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Differential vulnerability of anterior cingulate cortex cell types to diseases and drugs

Molecular Psychiatry volume 27, pages 4023–4034 (2022)Cite this article

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Abstract

In psychiatric disorders, mismatches between disease states and therapeutic strategies are highly pronounced, largely because of unanswered questions regarding specific vulnerabilities of different cell types and therapeutic responses. Which cellular events (housekeeping or salient) are most affected? Which cell types succumb first to challenges, and which exhibit the strongest response to drugs? Are these events coordinated between cell types? How does disease and drug effect this coordination? To address these questions, we analyzed single-nucleus-RNAseq (sn-RNAseq) data from the human anterior cingulate cortex—a region involved in many psychiatric disorders. Density index, a metric for quantifying similarities and dissimilarities across functional profiles, was employed to identify common or salient functional themes across cell types. Cell-specific signatures were integrated with existing disease and drug-specific signatures to determine cell-type-specific vulnerabilities, druggabilities, and responsiveness. Clustering of functional profiles revealed cell types jointly participating in these events. SST and VIP interneurons were found to be most vulnerable, whereas pyramidal neurons were least. Overall, the disease state is superficial layer-centric, influences cell-specific salient themes, strongly impacts disinhibitory neurons, and influences astrocyte interaction with a subset of deep-layer pyramidal neurons. In absence of disease, drugs profiles largely recapitulate disease profiles, offering a possible explanation for drug side effects. However, in presence of disease, drug activities, are deep layer-centric and involve activating a distinct subset of deep-layer pyramidal neurons to circumvent the disease state’s disinhibitory circuit malfunction. These findings demonstrate a novel application of sn-RNAseq data to explain drug and disease action at a systems level.

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Fig. 1: Characterization of ACC cell-types.
Fig. 2: Functional analysis of cell types in the control state.
Fig. 3: Distribution of CNS disorders by cell-specific enrichment.
Fig. 4: Cell type-specific functional state comparison between control and disease states.
Fig. 5: Functional clustering of cell types in control, disease, druggable, and actionable state.

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All data are available in the main text or the Supplementary Materials.

References

  1. Pankevich DE, Altevogt BM, Dunlop J, Gage FH, Hyman SE. Improving and accelerating drug development for nervous system disorders. Neuron. 2014;84:546–53.

    PubMed  PubMed Central  CAS  Google Scholar 

  2. Gribkoff VK, Kaczmarek LK. The need for new approaches in CNS drug discovery: why drugs have failed, and what can be done to improve outcomes. Neuro pharmacology. 2017;120:11–9.

    CAS  Google Scholar 

  3. Tatti R, Maffei A. Synaptic dynamics: how network activity affects neuron communication. Curr Biol. 2015;25:R278–R280.

    PubMed  CAS  Google Scholar 

  4. Shukla R, Prevot TD, French L, Isserlin R, Rocco BR, Banasr M, et al. The Relative Contributions of Cell-Dependent Cortical Microcircuit Aging to Cognition and Anxiety. Biol Psychiatry. 2019;85:257–67.

    PubMed  Google Scholar 

  5. Darmanis S, Sloan SA, Zhang Y, Enge M, Caneda C, Shuer LM, et al. A survey of human brain transcriptome diversity at the single cell level. Proc Natl Acad Sci U S A. 2015;112:7285–90.

    PubMed  PubMed Central  CAS  Google Scholar 

  6. Lake BB, Ai R, Kaeser GE, Salathia NS, Yung YC, Liu R, et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science. 2016;352:1586.

    PubMed  PubMed Central  CAS  Google Scholar 

  7. Hodge RD, Bakken TE, Miller JA, Smith KA, Barkan ER, Graybuck LT, et al. Conserved cell types with divergent features in human versus mouse cortex. Nature. 2019;573:61–68.

    PubMed  PubMed Central  CAS  Google Scholar 

  8. Nagy C, Maitra M, Tanti A, Suderman M, Théroux J-F, Davoli MA, et al. Single-nucleus transcriptomics of the prefrontal cortex in major depressive disorder implicates oligodendrocyte precursor cells and excitatory neurons. Nature Neuroscience. 2020;23:771–81.

    PubMed  CAS  Google Scholar 

  9. Velmeshev D, Schirmer L, Jung D, Haeussler M, Perez Y, Mayer S, et al. Single-cell genomics identifies cell type–specific molecular changes in autism. Science. 2019;364:685.

    PubMed  PubMed Central  CAS  Google Scholar 

  10. Pfisterer U, Petukhov V, Demharter S, Meichsner J, Thompson JJ, Batiuk MY, et al. Identification of epilepsy-associated neuronal subtypes and gene expression underlying epileptogenesis. Nat Commun. 2020;11:5038.

    PubMed  PubMed Central  CAS  Google Scholar 

  11. Pinero J, Ramirez-Anguita JM, Sauch-Pitarch J, Ronzano F, Centeno E, Sanz F, et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 2020;48:D845–55.

    PubMed  CAS  Google Scholar 

  12. Davis AP, Grondin CJ, Johnson RJ, Sciaky D, Wiegers J, Wiegers TC, et al. Comparative Toxicogenomics Database (CTD): update 2021. Nucleic Acids Res. 2021;49:D1138–43.

    PubMed  CAS  Google Scholar 

  13. Subramanian A, Narayan R, Corsello SM, Peck DD, Natoli TE, Lu X, et al. A Next Generation Connectivity Map: L1000 Platform and the First 1,000,000 Profiles. Cell. 2017;171:1437–52 e1417.

    PubMed  PubMed Central  CAS  Google Scholar 

  14. Smail MA, Wu X, Henkel ND, Eby HM, Herman JP, McCullumsmith RE, et al. Similarities and dissimilarities between psychiatric cluster disorders. Mol Psychiatry. 2021;26:4853–3.

    PubMed  PubMed Central  Google Scholar 

  15. Barthas F, Sellmeijer J, Hugel S, Waltisperger E, Barrot M, Yalcin I. The anterior cingulate cortex is a critical hub for pain-induced depression. Biol Psychiatry. 2015;77:236–45.

    PubMed  Google Scholar 

  16. Sanders GS, Gallup GG, Heinsen H, Hof PR, Schmitz C. Cognitive deficits, schizophrenia, and the anterior cingulate cortex. Trends Cogn Sci. 2002;6:190–2.

    PubMed  Google Scholar 

  17. Simms ML, Kemper TL, Timbie CM, Bauman ML, Blatt GJ. The anterior cingulate cortex in autism: heterogeneity of qualitative and quantitative cytoarchitectonic features suggests possible subgroups. Acta Neuropathol. 2009;118:673–84.

    PubMed  Google Scholar 

  18. Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184:3573–87. e3529

    PubMed  PubMed Central  CAS  Google Scholar 

  19. Markram H, Toledo-Rodriguez M, Wang Y, Gupta A, Silberberg G, Wu C. Inter-neurons of the neocortical inhibitory system. Nat Rev Neurosci. 2004;5:793–807.

    PubMed  CAS  Google Scholar 

  20. Chen EY, Tan CM, Kou Y, Duan Q, Wang Z, Meirelles GV, et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics. 2013;14:128.

    PubMed  PubMed Central  Google Scholar 

  21. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Research. 2016;45:D833–39.

    PubMed  PubMed Central  Google Scholar 

  22. Yoo M, Shin J, Kim J, Ryall KA, Lee K, Lee S, et al. DSigDB: drug signatures database for gene set analysis. Bioinformatics. 2015;31:3069–71.

    PubMed  PubMed Central  CAS  Google Scholar 

  23. Pfeffer CK. Inhibitory neurons: vip cells hit the brake on inhibition. Curr Biol. 2014;24:R18–r20.

    PubMed  CAS  Google Scholar 

  24. Pi HJ, Hangya B, Kvitsiani D, Sanders JI, Huang ZJ, Kepecs A. Cortical interneurons that specialize in disinhibitory control. Nature. 2013;503:521–4.

    PubMed  PubMed Central  CAS  Google Scholar 

  25. Letzkus JJ, Wolff SB. Lüthi A. Disinhibition, a circuit mechanism for associative learning and memory. Neuron. 2015;88:264–76.

    PubMed  CAS  Google Scholar 

  26. Tremblay R, Lee S, Rudy B. GABAergic interneurons in the neocortex: from cellular properties to circuits. Neuron. 2016;91:260–92.

    PubMed  PubMed Central  CAS  Google Scholar 

  27. Naskar S, Qi J, Pereira F, Gerfen CR, Lee S. Cell-type-specific recruitment of GABAergic interneurons in the primary somatosensory cortex by long-range inputs. Cell Rep. 2021;34:108774.

    PubMed  PubMed Central  CAS  Google Scholar 

  28. Eckenstein F, Baughman RW. Two types of cholinergic innervation in cortex, one co-localized with vasoactive intestinal polypeptide. Nature. 1984;309:153–5.

    PubMed  CAS  Google Scholar 

  29. Chu J, Anderson SA. Development of cortical interneurons. Neuropsycho pharmacology. 2015;40:16–23.

    Google Scholar 

  30. Takada N, Pi HJ, Sousa VH, Waters J, Fishell G, Kepecs A, et al. A developmental cell-type switch in cortical interneurons leads to a selective defect in cortical oscillations. Nature Communications. 2014;5:5333.

    PubMed  CAS  Google Scholar 

  31. Poorthuis RB, Muhammad K, Wang M, Verhoog MB, Junek S, Wrana A, et al. Rapid Neuromodulation of Layer 1 Interneurons in Human Neocortex. Cell Rep. 2018;23:951–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  32. Leong ATL, Chan RW, Gao PP, Chan Y-S, Tsia KK, Yung W-H, et al. Long-range projections coordinate distributed brain-wide neural activity with a specific spatiotemporal profile. Proceedings of the National Academy of Sciences. 2016;113:E8306.

    CAS  Google Scholar 

  33. Gerfen CR, Economo MN, Chandrashekar J. Long distance projections of cortical pyramidal neurons. J Neurosci Res. 2018;96:1467–75.

    PubMed  CAS  Google Scholar 

  34. Baker A, Kalmbach B, Morishima M, Kim J, Juavinett A, Li N, et al. Specialized Subpopulations of Deep-Layer Pyramidal Neurons in the Neocortex: Bridging Cellular Properties to Functional Consequences. The Journal of Neuroscience. 2018;38:5441.

    PubMed  PubMed Central  CAS  Google Scholar 

  35. Cauli B, Tong XK, Rancillac A, Serluca N, Lambolez B, Rossier J, et al. Cortical GABA interneurons in neurovascular coupling: relays for subcortical vasoactive pathways. J Neurosci. 2004;24:8940–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  36. Dong Y, Benveniste EN. Immune function of astrocytes. Glia. 2001;36:180–90.

    PubMed  CAS  Google Scholar 

  37. Norris GT, Kipnis J. Immune cells and CNS physiology: microglia and beyond. J Exp Med. 2019;216:60–70.

    PubMed  PubMed Central  CAS  Google Scholar 

  38. Huang Y, Xu Z, Xiong S, Sun F, Qin G, Hu G, et al. Repopulated microglia are solely derived from the proliferation of residual microglia after acute depletion. Nature Neuroscience. 2018;21:530–40.

    PubMed  CAS  Google Scholar 

  39. Wang H, Azuaje F, Bodenreider O, Dopazo J. Gene expression correlation and gene ontology-based similarity: an assessment of quantitative relationships. Proc IEEE Symp Comput Intell Bioinforma Comput Biol. 2004;2004:25–31.

    PubMed  PubMed Central  Google Scholar 

  40. Rangaraju S, Solis GM, Thompson RC, Gomez-Amaro RL, Kurian L, Encalada SE, et al. Suppression of transcriptional drift extends C. elegans lifespan by postponing the onset of mortality. Elife. 2015;4:e08833.

    PubMed  PubMed Central  Google Scholar 

  41. Kamme F, Salunga R, Yu J, Tran DT, Zhu J, Luo L, et al. Single-cell microarray analysis in hippocampus CA1: demonstration and validation of cellular heterogeneity. J Neurosci. 2003;23:3607–15.

    PubMed  PubMed Central  CAS  Google Scholar 

  42. Lee S, Kruglikov I, Huang ZJ, Fishell G, Rudy B. A disinhibitory circuit mediates motor integration in the somatosensory cortex. Nat Neurosci. 2013;16:1662–70.

    PubMed  PubMed Central  CAS  Google Scholar 

  43. Griemsmann S, Höft SP, Bedner P, Zhang J, von Staden E, Beinhauer A, et al. Characterization of Panglial Gap Junction Networks in the Thalamus, Neocortex, and Hippocampus Reveals a Unique Population of Glial Cells. Cerebral Cortex. 2014;25:3420–33.

    PubMed  PubMed Central  Google Scholar 

  44. Philippot C, Griemsmann S, Jabs R, Seifert G, Kettenmann H, Steinhäuser C. Astrocytes and oligodendrocytes in the thalamus jointly maintain synaptic activity by supplying metabolites. Cell Reports. 2021;34:108642.

    PubMed  CAS  Google Scholar 

  45. Park K, Lee SJ. Deciphering the star codings: astrocyte manipulation alters mouse behavior. Experimental & Molecular Medicine. 2020;52:1028–38.

    CAS  Google Scholar 

  46. Min R, Nevian T. Astrocyte signaling controls spike timing-dependent depression at neocortical synapses. Nat Neurosci. 2012;15:746–53.

    PubMed  CAS  Google Scholar 

  47. Heuser K, Nome CG, Pettersen KH, Åbjørsbråten KS, Jensen V, Tang W, et al. Ca2+ Signals in Astrocytes Facilitate Spread of Epileptiform Activity. Cereb Cortex. 2018;28:4036–48.

    PubMed  PubMed Central  Google Scholar 

  48. Voskuhl RR, Peterson RS, Song B, Ao Y, Morales LB, Tiwari-Woodruff S, et al. Reactive astrocytes form scar-like perivascular barriers to leukocytes during adaptive immune inflammation of the CNS. J Neurosci. 2009;29:11511–22.

    PubMed  PubMed Central  CAS  Google Scholar 

  49. Horchar MJ, Wohleb ES. Glucocorticoid receptor antagonism prevents microgliamediated neuronal remodeling and behavioral despair following chronic unpredictable stress. Brain Behav Immun. 2019;81:329–40.

    PubMed  CAS  Google Scholar 

  50. Xu Z-X, Kim GH, Tan J-W, Riso AE, Sun Y, Xu EY, et al. Elevated protein synthesis in microglia causes autism-like synaptic and behavioral aberrations. Nature Communications. 2020;11:1797.

    PubMed  PubMed Central  CAS  Google Scholar 

  51. Urrego D, Troncoso J, Múnera A. Layer 5 pyramidal neurons’ dendritic remodeling and increased microglial density in primary motor cortex in a murine model of facial paralysis. Biomed Res Int. 2015;2015:482023.

    PubMed  PubMed Central  Google Scholar 

  52. Shukla R, Henkel ND, Alganem K, Hamoud AR, Reigle J, Alnafisah RS, et al. Signature-based approaches for informed drug repurposing: targeting CNS disorders. Neuropsychopharmacology. 2021;46:116–30.

    PubMed  Google Scholar 

  53. Chiu CQ, Lur G, Morse TM, Carnevale NT, Ellis-Davies GC, Higley MJ. Compartmentalization of GABAergic inhibition by dendritic spines. Science. 2013;340:759–62.

    PubMed  PubMed Central  CAS  Google Scholar 

  54. Schulz JM, Knoflach F, Hernandez M-C, Bischofberger J. Dendrite-targeting interneurons control synaptic NMDA-receptor activation via nonlinear α5-GABAA receptors. Nat Commun. 2018;9:3576.

    PubMed  PubMed Central  Google Scholar 

  55. Karnani MM, Jackson J, Ayzenshtat I, Hamzehei Sichani A, Manoocheri K, Kim S, et al. Opening Holes in the Blanket of Inhibition: Localized Lateral Disinhibition by VIP Interneurons. J Neurosci. 2016;36:3471–80.

    PubMed  PubMed Central  CAS  Google Scholar 

  56. Pardi MB, Abs E, Letzkus JJ. Disinhibition goes spatial. Neuron. 2019;101:994–6.

    PubMed  CAS  Google Scholar 

  57. Turi GF, Li WK, Chavlis S, Pandi I, O'Hare J, Priestley JB, et al. Vasoactive Intestinal Polypeptide-Expressing Interneurons in the Hippocampus Support Goal-Oriented Spatial Learning. Neuron. 2019;101:1150-65.e1158.

    Google Scholar 

  58. Seasholtz A. Regulation of adrenocorticotropic hormone secretion: lessons from mice deficient in corticotropin-releasing hormone. J Clin Invest. 2000;105:1187–8.

    PubMed  PubMed Central  CAS  Google Scholar 

  59. Zhu Y, Zhu JJ. Rapid arrival and integration of ascending sensory information in layer 1 nonpyramidal neurons and tuft dendrites of layer 5 pyramidal neurons of the neocortex. J Neurosci. 2004;24:1272–9.

    PubMed  PubMed Central  CAS  Google Scholar 

  60. Hartung J, Letzkus JJ. Inhibitory plasticity in layer 1 -dynamic gatekeeper of neocortical associations. Curr Opin Neurobiol. 2021;67:26–33.

    PubMed  CAS  Google Scholar 

  61. Koob GF, Markou A, Weiss F, Schulteis G. Opponent process and drug dependence: neurobiological mechanisms. Semin Neurosci. 1993;5:351–8.

    Google Scholar 

  62. Chen C, Jiang Z, Fu X, Yu D, Huang H, Tasker JG. Astrocytes Amplify Neuronal Dendritic Volume Transmission Stimulated by Norepinephrine. Cell Rep. 2019;29:4349-.e4344.

    Google Scholar 

  63. Shukla R, Newton DF, Sumitomo A, Zare H, McCullumsmith R, Lewis DA, et al. Molecular characterization of depression trait and state. Mol Psychiatry. 2021;27:1083–94.

    PubMed  Google Scholar 

  64. Kuang Y, Walt DR. Monitoring “promiscuous” drug effects on single cells of multiple cell types. Anal Biochem. 2005;345:320–5.

    PubMed  CAS  Google Scholar 

  65. Baker A, Kalmbach B, Morishima M, Kim J, Juavinett A, Li N, et al. Specialized Subpopulations of Deep-Layer Pyramidal Neurons in the Neocortex: Bridging Cellular Properties to Functional Consequences. J Neurosci. 2018;38:5441–55.

    PubMed  PubMed Central  CAS  Google Scholar 

  66. Rajkowska G, Stockmeier CA. Astrocyte pathology in major depressive disorder: insights from human postmortem brain tissue. Curr Drug Targets. 2013;14:1225–36.

    PubMed  PubMed Central  CAS  Google Scholar 

  67. Anderson KM, Collins MA, Kong R, Fang K, Li J, He T, et al. Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder. Proceedings of the National Academy of Sciences. 2020;117:25138.

    CAS  Google Scholar 

  68. Arion D, Corradi JP, Tang S, Datta D, Boothe F, He A, et al. Distinctive transcriptome alterations of prefrontal pyramidal neurons in schizophrenia and schizoaffective disorder. Molecular Psychiatry. 2015;20:1397–405.

    PubMed  PubMed Central  CAS  Google Scholar 

  69. Cotter D, Mackay D, Landau S, Kerwin R, Everall I. Reduced glial cell density and neuronal size in the anterior cingulate cortex in major depressive disorder. Arch Gen Psychiatry. 2001;58:545–53.

    PubMed  CAS  Google Scholar 

  70. Cotter D, Mackay D, Chana G, Beasley C, Landau S, Everall IP. Reduced Neuronal Size and Glial Cell Density in Area 9 of the Dorsolateral Prefrontal Cortex in Subjects with Major Depressive Disorder. Cerebral Cortex. 2002;12:386–94.

    PubMed  Google Scholar 

  71. Anderson EM, Gomez D, Caccamise A, McPhail D, Hearing M. Chronic unpredictable stress promotes cell-specific plasticity in prefrontal cortex D1 and D2 pyramidal neurons. Neurobiol Stress. 2019;10:100152.

    PubMed  PubMed Central  CAS  Google Scholar 

  72. Gee S, Ellwood I, Patel T, Luongo F, Deisseroth K, Sohal VS. Synaptic activity unmasks dopamine D2 receptor modulation of a specific class of layer V pyramidal neurons in prefrontal cortex. J Neurosci. 2012;32:4959–71.

    PubMed  PubMed Central  CAS  Google Scholar 

  73. Alnafisah RS, Reigle J, Eladawi MA, O’Donovan SM, Funk AJ, Meller J, et al. Assessing the effects of antipsychotic medications on schizophrenia functional analysis: a postmortem proteome study. Neuropsychopharmacology 2022.

  74. Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. 2014;34:502–8.

    PubMed  Google Scholar 

  75. Reddy V, Sherif M, Shukla R. Integrating single-cell transcriptomics and microcircuit computer modeling. Curr Opin Pharmacol. 2021;60:34–39.

    PubMed  CAS  Google Scholar 

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Funding

MAS is supported by the National Institute of Mental Health predoctoral fellowship F31MH125541.

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Authors and Affiliations

  1. Department of Pharmacology and Systems Physiology, University of Cincinnati, Cincinnati, OH, USA

    Marissa A. Smail & James P. Herman

  2. Neuroscience Graduate Program, University of Cincinnati, Cincinnati, OH, USA

    Marissa A. Smail

  3. Department of Mathematics and Statistics, University of Toledo, Toledo, OH, USA

    Sapuni S. Chandrasena

  4. Department of Neurosciences, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA

    Xiaolu Zhang, Vineet Reddy, Robert E. McCullumsmith & Rammohan Shukla

  5. Program in Biomedical Engineering, SUNY Downstate Health Science University, Brooklyn, NY, USA

    Craig Kelley

  6. Veterans Affairs Medical Center, Cincinnati, OH, USA

    James P. Herman

  7. Department of Neurology, University of Cincinnati, Cincinnati, OH, USA

    James P. Herman

  8. Department of Psychiatry and Human Behavior, Carney Institute for Brain Science, Brown University, Providence, RI, USA

    Mohamed Sherif

  9. Neurosciences Institute, ProMedica, Toledo, OH, USA

    Robert E. McCullumsmith

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Contributions

RS and MAS conceptualized the study and together wrote the manuscript. SSC participated in formulating the density score. XZ, VR, and REM, participated with RS and MAS in developing drug-based ontology. CK, MS, and JPH participated with RS and MAS in developing disease-based ontology. All authors participated in writing the manuscript.

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Correspondence to Rammohan Shukla.

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Smail, M.A., Chandrasena, S.S., Zhang, X. et al. Differential vulnerability of anterior cingulate cortex cell types to diseases and drugs. Mol Psychiatry 27, 4023–4034 (2022). https://doi.org/10.1038/s41380-022-01657-w

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