Profiling gene expression in the human dentate gyrus granule cell layer reveals insights into schizophrenia and its genetic risk.
Adolescent
Adult
Aged
Aged, 80 and over
Aging
Bipolar Disorder
/ genetics
Dentate Gyrus
/ metabolism
Depressive Disorder, Major
/ genetics
Female
Gene Expression Profiling
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Male
Middle Aged
Neurons
/ metabolism
Quantitative Trait Loci
Schizophrenia
/ genetics
Transcriptome
Young Adult
Journal
Nature neuroscience
ISSN: 1546-1726
Titre abrégé: Nat Neurosci
Pays: United States
ID NLM: 9809671
Informations de publication
Date de publication:
04 2020
04 2020
Historique:
received:
27
03
2019
accepted:
05
02
2020
pubmed:
24
3
2020
medline:
8
7
2020
entrez:
24
3
2020
Statut:
ppublish
Résumé
Specific cell populations may have unique contributions to schizophrenia but may be missed in studies of homogenate tissue. Here laser capture microdissection followed by RNA sequencing (LCM-seq) was used to transcriptomically profile the granule cell layer of the dentate gyrus (DG-GCL) in human hippocampus and contrast these data to those obtained from bulk hippocampal homogenate. We identified widespread cell-type-enriched aging and genetic effects in the DG-GCL that were either absent or directionally discordant in bulk hippocampus data. Of the ~9 million expression quantitative trait loci identified in the DG-GCL, 15% were not detected in bulk hippocampus, including 15 schizophrenia risk variants. We created transcriptome-wide association study genetic weights from the DG-GCL, which identified many schizophrenia-associated genetic signals not found in transcriptome-wide association studies from bulk hippocampus, including GRM3 and CACNA1C. These results highlight the improved biological resolution provided by targeted sampling strategies like LCM and complement homogenate and single-nucleus approaches in human brain.
Identifiants
pubmed: 32203495
doi: 10.1038/s41593-020-0604-z
pii: 10.1038/s41593-020-0604-z
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
510-519Subventions
Organisme : NIBIB NIH HHS
ID : T32 EB025816
Pays : United States
Références
BrainSeq Consortium. BrainSeq: neurogenomics to drive novel target discovery for neuropsychiatric disorders. Neuron 88, 1078–1083 (2015).
Fromer, M. et al. Gene expression elucidates functional impact of polygenic risk for schizophrenia. Nat. Neurosci. 19, 1442–1453 (2016).
pubmed: 27668389
pmcid: 5083142
PsychENCODE Consortiumet al. The PsychENCODE project. Nat. Neurosci. 18, 1707–1712 (2015).
GTEx Consortium et al. Genetic effects on gene expression across human tissues. Nature 550, 204–213 (2017).
pmcid: 5776756
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature 511, 421–427 (2014).
pmcid: 4112379
Jaffe, A. E. et al. Developmental and genetic regulation of the human cortex transcriptome illuminate schizophrenia pathogenesis. Nat. Neurosci. 21, 1117–1125 (2018).
pubmed: 30050107
pmcid: 6438700
Collado-Torres, L. et al. Regional heterogeneity in gene expression, regulation, and coherence in the frontal cortex and hippocampus across development and schizophrenia. Neuron 103, 203–216 (2019).
pubmed: 31174959
pmcid: 7000204
Lake, B. B. et al. Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain. Nat. Biotechnol. 36, 70–80 (2018).
pubmed: 29227469
Lake, B. B. et al. Neuronal subtypes and diversity revealed by single-nucleus RNA sequencing of the human brain. Science 352, 1586–1590 (2016).
pubmed: 27339989
pmcid: 5038589
Zhu, Y. et al. Spatiotemporal transcriptomic divergence across human and macaque brain development. Science 362, eaat8077 (2018).
pubmed: 30545855
pmcid: 6900982
Li, M. et al. Integrative functional genomic analysis of human brain development and neuropsychiatric risks. Science 362, eaat7615 (2018).
pubmed: 30545854
pmcid: 6413317
Hodge, R. D. et al. Conserved cell types with divergent features in human versus mouse cortex. Nature 573, 61–68 (2019).
pubmed: 31435019
pmcid: 6919571
Bakken, T. E. et al. A comprehensive transcriptional map of primate brain development. Nature 535, 367–375 (2016).
pubmed: 27409810
pmcid: 5325728
Miller, J. A. et al. Transcriptional landscape of the prenatal human brain. Nature 508, 199–206 (2014).
pubmed: 24695229
pmcid: 4105188
Arion, D. et al. Transcriptome alterations in prefrontal pyramidal cells distinguish schizophrenia from bipolar and major depressive disorders. Biol. Psychiatry 82, 594–600 (2017).
pubmed: 28476208
pmcid: 5610065
Kempermann, G., Song, H. & Gage, F. H. Neurogenesis in the adult hippocampus. Cold Spring Harb. Perspect. Biol. 7, a018812 (2015).
pubmed: 26330519
pmcid: 4563705
Habib, N. et al. Massively parallel single-nucleus RNA-seq with DroNc-seq. Nat. Methods 14, 955–958 (2017).
pubmed: 28846088
pmcid: 5623139
Leutgeb, J. K., Leutgeb, S., Moser, M.-B. & Moser, E. I. Pattern separation in the dentate gyrus and CA3 of the hippocampus. Science 315, 961–966 (2007).
pubmed: 17303747
Neunuebel, J. P. & Knierim, J. J. CA3 retrieves coherent representations from degraded input: direct evidence for CA3 pattern completion and dentate gyrus pattern separation. Neuron 81, 416–427 (2014).
pubmed: 24462102
pmcid: 3904133
Hagihara, H., Takao, K., Walton, N. M., Matsumoto, M. & Miyakawa, T. Immature dentate gyrus: an endophenotype of neuropsychiatric disorders. Neural Plast. 2013, 318596 (2013).
pubmed: 23840971
pmcid: 3694492
Mertens, J. et al. Differential responses to lithium in hyperexcitable neurons from patients with bipolar disorder. Nature 527, 95–99 (2015).
pubmed: 26524527
pmcid: 4742055
Nakahara, S., Matsumoto, M. & van Erp, T. G. M. Hippocampal subregion abnormalities in schizophrenia: a systematic review of structural and physiological imaging studies. Neuropsychopharmacol. Rep. 38, 156–166 (2018).
pubmed: 30255629
pmcid: 7021222
Elvsåshagen, T. et al. Dentate gyrus–cornu ammonis (CA) 4 volume is decreased and associated with depressive episodes and lipid peroxidation in bipolar II disorder: longitudinal and cross-sectional analyses. Bipolar Disord 18, 657–668 (2016).
pubmed: 27995733
Hibar, D. P. et al. Novel genetic loci associated with hippocampal volume. Nat. Commun. 8, 13624 (2017).
pubmed: 28098162
pmcid: 5253632
Rasetti, R. et al. Altered hippocampal–parahippocampal function during stimulus encoding: a potential indicator of genetic liability for schizophrenia. JAMA Psychiatry 71, 236–247 (2014).
pubmed: 24382711
Weinberger, D. R., Berman, K. F., Suddath, R. & Torrey, E. F. Evidence of dysfunction of a prefrontal–limbic network in schizophrenia: a magnetic resonance imaging and regional cerebral blood flow study of discordant monozygotic twins. Am. J. Psychiatry 149, 890–897 (1992).
pubmed: 1609867
Pardiñas, A. F. et al. Common schizophrenia alleles are enriched in mutation-intolerant genes and in regions under strong background selection. Nat. Genet. 50, 381–389 (2018).
pubmed: 29483656
pmcid: 5918692
Gusev, A. et al. Integrative approaches for large-scale transcriptome-wide association studies. Nat. Genet. 48, 245–252 (2016).
pubmed: 26854917
pmcid: 4767558
Gandal, M. J. et al. Shared molecular neuropathology across major psychiatric disorders parallels polygenic overlap. Science 359, 693–697 (2018).
pubmed: 29439242
pmcid: 5898828
Eisch, A. J. & Petrik, D. Depression and hippocampal neurogenesis: a road to remission? Science 338, 72–75 (2012).
pubmed: 23042885
pmcid: 3756889
Overall, R. W., Paszkowski-Rogacz, M. & Kempermann, G. The mammalian adult neurogenesis gene ontology (MANGO) provides a structural framework for published information on genes regulating adult hippocampal neurogenesis. PLoS One 7, e48527 (2012).
pubmed: 23139788
pmcid: 3489671
Pollen, A. A. et al. Molecular identity of human outer radial glia during cortical development. Cell 163, 55–67 (2015).
pubmed: 26406371
pmcid: 4583716
Shapiro, E., Biezuner, T. & Linnarsson, S. Single-cell sequencing-based technologies will revolutionize whole-organism science. Nat. Rev. Genet. 14, 618–630 (2013).
pubmed: 23897237
Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).
pubmed: 26060301
pmcid: 4466750
Xu, X. et al. Species and cell-type properties of classically defined human and rodent neurons and glia. eLife 7, e37551 (2018).
pubmed: 30320555
pmcid: 6188473
Gandal, M. J. et al. Transcriptome-wide isoform-level dysregulation in ASD, schizophrenia, and bipolar disorder. Science 362, eaat8127 (2018).
pubmed: 30545856
pmcid: 6443102
Sekar, A. et al. Schizophrenia risk from complex variation of complement component 4. Nature 530, 177–183 (2016).
pubmed: 26814963
pmcid: 4752392
Li, M. et al. A human-specific AS3MT isoform and BORCS7 are molecular risk factors in the 10q24.32 schizophrenia-associated locus. Nat. Med. 22, 649–656 (2016).
pubmed: 27158905
Ma, L. et al. Schizophrenia risk variants influence multiple classes of transcripts of sorting nexin 19 (SNX19). Mol. Psychiatry https://doi.org/10.1038/s41380-018-0293-0 (2019).
Wang, D. et al. Comprehensive functional genomic resource and integrative model for the human brain. Science 362, eaat8464 (2018).
pubmed: 30545857
pmcid: 6413328
Dong, X. et al. Enhancers active in dopamine neurons are a primary link between genetic variation and neuropsychiatric disease. Nat. Neurosci. 21, 1482–1492 (2018).
pubmed: 30224808
pmcid: 6334654
Pietersen, C. Y. et al. Molecular profiles of parvalbumin-immunoreactive neurons in the superior temporal cortex in schizophrenia. J. Neurogenet. 28, 70–85 (2014).
pubmed: 24628518
pmcid: 4633016
Datta, D., Arion, D., Corradi, J. P. & Lewis, D. A. Altered expression of CDC42 signaling pathway components in cortical layer 3 pyramidal cells in schizophrenia. Biol. Psychiatry 78, 775–785 (2015).
pubmed: 25981171
pmcid: 4600637
Pietersen, C. Y. et al. Molecular profiles of pyramidal neurons in the superior temporal cortex in schizophrenia. J. Neurogenet. 28, 53–69 (2014).
pubmed: 24702465
pmcid: 4196521
Egan, M. F. et al. Variation in GRM3 affects cognition, prefrontal glutamate, and risk for schizophrenia. Proc. Natl Acad. Sci. USA 101, 12604–12609 (2004).
pubmed: 15310849
pmcid: 515104
Green, E. K. et al. The bipolar disorder risk allele at CACNA1C also confers risk of recurrent major depression and of schizophrenia. Mol. Psychiatry 15, 1016–1022 (2010).
pubmed: 19621016
Wagner, M. J., Kim, T. H., Savall, J., Schnitzer, M. J. & Luo, L. Cerebellar granule cells encode the expectation of reward. Nature 544, 96–100 (2017).
pubmed: 28321129
pmcid: 5532014
Huckins, L. M. et al. Gene expression imputation across multiple brain regions provides insights into schizophrenia risk. Nat. Genet. 51, 659–674 (2019).
pubmed: 30911161
pmcid: 7034316
Bigos, K. L. et al. Genetic variation in CACNA1C affects brain circuitries related to mental illness. Arch. Gen. Psychiatry 67, 939–945 (2010).
pubmed: 20819988
pmcid: 3282053
Eckart, N. et al. Functional characterization of schizophrenia-associated variation in CACNA1C. PLoS One 11, e0157086 (2016).
pubmed: 27276213
pmcid: 4898738
Deep-Soboslay, A. et al. Reliability of psychiatric diagnosis in postmortem research. Biol. Psychiatry 57, 96–101 (2005).
pubmed: 15607306
Lipska, B. K. et al. Critical factors in gene expression in postmortem human brain: focus on studies in schizophrenia. Biol. Psychiatry 60, 650–658 (2006).
pubmed: 16997002
Ziegenhain, C. et al. Comparative analysis of single-cell RNA sequencing methods. Mol. Cell 65, 631–643 (2017).
pubmed: 28212749
Babraham Bioinformatics. FastQC https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2016).
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).
pubmed: 24695404
pmcid: 4103590
Kim, D., Langmead, B. & Salzberg, S. L. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).
pubmed: 25751142
pmcid: 4655817
Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).
pubmed: 24227677
Feng, Y.-Y., et al. RegTools: integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer. Preprint at bioRxiv https://doi.org/10.1101/436634 (2018).
Kim, D. et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013).
pubmed: 23618408
pmcid: 4053844
Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419 (2017).
pubmed: 28263959
pmcid: 5600148
Wang, L., Wang, S. & Li, W. RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012).
pubmed: 22743226
Kent, W. J., Zweig, A. S., Barber, G., Hinrichs, A. S. & Karolchik, D. BigWig and BigBed: enabling browsing of large distributed datasets. Bioinformatics 26, 2204–2207 (2010).
pubmed: 20639541
pmcid: 2922891
Delaneau, O., Coulonges, C. & Zagury, J.-F. Shape-IT: new rapid and accurate algorithm for haplotype inference. BMC Bioinformatics 9, 540 (2008).
pubmed: 19087329
pmcid: 2647951
Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 1, 457–470 (2011).
pubmed: 22384356
pmcid: 3276165
Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).
pubmed: 24485249
pmcid: 4053721
Hu, P. et al. Dissecting cell-type composition and activity-dependent transcriptional state in mammalian brains by massively parallel single-nucleus RNA-seq. Mol. Cell 68, 1006–1015 (2017).
pubmed: 29220646
pmcid: 5743496
Habib, N. et al. Div-Seq: single-nucleus RNA-Seq reveals dynamics of rare adult newborn neurons. Science 353, 925–928 (2016).
pubmed: 27471252
pmcid: 5480621
Lacar, B. et al. Nuclear RNA-seq of single neurons reveals molecular signatures of activation. Nat. Commun. 7, 11022 (2016).
pubmed: 27090946
pmcid: 4838832
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
pubmed: 25867923
pmcid: 4430369
Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).
pubmed: 31042697
pmcid: 6865822
Houseman, E. A. et al. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics 13, 86 (2012).
pubmed: 22568884
pmcid: 3532182
Burke, E. E. et al. Dissecting transcriptomic signatures of neuronal differentiation and maturation using iPSCs. Nat. Commun. 11, 462 (2019).
Jaffe, A. E. et al. qSVA framework for RNA quality correction in differential expression analysis. Proc. Natl Acad. Sci. USA 114, 7130–7135 (2017).
pubmed: 28634288
pmcid: 5502589
Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012).
pubmed: 22455463
pmcid: 3339379
Shabalin, A. A. Matrix eQTL: ultra fast eQTL analysis via large matrix operations. Bioinformatics 28, 1353–1358 (2012).
pubmed: 22492648
pmcid: 3348564
Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E. & Storey, J. D. The sva package for removing batch effects and other unwanted variation in high-throughput experiments. Bioinformatics 28, 882–883 (2012).
pubmed: 22257669
pmcid: 3307112
Edlund, C. K., Conti, D. V. & Van Den Berg, D. J. raggr http://raggr.usc.edu/ (USC, 2017).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
pubmed: 21167468
pmcid: 3014363