Abstract Many (most!) genes/proteins linked to disease phenotypes remain severely understudied [1]. Distinct open-science initiatives are needed to promote the exploration of currently understudied proteins, from the proper identification of research reagents [2], to the development of a chemical probes for every human protein [1]. We focus on the proper characterization of antibodies to guide researchers in selecting the most specific/selective antibodies for their needed application(s) [3]. Antibodies are among the most commonly used reagents in cell biology. Generally, scientists purchase antibodies from commercial suppliers, and rely on the vendor’s quality control data to make their purchasing decisions. While there are many outstanding commercially-available antibodies, many other antibodies do not perform as advertised - and in the absence of an objective means to compare performance, it is impossible to tell one from the other. This is a widely known problem that plagues tens of thousands of scientists annually [4-6]. There is a scientific solution, enabled by CRISPR/Cas9 technology. By comparing signals from wild-type and isogenic knockout cells, one can readily test the specificity of antibodies. We applied this approach in a pilot study demonstrating that only three of the 16 commercially-available antibodies for C9ORF72, the protein product of a major amyotrophic lateral sclerosis disease locus, specifically recognized the protein. Distressingly, neither antibody had been used in a publication, and the antibody used most frequently in publications, which have been cited thousands of times, did not recognize the protein in any application [7]. We are now applying our antibody characterization pipeline to generate head-to-head comparisons of commercial antibodies for all human proteins. This work is performed in partnership with high-quality manufacturers that provide in-kind reagents (i.e. antibodies and knock-out lines). Finalized antibody characterization reports are progressively uploaded on a free open-science repository (https://zenodo.org/communities/ycharos/). We believe our initiative, Antibody Characterization through Open Science (YCharOS), will contribute to make science more reproducible and help illuminate the dark genome. The top 3 key questions that YCharOS can answer: 1. Do antibodies used in my field perform as advertised? 2. How do I identify the best performing antibody for my protein of interest? 3. Do I need to launch an expensive and time-consuming antibody generation study or do effective commercial antibodies already exist for my protein of interest? References (see https://dknet.org/about/blog/2334) Presenter: Carl Laflamme, PhD, Senior Postdoctoral Fellow at the Montreal Neurological Institute (The Neuro, McGill University). Chetan Raina, YCharOS, CEO. Upcoming webinars schedule: https://dknet.org/about/webinar

1. #OpenScienceinAction | www.theneuro.ca
YCharOS: Antibody Characterization
Through Open Science
Carl Laflamme, NeuroSGC, Team leader
Chetan Raina, YCharOS, CEO
Institut-Hôpital
neurologique de Montréal
Montreal Neurological
Institute-hospital
Join the conversation online! @TheNeuro_MNI #OpenScienceinAction

2. Would you use a navigation system that is accurate half of the time?
NO!
The antibody liability crisis:
-cost $1 billion/ann.,
-lead to wrong hypothesis,
-waste of research time,
-at the heart of the reproducibility crisis in biomedical research.

3. #OpenScienceinAction | www.theneuro.ca
Our rationale: they are high quality antibodies for most human protein
• Slow route (>5-10 years ago): generate your own antibodies and your own
characterization data
• 2003: ~50,000 commercially available antibodies1
• Few KO cell lines available
• Fast route (now): use of commercial antibodies
• ~2.5 millions antibodies from hundreds of suppliers (The Antibody Registry)
• 85% of human proteins are covered by >20 antibodies2
• KO cells are available or can be generated routinely
• Identification of high-quality antibodies is problematic
1Michaud et al., 2003
2Laflamme et al., New BIOTECHNOLOGY, 2021

4. #OpenScienceinAction | www.theneuro.ca
How do you select a commercial antibody to purchase?

5. Our solution: an industrialized antibody characterization platform

6. Identify best performing antibodies for nominated targets
Our antibody characterization reports are
available:
-on the Zenodo repository:
zenodo.org/communities/ycharos
-on the YCharOS website:
ycharos.com
-on The Antibody Registry
https://antibodyregistry.org/

7. Antibody characterization data for two protein targets
• Matrin-3
• ALS linked protein
• Poorly characterized protein; 93 pubmed entries
• KO line generated by the Horizon Discovery, an industry partner
• E3 ubiquitin-protein ligase parkin
• Parkinson’s disease target
• Heavily studied protein; 9,092 pubmed entries
• KO lines from The Neuro and Abcam, an industry partner

8. Matrin-3: reagents provided by partners

9. Matrin-3: antibody screening by Western blot

10. Matrin-3: antibody screening by immunoprecipitation

11. Matrin-3: antibody screening by immunofluorescence

12. High-quality antibodies were identified for most ALS-linked proteins
WB IP IF
C9ORF72 16 10 1 1
ATXN2 5 3 3 3
CHCHD10 4 4 4 3
FUS 12 11 3 7
NEK1 6 5 0 0
OPTN 8 7 4 5
SOD1 14 13 3 4
SQSTM1 21 19 11 11
TBK1 11 11 1 2
MATR3 11 11 6 7
SIGMAR1 5 4 0 0
SPAST 5 3 3 1
ANXA11 14 6 3 0
High quality antibodies
# antibodies
tested

13. Parkin: reagents provided by partners

14. Parkin: antibody screening by Western blot
125
[PRK8]
90
[PRK8]
41 0 2 1
PRK8: 446 citations
2 0 0

15. Parkin: antibody screening by immunoprecipitation

16. Parkin: antibody screening by immunofluorescence

17. #OpenScienceinAction | www.theneuro.ca
Characterization is key!
We have done studies on 50 proteins
Still 19,950 proteins to do!!

18. YCharOS (Antibody Characterization Through Open Science)

19. The Target Enabling Packages (TEPs) & Target 2035
TEPs provide a critical mass of reagents and knowledge on a protein
target to allow rapid biochemical and chemical exploration and
characterization of proteins with genetic linkage to key disease areas.
Open science initiative launched by the
Structural Genomics Consortium
Global movement

20. How confident are you about antibodies for NIDDK-linked proteins?
Are there understudied proteins in the NIDDK community?
Bakay et al., Current Diabetes Reports, 2019

21. Thank you!
-on the Zenodo repository:
zenodo.org/communities/ycharos
-on the YCharOS website:
ycharos.com
-on The Antibody Registry
https://antibodyregistry.org/