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Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their ‘readout’ of visual input

Nature Neuroscience volume 26, pages 470–480 (2023)Cite this article

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Abstract

The thalamus is the main gateway for sensory information from the periphery to the mammalian cerebral cortex. A major conundrum has been the discrepancy between the thalamus’s central role as the primary feedforward projection system into the neocortex and the sparseness of thalamocortical synapses. Here we use new methods, combining genetic tools and scalable tissue expansion microscopy for whole-cell synaptic mapping, revealing the number, density and size of thalamic versus cortical excitatory synapses onto individual layer 2/3 (L2/3) pyramidal cells (PCs) of the mouse primary visual cortex. We find that thalamic inputs are not only sparse, but remarkably heterogeneous in number and density across individual dendrites and neurons. Most surprising, despite their sparseness, thalamic synapses onto L2/3 PCs are smaller than their cortical counterparts. Incorporating these findings into fine-scale, anatomically faithful biophysical models of L2/3 PCs reveals how individual neurons with sparse and weak thalamocortical synapses, embedded in small heterogeneous neuronal ensembles, may reliably ‘read out’ visually driven thalamic input.

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Fig. 1: Specific labeling of thalamic excitatory afferents onto the full dendritic arbors of L2/3 pyramidal cells.
Fig. 2: Magnified analysis of the proteome allows validation of two-photon scoring criteria for true thalamic boutons.
Fig. 3: Every PSD95+ spine receives either thalamic or cortical input, with thalamic inputs favoring smaller spines. Inhibitory synapses target spines receiving either thalamic or cortical input.
Fig. 4: Distribution of thalamic versus cortical synapses onto L2/3 neurons.
Fig. 5: Thalamocortical and corticocortical synaptic variability results in large response variability of L2/3 cells to visual input.
Fig. 6: Heterogenous synaptic density among L2/3 cells improves readout accuracy of visually driven thalamic input.

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Data availability

Data necessary to interpret, verify or extend the research in the article are available upon request.

Code availability

Synapses were annotated using ObjectJ, a freely available ImageJ plugin (https://sites.imagej.net/Kpberry/).

References

  1. Jones, E. G. Thalamic circuitry and thalamocortical synchrony. Philos. Trans. R. Soc. Lond. B Biol. Sci. 357, 1659–1673 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Halassa, M. M. & Sherman, S. M. Thalamocortical circuit motifs: a general framework. Neuron 103, 762–770 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Douglas, R. J. & Martin, K. A. A functional microcircuit for cat visual cortex. J. Physiol. 440, 735–769 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Douglas, R. J., Koch, C., Mahowald, M., Martin, K. A. C. & Suarez, H. H. Recurrent excitation in neocortical circuits. Science 269, 981–985 (1995).

    Article  CAS  PubMed  Google Scholar 

  5. Gil, Z., Connors, B. W. & Amitai, Y. Efficacy of thalamocortical and intracortical synaptic connections: quanta, innervation and reliability. Neuron 23, 385–397 (1999).

    Article  CAS  PubMed  Google Scholar 

  6. Stratford, K. J., Tarczy-Hornoch, K., Martin, K. A. C., Bannister, N. J. & Jack, J. J. B. Excitatory synaptic inputs to spiny stellate cells in cat visual cortex. Nature 382, 258–261 (1996).

    Article  CAS  PubMed  Google Scholar 

  7. Bruno, R. M. & Sakmann, B. Cortex is driven by weak but synchronously active thalamocortical synapses. Science 312, 1622–1627 (2006).

    Article  CAS  PubMed  Google Scholar 

  8. Sedigh-Sarvestani, M., Palmer, L. A. & Contreras, D. Thalamocortical synapses in the cat visual system in vivo are weak and unreliable. Elife 8, e41925 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Wang, H. P., Spencer, D., Fellous, J. M. & Sejnowski, T. J. Synchrony of thalamocortical inputs maximizes cortical reliability. Science 328, 106–109 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Meyer, H. S. et al. Cell type-specific thalamic innervation in a column of rat vibrissal cortex. Cereb. Cortex 20, 2287–2303 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Meyer, H. S. et al. Number and laminar distribution of neurons in a thalamocortical projection column of rat vibrissal cortex. Cereb. Cortex 20, 2277–2286 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Oberlaender, M. et al. Cell-type-specific three-dimensional structure of thalamocortical circuits in a column of rat vibrissal cortex. Cereb. Cortex 22, 2375–2391 (2012).

    Article  PubMed  Google Scholar 

  13. Sermet, B. S. et al. Pathway-, layer- and cell-type-specific thalamic input to mouse barrel cortex. Elife 8, e52665 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Quiquempoix, M. et al. Layer 2/3 pyramidal neurons control the gain of cortical output. Cell Rep. 24, 2799–2807 (2018).

    Article  CAS  PubMed  Google Scholar 

  15. Benshalom, G. & White, E. L. Quantification of thalamocortical synapses with spiny stellate neurons in layer IV of mouse somatosensory cortex. J. Comp. Neurol. 253, 303–314 (1986).

    Article  CAS  PubMed  Google Scholar 

  16. Peters, A. & Payne, B. R. Numerical relationships between geniculocortical afferents and pyramidal cell modules in cat primary visual cortex. Cereb. Cortex 3, 69–78 (1993).

    Article  CAS  PubMed  Google Scholar 

  17. Ahmed, B., Anderson, J. C., Douglas, R. J., Martin, K. A. C. & Nelson, J. C. Polyneuronal innervation of spiny stellate neurons in cat visual cortex. J. Comp. Neurol. 341, 39–49 (1994).

    Article  CAS  PubMed  Google Scholar 

  18. Schoonover, C. E. et al. Comparative strength and dendritic organization of thalamocortical and corticocortical synapses onto excitatory layer 4 neurons. J. Neurosci. 34, 6746–6758 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Bopp, R., Holler-Rickauer, S., Martin, K. A. & Schuhknecht, G. F. An ultrastructural study of the thalamic input to layer 4 of primary motor and primary somatosensory cortex in the mouse. J. Neurosci. 37, 2435–2448 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Guillery, R. W. On counting and counting errors. J. Comp. Neurol. 447, 1–7 (2002).

    Article  CAS  PubMed  Google Scholar 

  21. Ku, T. et al. Multiplexed and scalable super-resolution imaging of three-dimensional protein localization in size-adjustable tissues. Nat. Biotechnol. 34, 973–981 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Park, J. et al. Epitope-preserving magnified analysis of proteome (eMAP). Sci. Adv. 7, eabf6589 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Subramanian, J., Michel, K., Benoit, M. & Nedivi, E. CPG15/neuritin mimics experience in selecting excitatory synapses for stabilization by facilitating PSD95 recruitment. Cell Rep. 28, 1584–1595 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Villa, K. L. et al. Inhibitory synapses are repeatedly assembled and removed at persistent sites in vivo. Neuron 89, 756–769 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Fremeau, R. T. Jr. et al. The expression of vesicular glutamate transporters defines two classes of excitatory synapse. Neuron 31, 247–260 (2001).

    Article  CAS  PubMed  Google Scholar 

  26. Balaram, P., Hackett, T. A. & Kaas, J. H. Differential expression of vesicular glutamate transporters 1 and 2 may identify distinct modes of glutamatergic transmission in the macaque visual system. J. Chem. Neuroanat. 50-51, 21–38 (2013).

    Article  CAS  PubMed  Google Scholar 

  27. Roth, M. M. et al. Thalamic nuclei convey diverse contextual information to layer 1 of visual cortex. Nat. Neurosci. 19, 299–307 (2016).

    Article  CAS  PubMed  Google Scholar 

  28. Kubota, Y., Hatada, S., Kondo, S., Karube, F. & Kawaguchi, Y. Neocortical inhibitory terminals innervate dendritic spines targeted by thalamocortical afferents. J. Neurosci. 27, 1139–1150 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Nahmani, M. & Erisir, A. VGluT2 immunochemistry identifies thalamocortical terminals in layer 4 of adult and developing visual cortex. J. Comp. Neurol. 484, 458–473 (2005).

    Article  CAS  PubMed  Google Scholar 

  30. Rodriguez-Moreno, J. et al. Quantitative 3D ultrastructure of thalamocortical synapses from the ‘lemniscal’ ventral posteromedial nucleus in mouse barrel cortex. Cereb. Cortex 28, 3159–3175 (2018).

    Article  PubMed  Google Scholar 

  31. Banitt, Y., Martin, K. A. & Segev, I. A biologically realistic model of contrast invariant orientation tuning by thalamocortical synaptic depression. J. Neurosci. 27, 10230–10239 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Huang et al. Relationship between simultaneously recorded spiking activity and fluorescence signal in GCaMP6 transgenic mice. eLife 10, 1–19 (2021).

  33. Shamir, M. Emerging principles of population coding: in search for the neural code. Curr. Opin. Neurobiol. 25, 140–148 (2014).

    Article  CAS  PubMed  Google Scholar 

  34. Hung, C. P., Kreiman, G., Poggio, T. & DiCarlo, J. J. Fast readout of object identity from macaque inferior temporal cortex. Science 310, 863–866 (2005).

    Article  CAS  PubMed  Google Scholar 

  35. Yan, Y. et al. Perceptual training continuously refines neuronal population codes in primary visual cortex. Nat. Neurosci. 17, 1380–1387 (2014).

    Article  CAS  PubMed  Google Scholar 

  36. Berens, P. et al. A fast and simple population code for orientation in primate V1. J. Neurosci. 32, 10618–10626 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Heyer, D. B. et al. Verbal and general IQ associate with supragranular layer thickness and cell properties of the left temporal cortex. Cereb. Cortex 32, 2343–2357 (2022).

    Article  CAS  PubMed  Google Scholar 

  38. Rah, J. C. et al. Thalamocortical input onto layer 5 pyramidal neurons measured using quantitative large-scale array tomography. Front Neural Circuits 7, 177 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Bagnall, M. W., Hull, C., Bushong, E. A., Ellisman, M. H. & Scanziani, M. Multiple clusters of release sites formed by individual thalamic afferents onto cortical interneurons ensure reliable transmission. Neuron 71, 180–194 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Harvey, C. D. & Svoboda, K. Locally dynamic synaptic learning rules in pyramidal neuron dendrites. Nature 450, 1195–1200 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Engert, F. & Bonhoeffer, T. Synapse specificity of long-term potentiation breaks down at short distances. Nature 388, 279–284 (1997).

    Article  CAS  PubMed  Google Scholar 

  42. Kastellakis, G. & Poirazi, P. Synaptic clustering and memory formation. Front Mol. Neurosci. 12, 300 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Suzuki, M. & Larkum, M. E. General anesthesia decouples cortical pyramidal neurons. Cell 180, 666–676 (2020).

    Article  CAS  PubMed  Google Scholar 

  44. Holler, S., Kostinger, G., Martin, K. A. C., Schuhknecht, G. F. P. & Stratford, K. J. Structure and function of a neocortical synapse. Nature 591, 111–116 (2021).

    Article  CAS  PubMed  Google Scholar 

  45. Guo, Z. V. et al. Maintenance of persistent activity in a frontal thalamocortical loop. Nature 545, 181–186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Wang, H. P., Garcia, J. W., Sabottke, C. F., Spencer, D. J. & Sejnowski, T. J. Feedforward thalamocortical connectivity preserves stimulus timing information in sensory pathways. J. Neurosci. 39, 7674–7688 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Rahimi, A. & Recht, B. Random features for large-scale kernal machines. in Proceedings of the 20th International Conference on Neural Information Processing Systems (eds. J. C. Platt et al.) 1177–1184 (Curran Associates, 2008).

  48. Rokach, L. Ensemble-based classifiers. Artif. Intell. Rev. 33, 1–39 (2010).

    Article  Google Scholar 

  49. Marder, E. Variability, compensation, and modulation in neurons and circuits. Proc. Natl Acad. Sci. USA 108, 15542–15548 (2011). Suppl 3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Reimann, M. W., Horlemann, A. L., Ramaswamy, S., Muller, E. B. & Markram, H. Morphological diversity strongly constrains synaptic connectivity and plasticity. Cereb. Cortex 27, 4570–4585 (2017).

    Article  PubMed  Google Scholar 

  51. Padmanabhan, K. & Urban, N. N. Intrinsic biophysical diversity decorrelates neuronal firing while increasing information content. Nat. Neurosci. 13, 1276–1282 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Tripathy, S. J., Padmanabhan, K., Gerkin, R. C. & Urban, N. N. Intermediate intrinsic diversity enhances neural population coding. Proc. Natl Acad. Sci. USA 110, 8248–8253 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mejias, J. F. & Longtin, A. Optimal heterogeneity for coding in spiking neural networks. Phys. Rev. Lett. 108, 228102 (2012).

    Article  CAS  PubMed  Google Scholar 

  54. Ramaswamy, S. et al. Intrinsic morphological diversity of thick-tufted layer 5 pyramidal neurons ensures robust and invariant properties of in silico synaptic connections. J. Physiol. 590, 737–752 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Rich, S., Moradi Chameh, H., Lefebvre, J. & Valiante, T. A. Loss of neuronal heterogeneity in epileptogenic human tissue impairs network resilience to sudden changes in synchrony. Cell Rep. 39, 110863 (2022).

    Article  CAS  PubMed  Google Scholar 

  56. Vue, T. Y. et al. Sonic hedgehog signaling controls thalamic progenitor identity and nuclei specification in mice. J. Neurosci. 29, 4484–4497 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Raymond, C. S. & Soriano, P. High-efficiency FLP and PhiC31 site-specific recombination in mammalian cells. PLoS ONE 2, e162 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Tabata, H. & Nakajima, K. Efficient in utero gene transfer system to the developing mouse brain using electroporation: visualization of neuronal migration in the developing cortex. Neuroscience 103, 865–872 (2001).

    Article  CAS  PubMed  Google Scholar 

  59. Chen, J. L. et al. Clustered dynamics of inhibitory synapses and dendritic spines in the adult neocortex. Neuron 74, 361–373 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Lee, W. C. et al. A dynamic zone defines interneuron remodeling in the adult neocortex. Proc. Natl Acad. Sci. USA 105, 19968–19973 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Kalatsky, V. A. & Stryker, M. P. New paradigm for optical imaging: temporally encoded maps of intrinsic signal. Neuron 38, 529–545 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Schindelin, J. et al. Fiji: an open-source platform for biological-image analysis. Nat. Methods 9, 676–682 (2012).

    Article  CAS  PubMed  Google Scholar 

  63. Ku, T. et al. Elasticizing tissues for reversible shape transformation and accelerated molecular labeling. Nat. Methods 17, 609–613 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Dickstein, D. L. et al. Automatic dendritic spine quantification from confocal data with neurolucida 360. Curr. Protoc. Neurosci. 77, 1.27.21–21.27.21 (2016).

    Article  Google Scholar 

  65. Carnevale, N. T. & Hines, M. L. The NEURON Book (Cambridge University Press, 2006).

  66. Segev, I. & Burke, R. E. in Methods in Neuronal Modeling (eds. C. Koch & I. Segev) (MIT Press, 1998).

  67. Brent, R. P. Algorithms for minimization without derivatives (Courier Corporation, 2013).

  68. Rapp, M., Yarom, Y. & Segev, I. The impact of parallel fiber background activity on the cable properties of cerebellar Purkinje cells. Neural Comput. 4, 518–533 (1992).

    Article  Google Scholar 

  69. Druckmann, S. et al. A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci. 1, 7–18 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Markram, H. & et al Reconstruction and simulation of neocortical microcircuitry. Cell 163, 456–492 (2015).

    Article  CAS  PubMed  Google Scholar 

  71. Spruston, N., Jonas, P. & Sakmann, B. Dendritic glutamate receptor channels in rat hippocampal CA3 and CA1 pyramidal neurons. J. Physiol. 482, 325–352 (1995).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Rhodes, P. The properties and implications of NMDA spikes in neocortical pyramidal cells. J. Neurosci. 26, 6704–6715 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Jahr, C. E. & Stevens, C. F. Voltage dependence of NMDA-activated macroscopic conductances predicted by single-channel kinetics. J. Neurosci. 10, 3178–3182 (1990).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  74. Doron, M., Chindemi, G., Muller, E., Markram, H. & Segev, I. Timed synaptic inhibition shapes NMDA spikes, influencing local dendritic processing and global I/O properties of cortical neurons. Cell Rep. 21, 1550–1561 (2017).

    Article  CAS  PubMed  Google Scholar 

  75. Hay, E., Hill, S., Schurmann, F., Markram, H. & Segev, I. Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput. Biol. 7, e1002107 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Salin, P. A. & Prince, D. A. Spontaneous GABAA receptor-mediated inhibitory currents in adult rat somatosensory cortex. J. Neurophysiol. 75, 1573–1588 (1996).

    Article  CAS  PubMed  Google Scholar 

  77. Iascone, D. M. et al. Whole-neuron synaptic mapping reveals spatially precise excitatory/inhibitory balance limiting dendritic and somatic spiking. Neuron 106, 566–578 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  78. Tang, J., Ardila Jimenez, S. C., Chakraborty, S. & Schultz, S. R. Visual receptive field properties of neurons in the mouse lateral geniculate nucleus. PLoS ONE 11, e0146017 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  79. Galton, F. Regression towards mediocrity in hereditary stature. J. Anthropological Inst. 15, 246–263 (1886).

    Google Scholar 

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Acknowledgements

We thank K. Flavahan and K. Villa for technical support, and J. Boivin and M. Helmstaeder for comments on the manuscript. This work was supported by ONR grant N00014-19-1-2036 (to E.N. & I.S.), NEI grants RO1-EY011894 and RO1-EY034419 (to E.N.), JBP Foundation PNDRF award (to E.N. & K.C.), Research Supplement to Promote Re-Entry into Biomedical and Behavioral Research Careers award RO1-EY011894-S1 (to A.B.), JBP Foundation postdoctoral fellowship and NINDS grant K08-NS107591 (to R.G.), The Gatsby Charitable Foundation (to I. S.), and University of Virginia College of Arts and Sciences research funds (to A.E.). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. This work is dedicated to the memory of L. Safra, a great supporter of brain research.

Author information

Author notes

  1. Rebecca Gillani

    Present address: Department of Neurology, Massachusetts General Hospital, Boston, MA, USA

  2. Michael Doron

    Present address: Broad Institute of Harvard University and MIT, Cambridge, MA, USA

  3. Taeyun Ku

    Present address: Graduate School of Medical Science and Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea

Authors and Affiliations

  1. Picower Institute for Learning and Memory, Cambridge, MA, USA

    Aygul Balcioglu, Rebecca Gillani, Kendyll Burnell, Taeyun Ku, Kwanghun Chung & Elly Nedivi

  2. Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Aygul Balcioglu, Rebecca Gillani, Kendyll Burnell & Elly Nedivi

  3. The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel

    Michael Doron & Idan Segev

  4. Department of Neurobiology, The Hebrew University of Jerusalem, Jerusalem, Israel

    Michael Doron, Kwanghun Chung & Idan Segev

  5. Institute for Medical Engineering and Science, Cambridge, MA, USA

    Taeyun Ku & Kwanghun Chung

  6. Department of Chemical Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

    Taeyun Ku

  7. Department of Psychology, University of Virginia, Charlottesville, VA, USA

    Alev Erisir

  8. Broad Institute of Harvard University and MIT, Cambridge, MA, USA

    Kwanghun Chung

  9. Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA

    Elly Nedivi

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Contributions

A.B. and E.N. designed the study. A.B. conducted all in vivo imaging experiments and data analysis. R.G. conducted all MAP experiments with T.K. and analyzed the data. R.G. developed two-photon scoring criteria based on MAP with A.B.’s help. T.K. established the MAP protocol under K.C.’s supervision. A.E. conducted all EM experiments and data analysis. K.B. contributed Extended Data Figs. 2 and 8. All modeling was done by M.D. with supervision by I.S. The manuscript was written by E.N. and I.S. with contributions from all authors.

Corresponding author

Correspondence to Elly Nedivi.

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Extended data

Extended Data Fig. 1 Crossing the Olig 3 Cre driver line to a Cre dependent Syn-Tdtomato reporter line faithfully and exclusively labels thalamic boutons from LGN but not LP.

Olig3/+ mice were crossed with Ai14 reporter mice expressing Cre-dependent Tdtomato. (a–c) Sections at different Bregma coordinates on the anterior posterior axis of the brain show cellular labeling exclusive to thalamic nuclei, with thalamic afferents visible in the internal capsule and layer 4 of primary sensory cortices. (d–f) Sections through visual cortex immunostained for Tdtomato (red) and VGlut2 (green) show perfect overlap in thalamic afferents in layers 4 and 1. g–j) Coronal sections at Bregma coordinates that include both LP and LGN show predominantly LGN labeling in Olig3−/+/Ai14 mice. Thal, thalamus; PoT, posterior thalamic nucleus; MGB, medial geniculate body; L4, cortical layer 4; L1, cortical layer1; BC, barrel cortex; LGN, lateral geniculate nucleus; IC, internal capsule; CP, caudate putamen; LV, lateral ventricle. (k–m) High magnification view in layer 2/3 of visual cortex showing beads-on-a-string appearance of thalamic boutons and specificity of VGlut2 staining to these boutons. (n-p) MAP processed sample labeled with n) anti-RFP to label genetically encoded tdTomato in thalamic boutons, and o) VGluT2. p) Composite image shows that all thalamic boutons genetically encoded with tdTomato, also are VGluT2 positive. q) Quantification of RFP and Vlut2 bouton co-labeling in MAP processed samples. Approximately 91% of Vglut2 boutons are RFP positive, with 9% potentially representing non-thalamic inputs. All (100%) RFP labeled boutons were Vlut2 positive. Brains from three animals were sectioned and imaged, and representative sections are shown in a–f, h, j, k–p). Scale bars: (a-c) 0.5 mm; (d–f) 100 µm; (h–j) 1 mm; (k–m) 2 µm; (n-p) 10 µm.

Extended Data Fig. 2 Two photon imaging system alignment is compatible with its theoretical diffraction limited resolution.

Two hundred nm fluorescent bead imaged on 2-photon system with a 1.0 NA objective at 915 nm with 100 nm/pixel XY sampling and 250 nm Z steps. a) Intensity plot on the XY axis (shown in red) with full width half maximum measured as 400 nm, as predicted by the Abbe limit. b) Intensity plot of the Z axis with full width half maximum measured as 1um.

Extended Data Fig. 3 Genetic background and plasmid system do not affect spine and synaptic densities, or spine volumes, on imaged neurons.

a) To confirm that the Olig3/Syntd genetic background and use of the Flp/FRT vs Cre/lox plasmid system does not influence synaptic densities, densities of all, PSD95 + , and PSD95- spines were compared with previously published data1. No difference was found between backgrounds and plasmid systems in any category of spines imaged imaged (Nested t test, t(4)= 0.2585, p = 0.808, n = 51 from 17 animals). b) Thalamic vs cortical dendritic spine volumes were compared in MAP images of L2/3 labeled neurons from B6 vs Olig3 mice, where thalamic synapses were identified by VGlut2 staining. Scatter plots of spine sizes shown per each MAP cell demonstrate that synaptophysin expression does not influence either thalamic (Nested t test, t(3) = 0.07544, p = 0.9446, n = 78 spines from 5 animals) or cortical dendritic spine volumes (Nested t test, t(3) = 0.6727, p = 0.5493, n = 721 spines from 5 animals).

Extended Data Fig. 4 Comparison of EM and MAP validates that MAP expands tissue uniformly.

a) Representative EM image showing a VGluT2+ terminal bouton (TC) that forms a perforated synapse on a spine (sp), and a small, non-perforated synapse on a dendrite shaft (d; identified by the presence of a mitochondria within). Yellow arrowheads mark synaptic zones from the postsynaptic side. An unlabeled terminal bouton (non-TC) that forms a perforated synapse on a spine is also marked. Scale bar = 250 nm. b) Magnification of 1 x-y plane of a MAP imaged dendritic spine, shown in white, with Vglut2 labeled terminal bouton (thalamic, magenta). Contours for measurement of spine area and terminal bouton area are shown. c) Same as b) but with a spine contacting a VGluT1 labeled bouton (cortical, green). Scale bars = 1 µm. d) Ratio of spine head area/terminal bouton area match for MAP and EM for thalamic synapses (Mann–Whitney U test, p = 0.9399, n = 76 for MAP and 33 for EM) and e) cortical synapses (Mann–Whitney U test, p = 0.1056, n = 69 for MAP and 61 for EM). Sp, dendritic spine; D, dendrite.

Extended Data Fig. 5 Distribution of thalamic vs cortical inputs onto L2/3 neurons.

Additional dendrograms showing the distribution of thalamic versus cortical inputs onto the dendritic arbors of four additional pyramidal neurons in L2/3 of binocular V1 not presented in Fig. 4. Apical and basal dendrites are shown on opposite sides of the cell body (marked as 0 on the scale). The scale illustrates the distance of each input from the cell body. Note that cells 5 & 3, are deeper, and shown on a different scale. Dashed lines depict branch segments that were traced, but not scored. Insets (top left) in each dendrogram show the traced skeleton of that cell with location of scored inputs.

Extended Data Fig. 6 Diverse morphology and statistics of the nine pyramidal cells analyzed.

Maximum intensity projections of cells imaged in vivo are presented in order of somal depth in relation to pia, with top left most superficial and bottom right deepest. Note that superficial cells have atypical apical dendrites as compared to deeper cells. They also tend towards more extensive basal dendrites (for example, Cell 1 has 13 basal dendrites while having only 2 apical dendrites. A similar morphology exists for Cell 2), while deeper cells have more apical and fewer basal dendrites (for example, Cell 9). Since distinction of basal vs apical is not always obvious in the maximum intensity projections, white arrows point to a basal dendrite for each cell, and an appended movie file shows a representative Z stack. A representative Supplementary video 1 shows a Z stack of Cell 7 encompassing both apical and basal dendrites. See Fig. 4c–d in main text for number of apical vs basal dendrites scored for each cell.

Extended Data Fig. 7 Thalamic inputs are heterogeneously distributed across individual branches of pyramidal cells, and across cells.

The overall percent of the thalamic vs. cortical synapses onto individual branches (marked as B followed by branch number) of all nine pyramidal cells imaged, with apical branches on the right and basal dendrites on the left for each cell. TA, TB, and Total summarize total apical and total basal, and total per cell, respectively.

Extended Data Fig. 8 Thalamic innervation is not correlated with dendritic branch depth or local bouton density.

a) Coronal section through visual cortex stained with DAPI (left) from Olig3/synTd mouse showing synTd labeling of thalamic pre-synaptic boutons (middle). SynTd boutons were counted per 100x25um bins and their density was plotted as a function of distance from the pial surface (right). Error bars: SEM n = 4 mice. Red triangles are a schematic representation of Cell 6 and Cell 9 with their respective % thalamic innervation. Their somal depth with basal and apical dendrite span are mapped in relation to the bouton density graph. While both Cell 6 & 9 are within L2/3, because of their different somal depth, their basal dendrites extend to to different depths. Although Cell 9 basal dendrites project much deeper than those of Cell 6 into a region with higher thalamic bouton density, Cell 6 has almost double the thalamic innervation. b) Diagram demonstrating how thalamic innervation onto specific dendritic branches was scored in relation to local thalamic bouton density, with 4x4um ROIs placed around each branch segment imaged in-vivo in the z-planes where it was brightest (green: synTd, cyan: PSD95-teal, red: YFP). c) Representative branches varying in depth were scored as shown in b, showing the number of thalamic synapses on each branch is unrelated branch depth or local thalamic bouton density. d) Linear regression comparing density of thalamic synapses on branches versus depth from pia (left) and local bouton density (right), including data from c, shows no correlation between branch depth, local thalamic bouton density, and number of thalamic contacts per branch.; r2 = 0.084 p = 0.449 and r2 = 0.209 p = 0.215 respectively.

Extended Data Fig. 9 Experimentally based model of L2/3 pyramidal cell in V1.

(a) Voltage response of typical cell (shown in inset) following a 3 msec and 0.6 nA subthreshold depolarizing step current. Blue, experimental trace; black, model. The morphology and physiology of this cell was taken from the Allen Institute Cell Atlas (see Methods). (b) Voltage response of the cell shown in a to three subthreshold long current pulses (0.01 nA, 0.03 nA and 0.05 nA, given for 1000 msecs). Blue, experimental trace; black, model. (c) Spiking response of the cell shown in a (in blue) to suprathreshold current injections (1.17 nA for 1000 msecs); model response is shown in black. (d) The response of a L4 cortical pyramidal cells model (inset, cell reconstructed by the Blue Brain Project, see Methods) to the activation of a single thalamo-cortical axon. The experimental somatic EPSP is shown in blue (courtesy of Randy Bruno, see Methods); thick black line shows the average EPSP in the biophysical model constructed for this L4 cell in response to the simultaneous activation of 7 synaptic contacts (red synapses). The gray area shows the standard deviation around the mean of the somatic EPSPs in repeated activation of 7 synaptic contacts that were randomly distributed repeatedly across the modelled dendritic tree. The best fit was achieved with peak conductance of 0.31 nS for both the AMPA- and the NMDA- components; see Methods for details of synapse and neuron models.

Supplementary information

Supplementary Information

Supplementary Tables 1–3

Reporting Summary

Supplementary Video

75-µm z-stack at 1-micron intervals of cell 7 showing both apical and basal dendrites.

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Balcioglu, A., Gillani, R., Doron, M. et al. Mapping thalamic innervation to individual L2/3 pyramidal neurons and modeling their ‘readout’ of visual input. Nat Neurosci 26, 470–480 (2023). https://doi.org/10.1038/s41593-022-01253-9

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