A novel method for reproducible fluorescent labeling of small amounts of antibodies on solid phase

Facile generation of monoclonal antibodies suitable for conjugation

Monoclonal antibodies (mAb) are unique tools in therapeutics and immunodiagnostics applications but many of these applications rely on conjugated mAbs. Whether conjugating drugs or tracers, the conjugation process, frequently taking advantage of primary amines on lysine residues, may affect the binding activity of the antibodies. Furthermore, due to the sticky nature of many mAbs, unfavorable interactions may become eminent, with the result of high background signals. The workload associated with producing mAbs, able to withstand conjugation, preserving stability and affinity and avoiding off-target interactions, is comprehensive and related with only incidental success. We designed a method, where uncloned hybridomas were pre-selected for secretion of mAbs with the above characteristics. Using human collectin K1 (CL-K1, alias CL-11, Colec11) as a model antigen, mAbs present in culture supernatant from uncloned hybridomas were immobilized on Protein A beads, followed by solid phase biotinylation and subsequent elution. ELISA was employed to compare the binding activity of conjugated vs. unconjugated mAbs, and furthermore for their application in combination with other antibodies. From a group of 96 uncloned hybridomas we accomplished in obtaining five suitable mAbs, among which, two mAbs were superior. The successful conjugation of the selected mAbs with fluorophores and subsequent applications in microscopy and flow cytometry were further demonstrated. In conclusion, pre-selection of uncloned hybridomas, by testing of their mAbs' ability to withstand conjugation with tracers or drugs, is a successful strategy to avoid a huge workload of cloning numerous hybridomas, in order to obtain conjugatable mAbs.

Antibody Labeling with Fluorescent Dyes Using Magnetic Protein A and Protein G Beads

Antibodies labeled with small molecules like fluorescent dyes, cytotoxic drugs, and radioactive tracers are essential tools in biomedical research, immunodiagnostics and more recently as therapeutic agents. Traditional methods for labeling antibodies with small molecules require purified antibodies at relatively high concentration, involve multiple dialysis steps and have limited throughput. However, several applications, including the field of Antibody Drug Conjugates (ADCs), will benefit from new methods that will allow labeling of antibodies directly from cell media. Such methods may allow antibodies to be screened in biologically relevant assays, for example, the receptor-mediated antibody internalization assay in the case of ADCs. Here, we describe a method (on-bead method) that enables labeling of small amounts of antibodies directly from cell media. This approach utilizes high capacity magnetic Protein A and Protein G affinity beads to capture antibodies from the cell media followed by labeling with small molecules using either amine or thiol chemistry and subsequent elution of the labeled antibodies. Taking fluorescent dyes as surrogates for small molecules, we demonstrate the on-bead labeling of three different mouse antibodies directly from cell media using both amine and thiol labeling chemistry. The high binding affinity of antibodies to Protein A and Protein G ensures high recoveries as well as high purity of the labeled antibodies. In addition, use of magnetic beads allows multiple samples to be handled manually, thereby significantly improving labeling throughput.

Magnetic bead assisted labeling of antibodies at nanogram scale

There are currently several initiatives that aim to produce binding reagents for proteome-wide analysis. To enable protein detection, visualization, and target quantification, covalent coupling of reporter molecules to antibodies is essential. However, current labeling protocols recommend considerable amount of antibodies, require antibody purity and are not designed for automation. Given that small amounts of antibodies are often sufficient for downstream analysis, we developed a labeling protocol that combines purification and modification of antibodies at submicrogram quantities. With the support of magnetic microspheres, automated labeling of antibodies in parallel using biotin or fluorescent dyes was achieved.

IL-6 adsorption dynamics in hemoadsorption beads studied using confocal laser scanning microscopy

Sepsis is characterized by a systemic inflammatory response caused by infection, and can result in organ failure and death. Removal of inflammatory mediators such as cytokines from the circulating blood is a promising treatment for severe sepsis. We are developing an extracorporeal hemoadsorption device to remove cytokines from the blood using biocompatible, polymer sorbent beads. In this study, we used confocal laser scanning microscopy (CLSM) to directly examine adsorption dynamics of a cytokine (IL-6) within hemoadsorption beads. Fluorescently labeled IL-6 was incubated with sorbent particles, and CLSM was used to quantify spatial adsorption profiles of IL-6 within the sorbent matrix. IL-6 adsorption was limited to the outer 15 microm of the sorbent particle over a relevant clinical time period, and intraparticle adsorption dynamics was modeled using classical adsorption/diffusion mechanisms. A single model parameter, alpha = q(max) K/D, was estimated by fitting CLSM intensity profiles to our mathematical model, where q(max) and K are Langmuir adsorption isotherm parameters, and D is the effective diffusion coefficient of IL-6 within the sorbent matrix. Given the large diameter of our sorbent beads (450 microm), less than 20% of available sorbent surface area participates in cytokine adsorption. Development of smaller beads may accelerate cytokine adsorption by maximizing available surface area per bead mass.

Automated Analysis of Human Protein Atlas Immunofluorescence Images

The Human Protein Atlas is a rich source of location proteomics data. In this work, we present an automated approach for processing and classifying major subcellular patterns in the Atlas images. We demonstrate that two different classification frameworks (support vector machine and random forest) are effective at determining subcellular locations; we can analyze over 3500 Atlas images with a high degree of accuracy, up to 87.5% for all of the samples and 98.5% when only considering samples in whose classification assignments we are most confident. Moreover, the features obtained in both of these frameworks are observed to be highly consistent and generalizable. Additionally, we observe that the features relating the proteins to cell markers are especially important in automated learning approaches.