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Quantitative and robust serology assays are critical measurements underpinning global COVID-19 response to diagnostic, surveillance, and vaccine development. Here, we report a proof-of-concept approach for the development of quantitative, multiplexed flow cytometry-based serological and neutralization assays. The serology assays test the IgG and IgM against both the full-length spike antigens and the receptor binding domain (RBD) of the spike antigen. Benchmarking against an RBD-specific SARS-CoV IgG reference standard, the anti-SARS-CoV-2 RBD antibody titer was quantified in the range of 37.6 µg/mL to 31.0 ng/mL. The quantitative assays are highly specific with no correlative cross-reactivity with the spike proteins of MERS, SARS1, OC43 and HKU1 viruses. We further demonstrated good correlation between anti-RBD antibody titers and neutralizing antibody titers. The suite of serology and neutralization assays help to improve measurement confidence and are complementary and foundational for clinical and epidemiologic studies.

Protein expression from human embryonic kidney cells (HEK 293) is an important tool for structural and clinical studies. It is previously shown that microRNAs (small, noncoding RNAs) are effective means for improved protein expression from these cells, and by conducting a high-throughput screening of the human microRNA library, several microRNAs are identified as potential candidates for improving expression. From these, miR-22-3p is chosen for further study since it increased the expression of luciferase, two membrane proteins and a secreted fusion protein with minimal effect on the cells' growth and viability. Since each microRNA can interact with several gene targets, it is of interest to identify the repressed genes for understanding and exploring the improved expression mechanism for further implementation. Here, the authors describe a novel approach for identification of the target genes by integrating the differential gene expression analysis with information obtained from our previously conducted high-throughput siRNA screening. The identified genes were validated as being involved in improving luciferase expression by using siRNA and qRT-PCR. Repressing the target gene, HIPK1, is found to increase luciferase and GPC3 expression 3.3- and 2.2-fold, respectively.

The ability to produce recombinant proteins by utilizing different “cell factories” revolutionized the biotherapeutic and pharmaceutical industry. Chinese hamster ovary (CHO) cells are the dominant industrial producer, especially for antibodies. Human embryonic kidney cells (HEK), while not being as widely used as CHO cells, are used where CHO cells are unable to meet the needs for expression, such as growth factors. Therefore, improving recombinant protein expression from mammalian cells is a priority, and continuing effort is being devoted to this topic. Non-coding RNAs are RNA segments that are not translated into a protein and often have a regulatory role. Since their discovery, major progress has been made towards understanding their functions. Non-coding RNA has been investigated extensively in relation to disease, especially cancer, and recently they have also been used as a method for engineering cells to improve their protein expression capability. In this review, we provide information about methods used to identify non-coding RNAs with the potential of improving recombinant protein expression in mammalian cell lines.

Stable cell lines can continuously produce a recombinant protein without the need to repeatedly engineer the genome. In a previous study HIPK1, Homeodomain-interacting Protein Kinase 1, was found to be a target of the microRNA miR-22 that, when repressed, improved expression of both an intracellular and a secreted protein. In this report, HEK293 cells stably over-expressing miR-22 were compared with HEK293 with knockout of HIPK1, executed by CRISPR/Cas9, for their ability to improve recombinant protein expression. In this model case of luciferase, overexpression of miR-22 improved overall activity 2.4-fold while the HIPK1 knockout improved overall activity 4.7-fold.

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A stochastic reaction–diffusion model was developed to describe the binding of labeled monoclonal antibodies (mAbs) to CD4 receptors on the surface of T cells. The mAbs diffused to, adsorbed on, and underwent monovalent and bivalent binding to CD4 receptors on the cell surface. The model predicted the time-dependent nature of all populations involved in the labeling process. At large time, the populations reached equilibrium values, giving the number of antibodies bound to the T cell (ABC) defined as the sum of monovalently and bivalently bound mAbs. The predicted coefficient of variation (CV%) of the (ABC) values translated directly to a corresponding CV% of the measured mean fluorescence intensity (MFI). The predicted CV% was about 0.2% from the intrinsic fluctuations of the stochastic reaction process, about 5% after inclusion of the known fluctuations in the number of available CD4 receptors, and about 11% when fluctuations in bivalent binding affinity were included. The fluorescence detection process is expected to contribute approximately 7%. The abovementioned contributions to CV% sum up to approximately 13%. Work is underway to reconcile the predicted values and the measured values of 17% to 22%.