Masahiro Ono

Fluorescent Timer proteins, which spontaneously change their emission spectra over time, are valuable tools for analyzing temporal changes in cellular activities at the single-cell level. Traditional analysis of Fluorescent Timer data has mostly relied on conventional flow cytometric methods, which lacks the sophistication needed for detailed quantitative analysis. Recently, we developed the Timer-of-Cell-Kinetics-and-Activity (Tocky) tools, employing transgenic reporter systems using an mCherry mutant Timer protein, Fast-FT, to analyze these changes, implementing data preprocessing methods for Timer fluorescence. Despite this advancement, the computational implementation of effective quantitative analysis methods for Fluorescent Timer data has been lacking. In this study, we introduce rigorous algorithms for a data categorization method, designated as the Tocky locus approach, which uses normalized and trigonometric-transformed Fluorescent Timer data, suitable for quantitative analysis. We found that the five-locus approach, which categorizes Timer fluorescence into five categories, optimally captures the dynamics of Timer profiles, thus enabling effective quantitative statistical analysis and biological interpretation. The current study has developed comprehensive tools for data categorization, visualization, and statistical analysis optimized to Tocky Locus data. These algorithms have been implemented in the TockyLocus package, a new R package designed to facilitate the quantitative analysis of Timer fluorescence data. This toolkit is expected to significantly enhance the utility of experimental systems using Fluorescent Timer proteins, particularly the Tocky tools, by standardizing methods for analyzing dynamic cellular events and responses.

Background: Fluorescent Timer proteins, which display time-dependent changes in their emission spectra, are invaluable for analyzing the temporal dynamics of cellular events at the single-cell level. We previously developed the Timer-of-cell-kinetics-and-activity (Tocky) tools, utilizing a specific Timer protein, Fast-FT, to monitor temporal changes in cellular activities. Despite their potential, the analysis of Timer fluorescence in flow cytometry is frequently compromised by variability in instrument settings and the absence of standardized preprocessing methods. The development and implementation of effective data preprocessing methods remain to be achieved. Results: In this study, we introduce the R package that automates the data preprocessing of Timer fluorescence data from flow cytometry experiments for quantitative analysis at single-cell level. Our aim is to standardize Timer data analysis to enhance reproducibility and accuracy across different experimental setups. The package includes a trigonometric transformation method to elucidate the dynamics of Fluorescent Timer proteins. We have identified the normalization of immature and mature Timer fluorescence data as essential for robust analysis, clarifying how this normalization affects the analysis of Timer maturation. These preprocessing methods are all encapsulated within the TockyPrep R package. Conclusions: TockyPrep is available for distribution via GitHub at this https URL, providing tools for data preprocessing and basic visualization of Timer fluorescence data. This toolkit is expected to enhance the utility of experimental systems utilizing Fluorescent Timer proteins, including the Tocky tools.

Advancements in cytometry technologies have led to a remarkable increase in the number of markers that can be analyzed simultaneously, presenting significant challenges in data analysis. Traditional approaches, such as dimensional reduction techniques and computational clustering, although popular, often face reproducibility challenges due to their heavy reliance on inherent data structures, preventing direct translation of their outputs into gating strategies to be used in downstream experiments. Here we propose the novel Gating Tree methodology, a pathfinding approach that investigates the multidimensional data landscape to unravel group-specific features without the use of dimensional reduction. This method employs novel measures, including enrichment scores and gating entropy, to effectively identify group-specific features within high-dimensional cytometric datasets. Our analysis, applied to both simulated and real cytometric datasets, demonstrates that the Gating Tree not only identifies group-specific features comprehensively but also produces outputs that are immediately usable as gating strategies for unequivocally identifying cell populations. In conclusion, the Gating Tree facilitates a comprehensive analysis of the multidimensional data landscape and provides experimentalists with practical, successive gating strategies that enhance cross-experimental comparisons and downstream analyses such as flow cytometric sorting.

Despite extensive regulatory T cell (Treg) research, fundamental questions on in vivo dynamics remain to be answered. The current study aims to dissect several interwoven concepts in Treg biology, highlighting the ‘self-reactivity’ of Treg and their counterparts, namely naturally-arising memory-phenotype T-cells, as a key mechanism to be exploited by a human retroviral infection. We propose the novel key concept, Periodic T cell receptor (TCR)-signalled T-cells, capturing self-reactivity in a quantifiable manner using the Nr4a3-Timer-of-cell-kinetics-and-activity (Tocky) technology. Periodic and brief TCR signals in self-reactive T-cells contrast with acute TCR signals during inflammation. Thus, we propose a new two-axis model for T-cell activation by the two types of TCR signals or antigen recognition, elucidating how Foxp3 expression and acute TCR signals actively regulate Periodic TCR-signalled T-cells. Next, we highlight an underappreciated branch of immunological research on Human T-cell Leukemia Virus type 1 (HTLV-1) that precedes Treg studies, illuminating the missing link between the viral infection, CD25, and Foxp3. Based on evidence by single-cell analysis, we show how the viral infection exploits the regulatory mechanisms for T-cell activation and suggests a potential role of periodic TCR signalling in infection and malignant transformation. In conclusion, the new perspectives and models in this study provide a working framework for investigating Treg within the self-reactive T-cell spectrum, expected to advance understanding of HTLV-1 infection, cancer, and immunotherapy strategies for these conditions.

Regulatory T cells (Treg) are negative regulators of the immune response; however, it is poorly understood whether and how Foxp3 transcription is induced and regulated in the periphery during T‐cell responses. Using Foxp3‐Timer of cell kinetics and activity (Tocky) mice, which report real‐time Foxp3 expression, we show that the flux of new Foxp3 expressors and the rate of Foxp3 transcription are increased during inflammation. These persistent dynamics of Foxp3 transcription determine the effector Treg programme and are dependent on a Foxp3 autoregulatory transcriptional circuit. Persistent Foxp3 transcriptional activity controls the expression of coinhibitory molecules, including CTLA‐4 and effector Treg signature genes. Using RNA‐seq, we identify two groups of surface proteins based on their relationship to the temporal dynamics of Foxp3 transcription, and we show proof of principle for the manipulation of Foxp3 dynamics by immunotherapy: new Foxp3 flux is promoted by anti‐TNFRII antibody, and high‐frequency Foxp3 expressors are targeted by anti‐OX40 antibody. Collectively, our study dissects time‐dependent mechanisms behind Foxp3‐driven T‐cell regulation and establishes the Foxp3‐Tocky system as a tool to investigate the mechanisms behind T‐cell immunotherapies.

Understanding the mechanisms of cellular differentiation is challenging because differentiation is initiated by signaling pathways that drive temporally dynamic processes, which are difficult to analyze in vivo. We establish a new tool, Timer of cell kinetics and activity (Tocky; or toki [time in Japanese]). Tocky uses the fluorescent Timer protein, which spontaneously shifts its emission spectrum from blue to red, in combination with computer algorithms to reveal the dynamics of differentiation in vivo. Using a transcriptional target of T cell receptor (TCR) signaling, we establish Nr4a3-Tocky to follow downstream effects of TCR signaling. Nr4a3-Tocky reveals the temporal sequence of events during regulatory T cell (Treg) differentiation and shows that persistent TCR signals occur during Treg generation. Remarkably, antigen-specific T cells at the site of autoimmune inflammation also show persistent TCR signaling. In addition, by generating Foxp3-Tocky, we reveal the in vivo dynamics of demethylation of the Foxp3 gene. Thus, Tocky is a tool for cell biologists to address previously inaccessible questions by directly revealing dynamic processes in vivo.

Thymus-derived regulatory T cells (Tregs) are considered to be a distinct T-cell lineage that is genetically programmed and specialised for immunosuppression. This perspective is based on the key evidence that CD25+ Tregs emigrate to neonatal spleen a few days later than other T cells and that thymectomy of 3-day-old mice depletes Tregs only, causing autoimmune diseases. Although widely believed, the evidence has never been reproduced as originally reported, and some studies indicate that Tregs exist in neonates. Thus we examine the consequences of the controversial evidence, revisit the fundamental issues of Tregs and thereby reveal the overlooked relationship of T-cell activation and Foxp3-mediated control of the T-cell system. Here we provide a new model of Tregs and Foxp3, a feedback control perspective, which views Tregs as a component of the system that controls T-cell activation, rather than as a distinct genetically programmed lineage. This perspective provides new insights into the roles of self-reactivity, T cell–antigen-presenting cell interaction and T-cell activation in Foxp3-mediated immune regulation.

CD4+ T cells that express the transcription factor FOXP3 (FOXP3+ T cells) are commonly regarded as immunosuppressive regulatory T cells (Tregs). FOXP3+ T cells are reported to be increased in tumor-bearing patients or animals and are considered to suppress antitumor immunity, but the evidence is often contradictory. In addition, accumulating evidence indicates that FOXP3 is induced by antigenic stimulation and that some non-Treg FOXP3+ T cells, especially memory-phenotype FOXP3low cells, produce proinflammatory cytokines. Accordingly, the subclassification of FOXP3+ T cells is fundamental for revealing the significance of FOXP3+ T cells in tumor immunity, but the arbitrariness and complexity of manual gating have complicated the issue. In this article, we report a computational method to automatically identify and classify FOXP3+ T cells into subsets using clustering algorithms. By analyzing flow cytometric data of melanoma patients, the proposed method showed that the FOXP3+ subpopulation that had relatively high FOXP3, CD45RO, and CD25 expressions was increased in melanoma patients, whereas manual gating did not produce significant results on the FOXP3+ subpopulations. Interestingly, the computationally identified FOXP3+ subpopulation included not only classical FOXP3high Tregs, but also memory-phenotype FOXP3low cells by manual gating. Furthermore, the proposed method successfully analyzed an independent data set, showing that the same FOXP3+ subpopulation was increased in melanoma patients, validating the method. Collectively, the proposed method successfully captured an important feature of melanoma without relying on the existing criteria of FOXP3+ T cells, revealing a hidden association between the T cell profile and melanoma, and providing new insights into FOXP3+ T cells and Tregs.

BACKGROUND: Currently, in the era of post-genomics, immunology is facing a challenging problem to translate mutant phenotypes into gene functions based on high-throughput data, while taking into account the classifications and functions of immune cells, which requires new methods. RESULTS: Here we propose a novel application of a multidimensional analysis, Canonical Correspondence Analysis (CCA), to reveal the molecular characteristics of undefined cells in terms of cellular differentiation programmes by analysing two transcriptomic datasets. Using two independent datasets, whether RNA-seq or microarray data, CCA successfully visualised the cross-level relationships between genes, cells, and differentiation programmes, and thereby identified the immunological features of mutant cells (Gata3-KO T cells and Stat3-KO T cells) in a data-oriented manner. With a new concept, differentiation variable, CCA provides an automatic classification of cell samples, which had a high sensitivity and a comparable performance to other classification methods. In addition, we elaborate how CCA results can be interpreted, and reveal the features of CCA in comparison with other visualisation techniques. CONCLUSIONS: CCA is a visualisation tool with a classification ability to reveal the cross-level relationships of genes, cells and differentiation programmes. This can be used for characterising the functional defect of cells of interest (e.g. mutant cells) in the context of cellular differentiation. The proposed approach fits with common hypothesis-oriented studies in immunology, and can be used for a wide range of molecular and genomic studies on cellular differentiation mechanisms.

The understanding of pathological processes is based on the comparison between physiological and pathological conditions, and transcriptomic analysis has been extensively applied to various diseases for this purpose. However, the way in which the transcriptomic data of pathological cells relate to the transcriptomes of normal cellular counterparts has not been fully explored, and may provide new and unbiased insights into the mechanisms of these diseases. To achieve this, it is necessary to develop a method to simultaneously analyse components across different levels, namely genes, normal cells, and diseases. Here we propose a multidimensional method that visualises the cross-level relationships between these components at three different levels based on transcriptomic data of physiological and pathological processes, by adapting Canonical Correspondence Analysis, which was developed in ecology and sociology, to microarray data (CCA on Microarray data, CCAM). Using CCAM, we have analysed transcriptomes of haematological disorders and those of normal haematopoietic cell differentiation. First, by analysing leukaemia data, CCAM successfully visualised known relationships between leukaemia subtypes and cellular differentiation, and their characteristic genes, which confirmed the relevance of CCAM. Next, by analysing transcriptomes of myelodysplastic syndromes (MDS), we have shown that CCAM was effective in both generating and testing hypotheses. CCAM showed that among MDS patients, high-risk patients had transcriptomes that were more similar to those of both haematopoietic stem cells (HSC) and megakaryocyte-erythroid progenitors (MEP) than low-risk patients, and provided a prognostic model. Collectively, CCAM reveals hidden relationships between pathological and physiological processes and gene expression, providing meaningful clinical insights into haematological diseases, and these could not be revealed by other univariate and multivariate methods. Furthermore, CCAM was effective in identifying candidate genes that are correlated with cellular phenotypes of interest. We expect that CCAM will benefit a wide range of medical fields.