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Convergent Molecular, Cellular, and Cortical Neuroimaging Signatures of Major Depressive Disorder

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Convergent Molecular, Cellular, and Cortical Neuroimaging Signatures of Major Depressive Disorder Convergent molecular, cellular, and cortical neuroimaging signatures of major depressive disorder Kevin M. Andersona,1, Meghan A. Collinsa, Ru Kongb,c,d,e,f, Kacey Fanga, Jingwei Lib,c,d,e,f, Tong Heb,c,d,e,f, Adam M. Chekroudg,h, B. T. Thomas Yeob,c,d,e,f,i, and Avram J. Holmesa,g,j aDepartment of Psychology, Yale University, New Haven, CT 06520; bDepartment of Electrical and Computer Engineering, National University of Singapore, Singapore; cCentre for Sleep and Cognition, National University of Singapore, Singapore; dClinical Imaging Research Centre, National University of Singapore, Singapore; eN.1 Institute for Health, National University of Singapore, Singapore; fInstitute for Digital Medicine, National University of Singapore, Singapore; gDepartment of Psychiatry, Yale University, New Haven, CT 06520; hSpring Health, New York, NY 10001; iGraduate School for Integrative Sciences and Engineering, National University of Singapore, Singapore; and jDepartment of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114 Edited by Huda Akil, University of Michigan, Ann Arbor, MI, and approved August 12, 2020 (received for review May 5, 2020) Major depressive disorder emerges from the complex interactions detail. To date, there have been few opportunities to directly of biological systems that span genes and molecules through cells, explore the depressive phenotype across levels of analysis—from networks, and behavior. Establishing how neurobiological pro- genes and molecules through cells, circuits, networks, and cesses coalesce to contribute to depression requires a multiscale behavior—simultaneously (14). approach, encompassing measures of brain structure and function In vivo neuroimaging has identified depression-related corre- as well as genetic and cell-specific transcriptional data. Here, we lates in brain anatomy, metabolism, and function. For example, examine anatomical (cortical thickness) and functional (functional discoveries linking amygdala–medial prefrontal cortex (mPFC) variability, global brain connectivity) correlates of depression and circuitry to emotional (15) and social processing (16) led to the negative affect across three population-imaging datasets: UK Bio- hypothesis that dysregulated interactions of cortical and sub- bank, Brain Genomics Superstruct Project, and Enhancing Neuro- cortical systems precipitate the onset of depression (2, 17). Imaging through Meta Analysis (ENIGMA; combined n ≥ 23,723). Integrative analyses incorporate measures of cortical gene ex- Disrupted metabolism and altered gray matter volume in the pression, postmortem patient transcriptional data, depression mPFC of patients is also a pronounced feature of the disorder (8, NEUROSCIENCE genome-wide association study (GWAS), and single-cell gene tran- 18) that may track illness chronicity (19). As sample sizes have scription. Neuroimaging correlates of depression and negative af- increased into the thousands, however, it is apparent that many fect were consistent across three independent datasets. Linking early identified effects are likely more subtle than initially expected ex vivo gene down-regulation with in vivo neuroimaging, we find that transcriptional correlates of depression imaging phenotypes Significance track gene down-regulation in postmortem cortical samples of pa- tients with depression. Integrated analysis of single-cell and Allen Major depressive disorder is a debilitating condition with di- Human Brain Atlas expression data reveal somatostatin interneu- verse neuroimaging correlates, including cortical thinning in rons and astrocytes to be consistent cell associates of depression, medial prefrontal cortex and altered functional connectivity of through both in vivo imaging and ex vivo cortical gene dysregu- cortical association networks. However, the molecular bases of lation. Providing converging evidence for these observations, these imaging markers remain ambiguous, despite a need for GWAS-derived polygenic risk for depression was enriched for treatment targets and mechanisms. Here, we advance cross- genes expressed in interneurons, but not glia. Underscoring the modal approaches to identify cell types and gene transcripts translational potential of multiscale approaches, the transcrip- associated with depression-implicated cortex. Across multiple tional correlates of depression-linked brain function and structure population-imaging datasets (combined N ≥ 23,723) and were enriched for disorder-relevant molecular pathways. These ex vivo patient cortical tissue, somatostatin interneurons and findings bridge levels to connect specific genes, cell classes, and astrocytes emerge as replicable cell-level correlates of depres- biological pathways to in vivo imaging correlates of depression. sion and negative affect. These data identify transcripts, cell types, and molecular processes associated with neuroimaging major depressive disorder | neuroimaging | gene expression | somatostatin markers of depression and offer a roadmap for integrating interneurons | astrocytes in vivo clinical imaging with genetic and postmortem patient transcriptional data. ajor depressive disorder (MDD) is a common and debili- Mtating illness with a moderately strong genetic basis (her- Author contributions: K.M.A. and A.J.H. designed research; K.M.A. performed research; itability, h2 ≈ 40%) (1). Clinical depression emerges through K.M.A., R.K., J.L., T.H., and B.T.T.Y. contributed new reagents/analytic tools; K.M.A. ana- lyzed data; K.M.A., A.M.C., and A.J.H. wrote the paper; and M.A.C., R.K., K.F., J.L., T.H., complex interactions spanning multiple biological systems and A.M.C., B.T.T.Y., and A.J.H. contributed analytic expertise, theoretical guidance, paper levels of analysis (2). The multiscale nature of depression is ev- revisions, and informed interpretation of the results. ident in the presence of disorder-relevant genetic loci (3), as well Competing interest statement: A.M.C. holds equity in Spring Care Inc, Fitbit Inc, and as shifts in gene expression (4, 5), cellular composition (6, 7), UnitedHealthcare Inc. He is lead inventor on three patent submissions relating to treat- cortical anatomy (8), and large-scale network function (9). ment for major depressive disorder (US Patent and Trademark Office [USPTO] docket no. Y0087.70116US00, USPTO provisional application no. 62/491,660, and USPTO provisional However, most human research on the pathophysiology of de- application no. 62/629,041). He has consulted for Fortress Biotech on antidepressant drug pressive illness focuses on select features of brain biology, often development. in isolation. For instance, in vivo neuroimaging studies link This article is a PNAS Direct Submission. symptom profiles in patients to brain anatomy and network This open access article is distributed under Creative Commons Attribution-NonCommercial- function (10, 11), but are largely divorced from insights about NoDerivatives License 4.0 (CC BY-NC-ND). underlying molecular and cellular mechanisms. By contrast, 1To whom correspondence may be addressed. Email: [email protected]. analyses of postmortem tissue samples characterize illness- This article contains supporting information online at https://www.pnas.org/lookup/suppl/ related cellular and biological processes (4, 5, 12, 13), but of- doi:10.1073/pnas.2008004117/-/DCSupplemental. ten focus on few regions and are limited by coarse diagnostic www.pnas.org/cgi/doi/10.1073/pnas.2008004117 PNAS Latest Articles | 1of12 Downloaded by guest on September 26, 2021 (8, 20). As a consequence, the stability of depression-relevant functional correlates of depression and negative affect that are profiles of brain anatomy and function across populations consistent across populations. Multiple biological hypotheses remains unclear. about the neural substrates of depression have been proposed, Complex clinical phenotypes like depression are tied to in- such as interneuron and glial cell dysfunction, as well as alter- teractions throughout the functional connectome (9, 10, 21). ations in glutamatergic signaling (12, 13, 30). However, most Supporting this perspective, biological subtypes and heteroge- neuroimaging modalities are not sensitive to underlying molec- neous presentations of depression may be revealed by consid- ular or transcriptional properties of brain tissue. To address this ering the collective set of functional connections in the brain (10, gap, we link cortical correlates of depression to normative pat- 11). Spatially diffuse correlates of depression across cortical terns of gene expression in the adult human brain, identifying anatomy and function could arise from a host of biological cell classes and gene transcripts expressed most within changes in patient populations, such as altered neurotransmis- depression-implicated brain regions. Indicating that normative sion (22), inflammation (23), and changes in cell abundance or patterns of gene expression may inform depression-related vul- morphology (6). Approaches that consider cross-level neurobi- nerability of cortex, the transcriptional associates of depression ological relationships would illuminate the biological bases of neuroimaging phenotypes correlated with gene dysregulation in large-scale neuroimaging correlates of depressive illness. The independent MDD ex vivo patient brain tissue. Postmortem emergence of whole-brain gene expression atlases (24) now case−control data also identified cell types tied to both in vivo permits more spatially comprehensive descriptions
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