The analysis of secreted antibody from large and diverse populations of B cells in parallel at the clonal level can reveal desirable antibodies for diagnostic or therapeutic applications. By immobilizing B cells in microdroplets with particulate reporters, decoding and isolating them in a microscopy environment, we have recovered panels of antibodies with rare attributes to therapeutically relevant targets. The ability to screen up to 100 million cells in a single experiment can be fully leveraged by accessing primary B-cell populations from evolutionarily divergent species such as chickens.

Raising functional antibodies against G protein-coupled receptors (GPCRs) is challenging due to their low density expression, instability in the absence of the cell membrane's lipid bilayer and frequently short extracellular domains that can serve as antigens. In addition, a particular therapeutic concept may require an antibody to not just bind the receptor, but also act as a functional receptor agonist or antagonist. Antagonizing the glucose-dependent insulinotropic polypeptide (GIP) receptor may open up new therapeutic modalities in the treatment of diabetes and obesity. As such, a panel of monoclonal antagonistic antibodies would be a useful tool for in vitro and in vivo proof of concept studies. The receptor is highly conserved between rodents and humans, which has contributed to previous mouse and rat immunization campaigns generating very few usable antibodies. Switching the immunization host to chicken, which is phylogenetically distant from mammals, enabled the generation of a large and diverse panel of monoclonal antibodies containing 172 unique sequences. Three-quarters of all chicken-derived antibodies were functional antagonists, exhibited high-affinities to the receptor extracellular domain and sampled a broad epitope repertoire. For difficult targets, including GPCRs such as GIPR, chickens are emerging as valuable immunization hosts for therapeutic antibody discovery.

Introduction Advances in the scientific understanding of the skin and characteristic genomic dermal signatures continue to develop rapidly. Nonetheless, skin diagnosis remains predicated on a subjective visual examination, frequently followed by biopsy and histology. These procedures often are not sufficiently sensitive, and in the case of many inflammatory diseases, biopsies are not justified, creating a situation where high-quality samples can be difficult to obtain. The wealth of molecular information available and the pace at which new data are acquired suggest that methods for minimally invasive biomarker collection could dramatically alter our understanding of skin disease and positively impact treatment paradigms. Methods A chemical method was optimized to covalently modify custom dermal patches with single-stranded DNA that could bind to messenger RNA. These patches were applied to ex vivo skin samples and penetration evaluated by histological methods. Patches were then applied to both the skin of normal human subjects (lower arm) as well as lesional skin of psoriasis patients, and the transcriptome captured ( N = 7; 33 unique samples). Standard RNA-Seq processing was performed to assess the gene detection rate and assessments made of the reproducibility of the extraction procedure as well as the overlap with matched punch biopsy samples from the same patient. Results We have developed a dermal biomarker patch (DBP) designed to be minimally invasive and extract the dermal transcriptome. Using this platform, we have demonstrated successful molecular analysis from healthy human skin and psoriatic lesions, replicating the molecular information captured with punch biopsy. Conclusion This DBP enables an unprecedented ability to monitor the molecular “fingerprint” of the skin over time or with various interventions, and generate previously inaccessible rich datasets. Furthermore, use of the DBP could be favored by patients relative to biopsy by limiting pain resulting from biopsy procedures. Given the large dynamic range observed in psoriatic skin, analysis of complex phenotypes is now possible, and the power of machine-learning methods can be brought to bear on dermatologic disease.