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.

  • Article
  • Published:

Transcriptomic pathology of neocortical microcircuit cell types across psychiatric disorders

Molecular Psychiatry (2024)Cite this article

Subjects

Abstract

Psychiatric disorders such as major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are characterized by altered cognition and mood, brain functions that depend on information processing by cortical microcircuits. We hypothesized that psychiatric disorders would display cell type-specific transcriptional alterations in neuronal subpopulations that make up cortical microcircuits: excitatory pyramidal (PYR) neurons and vasoactive intestinal peptide- (VIP), somatostatin- (SST), and parvalbumin- (PVALB) expressing inhibitory interneurons. Using laser capture microdissection followed by RNA sequencing (LCM-seq), we performed cell type-specific molecular profiling of subgenual anterior cingulate cortex, a region implicated in mood and cognitive control. We sequenced libraries from 130 whole cells pooled per neuronal subtype (VIP, SST, PVALB, superficial and deep PYR) in 76 subjects from the University of Pittsburgh Brain Tissue Donation Program, evenly split between MDD, BD and SCZ subjects and healthy controls (totaling 380 bulk transcriptomes from ~50,000 neurons). We identified hundreds of differentially expressed (DE) genes and biological pathways across disorders and neuronal subtypes, with the vast majority in interneurons, particularly PVALB. While DE genes were unique to each cell type, there was a partial overlap across disorders for genes involved in the formation and maintenance of neuronal circuits. We observed coordinated alterations in biological pathways between select pairs of microcircuit cell types, also partially shared across disorders. Finally, DE genes coincided with known risk variants from psychiatric genome-wide association studies, suggesting cell type-specific convergence between genetic and transcriptomic risk for psychiatric disorders. Our study suggests transdiagnostic cortical microcircuit pathology in SCZ, BD, and MDD and sets the stage for larger-scale studies investigating how cell circuit-based changes contribute to shared psychiatric risk.

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

Access options

Buy this article

  • Purchase on SpringerLink
  • Instant access to full article PDF
Buy now

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

Fig. 1: LCM-seq and differential expression testing identify cell type-specific transcriptomic perturbations in interneurons that are shared in part across psychiatric disorders.
Fig. 2: Cortical neuron subtypes exhibit disruption of biological pathways across psychiatric disorders.
Fig. 3: Cell type-specific differentially expressed genes overlap with known genetic risk variants from psychiatric genome-wide association studies.

Similar content being viewed by others

Data availability

The datasets generated are available from the corresponding author upon request and pending local and national protection and ethics regulations for human data.

Code availability

The custom code developed for this study is openly available at: https://github.com/keon-arbabi/psych_microcircuit_cells.

References

  1. Moncrieff J, Cooper RE, Stockmann T, Amendola S, Hengartner MP, Horowitz MA. The serotonin theory of depression: a systematic umbrella review of the evidence. Mol Psychiatry. 2022;28:3243–56. https://doi.org/10.1038/s41380-022-01661-0.

    Article  PubMed  PubMed Central  Google Scholar 

  2. Lin SH, Lee LT, Yang YK. Serotonin and mental disorders: a concise review on molecular neuroimaging evidence. Clin Psychopharmacol Neurosci. 2014;12:196–202.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Diehl DJ, Gershon S. The role of dopamine in mood disorders. Compr Psychiatry. 1992;33:115–20.

    Article  CAS  PubMed  Google Scholar 

  4. Grace AA. Dysregulation of the dopamine system in the pathophysiology of schizophrenia and depression. Nat Rev Neurosci. 2016;17:524–32.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Moret C, Briley M. The importance of norepinephrine in depression. Neuropsychiatr Dis Treat. 2011;7:9–13.

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Javitt DC. Glutamate as a therapeutic target in psychiatric disorders. Mol Psychiatry. 2004;9:984–97.

    Article  CAS  PubMed  Google Scholar 

  7. Schür RR, Draisma LWR, Wijnen JP, Boks MP, Koevoets MGJC, Joëls M, et al. Brain GABA levels across psychiatric disorders: a systematic literature review and meta‐analysis of 1H‐MRS studies. Hum Brain Mapp. 2016;37:3337–52.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Ramocki MB, Zoghbi HY. Failure of neuronal homeostasis results in common neuropsychiatric phenotypes. Nature. 2008;455:912–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Neto FL, Borges G, Torres-Sanchez S, Mico JA, Berrocoso E. Neurotrophins role in depression neurobiology: a review of basic and clinical evidence. Curr Neuropharmacol. 2011;9:530–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Najjar S, Pearlman DM, Alper K, Najjar A, Devinsky O. Neuroinflammation and psychiatric illness. J Neuroinflammation. 2013;10:816.

    Article  Google Scholar 

  11. Bauer ME, Teixeira AL. Inflammation in psychiatric disorders: what comes first? Ann N Y Acad Sci. 2019;1437:57–67.

    Article  CAS  PubMed  Google Scholar 

  12. Haroon E, Miller AH, Sanacora G. Inflammation, glutamate, and glia: a trio of trouble in mood disorders. Neuropsychopharmacology. 2017;42:193–215.

    Article  CAS  PubMed  Google Scholar 

  13. Nakao A, Matsunaga Y, Hayashida K, Takahashi N (2021): Role of oxidative stress and Ca2+ signaling in psychiatric disorders. Front Cell Dev Biol. 2023, 9, from https://www.frontiersin.org/articles/10.3389/fcell.2021.615569.

  14. Ng F, Berk M, Dean O, Bush AI. Oxidative stress in psychiatric disorders: evidence base and therapeutic implications. Int J Neuropsychopharmacol. 2008;11:851–76.

    Article  CAS  PubMed  Google Scholar 

  15. Muneer A, Shamsher Khan RM. Endoplasmic reticulum stress: implications for neuropsychiatric disorders. Chonnam Med J. 2019;55:8–19.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang J, Xie S, Chen Y, Zhou X, Zheng Z, Yang L, et al. (2022): Comprehensive analysis of endoplasmic reticulum stress and immune infiltration in major depressive disorder. Front Psychiatry 13. 2023, from https://www.frontiersin.org/articles/10.3389/fpsyt.2022.1008124.

  17. Pfaffenseller B, Wollenhaupt-Aguiar B, Fries GR, Colpo GD, Burque RK, Bristot G, et al. Impaired endoplasmic reticulum stress response in bipolar disorder: cellular evidence of illness progression. Int J Neuropsychopharmacol. 2014;17:1453–63.

    Article  CAS  PubMed  Google Scholar 

  18. Doucet GE, Janiri D, Howard R, O’Brien M, Andrews-Hanna JR, Frangou S. Transdiagnostic and disease-specific abnormalities in the default-mode network hubs in psychiatric disorders: a meta-analysis of resting-state functional imaging studies. Eur Psychiatry. 2020;63:e57.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Whitfield-Gabrieli S, Ford JM. Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol. 2012;8:49–76.

    Article  PubMed  Google Scholar 

  20. Liu Y, Ouyang P, Zheng Y, Mi L, Zhao J, Ning Y, et al. A selective review of the excitatory-inhibitory imbalance in schizophrenia: underlying biology, genetics, microcircuits, and symptoms. Front Cell Dev Biol. 2021;9:664535.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Sohal VS, Rubenstein JLR. Excitation-inhibition balance as a framework for investigating mechanisms in neuropsychiatric disorders. Mol Psychiatry. 2019;24:1248–57.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Selten M, van Bokhoven H, Nadif Kasri N. Inhibitory control of the excitatory/inhibitory balance in psychiatric disorders. F1000Research. 2018;7:23.

    Article  PubMed  PubMed Central  Google Scholar 

  23. Lewis DA, Hashimoto T, Volk DW. Cortical inhibitory neurons and schizophrenia. Nat Rev Neurosci. 2005;6:312–24.

    Article  CAS  PubMed  Google Scholar 

  24. Fee C, Banasr M, Sibille E. Somatostatin-positive gamma-aminobutyric acid interneuron deficits in depression: cortical microcircuit and therapeutic perspectives. Biol Psychiatry. 2017;82:549–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Newton DF, Oh H, Shukla R, Misquitta K, Fee C, Banasr M, et al. Chronic stress induces coordinated cortical microcircuit cell–type transcriptomic changes consistent with altered information processing. Biol Psychiatry. 2022;91:798–809.

    Article  CAS  PubMed  Google Scholar 

  26. Northoff G, Sibille E. Why are cortical GABA neurons relevant to internal focus in depression? A cross-level model linking cellular, biochemical and neural network findings. Mol Psychiatry. 2014;19:966–77.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Prévot T, Sibille E. Altered GABA-mediated information processing and cognitive dysfunctions in depression and other brain disorders. Mol Psychiatry. 2021;26:151–67.

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. 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. Nat Neurosci. 2020;23:771–81.

    Article  CAS  PubMed  Google Scholar 

  30. Maitra M, Mitsuhashi H, Rahimian R, Chawla A, Yang J, Fiori LM, et al. Cell type specific transcriptomic differences in depression show similar patterns between males and females but implicate distinct cell types and genes. Nat Commun. 2023;14:2912.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Chatzinakos C, Pernia CD, Morrison FG, Iatrou A, McCullough KM, Schuler H, et al. Single-Nucleus Transcriptome Profiling of Dorsolateral Prefrontal Cortex: Mechanistic Roles for Neuronal Gene Expression, Including the 17q21.31 Locus, in PTSD Stress Response. Am J Psychiatry. 2023;180:739–54.

  32. Ruzicka WB, Mohammadi S, Davila-Velderrain J, Subburaju S, Tso DR, Hourihan M, et al. (2020): Single-cell dissection of schizophrenia reveals neurodevelopmental-synaptic axis and transcriptional resilience. Psychiatry Clin Psychol. https://doi.org/10.1101/2020.11.06.20225342.

  33. Reiner BC, Crist RC, Stein LM, Weller AE, Doyle GA, Arauco-Shapiro G, et al. (2020): Single-nuclei transcriptomics of schizophrenia prefrontal cortex primarily implicates neuronal subtypes. Neuroscience. https://doi.org/10.1101/2020.07.29.227355.

  34. Ruzicka WB, Mohammadi S, Fullard JF, Davila-Velderrain J, Subburaju S, Tso DR, et al. (2022): Single-cell multi-cohort dissection of the schizophrenia transcriptome. Psychiatry Clin Psychol. https://doi.org/10.1101/2022.08.31.22279406.

  35. 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. Mol Psychiatry. 2015;20:1397–405.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Arion D, Huo Z, Enwright JF, Corradi JP, Tseng G, Lewis DA. Transcriptome alterations in prefrontal pyramidal cells distinguish schizophrenia from bipolar and major depressive disorders. Biol Psychiatry. 2017;82:594–600.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Enwright III JF, Huo Z, Arion D, Corradi JP, Tseng G, Lewis DA. Transcriptome alterations of prefrontal cortical parvalbumin neurons in schizophrenia. Mol Psychiatry. 2018;23:1606–13.

    Article  CAS  PubMed  Google Scholar 

  38. Hemby SE, Ginsberg SD, Brunk B, Arnold SE, Trojanowski JQ, Eberwine JH. Gene expression profile for schizophrenia: discrete neuron transcription patterns in the entorhinal cortex. Arch Gen Psychiatry. 2002;59:631.

    Article  CAS  PubMed  Google Scholar 

  39. Rocco BR, Oh H, Shukla R, Mechawar N, Sibille E. Fluorescence-based cell-specific detection for laser-capture microdissection in human brain. Sci Rep. 2017;7:14213.

    Article  PubMed  PubMed Central  Google Scholar 

  40. 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.

    Article  PubMed  Google Scholar 

  41. Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: ultrafast universal RNA-seq aligner. Bioinforma Oxf Engl. 2013;29:15–21.

    Article  CAS  Google Scholar 

  42. Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, et al. Software for computing and annotating genomic ranges ((A. Prlic, editor)). PLoS Comput Biol. 2013;9:e1003118.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. 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;573:61–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.

    Article  PubMed  PubMed Central  Google Scholar 

  45. Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6:461–4.

    Article  Google Scholar 

  46. Gandal MJ, Haney JR, Parikshak NN, Leppa V, Ramaswami G, Hartl C, et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science. 2018;359:693–7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Cahill KM, Huo Z, Tseng GC, Logan RW, Seney ML. Improved identification of concordant and discordant gene expression signatures using an updated rank-rank hypergeometric overlap approach. Sci Rep. 2018;8:9588.

    Article  PubMed  PubMed Central  Google Scholar 

  48. Blanchard JW, Akay LA, Davila-Velderrain J, von Maydell D, Mathys H, Davidson SM, et al. APOE4 impairs myelination via cholesterol dysregulation in oligodendrocytes. Nature. 2022;611:769–79.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinform. 2013;14:7.

    Article  Google Scholar 

  50. Howard DM, Adams MJ, Clarke T-K, Hafferty JD, Gibson J, Shirali M, et al. Genome-wide meta-analysis of depression identifies 102 independent variants and highlights the importance of the prefrontal brain regions. Nat Neurosci. 2019;22:343–52.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Wainberg M, Merico D, Keller MC, Fauman EB, Tripathy SJ. Predicting causal genes from psychiatric genome-wide association studies using high-level etiological knowledge. Mol Psychiatry. 2022;27:3095–106.

    Article  PubMed  Google Scholar 

  52. Als TD, Kurki MI, Grove J, Voloudakis G, Therrien K, Tasanko E, et al. Depression pathophysiology, risk prediction of recurrence and comorbid psychiatric disorders using genome-wide analyses. Nat Med. 2023;29:1832–44.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Mullins N, Forstner AJ, O’Connell KS, Coombes B, Coleman JRI, Qiao Z, et al. Genome-wide association study of more than 40,000 bipolar disorder cases provides new insights into the underlying biology. Nat Genet. 2021;53:817–29.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB, et al. Mapping genomic loci implicates genes and synaptic biology in schizophrenia. Nature. 2022;604:502–8.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Boxer EE, Seng C, Lukacsovich D, Kim J, Schwartz S, Kennedy MJ, et al. Neurexin-3 defines synapse- and sex-dependent diversity of GABAergic inhibition in ventral subiculum. Cell Rep. 2021;37:110098.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. George-Hyslop FS, Haneklaus M, Kivisild T, Livesey FJ (2023): Loss of CNTNAP2 alters human cortical excitatory neuron differentiation and neural network development. Biol Psychiatry 0. https://doi.org/10.1016/j.biopsych.2023.03.014.

  57. Lazaro MT, Taxidis J, Shuman T, Bachmutsky I, Ikrar T, Santos R, et al. Reduced prefrontal synaptic connectivity and disturbed oscillatory population dynamics in the CNTNAP2 model of Autism. Cell Rep. 2019;27:2567–78.e6.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Canali G, Garcia M, Hivert B, Pinatel D, Goullancourt A, Oguievetskaia K, et al. Genetic variants in autism-related CNTNAP2 impair axonal growth of cortical neurons. Hum Mol Genet. 2018;27:1941–54.

    Article  CAS  PubMed  Google Scholar 

  59. Avramopoulos D. Neuregulin 3 and its roles in schizophrenia risk and presentation. Am J Med Genet Part B Neuropsychiatr Genet Off Publ Int Soc Psychiatr Genet. 2018;177:257–66.

    Article  CAS  Google Scholar 

  60. Douet V, Chang L, Lee K, Ernst T. ERBB4 polymorphism and family history of psychiatric disorders on age-related cortical changes in healthy children. Brain Imaging Behav. 2015;9:128–40.

    Article  PubMed  PubMed Central  Google Scholar 

  61. Pickard BS, Malloy MP, Porteous DJ, Blackwood DHR, Muir WJ. Disruption of a brain transcription factor, NPAS3, is associated with schizophrenia and learning disability. Am J Med Genet B Neuropsychiatr Genet. 2005;136B:26–32.

    Article  PubMed  Google Scholar 

  62. Rossi JJ, Rosenfeld JA, Chan KM, Streff H, Nankivell V, Peet DJ, et al. Molecular characterisation of rare loss-of-function NPAS3 and NPAS4 variants identified in individuals with neurodevelopmental disorders. Sci Rep. 2021;11:6602.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Labonté B, Engmann O, Purushothaman I, Menard C, Wang J, Tan C, et al. Sex-specific transcriptional signatures in human depression. Nat Med. 2017;23:1102–11.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Zandi PP, Jaffe AE, Goes FS, Burke EE, Collado-Torres L, Huuki-Myers L, et al. Amygdala and anterior cingulate transcriptomes from individuals with bipolar disorder reveal downregulated neuroimmune and synaptic pathways. Nat Neurosci. 2022;25:381–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Rasmussen AH, Rasmussen HB, Silahtaroglu A. The DLGAP family: neuronal expression, function and role in brain disorders. Mol Brain. 2017;10:43.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Morris J, Leung SSY, Bailey MES, Cullen B, Ferguson A, Graham N, et al. Exploring the role of contactins across psychological, psychiatric and cardiometabolic traits within UK Biobank. Genes. 2020;11:1326.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Weeks EM, Ulirsch JC, Cheng NY, Trippe BL, Fine RS, Miao J, et al. (2023): Leveraging polygenic enrichments of gene features to predict genes underlying complex traits and diseases. Nat Genet. https://doi.org/10.1038/s41588-023-01443-6.

  68. O’Leary A, Fernàndez-Castillo N, Gan G, Yang Y, Yotova AY, Kranz TM, et al. Behavioural and functional evidence revealing the role of RBFOX1 variation in multiple psychiatric disorders and traits. Mol Psychiatry. 2022;27:4464–73.

    Article  PubMed  PubMed Central  Google Scholar 

  69. Lee PH, Anttila V, Won H, Feng Y-CA, Rosenthal J, Zhu Z, et al. Genomic relationships, novel loci, and pleiotropic mechanisms across eight psychiatric disorders. Cell. 2019;179:1469–82.e11.

    Article  Google Scholar 

  70. Vuong CK, Wei W, Lee J-A, Lin C-H, Damianov A, de la Torre-Ubieta L, et al. Rbfox1 regulates synaptic transmission through the inhibitory neuron specific vSNARE Vamp1. Neuron. 2018;98:127–141.e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Wamsley B, Jaglin XH, Favuzzi E, Quattrocolo G, Nigro MJ, Yusuf N, et al. Rbfox1 mediates cell-type-specific splicing in cortical interneurons. Neuron. 2018;100:846–59.e7.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Qin Z, Ren F, Xu X, Ren Y, Li H, Wang Y, et al. ZNF536, a novel zinc finger protein specifically expressed in the brain, negatively regulates neuron differentiation by repressing retinoic acid-induced gene transcription. Mol Cell Biol. 2009;29:3633–43.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Schwarz N, Uysal B, Welzer M, Bahr JC, Layer N, Löffler H, et al. Long-term adult human brain slice cultures as a model system to study human CNS circuitry and disease. Elife. 2019;8:e48417.

  74. Enwright JF, Sanapala S, Foglio A, Berry R, Fish KN, Lewis DA. Reduced labeling of parvalbumin neurons and perineuronal nets in the dorsolateral prefrontal cortex of subjects with schizophrenia. Neuropsychopharmacology. 2016;41:2206–14.

    Article  PubMed  PubMed Central  Google Scholar 

  75. Lewis DA, Curley AA, Glausier JR, Volk DW. Cortical parvalbumin interneurons and cognitive dysfunction in schizophrenia. Trends Neurosci. 2012;35:57–67.

    Article  CAS  PubMed  Google Scholar 

  76. Goodkind M, Eickhoff SB, Oathes DJ, Jiang Y, Chang A, Jones-Hagata LB, et al. Identification of a common neurobiological substrate for mental illness. JAMA Psychiatry. 2015;72:305–15.

    Article  PubMed  PubMed Central  Google Scholar 

  77. Kochunov P, Hong LE, Dennis EL, Morey RA, Tate DF, Wilde EA, et al. ENIGMA-DTI: translating reproducible white matter deficits into personalized vulnerability metrics in cross-diagnostic psychiatric research. Hum Brain Mapp. 2022;43:194–206.

    Article  PubMed  Google Scholar 

  78. Opel N, Goltermann J, Hermesdorf M, Berger K, Baune BT, Dannlowski U. Cross-disorder analysis of brain structural abnormalities in six major psychiatric disorders: a secondary analysis of mega- and meta-analytical findings from the ENIGMA consortium. Biol Psychiatry. 2020;88:678–86.

    Article  PubMed  Google Scholar 

  79. Hettwer MD, Larivière S, Park BY, van den Heuvel OA, Schmaal L, Andreassen OA, et al. Coordinated cortical thickness alterations across six neurodevelopmental and psychiatric disorders. Nat Commun. 2022;13:6851.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  80. Sprooten E, Franke B, Greven CU. The P-factor and its genomic and neural equivalents: an integrated perspective. Mol Psychiatry. 2022;27:38–48.

    Article  PubMed  Google Scholar 

  81. Brainstorm Consortium, Anttila V, Bulik-Sullivan B, Finucane HK, Walters RK, Bras J, et al. Analysis of shared heritability in common disorders of the brain. Science. 2018;360:eaap8757.

    Article  Google Scholar 

  82. Grotzinger AD, Mallard TT, Akingbuwa WA, Ip HF, Adams MJ, Lewis CM, et al. Genetic architecture of 11 major psychiatric disorders at biobehavioral, functional genomic and molecular genetic levels of analysis. Nat Genet. 2022;54:548–59.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Ghosal S, Duman CH, Liu R-J, Wu M, Terwilliger R, Girgenti MJ, et al. Ketamine rapidly reverses stress-induced impairments in GABAergic transmission in the prefrontal cortex in male rodents. Neurobiol Dis. 2020;134:104669.

    Article  CAS  PubMed  Google Scholar 

  84. Baker M, Hong S-I, Kang S, Choi D-S. Rodent models for psychiatric disorders: problems and promises. Lab Anim Res. 2020;36:9.

    Article  PubMed  PubMed Central  Google Scholar 

  85. Peedicayil J. Genome–environment interactions and psychiatric disorders. Biomedicines. 2023;11:1209.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  86. Datta D, Arion D, Corradi JP, Lewis DA. Altered expression of CDC42 signaling pathway components in cortical layer 3 pyramidal cells in schizophrenia. Biol Psychiatry. 2015;78:775–85.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  87. Seney ML, Huo Z, Cahill K, French L, Puralewski R, Zhang J, et al. Opposite molecular signatures of depression in men and women. Biol Psychiatry. 2018;84:18–27.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Ding Y, Chang L-C, Wang X, Guilloux J-P, Parrish J, Oh H, et al. Molecular and genetic characterization of depression: overlap with other psychiatric disorders and aging. Complex Psychiatry. 2015;1:1–12.

    Article  Google Scholar 

  89. Gabitto MI, Travaglini KJ, Rachleff VM, Kaplan ES, Long B, Ariza J, et al. Integrated multimodal cell atlas of Alzheimer's disease. Res Sq [Preprint]. 2023 May 23:rs.3.rs-2921860.

  90. Murray JD, Anticevic A, Gancsos M, Ichinose M, Corlett PR, Krystal JH, et al. Linking microcircuit dysfunction to cognitive impairment: effects of disinhibition associated with schizophrenia in a cortical working memory model. Cereb Cortex N Y N 1991. 2014;24:859–72.

    Google Scholar 

  91. Yao HK, Guet-McCreight A, Mazza F, Moradi Chameh H, Prevot TD, Griffiths JD, et al. Reduced inhibition in depression impairs stimulus processing in human cortical microcircuits. Cell Rep. 2022;38:110232.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by Canadian Institute of Health Research Project Grant No. 153175 (to ES), CAMH Foundation Discovery Fund (to SJT), CAMH Foundation Krembil Startup Fund (to SJT), Ontario Graduate Scholarship (to KA and DJN), and Canadian Open Neuroscience Platform Initiative (to DJN).

Author information

Author notes

  1. These authors contributed equally: Keon Arbabi, Dwight F. Newton.

  2. These authors jointly supervised this work: Shreejoy J. Tripathy, Etienne Sibille.

Authors and Affiliations

  1. The Krembil Centre for Neuroinformatics, Centre for Addiction & Mental Health, Toronto, ON, Canada

    Keon Arbabi, Melanie C. Davie, Michael Wainberg & Shreejoy J. Tripathy

  2. Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada

    Keon Arbabi, Shreejoy J. Tripathy & Etienne Sibille

  3. Campbell Family Mental Health Research Institute, Centre for Addiction & Mental Health, Toronto, ON, Canada

    Dwight F. Newton, Hyunjung Oh & Etienne Sibille

  4. Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA

    David A. Lewis

  5. Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, ON, Canada

    Michael Wainberg

  6. Department of Psychiatry, University of Toronto, Toronto, ON, Canada

    Shreejoy J. Tripathy & Etienne Sibille

  7. Department of Physiology, University of Toronto, Toronto, ON, Canada

    Shreejoy J. Tripathy

  8. Department of Pharmacology and Toxicology, University of Toronto, Toronto, ON, Canada

    Etienne Sibille

Authors
  1. Keon Arbabi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Dwight F. Newton
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hyunjung Oh
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Melanie C. Davie
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. David A. Lewis
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Michael Wainberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Shreejoy J. Tripathy
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Etienne Sibille
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Conceptualization, KA, DJN, SJT, and ES; Formal analysis, KA, DFN, and MCD; Investigation, DFN and HO; Resources, DAL and ES; Writing – original draft, KA, DJN, MW, SJT, and ES; Writing – Review & Editing; KA, DJN, MCD, DAL, MW, SJT, and ES; Visualization, KA; Supervision, SJT and ES.

Corresponding author

Correspondence to Etienne Sibille.

Ethics declarations

Competing interests

ES is co-founder and CSO of Damona Pharmaceutical Inc., a biotech company developing GABAergic molecules for the treatment of cognitive dysfunctions in depression. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethics approval

The authors confirm all relevant ethical guidelines have been followed, and necessary ethics committee approvals have been obtained from The University of Pittsburgh Committee for Oversight of Research and Clinical Training Involving Decedents and Institutional Review Board for Biomedical Research. All necessary patient consent has been obtained and the appropriate institutional forms have been archived, and sample identifiers included were not known to anyone outside the research group so cannot be used to identify individuals.

Additional information

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

Supplementary information

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Arbabi, K., Newton, D.F., Oh, H. et al. Transcriptomic pathology of neocortical microcircuit cell types across psychiatric disorders. Mol Psychiatry (2024). https://doi.org/10.1038/s41380-024-02707-1

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41380-024-02707-1

Search

Advanced search

Quick links