Precise immunofluorescence canceling for highly multiplexed imaging to capture specific cell states.

Cell states are regulated by the response of signaling pathways to receptor ligand-binding and intercellular interactions. High-resolution imaging has been attempted to explore the dynamics of these processes and, recently, multiplexed imaging has profiled cell states by achieving a comprehensive acquisition of spatial protein information from cells. However, the specificity of antibodies is still compromised when visualizing activated signals. Here, we develop Precise Emission Canceling Antibodies (PECAbs) that have cleavable fluorescent labeling. PECAbs enable high-specificity sequential imaging using hundreds of antibodies, allowing for reconstruction of the spatiotemporal dynamics of signaling pathways. Additionally, combining this approach with seq-smFISH can effectively classify cells and identify their signal activation states in human tissue. Overall, the PECAb system can serve as a comprehensive platform for analyzing complex cell processes.

Prospects for the computational humanization of antibodies and nanobodies.

To be viable therapeutics, antibodies must be tolerated by the human immune system. Rational approaches to reduce the risk of unwanted immunogenicity involve maximizing the 'humanness' of the candidate drug. However, despite the emergence of new discovery technologies, many of which start from entirely human gene fragments, most antibody therapeutics continue to be derived from non-human sources with concomitant humanization to increase their human compatibility. Early experimental humanization strategies that focus on CDR loop grafting onto human frameworks have been critical to the dominance of this discovery route but do not consider the context of each antibody sequence, impacting their success rate. Other challenges include the simultaneous optimization of other drug-like properties alongside humanness and the humanization of fundamentally non-human modalities such as nanobodies. Significant efforts have been made to develop in silico methodologies able to address these issues, most recently incorporating machine learning techniques. Here, we outline these recent advancements in antibody and nanobody humanization, focusing on computational strategies that make use of the increasing volume of sequence and structural data available and the validation of these tools. We highlight that structural distinctions between antibodies and nanobodies make the application of antibody-focused in silico tools to nanobody humanization non-trivial. Furthermore, we discuss the effects of humanizing mutations on other essential drug-like properties such as binding affinity and developability, and methods that aim to tackle this multi-parameter optimization problem.

Accurate prediction of antibody function and structure using bio-inspired antibody language model.

In recent decades, antibodies have emerged as indispensable therapeutics for combating diseases, particularly viral infections. However, their development has been hindered by limited structural information and labor-intensive engineering processes. Fortunately, significant advancements in deep learning methods have facilitated the precise prediction of protein structure and function by leveraging co-evolution information from homologous proteins. Despite these advances, predicting the conformation of antibodies remains challenging due to their unique evolution and the high flexibility of their antigen-binding regions. Here, to address this challenge, we present the Bio-inspired Antibody Language Model (BALM). This model is trained on a vast dataset comprising 336 million 40% nonredundant unlabeled antibody sequences, capturing both unique and conserved properties specific to antibodies. Notably, BALM showcases exceptional performance across four antigen-binding prediction tasks. Moreover, we introduce BALMFold, an end-to-end method derived from BALM, capable of swiftly predicting full atomic antibody structures from individual sequences. Remarkably, BALMFold outperforms those well-established methods like AlphaFold2, IgFold, ESMFold and OmegaFold in the antibody benchmark, demonstrating significant potential to advance innovative engineering and streamline therapeutic antibody development by reducing the need for unnecessary trials. The BALMFold structure prediction server is freely available at https://beamlab-sh.com/models/BALMFold.

Chromatin Immunoprecipitation of Retroviral Genomes with Antibodies Recognizing Modified Histones and Specific Viral Proteins.

Mammalian cells have developed and optimized defense mechanisms to prevent or hamper viral infection. The early transcriptional silencing of incoming viral DNAs is one such antiviral strategy and seems to be of fundamental importance, since most cell types silence unintegrated retroviral DNAs. In this chapter, a method for chromatin immunoprecipitation of unintegrated DNA is described. This technique allows investigators to examine histone and co-factor interactions with unintegrated viral DNAs as well as to analyze histone modifications in general or in a kinetic fashion at various time points during viral infection.

Unleashing the power of antibodies: Engineering for tomorrow's therapy.

Antibodies play a crucial role in host defense against various diseases. Antibody engineering is a multidisciplinary field that seeks to improve the quality of life of humans. In the context of disease, antibodies are highly specialized proteins that form a critical line of defense against pathogens and the disease caused by them. These infections trigger the innate arm of immunity by presenting on antigen-presenting cells such as dendritic cells. This ultimately links to the adaptive arm, where antibody production and maturation occur against that particular antigen. Upon binding with their specific antigens, antibodies trigger various immune responses to eliminate pathogens in a process called complement-dependent cytotoxicity and phagocytosis of invading microorganisms by immune cells or induce antibody-dependent cellular cytotoxicity is done by antibodies. These engineered antibodies are being used for various purposes, such as therapeutics, diagnostics, and biotechnology research. Cutting-edge techniques that include hybridoma technology, transgenic mice, display techniques like phage, yeast and ribosome displays, and next-generation sequencing are ways to engineer antibodies and mass production for the use of humankind. Considering the importance of antibodies in protecting from a diverse array of pathogens, investing in research holds great promise to develop future therapeutic targets to combat various diseases.

Engineering high affinity antigen-binders: Beyond conventional antibodies.

For decades, antibodies have remained the archetypal binding proteins that can be rapidly produced with high affinity and specificity against virtually any target. A conventional antibody is still considered the prototype of a binding molecule. It is therefore not surprising that antibodies are routinely used in basic scientific and biomedical research, analytical workflows, molecular diagnostics etc. and represent the fastest growing sector in the field of biotechnology. However, several limitations associated with conventional antibodies, including stringent requirement of animal immunizations, mammalian cells for expression, issues on stability and aggregation, bulkier size and the overall time and cost of production has propelled evolution of concepts along alternative antigen binders. Rapidly evolving protein engineering approaches and high throughput screening platforms have further complemented the development of myriads of classes of non-conventional protein binders including antibody derived as well as non-antibody based molecular scaffolds. These non-canonical binders are finding use across disciplines of which diagnostics and therapeutics are the most noteworthy.

Discrepant Phenotyping of Monocytes Based on CX3CR1 and CCR2 Using Fluorescent Reporters and Antibodies.

Monocytes, as well as downstream macrophages and dendritic cells, are essential players in the immune system, fulfilling key roles in homeostasis as well as in inflammatory conditions. Conventionally, driven by studies on reporter models, mouse monocytes are categorized into a classical and a non-classical subset based on their inversely correlated surface expression of Ly6C/CCR2 and CX3CR1. Here, we aimed to challenge this concept by antibody staining and reporter mouse models. Therefore, we took advantage of Cx3cr1GFP and Ccr2RFP reporter mice, in which the respective gene was replaced by a fluorescent reporter protein gene. We analyzed the expression of CX3CR1 and CCR2 by flow cytometry using several validated fluorochrome-coupled antibodies and compared them with the reporter gene signal in these reporter mouse strains. Although we were able to validate the specificity of the fluorochrome-coupled flow cytometry antibodies, mouse Ly6Chigh classical and Ly6Clow non-classical monocytes showed no differences in CX3CR1 expression levels in the peripheral blood and spleen when stained with these antibodies. On the contrary, in Cx3cr1GFP reporter mice, we were able to reproduce the inverse correlation of the CX3CR1 reporter gene signal and Ly6C surface expression. Furthermore, differential CCR2 surface expression correlating with the expression of Ly6C was observed by antibody staining, but not in Ccr2RFP reporter mice. In conclusion, our data suggest that phenotyping strategies for mouse monocyte subsets should be carefully selected. In accordance with the literature, the suitability of CX3CR1 antibody staining is limited, whereas for CCR2, caution should be applied when using reporter mice.

Prediction of Paratope-Epitope Pairs Using Convolutional Neural Networks.

Antibodies play a central role in the adaptive immune response of vertebrates through the specific recognition of exogenous or endogenous antigens. The rational design of antibodies has a wide range of biotechnological and medical applications, such as in disease diagnosis and treatment. However, there are currently no reliable methods for predicting the antibodies that recognize a specific antigen region (or epitope) and, conversely, epitopes that recognize the binding region of a given antibody (or paratope). To fill this gap, we developed ImaPEp, a machine learning-based tool for predicting the binding probability of paratope-epitope pairs, where the epitope and paratope patches were simplified into interacting two-dimensional patches, which were colored according to the values of selected features, and pixelated. The specific recognition of an epitope image by a paratope image was achieved by using a convolutional neural network-based model, which was trained on a set of two-dimensional paratope-epitope images derived from experimental structures of antibody-antigen complexes. Our method achieves good performances in terms of cross-validation with a balanced accuracy of 0.8. Finally, we showcase examples of application of ImaPep, including extensive screening of large libraries to identify paratope candidates that bind to a selected epitope, and rescoring and refining antibody-antigen docking poses.

Electrically Oriented Antibodies on Transistor for Monitoring Several Copies of Methylated DNA.

An antibody transistor is a promising biosensing platform for the diagnosis and monitoring of various diseases. Nevertheless, the low concentration and short half-life of biomarkers require biodetection at the trace-molecule level, which remains a challenge for existing antibody transistors. Herein, we demonstrate a graphene field-effect transistor (gFET) with electrically oriented antibody probes (EOA-gFET) for monitoring several copies of methylated DNA. The electric field confines the orientation of antibody probes on graphene and diminishes the distance between graphene and methylated DNAs captured by antibodies, generating more induced charges on graphene and amplifying the electric signal. EOA-gFET realizes a limit of detection (LoD) of ∼0.12 copy µL-1, reaching the lowest LoD reported before. EOA-gFET shows a distinguishable signal for liver cancer clinical serum samples within ∼6 min, which proves its potential as a powerful tool for disease screening and diagnosis.

Investigating surface proteins and antibody combinations for detecting circulating tumor cells of various sarcomas.

Circulating tumor cells (CTCs) have gathered attention as a biomarker for carcinomas. However, CTCs in sarcomas have received little attention. In this work, we investigated cell surface proteins and antibody combinations for immunofluorescence detection of sarcoma CTCs. A microfluidic device that combines filtration and immunoaffinity using gangliosides 2 and cell surface vimentin (CSV) antibodies was employed to capture CTCs. For CTC detection, antibodies against cytokeratins 7 and 8 (CK), pan-cytokeratin (panCK), or a combination of panCK and CSV were used. Thirty-nine blood samples were collected from 21 patients of various sarcoma subtypes. In the independent samples study, samples were subjected to one of three antibody combination choices. Significant difference in CTC enumeration was found between CK and panCK + CSV, and between panCK and panCK + CSV. Upon stratification of CK+ samples, those of metastatic disease had a higher CTC number than those of localized disease. In the paired samples study involving cytokeratin-positive sarcoma subtypes, using panCK antibody detected more CTCs than CK. Similarly, for osteosarcoma, using panCK + CSV combination resulted in a higher CTC count than panCK. This study emphasized deliberate selection of cell surface proteins for sarcoma CTC detection and subtype stratification for studying cancers as heterogeneous as sarcomas.

Anti-Drug Antibody Incidence Comparison of Therapeutic Proteins Administered Via Subcutaneous vs. Intravenous Route.

Subcutaneous (SC) administration of therapeutic proteins is perceived to pose higher risk of immunogenicity when compared with intravenous (IV) route of administration (RoA). However, systematic evaluations of clinical data to support this claim are lacking. This meta-analysis was conducted to compare the immunogenicity of the same therapeutic protein by IV and SC RoA. Anti-drug antibody (ADA) data and controlling variables for 7 therapeutic proteins administered by both IV and SC routes across 48 treatment groups were analyzed. RoA was the primary independent variable of interest while therapeutic protein, patient population, adjusted dose, and number of ADA samples were controlling variables. Analysis of variance was used to compare the ADA incidence between IV and SC RoA, while accounting for controlling variables and potential interactions. Subsequently, 10 additional therapeutic proteins with ADA data published for both IV and SC administration were added to the above 7 therapeutic proteins and were evaluated for ADA incidence. RoA had no statistically significant effect on ADA incidence for the initial dataset of 7 therapeutic proteins (p = 0.55). The only variable with a significant effect on ADA incidence was the therapeutic protein. None of the other controlling variables, including their interactions with RoA, was significant. When all data from the 17 therapeutic proteins were pooled, there was no statistically significant effect of RoA on ADA incidence (p = 0.81). In conclusion, there is no significant difference in ADA incidence between the IV and SC RoA, based on analysis of clinical ADA data from 17 therapeutic proteins.

Predicting class switch recombination in B-cells from antibody repertoire data.

Statistical and machine learning methods have proved useful in many areas of immunology. In this paper, we address for the first time the problem of predicting the occurrence of class switch recombination (CSR) in B-cells, a problem of interest in understanding antibody response under immunological challenges. We propose a framework to analyze antibody repertoire data, based on clonal (CG) group representation in a way that allows us to predict CSR events using CG level features as input. We assess and compare the performance of several predicting models (logistic regression, LASSO logistic regression, random forest, and support vector machine) in carrying out this task. The proposed approach can obtain an unweighted average recall of 71 % $71\%$ with models based on variable region descriptors and measures of CG diversity during an immune challenge and, most notably, before an immune challenge.

Cytokine/Antibody Fusion Protein Design and Evaluation.

Cytokines constitute a class of secreted proteins that activate transmembrane receptors to coordinate a vast array of physiological processes, particularly those related to immune activity. Due to their vital role in immune regulation, cytokines have garnered great interest as potential therapeutic agents. Unfortunately, the clinical success of cytokine drugs has been limited by their multifunctional activities, which hinder therapeutic performance and lead to harmful toxicities. In addition, the strikingly short circulation half-life of cytokines further hampers their efficacy as drugs. To overcome the translational challenges associated with natural cytokines, significant efforts have focused on engineering cytokines to target their activities and improve their pharmacological properties. One such strategy is the design of fusion proteins that tether a cytokine to an anti-cytokine antibody that selectively biases its functions and extends its serum half-life. These cytokine/antibody fusion proteins (termed immunocytokines) assemble intramolecularly to bias cytokine signaling behavior through multi-layered structural and molecular effects. Here, we present a detailed workflow for the design, production, and functional validation of intramolecularly assembled immunocytokines. In-depth procedures are presented for gene manipulation, mammalian cell-based expression and purification, binding analysis via bio-layer interferometry, and interrogation of cytokine signaling activity on human primary cells. In contrast with immunocytokines in which the tethered cytokine and antibody do not bind one another, intramolecularly assembled immunocytokines require special considerations with respect to their production to avoid oligomerization and/or aggregation. The protocol herein was developed based on experience with immunocytokines that incorporate interleukin-2 (IL-2); however, this modular approach can be extended to any cytokine of interest for a broad range of biomedical applications. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Design and generation of immunocytokine genes Basic Protocol 2: Immunocytokine expression and purification Basic Protocol 3: Validation of immunocytokine assembly and binding by bio-layer interferometry Basic Protocol 4: Analysis of immunocytokine signaling on human primary cells.

Identification of high-performing antibodies for the reliable detection of Tau proteoforms by Western blotting and immunohistochemistry.

Antibodies are essential research tools whose performance directly impacts research conclusions and reproducibility. Owing to its central role in Alzheimer's disease and other dementias, hundreds of distinct antibody clones have been developed against the microtubule-associated protein Tau and its multiple proteoforms. Despite this breadth of offer, limited understanding of their performance and poor antibody selectivity have hindered research progress. Here, we validate a large panel of Tau antibodies by Western blot (79 reagents) and immunohistochemistry (35 reagents). We address the reagents' ability to detect the target proteoform, selectivity, the impact of protein phosphorylation on antibody binding and performance in human brain samples. While most antibodies detected Tau at high levels, many failed to detect it at lower, endogenous levels. By WB, non-selective binding to other proteins affected over half of the antibodies tested, with several cross-reacting with the related MAP2 protein, whereas the "oligomeric Tau" T22 antibody reacted with monomeric Tau by WB, thus calling into question its specificity to Tau oligomers. Despite the presumption that "total" Tau antibodies are agnostic to post-translational modifications, we found that phosphorylation partially inhibits binding for many such antibodies, including the popular Tau-5 clone. We further combine high-sensitivity reagents, mass-spectrometry proteomics and cDNA sequencing to demonstrate that presumptive Tau "knockout" human cells continue to express residual protein arising through exon skipping, providing evidence of previously unappreciated gene plasticity. Finally, probing of human brain samples with a large panel of antibodies revealed the presence of C-term-truncated versions of all main Tau brain isoforms in both control and tauopathy donors. Ultimately, we identify a validated panel of Tau antibodies that can be employed in Western blotting and/or immunohistochemistry to reliably detect even low levels of Tau expression with high selectivity. This work represents an extensive resource that will enable the re-interpretation of published data, improve reproducibility in Tau research, and overall accelerate scientific progress.

Epitope binning for multiple antibodies simultaneously using mammalian cell display and DNA sequencing.

Epitope binning, an approach for grouping antibodies based on epitope similarities, is a critical step in antibody drug discovery. However, conventional methods are complex, involving individual antibody production. Here, we established Epitope Binning-seq, an epitope binning platform for simultaneously analyzing multiple antibodies. In this system, epitope similarity between the query antibodies (qAbs) displayed on antigen-expressing cells and a fluorescently labeled reference antibody (rAb) targeting a desired epitope is analyzed by flow cytometry. The qAbs with epitope similar to the rAb can be identified by next-generation sequencing analysis of fluorescence-negative cells. Sensitivity and reliability of this system are confirmed using rAbs, pertuzumab and trastuzumab, which target human epidermal growth factor receptor 2. Epitope Binning-seq enables simultaneous epitope evaluation of 14 qAbs at various abundances in libraries, grouping them into respective epitope bins. This versatile platform is applicable to diverse antibodies and antigens, potentially expediting the identification of clinically useful antibodies.

Understanding the Specific Implications of Amino Acids in the Antibody Development.

As the demand for immunotherapy to treat and manage cancers, infectious diseases and other disorders grows, a comprehensive understanding of amino acids and their intricate role in antibody engineering has become a prime requirement. Naturally produced antibodies may not have the most suitable amino acids at the complementarity determining regions (CDR) and framework regions, for therapeutic purposes. Therefore, to enhance the binding affinity and therapeutic properties of an antibody, the specific impact of certain amino acids on the antibody's architecture must be thoroughly studied. In antibody engineering, it is crucial to identify the key amino acid residues that significantly contribute to improving antibody properties. Therapeutic antibodies with higher binding affinity and improved functionality can be achieved through modifications or substitutions with highly suitable amino acid residues. Here, we have indicated the frequency of amino acids and their association with the binding free energy in CDRs. The review also analyzes the experimental outcome of two studies that reveal the frequency of amino acids in CDRs and provides their significant correlation between the outcomes. Additionally, it discusses the various bond interactions within the antibody structure and antigen binding. A detailed understanding of these amino acid properties should assist in the analysis of antibody sequences and structures needed for designing and enhancing the overall performance of therapeutic antibodies.

Antibody Design for the Quantification of Photosynthetic Proteins and Their Isoforms.

Antibodies are a valuable research tool, with uses including detection and quantification of specific proteins. By using peptide fragments to raise antibodies, they can be designed to differentiate between structurally similar proteins, or to bind conserved motifs in divergent proteins. Peptide sequence selection and antibody validation are crucial to ensure reliable results from antibody-based experiments. This chapter describes the steps for the identification of peptide sequences to produce protein- or isoform-specific antibodies using recombinant technologies as well as the subsequent validation of such antibodies. The photosynthetic protein Rubisco activase is used as a case study to explain the various steps involved and key aspects to take into consideration.

One-pot green synthesis of ZIF-8/IgG composite for the precise orientation and protection of antibody and its application in purification and detection of aflatoxins in peanut oil.

This study presents a novel approach toward the one-pot green synthesis of ZIF-8/IgG composite, focusing on its precise orientation and protection of the anti-aflatoxins antibody. The antibody orientation is achieved through the specific binding of IgG to the Fc region of the antibody, while the antibody protection is accomplished by the structural change restriction of ZIF-8 framework to the antibody. Consequently, the antibody exhibits enhanced target capability and significantly improved tolerance to organic solvents. The ZIF-8/IgG/anti-AFT was employed for the purification and detection of AFTs by coupling with UPLC. Under optimized conditions, the recoveries of spiked AFTs in peanut oils are between 86.1% and 106.4%, with relative standard deviations (RSDs) ranging from 0.8% to 8.8%. The linearity range is 0.5-20.0 ng for AFB1 and AFG1, 0.125-5.0 ng for AFB2 and AFG2, the limit of detection is 0.1 ng for AFB1 and AFG1, 0.03 ng for AFB2 and AFG2.

Central Chirality and Axial Chirality Recognition of the Enantioselective Antibodies to Herbicide Metolachlor.

Enantioselective antibodies have emerged as efficient tools in the field of chiral chemical detection and separation. However, it is complicated to obtain a highly stereoselective antibody due to the unclear recognition mechanism. In this study, the hapten of metolachlor was synthesized and enantio-separated. The absolute configuration of the four haptens obtained was identified by the computed and experimental electronic circular dichroism comparison. Five polyclonal antibodies against the Rac-metolachlor and its enantiomers were generated by immunization. The cross-activity of all the 5 antibodies with 44 structural analogues, including metolachlor enantiomers, was tested. It demonstrated that antibodies have higher specificity to recognize central chirality than axial chirality. Especially, αRR-MET-Ab exhibited excellent specificity and stereoselectivity. Accordingly, 3D-QSAR models were constructed and revealed that paired stereoisomers exhibited opposite interactions with the antibodies. It is the first time that the antibodies against four stereoisomers were prepared and analyzed, which will be conducive to the rational design of the stereoselective antibodies.

Basophil activation in insect venom allergy: comparison of an established test using liquid reagents with a test using 5-color tubes with dried antibody reagents.

BACKGROUND: Flow cytometry-based basophil activation tests (BAT) have been performed with various modifications, differing in the use of distinct identification and activation markers. Established tests use liquid reagents while a new development involves the use of tubes with dried antibody reagents. The aim of this pilot study was to compare these two techniques in patients with insect venom allergy. METHODS: Seventeen patients with an insect venom allergy were included in the study. The established "BAT 1" utilizes conventional antibody solutions of anti-CCR3 for basophil identification and anti-CD63 to assess basophil activation, whereas "BAT 2" uses dried anti-CD45, anti-CD3, anti-CRTH2, anti-203c and anti-CD63 for identification and activation measurement of basophils. Negative and positive controls as well as incubations with honey bee venom and yellow jacket venom at three concentrations were performed. RESULTS: Seven patients had to be excluded due to low basophil counts, high values in negative controls or negative positive controls. For the remaining 10 patients the overall mean (± SD) difference in activated basophils between the two tests was 0.2 (± 12.2) %P. In a Bland-Altman plot, the limit of agreement (LoA) ranged from 24.0 to -23.7. In the qualitative evaluation (value below/above cut-off) Cohen's kappa was 0.77 indicating substantial agreement. BAT 2 took longer to perform than BAT 1 and was more expensive. CONCLUSION: The BAT 2 technique represents an interesting innovation, however, it was found to be less suitable compared to an established BAT for the routine diagnosis of insect venom allergies.