Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications Article Swipe
Riham Ayoubi
,
Joël Ryan
,
Michael Biddle
,
Walaa Alshafie
,
Maryam Fotouhi
,
Sara González Bolívar
,
Vera Ruíz Moleón
,
Peter Eckmann
,
Donovan Worrall
,
Ian McDowell
,
Kathleen Southern
,
Wolfgang Reintsch
,
Thomas M. Durcan
,
Claire M. Brown
,
Anita Bandrowski
,
Harvinder Virk
,
A.M. Edwards
,
Peter S. McPherson
,
Carl Laflamme
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.06.01.543292
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1101/2023.06.01.543292
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, demonstrates that: i) more than 50% of all antibodies failed in one or more tests, 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 such 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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2023.06.01.543292
- https://www.biorxiv.org/content/biorxiv/early/2023/07/13/2023.06.01.543292.full.pdf
- OA Status
- green
- Cited By
- 22
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379208409
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4379208409Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2023.06.01.543292Digital Object Identifier
- Title
-
Scaling of an antibody validation procedure enables quantification of antibody performance in major research applicationsWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-06-02Full publication date if available
- Authors
-
Riham Ayoubi, Joël Ryan, Michael Biddle, Walaa Alshafie, Maryam Fotouhi, Sara González Bolívar, Vera Ruíz Moleón, Peter Eckmann, Donovan Worrall, Ian McDowell, Kathleen Southern, Wolfgang Reintsch, Thomas M. Durcan, Claire M. Brown, Anita Bandrowski, Harvinder Virk, A.M. Edwards, Peter S. McPherson, Carl LaflammeList of authors in order
- Landing page
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https://doi.org/10.1101/2023.06.01.543292Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2023/07/13/2023.06.01.543292.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2023/07/13/2023.06.01.543292.full.pdfDirect OA link when available
- Concepts
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Antibody, Polyclonal antibodies, Proteome, Monoclonal antibody, Antibody Repertoire, Computational biology, Computer science, Biology, Immunology, BioinformaticsTop concepts (fields/topics) attached by OpenAlex
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22Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 13, 2023: 4Per-year citation counts (last 5 years)
- References (count)
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28Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.antibodies. | 162 |
| abstract_inverted_index.comparisons | 94 |
| abstract_inverted_index.demonstrate | 222 |
| abstract_inverted_index.feasibility | 37 |
| abstract_inverted_index.information | 24 |
| abstract_inverted_index.performance | 84 |
| abstract_inverted_index.recombinant | 154 |
| abstract_inverted_index.recommended | 209 |
| abstract_inverted_index.repertoire, | 248 |
| abstract_inverted_index.specificity | 228 |
| abstract_inverted_index.Side-by-side | 93 |
| abstract_inverted_index.application, | 139 |
| abstract_inverted_index.characterize | 7 |
| abstract_inverted_index.demonstrates | 106 |
| abstract_inverted_index.significant; | 150 |
| abstract_inverted_index.standardized | 68 |
| abstract_inverted_index.Encouragingly, | 189 |
| abstract_inverted_index.manufacturers, | 202 |
| abstract_inverted_index.high-performing | 135 |
| abstract_inverted_index.underperforming | 166, 195 |
| abstract_inverted_index.characterization | 69 |
| abstract_inverted_index.neuroscience-related | 91 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5029919195 |
| countries_distinct_count | 3 |
| institutions_distinct_count | 19 |
| corresponding_institution_ids | https://openalex.org/I5023651 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/7 |
| sustainable_development_goals[0].score | 0.4000000059604645 |
| sustainable_development_goals[0].display_name | Affordable and clean energy |
| citation_normalized_percentile.value | 0.96783084 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |