According to the classical view in immunology, adaptive immune cells can generate antigen-specific long-lasting immunological memory to protect against subsequent challenges, whereas innate immune cells are unable to develop memory traits (Marshall et al., 2018; Netea et al., 2011; Netea et al., 2020). Nevertheless, some memory traits are found in organisms devoid of an adaptive immune system. Invertebrates, including earthworms, cockroaches, and corals, showed a form of innate immunological memory, exhibiting protection against reinfection, or accelerated rejection response upon subsequent exposure to the same foreign tissue in graft models (Cooper and Roch, 1986; Little et al., 2005; Kurtz and Franz, 2003). In plants, a similar process occurs whereby localised pathogenic infections can trigger systemic and nonspecific protection against reinfection, a process known as systemic acquired resistance (Fu and Dong, 2013). These findings, together with the beneficial nonspecific effects of vaccination (Aaby and Benn, 2012), challenged the classical paradigm and led to the discovery that innate immune cells that have encountered a first proinflammatory insult can develop an enhanced immune response to a second challenge of either the same or a different nature, a process called trained immunity (TI) (Netea et al., 2011; Netea et al., 2020). Additionally, innate immune cells have not only acquired memory traits with the consequence of enhancing subsequent responses, but can also display blunted secondary responses after certain primary stimuli, generally lipopolysaccharide (LPS), a process termed endotoxin tolerance (Liu et al., 2019).

TI can be induced in several mature, differentiated immune cell types, in a process known as peripheral TI (Ochando et al., 2023). The first studies of this phenomenon reported that mouse and human monocytes were able to develop innate immune memory (Quintin et al., 2012; Kleinnijenhuis et al., 2012). It has subsequently been demonstrated that TI can be induced in other innate immune cells from the myeloid lineage, such as neutrophils (Kalafati et al., 2020) and dendritic cells (Hole et al., 2019), and in lymphoid cells like natural killer (Kleinnijenhuis et al., 2014) cells and γδ T cells (Röring et al., 2024). Moreover, non-immune cells such as epithelial stem cells may also display immune memory features (Bigot et al., 2019). In vivo it was demonstrated that trained-immunity-inducing agonists can induce expansion of hematopoietic progenitors, specifically those promoting myelopoiesis, in a process termed central TI (Mitroulis et al., 2018; Kaufmann et al., 2018; Tran et al., 2025). To date, several compounds have been found to induce trained phenotypes. Among these, microbe-associated molecular patterns, such as β-glucans from the yeast cell wall (Quintin et al., 2012), bacterial flagellin (Ifrim et al., 2014), and muramyl dipeptide (Ifrim et al., 2014), induce a trained phenotype in myeloid cells. Live vaccines like Bacille Calmette-Guérin (BCG) employed against tuberculosis, and systemically disseminated microbiota that reach the bone marrow (particularly Enterococcus faecalis) induce TI and mediate protection against heterologous infections (Kleinnijenhuis et al., 2012; Robles-Vera et al., 2025). Moreover, mucosal administration of inactivated bacteria, such as MV130 polybacterial preparations, also leads to viral- and fungal-protective TI induction (del Fresno et al., 2021; Brandi et al., 2022). Not only microbial components but also endogenous sterile triggers of inflammation, like oxidised low-density lipoproteins (oxLDLs) (Bekkering et al., 2014; Sohrabi et al., 2019) or labile heme (Jentho et al., 2021), can induce trained phenotypes.

The diversity of TI inducers and immune cells in which TI can be exerted underlines the complexity and variety of mechanisms involved in the establishment of trained phenotypes. Moreover, the same stimuli may induce a non-linear/non-monotonic response based on the dose employed. For example, while LPS generally induces tolerance at higher concentrations (e.g. 100 ng/mL), very low doses (≤10 pg/mL) promote TI; similar dose-dependent effects have been reported for flagellin (Ifrim et al., 2014). Upon primary stimulation, TI inducers activate signalling cascades and downstream transcription factors (TFs) that promote the first-wave immune response. In addition, this signalling triggers two main processes that are required for TI induction: a profound metabolic reprogramming and the deposition of epigenetic marks on chromatin. These changes create a poised chromatin state that enables a heightened immune response to future stimuli (Netea et al., 2020; Ochando et al., 2023). Notably, despite leading to opposite functional states, tolerance is also regulated by chromatin modifications (Foster et al., 2007), indicating a shared cross-cutting mechanism of innate immune ‘memory’.

Epigenetics is defined as the heritable changes in gene expression that do not alter the DNA sequence, which are mediated by histone post-translational modifications and changes in DNA methylation (Allis and Jenuwein, 2016). Epigenetic reprogramming is believed to be one of the main molecular processes driving the enhanced cytokine production upon secondary stimulation in TI. This aligns with the similar concept of ‘transcriptional memory’ (Ostuni et al., 2013; Glass and Natoli, 2016). The metabolic and epigenetic rewiring that occurs during TI induction is highly interconnected (Martínez-Reyes and Chandel, 2020). It has been demonstrated that metabolic rewiring is a prerequisite for the epigenetic reprogramming that drives enhanced production of proinflammatory cytokines after secondary stimulation. In parallel, epigenetic rewiring drives metabolic changes that are required for macrophage function.

The timing of metabolic and epigenetic crosstalk in TI is crucial, as this interplay is both rapid and sustained (Arts et al., 2016b; van der Heijden et al., 2018). Shortly after the primary stimulation, a profound epigenetic reprogramming occurs. This epigenetic rewiring can influence the expression of metabolic enzymes, thereby modifying the metabolic adaptations required for macrophage immune responses. Nevertheless, the epigenetic changes are not completely fixed within the first hours after the primary stimulation (Novakovic et al., 2016), and changes in metabolism, such as increased glycolysis and production of intermediates like lactate, acetyl-CoA, and fumarate, can influence the activity of epigenetic enzymes (e.g. histone acetyltransferases [HATs] and demethylases). These metabolic changes can modulate epigenetic modifications in a period of time that spans from hours after the primary stimulation to even days after the initial trigger. In this review, we aim to highlight the epigenetic-metabolic crosstalk in TI in monocytes and macrophages, distinguishing the primary metabolic changes that influence early epigenetic adaptations driving TI induction from secondary metabolic adaptations that are a consequence of this epigenetic rewiring. Finally, we overview methodological techniques to explore the metabolic reprogramming during TI induction.

Cell metabolism can be seen not only as a means of macromolecule and energy synthesis but also as a way to communicate external cues to elicit specific functional responses. One way that this communication occurs is through changes in metabolite levels that can regulate epigenetic modifications (Martínez-Reyes and Chandel, 2020; Etchegaray and Mostoslavsky, 2016; Britt et al., 2020). Cellular metabolites and especially mitochondrial TCA cycle metabolites play a key role in this communication, acting as substrates or cofactors for enzymes that affect DNA methylation status and modification of histones. Thus, by retrograde signalling, cytosolic and mitochondrial metabolism influences the expression of nuclear-encoded genes by modulating epigenetic modifications (Martínez-Reyes and Chandel, 2020; Chandel, 2015). Another demonstrated mechanism of mitochondrial-nuclear communication is the nuclear translocation of mitochondrial proteins (Monaghan and Whitmarsh, 2015). While the cellular compartmentalisation of metabolites and proteins has not been addressed in the context of TI, this may be an important factor in metabolic regulation of transcription. Epigenetic rewiring, which increases the accessibility of innate immune genes and poises them for transcription, underlies the enhanced production of proinflammatory cytokines in TI. High-throughput sequencing technologies (Assay for Transposase-Accessible Chromatin using sequencing [ATAC-seq], ChIP-seq, and CUT&TAG) have revealed that TI-inducing stimuli trigger a genome-wide expansion of accessible chromatin (Novakovic et al., 2016; Saeed et al., 2014; Cai et al., 2025). For instance, β-glucan stimulation leads to a global epigenetic change at distal regulatory elements (enhancers) and promoters, which remain poised even after the initial stimulus is removed (van der Heijden et al., 2018; Novakovic et al., 2016; Saeed et al., 2014). In this context, multiple metabolic pathways that contribute to the initiation and/or maintenance of the epigenetic rewiring that assists the trained phenotype have been described.

Histone acylation: acetyl-CoA and lactate

One of the main epigenetic modifications described to contribute to the trained phenotype is histone acylation, with histone acetylation being one of the first and most commonly studied to date (Novakovic et al., 2016; Saeed et al., 2014; Cheng et al., 2014). HATs, assisted by transcription factors (TFs) that help to direct its activity, promote acetylation using acetyl-CoA as a substrate, generally enhancing transcriptional activity by generating H3K9ac and H3K27ac, which neutralise the positive charges on lysine residues, leading to a more open chromatin structure (Figure 1). In turn, acetyl marks also recruit TFs and chromatin remodelling complexes to activate transcription (Allis and Jenuwein, 2016). TI inducers such as β-glucan (Arts et al., 2016b; Novakovic et al., 2016; Saeed et al., 2014; Cheng et al., 2014), heme (Jentho et al., 2021), and BCG (Arts et al., 2018; Kaufmann et al., 2018; Vierboom et al., 2021), among others, promote the deposition of H3K27ac, which specifically marks active promoters and enhancers, triggering a massive reconfiguration of the epigenome. H3K27ac was initially described to be one of the key and highly dynamic marks upon β-glucan stimulation of human monocytes, more often at distal regions compared to regions nearby the transcription start site (Novakovic et al., 2016; Saeed et al., 2014). The initial deposition of histone marks is assisted by the signalling pathways elicited by the primary stimulation. TF motif analyses revealed that motifs for Basic Leucine Zipper (bZIP) subfamily members, specifically Activating Transcription Factor 1 (ATF1), ATF7, cAMP Responsive Element Binding Protein 3 (CREB3), and Jun dimerisation protein 2 (JDP2), along with the glucocorticoid receptor (NR3C1), were significantly enriched at dynamic H3K27ac sites induced by β-glucan (Saeed et al., 2014). Notably, tolerised macrophages can be, at least partially, rescued by β-glucan treatment that can induce most of the H3K27 deposition at the regions silent in tolerised macrophages (Novakovic et al., 2016). When comparing β-glucan and LPS-treated monocytes, β-glucan-induced enhancers were specifically enriched for TF motifs such as Early Growth Response 2 (EGR2), a downstream element of the Dectin-1 pathway. These EGR2-linked regions were associated with lipid metabolism and lysosomal genes. This global H3K27ac reprogramming across the genome suggests that TI does not merely switch on a few genes, but rather lowers the kinetic barrier for an entire network of thousands of loci involved in distinct processes, including cytokine production, leukocyte activation, phagocytosis, and cellular metabolism.

Figure 1

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These dynamic changes in histone acetylation are associated with accumulation of acetyl-CoA during TI induction by various stimuli (Arts et al., 2016b; Sohrabi et al., 2020; Findeisen et al., 2022). Acetyl-CoA can be generated from several metabolic pathways (Figure 1). For instance, increased glycolysis results in the production of pyruvate, which is transported into the mitochondria and converted to acetyl-CoA by the pyruvate dehydrogenase complex. Moreover, fatty acid β-oxidation breaks down fatty acids into acetyl-CoA molecules in the mitochondrial matrix. Nevertheless, for acetyl-CoA to impact histone acetylation, these molecules must act in the cytosolic/nuclear fraction (Wellen et al., 2009). Acetyl-CoA-derived citrate within the TCA cycle can be exported to the cytosol and cleaved by the enzyme ATP citrate lyase (ACLY) to generate acetyl-CoA as a precursor in mevalonate/cholesterol biosynthesis and for the acetylation of histones and other proteins (Wellen et al., 2009; Figure 1). Trained immune cells have been found to upregulate several of the pathways that lead to acetyl-CoA accumulation, such as glycolysis (Cheng et al., 2014; Arts et al., 2016a; Keating et al., 2020a), fatty acid oxidation (Groh et al., 2021), and fatty acid synthesis (FAS) (Arts et al., 2016b). This suggests that the upregulation of these metabolic pathways could contribute to the build-up of acetyl-CoA and histone acetylation during TI induction.

Several studies have also found evidence directly linking acetyl-CoA accumulation upon training induction to histone acetylation at relevant genes for innate immune function. In the case of primary oxLDL stimulation TI, the activity of Liver X Receptor (LXR) increased levels of acetyl-CoA. This accumulation was accompanied by increased expression of ACLY, which was reduced upon treatment with an LXR antagonist, correlating with heightened cytokine responses to secondary stimulation (Findeisen et al., 2022). In addition, the increased H3K27ac marks at TNF and IL6 promoters induced by oxLDL stimulation were reduced by pre-treatment with LXR antagonists, indicating that oxLDL-induced TI is partially mediated by LXR activity, increased acetyl-CoA, and H3K27ac deposition at the promoters of proinflammatory cytokines (Findeisen et al., 2022). In line with this study, direct LXR activation alone induced a trained phenotype in human monocytes by increasing acetyl-CoA and deposition of H3K27ac at the promoters of TNF and IL6 (Sohrabi et al., 2020). In addition, mevalonate training was accompanied by global changes in H3K27ac, including inflammatory and non-inflammatory pathways, as measured by whole-genome epigenetic analysis (Bekkering et al., 2018). Nevertheless, in this study, the accumulation of acetyl-CoA was not studied, and a causative role between H3K27ac and acetyl-CoA through mevalonate cannot be affirmed. Instead, the study indicates that mevalonate training induces global H3K27ac changes via the IGF1-R/mTOR axis, modulating key inflammatory pathways. In the case of β-glucan-induced TI, one study suggested that the accumulation of acetyl-CoA and increased histone acetylation observed after stimulation is primarily driven by lactate rather than glucose (Cai et al., 2025). Lactate is taken up by the cells and converted to acetyl-CoA through a pathway involving the enzymes lactate dehydrogenase (LDH), pyruvate dehydrogenase, and ACLY. Inhibiting any of these enzymes blocked the β-glucan-induced production of acetyl-CoA, and isotope tracing confirmed that lactate was the main carbon source for this process. Interestingly, while blocking ACLY reduced histone acetylation at specific sites (such as H3K27ac and H3K18ac), it did not affect the production of key inflammatory cytokines (IL-6 and TNF), nor did it impact bacterial clearance, a role that was more ascribed to histone lactylation (Cai et al., 2025). Moreover, acetyl-CoA-mediated histone acetylation has not only been reported to affect innate immune genes, but it can also promote upregulation of glycolytic genes such as hexokinase and LDH (Wellen et al., 2009), which could in turn affect TI induction (Cheng et al., 2014).

Nevertheless, while the correlation between acetyl-CoA accumulation and histone acetylation is well established, a significant knowledge gap remains regarding the direct mechanistic coupling of specific metabolic fluxes to global epigenetic reprogramming. On one hand, the epigenetic landscape of TI has been extensively mapped across various inductors, and metabolic studies have demonstrated the necessity of certain pathways for TI induction (Novakovic et al., 2016; Saeed et al., 2014; Arts et al., 2016b; Cheng et al., 2014; Keating et al., 2020b). Yet, these studies often rely on measuring histone modifications at a few specific loci (e.g. TNF or IL6 promoters) rather than assessing how specific metabolic disruptions, such as the inhibition of ACLY (Cai et al., 2025), alter the global deposition and maintenance of these marks. Integrating precise metabolic manipulations with unbiased, genome-wide profiling represents an exciting opportunity to move beyond correlative observations and decipher the systemic contribution of metabolites to the trained phenotype.

The recently identified modification, histone lactylation (Kla), provides a compelling example of how a specific metabolite fuels broader genomic impact over time. In this process similar to acetylation, lactyl-CoA derived from lactate is covalently attached to lysine residues on histone proteins via the acetyltransferase p300 (Zhang et al., 2019; Figure 1). The addition of lactyl groups to histones loosens chromatin structure and promotes the expression of specific genes, such as those involved in wound healing and tissue repair in macrophages (Zhang et al., 2019; De Leo et al., 2024; Desgeorges et al., 2024). Histone lactylation connects cellular metabolic activity, particularly lactate generation from glycolysis-derived pyruvate, to the control of gene expression. Given the common signature of increased glycolysis and lactate production after stimulation with several TI inducers (Cheng et al., 2014; Arts et al., 2016a; Keating et al., 2020a), it was proposed that histone lactylation could have a role in the epigenetic rewiring occurring during TI induction (Ziogas et al., 2025). Human monocytes from individuals vaccinated with BCG showed long-term H3K18 lactylation, displaying 4791 dynamic peaks upon BCG across the genome, predominantly at distal enhancer regions rather than promoters, enhancing transcription (Ziogas et al., 2025). A relevant example was the deposition of H3K18la in regulatory regions of the IL1B gene, including its enhancer and promoter, and also long-range contact sites and CTCF-binding sites, suggesting a potential involvement in modifying gene architecture and regulating IL-1β expression in trained macrophages (Ziogas et al., 2025). In addition to this important locus, and compared with LPS-tolerised macrophages, pathway analysis of peaks specific to BCG-trained macrophages revealed links to the endosomal ESCRT-III complex and NOD2 signalling. Inhibition of glycolysis or lactate production diminished the deposition of H3K18la and also TI induction, which was also suppressed by inhibitors of the HAT p300 (Ziogas et al., 2025). Similar to BCG, β-glucan stimulation of human monocytes led to global enhanced histone lactylation, specifically H3K27la in an LDHA-dependent manner, which resembled accessibility data by ATAC-seq. Immunoblot analyses showed that global histone lactylation levels were more elevated than global acetylation levels following β-glucan stimulation of monocytes (Cai et al., 2025). In addition to this widespread epigenetic change, H3K27la peaks were highly enriched at the promoters of genes associated with cell adhesion and innate immune response, such as TNF, IL6, PSGL, and ICAM1, which contributed to trained phenotypes (Cai et al., 2025; Figure 1). This histone lactylation mark was more associated with promoter regions than enhancer regions, as was the case for H3K18la, suggesting that different lactylation marks could occur preferentially at different genomic locations depending on the TI inducer (Cai et al., 2025; Ziogas et al., 2025). Beside the effect of lactate on histone lactylation, lactate can also have a profound effect on immune cell metabolism and function (Cappellesso et al., 2024). In β-glucan-treated macrophages, lactic acid fuels the TCA cycle to promote the production of citrate (Cai et al., 2025). The lactic acid-derived citrate is used as a primary source for H3K27 acetylation. In a context of LPS primary stimulation, which decreases global H3K27ac levels, lactate restores global H3K27ac while having no effect on other histone marks such as H3K28ac, H3K24ac, and H3K8ac, as observed by immunoblots. This study reported that H3K27ac driven by lactate promoted the expression of genes involved in chromatin regulation, such as the TF NR4A1 which, in turn, reduces the expression of inflammatory genes by altering chromatin accessibility. In this way, lactic acid can induce a form of long-term immunosuppression, named ‘trained immunosuppression’ (Shi et al., 2024). These studies highlight the close crosstalk between metabolic and epigenetic changes that together regulate innate immune memory.

Nevertheless, these studies leave several open questions. Mechanistically, it was identified that acetylation and lactylation operate on different timescales (Zhang et al., 2019). Histone acetylation acts as a rapid response, peaking within 3–6 hr to drive early proinflammatory genes, but these levels then decrease or slow down during the later stages of the response. In contrast, histone lactylation follows a slower trajectory, steadily increases over 24 hr to trigger homeostatic and M2-like genes, such as Arg1, which promotes wound healing during the late phase of infection (Zhang et al., 2019). This dual nature of lactylation acting as a ‘lactate clock’ to terminate initial inflammation in acute settings while functioning as a persistent memory mark that poises pro-inflammatory loci in TI raises a fundamental question. Analysing the specific contributions of acetylation and lactylation in long-term reprogramming is essential, especially given their distinct kinetics and that their actions may be context-dependent and stimulus-dependent rather than phenotypically fixed.

The fact that histone lactylation can potentiate proinflammatory genes during TI, while promoting homeostatic or immunosuppressive genes in other contexts, suggests a dual role. This indicates that the action of lactate and by extension the accumulation of acetate derived from lactate for histone acetylation do not seem to control the phenotypic polarisation that they exert, but they act as merely substrate reservoirs. According to this model, the metabolite environment provides the necessary fuel, while signalling cascades and the activation of specific signal-dependent TFs, such as the PERK-ATF4 axis in tumour-associated macrophages or AP-1 and ATF3 in BCG-trained cells, orchestrate the genomic specification, directing the marks to where they are functionally needed (De Leo et al., 2024; Ziogas et al., 2025). Additionally, the discovery of ‘trained immunosuppression’, where lactic acid drives a specific H3K27ac-NR4A1 axis to promote stable tolerance (Shi et al., 2024), suggests a need to investigate whether lactylation also poises homeostatic programmes in TI to balance the enhanced secondary response and prevent excessive tissue damage.

Histone deacetylases (HDACs) catalyse the removal of acetyl groups from histone tails, increasing the positive charge of histones leading to a more compact chromatin structure. This reduces accessibility for TFs and RNA polymerase, thus suppressing gene expression. However, these HDACs are not acetylation-specific and have been shown to remove other histone acylations, such as histone lactylation and succinylation (Moreno-Yruela et al., 2022; Li et al., 2023). Class III HDACs are composed of sirtuins, which catalyse the removal of acetyl groups in an NAD⁺-dependent manner (Xu et al., 2024; Figure 1). NAD⁺ and NADH are central redox cofactors that receive or donate electrons, participating in a wide range of metabolic pathways. The balance between this redox pair affects immunomodulatory functions and immune responses (Zheng et al., 2022; Quinn et al., 2020; Minhas et al., 2019). Regarding TI induction, it was previously described that an increase in the NAD⁺/NADH ratio was detected in human monocytes 3 and 6 days, but not 1 day, after β-glucan stimulation (Cheng et al., 2014), suggesting that NAD⁺ biology could impact trained responses. One of the pathways that contribute to NAD⁺ generation is the salvage pathway, which utilises enzymes like nicotinamide phosphoribosyltransferase (NAMPT) to convert nicotinamide to nicotinamide mononucleotide (NMN), a precursor to NAD⁺. The NAD⁺ salvage pathway contributes to macrophage inflammatory activity and primary macrophage responses (Cameron et al., 2019; Venter et al., 2014). Moreover, stimulation of microglia with B cell-activating factor to induce a proinflammatory memory-like response increased the NAD⁺/NADH ratio of microglia after 5 days of stimulation (Wang et al., 2021). Nevertheless, mechanistic experiments are needed to clarify how NAD⁺ contributes to the early changes in histone acetylation and their role in TI induction.

Finally, it is important to note that while histone acetylation and lactylation are more widely studied, the contribution of other histone acylations such as histone succinylation, malonylation, or β-hydroxybutyrylation may also play a role in TI (Baldensperger and Glomb, 2021; Xie et al., 2016). However, new tools will be required to decipher their specific roles.

While epigenetic changes in trained macrophages have been directly linked to increased cytokine induction, transcriptional regulation of metabolic pathways or changes in their activation could also indirectly allow superior immune responses in these cells due to increased availability of nucleotides, proteins, and lipids required to mount an immune response. Macrophages are known to be metabolically flexible cells, and this allows them to function in a wide range of physiological niches (Wculek et al., 2022). Upon activation by cytokines or PAMPs, macrophages increase their bioenergetic demands. While the epigenetic consequences of training are discussed above, we will now focus on the metabolic consequences that occur post-transcription that can affect the secondary response. Such adaptations provide the resources required for mRNA transcription, protein synthesis, membrane biogenesis, and redox homeostasis, thereby supporting prolonged and enhanced cytokine production that underlies a trained immune response (Figure 2). Further studies are required to investigate the role of distinct metabolic pathways in the TI response. Here, we aim to summarise what studies have revealed so far about the role of metabolism in supporting the TI phenotype.

Figure 2

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Until now, most analyses in TI are performed at a single timepoint, or at a limited set of time intervals at best. As discussed earlier, it is important to discriminate between early and late metabolic changes in TI and we would therefore advocate for more extensive kinetic profiling of the epigenetic and metabolic state of myeloid cells in future studies. Currently, the technologies that are applied to profile metabolic and subsequent epigenetic changes in TI range from very simple multi-well-plate-based assays to high-end OMICs analyses. To investigate epigenetic changes in response to metabolic reprogramming, western blotting can be performed using antibodies directed against histone modifications of interest. This approach has the capacity to detect broad epigenetic changes but doesn’t provide insight into the epigenetic landscape of regulatory region of specific (inflammatory) genes. To obtain such information, one should perform chromatin immunoprecipitation (ChIP) with antibodies targeting specific epigenetic marks of interest. Next, a quantitative PCR can be performed with specific primer pairs to compare DNA fragment abundance in precipitate versus non-precipitated (input) samples. A more sophisticated unbiased approach is to sequence the immunoprecipitated chromatin (ChIP-seq). These ChIP-seq experiments are frequently accompanied by ATAC-seq and RNA-seq to obtain complementary information about specific epigenetic marks, chromatin accessibility, and the expression of the associated genes. ChIP-based studies come with the difficulty that they rely on good antibody clones, and that protocols are not fully standardised and may require trial-and-error-based optimisations that complicate progress and comparison with the field. Also, it remains difficult to probe the relative abundance, and hence importance of, the multitude of histone modifications that can occur and how they influence each other, and eventually how they regulate TI.

Both ATAC-seq and RNA-seq are becoming regularly used at the single-cell level and together with the emergence of cell-based metabolic techniques, it is clear that TI research is making the transition towards investigations at single-cell resolution. Another apparent evolution in the field is the study of epigenetic architectures and long-range contact between regulatory regions in the genome. For example, Ziogas et al. recently showed how glycolysis-derived H3K18la marks affect long-range contacts in the regulatory regions of inflammatory genes like Il1b, thus identifying a role for metabolic rewiring and subsequent histone lactylation in modifying gene architecture (Ziogas et al., 2025).

Moreover, it should be emphasised that most current techniques are still largely candidate-based targeted approaches that come with the limitation that they provide a biased and non-comprehensive view. Newer mass spectrometry (MS)-based characterisation of PTMs can reveal a more complete and quantitative analysis of histone marks in TI (Noberini et al., 2022; Thomas et al., 2020). While MS can reveal the existing modifications and their relative abundance in great detail, it does not provide information about their position. As such, a combination of complementary techniques will be needed to uncover a complete picture containing unbiased quantitative information about histone marks and their relative contributions (MS), distribution of specific marks across the genome (ChIP-seq), how this affects DNA accessibility (ATAC-seq), and finally gene transcription (RNA-seq).

For immunometabolic profiling, commercial colourimetric or fluorimetric assay kits can be used to measure intracellular and extracellular levels of lactate, acetyl-CoA, and other key metabolites that govern epigenetic modifications in TI. A broader range of metabolites can be assessed using MS-based targeted and untargeted metabolomics. Importantly, those bulk techniques do not reveal the localisation of metabolites across different organelles in the cell. Subcellular fractionation, followed by metabolite measurements, can be performed to obtain such information. This is relevant since multiple metabolic enzymes that generate metabolites needed for histone modifications have been shown to travel to the nucleus where they can increase concentrations at the location where they are consumed by histone-modifying enzymes (Lazaropoulos et al., 2024; Shao et al., 2024).

While metabolomics data can provide a snapshot of metabolite abundance at a certain timepoint, applying ¹³C stable isotopes enables the quantification of intracellular fluxes through diverse metabolic pathways and is often termed fluxomics or ¹³C metabolic flux analysis. Most of the metabolic readouts applied in TI are bulk analyses and it should be noted that novel approaches that interrogate metabolism at the single-cell level are becoming more regularly applied in the immunometabolism field (Artyomov and Van den Bossche, 2020; Cosgrove et al., 2024). These include cytometry-based approaches like ‘Met-Flow’ (Ahl et al., 2020) and single-cell metabolic profiling (scMEP Hartmann et al., 2021) via CyTOF in which fluorescently labelled or heavy-metal-tagged antibodies against key transporters and rate-limiting enzymes across multiple metabolic pathways are applied to estimate the metabolic configuration of immune cells at a per-cell level. So far, both Met-Flow and scMEP haven’t been applied in the TI context, but the recent use of SCENITH and CENCAT approaches indicate the field is moving from bulk towards single-cell metabolic analyses.

Aside from analysis of expression levels, there also exist functional readouts of metabolic activity. The most commonly used of these is the Seahorse extracellular flux analysis. This technique is commonly used in immunometabolism and TI research to measure glycolytic and mitochondrial metabolism via real-time measurements of protons and oxygen to calculate the extracellular acidification rate and oxygen consumption rate, respectively. However, this analysis requires a certain cell number which can be difficult to achieve in scarce populations and does not give single-cell data. A complementary single-cell approach is SCENITH – a flow cytometry-based methodology that uses puromycin incorporation as a proxy for protein synthesis, a process that is responsible for a large proportion of total ATP usage by a cell (Argüello et al., 2020). Using the glycolysis inhibitor 2-deoxyglucose and mitochondrial ATP synthase inhibitor oligomycin and assessing their effect on puromycin incorporation, one can thus calculate the glycolytic versus mitochondrial dependencies of immune cell subsets. This approach was recently applied to explore metabolic changes in ‘trained’ γδ T cells upon measles, mumps, rubella (MMR) vaccination (Röring et al., 2024). CENCAT is an alternate click chemistry-based measurement of protein synthesis via biorthogonal noncanonical amino acid tagging to profile the metabolic dependencies of cell subsets (Vrieling et al., 2024). This new method was recently applied to measure the bioenergetic profile of BCG-trained versus LPS-tolerised macrophages (Ziogas et al., 2025), albeit at a bulk level. The power of the cytometry-based metabolic profiling approaches is that they allow the assessment of metabolic and immune phenotypes simultaneously and a single-cell or subset resolution. They thus have the potential to address key outstanding questions in the field by uncovering how specific monocyte subsets and phenotypes are ‘trained’ and differentiate into macrophages.

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Author details

1. Aitor Jarit-Cabanillas

   1. Immunobiology Laboratory, Centro Nacional de Investigaciones Cardiovasculares CNIC, Madrid, Spain
   2. Escuela de Doctorado, Universidad Autónoma de Madrid, Madrid, Spain

   Contribution

   Conceptualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5630-1134

2. Gillian Dunphy

   Immunobiology Laboratory, Centro Nacional de Investigaciones Cardiovasculares CNIC, Madrid, Spain

   Contribution

   Conceptualization, Supervision, Writing – original draft, Writing – review and editing

   For correspondence

   gillian.dunphy@cnic.es

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0991-4565

3. Federico Virga

   Immunobiology Laboratory, Centro Nacional de Investigaciones Cardiovasculares CNIC, Madrid, Spain

   Contribution

   Conceptualization, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2523-2070

4. Jan Van den Bossche

   Department of Molecular Cell Biology and Immunology, Amsterdam Institute for Immunology and Infectious Diseases, Amsterdam Cardiovascular Sciences, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, Netherlands

   Contribution

   Conceptualization, Writing – original draft, Writing – review and editing

   For correspondence

   j.vandenbossche@amsterdamumc.nl

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-7852-2891

5. David Sancho

   Immunobiology Laboratory, Centro Nacional de Investigaciones Cardiovasculares CNIC, Madrid, Spain

   Contribution

   Conceptualization, Supervision, Funding acquisition, Writing – original draft, Writing – review and editing

   For correspondence

   dsancho@cnic.es

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2890-3984

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