some stimuli. One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons. Same fish as Fig. 5h. NS = “not significant”, all p > 0.2. *p < 0.05. f, DIC image of embedded fish (agar around nose removed), with location of injection pipette entering from the fissure between the optic tectum and lateral cerebellum (left). Close-up image of injection area in DIC and fluorescence imaging to visualize drug/dye flow (right). Localized dye spread was confirmed across all injected fish. g, Maximum tail angle of responsive movements - summary data from in RoL1-injected fish (NBQX or vehicle). Mean ± s.e.m., individual fish are points. N = (6,6) – (vehicle, NBQX). One-tailed Mann-Whitney U tests, Benjamini-Hochberg false discovery rate correction for multiple comparisons. Same fish as Fig. 5k. NS = “not significant”, all p > 0.1. See Supplementary Table 2 for test statistics.

Supplementary information

Reporting Summary

Supplementary Tables

Supplementary Tables 1 and 2.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lovett-Barron, M., Chen, R., Bradbury, S. et al. Multiple convergent hypothalamus–brainstem circuits drive defensive behavior. Nat Neurosci 23, 959–967 (2020). https://doi.org/10.1038/s41593-020-0655-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-020-0655-1

This article is cited by

Search

Advanced search

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