Introduction

Resident memory T cells (T_RM) provide immune surveillance and protection in tissues and mucosal surfaces through-out the body (Szabo et al., 2019), but the mechanisms by which they are maintained are not well understood. Conventional CD4⁺ and CD8⁺ T_RM in mice and humans are not intrinsically long-lived, but appear to self-renew slowly as assessed by readouts of cell division such Ki67 expression or BrdU incorporation, at levels that vary across tissues (Gebhardt et al., 2009, Watanabe et al., 2015, Park et al., 2018, Strobl et al., 2020, Divito et al., 2020, Christo et al., 2021). After infection or immune challenge, the numbers of elicited T_RM may also be sustained by influx from precursor populations, although the extent to which this occurs is unclear, and is likely also cell subset- and tissue-dependent. For example, in the lung there is evidence both for (Zammit et al., 2006, Ely et al., 2006, Slütter et al., 2017, Van Braeckel-Budimir et al., 2018, Takamura and Kohlmeier, 2019) and against (Takamura et al., 2016, van Dorp et al., 2024) ongoing recruitment of new T_RM following respiratory virus infections. Within skin, T_RM may be renewed or supplemented slowly from precursors in the setting of graft-versus-host disease (Divito et al., 2020), and from circulating central memory T_CM or effector memory T_EM following infection or sensitisation (Gaide et al., 2015, Matos et al., 2022). In the small intestine, however, resident CD4⁺ and CD8⁺ T_RM appear to persist for months to years with slow self-renewal without appreciable influx (Bartolomé-Casado et al., 2019, 2021).

The dynamics of production and loss of T_RM in the steady state are even less well understood, and measuring these processes is important for several reasons. The balance of loss and self-renewal defines the persistence of clonal populations and hence the duration of protective immunity. Further, while self-renewal can at best preserve clonal diversity within a tissue site, any supplementation or replacement by immigrant T_RM will perturb the local TCR repertoire. In particular, any significant influx into T_RM niches in the absence of overt infection may be a competitive force, potentially reducing the persistence T_RM previously established in response to infection or challenge.

The kinetics of circulating memory T cells have been quantified extensively in both mice and humans, using dye dilution assays (Choo et al., 2010), deuterium labelling (Westera et al., 2013, 2015, del Amo et al., 2018, Baliu-Piqué et al., 2018, 2019, van den Berg et al., 2021), and BrdU labelling, either alone (Younes et al., 2011, Ganusov and De Boer, 2013) or in combination with fate reporters (Gossel et al., 2017, Hogan et al., 2019, Bullock et al., 2024) or T cell receptor excision circles (den Braber et al., 2012). Using mathematical models to interpret these data, these studies identified rates of production, cellular lifespans, and signatures of heterogeneity in turnover. Modelling has also established evidence for continuous replenishment of circulating memory CD4⁺ T cells from precursors throughout life in specific-pathogen-free mice, driven by a combination of environmental, commensal and self antigens (Gossel et al., 2017, Hogan et al., 2019, Bullock et al., 2024). Quantification of T_RM dynamics has to date been restricted largely to measuring the net persistence of CD8⁺ T_RM following infection in mice, in a variety of tissues (Morris et al., 2019, Wijeyesinghe et al., 2021). Far less is known regarding CD4⁺ T_RM, which typically outnumber their CD8⁺ counterparts (Szabo et al., 2019), and there have been very few attempts to dissect the kinetics of either subset (van Dorp et al., 2024).

In general, measuring these basic parameters in isolation is challenging, partly due to their sensitivity to assumptions made in the models (De Boer and Perelson, 2013), but also because division-linked labelling alone may not distinguish in situ cell division and the supplementation of a population from labelled precursors. A more powerful approach is to triangulate information from different readouts of cell fate simultaneously (Bains et al., 2009, den Braber et al., 2012, del Amo et al., 2018, De Boer and Yates, 2023, Bullock et al., 2024).

With these challenges in mind, here we integrated data from two independent inducible fate reporter systems to study CD4⁺ T_RM homeostasis in mice. Each system allows one to track the fates of defined populations of cells and their descendants. One labels all CD4⁺ T cell subsets at any given moment, which effectively provides an age ‘timestamp’. The other labels cells undergoing cell division within defined time windows. In combination, these systems allowed us to establish a quantitative model of the basal homeostatic properties of CD4⁺ T_RM within the skin and the lamina propria of the small intestine in healthy mice. In particular, we could unpick the contributions of self renewal and de novo cell production that underpin their maintenance, and explore their lineage relationships to circulating T cell subsets.

Results

Combining cell fate reporters and models to measure T_RM replacement, loss, and self-renewal

To study the homeostatic dynamics of tissue resident CD4⁺ memory T cells in healthy mice, we used in concert two genetic fate mapping tools in which cohorts of peripheral T cells and their offspring can be induced to express permanent fluorescent markers (Fig. 1A). These reporter strains were previously used separately to study the turnover of naive and circulating memory T and B cells (Verheijen et al., 2020, Lukas et al., 2023, Bullock et al., 2024). In the Ki67^{mCherry-CreERT} Rosa26^RcagYFP system, henceforth Ki67-DIVN, fluorescent reporters are linked to the expression of Ki67, a nuclear protein that is expressed during cell division and for 3 to 4 days afterwards (Gossel et al., 2017, Miller et al., 2018). Specifically, these mice express both a Ki67-mCherry fusion protein and inducible CreERT from the Mki67 locus, together with a Rosa26^RcagYFP Cre reporter construct. Treatment of mice with tamoxifen therefore induces YFP in cells expressing high levels of Ki67, and YFP is then stably expressed by these cells and their offspring. Expression of Ki67-used mCherry gives a constitutive live readout of Ki67 expression, independent of tamoxifen treatment and YFP expression. In the second fate reporter, CD4^CreERT Rosa26^RmTom mice (Cd4-FR), the Cre reporter is constructed such that cells expressing CD4 during tamoxifen treatment permanently and heritably express the fluorescent reporter mTomato (mTom).

Figure 1.

In a closed population of cells at steady state, self-renewal must be balanced by loss and so, following tamoxifen treatment, the frequencies of cells expressing YFP or mTom within any such population would remain constant. Therefore, any decline in the frequency of either reporter among T_RM after treatment must derive from the influx of label-negative cells from an upstream (precursor) population. In the Ki67-DIVN mice, these will be descendants of cells that were not dividing at the time of tamoxifen treatment; in the Cd4-FR mice, labelled CD4⁺ cells will slowly be replaced by the descendants of those generated in the thymus after treatment. The shape of this decline will be determined by the combination of the net loss rate of T_RM from the tissue (through death, egress, or differentiation, offset by any self-renewal), and the label content of immigrant T_RM (Fig. 1B). To refer to the persistence of individual T_RM cells we will use the term ‘residence time’ rather than lifespan, to reflect the multiple potential mechanisms of loss from tissues.

To quantify these processes, we treated cohorts of both reporter mice, aged between 4 and 15 weeks, with single 2mg pulses of tamoxifen (Fig. 1C). Over 9 week (Ki67 reporter) and 57 week (CD4 reporter) chase periods we measured the frequencies of labelled cells among antigen-experienced CD4⁺ T cell subsets isolated from skin and the lamina propria of the small intestine (henceforth LP), and within circulating naive and memory T cell subsets derived from lymph nodes (Fig. S1A). By combining these frequencies with measures of Ki67 expression, and describing the resulting set of time series with simple mathematical models, we aimed to estimate the basic parameters underlying T_RM kinetics.

CD4⁺ T_RM in skin and lamina propria are continuously replaced from precursors

We considered two populations within both skin and LP, identified as tissue-localised by virtue of their protection from short-term in vivo labelling (Methods; Fig. S1B). One was effector-memory (EM) phenotype (CD4⁺CD44^hi CD62L^lo) T cells in bulk, which we studied in order to gain the broadest possible picture of memory cell dynamics at these sites. We also considered the subset of these cells that expressed CD69, a canonical and consistent marker of CD4⁺ T cell residency across multiple tissues (Szabo et al., 2019). We saw no significant changes with mouse age in the numbers of either population within skin (Fig. 2A, p >0.67) or LP (Fig. 2B, p > 0.39). There were also no significant changes in any of these quantities with time since tamoxifen treatment (p > 0.24). We therefore assumed that the skin- and LP-localised T cell subsets we considered were at, or close to, homeostatic equilibrium during the chase period. For brevity, we refer to tissue-localised CD4⁺CD44^hiCD62L^lo in bulk as EM, and the CD69⁺ subset as T_RM.

Figure 2.

During the first few days after tamoxifen treatment, YFP and mTom expression increased continuously within the skin and LP subsets (Fig. 2C), as well as among CD4⁺ naive (CD44^lo CD62L^hi), central memory (T_CM, CD44^hi CD62L^hi) and effector memory (T_EM, CD44^hi CD62L^lo) T cells recovered from lymph nodes (Fig. 2D). These initial increases were driven in part by the intracellular dynamics of the induction of the fluorescent reporters. We therefore began our analyses at day 5 post-treatment, by which time induction was considered complete and the subsequent trajectories of label frequencies reflected only the dynamic processes of cell production and loss. A key observation was that mTom expression within the skin and LP subsets then declined slowly (roughly 7-to 8-fold over the course of a year, Fig. 2C), indicating immediately that these populations were being continuously replaced from precursors. Early in the chase period YFP⁺ T_RM expressed Ki67 at higher levels than YFP⁻ cells, as expected, but Ki67 expression in the two populations converged at later times. We return to the interpretation of these kinetics below.

We then investigated the extent to which the simple model illustrated in Fig. 1B could explain these trajectories. Given the observation of continued recruitment, any time-variation in the label content of T_RM precursors might leave an imprint on the label kinetics of skin or LP T_RM themselves, and thereby help us to identify their developmental pathways. We reasoned that plausible T_RM precursors might be LN-derived CD4⁺ naive, T_CM or T_EM; we also considered the possibilities that CD69⁻ cells within skin and LP are the direct precursors of the local CD69⁺ populations. Therefore, we used empirical functions to describe the timecourses of the frequencies of YFP⁺ and mTom⁺ cells within these five populations (Fig. 2D, black curves), and used them in turn as inputs to the model of YFP and mTom kinetics within the tissues.

For each tissue subset (‘target’) and precursor pair, we fitted the model simultaneously to six timecourses; the frequencies of (i) YFP expression and (ii) mTom expression among target cells, the proportions of Ki67^high cells among (iii) YFP⁺ and (iv) YFP⁻ target cells, and the (v) YFP and (vi) mTom expression kinetics within the precursor (Methods and Supporting Information Text S1). For each precursor/target pair we considered three modes of influx – one in which new immigrant T_RM are Ki67^low (‘quiescent’ recruitment); another in which their Ki67 expression directly reflects that of the precursor (‘neutral’ recruitment), and a third in which immigrants have recently divided (Ki67^high), perhaps through an antigen-driven process (‘division-linked’ recruitment).

Skin and LP CD4⁺ T_RM have similar residence times but exhibit distinct contributions of replacement and self-renewal

For each precursor and mode of recruitment, we were able to estimate rates of influx, residence times and interdivision times (Fig. 3 and Table S1). Overall, we found that the residence times of both EM and T_RM within skin and LP were around 3 weeks and 2 weeks respectively. The means of production of new cells differed at the two sites, however. In skin, around 2% of both populations were replaced daily by influx, comparable to the rates of constitutive replacement of circulating memory CD4⁺ T cell subsets (Gossel et al., 2017, Hogan et al., 2019, Bullock et al., 2024), and self-renewed every 6 and 7 weeks respectively, In contrast, within LP these subsets divided less often (every 7-9 weeks) and relied on higher levels of recruitment (4-6% per day) for their maintenance.

Figure 3.

From these basic quantities we could derive several other important measures of T_RM behaviour. First, the balance of the rates of loss (δ) and self-renewal (ρ) defines the persistence of a cohort of T cells, which is distinct from the lifepan of its constituent cells (del Amo et al., 2018, De Boer and Yates, 2023). Specifically, the quantity ln(2)/(δ− ρ) is the average time taken for a cohort and their descendents to halve in number. While we studied polyclonal populations here, this quantity applies equally well to measuring the persistence of a TCR clonotype, so we refer to it as a clonal half life (Bullock et al., 2024). The substantial rates of self-renewal in skin led to clonal half lives of just over a month. The lower levels of self-renewal and higher levels of replacement in LP resulted in shorter clonal half-lives of 2 weeks.

Importantly, our estimates of these quantities depended to varying degrees on the choice of precursor and mode of recruitment (Fig. 3). For example, intuitively, given the observed level of Ki67 within each target population, the greater the levels of Ki67 within newly recruited cells, the less must derive from self-renewal; hence, if one assumes that recruitment is division-linked, estimated division rates are reduced. Similarly, as discussed above, the label content of the precursor influences the net loss rate of label in the target, which was most clearly reflected in the loss of mTom⁺ cells over the longer chase period (Fig. 2C and D). For example, mTom was lost most rapidly within naive CD4⁺ T cells (Fig. 2D), due to export of label-negative cells from the thymus. Models in which naive T cells were the direct precursor of T_RM therefore predicted greater clonal persistence within tissues.

We saw very little decline in YFP expression levels during the 2 month chase period (Fig. 2C), due in part to the sustained levels of YFP within the putative precursor populations (Fig. 2D), which ‘topped up’ YFP-expressing T_RM. As a result, YFP kinetics within the tissues was not strongly informative regarding rates of replacement. However, the rate of convergence of Ki67 within YFP⁺ and YFP⁻ cells (Fig. 2C) put clear constraints on the duration of Ki67 expression, which at approximately 3 days (Fig. 3) was consistent with previous estimates. This quantity in turn was informative for estimating rates of self-renewal. Further, the observed convergence of Ki67 within these two subsets is consistent with the basic model assumption of homogeneity in the rates of division and loss within skin and LP.

CD4⁺ T_RM within skin and LP may derive predominantly from effector-memory cells in lymph nodes

To more precisely quantify the kinetics of each target population, we assessed the relative support for each combination of precursor and mode of recruitment (Fig. 4A). Each of these weights summarises the magnitude and uncertainty of a model’s out-of-sample prediction error of the label kinetics within the target population (Methods).

Figure 4.

From the candidate set, the weighting strongly favoured a lymph-node derived EM as the closest precursor to CD4⁺ EM within both skin and LP. However, within skin, CD69⁻ cells were the favoured precursor to CD69⁺ T_RM. The evidence was more equivocal regarding the mode in which cells are recruited into skin and LP, although for skin we saw substantial evidence (66% of model support) for a division-linked transition from CD69⁻ to CD69⁺ cells. Fig. 4B summarises the developmental trajectories and kinetics of T_RM in skin and LP that were supported most strongly by our analyses. Parameter estimates and credible intervals for these models are highlighted with vertical shaded regions in Fig. 3 and are detailed in Supporting Information, Table S1.

Validation of residence times through use of Ki67 expression directly

As a consistency check, when a population is at or close to steady state, bounds on the mean residence time of cells can be estimated using only the measured frequency of Ki67 within the target population, and the daily rate of replacement (Supporting Information, Text S2). In skin, both EM and T_RM are replaced at the rate of 2% per day, and have Ki67 expression frequencies of around 0.15 (Fig. 2C). The approximation then yields residence times in the range 22-28 days, depending on whether immigrant T_RM are Ki67^low or Ki67^high respectively. In LP, with 5.5% daily replacement and Ki67 frequencies of around 0.07, we estimate residence times of 14-25 days. Both estimates are in good agreement with those from the model fitting. Further validation of these results, and dissection of the kinetics, might be achieved my manipulating cell trafficking, although this would potentially impact multiple processes at once. For example, treating mice with the sphingosine 1-phosphate receptor agonist FTY720 would block tissue ingress and egress. This would leave self-renewal as the only means of T_RM production, and would also remove the component of the cell loss rate that is due to cells leaving the tissue. In principle one could then gain estimates of the intrinsic lifespan of T_RM, rather than their tissue residence time. However, parameter estimation would then require accurate measurements of cell numbers within the tissue.

Discussion

Our analysis indicates that CD4⁺ T_RM are not intrinsically long-lived, but instead are sustained by both self-renewal and supplementation from circulating precursors. By combining fate reporting methods with mathematical models, we also showed that it is possible to separately quantify the processes that underlie their persistence. We saw quite distinct contributions of recruitment and self-renewal of both subsets within skin and LP. The basis of this difference is unclear, but we speculate that the large antigenic burden within the small intestine drives the higher levels of T_RM recruitment and clonal erosion within the lamina propria. We showed that the precise values of these quantities depend on the identity of any precursor, whose label kinetics propagate downstream into the population of interest; and the extent of any cell division that occurs around the time of differentiation or ingress. However, by using simple, easily interpretable mathematical models, and assessing the statistical support for each, we were able to identify different pathways and modes of recruitment into each subset.

Our analysis was rooted in the observation of steady-state numbers of CD4⁺ subsets within these tissues, which dictates that immigration of new cells must be accompanied by loss of existing cells. Estimates of residence times may be quite different if homeostatic niches are flexible in size. For example, repeated vaccinia virus challenges can drive progressive increases in the numbers of virus-specific CD8⁺ T_RM in skin (Jiang et al., 2012), and heterologous challenges appear not to erode pre-existing LCMV-specific CD8⁺ T_RM (Wijeyesinghe et al., 2021). Whether the same principles apply to CD4⁺ T_RM following repeated challenges is unclear.

Our goal here was to assess the support for external replenishment of effector-memory-like CD4⁺ T cells in bulk, and the dominant CD69⁺ T_RM subset. With richer phenotyping of tissue-localised cells, we could in principle use the labelling trajectories to further dissect these differentiation pathways, in the same way that we saw evidence that CD69⁺CD4⁺ T_RM in skin may be fed from a local CD69⁻ precursor. One issue is that accurate measurement of label frequencies becomes more difficult as one resolves T_RM into finer-grained populations, and indeed we saw that label kinetics within the small CD69⁻ populations were relatively noisy. Another issue is that label kinetics in these reporter mice operate on the timescales of the net loss rate of cell populations – loss balanced by self-renewal. More frequent sampling would be required to resolve more transitory intermediates. Nevertheless, our study shines a light upon the highly dynamic nature of CD4⁺ T_RM, sustained throughout life by both self renewal and continued influx from precursors. The tissue-specific contributions of these processes may contribute to the differential longevity of immunity at different barrier sites.

Methods

Reporter mouse strains

Ki67^{mCherry-CreERT} Rosa26^RcagYFP (Ki67-DIVN) and CD4^CreERT Rosa26^RmTom (Cd4-FR) mice have been described previously (Bullock et al., 2024). Experimental Ki67-DIVN mice were homozygous for indicated mutations at both the Ki67 and Rosa26 loci. Experimental CD4-FR mice were heterozygous for the indicated mutations and both the CD4 and Rosa26 loci. Tamoxifen (Sigma) was diluted to 20mg/mL in corn oil (Fisher Scientific) and 100μl (2mg) was administered to mice via oral feeding on day 0. Ki67-DIVN mice were injected with 2μg Thy1.2-BV510 (53-2.1) (BioLegend) 3 minutes prior to sacrifice to label T cells in the circulation. Mice were subsequently taken down at specified timepoints post tamoxifen treatment for organ collection.

Cell preparation

All peripheral lymph nodes (LN), the small intestine (SI) and ear skin were taken from mice and processed into single cell suspensions. LNs were mashed through 2 pieces of fine gauze in a petri dish and washed with complete RPMI (ThermoFisher) supplemented with 5% FCS (ThermoFisher) (cRPMI). Cells were resuspended in cold PBS and counted using the CASY counter (Cambridge Bioscience). Peyer’s patches were excised from the antimesenteric side of the SI before being opened longitudinally and SI contents scraped out. SI pieces were placed in 20mL pre-warmed extraction media (cRPMI + 10mM HEPES (ThermoFisher) + 5mM EDTA (Sigma) + 1mM DTT (Abcam)) and incubated in 37^° C shaking incubator for 30 minutes at 200rpm. Cells were filtered over 70μm cell strainer (supernatant containing intra-epithelial lymphocytes not used), and SI pieces were placed in cold cRPMI supplemented with 10mM HEPES and allowed to settle. Supernatant was carefully poured off and SI pieces were finely minced, added to 20mL pre-warmed digestion media (RPMI + 10% FCS + 1.5mg/mL collagenase VIII (Sigma)) and incubated in 37^° C shaking incubator for 30 minutes at 200rpm. After digestion, cells were passed through 70μm cell strainer and washed with cRPMI + 10mM HEPES. The resulting cell suspension contains cells from the lamina propria (LP) of the SI. Ear skin was excised and separated into dorsal and ventral sides. Skin was finely minced, added to 4mL digestion buffer (cRPMI + 50mM HEPES + 37.5μg/mL Liberase TL (Merck)+ 3.125mg/mL collagenase IV (ThermoFisher)+ 1mg/mL DNAse I (Merck)) and incubated in 37^° C C shaking incubator for 2 hours at 200rpm. Cells were filtered over 70μm cell strainer and washed through with cRPMI.

Flow cytometry

All cells isolated from skin and LP, and 5 × 10⁶ LN cells, were stained for analysis by flow cytometry. Cells were stained in 100μl PBS with combinations of: CD8α-BUV395 (53-6.7), CD25-BUV395 (PC61), CD62L-BUV737 (MEL-14), TCRγδ-BV421 (GL3), CD103-BV786 (M290) (all BD Biosciences); CD25-BV650 (PC61), CD103-BV421 (2E7), CD8α-BV570 (53-6.7), TCRγδ-BV605 (GL3), NK1.1-BV650 (PK136), CD44-BV785 (IM7), CD45.1-BV605 (A20), CD4-BV711 (RM4-5), CD8b.2-APC (53-5.8) (all BioLegend); TCRβ-PerCPCy5.5 (104) (Cambridge Bioscience); CD44-APCef780 (IM7) (eBioscience); and CD3-APCef780 (2C11), CD3-biotin (145-2C11), CD69-PeCy7 (H1.2F3), CD45.2-AF700 (104), nearIR live/dead, blue live/dead, yellow live/dead (all ThermoFisher). Cells were fixed for 20 minutes with IC fix (Invitrogen) and washed twice in FACS buffer (PBS + 0.1% BSA). Flow cytometric analysis was performed on either a Cytek Aurora spectral flow cytometer or a conventional BD LSR-Fortessa and analysed using FlowJo software (Treestar).

Cell count calculations

Cell counts of LN populations were calculated by dividing the event count in a target population by the event count of live cells, multiplied by the total live cells in LN prep determined by CASY counter. Cell counts of LP and skin populations were calculated using AccuCount (Spherotech) counting beads that were spiked into the sample prior to acquisition, as per manufacturer’s instructions.

Mathematical Modeling and Statistical Analysis

We fitted simultaneously the kinetically homogeneous mathematical model illustrated in Fig. 1B, and described in Supporting Information Text S1, to the time courses of frequencies of YFP⁺, Ki67^high in YFP⁺, Ki67^high in YFP^-, and mTom⁺ cells in the target populations; and empirical descriptor functions describing the trajectories of the frequencies of YFP⁺ and mTom⁺ precursors (all data shown in Fig. 2C). We used a Bayesian estimation approach using Python and Stan (Stan Development Team, 2024) to perform these model fits. Code and data used to perform model fitting, details of the prior distributions for parameters, and figure generation notebooks are available at https://github.com/elisebullock/CD4TRM. Models were ranked based on information criteria estimated using the Leave-One-Out (LOO) cross validation method (Vehtari et al., 2017). See Supporting Information, Text S1 for full details.

Acknowledgements

This work was supported by the National Institutes of Health (R01 AI093870 and U01 AI150680) and the Medical Research Council (MR/P011225/1).

Additional files

Supporting Information

Note

This reviewed preprint has been updated to correct the author list.