1
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Metzdorf K, Jacobsen H, Kim Y, Teixeira Alves LG, Kulkarni U, Brdovčak MC, Materljan J, Eschke K, Chaudhry MZ, Hoffmann M, Bertoglio F, Ruschig M, Hust M, Šustić M, Krmpotić A, Jonjić S, Widera M, Ciesek S, Pöhlmann S, Landthaler M, Čičin-Šain L. A single-dose MCMV-based vaccine elicits long-lasting immune protection in mice against distinct SARS-CoV-2 variants. Front Immunol 2024; 15:1383086. [PMID: 39119342 PMCID: PMC11306140 DOI: 10.3389/fimmu.2024.1383086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 06/11/2024] [Indexed: 08/10/2024] Open
Abstract
Current vaccines against COVID-19 elicit immune responses that are overall strong but wane rapidly. As a consequence, the necessary booster shots have contributed to vaccine fatigue. Hence, vaccines that would provide lasting protection against COVID-19 are needed, but are still unavailable. Cytomegaloviruses (CMVs) elicit lasting and uniquely strong immune responses. Used as vaccine vectors, they may be attractive tools that obviate the need for boosters. Therefore, we tested the murine CMV (MCMV) as a vaccine vector against COVID-19 in relevant preclinical models of immunization and challenge. We have previously developed a recombinant MCMV vaccine vector expressing the spike protein of the ancestral SARS-CoV-2 (MCMVS). In this study, we show that the MCMVS elicits a robust and lasting protection in young and aged mice. Notably, spike-specific humoral and cellular immunity was not only maintained but also even increased over a period of at least 6 months. During that time, antibody avidity continuously increased and expanded in breadth, resulting in neutralization of genetically distant variants, like Omicron BA.1. A single dose of MCMVS conferred rapid virus clearance upon challenge. Moreover, MCMVS vaccination controlled two variants of concern (VOCs), the Beta (B.1.135) and the Omicron (BA.1) variants. Thus, CMV vectors provide unique advantages over other vaccine technologies, eliciting broadly reactive and long-lasting immune responses against COVID-19.
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MESH Headings
- Animals
- SARS-CoV-2/immunology
- SARS-CoV-2/genetics
- Mice
- COVID-19 Vaccines/immunology
- COVID-19/prevention & control
- COVID-19/immunology
- Spike Glycoprotein, Coronavirus/immunology
- Spike Glycoprotein, Coronavirus/genetics
- Antibodies, Viral/immunology
- Antibodies, Viral/blood
- Muromegalovirus/immunology
- Muromegalovirus/genetics
- Female
- Antibodies, Neutralizing/immunology
- Antibodies, Neutralizing/blood
- Mice, Inbred BALB C
- Humans
- Genetic Vectors
- Immunity, Cellular
- Immunity, Humoral
- Disease Models, Animal
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Affiliation(s)
- Kristin Metzdorf
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualized Infection Medicine, a Joint Venture of the Helmholtz Centre for Infection Medicine and the Hannover Medical School, Hannover, Germany
| | - Henning Jacobsen
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualized Infection Medicine, a Joint Venture of the Helmholtz Centre for Infection Medicine and the Hannover Medical School, Hannover, Germany
| | - Yeonsu Kim
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualized Infection Medicine, a Joint Venture of the Helmholtz Centre for Infection Medicine and the Hannover Medical School, Hannover, Germany
| | - Luiz Gustavo Teixeira Alves
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
| | - Upasana Kulkarni
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualized Infection Medicine, a Joint Venture of the Helmholtz Centre for Infection Medicine and the Hannover Medical School, Hannover, Germany
| | | | - Jelena Materljan
- Center for Proteomics, University of Rijeka, Faculty of Medicine, Rijeka, Croatia
- Department of Histology and Embryology, University of Rijeka, Faculty of Medicine, Rijeka, Croatia
| | - Kathrin Eschke
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - M. Zeeshan Chaudhry
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
| | - Markus Hoffmann
- Infection Biology Unit, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August-University Göttingen, Göttingen, Germany
| | - Federico Bertoglio
- Department of Medical Biotechnology, Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Maximilian Ruschig
- Department of Medical Biotechnology, Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Michael Hust
- Department of Medical Biotechnology, Institute for Biochemistry, Biotechnology and Bioinformatics, Technische Universität Braunschweig, Braunschweig, Germany
| | - Marko Šustić
- Center for Proteomics, University of Rijeka, Faculty of Medicine, Rijeka, Croatia
| | - Astrid Krmpotić
- Department of Histology and Embryology, University of Rijeka, Faculty of Medicine, Rijeka, Croatia
| | - Stipan Jonjić
- Center for Proteomics, University of Rijeka, Faculty of Medicine, Rijeka, Croatia
| | - Marek Widera
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt am Main, Germany
| | - Sandra Ciesek
- Institute for Medical Virology, University Hospital Frankfurt, Goethe University Frankfurt, Frankfurt am Main, Germany
- Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt am Main, Germany
- German Centre for Infection Research (DZIF), External Partner Site Frankfurt, Frankfurt, Germany
| | - Stefan Pöhlmann
- Infection Biology Unit, German Primate Center – Leibniz Institute for Primate Research, Göttingen, Germany
- Faculty of Biology and Psychology, Georg-August-University Göttingen, Göttingen, Germany
| | - Markus Landthaler
- Berlin Institute for Medical Systems Biology (BIMSB), Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC), Berlin, Germany
- Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Luka Čičin-Šain
- Department of Viral Immunology, Helmholtz Centre for Infection Research, Braunschweig, Germany
- Centre for Individualized Infection Medicine, a Joint Venture of the Helmholtz Centre for Infection Medicine and the Hannover Medical School, Hannover, Germany
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2
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Kissel LT, Pochareddy S, An JY, Sestan N, Sanders SJ, Wang X, Werling DM. Sex-Differential Gene Expression in Developing Human Cortex and Its Intersection With Autism Risk Pathways. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100321. [PMID: 38957312 PMCID: PMC11217612 DOI: 10.1016/j.bpsgos.2024.100321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2023] [Revised: 04/09/2024] [Accepted: 04/12/2024] [Indexed: 07/04/2024] Open
Abstract
Background Sex-differential biology may contribute to the consistently male-biased prevalence of autism spectrum disorder (ASD). Gene expression differences between males and females in the brain can indicate possible molecular and cellular mechanisms involved, although transcriptomic sex differences during human prenatal cortical development have been incompletely characterized, primarily due to small sample sizes. Methods We performed a meta-analysis of sex-differential expression and co-expression network analysis in 2 independent bulk RNA sequencing datasets generated from cortex of 273 prenatal donors without known neuropsychiatric disorders. To assess the intersection between neurotypical sex differences and neuropsychiatric disorder biology, we tested for enrichment of ASD-associated risk genes and expression changes, neuropsychiatric disorder risk genes, and cell type markers within identified sex-differentially expressed genes (sex-DEGs) and sex-differential co-expression modules. Results We identified 101 significant sex-DEGs, including Y-chromosome genes, genes impacted by X-chromosome inactivation, and autosomal genes. Known ASD risk genes, implicated by either common or rare variants, did not preferentially overlap with sex-DEGs. We identified 1 male-specific co-expression module enriched for immune signaling that is unique to 1 input dataset. Conclusions Sex-differential gene expression is limited in prenatal human cortex tissue, although meta-analysis of large datasets allows for the identification of sex-DEGs, including autosomal genes that encode proteins involved in neural development. Lack of sex-DEG overlap with ASD risk genes in the prenatal cortex suggests that sex-differential modulation of ASD symptoms may occur in other brain regions, at other developmental stages, or in specific cell types, or may involve mechanisms that act downstream from mutation-carrying genes.
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Affiliation(s)
- Lee T. Kissel
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, Wisconsin
| | - Sirisha Pochareddy
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Joon-Yong An
- Department of Integrated Biomedical and Life Science, Korea University, Seoul, Republic of Korea
- Transdisciplinary Major in Learning Health Systems, Department of Healthcare Sciences, Graduate School, Korea University, Seoul, Republic of Korea
- BK21FOUR R&E Center for Learning Health Systems, Korea University, Seoul, Republic of Korea
| | - Nenad Sestan
- Department of Neuroscience and Kavli Institute for Neuroscience, Yale School of Medicine, New Haven, Connecticut
| | - Stephan J. Sanders
- Institute of Developmental and Regenerative Medicine, Department of Paediatrics, University of Oxford, Oxford, United Kingdom
- Department of Psychiatry and Behavioral Sciences, UCSF Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California
| | - Xuran Wang
- Seaver Autism Center for Research and Treatment, New York, New York
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Donna M. Werling
- Laboratory of Genetics, University of Wisconsin-Madison, Madison, Wisconsin
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3
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Sotelo-Hitschfeld P, Bernal L, Nazeri M, Renthal W, Brauchi S, Roza C, Zimmermann K. Comparative Gene Signature of Nociceptors Innervating Mouse Molar Teeth, Cranial Meninges, and Cornea. Anesth Analg 2024; 139:226-234. [PMID: 38236765 DOI: 10.1213/ane.0000000000006816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/19/2024]
Abstract
BACKGROUND The trigeminal ganglion (TG) collects afferent sensory information from various tissues. Recent large-scale RNA sequencing of neurons of the TG and dorsal root ganglion has revealed a variety of functionally distinct neuronal subpopulations, but organ-specific information is lacking. METHODS To link transcriptomic and tissue-specific information, we labeled small-diameter neurons of 3 specific subpopulations of the TG by local application of lipophilic carbocyanine dyes to their innervation site in the dental pulp, cornea, and meninges (dura mater). We then collected mRNA-sequencing data from fluorescent neurons. Differentially expressed genes (DEGs) were analyzed and subjected to downstream gene set enrichment analysis (GSEA), and ion channel profiling was performed. RESULTS A total of 10,903 genes were mapped to the mouse genome (>500 reads). DEG analysis revealed 18 and 81 genes with differential expression (log 2 fold change > 2, Padj < .05) in primary afferent neurons innervating the dental pulp (dental primary afferent neurons [DPAN]) compared to those innervating the meninges (meningeal primary afferent neurons [MPAN]) and the cornea (corneal primary afferent neurons [CPAN]). We found 250 and 292 genes differentially expressed in MPAN as compared to DPAN and to CPAN, and 21 and 12 in CPAN as compared to DPAN and MPAN. Scn2b had the highest log 2 fold change when comparing DPAN versus MPAN and Mmp12 was the most prominent DEG when comparing DPAN versus CPAN and, CPAN versus MPAN. GSEA revealed genes of the immune and mitochondrial oxidative phosphorylation system for the DPAN versus MPAN comparison, cilium- and ribosome-related genes for the CPAN versus DPAN comparison, and respirasome, immune cell- and ribosome-related gene sets for the CPAN versus MPAN comparison. DEG analysis for ion channels revealed no significant differences between the neurons set except for the sodium voltage-gated channel beta subunit 2, Scn2b . However, in each tissue a few ion channels turned up with robust number of reads. In DPAN, these were Cacna1b , Trpv2 , Cnga4 , Hcn1 , and Hcn3 , in CPAN Trpa1 , Trpv1 , Cacna1a , and Kcnk13 and in MPAN Trpv2 and Scn11a . CONCLUSIONS Our study uncovers previously unknown differences in gene expression between sensory neuron subpopulations from the dental pulp, cornea, and dura mater and provides the basis for functional studies, including the investigation of ion channel function and their suitability as targets for tissue-specific analgesia.
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Affiliation(s)
- Pamela Sotelo-Hitschfeld
- From the Department of Anesthesiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Institute of Physiology and Millennium Nucleus of Ion Channel-Associated Diseases, Universidad Austral de Chile, Valdivia, Chile
| | - Laura Bernal
- From the Department of Anesthesiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Departamento de Biología de Sistemas, Facultad de Medicina, Universidad de Alcalá, Madrid, Spain
| | - Masoud Nazeri
- From the Department of Anesthesiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - William Renthal
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sebastian Brauchi
- Institute of Physiology and Millennium Nucleus of Ion Channel-Associated Diseases, Universidad Austral de Chile, Valdivia, Chile
| | - Carolina Roza
- Departamento de Biología de Sistemas, Facultad de Medicina, Universidad de Alcalá, Madrid, Spain
| | - Katharina Zimmermann
- From the Department of Anesthesiology, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
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4
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Rodriguez de Los Santos M, Kopell BH, Buxbaum Grice A, Ganesh G, Yang A, Amini P, Liharska LE, Vornholt E, Fullard JF, Dong P, Park E, Zipkowitz S, Kaji DA, Thompson RC, Liu D, Park YJ, Cheng E, Ziafat K, Moya E, Fennessy B, Wilkins L, Silk H, Linares LM, Sullivan B, Cohen V, Kota P, Feng C, Johnson JS, Rieder MK, Scarpa J, Nadkarni GN, Wang M, Zhang B, Sklar P, Beckmann ND, Schadt EE, Roussos P, Charney AW, Breen MS. Divergent landscapes of A-to-I editing in postmortem and living human brain. Nat Commun 2024; 15:5366. [PMID: 38926387 PMCID: PMC11208617 DOI: 10.1038/s41467-024-49268-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries offer more nuanced and accurate insights into the regulatory mechanisms of RNA editing in the human brain.
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Affiliation(s)
| | - Brian H Kopell
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Gauri Ganesh
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andy Yang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pardis Amini
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lora E Liharska
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric Vornholt
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F Fullard
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pengfei Dong
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric Park
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah Zipkowitz
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Deepak A Kaji
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ryan C Thompson
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Donjing Liu
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - You Jeong Park
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Esther Cheng
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kimia Ziafat
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Emily Moya
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lillian Wilkins
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hannah Silk
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lisa M Linares
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brendan Sullivan
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vanessa Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Prashant Kota
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Claudia Feng
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | | | - Joseph Scarpa
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Minghui Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bin Zhang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pamela Sklar
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Noam D Beckmann
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric E Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Michael S Breen
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
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5
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. SCIENCE ADVANCES 2024; 10:eadh2588. [PMID: 38781336 PMCID: PMC11114236 DOI: 10.1126/sciadv.adh2588] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 01/05/2024] [Indexed: 05/25/2024]
Abstract
Sample-wise deconvolution methods estimate cell-type proportions and gene expressions in bulk tissue samples, yet their performance and biological applications remain unexplored, particularly in human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk tissue RNA sequencing (RNA-seq), single-cell/nuclei (sc/sn) RNA-seq, and immunohistochemistry. A total of 1,130,767 nuclei per cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expressions. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk tissue or single-cell eQTLs did alone. Differential gene expressions associated with Alzheimer's disease, schizophrenia, and brain development were also examined using the deconvoluted data. Our findings, which were replicated in bulk tissue and single-cell data, provided insights into the biological applications of deconvoluted data in multiple brain disorders.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Ming Zhang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Xuan Wang
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M. Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F. Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E. Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Departments of Psychiatry and Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H. Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | | | - Chao Chen
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- MOE Key Laboratory of Rare Pediatric Diseases & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, and Department of Psychiatry, The Second Xiangya Hospital, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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6
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Nguyen H, Nguyen H, Tran D, Draghici S, Nguyen T. Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges. Nucleic Acids Res 2024; 52:4761-4783. [PMID: 38619038 PMCID: PMC11109966 DOI: 10.1093/nar/gkae267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Revised: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 04/16/2024] Open
Abstract
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the measurement of the expression of all genes in each individual cell contained in a sample. Information at the single-cell level has been shown to be extremely useful in many areas. However, performing single-cell experiments is expensive. Although cellular deconvolution cannot provide the same comprehensive information as single-cell experiments, it can extract cell-type information from bulk RNA data, and therefore it allows researchers to conduct studies at cell-type resolution from existing bulk datasets. For these reasons, a great effort has been made to develop such methods for cellular deconvolution. The large number of methods available, the requirement of coding skills, inadequate documentation, and lack of performance assessment all make it extremely difficult for life scientists to choose a suitable method for their experiment. This paper aims to fill this gap by providing a comprehensive review of 53 deconvolution methods regarding their methodology, applications, performance, and outstanding challenges. More importantly, the article presents a benchmarking of all these 53 methods using 283 cell types from 30 tissues of 63 individuals. We also provide an R package named DeconBenchmark that allows readers to execute and benchmark the reviewed methods (https://github.com/tinnlab/DeconBenchmark).
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Affiliation(s)
- Hung Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Ha Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
| | - Duc Tran
- Department of Medicine, Washington University School of Medicine, St. Louis, MO, USA
| | - Sorin Draghici
- Department of Computer Science, Wayne State University, Detroit, MI, USA
- Advaita Bioinformatics, Ann Arbor, MI, USA
| | - Tin Nguyen
- Department of Computer Science and Software Engineering, Auburn University, Auburn, AL, USA
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7
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de los Santos MR, Kopell BH, Grice AB, Ganesh G, Yang A, Amini P, Liharska LE, Vornholt E, Fullard JF, Dong P, Park E, Zipkowitz S, Kaji DA, Thompson RC, Liu D, Park YJ, Cheng E, Ziafat K, Moya E, Fennessy B, Wilkins L, Silk H, Linares LM, Sullivan B, Cohen V, Kota P, Feng C, Johnson JS, Rieder MK, Scarpa J, Nadkarni GN, Wang M, Zhang B, Sklar P, Beckmann ND, Schadt EE, Roussos P, Charney AW, Breen MS. Divergent landscapes of A-to-I editing in postmortem and living human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.05.06.24306763. [PMID: 38765961 PMCID: PMC11100843 DOI: 10.1101/2024.05.06.24306763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2024]
Abstract
Adenosine-to-inosine (A-to-I) editing is a prevalent post-transcriptional RNA modification within the brain. Yet, most research has relied on postmortem samples, assuming it is an accurate representation of RNA biology in the living brain. We challenge this assumption by comparing A-to-I editing between postmortem and living prefrontal cortical tissues. Major differences were found, with over 70,000 A-to-I sites showing higher editing levels in postmortem tissues. Increased A-to-I editing in postmortem tissues is linked to higher ADAR1 and ADARB1 expression, is more pronounced in non-neuronal cells, and indicative of postmortem activation of inflammation and hypoxia. Higher A-to-I editing in living tissues marks sites that are evolutionarily preserved, synaptic, developmentally timed, and disrupted in neurological conditions. Common genetic variants were also found to differentially affect A-to-I editing levels in living versus postmortem tissues. Collectively, these discoveries illuminate the nuanced functions and intricate regulatory mechanisms of RNA editing within the human brain.
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Affiliation(s)
| | - Brian H. Kopell
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Gauri Ganesh
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Andy Yang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pardis Amini
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lora E. Liharska
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric Vornholt
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - John F. Fullard
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pengfei Dong
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric Park
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Sarah Zipkowitz
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Deepak A. Kaji
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Ryan C. Thompson
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Donjing Liu
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - You Jeong Park
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Esther Cheng
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Kimia Ziafat
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Emily Moya
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brian Fennessy
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lillian Wilkins
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Hannah Silk
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Lisa M. Linares
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Brendan Sullivan
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Vanessa Cohen
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Prashant Kota
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Claudia Feng
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | | | - Joseph Scarpa
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Minghui Wang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Bin Zhang
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Pamela Sklar
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Noam D. Beckmann
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Eric E. Schadt
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | - Panos Roussos
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
| | | | - Michael S. Breen
- Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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8
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de Vries LE, Jongejan A, Monteiro Fortes J, Balesar R, Rozemuller AJM, Moerland PD, Huitinga I, Swaab DF, Verhaagen J. Gene-expression profiling of individuals resilient to Alzheimer's disease reveals higher expression of genes related to metallothionein and mitochondrial processes and no changes in the unfolded protein response. Acta Neuropathol Commun 2024; 12:68. [PMID: 38664739 PMCID: PMC11046840 DOI: 10.1186/s40478-024-01760-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 03/10/2024] [Indexed: 04/28/2024] Open
Abstract
Some individuals show a discrepancy between cognition and the amount of neuropathological changes characteristic for Alzheimer's disease (AD). This phenomenon has been referred to as 'resilience'. The molecular and cellular underpinnings of resilience remain poorly understood. To obtain an unbiased understanding of the molecular changes underlying resilience, we investigated global changes in gene expression in the superior frontal gyrus of a cohort of cognitively and pathologically well-defined AD patients, resilient individuals and age-matched controls (n = 11-12 per group). 897 genes were significantly altered between AD and control, 1121 between resilient and control and 6 between resilient and AD. Gene set enrichment analysis (GSEA) revealed that the expression of metallothionein (MT) and of genes related to mitochondrial processes was higher in the resilient donors. Weighted gene co-expression network analysis (WGCNA) identified gene modules related to the unfolded protein response, mitochondrial processes and synaptic signaling to be differentially associated with resilience or dementia. As changes in MT, mitochondria, heat shock proteins and the unfolded protein response (UPR) were the most pronounced changes in the GSEA and/or WGCNA, immunohistochemistry was used to further validate these processes. MT was significantly increased in astrocytes in resilient individuals. A higher proportion of the mitochondrial gene MT-CO1 was detected outside the cell body versus inside the cell body in the resilient compared to the control group and there were higher levels of heat shock protein 70 (HSP70) and X-box-binding protein 1 spliced (XBP1s), two proteins related to heat shock proteins and the UPR, in the AD donors. Finally, we show evidence for putative sex-specific alterations in resilience, including gene expression differences related to autophagy in females compared to males. Taken together, these results show possible mechanisms involving MTs, mitochondrial processes and the UPR by which individuals might maintain cognition despite the presence of AD pathology.
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Affiliation(s)
- Luuk E de Vries
- Department of Neuroregeneration, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands.
| | - Aldo Jongejan
- Amsterdam UMC Location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Jennifer Monteiro Fortes
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Rawien Balesar
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Annemieke J M Rozemuller
- Department of Pathology, Amsterdam Neuroscience, Amsterdam UMC - Location VUmc, Amsterdam, The Netherlands
| | - Perry D Moerland
- Amsterdam UMC Location University of Amsterdam, Epidemiology and Data Science, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands
- Amsterdam Public Health, Methodology, Amsterdam, The Netherlands
- Amsterdam Infection and Immunity, Inflammatory Diseases, Amsterdam, The Netherlands
| | - Inge Huitinga
- Department of Neuroimmunology, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
- Center for Neuroscience, Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands
| | - Dick F Swaab
- Department of Neuropsychiatric Disorders, Netherlands Institute for Neuroscience, Institute of the Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands
| | - Joost Verhaagen
- Department of Neuroregeneration, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, Meibergdreef 47, 1105 BA, Amsterdam, The Netherlands.
- Center for Neurogenomics and Cognitive Research, Neuroscience Campus Amsterdam, VU University, Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands.
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9
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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 : THE PREPRINT SERVER FOR BIOLOGY 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] [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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10
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Garmire LX, Li Y, Huang Q, Xu C, Teichmann SA, Kaminski N, Pellegrini M, Nguyen Q, Teschendorff AE. Challenges and perspectives in computational deconvolution of genomics data. Nat Methods 2024; 21:391-400. [PMID: 38374264 DOI: 10.1038/s41592-023-02166-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 12/26/2023] [Indexed: 02/21/2024]
Abstract
Deciphering cell-type heterogeneity is crucial for systematically understanding tissue homeostasis and its dysregulation in diseases. Computational deconvolution is an efficient approach for estimating cell-type abundances from a variety of omics data. Despite substantial methodological progress in computational deconvolution in recent years, challenges are still outstanding. Here we enlist four important challenges related to computational deconvolution: the quality of the reference data, generation of ground truth data, limitations of computational methodologies, and benchmarking design and implementation. Finally, we make recommendations on reference data generation, new directions of computational methodologies, and strategies to promote rigorous benchmarking.
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Affiliation(s)
- Lana X Garmire
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
| | - Yijun Li
- Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA
| | - Qianhui Huang
- Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
| | - Chuan Xu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Naftali Kaminski
- Pulmonary, Critical Care & Sleep Medicine, Yale University School of Medicine, New Haven, CT, USA
| | - Matteo Pellegrini
- Molecular, Cell and Developmental Biology, University of California, Los Angeles, Los Angeles, CA, USA
| | - Quan Nguyen
- Institute for Molecular Bioscience, The University of Queensland and QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Andrew E Teschendorff
- CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
- UCL Cancer Institute, University College London, London, UK
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11
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van den Bosch AMR, van der Poel M, Fransen NL, Vincenten MCJ, Bobeldijk AM, Jongejan A, Engelenburg HJ, Moerland PD, Smolders J, Huitinga I, Hamann J. Profiling of microglia nodules in multiple sclerosis reveals propensity for lesion formation. Nat Commun 2024; 15:1667. [PMID: 38396116 PMCID: PMC10891081 DOI: 10.1038/s41467-024-46068-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Accepted: 02/06/2024] [Indexed: 02/25/2024] Open
Abstract
Microglia nodules (HLA-DR+ cell clusters) are associated with brain pathology. In this post-mortem study, we investigated whether they represent the first stage of multiple sclerosis (MS) lesion formation. We show that microglia nodules are associated with more severe MS pathology. Compared to microglia nodules in stroke, those in MS show enhanced expression of genes previously found upregulated in MS lesions. Furthermore, genes associated with lipid metabolism, presence of T and B cells, production of immunoglobulins and cytokines, activation of the complement cascade, and metabolic stress are upregulated in microglia nodules in MS. Compared to stroke, they more frequently phagocytose oxidized phospholipids and possess a more tubular mitochondrial network. Strikingly, in MS, some microglia nodules encapsulate partially demyelinated axons. Taken together, we propose that activation of microglia nodules in MS by cytokines and immunoglobulins, together with phagocytosis of oxidized phospholipids, may lead to a microglia phenotype prone to MS lesion formation.
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Affiliation(s)
- Aletta M R van den Bosch
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
| | - Marlijn van der Poel
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Nina L Fransen
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Maria C J Vincenten
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Anneleen M Bobeldijk
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Aldo Jongejan
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Hendrik J Engelenburg
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
| | - Perry D Moerland
- Department of Epidemiology and Data Science, Amsterdam Public Health Research Institute, Amsterdam University Medical Center, Amsterdam, The Netherlands
| | - Joost Smolders
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands
- MS Center ErasMS, Department of Neurology and Immunology, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Inge Huitinga
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
- Swammerdam Institute for Life Sciences, University of Amsterdam, Amsterdam, The Netherlands.
| | - Jörg Hamann
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, Amsterdam, The Netherlands.
- Department of Experimental Immunology, Amsterdam Institute for Infection and Immunity, Amsterdam University Medical Center, Amsterdam, The Netherlands.
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12
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Islam M, Behura SK. Single-Cell Transcriptional Response of the Placenta to the Ablation of Caveolin-1: Insights into the Adaptive Regulation of Brain-Placental Axis in Mice. Cells 2024; 13:215. [PMID: 38334607 PMCID: PMC10854826 DOI: 10.3390/cells13030215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 01/17/2024] [Accepted: 01/22/2024] [Indexed: 02/10/2024] Open
Abstract
Caveolin-1 (Cav1) is a major plasma membrane protein that plays important functions in cellular metabolism, proliferation, and senescence. Mice lacking Cav1 show abnormal gene expression in the fetal brain. Though evidence for placental influence on brain development is emerging, whether the ablation of Cav1 affects the regulation of the brain-placental axis remains unexamined. The current study tests the hypothesis that gene expression changes in specific cells of the placenta and the fetal brain are linked to the deregulation of the brain-placental axis in Cav1-null mice. By performing single-nuclei RNA sequencing (snRNA-seq) analyses, we show that the abundance of the extravillious trophoblast (EVT) and stromal cells, but not the cytotrophoblast (CTB) or syncytiotrophoblast (STB), are significantly impacted due to Cav1 ablation in mice. Interestingly, specific genes related to brain development and neurogenesis were significantly differentially expressed in trophoblast cells due to Cav1 deletion. Comparison of single-cell gene expression between the placenta and the fetal brain further showed that specific genes such as plexin A1 (Plxna1), phosphatase and actin regulator 1 (Phactr1) and amyloid precursor-like protein 2 (Aplp2) were differentially expressed between the EVT and STB cells of the placenta, and also, between the radial glia and ependymal cells of the fetal brain. Bulk RNA-seq analysis of the whole placenta and the fetal brain further identified genes differentially expressed in a similar manner between the placenta and the fetal brain due to the absence of Cav1. The deconvolution of reference cell types from the bulk RNA-seq data further showed that the loss of Cav1 impacted the abundance of EVT cells relative to the stromal cells in the placenta, and that of the glia cells relative to the neuronal cells in the fetal brain. Together, the results of this study suggest that the ablation of Cav1 causes deregulated gene expression in specific cell types of the placenta and the fetal brain in mice.
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Affiliation(s)
- Maliha Islam
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA;
| | - Susanta K. Behura
- Division of Animal Sciences, University of Missouri, Columbia, MO 65211, USA;
- MU Institute for Data Science and Informatics, University of Missouri, Columbia, MO 65211, USA
- Interdisciplinary Reproduction and Health Group, University of Missouri, Columbia, MO 65211, USA
- Interdisciplinary Neuroscience Program, University of Missouri, Columbia, MO 65211, USA
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13
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Méar L, Hao X, Hikmet F, Damdimopoulou P, Rodriguez-Wallberg KA, Lindskog C. Transcriptomics and Spatial Proteomics for Discovery and Validation of Missing Proteins in the Human Ovary. J Proteome Res 2024; 23:238-248. [PMID: 38085962 PMCID: PMC10775140 DOI: 10.1021/acs.jproteome.3c00545] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 11/23/2023] [Accepted: 11/28/2023] [Indexed: 01/06/2024]
Abstract
Efforts to understand the complexities of human biology encompass multidimensional aspects, with proteins emerging as crucial components. However, studying the human ovary introduces unique challenges due to its complex dynamics and changes over a lifetime, varied cellular composition, and limited sample access. Here, four new RNA-seq samples of ovarian cortex spanning ages of 7 to 32 were sequenced and added to the existing data in the Human Protein Atlas (HPA) database www.proteinatlas.org, opening the doors to unique possibilities for exploration of oocyte-specific proteins. Based on transcriptomics analysis of the four new tissue samples representing both prepubertal girls and women of fertile age, we selected 20 protein candidates that lacked previous evidence at the protein level, so-called "missing proteins" (MPs). The proteins were validated using high-resolution antibody-based profiling and single-cell transcriptomics. Fourteen proteins exhibited consistent single-cell expression patterns in oocytes and granulosa cells, confirming their presence in the ovary and suggesting that these proteins play important roles in ovarian function, thus proposing that these 14 proteins should no longer be classified as MPs. This research significantly advances the understanding of MPs, unearthing fresh avenues for prospective exploration. By integrating innovative methodologies and leveraging the wealth of data in the HPA database, these insights contribute to refining our understanding of protein roles within the human ovary and opening the doors for further investigations into missing proteins and human reproduction.
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Affiliation(s)
- Loren Méar
- Department
of Immunology, Genetics and Pathology, Cancer Precision Medicine Research
Program, Uppsala University, Uppsala 751 85, Sweden
- Division
of Obstetrics and Gynecology, Department of Clinical Science, Intervention
and Technology, Karolinska Institutet, Stockholm 14186, Sweden
- Department
of Gynaecology and Reproductive Medicine, Karolinska University Hospital, Stockholm 171 77, Sweden
| | - Xia Hao
- Department
of Oncology-Pathology, Laboratory of Translational Fertility Preservation, Karolinska Institutet, BioClinicum, Stockholm 171 64, Sweden
| | - Feria Hikmet
- Department
of Immunology, Genetics and Pathology, Cancer Precision Medicine Research
Program, Uppsala University, Uppsala 751 85, Sweden
| | - Pauliina Damdimopoulou
- Division
of Obstetrics and Gynecology, Department of Clinical Science, Intervention
and Technology, Karolinska Institutet, Stockholm 14186, Sweden
- Department
of Gynaecology and Reproductive Medicine, Karolinska University Hospital, Stockholm 171 77, Sweden
| | - Kenny A. Rodriguez-Wallberg
- Department
of Gynaecology and Reproductive Medicine, Karolinska University Hospital, Stockholm 171 77, Sweden
- Department
of Oncology-Pathology, Laboratory of Translational Fertility Preservation, Karolinska Institutet, BioClinicum, Stockholm 171 64, Sweden
| | - Cecilia Lindskog
- Department
of Immunology, Genetics and Pathology, Cancer Precision Medicine Research
Program, Uppsala University, Uppsala 751 85, Sweden
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14
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Cai M, Zhou J, McKennan C, Wang J. scMD facilitates cell type deconvolution using single-cell DNA methylation references. Commun Biol 2024; 7:1. [PMID: 38168620 PMCID: PMC10762261 DOI: 10.1038/s42003-023-05690-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 12/08/2023] [Indexed: 01/05/2024] Open
Abstract
The proliferation of single-cell RNA-sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell-type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent developments in single-cell DNA methylation (scDNAm), there are emerging opportunities for deconvolving bulk DNAm data, particularly for solid tissues like brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create precise cell-type signature matrixes that surpass state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, CA, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA.
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15
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Maden SK, Kwon SH, Huuki-Myers LA, Collado-Torres L, Hicks SC, Maynard KR. Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets. Genome Biol 2023; 24:288. [PMID: 38098055 PMCID: PMC10722720 DOI: 10.1186/s13059-023-03123-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 11/24/2023] [Indexed: 12/17/2023] Open
Abstract
Deconvolution of cell mixtures in "bulk" transcriptomic samples from homogenate human tissue is important for understanding disease pathologies. However, several experimental and computational challenges impede transcriptomics-based deconvolution approaches using single-cell/nucleus RNA-seq reference atlases. Cells from the brain and blood have substantially different sizes, total mRNA, and transcriptional activities, and existing approaches may quantify total mRNA instead of cell type proportions. Further, standards are lacking for the use of cell reference atlases and integrative analyses of single-cell and spatial transcriptomics data. We discuss how to approach these key challenges with orthogonal "gold standard" datasets for evaluating deconvolution methods.
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Affiliation(s)
- Sean K Maden
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Sang Ho Kwon
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Louise A Huuki-Myers
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Leonardo Collado-Torres
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA
| | - Stephanie C Hicks
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, USA.
- Center for Computational Biology, Johns Hopkins University, Baltimore, MD, USA.
- Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA.
| | - Kristen R Maynard
- Lieber Institute for Brain Development, Johns Hopkins Medical Campus, Baltimore, MD, USA.
- The Solomon H. Snyder Department of Neuroscience, Johns Hopkins School of Medicine, Baltimore, MD, USA.
- Department of Psychiatry and Behavioral Sciences, Johns Hopkins School of Medicine, Baltimore, MD, USA.
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16
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Rahman S, Dong P, Apontes P, Fernando M, Kosoy R, Townsley KG, Girdhar K, Bendl J, Shao Z, Misir R, Tsankova N, Kleopoulos S, Brennand K, Fullard J, Roussos P. Lineage specific 3D genome structure in the adult human brain and neurodevelopmental changes in the chromatin interactome. Nucleic Acids Res 2023; 51:11142-11161. [PMID: 37811875 PMCID: PMC10639075 DOI: 10.1093/nar/gkad798] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 08/18/2023] [Accepted: 09/19/2023] [Indexed: 10/10/2023] Open
Abstract
The human brain is a complex organ comprised of distinct cell types, and the contribution of the 3D genome to lineage specific gene expression remains poorly understood. To decipher cell type specific genome architecture, and characterize fine scale changes in the chromatin interactome across neural development, we compared the 3D genome of the human fetal cortical plate to that of neurons and glia isolated from the adult prefrontal cortex. We found that neurons have weaker genome compartmentalization compared to glia, but stronger TADs, which emerge during fetal development. Furthermore, relative to glia, the neuronal genome shifts more strongly towards repressive compartments. Neurons have differential TAD boundaries that are proximal to active promoters involved in neurodevelopmental processes. CRISPRi on CNTNAP2 in hIPSC-derived neurons reveals that transcriptional inactivation correlates with loss of insulation at the differential boundary. Finally, re-wiring of chromatin loops during neural development is associated with transcriptional and functional changes. Importantly, differential loops in the fetal cortex are associated with autism GWAS loci, suggesting a neuropsychiatric disease mechanism affecting the chromatin interactome. Furthermore, neural development involves gaining enhancer-promoter loops that upregulate genes that control synaptic activity. Altogether, our study provides multi-scale insights on the 3D genome in the human brain.
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Affiliation(s)
- Samir Rahman
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pengfei Dong
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Pasha Apontes
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY 10468, USA
| | - Michael B Fernando
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Roman Kosoy
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kayla G Townsley
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kiran Girdhar
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Jaroslav Bendl
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Zhiping Shao
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Ruth Misir
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nadia Tsankova
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Pathology, Molecular, and Cell-based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Steven P Kleopoulos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Kristen J Brennand
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Black Family Stem Cell Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY 10468, USA
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA
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17
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Tran KA, Addala V, Johnston RL, Lovell D, Bradley A, Koufariotis LT, Wood S, Wu SZ, Roden D, Al-Eryani G, Swarbrick A, Williams ED, Pearson JV, Kondrashova O, Waddell N. Performance of tumour microenvironment deconvolution methods in breast cancer using single-cell simulated bulk mixtures. Nat Commun 2023; 14:5758. [PMID: 37717006 PMCID: PMC10505141 DOI: 10.1038/s41467-023-41385-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2022] [Accepted: 09/01/2023] [Indexed: 09/18/2023] Open
Abstract
Cells within the tumour microenvironment (TME) can impact tumour development and influence treatment response. Computational approaches have been developed to deconvolve the TME from bulk RNA-seq. Using scRNA-seq profiling from breast tumours we simulate thousands of bulk mixtures, representing tumour purities and cell lineages, to compare the performance of nine TME deconvolution methods (BayesPrism, Scaden, CIBERSORTx, MuSiC, DWLS, hspe, CPM, Bisque, and EPIC). Some methods are more robust in deconvolving mixtures with high tumour purity levels. Most methods tend to mis-predict normal epithelial for cancer epithelial as tumour purity increases, a finding that is validated in two independent datasets. The breast cancer molecular subtype influences this mis-prediction. BayesPrism and DWLS have the lowest combined numbers of false positives and false negatives, and have the best performance when deconvolving granular immune lineages. Our findings highlight the need for more single-cell characterisation of rarer cell types, and suggest that tumour cell compositions should be considered when deconvolving the TME.
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Affiliation(s)
- Khoa A Tran
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
| | - Venkateswar Addala
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Rebecca L Johnston
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - David Lovell
- School of Computer Science, Queensland University of Technology, Brisbane, QLD, 4000, Australia
- QUT Centre for Data Science, Brisbane, QLD, 4000, Australia
| | - Andrew Bradley
- Faculty of Engineering, Queensland University of Technology, Brisbane, QLD, 4000, Australia
| | - Lambros T Koufariotis
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Scott Wood
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Sunny Z Wu
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Daniel Roden
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Ghamdan Al-Eryani
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Alexander Swarbrick
- Cancer Ecosystems Program, Garvan Institute of Medical Research, Darlinghurst, NSW, 2010, Australia
- School of Clinical Medicine, Faculty of Medicine and Health, UNSW Sydney, Kensington, NSW, 2052, Australia
| | - Elizabeth D Williams
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia
- Australian Prostate Cancer Research Centre - Queensland (APCRC-Q) and Queensland Bladder Cancer Initiative (QBCI), Brisbane, QLD, 4000, Australia
| | - John V Pearson
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Olga Kondrashova
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia
| | - Nicola Waddell
- Cancer Program, QIMR Berghofer Medical Research Institute, Brisbane, QLD, 4006, Australia.
- School of Biomedical Sciences, Queensland University of Technology (QUT), Brisbane, QLD, 4000, Australia.
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18
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Wang J, Lu L, Zheng S, Wang D, Jin L, Zhang Q, Li M, Zhang Z. DeCOOC Deconvoluted Hi-C Map Characterizes the Chromatin Architecture of Cells in Physiologically Distinctive Tissues. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2023; 10:e2301058. [PMID: 37515382 PMCID: PMC10520690 DOI: 10.1002/advs.202301058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 07/06/2023] [Indexed: 07/30/2023]
Abstract
Deciphering variations in chromosome conformations based on bulk three-dimensional (3D) genomic data from heterogenous tissues is a key to understanding cell-type specific genome architecture and dynamics. Surprisingly, computational deconvolution methods for high-throughput chromosome conformation capture (Hi-C) data remain very rare in the literature. Here, a deep convolutional neural network (CNN), deconvolve bulk Hi-C data (deCOOC) that remarkably outperformed all the state-of-the-art tools in the deconvolution task is developed. Interestingly, it is noticed that the chromatin accessibility or the Hi-C contact frequency alone is insufficient to explain the power of deCOOC, suggesting the existence of a latent embedded layer of information pertaining to the cell type specific 3D genome architecture. By applying deCOOC to in-house-generated bulk Hi-C data from visceral and subcutaneous adipose tissues, it is found that the characteristic chromatin features of M2 cells in the two anatomical loci are distinctively bound to different physiological functionalities. Taken together, deCOOC is both a reliable Hi-C data deconvolution method and a powerful tool for functional extraction of 3D genome architecture.
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Affiliation(s)
- Junmei Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Lu Lu
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Shiqi Zheng
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
| | - Danyang Wang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
- Sars‐Fang Centre & MOE Key Laboratory of Marine Genetics and BreedingCollege of Marine Life SciencesOcean University of ChinaQingdao266100China
| | - Long Jin
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Qing Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
| | - Mingzhou Li
- Livestock and Poultry Multiomics Key Laboratory of Ministry of Agriculture and Rural AffairsCollege of Animal Science and TechnologySichuan Agricultural UniversityChengdu611130China
- Animal Breeding and Genetics Key Laboratory of Sichuan ProvinceInstitute of Animal Genetics and BreedingSichuan Agricultural UniversityChengdu611130China
| | - Zhihua Zhang
- CAS Key Laboratory of Genome Sciences and InformationBeijing Institute of GenomicsChinese Academy of Sciences and China National Center for BioinformationBeijing100101China
- School of Life ScienceUniversity of Chinese Academy of SciencesBeijing100049China
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19
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Johansen N, Hu H, Quon G. Projecting RNA measurements onto single cell atlases to extract cell type-specific expression profiles using scProjection. Nat Commun 2023; 14:5192. [PMID: 37626024 PMCID: PMC10457395 DOI: 10.1038/s41467-023-40744-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Accepted: 08/08/2023] [Indexed: 08/27/2023] Open
Abstract
Multi-modal single cell RNA assays capture RNA content as well as other data modalities, such as spatial cell position or the electrophysiological properties of cells. Compared to dedicated scRNA-seq assays however, they may unintentionally capture RNA from multiple adjacent cells, exhibit lower RNA sequencing depth compared to scRNA-seq, or lack genome-wide RNA measurements. We present scProjection, a method for mapping individual multi-modal RNA measurements to deeply sequenced scRNA-seq atlases to extract cell type-specific, single cell gene expression profiles. We demonstrate several use cases of scProjection, including identifying spatial motifs from spatial transcriptome assays, distinguishing RNA contributions from neighboring cells in both spatial and multi-modal single cell assays, and imputing expression measurements of un-measured genes from gene markers. scProjection therefore combines the advantages of both multi-modal and scRNA-seq assays to yield precise multi-modal measurements of single cells.
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Affiliation(s)
- Nelson Johansen
- Graduate Group in Computer Science, University of California, Davis, Davis, CA, USA.
| | - Hongru Hu
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA
| | - Gerald Quon
- Graduate Group in Computer Science, University of California, Davis, Davis, CA, USA.
- Integrative Genetics and Genomics Graduate Group, University of California, Davis, Davis, CA, USA.
- Department of Molecular and Cellular Biology, University of California, Davis, Davis, CA, USA.
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20
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Galvão IC, Kandratavicius L, Messias LA, Athié MCP, Assis-Mendonça GR, Alvim MKM, Ghizoni E, Tedeschi H, Yasuda CL, Cendes F, Vieira AS, Rogerio F, Lopes-Cendes I, Veiga DFT. Identifying cellular markers of focal cortical dysplasia type II with cell-type deconvolution and single-cell signatures. Sci Rep 2023; 13:13321. [PMID: 37587190 PMCID: PMC10432381 DOI: 10.1038/s41598-023-40240-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 08/07/2023] [Indexed: 08/18/2023] Open
Abstract
Focal cortical dysplasia (FCD) is a brain malformation that causes medically refractory epilepsy. FCD is classified into three categories based on structural and cellular abnormalities, with FCD type II being the most common and characterized by disrupted organization of the cortex and abnormal neuronal development. In this study, we employed cell-type deconvolution and single-cell signatures to analyze bulk RNA-seq from multiple transcriptomic studies, aiming to characterize the cellular composition of brain lesions in patients with FCD IIa and IIb subtypes. Our deconvolution analyses revealed specific cellular changes in FCD IIb, including neuronal loss and an increase in reactive astrocytes (astrogliosis) when compared to FCD IIa. Astrogliosis in FCD IIb was further supported by a gene signature analysis and histologically confirmed by glial fibrillary acidic protein (GFAP) immunostaining. Overall, our findings demonstrate that FCD II subtypes exhibit differential neuronal and glial compositions, with astrogliosis emerging as a hallmark of FCD IIb. These observations, validated in independent patient cohorts and confirmed using immunohistochemistry, offer novel insights into the involvement of glial cells in FCD type II pathophysiology and may contribute to the development of targeted therapies for this condition.
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Affiliation(s)
- Isabella C Galvão
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Ludmyla Kandratavicius
- Department of Pathology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Lauana A Messias
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Maria C P Athié
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Guilherme R Assis-Mendonça
- Department of Pathology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Marina K M Alvim
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Enrico Ghizoni
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Helder Tedeschi
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Clarissa L Yasuda
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Fernando Cendes
- Department of Neurology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, SP, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - André S Vieira
- Department of Structural and Functional Biology, Institute of Biology, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Fabio Rogerio
- Department of Pathology, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Iscia Lopes-Cendes
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Diogo F T Veiga
- Department of Translational Medicine, School of Medical Sciences, University of Campinas (UNICAMP), Campinas, Brazil.
- The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil.
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21
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van den Oord EJCG, Aberg KA. Fine-grained cell-type specific association studies with human bulk brain data using a large single-nucleus RNA sequencing based reference panel. Sci Rep 2023; 13:13004. [PMID: 37563216 PMCID: PMC10415334 DOI: 10.1038/s41598-023-39864-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Accepted: 08/01/2023] [Indexed: 08/12/2023] Open
Abstract
Brain disorders are leading causes of disability worldwide. Gene expression studies provide promising opportunities to better understand their etiology but it is critical that expression is studied on a cell-type level. Cell-type specific association studies can be performed with bulk expression data using statistical methods that capitalize on cell-type proportions estimated with the help of a reference panel. To create a fine-grained reference panel for the human prefrontal cortex, we performed an integrated analysis of the seven largest single nucleus RNA-seq studies. Our panel included 17 cell-types that were robustly detected across all studies, subregions of the prefrontal cortex, and sex and age groups. To estimate the cell-type proportions, we used an empirical Bayes estimator that substantially outperformed three estimators recommended previously after a comprehensive evaluation of methods to estimate cell-type proportions from brain transcriptome data. This is important as being able to precisely estimate the cell-type proportions may avoid unreliable results in downstream analyses particularly for the multiple cell-types that had low abundances. Transcriptome-wide association studies performed with permuted bulk expression data showed that it is possible to perform transcriptome-wide association studies for even the rarest cell-types without an increased risk of false positives.
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Affiliation(s)
- Edwin J C G van den Oord
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, McGuire Hall, Room 216A, 1112 East Clay Street, P. O. Box 980533, Richmond, VA, 23298-0581, USA.
| | - Karolina A Aberg
- Center for Biomarker Research and Precision Medicine, Virginia Commonwealth University, McGuire Hall, Room 216A, 1112 East Clay Street, P. O. Box 980533, Richmond, VA, 23298-0581, USA
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22
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Cai M, Zhou J, McKennan C, Wang J. scMD: cell type deconvolution using single-cell DNA methylation references. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.03.551733. [PMID: 37577715 PMCID: PMC10418231 DOI: 10.1101/2023.08.03.551733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/15/2023]
Abstract
The proliferation of single-cell RNA sequencing data has led to the widespread use of cellular deconvolution, aiding the extraction of cell type-specific information from extensive bulk data. However, those advances have been mostly limited to transcriptomic data. With recent development in single-cell DNA methylation (scDNAm), new avenues have been opened for deconvolving bulk DNAm data, particularly for solid tissues like the brain that lack cell-type references. Due to technical limitations, current scDNAm sequences represent a small proportion of the whole genome for each single cell, and those detected regions differ across cells. This makes scDNAm data ultra-high dimensional and ultra-sparse. To deal with these challenges, we introduce scMD (single cell Methylation Deconvolution), a cellular deconvolution framework to reliably estimate cell type fractions from tissue-level DNAm data. To analyze large-scale complex scDNAm data, scMD employs a statistical approach to aggregate scDNAm data at the cell cluster level, identify cell-type marker DNAm sites, and create a precise cell-type signature matrix that surpasses state-of-the-art sorted-cell or RNA-derived references. Through thorough benchmarking in several datasets, we demonstrate scMD's superior performance in estimating cellular fractions from bulk DNAm data. With scMD-estimated cellular fractions, we identify cell type fractions and cell type-specific differentially methylated cytosines associated with Alzheimer's disease.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jingtian Zhou
- Genomic Analysis Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA
- Bioinformatics and Systems Biology Program, University of California, San Diego, La Jolla, CA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
- Clinical and Translational Science Institute, University of Pittsburgh, Pittsburgh, PA, USA
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23
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Liharska LE, Park YJ, Ziafat K, Wilkins L, Silk H, Linares LM, Vornholt E, Sullivan B, Cohen V, Kota P, Feng C, Cheng E, Moya E, Thompson RC, Johnson JS, Rieder MK, Huang J, Scarpa J, Hashemi A, Polanco J, Levin MA, Nadkarni GN, Sebra R, Crary J, Schadt EE, Beckmann ND, Kopell BH, Charney AW. A study of gene expression in the living human brain. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.21.23288916. [PMID: 37163086 PMCID: PMC10168405 DOI: 10.1101/2023.04.21.23288916] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
A goal of medical research is to determine the molecular basis of human brain health and illness. One way to achieve this goal is through observational studies of gene expression in human brain tissue. Due to the unavailability of brain tissue from living people, most such studies are performed using tissue from postmortem brain donors. An assumption underlying this practice is that gene expression in the postmortem human brain is an accurate representation of gene expression in the living human brain. Here, this assumption - which, until now, had not been adequately tested - is tested by comparing human prefrontal cortex gene expression between 275 living samples and 243 postmortem samples. Expression levels differed significantly for nearly 80% of genes, and a systematic examination of alternative explanations for this observation determined that these differences are not a consequence of cell type composition, RNA quality, postmortem interval, age, medication, morbidity, symptom severity, tissue pathology, sample handling, batch effects, or computational methods utilized. Analyses integrating the data generated for this study with data from earlier landmark studies that used tissue from postmortem brain donors showed that postmortem brain gene expression signatures of neurological and mental illnesses, as well as of normal traits such as aging, may not be accurate representations of these gene expression signatures in the living brain. By using tissue from large cohorts living people, future observational studies of human brain biology have the potential to (1) determine the medical research questions that can be addressed using postmortem tissue as a proxy for living tissue and (2) expand the scope of medical research to include questions about the molecular basis of human brain health and illness that can only be addressed in living people (e.g., "What happens at the molecular level in the brain as a person experiences an emotion?").
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24
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Chiu Y, Ni C, Huang Y. Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients. Cancer Med 2023; 12:15736-15760. [PMID: 37366298 PMCID: PMC10417088 DOI: 10.1002/cam4.6197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 05/02/2023] [Accepted: 05/23/2023] [Indexed: 06/28/2023] Open
Abstract
BACKGROUND Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression. RESULTS A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes. CONCLUSIONS HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.
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Affiliation(s)
- Yen‐Jung Chiu
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Department of Biomedical EngineeringMing Chuan UniversityTaoyuanTaiwan
| | - Chung‐En Ni
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
| | - Yen‐Hua Huang
- Institute of Biomedical InformaticsNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
- Center for Systems and Synthetic BiologyNational Yang Ming Chiao Tung UniversityTaipeiTaiwan
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25
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Cain A, Taga M, McCabe C, Green GS, Hekselman I, White CC, Lee DI, Gaur P, Rozenblatt-Rosen O, Zhang F, Yeger-Lotem E, Bennett DA, Yang HS, Regev A, Menon V, Habib N, De Jager PL. Multicellular communities are perturbed in the aging human brain and Alzheimer's disease. Nat Neurosci 2023; 26:1267-1280. [PMID: 37336975 PMCID: PMC10789499 DOI: 10.1038/s41593-023-01356-x] [Citation(s) in RCA: 23] [Impact Index Per Article: 23.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Accepted: 05/10/2023] [Indexed: 06/21/2023]
Abstract
The role of different cell types and their interactions in Alzheimer's disease (AD) is a complex and open question. Here, we pursued this question by assembling a high-resolution cellular map of the aging frontal cortex using single-nucleus RNA sequencing of 24 individuals with a range of clinicopathologic characteristics. We used this map to infer the neocortical cellular architecture of 638 individuals profiled by bulk RNA sequencing, providing the sample size necessary for identifying statistically robust associations. We uncovered diverse cell populations associated with AD, including a somatostatin inhibitory neuronal subtype and oligodendroglial states. We further identified a network of multicellular communities, each composed of coordinated subpopulations of neuronal, glial and endothelial cells, and we found that two of these communities are altered in AD. Finally, we used mediation analyses to prioritize cellular changes that might contribute to cognitive decline. Thus, our deconstruction of the aging neocortex provides a roadmap for evaluating the cellular microenvironments underlying AD and dementia.
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Affiliation(s)
- Anael Cain
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Mariko Taga
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Cristin McCabe
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Gilad S Green
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Idan Hekselman
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | | | - Dylan I Lee
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Pallavi Gaur
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA
| | - Orit Rozenblatt-Rosen
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Feng Zhang
- Broad Institute, Cambridge, MA, USA
- Department of Brain and Cognitive Sciences, McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Esti Yeger-Lotem
- Department of Clinical Biochemistry and Pharmacology, Faculty of Health Sciences, Ben-Gurion University of the Negev, Beer-Sheva, Israel
- National Institute for Biotechnology in the Negev, Ben-Gurion University of the Negev, Beer-Sheva, Israel
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Hyun-Sik Yang
- Broad Institute, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Center for Alzheimer Research and Treatment, Department of Neurology, Brigham and Women's Hospital, Boston, MA, USA
| | - Aviv Regev
- Klarman Cell Observatory, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Biology, Koch Institute of Integrative Cancer Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Genentech, South San Francisco, CA, USA
| | - Vilas Menon
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA.
| | - Naomi Habib
- Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel.
| | - Philip L De Jager
- Center for Translational & Computational Immunology, Department of Neurology and Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University Irving Medical Center, New York, NY, USA.
- Broad Institute, Cambridge, MA, USA.
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26
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Sun J, Singh P, Shami A, Kluza E, Pan M, Djordjevic D, Michaelsen NB, Kennbäck C, van der Wel NN, Orho-Melander M, Nilsson J, Formentini I, Conde-Knape K, Lutgens E, Edsfeldt A, Gonçalves I. Spatial Transcriptional Mapping Reveals Site-Specific Pathways Underlying Human Atherosclerotic Plaque Rupture. J Am Coll Cardiol 2023; 81:2213-2227. [PMID: 37286250 DOI: 10.1016/j.jacc.2023.04.008] [Citation(s) in RCA: 14] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/20/2023] [Accepted: 04/03/2023] [Indexed: 06/09/2023]
Abstract
BACKGROUND Atherosclerotic plaque ruptures, triggered by blood flow-associated biomechanical forces, cause most myocardial infarctions and strokes. OBJECTIVES This study aims to investigate the exact location and underlying mechanisms of atherosclerotic plaque ruptures, identifying therapeutic targets against cardiovascular events. METHODS Histology, electron microscopy, bulk and spatial RNA sequencing on human carotid plaques were studied in proximal, most stenotic, and distal regions along the longitudinal blood flow direction. Genome-wide association studies were used to examine heritability enrichment and causal relationships of atherosclerosis and stroke. Associations between top differentially expressed genes (DEGs) and preoperative and postoperative cardiovascular events were examined in a validation cohort. RESULTS In human carotid atherosclerotic plaques, ruptures predominantly occurred in the proximal and most stenotic regions but not in the distal region. Histologic and electron microscopic examination showed that proximal and most stenotic regions exhibited features of plaque vulnerability and thrombosis. RNA sequencing identified DEGs distinguishing the proximal and most stenotic regions from the distal region which were deemed as most relevant to atherosclerosis-associated diseases as shown by heritability enrichment analyses. The identified pathways associated with the proximal rupture-prone regions were validated by spatial transcriptomics, firstly in human atherosclerosis. Of the 3 top DEGs, matrix metallopeptidase 9 emerged particularly because Mendelian randomization suggested that its high circulating levels were causally associated with atherosclerosis risk. CONCLUSIONS Our findings show plaque site-specific transcriptional signatures associated with proximal rupture-prone regions of carotid atherosclerotic plaques. This led to the geographical mapping of novel therapeutic targets, such as matrix metallopeptidase 9, against plaque rupture.
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Affiliation(s)
- Jiangming Sun
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Pratibha Singh
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Annelie Shami
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | - Ewelina Kluza
- Experimental Vascular Biology, Department of Medical Biochemistry, Amsterdam University Medical Center, Amsterdam, the Netherlands; Laboratory of Chemical Biology, Department of Biomedical Engineering and Institute for Complex Molecular Systems, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Mengyu Pan
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | | | - Natasha Barascuk Michaelsen
- Type 2 Diabetes and Cardiovascular Disease Research, Global Drug Discovery, Novo Nordisk A/S, Måløv, Denmark
| | - Cecilia Kennbäck
- Clinical Research Unit, Department of Internal Medicine, Skåne University Hospital, Malmö, Sweden
| | - Nicole N van der Wel
- Electron Microscopy Center Amsterdam, Department of Medical Biology, Amsterdam University Medical Center, Amsterdam, the Netherlands
| | | | - Jan Nilsson
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden
| | | | | | - Esther Lutgens
- Experimental Vascular Biology, Department of Medical Biochemistry, Amsterdam University Medical Center, Amsterdam, the Netherlands; Cardiovascular Medicine, Experimental Cardiovascular Immunology Laboratory, Mayo Clinic, Rochester, Minnesota, USA; Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians Universität, München, Germany; German Center for Cardiovascular Research (DZHK), partner site Munich Heart Alliance, Munich, Germany
| | - Andreas Edsfeldt
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden; Department of Cardiology, Skåne University Hospital, Malmö, Sweden; Wallenberg Center for Molecular Medicine, Lund University, Lund, Sweden
| | - Isabel Gonçalves
- Department of Clinical Sciences Malmö, Lund University, Malmö, Sweden; Department of Cardiology, Skåne University Hospital, Malmö, Sweden.
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27
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Toker L, Nido GS, Tzoulis C. Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data. Genome Med 2023; 15:41. [PMID: 37287013 DOI: 10.1186/s13073-023-01195-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 05/22/2023] [Indexed: 06/09/2023] Open
Abstract
BACKGROUND Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed. METHODS We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls. RESULTS We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition. CONCLUSIONS Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.
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Affiliation(s)
- Lilah Toker
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
| | - Gonzalo S Nido
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway
| | - Charalampos Tzoulis
- Neuro-SysMed Center of Excellence, Department of Neurology, Department of Clinical Medicine, Haukeland University Hospital, University of Bergen, 5021, Bergen, Norway.
- Department of Clinical Medicine, University of Bergen, Pb 7804, 5020, Bergen, Norway.
- K.G Jebsen Center for Translational Research in Parkinson's Disease, University of Bergen, Bergen, Norway.
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28
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Luo J, Wu X, Cheng Y, Chen G, Wang J, Song X. Expression quantitative trait locus studies in the era of single-cell omics. Front Genet 2023; 14:1182579. [PMID: 37284065 PMCID: PMC10239882 DOI: 10.3389/fgene.2023.1182579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Accepted: 04/26/2023] [Indexed: 06/08/2023] Open
Abstract
Genome-wide association studies have revealed that the regulation of gene expression bridges genetic variants and complex phenotypes. Profiling of the bulk transcriptome coupled with linkage analysis (expression quantitative trait locus (eQTL) mapping) has advanced our understanding of the relationship between genetic variants and gene regulation in the context of complex phenotypes. However, bulk transcriptomics has inherited limitations as the regulation of gene expression tends to be cell-type-specific. The advent of single-cell RNA-seq technology now enables the identification of the cell-type-specific regulation of gene expression through a single-cell eQTL (sc-eQTL). In this review, we first provide an overview of sc-eQTL studies, including data processing and the mapping procedure of the sc-eQTL. We then discuss the benefits and limitations of sc-eQTL analyses. Finally, we present an overview of the current and future applications of sc-eQTL discoveries.
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Affiliation(s)
- Jie Luo
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xinyi Wu
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Yuan Cheng
- Institute of Vegetables, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Guang Chen
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Jian Wang
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
| | - Xijiao Song
- State Key Laboratory for Managing Biotic and Chemical Threats to The Quality and Safety of Agro‐products, Zhejiang Academy of Agricultural Sciences, Hangzhou, China
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29
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Merchant JP, Zhu K, Henrion MYR, Zaidi SSA, Lau B, Moein S, Alamprese ML, Pearse RV, Bennett DA, Ertekin-Taner N, Young-Pearse TL, Chang R. Predictive network analysis identifies JMJD6 and other potential key drivers in Alzheimer's disease. Commun Biol 2023; 6:503. [PMID: 37188718 PMCID: PMC10185548 DOI: 10.1038/s42003-023-04791-5] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2021] [Accepted: 03/31/2023] [Indexed: 05/17/2023] Open
Abstract
Despite decades of genetic studies on late-onset Alzheimer's disease, the underlying molecular mechanisms remain unclear. To better comprehend its complex etiology, we use an integrative approach to build robust predictive (causal) network models using two large human multi-omics datasets. We delineate bulk-tissue gene expression into single cell-type gene expression and integrate clinical and pathologic traits, single nucleotide variation, and deconvoluted gene expression for the construction of cell type-specific predictive network models. Here, we focus on neuron-specific network models and prioritize 19 predicted key drivers modulating Alzheimer's pathology, which we then validate by knockdown in human induced pluripotent stem cell-derived neurons. We find that neuronal knockdown of 10 of the 19 targets significantly modulates levels of amyloid-beta and/or phosphorylated tau peptides, most notably JMJD6. We also confirm our network structure by RNA sequencing in the neurons following knockdown of each of the 10 targets, which additionally predicts that they are upstream regulators of REST and VGF. Our work thus identifies robust neuronal key drivers of the Alzheimer's-associated network state which may represent therapeutic targets with relevance to both amyloid and tau pathology in Alzheimer's disease.
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Affiliation(s)
- Julie P Merchant
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Neuroscience Graduate Group, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA, USA
| | - Kuixi Zhu
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Marc Y R Henrion
- Liverpool School of Tropical Medicine, Pembroke Place, Liverpool, Pembroke Place, L3 5QA, UK
- Malawi-Liverpool-Wellcome Trust Clinical Research Programme, PO Box 30096, Blantyre, Malawi
| | - Syed S A Zaidi
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Branden Lau
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
- Arizona Research Labs, Genetics Core, University of Arizona, Tucson, AZ, USA
| | - Sara Moein
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Melissa L Alamprese
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA
| | - Richard V Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - David A Bennett
- Rush Alzheimer's Disease Center, Rush University Medical Center, Chicago, IL, USA
| | - Nilüfer Ertekin-Taner
- Department of Neuroscience, Mayo Clinic Florida, Jacksonville, FL, USA
- Department of Neurology, Mayo Clinic Florida, Jacksonville, FL, USA
| | - Tracy L Young-Pearse
- Ann Romney Center for Neurologic Diseases, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
- Harvard Stem Cell Institute, Harvard University, Boston, MA, USA.
| | - Rui Chang
- The Center for Innovation in Brain Sciences, University of Arizona, Tucson, AZ, USA.
- Department of Neurology, University of Arizona, Tucson, AZ, USA.
- INTelico Therapeutics LLC, Tucson, AZ, USA.
- PATH Biotech LLC, Tucson, AZ, USA.
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30
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Chen Y, Hunter E, Arbabi K, Guet-McCreight A, Consens M, Felsky D, Sibille E, Tripathy SJ. Robust differences in cortical cell type proportions across healthy human aging inferred through cross-dataset transcriptome analyses. Neurobiol Aging 2023; 125:49-61. [PMID: 36841202 DOI: 10.1016/j.neurobiolaging.2023.01.013] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 01/22/2023] [Accepted: 01/24/2023] [Indexed: 02/01/2023]
Abstract
Age-related declines in cognitive function are driven by cell type-specific changes in the brain. However, it remains challenging to study cellular differences associated with healthy aging as traditional approaches scale poorly to the sample sizes needed to capture aging and cellular heterogeneity. Here, we employed cellular deconvolution to estimate relative cell type proportions using frontal cortex bulk gene expression from individuals without psychiatric conditions or brain pathologies. Our analyses comprised 8 datasets and 6 cohorts (1142 subjects and 1429 samples) with ages of death spanning 15-90 years. We found aging associated with profound differences in cellular proportions, with the largest changes reflecting fewer somatostatin- and vasoactive intestinal peptide-expressing interneurons, more astrocytes and other non-neuronal cells, and a suggestive "U-shaped" quadratic relationship for microglia. Cell type associations with age were markedly robust across bulk-and single nucleus datasets. Altogether, we present a comprehensive account of proportional differences in cortical cell types associated with healthy aging.
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Affiliation(s)
- Yuxiao Chen
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Emma Hunter
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Keon Arbabi
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Alex Guet-McCreight
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Micaela Consens
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Daniel Felsky
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Etienne Sibille
- Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Centre for Addiction and Mental Health, Campbell Family Mental Health Research Institute, Toronto, Ontario, Canada; Department of Pharmacology and Toxicology, University of Toronto, Toronto, Ontario, Canada
| | - Shreejoy J Tripathy
- The Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Institute of Medical Science, University of Toronto, Toronto, Ontario, Canada; Department of Physiology, University of Toronto, Toronto, Ontario, Canada.
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31
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Zeighami Y, Bakken TE, Nickl-Jockschat T, Peterson Z, Jegga AG, Miller JA, Schulkin J, Evans AC, Lein ES, Hawrylycz M. A comparison of anatomic and cellular transcriptome structures across 40 human brain diseases. PLoS Biol 2023; 21:e3002058. [PMID: 37079537 PMCID: PMC10118126 DOI: 10.1371/journal.pbio.3002058] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 03/02/2023] [Indexed: 04/21/2023] Open
Abstract
Genes associated with risk for brain disease exhibit characteristic expression patterns that reflect both anatomical and cell type relationships. Brain-wide transcriptomic patterns of disease risk genes provide a molecular-based signature, based on differential co-expression, that is often unique to that disease. Brain diseases can be compared and aggregated based on the similarity of their signatures which often associates diseases from diverse phenotypic classes. Analysis of 40 common human brain diseases identifies 5 major transcriptional patterns, representing tumor-related, neurodegenerative, psychiatric and substance abuse, and 2 mixed groups of diseases affecting basal ganglia and hypothalamus. Further, for diseases with enriched expression in cortex, single-nucleus data in the middle temporal gyrus (MTG) exhibits a cell type expression gradient separating neurodegenerative, psychiatric, and substance abuse diseases, with unique excitatory cell type expression differentiating psychiatric diseases. Through mapping of homologous cell types between mouse and human, most disease risk genes are found to act in common cell types, while having species-specific expression in those types and preserving similar phenotypic classification within species. These results describe structural and cellular transcriptomic relationships of disease risk genes in the adult brain and provide a molecular-based strategy for classifying and comparing diseases, potentially identifying novel disease relationships.
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Affiliation(s)
- Yashar Zeighami
- Douglas Research Centre, Department of Psychiatry, McGill University, Montreal, Canada
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Trygve E. Bakken
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Thomas Nickl-Jockschat
- Department of Psychiatry, Neuroscience and Pharmacology, Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa, United States of America
| | - Zeru Peterson
- Department of Psychiatry, Neuroscience and Pharmacology, Iowa Neuroscience Institute, University of Iowa, Iowa City, Iowa, United States of America
| | - Anil G. Jegga
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States of America
- Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States of America
| | - Jeremy A. Miller
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Jay Schulkin
- Department of Obstetrics and Gynecology, University of Washington, Seattle, Washington, United States of America
| | - Alan C. Evans
- Montreal Neurological Institute, McGill University, Montreal, Canada
| | - Ed S. Lein
- Allen Institute for Brain Science, Seattle, Washington, United States of America
| | - Michael Hawrylycz
- Allen Institute for Brain Science, Seattle, Washington, United States of America
- University of Washington, Department of Genome Sciences, Seattle, Washington, United States of America
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32
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Huang P, Cai M, Lu X, McKennan C, Wang J. Accurate estimation of rare cell type fractions from tissue omics data via hierarchical deconvolution. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.15.532820. [PMID: 36993280 PMCID: PMC10055056 DOI: 10.1101/2023.03.15.532820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Bulk transcriptomics in tissue samples reflects the average expression levels across different cell types and is highly influenced by cellular fractions. As such, it is critical to estimate cellular fractions to both deconfound differential expression analyses and infer cell type-specific differential expression. Since experimentally counting cells is infeasible in most tissues and studies, in silico cellular deconvolution methods have been developed as an alternative. However, existing methods are designed for tissues consisting of clearly distinguishable cell types and have difficulties estimating highly correlated or rare cell types. To address this challenge, we propose Hierarchical Deconvolution (HiDecon) that uses single-cell RNA sequencing references and a hierarchical cell type tree, which models the similarities among cell types and cell differentiation relationships, to estimate cellular fractions in bulk data. By coordinating cell fractions across layers of the hierarchical tree, cellular fraction information is passed up and down the tree, which helps correct estimation biases by pooling information across related cell types. The flexible hierarchical tree structure also enables estimating rare cell fractions by splitting the tree to higher resolutions. Through simulations and real data applications with the ground truth of measured cellular fractions, we demonstrate that HiDecon significantly outperforms existing methods and accurately estimates cellular fractions.
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Affiliation(s)
- Penghui Huang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Manqi Cai
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Chris McKennan
- Deparment of Statistics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jiebiao Wang
- Deparment of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA
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33
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Dai R, Chu T, Zhang M, Wang X, Jourdon A, Wu F, Mariani J, Vaccarino FM, Lee D, Fullard JF, Hoffman GE, Roussos P, Wang Y, Wang X, Pinto D, Wang SH, Zhang C, Chen C, Liu C. Evaluating performance and applications of sample-wise cell deconvolution methods on human brain transcriptomic data. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.13.532468. [PMID: 36993743 PMCID: PMC10054947 DOI: 10.1101/2023.03.13.532468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
Sample-wise deconvolution methods have been developed to estimate cell-type proportions and gene expressions in bulk-tissue samples. However, the performance of these methods and their biological applications has not been evaluated, particularly on human brain transcriptomic data. Here, nine deconvolution methods were evaluated with sample-matched data from bulk-tissue RNAseq, single-cell/nuclei (sc/sn) RNAseq, and immunohistochemistry. A total of 1,130,767 nuclei/cells from 149 adult postmortem brains and 72 organoid samples were used. The results showed the best performance of dtangle for estimating cell proportions and bMIND for estimating sample-wise cell-type gene expression. For eight brain cell types, 25,273 cell-type eQTLs were identified with deconvoluted expressions (decon-eQTLs). The results showed that decon-eQTLs explained more schizophrenia GWAS heritability than bulk-tissue or single-cell eQTLs alone. Differential gene expression associated with multiple phenotypes were also examined using the deconvoluted data. Our findings, which were replicated in bulk-tissue RNAseq and sc/snRNAseq data, provided new insights into the biological applications of deconvoluted data.
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Affiliation(s)
- Rujia Dai
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Tianyao Chu
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Ming Zhang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Xuan Wang
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | | | - Feinan Wu
- Child Study Center, Yale University, New Haven, CT, USA
| | | | - Flora M Vaccarino
- Child Study Center, Yale University, New Haven, CT, USA
- Department of Neuroscience, Yale University, New Haven, CT, USA
| | - Donghoon Lee
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - John F Fullard
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Gabriel E Hoffman
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Panos Roussos
- Center for Disease Neurogenomics, Departments of Psychiatry and Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yue Wang
- Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, VA, USA
| | - Xusheng Wang
- Department of Biology, University of North Dakota, Grand Forks, ND, USA
| | - Dalila Pinto
- Department of Psychiatry, Department of Genetics and Genomic Sciences, Mindich Child Health and Development Institute, and Icahn Genomics Institute for Data Science and Genomic Technology, Seaver Autism Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sidney H Wang
- Center for Human Genetics, The Brown foundation Institute of Molecular Medicine, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Chunling Zhang
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
| | - Chao Chen
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
| | - Chunyu Liu
- Department of Psychiatry, SUNY Upstate Medical University, Syracuse, NY, USA
- Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, China
- Department of Neuroscience & Physiology, SUNY Upstate Medical University, Syracuse, NY, USA
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34
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Campbell KA, Colacino JA, Puttabyatappa M, Dou JF, Elkin ER, Hammoud SS, Domino SE, Dolinoy DC, Goodrich JM, Loch-Caruso R, Padmanabhan V, Bakulski KM. Placental cell type deconvolution reveals that cell proportions drive preeclampsia gene expression differences. Commun Biol 2023; 6:264. [PMID: 36914823 PMCID: PMC10011423 DOI: 10.1038/s42003-023-04623-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2021] [Accepted: 02/22/2023] [Indexed: 03/14/2023] Open
Abstract
The placenta mediates adverse pregnancy outcomes, including preeclampsia, which is characterized by gestational hypertension and proteinuria. Placental cell type heterogeneity in preeclampsia is not well-understood and limits mechanistic interpretation of bulk gene expression measures. We generated single-cell RNA-sequencing samples for integration with existing data to create the largest deconvolution reference of 19 fetal and 8 maternal cell types from placental villous tissue (n = 9 biological replicates) at term (n = 40,494 cells). We deconvoluted eight published microarray case-control studies of preeclampsia (n = 173 controls, 157 cases). Preeclampsia was associated with excess extravillous trophoblasts and fewer mesenchymal and Hofbauer cells. Adjustment for cellular composition reduced preeclampsia-associated differentially expressed genes (log2 fold-change cutoff = 0.1, FDR < 0.05) from 1154 to 0, whereas downregulation of mitochondrial biogenesis, aerobic respiration, and ribosome biogenesis were robust to cell type adjustment, suggesting direct changes to these pathways. Cellular composition mediated a substantial proportion of the association between preeclampsia and FLT1 (37.8%, 95% CI [27.5%, 48.8%]), LEP (34.5%, 95% CI [26.0%, 44.9%]), and ENG (34.5%, 95% CI [25.0%, 45.3%]) overexpression. Our findings indicate substantial placental cellular heterogeneity in preeclampsia contributes to previously observed bulk gene expression differences. This deconvolution reference lays the groundwork for cellular heterogeneity-aware investigation into placental dysfunction and adverse birth outcomes.
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Affiliation(s)
- Kyle A Campbell
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Justin A Colacino
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | | | - John F Dou
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Elana R Elkin
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Saher S Hammoud
- Human Genetics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Department of Urology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Steven E Domino
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Dana C Dolinoy
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Jaclyn M Goodrich
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Rita Loch-Caruso
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Vasantha Padmanabhan
- Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Nutritional Sciences, School of Public Health, University of Michigan, Ann Arbor, MI, USA
- Pediatrics, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
- Obstetrics and Gynecology, Michigan Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Kelly M Bakulski
- Epidemiology, School of Public Health, University of Michigan, Ann Arbor, MI, USA.
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Song X, Ji J, Rothstein JH, Alexeeff SE, Sakoda LC, Sistig A, Achacoso N, Jorgenson E, Whittemore AS, Klein RJ, Habel LA, Wang P, Sieh W. MiXcan: a framework for cell-type-aware transcriptome-wide association studies with an application to breast cancer. Nat Commun 2023; 14:377. [PMID: 36690614 PMCID: PMC9871010 DOI: 10.1038/s41467-023-35888-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 01/05/2023] [Indexed: 01/25/2023] Open
Abstract
Human bulk tissue samples comprise multiple cell types with diverse roles in disease etiology. Conventional transcriptome-wide association study approaches predict genetically regulated gene expression at the tissue level, without considering cell-type heterogeneity, and test associations of predicted tissue-level expression with disease. Here we develop MiXcan, a cell-type-aware transcriptome-wide association study approach that predicts cell-type-level expression, identifies disease-associated genes via combination of cell-type-level association signals for multiple cell types, and provides insight into the disease-critical cell type. As a proof of concept, we conducted cell-type-aware analyses of breast cancer in 58,648 women and identified 12 transcriptome-wide significant genes using MiXcan compared with only eight genes using conventional approaches. Importantly, MiXcan identified genes with distinct associations in mammary epithelial versus stromal cells, including three new breast cancer susceptibility genes. These findings demonstrate that cell-type-aware transcriptome-wide analyses can reveal new insights into the genetic and cellular etiology of breast cancer and other diseases.
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Affiliation(s)
- Xiaoyu Song
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Jiayi Ji
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph H Rothstein
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Stacey E Alexeeff
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Lori C Sakoda
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Adriana Sistig
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ninah Achacoso
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Eric Jorgenson
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
- Regeneron Genetics Center, Tarrytown, NY, USA
| | - Alice S Whittemore
- Department of Epidemiology and Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Department of Biomedical Data Science, Stanford University School of Medicine, Stanford, CA, USA
| | - Robert J Klein
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Laurel A Habel
- Division of Research, Kaiser Permanente Northern California, Oakland, CA, USA
| | - Pei Wang
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Weiva Sieh
- Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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36
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Deng W, Li B, Wang J, Jiang W, Yan X, Li N, Vukmirovic M, Kaminski N, Wang J, Zhao H. A novel Bayesian framework for harmonizing information across tissues and studies to increase cell type deconvolution accuracy. Brief Bioinform 2023; 24:bbac616. [PMID: 36631398 PMCID: PMC9851324 DOI: 10.1093/bib/bbac616] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/28/2022] [Accepted: 12/14/2022] [Indexed: 01/13/2023] Open
Abstract
Computational cell type deconvolution on bulk transcriptomics data can reveal cell type proportion heterogeneity across samples. One critical factor for accurate deconvolution is the reference signature matrix for different cell types. Compared with inferring reference signature matrices from cell lines, rapidly accumulating single-cell RNA-sequencing (scRNA-seq) data provide a richer and less biased resource. However, deriving cell type signature from scRNA-seq data is challenging due to high biological and technical noises. In this article, we introduce a novel Bayesian framework, tranSig, to improve signature matrix inference from scRNA-seq by leveraging shared cell type-specific expression patterns across different tissues and studies. Our simulations show that tranSig is robust to the number of signature genes and tissues specified in the model. Applications of tranSig to bulk RNA sequencing data from peripheral blood, bronchoalveolar lavage and aorta demonstrate its accuracy and power to characterize biological heterogeneity across groups. In summary, tranSig offers an accurate and robust approach to defining gene expression signatures of different cell types, facilitating improved in silico cell type deconvolutions.
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Affiliation(s)
- Wenxuan Deng
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Bolun Li
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Jiawei Wang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
| | - Xiting Yan
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Ningshan Li
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Milica Vukmirovic
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
- Leslie Dan Faculty of Pharmacy, University of Toronto, 144 College St., ON, Canada
| | - Naftali Kaminski
- Section of Pulmonary, Critical Care and Sleep Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Jing Wang
- State Key Laboratory of Medical Molecular Biology, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Department of Pathophysiology, Peking Union Medical College, Beijing, China
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, 60 College Street, New Haven, CT, USA
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37
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Humphrey J, Venkatesh S, Hasan R, Herb JT, de Paiva Lopes K, Küçükali F, Byrska-Bishop M, Evani US, Narzisi G, Fagegaltier D, Sleegers K, Phatnani H, Knowles DA, Fratta P, Raj T. Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes. Nat Neurosci 2023; 26:150-162. [PMID: 36482247 DOI: 10.1038/s41593-022-01205-3] [Citation(s) in RCA: 37] [Impact Index Per Article: 37.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2021] [Accepted: 10/13/2022] [Indexed: 12/13/2022]
Abstract
Amyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. In this study, we investigated gene expression changes in ALS via RNA sequencing in 380 postmortem samples from cervical, thoracic and lumbar spinal cord segments from 154 individuals with ALS and 49 control individuals. We observed an increase in microglia and astrocyte gene expression, accompanied by a decrease in oligodendrocyte gene expression. By creating a gene co-expression network in the ALS samples, we identified several activated microglia modules that negatively correlate with retrospective disease duration. We mapped molecular quantitative trait loci and found several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how common genetic variants associated with splicing of C9orf72 act as proxies for the well-known repeat expansion, and we use the same mechanism to suggest ATXN3 as a putative risk gene.
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Affiliation(s)
- Jack Humphrey
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Sanan Venkatesh
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rahat Hasan
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jake T Herb
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Katia de Paiva Lopes
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Fahri Küçükali
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | | | | | | | - Delphine Fagegaltier
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
| | - Kristel Sleegers
- Complex Genetics of Alzheimer's Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium
- Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Hemali Phatnani
- New York Genome Center, New York, NY, USA
- Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA
- Department of Neurology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA
| | - David A Knowles
- New York Genome Center, New York, NY, USA
- Department of Computer Science, Columbia University, New York, NY, USA
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences & Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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38
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Okereke LC, Bello AU, Onwukwe EA. Toward Precision Radiotherapy: A Nonlinear Optimization Framework and an Accelerated Machine Learning Algorithm for the Deconvolution of Tumor-Infiltrating Immune Cells. Cells 2022; 11:cells11223604. [PMID: 36429031 PMCID: PMC9688486 DOI: 10.3390/cells11223604] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 11/03/2022] [Accepted: 11/09/2022] [Indexed: 11/16/2022] Open
Abstract
Tumor-infiltrating immune cells (TIICs) form a critical part of the ecosystem surrounding a cancerous tumor. Recent advances in radiobiology have shown that, in addition to damaging cancerous cells, radiotherapy drives the upregulation of immunosuppressive and immunostimulatory TIICs, which in turn impacts treatment response. Quantifying TIICs in tumor samples could form an important predictive biomarker guiding patient stratification and the design of radiotherapy regimens and combined immune-radiation treatments. As a result of several limitations associated with experimental methods for quantifying TIICs and the availability of extensive gene sequencing data, deconvolution-based computational methods have appeared as a suitable alternative for quantifying TIICs. Accordingly, we introduce and discuss a nonlinear regression approach (remarkably different from the traditional linear modeling approach of current deconvolution-based methods) and a machine learning algorithm for approximating the solution of the resulting constrained optimization problem. This way, the deconvolution problem is treated naturally, given that the gene expression levels of pure and heterogenous samples do not have a strictly linear relationship. When applied across transcriptomics datasets, our approach, which also allows the coupling of different loss functions, yields results that closely match ground-truth values from experimental methods and exhibits superior performance over popular deconvolution-based methods.
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Affiliation(s)
- Lois Chinwendu Okereke
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Correspondence:
| | - Abdulmalik Usman Bello
- Department of Pure and Applied Mathematics, Mathematics Institute (Emerging Regional Centre of Excellence (ERCE) of the European Mathematical Society (EMS)), African University of Science and Technology, Abuja 900107, Nigeria
- Department of Mathematics, Federal University Dutsin-Ma, Dutsin-Ma 821101, Nigeria
| | - Emmanuel Akwari Onwukwe
- Department of Theoretical and Applied Physics, African University of Science and Technology, Abuja 900107, Nigeria
- Inspired Innovative Sustainable (IIS) Projects & Solutions Limited, Abuja 900107, Nigeria
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39
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Chatre L, Ducat A, Spradley FT, Palei AC, Chéreau C, Couderc B, Thomas KC, Wilson AR, Amaral LM, Gaillard I, Méhats C, Lagoutte I, Jacques S, Miralles F, Batteux F, Granger JP, Ricchetti M, Vaiman D. Increased NOS coupling by the metabolite tetrahydrobiopterin (BH4) reduces preeclampsia/IUGR consequences. Redox Biol 2022; 55:102406. [PMID: 35964341 PMCID: PMC9389306 DOI: 10.1016/j.redox.2022.102406] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 07/04/2022] [Accepted: 07/11/2022] [Indexed: 11/24/2022] Open
Abstract
Preeclampsia (PE) is a high-prevalence pregnancy disease characterized by placental insufficiency, gestational hypertension, and proteinuria. Overexpression of the A isoform of the STOX1 transcription factor (STOX1A) recapitulates PE in mice, and STOX1A overexpressing trophoblasts recapitulate PE patients hallmarks in terms of gene expression and pathophysiology. STOX1 overexpression induces nitroso-redox imbalance and mitochondrial hyper-activation. Here, by a thorough analysis on cell models, we show that STOX1 overexpression in trophoblasts alters inducible nitric oxide synthase (iNOS), nitric oxide (NO) content, the nitroso-redox balance, the antioxidant defense, and mitochondrial function. This is accompanied by specific alterations of the Krebs cycle leading to reduced l-malate content. By increasing NOS coupling using the metabolite tetrahydrobiopterin (BH4) we restore this multi-step pathway in vitro. Moving in vivo on two different rodent models (STOX1 mice and RUPP rats, alike early onset and late onset preeclampsia, respectively), we show by transcriptomics that BH4 directly reverts STOX1-deregulated gene expression including glutathione metabolism, oxidative phosphorylation, cholesterol metabolism, inflammation, lipoprotein metabolism and platelet activation, successfully treating placental hypotrophy, gestational hypertension, proteinuria and heart hypertrophy. In the RUPP rats we show that the major fetal issue of preeclampsia, Intra Uterine Growth Restriction (IUGR), is efficiently corrected. Our work posits on solid bases BH4 as a novel potential therapy for preeclampsia.
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Affiliation(s)
- Laurent Chatre
- Institut Pasteur, Department of Developmental & Stem Cell Biology, Stem Cell & Development, 25-28 Rue du Dr. Roux, Paris, France; UMR 3738 CNRS, 25 Rue du Dr. Roux, Paris, 75015, France
| | - Aurélien Ducat
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Frank T Spradley
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Ana C Palei
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Christiane Chéreau
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Betty Couderc
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Kamryn C Thomas
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Anna R Wilson
- Department of Surgery, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Lorena M Amaral
- Department of Pharmacology & Toxicology, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Irène Gaillard
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Céline Méhats
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Isabelle Lagoutte
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Sébastien Jacques
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Francisco Miralles
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Frédéric Batteux
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France
| | - Joey P Granger
- Department of Physiology & Biophysics, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA
| | - Miria Ricchetti
- Institut Pasteur, Department of Developmental & Stem Cell Biology, Stem Cell & Development, 25-28 Rue du Dr. Roux, Paris, France; UMR 3738 CNRS, 25 Rue du Dr. Roux, Paris, 75015, France; Institut Pasteur, Molecular Mechanisms of Pathological and Physiological Ageing, 25-28 Rue du Dr. Roux, Paris, France
| | - Daniel Vaiman
- Institut Cochin U1016, INSERM UMR8104 CNRS, 24, rue du Fg St Jacques, Paris, France.
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40
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Shi C, Zhu J, Shen Y, Luo S, Zhu H, Song R. Off-Policy Confidence Interval Estimation with Confounded Markov Decision Process. J Am Stat Assoc 2022. [DOI: 10.1080/01621459.2022.2110876] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/15/2022]
Affiliation(s)
| | | | - Ye Shen
- North Carolina State University
| | | | - Hongtu Zhu
- University of North Carolina at Chapel Hill
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Cai M, Yue M, Chen T, Liu J, Forno E, Lu X, Billiar T, Celedón J, McKennan C, Chen W, Wang J. Robust and accurate estimation of cellular fraction from tissue omics data via ensemble deconvolution. Bioinformatics 2022; 38:3004-3010. [PMID: 35438146 PMCID: PMC9991889 DOI: 10.1093/bioinformatics/btac279] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 03/22/2022] [Accepted: 04/13/2022] [Indexed: 01/04/2023] Open
Abstract
MOTIVATION Tissue-level omics data such as transcriptomics and epigenomics are an average across diverse cell types. To extract cell-type-specific (CTS) signals, dozens of cellular deconvolution methods have been proposed to infer cell-type fractions from tissue-level data. However, these methods produce vastly different results under various real data settings. Simulation-based benchmarking studies showed no universally best deconvolution approaches. There have been attempts of ensemble methods, but they only aggregate multiple single-cell references or reference-free deconvolution methods. RESULTS To achieve a robust estimation of cellular fractions, we proposed EnsDeconv (Ensemble Deconvolution), which adopts CTS robust regression to synthesize the results from 11 single deconvolution methods, 10 reference datasets, 5 marker gene selection procedures, 5 data normalizations and 2 transformations. Unlike most benchmarking studies based on simulations, we compiled four large real datasets of 4937 tissue samples in total with measured cellular fractions and bulk gene expression from different tissues. Comprehensive evaluations demonstrated that EnsDeconv yields more stable, robust and accurate fractions than existing methods. We illustrated that EnsDeconv estimated cellular fractions enable various CTS downstream analyses such as differential fractions associated with clinical variables. We further extended EnsDeconv to analyze bulk DNA methylation data. AVAILABILITY AND IMPLEMENTATION EnsDeconv is freely available as an R-package from https://github.com/randel/EnsDeconv. The RNA microarray data from the TRAUMA study are available and can be accessed in GEO (GSE36809). The demographic and clinical phenotypes can be shared on reasonable request to the corresponding authors. The RNA-seq data from the EVAPR study cannot be shared publicly due to the privacy of individuals that participated in the clinical research in compliance with the IRB approval at the University of Pittsburgh. The RNA microarray data from the FHS study are available from dbGaP (phs000007.v32.p13). The RNA-seq data from ROS study is downloaded from AD Knowledge Portal. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Manqi Cai
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Molin Yue
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
| | - Tianmeng Chen
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
- Department of Pathology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Jinling Liu
- Department of Engineering Management and Systems Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
- Department of Biological Sciences, Missouri University of Science and Technology, Rolla, MO 65409, USA
| | - Erick Forno
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Xinghua Lu
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15206, USA
| | - Timothy Billiar
- Department of Surgery, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Juan Celedón
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Chris McKennan
- Department of Statistics, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Wei Chen
- Department of Pediatrics, University of Pittsburgh Medical Center Children’s Hospital of Pittsburgh, Pittsburgh, PA 15224, USA
| | - Jiebiao Wang
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA
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42
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Haeusler GM, Garnham AL, Li‐Wai‐Suen CSN, Clark JE, Babl FE, Allaway Z, Slavin MA, Mechinaud F, Smyth GK, Phillips B, Thursky KA, Pellegrini M, Doerflinger M. Blood transcriptomics identifies immune signatures indicative of infectious complications in childhood cancer patients with febrile neutropenia. Clin Transl Immunology 2022; 11:e1383. [PMID: 35602885 PMCID: PMC9113042 DOI: 10.1002/cti2.1383] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/03/2022] [Accepted: 03/10/2022] [Indexed: 12/13/2022] Open
Abstract
Objectives Febrile neutropenia (FN) is a major cause of treatment disruption and unplanned hospitalization in childhood cancer patients. This study investigated the transcriptome of peripheral blood mononuclear cells (PBMCs) in children with cancer and FN to identify potential predictors of serious infection. Methods Whole-genome transcriptional profiling was conducted on PBMCs collected during episodes of FN in children with cancer at presentation to the hospital (Day 1; n = 73) and within 8-24 h (Day 2; n = 28) after admission. Differentially expressed genes as well as gene pathways that correlated with clinical outcomes were defined for different infectious outcomes. Results Global differences in gene expression associated with specific immune responses in children with FN and documented infection, compared to episodes without documented infection, were identified at admission. These differences resolved over the subsequent 8-24 h. Distinct gene signatures specific for bacteraemia were identified both at admission and on Day 2. Differences in gene signatures between episodes with bacteraemia and episodes with bacterial infection, viral infection and clinically defined infection were also observed. Only subtle differences in gene expression profiles between non-bloodstream bacterial and viral infections were identified. Conclusion Blood transcriptome immune profiling analysis during FN episodes may inform monitoring and aid in defining adequate treatment for different infectious aetiologies in children with cancer.
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Affiliation(s)
- Gabrielle M Haeusler
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,The Victorian Paediatric Integrated Cancer ServiceVictoria State GovernmentMelbourneVICAustralia,Infection Diseases UnitDepartment of General MedicineRoyal Children's HospitalMelbourneVICAustralia
| | - Alexandra L Garnham
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Connie SN Li‐Wai‐Suen
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Julia E Clark
- Queensland Children's HospitalChild Health Research CentreThe University of QueenslandBrisbaneQLDAustralia
| | - Franz E Babl
- Department of Emergency MedicineRoyal Children's HospitalMelbourneVICAustralia,Murdoch Children's Research InstitutePaediatric Research in Emergency Departments International Collaborative (PREDICT)MelbourneVICAustralia,Murdoch Children's Research InstituteMelbourneVICAustralia,Department of PaediatricsFaculty of Medicine, Dentistry and Health SciencesUniversity of MelbourneMelbourneVICAustralia
| | - Zoe Allaway
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia
| | - Monica A Slavin
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Infection Diseases UnitDepartment of General MedicineRoyal Children's HospitalMelbourneVICAustralia,Victorian Infectious Diseases ServiceThe Peter Doherty Institute for Infection and ImmunityMelbourneVICAustralia
| | - Francoise Mechinaud
- Children's Cancer CentreThe Royal Children's HospitalMelbourneVICAustralia,Unité d'Hématologie Immunologie PédiatriqueHopital Robert DebréAPHP Nord Université de ParisParisFrance
| | - Gordon K Smyth
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,School of Mathematics and StatisticsUniversity of MelbourneMelbourneVICAustralia
| | - Bob Phillips
- Leeds Children's HospitalLeeds General InfirmaryLeedsUK
| | - Karin A Thursky
- Department of Infectious DiseasesPeter MacCallum Cancer CentreMelbourneVICAustralia,NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Sir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Department of Infectious DiseasesNational Centre for Antimicrobial StewardshipUniversity of MelbourneMelbourneVICAustralia
| | - Marc Pellegrini
- NHMRC National Centre for Infections in CancerSir Peter MacCallum Department of OncologyUniversity of MelbourneMelbourneVICAustralia,Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
| | - Marcel Doerflinger
- Walter and Eliza Hall Institute for Medical ResearchParkvilleVICAustralia,Department of Medical BiologyThe University of MelbourneMelbourneVICAustralia
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43
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Haimbaugh A, Meyer D, Akemann C, Gurdziel K, Baker TR. Comparative Toxicotranscriptomics of Single Cell RNA-Seq and Conventional RNA-Seq in TCDD-Exposed Testicular Tissue. FRONTIERS IN TOXICOLOGY 2022; 4:821116. [PMID: 35615540 PMCID: PMC9126299 DOI: 10.3389/ftox.2022.821116] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2021] [Accepted: 03/03/2022] [Indexed: 12/18/2022] Open
Abstract
In this report, we compare the outcomes and limitations of two methods of transcriptomic inquiry on adult zebrafish testes exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) during sexual differentiation: conventional or bulk RNA-seq (bulk-seq) and single cell RNA sequencing (scRNA-seq) data. scRNA-seq has emerged as a valuable tool for uncovering cell type-specific transcriptome dynamics which exist in heterogeneous tissue. Our lab previously showed the toxicological value of the scRNA-seq pipeline to characterize the sequelae of TCDD exposure in testes, demonstrating that loss of spermatids and spermatozoa, but not other cell types, contributed to the pathology of infertility in adult male zebrafish exposed during sexual differentiation. To investigate the potential for technical artifacts in scRNA-seq such as cell dissociation effects and reduced transcriptome coverage, we compared bulk-sequenced and scRNA-seq-paired samples from control and TCDD-exposed samples to understand what is gained and lost in scRNA-seq vs bulk-seq, both transcriptomically and toxicologically. We hypothesized that the testes may be sensitive to tissue disruption as they contain multiple cell types under constant division and/or maturation, and that TCDD exposure may mediate the extent of sensitivity. Thus, we sought to understand the extent to which this dissociation impacts the toxicological value of data returned from scRNA-seq. We confirm that the required dissociation of individual cells from intact tissue has a significant impact on gene expression, affecting gene pathways with the potential to confound toxicogenomics studies on exposures if findings are not well-controlled and well-situated in context. Additionally, a common scRNA-seq method using cDNA amplified from the 3’ end of mRNA under-detects low-expressing transcripts including transcription factors. We confirm this, and show TCDD-related genes may be overlooked by scRNA-seq, however, this under-detection effect is not mediated by TCDD exposure. Even so, scRNA-seq generally extracted toxicologically relevant information better than the bulk-seq method in the present study. This report aims to inform future experimental design for transcriptomic investigation in the growing field of toxicogenomics by demonstrating the differential information extracted from sequencing cells—despite being from the same tissue and exposure scheme—is influenced by the specific protocol used, with implications for the interpretation of exposure-induced risk.
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Affiliation(s)
- Alex Haimbaugh
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
| | - Danielle Meyer
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, United States
- Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
| | - Camille Akemann
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
| | - Katherine Gurdziel
- Genome Sciences Core, Office of the Vice President for Research, Wayne State University, Detroit, MI, United States
| | - Tracie R. Baker
- Department of Pharmacology, School of Medicine, Wayne State University, Detroit, MI, United States
- Institute of Environmental Health Sciences, Wayne State University, Detroit, MI, United States
- Department of Environmental and Global Health, University of Florida, Gainesville, FL, United States
- *Correspondence: Tracie Baker,
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44
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Studniberg SI, Ioannidis LJ, Utami RAS, Trianty L, Liao Y, Abeysekera W, Li‐Wai‐Suen CSN, Pietrzak HM, Healer J, Puspitasari AM, Apriyanti D, Coutrier F, Poespoprodjo JR, Kenangalem E, Andries B, Prayoga P, Sariyanti N, Smyth GK, Cowman AF, Price RN, Noviyanti R, Shi W, Garnham AL, Hansen DS. Molecular profiling reveals features of clinical immunity and immunosuppression in asymptomatic P. falciparum malaria. Mol Syst Biol 2022; 18:e10824. [PMID: 35475529 PMCID: PMC9045086 DOI: 10.15252/msb.202110824] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Revised: 03/30/2022] [Accepted: 03/31/2022] [Indexed: 01/12/2023] Open
Abstract
Clinical immunity to P. falciparum malaria is non-sterilizing, with adults often experiencing asymptomatic infection. Historically, asymptomatic malaria has been viewed as beneficial and required to help maintain clinical immunity. Emerging views suggest that these infections are detrimental and constitute a parasite reservoir that perpetuates transmission. To define the impact of asymptomatic malaria, we pursued a systems approach integrating antibody responses, mass cytometry, and transcriptional profiling of individuals experiencing symptomatic and asymptomatic P. falciparum infection. Defined populations of classical and atypical memory B cells and a TH2 cell bias were associated with reduced risk of clinical malaria. Despite these protective responses, asymptomatic malaria featured an immunosuppressive transcriptional signature with upregulation of pathways involved in the inhibition of T-cell function, and CTLA-4 as a predicted regulator in these processes. As proof of concept, we demonstrated a role for CTLA-4 in the development of asymptomatic parasitemia in infection models. The results suggest that asymptomatic malaria is not innocuous and might not support the induction of immune processes to fully control parasitemia or efficiently respond to malaria vaccines.
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Affiliation(s)
- Stephanie I Studniberg
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Lisa J Ioannidis
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Retno A S Utami
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia,Eijkman Institute for Molecular BiologyJakartaIndonesia
| | - Leily Trianty
- Eijkman Institute for Molecular BiologyJakartaIndonesia
| | - Yang Liao
- Olivia Newton‐John Cancer Research InstituteHeidelbergVic.Australia
| | - Waruni Abeysekera
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Connie S N Li‐Wai‐Suen
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Halina M Pietrzak
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Julie Healer
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | | | - Dwi Apriyanti
- Eijkman Institute for Molecular BiologyJakartaIndonesia
| | | | | | | | | | - Pak Prayoga
- Papuan Health and Community FoundationPapuaIndonesia
| | | | - Gordon K Smyth
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Alan F Cowman
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
| | - Ric N Price
- Global and Tropical Health DivisionMenzies School of Health Research and Charles Darwin UniversityDarwinNTAustralia,Centre for Tropical Medicine and Global HealthNuffield Department of MedicineUniversity of OxfordOxfordUK,Mahidol‐Oxford Tropical Medicine Research UnitMahidol UniversityBangkokThailand
| | | | - Wei Shi
- Olivia Newton‐John Cancer Research InstituteHeidelbergVic.Australia
| | - Alexandra L Garnham
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,School of Mathematics and StatisticsThe University of MelbourneParkvilleVic.Australia
| | - Diana S Hansen
- The Walter and Eliza Hall Institute of Medical ResearchParkvilleVic.Australia,Department of Medical BiologyThe University of MelbourneParkvilleVic.Australia
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45
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Girdhar K, Hoffman GE, Bendl J, Rahman S, Dong P, Liao W, Hauberg ME, Sloofman L, Brown L, Devillers O, Kassim BS, Wiseman JR, Park R, Zharovsky E, Jacobov R, Flatow E, Kozlenkov A, Gilgenast T, Johnson JS, Couto L, Peters MA, Phillips-Cremins JE, Hahn CG, Gur RE, Tamminga CA, Lewis DA, Haroutunian V, Dracheva S, Lipska BK, Marenco S, Kundakovic M, Fullard JF, Jiang Y, Roussos P, Akbarian S. Chromatin domain alterations linked to 3D genome organization in a large cohort of schizophrenia and bipolar disorder brains. Nat Neurosci 2022; 25:474-483. [PMID: 35332326 PMCID: PMC8989650 DOI: 10.1038/s41593-022-01032-6] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 02/09/2022] [Indexed: 12/19/2022]
Abstract
Chromosomal organization, scaling from the 147-base pair (bp) nucleosome to megabase-ranging domains encompassing multiple transcriptional units, including heritability loci for psychiatric traits, remains largely unexplored in the human brain. In this study, we constructed promoter- and enhancer-enriched nucleosomal histone modification landscapes for adult prefrontal cortex from H3-lysine 27 acetylation and H3-lysine 4 trimethylation profiles, generated from 388 controls and 351 individuals diagnosed with schizophrenia (SCZ) or bipolar disorder (BD) (n = 739). We mapped thousands of cis-regulatory domains (CRDs), revealing fine-grained, 104-106-bp chromosomal organization, firmly integrated into Hi-C topologically associating domain stratification by open/repressive chromosomal environments and nuclear topography. Large clusters of hyper-acetylated CRDs were enriched for SCZ heritability, with prominent representation of regulatory sequences governing fetal development and glutamatergic neuron signaling. Therefore, SCZ and BD brains show coordinated dysregulation of risk-associated regulatory sequences assembled into kilobase- to megabase-scaling chromosomal domains.
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Affiliation(s)
- Kiran Girdhar
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Samir Rahman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Pengfei Dong
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Will Liao
- New York Genome Center, New York, NY, USA
| | - Mads E Hauberg
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- iPSYCH, The Lundbeck Foundation Initiative for Integrative Psychiatric Research, Aarhus, Denmark
- Department of Biomedicine, Aarhus University, Aarhus, Denmark
| | - Laura Sloofman
- Seaver Autism Center for Research and Treatment, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Leanne Brown
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Olivia Devillers
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bibi S Kassim
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jennifer R Wiseman
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Royce Park
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elizabeth Zharovsky
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Rivky Jacobov
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Elie Flatow
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexey Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Thomas Gilgenast
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica S Johnson
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Lizette Couto
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Jennifer E Phillips-Cremins
- Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA, USA
| | - Chang-Gyu Hahn
- Department of Psychiatry, Vickie and Jack Farber Institute for Neuroscience, Jefferson University, Philadelphia, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, University of Pennsylvania School of Medicine, Philadelphia, PA, USA
| | - Carol A Tamminga
- Department of Psychiatry, The University of Texas Southwestern Medical School, Dallas, TX, USA
| | - David A Lewis
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA
| | - Vahram Haroutunian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Stella Dracheva
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA
| | - Barbara K Lipska
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Stefano Marenco
- Human Brain Collection Core, National Institute of Mental Health-Intramural Research Program, Bethesda, MD, USA
| | - Marija Kundakovic
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Biological Sciences, Fordham University, Bronx, NY, USA
| | - John F Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Yan Jiang
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institutes of Brain Science, State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
| | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Mental Illness Research Education and Clinical Center (MIRECC), James J. Peters VA Medical Center, Bronx, NY, USA.
- Center for Dementia Research, Nathan Kline Institute for Psychiatric Research, Orangeburg, NY, USA.
| | - Schahram Akbarian
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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46
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Comprehensive evaluation of deconvolution methods for human brain gene expression. Nat Commun 2022; 13:1358. [PMID: 35292647 PMCID: PMC8924248 DOI: 10.1038/s41467-022-28655-4] [Citation(s) in RCA: 42] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2019] [Accepted: 01/28/2022] [Indexed: 11/08/2022] Open
Abstract
Transcriptome deconvolution aims to estimate the cellular composition of an RNA sample from its gene expression data, which in turn can be used to correct for composition differences across samples. The human brain is unique in its transcriptomic diversity, and comprises a complex mixture of cell-types, including transcriptionally similar subtypes of neurons. Here, we carry out a comprehensive evaluation of deconvolution methods for human brain transcriptome data, and assess the tissue-specificity of our key observations by comparison with human pancreas and heart. We evaluate eight transcriptome deconvolution approaches and nine cell-type signatures, testing the accuracy of deconvolution using in silico mixtures of single-cell RNA-seq data, RNA mixtures, as well as nearly 2000 human brain samples. Our results identify the main factors that drive deconvolution accuracy for brain data, and highlight the importance of biological factors influencing cell-type signatures, such as brain region and in vitro cell culturing. Transcriptome deconvolution aims to estimate cellular composition based on gene expression data. Here the authors evaluate deconvolution methods for human brain transcriptome and conclude that partial deconvolution algorithms work best, but that appropriate cell-type signatures are also important.
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47
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Brown AL, Wilkins OG, Keuss MJ, Hill SE, Zanovello M, Lee WC, Bampton A, Lee FCY, Masino L, Qi YA, Bryce-Smith S, Gatt A, Hallegger M, Fagegaltier D, Phatnani H, Newcombe J, Gustavsson EK, Seddighi S, Reyes JF, Coon SL, Ramos D, Schiavo G, Fisher EMC, Raj T, Secrier M, Lashley T, Ule J, Buratti E, Humphrey J, Ward ME, Fratta P. TDP-43 loss and ALS-risk SNPs drive mis-splicing and depletion of UNC13A. Nature 2022; 603:131-137. [PMID: 35197628 PMCID: PMC8891020 DOI: 10.1038/s41586-022-04436-3] [Citation(s) in RCA: 186] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2021] [Accepted: 01/18/2022] [Indexed: 12/12/2022]
Abstract
Variants of UNC13A, a critical gene for synapse function, increase the risk of amyotrophic lateral sclerosis and frontotemporal dementia1-3, two related neurodegenerative diseases defined by mislocalization of the RNA-binding protein TDP-434,5. Here we show that TDP-43 depletion induces robust inclusion of a cryptic exon in UNC13A, resulting in nonsense-mediated decay and loss of UNC13A protein. Two common intronic UNC13A polymorphisms strongly associated with amyotrophic lateral sclerosis and frontotemporal dementia risk overlap with TDP-43 binding sites. These polymorphisms potentiate cryptic exon inclusion, both in cultured cells and in brains and spinal cords from patients with these conditions. Our findings, which demonstrate a genetic link between loss of nuclear TDP-43 function and disease, reveal the mechanism by which UNC13A variants exacerbate the effects of decreased TDP-43 function. They further provide a promising therapeutic target for TDP-43 proteinopathies.
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Affiliation(s)
- Anna-Leigh Brown
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Oscar G Wilkins
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | - Matthew J Keuss
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Sarah E Hill
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Matteo Zanovello
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Weaverly Colleen Lee
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Alexander Bampton
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Flora C Y Lee
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | | | - Yue A Qi
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Sam Bryce-Smith
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Ariana Gatt
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Martina Hallegger
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
| | - Delphine Fagegaltier
- Center for Genomics of Neurodegenerative Disease, New York Genome Center (NYGC), New York, NY, USA
| | - Hemali Phatnani
- Center for Genomics of Neurodegenerative Disease, New York Genome Center (NYGC), New York, NY, USA
| | - Jia Newcombe
- NeuroResource, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Emil K Gustavsson
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Great Ormond Street Institute of Child Health, Genetics and Genomic Medicine, University College London, London, UK
| | - Sahba Seddighi
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
- Medical Scientist Training Program, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Joel F Reyes
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
| | - Steven L Coon
- Molecular Genomics Core, Eunice Kennedy Shriver National Institute of Child Health and Human Development, NIH, Bethesda, MD, USA
| | - Daniel Ramos
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA
- Center for Alzheimer's and Related Dementias, National Institutes of Health, Bethesda, MD, USA
| | - Giampietro Schiavo
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- UK Dementia Research Institute, University College London, London, UK
| | - Elizabeth M C Fisher
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Maria Secrier
- Department of Genetics, Evolution and Environment, UCL Genetics Institute, University College London, London, UK
| | - Tammaryn Lashley
- Queen Square Brain Bank, UCL Queen Square Institute of Neurology, University College London, London, UK
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Jernej Ule
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK
- The Francis Crick Institute, London, UK
- Department of Molecular Biology and Nanobiotechnology, National Institute of Chemistry, Ljubljana, Slovenia
| | - Emanuele Buratti
- Molecular Pathology Lab, International Centre for Genetic Engineering and Biotechnology (ICGEB), Trieste, Italy
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Michael E Ward
- National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD, USA.
| | - Pietro Fratta
- UCL Queen Square Motor Neuron Disease Centre, Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, UCL, London, UK.
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48
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Hasan R, Humphrey J, Bettencourt C, Newcombe J, Lashley T, Fratta P, Raj T. Transcriptomic analysis of frontotemporal lobar degeneration with TDP-43 pathology reveals cellular alterations across multiple brain regions. Acta Neuropathol 2022; 143:383-401. [PMID: 34961893 PMCID: PMC10725322 DOI: 10.1007/s00401-021-02399-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2021] [Revised: 12/07/2021] [Accepted: 12/10/2021] [Indexed: 12/28/2022]
Abstract
Frontotemporal lobar degeneration (FTLD) is a group of heterogeneous neurodegenerative disorders affecting the frontal and temporal lobes of the brain. Nuclear loss and cytoplasmic aggregation of the RNA-binding protein TDP-43 represents the major FTLD pathology, known as FTLD-TDP. To date, there is no effective treatment for FTLD-TDP due to an incomplete understanding of the molecular mechanisms underlying disease development. Here we compared postmortem tissue RNA-seq transcriptomes from the frontal cortex, temporal cortex, and cerebellum between 28 controls and 30 FTLD-TDP patients to profile changes in cell-type composition, gene expression and transcript usage. We observed downregulation of neuronal markers in all three regions of the brain, accompanied by upregulation of microglia, astrocytes, and oligodendrocytes, as well as endothelial cells and pericytes, suggesting shifts in both immune activation and within the vasculature. We validate our estimates of neuronal loss using neuropathological atrophy scores and show that neuronal loss in the cortex can be mainly attributed to excitatory neurons, and that increases in microglial and endothelial cell expression are highly correlated with neuronal loss. All our analyses identified a strong involvement of the cerebellum in the neurodegenerative process of FTLD-TDP. Altogether, our data provides a detailed landscape of gene expression alterations to help unravel relevant disease mechanisms in FTLD.
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Affiliation(s)
- Rahat Hasan
- Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jack Humphrey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
| | - Conceição Bettencourt
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Jia Newcombe
- NeuroResource, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, UK
| | - Tammaryn Lashley
- Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Queen Square Brain Bank for Neurological Disorders, UCL Queen Square Institute of Neurology, London, UK
| | - Pietro Fratta
- Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK
| | - Towfique Raj
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Ronald M. Loeb Center for Alzheimer's Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Department of Genetics and Genomic Sciences and Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
- Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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49
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Danaher P, Kim Y, Nelson B, Griswold M, Yang Z, Piazza E, Beechem JM. Advances in mixed cell deconvolution enable quantification of cell types in spatial transcriptomic data. Nat Commun 2022; 13:385. [PMID: 35046414 PMCID: PMC8770643 DOI: 10.1038/s41467-022-28020-5] [Citation(s) in RCA: 97] [Impact Index Per Article: 48.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2021] [Accepted: 12/10/2021] [Indexed: 12/11/2022] Open
Abstract
Mapping cell types across a tissue is a central concern of spatial biology, but cell type abundance is difficult to extract from spatial gene expression data. We introduce SpatialDecon, an algorithm for quantifying cell populations defined by single cell sequencing within the regions of spatial gene expression studies. SpatialDecon incorporates several advancements in gene expression deconvolution. We propose an algorithm harnessing log-normal regression and modelling background, outperforming classical least-squares methods. We compile cell profile matrices for 75 tissue types. We identify genes whose minimal expression by cancer cells makes them suitable for immune deconvolution in tumors. Using lung tumors, we create a dataset for benchmarking deconvolution methods against marker proteins. SpatialDecon is a simple and flexible tool for mapping cell types in spatial gene expression studies. It obtains cell abundance estimates that are spatially resolved, granular, and paired with highly multiplexed gene expression data.
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Affiliation(s)
| | | | | | | | - Zhi Yang
- NanoString Technologies, Seattle, WA, USA
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50
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Hoffman GE, Ma Y, Montgomery KS, Bendl J, Jaiswal MK, Kozlenkov A, Peters MA, Dracheva S, Fullard JF, Chess A, Devlin B, Sieberts SK, Roussos P. Sex Differences in the Human Brain Transcriptome of Cases With Schizophrenia. Biol Psychiatry 2022; 91:92-101. [PMID: 34154796 PMCID: PMC8463632 DOI: 10.1016/j.biopsych.2021.03.020] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Revised: 03/16/2021] [Accepted: 03/18/2021] [Indexed: 01/03/2023]
Abstract
BACKGROUND While schizophrenia differs between males and females in the age of onset, symptomatology, and disease course, the molecular mechanisms underlying these differences remain uncharacterized. METHODS To address questions about the sex-specific effects of schizophrenia, we performed a large-scale transcriptome analysis of RNA sequencing data from 437 controls and 341 cases from two distinct cohorts from the CommonMind Consortium. RESULTS Analysis across the cohorts identified a reproducible gene expression signature of schizophrenia that was highly concordant with previous work. Differential expression across sex was reproducible across cohorts and identified X- and Y-linked genes, as well as those involved in dosage compensation. Intriguingly, the sex expression signature was also enriched for genes involved in neurexin family protein binding and synaptic organization. Differential expression analysis testing a sex-by-diagnosis interaction effect did not identify any genome-wide signature after multiple testing corrections. Gene coexpression network analysis was performed to reduce dimensionality from thousands of genes to dozens of modules and elucidate interactions among genes. We found enrichment of coexpression modules for sex-by-diagnosis differential expression signatures, which were highly reproducible across the two cohorts and involved a number of diverse pathways, including neural nucleus development, neuron projection morphogenesis, and regulation of neural precursor cell proliferation. CONCLUSIONS Overall, our results indicate that the effect size of sex differences in schizophrenia gene expression signatures is small and underscore the challenge of identifying robust sex-by-diagnosis signatures, which will require future analyses in larger cohorts.
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Affiliation(s)
- Gabriel E Hoffman
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York.
| | - Yixuan Ma
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | | | - Jaroslav Bendl
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Manoj Kumar Jaiswal
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - Alex Kozlenkov
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | | | - Stella Dracheva
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York
| | - John F Fullard
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Andrew Chess
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania
| | | | - Panos Roussos
- Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, New York; Icahn Institute for Data Science and Genomic Technology, Icahn School of Medicine at Mount Sinai, New York, New York; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, New York; Mental Illness Research, Education and Clinical Centers, James J. Peters VA Medical Center, Bronx, New York.
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