Jason L. Stein

Default Patterning Produces Pan-cortical Glutamatergic and CGE/LGE-like GABAergic Neurons from Human Pluripotent Stem Cells

Summary Default differentiation of human pluripotent stem cells has been promoted as a model of cortical development. In this study, a developmental transcriptome analysis of default-differentiated hPSNs revealed a gene expression program resembling in vivo CGE/LGE subpallial domains and GABAergic signaling. A combination of bioinformatic, functional, and immunocytochemical analysis further revealed that hPSNs consist of both cortical glutamatergic and CGE-like GABAergic neurons. This study provides a comprehensive characterization of the heterogeneous group of neurons produced by default differentiation and insight into future directed differentiation strategies.

Polygenic selection underlies evolution of human brain structure and behavioral traits

Seemingly paradoxical characteristics of psychiatric disorders, including moderate to high prevalence, reduced fecundity, and high heritability have motivated explanations for the persistence of common risk alleles for severe psychiatric phenotypes throughout human evolution. Proposed mechanisms include balancing selection, drift, and weak polygenic adaptation acting either directly, or indirectly through selection on correlated traits. While many mechanisms have been proposed, few have been empirically tested. Leveraging publicly available data of unprecedented sample size, we studied twenty-five traits (i.e., ten neuropsychiatric disorders, three personality traits, total intracranial volume, seven subcortical brain structure volume traits, and four complex traits without neuropsychiatric associations) for evidence of several different signatures of selection over a range of evolutionary time scales. Consistent with the largely polygenic architecture of neuropsychiatric traits, we found no enrichment of trait-associated single-nucleotide polymorphisms (SNPs) in regions of the genome that underwent classical selective sweeps (i.e., events which would have driven selected alleles to near fixation). However, we discovered that SNPs associated with some, but not all, behaviors and brain structure volumes are enriched in genomic regions under selection since divergence from Neanderthals ~600,000 years ago, and show further evidence for signatures of ancient and recent polygenic adaptation. Individual subcortical brain structure volumes demonstrate genome-wide evidence in support of a mosaic theory of brain evolution while total intracranial volume and height appear to share evolutionary constraints consistent with concerted evolution. We further characterized the biological processes potentially targeted by selection, through expression Quantitative Trait Locus (eQTL) and Gene Ontology (GO) enrichment analyses and found evidence for the role of regulatory functions among selected SNPs in immune and brain tissues. Taken together, our results suggest that alleles associated with neuropsychiatric, behavioral, and brain volume phenotypes have experienced both ancient and recent polygenic adaptation in human evolution, acting through neurodevelopmental and immune-mediated pathways.

Transcriptomic signatures of neuronal differentiation and their association with risk genes for autism spectrum and related neuropsychiatric disorders

Genes for autism spectrum disorders (ASDs) are also implicated in fragile X syndrome (FXS), intellectual disabilities (ID) or schizophrenia (SCZ), and converge on neuronal function and differentiation. The SH-SY5Y neuroblastoma cell line, the most widely used system to study neurodevelopment, is currently discussed for its applicability to model cortical development. We implemented an optimal neuronal differentiation protocol of this system and evaluated neurodevelopment at the transcriptomic level using the CoNTeXT framework, a machine-learning algorithm based on human post-mortem brain data estimating developmental stage and regional identity of transcriptomic signatures. Our improved model in contrast to currently used SH-SY5Y models does capture early neurodevelopmental processes with high fidelity. We applied regression modelling, dynamic time warping analysis, parallel independent component analysis and weighted gene co-expression network analysis to identify activated gene sets and networks. Finally, we tested and compared these sets for enrichment of risk genes for neuropsychiatric disorders. We confirm a significant overlap of genes implicated in ASD with FXS, ID and SCZ. However, counterintuitive to this observation, we report that risk genes affect pathways specific for each disorder during early neurodevelopment. Genes implicated in ASD, ID, FXS and SCZ were enriched among the positive regulators, but only ID-implicated genes were also negative regulators of neuronal differentiation. ASD and ID genes were involved in dendritic branching modules, but only ASD risk genes were implicated in histone modification or axonal guidance. Only ID genes were over-represented among cell cycle modules. We conclude that the underlying signatures are disorder-specific and that the shared genetic architecture results in overlaps across disorders such as ID in ASD. Thus, adding developmental network context to genetic analyses will aid differentiating the pathophysiology of neuropsychiatric disorders.

Imaging Genomics and ENIGMA

Abstract We review efforts worldwide to discover differences in genomic and medical imaging data that offer new insights into psychiatric and neurological illnesses in human populations. The vast cost to society of mental illness from depression, posttraumatic stress, the dementias, and disorders of childhood development and old age have led many geneticists to seek treatable causal mechanisms that affect our risk and prognosis in these disorders. Imaging data can help us discover and understand both genetic and nongenetic risk factors for brain diseases, revealing what brain systems they affect. However, the vast diversity of mental illness and the complexity of the factors that affect it require large-scale studies to avoid making premature conclusions that generalize poorly individuals and new populations. We show how Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA), a worldwide alliance studying 12 brain diseases in 35 countries, has merged neuroimaging and genetic data from over 30,000 people to yield new insights into a variety of brain diseases and conditions, and the factors that affect them.

A single cell transcriptomic analysis of human neocortical development

Defining the number, proportion, or lineage of distinct cell types in the developing human brain is an important goal of modern brain research. We defined single cell transcriptomic profiles for 40,000 cells at mid-gestation to identify cell types in the developing human neocortex. We define expression profiles corresponding to all known major cell types at this developmental period and identify multiple transcription factors and co-factors expressed in specific cell types, providing an unprecedented resource for understanding human neocortical development including the first single-cell characterization of human subplate neurons. We characterize major developmental trajectories during early neurogenesis, showing that cell type differentiation occurs on a continuum that involves transitions that tie cell cycle progression with early cell fate decisions. We use these data to deconvolute regulatory networks and map neuropsychiatric disease genes to specific cell types, implicating dysregulation of specific cell types, as the mechanistic underpinnings of several neurodevelopmental disorders. Together these results provide an extensive catalog of cell types in human neocortex and extend our understanding of early cortical development, human brain evolution and the cellular basis of neuropsychiatric disease. One Sentence Summary Comprehensive single cell transcriptomes in developing human cortex inform models of cell diversity, differentiation and disease risk.

Genetic Determinants of Cortical Structure (Thickness, Surface Area and Volumes) among Disease Free Adults in the CHARGE Consortium

Cortical thickness, surface area and volumes (MRI cortical measures) vary with age and cognitive function, and in neurological and psychiatric diseases. We examined heritability, genetic correlations and genome-wide associations of cortical measures across the whole cortex, and in 34 anatomically predefined regions. Our discovery sample comprised 22,822 individuals from 20 cohorts within the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium and the United Kingdom Biobank. Significant associations were replicated in the Enhancing Neuroimaging Genetics through Meta-analysis (ENIGMA) consortium, and their biological implications explored using bioinformatic annotation and pathway analyses. We identified genetic heterogeneity between cortical measures and brain regions, and 161 genome-wide significant associations pointing to wnt/β-catenin, TGF-β and sonic hedgehog pathways. There was enrichment for genes involved in anthropometric traits, hindbrain development, vascular and neurodegenerative disease and psychiatric conditions. These data are a rich resource for studies of the biological mechanisms behind cortical development and aging.

Accurate and High Throughput Cell Segmentation Method for Mouse Brain Nuclei Using Cascaded Convolutional Neural Network

Recent innovations in tissue clearing and light sheet microscopy allow rapid acquisition of three-dimensional micron resolution images in fluorescently labeled brain samples. These data allow the observation of every cell in the brain, necessitating an accurate and high-throughput cell segmentation method in order to perform basic operations like counting number of cells within a region; however, large computational challenges given noise in the data and sheer number of features to identify. Inspired by the success of deep learning technique in medical imaging, we propose a supervised learning approach using convolution neural network (CNN) to learn the non-linear relationship between local image appearance (within an image patch) and manual segmentations (cell or background at the center of the underlying patch). In order to improve the segmentation accuracy, we further integrate high-level contextual features with low-level image appearance features. Specifically, we extract contextual features from the probability map of cells (output of current CNN) and train the next CNN based on both patch-wise image appearance and contextual features, extending previous methods into a cascaded approach. Using (a) high-level contextual features extracted from the cell probability map and (b) the spatial information of cell-to-cell locations, our cascaded CNN progressively improves the segmentation accuracy. We have evaluated the segmentation results on mouse brain images, and compared conventional image processing approaches. More accurate and robust segmentation results have been achieved with our cascaded CNN method, indicating the promising potential of our proposed cell segmentation method for use in large tissue cleared images.

Genetic markers of ADHD-related variations in intracranial volume

Attention-Deficit/Hyperactivity Disorder (ADHD) is a common and highly heritable neurodevelopmental disorder with a complex pathophysiology, where genetic risk is hypothesized to be mediated by alterations in structure and function of diverse brain networks. We tested one aspect of this hypothesis by investigating the genetic overlap between ADHD (n=55,374) and (mainly subcortical) brain volumes (n=11,221-24,704), using the largest publicly available studies. At the level of common variant genetic architecture, we discovered a significant negative genetic correlation between ADHD and intracranial volume (ICV). Meta-analysis of individual variants found significant loci associated with both ADHD risk and ICV; additional loci were identified for ADHD and amygdala, caudate nucleus, and putamen volumes. Gene-set analysis in the ADHD-ICV meta-analytic data showed significant association with variation in neurite outgrowth-related genes. In summary, our results suggest new hypotheses about biological mechanisms involved in ADHD etiology and highlight the need to study additional brain parameters.