Esther A. Zaal

Metabolic dysfunction-associated steatohepatitis (MASH) is characterized by lipid-driven sterile inflammation and fibrosis that leads to liver failure and liver cancer. Metabolites are increasingly recognized as signaling molecules and regulate disease progression through cell-cell crosstalk. To overcome the challenges of studying metabolite crosstalk between different cell types in MASH livers, we developed a novel in vitro MASH model, “MASH-in-a-dish”, a Transwell co-culture system of mouse hepatocyte-derived organoids (HepOrgs), Kupffer cells (KCs), and hepatic stellate cells (HSCs) in a chemically defined liver co-culture medium. MASH-in-a-dish models key clinical features of clinical MASH, including steatosis, hepatocyte ballooning, inflammation and fibrosis. MASH-in-a-dish allowed for rapid and specific separation of co-cultured HepOrgs, KCs and HSCs, enabling cell type-specific metabolomics and stable-isotope fluxomics analysis. Using MASH-in-a-dish, we identified 19 MASH-associated, cell interaction-responsive metabolites and demonstrated that fatty acids, via the carnitine shuttle, drive KC itaconate production, which aggravated oxidative stress, steatosis, and cell damage in lipid-stressed HepOrgs. Inhibiting itaconate production by KC-specific Acod1 knockout in the MASH-in-a-dish system mitigated hepatocyte damage. Our results show that MASH-in-a-dish is a powerful in vitro platform to study cell-cell interactions in MASH pathogenesis and identify itaconate crosstalk between Kupffer cells and hepatocytes as a novel pathogenic mechanism and a potential therapeutic target in MASH.

Metabolite detection with mass spectrometry (MS) in untargeted metabolomics is limited by the wide concentration range of metabolites, where high-abundance ions can restrict the detection of low-abundance features. Full Scan enhanced Dynamic Range (eDR) MS addresses this limitation by partitioning the MS1 mass range into multiple subscans and mass windows, reducing ion competition during accumulation. Here, we systematically evaluated different eDR acquisition strategies for untargeted metabolomics. Across four hepatocellular carcinoma cell lines, Full Scan eDR MS increased detectable compounds up to ∼3.5-fold compared with Full Scan MS and improved signal-to-noise ratios of low-abundance compounds. Among equidistant configurations, 12 windows yielded the highest compound count, while custom window distributions enabled targeted enhancement of selected mass regions. In particular, allocating narrower windows to the low-m/z region selectively increased the detection of low-mass compounds. Increased MS1 coverage also translated into deeper MS2 coverage and increased metabolite identifications. This expanded coverage included cancer-relevant metabolites and improved the detection of metabolic differences between cancer cell lines. Overall, Full Scan eDR MS provides a flexible strategy to increase analytical depth in untargeted metabolomics while maintaining data quality. Customization of window size and distribution enables targeted expansion of the dynamic range within predefined mass regions, allowing MS acquisition to be tailored to sample composition and metabolites of interest.

Hepatocellular carcinoma (HCC) is the most prevalent primary liver cancer in adults and represents the third most common cause of cancer-related deaths worldwide. With limited treatment options, HCC remains a major global health problem. The majority of HCC develops in a cirrhotic liver, where the tumor microenvironment (TME) is enriched in fibroblasts derived from activated hepatic stellate cells (HSCs). Cell-cell interactions in TME play an important role in metabolic reprogramming, which is increasingly recognized as a crucial driver in HCC proliferation. The tumor-promoting and metabolic effects of HSCs on HCC cells however remain incompletely characterized. Understanding how HSC-HCC interaction regulates HCC proliferation can lead to identification of novel therapeutic targets. Here, we established a Transwell co-culture model of HSCs and HCC cells to investigate how HSC-HCC interactions regulate HCC metabolism and proliferation. Using isotope tracing metabolomics, we show that co-culture with HSCs increases proliferation of HCC cells by rewiring glucose metabolism to increase glycolysis and de novo synthesis of nucleotides. Transcriptomic and immunofluorescence staining analyses revealed that HSCs upregulate hypoxia-inducible factor (HIF) signaling in HCC cells, which contributes to metabolic rewiring and HCC proliferation. We further identified H ₂ O ₂ as a candidate crosstalk signal that could mediate HSC-dependent activation of HIF signaling in HCC cells. Together, these findings reveal a previously overlooked modulatory role of HSC-HCC interaction in metabolism-driven HCC progression. Targeting these pathways may represent a promising strategy to improve HCC treatment.

Background: Multiple myeloma is an aggressive plasma cell malignancy that remains incurable despite major therapeutic advances and depends on extensive metabolic remodeling to support malignant growth and drug resistance. Although tumor cell-intrinsic mechanisms of drug resistance are well characterized, the contribution of cells in the tumor microenvironment, particularly bone marrow stromal cells, to metabolic remodeling and therapeutic resistance remains poorly understood. Methods: We combined [U- ¹³ C]glucose tracing metabolomics with a metabolomics-compatible co-culture system of multiple myeloma and bone marrow stromal cells to assess the impact of co-culture on metabolic pathway activity across genetically distinct multiple myeloma cell lines. Cells were additionally subjected to cytokine analysis, gene expression analysis and drug response assays to investigate associations between metabolic changes, treatment responses and cytokine abundances. Results: Bone marrow stromal cell co-culture induced a convergent metabolic rewiring in multiple myeloma cells, enhancing pathways previously linked to bortezomib resistance, including glycolysis, tricarboxylic acid cycle activity, serine synthesis and nucleotide biosynthesis. In parallel, stromal cells acquired a cancer-associated fibroblast-like phenotype characterized by increased interleukin-6 and transforming growth factor-β production. This cytokine milieu promoted bortezomib resistance and stromal cell activation. Interleukin-6 alone was sufficient to rescue multiple myeloma cells from bortezomib-induced cytotoxicity and reproduced a substantial fraction of the co-culture-induced metabolic remodeling. Analysis of patient proteomics revealed a strong positive association between interleukin-6 abundance and expression of enzymes involved in mitochondrial metabolism, glycolysis, serine metabolism, nucleotide metabolism and redox homeostasis. Conclusions: These findings identify bone marrow stromal cell-derived interleukin-6 as a central driver of tumor microenvironment-mediated metabolic reprogramming and bortezomib resistance in multiple myeloma. Targeting interleukin-6-dependent metabolic signaling may represent a promising strategy to overcome stromal-driven therapeutic resistance. Clinical trial number: Not applicable.

Mutations or deletions affecting the TP53 gene predict a dismal outcome in relapsed acute lymphoblastic leukemia (ALL). Loss of p53 function compromises the response to many anti‐leukemic therapies, underscoring the need for agents that are effective in TP53‐ deficient cells. Leukemic blasts depend on de novo pyrimidine synthesis to sustain proliferation, and the mitochondrial enzyme dihydroorotate dehydrogenase (DHODH) is essential for this pathway. While DHODH inhibitors (DHODHis) show preclinical anti‐leukemic activity, the efficacy in TP53 ‐deficient contexts has not been tested. Using both isogenic TP53 ‐wildtype and knock‐out cell line models and patient‐derived xenografts (PDXs), we show that dual inhibition of DHODH and Ataxia Telangiectasia and Rad3‐related (ATR) produces pronounced anti‐leukemic effects, irrespective of the TP53 status. Through integrated transcriptomic and metabolomic analyses, we show that combined inhibition of DHODH and ATR leads to a reduced flux of glucose into the TCA cycle, accompanied by an increase in oxidative stress. This metabolic phenotype triggers cell death through a mechanism converging on Activating Transcription Factor 4 (ATF4), a key integrator of cellular responses to metabolic and oxidative stress, which operates largely independently of p53. Taken together, our findings identify ATF4‐mediated cell death as a previously unrecognized vulnerability in TP53 ‐deficient ALL.

Metabolite detection with mass spectrometry (MS) in untargeted metabolomics is limited by the wide concentration range of metabolites, where high-abundance signals dominate MS1 scans and suppress detection of low-abundance features. This reduces metabolite coverage and obscures biologically relevant signals, particularly in complex cellular systems. Full Scan enhanced Dynamic Range (eDR) MS addresses these limitations by partitioning the MS1 mass range into multiple subscans and mass windows, reducing saturation effects from dominant ions. Here, we systematically evaluate different eDR acquisition strategies for untargeted metabolomics. Across four hepatocellular carcinoma cell lines, Full Scan eDR MS increased detectable features up to ~3.5-fold compared to Full Scan MS. Among equidistant window configurations, 12 windows yielded the highest feature count and broadest dynamic range, while custom window distributions further improved detection in ion-dense regions. In particular, allocating smaller window sizes to the low m/z region selectively increased detection of low-mass features while preserving performance for higher mass ions. Full Scan eDR MS also improved data quality, reducing variation and increasing signal-to-noise ratios, especially for low-abundance metabolites. MS2 coverage and metabolite identifications increased substantially, resulting in unique detection of cancer-relevant metabolites. Importantly, the increased depth of metabolite detection enabled improved discrimination between cancer cell lines, supporting deeper interrogation of metabolic heterogeneity. Overall, these results establish Full Scan eDR MS as a flexible strategy to improve sensitivity and metabolome coverage in untargeted metabolomics. Customization of window size and distribution enable targeted expansion of dynamic range within predefined mass regions, allowing MS acquisition to be tailored to sample complexity and metabolites of interest.

Hydrolysed collagen is used as a supplement for horses with osteoarthritis, hoof horn growth problems, and gastric ulcers. To determine the oral availability of a specific hydrolysed collagen supplement and the appropriate dose, six Warmblood mares were fed two different concentrations of the supplement: 100 g HC (C_H), 50 g HC (C_L), and a control of 0 g HC (C_N) during one week in a randomised cross-over design. On day 7, 14 and 21, blood sampling for amino acid (AA) analysis was performed, just prior to feeding the supplement (t = 0) and every hour after feeding for 8 h (t = 1–8). Statistical analysis revealed differences in mean plasma AA concentrations between the CH and CN doses for alanine, arginine, glutamine, glycine, proline, serine and hydroxyproline. Similarly, statistical differences were observed between the CL and CN doses for arginine, glycine, proline and hydroxyproline. This study demonstrated the availability of amino acids from the supplemented hydrolysed collagen. Although clinical efficacy was not evaluated in this study, a dose of 100 g HC once daily resulted in higher plasma concentrations, which remained detectable for at least 24 h, suggesting greater clinical relevance.

T cells undergo many metabolic changes throughout the different phases of their response in lymphoid and nonlymphoid tissues. Cell metabolism meets demands for energy and biosynthesis, particularly during cell division and effector differentiation. As costimulatory receptors, CD28 and various TNF receptor (TNFR) family members shape T-cell clonal expansion, survival and effector functions and are important clinical targets. While CD28 is acknowledged as a metabolic regulator, little is known about how TNFRs shape T-cell metabolism. We here identify TNFR family member CD27 as a metabolic regulator in activated human CD4+ T cells. In the context of CD3 signaling and CD28 costimulation, CD27 proved to regulate specific metabolic functions, as determined by metabolomics and metabolic tracer experiments. CD27 costimulation supported upregulation of glycolysis, the pentose phosphate pathway and the TCA cycle, increasing the use of glucose-derived carbon and glutamine-derived nitrogen as building blocks for de novo nucleotide synthesis. It also promoted uptake of amino acids (AAs) and modulated pathways of AA metabolism. Accordingly, CD27 costimulation boosted protein translation in CD3- and CD3/CD28-activated CD4+ T cells, which proceeded via enhanced mTOR pathway activation. Remarkably, CD27, OX40 and 4-1BB all enhanced CD3-induced mTOR signaling, but only CD27 could overrule inhibitory PD-1 signaling. CD27 costimulation increased IL-2, IFNγ and TNFα production by CD3-activated CD4+ T cells, also in presence of PD-1 signaling. Next to previously defined beneficial effects of CD27 on activated T-cell survival and CTL differentiation and Th1 effector differentiation, these data support its essential contribution to T-cell metabolism and its relevance as a therapeutic target.

Metabolomics has emerged as a pivotal field in understanding cellular function, particularly in the context of disease. In numerous diseases, including cancer, alterations in metabolism play an essential role in disease progression and drug response. Hence, unraveling the metabolic rewiring is of importance to find novel diagnostic and therapeutic strategies. Isotope tracing is a powerful technique for delving deeper into the metabolic wiring of cells. By tracking an isotopically labeled substrate through biochemical reactions in the cell, this technique provides a dynamic understanding of cellular metabolism. This chapter outlines a robust isotope tracing protocol utilizing high-resolution mass spectrometry coupled to liquid chromatography in cell culture-based models. We cover essential aspects of experimental design and analyses, providing a valuable resource for researchers aiming to employ isotopic tracing.