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.

Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type

Nature Neuroscience volume 21, pages 1185–1195 (2018)Cite this article

Subjects

Abstract

We describe convergent evidence from transcriptomics, morphology, and physiology for a specialized GABAergic neuron subtype in human cortex. Using unbiased single-nucleus RNA sequencing, we identify ten GABAergic interneuron subtypes with combinatorial gene signatures in human cortical layer 1 and characterize a group of human interneurons with anatomical features never described in rodents, having large ‘rosehip’-like axonal boutons and compact arborization. These rosehip cells show an immunohistochemical profile (GAD1+CCK+, CNR1–SST–CALB2–PVALB–) matching a single transcriptomically defined cell type whose specific molecular marker signature is not seen in mouse cortex. Rosehip cells in layer 1 make homotypic gap junctions, predominantly target apical dendritic shafts of layer 3 pyramidal neurons, and inhibit backpropagating pyramidal action potentials in microdomains of the dendritic tuft. These cells are therefore positioned for potent local control of distal dendritic computation in cortical pyramidal neurons.

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

Prices vary by article type

from$1.95

to$39.95

Learn more

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

Fig. 1: Identification of transcriptomic cell types in layer 1 of human temporal cortex.
Fig. 2: Morphological phenotype of RCs in layer 1 of the human cerebral cortex.
Fig. 3: Molecular phenotype of RCs in layer 1 of the human cerebral cortex.
Fig. 4: Intrinsic electrophysiological properties of RCs.
Fig. 5: Connections of rosehip cells in the local microcircuit.
Fig. 6: Human rosehip interneurons perform segment-specific regulation of action potential backpropagation to apical dendritic tufts of pyramidal cells.

References

  1. DeFelipe, J. & Jones, E. G. Cajal on the Cerebral Cortex. (Oxford University Press, Oxford, UK, 1988).

    Google Scholar 

  2. Ascoli, G. A. et al. Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nat. Rev. Neurosci. 9, 557–568 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  4. Tasic, B. et al. Adult mouse cortical cell taxonomy revealed by single cell transcriptomics. Nat. Neurosci. 19, 335–346 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Zeisel, A. et al. Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq. Science 347, 1138–1142 (2015).

    Article  CAS  PubMed  Google Scholar 

  6. Macosko, E. Z. et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell 161, 1202–1214 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Jiang, X. et al. Principles of connectivity among morphologically defined cell types in adult neocortex. Science 350, aac9462 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Eyal, G. et al. Unique membrane properties and enhanced signal processing in human neocortical neurons. eLife 5, e16553 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Wang, B. et al. A subtype of inhibitory interneuron with intrinsic persistent activity in human and monkey neocortex. Cell Rep. 10, 1450–1458 (2015).

    Article  CAS  PubMed  Google Scholar 

  10. Molnár, G. et al. Complex events initiated by individual spikes in the human cerebral cortex. PLoS Biol. 6, e222 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Molnár, G. et al. Human pyramidal to interneuron synapses are mediated by multi-vesicular release and multiple docked vesicles. eLife 5, e18167 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Testa-Silva, G. et al. Human synapses show a wide temporal window for spike-timing-dependent plasticity. Front. Synaptic Neurosci. 2, 12 (2010).

    PubMed  PubMed Central  Google Scholar 

  13. Verhoog, M. B. et al. Mechanisms underlying the rules for associative plasticity at adult human neocortical synapses. J. Neurosci. 33, 17197–17208 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Szegedi, V. et al. Plasticity in single axon glutamatergic connection interneurons regulates complex events in the human neocortex to GABAergic. PLoS Biol. 14, e2000237 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Elston, G. N. Pyramidal cells of the frontal lobe: all the more spinous to think with. J. Neurosci. 20, RC95 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Morales, J. et al. Random positions of dendritic spines in human cerebral cortex. J. Neurosci. 34, 10078–10084 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Mohan, H. et al. Dendritic and axonal architecture of individual pyramidal neurons across layers of adult human neocortex. Cereb. Cortex 25, 4839–4853 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Grindberg, R. V. et al. RNA-sequencing from single nuclei. Proc. Natl. Acad. Sci. USA 110, 19802–19807 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Picelli, S. et al. Full-length RNA-seq from single cells using Smart-seq2. Nat. Protoc. 9, 171–181 (2014).

    Article  CAS  PubMed  Google Scholar 

  21. Kawaguchi, Y. & Kubota, Y. GABAergic cell subtypes and their synaptic connections in rat frontal cortex. Cereb. Cortex 7, 476–486 (1997).

    Article  CAS  PubMed  Google Scholar 

  22. Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Oláh, S. et al. Output of neurogliaform cells to various neuron types in the human and rat cerebral cortex. Front. Neural Circuits 1, 4 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Kisvárday, Z. F. et al. Synapses, axonal and dendritic patterns of GABA-immunoreactive neurons in human cerebral cortex. Brain 113, 793–812 (1990).

    Article  PubMed  Google Scholar 

  25. Oláh, S. et al. Regulation of cortical microcircuits by unitary GABA-mediated volume transmission. Nature 461, 1278–1281 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Varga, C., Tamas, G., Barzo, P., Olah, S. & Somogyi, P. Molecular and electrophysiological characterization of GABAergic interneurons expressing the transcription factor COUP-TFII in the adult human temporal cortex. Cereb. Cortex 25, 4430–4449 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Zemankovics, R., Káli, S., Paulsen, O., Freund, T. F. & Hájos, N. Differences in subthreshold resonance of hippocampal pyramidal cells and interneurons: the role of h-current and passive membrane characteristics. J. Physiol. (Lond.) 588, 2109–2132 (2010).

    Article  CAS  Google Scholar 

  28. Katona, I. & Freund, T. F. Multiple functions of endocannabinoid signaling in the brain. Annu. Rev. Neurosci. 35, 529–558 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Kerekes, B. P. et al. Combined two-photon imaging, electrophysiological, and anatomical investigation of the human neocortex in vitro. Neurophotonics 1, 011013 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Freund, T. F. & Buzsáki, G. Interneurons of the hippocampus. Hippocampus 6, 347–470 (1996).

    Article  CAS  PubMed  Google Scholar 

  31. Hawrylycz, M. J. et al. An anatomically comprehensive atlas of the adult human brain transcriptome. Nature 489, 391–399 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  33. Xu, Q., Cobos, I., De La Cruz, E., Rubenstein, J. L. & Anderson, S. A. Origins of cortical interneuron subtypes. J. Neurosci. 24, 2612–2622 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. DeFelipe, J. Types of neurons, synaptic connections and chemical characteristics of cells immunoreactive for calbindin-D28K, parvalbumin and calretinin in the neocortex. J. Chem. Neuroanat. 14, 1–19 (1997).

    Article  CAS  PubMed  Google Scholar 

  35. von Economo, C. Eine neue Art Spezialzellen des Lobus cinguli and Lobus insulae. Zschr. ges Neurol. Psychiatry 100, 706–712 (1926).

    Article  Google Scholar 

  36. Nimchinsky, E. A., Vogt, B. A., Morrison, J. H. & Hof, P. R. Spindle neurons of the human anterior cingulate cortex. J. Comp. Neurol. 355, 27–37 (1995).

    Article  CAS  PubMed  Google Scholar 

  37. Gabbott, P. L. A. Subpial Fan Cell” - a class of calretinin neuron in layer 1 of adult monkey prefrontal cortex. Front. Neuroanat. 10, 28 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Somogyi, P. A specific ‘axo-axonal’ interneuron in the visual cortex of the rat. Brain Res. 136, 345–350 (1977).

    Article  CAS  PubMed  Google Scholar 

  40. Tamás, G., Buhl, E. H. & Somogyi, P. Fast IPSPs elicited via multiple synaptic release sites by different types of GABAergic neuron in the cat visual cortex. J. Physiol. (Lond.) 500, 715–738 (1997).

    Article  Google Scholar 

  41. Kisvárday, Z. F., Martin, K. A. C., Whitteridge, D. & Somogyi, P. Synaptic connections of intracellularly filled clutch cells: a type of small basket cell in the visual cortex of the cat. J. Comp. Neurol. 241, 111–137 (1985).

    Article  PubMed  Google Scholar 

  42. Lee, A. J. et al. Canonical organization of layer 1 neuron-led cortical inhibitory and disinhibitory interneuronal circuits. Cereb. Cortex 25, 2114–2126 (2015).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gjorgjieva, J., Drion, G. & Marder, E. Computational implications of biophysical diversity and multiple timescales in neurons and synapses for circuit performance. Curr. Opin. Neurobiol. 37, 44–52 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Palmer, L. M. et al. The cellular basis of GABA(B)-mediated interhemispheric inhibition. Science 335, 989–993 (2012).

    Article  CAS  PubMed  Google Scholar 

  46. Klausberger, T. & Somogyi, P. Neuronal diversity and temporal dynamics: the unity of hippocampal circuit operations. Science 321, 53–57 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Borg, I. et al. Disruption of Netrin G1 by a balanced chromosome translocation in a girl with Rett syndrome. Eur. J. Hum. Genet. 13, 921–927 (2005).

    Article  CAS  PubMed  Google Scholar 

  48. Molinari, F. et al. Truncating neurotrypsin mutation in autosomal recessive nonsyndromic mental retardation. Science 298, 1779–1781 (2002).

    Article  CAS  PubMed  Google Scholar 

  49. Mak, I. W., Evaniew, N. & Ghert, M. Lost in translation: animal models and clinical trials in cancer treatment. Am. J. Transl. Res. 6, 114–118 (2014).

    PubMed  PubMed Central  Google Scholar 

  50. Cavanaugh, S. E., Pippin, J. J. & Barnard, N. D. Animal models of Alzheimer disease: historical pitfalls and a path forward. ALTEX 31, 279–302 (2014).

    Article  PubMed  Google Scholar 

  51. Krishnaswami, S. R. et al. Using single nuclei for RNA-seq to capture the transcriptome of postmortem neurons. Nat. Protoc. 11, 499–524 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Trapnell, C. et al. Differential gene and transcript expression analysis of RNA-seq experiments with TopHat and Cufflinks. Nat. Protoc. 7, 562–578 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).

    Article  CAS  PubMed  Google Scholar 

  56. DeLuca, D. S. et al. RNA-SeQC: RNA-seq metrics for quality control and process optimization. Bioinformatics 28, 1530–1532 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Anders, S., Pyl, P. T. & Huber, W. HTSeq--a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  58. Aevermann, B. et al. Production of a preliminary quality control pipeline for single nuclei RNA-seq and its application in the analysis of cell type diversity of post-mortem human brain neocortex. Pac. Symp. Biocomput. 22, 564–575 (2017).

    PubMed  Google Scholar 

  59. R Development Core Team. R: a Language and Environment for Statistical Computing. (R Found. Stat. Comput., Vienna, Austria, 2016).

    Google Scholar 

  60. Lawrence, M. et al. Software for computing and annotating genomic ranges. PLOS Comput. Biol. 9, e1003118 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).

    Article  CAS  PubMed  Google Scholar 

  62. Fan, J. et al. Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis. Nat. Methods 13, 241–244 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Van Der Maaten, L. J. P. & Hinton, G. E. Visualizing high-dimensional data using t-SNE. J. Mach. Learn. Res. 9, 2579–2605 (2008).

    Google Scholar 

  64. Ester, M., Kriegel, H. P., Sander, J. & Xu, X. A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 2, 226–231 (1996).

    Google Scholar 

  65. Liu, Y., Hayes, D. N., Nobel, A. & Marron, J. S. Statistical significance of clustering for high-dimension, low–sample size data. J. Am. Stat. Assoc. 103, 1281–1293 (2008).

    Article  CAS  Google Scholar 

  66. Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Preprint at bioRxiv https://doi.org/10.1101/229542 (2017).

  67. Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43, e47 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Zeng, H. et al. Large-scale cellular-resolution gene profiling in human neocortex reveals species-specific molecular signatures. Cell 149, 483–496 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Paxinos, G. & Franklin, K. The Mouse Brain in Stereotaxic Coordinates. (Academic Press, Cambridge, MA, USA, 2012).

    Google Scholar 

  70. Faragó, N. et al. Digital PCR to determine the number of transcripts from single neurons after patch-clamp recording. Biotechniques 54, 327–336 (2013).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors thank the Allen Institute for Brain Science founders, Paul G. Allen and Jody Allen, for their vision, encouragement, and support. The authors thank L. Christiansen and F. Zhang from Illumina, Inc. for their assistance with RNA sequencing. This work was supported by the ERC Interimpact project (G.T.), the National Institute of Mental Health (USA) RFA MH 17 210 (E.S.L., G.T.), the Hungarian Academy of Sciences (G.T.), the National Research, Development and Innovation Office of Hungary (GINOP-2.3.2-15-2016-00018, VKSZ-14-1-2015-0155), and by the National Brain Research Program, Hungary (G.T.).

Author information

Author notes

  1. These authors contributed equally to this work: Eszter Boldog, Trygve E. Bakken, Rebecca D. Hodge.

Authors and Affiliations

  1. MTA-SZTE Research Group for Cortical Microcircuits, Department of Anatomy, Physiology and Neuroscience, University of Szeged, Szeged, Hungary

    Eszter Boldog, Judith Baka, Sándor Bordé, Nóra Faragó, Ágnes K. Kocsis, Balázs Kovács, Gábor Molnár, Gáspár Oláh, Attila Ozsvár, Márton Rózsa & Gábor Tamás

  2. Allen Institute for Brain Science, Seattle, WA, USA

    Trygve E. Bakken, Rebecca D. Hodge, Jennie L. Close, Song-Lin Ding, Zoe Maltzer, Jeremy A. Miller, Soraya I. Shehata, Kimberly A. Smith, Susan M. Sunkin, Abby Wall & Ed S. Lein

  3. J. Craig Venter Institute, La Jolla, CA, USA

    Mark Novotny, Brian D. Aevermann, Francisco Diez-Fuertes, Jamison M. McCorrison, Danny N. Tran, Pratap Venepally, Nicholas J. Schork, Richard H. Scheuermann & Roger S. Lasken

  4. Department of Neurosurgery, University of Szeged, Szeged, Hungary

    Frank J. Steemers

  5. Laboratory of Functional Genomics, Department of Genetics, Biological Research Center, Hungarian Academy of Sciences, Szeged, Hungary

    László G. Puskás

  6. Illumina, Inc., San Diego, CA, USA

    Pál Barzó

  7. Department of Pathology, University of California, San Diego, CA, USA

    Richard H. Scheuermann

Authors
  1. Eszter Boldog
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Trygve E. Bakken
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rebecca D. Hodge
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mark Novotny
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Brian D. Aevermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Judith Baka
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Sándor Bordé
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jennie L. Close
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Francisco Diez-Fuertes
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Song-Lin Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Nóra Faragó
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ágnes K. Kocsis
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Balázs Kovács
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Zoe Maltzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jamison M. McCorrison
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Jeremy A. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Gábor Molnár
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Gáspár Oláh
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Attila Ozsvár
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Márton Rózsa
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Soraya I. Shehata
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Kimberly A. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Susan M. Sunkin
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Danny N. Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Pratap Venepally
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Abby Wall
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. László G. Puskás
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Pál Barzó
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Frank J. Steemers
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. Nicholas J. Schork
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. Richard H. Scheuermann
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Roger S. Lasken
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Ed S. Lein
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Gábor Tamás
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, E.S.L., R.S.L., G.T. Methodology, R.D.H., M.N., J.L.C., P.B., L.G.P., G.T. Validation, J.L.C., S.-L.D., G.M., G.T. Formal analysis, T.E.B., B.D.A., J.M.M., J.A.M., P.V., M.R., S.B., R.H.S., E.B., J.B., G.O., G.M. Investigation, R.D.H., M.N., J.L.C., F.D.-F., S.I.S., K.A.S., A.W., D.N.T., Z.M., E.B., J.B., Á.K.K., N.F., B.K., M.R., G.M., A.O.,G.O., G.T. Resources, E.S.L., F.J.S., N.J.S., R.H.S., R.S.L., G.T. Data curation, T.E.B., B.D.A., J.M.M., J.A.M., P.V., S.B. Writing – original draft, T.E.B. R.D.H., J.L.C., J.A.M., E.S.L., E.B., G.O., G.T. Writing – review and editing, T.E.B., R.D.H., J.A.M., R.H.S., R.S.L., E.S.L., G.T. Visualization, T.E.B., R.D.H., J.L.C., S.-L.D., J.A.M., E.B., G.M., G.O. Supervision, E.S.L., F.J.S., N.J.S., R.H.S., R.S.L, G.T. Project administration, E.S.L., S.M.S., G.T. Funding acquisition, E.S.L., F.J.S., R.S.L., G.T.

Corresponding authors

Correspondence to Ed S. Lein or Gábor Tamás.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

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

Integrated supplementary information

Supplementary Figure 1 Gating strategy for collection of single nuclei.

A, Nuclei were first gated based on size (forward scatter area, FSC-A) and granularity (side scatter area, SSC-A). B, Nuclei were then gated on DAPI fluorescence, followed by gates to exclude doublets and aggregates (C – FSC-single cells [SC], D – SSC-SC). E, Lastly, nuclei were gated based on NeuN Alexa Fluor 594 signal (NeuN-AF594-A) to differentiate neuronal (NeuN+) and non-neuronal (NeuN-) nuclei.

Supplementary Figure 2 Marker gene expression patterns across identified single nuclei clusters.

A, Heatmap of log-normalized expression (CPM) in single nuclei grouped by clusters that have been ordered by transcriptomic similarity. Canonical gene markers (SNAP25, GAD1, SLC17A7) classify clusters into broad classes of excitatory and inhibitory neuron and non-neuronal cell types. Within these broad types, many genes discretely mark individual clusters. B, Colorimetric ISH in mouse cortex of marker genes shown for human temporal cortex in Figure 1. Red arrows highlight cells with expression in layer 1. ISH experiments were repeated on multiple tissue specimens as follows: Sst, Lhx6,Ndnf (n=2); Slc17a7, Vip, Sema3c (n=3), Cck (n=4); Reln, Cnr1 (n=5); Gad1, Pvalb (n=20).

Supplementary Figure 3 Transcriptomic matching of clusters to previously reported clusters in mouse and human cortex.

A, Heatmap of expression correlations between pairs of clusters from this study and human cortical clusters reported by Lake et al. 22 based on median expression of [400] genes with cluster-specific expression in both studies. Reciprocal best matching cluster pairs are labeled. B, Proportion of nuclei expressing (CPM > 1) [400]168 genes for each pair of matching clusters labeled in A. Genes that are cluster-specific in both studies are labeled. C, Heatmap of median log2-expression for select marker genes in both studies with matching clusters labeled. D, Heatmap of expression correlations between pairs of clusters from this study and mouse cortical clusters reported by 4. Note that the same color scale is used for correlations as in A. E, Proportion of nuclei or cells expressing (CPM > 1) 212[400] genes for each pair of best matching clusters labeled in D. Pearson’s correlations are reported at the top of each plot.

Supplementary Figure 4 Diverse expression patterns of voltage-gated ion channels and receptors among GABAergic clusters.

Violin plots show the distribution of expression levels (CPM) within clusters and are scaled independently for each gene by the maximum expression across clusters for that gene. Expression is on a linear scale and dots indicate median expression. Numbers denote the number of nuclei in each cluster.

Supplementary Figure 5 Additional molecular phenotype information of RCs in layer 1 of the human cerebral cortex.

A, Further immunolabeling showed that RCs were immunopositive for gamma-aminobutyric acid (GABA) (n=2), and for chicken ovalbumin upstream promoter transcription factor II (NR2F2) (n=2). In addition, all tested RCs were negative for many common interneuron markers including parvalbumin (n=3), neuronal nitric oxide synthase (n=4), neuropeptide Y (n=2), calbindin (n=2), and choline acetyltransferase (n=3). B, Expression distributions of marker genes in all cell types in human temporal cortex layer 1 (left) and mouse primary visual cortex (right; data from 4). Expression is on a linear scale and dots indicate median expression. Numbers denote the number of nuclei in each cluster. C, Multiplex FISH validation of rosehip marker co-expression. Arrowheads show examples of RCs based on marker gene expression identified from RNA-Seq data. D, Multiplex FISH of approximately 2 mm section of layer 1 and upper layer 2 with rosehip interneurons labeled based on marker gene expression. 300 µm diameter circles approximate the maximum extent of rosehip axonal arbors (see Figure 2E). Multiplex FISH experiments were conducted on n=2 tissue donors.

Supplementary Figure 6 Multiplex FISH validation of rosehip marker co-expression in parietal (Brodmann Area 40) and frontal (Brodmann Area 9) regions of cortex.

A-F, Three distinct gene combinations were run in each brain region. Arrowheads show examples of RCs located in layer 1 of each brain region based on marker gene expression identified from RNA-Seq data. RCs were double positive for the marker genes shown and were either negative for CNR1 or had very low levels of CNR1 (C, E) in comparison to adjacent CNR1-positive cells (C). Multiplex FISH experiments were conducted on n=2 tissue donors.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boldog, E., Bakken, T.E., Hodge, R.D. et al. Transcriptomic and morphophysiological evidence for a specialized human cortical GABAergic cell type. Nat Neurosci 21, 1185–1195 (2018). https://doi.org/10.1038/s41593-018-0205-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41593-018-0205-2

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