1. Cell Biology
  2. Neuroscience

A validated antibody toolbox for ALS research

  1. Riham Ayoubi
  2. Emma J MacDougall
  3. Ian McDowell
  4. Michael S Biddle
  5. Bárbara T Ferreira
  6. CongYao Zha
  7. Marie-France Dorion
  8. Jay P Ross
  9. Sara Gonzalez Bolivar
  10. Vera Ruiz Moleon
  11. Charles Alende
  12. Vincent Francis
  13. Maryam Fotouhi
  14. Mathilde Chaineau
  15. Carol X Chen
  16. Valerio EC Piscopo
  17. Vincent Soubannier
  18. Irina Shlaifer
  19. Zhipeng You
  20. Michael Nicouleau
  21. Tracy Keates
  22. Wen Hwa Lee
  23. Brian D Marsden
  24. Leonidas Koukouflis
  25. Edvard Wigren
  26. Carolyn A Marks
  27. Luke M Healy
  28. Patrick A Dion
  29. Guy A Rouleau
  30. Edward A Fon
  31. Harvinder S Virk
  32. Susanne Gräslund
  33. Opher Gileadi
  34. Aled M Edwards
  35. Thomas M Durcan
  36. Peter S McPherson
  37. Carl Laflamme  Is a corresponding author
  1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Canada
  2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Canada
  3. NIHR Respiratory Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, United Kingdom
  4. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Canada
  5. Department of Genetics, Stanford University School of Medicine, United States
  6. Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, United Kingdom
  7. Centre for Human Genetics and Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, United Kingdom
  8. Nuffield Department of Medicine, University of Oxford, United Kingdom
  9. Structural Genomics Consortium, Department of Medicine, Karolinska Institute, Sweden
  10. Neuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Canada
  11. School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Canada
https://doi.org/10.7554/eLife.111349.3
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Cite this article
  1. Riham Ayoubi
  2. Emma J MacDougall
  3. Ian McDowell
  4. Michael S Biddle
  5. Bárbara T Ferreira
  6. CongYao Zha
  7. Marie-France Dorion
  8. Jay P Ross
  9. Sara Gonzalez Bolivar
  10. Vera Ruiz Moleon
  11. Charles Alende
  12. Vincent Francis
  13. Maryam Fotouhi
  14. Mathilde Chaineau
  15. Carol X Chen
  16. Valerio EC Piscopo
  17. Vincent Soubannier
  18. Irina Shlaifer
  19. Zhipeng You
  20. Michael Nicouleau
  21. Tracy Keates
  22. Wen Hwa Lee
  23. Brian D Marsden
  24. Leonidas Koukouflis
  25. Edvard Wigren
  26. Carolyn A Marks
  27. Luke M Healy
  28. Patrick A Dion
  29. Guy A Rouleau
  30. Edward A Fon
  31. Harvinder S Virk
  32. Susanne Gräslund
  33. Opher Gileadi
  34. Aled M Edwards
  35. Thomas M Durcan
  36. Peter S McPherson
  37. Carl Laflamme
(2026)
A validated antibody toolbox for ALS research
eLife 15:RP111349.
https://doi.org/10.7554/eLife.111349.3
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eLife Assessment

Overall, this is an important article that delivers an invaluable community resource. The execution is relatively simple, but the evidence of this resource is solid. The effort invested in generating the knockout lines that for validation experiments is a clear strength of the study, but it does not substitute for a careful evaluation of the same reagents in the end-users’ specific protocols before making any biological statement.

https://doi.org/10.7554/eLife.111349.3.sa0

Abstract

A substantial fraction of amyotrophic lateral sclerosis (ALS)-associated proteins remains poorly characterized, in part because of the limited availability of validated research antibodies. We established knockout (KO)-based antibody characterization workflows and demonstrated that widely used antibodies against the major ALS-associated protein C9orf72 lacked specificity (Laflamme et al., 2019). We subsequently scaled this framework to systematically benchmark research antibodies, revealing that up to 61% fail to perform as recommended by manufacturers (Ayoubi et al., 2023b). Here, we extend this approach by establishing the ALS-Reproducible Antibody Platform (ALS-RAP), a comprehensive effort to generate a publicly available dataset of KO-validated antibodies targeting proteins encoded by ALS risk genes. In total, we characterized 303 antibodies against 33 ALS-associated proteins to identify high-quality reagents for use in western blot, immunoprecipitation, and immunofluorescence. Using these antibodies, we profiled protein levels across human induced pluripotent stem cell-derived and primary neurological cell types. These analyses revealed diverse cellular distributions and higher levels of several ALS-associated proteins in glial populations, consistent with emerging evidence for immune contributions to ALS. Together, ALS-RAP provides a validated antibody toolbox and protein expression resource to support the study of ALS-associated proteins.

Introduction

Research antibodies are among the most widely used tools for protein detection and quantification in biomedical research, yet their performance and specificity are often insufficiently characterized (Kahn et al., 2023; Biddle et al., 2024; Baker, 2015; Goodman, 2018). Building on our previous studies establishing knockout (KO)-based antibody characterization workflows and benchmarking antibody performance (Laflamme et al., 2019; Ayoubi et al., 2023b), we applied this framework to proteins encoded by amyotrophic lateral sclerosis (ALS) risk genes, a genetically defined but mechanistically heterogeneous disease. More than 30 genes have been linked to familial and sporadic ALS (Nijs and Van Damme, 2024; Rehm et al., 2015), yet most ALS-associated proteins remain poorly characterized at the protein level. This gap reflects both the limited availability of validated reagents and the lack of systematic protein-level characterization across relevant neurological cell types.

To address this limitation, we established the ALS-Reproducible Antibody Platform (ALS-RAP), a systematic effort to characterize antibodies targeting ALS-associated proteins using KO-based workflows (Ayoubi et al., 2025), with a focus on renewable reagents (monoclonal and recombinant antibodies). ALS-RAP builds on antibody characterization strategies developed through the Antibody Characterization through Open Science (YCharOS) initiative, an open-science ecosystem that brings together academic laboratories and antibody manufacturers to systematically evaluate research antibodies and openly disseminate characterization datasets (Monteiro et al., 2025; Lund-Johansen, 2023; Laflamme et al., 2021). To expand renewable reagent coverage, the Structural Genomics Consortium (SGC) generated recombinant antibodies for several ALS targets lacking suitable commercial reagents (Hornsby et al., 2015).

Through ALS-RAP, we assembled and characterized antibodies targeting 33 ALS-associated proteins and identified high-quality renewable reagents suitable for common applications. These validated antibodies enabled us to examine protein levels across human induced pluripotent stem cell (iPSC)-derived and primary neurological cell types. Together, this work extends antibody characterization to a disease-focused protein landscape and provides a resource for investigating these proteins in relevant cellular contexts.

Results

Genetic prioritization and initial characterization of ALS-RAP targets

We prioritized 33 ALS-related genes supported by replicated genetic evidence: ACSL5, ALS2, ANG, ANXA11, ATXN2, C9orf72, CAV1, CCNF, CHCHD10, CHMP2B, FIG4, FUS, HNRNPA1, HNRNPA2B1, KIF5A, LGALS1, MATR3, NEK1, OPTN, PFN1, SETX, SIGMAR1, SOD1, SPG11, SQSTM1, TAF15, TARDBP, TBK1, TIA1, TUBA4A, UBQLN2, VAPB, VCP. Building on a previous classification framework (Nijs and Van Damme, 2024), these genes were grouped according to variant frequency and estimated pathogenic impact, including rare variants with high, intermediate, or small effect sizes, as well as rare or common variants with uncertain impact. Of these, 25 genes have also been curated by the Clinical Genome Resource (ClinGen) ALS expert panel and classified as having evidence of disease association (Table 1; Rehm et al., 2015).

Table 1
ALS-associated genes selected for the ALS-RAP study.
Gene nameUniProt IDEffect size (variants; effect size)ClinGen curated descriptionPubMed entries
(gene name +ALS)		PubMed entries
(gene name)
SOD1P00441Rare; highDefinitive467910,855
TARDBPQ13148Rare; highDefinitive28465061
C9orf72Q96LT7Rare; highDefinitive19522757
FUSP35637Rare; highDefinitive13325694
VCPP55072Rare; highDefinitive2672354
OPTNQ96CV9Rare; highDefinitive2061640
SQSTM1Q13501Rare; intermediateModerate1833848
TBK1Q9UHD2Rare; highDefinitive1782749
VAPBO95292Rare; intermediateDefinitive169417
ATXN2Q99700Rare; intermediateDefinitive163437
UBQLN2Q9UHD9Rare; highDefinitive139206
ALS2Q96Q42Rare; intermediateDefinitive127250
ANGP03950Rare; intermediateLimited991592
SETXQ7Z333Rare; intermediateNot available93257
CHCHD10Q8WYQ3Rare; intermediateModerate93174
HNRNPA1P09651Rare; unknownNot available91574
PFN1P07737Rare; intermediateDefinitive79356
TAF15Q92804Rare; intermediateLimited66251
NEK1Q96PY6Rare; highDefinitive64192
MATR3P43243Rare; unknownDefinitive61162
KIF5AQ12840Rare; intermediateDefinitive61259
CHMP2BQ9UQN3Rare; intermediateDefinitive48225
TIA1P31483Rare; intermediateLimited471146
ANXA11P50995Rare; intermediateDefinitive47124
CCNFP41002Rare; smallLimited41184
FIG4Q92562Rare; intermediateLimited40170
HNRNPA2B1P22626Rare; unknownModerate38535
SPG11Q96JI7Rare; intermediateDefinitive34283
TUBA4AP68366Rare; highModerate3295
SIGMAR1Q99720Rare; unknownNot available27202
CAV1Q03135Rare; smallLimited146065
ACSL5Q9ULC5Common; smallNot available4192
LGALS1P09382Rare; highNot available3308

To estimate how ALS-associated proteins are represented in the literature, PubMed searches were performed for each gene using the gene name alone or combined with “ALS” (Table 1, ordered by number of PubMed entries). As shown in a line-scan plot (Figure 1—figure supplement 1), four genes—SOD1, TARDBP, C9orf72, and FUS—dominate the ALS literature, accounting for 10,809 of 13,173 publications (>80%). In contrast, the remaining 29 genes are far less studied in ALS and collectively define the “dark ALS-ome,” referring to ALS-associated proteins with limited functional characterization. Notably, seven of these genes—VCP, OPTN, SQSTM1, TBK1, ANG, TIA1, and CAV1—have been investigated in other biological contexts.

Reagent evaluation and antibody development for ALS-RAP targets

According to the CiteAb database (citeab.com), only 3 of the 33 ALS-RAP targets have a top-cited renewable antibody characterized using genetic knockdown or KO approaches, highlighting a substantial gap in validated reagents. We therefore assessed renewable antibody availability across all targets prior to benchmarking with YCharOS characterization workflows, as batch-to-batch variability of polyclonal antibodies limits reproducibility (Ayoubi et al., 2023b; Marx, 2020). Six proteins—ANXA11, OPTN, MATR3, PFN1, UBQLN2, and VCP—had limited commercially renewable antibodies. To address this gap, the SGC generated recombinant single-chain variable fragment (scFv) antibodies optimized to recognize native, folded proteins, making them particularly well suited for IP (Preger et al., 2020). Recombinant full-length proteins or protein domains were produced, purified, site-specifically biotinylated, and used as antigens for phage-display selections. Individual scFv clones were initially screened by ELISA, homogeneous time-resolved fluorescence, and suspension bead assays to confirm target binding and specificity. Candidate binders were subsequently evaluated by immunoprecipitation coupled to mass spectrometry (IP-MS) using established protocols (Marcon et al., 2015), with at least three scFv clones screened for each target. The top-performing clone, selected based on IP-MS target enrichment, was then benchmarked alongside commercially available antibodies in the YCharOS validation pipeline. Antibody-encoding plasmids have been deposited at Addgene to ensure community access, and the amino acid sequences of reported scFvs are provided in Supplementary file 2.

In parallel, we evaluated KO cell line availability for antibody characterization. Twenty-one commercial KO lines were available from YCharOS partners and were selected based on parental expression levels above 2.5 log₂(TPM+1) in the DepMap database, a threshold suitable for antibody characterization studies (Ayoubi et al., 2025). To complete coverage, 12 additional custom KO lines were generated and are available upon request, while VCP was targeted with siRNA as it is an essential gene.

Antibody performance and coverage for ALS-RAP proteins

Antibody performance for ALS-RAP targets was systematically evaluated in western blot (WB), IP, and immunofluorescence (IF) using KO-based characterization protocols (Ayoubi et al., 2025). In total, 303 antibodies (297 commercial and 6 custom scFv antibodies) targeting 33 ALS-associated proteins were tested. All antibodies were assessed in WB. For five proteins—ALS2, FIG4, NEK1, SIGMAR1, and SPG11—the absence of a specific antibody precluded confident assessment of antibody performance, as we could not determine whether this reflected uniformly poor antibody performance or suboptimal IF conditions for these targets. These datasets were therefore not included in Zenodo reports. Antibodies against ANG, CAV1, CCNF, KIF5A, and SETX were not evaluated in IP and/or IF due to the recent availability of renewable reagents, specialized experimental requirements (e.g., CCNF and cell-cycle arrest), or the secreted nature of ANG (Figure 1A).

Figure 1 with 5 supplements see all
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Summary of antibody performance for ALS-RAP proteins.

(A) Overview of the identification of high-quality antibodies targeting ALS-RAP proteins. (B) Percentage of ALS-RAP targets covered by at least one specific renewable (monoclonal or recombinant) antibody for the indicated application. (C) Percentage of tested antibodies that met specificity criteria in each indicated application.

Antibodies were evaluated independently for each application as previously described (Ayoubi et al., 2023b). In WB, antibodies were considered specific when an immunoreactive band was absent in the corresponding KO lysate. In IP, antibodies were considered successful when the target protein was specifically enriched relative to the starting material, as confirmed by immunoblot detection of the immunoprecipitated protein. In IF, antibodies were considered specific when the mean fluorescence intensity in wild-type cells was at least 1.5-fold higher than that measured in KO cells using the mosaic WT/KO assay. For IF, a minimum of 250 WT and 250 KO cells were quantified for each antibody. Detailed experimental workflows have been described previously (Ayoubi et al., 2025).

Across all 33 targets, at least one renewable, KO-validated antibody was identified for 32 (97%), 23 (70%), and 20 (61%) proteins in WB, IP, and IF, respectively (Figure 1B). Overall, 243 of 303 antibodies (80%) detected their intended target in WB, 105 of 238 (44%) immunoprecipitated their target, and 107 of 231 (46%) demonstrated specific target staining in IF (Figure 1C).

The newly developed scFv antibodies filled key gaps; for example, the scFv against PFN1 was the only antibody—among 16 tested—capable of immunoprecipitating the protein (Ayoubi et al., 2023a). Collectively, these results demonstrate that renewable, KO-validated antibodies cover most ALS-associated proteins, enabling reproducible studies of ALS biology.

For transparency and reuse, we generated 33 antibody characterization reports (one per protein), reviewed with YCharOS industry partners and deposited on Zenodo, with a subset published through the F1000Research YCharOS Gateway (Biddle and Virk, 2023). All antibodies tested are listed in Supplementary file 1 with links to their corresponding characterization reports. Research Resource Identifiers (RRIDs) were obtained for all antibodies and KO cell lines to facilitate reagent traceability (Bandrowski et al., 2023; Bairoch, 2018). Antibodies used for downstream protein detection analyses were selected based on these characterization data, prioritizing renewable reagents. Catalogue numbers for these antibodies are provided in the ‘Key resources table’.

Antibody characterization case study: Galectin-1

To illustrate the YCharOS antibody characterization workflow, we present the characterization of antibodies targeting Galectin-1, encoded by the LGALS1 gene. Galectin-1 is a lectin implicated in immune and stress responses in the nervous system and has been reported in association with ALS in transcriptomic and functional studies (Cai et al., 2022; Kato et al., 2001). Antibodies were not assigned scores, consistent with the YCharOS philosophy that end users should evaluate raw data to select fit-for-purpose reagents based on their specific experimental needs.

Using DepMap transcriptomic data, the HAP1 cell line was selected for antibody characterization because it expresses LGALS1 at adequate levels (7.9 log₂(TPM+1)), above the 2.5 threshold used for antibody characterization studies (Ayoubi et al., 2023b). An LGALS1 KO HAP1 cell line was obtained from Revvity, and 10 renewable commercial antibodies from YCharOS partners were assembled for benchmarking (Table 2).

Table 2
Galectin-1 antibodies tested in this study.
CompanyCatalog numberLot numberRRID
(antibody registry)		ClonalityClone IDHostConcentration
(µg/µL)		Vendors recommended applications
Abcamab108389*1018000-29AB_10863417Recombinant monoEPR3205Rabbit1.35WB, IF
Abcamab138513†1086382-8AB_2894851MonoclonalEPR3206(2)Rabbit0.26WB, IF
Abcamab192842*1011456-2AB_3725515Recombinant monoSP247Rabbit0.04WB
ABclonalA21218*3522042922AB_3725523Recombinant monoARC53156Rabbit1.20WB
Aviva Systems BiologyARP58491_P050QC25810-42978AB_2046045Polyclonal-Rabbit0.50WB
Bio-Techne (R&D Systems)MAB1152†CJKC0325011AB_3657585Monoclonal933133Mouse0.50WB
Cell Signaling Technology12936*3AB_2798065Recombinant monoD608TRabbit0.10WB, IF
Thermo Fisher ScientificMA5-32779*79527780AB_2810056Recombinant monoJM13-37Rabbit1.00WB, IP, IF
Thermo Fisher ScientificMA5-35566*79531623AB_2849466Recombinant mono7W5Q6Rabbit2.00WB, IF
Thermo Fisher ScientificMA5-47904*79531680AB_3074348Recombinant monoSAIC-25B-112Rabbit1.00WB
  1. *

    Recombinant antibody.

  2. †

    Monoclonal antibody.

Antibody performance was evaluated across WB, IP, and IF using WT and KO HAP1 cells. In WB experiments, high-quality antibodies detected Galectin-1 in WT lysates with complete signal loss in KO lysates (Figure 2A). Galectin-1 was also detected in conditioned medium, consistent with its known secretion (Figure 2B). In IP experiments, antibodies were assessed for their ability to enrich Galectin-1 from cell lysates, with enrichment in the IP fraction and partial depletion from the unbound fraction indicating effective capture (Figure 2C). Finally, antibody performance in IF was evaluated using a mosaic strategy in which fluorescently labeled WT and KO cells were mixed and imaged in the same field of view, minimizing staining and imaging bias (Figure 2D). Complete experimental datasets and antibody characterization reports are available through Zenodo.

Figure 2
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YCharOS antibody characterization workflow for Galectin-1 (LGALS1).

Ten Galectin-1 antibodies were evaluated side-by-side across three applications using HAP1 WT and LGALS1 KO cells. (A) Whole-cell protein lysates from HAP1 WT and LGALS1 KO cells (20 µg per lane) were analyzed in WB using the indicated Galectin-1 antibodies. Ponceau-stained membranes are shown to assess protein loading and transfer efficiency. (B) Whole-cell protein lysates (10 µg) and conditioned media (10 µg) from HAP1 WT and LGALS1 KO cells were analyzed in WB using antibody 12936, as in (A), to detect intracellular and secreted Galectin-1. (C) Immunoprecipitation was performed from HAP1 WT cell lysates using the indicated Galectin-1 antibodies. Starting material (SM), unbound fractions (UB), and whole immunoprecipitates (IP) were analyzed by WB. Anti-Galectin-1 antibody A21218 was used as a primary antibody. (D) HAP1 WT and LGALS1 KO cells were differentially labeled with green and far-red fluorescent dyes, respectively, mixed at a 1:1 ratio, and plated in optically clear 96-well plates. Cells were stained with the indicated Galectin-1 antibodies, followed by Coralite 555–conjugated secondary antibodies and DAPI. Images were acquired in the blue (DAPI), green (WT), red (antibody staining), and far-red (KO) channels. Representative grayscale images of the blue and red channels are shown; WT and KO cells are outlined with green and magenta dashed lines, respectively. Scale bar, 10 µm. Recombinant antibodies are denoted by double asterisks (**) and monoclonal antibodies by single asterisks (*) after the catalogue numbers; polyclonal antibodies are unmarked. Antibody screening was performed in two independent experiments; representative data from one experiment are shown. Galectin-1 antibody characterization raw datasets are available at https://doi.org/10.5281/zenodo.21538630.

ALS-RAP antibody recommendations on the OGA website

In addition to dissemination through YCharOS, ALS-RAP antibody characterization datasets were independently evaluated by the Only Good Antibodies (OGA) initiative (Biddle et al., 2024), which did not participate in generating the experimental data. OGA integrates KO-based characterization datasets from YCharOS into a publicly accessible antibody assessment platform (https://onlygoodantibodies.co.uk/), where reagents are curated and presented to facilitate antibody selection by end users. Antibody entries include direct links to manufacturer purchasing pages. A screenshot of the Galectin-1 dataset illustrates the interface: a schematic representing high-quality antibodies is shown at the top, while all tested antibodies are listed below with application-specific recommendations derived from OGA analysis of the YCharOS data (Figure 3).

Figure 3
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Accessing curated antibody characterization data through the OGA website.

YCharOS antibody characterization data for ALS-RAP proteins are accessible through the Only Good Antibodies (OGA) website (https://onlygoodantibodies.co.uk/). The OGA homepage provides a searchable interface for the 33 ALS-RAP proteins, together with 35 additional neurological disease-associated proteins evaluated by YCharOS, and includes links to key OGA publications, data interpretation guidelines, antibody characterization protocols, and a portal for nominating new targets. (A) Representative results page generated by searching LGALS1, showing filters for host species, clonality, recombinant format, and OGA-recommended applications; the WB filter is applied. A schematic panel at the top illustrates ideal antibody performance across applications, while antibodies matching the selected filters are displayed in randomized order. (B) Number of antibodies returned following application of the selected filters.

By adding an interpretive layer to the raw characterization datasets, OGA improves the discoverability of validated antibodies against ALS-associated proteins while preserving full access to the underlying data. This integration complements the ALS-RAP resource by pairing systematic KO-based characterization with a user-oriented recommendation framework.

Neurological expression landscape of ALS-RAP proteins

Cellular context for ALS-RAP expression analysis

To provide context for protein expression analyses, we first examined RNA expression of ALS-associated genes using the Human Protein Atlas (HPA) single-nucleus brain dataset (Karlsson et al., 2021). Genes were categorized as neuron-enriched, glial-enriched (astrocytes, oligodendrocytes, and microglia), or broadly expressed based on relative RNA enrichment across cell types. Several genes showed preferential neuronal expression (ALS2, CCNF, KIF5A, and TUBA4A), whereas others were enriched in glial populations (ACSL5, ANXA11, C9orf72, CAV1, CHMP2B, FIG4, FUS, HNRNPA1, LGALS1, SETX, SQSTM1, and TBK1). The remaining genes were broadly expressed, while four (ANG, MATR3, SIGMAR1, and TIA1) were barely detected across neurological cell types (Figure 1—figure supplement 2). Together with previously published single-cell RNA sequencing datasets of the human brain (McKeever et al., 2025), these analyses highlight the heterogeneous expression of ALS-associated genes.

Because RNA abundance does not necessarily predict protein levels (Gygi et al., 1999; Vogel and Marcotte, 2012), we next examined ALS-RAP protein expression across neurological cell types. Human iPSC-derived motor neurons, dopaminergic neurons, oligodendrocytes, astrocytes, and microglia were analyzed, complemented with primary human fetal astrocytes, fetal microglia, and monocyte-derived macrophages (MDMs). Cell identity was confirmed using established marker analyses (Figure 1—figure supplements 3 and 4).

Protein profiling across neurological cell types

We profiled 30 ALS-RAP proteins in WB using two independent preparations of the same cell types: one analyzed by chemiluminescence-based detection and the other by fluorescence-based WB (Figures 4—6). For each target, the best-performing renewable antibody identified during YCharOS screening was used. KO-validated primary bands were consistently detected at the expected molecular weight across neurological cell types. Additional bands were occasionally observed (e.g., MATR3, SQSTM1, ANXA11, ACSL5, UBQLN2) but were not interpreted, as we could not determine whether they corresponded to the intended target or reflected antibody cross-reactivity. Three proteins were excluded from expression profiling: CCNF (requires cell-cycle arrest for detection), ANG (secreted protein not readily detected in lysates), and SETX (insufficient signal-to-noise). Relative signal intensities within each detection modality were compared to identify general patterns of protein detection across neuronal and glial populations.

Figure 4
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Detection of 13 ALS-RAP proteins across five human-derived neurological cell types and three primary cell types.

WB showing detection of 13 ALS-RAP proteins in eight lysates from the indicated five iPSC-derived cell types and three primary cell types. For each protein, two independent experiments are shown: chemiluminescence-based WB (left), performed using a defined lysate batch across all corresponding blots, and fluorescence-based WB (right), performed using a second independent lysate batch. Proteins are listed in alphabetical order. Vertical lines between the motor neuron and DA neuron lanes indicate that the blots were spliced to remove additional conditions not relevant to this study. Lane identities: 1, motor neurons; 2, DA neurons; 3, oligodendrocytes; 4, astrocytes; 5, microglia; 6, fetal astrocytes; 7, fetal microglia; 8, MDMs. DA, dopaminergic; MDM, monocyte-derived macrophages. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by chemiluminescent WB are available at https://doi.org/10.5281/zenodo.18484385. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by fluorescent WB are available at https://doi.org/10.5281/zenodo.18484483.

Figure 5
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Detection of 10 ALS-RAP proteins across five human-derived neurological cell types and two primary cell types.

WB showing detection of 10 ALS-RAP proteins in 7 lysates from the indicated five iPSC-derived cell types and 2 primary cell types. Fetal astrocyte lysate was not available for either chemiluminescence-based WB (left) and fluorescence-based WB (right). Proteins are listed in alphabetical order. Lane identities: 1, motor neurons; 2, DA neurons; 3, oligodendrocytes; 4, astrocytes; 5, microglia; 7, fetal microglia; 8, MDMs. DA, dopaminergic; MDM, monocyte-derived macrophages. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by chemiluminescent WB are available at https://doi.org/10.5281/zenodo.18484385. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by fluorescent WB are available at https://doi.org/10.5281/zenodo.18484483.

Figure 6
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Detection of seven ALS-RAP proteins across five human-derived neurological cell types and primary cell types.

(A) Chemiluminescence-based WB (left) showing detection of seven ALS-RAP proteins in seven lysates from the indicated five iPSC-derived cell types and two primary cell types. Fetal astrocyte lysate was not available for this WB. Fluorescence-based WB (right) showing detection of seven ALS-RAP proteins in eight lysates from the indicated five iPSC-derived cell types and three primary cell types. A representative Ponceau-stained membrane and total protein stain of all eight samples are shown to exemplify protein loading and transfer efficiency. Lane identities: 1, motor neurons; 2, DA neurons; 3, oligodendrocytes; 4, astrocytes; 5, microglia; 6, fetal astrocytes; 7, fetal microglia; 8, MDMs. DA, dopaminergic; MDM, monocyte-derived macrophages. (B) Summary of 16 targets for which WB data and Human Protein Atlas RNA expression profiles are concordant, grouped by expression category. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by chemiluminescent WB are available at https://doi.org/10.5281/zenodo.18484385. Raw WB data and supporting working sheet for the ALS-RAP protein expression profiling across neurological cell types by fluorescent WB are available at https://doi.org/10.5281/zenodo.18484483.

Many ALS-RAP proteins were detected across both neuronal and glial cell types, including ALS2, CHMP2B, FIG4, FUS, HNRNPA1, HNRNPA2B1, MATR3, NEK1, OPTN, SIGMAR1, SOD1, SQSTM1, TAF15, TARDBP, TBK1, TIA1, UBQLN2, and VCP. Several proteins showed higher signal in glial populations in both datasets, including ACSL5, ANXA11, C9orf72, CAV1, CHCHD10, LGALS1, PFN1, and SPG11. ACSL5, ANXA11, C9orf72, and CHCHD10 were detected at higher levels in microglial populations, while CAV1 showed stronger signal in astrocytes. These patterns were consistent between iPSC-derived and primary glial cells. In contrast, several proteins showed higher protein levels in neuronal populations. ATXN2 was strongly detected in motor and dopaminergic neurons, while KIF5A and TUBA4A were enriched in neuronal lineages. VAPB was primarily detected in neurons, with additional signal in glial cells in the fluorescence-based dataset. Overall, these analyses reveal diversity in the cellular distribution of ALS-RAP proteins. Notably, many ALS-associated proteins were detected across glial and immune cell populations, supporting the view that ALS genetics converges on multicellular disease mechanisms.

Overall, 16 of 27 proteins assessed at both the RNA and protein levels showed concordant cell type categorization (Figure 6B), highlighting both concordance and divergence between transcriptomic and protein-level datasets. Consistent with the limited protein-level characterization of ALS-associated genes, STRING network analysis revealed sparse evidence of direct interactions among the 33 proteins, with clusters primarily involving autophagy regulators (TBK1, OPTN, SQSTM1) and RNA-binding proteins (FUS, TARDBP, MATR3, HNRNPA1, HNRNPA2B1) (Figure 1—figure supplement 5). These clusters mainly comprise broadly expressed proteins, whereas interactions involving cell type-restricted ALS-RAP proteins remain largely unexplored.

Discussion

Large-scale genetic studies have identified hundreds of risk loci across neurodegenerative diseases, including Alzheimer’s disease (Bellenguez et al., 2022; Zhang et al., 2023), Parkinson’s disease (Leonard and Global Parkinson’s Genetics Program (GP2), 2025; Kim et al., 2024), and ALS (van Rheenen et al., 2021). Despite these advances, functional follow-up has lagged behind genetic discovery, in part because disease-associated proteins lack validated reagents and systematic protein-level characterization. As a result, mechanistic insight has remained concentrated on a limited subset of genes, while the majority of risk-associated proteins remain poorly studied (Edwards et al., 2011; Carter et al., 2019). In ALS, this gap is particularly evident, with many proteins remaining part of the “dark ALS-ome”.

Here, we address this gap through ALS-RAP, which provides a publicly available dataset of KO-characterized antibodies across three commonly used applications—WB, IP, and IF—enabling the selection of fit-for-purpose reagents for studying ALS-associated proteins. Using these reagents, we examined protein levels across iPSC-derived and primary neurological cell types, revealing diverse expression patterns, including frequent detection of ALS-associated proteins in glial populations, particularly microglia and macrophages.

Consistent with this gap, STRING network analysis revealed sparse connectivity among ALS-associated proteins. Our expression analyses identify cell type-specific patterns that may help contextualize potential biological relationships. For example, ANXA11, classified as a definitive ALS gene by ClinGen, was predominantly detected in microglial populations. Annexins regulate membrane dynamics and trafficking, yet the role of Annexin A11 in microglia or macrophages remains largely unexplored. Its enrichment in microglia places it in the same cellular context as several ALS-associated proteins involved in autophagy and vesicular pathways, including C9orf72, TBK1, UBQLN2, SQSTM1, and OPTN, supporting potential convergence on shared pathways in myeloid-lineage cells. C9orf72 also showed higher expression in microglial populations, in agreement with studies implicating C9orf72 in microglial functions such as phagocytosis and immune regulation (O’Rourke et al., 2016; Masrori et al., 2025), as well as evidence that microglia present C9orf72-derived peptides capable of activating CD4+ T cells (Michaelis et al., 2025). Together, these findings support the view that ALS genetics converges on multicellular disease mechanisms involving both neuronal and glial populations.

Materials and methods

Key resources table
Reagent type (species) or resourceDesignationSource or referenceIdentifiersAdditional information
AntibodyHRP-Goat polyclonal anti-Rabbit antibody Secondary Antibody (H+L)Thermo Fisher ScientificCAT#65-6120;
RRID:AB_2533967		0.05 µg/mL
AntibodyHRP-Goat polyclonal anti-Mouse antibody Secondary Antibody (H+L)Thermo Fisher ScientificCAT#62-6520; RRID:AB_25339470.1 µg/mL
AntibodyIRDye 680CW Goat polyclonal anti-Rabbit Secondary Antibody (H+L)LI-COR BiosciencesCAT#926-68071; RRID:NA0.05 µg/mL
AntibodyIRDye 680CW Goat polyclonal anti-Mouse Secondary Antibody (H+L)LI-COR BiosciencesCAT#926-68070; RRID:NA0.1 µg/mL
AntibodyIRDye 800CW Goat polyclonal anti-Rabbit Secondary Antibody (H+L)LI-COR BiosciencesCAT#926-32211; RRID:NA0.05 µg/mL
AntibodyIRDye 800CW Goat polyclonal anti-Mouse Secondary Antibody (H+L)LI-COR BiosciencesCAT#926-32210; RRID:NA0.1 µg/mL
AntibodyHRP-Goat multiclonal recombinant anti-Rabbit Secondary Antibody (H+L)ProteintechCAT#RGAR001; RRID:AB_30735050.05 µg/mL
AntibodyHRP-Goat multiclonal recombinant anti-Mouse Secondary Antibody (H+L)ProteintechCAT#RGAM001; RRID:AB_30683330.5 µg/mL
AntibodyCoraLite Plus 555-Goat multiclonal recombinant Anti-Rabbit Secondary Antibody (H+L)ProteintechCAT# RGAR003; RRID:AB_30735070.5 µg/mL
AntibodyCoraLite Plus 555-Goat multiclonal recombinant Anti-Mouse Secondary Antibody (H+L)ProteintechCAT# RGAM003; RRID:AB_30685390.5 µg/mL
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone EPR3205)AbcamCAT#ab108389;
RRID:AB_10863417		WB:1/100; IP 2 µg;
IF:1/100
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone EPR3206(2))AbcamCAT#ab138513;
RRID:AB_2894851		WB:1/10,000; IP 2 µg; IF:1/250
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone SP247)AbcamCAT#ab192842;
RRID:AB_3725515		WB:1/100; IP 2 µg;
IF:1/100
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone ARC53156)ABclonalCAT#A21218;
RRID:AB_3725523		WB:1/11,000; IP 2 µg; IF:1/500
AntibodyRabbit polyclonal anti-Galectin-1Aviva Systems BiologyCAT#ARP58491_P050; RRID:AB_2046045WB:1/100; IP 2 µg;
IF:1/250
AntibodyMouse monoclonal anti-Galectin-1 (clone 933133)Bio-Techne (R&D Systems)CAT# MAB1152;
RRID:AB_3657585		WB:1/250; IP 2 µg;
IF:1/500
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone D608T)Cell Signaling TechnologyCAT#12936;
RRID:AB_2798065		WB:1/1000; IP 2 µg;
IF:1/50
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone JM13-37)Thermo Fisher ScientificCAT#MA5-32779;
RRID:AB_2810056		WB:1/2000; IP 2 µg;
IF:1/2000
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone A7W5Q6)Thermo Fisher ScientificCAT#MA5-35566;
RRID:AB_2849466		WB:1/200; IP 2 µg;
IF:1/100
AntibodyRabbit recombinant monoclonal anti-Galectin-1 (clone SAIC-25B-112)Thermo Fisher ScientificCAT#MA5-47904;
RRID:AB_3074348		WB:1/1000; IP 2 µg;
IF:1/1000
AntibodyRabbit recombinant monoclonal anti-CHMP2B (clone JE54-35)Thermo Fisher ScientificCAT#MA5-36184; RRID:AB_2890433WB:1/500
AntibodyRabbit recombinant monoclonal anti-FIG4 (clone N202/7R)AddgeneCAT#192037; RRID:AB_2916241WB:1/100
AntibodyMouse monoclonal anti-FUS (clone CL0190)AbcamCAT#ab154141; RRID:AB_2885092WB:1/1000
AntibodyMouse monoclonal anti-HNRNPA1 (clone 4B10)Bio-Techne (Novus Biologicals)CAT#NB100-672; RRID:AB_524308WB:1/1000
AntibodyRabbit recombinant monoclonal anti-MATR3 (clone EPR10634(B))AbcamCAT#ab151739; RRID:AB_2885091WB:1/10,000
AntibodyRabbit polyclonal anti-NEK1GeneTexCAT#GTX130828; RRID:AB_2886357WB:1/500
AntibodyMouse monoclonal anti-OPTN (clone 6C1H4)ProteintechCAT#60293-1-Ig; RRID:AB_2881408WB:1/5000
AntibodyRabbit recombinant monoclonal anti-SIGMAR1 (clone D4J2E)Cell Signaling TechnologyCAT#61994; RRID:AB_2799617WB:1/1000
AntibodyMouse monoclonal anti-SOD1 (clone 2F10G1)ProteintechCAT#67480-1-Ig; RRID:AB_2882707Chemiluminescent WB:1/10,000
AntibodyRabbit polyclonal anti-SOD1GeneTexCAT#GTX100554; RRID:AB_10618670Fluorescent WB:1/1000
AntibodyRabbit recombinant monoclonal anti-SQSTM1 (clone EPR4844)AbcamCAT#ab109012; RRID:AB_2810880Fluorescent WB:1/1000
AntibodyMouse monoclonal anti-SQSTM1 (clone 2C11)AbcamCAT#ab56416; RRID:AB_945626Chemiluminescent WB:1/1000
AntibodyRabbit recombinant monoclonal anti-TAF15 (clone JE61-92)Thermo Fisher ScientificCAT#MA5-44822; RRID:AB_2931279WB:1/1000
AntibodyRabbit recombinant monoclonal anti-TARDBP (clone 11N20)ProteintechCAT#80001-1-RR; RRID:AB_2882933WB:1/10,000
AntibodyRabbit recombinant monoclonal anti-TBK1 (clone EP611Y)AbcamCAT#ab40676; RRID:AB_776632Chemiluminescent WB:1/10,000
AntibodyRabbit recombinant monoclonal anti-TBK1 (clone E8I3G)Cell Signaling TechnologyCAT#38066; RRID:AB_2827657Fluorescent WB:1/1000
AntibodyRabbit recombinant monoclonal anti-TIA1 (clone D1Q3K)Cell Signaling TechnologyCAT#86050; RRID:AB_2800070WB:1/1000
AntibodyMouse monoclonal anti-UBQLN2 (clone 6H9)AbcamCAT#ab190283; RRID:AB_2747782WB:1/1000
AntibodyRabbit recombinant monoclonal anti-VCP (clone 7F3)Cell Signaling TechnologyCAT#2649; RRID:AB_2214629Chemiluminescent WB:1/10,000
AntibodyRabbit recombinant monoclonal anti-VCP (clone EPR3307(2))AbcamCAT#ab109240; RRID:AB_10862588Fluorescent WB:1/500
AntibodyRabbit polyclonal anti-ANXA11Aviva Systems BiologyCAT#ARP87950_P050; RRID:AB_2894925WB:1/1000
AntibodyMouse monoclonal anti-C9orf72 (clone GT1553)GeneTexCAT#GTX634482; RRID:AB_2784545WB:1/1000
AntibodyRabbit polyclonal anti-CHCHD10ProteintechCAT#25671-1-AP; RRID:AB_2880187Chemiluminescent WB:1/500
AntibodyMouse monoclonal anti-CHCHD10 (clone OTI3B8)Thermo Fisher ScientificCAT#MA5-27535; RRID:AB_2724132Fluorescent WB:1/500
AntibodyRabbit recombinant monoclonal anti-LGALS1 (clone EPR3206(2))AbcamCAT#ab138513; RRID:AB_2894851WB:1/500
AntibodyRabbit recombinant monoclonal anti-PFN1 (clone ARC1469)ABclonalCAT#A9188; RRID:AB_2863682WB:1/10,000
AntibodyRabbit recombinant monoclonal anti-SPG11 (clone 2H25L11)Thermo Fisher ScientificCAT#702776; RRID:AB_2725406WB:1/200
AntibodyMouse monoclonal anti-ACSL5 (clone 5H8)AbnovaCAT#H00051703-M01; RRID:AB_509241WB:1/200
AntibodyRabbit recombinant monoclonal anti-CAV1 (clone SZ02-01)Thermo Fisher ScientificCAT#MA5-32020; RRID:AB_2809314Chemiluminescent WB:1/500
AntibodyRabbit recombinant monoclonal anti-CAV1 (clone ARC50848)ABclonalCAT#A19006; RRID:AB_2862498Fluorescent WB:1/500
AntibodyRabbit recombinant monoclonal anti-ATXN2 (clone EPR23630-49)AbcamCAT#ab254362; RRID:AB_2885130WB:1/1000
AntibodyRabbit recombinant monoclonal anti-TUBA4A (clone
EPR13478(B))		AbcamCAT#ab176560; RRID:AB_2860019Chemiluminescent WB:1/500
AntibodyRabbit recombinant monoclonal anti-TUBA4A (clone JM73-24)Thermo Fisher ScientificCAT#MA5-32738; RRID:AB_2810015Fluorescent WB:1/1000
AntibodyRabbit recombinant monoclonal anti-VAPB (clone EPR27026-50)AbcamCAT#ab315013; RRID:AB_3086770Chemiluminescent WB:1/500
AntibodyRabbit polyclonal anti-VAPBThermo Fisher ScientificCAT#PA5-53023; RRID:AB_2649391Fluorescent WB:1/200
AntibodyRabbit recombinant monoclonal anti-ALS2 (clone EPR11185)AbcamCAT#ab170896; RRID:AB_2885099WB:1/1000
AntibodyMouse monoclonal anti-HNRNPA2B1 (clone DP3B3)Bio-Techne (Novus Biologicals)CAT#NB120-6102; RRID:AB_790226WB:1/500
AntibodyRabbit recombinant monoclonal anti-KIF5A (clone 241124H1)ProteintechCAT#84013-4-RR; RRID:AB_3671581WB:1/500
AntibodyRabbit recombinant monoclonal anti-Synaptotagmin 1 (clone HL1626)GeneTexCAT#GTX637119; RRID:AB_3068350WB:1/500
AntibodyRabbit recombinant monoclonal anti-CD44 (clone EPR18668)AbcamCAT#ab189524; RRID:AB_2885107WB:1/1000
AntibodyRabbit recombinant monoclonal anti-ISL1 (clone EPR10362)AbcamCAT#ab178400; RRID:AB_2927537WB:1/500
AntibodyRat recombinant monoclonal anti-Myelin Basic Protein antibody (clone 12)AbcamCAT#ab7349; RRID:AB_305869WB:1/500
AntibodyRabbit polyclonal anti-Tyrosine HydroxylasePel-FreezeCAT#P40101-150: RRID:AB_2617184WB:1/500
AntibodyRabbit recombinant monoclonal anti-CD11b (clone EPR1344)AbcamCAT#ab133357; RRID:AB_2650514WB:1/500
AntibodyRabbit recombinant monoclonal anti-IBA1 (clone E4O4W)Cell Signaling TechnologyCAT#17198; RRID:AB_2820254WB:1/500
AntibodyChicken monoclonal recombinant anti-IBA1 (clone Ch311H9)Synaptic SystemsCAT#234009; RRID:AB_2891282IF:1/1000
AntibodyMouse monoclonal anti-CD11b, pre-conjugated with PE (clone ICRF44)BD BiosciencesCAT#555388; RRID:AB_395789IF:1/20
AntibodyMouse monoclonal anti-CD45, pre-conjugated with Alexa Fluor 700 (clone HI30)BioLegendCAT#304024; RRID:AB_493761IF:1/40
AntibodyRabbit polyclonal anti-PU.1Cell Signaling TechnologyCAT#2266; RRID:AB_10692379IF:1/500
Commercial assay or kitRNeasy Mini KitQIAGENCAT#74106
Commercial assay or kitSTEMdiff Forebrain Neuron Differentiation KitSTEMCELL TechnologiesCAT#08600
Commercial assay or kitSTEMdiff Forebrain Neuron Maturation KitSTEMCELL TechnologiesCAT#08605
Commercial assay or kitSTEMdiff Hematopoietic KitSTEMCELL TechnologiesCAT#05310
Commercial assay or kitSTEMdiff Hematopoietic KitSTEMCELL TechnologiesCAT#05310
Commercial assay or kitSTEMdiff Hematopoietic KitSTEMCELL TechnologiesCAT#05310
Cell line (human)HAP1 WTHorizon DiscoveryCAT#C631; RRID:CVCL_Y019Cells were confirmed mycoplasma-free
Cell line (human)HAP1 LGALS1 KOHorizon DiscoveryCAT#HZGHC005989c006; RRID:CVCL_XQ14Cells were confirmed mycoplasma-free
Cell line (human)Healthy control iPSCs from the AIW002-02 backgroundEDDU, McGill UniversityNACells were confirmed mycoplasma-free
Cell line (human)Healthy control iPSCs from the QPN929 backgroundEDDU, McGill UniversityNACells were confirmed mycoplasma-free
Software, algorithmLI-COR Image Studio Litelicorbio.com/image-studioNA
Software, algorithmFIJI Softwarefiji.sc/RRID:SCR_002285
Software, algorithmMetaXpress High-Content Screening Software version 6.7.1.157moleculardevices.com/products/cellular-imaging-systems/high-content-analysis/metaxpress - overviewRRID:SCR_016654
Software, algorithmPyCharm 2023.3.4jetbrains.com/pycharm/RRID:SCR_018221
Software, algorithmRStudio 2023.09.1cran.rstudio.com/RRID:SCR_000432
OtherAccutaseSTEMCELL TechnologiesCAT#07922
OtherAntibody Registryantibodyregistry.orgRRID:SCR_006397
OtherAstrocyte Growth Supplements (AGS)ScienCell Research LaboratoriesCAT#1852
OtherAstrocyte Medium (AM)ScienCell Research LaboratoriesCAT#1801-b
OtherBLUelf Prestained Protein LadderFroggaBioCAT#PM008
OtherCellosaurus—- a knowledge resource on cell linescellosaurus.orgRRID:SCR_013869
OtherCellTracker deep red dyeThermo Fisher ScientificCAT#C34565
OtherCellTracker green chloromethyl derivative of fluorescein diacetate (CMFDA) dyeThermo Fisher ScientificCAT#C2925
OtherCentrifugal filter unit, Amicon Ultra-15MilliporeSigmaCAT#UFC901096
OtherCiteAb—Reagent Search & Life Science Dataciteab.comRRID:SCR_009653
OtherClinGenhttps://clinicalgenome.org/RRID:SCR_014968
OtherDepMap: The Cancer Dependency Map Project at Broad Institutedepmap.org/portalRRID:SCR_017655
OtherLive/Dead Fixable Aqua viability dyeThermo Fisher ScientificCAT#L34965
OthermTeSR1STEMCELL TechnologiesCAT#85850
OtherNuPAGE Bis-Tris Midi Protein Gels, 10%, 1.0 mmThermo Fisher ScientificCAT#WG1201BOX
OtherNuPAGE MES SDS Running Buffer (20×)Thermo Fisher ScientificCAT#NP000202
OtherNuPAGE MOPS SDS Running Buffer (20×)Thermo Fisher ScientificCAT#NP000102
OtherPhenoPlate, 96-well, optically clear flat bottomPerkinElmerCAT#6055300
OtherPierce IP Lysis BufferThermo Fisher ScientificCAT#87788
OtherPubMedpubmed.ncbi.nlm.nih.govRRID:SCR_004846
OtherRIPA Lysis and Extraction BufferThermo Fisher ScientificCAT#89901
OtherStructural Genomics Consortiumhttps://www.thesgc.org/RRID:SCR_003890
OtherThe Human Protein Atlasproteinatlas.orgRRID:SCR_006710
OtherTRIzolThermo Fisher ScientificCAT#15596026
OtherTrueStain blocking reagentBioLegendCAT#422302
OtherZenodohttps://zenodo.org/RRID:SCR_004129

Analysis of PubMed entries for each ALS-associated gene

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Throughout the manuscript, we reference ALS-RAP targets using their official gene names as listed in UniProt, written in italics (e.g., SOD1). To refer to the corresponding proteins, we use the same gene names in regular font, as the official protein names are often too long or ambiguous for practical use.

Each target gene was queried in PubMed using two search strategies: (1) GENE_NAME[Title/Abstract] to retrieve the total number of publications associated with the gene, and (2) (GENE_NAME[Title/Abstract]) AND (“amyotrophic lateral sclerosis” [Title/Abstract]) to identify publications specifically linking the gene to ALS. Searches were performed on August 27, 2025.

Evaluation of significance of ALS-associated genes

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Each candidate gene was assessed for its potential association with ALS using the evidence published at the time of this article. Using the recent review of Nijs and van Damme as a model (Nijs and Van Damme, 2024), we used currently available evidence to prioritize genes for potential association with ALS in a similar manner. First, genes were qualitatively assessed for rarity of genetic variants in the general population and among individuals with ALS. Next, any reported odds ratios (ORs) for risk of ALS in variant carriers were used to approximate whether genes were considered to have “small” (OR <1.2), “intermediate” (1.2<OR < 10), or “high” (OR >10) effect sizes. Genes were assigned an “uncertain” OR estimate if no OR had been published at the time of analysis. Variant frequency was assessed considering the type(s) of inheritance previously described; variants reported through GWAS or genotyping studies were designated as “common”, whereas variants reported in autosomal family/pedigree-based studies were designated as “rare”. If the method of discovery was poorly described or through non-genetic analyses (e.g., transcriptomic or proteomic), genes were described as having “uncertain” frequency.

RNA expression analysis

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We accessed the single-cell transcriptomics resource of the HPA, specifically the single-nuclei brain dataset that includes single-cell RNA sequencing data generated from 2.5 million cells and mapped to 34 supercluster cell types. Data were generated from postmortem human brain samples digested to a single-cell suspension, and cell types were clustered into transcriptomic cell types (Siletti et al., 2023). Raw sequencing data was normalized using Trimmed mean of M values and expression values were presented as normalized transcripts per million (nTPM). RNA expression data for all 33 ALS genes were downloaded online from the HPA. Data were grouped together based on overall cell type (neuron, oligodendrocyte, astrocyte, microglia, etc.), and the average expression in each cell type was determined for each gene. The average neuron, oligodendrocyte, astrocyte, and microglia expression data were extracted from the overall dataset and used in downstream analyses.

In our analyses, genes were considered “expressed” if their expression values in at least three cell types were greater than the median expression value of the entire HPA dataset (9.2 nTPM). Genes were considered “barely detectable” if expression was less than the median value in at least two cell types. For a small subset of “barely detectable” genes, expression in one cell type was greater than the median value and substantially “enriched” in that cell type. We included these in the category of “expressed” genes despite the low expression in most cell types.

To describe the cell type expression patterns for each gene, we established criteria determining if mRNA expression levels for a gene is “enriched” or not in a given cell type. Cell types in which mRNA expression for a gene is “enriched” were defined as when the ratio of the expression value in that cell type to the average expression value across all cell types was greater than or equal to 1.5*. Each gene was categorized based on the “enriched” cell types. If either more than one cell type was “enriched” or no cell types were, the gene was categorized as having “low cell-type specificity”.

∗Expression value in given cell typeAverage across all cell types≥1.5

STRING analysis

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STRING analysis was performed by entering the UniProt IDs of the 33 ALS-RAP proteins into the “multiple proteins” search at string-db.org. The analysis was restricted to the “physical subnetwork”, with the meaning of network edges set at “confidence”, and the minimum required interaction score set to “highest confidence”.

Western blot (WB)

Primary cells, iPSC-derived cultures and cultured cells were collected in RIPA buffer (25 mM Tris-HCl pH 7.6, 150 mM NaCl, 1 % NP-40, 1% sodium deoxycholate, 0.1% SDS) supplemented with protease inhibitors. Lysates were sonicated briefly for 5–10 seconds depending on the volume. Following 30 minutes on ice, lysates were spun at ~110,000 × g for 15 minutes at 4°C and equal protein aliquots of the supernatants were analyzed by SDS-PAGE and WB.

Chemiluminescent WB

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WB were performed with precast 10% Bis-Tris polyacrylamide gels (MOPS or MES SDS buffer) and nitrocellulose membranes. Total proteins were visualized with a Ponceau staining. Blots were blocked with 5% milk in TBS with 0.1% Tween 20 (TBST). Antibodies were also diluted in 5% milk in TBST and incubated O/N at 4°C. Membranes were washed three washes in TBST the next day. The peroxidase conjugated anti-rabbit and anti-mouse secondary antibodies (Thermo Fisher Scientific) were diluted in 5% milk in TBST at 0.5 and 1 µg/mL, respectively. Secondaries were incubated with the membranes for 1 hour at room temperature followed by three washes. Membranes were incubated with ECL for 1 minute prior to detection with the iBright CL1500 Imaging System (Thermo Fisher Scientific).

Fluorescent WB

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WB were performed with large 5–16% gradient Tris-based polyacrylamide gels (Tris-Glycine/SDS Buffer) and nitrocellulose membranes. Total proteins were visualized with the REVERT total protein stain (LI-COR Biosciences). Blots were blocked with 5% milk in TBST, and antibodies were diluted in the same buffer and incubated O/N at 4°C followed by three washes. The secondary antibodies (Odyssey IRDye 800CW or IRDye 680RD) were diluted in 5% milk in TBST to a final concentration of 0.05 µg/mL for anti-rabbit and 0.1 µg/mL for anti-mouse. Secondaries were incubated with the membranes for 1 hour at room temperature followed by three washes while covered from the light. Immuno-reactive bands were detected using the LI-COR Odyssey Imaging System (LI-COR Biosciences), and data analysis was done using LI-COR Image Studio Lite Version 6.10.2.

Galectin-1 antibody screening in WB

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For cell lysis, HAP1 WT and LGALS1 KO cells were collected in RIPA buffer and processed as described above.

To collect the conditioned medium, HAP1 WT and LGALS1 KO cells were washed 3× with PBS 1× and starved for ~36 hours in Iscove’s Modified Dulbecco’s Medium supplemented with 1% Pen/Strep and 1% L-Glutamine. Culture media were collected and centrifuged for 10 minute at 500 × g to eliminate cells and larger contaminants, then for 10 minutes at 4500 × g to eliminate smaller contaminants. Culture media were then concentrated by centrifuging at 4000 × g for 30 minutes using the Amicon Ultra-15 Centrifugal Filter Units with a membrane NMWL of 10 kDa. Culture media were finally supplemented with 1× protease inhibitor cocktail mix.

10% Bis-Tris gels with MES buffer were used for SDS-PAGE and WB was performed using the primary antibodies (Table 2) at the following dilutions: ab108389 at 1/100, ab138513 at 1/10,000, ab192842 at 1/100, A21218 at 1/11,000, ARP58491 at 1/100, MAB1152 at 1/250, 12936 at 1/1000, MA5-32779 at 1/2000, MA5-35566 at 1/200, and MA5-47904 at 1/1000. Anti-rabbit and anti-mouse antibodies (Proteintech) were used at a final concentration of 0.05 µg/mL and 0.5 µg/mL, respectively.

Galectin-1 antibody screening in immunoprecipitation (IP)

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Antibody-bead conjugates were prepared by adding 2 µg to 500 µL of Pierce IP Lysis Buffer from Thermo Fisher Scientific in a microcentrifuge tube, together with 30 µL of Dynabeads protein A- (for rabbit antibodies). Tubes were rocked for 1 hour at 4°C followed by two washes to remove unbound antibodies. HAP1 WT were collected in Pierce IP buffer (25 mM Tris-HCl pH 7.4, 150 mM NaCl, 1 mM EDTA, 1% NP-40, and 5% glycerol) supplemented with protease inhibitor. Lysates were rocked 30 minutes at 4°C and spun at 110,000 × g for 15 minutes at 4°C. 0.2 mL aliquots at 1 mg/mL of lysate were incubated with each antibody-bead conjugate for 2 hour at 4°C. To run 4% SM (8 µL per SM) alongside each IP on the gel, a master mix of lysate was kept aside on ice for subsequent SM sample preparation. The UBs were collected separately and prepared for gel loading similar to the rest of the samples. Beads were washed three times with 1.0 mL of IP buffer and processed for SDS-PAGE on precast midi 10% Bis-Tris polyacrylamide gels. For WB, membranes were trimmed at 24 kDa and the lower part was incubated with the rabbit recombinant monoclonal anti-Galectin-1 antibody A21218 diluted at 1/3000 in 5% milk in TBST. HRP-conjugated anti-rabbit (Proteintech) diluted in 5% milk in TBST was used as a secondary antibody at 0.05 µg/mL.

Galectin-1 antibody screening in immunofluorescence (IF)

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WT and LGALS1 KO HAP1 cells were labeled with a green and a far-red fluorescence dye, respectively. WT and KO cells were plated in a 96-well plate with optically clear flat-bottom (PerkinElmer) as a mosaic and incubated for 24 hours in a cell culture incubator at 37°C, 5% CO2. Cells were fixed in 4% paraformaldehyde (PFA) in phosphate-buffered saline (PBS). Cells were permeabilized in PBS with 0.1% Triton X-100 for 10 minutes at room temperature and blocked with PBS with 5% BSA, 5% goat serum, and 0.01% Triton X-100 for 30 minutes at room temperature. Cells were incubated with IF buffer (PBS, 5% BSA, 0.01% Triton X-100) containing the primary Galectin-1 antibodies overnight at 4°C. Cells were then washed 3×10 minutes with IF buffer and incubated with the corresponding CoraLite Plus 555 secondary antibody in IF buffer at a dilution of 0.5 µg/mL for 1 hour at room temperature. Cells were washed 3×10 minutes with 1× PBS. The nuclei were labeled with DAPI fluorescent stain at a concentration of 5 ng/mL in 1× PBS for 3 minutes then washed once with 1× PBS.

Images were acquired on an ImageXpress micro confocal high-content microscopy system (Molecular Devices). The objective used is a water Apo LambdaS LWD with magnification of 20×, NA 0.95, and scientific CMOS cameras, equipped with 395, 475, 555, and 635 nm solid-state LED lights (lumencor Aura III light engine) and bandpass filters to excite DAPI, Cellmask Green, Coralite-555, and Cellmask Red, respectively. Images had pixel sizes of 0.68 × 0.68 microns, and a z-interval of 4 microns. For analysis and visualization, shading correction (shade only) was carried out for all images. Then, maximum intensity projections were generated using three z-slices. Segmentation was carried out separately on maximum intensity projections of Cellmask channels using CellPose 1.0, and masks were used to generate outlines and for intensity quantification.

iPSC differentiation protocols

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Healthy control iPSCs from the AIW002-02 and QPN929 backgrounds (Early Drug Discovery Unit-EDDU, McGill University) were selected for differentiation into the indicated neurological cell types. In all cases, iPSCs were cultured in mTeSR1 for 5–7 days prior to differentiation. Two batches of neurological cells were generated from the AIW002-02 background using identical protocols, with the exception that the QPN929 iPSC line was used for microglia differentiation in the batch used for chemiluminescent WB.

Motor neuron differentiation

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Motor neurons were differentiated from iPSCs following a previously published protocol (Deneault et al., 2022; Lépine et al., 2024). Basic neural medium (BNM, 1:1 mixture of DMEM/F12 and Neurobasal, 0.5X N2, 0.5X B27, 0.5X GlutaMAX, 1X antibiotic-antimycotic, and 100 μM ascorbic acid) serves as the base medium for all other formulations used in this protocol. iPSCs were seeded on matrigel-coated flasks in neural induction medium (BNM supplemented with 2 μM SB431542, 2 μM DMH1, 3 μM CHIR99021) with a full media change performed every other day. After 6 days, cells were passaged in patterning medium (BNM supplemented with 1 μM CHIR99021, 2 μM SB431542, 2 μM DMH1, 0.1 μM retinoic acid [RA] and 0.5 μM purmorphamine [PMN]) onto poly-L-ornithine- and laminin-coated flasks and cultured for a further 6 days with a full media change performed every other day. Cells were then passaged onto poly-L-ornithine- and laminin-coated flasks in expansion medium (BNM supplemented with 3 μM CHIR99021, 2 μM SB431542, 2 μM DMH1, 0.1 μM RA, and 0.5 μM PMN, and 0.5 mM valproic acid [VPA]), with a full media change performed every other day for 6 days to obtain motor neural progenitor cells (M-NPCs). M-NPCs were dissociated as single cells and seeded onto poly-L-ornithine- and laminin-coated plates in final differentiation medium (BNM supplemented with 0.5 μM RA, 0.1 μM PMN, 0.1 μM Compound E [CpdE], 10 ng/mL insulin-like growth factor-1 [IGF-1], brain-derived neurotrophic factor [BDNF], and ciliary neurotrophic factor [CNTF]), with a half media change once per week for final differentiation into motor neurons (MNs). MNs were considered mature and collected for WB after 4 weeks of differentiation.

Dopaminergic neuron differentiation

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Dopaminergic neurons were differentiated from iPSCs following a protocol of floor-plate induction with additional modifications (Kriks et al., 2011; Chen et al., 2022). Briefly, iPSCs were dissociated into single cells to form embryoid bodies (EBs). EBs were grown on low-attachment plates for 1 week in DMEM/F12 supplemented with 1X N2 and 1X B27, in the presence of 10 μM SB431542, 200 ng/mL Noggin, 200 ng/mL sonic hedgehog, 3 μM CHIR99021, and 100 ng/mL Fibroblast growth factor 8 (FGF8). On day 7, EBs were transferred, in the same media, to poly-L-ornithine- and laminin-coated plates. On day 14, rosettes were selected semi–manually and cultured as a monolayer in DMEM/F12 supplemented with 1X N2 and 1X B27 on poly-L-ornithine- and laminin-coated plates to generate dopaminergic neural progenitor cells (DA-NPCs). DA-NPCs were passaged at a ratio of 1:3 every 5–7 days. DA-NPCs were then cultured in neurobasal medium, supplemented with 1X N2 and 1X B27, in the presence of 1 μg/mL laminin, 500 μM dibutyryl-cAMP (db-cAMP), 20 ng/mL BDNF, 20 ng/mL glial-derived neurotrophic factor (GDNF), 200 μM ascorbic acid, 100 nM CpdE, and 1 ng/mL transforming growth factor β3 (TGF-β3), with a half media change twice per week for final differentiation into dopaminergic neurons. Dopaminergic neurons were considered mature and collected for WB after 4 weeks of differentiation.

Oligodendrocyte differentiation

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Oligodendrocytes were differentiated from iPSCs following a recently published protocol (Piscopo et al., 2024). The base media for all other formulations in this protocol is as follows: DMEM/F12 with GlutaMAX supplemented with 1X N2, 1X B27 without vitamin A, 1X NEAA, 55 mM β-mercaptoethanol, 25 mg/mL Insulin, and 2 mg/mL heparin. First, iPSCs were directed toward a neural progenitor fate in base media supplemented with 10 µM SB31542 and 2 µM DMH1, with daily media change over 8 days. Followed by patterning with 100 nM RA and 1 µg/mL PMN in base media with daily media change, for 4 days. Cells were then transferred from monolayer to suspension culture in the same medium for an additional 8 days to form neurospheres. Oligodendrocyte precursor cells were then generated and expanded in suspension using OPC media (base media supplemented with 10 ng/mL PDGF-AA, 10 ng/mL IGF-1, 10 ng/mL NT-3, 5 ng/mL HGF, 60 ng/mL 3, 3′, 5-Triiodo-L-thyronine [T3], 100 ng/mL biotin, and 1 µM db-cAMP) for 10 days. Following this, the cells were plated in OPC media onto poly-L-ornithine/laminin-coated vessels with half media changes performed every other day to support further differentiation toward the OPC lineage, which continues until day 75. At this stage, cells were detached and passaged using TrypLE Express Enzyme. In the final differentiation step, cells were exposed to ScienCell Astrocyte Medium supplemented with ScienCell Astrocyte Growth Supplements (AGS) for 10 days. AGS was then withdrawn, and final maturation completed by reintroducing IGF-1 and T3 at the previously described concentrations for a further 10 days, with half media changes performed every other day. At this point, oligodendrocytes were considered mature and collected for WB.

Astrocyte differentiation

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Astrocytes were differentiated from iPSCs following a previously published protocol (Soubannier et al., 2022). First, iPSCs were differentiated into neural progenitor cells following the steps described above for the generation of M-NPCs. M-NPCs were dissociated as single cells and plated at a low cell density (15,000 cells/cm2) onto a matrigel-coated vessel in M-NPC expansion media (as described above). The next day, medium was replaced with Astrocyte Differentiation Medium (ScienCell Astrocyte Medium containing AGS, 1% fetal bovine serum [FBS] [ScienCell Research Laboratories], 1% penicillin-streptomycin [Wisent Bioproducts]). Cells were passaged in the same media at a ratio of 1:2 once per week and were maintained under these culture conditions for 30 days, with a half media change performed every 3–4 days. FBS was then withdrawn to promote astrocyte maturation. Cells were maintained for an additional 4 weeks with a half media change performed twice per week prior to astrocyte collection for WB.

Microglia differentiation

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iPSC-derived microglia were differentiated following a published protocol (McQuade et al., 2018). Briefly, iPSCs were differentiated to hematopoietic precursor cells (iHPCs) using the STEMCELL Technologies STEMdiff Hematopoietic Kit. iHPCs were harvested on days 10 and 12 of iHPC differentiation and seeded on matrigel-coated 6-well plates in microglia differentiation media (MDM, DMEM/F12 HEPES no phenol red supplemented with 2X insulin-transferrin-selenite, 2X B27, 0.5X N2, 1X GlutaMAX, 1X NEAA, 400 µM monothioglycerol, and 5 µg/mL insulin)+three cytokine cocktail (25 ng/mL M-CSF, 100 ng/mL IL-34, and 50 ng/mL TGF-β1) for further differentiation into microglia. Cells were supplemented with MDM +three cytokine cocktail every other day, with a full media change performed on day 12, for 24 days of microglia differentiation. On day 25, a full media change to MDM + five cytokine cocktail (25 ng/mL M-CSF, 100 ng/mL IL-34, 50 ng/mL TGF-β1, 100 ng/mL CD200, and 100 ng/mL CX3CL1) was performed, followed by supplementation with MDM + five cytokine cocktail on day 27. Microglia were considered mature and utilized for downstream experiments after 28 days of microglial differentiation.

Flow cytometry analysis of CD45 and CD11b

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Mature iPSC-derived microglia were collected by combining non-adherent cells recovered from culture media with adherent cells gently detached in PBS. Undifferentiated iPSCs were dissociated into single-cell suspensions using Accutase. Cells were first incubated with Live/Dead Fixable Aqua viability dye in PBS for 30 minutes at room temperature, followed by washing in FACS buffer (PBS supplemented with 1% FBS and 0.1% sodium azide). Fc receptor blocking was performed using TrueStain blocking reagent for 10 minutes prior to antibody staining.

Cells were then incubated with fluorophore-conjugated antibodies against CD11b (PE; 1:20) and CD45 (Alexa Fluor 700; 1:40) for 15 minutes at room temperature. After washing, cells were resuspended in FACS buffer and analyzed using a Thermo Attune NxT flow cytometer equipped with 405, 488, and 561 nm lasers (NeuroEDDU Flow Cytometry Facility, McGill University). A minimum of 50,000 events per sample were acquired. Analysis was performed in FlowJo (BD Biosciences), with sequential gating applied to exclude debris and doublets, followed by selection of live cells prior to assessment of CD45 and CD11b expression.

Immunofluorescence staining for IBA1 and PU.1

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Mature iPSC-derived microglia were plated onto glass coverslips and fixed in 4% paraformaldehyde for 20 minutes at room temperature. Following three washes in PBS, cells were permeabilized with 0.2% Triton X-100 in PBS for 10 minutes and blocked in 5% normal donkey serum for 1 hour. Primary antibodies against IBA1 (Synaptic Systems 234 009, 1:1,000) and PU.1 (Cell Signaling Technology 2266, 1:500) were diluted in blocking solution and incubated overnight at 4°C. After washing in PBS, cells were incubated for 1 hour at room temperature with corresponding secondary antibodies, together with Hoechst (1 μg/mL). Coverslips were washed, mounted, and imaged using a Leica SP8 confocal microscope.

RNA extraction and quantitative RT-PCR

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Total RNA was isolated using TRIzol reagent followed by purification with the RNeasy Mini Kit (QIAGEN 74106) according to the manufacturer’s instructions. RNA concentration and purity were assessed by spectrophotometry. Reverse transcription was performed using 500 ng total RNA with M-MLV reverse transcriptase, random hexamers, RNase inhibitor, and dNTPs in first-strand buffer supplemented with DTT. Reactions were incubated at 42°C for 60 minutes followed by enzyme inactivation at 75°C for 10 minutes.

Quantitative PCR was performed using TaqMan gene expression assays (Thermo Fisher) on a QuantStudio 3 Real-Time PCR System. Relative expression levels were calculated using the comparative Ct (ΔΔCt) method with GAPDH as the reference gene.

The following TaqMan probes were used: GAPDH–Hs02786624_g1; AIF1 (IBA1)–Hs00610419_g1; OLFML3–Hs01113293_g1; SPI1 (PU.1)–Hs02786711_m1; CX3CR1–Hs01922583_s1; MERTK–Hs01031969_m1; TREM2–Hs00219132_m1; CSF1–Hs00174164_m1; P2RY12–Hs01881698_s1; RUNX1–Hs02558380_s1.

Data availability

All data generated during this study are publicly available through the YCharOS community on Zenodo (https://zenodo.org/communities/ycharos/). This includes raw datasets from antibody characterization studies for 33 ALS-associated proteins, as well as raw immunoblot data used for protein expression profiling. Data are organized as individual records corresponding to each antibody characterization study or immunoblot experiment. DOIs for antibody characterization datasets are provided in Table S1, and DOIs for expression profiling datasets are cited in the manuscript.

The following data sets were generated
    1. Moleón VR
    2. Fanti R
    3. Fotouhi M
    4. Alende C
    5. Ryan J
    6. Ayoubi R
    7. Durcan TM
    8. Brown CM
    9. McPherson PS
    10. Laflamme C
    (2024) Zenodo
    Antibody Characterization Report for ACSL5 (Long-chain-fatty-acid--CoA ligase 5).
    https://doi.org/10.5281/zenodo.10891256
    1. Ayoubi R
    2. Worrall D
    3. McPherson PS
    4. Laflamme C
    (2023) Zenodo
    Antibody Characterization Report for Angiogenin.
    https://doi.org/10.5281/zenodo.7671286
    1. Ayoubi R
    2. Bolívar SG
    3. McPherson PS
    4. Laflamme C
    (2025) Zenodo
    A guide to selecting high-performing antibodies for Caveolin-1 (UniProt ID: Q03135) for use in western blot.
    https://doi.org/10.5281/zenodo.17253403
    1. Moleón VR
    2. Ayoubi R
    3. McPherson PS
    4. Laflamme C
    (2023) Zenodo
    Antibody Characterization Report for Cyclin-F.
    https://doi.org/10.5281/zenodo.7971946
    1. Ayoubi R
    2. Alshafie W
    3. Southern K
    4. McPherson PS
    5. Laflamme C
    6. NeuroSGC/YCharOS/EDDU collaborative group
    (2023) F1000Research
    The identification of high-performing antibodies for Coiled-coil-helix-coiled-coil-helix domain containing protein 10 (CHCHD10) for use in Western Blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.133479.2
    1. Alshafie W
    2. Fotouhi M
    3. Ayoubi R
    4. Shlaifer I
    5. Southern K
    6. McPherson PS
    7. Laflamme C
    8. NeuroSGC/YCharOS/EDDU collaborative group
    (2023) F1000Research
    The identification of high-performing antibodies for Charged multivesicular body protein 2b for use in Western Blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.139755.1
    1. Alshalfie W
    2. Biddle M
    3. Fotouhi M
    4. Jones C
    5. Ayoubi R
    6. You Z
    7. Southern K
    8. McPherson PS
    9. Virk H
    10. Laflamme C
    11. NeuroSGC/YCharOS/EDDU collaborative group
    (2024) F1000Research
    The identification of high-performing antibodies for FUS (UniProt ID: P35637) for use in western blot, immunoprecipitation, immunofluorescence and flow cytometry.
    https://doi.org/10.12688/f1000research.133220.3
    1. Moleón VR
    2. Fotouhi M
    3. Alende C
    4. Ryan J
    5. Ayoubi R
    6. Reintsch W
    7. Durcan TM
    8. Brown CM
    9. McPherson PS
    10. Laflamme C
    (2024) Zenodo
    Antibody Characterization Report for hnRNP A1 (Heterogeneous nuclear ribonucleoprotein A1).
    https://doi.org/10.5281/zenodo.10815038
    1. Moleón VR
    2. Ayoubi R
    3. Alende C
    4. Fothouhi M
    5. Ryan J
    6. Bolívar SG
    7. Worrall D
    8. Durcan TM
    9. Brown CM
    10. Francis V
    11. McPherson PS
    12. Laflamme C
    (2025) F1000Research
    A guide to selecting high-performing antibodies for Heterogeneous nuclear ribonucleoproteins A2/B1 (hnRNP A2/B1) (UniProt ID: P22626) for use in western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.170886.1
    1. Ayoubi R
    2. Bolívar SG
    3. Laflamme C
    (2026) Zenodo
    A guide to selecting high-performing antibodies for KIF5A (Q12840) for use in western blot.
    https://doi.org/10.5281/zenodo.18612357
    1. Zha CY
    2. Ayoubi R
    3. Alende C
    4. Fotouhi M
    5. Bolívar SG
    6. Laflamme C
    (2026) Zenodo
    A guide to selecting high-performing antibodies for Galectin-1 (P09382) for use in western blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.5281/zenodo.18612277
    1. Zha CY
    2. Bolívar SG
    3. Ayoubi R
    4. McPherson P
    5. Laflamme C
    6. NeuroSGC/YCharOS/EDDU collaborative group
    (2025) Zenodo
    Antibody Characterization Report for NEK1 (UniProt ID: Q96PY6) for use in western blot.
    https://doi.org/10.5281/zenodo.17955305
    1. Moleón VR
    2. Ayoubi R
    3. Alende C
    4. Fothouhi M
    5. Ryan J
    6. Bolívar SG
    7. Worrall D
    8. Durcan TM
    9. Brown CM
    10. Francis V
    11. Francis V
    12. McPherson PS
    13. Laflamme C
    (2025) F1000Research
    A guide to selecting high-performing antibodies for Optineurin (UniProt ID: Q96CV9) for use in western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.169966.1
    1. Ayoubi R
    2. Bolívar SG
    3. McPherson PS
    4. Laflamme C
    (2025) Zenodo
    A guide to selecting high-performing antibodies for Senataxin (UniProt ID: Q7Z333) for use in western blot.
    https://doi.org/10.5281/zenodo.17253433
    1. Alshafie W
    2. Fotouhi M
    3. Ayoubi R
    4. Nicouleau M
    5. Durcan TM
    6. McPherson PS
    7. Laflamme C
    (2021) Zenodo
    Antibody Characterization Report for Sigma non-opioid intracellular receptor 1.
    https://doi.org/10.5281/zenodo.5644356
    1. Ayoubi R
    2. Alshafie W
    3. You Z
    4. Southern K
    5. McPherson PS
    6. Laflamme C
    (2023) F1000Research
    Identification of high-performing antibodies for Superoxide dismutase [Cu-Zn] 1 (SOD1) for use in Western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.132952.2
    1. Ayoubi R
    2. Alshafie W
    3. Shlaifer I
    4. Southern K
    5. McPherson PS
    6. Laflamme C
    7. NeuroSGC/YCharOS/EDDU collaborative group
    (2024) F1000Research
    The identification of high-performing antibodies for Sequestosome-1 for use in Western blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.132628.2
    1. Moleón VR
    2. Alende C
    3. Fotouhi M
    4. Ayoubi R
    5. Laflamme C
    6. NeuroSGC/YCharOS/EDDU collaborative group
    7. ABIF consortium
    (2025) F1000Research
    A guide to selecting high-performing antibodies for TAF15 (UniProt ID: Q92804) for use in western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.160371.1
    1. Worrall D
    2. Ayoubi R
    3. Fotouhi M
    4. Southern K
    5. McPherson PS
    6. Laflamme C
    7. NeuroSGC/YCharOS/EDDU collaborative group
    8. ABIF Consortium
    (2023) F1000Research
    The identification of high-performing antibodies for TDP-43 for use in Western Blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.131852.2
    1. Alshafie W
    2. Fotouhi M
    3. Shlaifer I
    4. Ayoubi R
    5. Edwards AM
    6. Durcan TM
    7. McPherson PS
    8. Laflamme C
    (2022) F1000Research
    Identification of highly specific antibodies for Serine/threonine-protein kinase TBK1 for use in immunoblot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.124632.2
    1. Fotouhi M
    2. Worrall D
    3. Ayoubi R
    4. Southern K
    5. McPherson PS
    6. Laflamme C
    7. NeuroSGC/YCharOS/EDDU collaborative group
    8. ABIF Consortium
    (2023) F1000Research
    A guide to selecting high-performing antibodies for RNA-binding protein TIA1 for use in Western Blot, immunoprecipitation and immunofluorescence.
    https://doi.org/10.12688/f1000research.133645.2
    1. Fotouhi M
    2. Alshafie W
    3. Shlaifer I
    4. Ayoubi R
    5. Durcan TM
    6. McPherson PS
    7. Laflamme C
    (2022) Zenodo
    Antibody Characterization Report for Tubulin alpha-4A chain.
    https://doi.org/10.5281/zenodo.5903719
    1. McDowell I
    2. Ayoubi R
    3. Fotouhi M
    4. Southern K
    5. McPherson PS
    6. Laflamme C
    7. NeuroSGC/YCharOS/EDDU collaborative group
    8. ABIF Consortium
    (2023) F1000Research
    The identification of high-preforming antibodies for Ubiquilin-2 for use in Western Blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.131851.1
    1. Bolívar SG
    2. Ayoubi R
    3. Alende C
    4. Fothouhi M
    5. Shlaifer I
    6. McPherson PS
    7. Laflamme C
    8. NeuroSGC/YCharOS/EDDU collaborative group
    9. ABIF Consortium
    (2024) F1000Research
    A guide to selecting high-performing antibodies for VAPB (UniProt ID: O95292) for use in western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.160226.1
    1. Moleón VR
    2. Fotouhi M
    3. Ryan J
    4. Worrall D
    5. Ayoubi R
    6. Francis V
    7. McPherson PS
    8. Laflamme C
    (2025) F1000Research
    A guide to selecting high-performing antibodies for VCP (UniProt ID: P55072) for use in western blot, immunoprecipitation, and immunofluorescence.
    https://doi.org/10.12688/f1000research.169502.1
    1. Ayoubi R
    2. Laflamme C
    (2026) Zenodo
    ALS-RAP immunoblot dataset_chemiluminescent-based experiment.
    https://doi.org/10.5281/zenodo.18484385
    1. Ayoubi R
    2. Laflamme C
    (2026) Zenodo
    ALS-RAP immunoblot dataset_fluorescence-based experiment.
    https://doi.org/10.5281/zenodo.18484483

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Article and author information

Author details

  1. Riham Ayoubi

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6633-655X

  2. Emma J MacDougall

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing
    Competing interests
    No competing interests declared

  3. Ian McDowell

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Data curation, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9031-4729

  4. Michael S Biddle

    NIHR Respiratory Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
    Contribution
    Conceptualization, Resources, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9853-8815

  5. Bárbara T Ferreira

    NIHR Respiratory Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0004-5582-2586

  6. CongYao Zha

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Data curation, Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-1667-9559

  7. Marie-France Dorion

    Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Data curation, Investigation, Methodology, Writing – original draft
    Competing interests
    No competing interests declared

  8. Jay P Ross

    Department of Genetics, Stanford University School of Medicine, Stanford, United States
    Contribution
    Conceptualization, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-8183-2524

  9. Sara Gonzalez Bolivar

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4299-8281

  10. Vera Ruiz Moleon

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  11. Charles Alende

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0009-0005-4611-6134

  12. Vincent Francis

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Resources, Data curation
    Competing interests
    No competing interests declared

  13. Maryam Fotouhi

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8937-4719

  14. Mathilde Chaineau

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9705-0754

  15. Carol X Chen

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6167-8083

  16. Valerio EC Piscopo

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-0602-2839

  17. Vincent Soubannier

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  18. Irina Shlaifer

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-3737-268X

  19. Zhipeng You

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  20. Michael Nicouleau

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9319-3355

  21. Tracy Keates

    Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  22. Wen Hwa Lee

    Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4098-5225

  23. Brian D Marsden

    Centre for Human Genetics and Centre for Medicines Discovery, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Resources, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-1937-4091

  24. Leonidas Koukouflis

    Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  25. Edvard Wigren

    Structural Genomics Consortium, Department of Medicine, Karolinska Institute, Solna, Sweden
    Contribution
    Investigation
    Competing interests
    No competing interests declared

  26. Carolyn A Marks

    Structural Genomics Consortium, Department of Medicine, Karolinska Institute, Solna, Sweden
    Contribution
    Data curation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6665-8572

  27. Luke M Healy

    Neuroimmunology Unit, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Resources, Supervision
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9496-2216

  28. Patrick A Dion

    Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization
    Competing interests
    No competing interests declared

  29. Guy A Rouleau

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Funding acquisition
    Competing interests
    No competing interests declared

  30. Edward A Fon

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Funding acquisition
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5520-6239

  31. Harvinder S Virk

    NIHR Respiratory Biomedical Research Centre, Department of Respiratory Sciences, University of Leicester, Leicester, United Kingdom
    Contribution
    Conceptualization, Resources, Writing – original draft
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-9739-9593

  32. Susanne Gräslund

    Structural Genomics Consortium, Department of Medicine, Karolinska Institute, Solna, Sweden
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-0077-8333

  33. Opher Gileadi

    1. Structural Genomics Consortium, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
    2. Structural Genomics Consortium, Department of Medicine, Karolinska Institute, Solna, Sweden
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration
    Competing interests
    No competing interests declared

  34. Aled M Edwards

    Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration
    Competing interests
    No competing interests declared

  35. Thomas M Durcan

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Early Drug Discovery Unit (EDDU), Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0003-3942-1956

  36. Peter S McPherson

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing – original draft, Project administration
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-7806-5662

  37. Carl Laflamme

    1. Structural Genomics Consortium, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    2. Neurodegenerative Diseases Research Group, Department of Neurology and Neurosurgery, Montreal Neurological Institute-Hospital (The Neuro), McGill University, Montreal, Canada
    3. School of Pharmaceutical Sciences, Faculty of Medicine, University of Ottawa, Ottawa, Canada
    Contribution
    Conceptualization, Supervision, Methodology, Writing – original draft, Project administration, Writing – review and editing
    For correspondence
    carl.laflamme@uottawa.ca
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-5906-025X

Funding

Motor Neurone Disease Association, Amyotrophic Lateral Sclerosis Association and ALS Society of Canada (ALS-RAP)

  • Susanne Gräslund
  • Opher Gileadi
  • Aled M Edwards
  • Thomas M Durcan
  • Peter S McPherson
  • Carl Laflamme

Genome Canada, Genome Quebec and Genome Ontario (OGI-210)

  • Peter S McPherson
  • Carl Laflamme

Québec Consortium for Drug Discovery (YCharOS: A platform enabling neuroscience target validation)

  • Peter S McPherson
  • Carl Laflamme

National Centre for the Replacement Refinement and Reduction of Animals in Research (NC/NAM0019/1)

  • Harvinder S Virk

Medical Research Council (MRC UKRI076)

  • Harvinder S Virk

Genome Canada and Ontario Genomics (OGI-196)

  • Aled M Edwards

EUbOPEN (grant no. 875510)

  • Aled M Edwards

Mitacs

  • Riham Ayoubi

Canadian Institutes of Health Research (PJT-195804)

  • Edward A Fon

Canada Research Chairs (Tier 1)

  • Edward A Fon

Sean M. Healey & AMG Center for ALS (ALS Scholars)

  • Jay P Ross

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

We would like to thank Kathleen Southern for the coordination of information exchange between laboratories involved in the ALS-RAP project. We would like to thank Abcam, ABCD Antibodies, ABclonal, Aviva Systems Biology, Cell Signaling Technology, Developmental Studies Hybridoma Bank, GeneTex, Revvity, Institute for Protein Innovation, MilliporeSigma, Novus Biologicals, Proteintech, R&D Systems, Thermo Fisher Scientific for providing antibodies and/or KO cell lines, free of charge, for ALS-RAP related targets. We would like to acknowledge the Birth Defects Research Laboratory, University of Washington, Seattle, USA for providing fetal tissues. This work was supported by a grant from the Motor Neurone Disease Association, the ALS Association, and ALS Canada to develop the ALS-Reproducibility Antibody Platform. This work was also supported by the Government of Canada through Genome Canada, Genome Quebec, and Ontario Genomics (OGI-210) and by a grant from the Quebec Consortium for Drug Discovery (CQDM), a grant from the Ministère de l’Économie, de l’Innovation et de l’Énergie du Québec. This work was supported by a grant from the National Centre for the Replacement, Refinement, and Reduction of Animals in Research (NC3Rs) and MRC (NC3Rs Ref: NC/NAM0019/1, MRC UKRI076) alongside support from the Leicester Institute for Precision Health. The Structural Genomics Consortium is a registered charity (no. 1097737) that receives funds from Bayer, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute (grant no. OGI-196), the European Union (EU), and European Federation of Pharmaceutical Industries and Associations through the Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN grant no. 875510), Janssen, Merck (also known as EMD in Canada and the USA), Pfizer, and Takeda. RA is supported by a Mitacs postdoctoral fellowship. This work was supported by a CIHR project grant (PJT-195804). EAF is supported by a Canada Research Chair (Tier 1) in Parkinson’s disease. JPR is supported by an ALS Scholars in Therapeutics postdoctoral fellowship from the Sean M Healey & AMG Center for ALS at Massachusetts General Hospital (MGH), ALS FindingACure, and FightMND.

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  1. Riham Ayoubi
  2. Emma J MacDougall
  3. Ian McDowell
  4. Michael S Biddle
  5. Bárbara T Ferreira
  6. CongYao Zha
  7. Marie-France Dorion
  8. Jay P Ross
  9. Sara Gonzalez Bolivar
  10. Vera Ruiz Moleon
  11. Charles Alende
  12. Vincent Francis
  13. Maryam Fotouhi
  14. Mathilde Chaineau
  15. Carol X Chen
  16. Valerio EC Piscopo
  17. Vincent Soubannier
  18. Irina Shlaifer
  19. Zhipeng You
  20. Michael Nicouleau
  21. Tracy Keates
  22. Wen Hwa Lee
  23. Brian D Marsden
  24. Leonidas Koukouflis
  25. Edvard Wigren
  26. Carolyn A Marks
  27. Luke M Healy
  28. Patrick A Dion
  29. Guy A Rouleau
  30. Edward A Fon
  31. Harvinder S Virk
  32. Susanne Gräslund
  33. Opher Gileadi
  34. Aled M Edwards
  35. Thomas M Durcan
  36. Peter S McPherson
  37. Carl Laflamme
(2026)
A validated antibody toolbox for ALS research
eLife 15:RP111349.
https://doi.org/10.7554/eLife.111349.3

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    1. Biochemistry and Chemical Biology
    2. Cell Biology

    Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications

    Riham Ayoubi, Joel Ryan ... Carl Laflamme
    Research Advance

    Antibodies are critical reagents to detect and characterize proteins. It is commonly understood that many commercial antibodies do not recognize their intended targets, but information on the scope of the problem remains largely anecdotal, and as such, feasibility of the goal of at least one potent and specific antibody targeting each protein in a proteome cannot be assessed. Focusing on antibodies for human proteins, we have scaled a standardized characterization approach using parental and knockout cell lines (Laflamme et al., 2019) to assess the performance of 614 commercial antibodies for 65 neuroscience-related proteins. Side-by-side comparisons of all antibodies against each target, obtained from multiple commercial partners, have demonstrated that: (i) more than 50% of all antibodies failed in one or more applications, (ii) yet, ~50–75% of the protein set was covered by at least one high-performing antibody, depending on application, suggesting that coverage of human proteins by commercial antibodies is significant; and (iii) recombinant antibodies performed better than monoclonal or polyclonal antibodies. The hundreds of underperforming antibodies identified in this study were found to have been used in a large number of published articles, which should raise alarm. Encouragingly, more than half of the underperforming commercial antibodies were reassessed by the manufacturers, and many had alterations to their recommended usage or were removed from the market. This first study helps demonstrate the scale of the antibody specificity problem but also suggests an efficient strategy toward achieving coverage of the human proteome; mine the existing commercial antibody repertoire, and use the data to focus new renewable antibody generation efforts.