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Huuki-Myers LA, Montgomery KD, Kwon SH, Cinquemani S, Eagles NJ, Gonzalez-Padilla D, Maden SK, Kleinman JE, Hyde TM, Hicks SC, Maynard KR, Collado-Torres L. Benchmark of cellular deconvolution methods using a multi-assay reference dataset from postmortem human prefrontal cortex. bioRxiv 2024:2024.02.09.579665. [PMID: 38405805 PMCID: PMC10888823 DOI: 10.1101/2024.02.09.579665] [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: 02/27/2024]
Abstract
Background Cellular deconvolution of bulk RNA-sequencing (RNA-seq) data using single cell or nuclei RNA-seq (sc/snRNA-seq) reference data is an important strategy for estimating cell type composition in heterogeneous tissues, such as human brain. Computational methods for deconvolution have been developed and benchmarked against simulated data, pseudobulked sc/snRNA-seq data, or immunohistochemistry reference data. A major limitation in developing improved deconvolution algorithms has been the lack of integrated datasets with orthogonal measurements of gene expression and estimates of cell type proportions on the same tissue sample. Deconvolution algorithm performance has not yet been evaluated across different RNA extraction methods (cytosolic, nuclear, or whole cell RNA), different library preparation types (mRNA enrichment vs. ribosomal RNA depletion), or with matched single cell reference datasets. Results A rich multi-assay dataset was generated in postmortem human dorsolateral prefrontal cortex (DLPFC) from 22 tissue blocks. Assays included spatially-resolved transcriptomics, snRNA-seq, bulk RNA-seq (across six library/extraction RNA-seq combinations), and RNAScope/Immunofluorescence (RNAScope/IF) for six broad cell types. The Mean Ratio method, implemented in the DeconvoBuddies R package, was developed for selecting cell type marker genes. Six computational deconvolution algorithms were evaluated in DLPFC and predicted cell type proportions were compared to orthogonal RNAScope/IF measurements. Conclusions Bisque and hspe were the most accurate methods, were robust to differences in RNA library types and extractions. This multi-assay dataset showed that cell size differences, marker genes differentially quantified across RNA libraries, and cell composition variability in reference snRNA-seq impact the accuracy of current deconvolution methods.
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Affiliation(s)
- Louise A. Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Kelsey D. Montgomery
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Sophia Cinquemani
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | | | - Sean K. Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, 21205, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, 21218, USA
| | - Kristen R. Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
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2
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Benjamin KJM, Chen Q, Eagles NJ, Huuki-Myers LA, Collado-Torres L, Stolz JM, Pertea G, Shin JH, Paquola ACM, Hyde TM, Kleinman JE, Jaffe AE, Han S, Weinberger DR. Genetic and environmental contributions to ancestry differences in gene expression in the human brain. bioRxiv 2023:2023.03.28.534458. [PMID: 37034760 PMCID: PMC10081196 DOI: 10.1101/2023.03.28.534458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Ancestral differences in genomic variation are determining factors in gene regulation; however, most gene expression studies have been limited to European ancestry samples or adjusted for ancestry to identify ancestry-independent associations. We instead examined the impact of genetic ancestry on gene expression and DNA methylation (DNAm) in admixed African/Black American neurotypical individuals to untangle effects of genetic and environmental factors. Ancestry-associated differentially expressed genes (DEGs), transcripts, and gene networks, while notably not implicating neurons, are enriched for genes related to immune response and vascular tissue and explain up to 26% of heritability for ischemic stroke, 27% of heritability for Parkinson's disease, and 30% of heritability for Alzhemier's disease. Ancestry-associated DEGs also show general enrichment for heritability of diverse immune-related traits but depletion for psychiatric-related traits. The cell-type enrichments and direction of effects vary by brain region. These DEGs are less evolutionarily constrained and are largely explained by genetic variations; roughly 15% are predicted by DNAm variation implicating environmental exposures. We also compared Black and White Americans, confirming most of these ancestry-associated DEGs. Our results highlight how environment and genetic background affect genetic ancestry differences in gene expression in the human brain and affect risk for brain illness.
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Affiliation(s)
- Kynon J M Benjamin
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Qiang Chen
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | | | | | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Geo Pertea
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Joo Heon Shin
- Lieber Institute for Brain Development, Baltimore, MD, USA
| | - Apuã C M Paquola
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Andrew E Jaffe
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Neumora Therapeutics, Watertown, MA, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD, USA
- Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
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3
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Eagles NJ, Wilton R, Jaffe AE, Collado-Torres L. BiocMAP: a Bioconductor-friendly, GPU-accelerated pipeline for bisulfite-sequencing data. BMC Bioinformatics 2023; 24:340. [PMID: 37704947 PMCID: PMC10498615 DOI: 10.1186/s12859-023-05461-3] [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] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 08/31/2023] [Indexed: 09/15/2023] Open
Abstract
BACKGROUND Bisulfite sequencing is a powerful tool for profiling genomic methylation, an epigenetic modification critical in the understanding of cancer, psychiatric disorders, and many other conditions. Raw data generated by whole genome bisulfite sequencing (WGBS) requires several computational steps before it is ready for statistical analysis, and particular care is required to process data in a timely and memory-efficient manner. Alignment to a reference genome is one of the most computationally demanding steps in a WGBS workflow, taking several hours or even days with commonly used WGBS-specific alignment software. This naturally motivates the creation of computational workflows that can utilize GPU-based alignment software to greatly speed up the bottleneck step. In addition, WGBS produces raw data that is large and often unwieldy; a lack of memory-efficient representation of data by existing pipelines renders WGBS impractical or impossible to many researchers. RESULTS We present BiocMAP, a Bioconductor-friendly methylation analysis pipeline consisting of two modules, to address the above concerns. The first module performs computationally-intensive read alignment using Arioc, a GPU-accelerated short-read aligner. Since GPUs are not always available on the same computing environments where traditional CPU-based analyses are convenient, the second module may be run in a GPU-free environment. This module extracts and merges DNA methylation proportions-the fractions of methylated cytosines across all cells in a sample at a given genomic site. Bioconductor-based output objects in R utilize an on-disk data representation to drastically reduce required main memory and make WGBS projects computationally feasible to more researchers. CONCLUSIONS BiocMAP is implemented using Nextflow and available at http://research.libd.org/BiocMAP/ . To enable reproducible analysis across a variety of typical computing environments, BiocMAP can be containerized with Docker or Singularity, and executed locally or with the SLURM or SGE scheduling engines. By providing Bioconductor objects, BiocMAP's output can be integrated with powerful analytical open source software for analyzing methylation data.
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Affiliation(s)
- Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, USA
| | - Richard Wilton
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, 21218, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, 21205, USA.
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4
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Sriworarat C, Nguyen A, Eagles NJ, Collado-Torres L, Martinowich K, Maynard KR, Hicks SC. Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments. Biol Imaging 2023; 3:e15. [PMID: 38487694 PMCID: PMC10936372 DOI: 10.1017/s2633903x2300017x] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Revised: 06/21/2023] [Accepted: 07/05/2023] [Indexed: 03/17/2024]
Abstract
High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.
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Affiliation(s)
| | - Annie Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | - Keri Martinowich
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Kristen R. Maynard
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
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5
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Sriworarat C, Nguyen A, Eagles NJ, Collado-Torres L, Martinowich K, Maynard KR, Hicks SC. Performant web-based interactive visualization tool for spatially-resolved transcriptomics experiments. bioRxiv 2023:2023.01.28.525943. [PMID: 36747726 PMCID: PMC9900917 DOI: 10.1101/2023.01.28.525943] [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] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
High-resolution and multiplexed imaging techniques are giving us an increasingly detailed observation of a biological system. However, sharing, exploring, and customizing the visualization of large multidimensional images can be a challenge. Here, we introduce Samui, a performant and interactive image visualization tool that runs completely in the web browser. Samui is specifically designed for fast image visualization and annotation and enables users to browse through large images and their selected features within seconds of receiving a link. We demonstrate the broad utility of Samui with images generated with two platforms: Vizgen MERFISH and 10x Genomics Visium Spatial Gene Expression. Samui along with example datasets is available at https://samuibrowser.com.
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Affiliation(s)
| | - Annie Nguyen
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | | | - Keri Martinowich
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
- The Kavli Neuroscience Discovery Institute, Johns Hopkins University, Baltimore, MD, USA
| | - Kristen R. Maynard
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Stephanie C. Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA
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6
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Mandell KAP, Eagles NJ, Deep-Soboslay A, Tao R, Han S, Wilton R, Szalay AS, Hyde TM, Kleinman JE, Jaffe AE, Weinberger DR. Molecular phenotypes associated with antipsychotic drugs in the human caudate nucleus. Mol Psychiatry 2022; 27:2061-2067. [PMID: 35236959 PMCID: PMC9133054 DOI: 10.1038/s41380-022-01453-6] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2021] [Revised: 01/03/2022] [Accepted: 01/14/2022] [Indexed: 11/09/2022]
Abstract
Antipsychotic drugs are the current first-line of treatment for schizophrenia and other psychotic conditions. However, their molecular effects on the human brain are poorly studied, due to difficulty of tissue access and confounders associated with disease status. Here we examine differences in gene expression and DNA methylation associated with positive antipsychotic drug toxicology status in the human caudate nucleus. We find no genome-wide significant differences in DNA methylation, but abundant differences in gene expression. These gene expression differences are overall quite similar to gene expression differences between schizophrenia cases and controls. Interestingly, gene expression differences based on antipsychotic toxicology are different between brain regions, potentially due to affected cell type differences. We finally assess similarities with effects in a mouse model, which finds some overlapping effects but many differences as well. As a first look at the molecular effects of antipsychotics in the human brain, the lack of epigenetic effects is unexpected, possibly because long term treatment effects may be relatively stable for extended periods.
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Affiliation(s)
- Kira A. Perzel Mandell
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM), Baltimore, MD 21205, USA
| | - Nicholas J. Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Amy Deep-Soboslay
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Ran Tao
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA
| | - Shizhong Han
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Psychiatry and Behavioral Sciences, JHSOM, Baltimore, MD, USA
| | - Richard Wilton
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA
| | - Alexander S. Szalay
- Department of Physics and Astronomy, Johns Hopkins University, Baltimore, MD, USA,Department of Computer Science, JHSOM, Baltimore, MD, USA
| | - Thomas M. Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Psychiatry and Behavioral Sciences, JHSOM, Baltimore, MD, USA,Department of Neurology, JHSOM, Baltimore, MD, USA
| | - Joel E. Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Psychiatry and Behavioral Sciences, JHSOM, Baltimore, MD, USA
| | - Andrew E. Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM), Baltimore, MD 21205, USA,Department of Psychiatry and Behavioral Sciences, JHSOM, Baltimore, MD, USA,Department of Neuroscience, JHSOM, Baltimore, MD, USA.,Department of Mental Health, Johns Hopkins Bloomberg School of Public Health (JHBSPH), MD 21205, USA,Department of Biostatistics, JHBSPH, Baltimore, MD, USA
| | - Daniel R. Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD 21205, USA,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM), Baltimore, MD 21205, USA,Department of Psychiatry and Behavioral Sciences, JHSOM, Baltimore, MD, USA,Department of Neurology, JHSOM, Baltimore, MD, USA,Department of Neuroscience, JHSOM, Baltimore, MD, USA.,
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7
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Page SC, Sripathy SR, Farinelli F, Ye Z, Wang Y, Hiler DJ, Pattie EA, Nguyen CV, Tippani M, Moses RL, Chen HY, Tran MN, Eagles NJ, Stolz JM, Catallini JL, Soudry OR, Dickinson D, Berman KF, Apud JA, Weinberger DR, Martinowich K, Jaffe AE, Straub RE, Maher BJ. Electrophysiological measures from human iPSC-derived neurons are associated with schizophrenia clinical status and predict individual cognitive performance. Proc Natl Acad Sci U S A 2022; 119:e2109395119. [PMID: 35017298 PMCID: PMC8784142 DOI: 10.1073/pnas.2109395119] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Accepted: 11/10/2021] [Indexed: 12/11/2022] Open
Abstract
Neurons derived from human induced pluripotent stem cells (hiPSCs) have been used to model basic cellular aspects of neuropsychiatric disorders, but the relationship between the emergent phenotypes and the clinical characteristics of donor individuals has been unclear. We analyzed RNA expression and indices of cellular function in hiPSC-derived neural progenitors and cortical neurons generated from 13 individuals with high polygenic risk scores (PRSs) for schizophrenia (SCZ) and a clinical diagnosis of SCZ, along with 15 neurotypical individuals with low PRS. We identified electrophysiological measures in the patient-derived neurons that implicated altered Na+ channel function, action potential interspike interval, and gamma-aminobutyric acid-ergic neurotransmission. Importantly, electrophysiological measures predicted cardinal clinical and cognitive features found in these SCZ patients. The identification of basic neuronal physiological properties related to core clinical characteristics of illness is a potentially critical step in generating leads for novel therapeutics.
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Affiliation(s)
| | | | | | - Zengyou Ye
- Lieber Institute for Brain Development, Baltimore, MD 21205
| | - Yanhong Wang
- Lieber Institute for Brain Development, Baltimore, MD 21205
| | - Daniel J Hiler
- Lieber Institute for Brain Development, Baltimore, MD 21205
| | | | | | | | | | - Huei-Ying Chen
- Lieber Institute for Brain Development, Baltimore, MD 21205
| | - Matthew Nguyen Tran
- Lieber Institute for Brain Development, Baltimore, MD 21205
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205
| | | | - Joshua M Stolz
- Lieber Institute for Brain Development, Baltimore, MD 21205
| | - Joseph L Catallini
- Lieber Institute for Brain Development, Baltimore, MD 21205
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | | | - Dwight Dickinson
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health Intramural Research Program, NIH, Bethesda, MD 20892
| | - Karen F Berman
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health Intramural Research Program, NIH, Bethesda, MD 20892
| | - Jose A Apud
- Clinical and Translational Neuroscience Branch, National Institute of Mental Health Intramural Research Program, NIH, Bethesda, MD 20892
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Baltimore, MD 21205
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins School of Medicine, Baltimore, MD 21205
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Neurology, Johns Hopkins School of Medicine, Baltimore, MD 21205
| | - Keri Martinowich
- Lieber Institute for Brain Development, Baltimore, MD 21205
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Baltimore, MD 21205
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205
| | | | - Brady J Maher
- Lieber Institute for Brain Development, Baltimore, MD 21205;
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD 21205
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD 21205
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8
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Eagles NJ, Burke EE, Leonard J, Barry BK, Stolz JM, Huuki L, Phan BN, Serrato VL, Gutiérrez-Millán E, Aguilar-Ordoñez I, Jaffe AE, Collado-Torres L. Correction to: SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor‑powered RNA‑seq analyses. BMC Bioinformatics 2021; 22:381. [PMID: 34294043 PMCID: PMC8299680 DOI: 10.1186/s12859-021-04283-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Affiliation(s)
- Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Emily E Burke
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Jacob Leonard
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico.,QuestBridge Scholar, Palo Alto, CA, 94303, USA
| | - Brianna K Barry
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Louise Huuki
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - BaDoi N Phan
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.,Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.,Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Violeta Larios Serrato
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico.,Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas,, Mexico City, CDMX, 11340, Mexico
| | | | - Israel Aguilar-Ordoñez
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico.,Department of Supercomputing, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, CDMX, 14610, Mexico
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.,Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.,Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA.,Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA.,Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.,Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA.,Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA. .,Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA.
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9
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Eagles NJ, Burke EE, Leonard J, Barry BK, Stolz JM, Huuki L, Phan BN, Serrato VL, Gutiérrez-Millán E, Aguilar-Ordoñez I, Jaffe AE, Collado-Torres L. SPEAQeasy: a scalable pipeline for expression analysis and quantification for R/bioconductor-powered RNA-seq analyses. BMC Bioinformatics 2021; 22:224. [PMID: 33932985 PMCID: PMC8088074 DOI: 10.1186/s12859-021-04142-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [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: 01/29/2021] [Accepted: 04/21/2021] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND RNA sequencing (RNA-seq) is a common and widespread biological assay, and an increasing amount of data is generated with it. In practice, there are a large number of individual steps a researcher must perform before raw RNA-seq reads yield directly valuable information, such as differential gene expression data. Existing software tools are typically specialized, only performing one step-such as alignment of reads to a reference genome-of a larger workflow. The demand for a more comprehensive and reproducible workflow has led to the production of a number of publicly available RNA-seq pipelines. However, we have found that most require computational expertise to set up or share among several users, are not actively maintained, or lack features we have found to be important in our own analyses. RESULTS In response to these concerns, we have developed a Scalable Pipeline for Expression Analysis and Quantification (SPEAQeasy), which is easy to install and share, and provides a bridge towards R/Bioconductor downstream analysis solutions. SPEAQeasy is portable across computational frameworks (SGE, SLURM, local, docker integration) and different configuration files are provided ( http://research.libd.org/SPEAQeasy/ ). CONCLUSIONS SPEAQeasy is user-friendly and lowers the computational-domain entry barrier for biologists and clinicians to RNA-seq data processing as the main input file is a table with sample names and their corresponding FASTQ files. The goal is to provide a flexible pipeline that is immediately usable by researchers, regardless of their technical background or computing environment.
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Affiliation(s)
- Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Emily E Burke
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Jacob Leonard
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico
- QuestBridge Scholar, Palo Alto, CA, 94303, USA
| | - Brianna K Barry
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
| | - Joshua M Stolz
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - Louise Huuki
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
| | - BaDoi N Phan
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA, 15213, USA
- Medical Scientist Training Program, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15213, USA
| | - Violeta Larios Serrato
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico
- Instituto Politécnico Nacional, Escuela Nacional de Ciencias Biológicas, Mexico City, CDMX, 11340, Mexico
| | | | - Israel Aguilar-Ordoñez
- Winter Genomics, Salaverry 874 int 100, Lindavista, CDMX, 07300, Mexico
- Department of Supercomputing, Instituto Nacional de Medicina Genómica (INMEGEN), Mexico City, CDMX, 14610, Mexico
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA
- Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
- Department of Genetic Medicine, McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, 21205, USA
- Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, 21205, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, 21205, USA.
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Perzel Mandell KA, Price AJ, Wilton R, Collado-Torres L, Tao R, Eagles NJ, Szalay AS, Hyde TM, Weinberger DR, Kleinman JE, Jaffe AE. Characterizing the dynamic and functional DNA methylation landscape in the developing human cortex. Epigenetics 2020; 16:1-13. [PMID: 32602773 DOI: 10.1080/15592294.2020.1786304] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022] Open
Abstract
DNA methylation (DNAm) is a key epigenetic regulator of gene expression across development. The developing prenatal brain is a highly dynamic tissue, but our understanding of key drivers of epigenetic variability across development is limited. We, therefore, assessed genomic methylation at over 39 million sites in the prenatal cortex using whole-genome bisulfite sequencing and found loci and regions in which methylation levels are dynamic across development. We saw that DNAm at these loci was associated with nearby gene expression and enriched for enhancer chromatin states in prenatal brain tissue. Additionally, these loci were enriched for genes associated with neuropsychiatric disorders and genes involved with neurogenesis. We also found autosomal differences in DNAm between the sexes during prenatal development, though these have less clear functional consequences. We lastly confirmed that the dynamic methylation at this critical period is specifically CpG methylation, with generally low levels of CpH methylation. Our findings provide detailed insight into prenatal brain development as well as clues to the pathogenesis of psychiatric traits seen later in life.
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Affiliation(s)
- Kira A Perzel Mandell
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM) , Baltimore, MD, USA
| | - Amanda J Price
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM) , Baltimore, MD, USA
| | - Richard Wilton
- Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University , Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Center for Computational Biology, Johns Hopkins University , Baltimore, MD, USA
| | - Ran Tao
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA
| | - Nicholas J Eagles
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA
| | - Alexander S Szalay
- Department of Physics and Astronomy, Krieger School of Arts and Sciences, Johns Hopkins University , Baltimore, MD, USA.,Department of Computer Science, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD, USA
| | - Thomas M Hyde
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Neurology, JHSOM , Baltimore, MD, USA.,Department of Psychiatry and Behavioral Sciences, JHSOM , Baltimore, MD, USA
| | - Daniel R Weinberger
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM) , Baltimore, MD, USA.,Department of Neurology, JHSOM , Baltimore, MD, USA.,Department of Psychiatry and Behavioral Sciences, JHSOM , Baltimore, MD, USA.,Department of Neuroscience, JHSOM , Baltimore, MD, USA
| | - Joel E Kleinman
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Psychiatry and Behavioral Sciences, JHSOM , Baltimore, MD, USA
| | - Andrew E Jaffe
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus , Baltimore, MD, USA.,Department of Genetic Medicine, Johns Hopkins University School of Medicine (JHSOM) , Baltimore, MD, USA.,Center for Computational Biology, Johns Hopkins University , Baltimore, MD, USA.,Department of Psychiatry and Behavioral Sciences, JHSOM , Baltimore, MD, USA.,Department of Neuroscience, JHSOM , Baltimore, MD, USA.,Department of Mental Health, Johns Hopkins Bloomberg School of Public Health (JHBSPH) , Baltimore, MD, USA.,Department of Biostatistics, JHBSPH , Baltimore, MD, USA
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