Two-photon fluorescence lifetime imaging microscopy (2P-FLIM) is a non-destructive modality that can interrogate exogenous and endogenous fluorophores. 2P-FLIM provides high spatial and temporal resolution to image murine and human cell types, 2D and 3D cell cultures, and biopsies in vitro and in vivo (Skala et al., 2007, Okkelman et al., 2019, Neto et al., 2020). 2P-FLIM allows reduced nicotinamide adenine dinucleotide (NAD(P)H) and flavin adenine dinucleotide (FAD⁺) to be studied and quantified using 2P-FLIM, giving insight into cellular metabolism (Lakowicz et al., 1992; Skala et al., 2007, Neto et al., 2020). The average time for a fluorophore to return to the ground state from the excited state while emitting fluorescence is known as the fluorescence lifetime. NADH and NADPH fluorescence properties are identical, and hence NAD(P)H refers to these intracellular pools combined (Huang et al., 2002). NAD(P)H is noted as having a long fluorescence lifetime when enzyme-bound and a short fluorescence lifetime when free in the cytoplasm. NAD(P)H fluorescence properties offer extended information into NAD(P)H protein/enzyme binding (Lakowicz et al., 1992). 2P-FLIM facilitates the quantification of NAD(P)H fluorescence lifetimes and their respective proportions. In addition, 2P-FLIM can be used as a microscopy-based spatial approach and applied to limited cell numbers, which is beneficial in assessing limited sample numbers or validating metabolism-based therapies (Peterson et al., 2018; Shields et al., 2020).

Macrophages are essential components of the host immune response and key regulators of homeostatic function. In addition to host defence, macrophages are intimately involved in tissue homeostasis and play a key role in pathologies including heart failure, diabetes, and cancer (Mosser and Edwards, 2008). Macrophages adopt specific polarisation states, ranging in a spectrum, to accomplish various functions and mechanisms of action. Here, classically activated (M1) and alternatively activated (M2) macrophages occupy opposite ends (Gordon, 2003; Mosser and Edwards, 2008). In vitro, M1 macrophage behaviour can be evoked using IFNγ and/or microbial products such as LPS. M1 macrophage phenotype is defined by secretion of a significant amount of pro-inflammatory cytokines: TNFα and IL-1β enhanced endocytosis and ability to kill intracellular pathogens (Adams, 1989; Martinez et al., 2008).

M2 macrophage behaviour exists more heterogeneously and can be triggered by IL-4 or IL-13, IL1-β, TGF-β, IL-6, phagocytosis of apoptotic cells, or association with a tumour microenvironment, respectively, generating M2a, M2b, M2c, M2f, and tumour-associated macrophages (TAM) (Mantovani et al., 2002; Mantovani et al., 2017; Graney et al., 2020). M2 macrophages exhibit decreased expression of protein membrane markers, such as CD14 and CCR5, and increased fibronectin-1 production. In addition, M2 macrophages are also characterised by the downregulation of pro-inflammatory cytokines (Wang et al., 1998; Gratchev et al., 2001; Martinez et al., 2008). Most often, macrophage behaviour is assessed by endpoint assays using cytokine measurements, gene analysis, and staining of surface markers. However, there is a gathering shift towards non-invasive modalities to speed up this process and obtain spatio-temporal analysis. Two recent examples of this include the use of Raman microscopy to map the lipidomic spatial signature of polarised macrophages (Feuerer et al., 2021), and the use of average fluorescent lifetime parameters to discern murine macrophage phenotype (Alfonso-García et al., 2016).

We use an imaging-based approach, primarily focusing on metabolic machinery characteristically employed by polarised human macrophages. Macrophage metabolism poses a huge potential in the next generation of therapeutics for inflammatory disease. Human macrophage function and metabolism are inextricably linked (Van den Bossche et al., 2017). IFNγ-activated human macrophages (IFNγ-M1) are primed for enhanced inflammatory responses by stabilising HIF1α levels, activation of the JAK-STAT pathway, and increased production of IL-1β, all of which are dependent on enhanced levels of glycolysis (Wang et al., 2018). Alternatively activated human anti-inflammatory macrophages, most often observed in vitro through stimulation with IL-4 (IL-4-M2), are defined by an intact tricarboxylic acid cycle (TCA), enhanced OxPhos, increased fatty acid synthesis (FAS), and fatty acid oxidation (FAO) (O’Neill et al., 2016, Van den Bossche et al., 2017). In a nutshell, IFNγ-M1 macrophages are more active in glycolysis, while IL-4-M2 macrophages are more dependent on oxidative phosphorylation for their energy production. When assessing metabolism and bioenergetics, most methods are based on extracellular flux assays, measurement of cellular oxygen consumption, exogenous staining, radio-labelling nutrients, and gas chromatography-mass spectrometry (GC-MS). Metabolism probing methods require a substantial amount of sample processing yet still pose limitations due to short-lived oxidative metabolites (Vivekanandan-Giri et al., 2011; Koo et al., 2016; Fall et al., 2020; Ma et al., 2020).

2P-FLIM imaging of NAD(P)H acquires five fluorescence lifetime variables (τ₁, τ₂, α₁, α₂, τ_avg) and one fluorescence intensity-based variable (optical redox ratio [ORR]) descriptive of the polarisation linked-metabolic state of IFNγ-M1 and IL-4-M2 macrophage phenotypes. Furthermore, additional information can be obtained from these measurements by real-time perturbation of basal metabolism and metabolic capacity. To achieve this, sequential knockdown of intercellular pathways and uncomplexing of mitochondrial coupling is performed in this study via metabolic inhibitors.

For data visualisation purposes, we applied the Uniform Manifold Approximate and Projection (UMAP) technique, in favour over principal component analysis (PCA), due to its processing speed, capability to preserve the global and local structures of the data, and ability to use non-metric distance functions (McInnes et al., 2018). For in-depth statistical analysis, machine learning algorithms are employed for classification/supervised pattern recognition (Mohri and Rostamizadeh, 2012). Machine learning algorithms have been used to characterise DNA- and RNA-binding proteins, determine genetic and epigenetic contributions of antibody repertoire diversity, and classify chronic periodontitis patients based on their immune cell response to ex vivo stimulation with ligands (Alipanahi et al., 2015; Bolland et al., 2016; Culos et al., 2020). In addition, machine learning algorithms can be used to group samples into different classes (e.g., in this study IFNγ-M1 vs. IL-4-M2 based on 2P-FLIM variables) whilst determining which variables are the most important for this task (Touw et al., 2013). For our work, we employed the random forests algorithm for the classification task due to its high prediction accuracy, robustness to outliers, and ability to obtain the relative importance of each variable (Breiman, 2001; Verikas et al., 2011). Another advantage of the random forests model is that we can use the out-of-bag (OOB) error estimate to determine the values of the hyper-parameters, no need to set aside an independent dataset for validation. This makes random forests especially suitable for small datasets, where we do not have additional data for validating the model. The hyper-parameters of the random forests model are the number of trees (ntree) and the number of variables selected for the best split at each node (mtry). The OOB error of the final optimal random forests model serves as the estimate of the model’s prediction error.

The trained random forests model will be used to distinguish between the different populations of macrophages and to measure which 2P-FLIM variables are the most important for this differentiation. In our study, we used all data points obtained from both full field-of-view (FoV) and donor-specific single-cell images for training and validating the random forests model. Thus, there is no extra data available for testing our trained model. Therefore, to demonstrate the predictive ability and efficiency of the trained model, we calculated the area under the receiver operating characteristics curve (ROC-AUC), in which the specificity and sensitivity of the trained model are plotted. The ROC-AUC statistic is a performance evaluation metric for (binary) classification models. ROC curves plot the true positive rate against the false-positive rate at various threshold values. The AUC is the measure of the ability of a classifier to distinguish between two classes and is used as a summary of the ROC curve. An ROC-AUC value less than 0.5 suggests no discrimination, 0.7–0.8 is considered acceptable, 0.8–0.9 is considered excellent, and more than 0.9 is considered outstanding (Mandrekar, 2010). Similar applications of machine learning methods have been explored by Walsh et al. for classification of activated t-cells and Qian et al. for quality control of cardiomyocyte differentiation (Walsh et al., 2020, Qian et al., 2021).

We hypothesise that 2P-FLIM of NAD(P)H and FAD⁺ provides quantitative information to evaluate and identify human-derived macrophage polarisation by proxy of their metabolism. 2P-FLIM of NAD(P)H and FAD⁺ has strong clinical potential fuelled by the emergence of metabolic approaches to treat disease and inflammation. To test our hypothesis, we derived human macrophages from blood-circulating monocytes and polarised them into IFNγ-M1 or IL-4-M2 macrophages. We confirmed human macrophage cytokine and gene expression-related polarisation, while human macrophage metabolic behaviour was assessed via traditional extracellular flux assays. Finally, 2P-FLIM was applied during which real-time responses of photonic variables to metabolism-challenging small molecules were measured. This study establishes 2P-FLIM as a method to discriminate IFNγ-M1 and IL-4-M2 macrophages, which is most pronounced by macrophages’ differential responses when treated with carbonyl cyanide-p-trifluoromethoxyphenylhydrazone (FCCP). This allows the accurate classification of macrophage polarisation using machine learning methods, for example, the random forests model, and to evaluate single-cell heterogeneity. Our work reports, for the first time, the classification of human macrophages by random forests using data obtained from real-time metabolic perturbations triggered during 2P-FLIM.

In this study, we use macrophage polarisation as a model system to demonstrate the feasibility and effectiveness of the random forests model for classification, applied to 2P-FLIM parameters influenced by metabolic perturbations (Figure 1). The polarisation of human macrophages is often crudely described as two opposite phenotypes: classical activation (IFNγ-M1-macrophages) and alternative activation (IL-4-M2 macrophages). The higher production of TNFα in IFNγ-treated human macrophages and low IL-10 production are evidence of a macrophage classical activation (Tokunaga et al., 2018; Figure 2A and B). In contrast, treating human macrophages with IL-4, an increase in IL-10 production and higher expression of MRC1 and CCL13, are characteristic of an alternative activation of macrophages (Martinez et al., 2006; Artyomov et al., 2016; Figure 2D–F). Furthermore, we performed flow cytometry of polarised macrophages using antibodies to detect CD80, CD86, CD163, and CD206 surface markers, further validating the intended macrophage polarisation (Figure 2—figure supplement 1). Extending from this, extracellular flux analysis was performed. Extracellular flux measurements revealed a higher acidification ratio (ECAR) and a lower OCR for IFNγ-M1 macrophages during the different stages of metabolic inhibition when compared with IL-4-M2 macrophages (Figure 2G and J). By calculating the basal glycolysis rate and maximum glycolysis, IFNγ-M1 macrophages presented higher glycolytic rates (Figure 2H and I). The acidification (from ECAR) is linked with the production of lactate as a by-product of glycolysis, which reduces extracellular pH (Wang et al., 2018). Regarding IL-4-M2 macrophages, a reduced ECAR and increased OCR during the extracellular flux treatments (Figure 2G and J), together with a higher max respiration potential, were observed when compared with untreated macrophages. However, no difference was observed at the basal respiration measures (Figure 2K and L). ECAR and OCR calculated results are associated with a higher dependence of OxPhos as a more active metabolic machinery in IL-4-M2 macrophages. In order to fuel the upregulation of the TCA cycle and the ETC, the mitochondria need to consume more oxygen at the complex IV site of the electron transport chain (Van den Bossche et al., 2015; O’Neill et al., 2016).

We next sought to underline 2P-FLIM as a complimentary and, more advantageously, a non-invasive spatial evaluation of macrophage metabolism reflecting macrophage-induced polarisation. Recapitulating the sequence of metabolic enzyme inhibition which formed a basis of the extracellular flux analysis, sequential 2P-FLIM micrographs of IFNγ- or IL-4-polarised macrophages were acquired (Figure 3A). A reduced τ_avg, observed with IFNγ-M1 macrophages, reflects a higher relative amount of free NAD(P)H (which has characteristic short fluorescence lifetimes), indicative of glycolysis (Walsh et al., 2013; Perottoni et al., 2021). In contrast, IL-4-M2 macrophages had higher τ_avg-reflective of OxPhos due to increased proportion of longer lifetime, protein-bound NAD(P)H (Okkelman et al., 2019, Walsh et al., 2020; Figure 3B). Varying interpretations of ORR are reported in previous studies (Varone et al., 2014; Walsh et al., 2020), and for this study, we adopt a lower ORR reflecting a higher fraction of NAD(P)H and a lower FAD⁺ associated with upregulation of OxPhos (Equation 3; Neto et al., 2020). IL-4-M2 macrophages exhibited a lower ORR across the treatments in contrast with IFNγ-M1 macrophages, validating a higher dependency of IL-4-M2 macrophages on OxPhos when compared with IFNγ-M1 macrophages. A heatmap overviewing the 2P-FLIM output variables was compiled, showcasing the shifts promoted by the different treatments in both phenotype-directed human macrophages (Figure 3D). With IL-4-M2 macrophages, there is a noticeable increase in τ₁, τ₂, and τ_avg and an appreciable decrease in ORR when compared with IFNγ-M1-treated macrophages. The trending increase of NAD(P)H fluorescence lifetimes is further exacerbated after treatment with FCCP in IL-4-M2 macrophages. Indeed, given the ability of FLIM to imaging and measure NAD(P)H and FAD⁺, others have employed small molecules to gain further information about the dynamics of metabolic machinery in states of disease and differentiation. For instance, during stem cell osteogenic differentiation, Guo et al. tested oligomycin A (mitochondrial respiration inhibitor) as an experimental treatment in parallel to standard osteogenic media. A decrease in NAD(P)H average lifetime was calculated reflective of reduced oxidative phosphorylation. In addition, oligomycin A treatment promoted an increase in lactate production, lower oxygen consumption, and lower osteogenic differentiation (Guo et al., 2015). The heterogeneous response towards metabolic inhibitors by IFNγ-M1 or IL-4-M2 macrophages yields clustering patterns in the two-dimensional projected space via the UMAP method (Figure 3E). When analysing each treatment individually, measurements emanating from the FCCP treatment yielded the highest segregation across all donors between IFNγ-M1 and IL-4-M2 macrophages (Figure 3F). In addition, our phasor analysis (Figure 3—figure supplement 3) also demonstrates a higher segregation of IFNγ-M1 and IL-4-M2 macrophages when using FFCP treatment. Albeit, the limitation in pre-defining values for the fluorescence lifetime variables such as τ₁ and τ₂ can mask important predictors for the classification problem.

Higher NAD(P)H fluorescence lifetimes such as τ₁, τ₂, and τ_avg are attributable to two major factors: increases in NADPH concentrations and microenvironmental shifts (Blacker et al., 2014; Schaefer et al., 2019). FCCP functions as an uncoupler of mitochondria inner membrane allowing unhinged proton flux to the mitochondria matrix. Consequently, this proton flux causes a reduction of mitochondrial pH, increasing effectively the fluorescence lifetime of NAD(P)H. The unhinged proton flux due to FCCP compliments existing studies whereby Schaefer et al. reported an increased NAD(P)H fluorescence lifetime due to reduced mitochondria pH elicited by FCCP treatment (Blinova et al., 2005; Schaefer et al., 2017). Another consequence of FCCP treatment is an increase in ETC activity indicated by increased oxygen consumption of IL-4-M2 macrophages (Figure 2J). Increased ETC activity promotes an increase of NADH and FAD⁺ directly impacting the ORR. One would have expected the ORR to increase after the FCCP treatment as observed in IFNγ macrophages. However, for IL-4-M2 macrophages, the ORR begins to decrease. FCCP induction of maximal ETC activity increases the demand for NADH and FADH₂ causing a concomitant increase of the FAO and TCA cycle activity already upregulated in IL-4-M2 macrophages (Ludtmann et al., 2014). This demand results in a reduced pool of FAD⁺ and an increase of NADH, effectively reducing ORR (Ludtmann et al., 2014; Akie and Cooper, 2015; Viola et al., 2019). The higher heterogeneity in FCCP response is due to the effect of FCCP on the mitochondrial membrane and the higher dependence of FAS/FAO on IL-4-M2 macrophages basally (O’Neill et al., 2016). Blacker et al. reported the FCCP impact in great detail where, when seeking to separate NADH and NADPH fluorescence in live cells and tissues using FLIM, inhibiting mitochondrial oxidative phosphorylation in wild-type HEK293 cells using rotenone (10 μM) or uncoupling using FCCP (1 μM) (Blacker et al., 2014). The study of Blacker et al. provides excellent insights into NADH and NADPH dynamics and separation, and the treatment of FCCP has a similar effect on HEK293 cells as the IL-4-M2 macrophages reported in our study. Here, FCCP uncoupling promotes higher ETC, higher TCA activity impacting the ORR and, at the same time, a decreased mitochondrial pH increases the fluorescence lifetimes of NAD(P)H (Blinova et al., 2005; Schaefer et al., 2017). Regarding IFNγ-M1 macrophages, their lower dependence on ETC, TCA, FAO, and lower mitochondria membrane potential while producing most of ATP by glycolysis makes the impact of FCCP on the mitochondria and fluorescence lifetimes less pronounced.

Random forests models were applied to study the endpoint variables of the 2P-FLIM obtained during the FCCP treatment of IFNγ-M1 macrophages and IL-4-M2 macrophages. 2P-FLIM measurements contain a minimum of 24 cells per FOV and culminate in a total of 36 data points used for random forests training. For the trained random forests model, a low OBB error estimate and a high ROC-AUC value were achieved when classifying a population of IFNγ-M1 and IL-4-M2 macrophages (Figure 3G and H, Table 1). When the random forests model was trained using only three predictors, τ₁, τ₂, and τ_avg, the ROC-AUC classification accuracy decreased slightly from 0.944 to 0.934 (Figure 3G). The feature importance ranking based on mean decrease in accuracy and mean decrease in Gini indicates that τ₁, τ₂, and τ_avg are the most important 2P-FLIM variables for classifying an IFNγ-M1 or an IL-4-M2 macrophage when subjected to the FCCP treatment (Figure 3H). The relative importance of the three features, τ₁, τ₂, and τ_avg, implies that these FLIM features are divergent in IFNγ-M1 and IL-4-M2 macrophages. This outcome agrees with our previous results showing that FCCP highly impacts the NAD(P)H fluorescence lifetimes in IFNγ-M1 and IL-4-M2 macrophages (Figure 3D). The high predictive power of 2P-FLIM variables for classifying cell phenotype compliments the machine learning approach of Walsh et al., on classifying CD3⁺ and CD3⁺CD8⁺ T-cell activation (Walsh et al., 2020).

Precise regulation of macrophage activation state is key to understanding disease control, tissue homeostasis, and implant response, with this regulation shown to be directly related to macrophage intracellular metabolism (O’Neill et al., 2016). Therefore, impaired macrophage metabolism results in compromised homeostasis such as the case of diabetes, the foreign body response to biomaterials, obesity, or cancer (Mantovani and Sica, 2010; McNelis and Olefsky, 2014). Depending on the investigation being applied, shifts observed in cellular metabolism, cytokine production, or gene expression are typically a cumulative output from a broad population. We investigated the clustering pattern of IFNγ-M1 macrophages and IL-4-M2 macrophages using single-cell data within individual donors and found that the IFNγ-M1 macrophage and IL-4-M2 macrophage appeared as separate clusters within each donor (Figure 4A and B). Classifying IFNγ-M1 and IL-4-M2 macrophages at a single-cell level yielded some varied results, with four donors providing acceptable predicting performance (OOB < 31%; ROC-AUC > 0.75) (Figure 4C and D). There is some cell-to-cell variability which could stem from the uptake capacity of FCCP and other treatments in our experiments as well as the underlying health of the donors which is not available (Smiley et al., 1991; Stiebing et al., 2017). Nonetheless, for the four donors with the most superior performance during classification, τ₁, τ₂, τ_avg, and α₂ are the most relevant variables for the classification problem, which agrees with the case of full FoV analysis. Future efforts to improve single-cell classification include increasing the overall number of cells analysed to ensure a stronger classification. In addition, by observing phenotypic IFNγ-M1 and IL-4-M2 macrophage cell surface markers (examples include CD80, CD86, CD163, and CD206, respectively) during the imaging process by immunofluorescence or other modes of tagging, it would be possible to improve the classification efficiency (Murray et al., 2014).

2P-FLIM imaging has several advantages when compared with traditional metabolic assays and methods to classify and validate macrophage metabolism. 2P-FLIM enables spatial and temporal resolution in a non-invasive manner, allowing single-cell and cell-to-cell evaluations into cellular heterogeneity in a basal and interrogated mode. 2P-FLIM requires no fixation nor staining of cells and can be performed in real time with only a small number of cells. In this work, we demonstrated the feasibility of using 2P-FLIM as a tool to distinguish and classify opposing human macrophage polarisation states based on cellular metabolism and fluorescence lifetimes variables. Visualisation of the data showed a clear clustering pattern of IFNγ-M1 and IL-4-M2 macrophages in response to FCCP during real-time imaging in a full FoV. The excellent performance of machine learning models, applied on the data extracted from the non-invasive technique, underlines further the efficiency of this workflow. This workflow can be easily adapted to non-invasively characterising macrophage polarisation in in vivo models and in vitro multicellular organoid models. These organoid models can be developed to study foreign body interactions, biomaterial assessment, pharmaceutical research and screening, and clinical applications such as disease diagnosis.

All data generated or analysed during this study are included in the manuscript and supporting files (uploaded as source data).

1. 1. Adams DO

   (1989) Molecular interactions in macrophage activation

   Immunology Today 10:33–35.

   https://doi.org/10.1016/0167-5699(89)90298-3

   • PubMed
   • Google Scholar
2. 1. Akie TE
   2. Cooper MP

   (2015) Determination of fatty acid oxidation and lipogenesis in mouse primary hepatocytes

   Journal of Visualized Experiments 5:e52982.

   https://doi.org/10.3791/52982

   • PubMed
   • Google Scholar
3. 1. Alfonso-García A
   2. Smith TD
   3. Datta R
   4. Luu TU
   5. Gratton E
   6. Potma EO
   7. Liu WF

   (2016) Label-Free identification of macrophage phenotype by fluorescence lifetime imaging microscopy

   Journal of Biomedical Optics 21:46005.

   https://doi.org/10.1117/1.JBO.21.4.046005

   • PubMed
   • Google Scholar
4. 1. Alipanahi B
   2. Delong A
   3. Weirauch MT
   4. Frey BJ

   (2015) Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

   Nature Biotechnology 33:831–838.

   https://doi.org/10.1038/nbt.3300

   • PubMed
   • Google Scholar
5. 1. Artyomov MN
   2. Sergushichev A
   3. Schilling JD

   (2016) Integrating immunometabolism and macrophage diversity

   Seminars in Immunology 28:417–424.

   https://doi.org/10.1016/j.smim.2016.10.004

   • PubMed
   • Google Scholar
6. 1. Blacker TS
   2. Mann ZF
   3. Gale JE
   4. Ziegler M
   5. Bain AJ
   6. Szabadkai G
   7. Duchen MR

   (2014) Separating NADH and NADPH fluorescence in live cells and tissues using FLIM

   Nature Communications 5:3936.

   https://doi.org/10.1038/ncomms4936

   • PubMed
   • Google Scholar
7. 1. Blinova K
   2. Carroll S
   3. Bose S
   4. Smirnov AV
   5. Harvey JJ
   6. Knutson JR
   7. Balaban RS

   (2005) Distribution of mitochondrial NADH fluorescence lifetimes: steady-state kinetics of matrix NADH interactions

   Biochemistry 44:2585–2594.

   https://doi.org/10.1021/bi0485124

   • PubMed
   • Google Scholar
8. 1. Bolland DJ
   2. Koohy H
   3. Wood AL
   4. Matheson LS
   5. Krueger F
   6. Stubbington MJT
   7. Baizan-Edge A
   8. Chovanec P
   9. Stubbs BA
   10. Tabbada K
   11. Andrews SR
   12. Spivakov M
   13. Corcoran AE

   (2016) Two mutually exclusive local chromatin states drive efficient V (D) J recombination

   Cell Reports 15:2475–2487.

   https://doi.org/10.1016/j.celrep.2016.05.020

   • PubMed
   • Google Scholar
9. 1. Breiman L

   (2001) Random forests

   Machine Learning 45:5–32.

   https://doi.org/10.1023/A:1010933404324

   • Google Scholar
10. 1. Culos A
    2. Tsai AS
    3. Stanley N
    4. Becker M
    5. Ghaemi MS
    6. McIlwain DR
    7. Fallahzadeh R
    8. Tanada A
    9. Nassar H
    10. Espinosa C
    11. Xenochristou M
    12. Ganio E
    13. Peterson L
    14. Han X
    15. Stelzer IA
    16. Ando K
    17. Gaudilliere D
    18. Phongpreecha T
    19. Marić I
    20. Chang AL
    21. Shaw GM
    22. Stevenson DK
    23. Bendall S
    24. Davis KL
    25. Fantl W
    26. Nolan GP
    27. Hastie T
    28. Tibshirani R
    29. Angst MS
    30. Gaudilliere B
    31. Aghaeepour N

    (2020) Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions

    Nature Machine Intelligence 2:619–628.

    https://doi.org/10.1038/s42256-020-00232-8

    • PubMed
    • Google Scholar
11. 1. Fall F
    2. Lamy E
    3. Brollo M
    4. Naline E
    5. Lenuzza N
    6. Thévenot E
    7. Devillier P
    8. Grassin-Delyle S

    (2020) Metabolic reprograming of LPS-stimulated human lung macrophages involves tryptophan metabolism and the aspartate-arginosuccinate shunt

    PLOS ONE 15:e0230813.

    https://doi.org/10.1371/journal.pone.0230813

    • PubMed
    • Google Scholar
12. 1. Feuerer N
    2. Marzi J
    3. Brauchle EM
    4. Carvajal Berrio DA
    5. Billing F
    6. Weiss M
    7. Jakobi M
    8. Schneiderhan-Marra N
    9. Shipp C
    10. Schenke-Layland K

    (2021) Lipidome profiling with Raman microspectroscopy identifies macrophage response to surface topographies of implant materials

    PNAS 118:e2113694118.

    https://doi.org/10.1073/pnas.2113694118

    • PubMed
    • Google Scholar
13. 1. Floudas A
    2. Neto N
    3. Marzaioli V
    4. Murray K
    5. Moran B
    6. Monaghan MG
    7. Low C
    8. Mullan RH
    9. Rao N
    10. Krishna V
    11. Nagpal S
    12. Veale DJ
    13. Fearon U

    (2020) Pathogenic, glycolytic PD-1+ B cells accumulate in the hypoxic RA joint

    JCI Insight 5:21.

    https://doi.org/10.1172/jci.insight.139032

    • PubMed
    • Google Scholar
14. 1. Gordon S

    (2003) Alternative activation of macrophages

    Nature Reviews. Immunology 3:23–35.

    https://doi.org/10.1038/nri978

    • PubMed
    • Google Scholar
15. 1. Graney PL
    2. Ben-Shaul S
    3. Landau S
    4. Bajpai A
    5. Singh B
    6. Eager J
    7. Cohen A
    8. Levenberg S
    9. Spiller KL

    (2020) Macrophages of diverse phenotypes drive vascularization of engineered tissues

    Science Advances 6:eaay6391.

    https://doi.org/10.1126/sciadv.aay6391

    • PubMed
    • Google Scholar
16. 1. Gratchev A
    2. Guillot P
    3. Hakiy N
    4. Politz O
    5. Orfanos CE
    6. Schledzewski K
    7. Goerdt S

    (2001) Alternatively activated macrophages differentially express fibronectin and its splice variants and the extracellular matrix protein betaig-h3

    Scandinavian Journal of Immunology 53:386–392.

    https://doi.org/10.1046/j.1365-3083.2001.00885.x

    • PubMed
    • Google Scholar
17. 1. Guo H-W
    2. Yu J-S
    3. Hsu S-H
    4. Wei Y-H
    5. Lee OK
    6. Dong C-Y
    7. Wang H-W

    (2015) Correlation of NADH fluorescence lifetime and oxidative phosphorylation metabolism in the osteogenic differentiation of human mesenchymal stem cell

    Journal of Biomedical Optics 20:017004.

    https://doi.org/10.1117/1.JBO.20.1.017004

    • PubMed
    • Google Scholar
18. 1. Huang S
    2. Heikal AA
    3. Webb WW

    (2002) Two-Photon fluorescence spectroscopy and microscopy of NAD (P) H and flavoprotein

    Biophysical Journal 82:2811–2825.

    https://doi.org/10.1016/S0006-3495(02)75621-X

    • PubMed
    • Google Scholar
19. 1. Koo SJ
    2. Chowdhury IH
    3. Szczesny B
    4. Wan X
    5. Garg NJ

    (2016) Macrophages promote oxidative metabolism to drive nitric oxide generation in response to Trypanosoma cruzi

    Infection and Immunity 84:3527–3541.

    https://doi.org/10.1128/IAI.00809-16

    • PubMed
    • Google Scholar
20. 1. Lakowicz JR
    2. Szmacinski H
    3. Nowaczyk K
    4. Johnson ML

    (1992) Fluorescence lifetime imaging of free and protein-bound NADH

    PNAS 89:1271–1275.

    https://doi.org/10.1073/pnas.89.4.1271

    • PubMed
    • Google Scholar
21. 1. Levitt JM
    2. McLaughlin-Drubin ME
    3. Münger K
    4. Georgakoudi I
    5. Egles C

    (2011) Automated biochemical, morphological, and organizational assessment of precancerous changes from endogenous two-photon fluorescence images

    PLOS ONE 6:e24765.

    https://doi.org/10.1371/journal.pone.0024765

    • Google Scholar
22. 1. Ludtmann MHR
    2. Angelova PR
    3. Zhang Y
    4. Abramov AY
    5. Dinkova-Kostova AT

    (2014) Nrf2 affects the efficiency of mitochondrial fatty acid oxidation

    The Biochemical Journal 457:415–424.

    https://doi.org/10.1042/BJ20130863

    • PubMed
    • Google Scholar
23. 1. Ma J
    2. Wei K
    3. Liu J
    4. Tang K
    5. Zhang H
    6. Zhu L
    7. Chen J
    8. Li F
    9. Xu P
    10. Chen J
    11. Liu J
    12. Fang H
    13. Tang L
    14. Wang D
    15. Zeng L
    16. Sun W
    17. Xie J
    18. Liu Y
    19. Huang B

    (2020) Glycogen metabolism regulates macrophage-mediated acute inflammatory responses

    Nature Communications 11:1769.

    https://doi.org/10.1038/s41467-020-15636-8

    • PubMed
    • Google Scholar
24. 1. Mahon OR
    2. Kelly DJ
    3. McCarthy GM
    4. Dunne A

    (2020) Osteoarthritis-Associated basic calcium phosphate crystals alter immune cell metabolism and promote M1 macrophage polarization

    Osteoarthritis and Cartilage 28:603–612.

    https://doi.org/10.1016/j.joca.2019.10.010

    • PubMed
    • Google Scholar
25. 1. Mandrekar JN

    (2010) Receiver operating characteristic curve in diagnostic test assessment

    Journal of Thoracic Oncology 5:1315–1316.

    https://doi.org/10.1097/JTO.0b013e3181ec173d

    • PubMed
    • Google Scholar
26. 1. Mantovani A
    2. Sozzani S
    3. Locati M
    4. Allavena P
    5. Sica A

    (2002) Macrophage polarization: tumor-associated macrophages as a paradigm for polarized M2 mononuclear phagocytes

    Trends in Immunology 23:549–555.

    https://doi.org/10.1016/s1471-4906(02)02302-5

    • PubMed
    • Google Scholar
27. 1. Mantovani A
    2. Sica A

    (2010) Macrophages, innate immunity and cancer: balance, tolerance, and diversity

    Current Opinion in Immunology 22:231–237.

    https://doi.org/10.1016/j.coi.2010.01.009

    • PubMed
    • Google Scholar
28. 1. Mantovani A
    2. Marchesi F
    3. Malesci A
    4. Laghi L
    5. Allavena P

    (2017) Tumour-Associated macrophages as treatment targets in oncology

    Nature Reviews. Clinical Oncology 14:399–416.

    https://doi.org/10.1038/nrclinonc.2016.217

    • PubMed
    • Google Scholar
29. 1. Martinez FO
    2. Gordon S
    3. Locati M
    4. Mantovani A

    (2006) Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression

    Journal of Immunology 177:7303–7311.

    https://doi.org/10.4049/jimmunol.177.10.7303

    • PubMed
    • Google Scholar
30. 1. Martinez FO
    2. Sica A
    3. Mantovani A
    4. Locati M

    (2008) Macrophage activation and polarization

    Frontiers in Bioscience 13:453–461.

    https://doi.org/10.2741/2692

    • PubMed
    • Google Scholar
31. 1. McInnes L
    2. Healy J
    3. Saul N
    4. Großberger L

    (2018) UMAP: uniform manifold approximation and projection

    Journal of Open Source Software 3:861.

    https://doi.org/10.21105/joss.00861

    • Google Scholar
32. 1. McNelis JC
    2. Olefsky JM

    (2014) Macrophages, immunity, and metabolic disease

    Immunity 41:36–48.

    https://doi.org/10.1016/j.immuni.2014.05.010

    • PubMed
    • Google Scholar
33. 1. McQuin C
    2. Goodman A
    3. Chernyshev V
    4. Kamentsky L
    5. Cimini BA
    6. Karhohs KW
    7. Doan M
    8. Ding L
    9. Rafelski SM
    10. Thirstrup D
    11. Wiegraebe W
    12. Singh S
    13. Becker T
    14. Caicedo JC
    15. Carpenter AE

    (2018) CellProfiler 3.0: next-generation image processing for biology

    PLOS Biology 16:e2005970.

    https://doi.org/10.1371/journal.pbio.2005970

    • PubMed
    • Google Scholar
34. Book

    1. Mohri M
    2. Rostamizadeh A

    (2012)

    Foundations of Machine Learning

    MIT press.

    • Google Scholar
35. 1. Mosser DM
    2. Edwards JP

    (2008) Exploring the full spectrum of macrophage activation

    Nature Reviews. Immunology 8:958–969.

    https://doi.org/10.1038/nri2448

    • PubMed
    • Google Scholar
36. 1. Murray PJ
    2. Allen JE
    3. Biswas SK
    4. Fisher EA
    5. Gilroy DW
    6. Goerdt S
    7. Gordon S
    8. Hamilton JA
    9. Ivashkiv LB
    10. Lawrence T
    11. Locati M
    12. Mantovani A
    13. Martinez FO
    14. Mege J-L
    15. Mosser DM
    16. Natoli G
    17. Saeij JP
    18. Schultze JL
    19. Shirey KA
    20. Sica A
    21. Suttles J
    22. Udalova I
    23. van Ginderachter JA
    24. Vogel SN
    25. Wynn TA

    (2014) Macrophage activation and polarization: Nomenclature and experimental guidelines

    Immunity 41:14–20.

    https://doi.org/10.1016/j.immuni.2014.06.008

    • PubMed
    • Google Scholar
37. Book

    1. Neto N
    2. Dmitriev RI
    3. Monaghan MG

    (2020) Seeing is believing: noninvasive microscopic imaging modalities for tissue engineering and regenerative medicine

    In: Gimble M, Presen D, Oreffo ROC, Wolbank S, Redl H, editors. Cell Engineering and Regeneration. Springer International Publishing. pp. 599–638.

    https://doi.org/10.1007/978-3-319-08831-0_40

    • Google Scholar
38. 1. Okkelman IA
    2. Neto N
    3. Papkovsky DB
    4. Monaghan MG
    5. Dmitriev RI

    (2019) A deeper understanding of intestinal organoid metabolism revealed by combining fluorescence lifetime imaging microscopy (FLIM) and extracellular flux analyses

    Redox Biology 30:101420.

    https://doi.org/10.1016/j.redox.2019.101420

    • PubMed
    • Google Scholar
39. 1. O’Neill LAJ
    2. Kishton RJ
    3. Rathmell J

    (2016) A guide to immunometabolism for immunologists

    Nature Reviews. Immunology 16:553–565.

    https://doi.org/10.1038/nri.2016.70

    • PubMed
    • Google Scholar
40. 1. Perottoni S
    2. Neto NGB
    3. Di Nitto C
    4. Dmitriev RI
    5. Raimondi MT
    6. Monaghan MG

    (2021) Intracellular label-free detection of mesenchymal stem cell metabolism within a perivascular niche-on-a-chip

    Lab on a Chip 21:1395–1408.

    https://doi.org/10.1039/d0lc01034k

    • PubMed
    • Google Scholar
41. 1. Peterson KR
    2. Cottam MA
    3. Kennedy AJ
    4. Hasty AH

    (2018) Macrophage-Targeted therapeutics for metabolic disease

    Trends in Pharmacological Sciences 39:536–546.

    https://doi.org/10.1016/j.tips.2018.03.001

    • PubMed
    • Google Scholar
42. 1. Qian T
    2. Heaster TM
    3. Houghtaling AR
    4. Sun K
    5. Samimi K
    6. Skala MC

    (2021) Label-Free imaging for quality control of cardiomyocyte differentiation

    Nature Communications 12:4580.

    https://doi.org/10.1038/s41467-021-24868-1

    • PubMed
    • Google Scholar
43. 1. Ranjit S
    2. Malacrida L
    3. Jameson DM
    4. Gratton E

    (2018) Fit-free analysis of fluorescence lifetime imaging data using the phasor approach

    Nature Protocols 13:1979–2004.

    https://doi.org/10.1038/s41596-018-0026-5

    • PubMed
    • Google Scholar
44. 1. Schaefer PM
    2. Hilpert D
    3. Niederschweiberer M
    4. Neuhauser L
    5. Kalinina S
    6. Calzia E
    7. Rueck A
    8. von Einem B
    9. von Arnim CAF

    (2017) Mitochondrial matrix pH as a decisive factor in neurometabolic imaging

    Neurophotonics 4:045004.

    https://doi.org/10.1117/1.NPh.4.4.045004

    • PubMed
    • Google Scholar
45. 1. Schaefer PM
    2. Kalinina S
    3. Rueck A
    4. von Arnim CAF
    5. von Einem B

    (2019) Nadh autofluorescence-A marker on its way to boost bioenergetic research

    Cytometry. Part A 95:34–46.

    https://doi.org/10.1002/cyto.a.23597

    • PubMed
    • Google Scholar
46. 1. Shields CW
    2. Evans MA
    3. Wang LLW
    4. Baugh N
    5. Iyer S
    6. Wu D
    7. Zhao Z
    8. Pusuluri A
    9. Ukidve A
    10. Pan DC
    11. Mitragotri S

    (2020) Cellular backpacks for macrophage immunotherapy

    Science Advances 6:eaaz6579.

    https://doi.org/10.1126/sciadv.aaz6579

    • PubMed
    • Google Scholar
47. 1. Skala MC
    2. Riching KM
    3. Bird DK
    4. Gendron-Fitzpatrick A
    5. Eickhoff J
    6. Eliceiri KW
    7. Keely PJ
    8. Ramanujam N

    (1992) In vivo multiphoton fluorescence lifetime imaging of protein-bound and free nicotinamide adenine dinucleotide in normal and precancerous epithelia

    Journal of Biomedical Optics 12:1–19.

    https://doi.org/10.1117/1.2717503

    • PubMed
    • Google Scholar
48. 1. Skala MC
    2. Riching KM
    3. Gendron-Fitzpatrick A
    4. Eickhoff J
    5. Eliceiri KW
    6. White JG
    7. Ramanujam N

    (2007) In vivo multiphoton microscopy of NADH and FAD redox states, fluorescence lifetimes, and cellular morphology in precancerous epithelia

    PNAS 104:19494–19499.

    https://doi.org/10.1073/pnas.0708425104

    • PubMed
    • Google Scholar
49. 1. Smiley ST
    2. Reers M
    3. Mottola-Hartshorn C
    4. Lin M
    5. Chen A
    6. Smith TW
    7. Steele GD
    8. Chen LB

    (1991) Intracellular heterogeneity in mitochondrial membrane potentials revealed by a J-aggregate-forming lipophilic cation JC-1

    PNAS 88:3671–3675.

    https://doi.org/10.1073/pnas.88.9.3671

    • PubMed
    • Google Scholar
50. 1. Stiebing C
    2. Meyer T
    3. Rimke I
    4. Matthäus C
    5. Schmitt M
    6. Lorkowski S
    7. Popp J

    (2017) Real-Time Raman and SRS imaging of living human macrophages reveals cell-to-cell heterogeneity and dynamics of lipid uptake

    Journal of Biophotonics 10:1217–1226.

    https://doi.org/10.1002/jbio.201600279

    • PubMed
    • Google Scholar
51. 1. Tokunaga R
    2. Zhang W
    3. Naseem M
    4. Puccini A
    5. Berger MD
    6. Soni S
    7. McSkane M
    8. Baba H
    9. Lenz HJ

    (2018) Cxcl9, CXCL10, CXCL11/CXCR3 axis for immune activation-a target for novel cancer therapy

    Cancer Treatment Reviews 63:40–47.

    https://doi.org/10.1016/j.ctrv.2017.11.007

    • PubMed
    • Google Scholar
52. 1. Touw WG
    2. Bayjanov JR
    3. Overmars L
    4. Backus L
    5. Boekhorst J
    6. Wels M
    7. van Hijum SAFT

    (2013) Data mining in the life sciences with random forest: a walk in the Park or lost in the jungle?

    Briefings in Bioinformatics 14:315–326.

    https://doi.org/10.1093/bib/bbs034

    • PubMed
    • Google Scholar
53. 1. Van den Bossche J
    2. Baardman J
    3. de Winther MPJ

    (2015) Metabolic characterization of polarized M1 and M2 bone marrow-derived macrophages using real-time extracellular flux analysis

    Journal of Visualized Experiments 5:53424.

    https://doi.org/10.3791/53424

    • PubMed
    • Google Scholar
54. 1. Van den Bossche J
    2. O’Neill LA
    3. Menon D

    (2017) Macrophage immunometabolism: where are we (going)?

    Trends in Immunology 38:395–406.

    https://doi.org/10.1016/j.it.2017.03.001

    • PubMed
    • Google Scholar
55. 1. Varone A
    2. Xylas J
    3. Quinn KP
    4. Pouli D
    5. Sridharan G
    6. McLaughlin-Drubin ME
    7. Alonzo C
    8. Lee K
    9. Münger K
    10. Georgakoudi I

    (2014) Endogenous two-photon fluorescence imaging elucidates metabolic changes related to enhanced glycolysis and glutamine consumption in precancerous epithelial tissues

    Cancer Research 74:3067–3075.

    https://doi.org/10.1158/0008-5472.CAN-13-2713

    • PubMed
    • Google Scholar
56. 1. Verikas A
    2. Gelzinis A
    3. Bacauskiene M

    (2011) Mining data with random forests: a survey and results of new tests

    Pattern Recognition 44:330–349.

    https://doi.org/10.1016/j.patcog.2010.08.011

    • Google Scholar
57. 1. Viola A
    2. Munari F
    3. Sánchez-Rodríguez R
    4. Scolaro T
    5. Castegna A

    (2019) The metabolic signature of macrophage responses

    Frontiers in Immunology 10:1462.

    https://doi.org/10.3389/fimmu.2019.01462

    • PubMed
    • Google Scholar
58. 1. Vivekanandan-Giri A
    2. Byun J
    3. Pennathur S

    (2011) Quantitative analysis of amino acid oxidation markers by tandem mass spectrometry

    Methods in Enzymology 491:73–89.

    https://doi.org/10.1016/B978-0-12-385928-0.00005-5

    • PubMed
    • Google Scholar
59. 1. Wahl M
    2. Röhlicke T
    3. Rahn H-J
    4. Erdmann R
    5. Kell G
    6. Ahlrichs A
    7. Kernbach M
    8. Schell AW
    9. Benson O

    (2013) Integrated multichannel photon timing instrument with very short dead time and high throughput

    The Review of Scientific Instruments 84:043102.

    https://doi.org/10.1063/1.4795828

    • PubMed
    • Google Scholar
60. 1. Walsh AJ
    2. Cook RS
    3. Manning HC
    4. Hicks DJ
    5. Lafontant A
    6. Arteaga CL
    7. Skala MC

    (2013) Optical metabolic imaging identifies glycolytic levels, subtypes, and early-treatment response in breast cancer

    Cancer Research 73:6164–6174.

    https://doi.org/10.1158/0008-5472.CAN-13-0527

    • PubMed
    • Google Scholar
61. 1. Walsh AJ
    2. Mueller KP
    3. Tweed K
    4. Jones I
    5. Walsh CM
    6. Piscopo NJ
    7. Niemi NM
    8. Pagliarini DJ
    9. Saha K
    10. Skala MC

    (2020) Classification of T-cell activation via autofluorescence lifetime imaging

    Nature Biomedical Engineering 5:77–88.

    https://doi.org/10.1038/s41551-020-0592-z

    • PubMed
    • Google Scholar
62. 1. Wang J
    2. Roderiquez G
    3. Oravecz T
    4. Norcross MA

    (1998) Cytokine regulation of human immunodeficiency virus type 1 entry and replication in human monocytes/macrophages through modulation of CCR5 expression

    Journal of Virology 72:7642–7647.

    https://doi.org/10.1128/JVI.72.9.7642-7647.1998

    • PubMed
    • Google Scholar
63. 1. Wang F
    2. Zhang S
    3. Jeon R
    4. Vuckovic I
    5. Jiang X
    6. Lerman A
    7. Folmes CD
    8. Dzeja PD
    9. Herrmann J

    (2018) Interferon gamma induces reversible metabolic reprogramming of M1 macrophages to sustain cell viability and pro-inflammatory activity

    EBioMedicine 30:303–316.

    https://doi.org/10.1016/j.ebiom.2018.02.009

    • PubMed
    • Google Scholar

Author details

1. Nuno GB Neto

   1. Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
   2. Trinity Centre for Biomedical Engineering, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Conceptualization, Data curation, Software, Formal analysis, Validation, Investigation, Visualization, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5467-8720

2. Sinead A O'Rourke

   1. Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
   2. Trinity Centre for Biomedical Engineering, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland
   3. School of Biochemistry & Immunology and School of Medicine, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Data curation, Formal analysis, Validation, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

3. Mimi Zhang

   School of Computer Science and Statistics, Trinity College Dublin, Dublin, Ireland

   Contribution

   Resources, Software, Formal analysis, Visualization, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

4. Hannah K Fitzgerald

   School of Biochemistry & Immunology and School of Medicine, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland

   Contribution

   Resources, Visualization

   Competing interests

   No competing interests declared

5. Aisling Dunne

   1. School of Biochemistry & Immunology and School of Medicine, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland
   2. Advanced Materials for BioEngineering Research (AMBER) Centre, Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland

   Contribution

   Resources, Writing – review and editing

   Competing interests

   No competing interests declared

6. Michael G Monaghan

   1. Department of Mechanical, Manufacturing and Biomedical Engineering, Trinity College Dublin, Dublin, Ireland
   2. Trinity Centre for Biomedical Engineering, Trinity Biomedical Science Institute, Trinity College Dublin, Dublin, Ireland
   3. Advanced Materials for BioEngineering Research (AMBER) Centre, Trinity College Dublin and Royal College of Surgeons in Ireland, Dublin, Ireland
   4. CURAM SFI Research Centre for Medical Devices, National University of Ireland, Galway, Ireland

   Contribution

   Conceptualization, Resources, Data curation, Software, Supervision, Funding acquisition, Visualization, Methodology, Writing – original draft, Project administration, Writing – review and editing

   For correspondence

   monaghmi@tcd.ie

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-5530-4998

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