Cell metabolism is the biochemical engine that determines how immune cells sense, survive, and respond to infection, inflammation, and tissue stress. At the center of this process is energy metabolism: the coordinated use of pathways such as glycolysis, oxidative phosphorylation (OXPHOS), fatty-acid oxidation, and anabolic biosynthesis. Because immune cells are heterogeneous and dynamically reprogram their metabolism over minutes to days, averaging measurements across bulk populations can obscure critical biology. This is why single-cell profiling methods have become central to modern immunometabolism.
Energy metabolism in immune cells is not merely “fuel use”; it is an information-processing layer that couples extracellular cues to intracellular fate decisions. For example, when innate immune cells such as macrophages encounter microbial products or danger signals, they can increase glycolytic flux to support rapid ATP production and metabolic intermediates used for biosynthesis and redox balance. In contrast, adaptive immune cells and some macrophage states rely more heavily on mitochondrial respiration, which supports efficient long-term function and memory-like phenotypes. Metabolic shifts also influence epigenetic remodeling: metabolites act as cofactors for enzymes that modify DNA and histones, thereby shaping transcriptional programs. Thus, energy metabolism regulates both immediate bioenergetic demands and longer-term gene expression.
A major technical challenge is measuring cellular energy metabolism with sufficient spatial and temporal resolution while preserving cell identity. Traditional assays (e.g., bulk metabolomics, flow cytometry with limited metabolic readouts) often require cell destruction, lack subcellular localization, or cannot resolve cell-to-cell variability. Imaging-based approaches address subcellular context but can generate high-dimensional data that are difficult to interpret without computational tools. Machine learning provides a solution by learning nonlinear patterns in imaging features and mapping them to metabolic states.
Single-cell imaging of metabolic parameters typically relies on proxies such as fluorescence-based reporters for redox state, mitochondrial membrane potential, oxygenation-related indicators, or other spectrally distinct signals that reflect energy handling. These signals can correlate with glycolytic activity or mitochondrial respiratory capacity, but calibration and biological interpretation require rigorous controls. In practice, imaging data can contain confounders including differences in cell size, optical density, probe loading, and microscope settings. Machine-learning models can mitigate these issues by performing feature extraction, normalization, denoising, and classification or regression across large cell cohorts.
The concept of “single-cell profiling of cellular energy metabolism” therefore combines three elements: (1) quantitative imaging to measure metabolic readouts at the individual-cell level, (2) computational modeling to convert complex image features into metabolic state scores, and (3) biological validation linking inferred metabolic states to functional immune phenotypes. Validation may include perturbations such as pharmacologic inhibition of glycolysis or OXPHOS, genetic modulation of metabolic enzymes, or measurement of downstream cytokine production and proliferation. Concordance between predicted and experimentally manipulated metabolic behavior strengthens causal interpretation.
In immunology, mapping energy metabolism at single-cell resolution enables identification of distinct metabolic programs within a single tissue or sample. Such programs can correspond to activation states, differentiation trajectories, or spatially organized microenvironments. For example, within a tumor or inflamed tissue, immune cells may experience gradients of glucose, oxygen, and lactate. These gradients drive heterogeneity in glycolysis versus respiration. Single-cell energy profiling can reveal subpopulations that are otherwise masked in bulk analyses, including cells with high glycolytic reliance, cells with preserved mitochondrial function, and hybrid states that use both pathways.
Machine-learning-enabled metabolic imaging can also support trajectory inference. Metabolic reprogramming often precedes transcriptional changes, so temporal or pseudo-temporal analysis of metabolic signatures can indicate how cells transition between states during immune activation, tolerance, or exhaustion. This is particularly relevant for chronic infections and cancer, where immune dysfunction emerges through progressive metabolic and transcriptional alterations.
From a clinical perspective, understanding energy metabolism at single-cell resolution offers pathways to biomarker discovery and therapeutic targeting. Metabolic phenotypes may predict responses to immunotherapies by reflecting the energetic capacity of immune effector cells and the suppressive capacity of metabolic-reprogrammed populations such as tumor-associated macrophages or regulatory T cells. Therapeutic strategies targeting metabolism must be informed by cell-type specificity, since the same pathway can support different functions depending on cell state.
Overall, advances that integrate high-content imaging with machine learning—such as imaging and machine-learning frameworks designed for single-cell profiling of cellular energy metabolism—are accelerating mechanistic immunometabolism research. They transform metabolism from a bulk average into a cell-resolved, data-driven phenotype, enabling more precise mapping of how energy use governs immune cell identity, function, and fate decisions.
Source: JCellBiol (Journal of Cell Biology) via the provided Creator update
Journal of Cell Biology: New #tools: Lecourieux, Bardou, Garcia, and Bousso (Institut Pasteur) present Met-Vision, an imaging and machine-learning approach enabling single-cell profiling of cellular energy #metabolism. #CellMetabolism #Immunology. #breaking
— @JCellBiol May 1, 2026
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