The aim of this site is to collect and to share experimental results on antibodies that would otherwise remain in laboratories, thus aiding researchers in selection and validation of antibodies.

Data pertaining to the publication "Can journal guidelines improve the reporting of antibody validation?". The project investigates the quality of antibody validation information provided in 120 biomedical publications and whether the introduction of journal validation guidelines improved the quality of this information.The data covers 60 publications before introduction of guidelines, and 60 after introduction, half of which from journals with guidelines. The quality of antibody validation information was coded by one author ("Antibody validation information data set.xlsx"), with a sample checked for interrater reliability by another ("Interrater reliability data set.xlsx"). Effects of journal guidelines introduction were tested statistically with a pseudo-experimental design. (Code for the statistical package R is provided.) The data package also includes detailed explanation of how coding was performed ("Coding protocol.docx") and an explanation of these files and data labels ("Data dictionary.docx").

This dataset contains underlying data from a study that evaluated twenty commercial antibodies agaisnt Huntingtin in western blot, immunoprecipitation and immunofluorescence. The study is accessible on our Zenodo community (DOI: 10.5281/zenodo.11582780) and serves as a research tool to facilitate reproducible and reliable Huntingtin resarch.

Open-access database of antibodies against human proteins developed through collaboration between Antibodypedia AB and the Nature Publishing Group. It aims to provide the scientific community and antibody distributors alike with information on the effectiveness of specific antibodies in specific applications--to help scientists select the right antibody for the right application. Antibodypedia's mission is to promote the functional understanding of the human proteome and expedite analysis of potential biomarkers discovered through clinical efforts. To this end, they have developed an open-access, curated, searchable database containing annotated and scored affinity reagents to aid users in selecting antibodies tailored to specific biological and biomedical assays. They envisage Antibodypedia as a virtual repository of validated antibodies against all human, and ultimately most model-organism, proteins. Such a tool will be exploitable to identify affinity reagents to document protein expression patterns in normal and pathological states and to purify proteins alone and in complex for structural and functional analyses. They hope to promote characterization of the roles and interplay of proteins and complexes in human health and disease. They encourage commercial providers to submit information regarding their inventory of antibodies with links to quality control data. Independent users can submit their own application-specific experimental data using standard validation criteria (supportive or non-supportive) developed with the assistance of an international advisory board recruited from academic research institutions. Users can also comment on specific antibodies without submitting validation data.

There is a need for standardized validation methods for antibody specificity and selectivity. Recently, five alternative validation “pillars" were proposed to explore the specificity of research antibodies using methods with no need for prior knowledge about the protein target. Here, we show that these principles can be used in a streamlined manner for enhanced validation of research antibodies in Western blot applications. More than 6,000 antibodies were validated with at least one of these strategies involving orthogonal methods, genetic knockdown, recombinant expression, independent antibodies and migration capture mass spectrometry analysis. The results show a path forward for efforts to validate antibodies in an application-specific manner suitable for both providers and users.

This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.

The dataset presented contains the following underlying raw data for study which evaluated twelve commercial antibodies against Synaptotagmin-1 in western blot, immunoprecipitation, immunofluorescence and flow cytometry. The original study is also accessible on the YCharOS Zenodo community (https://doi.org/10.5281/zenodo.12666747).

The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).

Supplementary dataset The folder contains individual antibody staining (~100) on frozen tonsil sections, fixed with acetone, 4% formaldehyde or antigen retrieved (AR) following formalin fixation. Each image is a stack of 3 TIFF unmodified fluorescence images on serial sections. Fixation and antibody name can be found in each image title. The collection is a compressed .zip file.The dataset can be downloaded at the Mendeley repository:Cattoretti, Giorgio; Bolognesi, Maddalena M; Bosisio, Francesca M; Faretta, Mario; Mascadri, Francesco (2020), “Antibodies validated for routinely processed tissue unpredictably stain frozen tissue sections.”, Mendeley Data, V1, doi: 10.17632/j88c2ftpsr.1http://dx.doi.org/10.17632/j88c2ftpsr.1References1. Sutherland BW, Toews J, Kast J. Utility of formaldehyde cross-linking and mass spectrometry in the study of protein-protein interactions. Journal of mass spectrometry : JMS, 43(6), 699-715 (2008).2. Bolognesi MM, Manzoni M, Scalia CR et al. Multiplex Staining by Sequential Immunostaining and Antibody Removal on Routine Tissue Sections. Journal of Histochemistry & Cytochemistry, 65(8), 431-444 (2017).3. Scalia CR, Boi G, Bolognesi MM et al. Antigen Masking During Fixation and Embedding, Dissected. Journal of Histochemistry & Cytochemistry, 65(1), 5-20 (2017).

Description of study and method This dataset contains measurement values from a multiplexed screen of antibodies (Abs) to determine their selective recognition of G protein-coupled receptors (GPCRs).

Therapeutic antibodies are being developed to modulate GPCR function. However, validating the selectivity of anti-GPCR Abs is challenging due to sequence similarities of individual receptors within GPCR subfamilies. Here we present data from multiplexed immunoassay to test >400 anti-GPCR Abs from the Human Protein Atlas targeting a customized library of 215 expressed and solubilized GPCRs representing all GPCR subfamilies.

The GPCRs were conjugated with 1D4 and FLAG epitope tags (common protein tags for Ab binding), were overexpressed in Expi293F cells and solubilized for the assay. The Abs were tested using the suspension bead array (SBA) technology from Luminex. The technology employs color-coded beads with Abs that bind the target (for capture) and fluorescently labelled Abs that bind the 1D4 epitope tag (for detection). The resulting output data are the median fluorescence intensity (MFI) values for each sample and Ab. MFI represents a relative measurement that allows for comparison within Abs but not between. The dataset also contains Z-scores and robust Z-scores for visualization and to provide a similar scale for all Abs. For Ab validation, Abs targeting GPCRs were used for capture. To verify that GPCRs were expressed at all, Abs targeting the FLAG tag were used for capture.

Each anti-GPCR Ab was evaluated against its target GPCR and phenotypically closely related GPCRs by using a population density-based threshold to map the on-target and off-target binding of the antibody.

The files contain the following data and information

abval_dat.csv: Sample information and Luminex measurements (MFI, Z-scores and robust Z-scores) for samples used in the antibody validation screen (GPCR capture, 1D4 detection). expr_dat.csv: Sample information and Luminex measurements for lysates used to evaluate GPCR expression (FLAG capture, 1D4 detection). prot.csv: Information about antibodies used in the study. ReadMe: Details about the files and description of columns

This antibody characterization dataset is related to the F1000 research article openly available at F1000Research.

This Dataset contains the following underlying data for a study which evaluated eight Rab10 antibodies by western blot, immunoprecipitation and immunofluorescence using a knockdown cell line as an isogenic control. The original study is also available on the Zenodo YCharOS community (https://doi.org/10.5281/zenodo.13684961).

The Dataset is in the format of a zip file. Once downloaded, please expand the zip file to access the folders containing the underlying data for Western blot (Wb), immunoprecipitation (IP) and immunofluorescence (IF).

This excel document presents a cursory landscape analysis of the commercially available monoclonal antibodies, mouse models, and cell lines available for the targets within the ASAP Technical Track callout. Monoclonal antibody landscape analysis was performed through searches of the online catalogs of Abcam, Thermofischer, Millipore Sigma, and Cell Signaling Technologies. Additional searches through CiteAb were performed when a large number of monoclonal antibodies were not available through the aforementioned vendors. Additionally, targets that have antibody validation datasets available through YCharOS (https://ycharos.com/data/) are denoted with "YCHAROS VALIDATED" in red. Mouse model landscape analysis was performed through searchers of the International Mouse Strain Resource (IMSR) at findmice.org. Cell line landscape analysis was performed through searchers of the online catalogs of Abcam, Horizon Discovery, The Jackson Laboratory, and C-BIG repository (https://cbigr-open.loris.ca/ipsc_catalogue/). If MJFF or ASAP have ongoing tool development efforts, the specific tools/models in development are listed in the table as well.

Defining predictors of antigen-binding affinity of antibodies is valuable for engineering therapeutic antibodies with high binding affinity to their targets. However, this task is challenging owing to the huge diversity in the conformations of the complementarity determining regions of antibodies and the mode of engagement between antibody and antigen. In this study, we used the structural antibody database (SAbDab) to identify features that can discriminate high- and low-binding affinity across a 5-log scale. First, we abstracted features based on previously learned representations of protein-protein interactions to derive ‘complex’ feature sets, which include energetic, statistical, network-based, and machine-learned features. Second, we contrasted these complex feature sets with additional ‘simple’ feature sets based on counts of contacts between antibody and antigen. By investigating the predictive potential of 700 features contained in the eight complex and simple feature sets, we observed that simple feature sets perform comparably to complex feature sets in classification of binding affinity. Moreover, combining features from all eight feature-sets provided the best classification performance (median cross-validation AUROC and F1-score of 0.72). Of note, classification performance is substantially improved when several sources of data leakage (e.g., homologous antibodies) are not removed from the dataset, emphasizing a potential pitfall in this task. We additionally observe a classification performance plateau across diverse featurization approaches, highlighting the need for additional affinity-labeled antibody-antigen structural data. The findings from our present study set the stage for future studies aimed at multiple-log enhancement of antibody affinity through feature-guided engineering.

ROIs of size 600 x 900 px (0.109 mm^2) size were chosen from 25 expert-specified tumor subregions within DLBCL biopsy specimens. Images were generated by Hamamatsu Nanozoomer 2.0 RS slide scanner. ROI no. 1 - 25 represent staining with CD14 antibody (imaged at 488 nm wavelength), ROI no. 26 -- 50 represent staining with CD163 antibody (imaged at 555 nm wavelength). Imaged regions in ROIs no. 1 and 26, 2 and 27, ... , coincide. Data were used for mutual comparison of automated macrophage recognition approaches and validation against manual counts.

This project contains the following underlying data included in a study which characterized antibodies for Charged multivesicular body protein 2b. The study is available on Zenodo (https://doi.org/10.5281/zenodo.6370501).

This dataset contains the generated training, validation and testing samples used in the paper "Antibody interface prediction with 3D Zernike descriptors and SVM".3D Zernike descriptors are used to represent protein surface shape, while Support-Vector Machine (SVM) is the binary classification technique employed to produce a model based on the training data which predicts the class labels of the test data given only the feature vectors of the test data.Data are archived in the format .tar.xz, which can be extracted by common archive utilities. The archive contains a number of text files in the SVM light format. Each line records 1331 colon-separated pairs of numbers (the first one being a feature index - an integer ranging from 1 to 1331, the second a floating point number). Background:In the related paper we present a novel method for antibody interface prediction from their experimentally-solved structures based on 3D Zernike Descriptors. Roto-translationally invariant descriptors are computed from circular patches of the antibody surface enriched with the physicochemical properties from the HQI8 amino acid index set, and are used as samples for a binary classification problem. A SVM classifier is used to distinguish interface surface patches from non-interface ones. By exploiting the spatial continuity of the antigen-binding regions, the Isolation Forest algorithm is used to discard false-positive patches isolated from the others. Each residue is assigned a score by the overlying predicted patches indicating its likelihood of belonging to the binding region: a residue is identified as belonging to the interface only if its score reaches a minimum threshold value. The proposed method was shown to outperform other state-of-the-art antigen-binding interface prediction software.

This project contains the following underlying data included in a study aiming at characterizing antibodies for Tyrosine-protein kinase SYK. The study is available on Zenodo (https://doi.org/10.5281/zenodo.6566940).

OMAP-26 was developed for CO-Detection by indEXing (CODEX) imaging of fresh frozen human kidney tissues, following imaging mass spectrometry (IMS), as described in (Pham et al. 2025). The panel includes 22 antibodies and the nuclear marker DAPI for image registration. This OMAP captures spatial context for all functional tissue units (FTUs) of the nephron, as well as basement membranes, vasculature, lymphatics, stromal, and adaptive immune cells (B and T cells). It does not include innate immune cells. The antibody panel in OMAP-26 significantly overlaps with other kidney panels including OMAP-3 (Neumann and Farrow 2024), OMAP-9 (Barwinska et al. 2024), OMAP-14 (de Caestecker 2024), and OMAP-20 (Esselman 2024). All antibodies in OMAP-26 are also included in the expanded CODEX kidney panel OMAP-27 (https://doi.org/10.48539/HBM228.CPGH.632). OMAP-26 supports spatial mapping of structures and cell types listed in the ASCT+B kidney table v1.6 (Jain et al. 2024). This panel was optimized and validated for use following MALDI IMS imaging, supporting multimodal data integration. Additional details on sample preparation and antibody validation are provided by the VU Biomolecular Multimodal Imaging Center and the Spraggins Research Group (2025).

Bibliography:

• Barwinska, Daria, Tarek M. El-Achkar, Angela R. Sabo, Michael J. Ferkowicz, Seth Winfree, and William S. Bowen. 2024.“9-Kidney-Codex (v1.3) Graph Data, v1.3.” https://lod.humanatlas.io/omap/9-kidney-codex/v1.3/.
• Caestecker, Mark de. 2024. “14-Kidney-Cycif (v1.0) Graph Data, v1.0.” https://lod.humanatlas.io/omap/14-kidney-cycif/v1.0/.
• Esselman, Allison. 2024. “20-Kidney-Ims-Cycif (v1.0) Graph Data, v1.0.” https://lod.humanatlas.io/omap/20-kidney-ims-cycif/v1.0/.
• Jain, Sanjay, M. Todd Valerius, Yongqun He, Tarek El-Achkar, Michael Eadon, and Jeffrey Spraggins. 2024. “Kidney (v1.6) Graph Data, v1.6.” https://lod.humanatlas.io/asct-b/kidney/v1.6/.
• Neumann, Elizabeth K., and Melissa Farrow. 2024. “3-Kidney-Codex (v1.3) Graph Data, v1.3.” https://lod.humanatlas.io/omap/3-kidney-codex/v1.3/.
• Pham, Thai, Lukasz Migas, Madeline E. Colley, Ellie Pringy, Anna Smith, Jamie Allen, Mark De Caestecker, et al. 2025. “Post MALDI IMS CODEX Staining and Imaging Fresh Frozen Tissue.” https://www.protocols.io/view/post-maldi-ims-codex-staining-and-imaging-fresh-fr-dzy277ye.
• VU Biomolecular Multimodal Imaging Center / Spraggins Research Group. 2025. “VU Biomolecular Multimodal Imaging Center / Spraggins Research Group - Research Workspace on Protocols.Io.” https://www.protocols.io/workspaces/BIOMIC.

Spaceflight is known to affect immune cell populations. In particular, splenic B-cell numbers decrease during spaceflight and in ground-based physiological models. Although antibody isotype changes have been assessed during and after spaceflight, an extensive characterization of the impact of spaceflight on antibody composition has not been conducted in mice. Next Generation Sequencing and bioinformatic tools are now available to assess antibody repertoires. We can now identify immunoglobulin gene- segment usage, junctional regions, and modifications that contribute to specificity and diversity. Due to limitations on the International Space Station, alternate sample collection and storage methods must be employed. Our group compared Illumina MiSeq sequencing data from multiple sample preparation methods in normal C57Bl/6J mice to validate that sample preparation and storage would not bias the outcome of antibody repertoire characterization. In this report, we also compared sequencing techniques and a bioinformatic workflow on the data output when we assessed the IgH and Igκ variable gene usage. Our bioinformatic workflow has been optimized for Illumina HiSeq and MiSeq datasets, and is designed specifically to reduce bias, capture the most information from Ig sequences, and produce a data set that provides other data mining options.

SARS-CoV-2 antibody testing is important for seroprevalence studies and for evaluating vaccine immune responses. We developed and validated a Luminex bead-based multiplex serology assay for measuring IgG levels of anti-SARS-CoV-2 antibodies against full-length spike (S), nucleocapsid (N), and receptor-binding domains (RBDs) of wild-type, RBD N501Y mutant, RBD E484K mutant, RBD triple mutant SARS-CoV-2 proteins, Sars-CoV-1, MERS-CoV, and common human coronaviruses, including SARS-CoV-2, OC43, 229E, HKU1, and NL63. Assay cutoff values, sensitivity, and specificity were determined using samples from 160 negative controls and 60 PCR-confirmed, SARS-CoV-2-infected individuals. The assay demonstrated sensitivities of 98.3%, 95%, and 100% and specificities of 100%, 99.4%, and 98.8% for anti-(S), -N, and -RBD, respectively. Results are expressed as IgG antibody concentrations in BAU/mL, using the WHO international standard (NIBSC code 20/136) for anti-SARS-CoV-2 IgG antibodies. When the multiplex assay was performed and compared with singleplex assays, the IgG antibody measurement geometric mean ratios were between 0.895 and 1.122, and no evidence of interference was observed between antigens. Lower and upper IgG concentration limits, based on accuracy (between 80% and 120%), precision (percent relative standard deviation, ≤25%), and sample dilutional linearity (between 75% and 125%), were used to establish the assay range. Precision was established by evaluating 24 individual human serum samples obtained from vaccinated and SARS-CoV-2-infected individuals. The assay provided reproducible, consistent results with typical coefficients of variation of ≤20% for all assays, irrespective of the run, day, or analyst. Results indicate the assay has high sensitivity and specificity and thus is appropriate for use in measuring SARS-CoV-2 IgG antibodies in infected and vaccinated individuals. IMPORTANCE The SARS-CoV-2 pandemic resulted in the development and validation of multiple serology tests with variable performance. While there are multiple SARS-CoV-2 serology tests to detect SARS-CoV-2 antibodies, the focus is usually either on only one antigen at a time or multiple proteins from only one SARS-CoV-2 variant. These tests usually do not evaluate antibodies against viral proteins from different SARS-CoV-2 variants or from other coronaviruses. Here, we evaluated a multiplex serology test based on Luminex technology, where antibodies against multiple domains of SARS-CoV-2 wild type, SARS-CoV-2 mutants, and common coronavirus antibodies are detected simultaneously in a single assay. This Luminex-based multiplex serology assay can enhance our understanding of the immune response to SARS-CoV-2 infection and vaccination.

Comparative specificity of multi-antibody assays at equivalent sensitivity.

A combinatorial antibody library cloning, and NGS-based quality control platform were developed in this study. This Illumina NGS dataset contain the raw data for the validation of our library cloning platform and PCR-free NGS method, and can be used to replicate the results shown in the manuscript "A versatile platform for combinatorial antibody library cloning and NGS based quality control with high accuracy".