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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/

References

  1. Ravits, J. M. & La Spada, A. R. ALS motor phenotype heterogeneity, focality, and spread: deconstructing motor neuron degeneration. Neurology 73, 805–811 (2009).

    Google Scholar 

  2. Byrne, S. et al. Rate of familial amyotrophic lateral sclerosis: a systematic review and meta-analysis. J. Neurol. Neurosurg. Psychiatry 82, 623–627 (2011).

    Google Scholar 

  3. Majounie, E. et al. Frequency of the C9orf72 hexanucleotide repeat expansion in patients with amyotrophic lateral sclerosis and frontotemporal dementia: a cross-sectional study. Lancet Neurol. 11, 323–330 (2012).

    CAS  Google Scholar 

  4. Renton, A. E., Chiò, A. & Traynor, B. J. State of play in amyotrophic lateral sclerosis genetics. Nat. Neurosci. 17, 17–23 (2014).

    CAS  Google Scholar 

  5. Cirulli, E. T. et al. Exome sequencing in amyotrophic lateral sclerosis identifies risk genes and pathways. Nat. Methods 347, 1436–1441 (2016).

    Google Scholar 

  6. Kenna, K. P. et al. NEK1 variants confer susceptibility to amyotrophic lateral sclerosis. Nat. Genet. 48, 1037–1042 (2016).

    CAS  Google Scholar 

  7. Nicolas, A. et al. Genome-wide analyses identify KIF5A as a novel ALS gene. Neuron 97, 1268–1283 (2018).

    CAS  Google Scholar 

  8. van Es, M. A. et al. Genome-wide association study identifies 19p13.3 (UNC13A) and 9p21.2 as susceptibility loci for sporadic amyotrophic lateral sclerosis. Nat. Genet. 41, 1083–1087 (2009).

    Google Scholar 

  9. Van Rheenen, W. et al. Genome-wide association analyses identify new risk variants and the genetic architecture of amyotrophic lateral sclerosis. Nat. Genet. 48, 1043–1048 (2016).

    Google Scholar 

  10. van Rheenen, W. et al. Common and rare variant association analyses in amyotrophic lateral sclerosis identify 15 risk loci with distinct genetic architectures and neuron-specific biology. Nat. Genet. 53, 1636–1648 (2021).

    Google Scholar 

  11. Elden, A. C. et al. Ataxin-2 intermediate-length polyglutamine expansions are associated with increased risk for ALS. Nature 466, 1069–1075 (2010).

    CAS  Google Scholar 

  12. Tazelaar, G. H. P. et al. ATXN1 repeat expansions confer risk for amyotrophic lateral sclerosis and contribute to TDP-43 mislocalization. Brain Commun. 2, fcaa064 (2020).

    Google Scholar 

  13. Lattante, S. et al. ATXN1 intermediate-length polyglutamine expansions are associated with amyotrophic lateral sclerosis. Neurobiol. Aging 64, 157.e1–157.e5 (2018).

    CAS  Google Scholar 

  14. Hirano, M. et al. Noncoding repeat expansions for ALS in Japan are associated with the ATXN8OS gene. Neurol. Genet. 4, e252 (2018).

    CAS  Google Scholar 

  15. Giambartolomei, C. et al. Bayesian test for colocalisation between pairs of genetic association studies using summary statistics. PLoS Genet. 10, e1004383 (2014).

    Google Scholar 

  16. Young, A. M. H. et al. A map of transcriptional heterogeneity and regulatory variation in human microglia. Nat. Genet. 53, 861–868 (2021).

    CAS  Google Scholar 

  17. Novikova, G. et al. Integration of Alzheimer’s disease genetics and myeloid genomics identifies disease risk regulatory elements and genes. Nat. Commun. 12, 1610 (2021).

    CAS  Google Scholar 

  18. Lopes, K. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat. Genet. 54, 4–17 (2022).

    CAS  Google Scholar 

  19. Pramatarova, A., Laganière, J., Roussel, J., Brisebois, K. & Rouleau, G. A. Neuron-specific expression of mutant superoxide dismutase 1 in transgenic mice does not lead to motor impairment. J. Neurosci. 21, 3369–3374 (2001).

    CAS  Google Scholar 

  20. Jaarsma, D., Teuling, E., Haasdijk, E. D., De Zeeuw, C. I. & Hoogenraad, C. C. Neuron-specific expression of mutant superoxide dismutase is sufficient to induce amyotrophic lateral sclerosis in transgenic mice. J. Neurosci. 28, 2075–2088 (2008).

    CAS  Google Scholar 

  21. Yamanaka, K. et al. Astrocytes as determinants of disease progression in inherited amyotrophic lateral sclerosis. Nat. Neurosci. 11, 251–253 (2008).

    CAS  Google Scholar 

  22. Lepore, A. C. et al. Focal transplantation-based astrocyte replacement is neuroprotective in a model of motor neuron disease. Nat. Neurosci. 11, 1294–1301 (2008).

    CAS  Google Scholar 

  23. Boillée, S. et al. Onset and progression in inherited ALS determined by motor neurons and microglia. Science 312, 1389–1392 (2006).

    Google Scholar 

  24. Wang, L., Sharma, K., Grisotti, G. & Roos, R. P. The effect of mutant SOD1 dismutase activity on non-cell autonomous degeneration in familial amyotrophic lateral sclerosis. Neurobiol. Dis. 35, 234–240 (2009).

    CAS  Google Scholar 

  25. Phatnani, H. P. et al. Intricate interplay between astrocytes and motor neurons in ALS. Proc. Natl Acad. Sci. USA 110, E756–E765 (2013).

    CAS  Google Scholar 

  26. Town, T., Nikolic, V. & Tan, J. The microglial ‘activation’ continuum: from innate to adaptive responses. J. Neuroinflammation 2, 24 (2005).

    Google Scholar 

  27. Chiu, I. M. et al. A neurodegeneration-specific gene-expression signature of acutely isolated microglia from an amyotrophic lateral sclerosis mouse model. Cell Rep. 4, 385–401 (2013).

    CAS  Google Scholar 

  28. Liddelow, S. A. et al. Neurotoxic reactive astrocytes are induced by activated microglia. Nature 541, 481–487 (2017).

    CAS  Google Scholar 

  29. Zhao, W. et al. Activated microglia initiate motor neuron injury by a nitric oxide and glutamate-mediated mechanism. J. Neuropathol. Exp. Neurol. 63, 964–977 (2004).

    CAS  Google Scholar 

  30. Haidet-Phillips, A. M. et al. Astrocytes from familial and sporadic ALS patients are toxic to motor neurons. Nat. Biotechnol. 29, 824–828 (2011).

    CAS  Google Scholar 

  31. Guttenplan, K. A. et al. Knockout of reactive astrocyte activating factors slows disease progression in an ALS mouse model. Nat. Commun. 11, 3753 (2020).

    CAS  Google Scholar 

  32. D’Erchia, A. M. et al. Massive transcriptome sequencing of human spinal cord tissues provides new insights into motor neuron degeneration in ALS. Sci. Rep. 7, 10046 (2017).

  33. Brohawn, D. G., O’Brien, L. C. & Bennett, J. P. Jr. RNAseq analyses identify tumor necrosis factor-mediated inflammation as a major abnormality in ALS spinal cord. PLoS ONE 11, e0160520 (2016).

    Google Scholar 

  34. Andrés-Benito, P., Moreno, J., Aso, E., Povedano, M. & Ferrer, I. Amyotrophic lateral sclerosis, gene deregulation in the anterior horn of the spinal cord and frontal cortex area 8: implications in frontotemporal lobar degeneration. Aging 9, 823–851 (2017).

    Google Scholar 

  35. Dols-Icardo, O. et al. Motor cortex transcriptome reveals microglial key events in amyotrophic lateral sclerosis. Neurol. Neuroimmunol. Neuroinflamm. 7, e829 (2020).

    Google Scholar 

  36. Thompson, A. G. et al. Cerebrospinal fluid macrophage biomarkers in amyotrophic lateral sclerosis. Ann. Neurol. 83, 258–268 (2018).

    CAS  Google Scholar 

  37. Tanaka, H. et al. The potential of GPNMB as novel neuroprotective factor in amyotrophic lateral sclerosis. Sci. Rep. 2, 573 (2012).

    Google Scholar 

  38. Oeckl, P. et al. Proteomics in cerebrospinal fluid and spinal cord suggests UCHL1, MAP2 and GPNMB as biomarkers and underpins importance of transcriptional pathways in amyotrophic lateral sclerosis. Acta Neuropathol. 139, 119–134 (2020).

    CAS  Google Scholar 

  39. Hüttenrauch, M. et al. Glycoprotein NMB: a novel Alzheimer’s disease associated marker expressed in a subset of activated microglia. Acta Neuropathol. Commun. 6, 108 (2018).

    Google Scholar 

  40. Murthy, M. N. et al. Increased brain expression of GPNMB is associated with genome wide significant risk for Parkinson’s disease on chromosome 7p15.3. Neurogenetics 18, 121–133 (2017).

    CAS  Google Scholar 

  41. Nalls, M. A. et al. Identification of novel risk loci, causal insights, and heritable risk for Parkinson’s disease: a meta-analysis of genome-wide association studies. Lancet Neurol. 18, 1091–1102 (2019).

    CAS  Google Scholar 

  42. Halter, B. et al. Oxidative stress in skeletal muscle stimulates early expression of Rad in a mouse model of amyotrophic lateral sclerosis. Free Radic. Biol. Med. 48, 915–923 (2010).

    CAS  Google Scholar 

  43. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    CAS  Google Scholar 

  44. Liberzon, A. et al. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 1, 417–425 (2015).

    CAS  Google Scholar 

  45. Mathys, H. et al. Single-cell transcriptomic analysis of Alzheimer’s disease. Nature 570, 332–337 (2019).

    CAS  Google Scholar 

  46. Keren-Shaul, H. et al. A unique microglia type associated with restricting development of Alzheimer’s disease. Cell 169, 1276–1290 (2017).

    CAS  Google Scholar 

  47. Habib, N. et al. Disease-associated astrocytes in Alzheimer’s disease and aging. Nat. Neurosci. 23, 701–706 (2020).

    CAS  Google Scholar 

  48. Zamanian, J. L. et al. Genomic analysis of reactive astrogliosis. J. Neurosci. 32, 6391–6410 (2012).

    CAS  Google Scholar 

  49. Chen, W.-T. et al. Spatial transcriptomics and in situ sequencing to study Alzheimer’s disease. Cell 182, 976–991 (2020).

    CAS  Google Scholar 

  50. Darmanis, S. et al. A survey of human brain transcriptome diversity at the single cell level. Proc. Natl Acad. Sci. USA 112, 7285–7290 (2015).

    CAS  Google Scholar 

  51. Wang, X., Park, J., Susztak, K., Zhang, N. R. & Li, M. Bulk tissue cell type deconvolution with multi-subject single-cell expression reference. Nat. Commun. 10, 380 (2019).

    CAS  Google Scholar 

  52. Hunt, G. J., Freytag, S., Bahlo, M. & Gagnon-Bartsch, J. A. dtangle: accurate and robust cell type deconvolution. Bioinformatics 35, 2093–2099 (2019).

    CAS  Google Scholar 

  53. Skene, N. G. & Grant, S. G. N. Identification of vulnerable cell types in major brain disorders using single cell transcriptomes and expression weighted cell type enrichment. Front. Neurosci. 10, 16 (2016).

    Google Scholar 

  54. Prudencio, M. et al. Distinct brain transcriptome profiles in C9orf72-associated and sporadic ALS. Nat. Neurosci. 18, 1175–1182 (2015).

    CAS  Google Scholar 

  55. Dickson, D. W. et al. Extensive transcriptomic study emphasizes importance of vesicular transport in C9orf72 expansion carriers. Acta Neuropathol. Commun. 7, 150 (2019).

    Google Scholar 

  56. Dolzhenko, E. et al. ExpansionHunter: a sequence-graph based tool to analyze variation in short tandem repeat regions. Preprint at https://www.biorxiv.org/content/10.1101/572545v2 (2019).

  57. Jackson, J. L. et al. Elevated methylation levels, reduced expression levels, and frequent contractions in a clinical cohort of C9orf72 expansion carriers. Mol. Neurodegener. 15, 7 (2020).

  58. Oldham, M. C. et al. Functional organization of the transcriptome in human brain. Nat. Neurosci. 11, 1271–1282 (2008).

    CAS  Google Scholar 

  59. Prudencio, M. et al. Truncated stathmin-2 is a marker of TDP-43 pathology in frontotemporal dementia. J. Clin. Invest. 130, 6080–6092 (2020).

    CAS  Google Scholar 

  60. Klim, J. R. et al. ALS-implicated protein TDP-43 sustains levels of STMN2, a mediator of motor neuron growth and repair. Nat. Neurosci. 22, 167–179 (2019).

    CAS  Google Scholar 

  61. Melamed, Z. et al. Premature polyadenylation-mediated loss of stathmin-2 is a hallmark of TDP-43-dependent neurodegeneration. Nat. Neurosci. 22, 180–190 (2019).

    CAS  Google Scholar 

  62. Ticozzi, N. et al. Paraoxonase gene mutations in amyotrophic lateral sclerosis. Ann. Neurol. 68, 102–107 (2010).

    CAS  Google Scholar 

  63. Li, Y. I. et al. Annotation-free quantification of RNA splicing using LeafCutter. Nat. Genet. 50, 151–158 (2018).

    CAS  Google Scholar 

  64. de Paiva Lopes, K. et al. Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies. Nat. Genet. 54, 4–17 (2022).

    Google Scholar 

  65. GTEx Consortium. The GTEx Consortium atlas of genetic regulatory effects across human tissues. Science 369, 1318–1330 (2020).

  66. Kim-Hellmuth, S. et al. Cell type-specific genetic regulation of gene expression across human tissues. Science 369, eaaz8528 (2020).

    CAS  Google Scholar 

  67. Li, Y. I., Wong, G., Humphrey, J. & Raj, T. Prioritizing Parkinson’s disease genes using population-scale transcriptomic data. Nat. Commun. 10, 994 (2019).

    Google Scholar 

  68. Kelley, K. W., Nakao-Inoue, H., Molofsky, A. V. & Oldham, M. C. Variation among intact tissue samples reveals the core transcriptional features of human CNS cell classes. Nat. Neurosci. 21, 1171–1184 (2018).

    CAS  Google Scholar 

  69. O’Rourke, J. G. et al. C9orf72 is required for proper macrophage and microglial function in mice. Science 351, 1324–1329 (2016).

    Google Scholar 

  70. Renton, A. E. et al. A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD. Neuron 72, 257–268 (2011).

    CAS  Google Scholar 

  71. DeJesus-Hernandez, M. et al. Expanded GGGGCC hexanucleotide repeat in noncoding region of C9ORF72 causes chromosome 9p-linked FTD and ALS. Neuron 72, 245–256 (2011).

    CAS  Google Scholar 

  72. Nakamura, R. et al. A multi-ethnic meta-analysis identifies novel genes, including ACSL5, associated with amyotrophic lateral sclerosis. Commun. Biol. 3, 526 (2020).

    CAS  Google Scholar 

  73. Paulson, H. Machado–Joseph Disease/Spinocerebellar Ataxia Type 3. in Genetic Instabilities and Neurological Diseases 2nd ed 363–377 (Academic Press, 2006).

  74. Seidel, K. et al. Axonal inclusions in spinocerebellar ataxia type 3. Acta Neuropathol. 120, 449–460 (2010).

    CAS  Google Scholar 

  75. Prudencio, M. et al. Toward allele-specific targeting therapy and pharmacodynamic marker for spinocerebellar ataxia type 3. Sci. Transl. Med. 12, eabb7086 (2020).

  76. Kang, S. H. et al. Degeneration and impaired regeneration of gray matter oligodendrocytes in amyotrophic lateral sclerosis. Nat. Neurosci. 16, 571–579 (2013).

    CAS  Google Scholar 

  77. Zondler, L. et al. Peripheral monocytes are functionally altered and invade the CNS in ALS patients. Acta Neuropathol. 132, 391–411 (2016).

    CAS  Google Scholar 

  78. Saul, J. et al. Global alterations to the choroid plexus blood–CSF barrier in amyotrophic lateral sclerosis. Acta Neuropathol. Commun. 8, 92 (2020).

    CAS  Google Scholar 

  79. Månberg, A. et al. Publisher correction: Altered perivascular fibroblast activity precedes ALS disease onset. Nat. Med. 27, 1308 (2021).

    Google Scholar 

  80. Brettschneider, J. et al. Microglial activation correlates with disease progression and upper motor neuron clinical symptoms in amyotrophic lateral sclerosis. PLoS ONE 7, e39216 (2012).

    CAS  Google Scholar 

  81. Varghese, A. M. et al. Chitotriosidase, a biomarker of amyotrophic lateral sclerosis, accentuates neurodegeneration in spinal motor neurons through neuroinflammation. J. Neuroinflammation 17, 232 (2020).

    CAS  Google Scholar 

  82. Pagliardini, V. et al. Chitotriosidase and lysosomal enzymes as potential biomarkers of disease progression in amyotrophic lateral sclerosis: a survey clinic-based study. J. Neurol. Sci. 348, 245–250 (2015).

    CAS  Google Scholar 

  83. Wainberg, M. et al. Opportunities and challenges for transcriptome-wide association studies. Nat. Genet. 51, 592–599 (2019).

    CAS  Google Scholar 

  84. Tam, O. H. et al. Postmortem cortex samples identify distinct molecular subtypes of ALS: retrotransposon activation, oxidative stress, and activated glia. Cell Rep. 29, 1164–1177 (2019).

    CAS  Google Scholar 

  85. Schroeder, A. et al. The RIN: an RNA integrity number for assigning integrity values to RNA measurements. BMC Mol. Biol. 7, 3 (2006).

    Google Scholar 

  86. Di Tommaso, P. et al. Nextflow enables reproducible computational workflows. Nat. Biotechnol. 35, 316–319 (2017).

    Google Scholar 

  87. Bolduc, B. Quality control of reads using Trimmomatic (Cyverse) V.1. https://doi.org/10.17504/protocols.io.ewbbfan (2016).

  88. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    CAS  Google Scholar 

  89. Harrow, J., Frankish, A., Gonzalez, J. M. & Frazer, K. A. GENCODE: the reference human genome annotation for the ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  Google Scholar 

  90. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).

    CAS  Google Scholar 

  91. Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Google Scholar 

  92. Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).

    CAS  Google Scholar 

  93. Hoffman, G. E. & Schadt, E. E. variancePartition: interpreting drivers of variation in complex gene expression studies. BMC Bioinformatics 17, 483 (2016).

    Google Scholar 

  94. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  Google Scholar 

  95. Law, C. W., Chen, Y., Shi, W. & Smyth, G. K. voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 15, R29 (2014).

    Google Scholar 

  96. Risso, D., Schwartz, K., Sherlock, G. & Dudoit, S. GC-content normalization for RNA-seq data. BMC Bioinformatics 12, 480 (2011).

    CAS  Google Scholar 

  97. Yu, G., Wang, L.-G., Han, Y. & He, Q.-Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16, 284–287 (2012)

    CAS  Google Scholar 

  98. Franzén, O., Gan, L.-M. & Björkegren, J. L. M. PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data. Database (Oxford) 2019, baz046 (2019).

  99. Mancarci, B. O. et al. Cross-laboratory analysis of brain cell type transcriptomes with applications to interpretation of bulk tissue data. eNeuro 4, ENEURO.0212-17.2017 (2017).

  100. Mancarci, O. & French, L. Package ‘homologeneʼ: quick access to homologene and gene annotation updates. https://cran.r-project.org/web/packages/homologene/homologene.pdf (2019).

  101. Reimand, J., Kull, M., Peterson, H., Hansen, J. & Vilo, J. g:Profiler—a web-based toolset for functional profiling of gene lists from large-scale experiments. Nucleic Acids Res. 35, W193–W200 (2007).

    Google Scholar 

  102. Love, M. I., Soneson, C. & Robinson, M. D. Importing transcript abundance datasets with tximport. https://bioconductor.statistik.tu-dortmund.de/packages/3.6/bioc/vignettes/tximport/inst/doc/tximport.html (2017).

  103. Stegle, O., Parts, L., Piipari, M., Winn, J. & Durbin, R. Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses. Nat. Protoc. 7, 500 (2012).

    CAS  Google Scholar 

  104. Taylor-Weiner, A. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol. 20, 228 (2019).

    Google Scholar 

  105. Feng, Y.-Y. et al. RegTools: integrated analysis of genomic and transcriptomic data for discovery of splicing variants in cancer. Preprint at https://www.biorxiv.org/content/10.1101/436634v5 (2018).

  106. Storey, J. D. The positive false discovery rate: a Bayesian interpretation and the q-value. Ann. Stat. 31, 2013–2035 (2003).

    Google Scholar 

  107. Oscanoa, J. et al. SNPnexus: a web server for functional annotation of human genome sequence variation (2020 update). Nucleic Acids Res. 48, W185–W192 (2020).

    CAS  Google Scholar 

  108. Myers, T. A., Chanock, S. J. & Machiela, M. J. LDlinkR: an R package for rapidly calculating linkage disequilibrium statistics in diverse populations. Front. Genet. 11, 157 (2020).

    Google Scholar 

  109. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer-Verlag, 2009).

  110. Slowikowski, K. ggrepel: repulsive text and label geoms for ‘ggplot2. https://cran.microsoft.com/snapshot/2016-08-05/web/packages/ggrepel/index.html (2016).

  111. Tang, Y., Horikoshi, M. & Li, W. ggfortify: unified interface to visualize statistical results of popular R packages. The R Journal 8, 474–485 (2016).

    Google Scholar 

  112. Pedersen, T. L. patchwork: the composer of plots. https://mran.revolutionanalytics.com/snapshot/2020-04-25/web/packages/patchwork/index.html (2019).

  113. Xu, S. et al. Use ggbreak to effectively utilize plotting space to deal with large datasets and outliers. Front. Genet. 12, 774846 (2021).

    CAS  Google Scholar 

  114. Yin, T., Cook, D. & Lawrence, M. ggbio: an R package for extending the grammar of graphics for genomic data. Genome Biol. 13, R77 (2012).

    Google Scholar 

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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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  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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