Spatiotemporal regulation of energetic charge dictates T cell function

Domain: chemistry
Published: 2026-01-21
Editorial outcome: major_revision

Abstract
Immune cell functions are dictated by their differentiation state and regulated by transcriptional and epigenetic changes. Immune cell differentiation also controls the preferential metabolic pathways used for energy production. However, whether the energy charge of individual immune cells itself varies across time and space and regulates cell function remains to be fully understood. Here, we show that T cells harbor distinct energetic resources and function in different anatomical locations and times of the day. To monitor ATP:ADP ratio, an indicator of cellular energetic resources, we rely on SPICE-Met, a method that dissects energy metabolism in complex cell populations in vivo. We find that cells with the highest glycolytic capacity, including effector T cells and NK cells, exhibit the highest ATP:ADP ratio. Importantly, effector T cells but not naïve T cells display higher energetic charge when present in the blood compared to lymph nodes due to differential glucose availability. Energetic resources are also regulated in a circadian manner, being highest at the early rest phase. Importantly, differences in energetic charge are directly translated at the level of T cell function, impacting IFN-γ production. Thus, modulation of energetic charge and nutrient availability dictates immune cell function across time and space.

Introduction
Immune cells exert a variety of effector functions based on their differentiation state, regulated by transcriptional and epigenetic changes. In addition, cell differentiation is also often associated with unique metabolic profiles, including specific metabolic pathways used for energy production1. For example, naïve or central memory T cells largely rely on OXPHOS while effector T cells primarily use glycolysis for energy production2. Within the cell, energy relies on generation of ATP, a key molecule used to store chemical energy and fuel a variety of cellular processes. Upon hydrolysis, ATP is converted to ADP, and energy is released3. Therefore, the cellular ATP:ADP ratio reflects the capacity of the cell to maintain its energetic resources and can be considered as a proxy for the cell energetic status or charge4–6. It remains, however, unclear whether a given immune cell, once differentiated, displays substantial changes in energetic charge during its lifespan, due, for example, to variations in nutrient availability. In fact, even at homeostasis, immune cells are likely to experience substantial nutrient fluctuations. For example, many immune cells, including T cells, migrate between secondary lymphoid organs, blood, and peripheral tissue7, which may each offer specific nutrient niches. Moreover, the niches themselves can potentially be subjected to nutrient oscillations due to circadian changes in physiology8. How changes in nutrient exposure across time and space impact the cellular energetic charge and function of immune cells remain to be explored.

Addressing these questions remains challenging. While a variety of approaches can provide information on immunometabolism9, few are dedicated to studying the cellular energetic charge and dependence on metabolic pathways and nutrients. We recently described SPICE-Met, a methodology to simultaneously profile energy metabolism in complex populations, providing single-cell information on ATP:ADP ratio and dependence in metabolic pathways and nutrients10. SPICE-Met is based on a transgenic mouse (SPICY mice) expressing a fluorescent reporter for ATP:ADP ratio11 in immune cells and the use of specific metabolic inhibitors10. SPICE-Met give results comparable to SeaHorse for example, regarding energy metabolic pathways, but can be performed on multiple defined subpopulations without cell sorting and with minimal sample processing time10.

Here, we uncovered that the energetic charge of effector T cells is a dynamic parameter, influenced by anatomical site, nutrient availability, and circadian time. Changes in energetic status across time and space critically impacted effector functions, revealing a new parameter tuning immune responses.

Results
Evaluating the relative and net contribution of metabolic pathways and substrates on cellular energetic resources
ATP:ADP ratio is an essential parameter of cellular energy status4. To explore how energetic resources impact immune cell function, we relied on SPICE-Met10, a method that used transgenic mice (SPICY mice) expressing the PercevalHR11 fluorescent reporter for ATP:ADP ratio. Competitive binding of ATP and ADP to the probe is reflected by changes in fluorescence signals collected by excitation at two different wavelengths, yielding an estimated value for the cellular energetic charge. These ratiometric measurements are therefore largely independent of the probe expression level. When used in flow cytometry, these mice allow for direct measurement of energetic resources in multiple cell populations simultaneously (Fig. 1A). Moreover, changes in ATP:ADP reporter activity upon treatment with metabolic inhibitors reveal dominant pathways and substrates contributing to energy production. For example, changes in ATP:ADP ratio upon treatment with oligomycin (which blocks mitochondrial respiration) reflect the dependence on OXPHOS versus glycolytic capacity (Fig. 1B). Similarly, treatment with iodoacetic acid or 2-DG (which block glucose metabolism) reveal cell dependence on glucose versus alternative energy substrates (amino-acids, or fatty acids) (Fig. 1C). Drops in ATP:ADP ratio upon inhibitor treatment can be expressed as percentage of total energy reserve (relative contribution) (Fig. 1B, C). Since ATP:ADP ratio may vary between cell types, it is important to consider how individual pathways quantitatively contribute to the raw value of ATP:ADP ratio, and we referred to these values as net contributions.

Diversity of energy metabolism of immune cells in the blood
We rely on this methodology to profile energetic resources in immune cells in the blood. Interestingly, we observed extensive variability in cellular ATP:ADP ratio among leukocyte populations with the highest values observed in B cells, neutrophils, and effector lymphocytes (T cells, NK cells and NKT cells) (Fig. 1D). By contrast, quiescent T cells (naive and central memory) exhibit lower ATP:ADP ratios. Next, we assessed the link between ATP:ADP ratios and metabolic pathways and substrates (Fig. 1E, F). Overall, we found that immune cells with the highest ATP:ADP ratio exhibited elevated net contribution of glycolysis and glucose metabolism. As expected, naive and central memory T cells almost exclusively relied on OXPHOS (high relative contribution) but the net contribution of OXPHOS to the ATP:ADP value remained similar (if not lower) to that seen in effector cells. This observation highlights the importance of measuring changes in ATP:ADP ratio both qualitatively (relative contribution) and quantitatively (net contribution). In search for possible correlation between ATP:ADP ratio and other metabolic parameters, we found that the net (but not the relative) contribution of glycolysis was best correlated (Fig. 1G).

In sum, we revealed extensive differences in ATP: ADP ratio of coexisting immune cells in the blood that originated for distinct quantitative input of metabolic pathways and nutrients.

Anatomical regulation of lymphocyte energetic resources
Most lymphocyte populations can recirculate between the blood, lymphoid organs and/or peripheral tissues. Yet, the extent to which energetic resources of individual immune cells are intrinsically determined or influenced by their anatomical location is unclear.

To address this question, we compared energetic status and metabolism of lymphocyte populations, isolated from blood and lymph nodes (LN) that both contain numerous lymphocyte subsets (Fig. 2A). While naïve and central memory T cells exhibited similar ATP:ADP ratio in both locations, we observed that the cellular energetic charge of effector lymphocyte populations including CD8+ effector T cells, NK and NKT cells were remarkably higher in the blood (Fig. 2B, C).

A deeper look into their metabolism revealed that effector T cells, NK, and NKT cells displayed a higher net contribution of glycolysis in the blood compared to LNs, and to a lower extent, an increase in the net contribution of OXPHOS. By contrast, the contribution of metabolic pathways in naïve T cells remained relatively similar between the blood and LNs (Fig. 2B, C).

To extend our results to an ongoing antigen-specific T cell response, we relied on a vaccine setting12 using the MVA vector (Fig. 2D). Briefly, mice were injected with MVA-based vaccine, and the ATP:ADP ratio was measured among MVA-specific effector-like (CD44+CD62L-) and central memory-like (CD44+CD62L+) CD8+ T cells at day 7. Reminiscent of what we observed at steady-state, antigen-specific T cells with an effector phenotype (but not central memory-like T cells) showed increased ATP:ADP ratio in the blood, compared to the LNs (Fig. 2E, F). These changes were accompanied by increased glycolysis, OXPHOS, and nutrient dependencies (Fig. 2E-F). Together, these data revealed that lymphocytes with similar phenotypes can possess different energetic charges in distinct anatomical locations.

Tissue microenvironment dictates T cell energetic resources
The different energetic resources observed for effector/effector memory T cells (CD44+CD62L-) present in the blood and lymph nodes could possibly be explained by preferential homing of distinct T cell subpopulations. In this case, an energetic resource would be an intrinsic property of T cells. Alternatively, a given T cell may harbor different energetic charge upon relocation in different anatomical sites. Under this hypothesis, the tissue microenvironment primarily dictates ATP:ADP ratio. To distinguish between these possibilities, we assess how energetic resources of effector T cells isolated from lymph nodes evolve upon adoptive transfer, as these cells redistribute between blood and lymph nodes of the recipient (Fig. 3A). We measured metabolic parameters from transferred lymph node cells in the blood and lymph nodes of the host as early as 3 hours after the transfer (Fig. 3B). Strikingly, effector CD4+ T and CD8+ T cells showed increased ATP:ADP ratio in the blood as compared to lymph nodes together with increased contribution of OXPHOS and glycolysis (Fig. 3C, D). On the other hand, transferred naïve CD4+ and CD8+ T cells maintained the same ATP:ADP ratio upon redistribution in the blood and lymph nodes, in good agreement with steady state results (Fig. 3C, D). Thus, the tissue microenvironment dictates cellular energetic resources in effector T cells.

Glucose availability contributes to the anatomical regulation of immune cell energetic resources
As glycolysis and glucose consumption a hallmarks of effector T cells (Fig. 1E), we tested whether glucose concentration could be a key microenvironmental parameter that accounted for the observed differences in the ATP:ADP ratio measured in the blood and lymph nodes. It has been shown that immune cells upregulate the glucose transporter GLUT1 in glucose-poor microenvironment13–16. We therefore evaluated the expression of GLUT1 in blood and LN lymphocytes (Fig. 4A). Interestingly, for all tested lymphocyte populations including T cells, NK cells and NKT cells, GLUT1+ cells were barely detectable in the blood. By contrast, GLUT1+ cells were readily found in lymph nodes, representing for example, up to 40% of CD8+ effector T cells (Fig. 4A–C). Similar results were obtained using 3 distinct anti-GLUT1 Abs (Supplementary Fig. 1). Consistent with the idea the GLUT1 expression is upregulated under energetic stress, we observed an overall decreased ATP:ADP ratio in GLUT1+ cells as compared to the GLUT1- counterpart (Fig. 4D–F). Consistently GLUT1+ CD8+CD44+CD62L- effector/effector memory T cells showed low mTOR activity (Supplementary Fig. 2A, B). On the other hand, adoptively transferred GLUT1+ effector CD8+ T cells exhibited increased intracellular glucose (Supplementary Fig. 2C). These results raise the possibility that lymphocytes in the LNs microenvironment are subjected to metabolic stress, leading to upregulation of GLUT1 to overcome glucose deprivation. Of note, naïve T cells only modestly upregulated GLUT1 in the lymph node (Fig. 4C), consistent with their low dependence on glucose for energy production (Fig. 1E, F). To directly assess glucose concentration within immune cells located either in the blood or lymph nodes, we employed a recently developed glucose reporter iGluco17 that changes fluorescent spectrum upon glucose binding. Specifically, we transferred activated T cells (containing both CD4+ and CD8+ cells) transduced with iGluco probe and monitored iGluco activity upon their relocation in the blood or lymph nodes (Fig. 4G, H). We found that T cells in the blood exhibited a higher intracellular glucose concentration compared to lymph nodes (Fig. 4I). Moreover, we noted that iGluco fluorescence was directly reflecting glucose concentration in the extracellular milieu, supporting the idea that the LN microenvironment is glucose-poor compared to the blood.

Having shown that effector T cells experience distinct glucose levels in blood versus lymph node, we assessed the impact of nutrient availability on T cell energetic charge using defined media compositions (Fig. 4J). Remarkably, blood effector T cells displayed a rapid drop in energetic resources in conditions of reduced glucose availability (glucose-free media, 2DG or Glutor (inhibitor of GLUT1-mediated glucose uptake) treatment) (Fig. 4K, L). This was not the case for effector T cells present in the lymph node. In these cells, we saw a strong upregulation of fatty acid/amino acid usage upon GLUT1 blockade that potentially explains the compensatory increase in ATP:ADP. Conversely, increasing the glucose concentration directly enhanced ATP:ADP levels in blood effector/effector memory CD8+CD44+CD62L- T cells (Supplementary Fig. 3). Effector T cells in lymph nodes exhibit low energetic resources at steady-state and were less sensitive to glucose deprivation (Fig. 4K, L). As expected, naïve T cell ATP:ADP ratio was largely independent on glucose (Supplementary Fig. 4), likely explaining their similar energetic charge in the blood and lymph nodes. Taken together, our data suggest that differential availability of glucose in the blood versus lymph node is the major determinant of differential energetic charges.

Circadian regulation of lymphocyte energetic charges
Immune cell trafficking and function have been shown to be regulated in a circadian manner18–20. We hypothesized that some aspect of the circadian regulation of immunity may be linked to fluctuations in energetic resources. We subjected mice to shifted light regimen for three weeks to be able to simultaneously profile energy metabolism at different zeitgeber time (ZT1 – 1 hour after beginning of the light phase, ZT5, ZT13 – 1 hour after beginning of the dark phase and ZT20) (Fig. 5A). Successful light entrainment was confirmed by observing expected circadian variations of immune cell counts (Supplementary Fig. 5). We next focused on cellular bioenergetics. For CD4+ and CD8+ effector T cells as well as NK and NKT cells, the energetic charge remained higher in the blood as compared to lymph nodes throughout the day (Fig. 5B, C). Remarkably, lymphocyte energetic resources substantially varied across the day with ATP:ADP ratio changing by two-fold between the minimal and maximal value (Fig. 5B, C). These changes appeared to be of circadian nature as they persisted in constant darkness (Supplementary Fig. 6). For effector cells, change in energetic resources reflected fluctuation in glucose metabolism (Fig. 5B, C) and correlated with diurnal oscillations of glucose concentration in the blood (Supplementary Fig. 7A). Our data suggested a link between oscillations in T cell energetic charge and nutrient intake. To address this point, we compared cellular energetic charge in fed versus fasted mice at ZT1 (corresponding to maximal values of ATP:ADP). As expected, fasted mice displayed lower blood glucose concentrations compared to fed animals (Supplementary Fig. 7B). Importantly, a substantial decrease in energetic charge was observed upon fasting for effector T cells and NK cells while naïve T cells were not affected (Supplementary Fig. 7C). The reduction of ATP:ADP values were also associated with diminished effector T cell size (Supplementary Fig. 7D). Together, our results reveal strong diurnal fluctuations in lymphocyte energetic status, possibly regulated by the availability of glucose or other nutrients.

T cell energetic charge controls effector function
Having shown extensive variations in immune cell energetic resources in time and space, we explored whether these changes would translate into different functional capacities. First, we thought to link the differences in T cell energetic charge in blood versus lymph nodes with their capacity to produce the effector cytokine IFN-γ (Fig. 6A). To this end, activated T cells were adoptively transferred and harvested a day later from the blood and lymph nodes of the recipient. As shown in Fig. 6B T cells recovered from the blood were larger and expressed higher level of IFN-γ and perforin as compared to their lymph node counterparts. A similar hierarchy was observed after in vitro restimulation (Fig. 6C–E).

Second, we tracked T cell production of IFN-γ throughout the day and night. We revealed that the highest levels of cytokine production were achieved at ZT1, corresponding to the time of maximal energetic charge (Fig. 6G). Of note, changes in IFN-γ production are slightly lagging behind variations of energetic charge, possibly because cytokine levels may result from the integration of cell bioenergetic capacity over the previous hours.

To directly manipulate cellular energetic resources, we altered effector T cell ATP:ADP ratio using multiple combinations of the metabolic inhibitors oligomycin and 2-DG (Fig. 6H, I, Supplementary Fig. 8). For each condition, we recorded the ATP:ADP value and IFN-γ production. Of note, we observed metabolic plasticity in effector T cells as energetic status is primarily affected upon simultaneous inhibition of OXPHOS and glycolysis (Fig. 6I, Supplementary Fig. 8) as previously reported21. Overall, IFN-γ production declined together with decrease in energetic status (Fig. 6I) with a dynamic range of two-fold as also observed in Fig. 6D. A similar pattern was observed when measuring T cell size and intracellular perforin expression as a function of the energetic charge (Supplementary Fig. 9) extending the importance of ATP:ADP ratio to additional hallmarks of T cell activation/function. Of note, reducing glucose concentration by 3-fold, was associated with a trend toward decreased intracellular IFN-γ content in effector CD8+ T cells (Supplementary Fig. 10), suggesting a contribution of glucose availability to T cell energetic charge and effector functions. Together, these experiments established local and temporal variation in energetic charge as a major determinant of T cell function.

Methods
Mice
C57BL/6J mice, aged 6 to 8 weeks, were purchased from ENVIGO and housed in the animal care facility at Institut Pasteur under specific pathogen-free conditions. Vav-iCre x Percevalfl/fl (SPICY), GFP-OT-I-Rag1-/-, CD45.1 mice were bred on-site at our facility. Experimental/control animals were co-housed. Both male and female mice were used, except for circadian experiments, which were conducted on males. Mice have been euthanized using a CO2 chamber in agreement with ethical requirements. All animal experiments were conducted with approval from the Institut Pasteur Safety Committee, in compliance with French and European guidelines (CETEA 230038) and were validated by the French Ministry of Education and Research.

Sample preparation
Blood was collected by 0.5G insulin syringes containing 50µL of 2mM EDTA by cardiac puncture. 1mL of blood sample was processed using RBC lysis buffer (eBioscience), following manufacturer's instructions. Lymph nodes were crushed on 70µm cell strainers (Corning) with Injekt Luer Solo 2mL syringes plug, lymphocytes have been collected in R-10 complete media (RPMI 1640 Glutamax without phenol-red; 10% heat-inactivated FCS,; 50µ/ml Penicillin-streptomycin; 10mM HEPES, 50µM b-mercaptoethanol, Gibco) and centrifuged. After the last centrifugation, blood and lymph node cells were resuspended in R-10 complete media for further staining.

SPICE-Met Flow cytometry
Cells expressing PercevalHR were analyzed by flow cytometry. ATP:ADP ratios (R) in individual cells were calculated from two fluorescence signals. In brief, excitation with a violet laser (405nm) and signal detection with a 525 band-pass filter were used to measure ADP contribution, while ATP levels were estimated by excitation with a blue laser (488nm) and signal detection with a 525 band-pass filter. To calculate ATP:ADP ratio, we derived a new parameter (FlowJo) corresponding to fluorescence signals collected in the ATP channel divided by the fluorescent signals from the ADP channel. To reveal OXPHOS/glycolysis contribution, cells have been treated for 10 min at 37°C with 1µM oligomycin (oligo, Sigma); to reveal glucose/fatty and amino acids contribution, cells have been treated with 1mM iodoacetic acid (IA, Sigma) or 100mM 2-deoxy-D-glucose (2DG, Sigma). A combination of oligomycin with iodoacetic acid treatment was used to reveal the minimal level of energetic resources. Control samples were treated with H2O and DMSO. Relative OXPHOS/glycolysis dependencies were calculated as ((R DMSO+H2O - R Oligomycin)/(R DMSO+H2O - R Oligo+IA)) × 100 and ((R Oligomycin - R Oligo+IA)/(R DMSO+H2O - R Oligo+IA)), respectively. Absolute OXPHOS/glycolysis dependencies were calculated as (R DMSO+H2O - R Oligomycin) and (R Oligomycin - R oligo+IA), respectively. Relative glucose/fatty and amino acids dependencies were calculated as ((R DMSO+H2O - R IA)/(R DMSO+H2O - R Oligo+IA)) × 100 and ((R IA - R Oligo+IA)/(R DMSO+H2O - R Oligo+IA)), respectively. Absolute glucose/fatty and amino acids dependencies were calculated as (R DMSO+H2O - R IA) and (R IA - R oligo+IA), respectively.

MVA vaccination
The recombinant MVA-HIV-B, which encodes full-length HIV Gag along with three Pol and two Nef fragments12 was supplied by the Agence Nationale de Recherche sur le Sida (ANRS). Mice were injected with 2 × 10⁶ p.f.u. of MVA-HIV-B via footpad injection.

Adoptive transfer
Lymphocytes were isolated from lymph nodes of Vav-iCre x Percevalfl/fl (SPICY) mice (expressing CD45.2 congenic marker), and the resulting cell suspension was injected intravenously (2 × 107 cells per mouse) in CD45.1 animals of the same sex. Recipients were killed 3h later, and lymphocytes isolated from blood and lymph nodes were processed for SPICE-Met profiling.

Generation of T cells expressing the glucose reporter iGluco
The construct of encoding the iGlucoSnFR-mRuby2 (iGluco) glucose probe17 was obtained from Addgene and subcloned into the retroviral vector MSCV. T cells were activated in plates coated with anti-CD3 monoclonal antibody (mAb) (2.5μg ml−1; clone 17.A2; BioLegend) in the presence of soluble anti-CD28 mAb (2.5μg ml−1; clone 37.51; BioLegend) and murine IL-12 (10ng ml−1; I8523; Sigma-Aldrich)36. Two rounds of spin infection were performed at 24 and 48 hours after T cell activation using retroviral particles supplemented with polybrene (8μg ml−1; Merck). T cells were cultured for four additional days in the presence of hIL-2 (10ng ml−1; 202-IL; R&D Systems). Calibration of the iGluco probe was performed in R10 glucose-free media, supplemented with different glucose concentrations (D-Glucose, Sigma).

Impact of nutrient availability
Lymphocytes, isolated from Vav-iCre/Percevalfl/fl (SPICY) mice, were isolated and stained as described above. Later, they were subjected to different media for at least 20 min at 37°C 5% CO2 incubator, and their ATP:ADP ratio was analyzed. Media were prepared based on the SILAC RPMI 1640 Flex Media without glucose (Gibco), with the following additives, according to experimental condition: D-glucose (Gibco), fatty acids supplement (1:100, Sigma), GlutaMAX (1:100, Gibco), Glutor (4µM, Sigma), 2DG (100mM, Sigma). HEPES, penicillin, streptomycin and b-mercaptoethanol were added in all conditions, as described above for complete R-10 media composition. The addition of metabolic inhibitors and the reading of ATP:ADP ratio were performed directly in the experimental media.

Circadian experiments
In all cases, Vav-iCre/Percevalfl/fl (SPICY) and C57BL/6J male mice were entrained to a 12-h light:12-h dark cycle for at least 3 weeks before experimentation, using Leddy programmable IVC lighting control system (Techniplast). Zeitgeber time (ZT) 0 was defined as the time of lights on, and ZT12 is the time of lights off.

Generation of effector OT-I CD8+ T cells
In vitro OT-I T cell stimulation was performed during 4h in complete R10 media, supplemented with 0.1% brefeldin (BD GolgiPlug, BD Biosciences) and, according to experimental conditions, supplemented with phormol 12-myristate 13-acetate (0.02µg/mL PMA, Sigma) and ionomycin calcium salt from Streptomyces conglobatus (0.5µg/mL, Sigma); or plated on anti-CD3 antibody coated plates (2.5µg/mL, clone 17A2, Biolegend) and, in this case, supplemented with anti-CD28 (2.5µg/mL, clone 37.51, Biolegend). In case of N4-peptide restimulation, SIINFEKL peptide (10µM, AS-60193-5, Anaspec) was used.

Flow cytometry
For standard cell surface staining, the cells were incubated in R-10 complete media. All antibodies were purchased from Biolegend, if not noted otherwise. For extracellular staining cells have been incubated for 20min at 4°C with constant agitation with CD8-BUV395 (1:200, clone 53-6.7, BD Biosciences), CD11b-BUV736 (1:200, clone M1/70, BD Biosciences), CD44-BV605 (1:200, clone IM7), CD4-BV786 (1:200, clone RM4-5), NK1.1-BV650 (1:200, clone PK136), TCRβ-APCCy7 (1:200, clone H57-597), Ly6G-Alexa647 (1:200, clone 1A8), CD19-PECy7 (1:200, clone 6D5), CD62L-BV421 (1:200, clone MEL-14), CD45.1-PECy7 (1:50, clone A20), CD45.2-BUV736 (1:50, clone 104, BD Biosciences), GLUT1 (1:50, Sigma 07-1401-AF647 or Almone labs AGT-041 or Biotechne clone 202915), H2-Kb-TSYKFESV MHC:peptide tetramers were generously provided by the National Institutes of Health Tetramer Core Facility. Tetramer staining was conducted first, followed by staining with the antibody cocktail. For intracellular staining, cells have been processed with BD Cytofix/Cytoperm™ Fixation/Permeabilization Kit, according to manufacturer instructions. Incubations with anti-IFN-γ (1:50, clone XMG1.2) and anti-Perforin (1:50, clone S16009B) antibodies were done overnight at +4°C with constant agitation. Isotype controls were PE-Rat IgG1, κ; clone RTK2071 (for IFN-γ staining) and APC-Rat IgG2a, κ; clone RTK2758 (for perforin staining). Dead cells were stained and excluded by fixable viability dye eF780 (1:1000, Invitrogen), added to extracellular antibodies cocktail mix. Data were collected using a Cytoflex LX flow cytometer (Beckman Culture) and analyzed using FlowJo software v10.8.1 (BD Biosciences).

Statistical analysis
All statistical analyses were conducted using Prism v.9.5.0 (GraphPad). Data are presented as mean ± SEM. The Wilcoxon signed-rank test was used to compare paired groups. The Mann-Whitney U test was used for comparing two independent groups or a paired t-test if normality was validated by a Shapiro-Wilk test. For comparison of multiple (>2) independent groups, we used Kruskal-Wallis-Dunn’s test or one-way ANOVA if normality was validated by a Shapiro-Wilk test. All statistical tests were two-tailed with a significance level of 0.05. ∗p<0.05, ∗∗p<0.01, ∗∗∗p<0.001; ns, non-significant.

Graphical design
Figures 1A, 2A, D, 3A, 4A, D, G, J, 5A, 6A, F, H, supplementary Fig. 6A, were created in-house using Adobe Illustrator software. Clock images in Figs. 5A, 6F, supplementary Fig. 6A, were created in BioRender, and are under license CHIKINA, A. (2025) https://BioRender.com/zx9rjpl.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Overall, the authors have addressed my concerns, but I have a few additional comments that should be addressed.\n1. Please state how effector T cells were defined in the new Supplemental Figures 2 and 3, and whether these were CD4+ or CD8+ T cells. Cells defined as effector cells based on CD44 expression under steady state conditions in vivo (i.e., non-inflammatory conditions, which is presumably what was done for Fig. S2A and B) are not equivalent to effector cells that have been activated and expanded in IL-2 prior to adoptive transfer into mice, as done for Fig. S2C. Is mTORC1 signaling and the ratio of ATP:ADP also reduced in Glut1+ compared to Glut1– cells shown in Fig. S2C? If not, the authors need to revise their new conclusions on page 8. This is important to address, as increased glucose transporter expression and glucose import are also characteristics of activated T cells and are not faithful markers of low extracellular glucose or intracellular energetic stress.\n2. There is an issue with Figure 1 that needs to be fixed.\n3. A figure callout needs to be added for this new sentence – “Of note, naive T cells only modestly upregulated GLUT1 in the lymph node, consistent with their low dependence on glucose for energy production.”", "reviewer_2": "The authors have answered adequately to my previous comments. The manuscript is now stronger for publication in Nature Communications\nThe following minor comments should be addressed.\n1. The answer to my previous comment (copy below) should also be incorporated into the manuscript and this will help the reader to interpret properly the results.\n« 6. Figure 4K. How do the authors explain that blocking Glut1 increase the ATP:ADP ratio. If I understand well, blocking Glut1 should decrease the glucose level and as such theATP:ADP ratio.\nBlocking of GLUT1 resulted in a strong drop in ATP:ADP ratio in blood effector T cells. As pointed out by the reviewers, this was not the case for effector T cells present in the lymph node. In these cells, we saw a strong upregulation of fatty acid/amino acid usage upon GLUT1 blockade that potentially explains the compensatory increase in ATP:ADP. »\n2. The correction to the previous comment below was not made in the revised manuscript.\n3.\n« The dot plot next to the heatmap correlates the IFNg RFI with ATP:ADP, I wonder if it is\nreally the RFI of IFNg that is shown as the number are much higher than in the previous\npanel of the figure.\nWe thank the reviewer for noticing this mistake in the scale that we now have corrected. »\nThis does not seem to be corrected, the RFI range from 100 to 250 in this panel while it is only up to up in the other panel of the figure", "reviewer_3": "In the study by Chikina et al., the authors investigated the spatiotemporal dynamics of cellular energetic charge—defined by the ATP/ADP ratio—in immune cells, particularly T cells. They demonstrated how anatomical location, environmental nutrient availability, and circadian rhythm influence the metabolic state and effector function of these cells. The primary tool employed was SPICE-Met, a technology developed to measure metabolic and energetic states in distinct immune cell populations. This approach utilizes transgenic “SPICY” mice that express a fluorescent reporter for ATP/ADP ratios, enabling real-time assessment of cellular energy status, particularly when used alongside metabolic inhibitors to infer pathway contributions.\nThe authors observed that effector T cells modulate their energetic state in response to environmental cues. For instance, T cells isolated from blood showed higher ATP/ADP ratios compared to those in lymph nodes, correlating with increased glucose availability. This metabolic shift was accompanied by functional changes, such as enhanced IFN-γ production.\nWhile the study provides spatial and temporal resolution and introduces an elegant technological tool, the biological insights into T cell metabolism and function largely reiterate well-established concepts. The finding that peripheral effector T cells are more glycolysis-dependent, while lymph node-resident or memory-like T cells rely more on oxidative phosphorylation, is consistent with a decade of prior literature.\nMany of the figures remain descriptive and repeatedly confirm these known paradigms without offering substantial mechanistic depth or novel insights. Moreover, a critical limitation of the study is the lack of in vivo manipulation of glucose availability. Altering systemic glucose levels through dietary interventions, insulin administration, or genetic approaches could have directly tested the causal relationship between glucose availability and energetic charge across different environments. Unfortunately, such mechanistic experiments are absent throughout the manuscript. Lastly, the dataset addressing circadian regulation appears loosely integrated with the rest of the study. This section would benefit from either a more thorough analysis and mechanistic exploration or should be removed to maintain a clearer focus. In summary, while the SPICE-Met platform is technically innovative and may prove useful in future studies, this work does not provide sufficient biological advances in our understanding of T cell metabolism.", "reviewer_4": "I have kept/limited my comments to those expressed by Reviewer 3, and in short there are a few things that the authors could do to expand their current analysis which would more directly address the key issues raised by Reviewer 3. (Please see my comments, linked through the rebuttal below in italics)\nHowever, looking at the comments from reviewer 1 and 2, and the corresponding rebuttal I feel that the authors have truly aimed to address and answer most comments, and successfully reflect this in their resubmission."}, "author_response": "Authors: We respectfully disagree with the comment. Our study does not focus on the known metabolic pathways used by immune cells but rather on oscillation in energetic charge in T cells in time and space and their impact on T cell functions. Specifically, we believe that the main novelties of our study are:\n- The differences in steady-state ATP:ADP ratio among various immune cell types\n- The dynamic changes in T cell energetic charge during T cell trafficking\n- The role of glucose availability in boosting T cell energetic charge in the blood\n- The circadian oscillation of energetic charge in immune cells\n- The causal role for fluctuation in energy status in shaping several T cell functions\nWe have modified the discussion to better highlight the novelty and focus of the paper.\n(Discussion section p.12)\nI appreciate the highlighting and clarification of the findings as explained by the authors.\nThis paper has done a beautiful job of clearly demonstrating the metabolic changes experienced by migrating cells. Yes, this has been hypothesized for a while now, but the data presented in this paper show direct, single cell readout of the energetic “status” in response to the microenvironment.\nReviewer3: Many of the figures remain descriptive and repeatedly confirm these known paradigms\nwithout offering substantial mechanistic depth or novel insights. Moreover, a critical\nlimitation of the study is the lack of in vivo manipulation of glucose availability. Altering\nsystemic glucose levels through dietary interventions, insulin administration, or genetic\napproaches could have directly tested the causal relationship between glucose availability\nand energetic charge across different environments. Unfortunately, such mechanistic\nexperiments are absent throughout the manuscript. Lastly, the dataset addressing\ncircadian regulation appears loosely integrated with the rest of the study. This section\nwould benefit from either a more thorough analysis and mechanistic exploration or should\nbe removed to maintain a clearer focus. In summary, while the SPICE-Met platform is\ntechnically innovative and may prove useful in future studies, this work does not provide\nsufficient biological advances in our understanding of T cell metabolism.\nAuthors: The referee is suggesting the reinforce the link between ATP:ADP status and functional capacity of T cells. To do so, we have now analyzed the relationship between energetic charge and either T cell size and the expression of the cytotoxic molecule perforin. Thus, in addition to the effect on IFN-g production, we now show that impairing T cell energetic charge with combination of metabolic inhibitors affects these additional hallmarks of effector T cells.\nHere I believe that the authors have aimed to address the concern raised by reviewer 3 as a lack of mechanistic experiments. The authors have now included heatmaps showing the impact of metabolic inhibitors on T cell size and perforin expression, as well as IFNg.\nIt would be helpful to see the actual flow data however as the heatmaps alone are difficult to understand. I believe this data may well be there, but it is difficult to understand. 80mM 2DG treatment before you see reduction in perforin expression? No reduction in IFNg even at 100mM 2DG? – these effects are often very marked/severe at far lower 2DG concentrations. I believe this is why I am finding the heatmaps hard to gauge.\nFurthermore the figure legend is not enough to understand the experiment. Were the cells rested post transduction? Or restimulated immediately? How long were the cells treated with metabolic inhibitor? I could not find/see the transduction protocol/method ?\nLastly, whilst this (metabolic inhibitors) experiment aims to unpick the relationship between effector function and energetic charge, it does not directly address the impact of glucose availability on these readouts. This was a clear issue raised by reviewer 3, and is a point highlighted in the abstract. Do the authors have any data that address this point?\nAuthors: Regarding circadian regulation, we have added two pieces of experimental data in the revised manuscript. First, we show that fluctuations in blood glucose levels during the day correlated with changes in energetic charge. As suggested by the reviewer, we directly tested whether dietary intervention could contribute to oscillation in energetic charge\nduring the day and found indeed that starvation decrease the high ATP:ADP ratio usually\nseen at ZT1. These new results suggest that feeding and oscillating blood glucose levels\ncontribute to variations of energetic charge.\nThe authors have added some dietary data, showing a difference in fed and fasted ATP:ADP ratios in effector cells. This is a good addition, but perhaps could be expanded a bit further?? – were the FSc different? Was anything the same between the fasted and fed states? What were the blood glucose levels at the FED and FASTED states? Is there a difference in the ATP:ADP ratios in other cells in the fed/fasted state? also from lymph node? As fasting intervention is not specific to just “glucose restriction”, there is likely a whole host of nutrients affected.\nFurthermore, please clarify how the blood effector cells are identified ? (I do not believe that this procedure was performed under or post a model infection?)\nIf identified on CD62L, please recall that this is a short-lived protein, constantly cleaved from the cell surface and reexpressed. Lack of nutrients could definitely impact CD62L expression levels in general.\nFinally, a small point - the Reporting Summary needs to be double checked and updated to include the OT1 mice and the CD45.1 mice used."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed my questions.", "reviewer_2": "Thank for the revision of the manuscript and for incorporating my suggestion to the manuscript.", "reviewer_4": "I appreciate the effort that the authors have shown in order to address my comments. In particular I appreciate the addition and text clarification around the dietary studies (see SuppFig7). And the information regarding CD62L surface staining.\nFurthermore, I thank the authors for including the effect of glucose availability on IFNg production by effector T cells in figure S10, however I find that the data in figure S10 is not presented quite clearly enough. The legend states production of IFNg was measured by flow cytometry. Is this total MFI? Or % cells expressing IFNg? Are these changes significant, or within biological variability??\nIf it is % of cells expressing IFNg, what is the MFI of the “positive” cells – is this lower? The actual flow plots would be more informative.\nAlso, as data has been provided as “RFI”, please list the isotype controls used to generate the “RFI” in the reagents/methods.\nFigure S8 shows flow data of expanded effector T cells treated with Oligomycin (10nM), 2DG (8mM) or both together (for how long??), showing that at these concentrations both metabolic interventions are required in order to impair IFNg production.\nThis addition does not directly address my query as to why the heatmap representation is showing that even at 100mM 2DG there is no effect on the ATP/ADP ratio, or reduction in IFNg?\nThis is where the flow plots (again) are more accurate and informative than the “normalised” heatmaps. Without seeing the data behind the heatmap, this is hard to interpret. The addition of Figure S8 does not address the underlying issue of interpreting the heatmap.\nWhat does the actual data look like in this array? Including the high 2DG concentrations? This is pertinent as you have shown earlier on in the paper (Fig4) that 2DG (assuming 100mM) does impact ATP/ADP ratios.\nThe violin plots shown earlier through the paper -depicting the data spread from each individual biological replicate- are far more accurate and informative than these heatmaps… without knowing the variability within the experiment, the heatmaps can be far too easily misinterpreted.\n(This is a point that I feel should be noted throughout with the heatmap representations, the biological variability is understandably high between experiments, but this spread itself is informative. I strongly recommend the inclusion of the underlying data in a transparent manner to enable correct interpretations.)\nThe legends still need some work for overall clarity.\nEg: Figure 4 legend “exposed to different media composition for at least 15 min” – what is the amount of time, this needs to be clarified.\nFigure S3- the legend (again) is insufficient to understand exactly what is being shown – I assume that the concentration of 2DG used here is 100mM? Does 100mM 2DG alter the ADP/ATP ratio in the blood effector (assuming these are CD44pos, CD62lneg) CD8 cells? Is this for 15mins? Does inhibiting glucose transport with Glutor alter the ADP/ATP ratio in the blood effector cells? Is this for 15 mins??\nQuery: The Stats section states that Wilcoxon test was used, whereas legends state Mann-Whitney, which was it?\nFurthermore, why are some Stats performed with Mann-Whitney (assuming non-normal distributions) and others using One-way Anova (here assuming normal distribution) ?\nRegarding GLUT1 antibody staining.\nThe use of a naïve cell (or even the rapamycin treated cells) as negative staining controls is flawed. The cell size is significantly different and as such not appropriate for comparisons relating to the of specificity of the antibodies – MANY proteins are expressed at lower levels in naïve (or rapamycin treated) T cells compared to activated ones. This would be true for non-specific binding as well."}, "author_response": "We are happy that the reviewer is satisfied by our revised manuscript and have now addressed the final comments.\n1. Please state how effector T cells were defined in the new Supplemental Figures 2 and 3, and whether these were CD4+ or CD8+ T cells. Cells defined as effector cells based on CD44 expression under steady state conditions in vivo (i.e., non-inflammatory conditions, which is presumably what was done for Fig. S2A and B) are not equivalent to effector cells that have been activated and expanded in IL-2 prior to adoptive transfer into mice, as done for Fig. S2C. Is mTORC1 signaling and the ratio of ATP:ADP also reduced in Glut1+ compared to Glut1– cells shown in Fig. S2C? If not, the authors need to revise their new conclusions on page 8. This is important to address, as increased glucose transporter expression and glucose import are also characteristics of activated T cells and are not faithful markers of low extracellular glucose or intracellular energetic stress.\nWe apologize for this omission and have now clarified that the effector T cells corresponded to CD8+ CD44+CD62L- T cells. Based on the referee comment, we have revised the phrasing of our conclusion to specify results obtained with endogenous CD8+ CD44+CD62L- T cells and in vitro activated OT-I effector T cells.\n2. There is an issue with Figure 1 that needs to be fixed.\nWe thank the referee. The problem probably happened during the pdf conversion and has now been corrected.\n3. A figure callout has been added for this new sentence – “Of note, naive T cells only modestly upregulated GLUT1 in the lymph node, consistent with their low dependence on glucose for energy production.”\nA figure callout (Figure 4C, Figure S4) has been added in the latest version of the manuscript. We are grateful to the reviewer for noticing this.\nReviewer #2 (Remarks to the Author):\nThe authors have answered adequately to my previous comments. The manuscript is now stronger for publication in Nature Communications\nWe thank the reviewer for appreciating our revisions and finding the current version of the manuscript being stronger for publication.\nThe following minor comments should be addressed.\n1. The answer to my previous comment (copy below) should also be incorporated into the manuscript and this will help the reader to interpret properly the results.\n« 6. Figure 4K. How do the authors explain that blocking Glut1 increase the ATP:ADP ratio. If I understand well, blocking Glut1 should decrease the glucose level and as such theATP:ADP ratio.\nBlocking of GLUT1 resulted in a strong drop in ATP:ADP ratio in blood effector T cells. As pointed out by the reviewers, this was not the case for effector T cells present in the lymph node. In these cells, we saw a strong upregulation of fatty acid/amino acid usage upon GLUT1 blockade that potentially explains the compensatory increase in ATP:ADP. »\nWe agree that that including this paragraph in the manuscript would be helpful, this has now been done in the latest version of the text.\n2. The correction to the previous comment below was not made in the revised manuscript.\n3.\n« The dot plot next to the heatmap correlates the IFNg RFI with ATP:ADP, I wonder if it is\nreally the RFI of IFNg that is shown as the number are much higher than in the previous\npanel of the figure.\nWe thank the reviewer for noticing this mistake in the scale that we now have corrected. »\nThis does not seem to be corrected, the RFI range from 100 to 250 in this panel while it is only up to up in the other panel of the figure\nThis reviewer initially noticed a mistake in the RFI scale. There was indeed an error in the color scale in our Prism file. We had corrected the RFI scale (with values now starting at approximately 100 instead of 500 in the original version). Still the referee is right that these values are higher compared to the ones shown in Figure 6D. We believe that absolute RFI values in Figure 6D and 6I may be difficult to directly compare as these data originate from different experimental set-ups (different stainings and acquisition dates) and/or different nature of cells (T cells were activated for 2 days (Figure 6D) versus 7 days (Figure 6I) with possibly more IFN-g production capacity). Importantly, the dynamic range between maximal and minimal values is very consistent between both experimental set-ups (around 2-fold).\nTo clarify this point, we have mentioned similarities in dynamic range in the revised manuscript.\nReviewer #4 (Mediator, Remarks to the Author):\nI have kept/limited my comments to those expressed by Reviewer 3, and in short there are a few things that the authors could do to expand their current analysis which would more directly address the key issues raised by Reviewer 3. (Please see my comments, linked through the rebuttal below in italics)\nHowever, looking at the comments from reviewer 1 and 2, and the corresponding rebuttal I feel that the authors have truly aimed to address and answer most comments, and successfully reflect this in their resubmission.\nWe thank the mediator for positive comments on our revised manuscript and for his/her valuable input.\nRegarding the novelty of the work:\nI appreciate the highlighting and clarification of the findings as explained by the authors.\nThis paper has done a beautiful job of clearly demonstrating the metabolic changes experienced by migrating cells. Yes, this has been hypothesized for a while now, but the data presented in this paper show direct, single cell readout of the energetic “status” in response to the microenvironment.\nWe thank the mediator for appreciating the important contribution of our study to the field.\nRegarding mechanistic experiments:\nHere I believe that the authors have aimed to address the concern raised by reviewer 3 as a lack of mechanistic experiments. The authors have now included heatmaps showing the impact of metabolic inhibitors on T cell size and perforin expression, as well as IFNg.\nIt would be helpful to see the actual flow data however as the heatmaps alone are difficult to understand. I believe this data may well be there, but it is difficult to understand. 80mM 2DG treatment before you see reduction in perforin expression? No reduction in IFNg even at 100mM 2DG? – these effects are often very marked/severe at far lower 2DG concentrations. I believe this is why I am finding the heatmaps hard to gauge.\nAs suggested by the mediator, we have now included flow data illustrating how the functional heatmaps were generated. These graphs are showing reduction of IFN-g content at the single cell level upon inhibition with oligomycin and 2DG. Moreover, these data highlight the metabolic plasticity of activated T cells. As described previously, they become sensitive to 2DG only if OXPHOS is blocked concomitantly suggesting their ability to rapidly switch to available metabolic pathways when oligomycin or 2DG is used alone (PMID 35997165 ), in particular in this 4-hours assay. A sentence of T cell metabolic plasticity has now been added for clarity.\n-> Example of flow data are shown in Figure S8 in the new version of the manuscript.\nFurthermore the figure legend is not enough to understand the experiment. Were the cells rested post transduction? Or restimulated immediately? How long were the cells treated with metabolic inhibitor? I could not find/see the transduction protocol/method ?\nWe thank the mediator for noticing these points. We have now detailed the protocol in the legend of Figure 6.\nLastly, whilst this (metabolic inhibitors) experiment aims to unpick the relationship between effector function and energetic charge, it does not directly address the impact of glucose availability on these readouts. This was a clear issue raised by reviewer 3, and is a point highlighted in the abstract. Do the authors have any data that address this point?\nThe mediator asks that we provide evidence for impact of the glucose availability on effector function and energetic charge. The link between glucose availability and energetic charge was made in Figure 4K-L and Figure S3. As suggested by the mediator, we have now included data linking glucose concentration and T cell function. Specifically, we have analyzed the production of IFN-g by activated T cells in the presence of 5 or 14mM glucose. We observed a (moderate) decrease in IFN-g content when the glucose concentration was lowered, supporting a contribution of glucose availability on T cell energetic charge and effector functions.\n-> The impact of glucose availability on IFN-g production by effector T cells is included in Figure S10.\nThe authors have added some dietary data, showing a difference in fed and fasted ATP:ADP ratios in effector cells. This is a good addition, but perhaps could be expanded a bit further?? – were the FSc different? Was anything the same between the fasted and fed states? What were the blood glucose levels at the FED and FASTED states? Is there a difference in the ATP:ADP ratios in other cells in the fed/fasted state? also from lymph node? As fasting intervention is not specific to just “glucose restriction”, there is likely a whole host of nutrients affected.\nWe agree with these suggestions. We now show the fasting impact blood glucose concentrations and FSC levels on effector T cells. We also include data for additional immune cells population indicating that fasting had a significant impact on energetic charge of CD4+ effector T cells, CD8 effector T cells and NK cells but no detectable effects on CD4+ and CD8+ naïve T cell populations. We favored blood over lymph node for this analysis since T cells show high sensitivity to glucose in the blood but not in lymph node.\nFollowing mediator suggestion, we have also discussed the fact that additional nutrients could also been affected by the fasting.\n-> Blood glucose concentrations, ATP:ADP ratio in various immune cell types and FSC (size) of T cells are shown in Figure S7.\nFurthermore, please clarify how the blood effector cells are identified ? (I do not believe that this procedure was performed under or post a model infection?)\nIf identified on CD62L, please recall that this is a short-lived protein, constantly cleaved from the cell surface and reexpressed. Lack of nutrients could definitely impact CD62L expression levels in general.\nThe mediator raises the possibility that lack of nutrients impacts CD62L expression levels and the definition of T cell subsets. We thank the mediator for this point. To address this possibility, we analyzed the percentage of CD62L+ and CD62L- T cells in fed and starved animals and found very similar values suggesting that the starvation did not exert a major effect on CD62L expression (results are shown below).\nFinally, a small point - the Reporting Summary needs to be double checked and updated to include the OT1 mice and the CD45.1 mice used.\nWe have updated the reporting summary as suggested.\nFrom the editor\n…another antibody is not a good validation for the Glut1 antibody specificity, as both could pick up background. Editorially we agree and ask you to show negative controls, eg. knockout cell lines or cells that have been reported as not expressing Glut1.\nThis is an important point. In Figure S1b, we have used naïve T cells known to express very low basal levels of Glut1 which become robustly expressed upon activation. With 3 distinct Abs, we confirmed that Glut1 staining is upregulated in activated T cells as compared to naïve T cells (approximating a negative control, as requested). Moreover, this upregulation is partly blocked with rapamycin that interferes with T cell activation. Altogether, these data support the specificity of Glut1 staining. This now mentioned the legend of Fig. S1."}, {"round": 3, "reviewer_comments": {"reviewer_4": "Thank you for these final clarifications on your manuscript. I appreciate the added data/information, and have addressed my comments."}, "author_response": ""}]

Decision letter
Peer Review File
Spatiotemporal regulation of energetic charge dictates T cell
function
Corresponding Author: Dr Philippe Bousso
This manuscript has been previously reviewed at another journal. This document only contains information relating to
versions considered at Nature Communications.
This file contains all reviewer reports in order by version, followed by all author rebuttals in order by version.

[ATTACHED REVIEWER REPORT — IMPORTANT
The text below is a complete reviewer report submitted as a PDF
attachment. It appears after the Creative Commons license block.
Assign it to whichever reviewer wrote 'see pdf' or had no inline text.
======================================================================

Point-by-point
Reviewer #1 (Remarks to the Author):
The study by Chikina and colleagues utilized their previously published method, SPICE-
Met, to examine the spatiotemporal regulation of T cell energy production and function.
Immune cells that were more glycolytic showed an increased energetic charge, based on
the increased ratio of ATP to ADP. Both CD4 and CD8 effector/memory T cells had higher
ATP:ADP ratios when localized in the blood compared to the lymph nodes (LN). The
reduction in ATP in T cells in the LN correlated with reduced glucose availability in the
microenvironment. Further, the study demonstrated that T cell energetic resources and
metabolism change with the light-dark cycle. Elevated ATP:ADP in T cells was associated
with an increased ability to produce perforin and cytokine production. Overall, the findings
of this study show that intracellular energetic charge is modulated by nutrient available in
the microenvironment and diurnal rhythms, which impact T cell function. However, there
are several concerns that need to be addressed.
1. In Fig. 4, the authors suggest that cells upregulate Glut1 expression to compensate for
low levels of glucose in the LN microenvironment, which correlates with a reduced
ATP:ADP ratio. Do the Glut1+ cells have increased intracellular glucose compared to
Glut1– cells in the LN, and if so, why do the cells still maintain a low ATP:ADP ratio?
Also, increased Glut1 expression can be correlated with increased mTORC1 signaling, a
central driver of cellular metabolism – what was the status of mTORC1 signaling in Glut1+
and Glut1– cells?
These are interesting suggestions. We have now analyzed intracellular glucose
concentration and mTORC1 activity in GLUT1+ versus GLUT1- effector T cells in the lymph
node. Using iGluco transduced T cells. We found that GLUT1+ T cells indeed showed
increased intracellular glucose, yet GLUT1+ T cells displayed decrease mTORC1 activity.
These results are consistent with upregulation of GLUT1 as results of low energetic status
and low mTORC1 activity, leading to increase glucose flux as a compensatory mechanism.
-> Data showing the level of intracellular glucose and mTORC1 activity are shown in
Figure S2
2. In Fig. 5, the energetic levels in immune cells are not fully restored at ZT20 compared
to ZT1. If sampled at ZT1 again, do the authors observe a more similar phenotype?
Showing the cyclical nature over several light-dark cycles would strengthen these
findings.
We thank the referee for this comment. The fact that energetic levels appeared to be not
fully restored at ZT20 is most likely due to a further increase between ZT20 and ZT1.
The referee suggested to sample the same mice over multiple light/dark cycles. While this
is an interesting suggestion, it is unfortunately not permitted by our ethical committee to
draw blood more than once per week. Also, we try to minimize the manipulation of the
animals to avoid stress (cortisol peaks that may alter the clock) and therefore only
analyzed one time point per mouse. We feel that the fact that oscillations are observed
using independent mice for each time points (and in multiple experiments) reinforce the
robustness of our findings.
3. IFN-g production is lowest in T cells taken at ZT20 (Fig. 6G), but ATP:ADP is lowest at
ZT5 and ZT13 (Fig. 5B). Can the authors discuss these differences?
In the revised manuscript, we have discussed the possibility that functional changes may
be delayed compared to ATP:ADP levels as cytokine typically require several hours (2-4h)
to accumulate and intracellular IFN-g is measured in Figure 6G without restimulation
(reflecting steady-state cytokine production). (Results section p.11)
4. Was there any impact of the diurnal rhythm on T cell energetic charge and
trafficking/localization in the blood and lymph nodes?
As suggested by the reviewer, we have now analyzed T cell trafficking at various time of
the day. These data show distinct trafficking patterns between blood and LN and are
largely consistent with the previous literature (PMID 28087238). Thus, both T cell
energetic charge and trafficking are influenced by diurnal rhythm but is unclear whether
these parameters are linked as increased trafficking correlates with energetic charge
in the blood but not in the lymph node. Energetic charge may not represent the main
determinant of trafficking, for example compared to previously described diurnal
fluctuations in S1P1R expression.
-> Data showing the diurnal oscillations of immune cell numbers in different locations are
shown in Figure S5.
5. The study does not test whether the changes in the ATP:ADP ratios are circadian – this
would need to be established without external changes in the light. Since the mice were
still sampled under a light-dark cycle, the authors should avoid the use of “circadian” in the
conclusions.
To address this point, we have entrained mice under light-dark cycles for 3 weeks and
subsequently put them in constant darkness for at least 2 weeks to evaluate whether
oscillations in ATP:ADP persist. When sampling mice kept in constant darkness at ZT1
and ZT5 (corresponding to maximal and minimal ATP:ADP under light dark cycles), we
still observed differences in ATP:ADP ratio, suggesting the oscillation of energic charge is
of circadian nature.
-> Data showing variations of ATP:ADP in constant darkness are shown in Figure S6
6. Will the authors clarify in the methods how the ATP:ADP ratio is represented as a single
histogram (e.g., Fig. 1B and 1C), given that PercevallHR-ATP and PercevallHR-ADP
fluorescence are collected in two different channels on the cytometer?
We apologize if the methodology was not completely clear. We have clarified in the method
section that the ATP and ADP fluorescence are collected individually in two different
channels and that the ratio is calculated by FlowJo software as ATP fluo/ADP fluo.
(methods section p.16)
Reviewer #2 (Remarks to the Author):
Synopsis of the paper
In this paper, the authors used a previously described mouse model, the SPICY mice,
which express a biosensor for ATP (binding of ATP to the biosensor changes in
fluorescence emission) to measure cellular energetic (ATP:ADP ratio) at the single cell
level (flow cytometry) in different immune cell types in the blood and lymph nodes. This
model also allowed the authors to examine the metabolic dependencies of immune cells
using changes in the ATP:ADP ratio following short treatment with metabolic inhibitors
(Oligomycin, Iodoacetic acid, 2DG…). Using this system, they show that effector/Tem cells
but not naive/Tcm cells have higher ATP levels when in the blood compared to lymph
nodes. They further relate this to differential glucose availability in these sites and that
these difference in energetic charge affects INF-gamma production. They also showed
that the ATP:ADP ratio, and the dependencies on OXPHOS and glycolysis, varies
according to the time of the day sampling.
Comments
This study provides an interesting approach to evaluate cellular energetic at the single cell
level. It brings novel information on cellular energetics in different immune cell types and
on the effect of cell location for some immune cell types(blood vs lymph node); and on the
daily variation of cellular energetics. These descriptive experiments are well performed but
the one aiming at defining a mechanism and the impact of changes of cellular energetic
are overinterpreted.
The data generally of quality but the presentation is often lacking details. The statistics are
done correctly.
1.It is very unfortunate that the authors assumed that everyone know about their mouse
model (SPICY mice) to measure ATP/ADP ratio in cells. I understand that they have a
previous publication described this model but they should take the time to explain how this
model allows to detect ATP:ADP ratio. They should also better described how they are
presenting the data, for example in Fig 1B, they show an histogram of ATP:ADP, it is not
clear how this was obtained, I guess that it is if the ratio of fluorescence of the biosensor
not bound to ATP to the one of the fluorescence of the biosensor bound to ATP for each
cells. Also, is the fluorescence detection of PercevalHR-ADP measuring the expression of
the biosensor irrespectively of its binding to ADP? If so, how can we say that this measure
ADP?
We apologize for omitting methodological details. We have now added a full paragraph to
more precisely describe our approach to reader that are not necessarily aware of our
previous publication. We also specify that it is the fact the biosensor is ratiometric provides
measurements that are independent on expression level of the biosensor, as described in
the original publication of the biosensor (PMID 24096541). (Results section p.5)
We have also clarified in the methods the ATP and ADP fluorescence are collected
individually in two different channels and that the ratio is calculated by FlowJo software as
ATP fluo/ADP fluo.
There are also inconsistencies on how the data is presented, in Fig 1D, the dot plot
showing PercevalHR-APT versus Percevall-ADP is in a linear scale while in all the other
figures it is shown using a log scale.
As suggested by the reviewer, we now present the data in Figure 1D using a log-scale, to
be consistent with the rest of the figures.
The panel G of Figure needs to be explained, I could not understand what is display.
In panel G, we look at possible correlation between the measured ATP:ADP ratio and the
other metabolic parameters as defined in Figure 1B and Figure 1C to identify potential
drivers of energy status differences. We have modified the results and figure legends
accordingly to clarify the panel. (Results section p6, legend for figure 1)
Figure 2E and F. The analysis was done at day 7 and therefore the cells are not Tem nor
Tcm yet.
We agree with the reviewer the fate of the T cells may not be fully determined at day 7. To
be more careful, we have changed the terminology to CD44+CD62L- phenotype (Tem-like)
and CD44+CD62L- (Tcm-like). (Results section, p7).
2. Figure 2 and 3 show that the ATP:ADP ratio is different in the blood and lymph nodes
for effector/Tem cells but not for naive cells. They further associate this with difference in
glucose accessibility in blood versus lymph nodes. But how do you explain that naive T
cells are not affected by this difference in glucose accessibility, they have a high ATP:ADP
ratio in both the blood and lymph nodes.
The fact that naïve T cells are not affected by anatomical difference in glucose accessibility
is most likely explained by the fact that naïve T cell ATP:ADP ratio is unaffected by glucose
deprivation but appeared dependent on fatty acids. We have added data using
reconstituted media (with or without glucose or fatty acids) to support this point.
-> Data showing naïve T cell ATP:ADP ratio in reconstituted media in the absence of
glucose or fatty acids are shown in Figure S4.
The authors should also consider the possibility that eff/Tem cells are not expressing the
proper homing molecules to enter in lymph nodes and that maybe to locate to a distinct
region of the lymph nodes than naive T cells.
We thank the reviewer for this interesting point, and we have now mentioned the possibility
of nutrient partitioning within lymph node. (Discussion section p. 14)
3.Figure 3. The authors need to specify if they transferred purified Eff/tem or naive or total
T cells and then gated on eff/Tem or naive cells.
We have now clarified that total polyclonal T cells were used for adoptive transfer (and
then gated). To extend our findings, a more homogeneous effector T cell population was
also adoptively transferred using in vitro activated OT-I T cells. This has been clarified in
Figure legends of the revised manuscript
4. Figure 4A-F. The authors used Glut1 expression as a proxy for energetic stress as it
was shown by others that under glucose deprivation cells upregulate the expression of
Glut1. They showed that eff/Tm in lymph nodes express more Glut1 than in the blood
supporting the idea that in the blood eff/Tem have less access to glucose. Again, when
naive T cells do not sense this difference in glucose between the blood and lymph nodes
by urpregulating Glut1 in the lymph nodes?
The referee is right that naïve T cells very modestly upregulated GLUT1 in the lymph node.
We explain this observation by the fact that naïve T cell energetic charge is poorly
dependent on glucose but rely mostly on fatty acids. The high dependence on glucose for
effector T cells likely explains why they are very sensitive to the low glucose availability in
the lymph node. We have provided this information in the revised manuscript.
-> Data showing the naïve T cell ATP:ADP ratio is largely independent on glucose are
shown in Figure S4.
I am also concerned about the antibody they used to detect Glut1. They used an antibody
that was not validated for flow cytometry and that is directed to an epitope at the C-terminal
of Glut1. This epitope is intracellular but the authors did not mentioned that they have
performed intracellular staining to detect Glut1 (07-1401 from Sigma).
We thank the referee for this important comment. To make sure that the surface Glut1
staining we performed using anti-GLUT1 (polyclonal Ab #07-1401 from Sigma) was
specific, we tested 2 additional anti-GLUT1 Ab (known to target the extracellular domain),
AGT-041 (Almone labs) and clone #202915 (Biotechne). First, we confirmed that all 3 Ab
could detected the known upregulation of surface GLUT1 during T cell activation, a
process that was blocked using rapamycin as expected (PMID: 30320109). Most
importantly, we show that the upregulation of GLUT1 in effector T cells in the lymph node
was detected with all three antibodies at similar levels.
The Ab we used initially is indeed described as targeting the C-terminal part of the
molecule by the manufacturer. However, it is a polyclonal Ab that appear either to label
surface GLUT1 directly or due to the known conformational change of the molecule during
glucose transfer. We now include data obtained with all 3 anti-GLUT1 Ab to reinforce our
findings.
-> The validation of 3 distinct anti-GLUT1 Abs for flow cytometry and the confirmation of
GLUT1 specific upregulation in effector T cells in the LN with 3 distinct anti-GLUT1 Abs
are shown in Figure S1.
5. Figure 4G-H-I. The use of the iGluco system to detect intracellular level of glucose is
interesting and nicely shows that activated T cells have higher intracellular glucose in the
blood than in the lymph nodes. However, this does not demonstrate that this is the
consequence of the difference in ATP:ADP ratio. The calibration experiment in G validate
that the IGluco reporter system is sensitive to the level of glucose in the media but I am
not sure that this can be used to estimate the concentration of glucose in the blood and
lymph nodes as the level of intracellular glucose level will also be affected by the ability of
the cells to transport it.
We agree with the referee and will not use iGluco to estimate glucose concentrations in
the extracellular milieu and but used iGluco to monitor intracellular glucose levels.
Also, in this in vitro system does the cells cultured I higher amount of glucose have an
increase in ATP:ADP ratio, which would validate the link between higher level of
intracellular glucose to higher ATP:ADP ratio.
As suggested by the reviewer, we have analyzed effector T cell energetic charges upon
culture with different concentrations of extracellular glucose. We now show a dose
dependent upregulation of ATP:ADP ratio in effector T cells.
-> The dose-dependent upregulation of ATP:ADP ratio in effector T cells in media
containing different concentrations of extracellular glucose is shown in Figure S3.
6.Figure 4K. How do the authors explain that blocking Glut1 increase the ATP:ADP ratio.
If I understand well, blocking Glut1 should decrease the glucose level and as such the
ATP:ADP ratio.
Blocking of GLUT1 resulted in a strong drop in ATP:ADP ratio in blood effector T cells. As
pointed out by the reviewers, this was not the case for effector T cells present in the lymph
node. In these cells, we saw a strong upregulation of fatty acid/amino acid usage upon
GLUT1 blockade that potentially explains the compensatory increase in ATP:ADP.
7. Figure 5. The experiments were done in light:dark cycle and as such the term circadian
cannot be used. As done, this shows time of the day variation of cellular energetic.
This is an important comment. To address this point, we have entrained mice under light-
dark cycles for 3 weeks and subsequently put them in constant darkness for at least 2
weeks to evaluate whether oscillations in ATP:ADP persist. When mice kept in constant
darkness were analyzed at ZT1 and ZT5 (corresponding to maximal and minimal ATP:ADP
ratio under light/dark cycles), we continued to observe differences in ATP:ADP ratio,
suggesting that the oscillations of energic charge are of circadian nature.
-> Data showing ATP:ADP values at ZT1 and ZT5 for mice kept in constant darkness are
shown in Figure S6
The indication of what is statistically significant in these graphs is not clear.
We have now explained that the comparisons are made for each time point to ZT1 time
point.
8. Figure 6B-C. It is very surprising to detect IFNgamma in effector/tem cells at the steady
state without TCR restimulation. Cytokine production in CD8 effector T cells requires TCR
signaling.
While intracellular cytokines are usually increased by TCR in vitro stimulation, effector T
cell can display intracellular staining for effector cytokine such IFN-g or TNF-a even in the
absence of in vitro restimulation (4h culture in the presence of BrefeldinA), reflecting the
basal production and possibly recent in vivo TCR engagement (PMID: 20850354).
Furthermore, cytokines are secreted and do not accumulate in cells, were the mice treated
with BFA to stop secretion of cytokine?
Yes, it was stated in the methods that brefeldin was used during a 4h incubation period.
We have now added this information also in the Figure legend.
How was the RFI calculated? An isotype control should be included as the cells making
more cytokine are bigger and therefore could be more autofluorescent.
We have clarified that RFI are calculated as MFI of the sample/MFI of the isotype control
in the Figure legend.
9. Figure 6F-G. What is the Y axis in the compilation? Was the cytokine measurement
done with blood or lymph nodes T cells. Without these informations, it is difficult to interpret
the data.
We apologize for omitting to label the Y axis (RFI of IFN-g) and thank the reviewer for
noticing it. This has now been corrected. We have clarified that results are shown for blood
cells.
The analysis of cytokine production according to the time of day sampling does not show
an exact correlation with the level of ATP:ADP. The production of cytokine is low at ZT20
although ATP:ADP ratio is similar at ZT5 and ZT13 in the lymph node and higher in the
blood.
In the revised manuscript, we have discussed the possibility that functional changes may
be delayed compared to ATP:ADP levels as cytokine typically require several hours (2-
4h) to accumulate and intracellular IFN-g is measured in Figure 6G without restimulation
(reflecting steady-state cytokine production).
10. Figure 6I. Activated T cells used both OXPHOS and glycolysis to make ATP.
Therefore, oligomycin treatment should decrease the ATP:ADP ratio but no effect is
observed in the heatmap. Same for 2DG treatment.
This is a very interesting point. We have discussed that this probably reflect metabolic
plasticity with effector T cells rapidly switching from Oxphos to glycolysis as we have
observed previously (Russo et al. EMBO 2022). We have now discussed this point (Result
section p.11).
The dot plot next to the heatmap correlates the IFNg RFI with ATP:ADP, I wonder if it is
really the RFI of IFNg that is shown as the number are much higher than in the previous
panel of the figure.
We thank the reviewer for noticing this mistake in the scale that we now have corrected.
Overall, the data presented in Figure 6 are not strong enough to conclude that the
ATP:ADP ratio affects T cell function (steady-state measurement is unexpected, difference
after restimulation in low and no exact correlation with the time of the day cellular
energetic)
The referee is suggesting the reinforce the link between ATP:ADP status and functional
capacity of T cells. To do so, we have now analyzed the relationship between energetic
charge and either T cell size or the expression of the cytotoxic molecule perforin. In
addition to the effect on IFN-g production shown in the original version of the manuscript,
we now show that impairing T cell energetic charge with combination of metabolic
inhibitors affect these additional hallmarks of effector T cells.
-> Data showing the impact of ATP:ADP ratio on effector T cell size and perforin content
are shown in Figure S8.
Reviewer #3 (Remarks to the Author):
In the study by Chikina et al., the authors investigated the spatiotemporal dynamics of
cellular energetic charge—defined by the ATP/ADP ratio—in immune cells, particularly T
cells. They demonstrated how anatomical location, environmental nutrient availability, and
circadian rhythm influence the metabolic state and effector function of these cells. The
primary tool employed was SPICE-Met, a technology developed to measure metabolic and
energetic states in distinct immune cell populations. This approach utilizes transgenic
“SPICY” mice that express a fluorescent reporter for ATP/ADP ratios, enabling real-time
assessment of cellular energy status, particularly when used alongside metabolic inhibitors
to infer pathway contributions.
The authors observed that effector T cells modulate their energetic state in response to
environmental cues. For instance, T cells isolated from blood showed higher ATP/ADP
ratios compared to those in lymph nodes, correlating with increased glucose availability.
This metabolic shift was accompanied by functional changes, such as enhanced IFN-γ
production.
While the study provides spatial and temporal resolution and introduces an elegant
technological tool, the biological insights into T cell metabolism and function largely
reiterate well-established concepts. The finding that peripheral effector T cells are more
glycolysis-dependent, while lymph node-resident or memory-like T cells rely more on
oxidative phosphorylation, is consistent with a decade of prior literature.
We respectfully disagree with the comment. Our study does not focus on the known
metabolic pathways used by immune cells but rather on oscillation in energetic charge in
T cells in time and space and their impact on T cell functions.
Specifically, we believe that the main novelties of our study are:
- The differences in steady-state ATP:ADP ratio among various immune cell types
- The dynamic changes in T cell energetic charge during T cell trafficking
- The role of glucose availability in boosting T cell energetic charge in the blood
- The circadian oscillation of energetic charge in immune cells
- The causal role for fluctuation in energy status in shaping several T cell functions
We have modified the discussion to better highlight the novelty and focus of the paper.
(Discussion section p.12)
Many of the figures remain descriptive and repeatedly confirm these known paradigms
without offering substantial mechanistic depth or novel insights. Moreover, a critical
limitation of the study is the lack of in vivo manipulation of glucose availability. Altering
systemic glucose levels through dietary interventions, insulin administration, or genetic
approaches could have directly tested the causal relationship between glucose availability
and energetic charge across different environments. Unfortunately, such mechanistic
experiments are absent throughout the manuscript. Lastly, the dataset addressing
circadian regulation appears loosely integrated with the rest of the study. This section
would benefit from either a more thorough analysis and mechanistic exploration or should
be removed to maintain a clearer focus. In summary, while the SPICE-Met platform is
technically innovative and may prove useful in future studies, this work does not provide
sufficient biological advances in our understanding of T cell metabolism.
The referee is suggesting the reinforce the link between ATP:ADP status and functional
capacity of T cells. To do so, we have now analyzed the relationship between energetic
charge and either T cell size and the expression of the cytotoxic molecule perforin. Thus,
in addition to the effect on IFN-g production, we now show that impairing T cell energetic
charge with combination of metabolic inhibitors affects these additional hallmarks of
effector T cells.
Regarding circadian regulation, we have added two pieces of experimental data in the
revised manuscript. First, we show that fluctuations in blood glucose levels during the day
correlated with changes in energetic charge. As suggested by the reviewer, we directly
tested whether dietary intervention could contribute to oscillation in energetic charge
during the day and found indeed that starvation decrease the high ATP:ADP ratio usually
seen at ZT1. These new results suggest that feeding and oscillating blood glucose levels
contribute to variations of energetic charge.
-> Data showing the impact of ATP:ADP ratio on effector T cell size and perforin content
are shown in Figure S8.
-> Data showing the oscillation of blood glucose during the day and the impact of
fasting on ATP:ADP ratio at ZT1 are shown in Figure S7.
Reviewer #1 (Remarks to the Author):
Overall, the authors have addressed my concerns, but I have a few additional comments that should
be addressed.
We are happy that the reviewer is satisfied by our revised manuscript and have now addressed the
final comments.
1. Please state how effector T cells were defined in the new Supplemental Figures 2 and 3, and whether
these were CD4+ or CD8+ T cells. Cells defined as effector cells based on CD44 expression under steady
state conditions in vivo (i.e., non-inflammatory conditions, which is presumably what was done for Fig.
S2A and B) are not equivalent to effector cells that have been activated and expanded in IL-2 prior to
adoptive transfer into mice, as done for Fig. S2C. Is mTORC1 signaling and the ratio of ATP:ADP also
reduced in Glut1+ compared to Glut1– cells shown in Fig. S2C? If not, the authors need to revise their
new conclusions on page 8. This is important to address, as increased glucose transporter expression
and glucose import are also characteristics of activated T cells and are not faithful markers of low
extracellular glucose or intracellular energetic stress.
We apologize for this omission and have now clarified that the effector T cells corresponded to CD8+
CD44+CD62L- T cells. Based on the referee comment, we have revised the phrasing of our conclusion
to specify results obtained with endogenous CD8+ CD44+CD62L- T cells and in vitro activated OT-I
effector T cells.
2. There is an issue with Figure 1 that needs to be fixed.
We thank the referee. The problem probably happened during the pdf conversion and has now been
corrected.
3. A figure callout needs to be added for this new sentence – “Of note, naive T cells only modestly
upregulated GLUT1 in the lymph node, consistent with their low dependence on glucose for energy
production.”
A figure callout (Figure 4C, Figure S4) has been added in the latest version of the manuscript. We are
grateful to the reviewer for noticing this.
Reviewer #2 (Remarks to the Author):
The authors have answered adequately to my previous comments. The manuscript is now stronger
for publication in Nature Communications
We thank the reviewer for appreciating our revisions and finding the current version of the manuscript
being stronger for publication.
The following minor comments should be addressed.
1. The answer to my previous comment (copy below) should also be incorporated into the manuscript
and this will help the reader to interpret properly the results.
« 6. Figure 4K. How do the authors explain that blocking Glut1 increase the ATP:ADP ratio. If I
understand well, blocking Glut1 should decrease the glucose level and as such theATP:ADP ratio.
Blocking of GLUT1 resulted in a strong drop in ATP:ADP ratio in blood effector T cells. As pointed
out by the reviewers, this was not the case for effector T cells present in the lymph node. In these cells, we
1
saw a strong upregulation of fatty acid/amino acid usage upon GLUT1 blockade that potentially
explains the compensatory increase in ATP:ADP. »
We agree that that including this paragraph in the manuscript would be helpful, this has now been
done in the latest version of the text.
2. The correction to the previous comment below was not made in the revised manuscript.
3.
« The dot plot next to the heatmap correlates the IFNg RFI with ATP:ADP, I wonder if it is
really the RFI of IFNg that is shown as the number are much higher than in the previous
panel of the figure.
We thank the reviewer for noticing this mistake in the scale that we now have corrected. »
This does not seem to be corrected, the RFI range from 100 to 250 in this panel while it is only up to
up in the other panel of the figure
This reviewer initially noticed a mistake in the RFI scale. There was indeed an error in the color scale
in our Prism file. We had corrected the RFI scale (with values now starting at approximately 100 instead
of 500 in the original version). Still the referee is right that these values are higher compared to the
ones shown in Figure 6D. We believe that absolute RFI values in Figure 6D and 6I may be difficult to
directly compare as these data originate from different experimental set-ups (different stainings and
acquisition dates) and/or different nature of cells (T cells were activated for 2 days (Figure 6D) versus
7 days (Figure 6I) with possibly more IFN-g production capacity). Importantly, the dynamic range
between maximal and minimal values is very consistent between both experimental set-ups (around
2-fold).
To clarify this point, we have mentioned similarities in dynamic range in the revised manuscript.
Reviewer #4 (Mediator, Remarks to the Author):
I have kept/limited my comments to those expressed by Reviewer 3, and in short there are a few
things that the authors could do to expand their current analysis which would more directly address
the key issues raised by Reviewer 3. (Please see my comments, linked through the rebuttal below in
italics)
However, looking at the comments from reviewer 1 and 2, and the corresponding rebuttal I feel that
the authors have truly aimed to address and answer most comments, and successfully reflect this in
their resubmission.
We thank the mediator for positive comments on our revised manuscript and for his/her valuable
input.
Regarding the novelty of the work:
I appreciate the highlighting and clarification of the findings as explained by the authors.
This paper has done a beautiful job of clearly demonstrating the metabolic changes experienced by
migrating cells. Yes, this has been hypothesized for a while now, but the data presented in this paper
show direct, single cell readout of the energetic “status” in response to the microenvironment.
We thank the mediator for appreciating the important contribution of our study to the field.
Here I believe that the authors have aimed to address the concern raised by reviewer 3 as a lack of
mechanistic experiments. The authors have now included heatmaps showing the impact of metabolic
inhibitors on T cell size and perforin expression, as well as IFNg.
It would be helpful to see the actual flow data however as the heatmaps alone are difficult to
understand. I believe this data may well be there, but it is difficult to understand. 80mM 2DG
treatment before you see reduction in perforin expression? No reduction in IFNg even at 100mM
2DG? – these effects are often very marked/severe at far lower 2DG concentrations. I believe this is
why I am finding the heatmaps hard to gauge.
As suggested by the mediator, we have now included flow data illustrating how the functional
heatmaps were generated. These graphs are showing reduction of IFN-g content at the single cell level
upon inhibition with oligomycin and 2DG. Moreover, these data highlight the metabolic plasticity of
activated T cells. As described previously, they become sensitive to 2DG only if OXPHOS is blocked
concomitantly suggesting their ability to rapidly switch to available metabolic pathways when
oligomycin or 2DG is used alone (PMID 35997165 ), in particular in this 4-hours assay. A sentence of T
cell metabolic plasticity has now been added for clarity.
-> Example of flow data are shown in Figure S8 in the new version of the manuscript.
Furthermore the figure legend is not enough to understand the experiment. Were the cells rested post
transduction? Or restimulated immediately? How long were the cells treated with metabolic
inhibitor? I could not find/see the transduction protocol/method ?
We thank the mediator for noticing these points. We have now detailed the protocol in the legend of
Figure 6.
Lastly, whilst this (metabolic inhibitors) experiment aims to unpick the relationship between effector
function and energetic charge, it does not directly address the impact of glucose availability on these
readouts. This was a clear issue raised by reviewer 3, and is a point highlighted in the abstract. Do the
authors have any data that address this point?
The mediator asks that we provide evidence for impact of the glucose availability on effector function
and energetic charge. The link between glucose availability and energetic charge was made in Figure
4K-L and Figure S3. As suggested by the mediator, we have now included data linking glucose
concentration and T cell function. Specifically, we have analyzed the production of IFN-g by activated
T cells in the presence of 5 or 14mM glucose. We observed a (moderate) decrease in IFN-g content
when the glucose concentration was lowered, supporting a contribution of glucose availability on T
cell energetic charge and effector functions.
-> The impact of glucose availability on IFN-g production by effector T cells is included in Figure S10.
The authors have added some dietary data, showing a difference in fed and fasted ATP:ADP ratios in
effector cells. This is a good addition, but perhaps could be expanded a bit further?? – were the FSc
different? Was anything the same between the fasted and fed states? What were the blood glucose
levels at the FED and FASTED states? Is there a difference in the ATP:ADP ratios in other cells in the
fed/fasted state? also from lymph node? As fasting intervention is not specific to just “glucose
restriction”, there is likely a whole host of nutrients affected.
FSC levels on effector T cells. We also include data for additional immune cells population indicating
that fasting had a significant impact on energetic charge of CD4+ effector T cells, CD8 effector T cells
and NK cells but no detectable effects on CD4+ and CD8+ naïve T cell populations. We favored blood
over lymph node for this analysis since T cells show high sensitivity to glucose in the blood but not in
lymph node.
Following mediator suggestion, we have also discussed the fact that additional nutrients could also
been affected by the fasting.
-> Blood glucose concentrations, ATP:ADP ratio in various immune cell types and FSC (size) of T cells
are shown in Figure S7.
Furthermore, please clarify how the blood effector cells are identified ? (I do not believe that this
procedure was performed under or post a model infection?)
If identified on CD62L, please recall that this is a short-lived protein, constantly cleaved from the cell
surface and reexpressed. Lack of nutrients could definitely impact CD62L expression levels in general.
The mediator raises the possibility that lack of nutrients impacts CD62L expression levels and the
definition of T cell subsets. We thank the mediator for this point. To address this possibility, we
analyzed the percentage of CD62L+ and CD62L- T cells in fed and starved animals and found very similar
values suggesting that the starvation did not exert a major effect on CD62L expression (results are
shown below).
Finally, a small point - the Reporting Summary needs to be double checked and updated to include the
OT1 mice and the CD45.1 mice used.
We have updated the reporting summary as suggested.
From the editor
…another antibody is not a good validation for the Glut1 antibody specificity, as both could pick up
background. Editorially we agree and ask you to show negative controls, eg. knockout cell lines or
cells that have been reported as not expressing Glut1.
This is an important point. In Figure S1b, we have used naïve T cells known to express very low basal
levels of Glut1 which become robustly expressed upon activation. With 3 distinct Abs, we confirmed
that Glut1 staining is upregulated in activated T cells as compared to naïve T cells (approximating a
negative control, as requested). Moreover, this upregulation is partly blocked with rapamycin that
interferes with T cell activation. Altogether, these data support the specificity of Glut1 staining. This
now mentioned the legend of Fig. S1.
Reviewer #1 (Remarks to the Author):
The authors have addressed my questions.
Reviewer #2 (Remarks to the Author):
Thank for the revision of the manuscript and for incorporating my suggestion to the manuscript.
Reviewer #4 (Remarks to the Author):
I appreciate the effort that the authors have shown in order to address my comments. In
particular I appreciate the addition and text clarification around the dietary studies (see
SuppFig7). And the information regarding CD62L surface staining.
We thank the referee for the positive comments on our revisions.
Furthermore, I thank the authors for including the effect of glucose availability on IFNg
production by effector T cells in figure S10, however I find that the data in figure S10 is not
presented quite clearly enough. The legend states production of IFNg was measured by flow
cytometry. Is this total MFI? Or % cells expressing IFNg? Are these changes significant, or
within biological variability?? If it is % of cells expressing IFNg, what is the MFI of the “positive”
cells – is this lower? The actual flow plots would be more informative.
Also, as data has been provided as “RFI”, please list the isotype controls used to generate the
“RFI” in the reagents/methods.
We have clarified that we measured IFN-γ by flow cytometry and the graph represents the RFI.
We observed lower intracellular IFN-γ content in 5mM versus 14mM glucose in 9 out of 10
mice although the changes were moderate (10%). The p value with the outlier mouse was 0.06
and 0.01 without the outlier (Wilcoxon test). To be rigorous, we have kept all data point and
clarified in the text that a trend is observed (p=0.06). We also included a representative FACS
plot as suggested. The references for the isotype controls are now included in the methods
section. The duration of the assay was 4h and is now indicated in the figure legend.
Figure S8 shows flow data of expanded effector T cells treated with Oligomycin (10nM),
2DG (8mM) or both together (for how long??), showing that at these concentrations both
metabolic interventions are required in order to impair IFNg production. This addition does not
directly address my query as to why the heatmap representation is showing that even at
100mM 2DG there is no effect on the ATP/ADP ratio, or reduction in IFNg? This is where the
flow plots (again) are more accurate and informative than the “normalised” heatmaps. Without
seeing the data behind the heatmap, this is hard to interpret. The addition of Figure S8 does not
address the underlying issue of interpreting the heatmap. What does the actual data look like
in this array? Including the high 2DG concentrations? This is pertinent as you have shown
earlier on in the paper (Fig4) that 2DG (assuming 100mM) does impact ATP/ADP ratios.The
violin plots shown earlier through the paper -depicting the data spread from each individual
biological replicate- are far more accurate and informative than these heatmaps… without
knowing the variability within the experiment, the heatmaps can be far too easily
misinterpreted. (This is a point that I feel should be noted throughout with the heatmap
representations, the biological variability is understandably high between experiments, but this
spread itself is informative. I strongly recommend the inclusion of the underlying data in a
transparent manner to enable correct interpretations.)
To address the reviewer comments, we have now included more histograms (corresponding
to 15 conditions) in Figure S8 showing in particular IFN-γ production upon treatment with the
highest inhibitor concentration. We have also included the raw data from 4 individual mice used
to create the heatmap in the source data file, for transparency as suggested.
The legends still need some work for overall clarity. Eg: Figure 4 legend “exposed to different
media composition for at least 15 min” – what is the amount of time, this needs to be clarified.
We have corrected the figure legend to indicate that the exposition was for 15-30 min.
Treatment with inhibitors was 10 min for each sample. Small differences in the exposition of a
given media were due to sequential FACS acquisition.
Figure S3- the legend (again) is insufficient to understand exactly what is being shown – I
assume that the concentration of 2DG used here is 100mM? Does 100mM 2DG alter the
ADP/ATP ratio in the blood effector (assuming these are CD44pos, CD62lneg) CD8 cells? Is
this for 15mins? Does inhibiting glucose transport with Glutor alter the ADP/ATP ratio in the
blood effector cells? Is this for 15 mins??
The concentration of 2DG was indeed 100mM and the assay was for 15 min. Glutor treatment
reduced ATP:ADP value to around 40% of maximum (last bar on the graph).
Query: The Stats section states that Wilcoxon test was used, whereas legends state Mann-
Whitney, which was it? Furthermore, why are some Stats performed with Mann-Whitney
(assuming non-normal distributions) and others using One-way Anova (here assuming normal
distribution)?
We thank the reviewer for catching this inconsistency. We apologize for the lack of clarity in
the initial version. In the revised version, we have revised and homogenized the statistics of
the Figures:
-for comparing 2 paired groups (eg. LN versus blood, Glut1+ cells versus Glut1- cells…)
Wilcoxon signed-rank test (no normality assumption). A paired t-test was used only if normality
was validated by a Shapiro-Wilk test.
-for comparing 2 independent groups (eg. ZT1 versus ZT5): Mann-Whitney U test
-for comparing >2 independent groups Kruskal-Wallis test (no normality assumption) or one-
way ANOVA if normality was valided by a Shapiro-Wilk test. These criteria have now been
explicitly stated in the revised Statistical Analysis section to clarify our rationale.
Regarding GLUT1 antibody staining.
The use of a naïve cell (or even the rapamycin treated cells) as negative staining controls is
flawed. The cell size is significantly different and as such not appropriate for comparisons
relating to the of specificity of the antibodies – MANY proteins are expressed at lower levels in
naïve (or rapamycin treated) T cells compared to activated ones. This would be true for non-
specific binding as well.
We appreciate the reviewer thoughtful comment regarding the choice of negative controls for
GLUT1 staining. We fully agree that naïve and rapamycin-treated T cells differ in size
compared to activated T cells, which could in principle confound the interpretation of staining
for various surface molecules, including GLUT1. However, several lines of evidence indicate
that this is not the case here.
First, our in vivo data show that GLUT1 expression is not correlated with cell size: GLUT1⁺ T
cells are not larger than GLUT1⁻ counterparts, as quantified in Figure 4 (see Figure R1, graph
and quantification below). Second, the staining pattern was reproducible across three
independent anti-GLUT1 antibodies, some of which have been previously validated for flow
cytometry by other groups (see for example PMID: 40510344, 39284072), supporting the
specificity of the signal. Third, as an additional biological control, we compared GLUT1 staining
between naïve T cells (known to express little to no GLUT1) and neutrophils (which
constitutively express high GLUT1 levels). As expected, we observed clear differential staining,
further confirming the specificity of the signal (see below Figure R2).
Together, these findings strengthen our confidence that the observed GLUT1 signal reflects
specific expression rather than size-dependent or non-specific binding artifacts.
Figure R1: Glut1 staining on T cells is not correlated to cell size.
Figure R2: Neutrophils but not naïve T cells express Glut1 on their surface