Advances in preclinical TCR characterization: leveraging cell avidity to identify functional TCRs.

T-cell therapy has emerged as an effective approach for treating viral infections and cancers. However, a significant challenge is the selection of T-cell receptors (TCRs) that exhibit the desired functionality. Conventionally in vitro techniques, such as peptide sensitivity measurements and cytotoxicity assays, provide valuable insights into TCR potency but are labor-intensive. In contrast, measuring ligand binding properties (z-Movi technology) could provide an accelerated processing while showing robust correlations with T-cell functions. In this study, we assessed whether cell avidity can predict functionality also in the context of TCR-engineered T cells. To this end, we developed a flexible system for TCR re-expression by generating a Jurkat-derived T cell clone lacking TCR and CD3 expression through CRISPR-Cas9-mediated TRBC knockout. The knockin of a transgenic TCR into the TRAC locus restored TCR/CD3 expression, allowing for CD3-based purification of TCR-engineered T cells. Subsequently, we characterized these engineered cell lines by functional readouts, and assessment of binding properties through the z-Movi technology. Our findings revealed a strong correlation between the cell avidities and functional sensitivities of Jurkat TCR-T cells. Altogether, by integrating cell avidity measurements with our versatile T cell engineering platform, we established an accelerated system for enhancing the in vitro selection of clinically relevant TCRs.

Three perspectives on the prediction of chemical effects in ecosystems.

The increasing production, use and emission of synthetic chemicals into the environment represents a major driver of global change. The large number of synthetic chemicals, limited knowledge on exposure patterns and effects in organisms and their interaction with other global change drivers hamper the prediction of effects in ecosystems. However, recent advances in biomolecular and computational methods are promising to improve our capacity for prediction. We delineate three idealised perspectives for the prediction of chemical effects: the suborganismal, organismal and ecological perspective, which are currently largely separated. Each of the outlined perspectives includes essential and complementary theories and tools for prediction but captures only part of the phenomenon of chemical effects. Links between the perspectives may foster predictive modelling of chemical effects in ecosystems and extrapolation between species. A major challenge for the linkage is the lack of data sets simultaneously covering different levels of biological organisation (here referred to as biological levels) as well as varying temporal and spatial scales. Synthesising the three perspectives, some central aspects and associated types of data seem particularly necessary to improve prediction. First, suborganism- and organism-level responses to chemicals need to be recorded and tested for relationships with chemical groups and organism traits. Second, metrics that are measurable at many biological levels, such as energy, need to be scrutinised for their potential to integrate across levels. Third, experimental data on the simultaneous response over multiple biological levels and spatiotemporal scales are required. These could be collected in nested and interconnected micro- and mesocosm experiments. Lastly, prioritisation of processes involved in the prediction framework needs to find a balance between simplification and capturing the essential complexity of a system. For example, in some cases, eco-evolutionary dynamics and interactions may need stronger consideration. Prediction needs to move from a static to a real-world eco-evolutionary view.

Gene Signatures of T-Cell Activation Can Serve as Predictors of Functionality for SARS-CoV-2-Specific T-Cell Receptors.

The importance of T cells in controlling SARS-CoV-2 infections has been demonstrated widely, but insights into the quality of these responses are still limited due to technical challenges. Indeed, understanding the functionality of the T-cell receptor (TCR) repertoire of a polyclonal antigen-specific population still requires the tedious work of T-cell cloning or TCR re-expression and subsequent characterization. In this work, we show that it is possible to discriminate highly functional and bystander TCRs based on gene signatures of T-cell activation induced by recent peptide stimulation. SARS-CoV-2-specific TCRs previously identified by cytokine release after peptide restimulation and subsequent single-cell RNA sequencing were re-expressed via CRISPR-Cas9-mediated gene editing into a Jurkat-based reporter cell line system suitable for high-throughput screening. We could observe differences in SARS-CoV-2 epitope recognition as well as a wide range of functional avidities. By correlating these in vitro TCR engineered functional data with the transcriptomic profiles of the corresponding TCR-expressing parental T cells, we could validate that gene signatures of recent T-cell activation accurately identify and predict truly SARS-CoV-2-specific TCRs. In summary, this work paves the way for alternative approaches useful for the functional analysis of global antigen-specific TCR repertoires with largely improved throughput.