Abstract

The local and long-range connectivity of cortical neurons are considered instrumental to the functional repertoire of the cortical region in which they reside. In cortical networks, distinct cell types build local circuit structures enabling computational operations. Computations in the medial prefrontal cortex (mPFC) are thought to be central to cognitive operation, including decision-making and memory. We used a retrograde trans-synaptic rabies virus system to generate brain-wide maps of the input to excitatory neurons as well as three inhibitory interneuron subtypes in the mPFC. On the global scale the input patterns were found to be mainly cell type independent, with quantitative differences in key brain regions, including the basal forebrain. Mapping of the local mPFC network revealed high connectivity between the different subtypes of interneurons. The connectivity mapping gives insight into the information that the mPFC processes and the structural architecture underlying the mPFC’s unique functions.

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Data availability

The data that support the findings of this study are available in Supplementary table 1 and from the corresponding author upon reasonable request.

Code availability

Data were collected with previously published custom MatLab script8. The code written to visualize the data of this study is available from the corresponding author upon request.

Additional information

Journal peer review information: Nature Neuroscience thanks Ian Wickersham and 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.

Change history

  • 15 April 2019

    The Supplementary Information is available in the online version of this Publisher Correction.

References

  1. 1.

    Heidbreder, C. A. & Groenewegen, H. J. The medial prefrontal cortex in the rat: evidence for a dorso-ventral distinction based upon functional and anatomical characteristics. Neurosci. Biobehav. Rev. 27, 555–579 (2003).

  2. 2.

    Van De Werd, H. J. J. M., Rajkowska, G., Evers, P. & Uylings, H. B. M. Cytoarchitectonic and chemoarchitectonic characterization of the prefrontal cortical areas in the mouse. Brain Struct. Funct. 214, 339–353 (2010).

  3. 3.

    Euston, D. R., Gruber, A. J. & McNaughton, B. L. The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057–1070 (2012).

  4. 4.

    Kepecs, A. & Fishell, G. Interneuron cell types are fit to function. Nature 505, 318–326 (2014).

  5. 5.

    Kim, H., Ährlund-Richter, S., Wang, X., Deisseroth, K. & Carlén, M. Prefrontal parvalbumin neurons in control of attention. Cell 164, 208–218 (2016).

  6. 6.

    Harris, K. D. & Shepherd, G. M. The neocortical circuit: themes and variations. Nat. Neurosci. 18, 170–181 (2015).

  7. 7.

    Callaway, E. M. & Luo, L. Monosynaptic circuit tracing with glycoprotein-deleted rabies viruses. J. Neurosci. 35, 8979–8985 (2015).

  8. 8.

    Pollak Dorocic, I. et al. A whole-brain atlas of inputs to serotonergic neurons of the dorsal and median raphe nuclei. Neuron 83, 663–678 (2014).

  9. 9.

    Allen, W. E. et al. Global representations of goal-directed behavior in distinct cell types of mouse neocortex. Neuron 94, 891–907.e6 (2017).

  10. 10.

    Lein, E. S. et al. Genome-wide atlas of gene expression in the adult mouse brain. Nature 445, 168–176 (2007).

  11. 11.

    Paxinos, G. & Franklin, K. The Mouse Brain In Stereotaxic Coordinates 3rd edn. (Elsevier/Academic Press, Amsterdam, 2008).

  12. 12.

    DeNardo, L. A., Berns, D. S., DeLoach, K. & Luo, L. Connectivity of mouse somatosensory and prefrontal cortex examined with trans-synaptic tracing. Nat. Neurosci. 18, 1687–1697 (2015).

  13. 13.

    Zingg, B. et al. Neural networks of the mouse neocortex. Cell 156, 1096–1111 (2014).

  14. 14.

    Zhang, S. et al. Organization of long-range inputs and outputs of frontal cortex for top-down control. Nat. Neurosci. 19, 1733–1742 (2016).

  15. 15.

    Gerfen, C. R. & Bolam, J. P. in Handbook of Behavioral Neuroscience Vol. 20 (eds. Steiner, H. & Tseng, K. Y.) 3–28 (Elsevier, Amsterdam, 2010).

  16. 16.

    Kristt, D. A. Organization and development of the cingulum: laminar arrangement of acetylcholinesterase-rich components in rat. Brain Res. Bull. 26, 789–798 (1991).

  17. 17.

    Salgado, S. & Kaplitt, M. G. The nucleus accumbens: a comprehensive review. Stereotact. Funct. Neurosurg. 93, 75–93 (2015).

  18. 18.

    Tremblay, R., Lee, S. & Rudy, B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron 91, 260–292 (2016).

  19. 19.

    Pi, H. J. et al. Cortical interneurons that specialize in disinhibitory control. Nature 503, 521–524 (2013).

  20. 20.

    Pfeffer, C. K., Xue, M., He, M., Huang, Z. J. & Scanziani, M. Inhibition of inhibition in visual cortex: the logic of connections between molecularly distinct interneurons. Nat. Neurosci. 16, 1068–1076 (2013).

  21. 21.

    Albisetti, G. W. et al. Identification of two classes of somatosensory neurons that display resistance to retrograde infection by rabies virus. J. Neurosci. 37, 10358–10371 (2017).

  22. 22.

    Bloem, B., Poorthuis, R. B. & Mansvelder, H. D. Cholinergic modulation of the medial prefrontal cortex: the role of nicotinic receptors in attention and regulation of neuronal activity. Front. Neural Circuit 8, 17 (2014).

  23. 23.

    Zaborszky, L., van den Pol, A. & Gyengesi, E. in The Mouse Nervous System (eds. Watson, C., Paxinos, G. & Puelles, L.) 684–718 (Academic Press, San Diego, CA, USA, 2012).

  24. 24.

    Kim, T. et al. Cortically projecting basal forebrain parvalbumin neurons regulate cortical gamma band oscillations. Proc. Natl Acad. Sci. USA 112, 3535–3540 (2015).

  25. 25.

    Vertes, R. P., Linley, S. B. & Hoover, W. B. Limbic circuitry of the midline thalamus. Neurosci. Biobehav. Rev. 54, 89–107 (2015).

  26. 26.

    Hunnicutt, B. J. et al. A comprehensive thalamocortical projection map at the mesoscopic level. Nat. Neurosci. 17, 1276–1285 (2014).

  27. 27.

    Mitchell, A. S. The mediodorsal thalamus as a higher order thalamic relay nucleus important for learning and decision-making. Neurosci. Biobehav. Rev. 54, 76–88 (2015).

  28. 28.

    Collins, D. P., Anastasiades, P. G., Marlin, J. J. & Carter, A. G. Reciprocal circuits linking the prefrontal cortex with dorsal and ventral thalamic nuclei. Neuron 98, 366–379 e4 (2018).

  29. 29.

    Delevich, K., Tucciarone, J., Huang, Z. J. & Li, B. The mediodorsal thalamus drives feedforward inhibition in the anterior cingulate cortex via parvalbumin interneurons. J. Neurosci. 35, 5743–5753 (2015).

  30. 30.

    Bolkan, S. S. et al. Thalamic projections sustain prefrontal activity during working memory maintenance. Nat. Neurosci. 20, 987–996 (2017).

  31. 31.

    Vertes, R. P., Linley, S. B., Groenewegen, H. J. & Witter, M. P. in The Rat Nervous System 4th edn (ed. Paxinos, G.) 335–390 (Academic Press, San Diego, CA, USA, 2015).

  32. 32.

    Van der Werf, Y. D., Witter, M. P. & Groenewegen, H. J. The intralaminar and midline nuclei of the thalamus. Anatomical and functional evidence for participation in processes of arousal and awareness. Brain Res. Brain Res. Rev. 39, 107–140 (2002).

  33. 33.

    Oh, S. W. et al. A mesoscale connectome of the mouse brain. Nature 508, 207–214 (2014).

  34. 34.

    Eichenbaum, H. On the integration of space, time, and memory.Neuron 95, 1007–1018 (2017).

  35. 35.

    Eichenbaum, H. Prefrontal-hippocampal interactions in episodic memory. Nat. Rev. Neurosci. 18, 547–558 (2017).

  36. 36.

    Soltesz, I. & Losonczy, A. CA1 pyramidal cell diversity enabling parallel information processing in the hippocampus. Nat. Neurosci. 21, 484–493 (2018).

  37. 37.

    Kerr, K. M., Agster, K. L., Furtak, S. C. & Burwell, R. D. Functional neuroanatomy of the parahippocampal region: the lateral and medial entorhinal areas. Hippocampus 17, 697–708 (2007).

  38. 38.

    Kitamura, T. et al. Engrams and circuits crucial for systems consolidation of a memory. Science 356, 73–78 (2017).

  39. 39.

    Hoover, W. B. & Vertes, R. P. Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat. Brain Struct. Funct. 212, 149–179 (2007).

  40. 40.

    Tsao, A. et al. Integrating time from experience in the lateral entorhinal cortex. Nature 561, 57–62 (2018).

  41. 41.

    Olucha-Bordonau, F. E., Fortes-Marco, L., Otero-García, M., Lanuza, E. & Martínez-García, F. in The Rat Nervous System 4th edn (ed. Paxinos, G.) 441–490 (Academic Press, San Diego, CA, USA, 2015).

  42. 42.

    Dilgen, J., Tejeda, H. A. & O’Donnell, P. Amygdala inputs drive feedforward inhibition in the medial prefrontal cortex. J. Neurophysiol. 110, 221–229 (2013).

  43. 43.

    McGarry, L. M. & Carter, A. G. Inhibitory gating of basolateral amygdala inputs to the prefrontal cortex. J. Neurosci. 36, 9391–9406 (2016).

  44. 44.

    Fadok, J. P., Markovic, M., Tovote, P. & Lüthi, A. New perspectives on central amygdala function. Curr. Opin. Neurobiol. 49, 141–147 (2018).

  45. 45.

    Seo, D. O. et al. A GABAergic projection from the centromedial nuclei of the amygdala to ventromedial prefrontal cortex modulates reward behavior. J. Neurosci. 36, 10831–10842 (2016).

  46. 46.

    Pardo-Bellver, C., Cádiz-Moretti, B., Novejarque, A., Martínez-García, F. & Lanuza, E. Differential efferent projections of the anterior, posteroventral, and posterodorsal subdivisions of the medial amygdala in mice. Front. Neuroanat. 6, 33 (2012).

  47. 47.

    Vaz, R. P., Cardoso, A., Sá, S. I., Pereira, P. A. & Madeira, M. D. The integrity of the nucleus of the lateral olfactory tract is essential for the normal functioning of the olfactory system. Brain Struct. Funct. 222, 3615–3637 (2017).

  48. 48.

    Wolff, S. B. et al. Amygdala interneuron subtypes control fear learning through disinhibition. Nature 509, 453–458 (2014).

  49. 49.

    Carlén, M. What constitutes the prefrontal cortex? Science 358, 478–482 (2017).

  50. 50.

    Wickersham, I. R. et al. Monosynaptic restriction of transsynaptic tracing from single, genetically targeted neurons. Neuron 53, 639–647 (2007).

  51. 51.

    Sena-Esteves, M., Tebbets, J. C., Steffens, S., Crombleholme, T. & Flake, A. W. Optimized large-scale production of high titer lentivirus vector pseudotypes. J. Virol. Methods 122, 131–139 (2004).

  52. 52.

    Glangetas, C. et al. NMDA-receptor-dependent plasticity in the bed nucleus of the stria terminalis triggers long-term anxiolysis. Nat. Commun. 8, 14456 (2017).

  53. 53.

    Yonehara, K. et al. The first stage of cardinal direction selectivity is localized to the dendrites of retinal ganglion cells. Neuron 79, 1078–1085 (2013).

  54. 54.

    Leinweber, M., Ward, D. R., Sobczak, J. M., Attinger, A. & Keller, G. B. A sensorimotor circuit in mouse cortex for visual flow predictions. Neuron 95, 1420–1432.e5 (2017).

  55. 55.

    Wall, N. R., Wickersham, I. R., Cetin, A., De La Parra, M. & Callaway, E. M. Monosynaptic circuit tracing in vivo through Cre-dependent targeting and complementation of modified rabies virus. Proc. Natl Acad. Sci. USA 107, 21848–21853 (2010).

  56. 56.

    Pringle, F. M. et al. A novel capsid expression strategy for Thosea asigna virus (Tetraviridae). J. Gen. Virol. 80, 1855–1863 (1999).

  57. 57.

    Southern, J. A., Young, D. F., Heaney, F., Baumgärtner, W. K. & Randall, R. E. Identification of an epitope on the P and V proteins of simian virus 5 that distinguishes between two isolates with different biological characteristics. J. Gen. Virol. 72, 1551–1557 (1991).

  58. 58.

    Wickersham, I. R., Sullivan, H. A. & Seung, H. S. Production of glycoprotein-deleted rabies viruses for monosynaptic tracing and high-level gene expression in neurons. Nat. Protoc. 5, 595–606 (2010).

  59. 59.

    Junyent, F. & Kremer, E. J. CAV-2—why a canine virus is a neurobiologist’s best friend. Curr. Opin. Pharmacol. 24, 86–93 (2015).

  60. 60.

    Neve, R. L., Neve, K. A., Nestler, E. J. & Carlezon, W. A. Jr. Use of herpes virus amplicon vectors to study brain disorders. Biotechniques 39, 381–391 (2005).

  61. 61.

    Liu, Z. et al. Systematic comparison of 2A peptides for cloning multi-genes in a polycistronic vector. Sci. Rep. 7, 2193 (2017).

  62. 62.

    Wang, F. et al. RNAscope: a novel in situ RNA analysis platform for formalin-fixed, paraffin-embedded tissues. J. Mol. Diagn. 14, 22–29 (2012).

  63. 63.

    Lee, S.-H. et al. Parvalbumin-positive basket cells differentiate among hippocampal pyramidal cells. Neuron 82, 1129–1144 (2014).

  64. 64.

    Dong, H. W., Swanson, L. W., Chen, L., Fanselow, M. S. & Toga, A. W. Genomic-anatomic evidence for distinct functional domains in hippocampal field CA1. Proc. Natl Acad. Sci. USA 106, 11794–11799 (2009).

  65. 65.

    Cembrowski, M. S. et al. Spatial gene-expression gradients underlie prominent heterogeneity of CA1 pyramidal neurons. Neuron 89, 351–368 (2016).

  66. 66.

    Fürth, D. et al. An interactive framework for whole-brain maps at cellular resolution. Nat. Neurosci. 21, 139–149 (2018).

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Acknowledgements

We thank F. Wahl, C. Henningson, and D. Fürth for technical assistance, and I. Wickersham, the McGovern Institutet for Brain Research, Massachusetts Institute of Technology, for generously sharing plasmids and cell lines. This research was supported by the Knut and Alice Wallenbergs Foundation (Wallenberg Academy Fellow grant no. KAW 2012.0208), Ragnar Söderbergs Stiftelse, and Karolinska Institutet.

Author information

Author notes

    • Iskra Pollak Dorocic

    Present address: Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, CA, USA

  1. These authors contributed equally: Sofie Ährlund-Richter, Yang Xuan.

Affiliations

  1. Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden

    • Sofie Ährlund-Richter
    • , Yang Xuan
    • , Hoseok Kim
    • , Cantin Ortiz
    • , Iskra Pollak Dorocic
    • , Konstantinos Meletis
    •  & Marie Carlén
  2. Department of Biosciences and Nutrition, Karolinska Institutet, Huddinge, Sweden

    • Josina Anna van Lunteren
    •  & Marie Carlén

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Contributions

M.C. conceived the study. M.C., K.M., Y.X., and S.Ä.-R. designed the experiments. Y.X. designed and cloned the single helper AAV. Y.X. and S.Ä.-R. performed the experiments and the mapping of the input neurons. S.Ä.-R. analyzed and visualized the data. J.A.v.L. performed the ex vivo electrophysiology experiments, and analyzed the data. H.K. generated the three-dimensional visualizations and interactive sunburst diagrams. C.O. analyzed the local connectivity between IN types, and plotted the data. I.P.D. cloned the AAV-CaMKIIa-eGFP-Cre used for mapping input to excitatory neurons. M.C. wrote the manuscript, with input from S.Ä.-R, X.Y., and K.M.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Marie Carlén.

Integrated supplementary information

  1. Supplementary Fig. 1 Outline of possible caveats and limitations in retrograde trans-synaptic RV tracing using two helper AAVs.

    (a) Efficient production and detection of starter neurons, that is, the neurons whose presynaptic inputs are to be traced, are pivotal to successful RV tracing of circuitry, as is specific labeling of true input neurons, that is, presynaptic neurons giving direct input to the starter neurons. The most commonly used RV systems employ RG-deleted RV particles pseudotyped to express the envelope protein EnvA. Viruses expressing EnvA use the TVA receptor for entry into cells, and selective expression of the TVA receptor in the starter neurons ensures that the initial RV transduction exclusively targets the neurons whose monosynaptic inputs are being traced. Production, and trans-synaptic retrograde spread of RV particles to presynaptic neurons are dependent on trans-complementation of RG in the starter neurons. Regularly, two helper AAV vectors are used for cell-type-specific Cre-dependent expression of TVA and RG – most often one AAV vector expresses the TVA receptor and a fluorescent marker (TVA-XFP) in a Cre-dependent manner, while the second AAV, with Cre-dependent expression of RG, lacks a detectable marker8,12,14. This strategy precludes confirmation of RG expression in the starter neurons. (b) The two AAVs are mixed and injected together, which does not guarantee co-expression of TVA and RG in all starter neurons. (c-f) Illustration of possible misidentification of starter and/or input neurons in RV tracing using two AAVs with Cre dependent expression of TVA-XFP (1st AAV), and RG (2nd AAV). In the examples the RV expresses eGFP (RV-eGFP). (c) True starter neurons express TVA and RG, are transduced by RV-eGFP, and can give rise to retrograde trans-synaptic RV-eGFP spread to presynaptic partners. False starter neurons cannot give rise to RV-eGFP labeled input. Neurons expressing TVA and RV-eGFP, but not RG, are an example of false starter neurons. Identification of starter neurons based of co-expression of the fluorophore in the TVA-XFP and the fluorophore encoded by the RV would include both true and false starter neurons, potentially resulting in an overestimation of the starter population and skewing of calculations of input neuron-starter neuron ratios. Further, reliable detection of (true) starter neurons is necessary for tracing of monosynaptic input in the local circuit55. (d) Expression of TVA in the absence of Cre has been reported, and low levels of TVA are sufficient for binding and integration of RV14. Importantly, the cell type specificity of the starter neurons can then be lost. As the low expression of the fluorophore in the TVA-construct cannot be detected14, the RV transduced neurons can only be detected by the fluorophore expressed from the RV, identifying them as input neurons rather than starter neurons (that is, they are false input neurons). (e) Impure RV batches can result in TVA-independent RV transduction of neurons at the injection site. The transduced neurons are falsely identified as local input neurons based on the expression of the fluorophore in the RV. (f) If a neuron expressing RG, but not TVA-XFP, is transduced by RV in an TVA-independent manner, it can give rise to RV labeling of its input neurons, and thus function as an invisible starter neuron. As the RG-expression is dependent on Cre recombination, trans-synaptically labeled input neurons would represent true input neurons. However, the invisible starter neuron (expressing RG) would be falsely identified as an input neuron, as only the fluorophore in the RV is expressed in this neuron.

  2. Supplementary Fig. 2 Evaluation of the RV system and mapping of starter neurons.

    (a) Evaluation of the viral batches used in the current study. AAV-DIO-TVA-V5-RG was injected into the mPFC of WT mice (n = 3). Three weeks later RV-eGFP was injected at the same location. No neurons co-expressing V5 and RV-eGFP were detected, but a few neurons expressing only RV-eGFP (4, 19, and 38, respectively) were detected at the injection site. This indicates a lack of expression of TVA in the absence of Cre, and minute RV transduction in the absence of TVA. Right: magnification of outlined area in left. (b-c) Evaluation of the AAV-CaMKIIa-eGFP-Cre. AAV-CaMKIIa-eGFP-Cre was injected into the mPFC of WT mice (n = 2), and RNA-FISH for vGAT was performed. Co-localization of eGFP-Cre and vGAT was detected in a proportion of the neurons (8.30 ± 0.99%; 107 / 1413 neurons, data from 2 mice). (b) Schematic of the localization of detected eGFP-Cre+/vGAT- (green) and eGFP-Cre+/vGAT+ (red) neurons, respectively, in the mPFC of one mouse (one 14 µm section). (c) Filled arrowhead: eGFP-Cre+/vGAT+ mPFC neuron. Open arrowhead: eGFP-Cre-/vGAT+ mPFC neuron. (d) Channel split of images in Fig. 1f. Filled arrowheads: starter neurons in the mPFC co-expressing V5 and RV-eGFP and the respective IN marker (PV, SST or VIP). Open arrowheads: excitatory starter neurons in the mPFC co-expressing V5 and RV-eGFP. Pink arrowhead: The fusion protein eGFP-Cre (expressed from the AAV-CaMKIIa-eGFP-Cre) is expressed in the nucleus of excitatory starter neurons. Local input neurons express only RV-eGFP. The V5 is fused to the TVA receptor and therefore detected wherever the receptor is expressed in the neurons (that is, not only at the cell bodies). Images from a representative animal from each group, replicated in PV: n = 5, VIP, SST, CA: = 4 animals / group. (e) Distribution of the starter neurons (expressing V5 and RV-eGFP) within the mPFC subregions in the animals (n = 17) included in the whole-brain mapping of inputs. 1.00 = all detected starter neurons detected within the mPFC. The mapping highlights the differential delineation of the mPFC subregions in the two atlases. In particular, Paxinos and Franklin places fewer neurons in the ILA and more neurons in the ACAv. (f) Boxplot of the proportion starter neurons detected within the mPFC in the animals (n = 17) included in the whole-brain mapping of inputs. Boxplots: center line (white), median; edges, upper and lower quartiles; whiskers, largest and smallest value no further than 1.5 x IQR of the edges; dot, outlier (a VIP animal; black dot). The data for the individual animals are also shown. One dot = one animal. (g) Distribution of starter neurons outside the mPFC subregions (n = 17 animals). Scale bars: 500 μm in (a), 50 μm in (c), and 20 μm in (d).

  3. Supplementary Fig. 3 Whole-brain mapping of input to the mPFC.

    (a) Whole-brain representation of the proportion RV-eGFP labeled input in discrete brain regions. The proportions represent the number of input neurons in a discrete brain region normalized to the total number of input neurons detected in a higher prioritized level (color-coded) in the brain structure hierarchal tree developed in the ARA. (b) The proportion RV-eGFP input in the divisions of the AI. (c) The proportion RV-eGFP input in the divisions of the SS. (d) The proportion RV-eGFP input in the divisions of the RSP. (e) The top 10 discrete input regions. 6/10 regions are cortical regions. 2% cutoff applied for all regions except isocortex in (a). Data from PV: n = 5, VIP, SST, CA: n = 4 animals / group in (a-e). Data shown as mean ± s.e.m, gray circles denote individual animals in (a-e). For abbreviations, see Supplementary Table 4.

  4. Supplementary Fig. 4 Dorsal–ventral division of the mPFC; differential cortical innervation of the divisions.

    (a) Based on the anatomical distribution of the starter neurons within the mPFC subregions, the animals were divided into a dorsal group (pink; n = 10 animals), and a ventral group (green; n = 7 animals), respectively (see Methods for details). (b) The density (normalized) of cortical RV-eGFP labeled input neurons along the A-P axis in the dorsal (pink) and ventral (green) groups (RV-eGFP labeled neurons in the mPFC are excluded). Bin width: 0.16 mm. (c) Detailed analysis of the cortical input pattern for the dorsal (pink), and the ventral (green), animal groups. A two-sided Student’s t-test was used to generate P values. No correction for multiple comparisons was applied. See Supplementary Table 2 for statistical values. (d) Spearman Correlation matrix and hierarchal clustering (average method) investigating the co-variance of the proportion of input neurons in the regions of the isocortex. Red lettering; mPFC subregions, black lettering; cortical regions, pink lettering; cortical regions with a significantly higher proportion input to the dorsal mPFC, green lettering; cortical regions with a significantly higher proportion input to the ventral mPFC. Data from 17 animals, sign. level 0.05. Red: positive correlation, blue: negative correlation. Data from dorsal: n = 10, ventral: n = 7 animals / group in (b-c). *P < 0.05, data shown as mean ± s.e.m, circles denote individual animals in (c).

  5. Supplementary Fig. 5 Investigation of monosynaptic projections from STR to mPFC.

    (a) Left: CAV2-Cre was abundantly injected (bilaterally) into the mPFC of TdTomato reporter mice (n = 2). Extensive TdTomato labeling could be detected throughout the brain, including in projections passing through the STR (ACB and CP). However, very few TdTomato labeled cell bodies were detected in the STR (ACB: n = 4 TdTomato+ neurons; CP: n = 7 TdTomato+ neurons). Note the prominent TdTomato labeling in for example, neurons in the CLA projecting to the mPFC (black arrows). Right: magnification of outlined area in left. Images from a representative animal, replicated in 2 animals. (b-d) HSV-LS1L-SwiChR-EYFP was injected (unilaterally) into the mPFC of vGAT-Cre mice. Images from a representative animal, replicated in 2 animals. (b) Left: HSV-1 labeling could be detected in VGAT-expressing (putative local inhibitory) neurons in the mPFC. Very few labeled neurons could be detected in the STR (ACB: n = 0 labeled neurons, CP: n = 2 labeled neurons in one animal, 1 in the second animal). Right: magnification of outlined area in the left. (c) One of the labeled neurons (white arrowhead) in the STR in the experiment in (b). Midline to the left. (d) HSV-1 labeling could be detected in VGAT-expressing long-projecting neurons in subcortical structures. White arrowhead: mPFC projecting VGAT-expressing neuron in the NDB. Scale bars: 500 μm in (a) and (b), 50 μm in (c) and (d).

  6. Supplementary Fig. 6 RV tracing of connectivity between IN subtypes in mouse mPFC and VIS.

    (a) Detection of monosynaptic connections between mPFC IN subtypes. Channel split of images in Fig. 3c. Filled arrowheads: starter neurons expressing V5 and RV-eGFP; open arrowheads: inhibitory input neurons expressing RV-eGFP and a specific IN marker. Images from a representative animal from each group, replicated in PV, SST, VIP: n = 2 animals / group, 15–22 sections / animal. (b) Distribution of starter neurons (black), RV-eGFP labeled input neurons (green) and RV-eGFP labeled input neurons of a specific IN subtype (orange or blue) throughout the depth of the mPFC. Absolute cell counts binned per 100 μm from the cortical surface. Data from PV, SST, VIP: n = 2 animals / group. The cell counts for the two animals are stacked in each bar (animal #1: dark colors, animal #2: light colors). (c-d) Immunohistochemical investigation of local SST input to mPFC IN subtypes. (c) Schematic of the localization of detected SST INs (purple), and RV-eGFP labeled local input neurons (green) in a representative PV-Cre mouse (22 superimposed brain sections). (d) Investigation of local SST input to mPFC PV INs (top row: PV-Cre mouse), and to VIP INs (bottom row: VIP-Cre mouse) White arrowheads: starter neurons (expressing V5 and RV-eGFP), yellow arrowheads: SST expressing INs. Images from a representative animal from each group, replicated in PV, VIP: n = 2 animals / group, 15–22 sections / animal. Note the lack of RV-eGFP labeling in the SST INs. (e-f) Immunohistochemical investigation of local SST input to PV INs in the VIS. Schematic of the localization of detected SST INs (purple), and RV-eGFP labeled local input neurons (green) in a representative PV-Cre mouse (13 superimposed brain sections). (f) White arrowheads: starter neurons (expressing V5 and RV-eGFP), yellow arrowheads: SST expressing INs. Note the lack of RV-eGFP labeling in the SST INs. Images from a representative animal, replicated in 2 animals, 13–14 sections / animal. (g) Investigation of local SST input to PV INs in the VIS using RNA-FISH to detect Sst expression. White arrowheads: starter neurons (expressing V5 and RV-eGFP), yellow arrowheads: Sst expressing INs. Note the lack of RV-eGFP labeling in the Sst-expressing INs. Image from a representative animal, replicated in 2 animals, 4 sections / animal. Scale bars: 25 μm in (a), (d), (f), and (g).

  7. Supplementary Fig. 7 MD targeting of mPFC and dissociation of the thalamic input to dorsal vs. ventral mPFC.

    (a-c) Input from the MD targeting the mPFC investigated in three different ways. (a) Proportion of the thalamic RV-eGFP labeled input being derived from the MD. (b) The number of RV-eGFP labeled input neurons detected in the MD. (c) The relationship between the number of starter neurons detected in the mPFC and RV-eGFP labeled input neurons detected in the MD (input neuron / starter neuron ratio). (d) The distribution of RV-eGFP labeled input neurons within the divisions of the MD, independent of mPFC cell type targeted (n = 17 animals). The MDm and the MDl provide significantly more input than the MDc. A two-sided Student’s t-test was used to generate P values. No correction for multiple comparisons was applied. See Supplementary Table 2 for statistical values. (e) Spearman Correlation matrix and hierarchal cluster analysis (average method) of the 17 animals based on their thalamic input distribution. No clusters are identified based on animal group, that is, based on the mPFC cell type targeted, but instead there is a high degree of positive correlation between all animals, indicating a high similarity in the animals’ thalamic input patterns. (The clusters identified (black outlines) reflect the dorsal (pink) vs. ventral (green) position of the starter neurons in the animals). Sign. level 0.05. Red: positive correlation, blue: negative correlation. Data from PV: n = 5, VIP, SST, CA: n = 4 animals / group in (a-c). *P < 0.05, data shown as mean ± s.e.m, gray circles denote individual animals in (a-d).

  8. Supplementary Fig. 8 Input from the hippocampal region.

    (a) Cell count density plot (Gaussian kernel) of the RV-eGFP labeled input in the hippocampal region, and in specific discrete regions therein, as detected along the A-P axis (graph for Hippocampal region replicated from Fig. 6d). Bin width: 0.05 mm. Shaded areas: A-P extent of the respective region. Data from PV: n = 5, VIP, SST, CA: n = 4 animals / group. (b) Left; Coronal section immunostained for NeuN for illustration of the dorsal (D) and ventral (V) parts of the CA1. Right; outlined areas in left. The sublayers of the CA1sp (superficial, turquoise; deep, pink) display variation within the CA1. The CA1sr - CA1sp border was used as reference point for the radial division of the CA1sp, and for anatomical mapping of the RV-eGFP labeled input neurons within the CA1sp. (c) The expression of calbindin in RV-eGFP labeled input neurons in the superficial and deep sublayers of the CA1sp. 1.00 represents all RV-eGFP labeled neurons in the deep and in the superficial sublayer of the CA1sp, respectively. Data from 12 animals; PV, VIP, SST, CA: n = 3 animals / group. Colors as in (d). (d) The expression of calbindin in RV-eGFP labeled input neurons in the dorsal (D), intermediate (I) and ventral (V) divisions of the CA1, respectively. 1.00 represents all RV-eGFP neurons in the respective division of the CA1sp. ND; not determined, that is, ambiguous expression. Data from 12 animals; PV, VIP, SST, CA: n = 3 animals / group. (e) A calbindin-expressing RV-eGFP labeled input neuron in the deep subdivision of the CA1sp providing input to mPFC SST INs (SST-Cre mouse). Image from a representative animal, replicated in PV, VIP, SST, CA: n = 3 animals / group, 6–12 sections / animal. Scale bar: 500 μm in (b), 50 μm in (e). Data shown is mean ± s.e.m, circles denote individual animals in (d).

Supplementary information

  1. Supplementary Figs. 1–8 and Supplementary Tables 2, 3 and 4.

  2. Reporting Summary

  3. Supplementary Table 1

    Whole-brain data of monosynaptic input targeting four different cell types in the medial prefrontal cortex of the mouse. Raw data regarding all detected RV-eGFP labeled input neurons. Each row represents one neuron. The columns state for each neuron (from left to right): animal group (genotype), individual animal number (animal.ID), manually mapped bregma coordinate (z.position), X and Y coordinate in the brain section (Xpos and Ypos, respectively), information regarding location in the contra (0) or ipsilateral (1) hemisphere (is.point.ipsi), and the location according to the hierarchical depths of the ARA (Depth 1–9).

  4. Supplementary Video 1

    3D representation of all detected RV-eGFP labeled input neurons in the isocortex and claustrum, color-coded based on input region.

  5. Supplementary Video 2

    3D representation of all detected RV-eGFP labeled input neurons in the cholinergic space, color-coded based on mPFC cell type targeted.

  6. Supplementary Video 3

    3D representation of all detected RV-eGFP labeled input neurons in the CA1, color-coded based on the mPFC cell type targeted.

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https://doi.org/10.1038/s41593-019-0354-y