Reprogramming of energy metabolism is an established hallmark of malignant tumors. Tumor cells adjust their metabolism to sustain uncontrolled proliferation and tumor progression, even in the conditions of low nutrient supply and hypoxia. There are two main metabolic pathways for energy production in the form of ATP – glycolysis and oxidative phosphorylation (OXPHOS). For a long time, enhanced glycolysis has been considered as a central feature of tumor metabolism (DeBerardinis and Chandel, 2016). Unlike most normal cells, tumor cells rely on glycolysis not only in hypoxia (anaerobic glycolysis), but also in normoxia (aerobic glycolysis, or the Warburg effect), which provides them with many metabolic intermediates and a high-speed production of ATP for fast growth. The glycolytic shift in the tumors traditionally correlates with negative prognosis (Zhou et al., 2022). At the same time, mitochondrial OXPHOS is as important in malignant cells as glycolysis. Although defective mitochondria and lower OXPHOS capacity are often observed in cancer cells, this is not an absolute phenomenon – many tumors preserve functional mitochondria and normal OXPHOS rate (Gentric et al., 2017). Along with glucose, tumors use glutamine and fatty acids as alternative substrates. Interestingly, tumor cells are capable of switching between different metabolic pathways depending on their own needs and local environment, thus demonstrating a high degree of metabolic plasticity.

Therefore, tumor metabolism is currently viewed as a dynamic, variable system with a diversity of cellular metabolic states. In this context, intratumor heterogeneity means that there are the cells with different metabolic profiles in a tumor concurrently, and intertumor heterogeneity means that tumors of the same type and stage are characterized by the different metabolic strategies (Kim and DeBerardinis, 2019).

Heterogeneity of tumor metabolism can be caused by various factors (Shirmanova et al., 2023). Some of them («intrinsic») are related to tumor cells themselves: these are histogenesis of the tumor, mutation profile, (epi)genetics factors, differentiation state and proliferation activity, to name a few (Pavlova and Thompson, 2016; Sengupta and Pratx, 2016; Seth Nanda et al., 2020; Farhadi et al., 2022). Other reasons («extrinsic») are associated with the nonuniform microenvironment, for example local hypoxia, nutrient distribution, heterogeneity of the vasculature network, interaction of tumor cells with the extracellular matrix and stromal cells, etc. (Marusyk and Polyak, 2010; Masson and Ratcliffe, 2014; Yoshida, 2015; Stine et al., 2015). Collectively, these factors lead to variability of tumor metabolism in space and time. Metabolic heterogeneity as a part of general heterogeneity of tumors imposes some difficulties in patients’ diagnosis and treatment and is believed to have a prognostic value (Liu et al., 2022a; Liu et al., 2022b; Pinho et al., 2020).

Although metabolic heterogeneity is a well-recognized feature of tumors, its characterization at the cellular level remains scarce. In part, this is associated with the lack of the highly sensitive and direct techniques for its observation. Clinical imaging modalities, such as metabolic PET with radiolabeled tracers (¹⁸F, ¹¹C) and MRI/MRS, allow to capture inter- and intratumor heterogeneity among patients with low (~4.5 mm) spatial resolution (Plathow and Weber, 2008). Recently evolved methods of single-cell sequencing deliver comprehensive information about the metabolic landscape of a tumor with a resolution up to 55–100 µm based on the expression of the metabolic genes, but these approaches are rather complex, laborious and not widely available (Evers et al., 2019; Huang et al., 2023).

Interrogation of cellular metabolism is possible with the laser scanning fluorescence microscopy that enables the detection of autofluorescence of the redox cofactors, such as the reduced form of the nicotinamide adenine dinucleotide (phosphate) – NAD(P)H and oxidized flavins (Georgakoudi and Quinn, 2023). Recording of the fluorescence decay parameters using the option of the fluorescence lifetime imaging (FLIM) allows the evaluation of the states of the cofactor molecules attributed to different metabolic pathways. The unbound state of NAD(P)H has a short lifetime (~0.4 ns), while the protein-bound state has a long lifetime (~1.7–3.0 ns; Lakowicz et al., 1992). Changes in the relative fractions of the unbound and bound NAD(P)H in cancer cells calculated from biexponential fitting of decay curve typically indicate the shifts between glycolysis and OXPHOS (Rück et al., 2014). FLIM of NAD(P)H is currently considered as a promising, label-free technique for the assessment of metabolic heterogeneity of tumors at the (sub)cellular scale. Several studies, including ours, demonstrate the possibilities of NAD(P)H FLIM to visualize metabolic heterogeneity in cultured cells, multicellular structures in vitro, tumor xenografts in vivo and patients’ tumors ex vivo (Shah et al., 2015; Skala et al., 2022; Lukina et al., 2019). However, quantifying the degree of tumor heterogeneity is lacking in most of these works.

In this paper, we present the results of assessment of cell-level metabolic heterogeneity of colorectal cancer using FLIM-microscopy of NAD(P)H. The distributions of the fluorescence decay parameters have been analyzed in the four colorectal cancer cell lines (HT29, HCT116, CaCo2, CT26), in the mouse tumor models in vivo generated from these cell lines and in the post-operative samples of patients’ colon tumors. Quantification of heterogeneity has been performed with the dispersion (D) and the bimodality index (BI) of the decay parameters. SHAP (SHapley Additive exPlanations) analysis has been conducted to find associations of the heterogeneity degree with clinical prognosis.

The altered energy metabolism is known to support tumor progression because tumor cells are critically in need of ATP for uncontrolled proliferation and growth. It has been established that tumor cells explore different metabolic programs and utilize multiple fuels even within one tumor, thus leading to metabolic heterogeneity. Until recently, direct observation of cell-level heterogeneity of tumor metabolism was a challenging task, but became possible with the evolution of advanced optical microscopic techniques, such as two-photon fluorescence lifetime microscopy, FLIM. In the present research, using FLIM of redox cofactor NAD(P)H, which possesses endogenous fluorescence, we compared metabolic features of colorectal cancer cells across in vitro and in vivo models and patients’ samples. For the first time, it was shown that heterogeneity of cellular metabolism increases with model complexity and is the highest at the level of patients’ tumors. The heterogeneity was quantified on the basis of FLIM data and correlated with clinical characteristics of the tumors, which has not been done before.

A lot of studies, including the works of our group, demonstrate the possibilities of FLIM for assessment of the intra- and intertumor heterogeneity of metabolism in different models. Using NAD(P)H FLIM, metabolic heterogeneity was revealed in colorectal cancer cell cultures obtained from the patients’ tumors, whereas the cells of the standard cell lines were uniform in their metabolism (Shirshin et al., 2022). J. Chacko and K. Eliceiri observed intercellular heterogeneity of metabolism in the MCF10A human breast epithelial cell line (Chacko and Eliceiri, 2019). Several studies show metabolic heterogeneity of 3D multicellular structures. For example, tumor spheroids obtained from the cervical cancer cell line HeLa (Lukina et al., 2018) or murine colorectal carcinoma cell line CT26 (Shirmanova et al., 2021) displayed the differences in metabolism between the outer and the inner cell layers with the outer layers being more glycolytic (higher NAD(P)H-a₁). The spheroids generated from the patients’ derived glioblastoma cultures did not show metabolic zonality, but generally had greater intercellular variations of NAD(P)H lifetime parameters than the spheroids from standard line U373 MG (Yuzhakova et al., 2023). A spectrum of works by M. Skala’ group demonstrates an opportunity of the optical metabolic imaging for heterogeneity assessment in patient tumor-derived organoids on the example of a breast, pancreatic (Sharick et al., 2020; Walsh et al., 2014; Walsh et al., 2017), head and neck (Shah et al., 2017), and colorectal cancers (Skala et al., 2022).

In the research in vivo, the heterogeneous structure of tumor and its microenvironment was shown on PyMT mammary tumors (Burkel et al., 2022). Analysis of the optical metabolic parameters revealed heterogeneity of the B78 mouse melanoma model, caused by different microenvironments (Heaton et al., 2023). The authors suggested that tumor heterogeneity could be induced by such factors as pH, nutrient and oxygen availability, or activation of immune cells. In the in vivo study on HeLa tumor xenografts, metabolically different cells were detected in the cellular and collagen-rich areas (Shirmanova et al., 2018). Recently, we demonstrated a high variability of cellular metabolic statuses in CT26 tumors of large size compared with small tumors, which correlated with heterogeneous oxygen distribution (Parshina et al., 2022). A significant intertumor heterogeneity of metabolism was observed in patient-derived glioblastoma xenografts, which differed them from standard U87 glioma (Yuzhakova et al., 2022).

In our study, we compared metabolic FLIM parameters of colorectal cell lines, mouse tumor models and patients’ colorectal tumors. As expected, intercellular heterogeneity of metabolism was the highest in patients’ tumor samples as followed from a wide and often bimodal distribution of NAD(P)H-a₁. This result is consistent with the study by J. Auman and H. McLeod using genome-wide gene expression data; the authors concluded that colorectal cancer cell lines lack the molecular heterogeneity of clinical colorectal tumors (Auman and McLeod, 2010).

Cell-level metabolic heterogeneity may be present in the cell population initially, as discussed above, or develop during the treatment. The heterogeneous metabolism after treatment was detected in tumor organoids (Walsh et al., 2016; Walsh et al., 2014; Gillette et al., 2021) and in animal models (Heaster et al., 2019). In any case, the presence of metabolically distinct cells led to the varying drug responses.

An assessment of metabolic heterogeneity in patients’ tumors seems valuable from a clinical point of view. The great efforts to translate FLIM in the clinical setting both in vivo and ex vivo have been done by several groups (Jo et al., 2018; Alfonso-Garcia et al., 2023; Weyers et al., 2022; Seidenari et al., 2012; Mycek et al., 1998; Herrando et al., 2024; Lagarto et al., 2020). Clinical translation is spearheaded through macroscopic implementation and point-spectroscopy approaches that are capable of large sampling areas and enable access to otherwise constrained spaces but lack cellular resolution, making the interpretation of the results a complicated task. Previously, using NAD(P)H FLIM-microscopy of postoperative samples, we observed a high degree of inter- and intratumor heterogeneity of T3 stage colorectal tumors in comparison with normal colon samples (Lukina et al., 2019). Unfortunately, most of the tumor types are inaccessible for microscopic investigation in patients using clinically approved techniques. The exception is the skin tumors that can be inspected with clinical systems, like MPTflex or MPTcompact (JenLab, Germany). At the same time, endoscopic FLIM-microscopy has been developing actively since the last ten years, which opens the prospects for in vivo examination of a wide spectrum of patients’ tumor types (Sun et al., 2013; Farhadi et al., 2022). In principle, an assessment of tumor metabolism in biopsy samples is also possible, which widens the potential clinical applications of FLIM.

Although metabolic heterogeneity at the cellular level has been reported for different models and patient-derived cells, the comparisons between different samples and studies are hard to make as most of them lack any quantification of the heterogeneity.

Different approaches have been proposed to quantify the metabolic differences identified by FLIM. For example, in the papers (Shah et al., 2015; Sharick et al., 2019) a heterogeneity index and its modified form the weighted heterogeneity index (wH-index) were used for quantitative analysis of cellular heterogeneity. The wH-index is based on the Gaussian distribution models and is a modified form of the Shannon diversity index. Using this index the authors described the variations of a combination of parameters, such as NAD(P)H fluorescence lifetime, FAD fluorescence lifetime and optical redox ratio, defined as the OMI-index. The methods developed in Heaster et al., 2019 established the combination of optical metabolic imaging variables and spatial statistical analysis (spatial proximity analysis, spatial clustering, multivariate spatial analysis, spatial principal components analysis) to quantify the spatial heterogeneity of tumor cell metabolism. Recently, we have proposed a new quantitative criterion – the bimodality index (BI) – to accurately discriminate between metabolically diverse cellular subpopulations on the basis of NAD(P)H FLIM data (Shirshin et al., 2022). The BI provides dimensionless estimation on the inherent heterogeneity of a sample by checking the hypothesis about approximation of a fluorescence decay parameter distribution by two Gaussians. Using the BI, the metabolic heterogeneity has been identified in standard and primary cancer cells cultures after chemotherapy with 5-fluorouracil.

Here, we used the dispersion of NAD(P)H D-a₁ and bimodality index BI-a₁ for quantifying metabolic heterogeneity of patient’s tumors and compared it with in vitro and in vivo models. Of these two metrics of heterogeneity, the dispersion appeared more valuable in terms of tumor prognosis. Our results on patients’ colorectal tumors revealed some associations between the dispersion of a₁ within a sample and tumor stage – the early-stage tumors (T1, T2) were metabolically more heterogeneous than the late-stage ones (T3, T4). A degree of heterogeneity was also associated with differentiation state, a stage-independent prognostic factor in colorectal cancer where the lower grade correlates with better the prognosis. The high-grade (G3) tumors had significantly higher dispersion of a₁, compared with low-grade ones (G1, G2). These results have a rational explanation from the point of view of biological significance of heterogeneity. In stressful and unfavorable conditions, to which the tumor cells are exposed, the spread of the parameter distribution in the population rather than the presence of several distinct clusters (modes) matters for adaptation and survival. The high diversity of cellular metabolic phenotypes provided the survival advantage, and so was observed in more aggressive (undifferentiated or poorly differentiated) and the least advanced tumors.

One of the possible reasons for metabolic heterogeneity could be the presence of stromal cells or diversity of epithelial and mesenchymal phenotypes of cancer cells within a tumor. Immunohistochemical staining of tumors for EpCam (epithelial marker) and vimentin (mesenchymal marker) showed that the fraction of epithelial, EpCam-positive, cells was more than 90% in tumor xenografts (Figure 2—figure supplement 1) and on average 76 ± 10% in patients’ tumors (Figure 3—figure supplement 1). However, the ratio of EpCam- to vimentin-positive cells in patients’ samples neither correlated with D-a₁ nor with BI-a₁, which means that the presence of cells with mesenchymal phenotype did not contribute to metabolic heterogeneity of tumors identified by NAD(P)H FLIM.

Metabolic heterogeneity of colorectal cancer is discussed in the literature. The focus of many of these studies is on the molecular classification of tumors by the analysis of their metabolic features. For example, Zhang et al., 2020 showed that colorectal tumors could be classified into three distinct metabolism-relevant subtypes and developed a metabolism-related signature consisting of 27 metabolic genes, which were expressed differentially among the three subtypes and correlated with patients’ overall survival . Varshavi et al., 2020 performed metabolic characterization of colorectal cancer cells depending on KRAS mutation status using ¹H NMR spectra of the metabolites. They revealed that some mutations led to an increase of glucose consumption and lactate release, while others, on the contrary, decreased it. Numerous studies have investigated the prognostic potential of key metabolic (mainly glycolytic) enzymes assessed from immunohistochemistry (IHC). For example, glucose transporter 3 (GLUT3) was highly expressed in colorectal cancer tissues of 63% of patients as relative to benign tissues and correlated with poor clinical outcomes (Dai et al., 2020). In the study by Offermans et al., 2022 a sum score based on the expression levels of six proteins (PTEN, p53, GLUT1, PKM2, LDHA, MCT4) in colorectal cancer showed worse survival of patients with a higher probability of the Warburg effect . Mizuno et al., 2020 found that expression of lactate dehydrogenase A (LDHA) at the invasive margin of the tumor was weaker than in the center. Note that standard molecular genetics and immunolabeling techniques identify the metabolic differences between tumors or between large areas within a tumor, while a single cell level metabolic information is lacking. We have verified the inability of IHC to detect intercellular differences in metabolic states based on the expression of LDHA and GLUT3. One can see from representative IHC images that the expression level of glycolytic enzymes provides some information about intertumor metabolic differences, but is insensitive to intercellular variability of metabolism registered by NAD(P)H FLIM (Figure 3—figure supplement 2).

Liu et al. found a correlation between intratumor metabolic heterogeneity parameters of ¹⁸F-FDG PET/CT and KRAS mutation status in colorectal cancer – KRAS mutant tumors had more ¹⁸F-FDG uptake and heterogeneity than wild-type KRAS (Liu et al., 2022a). In a different study they showed that intratumor metabolic heterogeneity assessed from ¹⁸F-FDG PET/CT is an important prognostic factor for progression-free survival and overall survival in patients with colorectal cancer (Liu et al., 2022b). The value of intratumoral metabolic heterogeneity in ¹⁸F-FDG PET/CT for prediction of recurrence in patients with locally advanced colorectal cancer was also demonstrated by Han et al., 2018. In the study by Zhang et al. intratumoral metabolic heterogeneity derived from ¹⁸F-FDG PET/CT was higher in colorectal tumors with a high microsatellite instability (MSI) – an important prognostic biomarker, and predicted MSI in stage I–III colorectal cancer patients preoperatively (Zhang et al., 2023). Lin et al., 2021 identified two subtypes of colorectal cancer using a metabolic risk score based on genes that were mostly involved in lipid metabolism pathways; this criterion was applied for survival prediction – patients with a higher metabolic risk score had worse prognosis. Therefore, these clinical studies consider intratumor metabolic heterogeneity as a useful prognostic factor.

In the context of metabolic heterogeneity assay using NAD(P)H FLIM, some limitations should be mentioned. If the investigation of patients’ tumor metabolism is performed on ex vivo tissue samples, one should be careful to work with freshly excised tissue or store the sample in the appropriate conditions. As we found previously, FLIM parameters of autofluorescence change very quickly (within 15 min) on air, but can be preserved in 10% BSA on ice during 3 h (Lukina et al., 2019). A limitation of our study is that microscopic FLIM images of the tissues were processed manually to obtain information about each individual cell, which made the analysis time-consuming. The methods for segmentation of tissue images have been continuously developed in digital histopathology (Ahmed et al., 2022; van der Laak et al., 2021). As for FLIM, there are approaches to automatic segmentation of FLIM images of cell cultures and some normal tissues on the basis of machine learning (Smith et al., 2019). So, in spite of the complex and heterogeneous tissue architecture, there are expectations that the task of identification of the individual cells in FLIM images of tumor tissues will be solved in the nearest future. Finally, a small size of the microscopic field of view (typically less 300 µm) limits the inspected area within the tumor sample. Taking into account a high spatial heterogeneity of clinical tumors, there is no confidence whether the inspected area is sufficiently representative.

Fluorescence of flavins can also serve as a biomarker of cellular metabolism independently of or in conjunction with NAD(P)H, for example in the optical redox ratio, OMI or FLIRR indexes (Walsh et al., 2014; Wallrabe et al., 2018; Kalinina et al., 2021). With the aim to assess metabolic heterogeneity in colorectal cancer, we have made an attempt to record the signal from flavins in addition to NAD(P)H. Figure 1—figure supplement 2, Figure 2—figure supplement 2 and Figure 3—figure supplement 3 show the examples of fluorescence images of flavins and NAD(P)H in colorectal cancer cell cultures and tissues. However, the fluorescence intensity of flavins was very low (~27 times lower than NAD(P)H) and insufficient to collect the required number of photons for correct fitting. Yet, this observation does not exclude that flavins can be useful in the heterogeneity assays of cancer of different origin.

Owing to the high (sub)cellular resolution (~200–500 nm), FLIM-microscopy provides unique information about metabolic heterogeneity, unavailable with any other methods, like ¹⁸F-FDG PET/CT, spatial transcriptomics or immunohistochemistry. Therefore, FLIM-microscopy of endogenous cofactors is not only a powerful research technique, which is capable of improving our understanding of cellular metabolic diversity, but also the tool with a great potential for clinical translation to predict disease outcome.

All data generated or analyzed during this study are included in the manuscript and supporting files. Source data files are provided for Figure 1, Figure 2, Figure 3. Raw FLIM data for cells and animal tumors are deposited in the BioStudies database (http://www.ebi.ac.uk/biostudies) under accession number S-BSST1536 (DOI:10.6019/S-BSST1536). The software is available at Becker & Hickl, 2023. Sharing restrictions are applied to the raw patients' data because original files may contain personal information. Processed versions of the dataset for the patients' files are provided as source data file for Figure 3 and Supplementary file 2.

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Author details

1. Anastasia D Komarova

   1. Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation
   2. Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation

   Contribution

   Validation, Investigation, Visualization, Writing – original draft

   Contributed equally with

   Snezhana D Sinyushkina and Ilia D Shchechkin

   Competing interests

   No competing interests declared

2. Snezhana D Sinyushkina

   Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Validation, Investigation, Visualization, Writing – original draft

   Contributed equally with

   Anastasia D Komarova and Ilia D Shchechkin

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9325-1746

3. Ilia D Shchechkin

   1. Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation
   2. Institute of Biology and Biomedicine, Lobachevsky State University of Nizhny Novgorod, Nizhny Novgorod, Russian Federation

   Contribution

   Data curation, Validation, Visualization, Methodology, Writing – original draft

   Contributed equally with

   Anastasia D Komarova and Snezhana D Sinyushkina

   Competing interests

   No competing interests declared

4. Irina N Druzhkova

   Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Formal analysis, Funding acquisition, Investigation

   Competing interests

   No competing interests declared

5. Sofia A Smirnova

   Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

6. Vitaliy M Terekhov

   Nizhny Novgorod Regional Oncologic Hospital, Nizhny Novgorod, Russian Federation

   Contribution

   Resources, Investigation

   Competing interests

   No competing interests declared

7. Artem M Mozherov

   Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Data curation, Investigation, Methodology

   Competing interests

   No competing interests declared

8. Nadezhda I Ignatova

   Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

   Contribution

   Investigation

   Competing interests

   No competing interests declared

9. Elena E Nikonova

   Laboratory of Clinical Biophotonics, Sechenov First Moscow State Medical University, Moscow, Russian Federation

   Contribution

   Investigation, Methodology

   Competing interests

   No competing interests declared

10. Evgeny A Shirshin

    1. Laboratory of Clinical Biophotonics, Sechenov First Moscow State Medical University, Moscow, Russian Federation
    2. Faculty of Physics, Lomonosov Moscow State University, Moscow, Russian Federation

    Contribution

    Formal analysis, Methodology, Writing – original draft

    Competing interests

    No competing interests declared

11. Liubov E Shimolina

    Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

    Contribution

    Investigation

    Competing interests

    No competing interests declared

12. Sergey V Gamayunov

    Nizhny Novgorod Regional Oncologic Hospital, Nizhny Novgorod, Russian Federation

    Contribution

    Resources, Data curation

    Competing interests

    No competing interests declared

13. Vladislav I Shcheslavskiy

    1. Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation
    2. Becker&Hickl GmbH, Berlin, Germany

    Contribution

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

    Competing interests

    The author was affiliated with Becker&Hickl GmbH at the time of research. The author has no other competing interests to declare

14. Marina V Shirmanova

    Institute of Experimental Oncology and Biomedical Technologies, Privolzhsky Research Medical University, Nizhny Novgorod, Russian Federation

    Contribution

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

    For correspondence

    Shirmanovam@mail.ru

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-3207-7227

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