Dec 09, 2024

Public workspaceA consensus platform for antibody characterization

This collection is a draft, published without a DOI.
  • Riham Ayoubi1,
  • Joel Ryan2,
  • Sara Gonzalez Bolivar1,
  • Charles Alende1,
  • Vera Ruiz Moleon1,
  • Maryam Fotouhi1,
  • Kathleen Southern1,
  • Walaa Alshafie1,
  • Matt R Baker3,
  • Alexander R Ball Jr4,
  • Danielle Callahan5,
  • Jeffery A Cooper6,
  • Katherine Crosby7,
  • Kevin J Harvey8,
  • Douglas W Houston9,
  • Ravindran Kumaran10,
  • Meghan Rego11,
  • Christine Schofield12,
  • Hai Wu13,
  • Michael S Biddle14,
  • Claire M Brown2,
  • Richard A Kahn15,
  • Anita Bandrowski16,
  • Harvinder S Virk14,
  • Aled M Edwards17,
  • Peter S McPherson1,
  • Carl Laflamme18
  • 1Montreal Neurological Institute, McGill University, Structural Genomics Consortium;
  • 2Advanced BioImaging Facility (ABIF), McGill University;
  • 3Thermo Fisher Scientific;
  • 4Genetex;
  • 5Proteintech Group;
  • 6Bio-Techne;
  • 7Cell Signaling Technology;
  • 8Aviva Systems Biology;
  • 9Developmental Studies Hybridoma Bank, University of Iowa;
  • 10Abcam;
  • 11Addgene;
  • 12Revvity;
  • 13Abclonal;
  • 14Leicester University;
  • 15Emory University School of Medicine;
  • 16UC San Diego;
  • 17University of Toronto, Structural Genomics Consortium;
  • 18McGill University
  • Riham Ayoubi: https://orcid.org/0000-0001-6633-655X;
  • Joel Ryan: https://orcid.org/0000-0002-7295-815X;
  • Sara Gonzalez Bolivar: https://orcid.org/0000-0002-4299-8281;
  • Charles Alende: https://orcid.org/0009-0005-4611-6134;
  • Vera Ruiz Moleon: https://orcid.org/0000-0003-3728-3158;
  • Maryam Fotouhi: https://orcid.org/0000-0001-8937-4719;
  • Kathleen Southern: https://orcid.org/0000-0002-4125-3608;
  • Walaa Alshafie: https://orcid.org/0000-0001-7999-1297;
  • Michael S Biddle: https://orcid.org/0000-0002-9853-8815;
  • Claire M Brown: https://orcid.org/0000-0003-1622-663X;
  • Anita Bandrowski: https://orcid.org/0000-0002-5497-0243;
  • Harvinder S Virk: https://orcid.org/0000-0002-9739-9593;
  • Peter S McPherson: https://orcid.org/0000-0001-7806-5662;
  • Carl Laflamme: https://orcid.org/0000-0001-5906-025X;
Carl Laflamme
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External link: https://doi.org/10.21203/rs.3.pex-2607/v1
Collection Citation: Riham Ayoubi, Joel Ryan, Sara Gonzalez Bolivar, Charles Alende, Vera Ruiz Moleon, Maryam Fotouhi, Kathleen Southern, Walaa Alshafie, Matt R Baker, Alexander R Ball Jr, Danielle Callahan, Jeffery A Cooper, Katherine Crosby, Kevin J Harvey, Douglas W Houston, Ravindran Kumaran, Meghan Rego, Christine Schofield, Hai Wu, Michael S Biddle, Claire M Brown, Richard A Kahn, Anita Bandrowski, Harvinder S Virk, Aled M Edwards, Peter S McPherson, Carl Laflamme 2024. A consensus platform for antibody characterization. protocols.io https://protocols.io/view/a-consensus-platform-for-antibody-characterization-dnum5eu6
License: This is an open access collection distributed under the terms of the Creative Commons Attribution License,  which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited
Protocol status: Working
Created: October 04, 2024
Last Modified: December 09, 2024
Collection Integer ID: 109165
Keywords: commercial antibodies, validation, characterization, immunoblot, western blot, immunoprecipitation, immunofluorescence, RRIDs, open science, YCharOS
Funders Acknowledgements:
National Institutes of Aging (NIA)
Grant ID: U54AG065187, RF1AG057443
Michael J. Fox Foundation for Parkinson’s Research
Grant ID: 18331
Government of Canada (through Genome Canada, Genome Quebec, and Ontario Genomics)
Grant ID: OGI-210
Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute
Grant ID: OGI-196
EU and EFPIA through the Innovative Medicines Initiative 2 Joint Undertaking
Grant ID: EUbOPEN 875510
Chan Zuckerberg Initiative (Silicon Valley Community Foundation)
Grant ID: 2020–225398
Abstract
Antibody-based research applications are critical for biological discovery. Yet, there are no industry standards to compare the performance of antibodies in various applications. We describe a knockout cell line-based antibody characterization platform, developed, and approved jointly by industry and academic researchers that enables the systematic comparison of antibody performance in western blot, immunoprecipitation, and immunofluorescence procedures. The scalable protocols consist of (i) the identification of appropriate cell lines for antibody characterization studies, (ii) development/contribution of isogenic knockout controls, validated at the protein level, and (iii) a series of antibody characterization procedures focused on the most common uses of antibodies in research. Guidelines for the assessment of antibody performance are presented along with the value of making the resulting data publicly available. This antibody characterization platform can be implemented with minimal technological limitations. The proposed antibody characterization platform may be performed readily on antibodies targeting a protein in roughly one month, with one person working less than full-time. Antibody characterization is needed to meet standards for resource validation and data reproducibility, increasingly required by journals and funding agencies.

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Guidelines
Introduction

Antibodies are fundamental tools in biomedical research, yet the absence of standardized performance evaluation methods poses challenges for researchers selecting appropriate reagents. Current reliance on published descriptions and commercial quality control data lacks detailed characterization and data inclusion1, hindering effective antibody selection. Additionally, the proliferation of commercially available antibodies targeting human proteins further complicates the process, often leading to time-consuming and inefficient searches for optimal reagents2. Moreover, instances of invalidated top-cited antibodies have tainted scientific literature3-9, underscoring the need for standardized comparison methods to improve data quality and reproducibility.

To address these challenges, we introduce "Antibody Characterization through Open Science" (YCharOS), a collaborative effort among academia, leading antibody manufacturers, and knockout (KO) cell line providers. YCharOS has developed an antibody characterization platform based on KO cell lines as isogenic controls for common applications: western blot (WB), immunoprecipitation (IP), and immunofluorescence (IF). This collaboration enables the evaluation of approximately 80% of renewable antibodies accessible in commercial catalogs, with the remaining 20% outside our scope due to their availability from sources beyond our partnerships. This setup facilitates direct comparisons among antibodies targeting a specific protein.

A key strength of the YCharOS platform is the support of industrial partners, who contribute antibodies and KO cell lines, to enable comprehensive antibody characterization10. The iterative validation of KO celllines and antibodies (ideally renewable) for their ability to recognize target proteins expressed at endogenous levels is another key component of the platform. The resulting data are shared openly10, benefiting the global biomedical community and promoting robust and reproducible research. By identifying specific antibodies and validating their performance, including the removal of non-specific antibodies from commercial catalogs11, YCharOS contributes to improving the reliability of scientific data. Moreover, the consensus protocols employed by YCharOS facilitate the creation of a publicly accessible database containing trusted antibody characterization data12, aiding researchers in antibody selection and reviewers in assessing antibody suitability. While independent researchers may not have access to as many antibodies for any given protein target, the feasibility of characterizing a wide array of available antibodies against a target protein has been demonstrated4,5,7,13-15.

In the following sections, we detail the development of the YCharOS platform, its comparison with other antibody validation methods, experimental procedures, expertise and equipment requirements, data dissemination strategies, and limitations. Through these efforts, YCharOS aims to address the critical need for standardized antibody characterization methods, ultimately enhancing the reliability and reproducibility of scientific research.

Development of the Platform

The establishment of the YCharOS platform stems from a collaborative effort between academic and industry partners16, aimed at addressing the critical need for standardized antibody characterization methods1,17-19. Co-authors of this article actively participate in the YCharOS public-private partnership,which forms the foundation of this endeavor. Central to the platform are protocols utilizing KO cell lines, with modifications to knockdown (KD) strategies when assessing antibodies targeting essential genes.

To ensure robust and comprehensive protocols, senior scientists from leading antibody manufacturers, including Abcam, Abclonal, Addgene, Aviva Systems Biology, Bio-Techne (comprising Novus Biologicals and R&D Systems), Cell Signaling Technology, Developmental Studies Hybridoma Bank, Genetex, Proteintech, and Thermo Fisher Scientific, collaborated in refining the methodologies. Furthermore, antibody manufacturers donate antibodies to the project, alongside contributions of KO cell lines from Abcam and Horizon Discovery (part of Revvity).

The platform's optimization (Fig. 1) enables the characterization of antibodies against a broad spectrum of human proteins, encompassing soluble, membrane-bound, and secreted proteins. Notably, as of March 2024, the platform has tested 859 antibodies targeting 96 human proteins.

A pivotal aspect of the YCharOS platform is its commitment to transparency and data sharing. To this end, all characterization data generated through collaborations are openly disseminated. Additionally, an analysis code for IF has been developed and made publicly available, facilitating the segmentation and direct comparison of fluorescence intensity between parental and KO cell lines.

Comparison with Other Methods

The International Working Group for Antibody Validation has recommended five antibodycharacterization methodologies20: 1) genetic strategies utilizing KO or KD cell lines as controls; 2) orthogonal strategies correlating antibody signals to known information about the protein of interest; 3) overlap of signals of two independent antibodies recognizing different epitopes in the same target; 4) antibody recognition of an overexpressed tagged version of the protein target; 5) employment of mass spectrometry to determine if the protein target captured by an antibody corresponds to the major signal in the immunoprecipitate20. Among these five recommended strategies, the genetic and orthogonal approaches are used approximately 90% of the time by antibody manufacturers, with 61% and 83% of antibodies validated using orthogonal strategies in WB and IF, respectively11. Genetic strategies account for 30% and 7% of validations in WB and IF, respectively11. Our analysis revealed discrepancies in the specificity of antibodies validated by suppliers, particularly with IF, where only 38% of antibodies validated by orthogonal strategies exhibited specificity11. This presents a compelling argument to prioritize genetic strategies for antibody validation.

Utilizing KO cell lines for antibody characterization offers several advantages, particularly in discerning between specific and non-specific binding. An antibody that selectively immunodetects its target protein in WB will produce a distinct band (or potentially multiple bands in presence of isoforms or posttranslational modifications) in the parental lysate that is absent in the KO lysate (Fig. 2a, case 1). A non-selective but specific antibody may recognize the target protein along with other unwanted proteins (Fig. 2a, case 2). Non-specific antibodies fail to recognize the target protein even in a cell line with confirmed target expression (Fig. 2a, case 3). Of the antibodies recommended for WB by their manufacturers, 35% detected their intended targets, as well as unwanted proteins, and 21% failed in detecting their intended target 11. This finding underscores the importance of utilizing KO cell lines to accurately distinguish between undesired non-specific binding and genuine isoforms, post-translational modifications, or degradation of the target protein.

Despite the validation of edited gene modifications in KO lines through genomic PCR and DNA sequencing, our WB analysis revealed that approximately 14% of KO lines were not null for the target protein. Some KO lines resulted in truncated proteins rather than complete loss (Fig. 2b), emphasizing the necessity of WB screening using both wild-type (WT) and KO lysates to validate target protein expression and antibody specificity, as well as to validate the KO clone.

Experimental Design

The workflow employed by YCharOS involves three common laboratory procedures, WB, IP and IF (Fig. 1). Each antibody is tested for all applications regardless of the manufacturers’ recommendations as the use of antibodies can be extended to applications not considered by the manufacturers.

Target protein expression in a particular cell line is assessed using RNA sequencing data available on the Cancer Dependency Map Portal (RRID:SCR_017655, “DepMap”, depmap.org). The DepMap Portal presents transcriptomic profiles of over a thousand cancer cell lines21. The target RNA expression value of 2.5 log2 (transcripts per million (TPM)+1) has emerged as a minimal threshold that is likely to yield detectable protein levels suitable for antibody screening11. While cell line selection can be straightforward for proteins with high, ubiquitous expression, this step can be difficult for proteins expressed at lower levels or only in specific cell types. For this latter situation, we combine orthogonal and independent antibody validation strategies20 to help in selecting an optimal cell line background for generating a KO cell line. Briefly, we select 4-8 available cancer cell lines with the highest RNA score, as well as one or two lines with an RNA score close to or equal to zero, and use at least two unique antibodies (i.e., different clone numbers if monoclonal antibodies) to assess protein expression in WB. This has proven useful when at least two antibodies show similar protein expression patterns in the cell lines with high RNA values and no signal in the lines with RNA close to zero, suggesting specificity to the target (Fig. 3a, case 1 and 2). A non-specific antibody would produce a different protein expression pattern than putative specific antibodies and would not correlate with RNA levels (Fig. 3a, case 3). The generation of a KO line remains essential to confirm the specificity of the signal and ascertain whether the selected parental cell line exhibits appropriate expression of the target protein (Fig. 3b). For instance, the SYT1 antibody utilized in case 1 selectively detects several SYT1 protein species, while the SY1 antibody in case 2 detects SYT1 along with undesired proteins, a scenario verifiable only using the KO cells and not through the orthogonal strategy (Fig. 3).

Sample preparation for WB and IP varies depending on whether the target of interest is an intracellular (Fig. 4, case 1) or secreted protein (Fig. 4, case 2). A secreted protein is defined as having a signal peptide and no transmembrane domains. It has been predicted that ~3000 human proteins are secreted (referred to as the secretome), representing ~15% of the human proteome22. These proteins are expected to be primarily identified in the conditioned medium of cell lines (Fig. 4, case 2). However, ~35% of the secretome may remain intracellular awaiting secretion in the absence of an appropriate releasing stimulus or are not released to the medium due to retention on the plasma membrane following secretion22. In the latter cases, the target protein can be detected both in the conditioned medium and in the cell lysate (Fig. 4, case 3). Subcellular annotation from UniProt (RRID:SCR_002380, uniprot.org) can be utilized to predict whether a protein is secreted, yet the identification of the target protein in the medium by mass spectrometry or using specific antibodies provides the definitive evidence that a protein can be secreted.

Cell preparation for Antibody Screening

The preparation of the parental and KO cell line samples for application testing can be carried out simultaneously or prior to each application. The procedures detailed below involve the use of adherent cell lines. One confluent 150 mm petri dish of the most common cancer cell lines corresponds to approximately 2 x 107 cells and 2 mg of protein lysate. Therefore, one confluent 150 mm dish is sufficient to test 14 antibodies by WB (20-50 µg per lane), two antibodies by IP (1.0 mg protein per IP) and 14 antibodies by IF (8,000 cells per well).

Procedure 1: Antibody Screening by WB

The radioimmunoprecipitation assay (RIPA) denaturing buffer extracts most intracellular proteins from culture cell lines, including cytoplasmic, nuclear, and membrane-bound proteins23. The commercial RIPA buffer used is composed of 25 mM Tris-HCl pH 7.8 (pH measured at 4°C), 150 mM NaCl, 1% NP-40, 1% sodium deoxycholate, and 0.1% SDS. Secreted proteins are harvested directly from the cell culture medium. To this end, cells are grown in a serum-free medium for 18 hrs, media are collected, and debris in suspension are removed by centrifugation. The cleared media are then concentrated by filtration. The desired amount of WT/KO lysates or WT/KO cell media are run on sodium dodecyl-sulfate polyacrylamide gel electrophoresis (SDS-PAGE). All antibodies are tested in parallel in WB.

Procedure 2: Antibody Screening by IP

Antibodies can be used to immunocapture target proteins from cell extracts or media. Antibody performance in IP can be assessed using mass spectrometry approaches24, which for most laboratoriesis costly and time-consuming. To assess if an antibody can IP the target, IPs are performed using cell lysates generated in a non-denaturing buffer or using cell media, followed by WB (IP-WB) with KO-validated antibodies identified in the previous WB screening. A successful antibody should enrich its intended target in the IP, as compared to the starting material, and deplete it from the unbound fraction (Fig. 5). The unbound fraction is collected once incubation of the protein sample with the bead/antibody conjugate is complete.

Cell lysates (starting material) are prepared using a non-denaturing commercial lysis buffer (IP buffer) composed of 25 mM Tris-HCl pH 7.8 (pH measured at 4°C), 150 mM NaCl, 1 mM EDTA, 1% NP-40, and 5% glycerol. This buffer allows the efficient extraction of all targets tested thus far, including cytosolic, nuclear and membrane-bound proteins. Cell lysates are first incubated with antibody-bead conjugates. After incubation with the lysate, an aliquot of the unbound fraction is collected. Antibody-bead conjugates are then washed with lysis buffer to remove or minimize unbound and non-specifically bound proteins. Following the final wash, bound protein(s) are eluted from the beads. Similar volumes from the starting material and unbound fractions are run on SDS-PAGE side-by-side with the eluted fraction, followed by WB to detect the target protein. We were able to identify at least one antibody that can capture its intended protein for 73 out of 95 proteins (77% success rate).

Procedure 3: Antibody screening by IF

In IF studies, fixation and cell permeabilization steps enable antibodies to reach their intracellular targets. Standardization of IF protocols is challenged by the diversity of fixation and permeabilization reagents and concentrations. A study comparing the suitability of six IF protocols with known specific antibodies targeting 18 proteins with distinct subcellular distributions revealed that fixation with paraformaldehyde (PFA) followed by permeabilization with Triton X-100 was adequate for detecting all proteins analyzed in their study, suggesting that a PFA/Triton X-100 based protocol is adequate for a significant number of human proteins25 . The processes described here use 4% PFA with 0.1% Triton X-100 for permeabilization and 0.01% for the later steps. While we recognize that this protocol will not be suitable for all human proteins, we were able to identify at least one specific antibody suitable in IF for 49 out of 82 intracellular proteins (60% success rate).

For IF, we use parental and KO cells labelled with fluorescent dyes of different wavelengths and then plated the cells together as a mosaic. Staining is performed with primary antibodies and a secondary antibody coupled to a fluorophore that emits at a different wavelength from that of the cell dyes. This mosaic strategy enables screening in a single well (or cover slip), thus avoiding imaging or user bias when comparing WT and KO cells in a single field of view.

The use of a high-content imaging system, designed to image numerous fields of view per well, enables rapid imaging of thousands of cells for all antibodies tested. The goal of the IF approach is not to determine the cellular location of the target protein but to determine whether there is a significant difference in overall signal coming from WT and KO cells. A larger collection of IF images make analysis more robust. We have developed a collection of scripts in Python and in ImageJ (RRID:SCR_003070) or FIJI (RRID:SCR_002285) made openly available on GitHub (https://github.com/ABIF-McGill/YCharOS_IF_characterization) to quantify and compare fluorescence from parental cells and KOcells. Generally, this quantitative analysis pipeline consists of object detection to generate masks of WT and KO cells, followed by background estimation and subtraction, followed by intensity measurement of antibody labeling intensity in each detected cell in each image. Antibody intensity in WT vs KO cells can be expressed as a ratio for each cell and plotted to compare antibody labeling intensity of several different antibodies for a given target. This more detailed analysis of numerous cells improves the comparison of performance between antibodies. Moreover, antibody titration is performed routinely on IF experiments, where two concentrations are tested, including the concentration recommended by the manufacturers, when available.

Expertise and Specialized Equipment Needed to Implement the Protocol

The protocols described here can be adapted to most standard molecular/cell biology laboratories. For cell culture, WB and IP, most trainees with prior biochemistry knowledge will be capable of performing these protocols or could learn them with appropriate training that would be of value for exploring or initiating a career in related research areas. IF steps require training in microscopy and in fluorescence image analysis. While all analyses can be performed on standard desktop computers with minimal software requirements (including the open access FIJI software) using the provided analysis code, the cellpose (RRID:SCR_021716) segmentation code works best on a system with a Compute Unified Device Architecture (CUDA)-capable graphics card.

Data Dissemination and Uptake by the Research Community

YCharOS’s data generation and dissemination are intended to benefit the global life sciences community, but its impact depends on real-world uptake of the data. To date, 859 different antibodies targeting 96 human proteins have been tested and characterization data are consolidated in the form of reports, with one report per protein. Reports are uploaded on ZENODO (RRID:SCR_004129), an open repository operated by the European Organization for Nuclear Research: https://zenodo.org/communities/ycharos/, and assigned a Digital Object Identifier (DOI). Datasets, which include raw data for all applications, can also be viewed and downloaded on ZENODO. To test the possibility of better outreach by indexation on PubMed, some ZENODO reports are converted into peer-review articles published in F1000 (www.f1000research/ycharos), accompanied by a guide to help interpret the antibody characterization data10.

To ensure the proper identification of each antibody tested, each YCharOS report presents detailed antibody information, including antibody concentration, batch number and Research Resource Identifier (RRID). An RRID is a unique and persistent tag assigned to an antibody (and other research resources) that integrates the following detailed information in the case of antibodies: the target antigen, antibody clonality, catalogue number and supplier, clone ID, application(s) recommended by the manufacturers, host organism and availability of third-party validation data. Over 2.5 million antibodies are registered with an RRID and listed in the Antibody Registry (RRID:SCR_006397, antibodyregistry.org) and in the RRID portal (RRID:SCR_003115, https://scicrunch.org). RRIDs represent the gold standard for research reagent identification and are requested by over 1000 journals26-28. They facilitate access to third-party characterization data through the RRID portal, and the integration of characterization data with RRIDs via Biomed Resource Watch (https://scicrunch.org/ResourceWatch) could potentially establish the RRID portal as the primary centralized database for genetically validated antibodies12.

Importantly, the participating antibody manufacturers, who have endorsed these protocols through extensive dialogue, and are represented as co-authors of this article, are also actively using the antibody characterization data in their marketing materials to help scientists select the most appropriate products for their research needs. In addition, these same companies are withdrawing or re-evaluating antibodies whose performance in these assays appears substandard11, underlining the importance of informing antibody manufacturers in the latter case. Finally, targets for which better antibodies are needed are identified and perhaps designated for the development of new antibodies.

Limitations

Antibodies are among the most useful reagents in the biomedical sciences due to their ability to bind proteins or other antigens with high affinity and specificity, providing information on target abundance, cellular location, binding partners, modifications, and other biochemical or cellular features. Beyond the described protocols, antibodies find extensive application in techniques like flow cytometry, ELISA, and immunohistochemistry.

When faced with a multitude of antibody options for a specific protein, users can utilize the following guidelines for selection: i) prioritize recombinant or monoclonal antibodies with designated clone numbers to prevent duplicate purchases from different suppliers and ensure reagent renewability, ii) give preference to primary manufacturers with rigorous internal validation standards that offer refund policies if users demonstrate antibody specificity issues, iii) choose antibodies characterized using KO or KD cells, with data provided by manufacturers or referenced in published articles. Typically, commercial antibody vials contain 50 to 100 µg of purified antibody at concentrations ranging from 0.1 to 1.0 mg/ml, sufficient for conducting the described protocols.

Several factors can influence the performance of antibodies in different applications, with the abundance of the target protein in the cell line used being a critical factor. For example, MDA-MB231 cells display, according to DepMap, a CD44 RNA level of 9.6 log2 (TPM+1) and a ~10-fold increase in CD44 protein expression as compared to HAP1 as observed by WB (Fig. 6a, case 1). While some antibodies can detect CD44 in both cell types (Fig. 6a, case 1), others can only detect CD44 in the higher expressing, MDA-MB231 line (Fig.6a, case 2). This example illustrates the inconsistency of antibody evaluation based on the use of a single cell line – the CD44 antibody in case 2 would have been evaluated as non-specific using HAP1, but specific in MDA-MB231. Protein abundance also affects antibody performance in IF. For example, the THP-1 cell line presents with a PLCG2 RNA level of 5.9 log2 (TPM+1) according to DepMap. THP-1 is a monocyte-like cell line that grows in suspension and can be differentiated into adherent macrophage-like cells following a treatment with phorbol 12-myristate 13-acetate (PMA)29. As antibodies are routinely tested in IF on adherent cell lines, a PMA treatment therefore enables PLCG2 antibodies to be tested on THP-1 cells. A WB analysis with a KO-validated PLCG2 antibody reveals that while PMA treatment slightly reduces the PLGC2 protein level in THP-1, the THP-1 treated cells still exhibit a ~3-fold increase in PLCG2 protein level as compared to HAP1 (Fig. 6b, WB). The same PLCG2 antibody used in IF on HAP1 detects a signal similar between the WT and KO cells. However, the signal generated by the antibody in THP-1 treated with PMA is selective as evidenced by the high signal observed in WT cells and the absence of signal in KO cells (Fig. 6b, IF).

Despite the robustness of the WB, IP, and IF protocols described, differences in buffers, blocking reagents, antibody dilutions, and other protocol details can influence antibody performance25,30. Nevertheless, utilizing these protocols offers a productive strategy for assessing the application-specific performance of antibodies and optimizing their selection.

The absence of a universal, public collection of KO human cell lines hinders the ability of scientists to immediately use the described protocols. However, a significant portion of human genes have already been targeted and knocked out in cell lines generated by academic researchers. Cellosaurus (RRID:SCR_013869, https://www.cellosaurus.org/) is a knowledge resource that assigns an RRID identifier to cell lines used in biomedical research, including KO cell lines, whether generated by academic laboratories or industry31. Searching Cellosaurus (release 48, February 1, 2024) indicates that 13,644 KO cell lines covering 4,873 human genes have been generated with the majority being commercially available. For example, Horizon Discovery has a collection of over 6,000 KO cell lines targeting more than 3,000 genes in the human HAP1 cell line. Horizon Discovery HAP1 KO lines are now part of Revvity’s portfolio. Abcam has a catalogue of about 5,300 KO cell lines covering 2,915 human genes in various cell line backgrounds. ATCC recently made available 280 KO cell lines covering solute carrier protein superfamily members, that were generated by the RESOLUTE consortium32. While commercial KO cell lines are well-documented, those generated by individual researchers are often not registered, highlighting a gap in data accessibility. Adding a cell line to Cellosaurus can be done by writing to https://www.cellosaurus.org/contact.

While the platform presented is suitable for most proteins, it may require optimization for certain cases to achieve the desired signal-to-noise ratio. Notably, a KO-based methodology may not be applicable for evaluating antibodies targeting posttranslational modifications or essential genes. Hence, end users are strongly encouraged to conduct validation experiments in their own laboratories, as differences in protocols and cell lines can influence results.

Procedure

The described platform is intended to screen up to 14 antibodies at a time against a single protein target and can be adapted to test hundreds. All timings listed below do not include cell culture time. The cell culture requirement is sufficient to screen, in parallel, 14 antibodies directed against the same protein target, in 3 applications.

Troubleshooting
Time Taken
Anticipated Results

We describe standardized, industry-approved protocols for comparing and evaluating the performance of a set of antibodies targeting selected human proteins in WB, IP and IF. Initial characterization data assessment allows for the identification of non-specific or poorly performing antibodies, facilitating their exclusion from future antibody selection by users. We strongly advocate for users to communicate feedback to antibody suppliers regarding underperforming antibodies, as most suppliers will evaluate user data and take proactive measures to withdraw or amend antibody descriptions accordingly.

It is inevitable that different antibodies will exhibit varying degrees of selectivity towards their intended targets. Whenever feasible, users should prioritize the use of recombinant antibodies, as these are renewable products that contribute to reducing the need for animal-based antibody production. Drawing from our prior research, we anticipate that widespread adoption of these protocols can facilitate the identification of selective, renewable antibodies for approximately 50-75% of human proteins, depending on the application11.

While the standardized protocols described herein may not yield optimal performance for all tested antibodies, we recommend users select one or two top-performing antibodies and optimize various parameters relevant to their chosen application and cell type. It is imperative to consider the endogenous protein expression level when determining the most suitable antibody concentration. Significant differences in protein expression between the cell line used for antibody characterization and the user's cell line may necessitate antibody titration.

The focus here remains on antibodies being tested against human targets. This step is essential before employing them on proteins from other species, which needs further validation using KO lines from the specific of interest.

The consensus protocols provided for antibody validation empower researchers to generate robust, reproducible, industry-standard data that can be readily disseminated for the benefit of the global biomedical community.

Acknowledgements

This work was supported by the Emory-Sage-SGC TREAT-AD center established by the National Institutes of Aging (NIA) U54AG065187 grant and additional support by RF1AG057443, by a grant from the Michael J. Fox Foundation for Parkinson’s Research (no. 18331), by a grant from the Motor Neurone Disease Association (UK), The ALS Association (USA) and ALS Canada to develop the ALS-Reproducibility Antibody Platform, by the Bill & Melinda Gates Foundation, and by the Government of Canada through Genome Canada, Genome Quebec and Ontario Genomics (OGI-210). The Structural Genomics Consortium is a registered charity (no. 1097737) that receives funds from Bayer AG, Boehringer Ingelheim, Bristol Myers Squibb, Genentech, Genome Canada through Ontario Genomics Institute (grant no. OGI-196), the EU and EFPIA through the Innovative Medicines Initiative 2 Joint Undertaking (EUbOPEN grant no. 875510), Janssen, Merck KGaA (also known as EMD in Canada and the United States), Pfizer and Takeda. RA is supported by a Mitacs postdoctoral fellowship. CMB is supported by the Chan Zuckerberg Initiative, an advised fund of Silicon Valley Community Foundation supports (Grant# 2020–225398). AB is the co-founder and serves as the CEO of SciCrunch Inc, a company that works with publishers to improve the rigor and transparency of scientific manuscripts. We would like to thank Amos Bairoch at the University of Geneva and manager of the Cellosaurus database, who helped us extract from Cellosaurus the number of KO cell lines and human genes covered by KO lines.

Figures


Figure 1

Experimental design of the antibody characterization workflow.

All antibodies are tested in all three applications. Antibodies are first tested in WB to iteratively validate the KO lines and the antibodies (Procedure 1). Antibodies are next tested in IP followed by WB to evaluate their performance to capture their intended target (Procedure 2). The antibody selected for WB in Procedure 2 was previously validated in Procedure 1. Antibodies against intracellular proteins are next screened in IF (Procedure 3).




Figure 2

Interpretation of antibody performance in WB

For each presented WB, the antibody related chemiluminescent signal is shown at the top of its corresponding ponceau S-stained membrane.

a) Three selected antibodies against the CD44 protein (Uniprot ID: P16070), expected at 82 kDa, are presented to illustrate various types of target specificity in WB. In case 1, the antibody selectively detected CD44 as determined by the presence of a band in the WT lysate and the complete absence of any band in the KO lysate (star). In case 2, the antibody specifically detected CD44 as determined by the absence of the main band in the KO lysate (star), but also detected unwanted proteins (bands present in both WT and KO lysates). In case 3, the antibody failed to recognize CD44 as the band detected in the WT lysate is also detected in the KO lysate.
b) A selective antibody against the CNN3 protein (Uniprot ID: Q15417) was used to characterize two independent commercial CNN3 KO clones generated in the same cell line background. CNN3 was detected at ~40 kDa in the WT lysate (star). In case 1, a truncated ~35 kDa protein was detected in the lysate derived from the putative CNN3 KO clone, defined here as a failed clone (arrowhead). In case 2, the antibody did not detect any form of residual CNN3 protein. 4-20% TG gels were used.




Figure 3
Identification of an adequate cell line background for KO generation

WBs are presented as in Figure 2.

a) The identification of an adequate cell line for the SYT1 protein (Uniprot ID: P21579, SYT1 is the corresponding gene). Seven cancer cell lines were selected with RNA expression spanning from 0.3 to 4.6 log2 (TPM+1). The RNA levels, in log2 (TPM+1), were extracted from DepMap.org and presented in blue below the corresponding cell line. Lysates were prepared, processed by SDS-PAGE and probed with three unique primary SYT1 antibodies (case 1, 2 and 3). In case 1 and case 2, both antibodies putatively identified Synatotagmin-1 at ~66 kDa in HCT116 as they provide a similar banding pattern with absence of signal in cells with low RNA value. In case 3, the antibody provided a signal that does not correlate with the signal of the other two antibodies.
b) An HCT116 Syt1 KO line was generated and used to validate that HCT116 expresses the endogenous SYT1 protein and the specificity of the SYT1 antibodies.



Figure 4
Antibody performance correlates with sample preparation

WBs are presented as in Figure 2. From a cell line endogenously expressing the corresponding intended target, proteins were prepared from both cell lysates and conditioned medium. Protein targets were searched through Uniprot to determine whether they are predicted to be secreted or not.

a) In case 1, the antibody targets ECE1 (Uniprot ID: P42892), a predicted intracellular protein. ECE1 was detected exclusively in the cell lysate sample (star). 4-20% TG gels were used.
b) In case 2, the antibody targets Angiogenin (Uniprot ID P03950), a predicted canonical secreted protein. Angiogenin was only detected in the medium (star). 10% BT gels with MES running buffer were used.
c) In case 3, the antibody targets the protein QPRT (Uniprot ID: Q15274), predicted to be secreted and to retain an intracellular distribution. QPRT was detected both in cell lysate (star) and medium (star). 4-20% TG gels were used.



Figure 5
Interpretation of antibody performance by IP

WBs are presented as in Figure 2. Three selected antibodies directed against the LRP1 protein (Uniprot ID: Q07954) illustrates different degrees of capture efficiency in IP. A selective LRP1 antibody in WB was used to detect the LRP1 protein level between three distinct fractions, namely the SM (4%), UB (4%) and the IP. In case 1, the antibody did not capture the target protein as determined by the absence of signal in the IP fraction and unchanged level of the LRP1 protein in the UB. In case 2, the antibody captured the target protein to slightly below the level of the SM and failed to deplete LRP1 from the UB. In case 3, the antibody enriched its intended target in the IP several folds over the SM and mostly depleted LRP1 from the UB. This antibody successfully immunocaptured its intended target in the conditions used. 4-20% TG gels were used. SM=4%, UB=4%.



Figure 6
Protein abundance influences antibody performance

WBs are presented as in Figure 2.

a)Two antibodies against the CD44 protein (Uniprot ID: P16070) were selected to illustrate the effect of protein abundance on antibody performance. Both selected CD44 antibodies are different from those shown in Figure 2. RNA levels corresponding to both cell lines were extracted from DepMap.org and presented as in Figure 3. In case 1, the antibody was able to selectively detect CD44 in both cell lines (star). In case 2, the antibody detected CD44 in MB231, but not in HAP1 (star). 4-20% TG gels were used. MB231=MDA-MB231.
b) The intracellular protein PLCG2 (Uniprot ID: P16885) was selected to illustrate the effect of protein abundance on antibody performance in IF. The same PLCG2 antibody was used in WB and in IF. PLCG2 was detected in WB (stars) using lysates from HAP1 WT and PLCG2KO as well as THP-1 WT and PLCG2 KO, treated or not with PMA. The RNA levels are showed as in a). The PLCG2 antibody was tested on HAP1 (left IF) and PMA-treated THP1 (right IF). WT (green outline) and KO (purple outline) cell lines were plated as a mosaic and were segmented post-image acquisition. The gray-scale antibody channel is shown (top panels), together with the corresponding DAPI stain (nucleus, bottom panels). THP-1 are small cells that adopt a round shape. 4-20% TG gel was used.



Figure 7
Boiling protein samples creates an aggregation artifact

WBs are presented as in Figure 2. Lysates of a cell line expressing endogenous levels of S1PR1 (Uniprot ID: P21453), a transmembrane protein, were produced and were either heated at 65°C or 95°C for 10 min. Single stars point at the major bands representing S1PR1, whereas the double star points at the aggregated form. 4-20% TG gel was used.



Figure 8
Choice of SDS-PAGE chemistry in WB
WBs are presented as in Figure 2. The chemistry of the SDS-PAGE modifies the reading of the antibody signal.

a) A KO-validated antibody against the large PLEC protein (Uniprot ID: Q15149, PLEC is the corresponding gene) was used in WB from WT and PLEC KO lysates ran on three gels with distinct chemistries, namely 4-20% TG, 8% BT (MOPS buffer) and 3-8% TA using TG-SDS, MOPS-SDS and TA SDS buffers, respectively. PLEC has 9 putative isoforms produced by alternative splicing with the canonical PLEC isoform expected at 532 kDa (Uniprot.org). The vertical line followed by a star indicates the region of the gels where the isoforms are identified. The PLEC KO cell line expresses residual PLEC protein isoforms.
b) A KO-validated antibody against the small FCER1G protein (Uniprot ID: P30273) was used in WB of WT and FCER1G KO THP-1 lysates, each PMA-treated or not, ran on either 4-20% TG or 10% BT using TG-SDS or MES-SDS, respectively. PMA treatment was used to differentiate THP-1 into adherent macrophage-like cells. FCER1G is expected at ~10 kDa. The BT gels improved both the antibody-based signal and the resolution.



Figure 9
Selection of secondary detection systems for IP-WB experiments

WBs are presented as in Figure 2. A rabbit

a) or mouse
b) antibody targeting human UBQLN2 (Uniprot ID: Q9UHD9) was used in IP in combination with different secondary WB detection systems. In case 1 and 2, a rabbit primary antibody was used in WB and detected using either a secondary anti-rabbit:HRP or prot A:HRP, respectively. In case 3, a mouse primary antibody was used in WB coupled with a secondary anti-mouse:HRP. In case 4 and 5, a mouse primary antibody was used in WB and detected using either a secondary anti-mouse:HRP or anti-mouse IgG for IP:HRP, respectively. In case 6, a rabbit primary antibody was used in WB coupled with a secondary anti-rabbit:HRP. SM=4%, UB=4%, HC=heavy chain, LC=light chain, bracket indicates different UBQLN2 protein species identified in the IP.



Figure 10 Semi-automated analysis of antibody performance in IF

Semi-automated image analysis of a mosaic culture of WT and KO cells was conducted using in-house developed codes that take advantages of the publicly available cellpose algorithm and FIJI (ImageJ) software. Two antibodies against the TGM2 protein (Uniprot ID: P21980) are presented. A TGM2 KO line, validated by WB, was used.

a) Images for all four channels corresponding to DAPI (nucleus), CellTracker Green CMFDA (WT cell mask), the antibody staining corresponding to anti-TGM2, case 1 (coupled to an Alexa 555 conjugated secondary antibody) and CellTracker Deep Red (KO cell mask) were acquired with an ImageXpress high-content microscope and prepared for analysis.
b) A python script that executes cell segmentation using Cellpose1.0 was ran on both cell mask channels.
c)An ImageJ macro was used to generate cell mask outlines, perform background signal subtraction on the antibody channel using minimum intensity projection. Processed images were overlayed with cell masks outlines in the antibody channel and intensity was quantified in the segmented cells. All scripts are openly available on the YCharOS GitHub page. Bars = 20 μm
d) Same processes as in a, b, and c were applied to the anti-TGM2 antibody, case 2.
e) Plot showing the antibody intensity ratio of WT to KO cells.

Supplementary Files
  • Figure S1: Order of sample loading for antibody screening in WB and IP-WB a) A scanned Ponceau S-stained membrane used for antibody screening in WB. A master mix of MWM, WT and KO lysates are prepared, and samples are loaded in the following order on a 12-well SDS-PAGE gel: MWM, WT and KO lysates. Commercial 12-well gels have two smaller wells on each side which can be used for MWM (+). Up to 4 antibodies can be tested in WB on a single 12-well gel. b) Scanned Ponceau S-stained membrane used for antibody screening in IP. Samples are loaded in the following order on a 12-wells SDS-PAGE gel. Up to 3 antibodies can be tested in IP on a single 12-well gel.


  • Data Availability

DataAvailability.docx

  • Table 1


  • Table 2


  • Table 3


  • Table 4

Table4.docx



Materials
Reagents

  • ReagentSafeSeal reaction tube 1.5 ml PP PCR Performance Tested Low protein-bindingSarstedtCatalog #72.706.600
  • Reagent15 mL Polypropylene Centrifuge TubesThermo Fisher ScientificCatalog #339658
  • ReagentPBS 1XWisent BioproductsCatalog #311-010 CL
  • ReagentMacroTubes, 5 mlWard's Natural Science Establishment, Inc.Catalog #470225-020
  • ReagentConical Tubes (50 mL) (racked)Thermo FisherCatalog #AM12501
  • Reagent96-well, black, optically clear flat-bottom, tissue-culture treated, lidsRevvityCatalog #6055300
  • ReagentAnti-mouse IgG for IP (HRP)AbcamCatalog #ab131368
  • BLUelf Prestained Protein Ladder (FroggaBio, cat. no. PM007-0500K)
  • ReagentBoric Acid Fisher ScientificCatalog #A73-3
  • ReagentBSA Fraction V pH 7,0 100gWisent BioproductsCatalog #800-095
  • ReagentBradford ReagentMerck MilliporeSigma (Sigma-Aldrich)Catalog #B6916
  • ReagentTissue Culture Dish with Grid, 150mmFisher ScientificCatalog #08-772-6
  • Cell scraper (Sarstedt, cat. no. 83.1830)
  • ReagentCellTracker™ Deep Red DyeThermo FisherCatalog #C34565
  • ReagentCellTracker™ Green CMFDA DyeThermo FisherCatalog #C2925
  • ReagentAmicon® Ultra-15 Centrifugal Filter UnitMerck Millipore (EMD Millipore)Catalog #UFC901096
  • Dako mounting medium (Dako, cat. no. S3023)
  • ReagentDAPIFisher ScientificCatalog #D3571
  • ReagentDMSO (dimethyl sulfoxide)Merck MilliporeSigma (Sigma-Aldrich)Catalog #D8418
  • ReagentFetal Bovine SerumWisent BioproductsCatalog #080-450
  • ReagentGoat anti-mouse alexaFluor-555 antibodyThermofisherCatalog #A21424
  • ReagentGoat anti-Mouse IgG (H+L) Secondary Antibody, HRPThermo Fisher ScientificCatalog #62-6520
  • ReagentGoat anti-Rabbit IgG (H+L) Highly Cross-Adsorbed Secondary Antibody, Alexa Fluor™ 555Thermo Fisher ScientificCatalog #A-21429
  • ReagentGoat anti-Rabbit IgG (H+L) Secondary Antibody, HRPThermo Fisher ScientificCatalog #65-6120
  • ReagentHiMark™ Pre-stained Protein StandardThermo FisherCatalog #LC5699
  • ReagentL-Glutamine SolutionWisent BioproductsCatalog #609-065 EL
  • ReagentNuPAGE™ LDS Sample Buffer (4X)Thermo Fisher ScientificCatalog #NP0007
  • ReagentLaemmli SDS sample buffer, reducing (6X)Thermo Fisher ScientificCatalog #J61337
  • MES-SDS running buffer (20X) (Thermo Fisher Scientific, cat. no. J62138.K2)
  • ReagentNitrocellulose MembraneBio-Rad LaboratoriesCatalog #1620097
  • ReagentGoat SerumGibco - Thermo FischerCatalog #16210-064
  • ReagentParaformaldehyde solution, 16% w/v in waterBeantown ChemicalCatalog #140770- 10ml
  • ReagentPenicillin Streptomycin 100XWisent BioproductsCatalog #450-201 EL
  • Peroxidase substrate, Femto (Thermo Fisher Scientific, cat. no. PI34096)
  • Peroxidase substrate, regular (Thermo Fisher Scientific, cat. no. PI32106)
  • PhenoPlate, 96-well, optically clear flat-bottom (Perkin Elmer, cat. no. 6055300)
  • ReagentPierce BCA Protein Assay Kit Thermo Fisher ScientificCatalog #23225
  • ReagentPoly-ʟ-Lysine HydrobromideMerck MilliporeSigma (Sigma-Aldrich)Catalog #P9155-5MG
  • ReagentPonceau SThermo Fisher ScientificCatalog #BP10310
  • ReagentNuPAGE™ 10% Bis-Tris Midi Protein Gels, 12+2-wellThermo FisherCatalog #WG1201BOX
  • ReagentNuPAGE™ 3-8% Tris-Acetate Midi Protein Gels, 12+2-wellThermo FisherCatalog #WG1601BOX
  • ReagentNovex™ 4-20% Tris-Glycine Plus Midi Gels, 12+2-wellThermo FisherCatalog #WXP42012BOX
  • Prestained molecular weight marker (FroggaBio, cat. no. PM007-0500K)
  • ReagentProtease Inhibitor CocktailMerck MilliporeSigma (Sigma-Aldrich)Catalog #P8340
  • ReagentDynabeads™ Protein A for ImmunoprecipitationThermo FisherCatalog #10001D
  • ReagentProtein A–PeroxidaseMerck MilliporeSigma (Sigma-Aldrich)Catalog #P8651
  • ReagentDynabeads™ Protein G for ImmunoprecipitationThermo FisherCatalog #10004D
  • ReagentPierce™ Protein L Magnetic BeadsThermo FisherCatalog #88850
  • ReagentRIPA Lysis and Extraction BufferThermo FisherCatalog #89901
  • ReagentSKIM MILK (NON FAT POWDER) 1kgBioshopCatalog #SKI400.1
  • Reagent10X Tris-Glycine bufferBio-Rad LaboratoriesCatalog #1610771
  • ReagentTrichloroacetic AcidFisher ScientificCatalog #SA433-500
  • ReagentTris Buffered Saline with Tween® 20 (TBST-10X)Cell Signaling TechnologyCatalog ##9997
  • ReagentNuPAGE™ Tris-Acetate SDS Running Buffer (20X)Thermo FisherCatalog #LA0041
  • Reagent10x Tris/Glycine/SDSBio-Rad LaboratoriesCatalog #1610772
  • Triton X-100 (Thermo Fisher Scientific, cat. no. BP151-500)
  • ReagentTrypsinWisent BioproductsCatalog #325-542

Reagent setup

  • Labelling culture medium The appropriate type of complete medium but supplemented with only 5% serum.
  • Serum-free medium The appropriate type of medium supplemented with all components except serum.
  • Borate buffer (0.15 M, pH 8.4) Weigh 4.64 g of boric acid powder. Add the boric acid to a glass beaker containing 450 ml of distilled water and stir until the powder is completely dissolved. Adjust the pH to 8.4 using 10N NaOH. Complete to 500 ml with distilled water.
  • CellTracker deep red dye, 1000x solution Dissolve 15 µg of CellTracker deep red dye with 40 µl of DMSO. Aliquot into 5 µl samples.
  • CellTracker green CMFDA dye stock, 1000x solution Dissolve 50 µg of CellTracker green CMFDA dye with 20 µl of DMSO. Aliquot into 5 µl samples.
  • Complete IP lysis buffer Add 10 µl of the protease inhibitor cocktail into 1.0 ml of ice-cold IP lysis buffer. Keep on ice and use immediately.
  • Complete RIPA lysis buffer Add 10 µl of the protease inhibitor cocktail into 1.0 ml of ice-cold RIPA lysis buffer. Keep on ice and use immediately.
  • DAPI stock concentration (5 mg/ml) Dissolve 10 mg of DAPI in 2.0 ml of deionized water. Aliquot and store at -20°C.
  • DAPI working concentration (5 µg/ml) Add 5 µl of the DAPI stock concentration (5 mg/ml) to 5 ml of water. Prepare 1 ml aliquot at store at -20°C for years.
  • IF blocking buffer (1x PBS, 0.01% Triton X-100, 5% BSA, 5% NGS) To 47.5 ml of IF incubation buffer, add 2.5 ml of NGS. Mix gently at 4°C and use immediately.
  • IF DAPI solution (1x PBS + 5 ng/ml DAPI) Add 5 µl of DAPI working concentration (5 µg/ml) to 5 ml of 1x PBS.
  • IF fix buffer (0.5x PBS, 8%PFA, 20% sucrose) Combine 5 ml of PBS 1x to 5 ml of PFA 16% in water. Dissolve 2 g of sucrose into a 10 ml solution made of 5 ml of PBS 1x and 5 ml of PFA 16% in water.
  • IF incubation buffer (1x PBS, 0.01% Triton X-100, 5% BSA) To 80 ml of 1x PBS, add 10 µl of Triton X-100 and 5 g of BSA. Rock gently at 4°C until the BSA is completely dissolved. Complete at 100 ml with 1x PBS. Keep on ice and store at 4°C for 1 week.
  • IF permeabilization buffer (1x PBS, 0.1% Triton X-100). Add 50 µl of Triton X-100 to 50 ml of 1x PBS. Mix gently and store at 4 °C for 1 week.
  • Poly-L-lysine stock solution (1.0 mg/ml) Dissolve 5 mg of poly-L-lysine in 4 ml of distilled water to make a stock at 1.0 mg/ml. Complete to 5 ml with distilled H2O.
  • Poly-L-lysine working solution (10 µg/ml) Dilute the poly-L-lysine stock to 1:100 with 0.15M borate buffer (pH 8.4) to generate a final concentration at 10 µg/ml. Sterilize by filtration using a 0.2 µm filter unit.
  • Ponceau S working solution Dissolve 1 g of Ponceau S powder in 485 ml of deionized water. Add 15 ml of trichloroacetic acid. Protect from light and store at room temperature.
  • Running buffer Tris-Acetate SDS 1x Add 50 ml of Tris-Acetate SDS running buffer 20x to 950 ml of distilled water.
  • Running buffer Tris-Glycine SDS 1x Add 100 ml of Tris-Glycine SDS running buffer 10x to 900 ml of distilled water.
  • TBST 1x Add 100 ml of TBST 10x to 900 ml of distilled water.
  • Transfer buffer Tris-Glycine 1x (20% Methanol) Add 150 ml of Tris-Glycine transfer buffer 10x to 1,050 ml of distilled water. Add 300 ml of Methanol before transfer.
  • WB blocking solution, used also for primary and secondary antibody preparation Dissolve 5 g of non-fat milk powder in 100 ml of TBST1x. Prepare just before use.

Equipment

  • Cell culture incubator (Forma Scientific, cat. no. 1998-081)
  • Megafuge™ 16 Centrifuge (Thermo Fisher Scientific, cat. No. 75004270)
  • Optima MAX-XP Ultracentrifuge (Beckman Coulter)
  • TLA-100.3 Fixed-Angle Rotor (Beckman Coulter, cat. no. 349490)
Equipment
TLA-100.3 Fixed-Angle Rotary
NAME
Fixed-Angle (Ultracentrifugation)
TYPE
Beckman Coulter Life Sciences
BRAND
349490
SKU
https://www.mybeckman.in/centrifuges/rotors/fixed-angle/349490
LINK

  • Open-Top Thickwall Polycarbonate Tube, 3.5 ml (Beckman Coulter, cat. no. 349622)
Equipment
3.5 mL, Open-Top Thickwall Polycarbonate Tube
NAME
Thickwall Polycarbonate Tubes
TYPE
Beckman Coulter Life Sciences
BRAND
349622
SKU
https://www.mybeckman.in/supplies/tubes-and-bottles/open-top-tubes/349622
LINK

  • DynaMag-2 Magnet (Thermo Fisher Scientific, cat. no. 12321D)
Equipment
Magnet
NAME
DynaMag™-2 Magnet
TYPE
Thermo Fisher
BRAND
12321D
SKU
https://www.thermofisher.com/order/catalog/product/12321D
LINK

  • Heat block (Fisher Scientific, cat. no. 11.718)
  • High content microscopy system, ImageXpress (Molecular Device)
  • iBright chemiluminescence instrument (Thermo Fisher Scientific, cat. no. CL1500)
Equipment
iBright™ Imaging System
NAME
Imaging System
TYPE
iBright1500
BRAND
CL1500
SKU
https://www.thermofisher.com/order/catalog/product/A44114?SID=srch-srp-A44114
LINK

  • pH meter (Thermo Fisher Scientific, cat. no. STAR2116)
Equipment
Benchtop pH Meter
NAME
pH Meter
TYPE
Orion Star™ A211
BRAND
STARA2116
SKU
https://www.thermofisher.com/order/catalog/product/STARA2116
LINK

  • Plate reader (Thermo Fisher Scientific, cat. no. A51119700C)
Equipment
Microplate Spectrophotometer
NAME
Plate reader
TYPE
Thermo Scientific™
BRAND
A51119700C
SKU
https://www.thermofisher.com/order/catalog/product/A51119700C?SID=srch-hj-A51119700C
LINK

  • Rotating mixer (Fisherbrand, cat. no. 88861041)
Equipment
Nutating Mixers - Fixed Speed
NAME
Rotating Mixers
TYPE
Fisherbrand™
BRAND
88861041
SKU
https://www.fishersci.ca/shop/products/fisher-scientific-nutrating-mixers-fixed-speed-1/88861041
LINK

  • Shaker, multiplatform (Fisherbrand, cat. no. 88861021)
Equipment
Multi-Platform Shaker
NAME
Shaker
TYPE
Fisherbrand™
BRAND
88-861-021
SKU
https://www.fishersci.com/shop/products/multi-platfrom-shakers/88861021
LINK

  • Sonicator (Thermo Fisher Scientific, cat. no. FB120A110
Equipment
Model 120 Sonic Dismembrator
NAME
Sonicator
TYPE
Fisherbrand™
BRAND
FB120A110
SKU
https://www.fishersci.com/shop/products/fisher-scientific-model-120-sonic-dismembrator-4/FB120A110
LINK

  • Wet protein transfer system – Criterion blotter (Bio-Rad, cat. no. 1704070)
  • XCell4 SureLock™ Midi-Cell electrophoresis system (Thermo Fisher Scientific, cat. no. WR0100)
Equipment
Midi-Cell
NAME
Electrophoresis system
TYPE
XCell4 SureLock™
BRAND
WR0100
SKU
https://www.thermofisher.com/order/catalog/product/WR0100
LINK

Files
Protocol
Icon representing the file Antibody screening by WB (Timing - 2 Days)
Name
Antibody screening by WB (Timing - 2 Days)
Version 1
, McGill University
Carl LaflammeMcGill University
Protocol
Icon representing the file Antibody screening by IP (Timing - 3 days)
Name
Antibody screening by IP (Timing - 3 days)
Version 1
, McGill University
Carl LaflammeMcGill University
Protocol
Icon representing the file Antibody screening by IF (Timing - 2 d)
Name
Antibody screening by IF (Timing - 2 d)
Version 1
, McGill University
Carl LaflammeMcGill University
Protocol references
1 Baker, M. When antibodies mislead: the quest for validation. Nature 585, 313-314 (2020). https://doi.org:10.1038/d41586-020-02549-1
2 Voskuil, J. L. A. et al. The Antibody Society's antibody validation webinar series. MAbs 12, 1794421 (2020). https://doi.org:10.1080/19420862.2020.1794421
3 Andersson, S. et al. Insufficient antibody validation challenges oestrogen receptor beta research. Nat Commun 8, 15840 (2017). https://doi.org:10.1038/ncomms15840
4 Ellis, M. J. et al. Validation of Tau Antibodies for Use in Western Blotting and Immunohistochemistry. bioRxiv, 2023.2004.2013.536711 (2023). https://doi.org:10.1101/2023.04.13.536711
5 Laflamme, C. et al. Implementation of an antibody characterization procedure and application to the major ALS/FTD disease gene C9ORF72. Elife 8 (2019). https://doi.org:10.7554/eLife.48363
6 Rot, A. et al. Murine bone marrow macrophages and human monocytes do not express atypical chemokine receptor 1. Cell Stem Cell 29, 1013-1015 (2022). https://doi.org:10.1016/j.stem.2021.11.010
7 Virk, H. S. et al. Validation of antibodies for the specific detection of human TRPA1. Sci Rep 9, 18500 (2019). https://doi.org:10.1038/s41598-019-55133-7
8 Elliott, S. et al. Anti-Epo receptor antibodies do not predict Epo receptor expression. Blood 107, 1892- 1895 (2006). https://doi.org:10.1182/blood-2005-10-4066
9 Schrohl, A. S., Pedersen, H. C., Jensen, S. S., Nielsen, S. L. & Brunner, N. Human epidermal growth factor receptor 2 (HER2) immunoreactivity: specificity of three pharmacodiagnostic antibodies. Histopathology 59, 975-983 (2011). https://doi.org:10.1111/j.1365-2559.2011.04034.x
10 Biddle, M. S. & Virk, H. S. YCharOS open antibody characterisation data: Lessons learned and progress made. F1000Res 12, 1344 (2023). https://doi.org:10.12688/f1000research.141719.1
11 Ayoubi, R. et al. Scaling of an antibody validation procedure enables quantification of antibody performance in major research applications. Elife 12 (2023). https://doi.org:10.7554/eLife.91645
12 Biddle, M. et al. Improving the integrity and reproducibility of research that uses antibodies: a technical, data sharing, behavioral and policy challenge. MAbs 16, 2323706 (2024). https://doi.org:10.1080/19420862.2024.2323706
13 Davies, P. et al. Comprehensive characterization and optimization of anti-LRRK2 (leucine-rich repeat kinase 2) monoclonal antibodies. Biochem J 453, 101-113 (2013). https://doi.org:10.1042/BJ20121742
14 Lis, P. et al. Development of phospho-specific Rab protein antibodies to monitor in vivo activity of the LRRK2 Parkinson's disease kinase. Biochem J 475, 1-22 (2018). https://doi.org:10.1042/BCJ20170802
15 Jong, T., Gehrlein, A., Sidransky, E., Jagasia, R. & Chen, Y. Characterization of Novel Human beta-glucocerebrosidase Antibodies for Parkinson's Disease Research. J Parkinsons Dis 14, 65-78 (2024). https://doi.org:10.3233/JPD-230295
16 Laflamme, C., Edwards, A. M., Bandrowski, A. E. & McPherson, P. S. Opinion: Independent third-party entities as a model for validation of commercial antibodies. N Biotechnol 65, 1-8 (2021). https://doi.org:10.1016/j.nbt.2021.07.001
17 Baker, M. Reproducibility crisis: Blame it on the antibodies. Nature 521, 274-276 (2015). https://doi.org:10.1038/521274a
18 Goodman, S. L. The antibody horror show: an introductory guide for the perplexed. N Biotechnol 45, 9- 13 (2018). https://doi.org:10.1016/j.nbt.2018.01.006
19 Bradbury, A. & Pluckthun, A. Reproducibility: Standardize antibodies used in research. Nature 518, 27- 29 (2015). https://doi.org:10.1038/518027a
20 Uhlen, M. et al. A proposal for validation of antibodies. Nat Methods 13, 823-827 (2016). https://doi.org:10.1038/nmeth.3995
21 Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503-508 (2019). https://doi.org:10.1038/s41586-019-1186-3
22 Uhlen, M. et al. The human secretome. Sci Signal 12 (2019). https://doi.org:10.1126/scisignal.aaz0274
23 Subedi, P. et al. Comparison of methods to isolate proteins from extracellular vesicles for mass spectrometry-based proteomic analyses. Anal Biochem 584, 113390 (2019). https://doi.org:10.1016/j.ab.2019.113390
24 Marcon, E. et al. Assessment of a method to characterize antibody selectivity and specificity for use in immunoprecipitation. Nat Methods 12, 725-731 (2015). https://doi.org:10.1038/nmeth.3472
25 Stadler, C., Skogs, M., Brismar, H., Uhlen, M. & Lundberg, E. A single fixation protocol for proteome-wide immunofluorescence localization studies. J Proteomics 73, 1067-1078 (2010). https://doi.org:10.1016/j.jprot.2009.10.012
26 Bandrowski, A. et al. The Resource Identification Initiative: A cultural shift in publishing. F1000Res 4, 134 (2015). https://doi.org:10.12688/f1000research.6555.2
27 Bandrowski, A. E. & Martone, M. E. RRIDs: A Simple Step toward Improving Reproducibility through Rigor and Transparency of Experimental Methods. Neuron 90, 434-436 (2016). https://doi.org:10.1016/j.neuron.2016.04.030
28 Bandrowski, A., Pairish, M., Eckmann, P., Grethe, J. & Martone, M. E. The Antibody Registry: ten years of registering antibodies. Nucleic Acids Res(2022). https://doi.org:10.1093/nar/gkac927
29 Schwende, H., Fitzke, E., Ambs, P. & Dieter, P. Differences in the state of differentiation of THP-1 cells induced by phorbol ester and 1,25-dihydroxyvitamin D3. J Leukoc Biol 59, 555-561 (1996).
30 Pillai-Kastoori, L., Heaton, S., Shiflett, S. D., Roberts, A. C., Solache, A. & Schutz-Geschwender, A. R. Antibody validation for Western blot: By the user, for the user. J Biol Chem 295, 926-939 (2020). https://doi.org:10.1074/jbc.RA119.010472
31 Bairoch, A. The Cellosaurus, a Cell-Line Knowledge Resource. J Biomol Tech 29, 25-38 (2018). https://doi.org:10.7171/jbt.18-2902-002
32 Superti-Furga, G. et al. The RESOLUTE consortium: unlocking SLC transporters for drug discovery. Nat Rev Drug Discov 19, 429-430 (2020). https://doi.org:10.1038/d41573-020-00056-6
33 Pillai-Kastoori, L., Schutz-Geschwender, A. R. & Harford, J. A. A systematic approach to quantitative Western blot analysis. Anal Biochem 593, 113608 (2020). https://doi.org:10.1016/j.ab.2020.113608
34 Wang, M., Herrmann, C. J., Simonovic, M., Szklarczyk, D. & von Mering, C. Version 4.0 of PaxDb: Protein abundance data, integrated across model organisms, tissues, and cell-lines. Proteomics 15, 3163- 3168 (2015). https://doi.org:10.1002/pmic.201400441
35 Bonifacino, J. S., Gershlick, D. C. & Dell'Angelica, E. C. Immunoprecipitation. Curr Protoc Cell Biol 71(2016). https://doi.org:10.1002/cpcb.3
36 Stringer, C., Wang, T., Michaelos, M. & Pachitariu, M. Cellpose: a generalist algorithm for cellular segmentation. Nat Methods 18, 100-106 (2021). https://doi.org:10.1038/s41592-020-01018-x