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Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes

Nature Neuroscience volume 26, pages 150–162 (2023)Cite this article

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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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Fig. 1: Differential gene expression in the ALS spinal cord is driven by cell type composition.
Fig. 2: Gene co-expression network in the ALS spinal cord.
Fig. 3: Gene expression correlations with duration of disease.
Fig. 4: QTLs co-localize with putative ALS risk variants.
Fig. 5: Annotating co-localized genes with cell type information.
Fig. 6: sQTLs illuminate genetic associations with repeat expansions in C9orf72 and ATXN3.

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Data availability

All raw RNA-seq data can be accessed via the National Center for Biotechnology Information’s Gene Expression Omnibus database (GSE137810, GSE124439, GSE116622 and GSE153960). Processed gene expression count matrices with de-identified metadata have been deposited on Zenodo (https://doi.org/10.5281/zenodo.6385747), and we provide an R Markdown vignette on downloading them and performing differential expression (see URLs). In addition, we provide an interactive R Shiny app to visualize the gene expression and other clinical variable associations (see URLs). Full summary statistics for expression and sQTLs have been deposited on Zenodo (https://doi.org/10.5281/zenodo.5248758). All TWAS weight files have been deposited on Zenodo (https://doi.org/10.5281/zenodo.5256613). All RNA-seq and whole-genome sequencing data generated by the NYGC ALS Consortium are made immediately available to all members of the Consortium and with other consortia with which we have a reciprocal sharing arrangement. To request immediate access to new and ongoing data generated by the NYGC ALS Consortium and for samples provided through the Target ALS Postmortem Core, complete a genetic data request form at CGND_help@nygenome.org. All whole-genome sequencing data will be deposited on dbGaP at the conclusion of the project in late 2023.

Code availability

All analysis code written in R is available in R Markdown workbooks in a GitHub repository, and specific data processing pipelines are in separate repositories (see URLs).

URLs

Website associated with this manuscript, including all code notebooks written for this project:

https://jackhump.github.io/ALS_SpinalCord_QTLs/

Gene expression counts and TPMs with de-identified metadata:

https://zenodo.org/record/6385747

Code vignette demonstrating how to download data and perform differential expression with R: https://jackhump.github.io/ALS_SpinalCord_QTLs/html/DE_Vignette.html

R Shiny app for visualization:

https://rstudio-connect.hpc.mssm.edu/als_spinal_cord_browser/

Full QTL summary statistics:

https://zenodo.org/record/5248758

Full TWAS weights:

https://doi.org/10.5281/zenodo.5256613

MSigDB:

http://www.gsea-msigdb.org/gsea/msigdb/index.jsp

Kelley et al.68 gene fidelity marker genes:

http://oldhamlab.ctec.ucsf.edu/data-download/

NeuroExpresso marker genes:

http://neuroexpresso.org/

PanglaoDB marker genes:

https://panglaodb.se/

ENCODE Blacklist:

https://github.com/Boyle-Lab/Blacklist/blob/master/lists/hg38-blacklist.v2.bed.gz

WGS QC pipeline:

https://github.com/jackhump/WGS-QC-Pipeline

QTL mapping pipeline:

https://github.com/RajLabMSSM/QTL-mapping-pipeline

DLPFC TWAS weights:

http://gusevlab.org/projects/fusion/#reference-functional-data

ExpansionHunter:

https://github.com/Illumina/ExpansionHunter

SNPnexus:

https://www.snp-nexus.org/v4/

VCFs of 1000 Genomes samples: ftp://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/release/20190312_biallelic_SNV_and_INDEL/

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Acknowledgements

We thank all members of the Raj laboratory for their feedback on the manuscript. This work was supported by National Institutes of Health (NIH) National Institute on Aging grants R56-AG055824 and U01-AG068880 (J.H. and T.R.), NIH National Institute of Neurological Disorders and Stroke grant U54NS123743 (J.H., T.R. and P.F.) and NIH Medical Scientist Training Program grant T3GM007280 (J.T.H.). P.F. is supported by a UK Medical Research Council Senior Clinical Fellowship and the Lady Edith Wolfson Fellowship (MR/M008606/1 and MR/S006508/1). F.K. is supported by a BOF DOCPRO fellowship from the University of Antwerp Research Fund. P.F. is supported by the UK Motor Neurone Disease Association, the Rosetrees Trust and the UCLH NIHR Biomedical Research Centre. This work was supported, in part, through the computational resources and staff expertise provided by Scientific Computing at the Icahn School of Medicine at Mount Sinai. Research reported in this paper was supported by the Office of Research Infrastructure of the NIH under award numbers S10OD018522 and S10OD026880. All NYGC ALS Consortium activities are supported by the ALS Association (19-SI-459) and the Tow Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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  1. A list of members and their affiliations appears in the Supplementary Information.

Authors and Affiliations

  1. Nash Family Department of Neuroscience & Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jack Humphrey, Sanan Venkatesh, Rahat Hasan, Katia de Paiva Lopes & Towfique Raj

  2. Ronald M. Loeb Center for Alzheimer’s Disease, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jack Humphrey, Rahat Hasan, Katia de Paiva Lopes & Towfique Raj

  3. 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

    Jack Humphrey, Sanan Venkatesh, Rahat Hasan, Katia de Paiva Lopes & Towfique Raj

  4. Estelle and Daniel Maggin Department of Neurology, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jack Humphrey, Rahat Hasan, Katia de Paiva Lopes & Towfique Raj

  5. Department of Psychiatry, Pamela Sklar Division of Psychiatric Genomics, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Sanan Venkatesh

  6. Graduate School of Biomedical Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA

    Jake T. Herb

  7. Complex Genetics of Alzheimer’s Disease Group, Center for Molecular Neurology, VIB, Antwerp, Belgium

    Fahri Küçükali & Kristel Sleegers

  8. Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium

    Fahri Küçükali & Kristel Sleegers

  9. New York Genome Center, New York, NY, USA

    Marta Byrska-Bishop, Uday S. Evani, Giuseppe Narzisi, Delphine Fagegaltier, Hemali Phatnani & David A. Knowles

  10. Center for Genomics of Neurodegenerative Disease, New York Genome Center, New York, NY, USA

    Delphine Fagegaltier & Hemali Phatnani

  11. Department of Neurology, Columbia University Irving Medical Center, Columbia University, New York, NY, USA

    Hemali Phatnani

  12. Department of Computer Science, Columbia University, New York, NY, USA

    David A. Knowles

  13. Department of Neuromuscular Diseases, UCL Queen Square Institute of Neurology, London, UK

    Pietro Fratta

Authors
  1. Jack Humphrey
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  2. Sanan Venkatesh
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  3. Rahat Hasan
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  4. Jake T. Herb
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  5. Katia de Paiva Lopes
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  6. Fahri Küçükali
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  7. Marta Byrska-Bishop
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  8. Uday S. Evani
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  9. Giuseppe Narzisi
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  12. Hemali Phatnani
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  13. David A. Knowles
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  14. Pietro Fratta
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  15. Towfique Raj
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Consortia

NYGC ALS Consortium

Contributions

J.H. and T.R. conceived and designed the project. J.H. led the main analysis, with S.V., R.H., J.T.H., K.P.L., F.K., K.S., M.B.B., G.N. and U.S.E. contributing code and performing additional data analyses. J.H. and T.R. oversaw all aspects of the study, with input from D.A.K., H.P. and P.F. D.F. and H.P. designed the sample collection methodology, reviewed sample and data quality and coordinated NYGC ALS Consortium postmortem RNA research activity. The NYGC ALS Consortium and the Target ALS Human Postmortem Tissue Core provided human tissue samples as well as pathological, genetic and clinical information. J.H. wrote the manuscript, with input from all co-authors.

Corresponding authors

Correspondence to Jack Humphrey or Towfique Raj.

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The authors declare no competing interests.

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Nature Neuroscience thanks Sali Farhan and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Supplementary information

Supplementary Information

Supplementary Results, Discussion and Methods and Supplementary Figs. 1–26

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Supplementary Table

Supplementary Tables 1–15 and Supplementary Acknowledgements

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Humphrey, J., Venkatesh, S., Hasan, R. et al. Integrative transcriptomic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes. Nat Neurosci 26, 150–162 (2023). https://doi.org/10.1038/s41593-022-01205-3

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  • DOI: https://doi.org/10.1038/s41593-022-01205-3

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