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Altered perivascular fibroblast activity precedes ALS disease onset

Nature Medicine volume 27, pages 640–646 (2021)Cite this article

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A Publisher Correction to this article was published on 02 June 2021

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Abstract

Apart from well-defined factors in neuronal cells1, only a few reports consider that the variability of sporadic amyotrophic lateral sclerosis (ALS) progression can depend on less-defined contributions from glia2,3 and blood vessels4. In this study we use an expression-weighted cell-type enrichment method to infer cell activity in spinal cord samples from patients with sporadic ALS and mouse models of this disease. Here we report that patients with sporadic ALS present cell activity patterns consistent with two mouse models in which enrichments of vascular cell genes preceded microglial response. Notably, during the presymptomatic stage, perivascular fibroblast cells showed the strongest gene enrichments, and their marker proteins SPP1 and COL6A1 accumulated in enlarged perivascular spaces in patients with sporadic ALS. Moreover, in plasma of 574 patients with ALS from four independent cohorts, increased levels of SPP1 at disease diagnosis repeatedly predicted shorter survival with stronger effect than the established risk factors of bulbar onset or neurofilament levels in cerebrospinal fluid. We propose that the activity of the recently discovered perivascular fibroblast can predict survival of patients with ALS and provide a new conceptual framework to re-evaluate definitions of ALS etiology.

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Fig. 1: Patients with ALS show increased transcriptional activity of perivascular fibroblast cell gene markers that occur at presymptomatic disease stage in SOD1G93A and TARDBPQ331K mice.
Fig. 2: Perivascular fibroblast marker proteins COL6A1 and SPP1 accumulate in enlarged perivascular spaces during ALS progression.
Fig. 3: Prognostic value of SPP1 protein in plasma of patients with ALS.

Data availability

Transcriptome datasets and analysis scripts from human patients with ALS are available at GitHub (https://github.com/NathanSkene/ALS_Human_EWCE), SOD1G93A mouse transcriptome datasets and analysis scripts are available at https://github.com/NathanSkene/ALS_Mouse_EWCE, and TARDBPQ331K/ Q331K mouse transcriptome datasets and analysis scripts are available at https://github.com/szczepinskaa/ALS_TDP-43.git. Images and analysis scripts for human histology are deposited and available under the following links: SPP1 in Red (https://doi.org/10.6084/m9.figshare.11632794), COL6A1 in Red (https://doi.org/10.6084/m9.figshare.11635887), SPP1 in DAB (https://doi.org/10.6084/m9.figshare.11628891), and COL6A1 in DAB (https://doi.org/10.6084/m9.figshare.11628585). Additional data tables are available at https://doi.org/10.6084/m9.figshare.14035616. Patient plasma data and analysis scripts for cutoff optimization and Cox proportional hazards model are available at GitHub (https://github.com/lewandowskilab/PVF_Manuscript).

Code availability

Transcriptome analysis scripts from human patients with ALS are available at GitHub (https://github.com/NathanSkene/ALS_Human_EWCE), SOD1G93A mouse transcriptome analysis scripts are available at https://github.com/NathanSkene/ALS_Mouse_EWCE, and TARDBPQ331K/ Q331K mouse transcriptome analysis scripts are available at https://github.com/szczepinskaa/ALS_TDP-43.git. Images and analysis scripts for human histology are deposited and available under the following links: SPP1 in Red training model (https://doi.org/10.6084/m9.figshare.11632794), COL6A1 in Red training model (https://doi.org/10.6084/m9.figshare.11635887), SPP1 in DAB training model (https://doi.org/10.6084/m9.figshare.11628891), and COL6A1 in DAB training model and macro (https://doi.org/10.6084/m9.figshare.11628585). Patient plasma analysis scripts for cutoff optimization and Cox proportional hazards model are available at GitHub (https://github.com/lewandowskilab/PVF_Manuscript).

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Acknowledgements

S.A.L. is supported by the Olle Engkvist Byggmästare Foundation (SLS-499431); Ulla-Carin Lindquists Stiftelse för ALS-forskning, Åhléns Foundation (mA2/h17, 203074); the Thierry Latran Foundation (FIB-ALS); and Neuroförbundet. N.S. was supported by the Wellcome Trust (108726/Z/15/Z), Edmond J. Safra Foundation, Lily Safra and UK Dementia Research Institute. We thank the ALS Stichting grant ‘The Dutch ALS Tissue Bank’ (E.A.) and Netherlands Brain Bank (E.H.) for providing ALS tissue samples. We acknowledge the team who helped in the collection of ALS tissue samples (D. Troost, M. de Visser, A.J. van der Kooi and J. Raaphorst). U.K. and C.I. are supported by Björklunds Fund, the Ulla-Carin Lindquist Foundation, Neuro Sweden and SLL Hälsa Medicin och Teknik. E.R.-V. is supported by the Swedish Alzheimer Foundation (Alzheimerfonden), Swedish Dementia Association (Demensfonden), Gun & Bertil Stohne’s Foundation and Gamla Tjänarinnor Foundation. This project has received funding from the European Research Council under the European Union’s Horizon 2020 Research and Innovation Program (grant agreement no. 772376 – EScORIAL) awarded to J.V. R.A.H. is supported by Alltid Litt Sterkere, AlzheimerFonden, Swedish Medical Research Council, Swedish Brain Foundation and the Karolinska Institutet. M.L. was supported by a grant from the Knut and Alice Wallenberg Foundation (2012.0091). This study was also supported by grants to P.N. from the Swedish FTD Initiative funded by the Schörling Family Foundation and the KTH Center for Applied Precision Medicine funded by the Erling-Persson Family Foundation. P.V.D. holds a senior clinical investigatorship of FWO-Vlaanderen and is supported by the E. von Behring Chair for Neuromuscular and Neurodegenerative Disorders, the ALS Liga België and the KU Leuven funds ‘Een Hart voor ALS’, ‘Laeversfonds voor ALS Onderzoek’ and the ‘Valéry Perrier Race against ALS Fund’. Several authors of this publication are members of the European Reference Network for Rare Neuromuscular Diseases. We also thank K. Hultenby at the Karolinska Institutet’s electron microscopy facility; G. Ella Thorlacius, M.-G. Hong, S. Bergström, J. Yousef, H. Sarlus and A. Manouchehrinia for support with data analysis; and T. Brännström and M. Marklund for assistance with selection and collection of SOD1G93A mouse tissues. S.A.L. thanks the unpaid interns on Erasmus scholarships for their efforts and contributions.

Author information

Author notes

  1. Annemarie Hübers

    Present address: Division of Neurology, Geneva University Hospital, Geneva, Switzerland

  2. These authors contributed equally: Anna Månberg, Nathan Skene.

Authors and Affiliations

  1. Division of Affinity Proteomics, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden

    Anna Månberg, Julia Remnestål, Jennie Olofsson, Peter Nilsson & Sebastian A. Lewandowski

  2. Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden

    Nathan Skene, Marta Trusohamn & Peter Lönnerberg

  3. Division of Neuroscience, Department of Brain Sciences, Imperial College London, London, UK

    Nathan Skene

  4. United Kingdom Dementia Research Institute, London, UK

    Nathan Skene

  5. Department of Clinical Neuroscience, Karolinska Institute, Centre for Molecular Medicine, Karolinska Hospital, Stockholm, Sweden

    Folkert Sanders, Anna Szczepińska, Inci Sevval Aksoylu, Stefan Wouters, Inti von Gohren Antequera, Aylin Domaniku, Robert A. Harris & Sebastian A. Lewandowski

  6. Department of Laboratory Medicine, Karolinska Institute, Stockholm, Sweden

    Lwaki Ebarasi

  7. Department of Integrative Medical Biology, Umeå University, Umeå, Sweden

    Manuela Lehmann & Jonathan D. Gilthorpe

  8. Laboratory for Neurobiomarker Research, Department of Neurology, Leuven Brain Institute, KU Leuven (University of Leuven), Leuven, Belgium

    Maxim De Schaepdryver & Koen Poesen

  9. Neurology Department and Center for Brain & Disease Research, KU Leuven, VIB, Leuven, Belgium

    Joke De Vocht & Philip Van Damme

  10. Laboratory Medicine, UZ Leuven (University Hospital Leuven), Leuven, Belgium

    Koen Poesen

  11. Division of Systems Biology, Department of Protein Science, KTH Royal Institute of Technology, SciLifeLab, Stockholm, Sweden

    Mathias Uhlén

  12. Department of Neuroscience, Karolinska Institute, Stockholm, Sweden

    Mathias Uhlén & Eva Hedlund

  13. Department of (Neuro)Pathology, Amsterdam UMC, University of Amsterdam, Amsterdam Neuroscience, Amsterdam, the Netherlands

    Jasper Anink, Caroline Mijnsbergen & Eleonora Aronica

  14. UMC Utrecht Brain Center, University Medical Center Utrecht, Department of Neurology, Utrecht University, Utrecht, the Netherlands

    Hermieneke Vergunst-Bosch & Jan Veldink

  15. University of Ulm, Neurology Clinic, Ulm, Germany

    Annemarie Hübers & Albert Ludolph

  16. Department of Neurology, Karolinska University Hospital, Stockholm, Sweden

    Ulf Kläppe & Caroline Ingre

  17. Division of Clinical Geriatrics, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm, Sweden

    Elena Rodriguez-Vieitez

  18. Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE), Ulm, Bonn, Germany

    Albert Ludolph

  19. Department of Clinical Neuroscience, Karolinska Institute, Stockholm, Sweden

    Caroline Ingre

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  1. Anna Månberg
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  2. Nathan Skene
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Contributions

N.S. and S.A.L. designed transcriptomics enrichment experiments. N.S. and A.S. performed the computational analysis of enrichment experiments. M.T., I.S.A., A.D. and P.L. performed additional transcriptomics analysis. M.L., J.D.G. and S.A.L. facilitated and performed mouse histology and immunostaining. I.V.G.A. performed histology validation experiments. L.E., M.T. and S.A.L. performed electron microscopy imaging and quantifications. R.A.H., E.H., J.A., C.M. and E.A. facilitated and performed human histology staining. Clinical centers in Ulm (A.H. and A.L.), Utrecht (H.V.B. and J.V.), Leuven (J.D.V., M.D.S., K.P. and P.V.D.) and Stockholm (C.I. and U.K.) designed patient and control cohorts and provided plasma or CSF samples. S.A.L., A.M. and J.R. designed and chose protein targets for plasma profiling. A.M., J.R., J.O., F.S., S.W., M.U., P.N., E.R.-W. and C.I. facilitated and performed plasma profiling, survival associations and clinical parameter statistics. S.A.L. wrote the manuscript with input from the co-authors. P.N. and S.A.L. oversaw all aspects of the study.

Corresponding author

Correspondence to Sebastian A. Lewandowski.

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Competing interests

The authors declare no competing interests.

Additional information

Peer reviewer information Nature Medicine thanks Robert Baloh and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Jerome Staal was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Cell type-specific transcripts regulated in SOD1G93A mice identified by EWCE rankings.

RNA expression dynamics graphs show genes activated at selected timepoints of ALS progression in spinal cords of SOD1G93A mice. Expression is shown as fold of change ± SEM. Cell type RNA specificity graphs show data from single cell transcriptomics of mouse cortex and oligodendrocyte lineage (Zeisel et al. 2015, Marques et al. 2016). Bars represent relative count of RNA per cell ± SEM and median values from the following n number of cells: Perivascular fibroblasts (PVF=76), Vascular smooth muscle (vSMC=62), Endothelium (EC=137), Pericytes (PC=21), Microglia (MG=98), Astrocytes (AC=224), Interneurons (IN=290), Excitatory neurons (EN=1338), Oligodendrocytes (OD=4528) and Oligodendrocyte precursors (OPC=449).

Extended Data Fig. 2 Vascular cell-enriched transcripts regulated in SOD1G93A mice identified by EWCE rankings.

a, RNA expression dynamics graphs show genes activated at selected timepoints of ALS progression in spinal cords of SOD1G93A mice. Cell type RNA specificity graphs show data from single cell transcriptomics of mouse cortex with oligodendrocyte lineage (set 1: Zeisel et al. 2015, Marques et al. 2016) and brain vascular resource (set 2: Vanlandewijck et al. 2018). Bars represent median count of RNA per cell ± SEM. b, P-values for two -tailed ANOVA and t-test for genes presented in Fig. 1e. c, P-values for EWCE scores for graphs presented in Fig. 1b.

Extended Data Fig. 3 Enlarged perivascular spaces coincide with separation of astrocyte and mural basement membranes in sporadic ALS patients.

a, COL6A1 histochemistry indicates enlarged perivascular spaces in sALS patient spinal cord grey matter (ROI 1). Suggested location of endothelial cell (EC - grey arrow), mural cell (MC - black arrow) and perivascular fibroblast (PVF - red arrow) in relation to COL6A1 layers (ROI 2,3), bar: 200 μm (overview), 10 μm (ROIs). b, Two color histochemistry indicates separation of astrocyte basement membrane (LAMA1) from mural basement membrane (COL4A1) in sALS patients, bar: 10 μm.

Extended Data Fig. 4 COL6A1 and SPP1 proteins accumulate in enlarged perivascular spaces in sporadic ALS patients’ spinal cords.

a, Two-color histochemistry for COL6A1 or SPP1 (red) relative to mural basement membrane outlined with COL6A1 (blue). Four sporadic ALS cases are represented with non neurological control spinal cords, bar: 100 μm. b, Correlation of SPP1 and COL6A1 histochemistry with pathological scores for GFAP, HLA-DR and motor neuron (MN) loss. Spearman test R-rho and two-tailed p-vales are indicated in each graph.

Extended Data Fig. 5 Basement membrane gene ontology groups are enriched in presymptomatic stages of ALS.

Enrichment of gene sets from gene ontology groups (basement membrane - GO 0005604, basement membrane organization - GO 0071711 and collagen metabolic process - GO 0032963) in sporadic ALS patients and SOD1G93A mice. Significantly regulated genes are shown as red circles (○). Mroast test analysis from the limma R package. Multiple test (FDR) adjusted gene expression enrichment is shown as blue bar. p - FDR adjusted two-tailed mroast test p-value.

Extended Data Fig. 6 ALS patient cohort summary statistics.

a, Proportions between ALS patient and control samples with age and gender balance. n numbers are indicated within bars. b, Proportions within ALS patients for onset location, survival status and genetic vs. sporadic disease. c, Distributions within ALS patients for plasma sampling delay, age at symptom onset, proportion of known survival and ALS-FRS-R scale at sampling.

Extended Data Fig. 7 Antibodies against perivascular fibroblast enriched proteins used in the plasma profiling.

a, Antibodies for PVF cell proteins ranked by the survival estimate p-value (Kaplan-Meier log rank test) between protein value quartiles in plasma from ALS patients in the Utrecht cohort. Age at onset, site of onset and gender were used as covariates. b, Aminoacid sequences of E.coli expressed protein epitope signature tags (PrESTs) for COL6A1 (UniProt ID P12109) and SPP1 (UniProt ID P10451) used for immunisation.

Extended Data Fig. 8 Prognostic value of SPP1 and COL6A1 proteins in plasma of ALS patients within individual country cohorts.

a, SPP1 (HPA027541), (b) SPP1 (HPA005562) - technical valifdation antibody and (c) COL6A1 (HPA01914). Relative levels of protein levels in ALS patients and age-matched controls. Red color indicates thresholded protein level. Automated threshold selection using maximally selected log rank statistics. Boxplots show median, 2nd-3rd quartile and whiskers show ±1.5 of the IQR. COL6A1 protein values in the Belgian cohort did not indicate an optimized threshold and were separated at the median value. Kaplan-Meier survival estimate of high and low protein groups with two-tailed K-M logrank test p-values.

Extended Data Fig. 9 Prognostic value of SPP1 (validation antibody HPA005562) or COL6A1 proteins in plasma and CSF of ALS patients.

a, Relative levels of COL6A1 and SPP1 proteins in plasma, threshold selection and Kaplan-Meier survival estimates of ALS patients in discovery (n=452) and replication (n=122) cohorts. Red color indicates thresholded protein level. Thresholds are established using maximally selected log rank statistics. All boxplots show median, 2nd-3rd quartile and whiskers show ±1.5 of the IQR. COL6A1 protein values in the replication cohort did not indicate an optimized threshold and were separated at the median. Two-tailed Kaplan-Meier logrank test p-values. b, Cox proportional hazard models for continuous increase of SPP1 (HPA005562) and COL6A1 relative to hazard ratios indicated by gender, bulbar onset type plasma sampling age and cohort identity. Hazard ratios for Neurofilament light in CSF are indicated in the replication cohort. Whiskers represent 95% CI. c, Relative levels of SPP1 protein in CSF from the Swedish cohort from the two antibodies (HPA027541 and HPA005562). Threshold selection and Kaplan-Meier survival estimates of ALS patients. Two-tailed Kaplan-Meier logrank test p-values.

Extended Data Fig. 10 Plasma and CSF content of COL6A1 and SPP1 proteins in the Swedish cohort.

a, Correlations between the COL6A1 and SPP1 antibodies in the early sampling. Two-tailed Spearman test Rho and p-values are indicated within each graph. Error bands represent 95% CI. b, Relative levels of COL6A1 (HPA019142 antibody) and SPP1 protein (HPA027541 and HPA005562 antibodies) in plasma and CSF as referenced to sibling or spouse controls. All boxplots show median, 2nd-3rd quartile and whiskers show ±1.5 of the IQR. Wilcoxon test p-values are indicated above brackets. c, COL6A1 and SPP1 protein values during longitudinal sampling of plasma and CSF. Two-tailed Wilcoxon test p-values are indicated above brackets.

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Månberg, A., Skene, N., Sanders, F. et al. Altered perivascular fibroblast activity precedes ALS disease onset. Nat Med 27, 640–646 (2021). https://doi.org/10.1038/s41591-021-01295-9

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