LAG3 is not expressed in human and murine neurons and does not modulate α‐synucleinopathies

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Abstract

While the initial pathology of Parkinson’s disease and other α‐synucleinopathies is often confined to circumscribed brain regions, it can spread and progressively affect adjacent and distant brain locales. This process may be controlled by cellular receptors of α‐synuclein fibrils, one of which was proposed to be the LAG3 immune checkpoint molecule. Here, we analysed the expression pattern of LAG3 in human and mouse brains. Using a variety of methods and model systems, we found no evidence for LAG3 expression by neurons. While we confirmed that LAG3 interacts with α‐synuclein fibrils, the specificity of this interaction appears limited. Moreover, overexpression of LAG3 in cultured human neural cells did not cause any worsening of α‐synuclein pathology ex vivo. The overall survival of A53T α‐synuclein transgenic mice was unaffected by LAG3 depletion, and the seeded induction of α‐synuclein lesions in hippocampal slice cultures was unaffected by LAG3 knockout. These data suggest that the proposed role of LAG3 in the spreading of α‐synucleinopathies is not universally valid.

Abstract

This study re‐evaluated the role of neuronal lymphocyte‐activation gene 3 (LAG3) in modulating the spreading of α‐synucleinopathies.

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Most cited references 77

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Comprehensive Integration of Single-Cell Data

Tim Stuart, Andrew Butler, Paul Hoffman … (2019)

Single-cell transcriptomics has transformed our ability to characterize cell states, but deep biological understanding requires more than a taxonomic listing of clusters. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets to better understand cellular identity and function. Here, we develop a strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities. After demonstrating improvement over existing methods for integrating scRNA-seq data, we anchor scRNA-seq experiments with scATAC-seq to explore chromatin differences in closely related interneuron subsets and project protein expression measurements onto a bone marrow atlas to characterize lymphocyte populations. Lastly, we harmonize in situ gene expression and scRNA-seq datasets, allowing transcriptome-wide imputation of spatial gene expression patterns. Our work presents a strategy for the assembly of harmonized references and transfer of information across datasets.

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

Rahul Satija, Efthymia Papalexi, Peter Smibert … (2018)

Computational single-cell RNA-seq (scRNA-seq) methods have been successfully applied to experiments representing a single condition, technology, or species to discover and define cellular phenotypes. However, identifying subpopulations of cells that are present across multiple data sets remains challenging. Here, we introduce an analytical strategy for integrating scRNA-seq data sets based on common sources of variation, enabling the identification of shared populations across data sets and downstream comparative analysis. We apply this approach, implemented in our R toolkit Seurat (http://satijalab.org/seurat/), to align scRNA-seq data sets of peripheral blood mononuclear cells under resting and stimulated conditions, hematopoietic progenitors sequenced using two profiling technologies, and pancreatic cell 'atlases' generated from human and mouse islets. In each case, we learn distinct or transitional cell states jointly across data sets, while boosting statistical power through integrated analysis. Our approach facilitates general comparisons of scRNA-seq data sets, potentially deepening our understanding of how distinct cell states respond to perturbation, disease, and evolution.

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An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.

Ye Zhang, Kenian Chen, Steven A Sloan … (2014)

The major cell classes of the brain differ in their developmental processes, metabolism, signaling, and function. To better understand the functions and interactions of the cell types that comprise these classes, we acutely purified representative populations of neurons, astrocytes, oligodendrocyte precursor cells, newly formed oligodendrocytes, myelinating oligodendrocytes, microglia, endothelial cells, and pericytes from mouse cerebral cortex. We generated a transcriptome database for these eight cell types by RNA sequencing and used a sensitive algorithm to detect alternative splicing events in each cell type. Bioinformatic analyses identified thousands of new cell type-enriched genes and splicing isoforms that will provide novel markers for cell identification, tools for genetic manipulation, and insights into the biology of the brain. For example, our data provide clues as to how neurons and astrocytes differ in their ability to dynamically regulate glycolytic flux and lactate generation attributable to unique splicing of PKM2, the gene encoding the glycolytic enzyme pyruvate kinase. This dataset will provide a powerful new resource for understanding the development and function of the brain. To ensure the widespread distribution of these datasets, we have created a user-friendly website (http://web.stanford.edu/group/barres_lab/brain_rnaseq.html) that provides a platform for analyzing and comparing transciption and alternative splicing profiles for various cell classes in the brain. Copyright © 2014 the authors 0270-6474/14/3411929-19$15.00/0.

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Author and article information

Contributors

Adriano Aguzzi:

ORCID: https://orcid.org/0000-0002-0344-6708

adriano.aguzzi@usz.ch

Journal

Journal ID (nlm-ta): EMBO Mol Med

Journal ID (iso-abbrev): EMBO Mol Med

Journal ID (doi): 10.1002/(ISSN)1757-4684

Journal ID (publisher-id): EMMM

Journal ID (hwp): embomm

Title: EMBO Molecular Medicine

Publisher: John Wiley and Sons Inc. (Hoboken )

ISSN (Print): 1757-4676

ISSN (Electronic): 1757-4684

Publication date (Electronic): 26 July 2021

Publication date (Print): 07 September 2021

Volume: 13

Issue: 9 ( doiID: 10.1002/emmm.v13.9 )

Electronic Location Identifier: e14745

Affiliations

[ ¹ ] Institute of Neuropathology University of Zurich Zurich Switzerland

[ ² ] Department of Quantitative Biomedicine University of Zurich Zurich Switzerland

[ ³ ] German Center for Neurodegenerative Diseases (DZNE) Tübingen Germany

[ ⁴ ] Department of Cellular Neurology Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany

[ ⁵ ] Yusuf Hamied Department of Chemistry Centre for Misfolding Diseases University of Cambridge Cambridge UK

[ ⁶ ] Cell Biology and Gene Expression Section Laboratory of Neurogenetics National Institute on Aging National Institutes of Health Bethesda MD USA

[ ⁷ ] Department of Physiology, Anatomy and Genetics Oxford Parkinson’s Disease Center (OPDC) Oxford University Oxford UK

[ ⁸ ] Department of Pathology and Laboratory Medicine and Center for Neurodegenerative Disease Research University of Pennsylvania Perelman School of Medicine Philadelphia PA USA

[ ⁹ ] Laboratory of Neurodegenerative Diseases CNRS Institut François Jacob (MIRCen) CEA Fontenay‐aux‐Roses France

[ ¹⁰ ] Department of Neurodegeneration Hertie Institute for Clinical Brain Research University of Tübingen Tübingen Germany

[ ¹¹ ] Cavendish Laboratory Department of Physics University of Cambridge Cambridge UK

Author notes

[*] [* ] Corresponding author. Tel: +41 44 255 21 07; E‐mail: adriano.aguzzi@ 123456usz.ch

[†]

These authors contributed equally to this work

Author information

Marc Emmenegger https://orcid.org/0000-0002-6073-8811

Elena De Cecco https://orcid.org/0000-0002-0148-2596

Marian Hruska‐Plochan https://orcid.org/0000-0002-9253-4362

Matthias M Schneider https://orcid.org/0000-0002-1894-1859

Elena Tantardini https://orcid.org/0000-0001-9189-3390

Mehtap Bacioglu https://orcid.org/0000-0003-0304-7026

Nora Bengoa‐Vergniory https://orcid.org/0000-0002-3700-0464

Merve Avar https://orcid.org/0000-0003-4665-5558

Daniel Heinzer https://orcid.org/0000-0002-3282-4042

Natalie Landeck https://orcid.org/0000-0002-8399-4009

Kelvin Luk https://orcid.org/0000-0002-6591-6269

Ronald Melki https://orcid.org/0000-0003-0000-7096

Simone Hornemann https://orcid.org/0000-0002-2674-9891

Mark R Cookson https://orcid.org/0000-0002-1058-3831

Mathias Jucker https://orcid.org/0000-0001-9045-1072

Adriano Aguzzi https://orcid.org/0000-0002-0344-6708

Article

Publisher ID: EMMM202114745

DOI: 10.15252/emmm.202114745

PMC ID: 8422075

PubMed ID: 34309222

SO-VID: c598ce1a-ac66-4ca6-8b72-8606b572dac9

License:

This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

History

Date revision received : 05 July 2021

Date received : 18 June 2021

Date accepted : 06 July 2021

Page count

Figures: 12, Tables: 2, Pages: 20, Words: 16627

Funding

Funded by: University of Zurich and University Hospital of Zurich

Funded by: Swiss Personalized Health Network

Award ID: 2017DRI17

Funded by: NOMIS Stiftung (NOMIS Foundation) , doi 10.13039/501100008483;

Funded by: European Research Council (ERC)

Award ID: ERC Prion2020

Award ID: 670958

Funded by: EU Horizon 2020

Award ID: ETN grant 674979‐Nanotrans

Funded by: National Centre for Competence in Research (NCCR)

Award ID: 51NF40‐182880

Funded by: Swiss National Science Foundation Project Grant

Award ID: 310030_192650

Funded by: EU/EFPIA/Innovative Medicines Initiative 2 Joint Undertaking

Award ID: 116060

Funded by: Forschungskredit University of Zurich

Custom metadata

source-schema-version-number 2.0

cover-date 07 September 2021

details-of-publishers-convertor Converter:WILEY_ML3GV2_TO_JATSPMC version:6.0.7 mode:remove_FC converted:07.09.2021

ScienceOpen disciplines: Molecular medicine

Keywords: lag3,neurodegeneration,prionoids,α‐synuclein,neuroscience

Data availability:

ScienceOpen disciplines: Molecular medicine

Keywords: lag3, neurodegeneration, prionoids, α‐synuclein, neuroscience

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LAG3 is not expressed in human and murine neurons and does not modulate α‐synucleinopathies

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Genes & Diseases

Most cited references 77

Comprehensive Integration of Single-Cell Data

Integrating single-cell transcriptomic data across different conditions, technologies, and species

An RNA-sequencing transcriptome and splicing database of glia, neurons, and vascular cells of the cerebral cortex.

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Cited by 23

Most referenced authors 1,398