Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants.

Genome-wide association studies (GWASs) have successfully identified 145 genomic regions that contribute to schizophrenia risk, but linkage disequilibrium makes it challenging to discern causal variants. We performed a massively parallel reporter assay (MPRA) on 5,173 fine-mapped schizophrenia GWAS variants in primary human neural progenitors and identified 439 variants with allelic regulatory effects (MPRA-positive variants). Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. Furthermore, 64% of MPRA-positive variants did not exhibit expressive quantitative trait loci signature, suggesting that MPRA could identify yet unexplored variants with regulatory potentials. To predict the combinatorial effect of MPRA-positive variants on gene regulation, we propose an accessibility-by-contact model that combines MPRA-measured allelic activity with neuronal chromatin architecture.

Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis.

The function of some genetic variants associated with brain-relevant traits has been explained through colocalization with expression quantitative trait loci (eQTL) conducted in bulk post-mortem adult brain tissue. However, many brain-trait associated loci have unknown cellular or molecular function. These genetic variants may exert context-specific function on different molecular phenotypes including post-transcriptional changes. Here, we identified genetic regulation of RNA-editing and alternative polyadenylation (APA), within a cell-type-specific population of human neural progenitors and neurons. More RNA-editing and isoforms utilizing longer polyadenylation sequences were observed in neurons, likely due to higher expression of genes encoding the proteins mediating these post-transcriptional events. We also detected hundreds of cell-type-specific editing quantitative trait loci (edQTLs) and alternative polyadenylation QTLs (apaQTLs). We found colocalizations of a neuron edQTL in CCDC88A with educational attainment and a progenitor apaQTL in EP300 with schizophrenia, suggesting genetically mediated post-transcriptional regulation during brain development lead to differences in brain function.

MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size.

Expression quantitative trait loci (eQTL) data have proven important for linking non-coding loci to protein-coding genes. But eQTL studies rarely measure microRNAs (miRNAs), small non-coding RNAs known to play a role in human brain development and neurogenesis. Here, we performed small-RNA sequencing across 212 mid-gestation human neocortical tissue samples, measured 907 expressed miRNAs, discovering 111 of which were novel, and identified 85 local-miRNA-eQTLs. Colocalization of miRNA-eQTLs with GWAS summary statistics yielded one robust colocalization of miR-4707-3p expression with educational attainment and brain size phenotypes, where the miRNA expression increasing allele was associated with decreased brain size. Exogenous expression of miR-4707-3p in primary human neural progenitor cells decreased expression of predicted targets and increased cell proliferation, indicating miR-4707-3p modulates progenitor gene regulation and cell fate decisions. Integrating miRNA-eQTLs with existing GWAS yielded evidence of a miRNA that may influence human brain size and function via modulation of neocortical brain development.

Focus on your locus with a massively parallel reporter assay.

A growing number of variants associated with risk for neurodevelopmental disorders have been identified by genome-wide association and whole genome sequencing studies. As common risk variants often fall within large haplotype blocks covering long stretches of the noncoding genome, the causal variants within an associated locus are often unknown. Similarly, the effect of rare noncoding risk variants identified by whole genome sequencing on molecular traits is seldom known without functional assays. A massively parallel reporter assay (MPRA) is an assay that can functionally validate thousands of regulatory elements simultaneously using high-throughput sequencing and barcode technology. MPRA has been adapted to various experimental designs that measure gene regulatory effects of genetic variants within cis- and trans-regulatory elements as well as posttranscriptional processes. This review discusses different MPRA designs that have been or could be used in the future to experimentally validate genetic variants associated with neurodevelopmental disorders. Though MPRA has limitations such as it does not model genomic context, this assay can help narrow down the underlying genetic causes of neurodevelopmental disorders by screening thousands of sequences in one experiment. We conclude by describing future directions of this technique such as applications of MPRA for gene-by-environment interactions and pharmacogenetics.

Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy.

Tissue clearing followed by light-sheet microscopy (LSFM) enables cellular-resolution imaging of intact brain structure, allowing quantitative analysis of structural changes caused by genetic or environmental perturbations. Whole-brain imaging results in more accurate quantification of cells and the study of region-specific differences that may be missed with commonly used microscopy of physically sectioned tissue. Using light-sheet microscopy to image cleared brains greatly increases acquisition speed as compared to confocal microscopy. Although these images produce very large amounts of brain structural data, most computational tools that perform feature quantification in images of cleared tissue are limited to counting sparse cell populations, rather than all nuclei. Here, we demonstrate NuMorph (Nuclear-Based Morphometry), a group of analysis tools, to quantify all nuclei and nuclear markers within annotated regions of a postnatal day 4 (P4) mouse brain after clearing and imaging on a light-sheet microscope. We describe magnetic resonance imaging (MRI) to measure brain volume prior to shrinkage caused by tissue clearing dehydration steps, tissue clearing using the iDISCO+ method, including immunolabeling, followed by light-sheet microscopy using a commercially available platform to image mouse brains at cellular resolution. We then demonstrate this image analysis pipeline using NuMorph, which is used to correct intensity differences, stitch image tiles, align multiple channels, count nuclei, and annotate brain regions through registration to publicly available atlases. We designed this approach using publicly available protocols and software, allowing any researcher with the necessary microscope and computational resources to perform these techniques. These tissue clearing, imaging, and computational tools allow measurement and quantification of the three-dimensional (3D) organization of cell-types in the cortex and should be widely applicable to any wild-type/knockout mouse study design.

SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images.

Image-based cell counting is a fundamental yet challenging task with wide applications in biological research. In this paper, we propose a novel unified deep network framework designed to solve this problem for various cell types in both 2D and 3D images. Specifically, we first propose SAU-Net for cell counting by extending the segmentation network U-Net with a Self-Attention module. Second, we design an extension of Batch Normalization (BN) to facilitate the training process for small datasets. In addition, a new 3D benchmark dataset based on the existing mouse blastocyst (MBC) dataset is developed and released to the community. Our SAU-Net achieves state-of-the-art results on four benchmark 2D datasets - synthetic fluorescence microscopy (VGG) dataset, Modified Bone Marrow (MBM) dataset, human subcutaneous adipose tissue (ADI) dataset, and Dublin Cell Counting (DCC) dataset, and the new 3D dataset, MBC. The BN extension is validated using extensive experiments on the 2D datasets, since GPU memory constraints preclude use of 3D datasets. The source code is available at https://github.com/mzlr/sau-net.

NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images.

Tissue-clearing methods allow every cell in the mouse brain to be imaged without physical sectioning. However, the computational tools currently available for cell quantification in cleared tissue images have been limited to counting sparse cell populations in stereotypical mice. Here, we introduce NuMorph, a group of analysis tools to quantify all nuclei and nuclear markers within the mouse cortex after clearing and imaging by light-sheet microscopy. We apply NuMorph to investigate two distinct mouse models: a Topoisomerase 1 (Top1) model with severe neurodegenerative deficits and a Neurofibromin 1 (Nf1) model with a more subtle brain overgrowth phenotype. In each case, we identify differential effects of gene deletion on individual cell-type counts and distribution across cortical regions that manifest as alterations of gross brain morphology. These results underline the value of whole-brain imaging approaches, and the tools are widely applicable for studying brain structure phenotypes at cellular resolution.

Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis.

Interpretation of the function of non-coding risk loci for neuropsychiatric disorders and brain-relevant traits via gene expression and alternative splicing quantitative trait locus (e/sQTL) analyses is generally performed in bulk post-mortem adult tissue. However, genetic risk loci are enriched in regulatory elements active during neocortical differentiation, and regulatory effects of risk variants may be masked by heterogeneity in bulk tissue. Here, we map e/sQTLs, and allele-specific expression in cultured cells representing two major developmental stages, primary human neural progenitors (n = 85) and their sorted neuronal progeny (n = 74), identifying numerous loci not detected in either bulk developing cortical wall or adult cortex. Using colocalization and genetic imputation via transcriptome-wide association, we uncover cell-type-specific regulatory mechanisms underlying risk for brain-relevant traits that are active during neocortical differentiation. Specifically, we identified a progenitor-specific eQTL for CENPW co-localized with common variant associations for cortical surface area and educational attainment.

Segmentor: a tool for manual refinement of 3D microscopy annotations.

BACKGROUND: Recent advances in tissue clearing techniques, combined with high-speed image acquisition through light sheet microscopy, enable rapid three-dimensional (3D) imaging of biological specimens, such as whole mouse brains, in a matter of hours. Quantitative analysis of such 3D images can help us understand how changes in brain structure lead to differences in behavior or cognition, but distinguishing densely packed features of interest, such as nuclei, from background can be challenging. Recent deep learning-based nuclear segmentation algorithms show great promise for automated segmentation, but require large numbers of accurate manually labeled nuclei as training data. RESULTS: We present Segmentor, an open-source tool for reliable, efficient, and user-friendly manual annotation and refinement of objects (e.g., nuclei) within 3D light sheet microscopy images. Segmentor employs a hybrid 2D-3D approach for visualizing and segmenting objects and contains features for automatic region splitting, designed specifically for streamlining the process of 3D segmentation of nuclei. We show that editing simultaneously in 2D and 3D using Segmentor significantly decreases time spent on manual annotations without affecting accuracy as compared to editing the same set of images with only 2D capabilities. CONCLUSIONS: Segmentor is a tool for increased efficiency of manual annotation and refinement of 3D objects that can be used to train deep learning segmentation algorithms, and is available at https://www.nucleininja.org/ and https://github.com/RENCI/Segmentor .

Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation.

Common genetic risk for neuropsychiatric disorders is enriched in regulatory elements active during cortical neurogenesis. However, it remains poorly understood as to how these variants influence gene regulation. To model the functional impact of common genetic variation on the noncoding genome during human cortical development, we performed the assay for transposase accessible chromatin using sequencing (ATAC-seq) and analyzed chromatin accessibility quantitative trait loci (QTL) in cultured human neural progenitor cells and their differentiated neuronal progeny from 87 donors. We identified significant genetic effects on 988/1,839 neuron/progenitor regulatory elements, with highly cell-type and temporally specific effects. A subset (roughly 30%) of chromatin accessibility-QTL were also associated with changes in gene expression. Motif-disrupting alleles of transcriptional activators generally led to decreases in chromatin accessibility, whereas motif-disrupting alleles of repressors led to increases in chromatin accessibility. By integrating cell-type-specific chromatin accessibility-QTL and brain-relevant genome-wide association data, we were able to fine-map and identify regulatory mechanisms underlying noncoding neuropsychiatric disorder risk loci.

Memo1-Mediated Tiling of Radial Glial Cells Facilitates Cerebral Cortical Development.

Polarized, non-overlapping, regularly spaced, tiled organization of radial glial cells (RGCs) serves as a framework to generate and organize cortical neuronal columns, layers, and circuitry. Here, we show that mediator of cell motility 1 (Memo1) is a critical determinant of radial glial tiling during neocortical development. Memo1 deletion or knockdown leads to hyperbranching of RGC basal processes and disrupted RGC tiling, resulting in aberrant radial unit assembly and neuronal layering. Memo1 regulates microtubule (MT) stability necessary for RGC tiling. Memo1 deficiency leads to disrupted MT minus-end CAMSAP2 distribution, initiation of aberrant MT branching, and altered polarized trafficking of key basal domain proteins such as GPR56, and thus aberrant RGC tiling. These findings identify Memo1 as a mediator of RGC scaffold tiling, necessary to generate and organize neurons into functional ensembles in the developing cerebral cortex.

Cross Modality Microscopy Segmentation via Adversarial Adaptation.

Deep learning techniques have been successfully applied to automatically segment and quantify cell-types in images acquired from both confocal and light sheet fluorescence microscopy. However, the training of deep learning networks requires a massive amount of manually-labeled training data, which is a very time-consuming operation. In this paper, we demonstrate an adversarial adaptation method to transfer deep network knowledge for microscopy segmentation from one imaging modality (e.g., confocal) to a new imaging modality (e.g., light sheet) for which no or very limited labeled training data is available. Promising segmentation results show that the proposed transfer learning approach is an effective way to rapidly develop segmentation solutions for new imaging methods.

Initiation of Behavioral Response to Antidepressants by Cholecystokinin Neurons of the Dentate Gyrus.

Selective serotonin reuptake inhibitors (SSRIs) are the most commonly used class of antidepressant drugs, but the cellular and molecular mechanisms by which their therapeutic action is initiated are poorly understood. Here we show that serotonin 5-HT1B receptors in cholecystokinin (CCK) inhibitory interneurons of the mammalian dentate gyrus (DG) initiate the therapeutic response to antidepressants. In these neurons, 5-HT1B receptors are expressed presynaptically, and their activation inhibits GABA release. Inhibition of GABA release from CCK neurons disinhibits parvalbumin (PV) interneurons and, as a consequence, reduces the neuronal activity of the granule cells. Finally, inhibition of CCK neurons mimics the antidepressant behavioral effects of SSRIs, suggesting that these cells may represent a novel cellular target for the development of fast-acting antidepressant drugs.

A contact line pinning based microfluidic platform for modelling physiological flows.

This work introduces a contact line pinning based microfluidic platform for the generation of interstitial and intramural flows within a three dimensional (3D) microenvironment for cellular behaviour studies. A contact line pinning method was used to confine a natively derived biomatrix, collagen, in microfluidic channels without walls. By patterning collagen in designated wall-less channels, we demonstrated and validated the intramural flows through a microfluidic channel bounded by a monolayer of endothelial cells (mimic of a vascular vessel), as well as slow interstitial flows within a cell laden collagen matrix using the same microfluidic platform. The contact line pinning method ensured the generation of an engineered endothelial tube with straight walls, and spatially uniform interstitial fluid flows through the cell embedded 3D collagen matrix. Using this device, we demonstrated that the breast tumour cells' (MDA-MB-231 cell line) morphology and motility were modulated by the interstitial flows, and the motility of a sub-population of the cells was enhanced by the presence of the flow. The presented microfluidic platform provides a basic framework for studies of cellular behaviour including cell transmigration, growth, and adhesion under well controlled interstitial and intramural flows, and within a physiologically realistic 3D co-culture setting.