The Tocky reporter system (Bending et al., J Cell Biol 2018) uses a blue-to-red maturing Fluorescent Timer protein driven by the Nr4a3 promoter to record the timing and frequency of T cell receptor (TCR)-driven transcription in individual cells in vivo, revealing persistent TCR signals during thymic regulatory T cell generation and at sites of autoimmune inflammation, and infrequent 'tonic' signals in self-reactive peripheral T cells. Its Discussion posed a composite of unresolved questions that together amount to one objective: a quantitative account of how TCR signaling dynamics are encoded in the temporal structure of downstream transcription. The problem has four facets: (1) whether and how TCR-ligand biophysical parameters such as affinity are translated into transcriptional duration, frequency, and amount of targets such as Nr4a3; (2) the molecular mechanism generating infrequent tonic signals in self-reactive T cells and their long-term pathogenic consequences; (3) how transcriptional time/frequency patterns generalize across immune contexts (infection, allergy, vaccination, other autoimmune diseases); and (4) the formal mathematical characterization of the relationship between the Tocky time and frequency domains, which merge at the Persistent locus.

Background

T cells are lymphocytes of the adaptive immune system that recognize peptide fragments bound to major histocompatibility complex (MHC) molecules through the T cell receptor (TCR). Engagement of a peptide-MHC (pMHC) ligand triggers a cascade of proximal signaling events - phosphorylation of the TCR complex by Src-family kinases, recruitment of the kinase Zap70, and downstream second messengers - that unfolds on a timescale of seconds. These signals ultimately reach the nucleus and induce transcription of genes such as the Nr4a family of orphan nuclear receptors (Nr4a1/Nur77, Nr4a2/Nurr1, Nr4a3/Nor1), which are rapidly and sensitively induced by TCR engagement and are widely used as distal reporters of TCR signal strength. One developmental decision that depends on this signaling is the generation of regulatory T (Treg) cells - Foxp3-expressing suppressor thymocytes selected for self-reactivity.

Bending et al. (2018) introduced the Tocky system (Timer of cell kinetics and activity) to make TCR-driven transcriptional dynamics visible in vivo. Tocky exploits a Fluorescent Timer protein, an engineered fluorescent protein whose chromophore matures irreversibly from blue to red emission with a blue-fluorescence half-life of roughly 4 hours: newly synthesized protein is blue and reddens as it ages, so the blue-to-red ratio of a cell reports how long ago transcription occurred. In Nr4a3-Tocky mice, the Timer protein is expressed under the Nr4a3 promoter, giving a timer-labeled, single-cell readout of TCR-driven transcription. Flow-cytometric blue and red fluorescence are converted by a trigonometric transformation into Timer Angle (a maturation-based readout of timing) and Timer Intensity (total reporter amount, i.e., transcriptional output). Three canonical regions of this two-dimensional space are defined: the New locus (blue-positive, red-negative: recent transcription onset), the Persistent locus (cells on the steady-state diagonal whose transcription is sustained and/or frequent), and the Arrested locus (red-dominant cells in which transcription has ceased). This yields two analysis modes: a time domain, which orders molecular events after a differentiation cue, and a frequency domain, which identifies cells undergoing repeated signaling events; the timer-derived chronological readout ('Tocky time') stands in a nonlinear relationship to real time.

Using Nr4a3-Tocky, the authors showed that thymic T cells receiving persistent TCR signals differentiate into Treg cells, and that antigen-specific T cells at the site of autoimmune inflammation - myelin oligodendrocyte glycoprotein-induced experimental autoimmune encephalomyelitis (EAE), a mouse model of multiple sclerosis - also show persistent TCR signaling. Conversely, self-reactive peripheral T cells, whose spontaneous, low-level 'tonic' TCR signals - basal receptor signaling arising from ongoing recognition of self-peptide-MHC complexes rather than foreign antigen, first recognized by Stefanová et al. (2002) - reside mostly in the Arrested locus, implying signal intervals several-fold longer than the approximately 4-hour blue-fluorescence half-life. At the time, the second-scale dynamics of proximal TCR signaling had been quantitatively characterized, and TCR-ligand affinity was known to shape those proximal dynamics (Stepanek et al. 2014); what remained unknown was how any of this is decoded at the level of transcription over time in vivo. The paper's Discussion accordingly posed these open questions.

Problem Statement

The problem, as posed in the Discussion of Bending et al. (2018), is to establish a quantitative account of how the dynamics of TCR signaling are encoded in the temporal structure of downstream transcription in vivo. It has four source-faithful facets that jointly serve this common objective: (1) determine whether and how distinct biophysical parameters of TCR-ligand interactions - for example, different TCR affinities, which are known to determine the dynamics of proximal TCR signaling molecules - are translated into distinct temporal patterns of transcription of downstream targets such as Nr4a3, i.e., into transcriptional duration, frequency, and output amount; (2) identify the molecular mechanism(s) that generate the infrequent tonic TCR signals observed in self-reactive peripheral T cells, and establish whether and how those infrequent signals influence long-term differentiation into pathogenic T cells in autoimmune conditions; (3) determine how antigen-specific T cell transcriptional time/frequency patterns vary across immune contexts other than the originally examined thymic Treg differentiation and autoimmune inflammation - specifically other autoimmune diseases, infection, allergy, and vaccination; and (4) formally characterize the relationship between the time domain and the frequency domain of Tocky measurements, which merge at the Persistent locus, so that transcriptional timing and signaling frequency can be quantitatively mapped onto one another. All four facets are jointly required to resolve this composite objective: facets (1), (2), and (4) form the mechanistic and mathematical core, and facet (3) is the empirical generality axis along which the same encoding question is tested. For facets (1) and (3), the target is a general encoding principle, for which the named parameters and immune contexts are the primary instantiations.

References

  1. Bending D, Prieto Martin P, Paduraru A, Ducker C, Marzaganov E, Laviron M, Kitano S, Miyachi H, Crompton T, Ono M (2018). 'A timer for analyzing temporally dynamic changes in transcription during differentiation in vivo.' Journal of Cell Biology 217(8):2931-2950. DOI 10.1083/jcb.201711048. https://doi.org/10.1083/jcb.201711048
  2. Stefanová I, Dorfman JR, Germain RN (2002). 'Self-recognition promotes the foreign antigen sensitivity of naive T lymphocytes.' Nature 420(6914):429-434. DOI 10.1038/nature01146. https://doi.org/10.1038/nature01146
  3. Stepanek O, Prabhakar AS, Osswald C, King CG, Bulek A, Naeher D, Beaufils-Hugot M, Abanto ML, Galati V, Hausmann B, et al. (2014). 'Coreceptor scanning by the T cell receptor provides a mechanism for T cell tolerance.' Cell 159(2):333-345. DOI 10.1016/j.cell.2014.08.042. https://doi.org/10.1016/j.cell.2014.08.042
  4. Jennings E, Elliot TAE, Thawait N, Kanabar S, Yam-Puc JC, Ono M, Toellner K-M, Wraith DC, Anderson G, Bending D (2020). 'Nr4a1 and Nr4a3 Reporter Mice Are Differentially Sensitive to T Cell Receptor Signal Strength and Duration.' Cell Reports 33(5):108328. DOI 10.1016/j.celrep.2020.108328. https://doi.org/10.1016/j.celrep.2020.108328
  5. Elliot TAE, Jennings EK, Lecky DAJ, Rouvray S, Mackie GM, Scarfe L, Sheriff L, Ono M, Maslowski KM, Bending D (2022). 'Nur77-Tempo mice reveal T cell steady state antigen recognition.' Discovery Immunology 1(1):kyac009. DOI 10.1093/discim/kyac009. https://doi.org/10.1093/discim/kyac009
  6. Bartleson JM, Viehmann Milam AA, Donermeyer DL, Horvath S, Xia Y, Egawa T, Allen PM (2020). 'Strength of tonic T cell receptor signaling instructs T follicular helper cell-fate decisions.' Nature Immunology 21(11):1384-1396. DOI 10.1038/s41590-020-0781-7. https://doi.org/10.1038/s41590-020-0781-7
  7. Zinzow-Kramer WM, Kolawole EM, Eggert J, Evavold BD, Scharer CD, Au-Yeung BB (2022). 'Strong Basal/Tonic TCR Signals Are Associated with Negative Regulation of Naive CD4+ T Cells.' ImmunoHorizons 6(9):671-683. DOI 10.4049/immunohorizons.2200051. https://doi.org/10.4049/immunohorizons.2200051
  8. Eggert J, Zinzow-Kramer WM, Hu Y, Kolawole EM, Tsai Y-L, Weiss A, Evavold BD, Salaita K, Scharer CD, Au-Yeung BB (2024). 'Cbl-b mitigates the responsiveness of naive CD8+ T cells that experience extensive tonic T cell receptor signaling.' Science Signaling 17(822):eadh0439. DOI 10.1126/scisignal.adh0439. https://doi.org/10.1126/scisignal.adh0439
  9. Almeida-Santos J, Berkachy R, Tye CA, Hassan J, Kalfaoglu B, Selkirk ME, Ono M (2022). 'Temporal profiling of CD4 T-cell activation and differentiation upon SARS-CoV-2 spike protein immunisation.' bioRxiv preprint. DOI 10.1101/2022.07.21.500987. https://doi.org/10.1101/2022.07.21.500987
  10. Hassan J, Appleton E, Kalfaoglu B, Pedersen M, Almeida-Santos J, Kanemaru H, Irie N, Foo S, Reda O, Tan BJY, Okazaki I-m, Okazaki T, Satou Y, Harrington K, Melcher A, Ono M (2022). 'Single-cell level temporal profiling of tumour-reactive T cells under immune checkpoint blockade.' bioRxiv preprint. DOI 10.1101/2022.07.19.500582. https://doi.org/10.1101/2022.07.19.500582
  11. Hassan J, Reda O, Irie N, Pedersen M, Foo S, Appleton L, Okazaki I-m, Okazaki T, Satou Y, Harrington K, Melcher A, Ono M (2026). 'Temporal Mechanisms of T-Cell Fate Decisions under Immune Checkpoint Blockade Resolved by CanonicalTockySeq.' bioRxiv preprint. DOI 10.64898/2026.03.10.710825. https://doi.org/10.64898/2026.03.10.710825
  12. Kurokawa R, Mtali CS, Daniel IJ, Einarsdottir T, Reda O, Irie N, Sakhor W, Niimura K, Takatori M, Sugata K, Tye CA, Satou Y, Ono M, Nomura T (2026). 'Dynamics of lung-infiltrating virus-specific T cells associated with age-dependent SARS-CoV-2 pneumonia severity.' PLOS Pathogens 22:e1013866. DOI 10.1371/journal.ppat.1013866. https://doi.org/10.1371/journal.ppat.1013866
  13. Irie N, Takeda N, Satou Y, Araki K, Ono M (2025). 'Machine learning-assisted decoding of temporal transcriptional dynamics via fluorescent timer.' Nature Communications 16:5720. DOI 10.1038/s41467-025-61279-y. https://doi.org/10.1038/s41467-025-61279-y
  14. Ono M (2024). 'Unraveling T-cell dynamics using fluorescent timer: Insights from the Tocky system.' Biophysics and Physicobiology 21(Suppl). DOI 10.2142/biophysico.bppb-v21.s010. https://doi.org/10.2142/biophysico.bppb-v21.s010
  15. Ono M (2025). 'TockyPrep: data preprocessing methods for flow cytometric fluorescent timer analysis.' BMC Bioinformatics 26:44. DOI 10.1186/s12859-025-06058-8. https://doi.org/10.1186/s12859-025-06058-8
  16. Ono M (2025). 'TockyLocus: quantitative analysis of flow cytometric fluorescent timer data in Nr4a3-Tocky and Foxp3-Tocky mice.' Biology Methods and Protocols 10(1):bpaf060. DOI 10.1093/biomethods/bpaf060. https://doi.org/10.1093/biomethods/bpaf060
  17. Clarke J, Pike J, Bending D, Owen D, Wraith DC, El Haj AJ (2025). 'Remote Force Modulation of the T-Cell Receptor Reveals an NFAT-Threshold for CD4+ T-Cell Activation.' European Journal of Immunology 55(6):e202451716. DOI 10.1002/eji.202451716. https://doi.org/10.1002/eji.202451716