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McAfee JC, Lee S, Lee J, Bell JL, Krupa O, Davis J, Insigne K, Bond ML, Zhao N, Boyle AP, Phanstiel DH, Love MI, Stein JL, Ruzicka WB, Davila-Velderrain J, Kosuri S, Won H. Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants. Cell Genom 2023; 3:100404. [PMID: 37868037 PMCID: PMC10589626 DOI: 10.1016/j.xgen.2023.100404] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Revised: 02/23/2023] [Accepted: 08/21/2023] [Indexed: 10/24/2023]
Abstract
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.
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Affiliation(s)
- Jessica C. McAfee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Sool Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jiseok Lee
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica L. Bell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Davis
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Quantitative and Computational Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Kimberly Insigne
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Quantitative and Computational Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Marielle L. Bond
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Nanxiang Zhao
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Alan P. Boyle
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA
| | - Douglas H. Phanstiel
- Curriculum in Genetics and Molecular Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Curriculum in Bioinformatics and Computational Biology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Thurston Arthritis Research Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I. Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - W. Brad Ruzicka
- Laboratory for Epigenomics in Human Psychopathology, McLean Hospital, Belmont, MA 02141, USA
- Harvard Medical School, Boston, MA 02115, USA
- Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | | | - Sriram Kosuri
- Department of Chemistry and Biochemistry, University of California, Los Angeles, Los Angeles, CA 90095, USA
- UCLA-DOE Institute for Genomics and Proteomics, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Quantitative and Computational Biology Institute, University of California, Los Angeles, Los Angeles, CA 90095, USA
- Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
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Aygün N, Krupa O, Mory J, Le B, Valone J, Liang D, Love MI, Stein JL. Genetics of cell-type-specific post-transcriptional gene regulation during human neurogenesis. bioRxiv 2023:2023.08.30.555019. [PMID: 37693528 PMCID: PMC10491258 DOI: 10.1101/2023.08.30.555019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
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.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Brandon Le
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jordan Valone
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael I. Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L. Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- UNC Neuroscience Center University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
- Lead contact
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3
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Lafferty MJ, Aygün N, Patel NK, Krupa O, Liang D, Wolter JM, Geschwind DH, de la Torre-Ubieta L, Stein JL. MicroRNA-eQTLs in the developing human neocortex link miR-4707-3p expression to brain size. eLife 2023; 12:e79488. [PMID: 36629315 PMCID: PMC9859047 DOI: 10.7554/elife.79488] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 01/10/2023] [Indexed: 01/12/2023] Open
Abstract
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.
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Affiliation(s)
- Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Niyanta K Patel
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Department of Cell Biology and Physiology, The University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Luis de la Torre-Ubieta
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los AngelesLos AngelesUnited States
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel HillChapel HillUnited States
- UNC Neuroscience Center, University of North Carolina at Chapel HillChapel HillUnited States
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel HillChapel HillUnited States
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Abstract
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.
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Affiliation(s)
- Jessica C. McAfee
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Jessica L. Bell
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Oleh Krupa
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Nana Matoba
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Jason L. Stein
- grid.10698.360000000122483208Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA ,grid.10698.360000000122483208UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599 USA
| | - Hyejung Won
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA. .,UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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5
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Kyere FA, Curtin I, Krupa O, McCormick CM, Dere M, Khan S, Kim M, Wang TWW, He Q, Wu G, Shih YYI, Stein JL. Whole-Brain Single-Cell Imaging and Analysis of Intact Neonatal Mouse Brains Using MRI, Tissue Clearing, and Light-Sheet Microscopy. J Vis Exp 2022:10.3791/64096. [PMID: 35969091 PMCID: PMC9912361 DOI: 10.3791/64096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
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.
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Affiliation(s)
- Felix A Kyere
- UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill
| | - Ian Curtin
- UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill
| | - Oleh Krupa
- UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill
| | - Carolyn M McCormick
- UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill
| | - Mustafa Dere
- Department of Psychiatry, University of North Carolina, Chapel Hill
| | - Sarah Khan
- Department of Psychiatry, University of North Carolina, Chapel Hill; Department of Computer Science, The University of North Carolina at Greensboro
| | - Minjeong Kim
- Department of Computer Science, The University of North Carolina at Greensboro
| | - Tzu-Wen Winnie Wang
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill
| | - Qiuhong He
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina, Chapel Hill
| | - Yen-Yu Ian Shih
- Center for Animal Magnetic Resonance Imaging, The University of North Carolina at Chapel Hill; Biomedical Research Imaging Center, The University of North Carolina at Chapel Hill; Department of Neurology, The University of North Carolina at Chapel Hill
| | - Jason L Stein
- UNC Neuroscience Center, University of North Carolina, Chapel Hill; Department of Genetics, University of North Carolina, Chapel Hill;
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Guo Y, Krupa O, Stein J, Wu G, Krishnamurthy A. SAU-Net: A Unified Network for Cell Counting in 2D and 3D Microscopy Images. IEEE/ACM Trans Comput Biol Bioinform 2022; 19:1920-1932. [PMID: 34133284 PMCID: PMC8924707 DOI: 10.1109/tcbb.2021.3089608] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
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.
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Krupa O, Fragola G, Hadden-Ford E, Mory JT, Liu T, Humphrey Z, Rees BW, Krishnamurthy A, Snider WD, Zylka MJ, Wu G, Xing L, Stein JL. NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images. Cell Rep 2021; 37:109802. [PMID: 34644582 PMCID: PMC8530274 DOI: 10.1016/j.celrep.2021.109802] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 07/07/2021] [Accepted: 09/15/2021] [Indexed: 01/18/2023] Open
Abstract
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.
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Affiliation(s)
- Oleh Krupa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27514, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Giulia Fragola
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ellie Hadden-Ford
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica T Mory
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyi Liu
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zachary Humphrey
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Benjamin W Rees
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ashok Krishnamurthy
- Renaissance Computing Institute, Chapel Hill, NC 27517, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - William D Snider
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mark J Zylka
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Jason L Stein
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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8
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Aygün N, Elwell AL, Liang D, Lafferty MJ, Cheek KE, Courtney KP, Mory J, Hadden-Ford E, Krupa O, de la Torre-Ubieta L, Geschwind DH, Love MI, Stein JL. Brain-trait-associated variants impact cell-type-specific gene regulation during neurogenesis. Am J Hum Genet 2021; 108:1647-1668. [PMID: 34416157 PMCID: PMC8456186 DOI: 10.1016/j.ajhg.2021.07.011] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 07/23/2021] [Indexed: 12/21/2022] Open
Abstract
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.
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Affiliation(s)
- Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Angela L Elwell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kerry E Cheek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Kenan P Courtney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica Mory
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ellie Hadden-Ford
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Luis de la Torre-Ubieta
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA; Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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9
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Liang D, Elwell AL, Aygün N, Krupa O, Wolter JM, Kyere FA, Lafferty MJ, Cheek KE, Courtney KP, Yusupova M, Garrett ME, Ashley-Koch A, Crawford GE, Love MI, de la Torre-Ubieta L, Geschwind DH, Stein JL. Cell-type-specific effects of genetic variation on chromatin accessibility during human neuronal differentiation. Nat Neurosci 2021; 24:941-953. [PMID: 34017130 PMCID: PMC8254789 DOI: 10.1038/s41593-021-00858-w] [Citation(s) in RCA: 32] [Impact Index Per Article: 10.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Accepted: 04/15/2021] [Indexed: 02/03/2023]
Abstract
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.
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Affiliation(s)
- Dan Liang
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Angela L Elwell
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Nil Aygün
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Oleh Krupa
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Justin M Wolter
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Felix A Kyere
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Michael J Lafferty
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kerry E Cheek
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kenan P Courtney
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Marianna Yusupova
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Melanie E Garrett
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
| | - Allison Ashley-Koch
- Duke Molecular Physiology Institute, Duke University, Durham, NC, USA
- Department of Medicine, Duke University, Durham, NC, USA
| | - Gregory E Crawford
- Center for Genomic and Computational Biology, Duke University, Durham, NC, USA
- Department of Pediatrics, Division of Medical Genetics, Duke University, Durham, NC, USA
| | - Michael I Love
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Luis de la Torre-Ubieta
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Daniel H Geschwind
- Neurogenetics Program, Department of Neurology, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Center for Autism Research and Treatment, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry and Biobehavioral Sciences, Semel Institute, David Geffen School of Medicine University of California, Los Angeles, Los Angeles, CA, USA
| | - Jason L Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
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10
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Borland D, McCormick CM, Patel NK, Krupa O, Mory JT, Beltran AA, Farah TM, Escobar-Tomlienovich CF, Olson SS, Kim M, Wu G, Stein JL. Segmentor: a tool for manual refinement of 3D microscopy annotations. BMC Bioinformatics 2021; 22:260. [PMID: 34022787 PMCID: PMC8141214 DOI: 10.1186/s12859-021-04202-8] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Accepted: 05/17/2021] [Indexed: 12/24/2022] Open
Abstract
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 .
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Affiliation(s)
- David Borland
- RENCI, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 540, Chapel Hill, NC, 27517, USA
| | - Carolyn M McCormick
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Niyanta K Patel
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Oleh Krupa
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Jessica T Mory
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Alvaro A Beltran
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Tala M Farah
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Carla F Escobar-Tomlienovich
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Sydney S Olson
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Minjeong Kim
- Department of Computer Science, University of North Carolina at Greensboro, Greensboro, NC, 27412, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, 334 Emergency Room Drive, 343 Medical Wing C, Chapel Hill, NC, 27599, USA.
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
| | - Jason L Stein
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, 116 Manning Drive, CB# 7250, Chapel Hill, NC, 27599, USA.
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA.
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11
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Nakagawa N, Plestant C, Yabuno-Nakagawa K, Li J, Lee J, Huang CW, Lee A, Krupa O, Adhikari A, Thompson S, Rhynes T, Arevalo V, Stein JL, Molnár Z, Badache A, Anton ES. Memo1-Mediated Tiling of Radial Glial Cells Facilitates Cerebral Cortical Development. Neuron 2019; 103:836-852.e5. [PMID: 31277925 DOI: 10.1016/j.neuron.2019.05.049] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2018] [Revised: 03/07/2019] [Accepted: 05/30/2019] [Indexed: 11/30/2022]
Abstract
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.
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Affiliation(s)
- Naoki Nakagawa
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA; Division of Neurogenetics, National Institute of Genetics, Mishima 411-8540, Japan; Department of Genetics, SOKENDAI (The Graduate University for Advanced Studies), Mishima 411-8540, Japan.
| | - Charlotte Plestant
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Keiko Yabuno-Nakagawa
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Jingjun Li
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Janice Lee
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Chu-Wei Huang
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Amelia Lee
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Oleh Krupa
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Aditi Adhikari
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Suriya Thompson
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Tamille Rhynes
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Victoria Arevalo
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Jason L Stein
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA
| | - Zoltán Molnár
- Department of Physiology, Anatomy, and Genetics, University of Oxford, Oxford, UK
| | - Ali Badache
- Centre de Recherche en Cancérologie de Marseille, CRCM, Inserm, Institut Paoli-Calmettes, Aix-Marseille Université, CNRS, 13009 Marseille, France
| | - E S Anton
- UNC Neuroscience Center and the Department of Cell Biology and Physiology, University of North Carolina School of Medicine, Chapel Hill, NC 27599, USA.
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12
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Guo Y, Wang Q, Krupa O, Stein J, Wu G, Bradford K, Krishnamurthy A. Cross Modality Microscopy Segmentation via Adversarial Adaptation. Bioinform Biomed Eng (2019) 2019; 11466:469-478. [PMID: 32154516 DOI: 10.1007/978-3-030-17935-9_42] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
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.
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Affiliation(s)
- Yue Guo
- Renaissance Computing Institute, Chapel Hill, NC, USA
| | - Qian Wang
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Oleh Krupa
- Department of Biomedical Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jason Stein
- Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Guorong Wu
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Kira Bradford
- Renaissance Computing Institute, Chapel Hill, NC, USA
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Yukalo V, Storozh L, Datsyshyn K, Krupa O. ELECTROPHORETIC SYSTEMS FOR PREPARATIVE FRACTIONATION OF PROTEIN PRECURSORS OF BIOACTIVE PEPTIDES FROM COW’S MILK. ХНТ 2018. [DOI: 10.15673/fst.v12i2.932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/22/2022]
Abstract
The article considers the possibility of obtaining purified fractions-precursors of bioactive peptides from milk proteins by the method of preparative electrophoresis. To choose an electrophoretic system, a comparative study has been carried out of four methods of electrophoresis in polyacrylamide gel that are used to analyse milk proteins (disc-electrophoresis without disaggregating agents, and disc-electrophoresis in the presence of sodium dodecylsulfate in homogeneous and gradient gel, and electrophoresis in homogeneous gel with urea). Electrophoresis of the total milk protein has shown that none of these systems allows separating effectively all protein precursors of bioactive peptides. The next stage was obtaining two main groups of milk proteins – caseins and serum proteins for electrophoretic fractionation. With the help of analytical electrophoresis, it has been established that each of the obtained groups had a typical proteins composition. Then, the proteins groups obtained were fractionated by preparative electrophoresis using the four electrophoretic systems listed above. In this case, the casein proteins that differ in the primary structure (αS1-, αS2-, β-, and ϰ-caseins) can be effectively separated by preparative electrophoresis on the basis of a homogeneous gel system in the presence of urea. The composition of this electrophoretic system was simplified. Unlike the analytical variant of a homogeneous polyacrylamide gel system, the toxic 2-mercaptoethanol was excluded, and the urea concentration was reduced. For the fractionation of serum proteins, a disc-electrophoresis without disaggregating agents can be used as a basis. It allows obtaining the main precursors of bioactive peptides from milk serum proteins: β-lactoglobulin, α-lactalbumin, serum albumin, and immunoglobulins. The protein precursors obtained by preparative electrophoresis were used to develop the biotechnology of obtaining bioactive phosphopeptides and inhibitors of the angiotensin-converting enzyme.
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Medrihan L, Sagi Y, Inde Z, Krupa O, Daniels C, Peyrache A, Greengard P. Initiation of Behavioral Response to Antidepressants by Cholecystokinin Neurons of the Dentate Gyrus. Neuron 2017; 95:564-576.e4. [DOI: 10.1016/j.neuron.2017.06.044] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2016] [Revised: 03/09/2017] [Accepted: 06/27/2017] [Indexed: 12/19/2022]
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15
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Tung CK, Krupa O, Apyadin E, Liou J, Diaz-Santana A, Kim BJ, Wu M. A contact line pinning based microfluidic platform for modelling physiological flows. Lab Chip 2013; 13:3876-85. [PMID: 23917952 PMCID: PMC4505921 DOI: 10.1039/c3lc50489a] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
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.
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Affiliation(s)
- Chih-kuan Tung
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA.; Tel: 1-607-255-9410
| | - Oleh Krupa
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Elif Apyadin
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA.; Tel: 1-607-255-9410
| | - Jiun Liou
- Department of Biomedical Engineering, Cornell University, Ithaca, NY, USA
| | - Anthony Diaz-Santana
- School of Chemical and Biomolecular Engineering, Cornell University, Ithaca, NY, USA
| | - Beum Jun Kim
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA.; Tel: 1-607-255-9410
| | - Mingming Wu
- Department of Biological and Environmental Engineering, Cornell University, Ithaca, NY, USA.; Tel: 1-607-255-9410
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16
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Guzik K, Bzowska M, Smagur J, Krupa O, Sieprawska M, Travis J, Potempa J. A new insight into phagocytosis of apoptotic cells: proteolytic enzymes divert the recognition and clearance of polymorphonuclear leukocytes by macrophages. Cell Death Differ 2006; 14:171-82. [PMID: 16628232 DOI: 10.1038/sj.cdd.4401927] [Citation(s) in RCA: 43] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022] Open
Abstract
The recognition of phosphatidylserine (PS) on the surface of any apoptotic cell is considered to be a key event for its clearance. We challenge this concept by showing that pretreatment of neutrophils with either host or bacterial protease affects their uptake by human monocyte-derived macrophages without having an effect on cell-surface PS presentation. Specifically, whereas preincubation of apoptotic neutrophils with cathepsin G or thrombin significantly inhibited their uptake, gingipains R or clostripain enhanced phagocytosis by macrophages. Moreover, bacterial proteinases sensitized healthy neutrophils for uptake by macrophages, whereas endogenous proteinases were unable to elicit this effect. This stimulation was apparently owing to the combined effect of proteolytic cleavage of an antiphagocytic signal (CD31) and the generation of a novel 'eat-me' signal on the neutrophil surface. These results argue that neutrophil recognition and phagocytosis by macrophages is mediated by a protein ligand whose proteolytic modification could affect the local inflammatory process.
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Affiliation(s)
- K Guzik
- Department of Immunology, Faculty of Biotechnology, Jagiellonian University, Krakow, Poland
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