Thymic epithelial cell fate and potency in early organogenesis assessed by single cell transcriptional and functional analysis.

During development, cortical (c) and medullary (m) thymic epithelial cells (TEC) arise from the third pharyngeal pouch endoderm. Current models suggest that within the thymic primordium most TEC exist in a bipotent/common thymic epithelial progenitor cell (TEPC) state able to generate both cTEC and mTEC, at least until embryonic day 12.5 (E12.5) in the mouse. This view, however, is challenged by recent transcriptomics and genetic evidence. We therefore set out to investigate the fate and potency of TEC in the early thymus. Here using single cell (sc) RNAseq we identify a candidate mTEC progenitor population at E12.5, consistent with recent reports. Via lineage-tracing we demonstrate this population as mTEC fate-restricted, validating our bioinformatics prediction. Using potency analyses we also establish that most E11.5 and E12.5 progenitor TEC are cTEC-fated. Finally we show that overnight culture causes most if not all E12.5 cTEC-fated TEPC to acquire functional bipotency, and provide a likely molecular mechanism for this changed differentiation potential. Collectively, our data overturn the widely held view that a common TEPC predominates in the E12.5 thymus, showing instead that sublineage-primed progenitors are present from the earliest stages of thymus organogenesis but that these early fetal TEPC exhibit cell-fate plasticity in response to extrinsic factors. Our data provide a significant advance in the understanding of fetal thymic epithelial development and thus have implications for thymus-related clinical research, in particular research focussed on generating TEC from pluripotent stem cells.

Combined multidimensional single-cell protein and RNA profiling dissects the cellular and functional heterogeneity of thymic epithelial cells.

The network of thymic stromal cells provides essential niches with unique molecular cues controlling T cell development and selection. Recent single-cell RNA sequencing studies have uncovered previously unappreciated transcriptional heterogeneity among thymic epithelial cells (TEC). However, there are only very few cell markers that allow a comparable phenotypic identification of TEC. Here, using massively parallel flow cytometry and machine learning, we deconvoluted known TEC phenotypes into novel subpopulations. Using CITEseq, these phenotypes were related to corresponding TEC subtypes defined by the cells' RNA profiles. This approach allowed the phenotypic identification of perinatal cTEC and their physical localisation within the cortical stromal scaffold. In addition, we demonstrate the dynamic change in the frequency of perinatal cTEC in response to developing thymocytes and reveal their exceptional efficiency in positive selection. Collectively, our study identifies markers that allow for an unprecedented dissection of the thymus stromal complexity, as well as physical isolation of TEC populations and assignment of specific functions to individual TEC subtypes.

Occam's razor gets a new edge: the use of symmetries in model selection.

We demonstrate the power of using symmetries for model selection in the context of mechanistic modelling. We analyse two different models called the power law model (PLM) and the immunological model (IM) describing the increase in cancer risk with age, due to mutation accumulation or immunosenescence, respectively. The IM fits several cancer types better than the PLM implying that it would be selected based on minimizing residuals. However, recently a symmetry-based method for model selection has been developed, which has been successfully used in an in silico setting to find the correct model when traditional model fitting has failed. Here, we apply this method in a real-world setting to investigate the mechanisms of carcinogenesis. First, we derive distinct symmetry transformations of the two models and then we select the model which not only fits the original data but is also invariant under transformations by its symmetry. Contrary to the initial conclusion, we conclude that the PLM realistically describes the mechanism underlying the colon cancer dataset. These conclusions agree with experimental knowledge, and this work demonstrates how a model selection criterion based on biological properties can be implemented using symmetries.

COVID-19 hospitalization rates rise exponentially with age, inversely proportional to thymic T-cell production.

Here, we report that COVID-19 hospitalization rates follow an exponential relationship with age, doubling for every 16 years of age or equivalently increasing by 4.5% per year of life (R2 = 0.98). This mirrors the well-studied exponential decline of both thymus volume and T-cell production, which halve every 16 years. COVID-19 can therefore be added to the list of other diseases with this property, including those caused by methicillin-resistant Staphylococcus aureus, MERS-CoV, West Nile virus, Streptococcus pneumoniae and certain cancers, such as chronic myeloid leukaemia and brain cancers. In addition, the incidence of severe disease and mortality due to COVID-19 are both higher in men, consistent with the degree to which thymic involution (and the decrease in T-cell production with age) is more severe in men compared to women. Since these properties are shared with some non-contagious diseases, we hypothesized that the age dependence does not come from social-mixing patterns, i.e. that the probability of hospitalization given infection rises exponentially, doubling every 16 years. A Bayesian analysis of daily hospitalizations, incorporating contact matrices, found that this relationship holds for every age group except for the under 20s. While older adults have fewer contacts than young adults, our analysis suggests that there is an approximate cancellation between the effects of fewer contacts for the elderly and higher infectiousness due to a higher probability of developing severe disease. Our model fitting suggests under 20s have 49-75% additional immune protection beyond that predicted by strong thymus function alone, consistent with increased juvenile cross-immunity from other viruses. We found no evidence for differences between age groups in susceptibility to infection or infectiousness to others (given disease state), i.e. the only important factor in the age dependence of hospitalization rates is the probability of hospitalization given infection. These findings suggest the existence of a T-cell exhaustion threshold, proportional to thymic output and that clonal expansion of peripheral T-cells does not affect disease risk. The strikingly simple inverse relationship between risk and thymic T-cell output adds to the evidence that thymic involution is an important factor in the decline of the immune system with age and may also be an important clue in understanding disease progression, not just for COVID-19 but other diseases as well.

Thymic involution and rising disease incidence with age.

For many cancer types, incidence rises rapidly with age as an apparent power law, supporting the idea that cancer is caused by a gradual accumulation of genetic mutations. Similarly, the incidence of many infectious diseases strongly increases with age. Here, combining data from immunology and epidemiology, we show that many of these dramatic age-related increases in incidence can be modeled based on immune system decline, rather than mutation accumulation. In humans, the thymus atrophies from infancy, resulting in an exponential decline in T cell production with a half-life of ∼16 years, which we use as the basis for a minimal mathematical model of disease incidence. Our model outperforms the power law model with the same number of fitting parameters in describing cancer incidence data across a wide spectrum of different cancers, and provides excellent fits to infectious disease data. This framework provides mechanistic insight into cancer emergence, suggesting that age-related decline in T cell output is a major risk factor.