NAb-seq: an accurate, rapid, and cost-effective method for antibody long-read sequencing in hybridoma cell lines and single B cells.

Despite their common use in research, monoclonal antibodies are currently not systematically sequenced. This can lead to issues with reproducibility and the occasional loss of antibodies with loss of cell lines. Hybridoma cell lines have been the primary means of generating monoclonal antibodies from immunized animals, including mice, rats, rabbits, and alpacas. Excluding therapeutic antibodies, few hybridoma-derived antibody sequences are known. Sanger sequencing has been "the gold standard" for antibody gene sequencing, but this method relies on the availability of species-specific degenerate primer sets for amplification of light and heavy antibody genes and it requires lengthy and expensive cDNA preparation. Here, we leveraged recent improvements in long-read Oxford Nanopore Technologies (ONT) sequencing to develop Nanopore Antibody sequencing (NAb-seq): a three-day, species-independent, and cost-effective workflow to characterize paired full-length immunoglobulin light- and heavy-chain genes from hybridoma cell lines. When compared to Sanger sequencing of two hybridoma cell lines, long-read ONT sequencing was highly accurate, reliable, and amenable to high throughput. We further show that the method is applicable to single cells, allowing efficient antibody discovery in rare populations such as memory B cells. In summary, NAb-seq promises to accelerate identification and validation of hybridoma antibodies as well as antibodies from single B cells used in research, diagnostics, and therapeutics.

Targeting the Human ßc Receptor Inhibits Contact Dermatitis in a Transgenic Mouse Model.

Allergic contact dermatitis (ACD) is a prevalent and poorly controlled inflammatory disease caused by skin infiltration of T cells and granulocytes. The beta common (ßc) cytokines GM-CSF, IL-3, and IL-5 are powerful regulators of granulocyte function that signal through their common receptor subunit ßc, a property that has made ßc an attractive target to simultaneously inhibit these cytokines. However, the species specificity of ßc has precluded testing of inhibitors of human ßc in mouse models. To overcome this problem, we developed a human ßc receptor transgenic mouse strain with a hematopoietic cell‒specific expression of human ßc instead of mouse ßc. Human ßc receptor transgenic cells responded to mouse GM-CSF and IL-5 but not to IL-3 in vitro and developed tissue pathology and cellular inflammation comparable with those in wild-type mice in a model of ACD. Similarly, Il3-/- mice developed ACD pathology comparable with that of wild-type mice. Importantly, the blocking anti-human ßc antibody CSL311 strongly suppressed ear pinna thickening and histopathological changes typical of ACD and reduced accumulation of neutrophils, mast cells, and eosinophils in the skin. These results show that GM-CSF and IL-5 but not IL-3 are major mediators of ACD and define the human ßc receptor transgenic mouse as a unique platform to test the inhibitors of ßc in vivo.

A guide to creating design matrices for gene expression experiments.

Differential expression analysis of genomic data types, such as RNA-sequencing experiments, use linear models to determine the size and direction of the changes in gene expression. For RNA-sequencing, there are several established software packages for this purpose accompanied with analysis pipelines that are well described. However, there are two crucial steps in the analysis process that can be a stumbling block for many -- the set up an appropriate model via design matrices and the set up of comparisons of interest via contrast matrices. These steps are particularly troublesome because an extensive catalogue for design and contrast matrices does not currently exist. One would usually search for example case studies across different platforms and mix and match the advice from those sources to suit the dataset they have at hand. This article guides the reader through the basics of how to set up design and contrast matrices. We take a practical approach by providing code and graphical representation of each case study, starting with simpler examples (e.g. models with a single explanatory variable) and move onto more complex ones (e.g. interaction models, mixed effects models, higher order time series and cyclical models). Although our work has been written specifically with a limma-style pipeline in mind, most of it is also applicable to other software packages for differential expression analysis, and the ideas covered can be adapted to data analysis of other high-throughput technologies. Where appropriate, we explain the interpretation and differences between models to aid readers in their own model choices. Unnecessary jargon and theory is omitted where possible so that our work is accessible to a wide audience of readers, from beginners to those with experience in genomics data analysis.