TockyLocus: quantitative analysis of flow cytometric fluorescent timer data in Nr4a3-Tocky and Foxp3-Tocky mice

Fluorescent Timer proteins undergo a time-dependent shift from blue to red fluorescence after translation, providing a temporal record of transcriptional activity in Timer reporter systems. While Timer proteins are well suited for studying dynamic cellular processes such as T cell activation using the Timer-of-Cell-Kinetics-and-Activity (Tocky) framework, quantitative analysis of Timer-based flow cytometry data has yet to be fully standardized. In this study, we optimize quantitative analysis methods for the key parameter within the Tocky framework, Timer Angle, and introduce TockyLocus, an open-source <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:mi>R</mml:mi></mml:math> package that implements a five-category scheme based on biologically grounded angular intervals (designated as Tocky Loci). This approach is validated using both simulated and experimental datasets and enables downstream statistical testing and visualization of transcriptional dynamics in flow cytometry data. Using computational modelling of Timer protein kinetics, we define transcriptional dynamics in relation to key anchoring points in Timer Angle values at <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:msup><mml:mrow><mml:mn>0</mml:mn></mml:mrow> <mml:mo>°</mml:mo></mml:msup> </mml:mrow> </mml:mrow> </mml:math> , <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mrow><mml:mrow><mml:mn>45</mml:mn></mml:mrow> </mml:mrow> <mml:mo>°</mml:mo></mml:msup> </mml:mrow> </mml:mrow> </mml:math> , and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mrow> <mml:msup> <mml:mrow><mml:mrow><mml:mn>90</mml:mn></mml:mrow> </mml:mrow> <mml:mo>°</mml:mo></mml:msup> </mml:mrow> </mml:mrow> </mml:math> . Comprehensive simulations with synthetic spike-in datasets further demonstrate the robustness of the five-locus approach, which captures the three key points and the intermediate regions between these points. Building on the TockyPrep preprocessing framework, we systematically evaluated categorization schemes ranging from three to seven loci on real-world datasets from Nr4a3-Tocky and Foxp3-Tocky mice. The five-locus model emerged as optimal, showing significant advantages in balancing biological interpretability and statistical robustness. Optimized algorithms implemented in the TockyLocus package now standardize quantitative analysis of Timer Angle data, enabling reproducible interpretation without reliance on arbitrary gating or complex assumptions. In summary, the five-locus categorization of Timer Angle data effectively links underlying biological dynamics to the percentage of cells in each Tocky Locus, providing a robust and interpretable framework for investigating transcriptional dynamics in immunology and related fields.